Accumalation of Human Capital & Its Impact of Salary Growth in China

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Education EconomicsVol. 14, No. 2, 155180, June 2006

The Accumulation of Human Capital Over Time and itsImpact on Salary Growth in China

ZEYUN LIU* and JIN XIAO***School of Economics and Business Administration, Beijing Normal University, China **Facultyof Education, the Chinese University of Hong Kong, ChinaTaylorandFrancis LtdCEDE_A_162272.sgm10.1080/09645290600622913Education Economics0964-5292 (print)/1469-5782 (online)OriginalArticle2006Taylor&Francis142000000June [email protected]

ABSTRACT This study compares the growth in salaries across three spatial regions inChina during the period 19931998, when economic reforms were implemented nation-wide. Our study compares the impact of three forms of education and training on salary growth, namely pre-job formal schooling, on-the-job-training provided by employers, andadult education paid for by the employees themselves. We used a three-level hierarchicallinear model to partition variance among individual, firm, and regional characteristics.The data were drawn from a 1998 survey of 16 485 employees from 365 firms in six provinces (two provinces in the eastern part of the country, two in the central part, andtwo in the western part). We found that: (1) regional disparities have a paramount impacton differences in salary; (2) individual characteristics defined by firm as well as firmcharacteristics are significantly related to salary decisions; (3) returns to formal schoolingincrease significantly in more market-based regions; and (4) employees also benefit byreceiving on-the-job-training and by participating in adult education programs outsidetheir firm.

KEY WORDS: Human capital; salary; hierarchical linear model; China

Introduction

The private benefits of education are an important issue in the process of economicdevelopment and educational expansion. A large number of empirical studies esti-mating the rates of returns on education (RORE) have supported the view thatformal schooling is a crucial factor in differences in personal income in developedcountries (Cohn and Addison, 1998) as well as in less developed countries(Psacharopoulos and Patrinos, 2002). However, as Mincer (1974) has claimed, because in many cases the data used do not include information about other formsof education and training (ET) received during the period of ones employment,most studies of RORE have focused on the formal schooling acquired at the initialphase of ones life and have not captured much of the learning acquired over apersons life. One survey of studies of RORE in developed countries (Cohn and


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156 Z. Liu & J. Xiao

Addison, 1998) has noted that ET beyond formal schooling is a way of accumulatinghuman capital in the course of production. Nevertheless, recent studies of ROREin China (Li, 2003; Li and Ding, 2003; Lai, 1998; Johnson and Chow, 1997) also donot include information on the participation of workers in adult education and on-the-job training in the workplace, which have become popular during the period

of Chinas economic transition. Estimations of RORE are not accurate, as participa-tion in ET activities beyond formal schooling is neglected.

Since the early 1990s, China has experienced dramatic changes during the trans-formation of the countrys economy from a planned one to a market-oriented one.Many countries have likewise experienced rapid transitions in a global economy.Some scholars (Ashton et al., 2000) have built a new framework to understand theprocess of the formation of skills in different cultural contexts. They have identi-fied four stakeholders: the state; its apparatus of formal education; employers asusers of skills from whom demands for skills arise; and workers, whose personalcapacities in the market determine the need for skills. All of these stakeholders

influence the supply of skills. Lauder (2001) further suggested that the issue of thediffusion of skills is related to ET systems and to the structure of the labor market.A recent survey by the Organization of Economic Cooperation for Developmenton the development of adult education in six member countries has indicated thatthe rise and disappearance of various types of jobs has created a demand for awhole range of adult education programs that can help deliver skills required atdifferent occupational levels (Belanger and Tuijnman, 1997). A study on the train-ing of workers in seven different occupations in Colombia, a less developed coun-try, identified a variety of channels through which the workers have chosen tosatisfy their training needs (Ziderman and Horn, 1995). Middleton and associatesalso found that employers in less developed countries provided ET to theiremployees to develop their skills (Middleton et al., 1993). Surveys conducted inShenzhen (Xiao and Tsang, 1999) and Shanghai (Xiao, 2002a) provide furtherevidence that both employers and employees have assumed responsibility fordeveloping skills needed in the workplace.

International experience also suggests that, in addition to ET systems, socio-economic factors are crucial to explaining productivity. Based on 10 years ofobservations in the United States, Levin and Kelley (1994) did not find eithermonetary or non-monetary benefits, in terms of average income, interest in politi-cal participation, as well as dependence in social welfare, to be positively relatedto formal education. They maintained that firm profitability and employee salaryare related to multiple factors such as economic and social policies, peoplesconfidence in government, educational expansion, management strategies inworkplace, and so forth.

This paper, with a survey data of employees in Chinas three regions, is a studyto capture the impact of: (a) formal schooling (FS), firm-provided on-the-jobtraining (OJT), and adult education (AE) outside of the firm, paid for byemployees themselves, on salary growth from 1993 to 1998, a crucial period in thetransformation of Chinas economy; and (b) the inequality of economic reforms inthe three regions.

Labor Markets, Education, and Training Systems in China

China is a huge country Both economic development and educational expansion


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Human Capital Impact on Salary Growth in China 157

partially, but significantly, due to government policies. The eastern coastalregions (the East) were opened up to economic development in the 1980s, whilethe central region (the Central), and the western region (the West) were openedup in the early 1990s and the late 1990s, respectively. Hu et al. (1995, p. 210),Zhang (2003) and Wu (2004) have argued that disparities across the three regions

can be attributed to the fact that developed regions possess most of the resourcesand that opportunities in less developed regions are limited. Regional disparitiescreate inequalities in China so that ET may not play the same role in labormarkets. Studies conducted by Wei et al. (1999) and Chen and Chen (2002), and by Johnston (1999), have noted the productivity of firms and inequality amongregions. Studies on RORE in China, employing the Mincerian earning equationand using the most recent urban household survey data of 1995, have found that,more than ever, the market economy is allowing workers to capture the benefitsgained from education (Li, 2003; Li and Ding, 2003). Li (2003) found higherreturns to schooling for those newly hired in the early 1990s, after economic

reforms had started; and both Li and Ding (2003) and Lai (1998) found that work-ers in non-western regions can earn significantly more due to their FS. However, both did not capture the effects of firms nor the magnitude of regional disparities.

Our study involves six provinces, namely Guangdong and Jiangsu in the East,Hebei and Hubei in the Central, and Yunnan and Shaanxi in the West. 1

Guangdong and Jiangsu are ranked as the richest, with per-capita Gross DomesticProducts (GDPs) of RMB 11 143 and RMB 10 021(in 1998 prices), 2 respectively.Hebei and Hubei are at an intermediate level, with per-capita GDPs of RMB 6525and RMB 6300, respectively. Yunnan and Shaanxi in the West lagged far behind,with per-capita GDPs of RMB 4355 and RMB 3834, respectively. 3 An analysis ofproductivity shall consider these determinants.

Educational development in China has followed a unique path. Before the1980s, under the planned economy, ET was under the full control of the state. Inorder to recruit resources to meet the huge demand for education arising duringthe period of economic transition, the government employed different measures.It devolved financing for education to local governments, to increase the provi-sion of compulsory education for the population (China Central Committee,1985). The government further strengthened its budgetary efforts and, since 1993,has focused on education in urban areas and on higher education as the stateenvisioned a blueprint for modernization through science and education (ChinaCentral Committee and the State Council, 1993). The government also advocatedET to improve the vocational skills of employees in order to adapt to changingneeds in the workplace (China Central Committee and the State Council, 1981).But this endeavor was left entirely to work units or to the users of skills. Duringthe course of the economic transition, investment in ET related to workplaceneeds has been influenced by government policies, by management reform at thefirm level, and by individual choice (Xiao and Tsang, 1999, 2004; Xiao, 2002a).

