income disparity and economic growth: evidence from people's republic of china

8/22/2019 Income Disparity and Economic Growth: Evidence from People's Republic of China

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October 2006

ERDECONOMICS AND RESEARCH DEPARTMENT

Working PapeSERIESNo.

84

Duo Qin, Marie Anne Cagas,Geoffrey Ducanes, Xinhua He,Rui Liu, and Shiguo Liu

Income Disparity and EconomGrowth: Evidence from

Peoples Republic of China

Income Disparity and EconomGrowth: Evidence from



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ERD Working Paper No. 84

INCOME DISPARITYAND ECONOMIC GROWTH:

EVIDENCEFROM PEOPLES REPUBLICOF CHINA

DUO QIN, MARIE ANNE CAGAS, GEOFFREY DUCANES,XINHUA HE, RUI LIU, AND SHIGUO LIU

OCTOBER 2006

Duo Qin is a professor at St. Marys College, London; Marie Anne Cagas and Geoffrey Ducanes are consultants at the

Economics and Research Department, Asian Development Bank; and Xinhua He, Riu Liu, and Shiguo Liu are academic

staff at the Department of World Economic Statistics, Institute of World Economics and Politics, Chinese Academy of

Social Sciences.


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Asian Development Bank6 ADB Avenue, Mandaluyong City

1550 Metro Manila, Philippineswww.adb.org/economics

2006 by Asian Development BankOctober 2006

ISSN 1655-5252

The views expressed in this paperare those of the author(s) and do notnecessarily reflect the views or policies

of the Asian Development Bank.


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FOREWORD

The ERD Working Paper Series is a forum for ongoing and recently

completed research and policy studies undertaken in the Asian DevelopmentBank or on its behalf. The Series is a quick-disseminating, informal publicationmeant to stimulate discussion and elicit feedback. Papers published under this

Series could subsequently be revised for publication as articles in professionaljournals or chapters in books.


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CONTENTS

Abstract vii

I. Introduction 1

II. Background on Income Inequality in the PRC 1

III. Theories on Income Inequality Growth Nexus 3

IV. Method of Investigation and Inequality Measures 4

V. Econometric Model Results 8

VI. Model Simulations 11

VII. Conclusion 17

Appendix: Main Data Sources and Variable Definition 18

References 19


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ABSTRACT

This pilot empirical study seeks to ascertain how income inequality affectsgrowth by incorporating panel data information into a macroeconometric model.Peoples Republic of China is used as the pilot field. Provincial urban and ruralhousehold data are used to construct income inequality measures, which are then

used to augment household consumption equations in a quarterly macroeconometricmodel. Model simulations are performed to study the inequality effect on grossdomestic product growth and its sectoral components. Results show that income

inequality forms robust explanatory variables of consumption and that the wayinequality develops carries certain negative consequences on gross domesticproduct and sectoral growth.


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I. INTRODUCTION

Since undertaking market reforms in 1978, the economy of Peoples Republic of China (PRC) hasachieved sustained high growth and rapid progress in poverty reduction. The World Bank estimatesthat in the more than two decades since reforms started, average income per capita in the countryhas quadrupled, while more than 270 million people have been lifted out of poverty (Chen and

Wang 2001). From 1978 to 2004, gross domestic product (GDP) growth in the PRC averaged nearly10% annually, the highest growth rate of any country in the world for the same period, and, atleast in the near term, it has not yet shown signs of slowing down.

If there seems to be a dark lining to these extraordinary achievements, it is that income

inequality in the countryseen as a whole, within/between urban and rural areas, and acrossprovinceshas also risen quite rapidly in the period (see for example World Bank 1997, Chen and

Wang 2001, Kanbur and Zhang 2005). For any given level of natural or human capital, the greaterthe inequality, the higher the poverty one could expect. Income inequality is also seen to affectlong-term economic growth, although there is no consensus on the direction of the effect.

If income inequality affects growth positively, it is possible that the poverty-reducing impactof this growth offsets the direct adverse effect of inequality on welfare, thus tolerating relativelyhigh inequality. On the other hand, if inequality affects growth negatively, then addressing itimmediately should be an important concern.

This paper investigates empirically how much and in what ways income inequality affects thePRCs economic growth by means of incorporating income disparity measures derived from provincial

panel data of urban and rural household income into a macroeconometric model, and simulating theeffects of changes in income inequality on growth. The rest of the paper is structured as follows.Section II gives a summary of the inequality situation in the PRC. Section III briefly surveys theliterature on the transmission mechanism between income inequality and economic growth. Section IV

describes our modeling approach and discusses available inequality measures that might be pertinentto our investigation. Section V describes the estimation results of incorporating income inequalityinto the macroeconometric model. Section VI presents the results of model simulations showing theeffects of inequality changes on other economic variables. The last section concludes.

II. BACKGROUND ON INCOME INEQUALITY IN THE PRC

Income inequality had remained fairly mild and stable under an egalitarian regime prior to

economic reforms that started in 1978. According to Li et al. (2000, 34), the Gini ratio of urbanworkers was 0.16; the Gini ratio for rural households was 0.21 in 1978;1 and the Gini ratio among

provinces was 0.14 in 1979. The situation has changed considerably since the reforms. Followingthe governments introduction of individual incentives (also known as the household responsibility1 Li et al. (2000, 3) list several estimates for rural households Gini ratio in 1978: 0.21 estimated by the National

Statistics Bureau of China, 0.22 estimated by Adelman and Sunding in 1987, and 0.31 estimated by the World Bankin 1983.


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system) and market forces in 1978, as these immediately began to increase returns to capital andland, diversify employment, and increase factor mobility (World Bank 1997), income inequality rosesignificantly for the country as a whole, within/between urban and rural areas, and across provinces.This is particularly interesting in view of the fact that inequality is found to remain fairly constant

over time in many economies (e.g., see Besley and Burgess 2003).Krongkaew (2003) reports the per capita income Gini ratio in the whole of the PRC to be at

0.29 in 1981, and to have risen progressively to 0.30 in 1984, 0.35 in 1989, 0.39 in 1995, and

0.46 in 2000. Li et al. (2000, 8) estimate that within rural areas the Gini ratio of household incomerose from 0.21 to 0.34 from 1979 to 1995, and that within urban areas the Gini ratio went upfrom 0.16 to 0.28 in the same period. Li et al. (2000) and Zhang (2003), meanwhile, report the

interprovincial per capita income Gini ratio to have been rising almost consistently from 0.32 in1978, to 0.28 in 1983, 0.38 in 1988, 0.39 in 1995, 0.40 in 1999, and 0.42 in 2000.

The household responsibility system initially resulted in rural income growth surpassing

urban income growth as farms achieved greater productivity, but this trend was soon reversedas agricultural productivity hit the ceiling and rural income fell further behind urban income. In

fact, one may divide the period 1981 up to the present for the PRC into three different subperiodsdifferentiated by the growthequity characteristic of the economy, as was done by the World Bank(1997). The period 19811984 could be classified as a period ofgrowth with equityas real meanincome increased by 12.6% a year during this period while the Gini ratio rose only marginally. Theperiod 19841989 could be classified as a period of income inequality with little growth as overallreal mean income increased by less than 1% a year for this period, and this was very unevenlydistributed as income of the richest decile increased while the income of the poorest decile fell(World Bank 1997).2 Finally, the period 1990 up to the present is a period ofgrowth with income

inequalityas both overall real mean income and the Gini ratio grew rapidly.

Some researchers have claimed that the PRC possibly has the largest income gap between therural and urban sectors in the world (e.g., see Lin 2003). Inequality decompositions done by the

government show that the ruralurban income gap explained one third of total inequality in 1995and one half the increase in inequality since 1985. Rural per capita income was 38.9% of urban percapita income in 1978, 53.8% in 1985, and was down to 35.9% in 2000 (Lin 2003). Li et al. (2000)note the same diverging path of rural and urban incomes. This does not even take into accountthe set of publicly provided services (housing, pensions, health, education, and other entitlements)

that augment urban incomes by an average of 80 percent. When these are considered, ruralurbandisparities accounted for an even greater share of total inequality (World Bank 1997). Figure 1shows for the period 19922003 the widening gap between urban and rural per capita incomes.

Across provinces, the biggest source of increasing income inequality appears to be betweencoastal and interior provinces. Coastal provinces benefited from their proximity to world markets,better infrastructure, and educated labor force as the PRC opened up to the outside world (World Bank

1997). Interprovincial inequality accounted for a quarter of total inequality in 1995 and explained athird of the increase since 1985. In 1985 residents in the interior of the PRC earned 75% as muchas their coastal counterparts; by 1995 this had dropped to 50% (World Bank 1997).

