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The Labor Market Structure of Knowledge-Based Industries: A KoreanCaseJin Hwa Jung a;Kang-Shik Choi ba Seoul National University, Seoul, Korea b Yonsei University, Seoul, Korea

To cite this Article Jung, Jin Hwa andChoi, Kang-Shik(2006) 'The Labor Market Structure of Knowledge-Based Industries:A Korean Case', Journal of the Asia Pacific Economy, 11: 1, 59 78

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Journal of the Asia Pacific Economy

Vol. 11, No. 1, 5978, February 2006

The Labor Market Structure ofKnowledge-Based Industries:A Korean Case

JIN HWA JUNG & KANG-SHIK CHOI

Seoul National University, Seoul, KoreaYonsei University, Seoul, Korea

ABSTRACT This paper analyses the labor market structure of knowledge-based industriesvis-a-vis industries with low knowledge intensity, in terms of employment and wage structures.Empirical evidence suggests that knowledge-based industries have been largely responsible foremployment growth and structural changes, not to mention output growth, in Korea, just as inmost other OECD countries. In comparison with other industries, knowledge-based industriesare characterized by a higher ratio of knowledge-intensive jobs, higher hourly wage rates (forworkers with comparable qualifications), and a higher wage premium of education. The rent ofknowledge-based industries, if any, is skewed toward more educated workers, implying a skillbias in knowledge-based industries. The foreseen structural shift from traditional industrialsectors to knowledge-based industrial sectors thus implies that labor demand will center morearound highly-skilled and high-paying jobs. This in turn will result in a widening gap in

employment opportunities and wages, between economic sectors with growing labor demandand those without.

KEY WORDS: Labor market, employment structure, wage premium, knowledge-based indus-

tries, Korea

JEL CLASSIFICATIONS: J210, J240, J310

Introduction

This paper aims to analyze the Korean labor market with special reference to

knowledge-based industries. It examines the employment and wage structures in dif-

ferent industries with varying knowledge intensities, and explores the implications of

the effects on the labor market of the transition toward a knowledge-based industrial

structure.Statistically well-documented (e.g., OECD, 1999a), the developed countries have

been transforming from industrial to knowledge-based structures through the contin-

uous development of new technology, especially in information and communications.

Correspondence Address: Jin Hwa Jung, Seoul National University, Seoul, Korea. E-mail: jhjung@

shu.ac.kr

ISSN 13547860 Print/14699648 Online/06/010005920 C 2006 Taylor & Francis

DOI: 10.1080/13547860500347810


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60 J. H. Jung & K.-S. Choi

In fact, it is now alleged that the observed gap in rich and poor countries national

wealth is mainly due to the knowledge gap between those countries (World Bank,

1999). What follows is that the knowledge-based economy has emerged as the new

economic paradigm for the developed countries, and thus will be eventually adopted

for the globalized world economy. For Korea, such a trend has already been observed

in the recent past, and is expected to accelerate in the years to come (Jung et al.,

1999).

Industrial restructuring toward knowledge-based industries leads to changes in

labor demand, which in turn bringsaboutsignificant changes in labor marketstructure.

The experiences of OECD countries lend support to the argument that labor demand

grows fast in knowledge-based sectors of the economy, especially for highly-skilled

work, accompanied by sluggish or decreasing demand in traditional sectors with lowknowledge intensity.

Given the current transition of the world economy toward a knowledge-basedstruc-

ture, it is of importance, from the academic and political perspectives, to investigate

the labor market outcome of such a transition and develop strategies to respond effi-

ciently to the expected outcome. That is the major intention of this study.

This paper is an extension of previous OECD studies in that it explicitly analyzes

the employment and wage structures of knowledge-based industries vis-a-vis other

industries, whereas previous OECD studies simply compared the employment size

and the average wage level of different industries. This paper, to the authors best

knowledge, is also the first empirical study that estimates the wage premium of

knowledge-based industries in Korea.

Transition toward Knowledge-based Industries

Definition, Measurement, and Data

Knowledge-based industries are defined as those industrial sectors that produce high-

technology goods, intensively use high technology, and have a relatively highly skilled

workforce able to benefit from technological innovation. Although all industries areto

some extent dependent on knowledge inputs, the term knowledge-based industries

refers to those that are relatively intensive in their inputs of technology and human

capital (OECD 1999a).

The measurement of knowledge-based industries, however, is challenging because

of the arbitrariness inherent in the definition of knowledge intensity, along with the

limited data availability. Indeed, the relative knowledge intensity of different indus-tries can vary over time as a result of differing degrees of technical development

among different industries. In addition, it is often quite difficult to perform industry-

specific analysis, due to the lack of detailed data. Nevertheless, the current gap in the

relative knowledge intensity of different industries appears to be reasonably large and

lasting. The data problems are also not uncommon in the empirical analysis of social


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The Labor Market Structure of Knowledge-Based Industries 61

studies and are likely to be less serious for the analysis of individual countries where

detailed data are available.

