Household food demand in urban China: a censored system approach

Journal of Comparative Economics 32 (2004) 564–585 www.elsevier.com/locate/jce

Household food demand in urban China: a censored system approach Steven T. Yen a,∗ , Cheng Fang b , Shew-Jiuan Su c a Department of Agricultural Economics, University of Tennessee, Knoxville, TN 37996-4518, USA b Commodities and Trade Division, Food and Agriculture Organization of the United Nations,

Via delle Terme di Caracalla, 00100 Rome, Italy c Department of Geography, National Kaohsiung Normal University, Kaohsiung 802, Taiwan, ROC

Received 13 March 2003; revised 21 April 2004 Available online 11 August 2004

Yen, Steven T., Fang, Cheng, and Su, Shew-Jiuan—Household food demand in urban China: a censored system approach Household food consumption in urban China is investigated, using data from the 2000 Survey of Urban Households. A translog demand system is estimated taking account of reported zero consumption. High expenditure elasticities are found for milk and most meat products suggesting that demand for these products will grow faster than demand for other products as the economy develops and incomes increase. As in other market economies, prices play important roles in food demand. Demand is more price-responsive for milk than all other food products, and net substitution is observed among most food products. Regional differences are found so that changing demographics will have an important impact on future food demand in China. Journal of Comparative Economics 32 (3) (2004) 564–585. Department of Agricultural Economics, University of Tennessee, Knoxville, TN 37996-4518, USA; Commodities and Trade Division, Food and Agriculture Organization of the United Nations, Via delle Terme di Caracalla, 00100 Rome, Italy; Department of Geography, National Kaohsiung Normal University, Kaohsiung 802, Taiwan, ROC.  2004 Association for Comparative Economic Studies. Published by Elsevier Inc. All rights reserved.

* Corresponding author: Department of Agricultural Economics, University of Tennessee, 308D Morgan Hall, 2621 Morgan Circle, TN 37996-4518, USA. E-mail address: [email protected] (S.T. Yen).

0147-5967/$ – see front matter  2004 Association for Comparative Economic Studies. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jce.2004.04.005

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

565

JEL classification: C34; D12; R22 Keywords: China; Food demand; Censoring; Tanslog demand system

1. Introduction Since economic reforms were initiated in the late 1970s, China’s economy has grown at between 7 and 8% and urban population has increased by 3.7% annually. Income growth and urbanization have boosted food demand considerably; the mix of food consumption has shifted away from staple grain and starches toward animal proteins and fish. As a result, direct food grain consumption has declined while feed grain consumption has increased substantially. Corn used for feed increased from 53 million metric tons (mmt) in 1990 to 93 mmt in 2002, for an annual growth rate of 4.7%. Soybean meal used for feed rose even faster, from 1.03 mmt in 1990 to 14 mmt in 2000 and 16.65 mmt in 2002, for an annual growth rate of 30% (USDA-FAS, 2003). Demand shifts have important implications for China’s agriculture and world food markets. In the last two decades, China has experienced a transition from a planned and selfsufficient economy to one that is market driven and globally integrated. Under the planning system, agricultural products were priced low to reduce the labor and raw material costs of industrial production. Compulsory procurement policies were applied to almost all major farm products. Since the early 1980s, the market has been liberalized, first for fruits and vegetables and then for fishery products, livestock products and oilseeds. Food rationing in urban China was eliminated officially in 1993. Until recently, agricultural trade in China had been controlled strictly by the government; the goal of self-sufficiency in food impeded imports of land-intensive grain. With its accession into the World Trade Organization (WTO) in December 2001, China has become a major player in the global market and integrated into the world economy. Under the terms of accession, China’s agricultural trade regime will be more open and responsive to global market conditions. Hence consumer preferences will dictate future food production in China. Previous studies of China’s food demand do not reflect the current situation in the globalized world market. Most studies use aggregate time series data (Kueh, 1988; Lewis and Andrews, 1989; Peterson et al., 1991; Fan et al., 1994), aggregate cross-sectional data at the city level (Wu et al., 1995), or provincial panel data (Wang and Chern, 1992; Fan et al., 1995; Gao et al., 1996a). Recent studies use household survey data collected by the National Statistical Bureau, which have important advantages over aggregate time series. The detailed demographic characteristics collected in these surveys allow an accommodation of heterogeneity in preference. The large sample size also allows estimation of a relatively large demand system. Among the studies based on household survey data, Halbrendt et al. (1994) and Gao et al. (1996b) focus on rural households in single provinces, namely Guangdong and Jiangsu. Fang and Beghin (2002) use household data from the national sample, stratified by regions, and focus on a small, three-equation system of fat and oil products. With the growing interest in China’s food demand and given the availability of national survey data, studying a larger category of foods is appropriate.

566

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

One interesting feature of micro-level data is the presence of zero values in observed consumption. Statistical estimation procedures that do not account for censoring in the dependent variables produce biased and inconsistent parameter estimates. This issue has not been addressed in the aforementioned studies, with the exception of Fang and Beghin (2002) who accommodate censoring with the two-step procedure of Heien and Wessells (1990). However, statistical efficiency is compromised when the demand system is estimated with a two-step procedure. In fact, the Heien and Wessells procedure is incorrect analytically and performs poorly in Monte Carlo simulations (Shonkwiler and Yen, 1999), which may cast doubt on the elasticity and welfare estimates based on the Heien and Wessells procedure. This study quantifies the determinants of food demand by urban households in China, using data from a recent survey of urban households. Manufacturers and traders in the US and around the world need a good understanding of such determinants to gain insights into future food expenditure patterns in China and trade potential. Our analysis provides a timely analysis of the determinants of food demand in the world’s most populous country and deals with the censoring issues in the consumption data by using a recent censored demand system estimator. Section 2 presents the translog demand system and the estimation procedure when the dependent variables are censored. Section 3 describes the data, sample and estimation results. Demand elasticities are reported and discussed in Section 4. Section 5 concludes with policy implications.

2. The translog demand system with censored dependent variables To focus on the demand for foods, we assume that these products are weakly separable from all other goods, i.e., nonfoods. Consider the utility function U (q), where q ≡ [q1, q2 , . . . , qn ] is an n-vector of food commodities with pricesp ≡ [p1 , p2 , . . . , pn ] . Assuming that U (q) is monotonic and regular strictly quasi-concave in q, the consumerchoice problem is characterized by the indirect utility function given by
 V (p, m) = max U (q) | p q
m , (1) q

where m is total food expenditure. Our empirical work uses the demand system derived from the translog indirect utility function (Christensen et al., 1975): log V (p, m) = α0 +

n  i=1

1 βij log(pi /m) log(pj /m). 2 n

αi log(pi /m) +

n

i=1 j =1

Applying Roy’s identity to (2) and imposing the normalization rule deterministic demand-shares, denoted si (θ ), are:  αi + nj=1 βij (pj /m)   , i = 1, 2, . . . , n, si (θ ) = −1 + nk=1 nj=1 βkj log(pj /m) where θ is a vector containing demand parameters α and β.

n

i=1 αi

(2)

= −1, the

(3)

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

567

Homogeneity is implicit in (2) and (3) because we use normalized prices, i.e., p/m, and the following symmetry restrictions are imposed: βij = βj i

∀i, j.

Demographic variables dk are incorporated in the demand equations in (3) by parameterizing αi such that:  αik dk , i = 1, 2, . . . , n, αi = αi0 + k

where αi0 and αik are parameters. Appending an error term εi to each deterministic share, we have: wi∗ = si (θ ) + εi ,

i = 1, 2, . . . , n,

(4)

to complete the stochastic specification. Hence, the stochastic expenditure shares can be viewed as latent shares. The consumer choice specified by (1) is subject to nonnegativity constraints on the quantities, qi ; therefore, observed consumption levels are subject to censoring. Several statistical procedures accommodate censored dependent variables in a consumer demand system (Lee and Pitt, 1986; Wales and Woodland, 1983). We use a nonlinear generalization of the multivariate linear tobit system (Amemiya, 1974), which is also used by Yen et al. (2003). In this approach, observed shares, denoted wi , relate to latent shares, given by wi∗ , in the following way: wi = max(wi∗ , 0),

i = 1, 2, . . . , n.

