Determinants of salary growth in Shenzhen, China: An analysis of formal education, on-the-job training, and adult education with a three-level model

Transcription

1 Determinants of salary growth in Shenzhen, China: An analysis of formal education, on-the-job training, and adult education with a three-level model Jin Xiao Faculty of Education the Chinese University of Hong Kong, Hong Kong Sha Tin, N.T. Hong Kong Special Administration Region, China xiaojin@cuhk.edu.hk Received 19 March 1999; Accepted 6 June 2001 Abstract Using 1996 surveyed data of 1,023 employees in Shenzhen, China, this study estimated the effects of three forms of human capital on employee salary, namely formal education, on-the-job training provided by employers, and adult education pursued by employees. Using a hierarchical linear model, the analysis estimated employee monthly salary growth over a maximum of six years due to (a) such temporal factors as work experience and improved performance, (b) individual-level characteristics, and (c) firm-level characteristics. This study found that (a) pre-work formal education was positively associated with salary only at hiring, (b) employees experience in changing production technology as well as on-the-job training were positively associated with salary increases through improved technical proficiency, formal education was not; (c) manufacturing firms introduced more new production technology than the service sector and provided more on-the-job training, thus improving workers performance and increasing their salary. JEL classification: [C00, I20, J31] Keywords: Salary growth, Educational economics, Human capital, Economic development, Productivity

2 Determinants of salary growth in Shenzhen, China: An analysis of formal education, on-the-job training, and adult education with a three-level model 1. Introduction Human capital theory suggests that education or training raises the productivity of workers by imparting useful knowledge and skills, hence raising workers future income by increasing their lifetime earnings (Becker, 1964). Becker (1964) and Mincer (1974) provide an explanation that links investment in training with workers wages. In particular, their theory draws a crucial distinction between general education and firm-specific training. Over the past thirty years or so, hundreds of studies have been conducted to estimate rates of return to education (RORE); most such studies show that formal schooling is a crucial factor in explaining variations of salary and wages in well developed countries (Cohn & Addison, 1998). Comparative studies have been conducted in some less developed countries, focusing on investment in formal education (Psacharopoulos, 1985, 1994). While formal education has expanded rapidly in many countries, a large portion of human capital accumulation in the forms of on-the-job training and other modes for working adults actually take place both inside and outside the workplace. Adult education development in developed countries in recent years has focused on a strengthening of vocational training to meet the needs of skill development across all occupational strata in the global economy (Belanger & Tuijnman, 1997). Studies in some developing countries find that a mix of education and training is available for skill acquisition and there are multiple paths to skill development for a given occupation (Middleton, Ziderman, & Adams, 1993; Ziderman and Horn, A study of education provision in Shenzhen, China shows that both firm-provided on-the-job training and adult education financed by employees offer substantial means to develop vocational/technical skills (Xiao & Tsang, 1994), and provided about 2.07 million head counts of education and training to the workforce of 2.5 million during the period of (Xiao, 1998a:13). Given that education and training programs for working adults have experienced significant expansion, it is important that they be included in estimations of returns to education and training. This paper attempts to estimate the effects of formal education, on-the-job training, and adult education both on employees performance and salary growth with data from a survey conducted in Shenzhen in 1996 (Xiao & Tsang, 1999). This study was conducted in the municipality of Shenzhen, China, which is situated on the border with Hong Kong. Prior to 1980, Shenzhen was a small, poor county. The county s population of 0.3 million was mainly engaged in farming and fishing. In 1980, the State Council, China, inaugurated the Shenzhen Special Economic Zone, which was designed as a prototype for economic development. The policies of reform and opening to the outside world were implemented to experiment market economy and help boost the Chinese economy. Over the last two decades, Shenzhen has developed into a large industrialized area with an emerging tertiary sector. Its economic growth is rapid. For example, in 1980, Shenzhen s per-capita real GDP (in 1978 prices) was RMB 719.8, less than one-third that of Shanghai (RMB 2,360.3). In 1994, Shenzhen s per-capita real GDP reached RMB 4,032, which exceeded that of 1