The industrial classification for the empirical analysis is primarily based on the

R&D intensity and employment structure of each industry. Manufacturing industries

were classified according to R&D intensity (as measured by R&D expenditures as a

percentage of value added), while services are classified by the ratio of 4-year college

graduates among the total workforce.

Non-agricultural industries are grouped into four sectors: knowledge-based man-

ufacturing (KBM), other manufacturing (OM), knowledge-based services (KBS),

and other services (OS). Knowledge-based manufacturing refers to high-technology

manufacturing such as electronics, and also to medium-high-technology manufac-

turing such as chemicals and machinery. Other manufacturing includes medium-low-technology manufacturing, covering rubber and plastic products, and also

low-technology manufacturing ranging from food and textiles to paper products.

Knowledge-based services include communications, finance, business, and educa-

tion services. Other services (OS) include utilities, construction, wholesale and retail,

hotels and restaurants, transport and storage, etc. These industrial groupings were

made based upon the two-digit Korean Standard Industrial Classification (KSIC). 1

The transition of the Korean economy toward a knowledge-based structure is ana-

lyzed using inputoutput tables for the years 1990, 1995, and 2000, published by the

Bank of Korea. The data contain flow matrices of intermediate and final goods in real

value terms in 2000 constant prices, along with employment figures.2

Growth of Knowledge-Based Industries

As presented in Figure 1, the growth of knowledge-based industries has been evident

in Korea for the last decade in terms of real value-added. The growth of real value-

added is most impressive in knowledge-based manufacturing, in which real value-

added rose by almost six times for the 10-year period between 1990 and 2000. For the

same period, the real value-added of knowledge-based services almost tripled, while

that of other types of manufacturing and services less than doubled. Accordingly, the

share of knowledge-based industries in real value-added has substantially risen from

20.3 percent to 33.9 percent from 1990 to 2000. The growth of real value-added is

most impressive in knowledge-based manufacturing, which almost tripled its share

of total real value-added. The share of knowledge-based services has also grown,

albeit not by a large margin. Other manufacturing and service industrial sectors have

declined as a percentage of real value-added in the last 10 years.The growing importance of knowledge-based industries in the Korean economy

has also been observed in terms of employment growth. As shown in Figure 2, em-

ployment growth has been mainly led by knowledge-based services, especially during

the first half of the 1990s. During the years 1990 to 2000, knowledge-based services

brought about a 1.5 times increase of its workforce, although its labor absorption


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Figure 1. Growth and share of real value-added by industry, 19902000.Note: KBM = knowledge-based manufacturing. OM = other manufacturing. KBS =knowledge-based services. OS = other services.Source: Bank of Korea (2003) 1990-1995-2000 Link Input-Output Tables.

capacity has slowed down recently. Although the number of workers has dwindled in

knowledge-based manufacturing since the mid-1990s, such a worker reduction is far

less substantial than in other manufacturing sectors, in which the number of workers

has been constantly and relatively-rapidly declining since 1990. In addition, high-

technology manufacturing, such as computer equipment and precision instruments,


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Figure 2. Growth and share of employment by industry, 19902000.Note: KBM = knowledge-based manufacturing. OM = other manufacturing. KBS =knowledge-based services. OS = other services.

Source: Bank of Korea (2003) 1990-1995-2000 Link Input-Output Tables.

still remains an important source for job creation, manifesting a continuing large job

creation capacity.

The recent development trend of knowledge-based industries in Korea basically

mirrors the overall trend observed in most OECD economies as they move toward a

knowledge-based economic structure (OECD, 1998a, 1998b). As in most otherOECD


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economies, knowledge-based industries have been substantially outpacing the growth

of other industrial sectors in Korea in terms of real value-added and employment.

In short, Korea has been transforming toward a knowledge-based economic struc-

ture, just as other OECD countries have. The importance of knowledge-based indus-

tries as a major driving force of economic growth is expected to accelerate over the

next decade. Knowledge-based industries are projected to grow substantially faster

than the remaining industrial sectors in every major aspect of the economy including

output and employment (KIET, 1998, 1999; Jung et al., 1999).