(5)

In this multivariate tobit specification (5), the adding-up restriction holds for the latent equations (4) with proper parametric restrictions but it does not hold for observed expenditure shares. To accommodate the adding-up restriction, we follow the approach in Yen et al. (2003), suggested by Pudney (1989), and estimate the first (n − 1) equations in the system (5). Demand elasticities for the nth good can be calculated from the adding-up property. Specifically, denote the Marshallian price elasticities, Hicksian price elasticities and the ∗ and expenditure elasticity for good i as eij , eij∗ and eim , respectively. Then, enj , enj enm can be calculated related to the budget constraint, namely n using elasticity restrictions n  n ∗ w e = 1, w e = −w and w j i=1 i im i=1 i ij i=1 i eij = 0 for j = 1, 2, . . . , n. Without loss of generality, we consider a regime in which the first  goods are consumed with observed (n − 1)-vector, given by w = [w1∗ , . . . , w∗ , 0, . . . , 0] . Denote the random error vector as ξ ≡ [ξ1 , ξ2 ] , partitioned such that ξ1 ≡ [ε1 , . . . , ε ] and ξ2 ≡ [ε+1 , . . . , εn−1 ] . Assume that ξ is distributed as an (n − 1)-variate normal with zero mean and covariance matrix Σ having elements ρij σi σj , where ρij (i, j = 1, . . . , n − 1) are the error correlation coefficients and σi (i = 1, 2, . . . , n − 1) are the error standard deviations. Denote u ≡ [−s+1 (θ ), −s+2 (θ ), . . . , −sn−1 (θ )] . Then, the censor mechanism in (5), along with the latent share equations in (4), implies the following regime-switching condition: ξ2
u,

568

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

from which the likelihood contribution can be constructed as   f (ξ1 , ξ2 ) dξ2 = g(ξ1 ) h(ξ2 |ξ1 ) dξ2 , Lc (w) = {ξ2 :ξ2
u}

(6)

{ξ2 :ξ2
u}

where ξ1 ≡ [wi − si (θ )] is an -vector, f (ξ1 , ξ2 ) is the joint density of [ξ1 , ξ2 ] , g(ξ1 ) is the marginal density of ξ1 , and h(ξ2 |ξ1 ) is the conditional density of ξ2 given ξ1 . In Eq. (6), partial integration of the multivariate normal density f (ξ1 , ξ2 ) over the range of ξ2 is accomplished by the conditioning of ξ2 on ξ1 such that f (ξ1 , ξ2 ) = g(ξ1 )h(ξ2 |ξ1 ), as Pudney (1989) suggests. The probability density functions (pdf) g(ξ1 ) and h(ξ2 |ξ1 ) are defined using components of the covariance matrix Σ, partitioned at the th row and column such that:   Σ11 Σ12 , Σ= Σ21 Σ22 where Σ11 is  ×  and Σ22 is (n −  − 1) × (n −  − 1). Specifically, using properties of the multivariate normal distribution as Kotz et al. (2000) suggest, ξ1 is an -variate normal with zero mean and covariance matrix Σ11 and ξ2 |ξ1 is an (n −  − 1)-variate normal with mean and covariance, respectively: −1 ξ1 , µ2·1 = Σ21 Σ11

(7)

−1 Σ12 . Σ22·1 = Σ22 − Σ21 Σ11

(8)

In evaluating the likelihood contribution given in (6), the term g(ξ1 ) is identical to that of an -equation seemingly unrelated regression model with Gaussian errors (Davidson and MacKinnon, 1993), and the integral {ξ2 : ξ2
u} h(ξ2 |ξ1 ) dξ2 is an (n −  − 1)-dimensional cumulative distribution function (cdf ) with a known mean vector given by (7) and a covariance matrix specified as (8) which simplifies to the standard multivariate normal cdf upon standardization, as in the multinomial probit model (Daganzo, 1980). Thus, the computational burden in estimating the censored system is equivalent to that of a non-censored nonlinear system and a multinomial probit model combined. Numerical procedures evaluating the multivariate normal cdf are described in Kotz et al. (2000) and can be evaluated by numerical routines contained in statistical packages such as Gauss and Matlab. Note that the likelihood contribution given by (6) reduces to Lc (w) = g(ξ ) if all n − 1 goods are positive, which is also the likelihood contribution for the conventional demand system if censoring is ignored.1 The other extreme  case is if all (n − 1) goods are zeros, for which the likelihood contribution is Lc (w) = {ξ : ξ
u} f (ξ ) dξ , with u ≡ [−s1 (θ ), . . . , −sn−1 (θ )] , which is an (n − 1)-dimensional cdf with zero mean and covariance matrix Σ. For observation t, denote a dichotomous indicator It (r) such that It (r) = 1 if observed consumption wt falls in demand regime r, and It (r) = 0 otherwise. Then, the sample likelihood function is:

I (r) Lr (wt ) t . L= (9) t

r

1 We report selected elasticities from the non-censored system for comparison.

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

569

Censoring of the dependent variables should be considered when calculating demand elasticities. For commodity i, the probability of a positive observation is Pr(wi > 0) = Φ[si (θ )/σi ] and the conditional mean of expenditure share wi is E(wi |wi > 0) = si (θ ) + σi φ[si (θ )/σi ]/Φ[si (θ )/σi ], so that the unconditional mean of wi is  



E(wi ) = Φ si (θ )/σi si (θ ) + σi φ si (θ )/σi , (10) where φ(·) and Φ(·) are univariate standard normal pdf and cdf, respectively (Maddala, 1983). Demand elasticities are derived by differentiating (10).2

3. The data and estimation results The data are obtained from an annual survey conducted by China’s National Statistical Bureau, which is the sole government agency that collects information on food consumption and expenditures in China. This agency has had a long history of collecting such data and has separated its data collection effort into rural and urban components. Taken from the 2000 Urban Household Survey, our sample includes households from 30 randomly selected cities in 29 provinces. One city is selected randomly from each province except for Guangdong, in which two cities were selected, and Tibet, for which data were not available. In addition to quantities and expenditures of purchases, information on household characteristics is also available in the survey. To economize on the number of parameters in this large system, we include only four demographic variables, namely, household size, age of the household head, and two dummy variables for education of the household head, along with five other dummy variables for regions, namely, Northeast, North, East, South, and West, with the reference region as Pastoral.3 Household size, education and age are also included in the previous study of fat and oil demand for Chinese households by Fang and Beghin (2002). We estimate a system with the following eleven food categories: beef (including mutton), pork, poultry, fish, other meat, grain, fats and oils, egg, fresh milk, vegetables, and fruits. Among these food products, grain and vegetables are consumed by all households in the sample, but the proportions of consuming households are also high for pork (97.5%), poultry (95.4%), fish (97.5%), fats and oils (93.1%), eggs (98.9%) and fruits (99.7%). Milk and other meat are the least popular food products, with only 69.1 and 80% of the households, respectively, consuming these products. Price for each food category is 2 Denote the denominator of the expenditure shares s (θ ) in Eq. (3) as D and let δ be the Kronecker delta i ij such that δij = 1 if i = j and δij = 0 otherwise. Then, for goods i = 1, 2, . . . , n, the Marshallian elasticities with  respect to the price of good j are eij = −δij + Φ[si (θ )/σi ]D −1 [βij − si (θ ) nk=1 βkj ]/E(wi ), the expenditure    elasticities are eim = 1 + Φ[si (θ )/σi ]D −1 [− nk=1 βik + si (θ ) nh=1 nk=1 βhk ]/E(wi ), and the elasticities with respect to the demographic variable z are ei = Φ[si (θ )/σi ]D −1 αi z /E(wi ). 3 The Northeast region includes cities from the provinces, with sample size in parentheses, of Liaoning (98), Jilin (100) and Heilongjiang (100). The North region includes City of Beijing (484), Tianjin (49), Hebei (98), Shandong (98) and Henan (100). The East includes City of Shanghai (483), Jiangsu (100), Zhejiang (93) and Anhui (98). The South includes Fujiang (95), Jiangxi (100), Hubei (100), Hunan (98), Guangdong with Cities of Guangzhou (288) and Zhanjiang (97), Guangxi (100) and Hainan (48). The West include Sichuan (99), Guizhou (100), Yunnan (98) and Shaanxi (99). Finally, the Pastoral region includes Shanxi (99), Inner Mongolia (100), Gansu (99), Qinghai (49), Ningxia (50) and Xinjiang (95). Tibet was excluded due to unavailability of data.