3 Shanghai by RMB 1,410, ranking first in China ever since (Xiao, 1998a). Economic reforms brought substantial change to the economic system. In addition to the two major forms of ownership in a planned economy, namely state-owned and collective firms, there have emerged other forms of ownership. They include cooperatives and firms with sole-investment from and joint ventures with firms in Hong Kong, Macau, and Taiwan, other countries, a large number of local private firms and corporate firms. These firms have created a competitive market and they make businesses in both domestic and international markets. In the period of 1980 and 1996, these firms had provided about 2.3 million new jobs. These new occupations required that employees have new knowledge, skills, and attitudes/values. Employees had to learn to treat their clients as kings in order to win market share; time became as valuable as gold and efficiency was vital for a firm s survival. Advanced production technology was imported to replace outdated hand-operated equipment. The local workforce was unable to meet the human-capital demands of the Shenzhen economy in the early 1980s. In addition to expanding its formal education system, Shenzhen has cultivated and nurtured the development of workplace training and adult education over time. Two types of institutions provide education and training to working adults (Xiao & Tsang, 1994). First, employee-oriented adult education/training centers, set up and financed by large firms, provide training to their own employees. Training is job-related, corresponding to the productivity requirements of the firm. The second type of training is run by community organizations that provide education and training to adults in the local community. This community-oriented education/training provide job-related practical skills programs, vocational/technical certificate programs and credential programs. Participants usually voluntarily attend the adult course and pay for the courses themselves, though some may receive subsidies from their firms. Xiao and Tsang (1999) found that among the 4,002 sampled employees, 59 percent received on-the-job training, and 31.1 percent attended adult education/training programs. This study distinguished three types of education and training programs: pre-job formal education, firm-based on-the-job training (OJT) provided by employers, and self-financed adult education/training (AET) outside the firm. By using a three-level hierarchical linear growth model, this study incorporates both individual and firm-level factors to estimate their impact on salary growth over time. The remainder of this paper is divided into five sections. The next section provides a framework to interpret the rising demand for jobrelated training and adult education in the fast growing economy of Shenzhen. Followed is a discussion on conceptualized issues with a three-level analytic method. The fourth presents the survey and data sets. The fifth section examines empirical results. Finally, the concluding section presents a discussion on findings and their implication in development policy. 2. A framework for human capital development in the workplace While Becker (1964) suggests that education or training raise the productivity of workers by imparting useful knowledge and skills, others provide different explanations for how education is related to worker 2

4 productivity. One is based on the argument that the higher earnings of educated workers simply reflect their superior ability acquired during the process of education, rather than through skills and knowledge. Spence (1973) argues that education is used as a market signal to indicate the potential productivity of workers. Thurow (1975) maintains that productivity is largely characteristic of jobs rather than of workers; employers use education credentials to select workers because better-educated workers can be trained for specific jobs more quickly and at a lower cost than their less-educated peers. Schultz (1975) suggests that education enhance an individual s ability to successfully deal with disequilibria in changing economic conditions. Such ability includes that of perceiving a given disequilibrium, analyzing information, and reallocating resources to act. Another argument is based on the conditions of production. For instance, Levin (1987) argues that the organization of production, such as the extent of discretion, participation in decision-making, responsibility sharing, and information available to employees, all affect employees utilization of their ability to act. Levin and Kelley (1994) suggest that education can improve productivity only if complementary inputs exit, which include training, contract terms, and management practices; they point out that economists and other social scientists have overestimated the payoffs resulted from increased formal education while they have ignored the complementary inputs and conditions. Recently, Hall and Jones (1999) maintain that differences in capital accumulation, productivity, and therefore output per worker are fundamentally related to differences in social infrastructure across countries. Such social infrastructure includes the institutions and government policies that determine the economic environment, within which individuals accumulate skills and firms accumulate capital and produce output. Lack of these conditions would cause loss in production. Tsang (1987) found that mismatch of workers characteristics such as over-education caused dissatisfaction among workers and this was associated with a loss of over 8% in an output for the $57 billion dominant U.S. telecommunication industry AT & T in In this article, I offer an interpretation for the large demand of learning in a workplace in a modern, fast changing economic state of Shenzhen, China. I argue that job-related training provided to working adults is a strategy to re-equilibrate in the changing economy. The economic reform, and new technologies have resulted in qualitative economic changes, which aim to boost economic development in Shenzhen. Employees, nested in a given production organization, face such changes together with their firms; both firms and employees are involved in an equilibrating process to increase productivity. As technological innovations and economic reforms have been continuously adopted in the workplace in order to speed up growth, the organization of production, and management practices of the firm that formerly worked well in a planned economy are now obsolete. Such qualitative changes create disequilibrium, causing a technical discrepancy between the firms new investments and their employees competence; employees are unable to perform to meet the new demand. They may have accumulated significant knowledge, skills, and attitudes/values (KSA) through both formal schooling and work experience, but as change accelerated, KSA gained in previous learning gradually became also largely obsolete. 3