Labor Market Structure in Knowledge-Based Industries

Data and MethodologyThe employmentand wage structure of industries with different knowledge intensities

is analyzed using the original data set of the 1997 and 2001 Wage Structure Surveys

of the Ministry of Labor. The survey provides extensive information on personal

characteristics of workers, and wage data for businesses that employ ten or more

workers in non-agricultural industries.3 After eliminating part-time and non-regular

employees, approximately 0.39 million and 0.48million full-time, year-round workers

were included in the final data sets for 1997 and 2001, respectively. The 1997 wage

data do not include the devastating effects of the financial crisis that occurred in late

1997, which triggered large wage decreases in virtually all economic sectors. The

2001 wage data is also considered to be relatively free from the irregularities caused

by the financial crisis, and is used to test the temporal consistency of the empirical

findings.The employment structure of knowledge-based industries in comparison with other

industries is analyzed in terms of the occupational and educational composition of

workers. For the inter-industry comparison of the wage structure, the average hourly

wage rate for men and women is compared for each industry cluster. Specifically,

the wage premium of knowledge-based industries is estimated from the modified

Mincerian earnings equation. The skill biasness of knowledge-based industries is

tested in terms of an educational wage premium, using the interaction term of years

of schooling and a dummy variable for knowledge-based industries.

Employment Structure

The employment structure is one of the important labor market indicators, for it is the

type of jobeach workerholdsin theoccupational hierarchythat exerts a large influenceon employment stability and earnings. As illustrated in Figure 3, knowledge-based

services demonstrate the most knowledge-intensive occupational structure among

all four industry clusters. In knowledge-based services, as of 2001, about half of

all workers are either professionals or technicians, implying an exceptionally high

knowledge intensity in the workforce. The ratio of professionals or technicians in


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knowledge-based services is almost twice the ratio in other services. The proportion

of professionals and technicians among the workforce is the lowest in other manu-

facturing sectors.

Unlike knowledge-based services in which men and women are both concentrated

in knowledge-intensive jobs, knowledge-based manufacturing exhibits quite a dif-

ferent picture for men and women concerning the occupational structure. For men,

knowledge-based manufacturing exhibits the relatively highly knowledge-intensive

occupational structure, which is not the case for women. That is, knowledge-based

manufacturing provides highly knowledge-intensive jobs for men, but not for women,

under the current employment structure (see Figures A.1 and A.2).

Overall, women are less concentrated in knowledge-intensive jobs than men in

all four industry clusters, implying that Korean knowledge-based industries are noexception to the occupational segregation prevalent across OECD countries (e.g.,

OECD, 1998c). Even in knowledge-based services that have a favorable occupational

structure for women, women tend to be more skewed to unskilled jobs than their

male counterparts. It goes without saying that all other industrial sectors are subject

to much heavier occupational segregation.

The occupational structure of workers in each industry cluster is closely related

to workers educational background, for education is a fundamental form of human

capital investment that bears upon labor qualifications. It is thus unsurprising to find

that knowledge-based services that are characterized by a highly knowledge-intensive

occupational structure are also extensive employers of highly educated personnel. In

knowledge-based services, around one-half of workers have completed 4-year college

degrees or have higherlevels of education. In other manufacturing sectors, on theother

hand, two out of ten workers are college graduates or above (see Figure 3).

As is the case for occupational composition, women are disadvantaged as com-

pared to men when it comes to educational attainment. Women are more centered

around lower education levels than men in all four industry clusters. Nevertheless,

the gender difference in educational attainment is much smaller in knowledge-based

services than in knowledge-based manufacturing and other industries. In other words,

knowledge-based services currently render substantially large employment opportu-

nities for highly educated women compared to other industrial sectors.

In a nutshell, as compared to other industries of low knowledge intensity,

knowledge-based industries are characterized by more of the knowledge-intensive oc-

cupational structure with a relatively high ratio of highly educated personnel. Gender

differences in the employment structure are also less noticeable in knowledge-based

industries than other industries. Within knowledge-based industries, the service sec-tor manifests a higher knowledge intensity of employment structure, with a smaller

gender gap, than the manufacturing sector. This conclusion should be interpreted with

a caveat, however, that it draws from those employees working for firms with ten or

more workers, excluding non-wage earners and employees of small firms with less

than nine workers.


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Figure 3. Ratio of knowledge-intensive jobs by industry, 1997, 2001.

Note: Ratio of professionals/technicians, and of 4-year college graduates to the total workforcein each industry cluster (%).Source: Ministry of Labor, 1997 Wage structure Survey, 2001 Wage structure Survey.


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Wage Structure

Regardless of gender, the average hourly wage rate is the highest in knowledge-based

services, and the lowest in other manufacturing sectors, in both 1997 and 2001. The

average hourly wage rate for male workers in knowledge-based services is 1.6 times

higher in both years. Thewage premium adherent to knowledge-basedservicesis even

larger for women than for men. In the case of women, the average hourly wage rate

in knowledge-based services is 1.81.9 times higher than that in other manufacturing

sectors (see Figure 4).