2 However, this description may not be so accurate if we look at the official statistics on per capita GDP.

2 OCTOBER 2006

INCOME DISPARITYAND ECONOMIC GROWTH: EVIDENCEFROM PEOPLESREPUBLICOF CHINA

DUO QIN, MARIE ANNE CAGAS, GEOFFREY DUCANES, XINHUA HE, RUILIU, AND SHIGUO LIU


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III. THEORIES ON INCOME INEQUALITY GROWTH NEXUS

One of the most famous postulates concerning income inequality and growth was put forwardby Kuznets (1955). In what has come to be known as Kuznetss hypothesis, it is postulated that inthe course of the development of a country, inequality first rises before eventually decliningtheinverted-U hypothesis. However, Kuznetss hypothesis implies a causal relationship of growth inequality at a macro level, i.e., relating inequality to the stages of economic development.

Theories concerning how income inequality affects economic growth are more micro oriented,

i.e., relating heterogeneous consumers behavior and investment indivisibility to aggregate demand(e.g., see Bagliano and Bertola 2004). Specifically, the theories demonstrate that unequal incomedistribution among householdsat a point in time but more especially as it changes over time 3

affects aggregate consumption and demand structure through heterogeneous propensities toconsume and to save, and these effects are then transmitted to investment allocations, especiallyinvestment in human capital (see for example Benabou 1996, Galor and Zeira 1993, Galor and Tsiddon1997). Consequent theories augment the transmission process by studying the effect of inequality

on redistributive policies and the possible inefficiencies those may bring.4 Broader sociologicalstudies also pinpoint inequality as a possible cause for socio-political instability or violence (see forexample Knack and Keefer 2000), and even for the differences in fertility rate (see Perotti 1996).

3 An early pioneer of this issue is Staehle (1937 and 1938), who demonstrated how visible income distribution changedin Germany using quarterly data and how such changes affected aggregate market demand. In this context, he pointed

out the weakness of Keyness aggregate propensity to consume for overlooking the implication of income distributionand endorsed Robinsons (1933) proposal to bring income distribution between classes into discussions of aggregate

output growth. Noticeably, Robinsons idea is precursory to the later development of growth models with two classes

(see Kaldor 1956 and 1957, Bourguignon 1981). An example of recent theories is Zweimller (2000), which shows howinequality can affect long-run growth negatively by depressing aggregate demand for innovative products.

4 For evidence or lack of this, see Alesina and Rodrik (1994), Persson and Tabellini (1994), Perotti (1996), and Deiningerand Squire (1998).

Note: Figures are in nominal terms. See Appendix for the data source.

Figure 1Per Capita Household Income (RMB per quarter)

0

50 0

1000

1500

2000

2500

3000

PCINCuPCINCr

93-11992-1 2000-199-198-197-196-195-194-1 04-103-102-101-1

ERD WORKINGPAPER SERIESNO. 84 3

SECTION III

THEORIESON INCOME INEQUALITY GROWTH NEXUS


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Many empirical studies have produced positive evidence about the link between growth andincome inequality (e.g., Aghion et al. 1999). These studies can roughly be divided into two strands:cross-country analyses and micro-based (usually household survey based) studies. Cross-countryanalyses are frequently carried out by running regressions of growth rates on various proxies for

income inequality and redistribution effects together with relevant control variables. These areoften criticized, however, for lack of structural models and thus methodological crudity (e.g., seeFigini 1999). Micro applied studies, on the other hand, while methodologically tighter, often lack

a systematic and direct link to the macro economy.5

One increasingly popular approach is to study the subject by means of computable generalequilibrium (CGE) models. The CGE approach has the attraction of providing a logically consistent

way of analyzing the link between aggregate economic growth and disaggregated income changes. Agood example of this is the model developed recently by the Poverty and Social Impact Analysis Groupat the World Bank (see Bourguignon et al. 2003). However, as CGE models are heavily calibrated, itis difficult to assess the model conclusions empirically. Moreover, CGE models tend to lack realistic

dynamic adjustment mechanisms and might not be able to account for the heterogeneous effectsof a given policy within assumed homogenous agent groups, thus making them miss importantsources of changes in income inequality.

IV. METHOD OF INVESTIGATION AND INEQUALITY MEASURES

In this study, we explore a novel route of augmenting a macroeconometric model of the PRCwith income inequality measures built upon panel data such that the impact of income inequalityon growth and the macro economy can be studied via model simulations.6

The macroeconometric model of the PRC was developed at the Asian Development Bank (ADB)jointly with the Institute of World Economics and Politics (IWEP) of the Chinese Academy of SocialSciences (CASS), following He et al. (2005). The model, hereafter referred to as the ADB PRC model,uses quarterly data and contains over 70 endogenous variables and less than 20 nonmodeled variables.

It is structured into six blocks: investment, foreign trade, government, banking, prices, and privateincome and consumption. The last block is built upon aggregate time series of urban and ruralhousehold per capita income. A detailed description of the model plus a full equation list is given

in Qin et al. (2006).7 To augment the model, panel data of urban and rural household per capitaincomes of 30 provinces and autonomous municipalities at annual frequency are collected.8

Two types of income inequality measures can be derived from the available data. One is

income inequality between urban and rural areas measured by the ratio of their per capita incomesusing solely the aggregate time-series data (see Figure 1). The other is income inequality withinurban and within rural areas, obtained by exploiting provincial panel data on per capita income.There are several possible measures to characterize income inequality of panel data according to

5 A good example of micro empirical studies relating to the PRC is carried out by Benjamin et al. (2004). Based onhousehold survey data, they find unambiguous deterioration of income distribution in rural PRC, but they acknowledge

the difficulty of drawing inferences from their micro findings to macro conditions.6 Due to panel data shortage, it is impossible to endogenise the inequality measures to study the effects of growth or

government fiscal policy on these inequality measures.7 Some of the model properties are exhibited in two applied studies: Qin et al. (2005); and Qin, Cagas, He, and Quising

(2006).8 Beijing, Shanghai, and Tianjin are counted as independent entries, but Chongqing, a relatively new autonomous

municipality, is still regarded as part of Sichuan in our sample.

4 OCTOBER 2006




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the literature. Table 1 gives the formula for some of the more prevalently used ones, which arethe Gini coefficient (G), variance (V), coefficient of variation (c), log of variance (v1), variance oflogarithms (v2), Atkinsons Index (A), Daltons Index (D), Theils Index (T), and Herfindahls Index(H). In Figure 2, time-series graphs of each of these measures are plotted using panel data set for

the period 19922003.TABLE 1

INCOME INEQUALITY MEASURESANDTHEIR PROPERTIES

MEASURE FORMULA PROPERTIES

Gini coefficient

Gar y r

ny=

( )2cov , y

a,b,c

Variance

Vn

y yii

n

= [ ]=1 2

1 a,c,d

Coefficient of variationc

V

y=a,b,c,d

Log of variance =

=

2n

i

i 1

y1v1 log

n yc,d

Variance of logarithms

vn

yn

yi ii

n

i

n

21 1

11

2

= ( ) ( )

= log log

c,d

Atkinsons Index

Ay

Un

U y ii

n

= ( )

=1 1 11

1 a,b,c,d

Daltons IndexD

nU y

U y

ii

n

=

( )

( )=

1

1

1

a,d

Theils Index

Tn

y

y

y

yi i

i

n

=

+1

1

log

a,b,c,d

Herfindahls Index

Hy

nyi=

2

a,b,d

a means weak principle of transfers.b means scale independence.

c means principle of population.d means decomposability.

Note: For all measures y denotes income. 1. ry is the correlation coefficient between income and the ranks of all

individuals according to their income from poorest to richest, n is the population size; 2. y is mean income;3. U() denotes a social utility function, which in this paper we assumed to take the form of a constant relative

inequality aversion utility function.


SECTION IV

METHODOF INVESTIGATIONAND INEQUALITY MEASURES


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FIGURE 2URBAN, RURALAND OVERALL INTERPROVINCIALPERCAPITA INCOME INEQUALITY

Average

4500

4000

35003000

2500

2000

15001000

500

0

2000000

1500000

1000000

500000

0

2500000

1992 93 94 95 96 97 98 99 2000 01 02 03 031992 93 94 95 96 97 98 99 2000 01 02

1992 93 94 95 96 97 98 99 2000 01 02 03 1992 93 94 95 96 97 98 99 2000 01 02 03

1992 93 94 95 96 97 98 99 2000 01 02 03 1992 93 94 95 96 97 98 99 2000 01 02 03

1992 93 94 95 96 97 98 99 2000 01 02 03 1992 93 94 95 96 97 98 99 2000 01 02 03

1992 93 94 95 96 97 98 99 2000 01 02 03 199293 94 95 96 97 98 99 200001 02 03

Variance

0.0450.0400.0350.030

0.050

0.0200.015

0.0000.0050.010

0.025

0.35

0.30

0.25

0.20

0.40

0.10

0.05

0.00

0.15

0.40

0.35

0.30

0.25

0.45

0.15

0.10

0.05

0.00

0.10

0.20

0.090.080.070.060.050.040.030.020.010.00

0.200.180.160.140.120.100.080.060.040.020.00

Thell

Gini ratio

Coefficient of Variation

Variance of Logarithm

Dalton (e=0.5)

0.35

0.300.25

0.20

0.40

0.10

0.05

0.00

0.15

0.60

0.70

0.50

0.40

0.30

0.70

0.10

0.00

0.20

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

Herfindahls Index

Log of Variance

Atkinson (e=0.5)

Urban Overall Rural

Source: Authors computations. Gini ratios were computed using the Distribution Alaysis Software (DAD).

