three essays on estimation of chinese textile demand system and

Three Essays on the Estimation of Chinese Textile Demand and Its

Implications for the World Cotton Market

by

Mouze M.Kebede

A Dissertation

In

AGRICULTURE AND APPLIED ECONOMICS

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Dr. Darren Hudson

Chair of Committee

Dr. Dean Ethridge

Dr. Benaissa Chidmi

Dr. Eric Walden

Peggy Gordon Miller

Dean of the Graduate School

August, 2012

©2012, Mouze Mulugeta Kebede

Texas Tech University, Mouze Mulugeta Kebede, August 2012

ii

Acknowledgments

My special thanks and appreciation goes to those who have made this work a lot

easier to complete than what would have been needed. A special word of appreciation

goes to my major advisor, Dr. Darren Hudson, for his constructive comments, support,

patience, and guidance during this research work. My special appreciation also goes to

my dissertation committee, Dr. Dean Ethridge, Dr. Benaissa Chidmi, Dr. Suwen Pan, and

Dr. Eric Walden who have been patient and constructive in this research. Their constant

support has made this work a success.

I would like to thank Cotton Council International (CCI) who provided the data and

USDA/ERS and CERI for providing financial support for this research. I would also like

to extend my thanks to the faculty, staff, and students of the Department of Agricultural

and Applied Economics at Texas Tech which have made my stay at Texas Tech easy, and

a lot enjoyable.

My special thanks also go to my family members in Ethiopia, especially, my father,

Mulugeta, my mother, Neber, and my siblings for their constant encouragement, support

and prayer during this research work.


iii

Table of Contents

Acknowledgements………………………………………………………………………ii

Abstract…………………………………………………………………………………..v

List of Tables……………………………………………………………………………viii

List of Figures……………………………………………………………........................ix

Chapter

Chapter one………………………………………………………………………………..1

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

1.1 Changes in Chinese Demographic Structure ......................................................................... 4

1.2 Changes in Shopping Habits of Chinese Households ............................................................. 6

1.3 Specific Problem .................................................................................................................... 8

1.5 Objectives of the Study .......................................................................................................... 9

Chapter Two .................................................................................................................................. 10

Review of Literature ...................................................................................................................... 10

2.1 Review of Demand Functional Forms ................................................................................. 10

2.2 Review of Empirical Literature on Textile Demand ............................................................ 19

2.3. Review of the Global Fiber Model ..................................................................................... 25

Chapter Three ................................................................................................................................ 10

Choice of Retail Outlets and Chinese Household Demand for Textiles and Footwear: Analysis

Using Alternative Demand Functional Forms ............................................................................... 29

Introduction ................................................................................................................................ 29

3.1 Conceptual Framework ........................................................................................................ 30

3.2 Methods and Procedures ...................................................................................................... 32

3.3 Data Source and Description ............................................................................................... 39

3.4 Results and Discussion ........................................................................................................ 44

3.4.1 Model Selection ............................................................................................................ 44

3.4.2 Empirical Results .......................................................................................................... 45

3.5 Summary and Conclusion .................................................................................................... 55


iv

Chapter Four .................................................................................................................................. 58

Demand for Apparel Products among Chinese Consumers Using a Semi-parametric Two Step

Procedure ....................................................................................................................................... 57

Introduction ................................................................................................................................ 57



4.3 Results and Discussion ........................................................................................................ 67

4.3.1 Estimation Results ........................................................................................................ 73

4.4. Summary and Conclusion ................................................................................................... 86

Chapter Five ................................................................................................................................... 10

Implications of Changes in Chinese Demographic Structure on World Cotton Market ................ 89

Introduction ................................................................................................................................ 89

5.1 Objectives of the study ......................................................................................................... 90


5.3 Data Source and Description ............................................................................................... 95


5.5 Results and Discussions ....................................................................................................... 97

5.6 Summary and Conclusion .................................................................................................. 106

Chapter Six…………………………………………………………………………………………………………………………… 109

Summary and Conclusion ............................................................................................................ 108

References .................................................................................................................................... 110

Appendix A .................................................................................................................................. 116

Appendix B .................................................................................................................................. 120


v

Abstract

Three Essays on the Estimation of Chinese Textile Demand and Its

Implications for the World Cotton Market

Chinese consumption of textiles has grown rapidly in recent years, along with the

increase in Chinese household income. This, in turn, has made China an important market

for textiles. Yet, China’s population has been growing steadily at a slower rate and has

become older compared to previous years. Such changes, according to recent literature,

could have negative implications in terms of the growth rate of Chinese textile

consumption. The objective of this study is to analyze the textile consumption pattern in

China by taking into account consumers’ socio-economic profiles and choice of retail

outlets and fibers for textiles. Textile expenditure data constituting over 6000 urban

Chinese households from a Cotton Council International (CCI) consumer tracking study

for 2009 is used. This study makes three important contributions to the empirical

investigation of household textile demand. First, it integrates household choice decisions,

which are likely to be concerns of decision-makers involved in direct product sales, into a

formal demand analysis. Second, it utilizes a semi-parametric regression to model a

system of censored equations where censored observations are common in household

studies. The advantage of this procedure over the parametric method is that it allows for a

more flexible functional relationship among variables than the traditional parametric

approach. Last, the study integrates results from Chinese textile demand and investigates


vi

the implications of increases in per capita income, declines in family size, and increases

in the proportion of the elderly on both the Chinese and global textile and cotton markets.

Results concerning commodity group demand (apparel, home-textiles, and footwear)

indicate significant effects of store choice and household demographics on household

textile consumption. Households that often buy their apparel from department and chain

stores bought more apparel when compared to households that buy their apparel from

hypermarkets, independent stores, and stores in the “others” category. In regards to

household demographics, households either headed by a ready-to-retire age person or

have an elderly member appear to spend more on home-textiles compared to the younger

age group, indicating the need for examining the Chinese market as several segments

instead of one. All demand elasticities with respect to total expenditure, own-prices, and

cross-prices are also estimated. The own-price elasticity for apparel products is higher

when compared to home-textiles and footwear suggesting more price sensitivity in

apparel purchase.

Results from product level demand (apparel) also indicate significant effects of

household fiber choice on apparel demand. For example, households who often favor

denim as their choice of fiber for their pants spend more for their pants when compared to

households who favor artificial fibers. Expenditure and price elasticity estimates are also

significant.

Finally, a simulation that involved a 20 percent increase in per capita income, 0.05

percent increases in a ready-to-retire age group and 1.2 percent decline in family size is

conducted to examine the implications on Chinese and world textile markets. Results


vii

suggest that such changes are likely to increase the domestic textile price index and

textile consumption. In regards to the global market, the changes in Chinese socio-

economic variables considered is expected to affect positively the A-index, world cotton

production, and world mill use.


viii

List of Tables

Table1.1. Composition of annual per capita expenditure in China in 2010 ................................................. 3

Table2.1. Summary table of empirical literature review ...................................................................... 21

Table 3.1. Descriptive statistics on apparel, textile and footwear consumption for Chinese urban

households, CCI survey, 2009 .................................................................................................................... 41

Table 3.2. Frequency distribution of household demographic characteristics (sample size: 6532) ............ 42

Table 3.3. J-test for model selection among LES, LINQUAD and AIDS .................................................. 45

Table 3.4. Summary of mean square error ............................................................................................. 45

Table 3.5. Estimated parameters of participation equation for home textiles and footwear ....................... 46

Table 3.6. AIDS parameter estimates ......................................................................................................... 48

Table 3.7. Elasticity estimates .................................................................................................................... 51

Table 3.8. Marginal effects of demographic variables on demand for apparel and home textiles .............. 52

Table 3.9. Marginal effect of store choice on demand for apparel and home textiles ................................ 54

Table 4. 1. Grouping of apparel products ................................................................................................... 69

Table 4.2. Frequency distribution of respondents on basis of demographic characteristic and

product choice ............................................................................................................................................. 70

Table 4.3.Summary statistics on quantity, price and expenditure ............................................................... 73

Table 4.4. Measures of model fit and predictive power for participation equation .................................... 77

Table 4.5. Mean square error associated with in sample data ..................................................................... 78

Table 4.6. Estimated parameters of participation equation for apparel model ........................................... 80

Table 4.7. Estimated parameters of apparel AIDS model ........................................................................... 84

Table 4.8. Estimated parameters of apparel AIDS model ........................................................................... 87

Table 5.1. Linkage between textile expenditure and income( dependent variable: log(Textile

expenditure) .............................................................................................................................................. 100

Table 5.2.Parameter estimates on Chinese textile production and trade ................................................... 101

Table 5.3. Effects of changes in Chinese demographic structure on Chinese textile market ................... 104

Table 5.4. Effects of changes in Chinese demographic structure on Chinese cotton market ................... 105

Table 5.5. Effect of changes in Chinese demographic structure on World cotton market ........................ 106

Table 5.6. Effects of changes in Chinese demographic structure on U.S. cotton market ......................... 107

Table A.1. Estimated parameters of participation equation for apparel model......................................... 120


ix

List of Figures

Figure 1.1. Per capita income and textile consumption in china 2000-08 ................................................... 5

Figure1.2.China population and its composition, 1978-2009 ....................................................................... 6

Figure1.3. Sales of apparel by major retail types, 2005-10 .......................................................................... 7

Figure3.1. Utility tree for household consumption in China ...................................................................... 32

Figure 4.1. Utility tree for household apparel consumption in China ......................................................... 61

Figure 4.2. Normal and KS estimate of density function for coats (hn=0.281) .......................................... 75

Figure 4.3. Normal and KS estimate of density function for pants (hn=0.281) .......................................... 76

Figure 5.1. China vs. World per-capita fiber consumption ......................................................................... 91

Figure 5.2.Effects of Chinese demographic and economic changes on World textile and cotton

market ......................................................................................................................................................... 95

Figure B.1.Probability density function for error terms of coats market participation

equation ................................................................................................................................................... 123

Figure B.2. Probability density function for error terms of pants market participation equation ............. 124


1

Chapter One

Introduction

Recent studies underscore the importance of investigating the potential of newly

industrialized economies (NICs)1 as markets for textiles given an increase in their

domestic per capita income (Dickens). A good case for these studies could be the

Chinese textile market where economic growth, coupled with changes in population

structure, has changed the composition of textiles consumed. Various indicators have

shown a change in the structure of textile consumption in China; for example, consumers

have developed a strong preference for imported apparel over time (Zhang et al.;

Abernathy et al.). In addition, household consumption has grown by approximately 7.5%

over the last decade and is even expected to overtake Japan’s household consumption, by

some estimates, in the next decade (Hansakul). A close look at household consumption

in urban and rural areas of China indicates that clothing expenditures are one of the most

important expenses amongst Chinese households (Table1) and constitute a higher share

of the household income when compared to similar figures for the U.S ($3.5 of $100

spent by a given household in 2009 in the U.S). China is also the world’s second largest

consumer market for cotton products and is slowly closing the gap with respect to the

U.S. in market size (PCI Fibers).

1 UNIDO (2009) defines NICs as major exporters of manufacturing among developing countries

in the mid 1970s and 1980’s, with a share of manufactures in total merchandise exports exceeding

20%. These economies include, in Asia: the Chinese Economic Area (China, Hong Kong and

Taiwan), South Korea, Singapore, Indonesia, Malaysia and Thailand and India. In Latin

America: Mexico, Brazil and Argentina.


2

Despite the increasing evidence that signal the importance of China as a consumer in

global textile and cotton markets, information and research about consumers in China has

been extremely limited. Given China’s status as one of the world’s largest markets for

cotton products (Pan et al., 2005), which underscores its relative importance as a major

player in global cotton markets, trends in Chinese demographics and associated changes

in domestic consumption patterns are likely to have impacts beyond the textile industry.

The growing importance of this poorly understood component of China’s cotton demand

suggests that an empirically based analysis of China’s household consumption would

bear important insights into the future evolution of world textile demand, exports and

prices.


3

Table 1.1. Composition of annual per capita expenditure in China in 2010.

Urban Residents Expenditure

Average Low Income

Middle

Income High Income

Food 36.52 46.47 38.84 30.53

Clothing 10.47 9.45 10.96 9.95

Residence 10.02 11.61 10.02 9.54

Household Facilities 6.42 4.71 6.23 7.09

Health Care and Medical

Services 6.98 7.65 7.26 6.44

Transport and

Communications 13.72 7.99 11.63 18.36

Recreation Education, &

Cultural Services 12.01 9.46 11.47 13.48

Other 3.87 2.66 3.59 4.60


4

Table 1.1. Composition of annual per capita expenditure in China in 2010 (continued…)

Rural Residents Expenditures

Average Low Income

Middle

Income High Income

Food 41.71 47.00 43.38 34.76

Clothing 5.80 5.75 5.83 5.83

Residence 20.09 18.28 18.86 23.12

Household Facilities 5.09 5.01 5.12 5.14

Health Care and Medical

Services 9.84 8.08 9.28 12.17

Transport and

Communications 8.17 6.63 8.25 9.65

Recreation Education, &

Cultural Services 7.24 7.49 7.27 6.96

Other 2.05 1.76 2.02 2.37

Source: National Bureau of Statistics of China (NBS).

1.1 Changes in Chinese Demographic Structure

The Chinese economy has undergone many changes in its demographic topography

over the past decades. A notable change, important to tracking the economic layout of a

country, occurred in the growth in per capita income. On average, urban and rural per

capita annual income has grown by 15% and 12%, respectively, over the last three

decades (Hansakul)2. Multiple studies on consumer demand for textile products (Winkor;

Zhang et al.; Jones) have identified household income as the most important factor that

2 It’s important to note that these growth rates are from a small initial base.


5

affects textile consumption. Bearing this in mind, growth in domestic per capita income

is expected to influence textile markets. A casual observation of textile markets appears

to support a notion that a per capita income-textile consumption relationship holds in

China. Figure 1.1, shows textile consumption moving along with per capita income in

both urban and rural regions of the country since 2000.

Figure 1.1. Per capita income and textile consumption in China 2000-08.


Additionally, China is experiencing changes in its demographic structure in three

important ways: decline in household size, aging of its population, and rapid rate of

urbanization. Since the implementation of the “One Child” policy in 1979, Chinese

family size has been shrinking, from an average of 4.43 persons per family in 1964 to

4.41 in 1982, 3.96 in 1990, 3.44 in 2000, and 3.1 in 2010. Alongside the overall decline

in family size are significant changes in the age structure of the Chinese population. The

adult population, aged between 20 and 29, is expected to decrease by almost 25% from

200 to 160 million by the middle of the next decade. The adult population group

estimated to undergo the largest decline is aged 20 to 24 declining by more than half

within the next decade. In contrast, the proportion of the elderly population (65+) has

0

5

10

15

20

2000 2001 2002 2003 2004 2005 2006 2007 2008

Gro

wth

rat

e(%

)

Years

Growth in per capita income (urban)

Growth in textile consumption (urban)

Growth in textile consumption (rural)

Growth in per capita income (rural)


6

increased over the years from an average of 3.56% in 1964 to 4.9% in 1982, 6.2% in

1995, 7% in 2000, and 8.5% in 2009. Moreover, due to the dramatic fertility declines

over the last 30 years, the median age of the population is expected to increase from 30

years in 2000 to 47 in the next four decades (Wang). One effect of such a rapid increase

in size of the elderly population will be on domestic demand for textiles as the young

population is identified as the most active consumers of textiles in the literature (Wagner;

Lee et al.; Wagner and Mokhtari). A rapid rate of urbanization is also another feature of

the Chinese economy as depicted in Figure 1.2. The figure shows the urban population

growing, on average, by 3.9 percent, which is larger than the national average of 0.8

percent, while rural population has been declining both in proportion and size since 1978.

Results regarding the impact of geographic location and family size on textile demand,

however, are inconclusive in the literature with some reporting significant while others

indicating negligible effects.

Figure 1.2. China population and its composition, 1978-2009.


1.2 Changes in Shopping Habits of Chinese Households

Traditionally, the role of retailers is often limited to a link between manufacturer and final

consumers. Yet, with continuous change in consumer taste for products, the role retailers play has

0.00

20.00

40.00

60.00

80.00

100.00

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78

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80

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85

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90

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91

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Rat

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urban

rural


7

increased beyond that of intermediary over the years. In doing so, a variety of marketing

strategies are used by these retailers that ranges from expert knowledge on the product they sell

in the case of specialty stores to one-stop shopping experience for consumers in the case of

hypermarkets and department stores. Such strategies are implemented with the main objective of

increasing sales of retail store products, but little is known whether store strategies such as in-

store service, store design, and ambiance translate to increased sales of apparel and textiles.

China, with joining of WTO in 2001, has opened its retail sector to more foreign investment.

This, in turn, has resulted in a rapid increase in the numbers of western-style retail outlets (Kim

and Kincade). Many of the new retailers offer both domestic and foreign brands, and Chinese

consumers’ view shopping at these retail outlets and dressing foreign brands as superior as the

price they command is relatively higher. As shown in Figure 1.3, with an increase in the living

standard of Chinese citizens, specialty and department stores have increased their market share

for apparel products constituting over 60 percent of sales revenue in 2010 (Li and Fung Research

Centre). Another point worth noting from the figure below is that hyper and wholesale markets

have seen their share declining or unchanged over recent years.

Figure 1.3. Sales of apparel by major retail types: 2005-10.

Source: Li and Fung Research Centre (2011).

0

5

10

15

20

25

30

35

40

2004 2005 2006 2007 2008 2009 2010 2011

% R

eta

il sa

les

Years

Hyper markets

Department stores

Speciality stores

Other non grocery retailing (e.g:factory outlets, free markets) Non store retailing(e.g: internet)


8

1.3 Specific Problem

Given the relatively higher budget share of clothing of Chinese households ($5.8 for every $100)

compared to the developed world such as the U.S ($3.5 for every $100) and the rapid increase in

Chinese household income observed in recent years, domestic demand for apparel is expected to

grow rapidly. According to the National Bureau of statistics (NBS), per capita apparel

expenditure of urban households has increased on average by 12.5% to 1444.34 Yuan in 2010,

while cash expenditure on apparel of the lowest income rural households rose to 150.84 Yuan, up

by 11.9% from its 2009 value. Such a rapid growth rate, according to some analysts, is expected

to make China one of the largest markets for apparel by 2020 outpacing Japanese consumption by

over 120% (Kurt Salmon Associates as cited in Zhang et al.). Moreover, the structure of

consumption has changed significantly over recent years: sales in the high-end apparel

segments have increased by over 30 percent, higher than the growth rate in the low-end

segment (18.4%) and the national average (21.2%) in 2010 (Li and Fung Research Centre,

2011). On the other side of the picture, however, China is also experiencing a dramatic

decline in family size and an increase in the percentage of old population. Such changes

in population structure, according to previous literature, are likely to have an inverse

impact on the consumption pattern of textiles.