5 In a market-oriented economy, the competitiveness of a firm depends on its stock of both physical and human capitals as well as the uniqueness of management practices that can bring the firm s capacity into full play. In response, OJT provided by employers has thus become a firm strategy to develop human capital in order to adapt to changes in the workplace. OJT, while upgrading job skills of employees, also develops shared values and ways of working together to strengthen a firm s unique competitiveness in a transforming economy. Therefore, in-firm training, as opposed to formal education, as a strategy to develop human capital can over time compensate for competence gaps of the internal market and the increasingly complex technological demands of the workplace. By receiving OJT, the competence of employees is to be re-established. In Shenzhen, individual employees are free to change jobs whenever they find better opportunities to realized their personal potentials. If their job performance is not up to an expected level over time, employees may also be dismissed. Employees are cognizant of both changes in the firm and the increased discrepancy in their KSA and of opportunities outside the firm. They seek learning to close the gap, either to remain competent in their current jobs, or to change to new jobs. When employer-provided training does not fulfill the employees personal expectations, external open-market AET becomes an option. By taking AET, employees can close up the gap between competence and expectations. Employees look to OJT or AET to enable them to disengage from obsolete sets of KSA and to regain job competence. Given that firms constitute an economic setting where human capital is utilized, training and education related to the job setting for employees develop unique sets of KSA that can engage them in organized production. Therefore it is argued that in a fast-changing economic context, education and training programs related to jobs are a means to readjust to the changing workplace and improve productivity. 3. Measuring salary growth with a three-level model An estimation of human capital effects is complicated by the existence of different types of human capital (Chapman, 1993:69-72). Training can be taken in the pre-work form of formal education. Individuals can also train themselves through learning-by-doing in their jobs. The Mincerian method has been commonly used to estimate RORE to formal education in the earning function equation. It might measure the second type of human capital by using job experience, as proxied by years of working. However, various vocational adult education and training have been widely used in both OECD countries and other less developed countries to develop human capital. Recently Cohn and Addison (1998) conducted a comprehensive review of the literature on returns to both formal schooling and various vocational training programs for youth and adults. Regarding the former, they concluded that RORE is substantial across levels of schooling. Regarding the latter, they found that returns to training investment are mixed. Of course, education and training take place in more heterogeneous forms, namely, provided by different agencies to different age and occupations groups or delivered in various modes and settings. In 4

6 addition, the sample selection problems present greater ambiguity for an empirical investigation of training for adults than for formal education. It is unclear to what extent ability factors endogenous, determined by training and experience, and to what extent they are exogenous to other influences such as the market (Chapman, 1993:71-72). To date, most studies have used the three conventional methods to estimate returns of investment in education and training, namely short-cut methods, internal RORE or the Mincerian approach (see Cohn & Addison, 1998; Psacharopoulos and Woodhall, 1985). By focusing on the end result of earnings, few methods make allowances for the endogeneity of training decisions in a workplace setting. 1 In order to understand the occurrence and benefits of training, it is interesting to combine an analysis of the decisions of employers and employees regarding various education and training programs with the presence of influences of workplace characteristics within a single study. Such a study draws on recent advances in multilevel statistical theory by Bryk & Raudenbush (1992) and attempts to measure changes in earnings due to the effects of different types of education and training. Since employees are not randomly assigned to firms, the task of measuring changes in earnings becomes challenging. For instance, hirings and earnings increase over time are firm decisions affected by the firm s characteristics, as well as by individual characteristics pre-embodied and carried to the firm (e.g., formal schooling, sex, age, previous work experience, etc.) or developed while working (e.g., improved job skills and then performance through OJT). An aggregation bias can occur when a variable takes on different meanings, and therefore it may have a different effect at a different level of analysis. For example, receiving OJT may have an impact on one s job skills at the individual level, which can be measured by performance assessment and increased earning increase. At the firm level, providing OJT is a proxy measure of a firm s capacity and normative management practices. The recent statistics advancement in hierarchical linear models (HLM) resolves this problem by facilitating a decomposition of any observed relationship between variables into different level components (Bryk & Raudenbush, 1992). Again, within the firm, employees may develop dependence among themselves while working in the same normative environment. HLM incorporates a unique random effect for each organization unit and the variability in these random effects is taken into account to estimate standard errors. HLM also resolves the problem of heterogeneity of regression by estimating a separate set of regression coefficients for each organizational unit and by then modeling variations among firms in their sets of coefficients. The other advantage of HLM is that it accommodates multiple-time-point observations of an individual over time. The analysis employs a three-level growth model (Bryk & Raudenbush, 1992: , ), which offers an integrated approach for studying determinants of salary growth in an organized structure and presents both individual and firm predictors of salary growth from a multiple-time-point design. The analysis consists models at three levels: (a) salary observations at three time points, (b) employee factors, and (c) firm factors. At Level-1 (L1), the within-individual level, such variables as employee salary growth within a span of a maximum of six years at three observations and gains in one s technical proficiency level at corresponding observation time points are examined. At Level-2 (L2), the individual level, such variables as sex, age, formal schooling, OJT and AET, and experience of changes in the firm are 5

