ECONOMIC TRANSFORMATION AND ITS

IMPACT ON INCOME INEQUALITY: EVIDENCE

FROM HONG KONG

BY

Kong Hung 04012259

Applied Economics Major

An Honours Degree Project Submitted to the School of Business in Partial Fulfilment

of the Graduation Requirement for the Degree of Bachelor of Business Administration (Honours)

Hong Kong Baptist University Hong Kong

April 2007

1

Acknowledgement

I would like to express my sincere gratitude to my supervisor Prof. Tang Shu Hung.

His invaluable advice and genuine support guided my way in doing research. His

encouragement helped me through hard time. His supervision is very important for

me to complete the project.

I would also like to thank Dr. Ng Ying Chu for her precious comment and patient

in answering all my questions.

Finally, I would like to thank my classmates for their helping hand and my beloved

parents for their gracious support.

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Abstract

The persistence of rising income inequality in Hong Kong has provided

economists a hot topic for debate. During the same time period, Hong Kong

transformed from a manufacturing-based economy to a service-oriented economy and

further to a knowledge-based economy. It has been proposed that income inequality

can be attributed in great part to disparity in returns to educational attainment,

working industry, age and gender. The present study uses Hong Kong 1996 By-

Census and 2001 Census datasets, and the Mincerian equation to estimate the

relevance between income inequality and economic transformation. The empirical

findings suggest that except working industry, the other three do not completely

support the relevance, which reveal that the link exists but is rather weak.

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Table of Contents Acknowledgement 2 Abstract 3 Table of Contents 4 Chapter 1. Introduction 5 1.1 Widening Income Inequality Trend 5 1.2 Economic Transformation in Hong Kong 6 Chapter 2. Effect of Economic Transformation on Income 8 2.1 Effect on Income to Education 8 2.2 Effect on Income across Sectors 9 2.3 Effect on Income to Elderly 9 2.4 Effect on Income to Gender Earning Gap 9 Chapter 3. Methodology 10 Chapter 4. Data Description 11 Chapter 5. Results 14 5.1 Empirical Findings 14 5.2 Discussions 17 Chapter 6. Concluding Remarks 19 6.1 Summary 19 6.2 Limitations and Recommendations 19 References 21

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Chapter 1. Introduction

1.1 Widening Income Inequality Trend

In the past several decades, there are raising concerns on income inequality

internationally. Many studies (see, for example, Tsang, 1993; Lui, 1997) pointed out

that there is increasing income disparity between the rich and the poor in Hong Kong.

A quick glance at the official Gini Coefficient calculated by the Census and Statistics

Department (see Table 1) tells that it has been rising gradually. Especially after the

year 1986, it rose sharply from 0.453 in 1986 to 0.525 in 2001.

Table 1. Official Gini Coefficient 1981 1986 1991 1996 2001

Gini Coefficient 0.451 0.453 0.476 0.518 0.525 Source: Census and Statistic Department

According to the World Bank (2004), when comparing Gini Coefficient for 127

economies in the world, Hungary (at 0.244), Denmark (at 0.247) and Japan (at 0.249)

rank at the top while Hong Kong (at 0.525) is one of the economies which has the

widest income disparity. Not only is the Gini Coefficient for Hong Kong much higher

than other developed countries but also higher than China (at 0.447). It was more

shocking that its value is close to third-world economies, for instance, Nigeria (at

0.506) and Mexico (at 0.546).

The summary report of World Development Indicators (The World Bank, 2004)

aroused an urge to explore income inequality in Hong Kong. Income disparity may

affect social stability negatively through increasing crime rate etc. Many researchers

have discovered a negative relationship between income inequality and economic

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growth (see, for example, Deininger and Squire, 1998). Ultimately, economic growth

will also be influenced.

Politicians have long been attributed income disparity to structural transformation

and claimed that this phenomenon is inevitable in the period of economic

restructuring evidenced in many other developed economies. For instance, Mr. Yam,

the Chief Executive of Hong Kong Monetary Authority, has made a speech in 2003

saying that

"I believe that this development is one reason why the measured distribution of

income in Hong Kong has become more skewed ...... the current process of market-

led structural change in Hong Kong is not something that can be resisted or

reversed. ...... Hong Kong has to cope with it and make the best of it."

A brief history of economic transformation in Hong Kong will be presented in the

following section.

1.2 Economic Transformation in Hong Kong

Hong Kong has been undergoing economic transformation since 1950s.