570

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

derived by dividing its expenditure with the corresponding quantity, a procedure followed in many previous studies (e.g., Pitt, 1983). Originally 3800 households were in the sample; we excluded 79 households with prices exceeding five standard deviations above the means and 6 households reporting zero expenditures for six or more products.4 Our final sample consists of 3715 households, of which 1653 (44.5%) households consumed all food products. With respect to expenditures on food items, 1194 (32.1%) households contained one zero, 604 (16.3%) contained two zeros, and 194 (5.2%) contained three zeros. For a small number of households, 70 (1.9%), expenditures were zero for more items. For these households, the likelihood function was evaluated by the simulation procedure described below. The sample statistics for the quantities, prices and demographic variables are presented in Table 1. The translog demand system given by (3) is estimated by programming the likelihood function specified in (9), using the maxlik optimization routines in Gauss. Numerical optimization is carried out with the BHHH algorithm (Berndt et al., 1974), using the numerical gradient of the log-likelihood. Parameter estimates for the conventional translog without censoring are used as the initial values for the censored system. For a small number of households having four or more zeros in expenditures, evaluation of the normal cdf was slow with conventional methods so these cdf were evaluated with a smooth probability simulator known as the GHK simulator (Hajivassiliou, 1994), which is also described in Yen et al. (2003). Parameter estimates of the translog demand system are presented in Table 2. Among the demographic variables considered, household size is significant at the 10% level or lower in the other meat, grain, fats and oils, milk, and vegetables equations. Age is sigTable 1 Sample statistics Variable Quantities (kg per person per annum) Beef (mutton included) Consuming households Pork Consuming households Poultry Consuming households Fish Consuming households Other meat Consuming households Grain Fats and oils Consuming households Egg Consuming households

% consuming 81.1 97.5 95.4 97.5 80.0 100.0 93.1 98.9

Mean

Std. dev.

4.18 5.15 17.60 18.05 8.39 8.79 15.06 15.45 3.35 4.21 81.09 9.15 9.83 12.10 12.23

5.46 5.63 11.30 11.08 7.39 7.33 14.82 14.81 3.59 3.54 47.51 6.74 6.49 8.47 8.42

(continued on next page)

4 Although the simulated maximum-likelihood procedure is capable of accommodating many zeros, reported zeros for over half of the products were considered to be outliers during a one-year sampling period.

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

571

Table 1 (Continued) Variable Milk (fresh) Consuming households Vegetables Fruits Expenditures (Yuan per person per annum) Beef Consuming households Pork Consuming households Poultry Consuming households Fish Consuming households Other meat Consuming households Grain Fats and oils Consuming households Egg Consuming households Milk (fresh) Consuming households Vegetables Fruits Prices (Yuan/kg) Beef Pork Poultry Fish Other meat Grain Fats and oils Egg Milk (fresh) Vegetables Fruits Age (of household head) Size (of household) Dummy variables (1 = yes; 0 otherwise) College High school (junior/senior, tech school) Less than high school (reference) Northeast North East South West Pastoral (reference)

% consuming 69.1 100.0 99.7

Source: Urban Household Survey, National Statistical Bureau, 2000.

Mean

Std. dev.

15.58 22.56 122.04 64.97

22.79 24.39 62.51 41.12

55.88 68.36 197.47 202.41 133.35 139.68 210.32 220.60 66.96 83.53 210.32 73.71 79.11 58.45 59.09 63.63 92.02 225.80 161.46

70.52 72.85 134.49 132.42 133.12 133.10 274.06 275.39 74.30 74.70 108.86 54.53 52.67 37.43 37.14 96.53 104.32 127.23 117.96

13.78 11.17 14.88 12.17 19.31 2.85 8.36 5.16 4.39 1.93 2.61 47.89 3.10

3.70 2.54 4.71 6.01 7.45 1.14 2.19 1.31 2.29 0.70 1.32 11.71 0.82

0.23 0.69 0.08 0.08 0.22 0.21 0.25 0.11 0.13

572

Beef Demographic variables (αij ) Constant −0.015 (0.011) Age 0.000 (0.001) Size −0.004*** (0.001) College −0.001 (0.003) High school −0.003 (0.002) Northeast 0.035*** (0.004) North 0.037*** (0.003) East 0.059*** (0.005) South 0.058*** (0.005) West 0.060*** (0.005)

Pork

Poultry

Fish

Oth. meat

Grain

Fats–oils

Egg

Milk

Veg.

−0.096*** (0.014) −0.003*** (0.001) 0.001 (0.001) 0.002 (0.003) 0.001 (0.003) −0.027*** (0.004) −0.023*** (0.003) −0.030*** (0.004) −0.047*** (0.005) −0.075*** (0.006)

0.005 (0.012) 0.003*** (0.001) −0.001 (0.001) −0.002 (0.002) −0.001 (0.002) 0.000 (0.004) −0.020*** (0.003) −0.039*** (0.004) −0.042*** (0.004) −0.025*** (0.003)

0.125*** (0.021) 0.003*** (0.001) −0.006*** (0.001) −0.004 (0.003) −0.003 (0.003) −0.026*** (0.005) −0.017*** (0.003) −0.080*** (0.006) −0.076*** (0.006) −0.006) (0.004)

−0.033*** (0.009) 0.002*** (0.001) −0.001* (0.001) −0.005** (0.002) −0.004** (0.002) −0.001 (0.003) −0.025*** (0.002) 0.002 (0.003) 0.005** (0.002) −0.006*** (0.002)

−0.268*** (0.014) −0.005*** (0.001) 0.000 (0.001) 0.018*** (0.003) 0.011*** (0.002) 0.025*** (0.003) 0.049*** (0.004) 0.077*** (0.006) 0.071*** (0.005) 0.043*** (0.004)

−0.062*** (0.008) −0.001 (0.001) 0.000 (0.001) 0.011*** (0.002) 0.004*** (0.002) 0.008*** (0.002) 0.011*** (0.002) 0.009*** (0.002) 0.012*** (0.002) 0.004* (0.002)

−0.050*** (0.005) −0.001*** (0.000) 0.002*** (0.000) −0.001 (0.001) −0.001 (0.001) −0.010*** (0.001) −0.013*** (0.001) −0.011*** (0.002) −0.003** (0.001) −0.004*** (0.001)

0.040*** (0.013) 0.000 (0.001) −0.003*** (0.001) −0.018*** (0.003) −0.009*** (0.003) 0.024*** (0.004) −0.008*** (0.003) −0.017*** (0.003) 0.007** (0.003) 0.003 (0.003)

−0.264*** (0.012) −0.003*** (0.001) 0.004*** (0.001) 0.003 (0.002) 0.004** (0.002) −0.014*** (0.003) 0.004** (0.002) 0.016*** (0.003) 0.008*** (0.003) −0.002 (0.003)

(continued on next page)

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

Table 2 Maximum-likelihood estimates of censored translog demand system

Table 2 (continued)

Pork Poultry Fish Other meat Grain Fats & oils Egg Milk Veg. Fruits

Pork

Poultry

Fish

Oth. meat

Grain

Fats–oils

Egg

Milk

Veg.

