More proficiency, higher pay? Evidence from IT adoption in Taiwan

This article was downloaded by: [UQ Library]On: 24 November 2014, At: 07:06Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Statistics and Management SystemsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tsms20

More proficiency, higher pay? Evidence from ITadoption in TaiwanLong-Hwa Chen a , Tor Eriksson b & Tsorng-Chyi Hwang ca National Defense University , No. 70, Sec. 2, Zhongyang N. Rd. Beitou, Taipei Taiwan ,112 , R.O.C. E-mail:b Department of Economics , Aarhus School of Business University of Aarhus , Denmark E-mail:c Department of Applied Economics , National Chung-Hsing University Taiwan , R.O.C. E-mail:Published online: 14 Jun 2013.

To cite this article: Long-Hwa Chen , Tor Eriksson & Tsorng-Chyi Hwang (2010) More proficiency, higher pay?Evidence from IT adoption in Taiwan, Journal of Statistics and Management Systems, 13:1, 111-129, DOI:10.1080/09720510.2010.10701459

To link to this article: http://dx.doi.org/10.1080/09720510.2010.10701459

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose ofthe Content. Any opinions and views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be reliedupon and should be independently verified with primary sources of information. Taylor and Francis shallnot be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and otherliabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/loi/tsms20

http://www.tandfonline.com/action/showCitFormats?doi=10.1080/09720510.2010.10701459

http://dx.doi.org/10.1080/09720510.2010.10701459

http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/page/terms-and-conditions

More proficiency, higher pay? Evidence from IT adoption in Taiwan

Long-Hwa Chen ∗2

National Defense UniversityNo. 70, Sec. 2, Zhongyang N. Rd.4

Beitou, Taipei 112Taiwan, R.O.C.6

Tor Eriksson †

Department of Economics8

Aarhus School of BusinessUniversity of Aarhus10

Denmark

Tsorng-Chyi Hwang ‡12

Department of Applied EconomicsNational Chung-Hsing University14

Taiwan, R.O.C.

Abstract16

As IT has developed and become more and more user friendly, there is no extrapremia for complex computer skills in the new industrialized country — Taiwan — where18

IT industries are especially intensive. Workers with complex computer skills have evennegative returns for advanced level. Most basic skills have positive returns. However,20

we find no additional premia beyond two years of experience of computer use. There aresignificant returns to English proficiency when control for ability of learning computer for22

non-user as well as using experience.

Keywords and phrases : Computer use, computer skill, wage differential, Taiwan.24

∗E-mail: [email protected]†E-mail: [email protected]‡E-mail: [email protected]

——————————–Journal of Statistics & Management SystemsVol. 13 (2010), No. 1, pp. 111–129c© Taru Publications

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014

112 L. H. CHEN, T. ERIKSSON AND T. C. HWANG

1. Introduction

During the past two decades Taiwan has experienced a dramatic2

increase in earning inequality after a fast development of economy growthand Taiwan enjoyed an equalized distribution in mid-1970s. There are two4

main explanations of these developments. One is the internationalization,in particular the consequences of the Chinese Economy Openness Policy6

since 1990 which attracted Taiwanese enterprises to invest in China.Another conjecture is the skill biased technological change hypothesis,8

which has not, however, been verified due to lack of data of Taiwan.The literature in Western countries was started induced by the debate10

between Krueger (1993) and DiNardo and Pischike (1997). The majority ofthe subsequent researches on this issue focus on Western countries, most12

of which are English speaking countries. In an open economy like theTaiwanese, English proficiency is as important as computer skills in non-14

English speaking countries. The empirical work on non-English speakingcountries has seldom taken English proficiency into consideration when16

discussing the wage premium, mainly due to the unavailability of dataon employees’ English skills. As a result, neglecting English proficiency in18

the wage equation could cause a potentially serious omitted variable biasproblem in the regression analysis.20

Since Taiwan has been an open economy from the early 1960s,English has for a long time been a valuable asset in the labor market.22

When the new economy emerged in late 1980s, Taiwan not only alteredits labor-intensive industries into high tech industries, but also became24

an important base for production of IT products for the world market.From the point of view of demand, both computer skills and English26

skills have become two important success determinants in the Taiwaneselabor market. According to statistics from the largest online manpower28

companies, in 2000 more than 25% of the vacancies require fair skills inEnglish and about 20% ask for excellent English skills as well as computer30

skills1. However, far from every worker has acquired the two importantskills.32

This study provides estimates of the wage premium of computeruse in Taiwan. Using the Digital Divide Survey of Taiwan 2006, we34

estimate the returns to computer skills and English ability. Accountingfor potential selectivity bias with respect to labor force participation,36

our empirical result suggests, in line with prior studies, that the wage

1http://www.104.com.tw/cfdocs/2000/pressroom/viewp 03.htm (14th May 2007)