Immediately after the postwar period in the 1950s, other countries imposed an

embargo on commerce with China. This forced Hong Kong to transform from an

entrepot trading center for China into an export oriented light manufacturing center.

The second economic transformation started at 1978 because of China's open door

policy. This allowed the manufacturers to migrate their production base to China, in

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particular to the Pearl River Delta area in Guangdong Province, to enjoy lower labour

cost. Hong Kong, then, transformed to be the management and coordination center

serving the manufacturing base that had moved. At the same time, Hong Kong's

financial sector began to emerge to be one of the important financial centres in the

world. In the second stage of economic transformation, Hong Kong gradually

transformed from a manufacturing-based to a service-oriented economy.

In recent years, the word "globalization" is gaining popularity. Globalization refers

to the increasing economic dependence among countries through import and export of

goods and services, free flow of capital, and diffusion of information and technology.

Given its impact, Hong Kong is evolving further towards a highly service-oriented

economy that serve as the close trading partner for China with the rest of the world

and a knowledge-based economy.

Table 2 presents the share of the manufacturing and services sectors in total GDP.

From the table, we can see that the share of manufacturing sector diminished

considerably from 21% to 5% from 1981 to 2001. In reverse, the share of services

sector increased sharply from 66% to 84% during the same period. The huge change

in the shares of both sectors in total GDP is a clear evidence of economic

transformation in Hong Kong.

Table 2. Share of manufacturing and services sector in total GDP

1981 1986 1991 1996 2001 Manufacturing 0.21 0.21 0.14 0.06 0.05 Services 0.66 0.67 0.73 0.82 0.84

Source: Census and Statistic Department

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Economic transformation (sectoral shifts) of the Hong Kong economy may affect

elderly and unskilled labour negatively and lead to social problems that range from

labor shortage to unemployment. Ultimately, these will affect income distribution,

and more specifically, widen income inequality. The present study aims at examining

(1) What were the effect of economic transformation on income? (2) In terms, could

economic transformation explain the trend of widening income inequality?

Chapter 2. Effect of Economic Transformation on

Income

Economic transformation of Hong Kong can be summarized as an evolvement

from a manufacturing center to a service center or from a labour-intensive economy to

a knowledge-based economy. It affected income through four channels.

2.1 Effect on Income through Education

Transforming from a labour-intensive (manufacturing) industry towards education-

intensive (services, especially financial) industries, there will be arising demand on

skilled and educated labour. The growing demand for educated labour will raise their

returns relative to labour with low education level. Bartel & Lichtenberg (1987) and

Suen (1995) suggested the reason of discrimination against less educated labour is

that highly educated labour are more adaptable to changes. Their flexibility is

important and thus highly rewarded because rapid change is foreseen around the

period of economic transformation.

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2.2 Effect on Income across Sectors

Suen (1995) pointed out the two-sided argument on impact of economic

transformation to sectoral returns. On one side, if sectoral shifts from the

manufacturing-based economy to service-oriented economy are in consequence of the

change in final demand, then, we can expect returns to the expanding (services) sector

will rise relative to returns to the shrinking (manufacturing) sector. On the contrary,

when decreasing number of people are willing to work in the declining

(manufacturing) sector, the reduction in labour supply will raise its returns.

2.3 Effect on Income through Elderly

Suen (1995) stated that senior people are less adaptable and less willing to change

when compared to younger people. In the period of economic transformation, senior

people will not be able to cope with the swift change. They have less intention to

leave the shrinking (manufacturing) sector if they are already working in it (i.e.

inadaptable and unwilling to change). On the other hand, younger people are less

willing to enter this sector. Therefore, senior people will suffer from decreasing

returns from employment relative to younger people because of reduction in returns in

the declining sector that they stayed.

2.4 Effect on Income through Gender Earning Gap

By assuming that male and female are equally endowed with mental labour with

the difference that male is endowed with more physical labour than female, Fan & Lui

(1999) and Galor & Weil (1996) found that when an economy transforms from a

labour-intensive economy to a knowledge-based economy, there will be a reduction in

gender earning gap. The reason behind is that man may have advantage in physical

9

strength to benefit himself when working in a labour-intensive (manufacturing)

industry, but that would make no difference when working in a knowledge-based

(service) industry. Therefore, the returns to female will increase relative to the returns

to male over time.