7 considered. At Level-3 (L3), such firm characteristics as firm size, location, ownership, industrial sector and firms capacity to provide OJT are included in the analysis. It is then assumed that Y tij, the observed log salary at time t for employee i, is a function of a systematic growth curve plus random error. The systematic growth over time is represented as a polynomial of degree P. Then, the L1 Model is: Y tij = π 0ij + π 1ij (OBSERVATION) tij + π 2ij (OBSERVATION 2 ) tij + π 3ij (OBSERVATION 3 ) tij + π 4ij (PERF GAIN) tij + e tij (1) For i = 1,., 1,023 employees in firm j (j =1, 71), where Y tij is the outcome variable of log monthly salary at time t for employee i in firm j; the total t consists of three observations, the first in the initial working year, the second in the third year, and the third in 1996 (see Table 1); π 0ij is the mean log salary of employee ij at the second observation, which is coded as 0 (see discussion on data coding in the next section); π 1ij is the parameter of mean annual salary growth rate for employee ij ; π 2ij is the parameter of the accelerated rate of salary growth based on a squared term; π 3ij is the parameter of the accelerated rate of salary growth based on a cubic term; π 4ij is the parameter of a gain in employee s technical proficiency level; e tii is the error which is independent, with a mean of zero and normally distributed with a common variance σ 2. It is assumed that each e tij is independently and normally distributed with a mean of zero and constant variance, σ 2. Equation 1 is the assumption that the growth parameters vary across employees. At L2, the parameters of L1 (Equation 1) become outcomes variables and the L2 model represents the growth variation due to individual factors as: Q p π pij = β p 0 j + Σ β pqj X qij + r pij (2) q=1 β p0j is the intercept term for firm j in modeling the employee effect π pij ; X qij is a measured characteristic of the individual background for employee i in firm j (e.g., sex, formal education, OJT, or AET, etc.). β pqj represents the effect of X qij on the pth growth parameter; and γ pij is a random effect with a mean of zero. The set of P + 1 random effects for employee i are assumed multivariate normally distributed with full covariance matrix, T, dimensioned (P + 1) x (P + 1). 6