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014

MORE PROFICIENCY, HIGHER PAY 113

premium of computer use at work is about 15%. Furthermore, controllingfor the ability of computer use for those who does not use computer2

at work, the wage premium becomes 22%. The eight point per centdifference captures the ability of non-computer-user. This gives evidence4

for explaining some unobservable ability which many researchers arguethat the estimation of computer using premium in cross section data is6

just capturing selection bias. The premium for computer users with fairand advanced English ability is about 4% and 16% respectively. Most of8

the relatively complicated computer skills such as computer maintenanceand homepage design have no or even negative effects on wages.10

2. Literature review

An early and frequently cited article on computer using premium12

is Krueger (1993). This article estimates that use of computers at workearns 10 to 15 per cent higher wages and concludes that this technological14

change has significantly affected the wage structure. Moreover, use of e-mail at work shows the highest premiums among the specific tasks and16

Krueger argues that this reflects high level executives’ frequent use of e-mail at work.18

DiNardo and Pischike (1997) explore West German Qualificationand Career Survey data which provides information about various of20

tools used at work to test whether the computer is a better appliance atwork. They find that not only is using a computer but also using pens22

associated with higher wages and argue that computers are productiveand valuable only in connection with specific sets of skills or in certain24

jobs. The explanation is that higher level occupations are more likelyto use computer at work. They also propose that computer knowledge26

might be a better variable to test the impact of computer revolution onthe labor market. Interestingly, Splitz-Oener (2007) shows that the effect of28

using pens disappears by analyzing a newer wave of the same data set asDiNardo and Pischike (1997) used. She concludes that the computer had30

substituted routine and interactive jobs which require simple skills likeusing a pen but are still complementary with analytical tasks. Therefore,32

the wage premium of using pens vanished after 1998 while computer usestill provides a positive and significant return.34

Pro and con evidences have been presented in a number of papers.Entorf et al. (1997) find that computer-related new technology in France36

yields positive premiums when analyzed by cross-section data. They

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


divide the data into two parts: one is for pure computer use without anyexperience, and the other is for additional years of experience of computer2

use. The pure using effect disappears when fixed effects estimation iscarried out. Thus, they argue that firms tend to choose better workers4

to use computers and computer users are already better paid than thosewho do not use computers at work. However, without information of6

individual ability on computer use, the results can only explain thedifference in returns for every additional year of using a certain new8

technology in a certain job.Borghans and Weel (2004) demonstrate that although computer users10

earn more than non-users, returns for computer skills are smaller thanwriting and math skills. Computer skills are productive and useful only in12

higher level jobs. Wolday (2006) shows that computer premium estimatesbecome unstable as more workplace variables are included in the analysis14

and tend to disappear when using two income levels as a threshold. Onthe other hand, literacy skills are more important in reflecting unobserved16

skills. Dolton et al. (2007) argue that computer use has little impact onwages because the labor market reacts efficiently to the technological18

change. There should be some response time for the worker to adoptthis new technology and that causes a lagged adjustment process. They20

conclude that computer use is an indicator of equipment capital whichonly increases the marginal product of high quality workers.22

Some studies have discussed the returns to language skills as wellas adoption to IT, but cultural and multi-lingual background is not24

comparable across countries. For example, Chiswick and Miller (2007)find that English is important for computer use in Australia. Hiroshi and26

Madeline (2007) discuss the English ability and adoption of IT, includinguse of computer and internet, for immigrants in the U.S. and conclude28

that English proficiency is strongly associated with adoption of IT. Thetwo studies are based on the English speaking countries and compare the30

differences between immigrants and natives for the majority of websites inthe two countries is in English. Immigrants might not be familiar with the32

language as well as the natives. However, in our study, most of Taiwanesewebsites is in the country’s own official language and thus, the linkage34

between English and adoption of IT might not as important as in theprevious studies.36

KuKu et al. (2007) study the returns to computer use as well aslanguage skills in nine former central planed economies. They argue38

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


that language competency not only affects wage but also the adoptionof computers at work since eighty per cent of websites are in English.2

Controlling for computer adoption is in our study not as essential asthe cross-borders comparison study, since computers and access to the4

Internet in Soviet-block countries were relatively more expensive than inTaiwan.6

Most prior research has focused on developed Western countries.Newly industrialized countries, like Taiwan, also widely apply computers8

in many workplaces but have seldom been discussed in the literature.An exception is Liu et al. (2004), who use the 1999 Taiwan Social Change10

Survey data, also find a positive correlation between computer use andwages — the elasticity is 0.14. Nevertheless, their study differs in many12

respects from ours. First, the purpose of the Social Change Survey wasto examine the citizens’ life styles, whereas our data set was explicitly14

collected to focus on digital dividing. Thus, only a few questions inthe Social Change Survey relate to use of computer technology. The16