The restructuring of markets and the supply of ET in China are attributed to thetransformation of the economy, and thus to the management as well as the intro-duction of technology. In the planned economy, the Party, through the govern-ment, controlled the organization of production in the factory floor through

directives on labor and quotas on production and supplies. The market orienta-tion of the economy has pushed the state to give up some powers so that firmscan act independently to achieve efficiency (Kang 1999 pp 60 82) That firms


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daily operations are indicators of a changed socio-economic environment. Theeconomic sectors have become detached from the state in terms of administrationand have to build their competence to survive in the market. At the same time,continuing integration into the international market has prompted firms tointroduce technological innovations in the workplace. This has become another

important factor behind the demand for learning in the workplace. As govern-ment-funded FS is mainly responsible for the initial education received byindividuals, there is a need for ET that is responsive to changes in the workplace.

Benson and Zhu (2002) recently found that firms in Beijing and Shanghai makeuse of both internal and external labor markets. When the external market forhiring cannot meet their human resource needs, they use an internal strategy ofOJT to produce the required human resources. We may assume that firms thathave gained control over production expect their employees to learn new sets ofknowledge, skills, and values to become proficient in major work roles that differfrom those in the planned economy. Due to the firms own mission of survival

and ideology of management, the OJT of each firm serves to better to diffusespecific job skills to employees and socialize them into the firms culture(Bowman, 1996 ; Hake, 1999, pp. 8384).

The breaking of the state-guaranteed iron rice bowl has opened up occupa-tional horizons for individual workers. But they can also be dismissed if their jobperformance is not up to expectations or if the firm is downsizing. As argued byHake (1999, pp. 8587), individuals increasingly have to assume personal respon-sibility for formulating their identities in life courses. Studies in other parts ofChina have found that improving job-related skills is ranked as more importantthan other reasons for taking AE courses (Jones and Wallis, 1992). Therefore,paying for AE is becoming another important avenue to advance ones career.Individuals can realize their occupational aspirations through the external marketif they fail to do so in the internal market.

Both firms as employers and workers as employees are assuming the responsibility to develop skills in workers, making both OJT and AE responsive tochanges in the workplace and to their needs. This has enabled a market for skillformation to evolve outside the government education system. Both OJT and AEare becoming new forms of the accumulation of human capital that are close tothe workplace. They are a crucial factor affecting returns to education as well.Hence, we assume that, in addition to FS, OJT and AE are also rational choices ofinvestment in human capital and can improve individual productivity. Therefore,we maintain that any estimation of the returns to education should include thesetwo kinds of investment in human capital in Chinas context.

Of the three major sources for the supply of ET, the government mainlyfinances pre-job FS; while firms organize up to nine types of OJT training for theirown workers in a firm-based mode. 4 AE has come to involve many job-relatedprograms for working adults who often voluntarily pay for them. Therefore,Chinese workers have a mixed path of ET and receive three types of education,which are funded differently (Xiao and Tsang, 1999; Xiao, 2004).

OJT and AE are widely used in China nowadays to improve job skills. Forexample, during the period 19921997, 59%, 60%, and 40% of the sampled

workers in three metropolitan cities received OJT (Shenzhen, Shanghai, andChongqing); and the percentage of workers who attended AE programs are 31%,38% and 36% respectively (Xiao 2004 p 174) Our survey shows that 65% 54%


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West, respectively, during the period from 1993 to 1998. At the same time, 21%,25%, and 23% of the sampled employees attended AE programs, respectively(Xiao, 2004).

We therefore hypothesize that: (1) in addition to FS, both AE and OJT have animpact on decisions on employee salaries; (2) change in the workplace is another

important learning experience and influences decisions on salaries; (3) firm-levelfactors play an important role in salary decisions; (4) all of these factors vary fromregion to region because inequalities in China have an impact on the firmsdecisions on salaries. This study adds information about the ET received byworkers beyond FS. It compares the impact of different forms of human capitaldevelopment in the course of production. The analysis also reveals magnitudes ofdiscounted RORE when inequalities cause a region to lag behind in marketreforms.

Our two tasks are: (a) to measure the effects of individual-level, firm-level, andregional-level characteristics on salary determinations in the period of the first

five years of reform to a market-oriented economy (19931998); and (b) to exam-ine the impact of FS, OJT, and AE on salary growth. In the next section, we willdiscuss a modified earning equation with a hierarchical linear model and ourdata. The third section presents the empirical results. The concluding section willprovide a discussion of the implications of the findings.

Modeling Salary Growth and Data

Data in social studies are often stratified. For instance, an employee is nested in awork-unit/firm and a work-unit/firm is nested in a region/province. Thesefeatures form a multilevel structure. Two methods are often used to deal withmultilevel data in a traditional regression analysis. One is to use higher-levelvariables (i.e., the characteristics of an organization) as dummy variables to makea regression at the lower level (i.e., the individual level). An estimation bias willoccur because it is not correct to assume that individuals within the same organi-zation are independent and that the characteristics of a firm have the same effecton all employees from different firms. The other method is to aggregate lower-level variables (i.e., often individual) and integrate them in a regression at thehigher level (i.e., the level of the organization). This treatment will cause someuseful information to be lost; that is, the variance among employees within thesame firm, which accounts for a large part of the total variance of a dependentvariable.

The Mincerian earning equation (MEE) (Mincer, 1974) has been a standardmethod to estimate the private benefits of education as argued by human capitaltheory. In order to capture the complicated context of economic transition inChina as discussed earlier, this study will use a three-level hierarchical linearregression model (HLM), a modification of the MEE method, to partition the totalvariance into three levels: (a) differences in salary growth over the years for aparticular person; (b) differences in individual characteristics; and (c) differencesin firm characteristics. This is important in Chinas context, where inequality ineducational resources and policies has been a fundamental issue.

At level one, the HLM growth model (Raudentbush and Bryk, 2002, pp. 160204)will be employed to capture increases in salary at three points in time over a periodof five years of the early stage of economic transition much as with the MEE


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will portray the slope of the increases in salary over the years due to the experiencegained from work, or learning by doing. At the second and third levels of theregressions of HLM, individual characteristic variables and firm characteristicvariables will be put in, respectively, to differentiate the variance and source of theimpacts. By using the modified models, Xiao (2001, 2002b), in her studies on the

determinants of salary growth in Shenzhen and Shanghai, found that: (1) FS affectsthe initial salary but not the growth in salary; (2) participation in AE affects salarygrowth in the case of Shanghai, while received OJT correlates significantly withsalary growth in Shenzhen; and (3) a firms characteristics, such as ownership andeconomic sector, play a decisive role in salary growth.

Analysis with HLM contains variance at each level and introduces variables ofeach level to explain the variance in the dependent variable at that level (Rauden- bush and Bryk, 2002). The advantage of a modified MEE as demonstrated byXiaos studies (2001, 2002b) is that it can be used to differentiate sources of impactand their magnitude on wage growth due to maturation in work, individual

characteristics, and firm characteristics. This delineates the structure of oneswork life as: one matures over the years; how this maturation is affected by onespersonal characteristics; and how ones job-related characteristics are influenced by the characteristics of ones work unit. This paper will employ the samemodified models to portray salary growth using data from 12 counties acrossChina during the early period of the economic transition.

Survey and Data

This study adopted the reverse tracer study technique (RTST) to investigate anemployees salary and educational experience. RTST, as illustrated by Zidermanand Horn (1995), begins with the current employment status of each employeeand seeks to identify each major alternative ET route pursued by the employee.Thus, RTST permits the analysis of an array of education and training optionsutilized both by firms and individual employees for the accumulation of humancapital. This technique also allows a comparison of profitability among types ofET programs. In addition, it has a lower study cost.