6 OCTOBER 2006




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Four basic properties are commonly used to evaluate the goodness of various income inequalitymeasures (e.g., see Cowell 1995, Deininger and Squire 1998, Chakravarty 1999, and Fields 2001).These properties are most widely known as anonymity; population homogeneity or populationindependence; transfer principle or the Pigou-Dalton condition; and income homogeneity or

scale independence or normalization. Anonymity means that the names of the individuals areirrelevant to the question of inequality. Population homogeneity refers to the situation whereone income distribution is an n-fold replication of another, and the two distributions are regarded

as equal. Transfer principle is an income transfer from a rich person to a poor person that doesnot make the poor the richer of the two, and which reduces inequality. Income homogeneity isa relative inequality measure that is scale-invariant or homogeneous of degree zero in incomes. 9Alternatively, these properties are referred to, respectively, as weak principle of transfers, principle

of population, decomposability, and income scale independence.10

Of the above income inequality measures, the weak principle of transfers is satisfied by G, V,

c, T, H,A, and D and failed by v1 and v2. The property of income scale independence is satisfied

by all aforementioned inequality measures except Vand D. All measures except D and H satisfy theprinciple of population. Decomposability is satisfied by all except v1, v2, and G. Although the Ginicoefficient is the most popular income inequality measure, we consider only the three measuresthat satisfy all four properties, c, T, andA.

Once the income inequality measures are selected, we incorporate them into the model throughthe private consumption equations, similar to what Qin (2003) has done. The consumption equations

are of the form:

PCON f PCINC IR P C

PCINC

PCINCINEQ INEQ i

it it t t= =( , % , # , , , )ut

rt

rt ut rr u,

(1)

where PCONdenotes per capita consumption, PCINCdenotes per capita income, P#Cdenotes theconsumer price index,INEQ denotes income inequality measures,IR% denotes the interest rate on

demand deposits, the subscript idenotes subgroup of rural and urban households, and tdenotestime. To get rid of the scale factors, we assume there are log-linear relationships between the

variables, except for the interest rate and income inequality variables.

The effect of income inequality on consumption (and therefore savings) is of uncertain sign apriori; see for example, Ray (1998). If marginal savings increase with income, then an increase in

income inequality, insofar as it is equivalent to a transfer of income to the relatively rich, will meanan increase in aggregate savings ceteris paribus. On the other hand, if marginal savings decreasewith income, an increase in income inequality could lower savings.

In practice, regressions directly based on the log-linear form of equation (1) risks nonsenseresults due to the nonstationarity of most economic time series. Hence, we adopt the general specific dynamic modeling approach of Hendry (1995). Specifically, we start with an autoregressive

distributed-lag model based on equation (1), gradually reduce and re-parameterize it into a

9 Absolute inequality and relative inequality are not alternative measures of the same underlying concepts; they measure

fundamentally different concepts. Absolute inequality relies on dollar differences in real incomes, whereas relative

inequality is measured in terms of income ratios.10 Cowell (1995) identifies an additional property: strong principle of transfers. An inequality measure satisfies this

property if any transfer of income from a rich household to a poor one generates a reduction in inequality thatincreases as the distance between the two households incomes increases.


SECTION IV

METHODOF INVESTIGATIONAND INEQUALITY MEASURES


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parsimonious, data-congruent, and economically interpretable ECM. Statistical diagnostic tests,parameter constancy tests, and economic interpretability of individual coefficients are usedextensively as the main criteria in the model reduction.11 We pay particular attention to individualparameter constancy, as this is vital in ensuring relatively high confidence in model forecasts and

simulations.

V. ECONOMETRIC MODEL RESULTS

As mentioned in Section 4, three types of income inequality measures that satisfy all the fourproperties are chosen to be included in the PRC model, and these are the coefficient of variation,

Theils index, and Atkinsons index. Through modeling experiments, we find that the results areconsistent for all three measures but that using Theils index generally produces a more parsimoniousmodel. For this reason, we decide to use Theils index as the inequality measure in the model. Thesample size is 1992Q12003Q4. A simple smoothing method is used to interpolate annual incomeinequality measures into quarterly series.

Tables 2 and 3 present the final model reduction and estimation results of the two consumption

equations based on equation (1) together with the original equations and the relevant diagnostic teststatistics.12 The original equations are simply formulated without the inequality measures, i.e.:

PCON f PCINC IR P C i r uit it t t = =( , % , # ) , (2)A number of interesting observations are discernible from the tables. First, the encompassing test

statistics show that the inclusion of income inequality measures improves the equations significantly.

Second, changes in Theils inequality measures are found to exert only short-run impact. These tworesults corroborate the classical finding of Staehle (1937 and 1938) that income inequality cannotbe ignored in determining aggregate consumption, especially when income inequality is changingover time. Noticeably, urban and rural households react to the changing gap between urban and

rural income inequality measures and, in addition, urban households also react to changes in urbanincome inequality. The short-run, positive reaction of rural households toward consumption withrespect to increasing income inequality may be explained by the fact that the rural households

became more dependent on cash consumption rather than self-sustained consumption during thesample period. Finally, an increase in average income level of the urban/rural households relative tothe rural/urban households is found to exert significantly negative long-run impact on the urban/rural consumption. This suggests that urban/rural households exhibit a greater savings motive when

they perceive their income growing steadily faster than their rural/urban counterparts. This resultpartly conforms to the theory that income inequality affects aggregate demand negatively in thelong run (see Zweimller 2000).

11 The softwares PcGive 10.0 and PcGETS 1.0 are used in the model reduction and estimation (see Doornik and Hendry2001).

12 The original equations are from the 2004 version of the PRC model, which is also used for the later simulations. Themodel presented in Qin et al. (2006) contains some modifications of that version.

8 OCTOBER 2006




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TABLE 2PER CAPITA CONSUMPTIONOF URBAN HOUSEHOLDS

Withoutincome

inequalitymeasure

(Model 1)

Residual (standard error) 0.0245413

diagnostics No autocorrelation F(3,32) = 1.5187 [0.2285]

Normality 2

(2) = 3.2946 [0.1926]Homoscedasticity F(9,25) = 1.7550 [0.1285]

RESET F(1,34) = 1.5233 [0.2256]

Withincome

inequalitymeasure(Model 2)

Residual (standard error) 0.01798Diagnostics No autocorrelation F(3,28) = 1.6566 [0.1989]

Normality 2(2) = 0.4401 [0.8025]

Homoscedasticity F(16,14) = 1.3718 [0.2790]RESET F(1,30) = 0.9166 [0.3460]

Encompassing Cox Model 1 versus Model 2 Model 2 versus Model 1test Ericsson IV N(0,1) = -8.620 [0.0000]** N(0,1) = -0.4500 [0.6527]

Sargan N(0,1) = 5.032 [0.0000]** N(0,1) = 0.1229 [0.7023]

Joint Model 2 (7) = 18.859 [0.0086]** 2 (3) = 0.92356 [0.8197]F(7,28) = 4.6736 [0.0014]** F(3,28) = 0.2866 [0.8347]

Note: The variable notations are given in the appendix. As 2 denotes second-order difference, i.e., xt-xt-2. SQ denotes quarterly seasonaldummy. The statistics in the brackets below coefficient estimates are standard errors. The statistics in the squared brackets followingtest statistics are the associated probabilities; the ** mark indicates that the probability is smaller than 1%, i .e., strongly rejectingthe null hypothesis.

2 0 03050 01070 38540 0751 2 2

ln( ) .( . )

.( . )

* ln( )PCONut

PCONut

=

+

0 03410 0139

2 0 060 01

0 37420 0523 2

.( . )

* .( .

.( . )

* l

SQ

nn( ) .( . )

* ln( )

.( . )

*

PCINCut

PCINCut

+

+

0 26280 0608 4 1

0 58050 1109

2 4 18 57716 306 1

43 7211 1

ln( # ) .( . )

*

.( .

P Ct

INEQu INEQr t+

( )

00 10 73330 0752

0 005 100

)* .

( . )*

ln . * % *

INEQut

PCONu

PCINCuIR

+ 4 1

0 13ln( # ) . *lnP CPCIN

PCIN( )

+

3 1 00560 06290 23380 0329

1 0 00950 0

ln( ) .( . )

.( . )

* .( .