−0.001 (0.003) 0.004 (0.003) 0.005*** (0.002) 0.010*** (0.002) 0.009*** (0.002) −0.008*** (0.002) −0.002 (0.002) −0.004*** (0.001) 0.009*** (0.002) −0.002 (0.002) −0.006*** (0.002)

−0.069*** (0.006) 0.008*** (0.002) 0.020*** (0.003) 0.019*** (0.002) 0.003 (0.003) −0.001 (0.002) −0.010*** (0.002) 0.003 (0.002) −0.010*** (0.003) 0.022*** (0.003)

−0.013*** (0.003) 0.009*** (0.002) 0.006*** (0.001) −0.001 (0.002) 0.005*** (0.001) −0.003*** (0.001) 0.004** (0.002) 0.001 (0.002) −0.010*** (0.002)

−0.017*** (0.004) 0.004*** (0.001) 0.016*** (0.002) 0.010*** (0.002) 0.010*** (0.001) 0.003 (0.002) 0.009*** (0.002) −0.017*** (0.002)

0.000 (0.002) −0.020*** (0.002) 0.002 (0.001) 0.003*** (0.001) 0.001 (0.001) −0.004*** (0.002) −0.012*** (0.002)

−0.014*** (0.003) 0.007*** (0.002) −0.002* (0.001) −0.016*** (0.002) 0.012*** (0.002) −0.001 (0.002)

−0.017*** (0.002) −0.009*** (0.001) 0.002 (0.001) −0.002 (0.002) 0.002 (0.001)

−0.008*** (0.002) 0.006*** (0.001) 0.005*** (0.001) 0.004*** (0.001)

0.015*** (0.003) −0.004** (0.002) −0.009*** (0.002)

−0.032*** (0.003) 0.001 (0.002)

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

Prices (βij ) Beef

Beef

(continued on next page)

573

574

Table 2 (continued) Beef

Poultry

Fish

Oth. meat

Grain

Fats–oils

Egg

Milk

Veg.

0.065*** (0.001)

0.050*** (0.001)

0.059*** (0.001)

0.042*** (0.001)

0.062*** (0.001)

0.039*** (0.000)

0.024*** (0.000)

0.058*** (0.001)

0.050*** (0.000)

Error correlation (ρij ) Pork −0.233*** (0.018) Poultry −0.005 (0.020) Fish −0.059*** (0.023) Other meat −0.009 (0.019) Grain −0.207*** (0.016) Fats & oils −0.001 (0.016) Egg −0.156*** (0.017) Milk 0.025 (0.021) Veg. −0.194*** (0.018)

−0.062*** (0.018) −0.222*** (0.019) −0.063*** (0.019) −0.148*** (0.018) −0.145*** (0.018) 0.006 (0.018) −0.185*** (0.019) −0.013 (0.018)

0.019 (0.018) 0.189*** (0.017) −0.269*** (0.017) −0.250*** (0.018) −0.069*** (0.017) −0.061*** (0.019) −0.240*** (0.018)

0.016 (0.021) −0.213*** (0.021) −0.162*** (0.021) −0.134*** (0.020) −0.105*** (0.018) −0.145*** (0.020)

−0.282*** (0.018) −0.217*** (0.016) −0.100*** (0.018) 0.003 (0.020) −0.182*** (0.018)

0.288*** (0.015) 0.023 (0.016) −0.169*** (0.019) −0.015 (0.016)

0.023 (0.016) −0.123*** (0.020) −0.045*** (0.017)

−0.010 (0.019) −0.017 (0.016)

−0.223*** (0.018)

Log-likelihood = 54337.147 Notes. 1. Asymptotic standard errors are in parentheses. 2. The parameter estimate for the own-price coefficients for fruits (β11,11 ) is −0.020, with a standard error of 0.003. * Significant at the 10% level. ** Idem., 5%. *** Idem., 1%.

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

Pork

Error standard deviations 0.053*** σi (0.001)

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

575

nificant in the pork, poultry, fish, other meat, grain, egg and vegetables equations. College education is significant in beef, fish, other meat, egg, milk, and vegetables equation; while high school education is significant in only three equations, namely grain, fats and oils and milk. The regional variables are significant in most equations, which justifies their inclusion along with the demographic variables in accommodating heterogeneous preferences. Among the 66 quadratic price coefficients estimated, over two-thirds (48 parameters) are significant at the 10% level or lower. All the error standard deviations are estimated with high precision, each having a significance level less than 1%. Finally, among the 45 error correlation coefficients, 31 are significant at the 1% level. Thus, apart from the need to impose cross-equation restrictions, the significance of these error correlation coefficients justifies estimation of the demand equations as a system.

4. The demand elasticities To evaluate the effects of prices, total expenditure, and demographic variables on consumption, we calculate the demand elasticities at the sample means of the explanatory variables. For statistical inference, standard errors for these elasticities are approximated by the delta method (Spanos, 1999). The Marshallian price and expenditure elasticities are presented in Table 3. All own-price elasticities are significant and negative at the 1% level. With some exceptions, i.e., milk and, to a less extent, beef and other meat, most own-price elasticities are significantly less than unity in absolute values.5 Interestingly, milk is very price-responsive, having an own-price elasticity of −1.40. This large elasticity suggests that governmental price-support programs can be effective in promoting calcium intake from milk consumption. At the other extreme, pork has the lowest own-price elasticity at −0.21 among all food products considered. The cross-price elasticities suggest a mixture of gross complements and substitutes among the food products. While it is hard to discern a pattern among these elasticities, gross complementarity is predominant among all meat products, which is a counterintuitive finding. Moreover, beef, pork and poultry are gross substitutes for egg products, which are another protein source, while somewhat surprisingly, fish and other meat are gross complements with egg products. In addition, egg products are gross substitutes for fats and oils and three of the meat products, namely beef, pork and poultry, but gross complements with all other foods. Interestingly, if the price of fish increases (decreases), the quantities demanded of all products except fruits decrease (increase), suggesting that these products are all gross complements with fish. Similarly, poultry is a gross complement with most other products. Relative to the own-price and expenditure elasticities, the cross-price effects are less pronounced, with the largest elasticity being between pork and other meat at −0.59. 5 The demand elasticities derived from the current partial food demand system are conditional elasticities. Without elasticity estimates for aggregate consumption of food, we cannot assert that the demand for milk is price-elastic. Estimation of unconditional demand elasticities requires data for foods as well as nonfoods and is beyond the scope of the current investigation.

576

Table 3 Uncompensated elasticities

Pork Poultry Fish Other meat Grain Fats & oils Egg Milk Veg. Fruits

Beef

Pork

Poultry

Fish

Oth. meat

Grain

Fats–oils

Egg

Milk

Veg.

Fruits

Expend.

−0.96*** (0.09) −0.02 (0.03) −0.08*** (0.03) −0.11*** (0.02) −0.25*** (0.04) 0.10*** (0.02) 0.08* (0.04) 0.16*** (0.04) −0.24*** (0.05) 0.04** (0.02) 0.06*** (0.02)

−0.11 (0.08) −0.21*** (0.06) −0.16*** (0.04) −0.26*** (0.03) −0.59*** (0.06) −0.04* (0.03) 0.01 (0.06) 0.36*** (0.06) −0.10 (0.07) 0.08*** (0.03) −0.39*** (0.03)

−0.13*** (0.04) −0.08*** (0.02) −0.75*** (0.04) −0.09*** (0.02) −0.16*** (0.04) 0.03* (0.02) −0.12*** (0.04) 0.12*** (0.04) −0.10** (0.04) 0.00 (0.02) 0.12*** (0.02)

−0.25*** (0.05) −0.19*** (0.03) −0.11*** (0.03) −0.37*** (0.03) −0.10*** (0.04) −0.12*** (0.02) −0.24*** (0.04) −0.31*** (0.04) −0.05 (0.05) −0.05*** (0.02) 0.23*** (0.02)