8 At L3, a similar modeling process is repeated for firm factors. Each L2 outcome (i.e., each β pqj coefficient) may be predicted by some firm-level characteristics as, S pq β pqj = γ pq0 + Σ γ pqs W sj + u pqj (3) s = 1 where γ pq0 is the intercept term in the firm-level model for β pqj ; W sj is a firm characteristic used as a predictor for the firm effect on β pqj (note that each β pq may have a unique set of the L3 predictor, W sj s = 1,, S pq ); γ pqs is the corresponding L3 coefficient that represents the direction and strength of association between firm characteristic W sj and β pqj ; and u pqj is a L3 random effect that represents the deviation of firm j s coefficient, β pej, from its predicted value based on a firm-level model. Note that for each form there are Σ P p = 0 (Q + 1) equations in the L3 model. The residuals from these equations are assumed multivariate normally distributed. Each is assumed to have a mean of zero, some variance, and covariance among all pairs of elements. 4. The survey and data set In order to investigate the extent of OJT and AET provided to working adults and their effects, Xiao and Tsang (1999) conducted a reverse tracer study survey in The reverse tracer technique focuses on the analysis of employees who are currently employed in certain occupations and traces back the education and training histories pursued by employees (Ziderman & Horn, 1995). The reverse tracer study in Shenzhen began with the current job destinations and sought to identify each major alternative education and training route pursued by employees to reach the current destination in the previous five years. The 1996 survey questionnaire consisted of five groups of questions on: (a) an employee s pre-job formal education, (b) technological changes experienced in the workplace, (c) OJT provided by firms to employees; (d) AET courses that an employee attended outside the firm; and (e) an employee s position technical proficiency level and salary at three time points over six years. This questionnaire was administrated to a stratified random sample of 6,200 employees, slightly less than one percent of the registered workforce in Shenzhen. 2 The sample included (a) firms in both manufacturing and service sector; (b) firms of eight types of ownership; 3 (c) firms of different sizes, and (d) one or two major production lines in the firms, which included all the personnel from managers, clerk, technicians to front workers. In both manufacturing and service sectors, three large-size firms (see Table 1 for definition), two medium-size firms and one to two small-size firms in each of the 8 types of ownership were to be sampled (96 firms). The Yearbook of Registered Firms (Shenzhen AE, 1996) in these classifications was obtained 7

9 from the Association for Shenzhen Enterprises. Representatives of industrial and service firms were randomly selected. Managing directors and personnel offices were contacted about ownership, size, and acceptance of the study in their firms. Replacement was made with another randomly selected firm when either of the condition was not agreed. After deleting some types, which did not exist (large private service and manufacturing, for instance), finally 76 firms were selected and agreed to participate in the study. The survey was conducted during later Information was collected from 4,002 workers in these 76 firms, corresponding to a 65% response rate. Among the returned questionnaires, it is found that the state-owned firms were under-represented while corporation firms were over-sampled due to that their production lines were big. Weights were then applied to analysis in order to arrived at estimation for a representative sample of the overall workforce. As the first work year in the current firm for each employees varied from 1980 to 1996, this study selected a sub-sample of 1,023 employees who were hired by their current firm in 1990, 1991, 1992, or One advantage of HLM is that it allows observation time points and interval lengths to vary, especially those associated with growth and change. With employees hired within a short span of four years in the beginning of the third economic development stage in Shenzhen 5 and observations over a time span of a minimum of four years and maximum of six years, biases in growth rates should be reduced to a minimum. A sub-sample with all newly employed workers allows us to examine the effects of formal education, OJT, and AET on performance improvement and salary growth in the first few years of work. Table 1 presents descriptions of temporal variables that reflect individual change over time for the L1 model. The sub-sample of 66 firms, with an average of 15.5 employees in each, had three observations of salary and gain in technical proficiency level: for the first work year, the end of the third year, and the end of 1996 for each employee. 6 OBSERVATIONS. OBSERVATIONS are the number of years that elapsed from the initial recruiting year to the time when salary and performance data were reported (see Table 1). To avoid collinearity in the analysis that occurred with other temporal variables in the estimation, the secondary observation is coded as 0, and the first occasion is coded in negative number of years that elapsed from the initial to the second occasion. The third occasion is coded in the number of years that elapsed from the second to the third occasion. SALARY. SALARY refers to actual monthly salary in 1996 price. Shenzhen was the first city in China to restructure salaries to suit the market-oriented economy in 1985 (Shenzhen ESRC, 1989). Salary consists of three components: basic salary, seniority, and position-based salary. Basic salary is mostly fixed at RMB 75 (1985 price) for all managerial /professionals, supporting staff, as well as workers (Shenzhen Government, 1989: ). Seniority is rewarded at about RMB 10.5 for every additional year of work. Position-based salary can correspond to at least 60 percent of the total monthly payment. It consists of two categories, performance-based and profitability-based. Performance-based salary relates to the annual assessment of performance of task accomplishment, including technical skills, quality, quota, attendance, and security. Salary can be reduced if one s job tasks are not accomplished. The profitability-based salary 8