Digital Dividing Survey includes several questions concerning computerrelated skills beyond computer use as for example English proficiency and18

computer ability for non-users. Second, our data set is of more recent date.In 1997, only 30% of all families had computers at home and less than20

20% of them had internet access. In 2005, when the Digital Divide Surveywas carried out, 63% of all families owned computers and 56% of the22

households had access to the internet.2 This reflects the strong increase incomputerization in Taiwan during the period. In fact, 52% used computers24

at work in 1997 according to Liu et al. (2004), whereas 66% used computersat work in 2006. Third, our data set is based on a larger sample size and26

thus allows for more reliable comparisons of sub-samples. Finally, but notleast important, our dataset is the first one in Taiwan to have information28

about English language skills at the national level.

3. Data and descriptive statistics30

The purpose of the Digital Dividing Survey of Taiwan, which wasconducted by The Research, Development and Evaluation Commission32

of Executive Yuan, Taiwan was to establish a digital divide database toinform public debate and policy making. Data were collected through34

telephone interviews. A random 0.1% sample of households consistingof 27,702 individuals aged above 12 years were interviewed.36

2See The Report of Family Income and Expenditure 1997, 2006 Taiwan.

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


The data include socio-demographic information like the individual’sbirth year, education, occupation, employment statutes, working hours2

and monthly earnings. Most workers in Taiwan are paid on monthlybasis and we create an hourly wage rate by dividing monthly earning4

by monthly working hours.3 Furthermore, this data set provides detailsabout personal computer skills as well as English language. Computer6

skills include operating or maintaining computer as well as applicationssuch as doing office work, homepage designing, using E-mail, and surfing8

on the internet. After excluding respondents with missing values onrelated variables such as age, education and occupations, we have 10,42310

individuals aged between 21 and 65 years for our analysis. Thereof, 7,633participated in the labor market.12

Table 1 presents some descriptive statistics for computer users andnon-users, respectively. On average, workers who use a computer at work14

have higher education, higher incomes and fewer working hours thannon-users. The computer users earn 34% more than non-users, but work16

6.1% less hours. The years of schooling are also about 34% higher forcomputer users than for non-users. We use number of students and other18

family members’ earnings as controls for the labor participation.

Table 1Descriptive statistics for labor participator20

All Use computer User/Mean Std. dev. User Non-user Non-user

Schooling 13.20 3.00 14.47 10.78 34.3%Age 39.90 9.68 38.20 43.16 – 11.5%Earning 45.297 27.116 49,670 36,970 34.4%Wage 245.51 189.63 269.11 200.55 34.2%Working hours 46.78 13.71 45.77 48.72 – 6.1%Number of Students 0.94 1.13 0.90 1.03 – 13.4%Other member’s earning 37,467 33.282 42,762 27.382 56.2%Observations 7,633 5,005 2628

Source: Author’s calculations of 2006 Taiwan Digital Dividing Survey22

Many studies argue that returns to computer use could merelycapture the selection bias of the unobservable ability. In most cases24

information about ability to use computers, as opposed to actual use atwork, is not unavailable in the data analyzed. In this survey, non-users26

3Wage rate = monthly earning/(weekly working hours ∗4.28 )

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


are asked about what difficulties they would meet it they learn to usecomputers. The choice alternatives include:2

(a) Computer is difficult to learn (13.7%).

(b) Computer monitor is too small to read (15.9%).4

(c) Keyboard and mouse are difficult to control (9.7%).

The proportion of the non-user workers is given in parenthesis. We6

create a new variable — “ability to learn computer” — by regroupingthose who not choose any of the three alternatives above. 75% of workers8

that do not use computers at work do not have any problems in learningto use computers. Since computers have been applied to many tasks,10

especially most routine tasks, it is not surprising that computer use is nota difficult skill for workers to learn.12

Table 2 gives the percentages of workers using computers at work bydemographic characteristics. The first column contains the rates of users in14

each category and the second column shows the share of workers in eachgroup who use computers at work. Among the labor participants, males16

are 9% less likely than females to use computers at work. Over ninetypercent of professional jobs involve computer at work and almost the same18

rate of office clerks use computers. Office clerks use computers for routinetasks like sending E-mails or word processing, whereas, professional20

workers use computers for more advanced and complex tasks. Mostgovernment employees use computers at work because most civil service22

tasks have been computerized and the government had also invested largeresources on training its employees.24

Table 3 lists the various computer skills possessed by those who use acomputer at work. As expected, basic skills such as using the keyboard or26

the mouse, sending e-mails, searching, downloading, setting up softwareand using software packages are widely spread among workers. More28

professional computer knowledge and skills, maintaining or homepagedesigning, are relatively scarce skills.30