On average, women earn a little over 60 percent of what men earn in Korea (61

percent in 1997, and 65 percent in 2001). Although there is compelling evidence for

the substantial degree of gender wage gaps all over the world, the gender wage gap

in Korea looms exceptionally large from an international perspective. 4 Although a

gender wage gap is persistent in all industry sectors, it is most noticeable in manu-

facturing, where the average wage rates of male and female workers are the lowest

among all four industry clusters. More importantly, the effect of discrimination is

estimated to be far smaller in knowledge-based industries than other industries, when

estimated by the residual left after subtracting the effects of differences in individual

characteristics from overall wage differentials (Jung, 2001).

Empirical evidence indicates that knowledge-based industries in Korea are char-

acterized by higher wages for men and women alike. The fact that workers employed

in knowledge-based industries are paid higher wages than those employed in other

industries may imply that, for comparable worker groups, a substantial wage premium

persists for knowledge-based industrial sectors. On the other hand, it may simply re-

flect the individual differences in labor productivity, either measured or unmeasured,for workers employed in different industries.

Wage Premium of Knowledge-Based Industries

The wage premium of knowledge-based industries is estimated from the following

earnings equation:

ln Wi = Xi + Zi+i , i = 1, . . . ,n (1)

where Wi is the hourly wage rate of the i th worker, X is a vector of individual

characteristics, Z is a dummy variable for knowledge-based industries, and are

vectors of coefficients, and i is a disturbance term.

Table 1 describes the selected explanatory variables in the earnings equation. In-cluded herein are human capital variables (education, tenure, and skills), personal

characteristics (age and marital status), and job-related characteristics (company size,

union membership and occupation). The age of workers is expected to reflect in part

human capital accumulated from work experience and the non-productivity-related

wage-raising effect under the current earnings system.5 Marital status bears upon the


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Table 1. Description of explanatory variables in earnings equation

Variables Description

EDU2 EDU4 Dummy variables for high school graduates (EDU2), 2-year collegegraduates (EDU3), college graduates (EDU4), with workers withmiddle school education and under as the reference group

TEN Number of years worked in current jobTENSQ TEN TEN/100KBI Dummy variable for knowledge-based industries, with other industries

as the reference groupEDU KBI Interaction term of KBI dummy and years of schooling completedSKILL1 SKILL3 Dummy variables for workers with technical or craft certificates

(SKILL1), workers with other certificates (SKILL2), workers in

jobs where skill certificates are not applicable (SKILL3), withnon-certified skilled workers and unskilled workers as the referencegroup

AGE Age of respondent in yearsAGESQ AGEAGE/100SIZE1 SIZE4 Dummy variables for establishments with 3099 employees (SIZE1),

establishments with 100299 employees (SIZE2), establishmentswith 300499 employees (SIZE3), establishments with 500employees or more (SIZE4), with establishments with less than30 employees as the reference group.

MARR Dummy variable equal to 1 if married, zero otherwiseUNION Dummy variable equal to 1 if unionized, zero otherwiseOCC1 OCC4 Dummy variables for professionals and managers (OCC1),

technicians and associate professionals (OCC2), clerks (OCC3),salespersons and service workers (OCC4), with skilled and

unskilled production workers as the reference group

knowledge-based industries tend to be more highly educated, younger but with longer

tenure, more unionized, and more likely to be professional or white-collar workers

vis-a-vis blue-collar workers, than workers in other industries of low knowledge in-

tensity. This tendency holds for men and women alike.

The ordinary least squares (OLS) estimation results of the earnings equation are

presented in Table 3. Without any doubt, human capital variables such as educational

attainment, tenure, and skill level exert a significant influence on wage determination.

Workers age also has a significant effect on wage determination, as do union mem-

bership and occupation. Other things being equal, married men earn higher wages

than single men, which is not the case for women.

For both men and women, a substantial wage premium is observed for knowledge-based industries. Even controlled for measured worker qualification and job-related

factors like company size, knowledge-based industries tender a fairly large wage pre-

mium to their workers as compared to other industries with low knowledge intensity.

As it turns out, other things being equal, male workers in knowledge-based industries

earn 67 percent higher wages than those in other industries, and female workers in


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Table 2. Mean statistics of the variables

1997 2001

All KBI Other All KBI Other

ln (Hourly Wage) 8.798 8.916 8.719 8.926 9.058 8.840EDU2 0.491 0.491 0.491 0.467 0.437 0.487EDU3 0.107 0.098 0.114 0.146 0.145 0.147EDU4 0.220 0.288 0.174 0.256 0.335 0.204TEN 5.66 6.42 5.15 5.97 6.66 5.52SKILL1 0.108 0.132 0.093 0.130 0.145 0.120SKILL2 0.146 0.081 0.190 0.134 0.090 0.163SKILL3 0.437 0.486 0.405 0.462 0.505 0.434