10 can be zero if the firm fails to generate profits, there is no ceiling because it is based on firm profitability. Fast economic growth in Shenzhen led to rapid profitability-based salary increases. The Shenzhen Statistical Bureau has created salary index to indicate the rate of salary increase not due to personnel factors (e.g., performance improvement, promotion, etc.), but due to increases in bonuses and overall firm profits. Therefore, SALARY in this study is transformed into actual salaries in 1996 price, with both a price index and salary index control (Shenzhen Statistical and Information Yearbook Committee, 1997:335) and log SALARY is used in the analysis. (Insert Table 1 about here) PERF GAIN. PERF GAIN refers to a gain in job position s technical proficiency level from the previous observation time point, which is a reference for performance-based salary. In China, there are three payroll categories: cadre/managerial/professional staff, supporting staff, and workers. Among the three categories, managerial/professional ranks were the most prestigious followed by supporting staff and the workers. Firms in Shenzhen, use three levels to define one s technical proficiency: entry, intermediate, and senior for each category. Shenzhen has adopted performance assessments to determine one s performance, which are carried out annually. The assessment includes the technical skills that the employee could perform, quality of work accomplished (e.g., scrap rates), quota accomplished, ability to deal with diverse tasks, attendance, security records, technical and innovative suggestions made during a year, attitude and co-operativeness. 7 The level of one s technical proficiency is an official record of the assessment results and is reflected in performance-based salary. In this study, PERF GAIN for the initial observation at the hiring trial point is coded as zero. A gain during the first two observation points is coded as 1, two gains codes as 2, etc., for the second observation point. For the third observation point, any gains during the second interval of observations are added to values at the second observation point. An outstanding performance may give one a promotion from worker category to staff, or from staff to managerial/professional category. In such case, 4 is coded as the former across-category promotion occurs, and 5 is coded as the latter across-category promotion occurs. Table 2 contains individual-level variables. Shenzhen is a newly industrialized. Generally, female employees outnumber male employees, and the workforce is very young. 8 EXPERIENCE refers to whether an employee had any work experience prior to work in the current firm. POSITION refers to the current job position. Front-line workers are those employees who work on the floor. They can be either unskilled or skilled workers. Supporting staff includes clerical personnel and salespersons. Managerial/professionals staff refers to managers, senior supervisors, engineers, and senior technicians. (Insert Table 2 about here) CHANGE. CHAGNE refers to the amount of change employees experienced on the job. In the 9

11 survey, employees were asked if they had experienced changes of three kinds: the introduction of new production technology, production of a new product, and new requirements for increased job skills. If they experienced a single kind, they were coded 1; two kinds were coded 2; and three kinds of changes on the job were coded 3. EDUCATION. EDUCATION refers to formal schooling before the first job. PRE-JOB TRAINING refers to short-term vocational/technical training before applying for the first job. ADULT EDUCATIN refers to AET attended by employees at a local community-oriented center. TRAINING is a dummy variable with 1 referring to having received OJT and 0 to no. Many employees had OJT more than once. TRAINING AMOUNT ranks the amount of OJT received in the firm, with 0 for having received no OJT, 1 for one session, and 6 for six sessions. (Insert Table 3 about here) Table 3 presents firm-level variables. SECTOR, and LOCATION are dummy variables. Firm SIZE is coded in an ordered manner, with small firms coded as 0, medium as 1, and large as 2. OWNERSHIP refers to types of investment owners. Local private and collective firms, coded as 0, stateowned and newly created corporate firms, mostly coming from the state-owned sector, are coded as 1. Those firms with sole-investment from Hong Kong, Macau, and Taiwan, and other countries, or jointventures with firms from outside the PRC, are coded as 2. The first two types are the least formal in terms of personnel and production management. The second two types have formal management, and the last two types are most formal in management, in terms of recruitment, selection for training, performance assessment, cost-analysis, and production control. This ranking accords with per employee productivity (see Xiao, 1996b). 9 TRAINING EXTENT is an aggregated proxy variable made from the overall OJT that employees received in the firm. It is coded as 0 if less than one-third of the employees in a firm received OJT, 1 if between one- and two-thirds had received OJT, and 2 if over two-thirds received OJT in the job. 5. Empirical results The results are presented in three groups. An analysis of salary growth over time as a baseline model is conducted first, in which variance is left randomly in the L2 model and L3 model. Thereafter, follows an explanatory model that allows for an estimation of the separate effects of employee characteristic variables on employee initial salary, the annual salary increase rate, and PERF GAIN. Finally, the L3 model estimates the explanatory effects of the firm s characteristics variables to define employee performance in the firm context. 5.1 Unconditional model 10