Despite such considerable changes in the Chinese apparel market, research concerning

Chinese household demand for textiles is currently limited. To better meet consumer

demand and understand the implications of these changes on global markets, however, it

is critical to understand the factors that shape consumer preferences in China.


9

1.5 Objectives of the Study

The general purpose of this study is to examine the effects of China’s economic growth

and the changes in China’s demographic composition on its textile, apparel, and cotton

markets. Specifically, this study:

examines the impact of the socio-economic changes, product quality attributes,

and shopping habits on the aggregated and disaggregated textile product mix, and

analyzes the effect of the changes in Chinese textile consumption pattern on

global cotton markets.


10

CHAPTER TWO

Review of Literature

The first section of this chapter provides a review of the various theoretical and empirical models

used in demand analysis. The advantages and limitations associated with each of the models are

also explained. The second section reviews research studies that have used the Global Fiber

Model (GFM) developed by the Cotton Economic Research Institute at Texas Tech

University. The GFM is also be used in this study to explain the implications of Chinese

socio-economic changes on the global cotton market.

2.1 Review of Demand Functional Forms

This section surveys four major complete demand functional forms and one incomplete

demand system: the Linear Expenditure System (LES), Rotterdam model, Translog

Demand System, Almost Ideal Demand System (AIDS), and LINQUAD demand system,

respectively. Using the pure theory of consumer behavior as a starting point, where

consumer choice of quantity consumption is subject to a budget constraint, the earliest

approach in demand analysis used was a logarithmic functional form.

Denoting q1…. qn as quantities consumed of n goods and p1….pn as the corresponding

prices, and total expenditure as the summation of expenditure on each commodity

purchased, the demand for a good is specified as:

2.1

where is the intercept; is product i income elasticity; and is the cross price

elasticity of jth

price on ith

demand. To reduce the number of variables for estimation,


11

Stone used the Slutsky equation for decomposing the cross-price elasticities. That is, the

double log equation can further be rewritten by decomposing the uncompensated cross

price elasticity using the Slutsky equation, , where

is the

compensated price elasticity and

is the budget share of good j . This derivation

has the following result:

2.2

Here, however, can be used as a general price index; based on this

specification, the demand function can be expressed in terms of real income and

compensated prices:

2.3

In estimating this model, the homogeneity restriction3 can be imposed as shown in

equation 2.4, but the adding up restrictions would not hold unless all income elastcities

are set to one. This limitation, in turn, has limited the use of this model for empirical

analysis.

2.4

In 1954, Stone was able to use the first system of demand equations that was consistent

with theoretical restrictions of demand analysis. According to the LES, proposed by

3 Homogeneity restriction relates to the fact that for any good, the sum of its own-price

elasticity, all of the related cross-price elastcities and its income elasticity should be zero.

i.e: no money illusion.


12

Klein and Rubin, expenditure on a given commodity can be specified as a linear function

of n prices and income:

2.5

where is the coefficient for the price of the specific good analyzed. The

first component of this equation is referred to as “subsistence consumption”, while the

second term on the right-hand side of the above equation is referred to as “supernumerary

income.” The LES is derived from Stone-Geary utility function:

For his analysis, Stone used British consumers’ purchased goods data over the years

1920-38 and applied the theoretical restrictions of adding up, homogeneity, and

symmetry to limit the number of parameters to be estimated. These theoretical

restrictions gave the LES an attractive feature for use in demand analysis; yet, empirical

investigations of the model revealed some limitations for use in practice. A primary

limitation is that the model does not allow net complementary4 interaction among goods.

The Rotterdam model, proposed by Thiel and Barten, is an extension to the double

logarithmic model. This model uses differentials in contrast to level logarithmic forms

used in Stone’s model. This modification has enabled researchers to overcome many of

the limitations in the double logarithmic form, especially in estimating the substitution

matrix and determining substitutes and complements from direct estimation.

4 Two goods, xi and xj, are referred as net complements if


13

Derivation of the model starts by totally differentiating equation 2.1, which has the

following result:

2.6

After incorporating the Slutsky decomposition, as in equation 2.2, and multiplying the

result with the budget share wi to impose symmetry, the final equation becomes:

2.7

The testable restrictions of this model are: Empirical

investigation of this model initially carried out by Barten and then Deaton, however,

showed that the Rotterdam model failed in satisfying the homogeneity restriction, which

triggered the search for a more theoretically consistent demand system.

Unlike the previous two demand models, the indirect Translog demand system of

Christensen, Jorgenson, and Lau uses duality theory and builds its analysis on an indirect

utility or a cost function. It specifies the log of an indirect utility function as a function of

the log of prices to expenditure ratios as shown in equation 2.8. The function satisfies

homogeneity in prices without directly imposing the restriction.

2.8

Using Roy’s identity, the Marshallian demand functions can be specified as shown in

Equation 2.9. Despite its flexible functional form, Clements and Selvanathan indicate

that the model has limited use in empirical work as parameter estimates are difficult to

interpret because of the complex form of the variables.


14

2.9

Given such drawbacks of the above models, Deaton and Muelbauer developed a model

they called the “Almost Ideal Demand System (AIDS)” that would overcome those

limitations. The authors started by specifying some arbitrary preferences which allow the

use of a representative consumer. The expenditure function, which enables the derivation

of specific utility given product prices, is specified as:

2.10

where

, and 2.11

2.12

They then derived the demand function from the cost function, which in this case would

be the budget share of the specific good:

2.13

where P is price index specified as

. Given the above budget share

equation for AIDS, the following restrictions invoke homogeneity, adding up and

symmetry properties of demand function, respectively:

2.14

Elasticities implied by the model are given as:


15

2.15

2.16

where is the Kronecker delta, defined as . The

authors used the model to estimate effects of income and prices on British post war data

on non-durable goods from 1954 to 1974. Results from the study indicate rejection of the

homogeneity restriction. Despite the consistent rejection of homogeneity in other

applications, the model has wider use in empirical work because it possesses certain

properties not shared by others. The AIDS model has the ability to test homogeneity and

symmetry restrictions, the ability to derive aggregates perfectly from a representative

consumer, and the ability to give first order approximation to any other demand system.

The LinQuad demand system, forwarded by Agnew and Lafrance, is one of the model

that has gained prominence in recent years among applied economists. The model is

linear in income and linear and quadratic in prices. The model imposes a few restrictions

on underlying preferences, which, in turn, helps in reducing computation complexity in

large data sets.

A demand function for the commodity of interest could be derived from constrained

utility maximization given the following: a vector of consumption levels for commodities

of interest x = [x1,…. xn] ; their corresponding price vector p = [p1,…. pn]’; the price

vector q = [q1,…. qm] for consumption level of all other commodities z = [z1,…. zm]’

with m ≥ 2; and income Y. The resulting demand function for commodities of interest

will have the following four properties:


16

(i) The demands are positive valued, hx (p,q,y)≥0

(ii) The demands are zero degree homogeneous in all prices and income,

hx (p,q,y)= h

x (tp,tq,ty) fr all t≥0

(iii) The n x n matrix of compensated substitution effects for x, ∂hx/∂p’+∂h

x/∂y h

x’

is symmetric, negative semidefinite, and

(iv) Income is greater than total expenditure on a proper subset of the goods

consumed, p’hx(p,q,y)<y

The first three properties are identical for both complete and incomplete demand

systems, while the last property is a feature of an incomplete demand system that

distinguishes it from complete demand systems. However, such a difference could be

removed with the use of a composite good for goods not being included in the analysis.

Expenditure on the composite good is expressed as S=q’z=y-p’x. The four properties

identified above and the budget identity will, in turn, give a quasi-expenditure function

that is increasing and concave in p, and linearly homogeneous in p and q. The quasi-

expenditure function is related to expenditure function using the following identity:

2.17

2.18

2.19

where p is the vector of prices, is an arbitrary real value function for all variables in

q, is the constant of integration, and are vectors of parameters to be

estimated. Using Shepherd’s Lemma, the demand function can be specified as:


17

2.20

And the corresponding expenditure function could be obtained by multiplying both sides

of 2.20 by commodity corresponding prices:

2.21

The advantage of this demand specification is that it is theoretically consistent and can

directly provide an exact measure of welfare. Homogeneity is imposed by using real

prices and income, symmetry is imposed in the B matrix with each element Bij=Bji; and

adding-up is always satisfied as a property of an incomplete demand system.

The standard Marshallian income and price elasticity formula for the LinQuad are;

2.22

In contrast to the complete demand models discussed above that focused only on

income and price effects, demand analysis also needs to incorporate household and

product characteristics that are likely to generate differences in consumption patterns

across household and products with different characteristics. Pollak and Wales describe

Barten as the first to pioneer the use of demographic incorporated demand systems

consistent with economic theory. Pollak and Wales identify two ways of adding this

information into complete demand systems: the first uses un-pooled data while the

second makes use of pooled data. The first method involves separating households into

different sub samples on the basis of demographic profiles and using the above demand

models for each sample separately. This approach results in different parameter estimates


18

for households with different demographic variables. Accordingly, the effect of each

demographic variable on consumption decisions could be inferred without even having

the demographic variables included in the demand model.

The second approach introduces two procedures of incorporating demographic

variables to complete demand systems: translating and scaling procedures. The first

procedure, which Pollak and Wales named demographic translation, introduces N

translation parameters into each demand system and assumes that the demographic

variables affect expenditure only through its effect on these parameters. Starting from a

basic cost function of a reference household, demographic variables are introduced into

the cost function by adding or deducting fixed costs for the household with additional

demographic features as compared to a reference household. That is, if the original

demand system is given by qi (u, p), the translating procedure modifies this for the

household with demographic features of as:

2.23

where the P, q, and M denote prices, quantities and expenditure, respectively. The “d”s,

on the other hand, is parameters that depend on demographic variables, and their

functional relationship is specified as:

2.24

A change in causes a reallocation of expenditure among consumption goods while

leaving total expenditure constant. Their second procedure, named demographic scaling,

involves modifying a given expenditure function by substituting each price by a function

that includes all prices and demographic variables. The resulting expenditure function


19

depends on all prices and demographic variables. Pollak and Wales’ empirical

investigation included the number of children and ages as demographic variables, and

used 1966 and 1972 data from the British Family Expenditure Survey series for analyzing

consumption decisions on food, clothing, and miscellaneous categories. Results from

their study strongly supported the inclusion of these variables. Food and clothing budget

shares increased with family size, while the miscellaneous share decreased implying

reallocation of expenditure. Child’s age was also found significant in the model,

affecting directly the budget share for food and clothing and inversely affecting for

miscellaneous. The scaling procedure resulted in a higher likelihood function value as

compared to the translating procedure for both quadratic and translog models.

Given the limitation of each of the demand models discussed above, this study nests two

of the complete demand systems (the LES and AIDS) with the LINQUAD model and

tests using a statistical procedure to identify the model that best fits in the household data

used in this study.

2.2 Review of Empirical Literature on Textile Demand

Demand for textile products has traditionally been investigated by using either per

capita textile consumption or the textile budget share as a dependent variable. The

selection of factors influencing consumption, on other hand, has largely been based on

economic theory or previous research findings. Research on textile expenditure has

identified household income as one of the most important determinants affecting textile

consumption. Results from these studies have found income elasticities ranging between

0.41 and 2.5, depending on the textile products considered and the demographic group

analyzed as shown in Table 2.1.


20

Table 2.1. Summary table of empirical literature review.

Results Model used Data used Author

Income elasticity range : 0.5-2

Price elasticity range : 0.37-1.1

Single equation model

on clothing

U.K 1987-

2000 , time

series

Jones and

Hayes (2002)

Income elasticity : 0.48-0.5

Price elasticity : 1-1.9

Single equation on

clothing expenditure

U.S 1929-

1987

Mokhtari

(1992)

Expenditure elasticity: 1.01 for girls -2.01 for fathers

Lower expenditure for older children than younger ones

Mothers education increases expenditure on herself and father

No significant effect of fathers education

No significant effect of mothers or fathers age on their own expenditure

Young mothers with increased expenditure on girls and older mothers on

boys

Young fathers with increased expenditure on mothers and boys and older

fathers with decrease expenditure on boys

Single equation on

clothing ( double log)

CES1986 Nelson (1989)

Income elasticity: 0.4-0.62

Women headed household spend more on apparel than male headed

households

Household heads with more education have higher expense on apparel

than those with less education

Negative or no significant effect of household size

Single equation on

clothing ( double log)

CES 1990 Wagner and

Mokhtari

(2000)

Income elasticity: 0.72-0.80

Positive effect of family size

Negative effect of household head age

Single equation on

home textiles

CES1973 Wagner (1986)


21

Table 2.1. Summary table of empirical literature review (continued …)

Results Model used Data used Author

Positive effect of a younger child at home( aged less than 6)

Expenditure elasticity : 0.41-1.19

Price elasticity: 0.33-3.38

Higher expenditure by women compared to men

Systems of

equations on

clothing

American

Shoppers Panel

Survey(1990-

1999)

Fadiga, Misra, and

Ramirez (2005)

Positive effect of age on men’s and boy’s clothing and shoes but

insignificant effect on women’s and children’s clothing

Expenditure elasticity : 1.10-1.16

Price elasticity: 0.39-0.89

Systems of

equations on

clothing and

footwear

U.S 1929-94 from

NIPA(National

Income and

Products

Account)

Kim (2003)

Decline on apparel spending with increase in age

Increase in apparel spending with education

Single equations on

clothing

CES 1991 Lee, Hanna, Mok ,

Wang (1997)


22

Winakor, using survey data from three states (Nebraska, Iowa, and Illinois), found that

expenditure elasticities were inelastic for household textiles. Supporting results were also

reported by Wagner from his analysis of consumer expenditure survey data from 1973.

Results in regards to apparel products, on other hand, indicated mixed findings. Nelson,

using U.S. expenditure survey data from 1985, found higher expenditure elasticities for

apparel products with the fathers’ apparel having the highest elasticity when compared to

the children’s and mothers’ apparel. Higher apparel expenditure elasticities were also

reported from time series studies on U.K. consumers by Jones and Hayes and on U.S.

consumers by Kim. Fadiga, Misra, and Ramirez, on other hand, found lower expenditure

elasticities for some apparel products when analyzing the products individually rather

than as a group.

Other important demographic variables identified in the literature include age of the

household members, family size, family composition, geographic location, and the

gender, education, and occupation of the household head. Elderly consumers were

reported to spend less money in general for both home textiles and apparel (Wagner; Lee

et al.; Wagner and Mokhtari). Lee et al. focused on the influence of age, especially of the

elderly, on the demand for apparel. Using a nonlinear demand system and a life cycle

approach5 to consumption, they found declining expenditure levels for apparel after the

age of 68, which they attributed largely to increase expense for health and other age

related services. Similar results were also reported by Wagner and Mokhtari in their

analysis of quarterly U.S. household apparel expenditure. The effect of marital status on

textile expenditure is difficult to ascertain from the literature as some reported a

5 The life cycle hypothesis states that consumption decisions of households depend not

only on current income, but also on future anticipated circumstances.


23

significant and positive effect on textile expenditure (Wagner; Lee et al.) while others

reported no statistically significant effect (Wagner and Mokhtari).

The effect of family size on textile consumption was inconclusive in the literature with

some reporting a positive correlation (Winakor; Wagner), while others an inverse effect

(Wagner and Mokharti). Though the larger a family the more the textile requirements, the

effect might be minimized owing to the fact that economies of scale may be operative in

a large family as reported in Wagner and Mokharti. Winakor found that expenditure on

household textiles increased by $3.40 for each additional family member, while Wagner

and Mokharti reported an inverse relationship between apparel expense and family size

during the winter season and no statistically significant relationship for other seasons.

The effect of family composition indicated higher textile expenditure for both apparel

and home textiles for each female member in a household when compared to a male

counterpart. Winakor reported that expenditures for household textiles increased by a

different amount for each additional adult woman in a family: by $35 for farm families

and $45 for city families. Supporting results for apparel expenditure were also reported

by Nelson in his analysis of individual clothing consumption within a household.

Clothing expenditures on boys constituted only 81% of the spending on girls, while

expenditure on fathers’ clothing constituted only 62% of expenditures on mothers’.

Wagner also reported a significant effect of age of the youngest child on home textiles,

with families having children under six spending more on home textiles than families

with no children below six. In regards to gender, female headed households, in general,

spent more on apparel expenditures than did male headed households (Wagner and

Mokharti; Lee et al.; Fadiga, Misra and Ramirez).


24

On another dimension, evaluation of the effects of product attributes indicates that a

product’s price as the most important determinant in textile consumption. Fadiga, Misra,

and Ramirez reported demand for apparel products is own-price elastic. Cross-price

elasticities among apparel products reported were less than one and negative implying

complements. Supporting results were also reported by Mokhatri from his time series

analysis of U.S clothing expenditure and by Jones and Hayes from their time series

analysis of U.K clothing consumption. Other product attributes identified important in the

literature include fiber content (Fadiga, Misra, and Ramirez) and product origin (Zhang et

al.). Fadiga, Misra, and Ramirez reported higher expenditure shares for most apparel

products, with exception of male slacks, with 100 percent cotton than products with less

than 50 percent cotton blend. Zhang et al found country of origin as an important

attribute in the consumer’s purchasing decision.

Much of the economic studies discussed above were conducted for households living in

the developed world and there exist limited information on households’ textile

consumption behavior regarding the developing world. This study helps in bridging this

gap as it mainly focuses on Chinese households’ textile consumption patterns, one of the

largest and most rapidly developing part of the world. In addition, most of the studies

reviewed above, with exception to Kim; and Fadiga, Misra, and Ramirez work, are not

presented in a system of demand equations framework. This study attempts to address

those limitations and develops household demand models for textile products using a

framework consistent with economic theory. In addition, this study analyzes the demand

for textiles at a disaggregate level, thus identifing potential relationships between textile

products.