PCONrt

SQ= + 0017 3

1004

0 00580 0016 3

1004

)* % * ln( # )

.( . )

* % * ln

IR P Ct

IR

+

( )

(( # ) .( . )

* ln( )

.( . )

*

P Ct

PCINCrt( ) +

+

30 37550 0257 3

0 35370 0592

33

10 15250 0258

ln ln.( . )

*PCINCr

PCINCu

PCINCr

PCINCut

+

( )

+

t

INEQr INEQu t

PC

2

16 9396 059

0 99210 0615

.( . )

* .( . )

*

ln

OONr

PCINCrIR P C

PCINCr

PCINCu+ +( )

0 008 100 4 0 4. * % * ln( # ) . * ln

t 3


SECTION V

ECONOMETRIC MODEL RESULTS


16/37

3

1 00560 0629

0 23380 0329

1 0 00950 0

ln( ) .( . )

.( . )

* .( .

PCONrt

SQ= + 0017 3

1004

0 00580 0016 3

1004

)* % * ln( # )

.( . )

* % * ln

IR P Ct

IR

+

( )

(( # ) .( . )

* ln( )

.( . )

*

P Ct

PCINCrt( ) +

+

30 37550 0257 3

0 35370 0592

33

10 15250 0258

ln ln.( . )

*PCINCr

PCINCu

PCINCr

PCINCut

+

( )

+

t

INEQr INEQu t

2

16 9396 059

0 99210 0615

.( . )

* .( . )

*

3

1 00560 0629

0 23380 0329

1 0 00950 0

ln( ) .( . )

.( . )

* .( .

PCONrt

SQ= + 0017 3

1004

0 00580 0016 3

1004

)* % * ln( # )

.( . )

* % * ln

IR P Ct

IR

+

( )

(( # ) .( . )

* ln( )

.( . )

*

P Ct

PCINCrt( ) +

+

30 37550 0257 3

0 35370 0592

33

10 15250 0258

ln ln.( . )

*PCINCr

PCINCu

PCINCr

PCINCut

+

( )

+

t

INEQr INEQu t

PC

2

16 9396 059

0 99210 0615

.( . )

* .( . )

*

ln

OONr

PCINCr IR P C

PCINCr

PCINCu+ +( )

0 008 100 4 0 4. * % * ln( # ) . * ln

t 3

TABLE 3PER CAPITA CONSUMPTIONOF RURAL HOUSEHOLDS

Withoutincome

inequalitymeasure

(Model 1)


diagnostics No autocorrelation F(3,30) = 1.7641 [0.1753]Normality 2(2) = 0.2127 [0.8991]

Homoscedasticity F(11,21)= 0.9133 [0.5451]

RESET F(1,32) = 0.1853 [0.6697]

With

incomeinequality

measure(Model 2)


diagnostics No autocorrelation F(3,26) = 0.8101 [0.4998]Normality 2(2) = 1.3326 [0.5136]

Homoscedasticity F(15,13) = 0.6528 [0.7869]RESET F(1,28) = 0.2033 [0.6556]

Encompassing Cox Model 1 versus Model 2 Model 2 versus Model 1test Ericsson IV N(0,1) = -6.590 [0.0000]** N(0,1) = -0.5074 [0.6119]

Sargan N(0,1) = 3.871 [0.0001]** N(0,1) = 0.4279 [0.6687]Joint Model 2 (6) = 15.470 [0.0169]* 2 (4) = 2.5040 [0.6439]

F(6,25) = 4.1504 [0.0050]** F(4,25) = 0.5907 [0.6725]

Note: See the note in Table 2.

10 OCTOBER 2006




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Populationand Labor force

Of course, it is inconclusive to infer that income inequality is definitely harmful for long-run aggregate demand by looking at the consumption effects alone. One also needs to take intoaccount the effect of income inequalityeven indirect oneson the other variables. For instance,suppressed consumption will raise savings and hence may encourage investment, and eventually

enhance future consumption. To investigate the overall macro impact of income inequality, we carryout a number of model simulations, which are presented in the next section.

VI. MODEL SIMULATIONS

In order to study how income inequality affects growth and the macro economy, we substitute,

in the ADB PRC model, the two consumption equations without inequality measures (Model 1 inTables 2 and 3) by the two equations with the measures (Model 2 in Tables 2 and 3), and carryout two sets of model simulations.13 The base run assumes that income inequality is constant atthe 2003Q4 level from the beginning of 2004 up to the end of the simulation period, which covers

2005Q12010Q4.14

FIGURE 3

A FLOW CHARTOFTHE ADB PRC MODEL

13 The simulations are carried out using WinSolve. See Pierse (2001) for a detailed description of the software.14 The simulations start from 2005Q1 to avoid the periods where there are already actual values for the variables of

interest, even though the data series of the inequality measures end at 2003Q4.

Employment

Priceand Wage

block GDP

Capital

Long-termGDP trendPrimary

sector

Secondary

sector

Tertiary

sector

Banking

block

Monetarypolicy

Private consumption + Government consumption + Capital formation + Exports Imports

Budgetary

investment

Business

investment

FDIWorld trade

World priceExchange rate

Urban and rural householdconsumption and income

Government block:

Fiscal policy


SECTION VI

MODEL SIMULATIONS


18/37

The first set of simulations is the conventional impulse, step, and trend shocks on each ofthe urban and rural inequality measures. These shocks are defined below.

(i) Impulse shock is the increase in inequality for one year (2005Q12005Q4) by 10% from

its 2003Q4 level; upon which it returns to the 2003Q4 level at 2006Q1 and remains

so to the end of the simulation period.

(ii) Step shock is the increase in inequality by 10% from its 2003Q4 level at 2005Q1, and

remains so to the end of the simulation period.

(iii) Trend shock is the linear increase in inequality at 10% per annum starting from 2005Q1.The growth rate is chosen with reference to the increase in overall Theil index during

recent years, e.g., 9% in 2002 and 13% in 2003 (see also the bottom left panel ofFigure 2).

Notice thatINEQu andINEQr, the inequality measures, are calculated from panel data whereas

PCINCu and PCINCr, the household per capita income series, are taken from China Monthly EconomicIndicators (NBS 2005). Therefore, the shocks defined above implicitly assume that changes inINEQu

andINEQrdo not affect PCINCu and PCINCrdirectly. However, as the income variables are endogenousin the model, they will be affected indirectly by changes in the inequality measures. Hence, theonly assumption we need to make is that the shocks do not affect the income levels at the initialpoint when they first occur. In Figures 4 and 5, the indirect effect of the shocks on PCINCu and

PCINCrare plotted (top panels). It is interesting to see that increasing urban income inequality

would, to a large extent, stimulate the growth of both urban and rural average income level whereasboth urban and rural average incomes would decline from increasing rural income inequality. Moreinterestingly, the rural household income growth would not be significantly affected downward if

the inequality situation keeps evolving at the present pace (the trend shock case), unless thereis an abrupt deterioration in income inequality (the impulse and step shock cases). Nevertheless,this result highlights the need, at the macro level, to address rural income inequality, especiallyconsidering that much of the poverty is concentrated in the rural areas in the PRC.

Figures 4 and 5 also demonstrate, for the urban and rural sectors respectively, the effectsof the inequality shocks on GDP, its supply-side components of the three sector output, and itsdemand-side components of private consumption and capital formation, all measured in constant

price. In the urban scenario (Figure 4), rural inequality is assumed to remain at its 2003Q4 levelthroughout the simulation period. The same applies to the rural scenario (Figure 5). It can be seenthat the overall effects of rising inequality on GDP growth are almost negligibly small. In the urbanscenario, rising inequality appears to stimulate slightly the production of the industry and services

sectors if viewed from the supply side of GDP. Agriculture seems to be the only sector that wouldget virtually no long-run benefit. In comparison, agriculture is slightly worse off and the other twosectors are significantly worse off in the rural scenario.

From the demand side of GDP, private consumption is the most responsive to income inequalityshocks in terms of both velocity and volatility, with the response rapidly tapering off to virtuallyzero. This is not surprising given the household consumption equations reported in Tables 2 and 3.