12 Employees mature in performance and their salaries tend to increase over time. The L1 model (see Equation 1) estimates salary growth of an employee over time. There are no predictions in the L2 and L3 models on salary growth. This provides useful empirical evidence to determine a proper specification of the individual growth equation and baseline statistics for evaluating the effects of subsequent the L2 and L3 models later (Bryk & Raudenbush, 1992:135). The unconditional models can partition variability in the individual growth parameters into L2 and L3 components: Level-2 Model, π 0ij = β 00 j + r 0ij (2.0a) π 1ij = β 10 j + r 1ij (2.1a) π 2ij = β 20 j (2.2a) π 3ij = β 30 j (2.3a) π 4ij = β 40 j (2.4a) and the Level-3 Model, β 00j = γ u 00j (3.00a) β 10j = γ u 10j (3.10a) β 20j = γ 200 (3.20a) β 30j = γ 300 (3.30a) β 40j = γ u 40j (3.40a) Where π pij become the outcome variables in the L2 Model, and β pqj become the outcome variables in L3 Model. β 00j is the mean salary within firm j at the initial occasion while γ 000 is the grand mean salary of 1,023 employees in 66 firms at the initial observation occasion. β 10j is the mean annual growth rate of salary within firm j, while γ 100 is the grand mean growth rate for all employees; and β 40j is the mean rate of performance increase within firm j, while γ 400 is the grand mean rate of performance gain among 1,023 employees. β 20 and β 40 are the mean accelerated rates of salary increase within firm j in squared and cubic terms respectively; and γ 200 and γ 400 are the grand mean accelerating rates in squared and cubic terms respectively. Because there are only three occasions in the observations, variance at the individual level can only allow t-1 random variance for freedom in calculation, in this case, two Level-2 random effects, r 0ij and r 1ij with variances τ 00j and τ 11j, respectively, and with a covariance of τ 01j. Level-2 random effects represent the deviation of employee ij s coefficients (π pij ) from its predicted value based on the individual level model. There are three random effects, τ 00j, τ 10j, and τ 20j in the L3 model, which represent the deviation of firm, j s coefficients (β pqj ) from its predicted value based on the firm-level model. (Insert table 4 about here) 11

13 Table 4 presents the results of salary growth with unconditional modes at Level-2 and Level-3. The fixed effects, as presented in the top panel, indicate a strong positive salary growth trajectory. The estimated grand mean SALARY for π 0 parameter in the linear growth model represents the true average salary of the employee at the second time point, (e.g., the third year in the current firm, which is coded as 0) ^ γ 000 is ln 6.89 (about RMB ; t = and p =0.000), significantly different from that at the initial and third observation time points and therefore, the true initial salaries are γ 000 minus three times of γ Over time, SALARY appears as an positive growing trend. The average annual SALARY increase was estimated a 14.3 percent (i.e., γ 100 ), with another increase of 7.7 percent if an employee had a gain in one s technical proficiency level (i.e., γ 400 ). The estimate of the growth rate in a cubic acceleration (γ 300 < 0) indicates that salary growth is in a nonlinear function over times. The growth of SALARY over time is a parabola in a concave downward direction. Estimates for the random effects testing appear in the lower panel of the table, which separate the variance in employee initial salary and mean growth rate into within-firm and between-firm components. Residual variance at the growth level (σ 2 ) is The residual variance among employees, τ π, is (R 0ij) for average initial salary and (R 1ij ) for annual growth slope. The residual variance between firms, τ β is (U 00j ) for average initial salary, (U 10j ) for annual growth, and (U 40j ) for FERF GAIN. The χ 2 statistics accompanying these variances indicate that there is significant variation among employees within firms in initial salary and annual growth (π 0ij, and π 1ij ). There is also significant variation between firms in terms of mean initial salary, annual salary increase, and gain in proficiency level (i.e., β 00j, β 10j and β 40j ). Analysis at L2 should explain these variances. Table 4 shows that the reliability for the initial salary intercept is.760, based on an average of 15.5 employees in each firm, and.451 for the annual salary increase slope, based on the three temporal observations. Due to the small number of observations, the annual salary increase appears to be less reliable, but it is still fairly acceptable. The reliability of the initial salary intercept (i.e.,.580) and the annual increase rate intercept (i.e.,0.466) at the firm level are acceptable; but the reliability of the performance improvement intercept (i.e.,.351) seems a bit low with only three observations, but still can be considered to be fair Conditional L2 model with individual characteristics as predictors Now, the L1 model being the same as in Equation 1, the parameters in L1 model become outcome variables in the L2 Model, and their variability will be predicted by the employee characteristic variables. The specific L2 model is: π 0ij = β 00j + β 01j (SEX) ij + β 02j (EDUCATION) ij + β 03j (POSITION) ij + β 04j (EXPERIENCE) ij + β 05j (PRE-JOB TRAINING) ij + r 0ij (2.0b) π 1ij = β 10j + β 11j (AGE) ij + β 12j (EDUCATION) ij + β 13j (POSTION) ij 12