Ability to read an English website is a skill that has not been recordedbefore in a nationwide survey in Taiwan. Basically, reading a website32

should not differ from any other reading, it is equivalent to literacy.The Taiwanese literacy rate exceeds 97% in the survey year. Hence the34

respondents who cannot read an English website are those who lackEnglish reading skills. Therefore, this variable has nothing to do with36

computer ability but can be regarded as English reading skills. Because

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


Table 2Percentage of using computers at work

% Use computers at work

All 100% 65.57%Gender

male 56.10% 61.54%female 43.90% 70.73%

Occupationpolice 2.16% 82.42%managers 15.43% 63.07%professionals 19.78% 92.52%technicians 12.31% 77.87%clerks 19.51% 91.40%services 8.91% 42.21%agricultural 3.01% 10.00%craftsman 8.59% 26.83%operators 3.86% 21.36%laborers 6.42% 17.76%

Employment Statusprivate 60.57% 76.68%government 22.00% 90.53%self- 17.44% 37.19%

Observations 7633 76332

Source: Author’s calculations of 2006 Taiwan Digital Dividing Survey

this question is based on computer use in the survey, the interpretation4

for this variable is an interaction of computer use and English languageability. Only less than 10% of workers who use computers at work can6

read English very well and 37% of them to a moderate degree.

4. Empirical model8

We make use of an estimation method similar to that of Krueger(1993). The regression model is:10

ln Wi = α + βXi +θCUi + NU ablei + δλi +εi , (1)

where Wi is the workers’ wage rates. Xi represents the individual’s char-12

acteristics such as age, gender, schooling, occupation and employmentstatus. CUi is a computer use dummy equal to 1 if the employee uses14

computers at work and 0 otherwise. NU ablei , is the control for computerability of the non-user: 1 if the non-user reports no having difficulties in16

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


using computers, and 0 otherwise. λi is the inverse Mills’ ratio from theprobity selection model proposed by Heckman (1979). The independent2

variables in the probit model include gender, age, schooling, number ofstudents in the family, other household members’ income and a dummy4

variable for a foreign spouse. Number of students is a proxy to controlfor the family status. Other family member’s income can measure the6

substitution effect among members. Foreign spouses (mainly brides) fromSouth-East Asia countries such as Thailand, Vietnam, Philippines and8

China have become popular in Taiwanese disadvantaged families. The lastthree variables are constraints of labor supply for an individual. All signs10

in the model are consistent with our expectations and significant at the 1%level, except for other family members’ earnings which differ from zero at12

the 10% significance level.In equation (2), Cski represents the worker’s computer skills. The14

other variables are as in equation (1).

ln Wi = α + βXi + γCski +εi (2)16

In most cases, workers are not using computer skills separately. Themost common way is to perform tasks combining certain skills. Estimat-18

ing those skills together in a regression might cause a multicollinearityproblem. Therefore, we pursue a cluster strategy; see Borland et al. (2004).20

The hierarchical clustering algorithms are measured by the Ward Methodas suggested by Kuiper and Fisher (1975). Figures 1 and 2 display the22

dendrograms of males and females, respectively. We choose 5 clusters formales and 6 clusters for females in our regression analysis. The first male24

group is the office workers who can use the electronic mail, office docu-ment software and are good at typing. They can also setup the software26

package as well as some basic knowledge in searching information on thenet. In addition, male workers are able to fix or maintain computer in28

some simple cases. The second group includes those who can read simpleEnglish, have basic knowledge on homepage designing and no problem30

in searching information on the net. They can always fix and maintaincomputer themselves. The other three groups have totally different skills32

attributes and we treat them accordingly. The main difference betweenmales and females is that “fix2” is clustered to second group and “fix1” is34

clustered out of second group females. Obviously, fixing and maintaininga computer is a more common skill among men than women.36

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


2

Figure 1Dendrogram of the clustering skills of male rescaled distancecluster combine

4

6

Figure 2Dendrogram of the clustering skills of female

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


5. Results and discussion

5.1 Returns to computer use2

Column (1) of table 4 gives results for using computers at work. Theestimates are 15.6% of log wage or 16.9% of wage, which is somewhat4

higher than Kruegers’ (1993) estimate for the U.S. and also a little higherthan the 14.5% estimate of Liu et al. (2004) obtained by using the 19996

Taiwan Social Change Survey data. In column (2), both English languageproficiency and ability to learn computer are included. English language8

ability plays an important role in Taiwan’s labor market. The 37% ofcomputer users with a fair degree of English language skills earn 4.4%10

more than those who do not understand English, The computer userswith excellent English capacity earn about 16% more than other workers.12

Education is correlated to the English skills, but not every highly educatedworker using a computer at work has better English ability. The coefficient14

for years of schooling drops by 0.01 of log wages when we add Englishability in the wage equation. Thus, not controlling for it will lead to a slight16

overestimation of the returns to education.