SIZE1 0.267 0.262 0.271 0.234 0.246 0.226SIZE2 0.208 0.184 0.224 0.166 0.164 0.167SIZE3 0.070 0.079 0.064 0.053 0.062 0.047SIZE4 0.216 0.279 0.174 0.155 0.200 0.124AGE 35.7 34.5 36.6 36.5 35.1 37.4MARR 0.679 0.657 0.693 0.676 0.644 0.698UNION 0.414 0.451 0.389 0.343 0.376 0.321OCC1 0.156 0.194 0.130 0.163 0.209 0.133OCC2 0.113 0.131 0.100 0.160 0.182 0.145OCC3 0.242 0.264 0.228 0.246 0.266 0.233OCC4 0.038 0.014 0.055 0.048 0.130 0.071

knowledge-based industries earn more than 10 percent higher wages than those in

other industries.

Sources for KBIs Wage Premium

The wage premium of knowledge-based industries, observed after controlling for the

measured worker characteristics and job-related properties, can stem from the sys-

tematic differences in the unobserved worker characteristics, or from the industrial

premium unrelated to worker productivity. If those workers with higher yet unmea-

sured qualifications are more likely to enter the knowledge-based industries than their

counterparts with comparable measured qualifications, they would be paid higher

wages than their counterparts in other industries for their higher, unmeasured but

performed, productivity. The non-productivity-related wage premium of knowledge-

based industries may be ascribed to rent sharing, caused by the skill-biasness of

labor demand and enabled by the firms higher ability to pay in knowledge-basedindustries. That is, on the labor demand side, firms in knowledge-based industries

may have a higher incentive and ability to pay higher wages (efficiency wages)

to attract a high-quality workforce, resulting in the observed industrial wage pre-

mium. Institutional factors, such as unionization, can also affect the industrial wage

premium.6


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Table 3. OLS estimates of earnings equation: Model 1

Dep. Var: ln (hourly wage)

All Men Women

1997 2001 1997 2001 1997 2001

Constant 7.12 6.96 7.25 6.91 7.27 7.47

EDU2 0.234 0.198 0.131 0.148 0.160 0.124

EDU3 0.269 0.228 0.171 0.185 0.189 0.147

EDU4 0.447 0.439 0.314 0.361 0.406 0.367

TEN 0.058 0.058 0.050 0.051 0.074 0.070

TENSQ 0.112 0.101 0.091 0.084 0.166 0.141

SKILL1 0.134

0.134

0.074

0.088

0.061

0.035

SKILL2 0.069 0.113 0.005 0.109 0.041 0.002SKILL3 0.099 0.161 0.076 0.154 0.045 0.089

SIZE1 0.032 0.037 0.036 0.035 0.010 0.054

SIZE2 0.029 0.078 0.031 0.068 0.033 0.101

SIZE3 0.022 0.125 0.035 0.124 0.065 0.140

SIZE4 0.137 0.225 0.147 0.206 0.174 0.291

AGE 0.044 0.055 0.049 0.066 0.028 0.022

AGESQ 0.048 0.067 0.053 0.080 0.036 0.028

MARR 0.070 0.060 0.083 0.077 0.005 0.002UNION 0.029 0.118 0.016 0.103 0.074 0.098

OCC1 0.330 0.383 0.337 0.416 0.436 0.488

OCC2 0.223 0.226 0.209 0.212 0.314 0.354

OCC3 0.110 0.120 0.113 0.132 0.245 0.261

OCC4 0.035 0.091 0.065 0.121 0.231 0.251

KBI 0.065

0.065

0.066

0.062

0.119

0.102

N 394,264 481,750 287,567 352,855 106,697 128,895

R2 0.595 0.590 0.571 0.579 0.622 0.575

Note: p < 0.01.

The literature on the inter-industry wage differentials depicts a rather mixed picture

with regard to the sources for such wage differentials. For example, Keane (1993)

showed that the inter-industry wage differences, after controlling for observable

worker characteristics, were explained mostly by individual heterogeneity, rather

than industrial dummies. On the other hand, Krueger & Summers (1988) asserted

that workers in high wage industries receive non-competitive rents. Gibbons & Katz

(1992) concluded that no single theory measured-ability, efficiency-wage, and rent-

sharing provides a complete explanation of inter-industry wage differences.It should be of interest, from academic and political perspectives, to single out the

major factors underlying the observed wage premium of knowledge-based industries

and measure their relative importance. We do not delve into the decomposition of

the wage premium, however, due to the limitation of data, such as the non-existence

of employeeemployer matched panel data. Instead, we test the skill biasness of


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knowledge-based industries that props up the sustained wage premium tendered to

workers in knowledge-based industries, whether it comes from worker heterogeneity

or rent-sharing, in terms of educational wage premium. For this, we add an interac-

tion term of years of schooling (EDU) and a dummy variable for knowledge-based

industries (Z) in equation (1).