−0.23*** (0.04) −0.21*** (0.02) −0.09*** (0.02) −0.04*** (0.02) −1.00*** (0.06) 0.21*** (0.02) −0.03 (0.03) −0.09*** (0.03) −0.01 (0.04) 0.05*** (0.01) 0.13*** (0.02)

0.19*** (0.06) −0.07** (0.03) −0.01 (0.03) −0.24*** (0.02) 0.57*** (0.05) −0.90*** (0.03) −0.23*** (0.05) 0.04 (0.04) 0.41*** (0.05) −0.16*** (0.02) 0.08*** (0.03)

0.06 (0.04) 0.00 (0.03) −0.10*** (0.03) −0.13*** (0.02) −0.06 (0.04) −0.08*** (0.02) −0.55*** (0.06) 0.32*** (0.04) −0.05 (0.04) 0.01 (0.02) −0.01 (0.02)

0.09*** (0.03) 0.11*** (0.02) 0.04** (0.02) −0.13*** (0.01) −0.09*** (0.02) 0.01 (0.01) 0.23*** (0.03) −0.70*** (0.06) −0.17*** (0.03) −0.07*** (0.01) −0.08*** (0.01)

−0.23*** (0.05) −0.02 (0.03) −0.05* (0.03) −0.01 (0.02) −0.01 (0.04) 0.17*** (0.02) −0.03 (0.04) −0.20*** (0.04) −1.40*** (0.07) 0.06*** (0.02) 0.06*** (0.02)

0.03 (0.05) 0.08*** (0.03) −0.06** (0.03) −0.15*** (0.02) 0.08* (0.04) −0.15*** (0.02) 0.01 (0.04) −0.23*** (0.04) 0.08* (0.05) −0.72*** (0.03) −0.05** (0.02)

0.12*** (0.04) −0.33*** (0.02) 0.11*** (0.03) 0.14*** (0.02) 0.28*** (0.04) −0.06*** (0.02) −0.10*** (0.04) −0.23*** (0.03) 0.22*** (0.05) −0.08*** (0.02) −0.76*** (0.03)

1.41*** (0.05) 0.94*** (0.02) 1.26*** (0.03) 1.41*** (0.02) 1.31*** (0.04) 0.82*** (0.02) 0.98*** (0.03) 0.77*** (0.03) 1.40*** (0.05) 0.83*** (0.02) 0.60*** (0.03)

Note. Asymptotic standard errors are in parentheses. * Significant at the 10% level. ** Idem., 5%. *** Idem., 1%.

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

Beef

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

577

Table 3 also reports the total food expenditure elasticities, which are all positive and significant at the 1% level. Since aggregate food should be a normal good, these positive expenditure elasticities suggest that all products are considered normal goods. The expenditure elasticities are considerably high, at 1.4, for beef, fish and milk; they are also greater than unity for poultry and other meat. The expenditure elasticities are slightly less than unity for pork and fats and oils, and considerably below unity for grain, egg, vegetables and fruits. These large expenditure elasticities have important marketing and trade implications. During the last decade China experienced rapid growth in per capita real income and urban population, with annual growth rates of 7 and 2.3%, respectively. These trends are expected to continue or even accelerate with China’s admission to the WTO. As income continues to grow, expenditures for these food products are expected to increase. According to our estimated expenditure elasticities, the most substantial increases will be in beef, fish and milk, with other meat, poultry, fats and oils, pork, vegetables and fruits, grain, eggs and fruits following in order. Contrary to the empirical studies for other Asian countries suggesting that grain is an inferior good (Ito et al., 1989) and the conclusion that rice and cereals are inferior goods in Japan (Kanai et al., 1993), our results support the findings of Chern (2001), Gao et al. (1996b), and Halbrendt et al. (1994) that grain is a normal good in China. Our estimated expenditure elasticity for grain at 0.82 is much greater than the elasticities reported in the above studies, which range from 0.11 to 0.58 for China.6 The large expenditure elasticity for fish implies that the demand for fish and seafood will increase more than proportionately to total expenditures, which is consistent with studies of other Asian countries. Shono et al. (2000) suggest that China’s dietary pattern is moving toward those of the Asian developed countries, e.g., Japan, and not toward those of the US and the EU-15 countries. During 1997, food fish consumption was 62.5 kg per capita in Japan, which is much higher than in the US (19.7 kg) or the EU-15 (23.6 kg). China is the world’s largest fish producer, accounting for about 36% of total production, and also the world’s largest consumer. Aquaculture is the fastest growing source of food in China, where seafood is king and no celebration is complete without fish (Bean, 2003). Regardless of the price, fish and seafoods must be on the table at special dining occasions. Our estimated high expenditure elasticity suggests that the demand for fish products will grow as the standard of living increases among urban families. Delgado et al. (2002) project that China’s annual per capita food fish consumption will increase to 35.9 kg by 2020, representing an annual growth rate of 1.3%. The large own-price and expenditure elasticities for milk are also interesting. Chinese people by tradition are not regular milk consumers, but milk has been considered a luxury good by the common people for a long time. Dairy products appear to be income6 These studies are based on earlier survey data from the National Statistical Bureau so the definition of grain

is compatible. However, Gao et al. (1996b) and Halbrendt et al. (1994) limit their attention to rural households in single provinces. Unlike other two-step estimations that are motivated by selectivity correction, the censored system in Chern (2001) is estimated with multiple Mills’ ratios in all demand share equations, for which the justification is unclear. Contrary to most other findings, Halbrendt et al. (1994) report gross substitution among all food products. However, elasticities in Halbrendt et al. (1994) appear to be incorrect because adding-up and homogeneity restrictions are violated.

578

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

responsive because dairy expenditures are growing much more rapidly than total expenditure and this trend is expected to continue into the future. Dairy consumption has grown rapidly. Over the last decade, consumption of dairy products has increased from 1.37 mmt in 1980 to 12.06 mmt in 2002, with the annual rate of growth reaching about 10.4% for fluid milk consumption (USDA-FAS, 2003).7 In addition, if the newly introduced school milk program reaches its full potential, each of 200 million students will drink an average of 200 milliliters of milk each day, calling for about 9 mmt of milk each year, which is roughly double the level of current production.8 This expected growth in consumption indicates that the Chinese dairy sector has great market potential. China’s livestock production has grown rapidly in response to higher consumer demand and the species composition is changing substantially toward beef and poultry. The share of pork output in total meat production of pork, poultry, and beef and veal has declined from 88.54% in 1987 to 73.94% in 2002. During the same period, beef has increased from 3.83 to 9.56% and poultry from 7.63 to 16.50% (USDA-FAS, 2003). Our estimated elasticities indicate that this structural change in the livestock sector will continue because of future demand-driven effects. Our results also suggest that the shift from staple grain products toward high-protein livestock and fish products will continue, because high meat demand implies an increased demand for feed grain and soy meal as one unit of meat requires more than one unit of feed as an input.9 This shift of food from grain products toward livestock and fish products has important implications for world trade in feed grain, especially corn, and feed meal, especially soybean meal. China has become the world’s largest soybean meal importer in the last few years and, with expected economic growth of more than 7.4%, it may become a large importer of both corn and soy meal in the next decade. The elasticities with respect to the demographic and regional variables are presented in Table 4. On a per-capita basis, larger households consume more beef, fish, other meat and milk, but less egg products, vegetables and fruits than smaller households. Due to the one-child policy in China, the proportion of urban families having three or fewer people increased from 52% in 1992 to 63% in 1998 according to household survey data. As household size continues to decline, the consumption of egg products, vegetables and fruits will increase, and the consumption of beef, fish and other meat will decrease, all else equal. Relative to other households, households headed by an older individual consume more staple foods, e.g., grain and vegetables, as well as more pork and egg products, but less of other meat products and fruits. In addition, households headed by a better-educated person consume more milk and other meat but less grain and fats and oils, according to the estimated elasticities with respect to college and high school education. However, the effects of these demographic variables are small; the largest demographic effect is for household 7 During 2001, China’s per capita milk consumption, excluding butter, was only about 10.1 kg annually, which is low compared to consumption during the same period in other countries such as Taiwan (43.0 kg), Japan (65.8 kg), South Korea (28.6 kg) and the United States (256.6 kg) (FAO, 2003). These figures include fruit milk and manufactured milk but exclude butter. 8 Per capita milk production was approximately 7 kg in 2002. Milk is the only livestock that is not overproduced (FAO, 2003). 9 The fine feed conversion ratio, which is the ratio of fine feed consumption to net output, is 3.24 for pork, 2.70 for beef, 1.13 for mutton, 2.35 for chicken, and 2.96 for eggs in China according to survey data (Wailes et al., 1998).