Chapter 3. Methodology

Mincer (1974) developed a schooling model to estimate returns on the number of

years of schooling across individuals by a set of individual characteristics, including

age and gender. This model has been widely used in the subsequent studies. His

model has several variant forms, the one which are applicable to this study is written

as

lnY = βXi + ε (1)

where lnY is the natural logarithim of main employment income, X is a vector of

relevant characteristics that affect income and ε is an error term. This specification

assumes that an individual's monthly income are determined by the endowments or

characteristics, including education (no education, primary, lower secondary, upper

secondary or university), industry (manufacturing, construction, wholesale, transport,

financial or community services), age, age squared, gender (male or female), marital

status (now married or single), and whether the person was born in China or Hong

Kong.

Human capital theory tells us that education increases the productivity of an

individual so that we expect the coefficients to be positive. The coefficient of the age

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variable should be positive while the coefficient of the age squared variable should be

negative. This is because an individual can accumulate human capital through job

experience with his/her age, thus, the expected signs should be the same as that of

experience and experience squared. Other relevant characteristics, such as gender,

marital status, place of birth and industry that works, will have their own effects on

income independent of schooling.

Throughout this paper, we take monthly income from main employment as the

income measure.

Chapter 4. Data Description

The present study uses Hong Kong 1996 By-Census and 2001 Census datasets

with 26360 observations and 27751 observations respectively. Only employees of age

between 15 and 64 with positive monthly income from the main employment are

included in the analysis. To focus on the local employees as well as to eliminate the

influence of identical income of domestic helpers from abroad, foreigners (i.e. people

not born in Hong Kong or China) were excluded in the sample. Individual workers in

agriculture, fishing, mining and quarrying industries and those industries that are not

classified are excluded from the sample.

The dependent variable in the Mincerian equation used is the natural logarithm of

monthly income from main employment (lnMEARN). Other dependent variables

include education level completed (PRIM, LOWSEC, UPSEC, POSTSEC and UNIV)

with the reference group being people with no education, industry dummy variables

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(CONST, WHOL, TRAN, FIN and SER) with the reference group being the

manufacturing industry, the gender of an individual (MALE), the marital status

(MARRIED), the place of birth (CHINA), age and age squared.

Table 3. Sample statistics by year 1996 2001 (real)

MEARN 13102.10 (14968.41)

15426.91 (17023.87)

lnMEARN 9.183 (0.707)

9.321 (0.769)

SCH 9.290 (3.950)

10.505 (3.890)

EXP 20.671 (12.450)

20.161 (12.089)

EXP2 582.274 (628.357)

552.606 (555.699)

MALE 0.575 (0.494)

0.523 (0.499)

age 35.959 (10.755)

36.663 (10.279)

age2 1408.68 (838.209)

1449.84 (779.264)

MARRIED 0.598 (0.490)

0.586 (0.493)

CHINA 0.286 (0.452)

0.250 (0.433)

NOEDU 0.023 (0.149)

0.018 (0.133)

PRIM 0.172 (0.378)

0.143 (0.351)

LOWSEC 0.202 (0.402)

0.193 (0.394)

UPSEC 0.431 (0.495)

0.416 (0.493)

POSTSEC 0.151 (0.358)

0.070 (0.256)

UNIV 0.020 (0.142)

0.159 (0.365)

MANU 0.188 (0.391)

0.117 (0.321)

CONST 0.089 (0.284)

0.081 (0.273)

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1996 2001 (real) WHOL 0.234

(0.424) 0.249 (0.433)

TRAN 0.107 (0.310)

0.108 (0.310)

FIN 0.142 (0.349)

0.168 (0.374)

COMM 0.161 (0.367)

0.183 (0.387)

N 26360 27751

Official Gini Coefficient 0.518 0.525 Note: Standard deviations are in parentheses. where MEARN = monthly income from main employment lnMEARN = log of MEARN SCH = number of years of schooling completed EXP = potential years of experience = age – SCH – 6 EXP2 = EXP * EXP age2 = age * age CHINA = whether the person is born in China NOEDU = no education PRIM = primary LOWSEC = lower secondary UPSEC = upper secondary POSTSEC = post secondary UNIV = university MANU = manufacturing CONST = construction, electricity, gas and water WHOL = wholesale, retail and import/export trades, restaurants and hotels TRAN = transport, storage and communication FIN = financing, insurance, real estate and business services COMM = community, social and personal services

Table 3 provides the sample statistics of the variables by year. The monthly

income from main employment in 2001 sample is adjusted by consumer price index

to obtain the real amount in 1996 dollar. Completed years of schooling increased by

1.2 over the five-year period, while labour market experience remains stable. Take a

closer look into the educational dummy variables would provide the reason for

increment in average years of schooling, the percentage of workers completed

university level or above had increased dramatically from 2% to 15.9% while the

percentage of workers completed post secondary or below showed a downward trend.