ln Wi = Xi + 1Zi + 2Zi E DUi + i , i = 1, . . . ,n (2)

If there exists a skill-bias of knowledge-based industries, then the educational wage

premium in knowledge-based industriesshould be higher than in otherindustries.That

is,the coefficient of theinteractionterm in equation (2) shouldbe positive (2 > 0). As

is seen in Table 4, the estimated value of2 is significantly different from zero and is

also positive in every model. Moreover, in 1997, the coefficient of Zi (1) is negative

in the case of men and is insignificant in the case of women. In particular, in the case

Table 4. OLS estimates of earnings equation: Model 1

Dep. Var: ln (hourly wage)

All Men Women

1997 2001 1997 2001 1997 2001

Constant 7.14 6.964 7.26 6.91 7.28 7.47

EDU2 0.220 0.195 0.125 0.148 0.147 0.121

EDU3 0.244 0.222 0.157 0.184 0.172 0.143

EDU4 0.405 0.429 0.290 0.359 0.373 0.360

TEN 0.057 0.058 0.050 0.051 0.074 0.070

TENSQ 0.112 0.101 0.091 0.084 0.166 0.141

SKILL1 0.134 0.134 0.074 0.088 0.059 0.035

SKILL2 0.065 0.112 0.008 0.109 0.044 0.002SKILL3 0.098 0.161 0.076 0.154 0.046 0.089

SIZE1 0.031 0.037 0.035 0.035 0.011 0.054

SIZE2 0.027 0.079 0.029 0.068 0.035 0.102

SIZE3 0.026 0.126 0.036 0.124 0.069 0.140

SIZE4 0.142 0.226 0.150 0.206 0.179 0.292

AGE 0.044 0.055 0.048 0.065 0.028 0.022

AGESQ 0.048 0.067 0.053 0.080 0.036 0.028

MARR 0.070 0.060 0.083 0.077 0.004 0.002UNION 0.026 0.117 0.018 0.103 0.072 0.098

OCC1 0.328 0.382 0.335 0.416 0.436 0.488

OCC2 0.224 0.227 0.210 0.212 0.315 0.354

OCC3 0.111 0.121 0.114 0.132 0.246 0.261

OCC4 0.035 0.091 0.064 0.121 0.232 0.251

KBI 0.115 0.017 0.049 0.049 0.008 0.074

EDUKBI 0.014 0.004 0.009 0.001 0.010 0.002

N 394,264 481,750 287,567 352,855 106,697 128,895R2 0.596 0.590 0.571 0.579 0.623 0.575

Note: p < 0.01.


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of men, the partial derivative of log hourly wage with respect to Zi ( 1 + 2E DUi )

is positive when the years of schooling of an individual are higher than 9 years,

i.e. middle school graduates or a higher level of schooling. This implies that not

every worker in knowledge-based industries is paid higher than the workers in other

industries, who are comparable in terms of the observed human capital characteristics.

To be granted higher wages in knowledge-based industries, in 1997, a worker should

have attained at least nine years of schooling. In 2001, however, the coefficient of Zi(1), as well as the coefficient of the interaction term (2), is positive.

These wage premiums, as well as the educational wage premiums, of knowledge-

based industries may reflect unmeasured productivity differences among workers

who work in different industries. That is, workers in knowledge-based industries may

have comparable qualifications in measured terms but higher productivity (e.g., sameeducational attainment level but higher ability) and so earn higher wages than those in

other industries. On theotherhand, thewage premiums of knowledge-basedindustries

may reflect a non-productivity related premium enabled by the high value-added and

profitability of these industries. In otherwords, knowledge-based industries can afford

higher wages than other industries due to their high rent, and so workers can enjoy

higher wages when they move to these industries though their productivity remains

unchanged.

In reality, both factors are likely to be responsible to some extent for the observed

wage premium of knowledge-based industries. However, the rent of knowledge-based

industries, if any, is not equally distributed among all workers. More educated work-

ers enjoy a higher wage premium in knowledge-based industries than less educated

workers, reflecting the skill biasness of knowledge-based industries.

Conclusions

The empirical analysis in this paper confirms that knowledge-based industries have

emerged as the leading industrial sector in Korea throughout the 1990s, just as is

the case in most other OECD countries. The share of knowledge-based industries

in Korea has substantially risen in every important facet of the economy, including

production and employment. It is further projected that knowledge-based industries

will continue to grow rapidly as a driving force for economic growth in the next

decade, implying their ever-increasing role in the labor market, among other things.