The indicator for ability to learn using computers separates the non-18

computer users into two groups: those who cannot and those who do not.It is clear that those who do not use computers but have not problems20

in learning it are more able than those who cannot and their wage isaccordingly about 8% higher. It is interesting to note that the computer22

use premium increases by 22 per cent. That is, those who use computersearn 22% more than those who do not use computers and have difficulties24

learning using computers. Regressions which do not control for abilitydifferences may thus seriously under-estimate wage differences between26

computer users and those who are unable to use them. Difference betweencomputer users and those who do not use computers but have no problem28

in learning to do so is about 14%. This implies that not controlling for itleads to an overestimate of the difference between computer users and30

those who can use it.

We combine one and two years of experience of computer use as a32

reference group in the estimations because there is no difference whenthey are estimated separately. Column (3) shows that those who have used34

computers for more than two years obtain about 6 to 9% higher wages.Further experience beyond two years is not associated with additional36

returns. The last column shows, as a comparison, the estimates for em-

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


ployees in private sector only.4 Wages of private employees are apparentlymore related to their productivity. Therefore, returns to education and all2

the skill variables are higher than for overall workers. The coefficient ofcomputer use in private sectors is slightly higher. There is, however, no4

statistically significant differences among those who have more experiencethan two years. This result is different from Entorf et al. (1997) who find a6

computer use premium of about 1 per cent for each year of experience ofcomputer use.8

Unlike previous research that found computer using experience isassociated with higher productivity, our results show that there is no10

significant productivity gain for users with more than two years ofexperience. This could be due to the fact that computers have increasingly12

been designed to be user friendly which has shorten the learning process.This also explains why we arrive at different results than Borland et al.14

(2004) and Lex and Bas (2007), which both use data from a decade ago.

5.2 Return on computer skills16

The literature on computer use has been criticized for that computeruse may not be a good proxy measure for the computer technology impact18

on the labor market. An alternative proxy is the computer knowledge orskills. Computer skills are superior to computer use because computer use20

premium at work might merely a return to capital, or equipments which isuncorrelated with the human capital (DiNardo and Pischike, 1997; Hamil-22

ton, 1997). Borghans and Weel (2004, 2006) found that computer skills didnot yield return in most of the instances they examined for the UK. The24

only returns on computer skills are those needed for using computers foradvanced and complex purposes. We make use of the information about26

various computer skills in the survey to examine whether it is the specificcomputer skills rather than using a computer at work that are associated28

with wage primia. The first column in table 5 shows the wage premiums ofcomputer skills for male employees in private sector. Reading the English30

homepage is no doubt the skill with highest significant positive return.Skills like “e-mail”, “setup” and “searching”, which need “click” only32

with one’s fingers, have no premium on wage comparing to the non-users.Only skills like quick typing (“oper1”) and the office work software (i.e.34

use of spread-sheet for calculations and word-processing tools) yield somewage premiums. These skills for which employees need some training or36

4This column is estimate by the OLS for there is no selection problem.

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


experience are widely applied in modern offices. In fact, more complicatedskills like homepage designing, which needs more professional training, is2

negative and significant. Fixing or maintaining computers, for which mostemployees rely on their IT department, are also negatively related to their4

wages. The negative premia might reflect relatively low wage in certainindustries because those relatively scarce skills might not be an essential6

computer skill in most of workplaces.5

Females (see column 3) have significant returns to most of the8

computerized office skills like e-mail and fluent use of computer interface.The most popular documentary skills like spread-sheet for calculations10

and word-processing tools are not associated with a significant return.The more complicated skills, such as “Fix” and “homepg2”, are associated12

with negative returns and less insignificant. The control for computerindustries is insignificant. Only “homepage1” has a negative return at the14

10% significant level. These results reflect that considerably fewer femaleworkers in the computer related industries.16

The skills variables measure how a worker uses computers. Anotherquestion is whether more adeptness leads to higher earnings when mak-18

ing use of certain skills. In the survey, five skills are further divided intotwo skill levels, enabling us to compare the differences between them.20

The results show that males apparently obtain a different premium fromkeyboard control and English proficiency. Conversely, male workers are22

more sophisticated at “Fix” and “Homepg” skills. A possible explanationfor these results is that we include too many skills in the regressions, not24

accounting for the fact that workers usually do not use only a single skill intheir jobs. The regressions on clustered groups can be seen as a sensitivity26

test of this possibility.

Columns (2) and (4) in table 5 gives results of clustered skills. Workers28

with good English skills have the highest returns, but they are lower thanin columns (1) and (3). The most common skills in the modern office have30

significant returns for both male and female workers. For male workers,group 2 does not have any wage premium implying that the wages of men32

with fixing and maintenance skills do not differ from those who lack theseskills. Women with some basic skills (Eng2, Homepg2 and fix2) enjoy a34

5Hence, we also control the computer related industries: the Computer Manufacturingindustries which require workers with fixing and maintaining skills and the ComputerService industries where workers with homepage design skills work in. Male workers inthese industries also have negative wage premium. The results do not change if interactiveterms are added to the regressions.