25

2.3. Review of the Global Fiber Model

The global fiber model was mainly developed to explain the impacts of global trade liberalization

on the competitiveness of U.S. cotton industry. To this effect, the model incorporates inter-fiber

competition between artificial and natural fibers in textile mill use, regional production

heterogeneity within major cotton producing countries and a linkage between upstream and raw

fiber sector for major cotton producing countries. The partial equilibrium model includes

production, demand, ending stocks and market clearing conditions for both cotton and artificial

fibers for 24 countries analyzed within the model.

Cotton production, Equation 2.25 through 2.27, is specified as a product of yield (YLDi) and

harvested area (ACRi). Cotton harvested area (ACRic) in the i

th region is specified as a

function of the ratio of expected net return of cotton (ENRic) to competing crops (ENRi

o)

and a time trend (T). Previous year’s net return is used as a proxy for expected return in

the current period. Assuming constant returns to scale in production, it is modeled as a

function of a lag of rainfall (LRFi), expected farm price ( ), and a time trend (T).

PRDic= YLDi

c * ACRi

c, 2.25

, and 2.26

2.27

The next step involves estimation of fiber demand models. The demand for all fibers

(DMfi) is specified as a function of a constant term for autonomous consumption (DMi),

the fiber price index (FPRi), and gross domestic product (GDPi):

2.28


26

In the second step, total fibers are divided into cotton, wool, and man-made fibers.

Demand for cotton ( ) is, thus, a product of aggregate fiber demand and a proportion

of cotton in total fiber use. A share equation for cotton (DSc) is modeled as a function of

the ratio of the domestic price for cotton (PDci) and the domestic man-made fiber price

(PDm

i):

2.29

2.30

For man-made fibers, the supply function (PDRim

) is modeled through the estimation of

production capacity (CPTi) and capacity utilization (CPUi). Man-made fibers production

capacity is modeled as a function of the lag of the man-made fiber domestic price

(LPDm

i), the lag of the oil price (LPDli) and a lag of capacity (LCPTi):

2.31

Total capacity utilization, on the other hand, is modeled as a function of a ratio of the

current domestic man-made fiber price (PDm

i) and current oil price (PDli) and a lag of

capacity utilization (LCPUm

i):

. 2.32

Multiplying production capacity by capacity utilization yields total man-made fiber

production:

2.33


27

Cotton export demand (XPDi) is modeled as a function of the ratio of international cotton

price (Pw) converted to domestic currency by applicable exchange rate (XRi) and

domestic cotton prices (PDci):

2.34

The import demand equation for cotton (IMDi) for cotton is expressed as a function of

international cotton price, exchange rates, tariff rates (ti), and quota restrictions:

2.35

Domestic market equilibrium is obtained by equalizing demand and supply side

equations: ending stocks plus domestic demand and exports equal beginning stocks plus

production and imports:

2.36

Solving this equilibrium yields the domestic price for cotton. The world price for cotton

(A-index), the cotton textile price index, and man-made fiber price are solved by

equalizing world imports ( to world exports ( ).

2.37

Much of the research studies based on this model focus on changes on the supply side policies.

Notable among these include the Pan et al. (2007) study that analyzed removal of domestic

subsidies and border tariffs for cotton in the international market; Fadiga et al. (2008) who

examined the impact of unilateral removal of the total U.S aggregate measure of support (AMS)

and a multilateral trade reform where U.S AMS payment reductions are matched by multilateral

tariff and subsidy elimination from the rest of the world; and Pan et al. (2006) who looked into


28

the effects of Chinese currency evaluation on the world fiber market. This study, on the other

hand, will use the global fiber model to analyze effects of changes in Chinese textile demand

structure on global fiber market.


29

Chapter Three

Choice of Retail Outlets and Chinese Household Demand for Textiles

and Footwear: Analysis Using Alternative Demand Functional Forms

Introduction

This study focuses on the potential impact of households shopping habits and socio-economic

profiles on the demand for textiles. Research regarding the impact of household shopping

behavior on demand for textiles is limited. Quantifying the impact is important for both retailers

looking for optimal marketing strategies and manufacturers considering choice of distribution

channel for their products. Besides the changes in the structure of retail outlets for textiles,

marked changes have occurred in the socioeconomic profile of Chinese population in

recent years. These changes are likely to have important implications not only for the

textile industry, but also for upstream production sectors. Given this fact, the study looks

in the effects of changes in the socioeconomic structure of Chinese population on demand

for textile products. The research, along the way, implements a statistical model that nests

three demand functional forms (LES, AIDS and LinQuad incomplete demand systems) to

choose the functional form that best fits the data.

The major objective of this study is to determine whether household choice of retail outlets has an

impact on Chinese demand for textiles. In particular, this study attempts to answer two

questions:

How Chinese aggregate demand for each category of textile (apparel, home

textile, and footwear) is affected by household choice of retail outlets, and

changes in prices?


30

How does the change in Chinese demographic structure (increased proportion of

elderly, family size) affect Chinese aggregate expenditure shares for apparel,

home textiles, and footwear?

3.1 Conceptual Framework

In constructing a demand model for commodities, a two-step budgeting structure is

used as illustrated by Deaton and Mullbauer (1980). Preferences are assumed weakly

separable across broad consumer goods, and weakly separable across time. As shown in

Figure 3.1, the consumer allocates his expenditure to broad group of products such as

food, clothing or housing in the first stage. And in second stage, expenditures allocated

for broad groups are reallocated again among elements of the broad group in such a way

that the preference structure within the sub-utility functions are determined independent

of goods belonging to other broad group. Given such a structure, the consumer’s utility

function can be specified as:

U [Q1, Q2… QN),] =U [u (q1), u (q2)… u (qN)] 3.1

Where U [.] is utility from the broad commodity groups while u (.) is a sub-utility

function within the broad group. Using Price (PB =YB/QB) and quantity (QB=u (q))

indices for a broad group as in Lewbel (1989), utility maximization for a broad group can

be written as:

Max U [u (q1), u (q2)… u (qN] s.t ∑ PB QB = YB 3.2

The solution to this problem gives us Marshallian demand for each broad group and the

corresponding expenditure associated for each broad commodity group. Given optimal


31

expenditure for each broad group, the consumer maximizes its sub-utility in the second

stage.

Max u (q) s.t ∑ pi qi= YB. 3.3

The solution to this problem gives us Marshallian demand for each element in the broad

commodity group. The advantage of such a structure is that it avoids the need to include

all commodities consumed by a household in estimating demand for a specific consumer

good or set of goods.

As the above framework puts no restriction on the sub-utility functions in analyzing

effects of socio-demographic factors, this process is used to develop the empirical

econometric procedure to recover elasticity estimates for textile consumption in China. A

diagrammatical explanation of the procedure is shown in Figure 3.1.

Household Consumption Expenditure

Textile and Footwear Expenditure Non- Textile & Footwear Expenditure

Apparel Footwear Home-Textiles

Figure 3.1. Utility tree for household consumption in China


32

3.2 Methods and Procedures

Demand systems (here after called systems) estimation most often addresses the

optimal allocation of goods and services and is concerned with how changes in income,

relative commodity prices, and tastes and preferences affect the consumers’ choice of

goods in a given period. Its history in applied work, according to Stigler as cited in

Clements and Selvanathan, can be traced back to Engle’s famous budget study of 1857.

Yet, it was almost after 100 years that Klein and Rubin developed a linear expenditure

system (LES) that was in line with the basic principles of consumer utility maximization.

Stone pioneered its empirical use, which has resulted in a breakthrough in the study of

demand analysis. His analysis was able to reduce the number of parameters to be

estimated and he was also able to test whether the resulting functional forms satisfy the

basic theoretical properties of demand functions. A number of models have also been

developed which include, the Rotterdam model (Thiel; Barten), the Translog model

(Christensen, Jorgensen and Lau), the Almost Ideal Demand System (Deaton and

Muellbauer), and the LinQuad incomplete demand system (Lafrance and Agnew).

These models were developed after the LES to circumvent problems associated with

previous models. Yet, the advantage that these models exhibit also prevents them in

certain ways from satisfying the theoretical conditions. For example, the great attraction

of the LES in empirical use relates to its linearity, simplicity and economy of

parameterization, which makes it easy to use and also satisfy regular conditions globally,

but fails from modeling behaviors having complex functional forms. On the other hand,

relatively complex demand functional forms are able to approximate fairly complicated

and flexible behaviors. But, their flexible and complex nature makes results from these


33

models difficult to interpret (Clements, Selvanathan) and sometimes these models are not

well behaved globally (Guilkey, Lovell and Sickles).

Given the above limitations, Alston and Chalfant discuss the importance of using a

statistically based model selection to explain consumption patterns of households.

Relying on the Alston and Chalfant argument, this study compares three common

demand specifications for analyzing Chinese household allocation of total textile

expenditures among broad categories of textile products and footwear. Three

approaches for comparison have been proposed in the literature. The encompassing

model, the J test, and the Cox test in choosing the better performing model on statistical

grounds (Greene). For the purpose of this analysis, the J-test approach initially proposed

by Davidson and Mackinnon is used because of its ease of implementation.

According to Davidson and Mackinnon, when two competing models, say A and B

exist, the following compound procedure can be used in choosing between the models.

Hypothesis 0: (Model A) 3.4a

and

Hypothesis 1: (Model B) 3.4b

where Xi and Zi are vectors of observations on exogenous variables, are vectors

of parameters to be estimated, yi is the ith

observation on the dependent variable, and

are assumed to be NID (0, σ02)6 . Here we assume that H1 is not nested within

H0 and H0 is not nested within H1. Thus, the truth of H0 falsifies H1, and vice versa. The

J-test procedure for testing the validity of model A or B is:

6 NID refers that observations are identically and independently distributed.


34

3.5

where is and is the maximum likelihood estimates of . If model A is true

then the value of is zero. The primary problem with this structure is that both models

could be accepted or rejected, which Davidson and Mackinnon attribute as a finite sample

problem.

Before proceeding to the estimation stage, as described below in the data description,

the data set used for this study has some zero observations for home textile and footwear

budget shares. The non-consumption can be as a result of a corner solution7 or due to

infrequency of purchase, or can be as a result of no taste for the product. In such cases,

estimation that fails to accommodate the censoring of the data set will result in biased

parameter estimates of the demand system, and excluding the null observations also

causes inefficiency and sometimes inconsistent results if positive observations are not of

a random sample nature (Lee and Pitt). Shonkwiler and Yen (henceforth, SY) proposed a

two-step estimation procedure to overcome this problem. Under this procedure, a probit

regression is first estimated to determine whether a household is participating in the

market or not. Coefficients of the explanatory variables in the participation equation are

estimated to calculate estimated values of a standard normal density function

and the corresponding normal cumulative distribution function , respectively.

3.6

7 A solution to a consumer utility maximization problem in which zero consumption of at

least one good of the bundle of goods in a basket is a solution of an agent’s maximization

problem.


35

where subscripts i and h denote, product and household observation, are

observed dependent variables,

are corresponding latent variables,

are vectors of exogenous variables, are parameter vectors, and are

random errors, respectively. Following the calculation of cumulative and density

functions, the final demand equation is estimated by incorporating the cumulative and

density functions to correct the selectivity bias as described in the Equation 3.8:

For the positive consumption levels, the conditional expectation is:

3.7

and for a zero consumption level, . Thus, the expectation of

is:

3.8

here, for ith

equation and jth

observation, yij is the observed dependent variable while

is the random error. Furthermore, to incorporate shopping behavior in demand models

such as choice of retail outlets where these products are sold, Ray’s specification of a


36

‘basic’ equivalence scale was used. The scales were normalized at unity for a household

that shops at non-chain or independent stores. The equivalence scale used here is constant

across price distributions and utility levels, thus the modified system with shopping

behavior incorporated is theoretically feasible given that the original model is feasible.

Starting from a household expenditure function in terms of a reference expenditure

function that represents a household who shops from independent stores, a given

household expenditure can be specified as:

3.9

where is the cost function for the reference household, is a general

equivalence scale formulated as where and are retail

choice by households and their coefficients, respectively. Respective demand forms

(LES, AIDS, and LINQUAD) in the second stage with inclusion of demographic

variables and product attributes are discussed below. Pollak and Wales (1992) modified

LES specification in budget share form as:

for home-textiles, and

for apparel 3.10

where the p’s denote prices, M denotes total textile expenditure, ‘s denotes the

translating demographic variable, w’s denote the budget share devoted to good i and


37

is the equivalence scaling factor. The AIDS model, after taking logs of household

expenditure as suggested by Deaton and Muelbauer, has the following form:

3.11

for home-textile

for apparel . 3.12

Where P is the price index specified as

.

Lafrance, Beatty and Pope have proposed the following specification for transforming the

LINQUAD in budget share form with inclusion of demographic variables and shopping

behavior

for home textiles and footwear

for apparel. 3.13

Each model is first estimated separately. A compound model incorporating two of the

above models is then estimated to determine the “best” model. For instance, in the first


38

step, the LES model is estimated separately. Fitted values from the LES are then nested

in the compound model involving the AIDS and the LES as shown in Equation 3.14a. If

the AIDS model is true, then the coefficient for fitted values of the LES ( ) will not be

statistically significant. As a result, six compound models are estimated: the compound

model incorporating the AIDS and fitted values of the LES and fitted values of

LINQUAD, the compound model incorporating the LES and fitted values of the AIDS

and fitted values of the LINQUAD, and the compound model incorporating the

LINQUAD and fitted values of the AIDS and fitted values of the LES:

3.14a

3.14b

3.14c

In deriving the elastcities and the marginal effects, the following derivation is used.

Using the AIDS model specified in equation 3.12 as an example, own-price elasticity for

home-textile can be computed as:

3.15

Where


39

Here refers to the first step (probit) equation of the estimation process.

3.3 Data Source and Description

The data used for this analysis is obtained from a 2009 cotton consumer tracking study

conducted by Cotton Council International through a local market research firm in China.

In total, a survey response from 6532 households in 24 cities from 4 waves is used for

this analysis. The main content of the survey is based on household heads ages 15-54 that

lived in the city for at least one year. The data set includes extensive household

demographic profile information associated with textile consumption, quantities, and

prices. In total, there are 1,378 variables describing each household.

The data on textile consumption for households in the survey are aggregated into three

categories: apparel, home textiles, and footwear. Here, the data set used has some zero

observations for quantity consumed and prices for items not consumed by a particular

household. To estimate a complete system, however, observations on prices for all goods

for all households must be available. A regression model is used to impute data for

missing prices. That is, prices for those households consuming each textile product are

regressed on household characteristics and regional dummies. The regression is then used

to estimate the missing prices for households with corresponding explanatory variables

but who were not consuming the textile product. Demographic variables used include

household income, age, occupation, education, and sex of the household head, and

household size.


40

During 2009, apparel is purchased by nearly all households (99.68%), footwear is

purchased by over 75.5% of the households, and only 38% of the households made a

purchase of home textiles. Table 3.1 provides a descriptive statistics of the data used in

the analysis. On basis of average prices, home textiles are the least expensive; while

apparel has the highest price variability and is the most purchased. A majority of

households (71.56%) in the sample have a monthly income in the range below 5000

Yuan ($793) and have a family size over two people (78%) as shown in Table 3.2. Over

70% of the sampled households have completed primary school, and over 65% of the

sampled households belong to the young demographic group (aged between 20-40). Also

from the data set, most of the sampled households (33%) shop for their textile and shoe

products from department stores. Over one third of sampled households have reported

having children of a younger age (<15 years of age), while over 16% of the sampled

households have reported having an adult over age 55 in their home.


41

Table 3.1. Descriptive statistics on apparel, textile and footwear consumption for Chinese

urban households, CCI survey, 2009.

Variable Mean Standard

Deviation

Quantity

Apparel (consuming households: 99.69% of the sample)

Textile (consuming households: 38% of the sample)

Foot wear(consuming households: 75% of the sample)

5.2381

1.0346

0.9984

4.9068

1.7743

0.8202

Retail price

Apparel

Home Textile

Foot wear

56.0995

27.8487

58.8522

63.6815

36.8369

48.1609

Expenditure (Yuan per household per year)

Apparel

Home textile

Footwear

361.8202

260.1946

35.7408

65.8847

562.5819

419.4281

201.5575

127.2654

Budget share

Apparel

Home Textile

Foot wear

0.7359

0.0667

0.1973

0.2088

0.1295

0.1859


42

Table 3.2. Frequency distribution of household demographic characteristics (sample size:

6532)

Description Categories Frequency Percentage

Age of household

head

Less than 20

20-29

30-39

40-49

Over 50

461

2046

1783

1541

701

7.06

31.32

27.30

23.59

10.73

Education ≤Primary

Junior, Technical &high school

College and over

1939

655

3938

29.68

10.03

60.29

Income < 5000 Yuan

5000-10000 Yuan

10000-15000 Yuan

Over15000 Yuan

4674

1586

204

68

71.56

24.28

3.12

1.04

Gender Male

Female

2622

3910

40.14

59.86

Retail

choice(apparel)

Department store

Chain and specialty stores

Warehouse

Small Independent (non-chain,

tailor made)

Others(discount stores, internet,

factory outlet, TV home

shopping, door to door, bazaars)

2833

2185

487

652

375

43.37

33.45

7.46

9.98

5.74


43


6532) continued....

Retail choice (home

textile)

Department store


Warehouse

Small Independent (non-chain)


factory outlet, TV home

shopping, door to door, bazaars,

none)

2062

790

902

278

2500

31.57

12.09

13.81

4.26

38.27

Retail choice

(footwear)

Department store


Warehouse

Small Independent (non-chain)


sporting stores, factory outlet, TV

home shopping, door to door,

bazaars)

2725

2629

375

386

417

41.72

40.25

5.74

5.91

6.38

Household size Less or equal to 2

3-5

Over 6

1397

4929

206

21.38

75.46

3.17


44


6532) continued....