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

579

Table 4 Demographic elasticities Age −0.03 (0.07) Pork 0.17*** (0.04) Poultry −0.23*** (0.05) Fish −0.20*** (0.04) Other meat −0.27*** (0.07) Grain 0.22*** (0.03) Fats & oils 0.08 (0.06) Egg 0.16*** (0.04) Milk −0.03 (0.08) Veg. 0.12*** (0.03) Fruits −0.18*** (0.04) Beef

Size

College

0.33*** 0.00 (0.06) (0.02) −0.04 0.00 (0.03) (0.01) 0.05 0.01 (0.04) (0.01) 0.24*** 0.01 (0.03) (0.01) 0.10* 0.03*** (0.06) (0.01) −0.01 −0.04*** (0.03) (0.01) 0.02 −0.07*** (0.05) (0.01) −0.23*** 0.01 (0.04) (0.01) 0.22*** 0.12*** (0.07) (0.02) −0.13*** −0.01 (0.02) (0.01) −0.27*** 0.01 (0.03) (0.01)

H. schl.

Northeast North

East

South

West

0.06 (0.04) −0.01 (0.02) 0.01 (0.03) 0.03 (0.02) 0.07** (0.04) −0.07*** (0.02) −0.08*** (0.03) 0.02 (0.02) 0.16*** (0.05) −0.03** (0.01) −0.01 (0.02)

−0.08*** (0.01) 0.03*** (0.00) 0.00 (0.01) 0.03*** (0.00) 0.00 (0.01) −0.02*** (0.00) −0.02*** (0.00) 0.03*** (0.00) −0.05*** (0.01) 0.01*** (0.00) 0.01*** (0.00)

−0.33*** (0.02) 0.07*** (0.01) 0.15*** (0.01) 0.21*** (0.01) −0.01 (0.02) −0.16*** (0.01) −0.05*** (0.01) 0.08‡ (0.01) 0.10*** (0.02) −0.03*** (0.01) −0.04*** (0.01)

−0.39*** (0.02) 0.14*** (0.01) 0.19*** (0.01) 0.24*** (0.01) −0.04** (0.02) −0.17*** (0.01) −0.08*** (0.01) 0.03** (0.01) −0.05** (0.02) −0.02*** (0.01) −0.02** (0.01)

−0.17*** (0.01) 0.09*** (0.00) 0.05*** (0.01) 0.01 (0.01) 0.02*** (0.01) −0.05*** (0.00) −0.01* (0.01) 0.01*** (0.01) −0.01 (0.01) 0.00 (0.00) −0.01** (0.00)

−0.22*** (0.01) 0.06*** (0.01) 0.08*** (0.01) 0.05*** (0.01) 0.16*** (0.01) −0.11*** (0.00) −0.06*** (0.01) 0.10*** (0.01) 0.05*** (0.02) −0.01** (0.00) −0.01 (0.01)

Note. Asymptotic standard errors are in parentheses. * Significant at the 10% level. ** Idem., 5%. *** Idem., 1%.

size with a 1% increase in household size resulting in a 0.33% increase in the consumption of beef. The elasticities of the regional dummy variables indicate obvious regional differences.10 Relative to the pastoral region, households in all other regions consume less beef, due to the relative abundance of beef in the pastoral region. Households in the South, East and West regions consume less grain but more meat, especially poultry and pork, and fish products. Many crushing facilities and large scale livestock farms have emerged in these feed-grain deficit regions, especially the South, due to the strong demand for meat and fish. Corn and soybean farming in the South and West regions is much less productive than in the US and in the North and Northeast regions of China (Fang and Fabiosa, 2002). High demand and low productivity will dictate future trade patterns in these regions. However, the regional differences in food consumption are unlikely to be explained entirely by the regional variables; regional food consumption pattern in China requires a more careful analysis in future research. 10 With the nonlinear functional form in the translog system, the effects of demographic variables on expen-

diture shares are not trivial. Although elasticities with respect to dummy variables are not strictly correct, these elasticities offer a convenient way to assess the directional effects and significance of the dummy variables.

580

Table 5 Compensated elasticities

Pork Poultry Fish Other meat Grain Fats & oils Egg Milk Veg. Fruits

Beef

Pork

Poultry

Fish

Oth. meat

Grain

Fats–oils

Egg

Milk

Veg.

Fruits

−0.89*** (0.09) 0.02 (0.03) −0.02 (0.03) −0.04** (0.02) −0.19*** (0.04) 0.14*** (0.02) 0.12*** (0.04) 0.19*** (0.04) −0.17*** (0.05) 0.08*** (0.02) 0.09*** (0.02)

0.08 (0.08) −0.08 (0.06) 0.01 (0.04) −0.08*** (0.03) −0.41*** (0.06) 0.06** (0.03) 0.14** (0.06) 0.46*** (0.06) 0.09 (0.07) 0.19*** (0.03) −0.31*** (0.03)

−0.02 (0.04) 0.00 (0.02) −0.64*** (0.04) 0.03 (0.02) −0.05 (0.04) 0.10*** (0.02) −0.04 (0.04) 0.18*** (0.04) 0.02 (0.04) 0.07*** (0.02) 0.17*** (0.02)

−0.07 (0.05) −0.07*** (0.03) 0.04 (0.03) −0.20*** (0.03) 0.07* (0.04) −0.02 (0.02) −0.12*** (0.04) −0.22*** (0.04) 0.13*** (0.05) 0.05*** (0.02) 0.30*** (0.02)

−0.17*** (0.04) −0.17*** (0.02) −0.04* (0.02) 0.02 (0.02) −0.94*** (0.06) 0.25*** (0.02) 0.01 (0.03) −0.05* (0.03) 0.05 (0.04) 0.09*** (0.01) 0.16*** (0.02)

0.41*** (0.06) 0.08*** (0.03) 0.19*** (0.03) −0.02 (0.02) 0.78*** (0.05) −0.77*** (0.03) −0.07 (0.05) 0.16*** (0.04) 0.63*** (0.05) −0.02 (0.02) 0.18*** (0.03)

0.13*** (0.04) 0.06** (0.03) −0.03 (0.03) −0.06*** (0.02) 0.02 (0.04) −0.03* (0.02) −0.49*** (0.06) 0.36*** (0.04) 0.02 (0.04) 0.06*** (0.02) (0.02 (0.02)

0.15*** (0.03) 0.14*** (0.02) 0.09*** (0.02) −0.08*** (0.01) −0.04 (0.02) 0.04*** (0.01) 0.27*** (0.03) −0.67*** (0.06) −0.12*** (0.03) −0.03*** (0.01) −0.06*** (0.01)

−0.17*** (0.05) 0.02 (0.03) 0.00 (0.03) 0.04** (0.02) 0.05 (0.04) 0.21*** (0.02) 0.01 (0.04) −0.17*** (0.04) −1.34*** (0.07) 0.09*** (0.02) 0.08*** (0.02)

0.26*** (0.05) 0.22*** (0.03) 0.14*** (0.03) 0.07*** (0.02) 0.29*** (0.04) −0.02 (0.02) 0.16*** (0.04) −0.11*** (0.04) 0.31*** (0.05) −0.59*** (0.03) 0.05** (0.02)

0.28*** (0.04) −0.22*** (0.02) 0.26*** (0.03) 0.30*** (0.02) 0.43*** (0.04) 0.03* (0.02) 0.01 (0.04) −0.14*** (0.03) 0.38*** (0.05) 0.01 (0.02) −0.69*** (0.03)

Note: Asymptotic standard errors are in parentheses. * Significant at the 10% level. ** Idem., 5%. *** Idem., 1%.

