ECONOMIC TRANSFORMATION AND ITS IMPACT ON INCOME INEQUALITY: EVIDENCE FROM HONG KONG

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. 2

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. 3

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 4

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 5

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 marketled 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 6

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 7

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 educationintensive (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. 8

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 = βx i + 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 10

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 11

(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 MEARN 13102.10 (14968.41) lnmearn 9.183 (0.707) SCH 9.290 (3.950) EXP 20.671 (12.450) EXP 2 582.274 (628.357) MALE 0.575 (0.494) age 35.959 (10.755) age 2 1408.68 (838.209) MARRIED 0.598 (0.490) CHINA 0.286 (0.452) NOEDU 0.023 (0.149) PRIM 0.172 (0.378) LOWSEC 0.202 (0.402) UPSEC 0.431 (0.495) POSTSEC 0.151 (0.358) UNIV 0.020 (0.142) MANU 0.188 (0.391) CONST 0.089 (0.284) 1996 2001 (real) 15426.91 (17023.87) 9.321 (0.769) 10.505 (3.890) 20.161 (12.089) 552.606 (555.699) 0.523 (0.499) 36.663 (10.279) 1449.84 (779.264) 0.586 (0.493) 0.250 (0.433) 0.018 (0.133) 0.143 (0.351) 0.193 (0.394) 0.416 (0.493) 0.070 (0.256) 0.159 (0.365) 0.117 (0.321) 0.081 (0.273) 12

WHOL 0.234 (0.424) TRAN 0.107 (0.310) FIN 0.142 (0.349) COMM 0.161 (0.367) 1996 2001 (real) 0.249 (0.433) 0.108 (0.310) 0.168 (0.374) 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 EXP 2 = EXP * EXP age 2 = 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. 13

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***) PRIM 0.14334 (5.90***) LOWSEC 0.29202 (11.85***) UPSEC 0.60926 (25.09***) 6.21917 (122.88***) 0.13712 (5.19***) 0.32723 (12.44***) 0.62773 (24.19***) 14

POSTSEC 1.03016 (40.74***) UNIV 1.51630 (44.84***) CONST 0.19463 (14.38***) WHOL 0.21389 (22.22***) TRAN 0.22000 (17.72***) FIN 0.37914 (33.24***) SER 0.42362 (38.81***) MALE 0.33042 (45.28***) MARRIED 0.11904 (13.91***) CHINA - 0.12666 (- 15.3***) age 0.07314 (33.66***) age 2-0.00087 (- 31.83***) 1996 2001 1.04665 (36.65***) 1.29351 (47.87***) 0.30135 (20.52***) 0.28957 (28.58***) 0.31340 (24.18***) 0.44700 (39.07***) 0.52976 (47.89***) 0.37132 (51.08***) 0.11609 (13.53***) - 0.13043 (- 15.16***) 0.09671 (39.24***) - 0.00110 (- 34.48***) N 26360 27751 R 2 0.3928 0.4560 Adjusted R 2 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 R 2 of 0.39 in the 1996 model, indicates that 39 percent of the 15

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 R 2 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. 16

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 wellpaid 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 firstdegree 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 18

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. 19

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 950 1 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 20

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