The volatility in aggregate consumption undulates on to aggregate investment, whose responsesoscillate more persistently and more strongly than those of the three sectors. In a recent studyby Qin, Cagas, He, and Quising (2006), impulse shocks in investment are found to affect the

12 OCTOBER 2006




19/37

2010

TrendStepImpulse

TrendStepImpulse

2005 2006 2007 2008 2009 2010

2005 2006 2007 2008 2010

TrendStepImpulse

2005 2006 2007 2008 2010

2005 2006 2007 2008 2009 2010

Annual Growth in Per Capita Urban Income

(as percentage deviation from base growth)

Annual Growth in GDP(as percentage deviation from base growth)

Annual Growth in Industry(as percentage deviation from base growth)

Annual Growth in Private Consumption(as percentage deviation from base growth)

0.08

0.06

0.04

0.02

0.10

-0.02

-0.04

0.00

0.20

0.10

0.00

-0.10

0.30

-0.30

-0.20

0.10

0.00

0.05

0.15

-0.10

-0.05

0.04

-0.04

0.00

0.08

-0.08

0.02

-0.01

0.01

0.03

-0.02

TrendStepImpulse

2005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

10.00

-10.00

0.00

20.00

-20.00

Annual Growth in Per Capita Rural Income

(as percentage deviation from base growth)

Annual Growth in Agriculture(as percentage deviation from base growth)

Annual Growth in Services(as percentage deviation from base growth)

Annual Growth in Investments(as percentage deviation from base growth)

0.40

0.00

0.80

-0.80

-0.40

0.00

0.00

1.50

-0.50

1.00

0.50

-1.00

-1.50

2009

2009

FIGURE 4URBAN INCOME INEQUALITY SHOCKS


SECTION VI

MODEL SIMULATIONS


20/37

2010




0.00

-0.02

-0.04

0.02

-0.06

0.02

-0.02

0.06

-0.10

-0.06

0.04

-0.04

0.02

0.06

-0.02

0.01

-0.01

0.01

0.02

-0.01

2005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

15.00

5.00

10.00

20.00

0.00

Annual Growth in Per Capita Urban Income(as percentage deviation from base growth)




0.25

0.00

0.50

-0.50

-0.25

0.00

0.00

1.00

-0.50

0.50

-1.00

-1.50

20102005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

20102005 2006 2007 2008 2009

TrendStepImpulse

Annual Growth in Per Capita Rural Income(as percentage deviation from base growth)

-0.02

0.00

-0.06

-15.00

-5.00

-10.00

0.08

0.01

-0.06

0.15

-0.13

-0.20

FIGURE 5RURAL INCOME INEQUALITY SHOCKS

14 OCTOBER 2006




21/37

output growth in the industry and services sectors far more than that of the agricultural sector.This helps to explain why the output growth of the three sectors responds so differently to theinequality shocks. If judged by economic stability, the simulation results show that changinginequality adversely affects the stability by encouraging more volatility in the growth of aggregate

consumption, investment, and GDP.The second set of simulations tests the effects of income inequality as measured by the urban

and rural income gap, PCINCu/PCINCr. Default forecasting shows that this gap remains at about 2.5

(i.e., urban per capita income is two and half times of rural per capita income on average) for theforeseeable future. Here the interest is to see what happens to the economy when the current gapnarrows versus the situation when it widens. The simulations are designed as follows: the narrowing

gap scenario assumes that beginning 2005Q1, the government gives a lump-sum transfer to therural households equivalent to 1% of the previous years GDP; the widening gap scenario, on theother hand, assumes that the government taxes the rural household income an amount equivalentto 1% of the previous years GDP.15 The two scenarios amount to shifting the existing income gap

by 0.15 roughly during the simulation period, as shown in Figure 6. It is clear from the figurethat, for the most part, the two scenarios result in opposite effects. Narrowing the income gap hasan immediate and sustained positive impact on GDP growth over the simulation period, whereaswidening the gap has a negative immediate effect that eventually tapers off and becomes positive

toward the end of the simulation period. On the whole, the narrowing-gap scenario results in weakermacroeconomic responses in terms of magnitude as rural households account for a smaller share ofaggregate private consumption than urban households.

In terms of dynamics, the step shocks of changing urban and rural income gaps affect privateconsumption almost immediately and very significantly, and in turn its responses are transmittedto investment, agricultural production, industrial production, and services supply. The narrowing-

gap scenario initially boosts aggregate private consumption by up to around 2.7% in the shortrun, whereas the widening-gap scenario depresses it, and both effects recede quickly to about0.15~0.19% in absolute value in the long run. Consumption responses in turn are transmitted todemand for sectoral output, especially for agricultural produce and services, and these undulate on

to investment demand as well. Notice that the responses of the secondary sector follow closely thoseof investment, only on a smaller scale, as this is the sector that is most dependent on investment.In comparison, the primary and tertiary sectors are more demand-driven. Hence their growths are

buoyed by the increase in disposable income in the narrowing-gap scenario, and vice versa in thewidening-gap scenario.

Compared to the results from the first set of simulations, the results of the second set show

evidently that further widening of rural-urban income inequality would hinder economic growth,while narrowing the inequality would actually boost long-run growth. Overall, the simulationssuggest that further disparate income distribution is unlikely to contribute favorably to sustainingsteady economic growth in the long run, even though the likely adverse impact may not yet be

very significant at the macro level.

15 The simulations implicitly assume that the taxes and transfers are proportional to household incomes so that within-urban and within-rural inequalities are unaffected.


SECTION VI

MODEL SIMULATIONS


22/37




201020052004 2006 2007 2008 2009 201020052004 2006 2007 2008 2009

201020052004 2006 2007 2008 2009 201020052004 2006 2007 2008 2009

201020052004 2006 2007 2008 2009 201020052004 2006 2007 2008 2009

201020052004 2006 2007 2008 2009 201020052004 2006 2007 2008 2009

Per Capita Income Gap: PCINCu/PCINCrPer Capita Income Gap: PCINCu/PCINCr




1.00

3.00

0.00

2.00

-1.00-2.00

-2.00

-3.00

Narrowing gapBase run Widening gapBase run

Wide gapNarrow gap Wide gapNarrow gap



0.60

1.00

0.40

0.80

0.20

0.00

-2.00

-6.00

-4.00

0.20

0.40

0.10

0.30

0.00

-0.10

-0.20

-0.40

-0.30

0.10

0.00

0.20

0.30

-1.00

-2.00

3.00

2.00

4.00

1.00

3.00

2.00

4.00

1.00

0.00

0.050.10

0.15

0.20

-0.05

-0.10

-0.15

-0.20

0.00

0.05

0.10

0.15

0.20

0.25

-0.05

-0.10

-0.15

-0.20

FIGURE 6RURAL-URBAN INCOME GAP SHOCKS

16 OCTOBER 2006




23/37

VII. CONCLUSION

Macroeconometric models are incapable of tackling the issue of how income inequality affectsgrowth empirically for lack of explicit channels relating aggregate income and consumption to

income distribution. The present study runs a pilot experiment to incorporate panel data informationinto a macro econometric model so that the issue of how income inequality affects growth can bestudied through model simulations. The PRC is used as the pilot field. A panel of provincial urbanand rural household income data is used to construct income inequality measures, which are used

to augment the urban and rural consumption equations of the ADB PRC model. Simulations arethen carried out on the modified model to show how future changes in income inequality wouldaffect the macro economy.

Through model augmentation, the rapidly changing income inequality is found to exert significantimpact on consumption of both urban and rural households. While rising urban income inequalityholds back urban consumption in the short run, increase in the relative income level between rural

and urban areas is found to stimulate household savings in the long run.

Through model simulations, we observe several interesting results. We find that significantchanges in income inequalitywhether within-urban, within-rural, or urban-ruralcarry negative

effects on macroeconomic stability as they cause consumption and then investment to undulate.Comparing the effects of shocking each of the urban and rural inequality measures, we find thatincreases in urban inequality carry more favorable (or less negative) effects to the macro economythan increases in rural inequality. In simulating the impact of changing urban-rural average income

disparity, we see that GDP growth is highest in the long run when urban-rural income gap isnarrowed (i.e., rural-favorable growth), as compared with the scenario where it is widened; and thatthe urban-favorable growth scenario (widening urban-rural gap) would only benefit the industrialsector in the long run.

Several extensions of the present study are desirable. First, it is desirable to extend the

consumption block to base it on panel data entirely so as to achieve data consistency betweenaggregate income levels and income inequality measures. Secondly, it is desirable to explore explicitlinks between income inequality and employment distribution among the three sectors of GDP. Moredata would be needed for this extension. Thirdly, it is desirable to extend the fiscal block of themodel to establish explicit links between income inequality and income redistribution policies. The

last aspect is especially important since significant alleviation of income inequality entails powerfulfiscal policy measures (see Besley and Burgess 2003). Whichever direction of extension, a wider mixof time-series and panel data in macroeconometric modeling seems the desirable way forward.


SECTION VII

CONCLUSION


24/37

APPENDIXMAIN DATA SOURCES AND VARIABLE DEFINITION

VARIABLES DEFINITION SOURCE

GDPcGross domestic product, quarterly frequency (million yuan,in 1992Q1 price)

CMEI

INEQu, INEQrIncome inequality measures, quarterly frequency interpolated

from annual data (Theils index is the final choice)

Authors

calculations

IR% Interest rate on demand deposits, quarterly frequency CMEI

P#C Consumer price index, quarterly frequency 1992Q1=100 CMEI

PCINCri Provincial per capita net income of rural households of the PRC,annual frequency (yuan)

CSDM

PCINC

rPer capita net income of rural households of the PRC,

quarterly frequency (yuan)

CMEI

PCINCui Provincial per capita disposable income of urban householdsof the PRC, annual frequency (yuan)

CSDM

PCINCu Per capita disposable income of urban households of the PRC (yuan) CMEI

PCONr Per capita living consumption of rural households of the PRC,quarterly frequency (yuan)

CSDM & CMEI

PCONu Per capita living consumption of urban households of the PRC,

quarterly frequency (yuan)

CSDM & CMEI

POPr Population of rural PRC, annual frequency (1000 persons) SYC

POPr

iProvincial population of rural PRC, annual frequency (1000 persons) CSDM

POPu Population of urban PRC, annual frequency (1000 persons) SYC

POPui Provincial population of urban PRC, annual frequency (1000 persons) CSDM

Sources: National Bureau of Statistics: China Statistical Yearbook(SYC), China Monthly Economic Indicators (CMEI), ComprehensiveStatistical Data and Materials on 50 Years of New China (CSDM 1999). Some of the historical data are directly from NationalBureau of Statistics.