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

Beef

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

581

Table 6 Comparison of selected elasticities with and without accounting for censoring Model Marshallian own-price elasticities Censored translog Non-censored translog Expenditure elasticities Censored translog Non-censored translog Hicksian own-price elasticities Censored translog Non-censored translog

Beef

Other meat

Milk

−0.96 (0.09) −0.78 (0.11)

−1.00 (0.06) −0.81 (0.06)

−1.40 (0.07) −1.29 (0.08)

1.41 (0.05) 1.33 (0.06)

1.31 (0.04) 1.18 (0.04)

1.40 (0.05) 1.30 (0.06)

−0.89 (0.09) −0.72 (0.11)

−0.94 (0.06) −0.76 (0.06)

−1.34 (0.07) −1.24 (0.08)

Note: Asymptotic standard errors are in parentheses.

Table 5 presents compensated price elasticities. Similar to the uncompensated elasticities, all compensated own-price elasticities are significant and negative, except for pork, which is very small and significant at only the 13% level. Unlike their Marshallian counterparts indicating a mix of gross substitutes and complements, the compensated elasticities suggest that net substitution is the prevalent pattern. Specifically, among the 110 compensated cross-price elasticities, about half (54) are positive and significant indicating net substitution while less than one quarter are negative and significant indicating net complementarity. However, the compensated cross-price elasticities are considerably smaller than the compensated own-price elasticities and are also lower in absolute values than their uncompensated counterparts. Censoring is not prevalent in our data for many of the products. The percentage of zero observations is large only for beef at 18.9%, other meat at 20% and milk at 30.9%. To investigate the relevance of the censored system estimator, we also estimate the translog demand system using the conventional procedure which ignores censoring.11 Table 6 presents the uncompensated and compensated own-price elasticities and expenditure elasticities from the censored and non-censored translog demand systems for beef, other meat and milk, which are the products that would be more sensitive to censoring. These elasticities suggest that significant differences do exist between the two sets of estimates, confirming the importance of using the censored system estimator.12 Table 7 presents a comparison of our uncompensated own-price and food expenditure elasticities with those found in previous studies. Our own-price elasticity for grain (−0.90) is only slightly below that (−0.99) reported by Gao et al. (1996b). Most of our other elasticities are considerably different from those in the literature. For example, our estimated own-price elasticity for pork of −0.21 is considerably below the estimates of −0.65 reported by Wu et al. (1995) and of −0.98 reported by Gao et al. (1996b). Our expenditure elasticity for fish of 1.41 is above the ones reported by Wu et al. (1995) at 0.20 and by Gao et al. (1996b) at 0.89. However, unlike our study based on a recent national survey, re11 A complete set of parameter and elasticity estimates for the non-censored translog system is available upon request from the authors. 12 A priori, censored system estimates need not produce higher or lower elasticity estimates than non-censored system estimates.

582

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

Table 7 Comparison of Marshallian own-price and expenditure elasticities Beef

Pork

Poultry Fish

Own-price elasticities This study −0.96 −0.21 −0.75 Wu et al. (1995) −0.65 Gao et al. (1996b) −1.04 −0.98 −0.53 Fan et al. (1995) Fan et al. (1994) Fang and Beghin (2002) Expenditure elasticities This study 1.41 0.94 1.26 Wu et al. (1995) 1.17 Gao et al. (1996b) 0.78 1.15 0.29 Fan et al. (1995) Fan et al. (1994) Fang and Beghin (2002)

Grain Egg

Veg.

−0.37 −0.90 −0.70 −0.72 −1.40 −0.47 −0.88 −0.81 −0.99 −0.90 −0.83 −0.41 −0.47

1.41 0.20 0.89

0.82 0.52

0.77 0.54 0.91

0.83 1.26 0.95 0.99–1.20

Fruits Fats–oils −0.76 −0.55 −1.14 −0.96 −0.32 to − 1.32 0.60 1.45 0.72

0.98

0.04 to 0.32

Notes: Wu et al. (1995) is based on a cross section of 33 cities in 1990. Gao et al. (1996b) is based on 1990 household data in Jiangsu. Fan et al. (1994, 1995) were based on provincial panel (1982–1990) in rural China.

sults from these other studies are based on either regional data or earlier time periods. Our elasticity estimates for fats and oils differ from those reported by Fang and Beghin (2002), which is a more recent study in which censored dependent variables are considered. Although our own-price elasticity of aggregate fats and oils lies within the range reported by Fang and Beghin (2002) for disaggregate fats and oils products, our expenditure elasticity lies considerably above the range reported by Fang and Beghin (2002).

5. Conclusions With increasing globalization of the food market, analyzing food demand in China is crucial for its policy implications. We investigate the effects of prices, income and demographic characteristics on food demand, using a national sample from the 2000 Urban Household Survey. Our censored demand system estimator accommodates zeros in food expenditures and produces different elasticities from those of a conventional estimator. Price and food expenditure elasticities vary across products, but all foods are normal goods. The strong and positive estimated expenditure elasticities suggest that income is a driving force of changing food expenditures and that the consumption of all food products will grow substantially as China develops further. The different expenditure elasticities across food categories indicate more diversified food consumption in the future. The consumption of beef, fish and milk will grow at the highest rate, followed by other meat and poultry. The results also suggest that the demand for grain will continue to change in composition shifting away from food grain towards feed grain and soy meal as income grows. Hence, China will become a large importer of corn and soy meal in the next decade. Our findings suggest that changing demographics will have an important impact on China’s future food demand. Younger households consume more meat and fruits, but less staple foods like grain and vegetables than their older counterparts. Smaller families con-

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

583

sume more of the easy-to-fix food categories, such as eggs, vegetables and fruits, and less of the time-consuming foods, such as meat and fish, than do larger families. Better-educated households consume more milk and other meat, but less grain and fats and oils, than do less-educated households. As the population becomes wealthier and more educated, family size becomes smaller, and the younger generation begins to make up a larger proportion of the society in the coming decade, food consumption patterns will change considerably. Similar to results for other market economies, prices have a considerable impact on food demand in China as substitution occurs among the food products. Although the Marshallian price elasticities indicate a mix of gross complements and substitutes, compensated price elasticities suggest that most food products, except for meats and fish, are net substitutes. However, the cross-price effects are less pronounced than own-price and expenditure effects. During the last decade, China’s urban population increased from 302 million in 1990 to 481 million in 2001, a 59% increase overall and more than 3% annually. As China industrializes and migration policy changes toward a high priority on urbanization, about half of the population are expected to be living in urban areas by 2020 (Hsu et al., 2002). This trend is expected to continue so that another 200 million may be added to China’s urban population. Our results for urban households have important implications for future food consumption and the potential for food trade with China, because households in cities have higher incomes than those in rural areas. Our results have important implications for the organizational structure of the food industry in China and for the diversifications of agriculture, food processors, retailers, and other participants in the food production and marketing system given the consumer-driven agriculture in the 21st century. In addition to recent work on the impact of projected changes in food consumption for the US to 2020 (Blisard et al., 2002), our demand estimates can be useful for world agricultural projections to assess the impacts of urbanization and changing income and demographic profiles on future global consumption and world trade.

Acknowledgments

An earlier version of this paper was presented at the fifth biennial conference of the Asian Consumer and Family Economics Association, Taipei, Taiwan, December 17–20, 2003. The authors thank participants from that conference and two anonymous referees for helpful comments and suggestions. Yen’s research was funded in part by USDA’s Economic Research Service under Cooperative Agreements No. 43–3AEM–0–80042 and No. 43–3AEM–2–80063. Su’s research was funded by the National Science Council of Taiwan Grant NSC93-2415-H-017-004-. The views expressed in this publication are those of the authors and do not necessarily reflect the views of the US Department of Agriculture or the Food and Agriculture Organization of the United Nations, or of the Government of Taiwan.