The main purpose of this paper was to examine the nexus formed between this tran-

sition toward knowledge-based industries and labor market outcomes. It highlighted

the major characteristics of the Korean labor market in knowledge-based industries bycomparing the employmentand wagestructures in industrieswith differingknowledge

intensities. It turns out that the labor market structure of knowledge-based industries

contrasts with those of other industries, implying the large and significant influence

on the labor market of the transition toward knowledge-based industries in Korea.

Apart from their large and growing significance in labor absorption capacity,

the development of knowledge-based industries leads to qualitative changes in the


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employment structure. The nature of jobs in knowledge-based industries is more

highly-skilled and higher-paying than other industries. In comparison with other in-

dustries, knowledge-based industriesare characterized by a higher ratio of knowledge-

intensive jobs, and higher hourly wage rates (even after controlling for human capital

and some other wage-determining factors). To sum up, knowledge-based industries

require differenttypesof laborqualifications than otherindustriesand offer substantial

wage premiums for workers comparable in (measured) worker qualifications.

The wage premium rendered in knowledge-based industries may in part be due

to the unmeasured but productivity-enhancing worker qualifications, and in part due

to knowledge-based industries inherent properties, which enable rent-sharing for

workers, especially for highly educated workers. The educational wage premium is

larger in knowledge-based industries, reflecting the skill biasness of labor demand inknowledge-based industries. The complete decomposition of the sources for the ob-

served wage premium of knowledge-based industries remains a topic of further study.

Notes

1. For a detailed description of each industrial cluster, see Table A.1 in the Appendix.

2. The growth of knowledge-based industries in terms of value-added and employment was calculated

based on the I/O code. For the I/O code of knowledge-based industries, see Table A.2 in the Appendix.

3. The 2001 Wage Structure Survey covers firms with more than 5 workers, yet for consistency, firms

employing 59 workers were excluded from the data set for the empirical analysis. The empirical

analysis of the employment and wage structure in this paper should be interpreted with a caveat, in that

it draws from those employees working for firms with ten or more workers, excluding the non-wage

earners and employees of small firms with less than nine workers.

4. For example, see Blau & Kahn (1996) and Gunderson (1989) for the worldwide prevalence of genderwage gaps. According to OECD (1999b), the median earnings of Korean women full-time workers

were less than 60 percent of those of men in 1996, the largest wage differential among the 21 OECD

countries for which earnings data were available. In all OECD countries except Korea and Japan,

womens earnings amounted to 70 to 90 percent of male earnings.

5. Although Korean firms are now increasingly adopting the annual pay system with worker productivity

as the single most important determinant of wages, prevalent until quite recently was a traditional wage

system in which age was a key wage determinant regardless of the age-productivity nexus.

6. For example, the substantially larger inter-industry wage differentials in the United States as compared

to Sweden is, in part, attributed to the two countries different institutional conditions, such as the

unions bargaining power and patterns. See Edin & Zetterberg (1992).

References

Blau, F. D. & Kahn, L. M. (1996) Wage structure and gender earning differentials: an international com-

parison, Economica, 63(supplement), pp. S29S62.Edin, P-A & Zetterberg, J. (1992) Inter-industry wage differentials: evidence from Sweden and a Compar-

ison with the United States, American Economic Review, 82, pp. 13411349.

Gibbons, R. & L. Katz (1992) Does unmeasured ability explain inter-industry wage differentials? Review

of Economic Studies, 59, pp. 515535.

Gunderson, M. (1989) Malefemale wage differentials and policy response, Journal of Economic Litera-

ture, 27, pp. 4672.


18/21


Jung, J. H. (2001) Are women better-off in knowledge-based industries?: the gender wage gap in Korea,

KIET Occasional Paper, 49.

Jung, J. H., Jang, S. I., Lee, J. H. & Choi, K.-S. (1999) Development Strategies for Women Labor Force in

the Knowledge-Based Society (in Korean) (Seoul: Presidential Commission on Womens Affairs).

Keane, M. (1993) Individual heterogeneity and inter-industry wage differentials, Journal of Human

Resources, 28, pp. 134161.

KIET (1998) Industrial Restructuring for the 21st Century: Shift toward Knowledge-based Industries (in

Korean) (Seoul: Korea Institute for Industrial Economics and Trade).

KIET (1999) Industrial Projections and Policy Directions after the Reform of the Chaebol in Korea (in

Korean) (Seoul: Korea Institute for Industrial Economics and Trade).

Krueger, A. B. & Summers, L. J. (1988) Efficiency wages and the inter-industry wage structure, Econo-

metrica, 56, pp. 259293.

OECD (1998a) Technology, Productivity, and Job Creation: Best Policy Practices (Paris: OECD).

OECD (1998b) Science, Technology, and Industry Outlook(Paris: OECD).OECD (1998c) The Future of Female-dominated Occupations (Paris: OECD).