Family composition

Number of families with child

age less than 7 years

Number of families with child

aged between 7-14 years

Number of families with adult

aged (15-54)

Number of families with people

aged Over 55 years

1208

1257

6524

1063

18.49

19.24

99.88

16.27

3.4 Results and Discussion

3.4.1 Model Selection

The J-test procedure is used to determine which demand model best fits consumer

purchasing behavior on a statistical basis. As can be observed from Table 3.3, reported t

statistics associated with the each of the models analyzed in this study in all of the

compound models is significant indicating the addition of each model to competing

models results in statistically significant improvement in model fit. The mean square

error, as a result, is used as a criterion for model selection. The AIDS model has the

smallest mean square error as compared to the LES and LINQUAD as shown in Table

3.4. Empirical results presented, as a result, are based on the AIDS model.


45

Table 3.3. J-test for model selection among LES, LINQUAD and AIDS

Alternative

Hypothesis (H1)

LES (H0) AIDS(H0) LINQUAD(H0)

LES - 0.3694**

(0.0452)

(8.18)

0.9696**

(0.0047)

(204.38)

AIDS 0.9095**

(0.0232)

(39.13)

- 0.9657**

(0.0046)

(216.88)

LINQUAD 0.1474**

(0.0118)

(12.48)

0.1002**

(0.0092)

(10.89)

-

Note: the first element in each row is the value of ’s for the alternative hypothesis, the

second is the standard error, and the third is T- test statistic. ** Significance at 5%.

Table 3.4: Summary of mean square error

Model Apparel budget share Home-textiles budget

share

LES 0.0409 0.0171

LINQUAD 0.2607 0.0154

AIDS 0.0379 0.0150

3.4.2 Empirical Results

Results of the probit model presented in Table 3.5 indicate that all of the price and

expenditure variables are statistically significant at 5% level, while only 13 out of 32

coefficients for demographic variables are statistically significant at 10%. All own- and

cross-price coefficients are negative indicating the probability of textile purchase is

influenced by not only its own price but the price of related textiles and footwear

products. The coefficients for total expenditure in both home-textiles and footwear

purchase decisions are positive and significant, indicating positive correlation between

purchase decisions of textiles with income.

Among the store choice variables, parameter estimates for department stores in footwear,

chain stores in home-textiles and footwear, hypermarkets in home textiles, and stores


46

under the “others” category in home-textiles are all statistically significant. Effects of

shopping behavior such as the decision of where to purchase textile products indicated

that the probability of home-textile purchase is positively related to chain stores and

hyper-markets, while the probability of home-textiles purchase is inversely related to

stores categorized under the “others”. A purchase decision of footwear, on other hand, is

positively related to department stores and chain stores.

Table 3.5. Estimated parameters of participation equation for home textiles and footwear

participation equation parameters Home textile Footwear

Intercept

Apparel price

0.3476**

-0.0045**

0.4789**

-0.0039**

Home textile price -0.0038** -0.0064**

Footwear price -0.0029** -0.0027**

Income 0.0011** 0.0017**

Age 15-29(reference over50) -0.0550 0.1966**

Age 30-39 0.0653 0.1032

Age 40-49 0.0203 0.0231

Single -0.1651** 0.1159*

Divorced/widowed 0.0876 0.0395

Technical, junior, high school(reference Primary education) 0.0420 0.0555

College and over 0.0779* 0.1104**

Gender(reference female) -0.5363** 0.0170

Department store(reference independent store) 0.1194 0.1278*

Chain/specialty store 0.2147** 0.1449*

Warehouse 0.2275** 0.0949


47

Table 3.5. Estimated parameters of participation equation for home textiles and footwear

(continued...)

Others stores -1.1288** 0.0172

Child less than 7 year old(reference age b/n 15-54) -0.1053* -0.1369**

Child between 7-15 -0.0510 -0.0348

Old over age 55 0.0477 -0.0513

Family size -0.0302 -0.0105

Note: * indicates significance at 10% level while ** significance at 5% level

Effects of demographic variables on purchase decisions were also estimated. Results

indicate that the probability of home-textiles purchase is lower if the household head is a

man, is single, or has a child less than 7 years old. Footwear purchase probablity, on the

other hand, is positively related to household heads in the age group 15-29, to single

households, or to household heads who finished some college education, while it is lower

in households with a younger child (aged less than 7 years).

Results from the probit model are used to calculate the inverse Mills ratio that is

incorporated in the structural component of the model. Table 3.6 presents parameter

estimates for the AIDS model. Theoretical restrictions of homogeneity and symmetry are

maintained during estimation, but the theoretical restriction of adding up on parameter

estimates, in general, does not hold as is the case in many demand studies using the AIDS

model. As a result, estimation on budget equations is made by treating the footwear

category as a residual good as suggested by Yen, Lin, and Smallwood and estimating n-1

equations with an identity . Many of the coefficients on demographic

variables are not statistically significant. Own-price, cross-price, and total expenditure

coefficients are all statistically significant. In regards to shopping behavior such as retail

choice, only the coefficient for the“others” store is significant. This result, in turn,


48

indicates that under price invariance of the scale, households who often purchase from

locations categorized under “others”, which includes bazaars, discount stores, internet

purchases, and factory outlets, have lower expense for apparel products when compared

to households who purchase from independent, department, and chain stores. Households

who often purchase from chain, department and independent stores also have a lower

expense on home-textiles when compared to customers who buy from stores under the

“others” category.

Table 3.6. AIDS parameter estimates

Variables Apparel Home textile

Intercept 0.8820** -0.0440

(0.0382) (0.0522)

Age 15-29 (ref:over50) 0.0171 -0.0776**

(0.0314) (0.039)

Age 30-39 0.0144 -0.0192

(0.0364) (0.0450)

Age 40-49 -0.0170

(0.0328)

0.0321

(0.0404)

Technical, junior, high school ( ref: Primary education) -0.0275 0.0091

(0.0415) (0.0517)

College and over 0.0099 -0.0376

(0.0229) (0.0275)

Child less than 7 year old( ref: no dependent) 0.0061 0.0355

(0.0283) (0.0349)


49

Table 3.6. AIDS parameter estimates (continued...)

Child between 7-15 0.0664** -0.0720**

(0.0294) (0.0366)

Old over age 50 -0.0101 0.0218

(0.0276) (0.0342)

Gender(ref: female) -0.0608** 0.0165

(0.0209) (0.0261)

Family size 0.0043 -0.0017

(0.0096) (0.0112)

Home textile price -0.0054** 0.0464**

(0.0025) (0.0038)

Apparel price 0.0521 ** -0.0054**

(0.0035) (0.0025)

Expenditure -0.0492** 0.0608**

(0.0030) (0.004)

Department store 0.0591 0.4340

(0.3760)

(0.1650)

Chain store 0.0352 0.1821

(0.3345)

(0.1661)

Warehouse -0.0753 0.0461

(0.1954) (0.2926)

Others stores -0.3493** 3.3160**

(0.1501) (1.2408)

Note: * indicates significance at 10% level while ** significance at 5% level


50

Table 3.7 presents price and expenditure elasticity estimates along with their associated

bootstrapped standard errors. Lazaridis provides an approach to deriving elasticities from

Heckman type models as estimated here. Engle, Cournot, and Euler aggregations are used

in computing expenditure and uncompensated elasticities for footwear products, which is

a residual good in the AIDS model. Compensated elastcities, on the other hand, are

calculated using the Slutsky equation. All uncompensated own-price elastcities are

negative and significant at the 5% level. All expenditure elasticities are also significant

and positive. In line with results found in previous studies (Mokhtari; Jones and Hayes;

Fadiga, Misra and Ramirez), apparel products have a higher own-price elasticity (-0.82)

as compared to home-textiles (-0.62) and foot-wear (-0.21). All cross-price elastcities are

also significant with exception to home-textile prices on footwear products. The cross–

price elasticities suggest that apparel is a gross-complement for home-textile, and foot

wear, and vice versa. Expenditure elasticities are within the range found in previous

studies. Estimated expenditure elasticities indicate footwear (0.78) has the lowest

expenditure elasticity compared to home-textiles (1.55) and apparel (0.91). Based on

these results, all the textile products can be described as normal goods. In regards to the

compensated elastcities, all with exception to apparel price on footwear, home-textile

price on footwear, and footwear price on footwear are significant. Compensated cross-

price elastcities indicate net substitution between apparel and home-textiles.


51

Table 3.7. Elasticity estimates

Price

Expenditure

Product Apparel Home textile Footwear

Uncompensated elasticity

Apparel

-0.8197**

(0.0043)

-0.0218**

(0.0004)

-0.0661**

(0.0018)

0.9078**

(0.0022)

Home textile

-0.4154**

(0.0250)

-0.6213**

(0.0194)

-0.5119**

(0.0309)

1.5487**

(0.0450)

Footwear

-0.5884**

(0.0185)

0.0127

(0.0471)

-0.2062*

(0.0910)

0.7820**

(0.1283)

Compensated elasticity

Apparel

-0.2298**

(0.0045)

0.1201**

(0.0031)

0.1097**

(0.0031)

Home textile

0.6396**

(0.0293)

-0.4148**

(0.0182)

-0.2248**

(0.0299)

Footwear

-0.0082

(0.0361)

-0.0205

(0.0894)

-0.0028

(0.0894)

Note: bootstrap standard errors in parenthesis. Single asterisks (*) indicates significance

at 10% level while double asterisks (**) significance at 5% level.

With regards to marginal effects of store choices, results from this study indicate that

households who bought their apparel from department and chain stores bought more

apparel when compared to households who buy their apparel from hypermarkets,

independent stores, and stores in the “others” category. Also on basis of results from

Table 3.8, households who bought their home-textiles from department and stores in the

“others” category, on average, bought less of home-textiles when compared to


52

households who bought their home-textiles from independent stores, chain stores, or

hypermarkets.

Table 3.8. Marginal effect of store choice on demand for apparel and home textiles

Apparel Home-textile

Store choice Quantity Quantity

Department Store 0.0342** -0.1466**

(0.0126) (0.0087)

Chain Store 0.0206** 0.1395**

(0.0008) (0.0084)

Warehouse/Hyper market -0.0467** 0.2590**

(0.0017) (0.0107)

Others -0.2565** -2.562**

(0.0094) (0.0908)



Marginal effects of demographic variables, as shown in Table 3.9, are significant in

almost all cases. Consumption of apparel products is significantly lower for the (15-29)

age group when compared to the reference category age group (50-54), while

consumption of apparel is significantly higher for individuals in the age group of 30-39

and for age group of 40-49 when compared to the reference category. Consumption of

home-textiles, on the other hand, is significantly lower for all age group (15-29, 30-39,

and 40-49) when compared to the reference age group (50-54). Mid-age group

households (30-39) have less expense on home-textiles while their spending on apparel is

the highest. This result, in turn, refutes the conventional view that the older people get,

the less they spend on textiles. As shown in Table 3.9, the ready-to-retire age group has


53

the highest expense on home-textiles when compared to all the age groups and appears to

have a higher apparel expense than the young age group (15-29). Another important

demographic variable explaining consumption patterns in apparel and home-textiles is

family composition. As reported in Table 3.9, the presence of a child aged less than 15 in

a household increases apparel consumption and decreases home-textile consumption.

Similarly, households with an elderly member (aged over 50) consume less apparel

products and spend more on home-textiles when compared to households with no

members in this age group. In general, households either headed by ready-to-retire age

group or having an elderly member appear to spend more on home-textiles than apparel

products, while households headed by mid-age group (30-39) or who have a child less

than seven years old appear to spend more on apparel than on home-textiles compared to

their counterparts. Consumption of apparel increases with increases in head of household

education level, while consumption of home-textiles declines with increase in education

level of the household head. Men headed households consume less apparel and home-

textiles when compared to female headed households. Family size, on the other hand, has

an inverse effect on home textiles and positive and significant effect on apparel

consumption. The inverse effect of family size on home textiles is consistent with

previous results such as those reported by Wagner and Mokharti. They indicated that

such results are possible either because of operation of economies of scale in large

families or the need for other necessities such as food, thus forcing them to allocate less

for home-textiles.


54

Table 3.9. Marginal effects of demographic variables on demand for apparel and home

textiles

Demographic Variables

Apparel

Home-textile

Expenditure Quantity Expenditure Quantity

Age 15-29(ref: over 50) -9.2803** -0.1730** -10.4217** -0.6763**

(0.4131) (0.0128) (0.3731) (0.0250)

Age 30-39 9.5570** 0.2004** -2.577** -0.2384**

(0.2802) (0.0059) (0.1356) (0.0114)

Age40-49 9.3714** 0.1733** -1.3771** -0.0410**

(0.3912) (0.0098) (0.1118) (0.0058)

High school , technical school(ref: primary) 3.0423** 0.0385 -5.5172** -0.3382**

(0.2188) (0.0041) (0.2545) (0.0166)

College and over 2.2846** 0.0519** -3.1935** -0.3568**

(0.0670) (0.0015) (0.1809) (0.0161)

Gender(ref: female) -16.234** -0.3703** -25.8618** -0.9744**

(0.4153) (0.0105) (1.3271) (0.0372)

Family with child aged <7( ref: no

dependent) 32.5827** 0.6524** -10.6213** -0.4530**

(1.1089) (0.0274) (0.5249) (0.0210)

Family with child aged 7-15 9.5884** 0.2409** -4.6021** -0.3379**

(0.2601) (0.0044) (0.1755) (0.0128)

Family with elderly( over 50) -6.8653** -0.1435** 7.3283** 0.4159**

(0.2005) (0.0038) (0.2941) (0.0162)

Family size 5.9181** 0.1207** -2.2479** -0.1257**

(0.1890) (0.0044) (0.0956) (0.0054)


55

3.5 Summary and Conclusion

This study has compared three demand systems for estimating the Chinese household

textile demand structure. On basis of the mean-square error, the AIDS specification

provides the best approximation of Chinese textile consumption patterns in statistical

terms. Results from this study indicate that the price of products and household income

affect both decisions to participate in the textile market and quantity of textiles

purchased. Shopping behavior such as consumer’s decision of where to buy also plays a

role in household participation decisions in the home-textile market and in budget

allocation of apparel and home-textiles. Accordingly, department stores are most likely to

be successful if they target apparel products. But, these stores are less attractive in selling

home-textile products when compared to chain stores and hypermarkets.

In regards to demographic variables, women of middle age group (30-39), or

household heads who completed secondary education, or who have a younger child (aged

less than seven) in their family have the largest apparel expense while women who are

over the age of 50, or household heads who have only a primary education, or who have

an elderly member in their household have the largest home-textile expense as compared

to any other demographic group

All the products considered here are more likely to be purchased with increases in

income. Applying income elasticity estimates from Table 3.7, and a per capita income

growth rate of 10 percent per year, Chinese consumers are expected to increase their

textile spending at least by 8 percent every year. Such growth in textile spending by

Chinese consumers, given a per capita income of $4210 according to World Bank recent


56

estimates, could amount in aggregate to over $29 billion each year highlighting the

potential for growth of the Chinese textile market.

In conclusion, though the Chinese population is growing old and declining in growth

rate, the negative effect as a result of demographic changes on textile demand could be

overshadowed if China is able to sustain its current economic growth rate. Considering

the low rate of current fiber consumption when compared to the developed world and a

relatively high income elasticity, results obtained from this study, suggest the potential

for expansion of textile markets in China is promising.


57

Chapter Four

Demand for Apparel Products among Chinese Consumers Using a

Semi-parametric Two Step Procedure

Introduction

China, with its growing population and rising per capita income, has become an

attractive market for apparel products. Many foreign brands and retailers are entering this

market, given its huge potential. To capture a bigger share of this market, however,

requires an understanding of consumers’ taste and selection criteria in purchase

decision making. While the previous chapter provides useful information about aggregate

textile demand, the information needed for specific product marketing decision is less

likely to be acquired from analysis of aggregate commodities. Decision makers who often

are involved with new product developments and advertising, thus, require information

on actual product purchase. Such information helps, for instance, in designing their

marketing strategies; which includes, but is not limited to, understanding of the effects of

quality attributes, store characteristics, relative prices and consumers’ socio-economic

profiles. Despite such benefits, examination of individual product demand is often

complicated owing to zero consumption levels of the various products by many

households. Use of conventional demand models without accounting for the zero

observations will result in biased parameter estimates (Amemiya, 1984).

To account for the positive probability of zero consumption, two approaches are

commonly used: a Tobin type censored regression model and a two-step procedure

proposed by Heckman. The Heckman two-step procedure for household demand,


58

proposed by Heien and Wessell and extended by Shonkwiler and Yen, requires the

computation of a probit model as a first step to analyze the decision of whether or not to

purchase the good. In the second step, expenditure shares are augmented with a “Mills

Ratio” regressor to account for the censoring, or more, specifically, the probability that a

household does not consume that product.

Despite wide use of parametric censored demand models in empirical research, results

from such models are often criticized for being sensitive to the assumed parametric

distribution of the error terms. In parametric models, one cannot immediately detect

whether measurements are driven by data or by the imposed parametric functional forms

- where the functional form of the relationship is predetermined. In addition, more often

than not, the distribution of the latent variable error is unknown and thus may be related

to the regressors leading to conditional heteroscedastcity problems of an unknown form

(Yen and Lin). According to Hurd, in cases where errors from a latent regression are

heteroscedastic, regression based on the homoscedastic assumption is likely to result in

inconsistent estimates. Yet, a non-parametric approach also has its own flaws. Given the

limited structure of such estimation techniques, inference based on such estimation

techniques is often very limited (Greene). That is, estimates from a nonparametric

approach are difficult to interpret and the only result that one can infer from such analysis

is an estimate of the density function. A semi-parametric approach, on the other hand,

serves as a bridge between the two approaches and has an advantage over the parametric

approach in cross sectional analysis because it relies less on assumptions such as

normality and homoscedasticity. Given such advantages, non-parametric smoothing

methods have been used in applied demand analysis studies by Sam and Zheng, and


59

Belasco, Ghosch, and Chidmi. Because consumption data patterns behave nonlinearly in

relation to demographic variables and to prices, this study aims to exploit the advantages

of a semi-parametric approach; here, the study intends to implement a semi-parametric

approach to analyze effects of changes in Chinese demographic structure on apparel

demand. Furthermore, given the increasing taste for cotton among Chinese consumers in

recent years (Qui, 2005), this analysis explores whether choice of fiber content of apparel

products affects in the consumption of apparel.