Two questionnaires were distributed in autumn 1998 to collect the data. Thefirm questionnaire investigated firm characteristics and the employee question-naire was designed for respondents to recall their relevant experiences over theperiod 19931998. The employee questionnaire consisted of five groups of data:(1) the employees demographic information and work experience; (2) FS beforethe first job; (3) OJT provided by the firm; (4) AE attended outside of the firm; and(5) characteristics reflecting individual productivity including job position, levelof technical proficiency, experience of changes in the workplace, and salary. 5

Table 1 presents descriptions of level 1 variables reflecting changes in individ-ual salaries over time. TIME is the number of years that have elapsed from 1993 tothe year when the data were reported. SALARY refers to the monthly salary in1993 prices at three time points, 1993, 1995, and 1998. In this study, salary consistsof three components: basic salary, bonus, and allowance. In the analysis, Y is thenatural logarithmic value of SALARY.6

Table 2 presents descriptions of individual variables for a level 2 analysis. SEXis a dummy variable, with 0 for female and 1 for male. EDUYEAR is coded as theactual number of years of FS that the employee received An employee who


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secondary school (including high school and upper secondary vocational school)as 12, junior college as 14, and university as 16 and graduate study as 19. EXPrefers to the total work experience in years up to 1993, which is the sum of bothEXPFIRM, the work experience in the current firm, and EXPOTHER, the workexperience in other firms. JOB refers to the employees job position in 1993 andthe codes reflect a hierarchical order. A front-line worker is coded as 0, a memberof staff such as salesperson or clerical personnel is coded as 1, and a member ofmanagerial or professional staff is coded as 2. RANK refers to the employees levelof proficiency in the JOB position in 1993. We use three levels to define the indi-viduals technical proficiency: entry, intermediate, and senior, which are coded as0, 1, and 2, respectively. TRAIN is a dummy variable, with 1 referring to havingreceived OJT during the period 19931998 and 0 to having received no such train-ing. AE is a dummy variable with 1 indicating attendance in AE courses from1993 to 1998 and 0 for not having attended such courses. CHANGE refers to theamount of changes the employee experienced in the workplace from 1993 to 1998.In the survey, the employees were asked whether they had experienced fourkinds of changes: the introduction of new products or services, new equipment,new production technology, and new job positions. If they experienced nochanges they were coded as 0, if they had experienced one kind of change theywere coded as 1, two kinds as 2, three kinds as 3, and four or more kinds as 4.IN_GAIN refers to advancements in an employees proficiency ranking in thesame job position during the five years. A gain from entry-level to intermediate-level or from intermediate-level to senior-level is coded as 1, a gain from entry-level to senior-level is coded as 2, and no gain in the five years is coded as 0.BE_GAIN refers to promotion in an employees job position in the five-yearperiod from 1993 to 1998. Promotion from worker to salesperson/clerical person-nel or from salesperson/clerical personnel to managerial/professional staff iscoded as 1, promotion from worker to managerial/professional staff is coded as 2,and no promotion in the five years is coded as 0.

Firm-level variables are presented in Table 3. REGION refers to the geographic

localities of the East, the Central, or the West of China. PROVINCE refers to thetwo provinces in each region; namely, Jiangsu and Guangdong in the East, Hubeiand Hebei in the Central and Shaanxi and Yunnan in the West Ownership refers

Table 1. Descriptions of level 1 variables

Variable Definition Year Code Overall East Central West

TIME Years elapsedfrom 1993

1993 0 0 0 0 0

1995 2 2 2 2 21998 5 5 5 5 5

SALARY Actual monthlysalary in 1993prices (RMB)

1993 Mean (standarddeviation)

253(251) 327(275) 215(251) 156(92)


350(318) 445(339) 297(333) 238(115)


389(310) 482(323) 322(313) 305(190)

N 16 485 7181 6151 3153


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local private or collective firm. STA_CORP is dummy variable with 1 referring toa state-owned or corporate firm. FOREIGN is a dummy variable with 1 for a firm,which had investment from Hong Kong, Macao, Taiwan, or other countries ineither mode of sole investment or joint venture. SECTOR is a dummy variable,with 1 referring to firms in the service sector and 0 referring to those in the indu-sial sector. SIZE is coded in an ordinal manner, with 0 for small firms (those with

fewer than 300 employees), 1 for medium firms (301800 employees), and 2 forlarge firms (those with more than 800 employees). AV_EDU refers to the averagenumber of years of schooling of the sampled employees within a firm TREX


Variable Definition Code Overall East Central West

SEX Female, male (%) 0 42.3 45.8 39.9 39.01 57.7 54.2 60.1 61.0

EDUYEAR Years of formal schooling Mean 11.1 11.0 11.3 10.9EXP Years of work experience up to 1993 Mean 12.4 13.3 11.7 11.7EXP_SQ The square of work experience Mean 228.4 257.1 205.8 207.0EXPFIRM Years of work experience in the firm up

to 1993Mean 9.2 9.2 9.0 9.2

EXPOTHER Years of work experience in other firmsup to 1993

Mean 3.2 4.1 2.6 2.5

JOB Job position in 1993 (%)Front-line worker 0 66.1 67.1 63.0 69.8Salesperson/clerical personnel 1 9.1 9.2 9.0 9.2Managerial/professional staff 2 24.8 23.8 28.0 21.0

RANK Proficiency level in job position in1993 (%)Entry-level 0 77.1 76.8 75.9 79.9Intermediate-level 1 19.5 19.6 20.6 17.1Senior-level 2 3.4 3.6 3.4 3.0

CHANGE Experience of changes in workplace (%)None 0 37.4 36.3 36.3 42.1One kind of change 1 25.8 24.9 26.4 26.6Two kinds of changes 2 13.3 14.0 13.5 11.6Three kinds of changes 3 12.4 12.6 12.8 10.9Four kinds of changes 4 11.1 12.3 10.9 8.7

TRAIN Received OJT during 19931998 (%) 1, 0 63.9 70.5 60.4 55.9 AE Attended AE courses during19931998 (%)

1, 0 21.7 18.7 24.6 22.8

IN_GAIN Gain in proficiency level within thesame job position during 19931998 (%)

None 0 86.6 88.1 84.5 87.0One gain in proficiency level 1 12.1 11.0 13.8 11.2Two gains in proficiency level 2 1.4 0.9 1.6 1.8

BE_GAIN Promotion between job positionsduring 1993 to 1998 (%)

None 0 90.1 91.9 88.0 90.0One-level promotion 1 3.7 3.0 4.2 4.1Two-level promotion 2 6.3 5.1 7.8 5.9

N 16 485 7181 6151 3153


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employees who received OJT during 19931998. Finally, AEEXTENT refers to theproportion of employees who participated in AE within the firm. The latter threevariables measure the average human capital stock accumulated in the firm.

Measuring Salary Determination in three Regions

The analysis consists of three levels of models: (a) the level 1 model containstemporal variables relating to the individual, taking employee salary at three timepoints as the dependent variable; (b) the level 2 model contains individual vari-ables including sex, FS, OJT, AE, work experience, job position, proficiency level,advancement in proficiency level, job promotion, and experience of changes inthe firm; and (c) the level 3 model contains firm-level variables including region,size, sector, ownership, average years of schooling, extent of OJT provided by thefirm, and extent of AE pursued by employees. The level-1 model is: 7

where t = 0, 2, 5 refers to the years 1993, 1995, and 1998, respectively; i = 1, 2, , I (I is the number of employees in firm j used for the analysis); j = 1, 2, , J ( J is the

number of firms used for the analysis); Ytij is the outcome variable (i.e., the logmonthly salary at time t for employee i nested in firm j); 0ij is the parameter ofthe initial salary for employee ij at the beginning of 1993; is the parameter of

Y Time Time etij ij ij tij ij tij tij= + + + 0 1 22 1( ) ( ) ( )


Variable Definition Code Overall East Central West

REGION a The East, the Central, the West (%) 2, 1, 0 37.0 38.6 24.4PROVINCE b Jiangsu, Guangdong (%) 0, 1 37.1

Hubei, Hebei 0, 1 49.6Shaanxi, Yunnan 0, 1 50.6

PRI_COLL Private or collective (%) 0, 1 34.5 32.6 29.1 46.1STA_CORP State-owned or corporate (%) 0, 1 51.5 51.1 53.2 49.4FOREIGN HK, Macao, Taiwan, or foreign (%) 0, 1 14.0 16.3 17.7 4.5SECTOR Economic sector (%)

Industrial 0 69.9 67.4 73.8 67.4Service 1 30.1 32.6 26.2 32.6

SIZE Size of firm (%)Small (fewer than 300 employees) 0 44.9 37.8 39.7 64.0Medium (301800 employees) 1 26.0 34.1 24.8 15.7

Large (more than 800 employees) 2 29.0 28.2 35.5 20.2TREXTENT Extent of training during 19931998

Employees who received OJT (%) 60.2 68.5 56.7 52.9 AEEXTENT AE extent during 19931998

Employees who attended AE (%) 22.6 19.7 25.7 21.9 AV_EDU Average years of formal schooling

(year)Mean 11.2 11.2 11.5 10.9

N 365 135 141 89

a The Central is the reference group. b Jiangsu is the reference group, Hubei is the reference group, andShaanxi is the reference group.