584

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

References Amemiya, Takeshi, 1974. Multivariate regression and simultaneous equation models when the dependent variables are truncated normal. Econometrica 42 (6), 999–1012. Bean, Ralph, 2003. In China, fish means prosperity. AgExporter 14 (1), 17–19. Berndt, Ernst K., Hall, Bronwyn H., Hall, Robert E., Hausman, Jerry A., 1974. Estimation and inference in nonlinear structural models. Annals of Economic and Social Measurement 3 (4), 653–665. Blisard, Noel, Lin, Biing-Hwan, Cromartie, John, Ballenger, Nicole, 2002. America’s changing appetite: food consumption and spending to 2020. Food Review 25 (1), 2–9. Chern, Wen S., 2001. Assessment of demand-side factors affecting global food security. Chapter 6 in: Chern, Wen S., Carter, Colin A., Shei, Shun-Yi (Eds.), Food Security in Asia: Economics and Policies. Edward Elgar, Northampton, MA, pp. 83–118. Christensen, Laurits R., Jorgenson, Dale W., Lau, Lawrence J., 1975. Transcendental logarithmic utility functions. American Economic Review 65 (3), 367–383. Daganzo, Carlos, 1980. Multinomial Probit: The Theory and Its Application to Demand Forecasting. Academic Press, New York. Davidson, Russell, MacKinnon, James G., 1993. Estimation and Inference in Econometrics. Oxford Univ. Press, Oxford, UK. Delgado, Christopher, Rosegrant, Mark, Wada, Nikolas, Meijer, Siet, Ahmed, Mahfuzuddin, 2002. Fish as food: projections to 2020 under different scenarios. Discussion paper No. 52. Markets and Structural Studies Division (MSSD). International Food Policy Research Institute (IFPRI), Washington, DC. Fan, Shenggen, Cramer, Gale L., Wailes, Eric J., 1994. Food demand in rural China: evidence from rural household survey. Agricultural Economics 11 (1), 61–69. Fan, Shenggen, Wailes, Eric J., Cramer, Gale L., 1995. Household demand in rural China: a two-stage LES–AIDS model. American Journal of Agricultural Economics 77 (1), 54–62. Fang, Cheng, Beghin, John C., 2002. Urban demand for edible oils and fats in China: evidence from household survey data. Journal of Comparative Economics 30 (4), 732–753. Fang, Cheng, Fabiosa, Jay, 2002. Does the US Midwest have a cost advantage over China in producing corn, soybeans, and hogs? Research paper 02-MRP 4. Midwest Agribusiness Trade Research and Information Center, Iowa State University, Ames, IA. Food and Agriculture Organization of the United Nations (FAO), 2003. FAOSTAT: FAO Statistical Databases. FAO, Rome. Available from: http://apps.fao.org/. Gao, X.M., Wailes, Eric J., Cramer, Gale L., 1996a. Partial rationing and Chinese urban household food demand analysis. Journal of Comparative Economics 22 (1), 43–62. Gao, X.M., Wailes, Eric J., Cramer, Gale L., 1996b. A two-stage rural household demand analysis: microdata analysis from Jiangsu Province, China. American Journal of Agricultural Economics 78 (3), 604–613. Hajivassiliou, Vassilis A., 1994. Classical estimation methods for LDV models using simulation. Chaper 40 in: Engle, Robert F., McFadden, Daniel L. (Eds.), Handbook of Econometrics, vol. 4. North-Holland, Amsterdam, pp. 2383–2441. Halbrendt, Catherine, Tuan, Francis, Gempesaw, Conrado, Dolk-Etz, Dimphna, 1994. Rural Chinese food consumption: the case of Guangdong. American Journal of Agricultural Economics 76 (4), 794–799. Heien, Dale, Wessells, Cathy R., 1990. Demand systems estimation with microdata: a censored regression approach. Journal of Business and Economic Statistics 8 (3), 365–371. Hsu, Hsin-Hui, Chern, Wen S., Gale, Fred, 2002. How will rising income affect the structure of food demand?. In: Gale, Fred (Ed.), China’s Food and Agriculture: Issues for the 21st Century. ERS Agricultural Information Bulletin No. AIB775. US Department of Agriculture, Washington, DC. Ito, Shoichi, Peterson, E. Wesley F., Grant, Warren R., 1989. Rice in Asia: is it becoming an inferior good? American Journal of Agricultural Economics 71 (1), 32–42. Kanai, Michio, Sawada, Yutaka, Sawada, Manabu, 1993. Japanese consumer demand. In: Tweeten, Luther, Dishon, Cynthia L., Chern, Wen S., Imamura, Naraomi, Morishima, Masaru (Eds.), Japanese and American Agriculture: Tradition and Progress in Conflict. Westview Press, Boulder, CO. Kotz, Samuel, Balakrishnan, N., Johnson, Norman L., 2000. Continuous Multivariate Distributions, vol. 1: Models and Applications, second ed. Wiley, New York. Kueh, Y.Y., 1988. Food consumption and peasant incomes in the post-Mao era. China Quarterly 116, 634–670.

S.T. Yen et al. / Journal of Comparative Economics 32 (2004) 564–585

585

Maddala, G.S., 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge Univ. Press, Cambridge, UK. Lee, Lung-Fei, Pitt, Mark M., 1986. Microeconometric demand systems with binding nonnegativity constraints: the dual approach. Econometrica 54 (5), 1237–1242. Lewis, Phillip, Andrews, Neil, 1989. Household demand in China. Applied Economics 21 (6), 793–807. Peterson, E. Wesley F., Jin, Lan, Ito, Shoichi, 1991. An Econometric analysis of rice consumption in the People’s Republic of China. Agricultural Economics 6 (1), 67–78. Pitt, Mark M., 1983. Food preferences and nutrition in rural Bangladesh. Review of Economics and Statistics 65 (1), 105–114. Pudney, Stephen, 1989. Modelling Individual Choice: The Econometrics of Corners, Kinks, and Holes. Blackwell Publishers, Cambridge, UK. Shonkwiler, J. Scott, Yen, Steven T., 1999. Two-step estimation of a censored system of equations. American Journal of Agricultural Economics 81 (4), 972–982. Shono, Chizuru, Suzuki, Nobuhiro, Kaiser, Harry M., 2000. Will China’s diet follow Western diets? Agribusiness: An International Journal 16 (3), 271–280. Spanos, Aris, 1999. Probability Theory and Statistical Inference: Econometric Modeling with Observational Data. Cambridge Univ. Press, Cambridge, UK. US Department of Agriculture, Foreign Agriculture Service (USDA-FAS), 2003. Production, Supply & Distribution: PSD on Line. USDA, Washington, DC. Available from: http://www.fas.usda.gov/psd/about_psd.asp. Wailes, Eric J., Fang, C., Zhang, X., Cao, L., Chen, H., Wu, Z., Guo, J., 1998. China’s livestock feed use relationships: preliminary results from a survey in seven provinces. Selected paper for the WCC101 Workshop. Honolulu, Hawaii, January 12–13. Wales, T.J., Woodland, A.D., 1983. Estimation of consumer demand systems with binding non-negativity constraints. Journal of Econometrics 21 (3), 263–285. Wang, Zhi, Chern, Wen S., 1992. Effects of rationing on the consumption behavior of Chinese urban households during 1981–1987. Journal of Comparative Economics 16 (1), 1–26. Wu, Yanrui., Li, Elton, Samuel, S. Nicholas, 1995. Food consumption in urban China: an empirical analysis. Applied Economics 27 (6), 509–515. Yen, Steven T., Lin, Biing-Hwan, Smallwood, David M., 2003. Quasi and simulated likelihood approaches to censored demand systems: food consumption by food stamp recipients in the United States. American Journal of Agricultural Economics 85 (2), 458–478.