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


small wage premium. However, more professional homepage designingskills are still associated with lower pay for both genders.2

6. Concluding remarks

In this paper we analyze empirically the impact of computer use as4

well as different computer skills on employees’ wages using Taiwanesesurvey data. Employing the same regression model as Krueger (1993) and6

Liu et al. (2004) we find that the computer use premium is 15.6 per cent.This estimate is close to previous estimates in studies of wage differentials8

between computer user and non-user. Moreover, returns to computer useincrease when we account for the ability to learn use of computer of the10

non-users. This variable captures the ability for the non-users and thusdistinguishes between the computer users and those who are really unable12

to use computers. The results strengthen the conjecture that computertechnology has widened the wage gap not only due to adoption of14

computers at work, but also due to individual ability differences. Thus, theresults provide some additional support for the skill biased technological16

change hypothesis for the explanation of the widened wage dispersion inTaiwan. Notably, the increased wage inequality is not only due to adoption18

of computers at work, but also due to individual differences in computerknowledge (irrespective of whether this is used at work or not).20

There are no significant differences for more than two years of expe-rience in using computers. Furthermore, in many cases, more advanced22

computer skills do not bring more returns. Male workers get negativereturns to complex skills like fixing and Homepage designing. Female24

workers benefit from the most common skills, but obtain no extra returnsfor higher level skills. The cluster analysis shows that more advanced26

computer skills do not bring more returns either. These results could beattributed to the rapid and well developed of computer and thus more28

and more convenient for computer user to adopt in their workplaces.

Another key human capital variable we account for in our analysis is30

the employee’s English proficiency. Unlike most previous studies focusingon Western countries, English proficiency plays as a central role as32

computer skills in Taiwanese labor market. The extra returns to excellentand fair English language skills are about 16% and 4% respectively. This34

shows the importance of English proficiency on wage inequality in a non-English speaking open economy. As the economy has become increasingly36

international, more English-literate workers are demanded. Hence, inter-

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


T abl

e3

Defi

niti

ons

ofco

mpu

ter

skil

lsdu

mm

y

Que

stio

nA

nsw

erD

umm

yA

llPr

ivat

ese

ctor

vari

able

=1

Mal

eFe

mal

eQ

1W

hich

skill

leve

lsar

cyo

ube

long

toV e

ryw

ell

Ope

r188

.91

65.9

071

.59

inus

ing

mou

sean

dke

yboa

rd?

Not

very

wel

lO

per2

11,0

915

.33

15.2

5Q

2W

hen

your

frie

nds

need

som

epa

rtic

ular

V ery

wel

lSc

areh

127

.41

20.3

517

.34

info

rmat

ion,

can

you

sear

chfo

rth

emon

Fair

Scar

eh2

57.0

843

.61

48.9

5th

ein

tern

et?

Q3

Ar e

you

able

tous

eE-

mai

l?Y e

sEm

ail

91.0

568

.04

72.4

3Q

4D

oyo

ukn

owho

wto

use

soft

war

epa

ckag

e,fo

rex

ampl

e.W

ord

and

exce

l?Y e

sPa

perw

ork

89.2

363

.11

70.5

3

Q5

Do

you

know

how

todo

wnl

oad

and

setu

pth

eso

ftw

are

orfil

esw

hich

you

need

?Y e

sSe

tup

76.1

460

.88

55.7

4

Q6

Wha

tw

ould

you

dow

hen

your

com

pute

rne

edlo

Setu

pN

ewSo

ftw

are.

T ota

llyby

mys

elf

Fix1

14.3

117

.76

5.53

Brea

kdow

n,da

mag

eof

eith

erha

rdw

are

orso

ftw

are,

viru

sin

flect

ed?

Mai

ntai

ning

bym

ysel

fin

easy

case

.Pr

ofes

sion

alca

seby

othe

rFi

x245

.19

35.1

535

.38

Q7

Do

you

know

how

lose

tup

hom

page

s?V e

ryw

ell

Hom

pg1

4.62

364

1.72

Alit

tle

Hom

pg2

28.8

919

2219

.39

Q8

Can

you

read

the

Engl

ish

hom

page

?V e

ryW

ell

Eng1

8.61

8.21

4.88

Fair

Eng2

36.5

626

.62

24.7

8Q

9N

ovem

ber

man

yye

ars

have

you

Less

2ye

arC

exp

21.

101.

701.

53ev

erbe

enus

ing

com

pute

r?2-

5ye

ars

Cex

p25

5.37

7.97

7.07

5-7

year

sC

cxp5

77.

658.

867.