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the parameter of the accelerated rate of salary growth based on a squared term foremployee ij; and etij is the error item. It is assumed that each etij is independentlyand normally distributed with a mean of zero and a constant variance, 2.

The variable, schooling in the MEE (Mincer, 1974), is treated as an individualcharacteristic and placed in level 2. This modified level 1 growth model, retaining

only temporal variables, captures salary growth and ones maturation or learning by doing on the job.

Then, the parameters in the level 1 model become the outcome variables of thelevel 2 model; and the variability of ones initial salary and growth in salary will be predicted by the variables for employee characteristics. The level 2 model is asfollows:

where p = 0, 1, 2 are the first-level parameters; q = 1, 2, , Q (Q is the number ofindividual-level predictors); p0 j is the intercept term for pij; Xqij is a characteristicof employee i in firm j (e.g., sex, FS, OJT, AE, etc.); pqj is the effect of Xqij on the pthgrowth parameter; and r pij is the error item. The set of P + 1 random effects areassumed to be multivariates, normally distributed with a full covariance matrix,T , dimensioned ( P + 1) (P + 1). Each has a mean of zero.

The parameters in the level 2 model then become outcome variables in the level3 model, and their variability will be predicted by the variables for firm character-istics to determine how firms shape the behavior of their employees. The level 3model is as follows:

where s = 1, 2, , S (S is the number of firm-level predictors); pq0 is the interceptterm for pqj; W sj is a firm characteristic used as a predictor for the firms effect( pqs) on pqj; and u pqj is a level 3 random effect that represents the deviation of firm js parameter, pqj, from its predicted value based on a firm-level model. For eachfirm, there are Q + 1 equations in the level 3 model. The residuals are assumed to be multivariates normally distributed. Each is assumed to have a mean of zero,some variance, and covariance among all pairs of elements. This three-levelmodel presents the determinants of salary, including effects due to time, individ-ual characteristics as well as firm characteristics in an organizational setting.

The Empirical Results

The empirical results are presented in three groups. First, the level 1 model estab-lishes a baseline model to capture the unconditional growth curve of the increasein salary over the period 19931998 for each individual workers. Then variables ofindividual characteristics are put into the conditional level 2 model to predict the

initial salary and salary growth. Finally, variables of firm characteristics andregions are put in the full level 3 model to predict variations in individualemployees across firms This delineates an employees work life as follows: a

pij p j pqjq

Q

qij pijX r= + +=0

12( )

pqj pq pqss

S

sj pqjW u= + +=0

13( )


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his/her personal characteristics; and a persons job-related characteristics areinfluenced by the characteristics of his/her work unit.

Unconditional Level 1 Model

Using equation (1) without introducing any individual-level variables to predictany pij and without firm-level variables to predict pqj, we obtain the followingunconditional level 1 model:

The result of unconditional level 1 model consists of two components: fixedeffects and random effects. Fixed effects offer estimated parameters for temporalvariables only, and capture salary growth and ones maturation or learning bydoing on the job. Because there are only three occasions in the observations,variance at the individual level can only allow t 1 random variance for freedomin calculation, in this case, only two level 2 random effects, r0ij and r1ij withvariances 00 j and 11 j, respectively, and with a covariance of 01 j.

Table 4 presents both fixed and random effects for the overall sample as well asfor each of the three regions. The estimated grand-mean of the monthly salary atthe initial point in 1993 for employees in the East was RMB 291.82 (= e 5.676153), foremployees in the Central was RMB 182.27 (= e 5.205472), and for employees in theWest was RMB 139.49 (= e 4.937981), about US$39.3, US$24.6 and US$18.6, respec-tively. The estimated grand-mean of the initial salary in the West was not yet halfof that in the East, and the initial salary in the Central was not yet two-thirds ofthat in the East. Over the five-year period, the estimated average annual growthrates of salaries were 22.1% in the East, 24.4% in the Central, and 29.6% in theWest. The estimated growth rates in a squared acceleration were all negative ineach region, indicating that salary growth decelerated during the five years. Thefixed effects indicate a concave downward trajectory of salary growth the longeran employee stayed in a firm.

The lower panel of Table 4 presents the random effects. With regard to theemployees initial salary, the variance within firms (46.2%) was smaller than vari-

ance among firms (53.8%). There fore, we decided to run a test of the variance forthe sample for each region; the variance within the firm (59.23% for East, 71.84%for Central and 61 61% for West) turned out greater than the variance across


0 00 0 5ij j ijr= + ( )

1 10 1 6ij j ijr= + ( )

2 20 7ij j= ( )

00 000 00 8 j y u= + ( )

10 100 10 9 j j y u= + ( )

20 200 10 j y= ( )


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three regions. This indicates that the difference in initial salary was greater amongregions than within each region. For salary growth rate, again, the varianceamong firms (53.1%) was greater than the variance within firms (46.9%) for theoverall sample. For each region, the variance was more due to difference withinthe firm in the East and Central, but the situation was the reverse in the West. 8 Allof this indicates that (a) regional inequalities explain most of the differences insalary and in salary growth, but (b) within each region individual differenceswithin a firm explain more than half of the differences in salary. This means that ifthe same person moved from West to Central, or from Central to East, he/shewould automatically get a higher salary, while within the same region individualdifference mattered more than firm factors with regard to decisions relating tosalary.

Nevertheless, the proportion of between-firm variance in the total variance(2853%) was bigger than that noted in Shanghai and Shenzhen, which was 1626% (See: Xiao, 2001, Table 7; Xiao, 2002b, Table 6). 9 Shanghai and Shenzhen rankas the most developed cities in China. Our sample consists of firms in counties,which are much less developed. This comparison of Shanghai and Shenzhen withour analysis may indicate that firm-level factors have a greater effect on salarydecisions in county-level firms than those in a metropolis. We suppose that sucha difference may be due to inequalities among segregated labor markets in China.This is a matter that has not been given enough attention in previous studies onlabor markets as the use of the MEE method and urban survey data.

Conditional Level 2 Model

At level 2 individual variables are introduced to predict We propose that

Table 4. Fixed and random effects of unconditional level 1 model

Overall East Central West

Fixed effects: estimated parametersFor initial salary 0ij

For intercept 00For intercept 000 5.3227*** 5.6762*** 5.2055*** 4.9380***

For salary growth rate 1ijFor intercept 10 jFor intercept 100 0.2457*** 0.2210*** 0.2438*** 0.2956***

For accelerated rate of salary growth 2ijFor intercept 20 j

For intercept 200 0.0288*** 0.0268*** 0.0300*** 0.0313***Random effects: variance partitioning

Initial salaryVariance within the firm (%) 46.2 59.2 71.8 61.6

Variance among firms (%) 53.8 40.8 28.2 38.4Salary growth rate

Variance within the firm (%) 46.9 51.5 52.8 46.9Variance among firms (%) 53.1 48.5 47.3 53.1

*** p < 0.001.


13/27


associated with individual predictors. Fixed effects will present the impact ofthese individual predictors on salary and salary growth. Random effects allow usto find how much of the variance in initial salary and in salary growth rate theindividual predictors would explain. Firm-level predictors are not introduced for pqj at this step, so level 3 equations are not presented here with the conditional

level 2 model.

Table 5 presents the fixed effects. For SEX, the initial salary of male employeeswas significantly higher than that of female employees. The coefficient showinggender differences in the West (0.105) was higher than that in the Central (0.074),and that in the Central was higher than that in the East (0.071). The fact thatgender differences in salary were greater in poorer regions is accordance withMengs finding (Meng, 1998). The rate of salary growth, however, did not differsignificantly between male and female employees.