OECD (1999a) OECD Science, Technology, and Industry Scoreboard 1999: Benchmarking Knowledge-

based Economies (Paris: OECD).

OECD (1999b) OECD in Figures (Paris: OECD).

World Bank (1999) World Development Report: Knowledge for Development (Washington, DC: World

Bank and Oxford University Press).

Appendix

Table A1. R&D intensity and employment structure of knowledge-based industries

R&DIntensity1)

(1999)

CollegeGraduates2)

(2001)

Knowledge-based manufacturing (KBM)

Chemicals (D24) 3.63) 29.6Non-electrical machinery (D29) 3.6 12.5Office/accounting/computing machinery (D30) 7.0 17.0Electronic machinery (D31) 10.6 16.8Communication equipment (D32) 17.9 19.8Precision instruments (D33) 4.1 8.9Motor vehicles (D34) 8.9 11.8

Other manufacturing (OM)Food, beverages, tobacco (D15D16) 0.7 10.0Textiles, apparel, leather (D17D19) 0.9 6.7

Wood and paper products (D20D21) 0.54) 14.1Printing (D22) 29.0Petroleum refineries/products (D23) 0.5 44.7

Rubber/plastic products (D25) 3.5 12.1Non-metallic mineral products (D26) 1.9 11.5Metals (D27) 1.0 13.6Fabricated metal products (D28) 1.0 8.9Other transport equipment (D35) 1.1 26.5Furniture, and Manufacturing n.e.c (D36) 1.6 10.2Recycling (D37) 3.8

(Continued on the next page)


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Table A1. R&D intensity and employment structure of knowledge-based industries(Continued)

R&D

Intensity1)

(1999)

College

Graduates2)

(2001)

Knowledge-based services (KBS)Communications (I64) 5.0 29.2Financial services (J65- J 67) 31.0Business services (K72-K75) 35.2Education services (M80) 59.5Electricity, gas, water supply (E40-E41) 0.9 30.7

Other services (OS)Construction (F45) 0.7 13.2Wholesale/retail trade (G50-G52) 15.4Hotels and restaurants (H55) 4.7Transport and storage (I60-I63) 10.9Real estate activities (K70-K71) 15.0Health services/social work (N85) 27.6Culture/recreation (O92) 23.3Other services (O90-O91,O93) 18.7

Manufacturing 4.5 13.2Services 0.4 20.7

All Industries5) 1.8 19.0

Note: 1) R&D expenditures as a percentage of value added in each industry. 2) The ratio of 4-year collegegraduates to the total employed (%). 3) Includes printing industry. 4) Pharmaceuticals (R&D intensity3.9%) excluded. 5) Manufacturing and service industries. 6) Korean Standard Industrial ClassificationCode 6th edn, (1991) in parentheses.Source: OECD, Science, Technology and Industry Outlook(2002). National Statistics Office, Korea, 2001

Economically Active Population Survey (2002).

Table A2. Scope of knowledge-based industries: Comparison of KSIC and I/O codes

KSIC I/O

Knowledge-basedmanufacturing(KBM)

Chemicals (D24)Non-electrical machinery (D29)Office/accounting/computing

machinery (D30)Electronic machinery (D31)Communication equipment (D32)Precision instruments (D33)Motor vehicles (D34)

Chemical products (2930, 3435)Machinery and equipment (46)Electronic machinery/components

(4849)Communications equipment (50)Computer/office equipment (51)Household electrical appliances (52)Precision instruments (53)Motor vehicles (54)

Knowledge-based

services (KBS)

Communications (I64)

Financial services (J65-J 67)Business services (K72K75)Education services (M80)

Communications and broadcasting (66)

Finance and insurance (67)Business services (69)Education and research services (71)

Note: 1) KSIC refers to the Korean Standard Industrial Classification Code 6th edn (1991).2) I/O refers to the I/O classification code (77 sectors, 2000).Source: National Statistics Office(2002), Korea, 2001 Economically Active Population Survey.Bank or Korea(2003), 1990-1995-2000 Link Input-Output Tables.


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Figure A1. Ratio of knowledge-intensive jobs by industry (Men), 1997, 2001.Note: Ratio of professionals/technicians, and of 4-year college graduates to the total workforce

in each industry cluster (%).Source: Ministry of Labor, 1997 Wage structure Survey, 2001 Wage structure Survey.


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Figure A2. Ratio of knowledge-intensive jobs by industry (Women), 1997, 2001.

Note: Ratio of professionals/technicians, and of 4-year college graduates to the total workforcein each industry cluster (%).Source: Ministry of Labor, 1997 Wage structure Survey, 2001 Wage structure Survey.

labor market structure in knowledge based economy

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