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The largest decline, from 15.1% to 7%, occurred in the percentage of workers

completed post secondary.

The gender makeup within the sample is almost equal, with slightly more male

(ranging from 57.5% of the sample are male in 1996 to that of 52.3% in 2001) than

female, but the difference is narrowing.

Average age of the sample increased by nearly 1 year, which is consistent with the

growing aging population trend. The percentage of labour who are married or born in

China remains rather stable throughout the five-year period.

The industry dummy confirms the fact that labour engaged in the manufacturing

industry was declining (from 18.8% to 11.7%) while that in financial, and community

and personal services both rose by about 2%. The average real income increased by

more than $2300 in five years. The standard deviation of average income also go up,

which cohered with ascending Gini Coefficient Index.

Chapter 5. Results

5.1 Empirical Findings

Table 4. Regression results 1996 2001

Dependent Variable lnMEARN lnMEARN

Independent Variables intercept 6.80464

(151.39***) 6.21917 (122.88***)

PRIM 0.14334 (5.90***)

0.13712 (5.19***)

LOWSEC 0.29202 (11.85***)

0.32723 (12.44***)

UPSEC 0.60926 (25.09***)

0.62773 (24.19***)

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1996 2001

POSTSEC 1.03016 (40.74***)

1.04665 (36.65***)

UNIV 1.51630 (44.84***)

1.29351 (47.87***)

CONST 0.19463 (14.38***)

0.30135 (20.52***)

WHOL 0.21389 (22.22***)

0.28957 (28.58***)

TRAN 0.22000 (17.72***)

0.31340 (24.18***)

FIN 0.37914 (33.24***)

0.44700 (39.07***)

SER 0.42362 (38.81***)

0.52976 (47.89***)

MALE 0.33042 (45.28***)

0.37132 (51.08***)

MARRIED 0.11904 (13.91***)

0.11609 (13.53***)

CHINA - 0.12666 (- 15.3***)

- 0.13043 (- 15.16***)

age 0.07314 (33.66***)

0.09671 (39.24***)

age2 - 0.00087 (- 31.83***)

- 0.00110 (- 34.48***)

N 26360 27751

R2 0.3928 0.4560 Adjusted R2 0.3925 0.4557

F-value 1136.25 1549.99

Pr > F < 0.0001 < 0.0001 Note: T-values are below each coefficient and in parentheses. The asterisks *, **, ***

indicate statistical significance at the 10%, 5%, 1% levels respectively.

Table 4 presents the results of the regression of the earnings model described in

Chapter 3. As expected, education, working industry, gender, marital status, age and

the birth place are important determinants of earnings in Hong Kong since all of them

are significant at 1% level..

The adjusted R2 of 0.39 in the 1996 model, indicates that 39 percent of the

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variation in the lnMEARN is explained by the independent variables included in the

model. The explanatory power of the 2001 model is higher with the adjusted R2

reported is 0.456. For both models, the high F-values confirm that the independent

variables used are jointly significant and different to zero.

The findings suggest that while completing primary education only provides

around 14% more of income relative to those without education, completing post

secondary education or above would enhance an individual's income by more then

100% relative to those with no education. There is an interesting finding that, for both

models, returns to upper secondary education is nearly a double to the returns to lower

secondary education (60.9% : 29.2% for 1996 and 62.7% : 32.7% for 2001). Likewise,

returns to lower secondary education also equal nearly two times of the returns to

primary education (29.2% : 14.3% in 1996 and 32.7% : 13.7% in 2001). The returns

to education level is rather stable in the five-year period, except for labour completed

a degree or above, this group of people suffer a reduction of approximately 20% in

earnings relative to the reference group across the five-year's time.

In line with our expectation, all industries' return enhances corresponding to the

reference group during the five-year period. Relative to manufacturing industry,

earnings to community services ranks number one in both years, which is 42% and

53% higher than it; financial industry ranks second, which is 38% and 45% higher

than it in 1996 and in 2001 respectively. This implies that the returns to other

industries continue to outpace the returns to manufacturing industry, and with a faster

and faster pace.

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Males earn 33% more than females in 1996 and the gender earnings gap enlarged

slightly by 4% to 37% in 2001.