58M

ore

than

7ye

ars

Cex

p785

.29

61.5

368

.90

Obs

erva

tion

s50

0524

7221

51So

urce

:Aut

hors

calc

ulat

ions

of20

06Ta

iwan

Dig

ital

Div

idin

gSu

rvey

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


T abl

e4

Reg

ress

ion

resu

lts

ofco

mpu

ter

use

onw

age

y=

l n(w

age)

Basi

cco

ntr o

lA

ddA

bilit

ies

Add

expe

rien

cePr

ivat

eem

ploy

eeC

oef f

.t-

rati

oC

oef f

.t-

rati

oC

oef f

.t-

rati

oC

oef f

.t-

rati

o

Gen

der

(Mal

e=

1)0.

074

0.04

71.

590

0.04

50.

960

0.04

30.

910‡

0.17

314

.06‡

scho

olin

g0.

052

0.00

69.

060‡

0.04

27.

140‡

0.04

06.

740‡

0.04

613

.71‡

Exp.

0.03

30.

002

20.6

20‡

0.03

420

.840‡

0.03

520

.860‡

0.02

817‡

Squa

r eof

Exp.

/100

0.00

00.

000

–11

.490‡

0.00

0–

11.6

00‡

0.00

0–

11.3

80‡

0.00

0–

7.52‡

Empl

oyst

atus

(Ref

.Pri

vate

)G

over

nmen

t0.

172

0.01

511

.160‡

0.18

011

.630‡

0.17

911

.6‡

Self

-em

ploy

ed0.

039

0.01

92.

100‡

0.04

52.

430‡

0.04

82.

55‡

Com

pute

rus

e0.

156

0.01

510

.100‡

0.22

09.

030‡

0.15

85.

5‡0.

162

5.24

0‡

Engl

ish

(Ref

.Wit

hout

)V e

ryw

ell

0.16

26.

220‡

0.16

26.

19‡

0.19

37.

640‡

Fair

0.04

43.

080‡

0.04

12.

900‡

0.04

63.

110‡

Abi

lity

tole

arn

0.08

13.

730‡

0.04

11.

720∗

0.05

52.

130†

Usi

ngEx

peri

ence

(Ref

.bel

ow2

year

s)2-

5ye

ars

0.08

53.

28‡

0.04

81.

740∗

5-7

year

s0.

063

2.31†

0.05

31.

880∗

Abo

ve7

year

s0.

090

4.07‡

0.06

42.

7.30‡

Occ

upat

ion

grou

ps10

1010

9C

onst

ant

3.93

0.15

26.8

7‡4.

0126

.66‡

4.03

26.7

4‡3.

7274

.45‡

Lam

bda

–0.

382

0.14

4–

2.65

00.

463

–3.

180‡

–.4

73–

3.25

0‡

Wal

dch

i2/A

dj-R

239

32.2

739

53.1

439

63.6

0.40

0

Num

ber

ofob

serv

atio

ns76

3346

23‡ ,†

and∗

r epr

esen

tsta

tist

ical

sign

ifica

ntle

vels

at1%

,5%

and

10%

,res

pect

ivel

y

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


T abl

e5

OLS

resu

lts

ofco

mpu

ter

skil

lsan

dcl

uste

red

skil

lsin

priv

ate

sect

or

Mal

eFe

mal

ey

=l n

(wag

e)C

ompu

ter

skill

sC

lust

ered

skill

Com

pute

rsk

ills

Clu

ster

edsk

illC

oef f

.t-

rati

oC

oef f

.t-

rati

oC

oef f

.t-

rati

oC

oef f

.t-

rati

oBa

sic

cont

rol

Scho

olin

g,Ex

peri

ence

,Squ

are

ofEx

peri

ence

oper

10.

058

1.69

0∗0.

123

2.82

0‡op

er2

0.03

51.

160

0.11

13.

020‡

emai

l0.

002

0.08

00.

081

2.27

0†pa

perw

ork

0.05

72.

060†

0.04

11.

190

setu

p0.

021

0.82

00.

010

0.41

0se

arch

10.

019

0.60

00.

091

2.61

0‡se

arch

20.

008

0.32

00.

066

2.33

0†en

g10.

192

5.73

0‡0.

270

6.10

0‡en

g20.

037

1.76

0∗0.

072

3.15

0‡fix

1–

0.07

3–

2.58

0†–

0.03

2–

0.77

0fix

2–

0.04

5–

2.10

0†–

0.02

9–

1.39

0ho

mep

g1–

0.10

2–

2.25

0†–

0.13

1–

1.91

0∗ho

mep

g2–

0.02

6–

1.18

0–

0.01

5–

0.62

0Em

ail P

aper

wor

kse

tup

Ope

r1Se

arch

20.

066

2.77

0‡0.