18 OCTOBER 2006




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REFERENCES

Aghion, P., E. Caroli, and C. Garcia-Pealosa. 1999. Inequality and Economic Growth: the Perspective of the

New Growth Theories.Journal of Economic Literature 37:161560.

Alesina, A., and D. Rodrik. 1994. Distributive Politics and Economic Growth. The Quarterly Journal of

Economics 109:46589.

Bagliano, F.-C., and G. Bertola. 2004. Models for Dynamic Macroeconomics. Oxford: Oxford University Press.

Benabou, R. 1996. Inequality and Growth. In B. Bernanke and J. Rotemberg eds., NBER Macro Annual 1996.

Cambridge: MIT Press.

Benjamin, D., L. Brandt, and J. Giles. 2004. The Evolution of Income Inequality in China. William Davidson

Institute Working Papers Series No. 654, University of Michigan, Ann Arbor.

Besley, T., and R. Burgess. 2003. Halving Global Poverty. Journal of Economic Perspective 17:322.

Bourguignon, F. 1981. Pareto-Superiority of Unegalitarian Equilibria in Stiglitz Model of Wealth Distribution

with Convex Savings Function. Econometrica 49:146975.

Bourguignon, F., L. A. Pereira, and D. Silva. 2003. The Impact of Economic Policies on Poverty and Income

Distribution: Evaluation Techniques and Tools. Oxford: Oxford University Press.

Chakravarty, S. R. 1999. Measuring Inequality: The Axiomatic Approach. In J. Silber, ed., Handbook of IncomeInequality Measurement. Boston/Dordrecht/London: Kluwer Academic Publishers.

Chen, S., and Y. Wang. 2001. Chinas Growth and Poverty Reduction: Recent Trends Between 1990 and 1999.

World Bank Policy Research Working Paper No. 2651, Washington, DC.

Cowell, F. A. 1995. Measuring Inequality. London: Prentice Hall-Harvester Wheatsheaf.

Deininger, K., and L. Squire. 1998. New Ways of Looking at Old Issues. Journal of Development Economics

57:25987.

Doornik, J. A., and D. F. Hendry. 2001. Empirical Econometric Modeling Using PcGive. Timberlake Consultants

Ltd., London.

Fields, G. S. 2001. Distribution and Development: A New Look at the Developing World. Cambridge: Russell

Sage Foundation and MIT Press.

Figini, P. 1999. Inequality and Growth Revisited. Economics Department Economic Papers No. 99/2, Trinity

College, The University of Dublin.

Galor, O., and D. Tsiddon. 1997. Technological Progress, Mobility, and Economic Growth.American Economic

Review87:36382.

Galor, O., and J. Zeira. 1993. Income Distribution and Macroeconomics. Review of Economic Studies

60:3552.

He, X.-H., H.-Y. Wu, Y.-F. Cao, and R. Liu. 2005. China_QEM: A Quarterly Macroeconometric Model of China.

Social Sciences Academic Press (in Chinese), Beijing.

Hendry, D. F. 1995. Dynamic Economics. Oxford: Oxford University Press.

Kaldor, N. 1956. Alternative Theories of Distribution. Review of Economic Studies 23:83100.

. 1957. A Model of Economic Growth. Economic Journal 57:591624.

Knack, S., and P. Keefer. 2000. Polarization, Politics, and Property Rights: Links between Inequality and

Growth. World Bank Policy Research Working Paper No. 2418, Washington, DC.Krongkaew, M. 2003. Income Distribution and Sustainable Economic Development in East Asia: A Comparative

Analysis. Paper presented at the Annual Meeting of the East Asian Development Network, 1011

October, Singapore.

Kuznets, S. 1955. Economic Growth and Income Inequality. American Economic Review45:128.

Li, S., P. Zhang, Z. Wei, and J. Zhong. 2000. Positive Analysis on Distribution of Income on China (in Chinese).

Beijing: Social Science Documentation Press.


REFERENCES


26/37

Lin, B. 2003. Economic Growth, Income Inequality, and Poverty Reduction in Peoples Republic of China.

Asian Development Review20:10524.

Liu, S. 2004. Income Disparity and Macroeconomic Growth in PRC. ADM/03-508 Project Report. Asian

Development Bank, Manila.

National Bureau of Statistics. 1999. Comprehensive Statistical Data and Materials on 50 Years of New China.

Peoples Republic of China.

. 2004. China Statistical Yearbook. Peoples Republic of China.

. Various years. China Monthly Economic Indicators Peoples Republic of China.

Perotti, J. 1996. Growth, Income Distribution and Democracy: What the Data Say. Journal of Economic

Growth 1:14987.

Persson, T., and G. Tabellini. 1994. Is Inequality Harmful for Growth: Theory and Evidence. American Economic

Review84:60021.

Pierse, R. 2001. Winsolve Manual. Department of Economics, University of Surrey.

Qin, D. 2003. Determinants of Household Savings in China and Their Role in Quasi-money Supply. Economics

of Transition 11:51337.

Qin, D., M. A. Cagas, X-H. He, and P. Quising. 2006. How Much Does Investment Drive Economic Growth in

China.Journal of Policy Modeling28. Forthcoming.Qin, D., X.-H. He, S.-G. Liu, and P. Quising. 2005. Modeling Monetary Transmission and Policy in Peoples

Republic of China.Journal of Policy Modeling27:15775.

Qin, D., X. He, R. Liu, M. A. Cagas, G. Ducanes, P. Quising, and N. Magtibay-Ramos. 2006. A Small Macroeconometric

Model of the PRC. ERD Working Paper No. 81, Economics and Research Department, Asian Development

Bank, Manila. Available: http://www.adb.org/Documents/ERD/Working_Papers/WP081.pdf.

Ray, D. 1998. Development Economics. Princeton: Princeton University Press.

Robinson, J. 1933. The Economics of Imperfect Competition. London: Macmillan.

Staehle, H. 1937. Short-period Variations in the Distribution of Income. Review of Economic Statistics

19:13343.

. 1938. New Considerations on the Distribution of Incomes and the Propensity to Consume. Review of

Economic Statistics 20:13441.

World Bank. 1997. Sharing Rising Incomes. Washington, DC.

Zhang, P. 2003. Growing and Sharing Income Distribution Theory, Events and Policy. Beijing: Social Science

Documentation Press.

Zhang, X., and R. Kanbur. 2005. Spatial Inequality in Education and Health Care in China. China Economic

Review16:189-204.

Zweimller, J. 2000. Inequality, Redistribution and Economic Growth. Institute for Empirical Research in

Economics Working Paper No. 31, University of Zurich.

20 OCTOBER 2006




27/37

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No. 31 New Economy and the Effects of Industrial Structures on

International Equity Market Correlations

Cyn-Young Park and Jaejoon Woo, December 2002

No. 32 Leading Indicators of Business Cycles in Malaysia and

the Philippines

Wenda Zhang and Juzhong Zhuang, December 2002

No. 33 Technological Spillovers from Foreign Direct

InvestmentA Survey

Emma Xiaoqin Fan, December 2002

No. 34 Economic Openness and Regional Development in the

Philippines

Ernesto M. Pernia and Pil ipinas F. Quising, January 2003

No. 35 Bond Market Development in East Asia: Issues and

ChallengesRaul Fabella and Srinivasa Madhur, January 2003

No. 36 Environment Statistics in Central Asia: Progress and

Prospects

Robert Ballance and Bishnu D. Pant, March 2003

No. 37 Electricity Demand in the Peoples Republic of China:

Investment Requirement and Environmental Impact

Bo Q. Lin, March 2003

No. 38 Foreign Direct Investment in Developing Asia: Trends,

Effects, and Likely Issues for the Forthcoming WTO

Negotiations

Douglas H. Brooks, Emma Xiaoqin Fan,

and Lea R. Sumulong, April 2003

No. 39 The Political Economy of Good Governance for Poverty

Alleviation Policies

Narayan Lakshman, April 2003

No. 40 The Puzzle of Social Capital A Critical Review

M. G. Quibria, May 2003No. 41 Industrial Structure, Technical Change, and the Role of

Government in Development of the Electronics

and Information Industry in Taipei,China

Yeo Lin, May 2003

No. 42 Economic Growth and Poverty Reduction in Viet Nam

Arsenio M. Balisacan, Ernesto M. Pernia,

and Gemma Esther B. Estrada, June 2003

No. 43 Why Has Income Inequality in Thailand

Increased? An Analysis Using 1975-1998 Surveys

Taizo Motonishi, June 2003


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No. 44 Welfare Impacts of Electricity Generation Sector Reform

in the Philippines

Natsuko Toba, June 2003

No. 45 A Review of Commitment Savings Products in


Nava Ashraf, Nathalie Gons, Dean S. Karlan,

and Wesley Yin, July 2003

No. 46 Local Government Finance, Private Resources,

and Local Credit Markets in Asia

Roberto de Vera and Yun-Hwan Kim, October 2003

No. 47 Excess Investment and Efficiency Loss During Reforms:

The Case of Provincial-level Fixed-Asset Investment in


Duo Qin and Haiyan Song, October 2003No. 48 Is Export-led Growth Passe? Implications for

Developing Asia

Jesus Felipe, December 2003

No. 49 Changing Bank Lending Behavior and Corporate

Financing in AsiaSome Research Issues

Emma Xiaoqin Fan and Akiko Terada-Hagiwara,

December 2003

No. 50 Is Peoples Republic of Chinas Rising Services Sector

Leading to Cost Disease?