Estimating a complete demand system derived from pure neoclassical theory is often

difficult because it requires large quantities of data. The usual method of addressing such

a problem is to assume some form of a structure of consumer preferences. Here, the study

uses a three stage budgeting procedure which allows households to allocate total

expenditure in sequential stages.

For this study, the first stage involves income allocation between textile and non-textile

products. The first stage of the demand relation is not estimated in this study as the data

collected doesn’t provide information on expense made by households on non-textile

products. The study uses the weak seperability assumption to overcome data limitation

problems of non-textile consumption. In the second stage, a demand system is specified

for three sub group of textiles and footwear: home-textiles, apparel, and footwear. In the

third stage, a demand system for apparel products is specified consisting of shirts, coats

and suits, dresses, pants, and “other” clothing products.

At the first stage of the three stage allocation process, there are N commodity groups

where one of these commodities is textiles and footwear as shown in Figure 4.1. The


60

second step of budgeting results in a system for the allocation of textiles and footwear

expenditure among three sub-groups, indexed by M=1,..,m. Using the Edgerton

specification as a theoretical basis, the first stage can formally be expressed as :

4.1

where X is nx1 vector of group expenditures and P is nx1 vector of group price indices, A

is nx1 vector of demographic variables and Y= Q’P is total expenditure. The second stage

consists of allocating the group expenditures between the subgroups. Given an optimal

quantity index for a given group, the sub-group allocation problem in the second stage

can be computed as:

4.2

where X is total expenditure for a given group, and pi and qi are price and quantity of the

ith

sub-group in a given group. The cost minimization problem solves the conditional

Hicksian compensated demand system8 for the sub-group. By duality the Marshalian

demand system can be expressed as:

4.3

where pij is vector of prices in a given group. The third stage of budget allocation

involves the same procedure as the second one, though sub-group expenditure is

allocated among different goods.

8 The conditional demand here refers to the fact that the demand system is determined

given the first stage utility level is known


61


Zero consumption levels in cross-sectional data analysis are common and may occur for

three main reasons: sufficient inventory of the product by the household thus no need for

further purchase during the survey period, no-taste for the product (abstention), or a

Household Consumption Expenditure

Textile and Footwear Expenditure Non- Textile & Footwear Expenditure

Apparel Footwear Home-Textiles

Coats

Suits

Jacket

Sweaters

Pants

Shorts

Jeans

Shirts

T-shirts

Under shirts

Athletic shirts

Dresses

Skirts

Others

Figure 4.1. Utility tree for household apparel consumption in China


62

corner solution (given a consumer income level, he/she is not willing to buy the product

with the prevailing market price). In analyzing such observations, ordinary least square

regression (OLS) often produces inconsistent estimates as OLS treats the dependent

variable observations as actual values and not as an upper or lower limit of some

threshold value. To avoid such a problem, a framework involving a two-step procedure

for analyzing censored observations was suggested by Shonkwiler and Yen. The first step

of the procedure, which is used to determine whether an observation will be participating

in the market or not, is estimated using a standard probit model as shown in equation 4.4.

Coefficients of the explanatory variables in the participation equation are estimated to

calculate

. These are the estimated values of a standard normal

density function and the corresponding normal cumulative distribution function,

respectively.

4.4

where subscripts i and h denote, the product and household observation, are

the observed dependent variables,

are the corresponding latent variables,

are vectors of exogenous variables, are parameter vectors, and

are random errors, respectively.


63

Following the calculation of the cumulative and density functions, the final demand

equation is estimated by incorporating the cumulative and density functions to correct the

selectivity bias as described in Equation4.6:

For the positive consumption levels, the conditional expectation is:

4.5

and for a zero consumption level, . Thus, the expectation of

is:

4.6

Here, for ith

equation and jth

observation, yij is the observed dependent variable while

is the random error. Yet, the Shonkwiler and Yen approach (henceforth SY approach)

would yield consistent and unbiased estimates if the underlying distribution of the error

term is bi-variate normal. The study uses semi-parametric technique outlined by Newey,

Powell and Walker to circumvent possible misspecification and heteroscedasticity

problem in SY approach. In deriving the semi-parametric censored regression model, this

study uses the following specifications:

4.7

4.8


64

where I(.) is an indicator function,

9, and

are vectors of parameters. Using as the unknown cumulative distribution

function of the error term , the system of regression equations can be specified as:

4.9

where

. First will

be estimated using the Klien and Spady single index model, which is obtained by

maximizing the quasi-loglikelihood function:

4.10

where

and h is a smoothing parameter

satisfying the condition j-1/6

<h< j-1/8

. Location and scale parameterization requires setting

the intercept to zero and one of coefficients on a continuous explanatory variable to one.

Once estimates of are obtained using the Klein and Spady method, the

estimates will be incorporated to equation 4.9 to estimate the system of demand

equations. To estimate the unknown selectivity term , Newey, Powel and

Walker use a series approximation based on orthogonal polynomials,

i.e,

10 and k are allowed to increase with the

sample size. The system of equations can be estimated consistently as:

9 iid refers to the fact that errors terms from each observation are independent and are

identically distributed.


65

for i=1,2..n; j=1,2, J. 4.11

On basis of estimation results from 4.11, estimated uncompensated price and expenditure

elastcities for coats, pants, dresses, and “others” equations are calculated as follows:

, (A) 4.12

, (B)

where

(C) .

Let

.

4.13

where

11is the derivative of

and is the coefficient for from

the binary component of the model. The derivative of A1 and A2 is derived as:

and

. 4.14

10

Martins (2001) uses this approach in calculating unknown selectivity term:

11

The study uses numerical differentiation in calculating the derivative of at

the midpoint of each price. That is,


66

To find expenditure elasticity of dress (the residual commodity in the model),on the other

hand, the study used Engle aggregation. That is:

, 4.15

where are product price, quantity consumed, budget share, and total

expenditure, respectively. Euler aggregation is used to find the cross-price elasticity of

dress price on the other apparel products. That is:

4.16

At last, the study uses Cournot aggregation to calculate the cross-price effect of coat,

pants, shirts, and “others” price on dress.

4.17


67

The study uses the NP package, developed by Hayfield and Racine, under R software to

compute parameter estimates of the binary component of the model. For a non-parametric

model, estimates are obtained by slicing the data into different bins, then estimating the

behavior within the bin. The size of the bin is estimated using kernel density. Results

from the binary component then will be incorporated to the AIDS model to determine

households apparel consumption behavior.

4.3 Results and Discussion

The data used for this analysis were obtained from a 2009 cotton consumer tracking study

conducted by Cotton Council International through a local market research firm. In total,

a survey response from 2062 households in 24 cities from 2 waves was used for this

analysis. The main content of the survey is based on household heads ages15-54 who

lived in the city for at least one year. The data set includes extensive household

demographic information associated with apparel consumption, quantities, prices and

household expenditures on apparel products. In total, there are 1,378 variables describing

each household.

Due to the sparse nature of the dependent variables in the data set, products were

aggregated into five groups: suits and coats, shirts, pants, dresses, and “others” as shown

in Table 4.1. Still, the data set used has some missing observations on quantity consumed

and prices of the apparel products described above. Hence, a regression model is used to

impute the missing prices. That is, prices for those households consuming each apparel

product were regressed on household characteristics and regional dummies. The

regression was then used to estimate the missing prices for households who are not

consuming the apparel product. From the sampled data, 57 percent of the respondents


68

were women and over 46 percent of the households have no dependent in their home. The

majority of the households, over 69 percent, has a monthly income less than 4400 RMB

($690) and has completed primary education (over 72 percent) as shown in Table 4.2.

Also from the data, cotton is the preferred fiber for shirts and products under “others”

category for majority of the respondents (over 52 percent), while most households

preferred man-made fibers for their dresses and pants as compared to other fibers (over

51 percent). Wool, on the other hand, was the most preferred fiber for coats and suits

among respondents (47 percent).

Table 4.1 Grouping of apparel products

Group Apparel products

Suits and coats Coats, jacket, suits and sweaters

Shirts Shirts, T-shirts, Under t-shirts, athletic shirts

Pants Pants, shorts, Jeans

Dress Dress, skirts

Others Bras, underwear, socks, sleepwear,

undergarments


69

Table 4.2. Frequency distribution of respondents on basis of demographic characteristic

and product choice

Description Categories Frequency Percent

Fiber choice (pants) Cotton 532 25.80

Denim 90 4.36

Wool 12 0.58

Silk 3 0.15

Man-made 1274 61.78

Cotton blend 151 7.32

Fiber choice (shirts) Cotton 1084 52.57

Denim 7 0.34

Wool 13 0.63

Silk 14 0.68

Man-made 892 43.26

Cotton Blend 52 2.52

Fiber choice (dress) Cotton 391 18.96

Denim 75 3.64

Wool 81 3.93

Silk 330 16

Man-made 1061 51.45

Cotton Blend 124 6.01


70


and product choice (continued...)

Description Categories Frequency Percent

Income <5000RMB 1423 69.01

Income 5000-

10000RMB

546 26.48

Income Income 10000-

15000RMB

64 3.10

Income over 15000RMB 29 1.41

Family size Less or equal to 2 433 21

3-5 1560 75.65

Over 6 69 3.35

Fiber choice (coats) Cotton 753 36.52

Denim 197 9.55

Wool 971 47.09

Silk 34 1.65

Man-made 89 4.32

Cotton blend 18 0.87


71


and product choice (continued...)

Description

Categories Frequency Percent

Fiber choice (others) Cotton 1866 90.49

Denim 6 0.29

Wool 15 0.73

Silk 22 1.07

Man-made 144 6.98

Cotton Blend 9 0.44

Gender

Female 1184 57.42

Male 878 42.58

Education

Primary education and less 563 27.30

Junior or senior high school,

Technical school

219 10.62

College and over 1280 62.08

Family

Composition

Number of families with a child aged less

than 7 years

362 17.56

Number of families with a child aged

between 7-14 years

374 18.14

Number of families with people aged

Over 55 years

362 17.56


72

Summary statistics of quantity, price, and expenditure made on apparel products are

shown under Table 4.3. In general, products under the coats category have the highest

mean price RMB 114.17 per unit, followed by dresses and shirts costing RMB 71.63 and

RMB 50.54, respectively. In regards to the expenditure made by households, respondents,

on average, have made an expenditure equivalent to RMB 249.03 on apparel, with

products under the coat category having the highest expenditure. Taken together, these

findings indicate that the fiber choice of Chinese consumers differ depending on the

products consumed with cotton, man-made, and wool being the preferred fiber choices

for apparel. Products under the “others” category are the least expensive, and, thus, the

most purchased apparel among Chinese households.

Table 4.3. Summary statistics on quantity, price and expenditure

Variable Mean Std Dev

Price

Coat 114.1725 80.6456

Pants 49.5397 35.6002

Shirts 50.5489 35.8787

Dresses 71.6255 36.4273

Others 19.7491 20.7522

Quantity

Coat (consuming households: 54.85% of the sample) 0.8962 1.0880

Pants (consuming households: 53.30% of the sample) 0.8457 1.0602

Shirts (consuming households: 53.54% of the sample) 0.9907 1.2924


73

Table 4.3. Summary statistics on quantity, price and expenditure (continued ...)

Dress (consuming households: 14.50% of the sample) 0.1886 0.5201

Others (consuming households: 45.83% of the sample) 1.6023 2.6710

Expenditure 249.0311 359.4421

Coat 112.6486 246.6734

Pants 43.4346 89.4594

Shirt 51.3397 112.0734

Dress 14.7195 72.0521

Others 26.8886 86.6045

4.3.1 Estimation Results

As discussed above, the estimation is conducted in two steps to correct the inconsistency

problem of OLS parameter estimates. First, estimation is performed on the binary

component using both the probit and semi-parametric methods on four of the products for

this study: coat and suits, pants, shirts and “others”. The dependent variables in the first

step are binary, which take the value of 1 if the household made a purchase and zero

otherwise. The explanatory variables are family size, family composition, age, education,

and gender of the household head and the logarithmic prices of apparel products and

apparel expenditure. A comparison among probit and semi-parametric (Klein-Spady)

model was conducted first using the exploratory method. As shown in Figure 4.2 and

4.3, differences in probability density functions of the predicted dependent variables for

coats and pants can be observed between normal and Klein-Spady (hence forth KS)


74

estimates. Next, the overall correct classification ratio and R-squared12

were calculated to

determine if the semi-parametric (KS) procedure better predicts the binary outcome. The

semi-parametric procedure, as shown in Table 4.4, is able to predict apparel purchases in

all apparel categories better than the probit model. Both correct classification ratio (CCR)

and R2

of KS is higher in all categories indicating a better fit for the KS procedure over

the probit model. In performing CCR procedure, if relates to

and relates to 0, then the prediction is classified as correct. Results

from the second stage estimation also confirm the result found in the first step. Censored

equations based on KS in all the cases have lower mean square error outperforming the

censored equations based on the probit model as shown in Table 4.5.

Figure 4.2. Normal and KS estimate of density function for coats (hn=0.281)

12

As explained by Hayfield and Racine (2008), R2 comparison for parametric and semi-

parametric models can be done using

which is bounded by

[0, 1].


75

Figure 4.3. Normal and KS estimate of density function for pants (hn=0.281)


76

Table 4.4: Measures of model fit and predictive power for participation equation

Note: A/P, and CCR refer to actual/predicted and correct classification ratio values, respectively.

Coat

Pants

Shirts

Dress

Others

Model Probit KS Probit KS Probit KS Probit KS Probit KS

A/P 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1

0

935 0 661 273 630 334 964 0 572 389 961 0 1764 4 1768 0 897 220 1117 0

1 74 1060 177 958 300 805 75 1030 359 749 86 1022 211 90 12 289 307 645 57 895

CCR 0.7820 0.7825 0.6936 0.9638 0.6384 0.9224 0.8960 0.9942 0.7456 0.9724

R2 0.3448 0.8862 0.2377 0.8743 0.1126 0.8698 0.2879 0.9583 0.3139 0.9019


77

Table 4.5: Mean square error associated with in sample data

Products

Mean Square Error

Semi-parametric model Probit model

Coats and suits 0.0336 0.1261

Shirts 0.0333 0.1045

Pants 0.0190 0.0678

Others 0.0170 0.0421


78

Results from the KS procedure and probit model for the coat and pants category are

shown in Table 4.6.13

As per the location and scale restriction, the KS estimator does not

have an intercept and the coefficient for family size was set to 1. The coefficient for

expenditure is positive in the pants equation and is negative in the coats equation under

the KS procedure, but is not significant for both cases. The own-price effect is negative

and significant for both coats and pants equation. Result from KS procedure indicates

that pant price is a significant factor in the household decision to buy coats. The KS

estimate also shows that household heads younger than the ready-to-retire age group, or

who have more than a primary education, or who have a child aged 7-15, or who have an

elderly at home have a higher probability of purchasing coats when compared to the other

household group. In regards to the pant equation, KS procedures indicate that household

heads in the age group of 15-29 are more likely to purchase pants when compared to the

ready-to retire-age group.

13

Results regarding shirts, dress, and “others” equation are reported in Appendix A,

Table 1.


79

Table 4.6: Estimated parameters of participation equation for apparel model

Klein-Spady Probit

Variables Coat Pants Coat Pants

Intercept n/a n/a 1.4967**

(0.5494)

4.1533**

(0.5490)

Family size n/a n/a 0.0030

(0.0347)

0.0305

(0.0331)

Coat price -0.4467**

(0.0032)

-0.0025

(0.0021)

-1.4403**

(0.0767)

-0.2972**

(0.0623)

Pants price 0.0059**

(0.0027)

-0.4267**

(0.0026)

0.0275

(0.0711)

-1.0847**

(0.0757)

Shirt price 0.001

(0.0024)

-0.0012

(0.0017)

-0.2177**

(0.0634)

-0.1958**

(0.0583)

Dress price

-0.0035

(0.0031)

0.0023

(0.0027)

0.2022**

(0.1005)

-0.2953**

(0.0986)

Others price 0.0023

(0.0019)

0.0005

(0.0016)

0.1098

(0.0452)

-0.1379**

(0.0428)

Expenditure -0.0006

(0.0017)

0.001

(0.0015)

0.9440**

(00478)

0.7002**

(0.0399)


80

Table 4.6: Estimated Parameters of Participation Equation for Apparel Model

(continued…)

Age 15-29

(ref: over50)

0.0074*

(0.0041)

0.0069*

(0.0039)

-0.031

(0.1170)

0.2789**

(0.1130)

Age 30-39 0.0086* 0.0041 -0.0599 0.2833**

(0.0050) (0.0043) (0.1333) (0.1284)

Age 40-49 0.0133**

(0.0044)

0.0093**

(0.0044)

0.1390

(0.1235)

-0.0625

(0.1188)

High school education ( ref:

primary education

0.0188**

(0.0048)

-0.0033

(0.0041)

0.2392**

(0.1195)

0.0031

(0.1134)

College and over 0.0175**

(0.0032)

-0.0005

(0.0032)

-0.0402

(0.0795)

0.1180

(0.0762)

Have a child less than 7

years old (ref: no dependent)

0.0020

(0. 0039)

0.0113**

(0.0027)

0.0087

(0.0998)

-0.2739**

(0.0966)

Have a child 8-15 years old 0.0146**

(0.0037)

-0.0057*

(0.0033)

-0.1611

(0.0997)

-0.1495

(0.0966)

Have a elderly(aged over 50) 0.0140**

(0.0033)

-0.0001

(0.0027)

-0.1090

(0.0970)

0.1349

(0.0926)

Gender(ref: female) -0.0040 -0.0037 0.3262** -0.0083

(0.0026) (0.0025) (0.0671) (0.0630)

Note: Single asterisks (*) indicates significance at 10% level while double asterisks (**)

significance at 5% level

In the second stage, an Almost Ideal Demand System is used to model household apparel

consumption behavior. To incorporate product attributes in demand models such as

choice of textile fiber, Ray’s (1983) specification of a‘basic’ equivalence scale was used.