Married workers continue to earn more than single workers and remain stable at

around 11 – 12%. Similarly, Hong Kong workers keep on to earn about 13% more

than those who born in China, from 1996 to 2001.

From the table, it shows that for every year you grow up, there will be a positive

returns, from 7.3% in 1996 to 9.6% in 2001. Although there is an increase in earnings

to age, the diminishing rate of returns speed up from – 0.08% to – 0.11%.

5.2 Discussions

The evidence on the diverging returns incorporated to worker characteristics are

closely related to income inequality. Wage inequality (an approximation of income

inequality) can be decomposed into changes in workers' characteristics and their

equivalent changes in the returns. In other words, the previous findings on deviation

of returns to worker characteristics contribute to an important source of the rising

income inequality during economic transformation in Hong Kong.

The phenomenon of knowledge bias under economic transformation is not that

serious as asserted. The empirical findings, although show a small reduction of

earnings to primary level workers from 14% to 13%, as well as a slight increment of

1 – 3% towards earnings to secondary and post secondary level workers. University

level workers face a large drop in earnings. Coincide with the conclusions of Suen

17

(1995), while its shows some evidence that people with little education are less well-

paid than the others, the link between structural change and broadening earnings gap

against less educated cannot be clearly established. The study of Ng (2001) may bring

us an explanation to the unexpected change for workers that had completed first-

degree or above. She stated that there is an oversupply of postgraduates recently. The

trend will endure and worsen in 2007, according to the Report on Manpower

Projection to 2007. Other things being equal, oversupply would result in reduce in

price (i.e. return to education).

The empirical results support the first argument of Suen (1995) such that the

expected returns on the expanding (service) sector rise relative to that of the shrinking

(manufacturing) sector. Enlarging deviation of returns to workers working in different

industries, thus, may be one of the source of the rising income inequality during

economic transformation in Hong Kong

Similar to the findings of Suen (1995) and Ng (2001), it does not show a

significant deterioration on earnings of elderly. The positive change of returns to age

from 1996 to 2001 even indicates an improvement on the well being of them. In

contrast, diminishing return to age worsens. These two opposite changes leave the

effect of economic transformation to the income of elderly remain question.

The gender earnings gap widens by 4 % in the five-year period. In 2001, males

earn 37% more than females, ceteris paribus. Different to the results of the study of

Fan & Lui (1999) and Sung et al. (2001), they all found a narrowing gender earnings

gap during economic transformation. They reasoned their outcome that the rising

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educational attainment of females and transformation of the economy from

manufacturing to services facilitates females to shift from crafts or operators to clerks,

who are better paid. Broadening earnings differential between male and female may

be another source of the growing income inequality in the period of economic

transformation in Hong Kong.

Chapter 6. Concluding Remarks

6.1 Summary

Given the fact that Hong Kong has been undergoing economic transformation from

a labour-intensive economy to a knowledge-based economy and the increasing

income inequality trend, the present study’s purpose is to establish the links between

them.

There are four possible channels that economic transformation can affect income

inequality, namely, through influence on sectoral earnings, education returns, senior

people's earnings and gender earnings gap. Despite a clear link exists between

economic transformation – sectoral earnings – income inequality, the link through

gender earnings gap is weak and the link through the rest two channels remains to be

established.

6.2 Limitations and Recommendations

While the results show some evidence between economic transformation and the

increasing income inequality trend, it is difficult to construct a trend from five year

data only.

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The sectoral shifts theory of unemployment holds that periods of accelerated

structural change generate labor market mismatch and increase the extent of frictional

unemployment. Lilien (1892) pointed out the relationship between sectoral shifts and

cyclical unemployment in the United States. In the periods of fastening economic

transformation, displaced workers would find difficulties in finding new jobs, this

would lead to increase in frictional unemployment or industry-specific unemployment

rate. It is recommended that the future studies may consider adding unemployment as

an independent variable.

The analysis focuses on employees only, those who are self-employed are not

included. The future studies are recommended to include a broader sense data.

Variables such as the quality of education and parental factors were not

considered in this study. These variables are likely to influence an individual's

earnings, but these information is not readily available.

The sample of the analysis has excluded all the foreigners. But in an international

city such as Hong Kong, the results may be biased. Future studies may consider using

other method to exclude the effect of domestic helpers to minimize bias. For example,

delete industry code 9501 instead of excluding all the foreigners.

1 Industry Code 950 refers to repair services, laundry, dry cleaning and garment

services, domestic services and miscellaneous personal services

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