For EDUYEAR, years of FS had a significantly positive effect on initial salary inthe three regions. For every one more year of FS they had received, employeeswould have a salary premium of 1.7% at the initial point in the East, 1.1% in theCentral, and 1.5% in the West. 10 In regard to salary growth ( 12 j), the coefficientsfor FS were significantly positive in the East and the Central; that is, for every onemore year of FS, an employee would receive 0.05% and 1% more of an increase insalary each year, respectively, in the East and Central. But FS was not significantlyassociated with salary growth in the West. Considering that the West is the poor-est region in China, we argue that because it had been opened up for fewer yearsand a market-based economy had not yet been established, firms in the West hadyet to associate FS with individual productivity.

If we assume that the positive impact of FS on initial-point salary reflects thesignaling function of education (Spence, 1973) and that the positive impact of FSon salary growth reflects the function of education in improving ones productiv-ity and/or ability to deal with disequilibria (Shultz, 1961, 1975), results from thisstudy might indicate that both of these two functions exist at the county level inEast and Central China. The results also support the weak version of the screen-

ing hypothesis put forward by Psacharopoulos (1979). Nevertheless, in thestudies of Shanghai and Shenzhen, the most developed cities in China, Xiao (2001,2002b) found FS to be irrelevant to the growth in employee salaries Therefore we


0 00 01 02 03

204

05 06 07 0 12ij j j ij j ij j ij j ij

j ij j ij j ij ij

Sex Eduyear firm

other Jobrank Rank r

= + + + +

+ + + +

( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

Exp Exp

Exp

1 10 11 12 13 14

15 16 17 18 19

110 1 13

ij j j ij j ij j ij j ij

j ij j ij j ij j ij j ij

j ij ij

Sex Eduyear o

Change Train Ae In Gain Be Gain Jobrank r

= + + + +

+ + + + ++ +

( ) ( ) ( ) ( )

( ) ( ) ( ) ( _ ) ( _ )( ) ( )

Expfirm Exp ther

2 20 21 22 23 14ij j j ij j ij j ijEduyear Train Ae= + + +( ) ( ) ( ) ( )


14/27


context of China, even though we can accept that FS is a useful signal in labormarkets. Employee salary growth is related to some other firm-level characteris-tics. We will illustrate this shortly.

With regard to work experience counted in years, we identified three types: thetotal worker experience before 1993 ( EXP_SQ),11 which includes work experiencein the current firm ( EXPFIRM) and work experience in other firms ( EXPOTHER).For salary at the initial point, we have three major findings. First, work experiencesquared to 1993 does not have an impact. Second, both EXFIRM and EXPOTHERsignificantly increase ones salary at the initial point, but employees get more benefits as they acquire experience working in the current firm than fromworking in other firms. This is illustrated by coefficients of 0.0246 for EXPFIRM being greater than 0.0205 for EXPOTHER in the West; 0.0192 for EXPFIRM beinggreater than 0.0137 for EXPOTHER in the Central; and 0.0172 for EXPFIRM being

greater than 0.0143 for EXPOTHER in the East. This finding is consistent with theresults form Mengs study of Chinese firms (Meng, 1998) and Huangs study ofTaiwanese firms (Huang 2001) If we regard work experience as a type of job

Table 5. Fixed effects of conditional level 2 model

Overall East Central West

For individual initial salary 0ijFor intercept 00 j

For intercept 000 4.859974*** 5.205833*** 4.777103*** 4.440681***SEX 01 j 0.077261*** 0.070718*** 0.074378*** 0.104565***EDUYEAR 02 j 0.014153*** 0.016463*** 0.010846** 0.014926**EXP_SQ 03 j 0.000059* 0.000061 0.000062 0.000218EXPFIRM 04 j 0.019697*** 0.017219*** 0.019255*** 0.023579***EXPOTHER 05 j 0.015568*** 0.014294*** 0.013685*** 0.020528***

JOB 06 j 0.051451*** 0.051560*** 0.050706*** 0.052943***RANK 07 j 0.067764*** 0.077697*** 0.053175*** 0.079474***

For individual salary growthrate 1ij

For intercept 10 j

For intercept 100 0.186014*** 0.166818*** 0.142519*** 0.310376***SEX 11 j 0.001498 0.000429 0.003235 0.003218EDUYEAR 12 j 0.005872*** 0.005079*** 0.010031*** 0.001484EXPFIRM 13 j 0.001489*** 0.001235*** 0.001852*** 0.001261***EXPOTHER 14 j 0.000934*** 0.000851*** 0.001261*** 0.000576*CHANGE 15 j 0.001724*** 0.000753 0.002343*** 0.003018***TRAIN 16 j 0.004968 0.004867 0.000350 0.025635***

AE 17 j 0.011337** 0.015909** 0.015919** 0.013449IN_GAIN 18 j 0.008966*** 0.014681*** 0.005927** 0.004077BE_GAIN 19 j 0.008440*** 0.009607*** 0.006320*** 0.010055***

JOB 110 j 0.000029 0.002039 0.001335 0.002119

For individual accelerate rate ofsalary growth 2ijFor intercept 20 j

For intercept 200 0.017104*** 0.016675*** 0.010348*** 0.031836

*** p < 0.001, ** p < 0.01, * p < 0.05.


15/27


capital, we can say that firms value it and will pay more for the more relevantexperience gain from working in the current firms. Firms in poorer regions putmore weight on work experience in determining salaries. For salary growth rate,work experience of any type before 1993 was negatively associated with thedecision of a firm to increase an employees salary. This indicates that for those

who had more years of work experience before 1993, the rate of increase would besmaller than that for those who had fewer years of work experience before 1993,maybe more years to come.

Experience of change ( CHANGE) in the workplace predicts salary growth in theWest and the Central, not in the East. For example, for employees overall, thepremium in the salary growth rate for those who experienced four kinds ofchanges than those who experienced no change during the period 19931998 was0.9% (0.009372 = 0.002343 4) in the Central and 1.2% (0.012072 = 0.003018 4) inthe West. That is, employees who underwent more changes in the workplace hada steeper salary growth rate. This finding allies with findings in Shanghai and

Shenzhen (Xiao, 2001, 2002b). Changes in the workplace offer an opportunity foremployees to learn and develop ones ability while the firm is transforming. 12

Firms in the West and Central will acknowledge such experience in due course, but firms in the East will not.

With regard to OJT and AE, their effects on salary growth differ among regions.For instance, receiving OJT only positively affects salary growth in the West ( 16 j= 0.025635, p < 0.001), while experience of AE might increase employee salaries inthe East ( 17 j = 0.015909, p < 0.01) and the Central ( 17 j = 0.015919, p < 0.01).

13 OJThas been a common strategy in the East and Central for developing humanresources. Since most of the employees received OJT, 14 OJT cannot make aremarkable difference in the level of technical proficiency between employeesand has no significant effect on salary increase decisions. Firms in the East and theCentral, therefore, recognize employees who pursue learning opportunitiesoutside firms. On the other hand, employees in the West have fewer opportuni-ties to gain access to OJT, so employees who have received OJT are more likely toget an increase in salary. That AE is not associated with salary growth in the Westmay be partly because firms in the West do not recognize its relevance to the jobperformance of employees. These findings may indicate that firms use differentstrategies to develop their human resources.

The productive function of AE and OJT may vary in different localities. Xiaofound that the decision made by employees to participate in AE will affect salarygrowth in Shanghai, while the OJT provided by employers is significantly corre-lated with the growth in salaries in Shenzhen (Xiao, 2001, 2002b). Considering allof these findings, we argue that locality forms a complicated context and that eachChinese firm seeks a unique way to cope with it. They may find alternatives toaccumulating human capital and increasing productivity. So it is impossible tocome to a universal conclusion on the role of education (FS, OJT, or AE) inimproving productivity without taking into account the context of the decisionsof firms.

Let us look at the impact of job position and level of proficiency. In order totake into account the complexity of competence in the workplace, we define four

variables. Job position ( JOB) is a workplace hierarchical factor and reflects anemployees work task assigned by the firm. Proficiency level ( RANK) is the rankof technical efficiency indicating an employees competence in a particular


16/27


in job position ( BE_GAIN ) and a promotion in improved proficiency ( IN_GAIN )for the five-year period.