The scales were normalized at unity for a household that favors artificial fiber for its


81

apparel. Starting from a household expenditure function that represents a household who

favors artificial fiber for its apparel, a given household expenditure can be specified as:

4.18

where is the cost function for the reference household, is a general

equivalence scale formulated as where and are fiber

choice by households and their coefficients, respectively. Then

will be

a cost index for fiber type in relation to artificial fibers. Furthermore, the analysis uses the

translation approach proposed by Pollak and Wales to incorporate demographic variables

into the AIDS system. That is, the intercept in the budget share equation is augmented by

where is the value of the demographic variable. Theoretical

restrictions of homogeneity and symmetry were maintained during estimation, but the

theoretical restriction of adding up on parameter estimates, in general, does not hold as in

the conventional case as discussed by Yen, Kan, and Jiuan Su. As a result, estimation on

the budget equation was made by treating the dress category as a residual good as

suggested by Yen, Lin, and Smallwood and estimating n-1 equations with an

identity . To ensure our elasticity estimates are in line with economic

theory, this study used, as suggested by Yen, Kan, and Jiuan Su, Euler aggregation

(

14to recover cross price effects of dress goods group on coats, pants,

shirts and “others” categories. Engle aggregation was also used to

recover the income elasticity of the dress group. Cournot aggregation

was used to calculate the elasticity of dress group demand to coat, pants,

14

Here refer to cross price elasticity, budget share and income elasticity of a

given product, respectively.


82

“others” and shirt price. The AIDS model, after taking logs of household expenditure as

suggested by Deaton and Muelbauer and incorporating product attributes and household

characteristics has the following form:

15

4.19

The effect of the product attribute, which is fiber content choice in this case, is positive

and significant for cotton blends in the coat equation, for denim in the pants equation, for

wool in the shirts equation, and for cotton in the “others” category equation. On the other

hand, the effect of fiber choice is negative and significant at the 10 percent level for silk

in the coat equation. On basis of equation 4.18, the implication of this result is that

households who favor cotton blend as their fiber choice for their coats, in general, spend

more for their coats than households who favor artificial fibers for their pants. On the

other hand, households who choose denim for their pants spend more on their pants as

compared to households who favor artificial fiber. Households who favor silk fiber for

their coats spend less on their pants when compared to households who favor man-made

fibers. Consumers of products in the “others” category that preferred cotton also spent

more when compared to “others” products made of artificial fibers. Though not

significant in all cases, results from fiber content of textile products to indicate a

significant effect of fiber content on the expenditures for textile products. In regards to

the effect of age in the apparel budget share allocation, shirts and “others” budget shares

of the ready-to-retire age group were significantly lower than the 30-39 age groups. Yet,

the ready-to-retire age group appears to have no statistically significant difference in

budget share allocation with other age groups in any of the other apparel product. This

15

K was set to be 2 on basis of mean square error for all products in our study


83

result augments the resulted reported for the aggregate case. Based on the previous

chapter, the ready-to-retire age group had lower expense in apparel when compared to

households in the age group of 30-39. According to results reported here, much of the

decline in apparel spending is a result of their lower expense on shirts and “others” group.

Table 4.7: Estimated parameters of apparel AIDS model

Budget share equation parameters Coats Pants Shirts Others

Intercept 0.1831** 0.2252** 0.1020* 0.0688

(0.0454) (0.0507) (0.0562) (0.0507)

Age 15-29 reference (over 54) -0.0153 -0.0082 0.0447 0.0205

(0.0273) (0.0250) (0.0282) (0.0206)

Age 30-39 -0.0093 -0.0168 0.0744** 0.0483**

(0.0309) (0.0282) (0.0314) (0.0231)

Age 40-49 -0.0048 -0.0495* 0.0417 0.0315

(0.0289) (0.0270) (0.0300) (0.0217)

High school education ( ref: primary education -0.0117 -0.0528** -0.1128** -0.0592**

(0.0282) (0.0264) (0.0297) (0.0229)

College and over -0.0264 -0.0429** -0.0575** -0.0414**

(0.0184) (0.0166) (0.0185) (0.0139)

Have a child aged < 7(ref: no dependent) -0.0305 0.0175 0.0130 -0.0122

(0.0220) (0.0193) (0.0215) (0.0166)

Have a child aged b/n 7-15 -0.0227 0.0077 -0.0279 -0.0141

(0.0238) (0.0213) (0.0230) (0.0178)

Have a elderly at home aged>54 -0.0299 -0.0538** -0.0650** -0.0511**

(0.0219) (0.0189) (0.0216) (0.0162)


84

Table 4.7: Estimated parameters of apparel AIDS model (continued…)

Gender(ref: female) 0.0363** 0.0153 0.0776** -0.0061

(0.0167) (0.0151) (0.0167) (0.0128)

Family size 0.0176** 0.0132* 0.0201** 0.0092

(0.0079) (0.0077) (0.0081) (0.0062)

Pants Price -0.0530** 0.0627** -0.0524** -0.0133*

(0.0114) (0.0215) (0.0098) (0.0077)

Shirt Price -0.0100** -0.0524** 0.0427** -0.0186**

(0.0117) (0.0098) (0.0218) (0.0073)

Others Price -0.0265** -0.0133* -0.0186** 0.0068

(0.0077) (0.0077) (0.0073) (0.0156)

Expenditure -0.2562** -0.2373** -0.2535** -0.1632**

(0.0091) (0.0057) (0.0067) (0.0051)

Cotton fiber(ref: artificial) 0.0191 0.0419 -0.0144 0.1173**

(0.0621) (0.0300) (0.0240) (0.0491)

Denim fiber 0.0612 0.2126** -0.0632 -0.0239

(0.0717) (0.0680) (0.2066) (0.2258)

Wool 0.0279 0.0621 0.6164** 0.4348**

(0.0613) (0.1793) (0.2811) (0.2095)

Silk -0.1504* 0.0829 0.1817 0.0086

(0.0854) (0.2993) (0.1734) (0.1076)

Cotton Blend 0.4087** 0.0651 -0.0878 0.3772

(0.1672) (0.0473) (0.0687) (0.2408)

Note: Single asterisks (*) indicates significance at 10% level while double asterisks (**)

significance at 5% level.


85

Households with primary education appear to allocate more for pants, shirts, and

“others” when compared to households with high school or college education. Family

composition, such as the presence of a child or elderly, did not have statistically

significant effects on budget allocation for the coat category. But, in line with results

from Lee et al., and Wagner, the presence of an elderly (aged over 50) in a household had

a significant and inverse impact on pants, shirts, and “others” budget allocation. Men

headed households had a significantly higher budget share for coats and shirts than their

female counterpart. The effect of family size on apparel expenditure share is significant

and positive for all apparel products.

Marshallian elastcities were calculated on conditional budget shares using the approach

employed by Sam and Zheng. Accordingly, the uncompensated own-price elasticities for

all apparel products are significant and negative. This indicates that apparel products

have a downward slopping demand curve as expected. Furthermore, own-price elasticity

estimates suggest that “others” consumption is more sensitive to own-price changes as it

has the highest own price elasticity value when compared to other apparel products.

Expenditure elastcities are positive and significant for all apparel products. Products

under the dress category have the highest expenditure elasticity indicating they are the

most responsive to changes in apparel spending by consumers. The cross-price elasticity

of coats is significant and positive with pants and shirts indicating gross substitutes. In

particular, a one percent decline in coat price will cause 5% decline in pants and 12%

percent decline in expenditure for shirts products. On the other hand, a one percent

increase in price for pants is likely to cause a 0. 4 % increase in coats consumption, while

a one percent increase in price for shirts will cause an increase in coats consumption by


86

around 6 %. Products under pants and shirts are also gross substitute with products under

“others” category. Products under dress, on the other hand, are gross complements with

pants and shirts.

Table 4.8: Elasticity of apparel products with respect to prices and total expenditure

Product

Price

Expenditure

Coat

Pant

Shirt

Dress

Others

Coat

-0.4772**

(0.0094)

0.0044**

(0.0007)

0.0641**

(0.0013)

-0.5583**

(0.0033)

-0.0101**

(0.0004)

0.9772**

(0.0085)

Pant

0.0511**

(0.0013)

-0.3061**

(0.0122)

-0.0013

(0.0014)

-0.1074**

(0.0034)

0.0116*

(0.0007)

0.3519**

(0.0114)

Shirt

0.1256**

(0.0027)

0.0067**

(0.001)

-0.4641**

(0.0115)

-0.1800**

(0.0042)

0.0014**

(0.0005)

0.5102**

(0103)

Dress

-0.6762**

(0.0089)

-0.4023**

(0.0069)

-0.4498**

(0.0085)

-0.2386**

(0.0058)

0.6977**

(0.0040)

1.0693**

(0.0133)

Others

0.0992**

(0.0025)

0.0995**

(0.0027)

0.0612

(0.0018)

-0.0318**

(0.0049)

-0.5620**

(0.0127)

0.3302**

(0.0147)



4.4. Summary and Conclusion

Household demand on four apparel products is analyzed using the cross sectional data

obtained from a 2009 cotton consumer tracking study conducted by Cotton Council

International on Chinese consumers. The effect of demographic variables on household

budget shares for apparel is examined using both censored parametric and semi-

parametric methods. Results from model fit statistics showed better fit of the semi-


87

parametric approach indicating the importance of distributional assumptions in sample

selection models.

In line with the theory, results from semi-parametric models indicated that market

participation for apparel products (here for coats and pants) is inversely related to own-

prices. Demographic variables such as household head age and education, and family

composition do appear to have impact in the in household market participation according

to the KS estimate. This is one of the significant differences between the two methods

which could be important for marketers looking for new customers for their product.

Results from the estimated demand model revealed that product attributes such as fiber

content choice, though not always, are important determinants of apparel expenditure.

The reduced expense of the ready- to- retire age group on apparel reported in the

previous chapter is a result of the less expense this age group has on shirts and “others”

when compared to household heads in the age group of 30-39. Family size has

significant and positive effect on all apparel products, with the largest effect being on

shirts budget allocation supporting the results reported in the aggregate model. Presence

of a child had no significant effect in any of apparel budget shares, while presence of an

elderly appear to have negative and significant effect on pants, shirts, and “other” budget

share allocation. In all the budget share equation analyzed here, consumers are relatively

more responsive for own-prices compared to the cross-price effects which would give

important insight to marketers in designing price strategies for apparel in China. Large

positive and statistically significant expenditure elastcities for dress and coat indicate

income as the most important factor influencing change in dress and coats consumption


88

pattern. Given the prevailing price structure, expenditure on dress and coats will increase

with increases in household income.


89

Chapter Five

Implications of Changes in Chinese Demographic Structure on World

Cotton Market

Introduction

China is one of the leading cotton fiber consumers accounting for over39 percent of world

consumption in aggregate terms in 2008 (ICAC). Moreover, per capita domestic consumption of

textile fiber grew at a rate of 7.15% per annum between 2000-2008. By comparison, however,

total consumption figures are still far below the developed world consumption level on per capita

basis, as shown in Figure 5.1. Given the relatively higher Chinese budget share for clothing ($5.8

for every $ 100) compared to the developed world such as the U.S ($3.5 for every $100) and the

rapid increase in the Chinese household income observed in recent years, domestic demand for

textiles is expected to grow rapidly. According to the National Bureau of Statistics (NBS), per

capita textile expenditure of urban households has increased, on average, by 12.5% to 1444.34

Yuan in 2010, while cash expenditure on textiles of the lowest income rural households increased

to 150.84 Yuan, up by 11.9% from its 2009 value. Such a rapid growth rate, according to some

analysts, is expected to make China one of the largest markets for textiles by 2020, making it

20% larger than the Japan market (Kurt Salmon Associates as cited in Zhang et al.). However,

China is also experiencing dramatic changes in its demographic structure since launching of its

population policy in 1979, which has ramifications that could threaten the path of the per capita

income rate growth. Notable changes in demographic structure, according to Gao and Zhai,

include increases in urban population (to over 45 percent of the total population), increases in

proportion of educated people (8.9 %), increases in the proportion of elderly people (8.16 percent

in 2002), and a decline in the household size (3.1 people per household).


90

Figure 5.1. China vs. World per-capita fiber consumption

Source: ICAC (2009) and Fiber Organon (2004, 2010)

Changes in the country’s demographic structure are likely to affect consumption

patterns of textiles in China. Bearing in mind China’s status as one of the largest fiber

consumers in the world and one of the major cotton importers, the country’s changing

pattern of textile consumption has the potential to significantly affect the global market

for textiles. In this light, textile market studies are needed to yield a better understanding

of how changes in China’s demographic structure affect local and international textile

markets, and, by extension, cotton markets.

5.1 Objectives of the study

The major objective of this study is to investigate the implications of Chinese

demographic changes on the Chinese textile market and global cotton markets. In the

process, the study will use a framework that combines the Chinese end-textile

consumption changes to the partial equilibrium World Fiber Model developed by the

Cotton Economic Research Institute at Texas Tech University. Using this framework, the

study will compare alternative scenarios of income and demographic changes and

0

5

10

15

20

25

30

35

1995 2000 2005 2010

Pe

r ca

pit

a fi

be

r co

nsu

mp

tio

n

Years

China per capita

Industrial countries per capita

Developing countries per capita

World


91

examine their effect on textile and cotton markets. The study will specifically analyze

two sets of scenarios:

First, the impact of growth in Chinese per capita income on global cotton and

textile trade markets is examined. Domestic demand is expected to grow in

tandem with increases in per capita income; conversely the cost of textile

production, through wage rate increases (per capita income increases), is likely to

be higher for textile firms, thereby increasing textile prices and reducing per

capita fiber consumption.

Second, the impact of an increase in the proportion of Chinese elderly population

and decline in family size on global textile and fiber markets is analyzed. Changes

in these variables, in general, will likely have an inward shift of Chinese textile

demand which would, in turn, inversely affect global demand for fiber.


Changes in demand and supply factors have implications beyond the immediate

market. Just, Hueth and Schmitz developed a model that can be used for analyzing

interlinked markets that overcomes some of the weakness of analyzing single markets

while requiring less data than general equilibrium models. This research, in turn, has

motivated a number of empirical studies in policy analysis. Notable works within the

textile markets literature include studies by Hudson and Ethridge and Sumner. Given the

use of such an approach to analyze policy changes across markets, a theoretical

framework on the basis of such existing works was developed.


92

The model used in this analysis is based on a partial equilibrium framework and

static16

world trade models. In Figure 5.2, the eight panels show the price-quantity

relationship for homogeneous cotton and end-use textile products based on supply-

demand interactions. Two commodities are assumed: raw cotton and end-use textile

products, and three regions of the world: U.S., China, and a small textile exporting-cotton

importing country. In figures 5.2a, b, c and d, the lines Si and Di represent domestic

supply and demand curves for end-use textiles in each of the three countries. Assuming a

closed economy, price-quantity equilibrium is shown by Pd0 and Q0 for each of the three

countries in the analysis. On the other hand, panel 5.2e, f, g and h, the lines Si and Di

represent domestic supply and demand curves for cotton in each of the respective

countries.

Assuming ceteris paribus, Figure 5.2 describes how changes in Chinese per capita

income, size of elderly population and family size affect the world textile and cotton

markets. An increase in per capita income in China shifts the demand for textiles

outward to D1 and, at the same time, shifts domestic production of textiles inward to S1

due increases in labor cost. That is, an increase in per capita income is likely to raise

textile production cost, thus moving inward the textile supply curve. Free trade among

each of these countries moves world price for textiles to PW0. With increase in Chinese

per capita income, world textile price rises or falls depending on the relative elasticity of

the Chinese supply and demand curves and the magnitude of the shifts. Here, world price

for textiles is expected to rise to PW1. The initial equilibrium for world textile markets is

shown by excess supply curve (ES0) and excess demand curve (ED0). The quantity traded

16

Refers to analysis on relationships between variables with the same time period.


93

for textiles as a result of income changes is expected to be at (QT1) as excess supply

moves inward from ES0 to ES1. In regards to the cotton market, the effects of increases in

per capita income on Chinese domestic mill consumption is likely to be inverse, thus

moving domestic consumption of cotton from QSC0 to QSC1. This occurs as a result of a

decline in the domestic textile production, which as discussed above occurs owing to

increase in the labor cost. This move, in turn, would put downward pressure on world

cotton price moving it from PWC0 to PWC1. U.S. exports of cotton would, as a result,

decline from QSC0 to QSC1. In this scenario, however, the small country competing in

the textile export market and importing cotton is anticipated to increase its exports of

textiles from (QS0 to QS1) and imports of cotton from (QSC0-QDC0) to (QSC1-QDC1).

An inclusion of the elderly proportion and family size effect would have the following

impacts: domestic demand for textiles would shrink to D2, which would lower world

textile price and move it to PW2; Traded textiles on world markets would increase as a

result of lower world textile price; the Chinese cotton demand is expected to shift further

inward to QSC2 and, thus, the world price for cotton as demand for cotton declines in

China; Quantity traded of cotton increases from QTC1 to QTC2. Additionally, U.S

exports of cotton would also decline as a result of a decline in world price for cotton. In

regards to the small trading country, exports for textiles would decline while cotton

imports would be expected to increase as a result of the lower world price for cotton.

Texas Tech University, Mouze Mulugeta Kebede , August 2012

94

Figure 5.2. Effects of Chinese demographic and economic changes on World textile and cotton market


95

5.3 Data Source and Description

Raw data from a cotton consumer tracking study conducted by Cotton Council

International is used to estimate the effects of income and demographic variables on

Chinese textile consumption. In total, a survey response from 6539 households in 24

cities is used for this analysis. The estimates of household textile demand presented in

Chapter Three, Table 3.6 and 3.7, are used as a basis for the estimation. Time series data

used for this study are compiled from different sources: annual data on the Chinese

textile index, Chinese population size, Chinese old age proportion, Chinese family size

and per capita income is obtained from National Bureau of Statistics of China (NBS);

cotton prices, production, consumption, stocks, and trade is taken from the USDA

(Production, Supply, and Distribution Database); and fiber mill consumption, and man-

made fiber data are obtained from the Food and Agricultural Organization (FAO), World

Fiber Consumption Survey and Fiber Organon. Data on the world textile price index is

taken from International Cotton Advisory Committee (ICAC), while macroeconomic data

such as CPI, exchange rate, and oil prices are collected from Food and Agricultural

Policy Research Institute (FAPRI).