Duo Qin, March 2004

No. 51 Poverty Estimates in India: Some Key Issues

Savita Sharma, May 2004

No. 52 Restructuring and Regulatory Reform in the Power

Sector: Review of Experience and Issues

Peter Choynowski, May 2004

No. 53 Competitiveness, Income Distribution, and Growth in the

Philippines: What Does the Long-run Evidence Show?Jesus Felipe and Grace C. Sipin, June 2004

No. 54 Practices of Poverty Measurement and Poverty Profile of

Bangladesh

Faizuddin Ahmed, August 2004

No. 55 Experience of Asian Asset Management

Companies: Do They Increase Moral Hazard?

Evidence from Thailand

Akiko Terada-Hagiwara and Gloria Pasadilla,

September 2004

No. 56 Viet Nam: Foreign Direct Investment and Postcrisis

Regional Integration

Vittorio Leproux and Douglas H. Brooks, September 2004

No. 57 Practices of Poverty Measurement and Poverty Profile

of Nepal

Devendra Chhetry, September 2004

No. 58 Monetary Poverty Estimates in Sri Lanka: Selected Issues

Neranjana Gunetilleke and Dinushka Senanayake,October 2004

No. 59 Labor Market Distortions, Rural-Urban Inequality, and

the Opening of Peoples Republic of Chinas Economy

Thomas Hertel and Fan Zhai, November 2004

No. 60 Measuring Competitiveness in the Worlds Smallest

Economies: Introducing the SSMECI

Ganeshan Wignaraja and David Joiner, November 2004

No. 61 Foreign Exchange Reserves, Exchange Rate Regimes,

and Monetary Policy: Issues in Asia

Akiko Terada-Hagiwara, January 2005

No. 62 A Small Macroeconometric Model of the Philippine

Economy

Geoffrey Ducanes, Marie Anne Cagas, Duo Qin,

Pilipinas Quising, and Nedelyn Magtibay-Ramos,

January 2005

No. 63 Developing the Market for Local Currency Bonds by

Foreign Issuers: Lessons from AsiaTobias Hoschka, February 2005

No. 64 Empirical Assessment of Sustainability and Feasibility of

Government Debt: The Philippines Case

Duo Qin, Marie Anne Cagas, Geoffrey Ducanes,

Nedelyn Magtibay-Ramos, and Pilipinas Quising,

February 2005

No. 65 Poverty and Foreign Aid Evidence from

Cross-Country Data

Abuzar Asra, Gemma Estrada, Yangseom Kim,

and M. G. Quibria, March 2005

No. 66 Measuring Efficiency of Macro Systems: An Application

to Millennium Development Goal Attainment

Ajay Tandon, March 2005

No. 67 Banks and Corporate Debt Market Development

Paul Dickie and Emma Xiaoqin Fan, April 2005

No. 68 Local Currency FinancingThe Next Frontier

for MDBs?

Tobias C. Hoschka, April 2005

No. 69 Export or Domestic-Led Growth in Asia?

Jesus Felipe and Joseph Lim, May 2005

No. 70 Policy Reform in Viet Nam and the Asian Development

Banks State-owned Enterprise Reform and Corporate

Governance Program Loan

George Abonyi, August 2005No. 71 Policy Reform in Thailand and the Asian Development

Banks Agricultural Sector Program Loan

George Abonyi, September 2005

No. 72 Can the Poor Benefit from the Doha Agenda?

The Case of Indonesia

Douglas H. Brooks and Guntur Sugiyarto, October 2005

No. 73 Impacts of the Doha Development Agenda on Peoples

Republic of China: The Role of Complementary

Education Reforms

Fan Zhai and Thomas Hertel, October 2005

No. 74 Growth and Trade Horizons for Asia: Long-term

Forecasts for Regional Integration

David Roland-Holst, Jean-Pierre Verbiest, and Fan Zhai,

November 2005

No. 75 Macroeconomic Impact of HIV/AIDS in the Asian

and Pacific Region

Ajay Tandon, November 2005No. 76 Policy Reform in Indonesia and the Asian Development

Banks Financial Sector Governance Reforms Program

Loan

George Abonyi, December 2005

No. 77 Dynamics of Manufacturing Competitiveness in South Asia:

Analysis through Export Data

Hans-Peter Brunner and Massimiliano Cal,

December 2005

No. 78 Trade Facilitation

Teruo Ujiie, January 2006

No. 79 An Assessment of Cross-country Fiscal Consolidation

Bruno Carrasco and Seung Mo Choi, February 2006

No. 80 Central Asia: Mapping Future Prospects to 2015

Malcolm Dowling and Ganeshan Wignaraja, April 2006

No. 81 A Small Macroeconometric Model of the Peoples

Republic of China

Duo Qin, Marie Anne Cagas, Geoffrey Ducanes,Nedelyn Magtibay-Ramos, Pilipinas Quising, Xin-Hua He,

Rui Liu, and Shi-Guo Liu, June 2006

No. 82 Institutions and Policies for Growth and Poverty

Reduction: The Role of Private Sector Development

Rana Hasan, Devashish Mitra, and Mehmet Ulubasoglu,

July 2006

No. 83 Preferential Trade Agreements in Asia:

Alternative Scenarios of Hub and Spoke

Fan Zhai, October 2006

No. 84 Income Disparity and Economic Growth: Evidence from


Duo Qin, Marie Anne Cagas, Geoffrey Ducanes, Xinhua

He, Rui Liu, and Shiguo Liu, October 2006


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ERD POLICY BRIEF SERIES (PBS)(Published in-house; Available through ADB Office of External Relations; Free of charge)

Development

Ifzal Ali and Xianbin Yao, May 2004

No. 28 Higher Oil Prices: Asian Perspectives and Implications

for 2004-2005

Cyn-Young Park, June 2004

No. 29 Accelerating Agriculture and Rural Development for

Inclusive Growth: Policy Implications for Developing

Asia

Richard Bolt, July 2004

No. 30 Living with Higher Interest Rates: Is Asia Ready?

Cyn-Young Park, August 2004

No. 31 Reserve Accumulation, Sterilization, and Policy Dilemma

Akiko Terada-Hagiwara, October 2004

No. 32 The Primacy of Reforms in the Emergence of

Peoples Republic of China and India

Ifzal Ali and Emma Xiaoqin Fan, November 2004

No. 33 Population Health and Foreign Direct Investment:

Does Poor Health Signal Poor Government Effectiveness?

Ajay Tandon, January 2005

No. 34 Financing Infrastructure Development: Asian Developing

Countries Need to Tap Bond Markets More Rigorously

Yun-Hwan Kim, February 2005

No. 35 Attaining Millennium Development Goals in Health:

Isnt Economic Growth Enough?

Ajay Tandon, March 2005

No. 36 Instilling Credit Culture in State-owned Banks

Experience from Lao PDR

Robert Boumphrey, Paul Dickie, and Samiuela Tukuafu,

April 2005

No. 37 Coping with Global Imbalances and Asian Currencies

Cyn-Young Park, May 2005

No. 38 Asias Long-term Growth and Integration: Reaching

beyond Trade Policy Barriers

Douglas H. Brooks, David Roland-Holst, and Fan Zhai,

September 2005

No. 39 Competition Policy and Development

Douglas H. Brooks, October 2005

No. 40 Highlighting Poverty as Vulnerability:

The 2005 Earthquake in Pakistan

Rana Hasan and Ajay Tandon, October 2005

No. 41 Conceptualizing and Measuring Poverty as Vulnerability:

Does It Make a Difference?