We find that, in all three regions, both JOB and RANK were positively related tosalary at the initial point. However, job position ( JOB) in 1993 was not associatedwith salary growth during the next five years. Nevertheless, promotion in job

position ( BE_GAIN ) significantly affects salary growth: the estimated coefficientswere 0.0096 for the East, 0.0063 for the Central, and 0.01 for the West. In regard toones level of proficiency ( IN_GAIN ), firms in the East (0.015) gave more weight toimprovements to ones proficiency than firms in the Central (0.006). But in theeconomically lagging West, the link between improvements in job proficiencyand salary growth has not yet been established. These findings reflect a firm-levelor micro-level difference in regional development. Another important finding ofthis study is that estimates from the overall sample do not reflect differentpatterns for firms in different spatial localities.

The random effects of the conditional level 2 model (see column 2 in Table 6)

indicate that individual predictors explained substantial variance in the initialsalaries for employees (19% for overall sample; 18%, 19%, and 22% for the East,the Central, and the West, respectively). But the explanatory power for the vari-ance in the growth rate of salaries for employees is smaller (5% for overall sample;3%, 7%, and 3% for the East, the Central, and the West, respectively). The resultsare acceptable compared with those from other studies using a three-level growthmodel (for example, Boyle and Willms, 2001; Raudenbush and Chan, 1992;Raudenbush, 2001).

Conditional Level 3 Model

We put firm-level variables in the level 3 model to predict pqj, outcome variablesin the level 2 model. Theoretically, each pqj parameter has a regression function.For instance, we add region, ownership, sector, size, average FS years, and OJTextent to explain variability of the firms mean initial salary ( 00 j) and firm meansalary growth ( 10 j).

15 The estimation takes three steps. First, each firm-levelvariable indicating location (region or province), ownership, sector, or size will beput in level 3 models respectively to discern the effect of each variable. Second, based on the results from the former step, we select one or several firm-levelvariables to explore the explanatory power of these conventional characteristics. 16

Third, based on the former step, variables representing the average stock ofhuman capital in a firm (e.g., average years of schooling, extent of OJT, and extentof AE) will be added to form the final model.

Random effects. Let us begin by looking at random effects to get a sense of theexplanatory power of level 3 variables. In Table 6, column 1 presents variancepartitioning between individual-level and firm-level in the baseline model asillustrated in Table 4; column 2 presents variance explained by employee charac-teristics; column 3-1a presents variance explained by the variable of region (in theoverall model) or province (in the East, the Central, or the West model); columns3-1b3-1d present variance explained by ownership, size, and sector, respectively;

column 3-2 (conventional characteristics model) presents variance explained byall of the variables of region, province, ownership, size, and sector together; andcolumn 3 3 presents variance explained with a full model by adding the average


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T a

b l e 6

. R a n d o m e f

f e c t s

( % )

L e v e l 1

L e v e l 2

L e v e l 3 m o d e l

V a r i a n c e

p a r t i t i o n i n g

V a r i a n c e

e x p l a i n e d

A d d i t i o n a l v a r i a n c e e x p l a i n e d

U n c o n d i t i o n a l

m o d e l

W i t h e m p l o y e e

c h a r a c t e r i s t i c s

W i t h f i r m c h a r a c t e r i s t i c s

R e g i o n a n d

p r o v i n c e

O w n e r s h i p

S e c t o r

S i z e

C o n v e n t i o n a l

c h a r a c t e r i s t i c s

F i r m s a v e r a g e

h u m a n c a p i t a l

C o l u m n 1

C o l u m n 2

C o l u m n

3 - 1 a

C o l u m n

3 - 1 b

C o l u m n

3 - 1 c

C o l u m n

3 - 1 d

C o l u m n

3 - 2

C o l u m n

3 - 3

e r a l

l s a m

p l e a

e v e l

2 ( w

i t h i n a

f i r m

)

I n d i v i

d u a l

i n i t i a l s a

l a r y

0

4 6 . 2

1 9

I n d i v i

d u a l g r o w

t h r a

t e

1

4 6 . 9

5

e v e l

3 ( a m o n g

f i r m s )

F i r m m

e a n

i n i t i a l s a

l a r y

0

5 3 . 8

3

4 9

3

0

2

5 1

5 7

F i r m m

e a n g r o w t h r a t e

1

5 3 . 1

6

1 5

1

1

0

1 7

1 9

e E a s t s a m p l e b

e v e l

2 ( w

i t h i n f i r m

)

I n d i v i

d u a l

i n i t i a l s a

l a r y

0

5 9 . 3

1 8

I n d i v i

d u a l g r o w

t h r a

t e

1

5 1 . 5

3

e v e l

3 ( a m o n g

f i r m s )

F i r m m

e a n

i n i t i a l s a

l a r y

0

4 0 . 8

1 1

3 4

0

3

1

3 8

4 1

F i r m m


1

4 8 . 5

5

0

5

4

0

9

1 2

e C e n

t r a l s a m p l e c

e v e l

2 ( w

i t h i n a

f i r m

)

I n d i v i d u a l i n i t i a l s a l a r y

0

7 1 . 8

1 9

I n d i v i

d u a l g r o w

t h r a

t e

1

5 2 . 8

7

e v e l 3 ( a m o n g f i r m s )

F i r m m

e a n i n i t i a l s a l a r y

0

2 8 . 2

1 3

2

5

6

1

1 0

1 7

F i r m m


1

4 7 . 3

1 4

0

5

1

2

4

7


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T a

b l e 6

.

( c o n

t i n

u e

d )

L e v e

l 1

L e v e

l 2

L e v e l

3 m o d e l

V a r i a n c e

p a r t

i t i o n i n g

V a r

i a n c e

e x p l a i n e

d

A d d i t i o n a l v a r i a n c e e x p l a i n e

d

U n c o n

d i t i o n a l

m o d e l

W i t h e m p l o y e e

c h a r a c

t e r i s t

i c s

W i t h f i r m c h a r a c

t e r i s t

i c s

R e g i o n a n

d

p r o v i n c e

O w n e r s

h i p

S e c t o r

S i z e

C o n v e n t

i o n a l

c h a r a c

t e r i s t

i c s

F i r m

s a v e r a g e

h u m a n c a p i

t a l

C o l u m n

1

C o l u m n

2

C o l u m n

3 - 1 a

C o l u m n

3 - 1 b

C o l u m n

3 - 1 c

C o l u m n

3 - 1 d

C o l u m n

3 - 2

C o l u m n

3 - 3

e W e s

t s a m p l e d

e v e l

2 ( w

i t h i n a

f i r m

)

I n d i v i

d u a l

i n i t i a l s a

l a r y

0

6 1 . 6

2 2

I n d i v i

d u a l g r o w

t h r a

t e

1

4 6 . 9

3

e v e l

3 ( a m o n g

f i r m s )

F i r m m

e a n i n i t i a l s a l a r y

0

3 8 . 4

3

6

9

2

6

1 4

1 6

F i r m m

e a n g r o w

t h r a

t e

1

5 3 . 1

5

1 8

0

2

1

2 1

2 6

he C e n t r a l

i s t h e r e

f e r e n c e g r o u p .

b J i a n g s u i s

t h e r e

f e r e n c e g r o u p .

c H u b e i

i s t h e

r e f e r e n c e g r o u p .

d S h a a n x

i i s

t h e r e f e r e n c e g r o u p .


19/27


First, let us concentrate on the overall model. If differences among regions arenot taken into account, the firm characteristic variable ownership ( FOREIGN )accounts for at most an additional 3% of the variance in firm mean salary at theinitial point, and SECTOR counts for 0 and SIZE counts for 2% rate (see columns3-1b3-1d, for variance, 0 explained by level 3 variables in the overall sample).