The econometric model developed here will mainly address the demand side of the

Chinese textile market and incorporate the results from the Chinese demand model to the

World Fiber Model developed by the Cotton Economics Research Institute at Texas Tech

University. The model is described in detail in Pan et al. (2004) and includes 35 major

cotton exporting and importing countries of the world. The model incorporates

productivity differences within some regions of the world, fiber substitutability and


96

linkages between upstream (apparel industry) and downstream (cotton production)

production sectors within these regions.

Textile Supply and Demand

The Chinese cotton-textile market is modeled with three types of agents as major

players in the markets: cotton producers, textile producers and final consumers. These

agents interact in two vertically integrated markets. The first of these markets is the

textile sector market, which is composed of domestic textile demand, textile trade, and

cotton mill use or textile production components. These three components are joined by

the identity:

textileqi +Imtt= Dctt+Extt 5.1

where textileqi is domestic production and Imtt is import demand, both representing the

supply side, and Dctt is domestic consumption and Extt is export supply for textiles, both

representing the supply side.

Domestic textile production is modeled as a function Chinese textile price index

( ), Chinese per capita income ( ) as a proxy for wage rate, and Chinese

domestic cotton price ( :

The other element in the textile supply equation is the net export supply function.

The net export supply function (NEXTt) is specified as function of the Chinese textile

price index ( ), world textile price index (Pitwt) and price of petroleum (PPt) as a

measure of transportation cost and lagged textile net exports (Lnextt).


97

5.3

After identifying the textile supply function, the next step is estimation of demand

functions for textiles. In estimating demand for textile products (apparel, home-textiles,

and others), the results of the AIDS model presented in chapter Three under Table 3.7

and 3.9 are used.

Cotton Supply and Demand

Results from the textile sector model are incorporated into the partial equilibrium

World Fiber Model developed by the Cotton Economics Research Institute at Texas Tech

University. In this framework, each country is modeled as an individual buyer and seller

in the global market. On the demand side, because cotton is an input in the production of

textiles, its demand is derived from the demand for textiles. Domestic textile fiber

consumption is computed using the conventional approach used by USDA (US

Department of Agriculture) and ICAC (International Cotton Advisory Committee). It is

calculated as the difference between fiber spun by Chinese mills and the amount of fiber

contained in Chinese net-exports. On the supply side, cotton production is defined from

structural equations of yield and area allocation. Section 2.3 provides a detail description

of the global fiber model (or sees Pan et al. for further discussion of the model).

5.5 Results and Discussions Income and textile expenditure relationships are presented in Table 5.1. The income

elasticity of textile expenditure shows that textile consumption would increase by 0.59

percent with 1 percent increase in income. Estimates of household textile consumption,

on basis of AIDS model, are indicated in Table 3.7 and 3.9. The expenditure elasticities


98

are 0.91 and 1.54 for apparel and home-textiles, respectively, indicating an increase of at

least 0.91 percent in textile consumption for 1 percent increase in income allocated to

textiles. Using the income elasticity of textile expenditure from Table 5.1, the estimated

income elasticity for apparel and home-textiles are 0.54, and 0.91, respectively. Results

from Table 3.7 and 3.9 also indicate that household expenditure increases, on average, by

5.91 Yuan for apparel and declines by 2.25 Yuan for home-textiles for every additional

household member in the family. Consumers in the age group of 50-54 spend less on

apparel by almost 9 Yuan, on average, and spend more on home textiles, on average, by

2.58 Yuan when compared to reference consumer group of age 30-39.

Table 5.1. Linkage between textile expenditure and income (dependent variable: log (Textile

expenditure)

Parameters Coefficient Standard Error

Intercept 0.4951** 0.1589

Log(Income) 0.5949** 0.0201

Age (15-29) (ref: 50-54) 0.2712** 0.0491

Age (30-39) 0.2693** 0.0468

Age (40-49) 0.1864** 0.0432

Married (ref: single) -0.2226** 0.0402

Divorced/separated -0.0820 0.0609

Kids less than 7 years old (ref: no dependent) 0.0999** 0.03770

Kids 7-15 years old 0.1037** 0.0357

Elderly (over 50) -0.0001 0.0352

High school education ( ref: primary education) 0.2956** 0.0439


99

Table 5.1. Linkage between textile expenditure and income (dependent variable: log(Textile

expenditure) continued.....

College and over 0.2791** 0.0288

Gender(ref: women) -0.3664** 0.0238

Family size -0.0497** 0.0126

East region(ref: north) -0.059* 0.0327

Central region -0.3558** 0.0359

South region -0.3101** 0.0378

South-west region 0.0089 0.0365

R2 0.23

** significant at 5% level and * significant at 10% level.

Estimates on Cotton Textile Trade and Production

Estimates in regards to Chinese textile production and trade are presented in Table 5.2.

Parameter estimates for textile production indicate that changes in cotton prices and the

wage rate have negative impacts on textile production, while the textile price index has

positive and statistically significant effect on textile production. Among explanatory

variables, changes in cotton prices affects textile production more when compared to the

wage rate and the price for textiles, indicating more sensitivity of the industry to input

than output prices. Chinese textile net exports, on the other hand, are most influenced by

the domestic textile price index as for every 1 percent increase in domestic prices, net

exports decline by over 1 percent.


100

Table 5.2. Parameter estimates on Chinese textile production and trade (Double-log model)

Variables Log (net export) Log (mill use)

Intercept -5.05* 2.08*

(3.52) (0.55)

Chinese textile price index -1.05* 0.07*

(0.26) (0.01)

World textile price index 0.32*

(0.18)

Average wage rate -0.02*

(0.003)

Oil price -0.16*

(0.08

Chinese cotton price -0.26*

(0.08)

Trend 3.24*

(1.04)

Lag of independent variable 0.67* 0.87*

0.12 (0.08)

WTO dummy 0.17*

(0.05)

Adj R-square 0.97 0.99

F-value 182.32 304.88

Note: * significant at 10 percent. (MacDonald et al., 2011)

Simulation Results

Simulation results of the effects of changes in Chinese demographic structure on the

world fiber markets are presented in Table 5.3, 5.4, 5.5 and 5.6. The baseline scenario is

conducted under the assumption of no changes in any of the demographic variables

considered under this analysis. The first alternative scenario assumes a 20 percent change

in per capita income (wage rate) per year for the period 2012/13-2016/17 and no changes

in other demographic structures (family size and proportion of ready to retire age group).

In addition to the assumption made under scenario one, the second scenario is built with a


101

0.05 percent increase in proportion of age group (50-54) in the total population, and 1.2

percent decline in family size per year.

Scenario 1: Increase in per-capita income by 20 percent

An increase in the Chinese per capita income by 20 percent will result in an increase

in textile price by 44 percent each year, on average, over the simulated period when

compared to the baseline scenario as shown in Table 5.3. Domestic consumption will also

increase, on average, by 86 percent while textile trade will decline, on average, by 58

percent over the five year period. As described in Table 5.4, Chinese cotton production,

mill use and imports will also increase by 3.86, 3.04 and 1.81 percent, respectively, as a

result of the increase in per capita income when compared to the baseline scenario over

the simulation period. Chinese cotton price is also expected to increase, on average, by 14

percent over the five years. This result, in turn, indicates that the effect of changes in per

capita income is more expansionary to the Chinese textile industry as increase in demand

more than offsets the decline in supply as a result of the increased input cost.

In regards to the world cotton market, world cotton production, and mill-use is

expected to increase slightly over the simulation period as shown in Table 5.5. World

cotton trade declines in earlier years of the simulation period indicating a contraction

effect of the increase in per capita income, but will eventually returns to grow again over

the latter periods of simulation years as increase in demand as a result of increase income

offsets the reduction in supply. In regards to U.S cotton market shown in Table 5.6, U.S

mill use and production are expected to increase, on average, by 25 and 2 percent while

exports is expected to decline on average by 3 percent. The A-index and U.S farm price


102

are expected to increase, on average, by 1.29 and 7 percent over the five year period,

respectively.

Scenario 2: Increase in per-capita income by 20 percent, a 0.05 percent increase in

proportion of age group (50-54) in total population, and 1.2 percent decline in family

size

Under scenario two, the Chinese textile price index and textile domestic consumption

are still expected to increase by 40 and 78 percent, respectively but lower than the

increases observed under scenario one. On other hand, textile net-trade declines by lower

amount as demand shrinks as a result of smaller family size and growing old age

population. The effect of the demographic changes, however, gets smaller on domestic

consumption, textile price and textile trade as the large increase in per capita income

over later years offsets the decline in demand from the demographic changes. Similar

results are also indicated for Chinese domestic cotton market where in the demographic

changes inversely affect the increase in demand as a result of an income increase. The

Chinese domestic cotton price, cotton production, mill use and import increase by 11, 3, 2

and 1.64 percent, respectively in second scenario when compared to results from the

baseline scenario.

In regards to world market, the A-index, world cotton production, and mill use all will

increase compared to baseline scenario but by smaller amounts when compared to results

under scenario one. World trade, on the other hand, shrinks by smaller amount under

scenario two in earlier periods when compared to scenario one, indicating the strong

influence of the demographic changes in earlier years coupled with the increase in


103

production cost as a result of a wage increase. However, the large loss in trade in earlier

periods under scenario one will be offset in later years owing to the strong effect of the

increase in income on textile demand. U.S farm price, cotton production, mill use will

also increase under scenario two, while exports decline on average by 2.87% over the

five year period. Here, also, the difference between scenario one and two gets smaller in

later years as demand growth as result of income growth outweighs the decline in

demand as a result of the demographic changes.

Table 5.3. Effects of changes in Chinese demographic structure on Chinese textile market

China 2012/13 2013/14 2014/15 2015/16 2016/17 Average

Textile price index Base 247.84 269.68 254.66 220.27 207.97 240.09

Scenario 1 342.21 414.28 369.99 318.83 292.78 347.62

Percentage 38.07 53.62 45.29 44.74 40.78 44.50

Scenario2 318.71 378.76 382.12 318.84 289.24 337.53

Percentage 28.59 40.45 50.05 44.75 39.08 40.58 Million pounds

Textile net trade Base 14439.34 13942.64 13818.35 13800.24 13954.75 13991.06

Scenario 1 8077.21 6019.77 5318.16 4937.00 4894.58 5849.34

Percentage -44.06 -56.82 -61.51 -64.23 -64.93 -58.31

Scenario2 9196.37 7214.56 5804.18 5234.76 5155.82 6521.14

Percentage -36.31 -48.26 -58.00 -62.07 -63.05 -53.54 Domestic consumption Base 9083.00 10089.03 10282.37 10781.25 10775.35 10202.20

Scenario 1 15830.57 18611.19 19556.77 20549.45 20854.42 19080.48

Percentage 74.29 84.47 90.20 90.60 93.54 86.62

Scenario2 14603.05 17264.1 18955.4 20182.12 20528.573 18306.64

Percentage 60.77 71.12 84.35 87.20 90.51 78.79


104

Table 5.4. Effects of changes in Chinese demographic structure on Chinese cotton market

2012/13 2013/14 2014/15 2015/16 2016/17 Average

Yuan/lb China cotton Price Base 11.00 11.79 12.75 13.89 14.82 12.85

Scenario 1 12.00 13.45 14.34 15.70 16.61 14.42

Percentage 9.03 14.05 12.48 13.04 12.08 12.21

Scenario2 11.74 13.06 14.43 15.62 16.56 14.28

Percentage 6.69 10.73 13.14 12.47 11.77 11.13

000 Bales China production Base 31503.80 31909.27 31228.73 31640.56 32249.39 31706.35

Scenario 1 32194.93 32820.36 32545.98 33122.70 33972.96 32931.39

Percentage 2.19 2.86 4.22 4.68 5.34 3.86

Scenario2 31994.43 32580.58 32264.78 33030.06 33848.37 32743.64

Percentage 1.56 2.10 3.32 4.39 4.96 3.27

China Mill use Base 49006.01 50068.57 50213.92 51217.19 51528.35 50406.81

Scenario 1 49807.89 51314.53 51822.78 53096.77 53643.76 51937.15

Percentage 1.64 2.49 3.20 3.67 4.11 3.04

Scenario2 49582.15 50997.13 51582.50 52951.83 53509.16 51724.55

Percentage 1.18 1.85 2.73 3.39 3.84 2.61

China import Base 17479.77 18342.74 19039.68 19748.53 20193.06 18960.76

Scenario 1 17662.11 18673.60 19390.83 20165.34 20624.15 19303.21

Percentage 1.04 1.80 1.84 2.11 2.13 1.81

Scenario2 17613.63 18596.09 19395.07 20142.75 20611.70 19271.85

Percentage 0.77 1.38 1.87 2.00 2.07 1.64


105

Table 5.5. Effect of changes in Chinese demographic structure on World cotton market

World 2012/13 2013/14 2014/15 2015/16 2016/17 Average

Cents/lb

A-index Base 93.02 91.89 94.26 94.24 94.81 93.65

Scenario 1 94.81 94.14 95.40 94.98 94.93 94.85

Percentage 1.92 2.45 1.20 0.78 0.13 1.29

Scenario2 94.38 93.59 95.91 95.09 94.97 94.79

Percentage 1.45 1.85 1.75 0.90 0.17 1.22

000 Bales

Production Base 135489.97 136275.52 137944.2 160140.22 140997.2 138970.08

Scenario 1 136604.33 137672.49 139909.49 143112.47 146438.01 140747.36

Percentage 0.82 1.03 1.42 1.50 1.59 1.28

Scenario2 136286.77 137305.52 139463.94 142978.34 146292.32 140465.38

Percentage 0.59 0.76 1.10 1.41 1.49 1.08 Mill use Base 130682.11 132632.76 135099.68 137950.57 140288.19 135330.66

Scenario 1 131775.22 134182.80 136965.76 139957.63 142438.64 137064.01

Percentage 0.84 1.17 1.38 1.45 1.53 1.28

Scenario2 131474.01 133779.71 136674.42 139805.98 142295.03 136805.83

Percentage 0.61 0.86 1.17 1.34 1.43 1.09 Trade Base 48646.55 48636.21 49823.13 50796.77 51442.20 49868.97

Scenario 1 48600.10 48550.01 49749.79 50818.11 51514.82 49846.57

Percentage -0.10 -0.18 -0.15 0.04 0.14 -0.04

Scenario2 48608.94 48570.49 49746.98 50806.41 51510.99 49848.76

Percentage -0.08 -0.04 -0.15 0.02 0.13 -0.04


106

Table 5.6. Effects of changes in Chinese demographic structure on U.S. cotton market

2012/13 2013/14 2014/15 2015/16 2016/17 Average

U.S Cents/lb

Farm price Base 0.80 0.81 0.84 0.84 0.84 0.83

Scenario 1 0.86 0.89 0.91 0.90 0.89 0.89

Percentage 6.57 9.33 8.59 7.20 5.70 7.48

Scenario2 0.84 0.87 0.91 0.90 0.89 0.88

Percentage 4.87 6.91 8.48 7.12 5.50 6.58

000 bales

Production Base 19137.13 18185.21 18393.09 18610.08 19153.71 18695.84

Scenario 1 19396.62 18502.32 18821.23 19044.45 19541.44 19061.21

Percentage 1.36 1.74 2.33 2.33 2.02 1.95

Scenario2 19325.40 18418.69 18709.91 19011.15 19533.19 18999.67

Percentage 0.98 1.28 1.72 2.16 1.98 1.63 Mill use Base 4023.90 3688.03 3444.06 3156.10 2865.30 3435.48

Scenario 1 4688.59 4660.10 4436.23 4024.26 3622.96 4286.43

Percentage 16.52 26.36 28.81 27.51 26.44 25.13

Scenario2 4519.06 4415.84 4374.64 3994.57 3590.19 4178.86

Percentage 12.31 19.73 27.02 26.57 25.30 21.64 Export Base 14540.85 14506.35 14969.80 15369.10 16285.96 15134.41

Scenario 1 14146.37 13925.97 14394.57 14889.10 15868.78 14644.96

Percentage -2.71 -4.00 -3.84 -3.12 -2.56 -3.23

Scenario2 14244.44 14067.54 14406.51 14892.56 15890.68 14700.34

Percentage -2.04 -3.02 -3.76 -3.10 -2.43 -2.87

5.6 Summary and Conclusion Results from aggregate model of Chinese textile production revealed that input prices

(cotton price) was the most important determinant when compared to output prices

(domestic textile price index); and that the wage rate indicating sensitivity of the industry

to input prices. The aggregate Chinese textile export model also indicated the large

impact of domestic prices over world textile prices, and oil prices underscoring the

importance of domestic inflation in Chinese export market.


107

Simulation results from this study also supported the findings obtained under the

aggregate model. In general, the impact of income changes on demand for textiles is

stronger than the effect of income growth on textile production cost, thus pointing toward

an expansion of both the Chinese domestic textile and cotton sector. On the other hand,

the effect on the textile trade sector is negative, largely owing to a significant increase in

domestic textile price as a result of supply decreases and demand increases. The spillover

effect on the world and U.S market is, in most cases, expansionary causing the A-index,

world mill use, cotton production, U.S farm price, mill use, and cotton production to

increase, especially in later years of the simulation period. The trade effect, on other

hand, is negative and gets minimized in later years.

Demographic changes such as increase in elderly population and smaller family size

analyzed under scenario two have implications for textile and cotton markets. Cotton

farmers in India, other Asian countries, Argentina, and West African countries are likely

to be beneficiaries of these changes as exports of cotton is expected to grow from these

countries during the simulation period. Similar results can also be generalized regarding

the effect of demographic changes on Chinese and the U.S cotton market.


108

Chapter Six

Summary and Conclusion

This study has analyzed household textile expenditure patterns in China, taking into account fiber

choice, shopping behavior, and differences in preferences across different demographic groups.

The dissertation is motivated by the need to understand the evolution, behavior and characteristics

of Chinese consumers given the large and growing importance of Chinese textile industry on the

global market. To accomplish this task, the study used cross-sectional household data which

were obtained from a 2009 cotton consumer tracking study conducted by Cotton Council

International.