Ajay Tandon and Rana Hasan, October 2005

No. 42 Potential Economic Impact of an Avian Flu Pandemic on

Asia

Erik Bloom, Vincent de Wit, and Mary Jane Carangal-

San Jose, November 2005

No. 43 Creating Better and More Jobs in Indonesia:

A Blueprint for Policy Action

Guntur Sugiyarto, December 2005

No. 44 The Challenge of Job Creation in Asia

Jesus Felipe and Rana Hasan, April 2006

No. 45 International Payments Imbalances

Jesus Felipe, Frank Harrigan, and Aashish Mehta,

April 2006

No. 46 Improving Primary Enrollment Rates among the Poor

Ajay Tandon, August 2006

No. 1 Is Growth Good Enough for the Poor?

Ernesto M. Pernia, October 2001

No. 2 Indias Economic Reforms What Has Been

Accomplished? What Remains to Be Done?

Arvind Panagariya, November 2001

No. 3 Unequal Benefits of Growth in Viet Nam

Indu Bhushan, Erik Bloom, and Nguyen Minh Thang,

January 2002

No. 4 Is Volatility Built into Todays World Economy?

J. Malcolm Dowling and J.P. Verbiest, February 2002

No. 5 What Else Besides Growth Matters to Poverty Reduction?

Philippines

Arsenio M. Balisacan and Ernesto M. Pernia,

February 2002

No. 6 Achieving the Twin Objectives of Efficiency and Equity:

Contracting Health Services in Cambodia

Indu Bhushan, Sheryl Keller, and Brad Schwartz,

March 2002

No. 7 Causes of the 1997 Asian Financial Crisis:

What Can an Early Warning System Model Tell Us?

Juzhong Zhuang and Malcolm Dowling, June 2002

No. 8 The Role of Preferential Trading Arrangements in Asia

Christopher Edmonds and Jean-Pierre Verbiest, July 2002

No. 9 The Doha Round: A Development Perspective

Jean-Pierre Verbiest, Jeffrey Liang, and Lea Sumulong,

July 2002

No. 10 Is Economic Openness Good for Regional Development

and Poverty Reduction? The Philippines

E. M. Pernia and Pilipinas Quising, October 2002

No. 11 Implications of a US Dollar Depreciation for Asian


Emma Fan, July 2002

No. 12 Dangers of Deflation

D. Brooks and Pilipinas Quising, December 2002

No. 13 Infrastructure and Poverty ReductionWhat is the

Connection?

Ifzal Ali and Ernesto Pernia, January 2003

No. 14 Infrastructure and Poverty Reduction

Making Markets Work for the Poor

Xianbin Yao, May 2003

No. 15 SARS: Economic Impacts and Implications

Emma Xiaoqin Fan, May 2003

No. 16 Emerging Tax Issues: Implications of Globalization and

Technology

Kanokpan Lao Araya, May 2003

No. 17 Pro-Poor Growth: What is It and Why is It Important?

Ernesto M. Pernia, May 2003

No. 18 PublicPrivate Partnership for Competitiveness

Jesus Felipe, June 2003

No. 19 Reviving Asian Economic Growth Requires Further

Reforms

Ifzal Ali, June 2003

No. 20 The Millennium Development Goals and Poverty:

Are We Counting the Worlds Poor Right?

M. G. Quibria, July 2003

No. 21 Trade and Poverty: What are the Connections?

Douglas H. Brooks, July 2003

No. 22 Adapting Education to the Global Economy

Olivier Dupriez, September 2003

No. 23 Avian Flu: An Economic Assessment for Selected

Developing Countries in Asia

Jean-Pierre Verbiest and Charissa Castillo, March 2004

No. 25 Purchasing Power Parities and the International

Comparison Program in a Globalized World

Bishnu Pant, March 2004

No. 26 A Note on Dual/Multiple Exchange Rates

Emma Xiaoqin Fan, May 2004

No. 27 Inclusive Growth for Sustainable Poverty Reduction in

Developing Asia: The Enabling Role of Infrastructure


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24

SPECIAL STUDIES, COMPLIMENTARY(Available through ADB Office of External Relations)

19. The Role of Small and Medium-Scale Manufacturing

Industries in Industrial Development: The Experience of Selected

Asian Countries January 1990

20. National Accounts of Vanuatu, 1983-1987 January 1990

21. National Accounts of Western Samoa, 1984-1986 February 1990

22. Human Resource Policy and Economic Development:

Selected Country Studies July 1990

23. Export Finance: Some Asian Examples September 1990

24. National Accounts of the Cook Islands, 1982-1986

September 1990

25. Framework for the Economic and Financial Appraisal of Urban

Development Sector Projects January 1994

26. Framework and Criteria for the Appraisal and Socioeconomic

Justification of Education Projects January 1994

27. Investing in Asia 1997 (Co-published with OECD)

28. The Future of Asia in the World Economy 1998 (Co-published

with OECD)

29. Financial Liberalisation in Asia: Analysis and Prospects

1999 (Co-published with OECD)

30. Sustainable Recovery in Asia: Mobilizing Resources for

Development 2000 (Co-published with OECD)

31. Technology and Poverty Reduction in Asia and the Pacific

2001 (Co-published with OECD)

32. Asia and Europe 2002 (Co-published with OECD)

33. Economic Analysis: Retrospective 2003

34. Economic Analysis: Retrospective: 2003 Update 2004

35. Development Indicators Reference Manual: Concepts and

Definitions 2004

35. Investment Climate and Productivity Studies

Philippines: Moving Toward a Better Investment Climate 2005

The Road to Recovery: Improving the Investment Climate in

Indonesia 2005

Sri Lanka: Improving the Rural and Urban Investment Climate 2005

ERD TECHNICAL NOTE SERIES (TNS)(Published in-house; Available through ADB Office of External Relations; Free of Charge)

No. 1 Contingency Calculations for Environmental Impacts with

Unknown Monetary Values

David Dole, February 2002

No. 2 Integrating Risk into ADBs Economic Analysis

of Projects

Nigel Rayner, Anneli Lagman-Martin, and Keith Ward,

June 2002

No. 3 Measuring Willingness to Pay for Electricity

Peter Choynowski, July 2002

No. 4 Economic Issues in the Design and Analysis of a

Wastewater Treatment Project David Dole, July 2002

No. 5 An Analysis and Case Study of the Role of Environmental

Economics at the Asian Development Bank

David Dole and Piya Abeygunawardena, September 2002

No. 6 Economic Analysis of Health Projects: A Case Study in

Cambodia

Erik Bloom and Peter Choynowski, May 2003

No. 7 Strengthening the Economic Analysis of Natural Resource

Management Projects

Keith Ward, September 2003

No. 8 Testing Savings Product Innovations Using an

Experimental Methodology

Nava Ashraf, Dean S. Karlan, and Wesley Yin,

November 2003

No. 9 Setting User Charges for Public Services: Policies and

Practice at the Asian Development Bank

David Dole, December 2003

No. 10 Beyond Cost Recovery: Setting User Charges for

Financial, Economic, and Social Goals

David Dole and Ian Bartlett, January 2004

No. 11 Shadow Exchange Rates for Project Economic Analysis:

Toward Improving Practice at the Asian Development

Bank

Anneli Lagman-Martin, February 2004

No. 12 Improving the Relevance and Feasibility of Agriculture

and Rural Development Operational Designs:

How Economic Analyses Can Help

Richard Bolt, September 2005No. 13 Assessing the Use of Project Distribution and Poverty

Impact Analyses at the Asian Development Bank

Franklin D. De Guzman, October 2005

No. 14 Assessing Aid for a Sector Development Plan: Economic

Analysis of a Sector Loan

David Dole, November 2005

No. 15 Debt Management Analysis of Nepals Public Debt

Sungsup Ra, Changyong Rhee, and Joon-Ho Hahm,

December 2005

No. 16 Evaluating Microfinance Program Innovation with

Randomized Control Trials: An Example from Group

Versus Individual Lending

Xavier Gin, Tomoko Harigaya,Dean Karlan, and

Binh T. Nguyen, March 2006

No. 17 Setting User Charges for Urban Water Supply:

A Case Study of the Metropolitan Cebu Water District

in the Philippines David Dole and Edna Balucan, June 2006

1. Improving Domestic Resource Mobilization Through Financial

Development: Overview September 1985


Development: Bangladesh July 1986


Development: Sri Lanka April 1987


Development: India December 1987

5. Financing Public Sector Development Expenditure in Selected

Countries:

Overview January 1988

6. Study of Selected Industries: A Brief Report April 19887. Financing Public Sector Development Expenditure

in Selected Countries: Bangladesh June 1988

8. Financing Public Sector Development Expenditure

in Selected Countries: India June 1988


in Selected Countries: Indonesia June 1988


in Selected Countries: Nepal June 1988


in Selected Countries: Pakistan June 1988


in Selected Countries: Philippines June 1988


in Selected Countries: Thailand June 1988

14. Towards Regional Cooperation in South Asia:

ADB/EWC Symposium on Regional Coo