These three firm variables at most explain an additional 1% of the variance in firmmean salary growth ( 1). However, when we put in the locality dummy variable(the Central is the reference group), an amazing 49% of the variance in firm meansalary at the initial point and 15% of the variance in firm mean salary growth ratewill be predicted (column 3-1a). Therefore, the conclusion is that the differencesin both the initial salary and in the salary growth among firms are mainly due todifferences between the three regions. Compared with localities, the contributionof ownership, sector, or size can almost be ignored in this context. 17 This findinghas a significant implication: as far as an income distribution system within thefirm is concerned, it is impossible for firms and their employees in less developed

regions to catch up with firms in more developed regions (e.g., the Central withthe East and the West with the Central) by management reforms to increase effi-ciency at the firm level (such as changing ownership, altering economic sector,and increasing economies of scale). In order to equalize the regional disparities,some macro-level policies are necessary. 18

As the variable REGION would explain most of the variance, we ran the modelwith the sample of each region to detect whether firm characteristics would havegreater impact within a region. The lower panel provides information on randomeffects. Localities (provinces) within the same region have significant explanatorypower in the East and West. In the East, the region dummy variable (Jiangsuprovince is the reference group) can explain 34% of the variance between firmmean salaries at the initial point. In the West, the region dummy variable(Shaanxi province is the reference group) can explain 6% of the variance in firmmean salary at the initial point and 18% of the variance in the rate of increase infirm mean salary. In the Central, the region dummy variable (Hubei province isthe reference group) has little explanatory power. Variance explained by conven-tional model, including all the firm variables (e.g., REGION or PROVINCE,ownership, section and size), is about the sum of variance explained by thesevariable entered one at a time (see column 3-2).

Finally, after the average stock of human capital variable of each firm is put intothe equation over the firm characteristic variables (column 3-3), the variancesexplained an increase of 6% in mean salary at the initial point and an increase of2% in the mean growth rate for the overall sample, 3% and 3% for the East, 7%and 3% for the Central, and 3% and 5% for the West, respectively. This compari-son shows that the average human stock has more or less the same explanatorypower as the three variables of firm characteristics; namely, ownership, sectorand size together.

Fixed effects. We shall sum up our major findings here. 19 First, for provincialdifferences within a region, as compared with Jiangsu Province in the East, firmsin Guangdong province offered a significantly higher mean initial salary ( 001 =

0.280587, p = 0.001), but gave a significantly lower rate of increase in salary ( 101 =0.075574, p < 0.0001). In the East, firm size has a positive effect on gender in initialsalary ( = 0 019119 p = 0 024) and salary growth rate for those employees who


20/27


Central, there are no insignificant differences in firm mean salary at the initial pointneither in the growth rate of the firm mean salary between the two provinces, Hebeiand Hubei. Firms in Yunnan and Shaanxi have almost the same initial salary;however, Yunnan has a faster growth rate ( 101 = 0.045026, p < 0.001).

With regard to ownership, in the East firms with foreign investment offered a

lower initial salary than state-owned or corporate firms ( 004 = 0.243182, p =0.026), but a very higher rate of growth in salary ( 104 = 0.045143, p = 0.020).Private/collective firms and state-owned or corporate firms offered almost thesame initial salary, but the former had a slower rate of salary growth ( 103 = 0.033406, p = 0.034). In firms in the East with foreign investment, employeesearned more at the initial point ( 023 = 0.020247, p = 0.005) than those in state-owned or corporate firms with the same level of FS and work experience. In theWest, job position and level of proficiency may have had a greater effect on salaryat the initial point in private or collective firms than in state-owned or corporatefirms ( 061 = 0.026671, p = 0.031; 071 = 0.051122, p = 0.014).

For sector, in the East firms in the service sector gave a higher initial salary thanthat of firms in the industrial sector ( 002 = 0.113114, p = 0.024), but a lower growthrate in salary ( 102 = 0.020669, p = 0.019). Employees in the service sector had alower rate of increase in salary for their improved job proficiency ( 181 = 0.007123, p = 0.035). In the Central, firms in the service sector also gave a highersalary at the initial point than those firms in the industrial sector ( 002 = 0.592553, p < 0.001), but their salary growth rates were not different.

For the stock of human capital in a firm, the average FS was positively relatedto the firm mean salary at the initial point in the East ( 005 = 0.062332, p = 0.007)and in the West ( 004 = 0.075348, p < 0.001). In the Central and the West, the overallproportion of OJT received among employees in a firm had a positive effect onthe firm mean rate of salary growth ( 104 = 0.038230, p = 0.019 and 103 = 0.045493, p = 0.028), indicating that firms value OJT for its role in improving the job compe-tence of their employees. But the overall proportion of employees attending AEdid not have much effect on salary decisions in all three regions.

Concluding Discussion

This study has focused on how education and training are contributing to thegrowth in employee salaries in the transition from a planned economy to amarket-based economy. Economic returns to human capital are achieved in thecomplicated context of regional disparities. Thus, we placed the analysis of deter-minants of the salary of employees in a multi-level framework that considersregional factors, the features of firms, and individual characteristics, as well aspersonal maturation. Methodologically, we set up models both for the overallsampled employees and for employees in each of the three regions for compari-son. The findings give strong support to our assumptions.

The main findings of this study reveal that: (1) regional disparities have a para-mount impact on differences in salary and, in comparison, firm characteristics playvery limited roles; (2) individual characteristics matched with job characteristics(e.g., JOB,RANK, work experience, improved proficiency and promotion) are signif-

icantly related to firms salary decisions; (3) formal schooling has significant impacton salary decisions, too; and (4) finally, employees benefit by alternatives of continu-ing learning including being involved in workplace change receiving on the job


21/27


As far as research hypotheses 1 and 2 are concerned, we found that both FS andlearning experiences in the workplace including OJT, AE, and changes experi-enced in the job (learning-by-doing) are determinants of salary growth in China.However, they may have different roles in human resource development andtheir roles may vary in different localities. FS has a significant impact in the East

and Central. In the period under study, for every additional year of FS received,each employee in the East received a salary increase of an average annual rate of0.5%; while a person who worked in the Central received an increase of an annualrate of 1%. Unfortunately, employers in the West do not take FS into muchaccount when making decisions on increasing salaries. If one received AE duringthe five-year period, he/she would get a salary increase at the annul rate of 1.6%in either the East or Central. In the West, receiving OJT during the five-yearperiod is likely to get an employee an annual increase in salary of 2.6%. Learning- by-doing ( CHANGE) has an impact on increases in salary of annual rate of 0.2% inthe Central and 0.3% in the West. These are alternatives to accumulate human

capital through continued learning while working.In a market-based economy, firms introduce new technology or new productsand services and to transform themselves to meet the needs of the market. Firmsare also transforming. Such change in the workplace will impel employees tocontinue to learn and thus increase their competence. It is rational for employeesto acquire job-related abilities by way of OJT, AE, and so forth. Learning is there-fore resulting from change. Our findings indicate that employers in firms at thecounty level are aware of such issue and pay attention to the learning experiencesof employees in the workplace. Our findings suggest a transformation in thehuman resource development strategies of firms during economic transition. FSas an educational credential may be used as only as a signal of potential produc-tivity when employers make hiring decisions. An analysis of the returns tohuman capital should include the role of human capital accumulated in thecourse of production.

Hypothesis 3 is focused on the role of firm characteristics in salary decisions.Firm characteristics affect employee salaries in two aspects, at the individual levelas well at the firm level. At the individual level, firms assign employees to a jobposition and determine their level of technical proficiency, something that has aparamount impact on a persons career. We found that a persons job position andlevel of technical proficiency have a greater impact on the level of his/her salary.For instance, for the period under study, the coefficients for JOB and RANK onsalary at the initial point are 5.1% and 7.8%, respectively, for the East, 5.1% and5.3% for the Central, and 5.3% and 7.9% for the West. During the five-year period,recognition of improved proficiency ( IN_GAIN ) or a promotion ( BE_GAIN )would get a salary increase at an annual rate of 1% and 1.5%, respectively, for fiveyears for the employee in the East and the figures were 0.6% and 0.5% for theCentral. In the West, where the economy was lagged far behind, the figures were1% and 0%. An increase in salary will occur when employee characteristics match job requirements.

In the overall sample, the variable of region signifies inequality of as much as49% of the variance for salary at the initial point and 15% of the variance for

salary growth. In comparison, the conventional characteristics of firm (owner-ship, size, and sector) as well average firm human capital play a very limited rolein determining salaries between regions However firm characteristics and


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The story is that taking China as a whole for analysis or policy-making is meth-odologically erroneous, and will be misleading when interpreting achievementsand issues in the transitional period of development.