Results in regards to marginal effects of socioeconomic variables indicated that the ready-to-retire

age group tends to have a different consumption pattern than the other age groups. Consumers in

ready-to-retire age group are likely to spend more on home-textile and less on apparel compared

to the other age groups. This result disputes the long standing view that the elderly spend less on

textiles. Given the increasing proportion of the elderly consumer group, the high income elasticity

of home-textiles and double digit economic growth rate sustained by China, it appears that home-

textile marketers need to align marketing strategies that are designed to consider the elderly.

Another significant finding relates to apparel shopping behavior of Chinese households. Though

modern retail formats such as chain/ specialty stores have just started to evolve in china, attribute

they offer such as better in-store services, and a wide range of quality and fashionable products

appears to be appealing to Chinese apparel consumers. Such findings appear to mirror the retail

evolution experienced in the western developed world where the popularity of factory outlets,

discount and department stores declining over the years in favor of specialty stores.

Another important finding from this analysis relates to the potential for a decline in

dominance of China’s apparel industry in the global textile market. Though China still


109

leads the world in apparel production, recent rise in domestic labor costs has put the

industry lose its comparative advantage of lower labor cost. To retain this competitive

advantage in apparel production, however, producers need to emphasize on product

development and design, which are likely to be more profitable.

Limitations exist for this study. The data used for this analysis is from urban consumers

and is based on a one year study on household textile consumption pattern. The findings,

as a result, are limited in terms of explaining the evolution of Chinese household

consumption behavior; however the research provides a foundation for further research

for textile market in China. In addition, future research in other emerging economies such

as India and Brazil would give a better picture in understanding the global textile market.


110

References

Abernaty, F.H., J.T. Dunlop, J.H. Hammond, D. Weil(1999).The Development of the

China Apparel Industry, Harvard Center for Textile and Apparel Research.

Agnew , G.K. 1998. “LinQuad: An Incomplete Demand Systems Approach to

Demand Estimation and Exact Welfare Measures.” Thesis. Department of

Agricultural and Resource economics, University of Arizona.

Alston J.M. and Chalfant J.A. (1993). The Silence of the Lambdas: a Test of the

Almost Ideal and Rotterdam Models. American Journal of Agricultural Economics,

75: 304-313.

Amemiya, T. 1984. Tobit Models: A Survey. Journal of Econometrics, 24:3-61.

Barten, A.P. 1964. Consumer Demand Functions Under Conditions of Almost

Additive Preferences. Econometrica 32, 1-38.

_________ 1969. Maximum Likelihood estimation of a complete system of demand

equation, European economic Review 1:7-73.

Belasco, E., S. Ghosh, and Chidmi B. 2010. An Evaluation of the Soda Tax with

Multivariate Nonparametric Regressions. Selected Paper. American Agricultural

Economic Association Annual Meeting, Denver, Colorado.

Clements K.W., Selvanathan A., and Selvanathan S. (1996). Applied Demand

Analysis: A Survey. The Economic Record 72,63-81.

Christensen, L.R., Jorgenson, D.W. and Lau, L.J. (1975). Transcendental Logarthmic

Utility Functions. American Economic Review 65, 367-83.

CCI (Cotton Council International). Estimating Cotton Equivalence of Chinese End-

Use Consumption. Memo, November 2010.

Davidson, R. and J.G. MacKinnon. 1981.Several Tests for Model Specification in

Presence of Alternative Hypothesis. Econometrica, 49(3):781-793.

Deaton, A. and Muellbauer. J. 1980. An Almost Ideal Demand System. The American

Economic Review, 70, 312-326.

Deaton, Angus, and John Muellbauer. 1980. Economics and Consumer Behavior

(Cambridge: Cambridge University Press, 1980).

Dicken, P. (1998).Global Shift: Transforming the World economy. Paul

Chapman,London.

Edgerton D.L.1997. Weak Separability and the Estimation in Multistage Demand

Systems. American Journal of Agricultural Economics,79:62-79.


111

Fadiga M.L, Misra S.K. and Ramirez O.A. U.S Consumer Purchasing Decisions and

Demand for Apparel. Journal of Fashion Marketing and Management, 9(2005):1361-

2026.

Fadiga M.L, Mohanty S, Welch M, Pan S. 2008. Doha Development Agenda:

Implications for the U.S. and World Cotton Markets. The Journal of International

Trade and Economic Development, 17:135-153.

FAPRI.2001. Documentation of World Cotton Model. FAPRI-UMC Technical Data

Report 00-01.

Food and Agricultural Organization, World Apparel Fiber Consumption Survey,

various issues.

Fiber Organon. Fiber Economic Bureau, selected issues.

Gao Q. and Zhai F. Demographic Changes and Household Income in Urban China.

Paper Prepared for the 29th

General Conference of the International Association for

Research in Income and Wealth, Joensuu, Finland, August 2006.

Greene,W.H. 2007. Econometric Analysis. Upper Saddle River, N.J.: Prentice Hall.

Guilkey,D., Lovell C.A.K, and Sickles R.C. 1983. A Comparison of the Performance

of Three Flexible Functional Forms. International Economic Review 24:591-616.

Hanskul S. Understanding China’s Consumers. Deutsche Bank Research. September

2010.

Hayfield , R., and J. Racine. 2008.Nonparametric Econometrics: the NP Package.

Journal of Statistical Software 27:1-32.

Heien, D., and C. Wessells.1990. Demand Systems Estimation with Microdata: A

Censored Regression Approach. Journal of Business and Economics Statistics 8: 365-

371.

Hudson D. and Ethridge D. 2000. Income distributional impact of trade policies in a

multi market framework: a case in Pakistan. Journal of Agricultural and Applied

Economics 32(1), 49-61.

Hurd, M. 1979. Estimation in Truncated Samples When There is Hetroscedasticity.

Journal of Econometrics, 11:247-258.

International Cotton Advisory Committee (ICAC).2009. World Textile Demand.

Washington, DC.

Jones M and Hayes S.G. The Economic Determinants of Clothing Consumption in

UK 1987-2000. Journal of Fashion Marketing and Management, 6(2002):326-339.


112

Just, R.E, D.L Hueth, and A. Schmitz .2004. The Welfare Economics of Public

Policy. Edward Elgar Publishing Inc., Northampton, Massachusetts.

Klein, L.R. and H.Rubin. 1947-1948. A constant Utility Index of the Cost of Living.

Review of economic studies 15:84-87.

Klein R. L. and R.H. Spady. 1993. An Efficient Semiparametric Estimator for Binary

Response Models. Econometrica 63: 387-421.

Kim, K.,2003. U.S. Aggregate Demand for Clothing and Shoes, 1929-1994: Effects

of Nondurables Expenditures, Price and Demographic Changes. International Journal

of Consumer Studies, 27:11-125.

Kim S. and Kincade D. H. 2007. Evolution of Retail Institution Types and

Consumers’ Store Patronage Behavior: A Cross-Cultural Comparison Among

Consumers in China, India, and the United States.

LaFrance, J. T., 1990. Incomplete Demand Systems and Semilogarthmic Demand

Models. Australian Journal of Agricultural Economics, 34: 118-131.

Lafrance, J.T, Beatty T,K.M, and Pope R.D. 2006. Gorman Engle Curves for

Incomplete Demand Systems. Exploring Frontiers in Applied Economics: Essays in

Honor of Stanley R. Johnson. Berkley: Berkley electronic Press.

Lazaridis P. 2004. Demand Elastcities Derived from Consistent Estimation of

Heckman Type Models. Applied Economic Letters, 11:523-527.

Lee, J., D. Sherman, and H. Wang. Apparel Expenditiires Pattems of Elderly

Consumers: A Life-Cycle Consumption Model. Family and Consumer Science

Research Journal, 26 (December 1997): 109-147.

Lee , L. and M. M. Pitt. 1987. Micro-econometric Models of Rationing, Imperfect

Markets, and Non –negativity Constraints. Journal of econometrics 36: 89-110.

Lewbell A. 1989. Identification and Estimation of Equivalence Scales under Weak

Separability. Review of Economic Studies, 56: 311-316.

Li and Fung Research Centre .2011. China’s Apparel Market, available at

http://www.lifunggroup.com/eng/knowledge/research/industry_series19.pdf

MacDonald S., Pan S., Hudson D., Tuan F. 2011. Toward a Consumer Economy in

China: Implications of Changing Wage Policies for U.S. Cotton Exports. Selected

Paper for Presentation at the American Agricultural Economics Association annual

Meeting, Pittsburgh, July 24-26.

Martins M. F.2001. Parametric and Semiparametric Estimation of Sample Selection

Models: An Empirical Application of Female Labour Force in Portugal. Journal of

Applied Econometrics,16:23-29.

http://www.lifunggroup.com/eng/knowledge/research/industry_series19.pdf


113

Mokhtari M. An Alternative Model of US Clothing Expenditures: An application of

Co- integration Techniques. The Journal of Consumer Affairs, 26(Winter 1992):305-

323.

National Bureau of Statistics of China (NBS). China Statistical Yearbook, China

Statistical Publishing House, Beijing, 2003-2009.

Nelson J.A. Individual Consumption within the Household: a Study of Expenditures

on Clothing. The Journal of Consumer Affairs, 23(Summer 1989)2:21-44.

Newey W.K., Powell J.L., Walker J.R.(1990).Semi-parametric Estimation of

Selection Models: Some Empirical Results. The American Economic

Review,80:324-328.

Pan, S., D.Hudson, M.Mutuc and D. Ethridge. Structural models of the U.S. and rest

of the world natural fiber markets. Working Paper CER 04-03, Department of

Agricultural and Applied Economics, Texas Tech University 2004(updated 2011).

Pan, S., M. Welch , S. Mohanty, D. Ethridge, M. Fadiga.2005. Chinese Tariff Rate

Quota vs. U.S. Subsidies: what affects the world Cotton Market More? Selected

Paper for Presentation at the American Agricultural Economics Association annual

Meeting, Rohde Island, July 24-27.

Pan, S., M.L. Fadiga, S. Mohanty, and M. Welch. Cotton in a Free Trade

World. Economic Inquiry, 45 (1, 2007): 188-197.

Pan. S., S. Mohanty, D. Ethridge, and M. L. Fadiga. Effects of Chinese Currency

Revaluation on World Fiber Markets. Contemporary Economic Policy, 25 (2): 185-

205, June 2006.

[PCIFibre], “Textile Fiber Demand, 2010, memo, available at

http://pcifibres.com/images/Peter%20Driscoll%20HK08%20Paper.pdf.

Pollak, Robert A., and Terence J. Wales (1978). Estimation of Complete Demand

Systems from Household Budget Data: The Linear and Quadratic Expenditure

Systems, American Economic Review 68 (June 1978), 348-359.

____________ (1992). Demand System Specification and Estimation. Oxford

University Press.

___________ (1978). Comparison of the Quadratic Expenditure system and Translog

Demand Systems with Alternative Specifications of Demographic Effects.

Econometrica 48, 595-612.

Qui L.D. 2005. China’s Textile and Clothing Industry. http://s3.amazonaws.com/zanran_storage/www.bm.ust.hk/ContentPages/18112599.pdf

Ray R. 1983. Measuring the Cost of Children.: An alternative Approach. Journal of

Public Economics.22:89-102.

http://www.aaec.ttu.edu/ceri/Published%20Papers/Journal%20Article/CottonFreeTradeWrld.pdf

http://www.aaec.ttu.edu/ceri/Published%20Papers/Journal%20Article/CottonFreeTradeWrld.pdf

http://www.aaec.ttu.edu/ceri/Published%20Papers/Journal%20Article/EffectsChineseCurrency.pdf

http://www.aaec.ttu.edu/ceri/Published%20Papers/Journal%20Article/EffectsChineseCurrency.pdf

http://pcifibres.com/images/Peter%20Driscoll%20HK08%20Paper.pdf

http://s3.amazonaws.com/zanran_storage/www.bm.ust.hk/ContentPages/18112599.pdf


114

Sam, A., and Zheng Y. 2010. Semi-parametric Estimation of Consumer Demand

Systems with Micro Data. American Journal of Agricultural Economics 92:246-257.

Shonkwiler, J. S. and S. Yen. "Two-Step Estimation of a Censored System of

Equations."'American Journal of Agricultural Economics, 81(1999): 972-982.

Stone, R. (1954). Linear expenditure Systems and Demand Analysis: An Application

to the Pattern of British Demand. Economic Journal 64, 511-27.

Sumner, D. A.2003. A quantitative simulation analysis of the impacts of U.S. cotton

subsidies on cotton prices and quantities. Mimeo, Department of Agricultural and

Resource Economics, University of California Davis.

Thiel, H. (1965). The Information Approach to demand analysis. Econometrica 33,

67-87.

UNIDO (2009). Industrial Development Report 2009, United Nations Industrial

Development Organization, Vienna, Austria.

United States Department of Agriculture (USDA). Cotton and Wool Situation and

Outlook Yearbook, various issues.

Wang F.2011. The Future of Demographic Overachiever: Long Term Implications of

the Demographic transition in China. Population and development Review

37(supplement).

Wagner J. Expenditure for Household Textiles and Textiles Home Furnishings: An

Engle curve Analysis. Home economics research Journal 15(September 1986):21-31.

Wagner J. and Mokhtari M. The Moderating effect of Seasonality on Household

Apparel Expenditure. The Journal of Consumer Affairs 34(Winter 2000):314-328.

Winakor, G. Household Textile Expenditure by Farm and City Families: Assortment

Owned, annual Expenditures, and Sources. Family and Consumer Science Research

Journal, 4(September 1975):1-26.

Yen S.T., Kan Kamhon and Jiuan S.S (2002). Household Demand for Fats and Oils:

Two-Step estimation of a Censored Demand System. Applied Economics, 14:1799-

1806.

Yen, Steven T., Biing-Hwan Lin, and David M. Smallwood (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, pp. 458–478

Yen S.T., Lin, B.H. (2004). A Sample Selection Approach to Demand Systems.

Selected Paper for Presentation at the American Agricultural Economic Association,

Denver, Colorado, August 2004.


115

Zhang Z., Ching G., Gong C., Moody J, and Liu W.S. Investigation of Denim Wear

Consumption in China. Research Journal of Textiles and Apparel, 1999(3):60-6


116

Appendix A

TableA.1. Estimated parameters of participation equation for apparel model

Klein-Spady Probit

Variables Shirt Dress Other Shirt Dress Other

Intercept n/a n/a n/a 2.498**

(0.5092)

3.6099**

(0.5944)

3.135**

(0.5349)

Family size n/a n/a n/a -0.0122

(0.0312)

-0.0767*

(0.0443)

0.0097

(0.0341)

Coat price 0.0009

(0.0015)

0.0017

(0.0014)

-0.0063**

(0.0029)

0.1552**

(0.0572)

-0.0038

(0.0748)

-0.3119**

(0.0622)

Pants price -0.0007

(0.0018)

-0.0025

(0.0016)

-0.0071**

(0.0027)

-0.0599

(0.0624)

-0.1443*

(0.0779)

-0.1528**

(0.0673)

Shirt price -0.3068**

(0.0020)

0.0012

(0.0014)

0.0045

(0.0033)

-0.7223**

(0.0613)

-0.2067**

(0.0672)

-0.3577**

(0.0607)


117

TableA.1. Estimated parameters of participation equation for apparel model (continued….)

Dress price

-0.0077**

(0.0015)

-0.2557**

(0.0021)

-0.0094**

(0.0031)

-0.2269**

(0.0941)

-1.1550**

(0.1092)

-0.1328

(0.0972)

Others price -0.0008

(0.0011)

0.0002

(0.0014)

-0.3199**

(0.0019)

-0.1153

(0.0401)

-0.0912*

(0.0495)

-0.8506**

(0.0502)

Expenditure -0.0002

(0.0011)

-0.0005

(0.0011)

0.0015

(0.0017)

0.2049**

(0.0333)

0.4098**

(0.0487)

0.6164**

(0.0388)

Age 15-29

(ref: over50)

0.0310**

(0.0027)

0.0081**

(0.0022)

0.0044

(0.0058)

0.0458

(0.1068)

-0.0821

(0.1479)

-0.0538

(0.1164)

Age 30-39 0.0275** 0.0113** 0.0039 -0.0107 -0.00069 0.0348

(0.0030) (0.0027) (0.0060) (0.1210) (0.1648) (0.1315)

Age 40-49 0.0372**

(0.0031)

0.0085**

(0.0025)

0.0077

(0.0055)

-0.0812

(0.1121)

0.0044

(0.1534)

0.0002

(0.1224)

High school education ( ref:

primary education

0.0029

(0.0039)

0.0009

(0.0045)

0.0312**

(0.0053)

-0.1233

(0.1078)

0.1556

(0.1485)

-0.0538

(0.1184)


118

TableA.1. Estimated parameters of participation equation for apparel model (continued….)

College and over

0.0033

(0.0024)

-0.0009

(0.0022)

0.0078*

(0.0045)

0.1143

(0.0714)

0.2129**

(0.0987)

0.1885**

(0.0781)

Have a child less than 7

years old (ref: no dependent)

0.0026

(0.0028)

-0.0052**

(0.0021)

-0.0020

(0.0044)

0.1578*

(0.0902)

-0.0709

(0.1227)

0.0241

(0.0973)

Have a child 8- 15 years old

-0.0008

(0.0025)

-0.0024

(0.0025)

0.0016

(0.0047)

0.2208**

(0. 0906)

-0.0730

(0.1227)

-0.1227

(0.0976)

Have a elderly(aged over 50) 0.0015

(0.0029)

0.0009

(0.0028)

-0.0023

(0.0041)

-0.1470*

(0.0873)

0.1011

(0.1188)

-0.0074

(0.0951)

Gender(ref: female) 0.0004

(0.0020)

-0.1749**

(0.0020)

0.0023

(0.0035)

0.0967

(0.0598)

-0.9095**

(0.0967)

-0.3872**

(0.0644)


119

APPENDIX B

Figure B.1. Probability density function for error terms of coats market participation equation


120

Figure B.2. Probability density function for error terms of pants market participation equation

Documents

three essays on estimation of chinese textile demand system and