178

6.24‡

Eng2

Fix1

Fix2

Hom

epg2

Sear

ch1

0.01

10.

600

Eng2

Fix2

Hom

epy2

Sear

ch1

0.05

52.

590†

Fix1

–0.

004

–0.

100

Hom

epg1

–0.

097

–2.

290†

–0.

125

–1.

840∗

Eng1

0.16

25.

460‡

0.23

95.

760‡

Ope

r2–

0.00

9–

0.43

00.

011

0.43

0C

ompu

ter

Man

ufac

ture

indu

stri

es–

0.06

5–

2.76

0‡-0

.065

-2.7

90†

0.00

10.

020

0.01

20.

410

Com

pute

rSe

rvic

ein

dust

ries

–0.

090

–1.

880∗

–0.

099

–2.

070∗

–0.

081

–1.

190

–0.

071

–1.

030

Occ

upat

ion

grou

ps10

109

9C

onst

ant

3.94

063

.610‡

3.90

864

.610∗

3.69

450

.060‡

3.67

349

.930‡

Adj

-R2

0.40

20.

399

0.36

90.

362

Num

ber

ofob

serv

atio

ns24

7221

51‡ ,†

and∗

r epr

esen

tsta

tist

ical

sign

ifica

ntle

vels

at1%

,5%

and

10%

,res

pect

ivel

y

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


nationalization contributes to increased wage disparity. A disadvantage isthat the English language skills are conditioned on computer use. Thus, we2

cannot directly compare the returns to computer use and English languageskills. However, occupations like professionals and clerks, which tend to4

require English language ability, all have a high proportion of computerusers. Those who possess English skills but do not use computers are6

relatively scarce. Hence, this variable can at least to some extent be pickingup returns to English language skills, too.8

References

[1] L. Borghans and B. ter Weel (2004), Are computer skills the new basic10

skills? The returns to computer, writing and math skills in Britain,Labour Economics, Vol. 11, pp. 85–98.12

[2] L. Borghans and B. ter Weel (2006), Do we need computer skills touse a computer? Evidence from Britain, Labour, Vol. 20(3), pp. 505–14

532.

[3] J. Borland, J. G. Hirschberg and J. N. Lye (2004), Computer knowl-16

edge and earnings: evidence for Australia, Applied Economics, Vol. 36,pp. 1979–1993.18

[4] B. C. Chiswick and P. W. Miller (2007), Computer usage, destinationlanguage proficiency and the earnings of natives and immigrants,20

Review of Economics of the Household, Vol. 5, pp. 129–157

[5] J. E. DiNardo and J. S. Pischke (1997), The return to computer use22

revisited: have pencils changed the wage structure Too?, QuarterlyJournal of Economics, Vol. 112, pp. 291–303.24

[6] P. Dolton and G. Makepeace and H. Robinson (2007), Use IT or loseIT? The impact of computer on earnings, IZA Discussion paper No.26

2588, IZA.

[7] H. Entorf and F. Kramarz (1997), Does unmeasured ability explain28

the higher wages of new technology workers?, European EconomicsReview, Vol. 41, pp. 1489–150930

[8] B. H. Hamilton (1997), Returns to Computer Skills and Black-WhiteWage Differentials, John M. Olin School of Business, Mimeo March32

[9] J. J. Heckman (1979), Sample selection bias as a specification error,Econometrica, Vol. 47(1), pp. 153–162.34

[10] O. Hiroshi and Z. Madeline (2007), Immigrants, English ability anddigital divide, IZA Discussion paper No. 3124, IZA.36

[11] HRS (2006), Human Resources Survey, DGBAS, Taipei, Taiwan.

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014


[12] A. B. Krueger (1993), How computers change the wage structure:evidence from microdata, 1984-1989, Quarterly Journal of Economics,2

Vol. 108, pp. 33–60.[13] Y. KuKu, P. F. Orazem and R. Singh (2007), Computer adoption and4

returns in transition, Economics of Transition, Vol. 15(1), pp. 33–56.[14] F. K. Kuiper and L. Fisher (1975), A Monte Carlo comparison of six6

clustering procedures, Biometrics, Vol. 31, pp. 777–783[15] J. T. Liu, M. W. Tsou and J. K. Hammitt (2004), Computer use and8

wage: evidence from Taiwan, Economics Letters, Vol. 82, pp. 43–51.[16] A. Spitz-Oener (2007), The Return to Pencil Use Revisited, IZA10

Discussion paper No. 2729, IZA.[17] F. H. Wolday (2006), The Wage Effect of Computer-Use in Norway,12

Working paper, Ragnar Frisch Centre for Economic Research.

Received November, 200814

Dow

nloa

ded

by [

UQ

Lib

rary

] at

07:

07 2

4 N

ovem

ber

2014

Documents

More proficiency, higher pay? Evidence from IT adoption in Taiwan