Hypothesis 4 concerns varied roles of ET variables (e.g., SF, OJT or AT), indi-vidual characteristics, and firm characteristic in different regions. Our analysis

demonstrates that regional disparities due to differences in regional developmentpolicies (as discussed in the second section) have the most impact on employeesalaries: that is, the economic zones where firms are located is the major determi-nant of employee salaries. Altogether, ET variables, the individual variables aswell as firm variables cannot change the distribution of wages to counterbalancethe effects of salary inequalities due to regional disparities in the labor markets.Within each region, firms may have adopted different strategies to keepemployee competent. The results show that the pattern of salary determination ineach region is different from each other.

Using this multilevel model makes it possible to examine the role of regional

factors, firm-level features, OJT, AE, and job-related individual characteristics indetermining salaries. The multilevel analysis technique accords with socialstructures, and the partitioning of random effects is informative in determiningthe explanatory powers of variables at different levels, while the reverse tracestudy technique is suitable for making comparisons between educationalprograms. The findings from our study have some important implications forfuture research and policy-making in China.

First, disparities across the three regions in China are overwhelming. This can be attributed to preferable economic policies for the East and coastal region,which receive more resources and opportunities for development than the otherregions (Hu et al., 1995, p. 210; Zhang, 2003; Wu, 2004). In the twenty-first century,the Chinese government should make great efforts to narrow regional gaps byallocating more government investment to the central and western regions.Resources can go to infrastructure and social welfare to alleviation of poverty.That will allow individual workers to capture benefits from economic develop-ment. On the other hand, we shall not overstress the role of FS in capture privatereturns; our study has illustrated that private RORE is dramatically higher in theregions in which government has poured more public resources and to which ithas given preferable policies.

Second, our analysis provides an empirical case for the argument that humancapital continues to accumulate in the course of production when rapid transitionis undertaken in China. This study suggests that analysis and policy-making onFS, without referring to other educational programs, would lead to a neglect ofthe effects of other forms of ET on the accumulation of human capital. In policy-making, the provision of ET by local institutions, firms, non-profit organizations,and so forth should receive more favorable policy guidance. For instance, OJTand AE for adults, which are flexible alternatives to FS, should be integrated intosocioeconomic policies, along with an expansion and consolidation of compul-sory basic education. Firms that provide a large amount of OJT or give subsidiesto employees who participate in AE should be accorded a preferential rate oftaxation.

Third, the government should continue to decentralize its administrationespecially in the central and western regions. This would allow these laggardregions to avail themselves of the resources and opportunities of a market based


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respected and guaranteed so that salaries can reflect marginal productivity ofemployees, and an optimal allocation of resources in the labor market beachieved.

In regard to education and training, a high quality of basic education matters.The government should allocate more resources on compulsory education at the

county level in the Central and the West so that the population in rural will beable to have an adequate initial education. A working adult without sufficient FSis at a disadvantage in gaining access to OJT and AE as well as in many otheraspects of work life (Xiao and Tsang, 2004).

Acknowledgements

Both authors contributed equally to this work and their names are stated in alpha- betical order. This paper is part of a research project entitled Education andWork: The Efficacy of Schooling in Human Resource Development in Three

Regions in China, which is partially funded by the Research Grants Council ofHong Kong (CUHK 4379/00H). Jin Xiao is the principal investigator for thisresearch project.

Notes

1. The classification of provinces is in accordance with Hu et al. (1995, pp. 1850).2. At an exchange rate of 7.4, per-capita GDPs are corresponding to about US$842.6 and US$741.5,

respectively, in the East; US$482.9 and US$466.2 in the Central; and US$322.3 and US$283.7 in theWest.

3. Another indicator also shows clear regional inequality in China. In 1998, per-capita foreign directinvestment in Guangdong was RMB 168, and was RMB 92 in Jiangsu, RMB 22 in Hebei, RMB 16 inHubei, RMB 4 in Yunnan, and RMB 8 in Shaanxi (National Bureau of Statistics of China, 1999).

4. Please see Xiao (2004) for specific types of OJT organized by firm.5. Stratified random sampling was used to select the participating firms and employees. First, we

selected two provinces from each of the three regions, the East, Central, and West as classified byHu et al. (1995), using major economic indicators (see discussion about labor market in the secondsection). They are: Guangdong and Jiangsu in the East, Hebei and Hubei in the Central, andYunnan and Shaanxi in the West. Second, two counties with a medium level of economic develop-ment in each of the six provinces were selected. They were Jiangmen and Heshan in Guangdongprovince, Wuxi and Jiangyin in Jiangsu province, Handan and Cixian in Hebei province, Yichangand Zhijiang in Hubei province, Mile and Luxi in Yunnan province, and Baoji and Fengxiang inShaanxi province. Third, in each county, firms were randomly selected from among those of

different ownership types, sizes, and industrial sectors. Finally, in each firm, one or two intactworkgroups or production lines on the floor were selected to obtain a sample, which consisted ofall of the personnel at a work site, from managerial staff, frontline workers to supporting staff.Altogether, information was collected from 37 316 employees in 401 firms, corresponding to a92.0% response rate. To analyze the three-level model, 16 485 individual cases and 365 firms wereused, after deleting cases that contained missing values in any of the variables. Some firms did notwant provide information about the productivity of either the firm or their employees. Therefore,we lost cases largely due to unwillingness to reveal salary data. Therefore, the data may underesti-mate the salary of employees during the transitional period.

6. The salary is recalled data and bias can exist. Salaries were universally very low in an egalitarianplanned economy and at the beginning of the economic reforms in 1993. As part of the economicreforms, increases in salary came to be determined according to proficiency of performance and abil-ity. This was a big event for everyone in China, as such an approach was novel. Therefore, we assumedthat our sampled employees would best be able to recall their salary in 1993, the baseline point, andin 1995 when they had their first raise. We also compared our data with the national census data of


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8. In the West, the variance within the firm was smaller (46.9%) than the variance (53.1%) amongfirms for salary growth rate.

9. A study on school culture in the United States showed that between-school variance for leadershipaccounted for 23% of the total variance (Rowan et al., 1991, p. 254).

10. The coefficients 02 j for initial-point salary in 1993 accompanied with one more year of FS are0.0165, 0.0108, and 0.0149 in the East, the Central, and the West, respectively.

11. As EXP is the sum of EXPFIRM and EXPOTHER , our model did not include EXP to avoidmulticollinearity.12. Xiao and Tsang (2004) found that technological change in the workplace is the most important

factor prompting employees to engage in continuous learning on the job, and that such learningeffectively improves individual productivity.

13. In our trial analysis, we put types of OJT as predictors in models, but the coefficients had the similarresults.

14. More than 70% and 60% of employees in the sampled firms in the East and the Central,respectively, received at least one kind of OJT during the five years, while the figure in the Westwas less than 56% (see Table 2).

15. 00 j = 000 + 001(Region) j+ 002(Ownership) j + 003(Section) j + 004(Size) j + 005(Av_edu) j + u00 j; 10 j =100 + 101(Region) j + 102(Ownership) j + 103(Section) j + 104(Size) j + 105(Trextent) j + u10 j.

16. Conventional characteristics refer to those characteristics that are usually used to describe a firmsfeatures, such as geographic location, ownership, size, and sector (Naderi and Mace, 2003 ).

17. Of the three firm characteristicsnamely, ownership, sector, and sizeownership is the mostpowerful explanatory variable (see columns 31b, 31c and 31d). It can predict an additional 5%and 9% of the variance in the firm mean salary at the initial point in the Central and the West,respectively, and 5% of the variance in the firm mean salary growth rate in the Central region.

18. This finding helps to explain why the phenomena of an internal brain drain and floating popula-tion continue, because moving across regions to the east will automatically result in at least a 50%increase in salary, according to Table 4. Education might have another screening function as aticket to get into more developed labor markets.

19. As the full model is too long, we do not list out all the result. But statistics are available from theauthors.

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Accumalation of Human Capital & Its Impact of Salary Growth in China