14 + β 14j (ADUTL EDUCATION) ij + β 15j (TRAINING) ij + r 1ij (2.1b) π 2ij = β 20j π 3ij = β 30j (2.2b) (2.3b) π 4ij = β 40j + β 41j (EDUCATION) ij + β 42j (CHANGE) ij + β 43j (TRAINING6) ij + β 44j (ADULT EDUCAITON) ij (2.4b). It is hypothesized (1) that sex, years of formal education, job position assigned, work experience, and prejob vocational/technical training are related to initial salary; (2) that age, formal education, job position, adult education, and training are all related to annual salary increases over time; and (3) that sex, formal education, amount of change experienced in the job, amount of on-the-job training received within the firm, and adult education pursued outside the firm are all related to the performance gain. We leave the L3 model still unpredicted at this stage. Table 5 shows the results with the individual explanatory variables in the L2 model. The SALARY parameter remains positive and significant. SEX, FORMAL EDUCATION, and POSITION show a significantly positive effect on the grand mean SALARY, the initial hiring salary, but EXPERIENCE and PRE-JOB TRAINING do not. ^ On average, an employee earned ln RMB (γ 000, t = and p =.000) at the third year of working in the firm (about RMB ). However, male employees on average start with 5.9 percent (γ 010 ) more than their female counterparts, indicating a difference in the initial salary between male and female employees, all other things being equal. Formal education has a significant positive effect on being hired. For every additional year of education, one can receive about 2.37 percent (γ 020 ) more initial salary when hired by a firm. This means that formal education is an important factor that affects employers hiring decisions. The assigned job position (γ 030 ) has a significantly positive effect on initial salary. Support staff are likely to received 10 percent more salary at time of hire than front-line workers, and professional/managerial staff receive another 10 percent more. (Insert Table 5 about here) Work experience (γ 040 ) before coming to the current firm does not have an effect on initial salary. As discussed earlier, most jobs in Shenzhen resulted from the economic changes, thus they were new jobs. Previous work experience may have been less relevant for the current jobs. Prospective employees and employers often did not find the right match for the specific jobs in the market. We will see below that onthe-job training has an effect on current job skill improvements. Vocational training before hiring became a national labor policy from 1993 (China CC & SC, 1993). Some employees received pre-job vocational training before coming to the firm. However, the coefficient indicates that, PRE-JOB TRAINING (γ 050 ) did not have a job preparation effect on the firms hiring decision. 13

15 The salary increase parameter (γ 100 ) had an average of 18.7 percent slope, which is substantially high. Age group (γ 110 ) had no effect on the annual salary increase. Shenzhen has a very young workforce and the salary reform favors those with job competence instead of seniority or age. Though formal education (γ 120 ) had a strong positive effect on one s initial salary, it showed a negative effect of 0.45 percent less salary increase for employees with every additional year of formal education. In Shenzhen, salary scheme is set up in such a way (see discussion on data set) that formal education only counts in computing the fixed basic salary and employers can decide on the salary increase based on performance. This statistic reflects the effect of the implementation of such salary scheme. It indicates that employees with less formal education tend to have a higher annual salary increase slope than those with more years of education do though they have a higher salary at the time of hiring. The position assigned (γ 130 =.0129, t =2.624, p =.009) had a significantly positive effect on the annual salary increase. Support staff received a 1.3 percent more annual salary increase and managerial/professional staff received another 1.3 percent more than front-line workers. Therefore, the positions, as determined by the firm, have an effect on salary. Adult education (γ 140 ) selected by employees outside the firm shows no relation to the annual salary increase, nor does on-the-job training (γ 150 ) have an effect on the annual salary increase. Although the actual salary is used to control for salary inflation, the annual salary increase is still substantial. Nevertheless, the annual increase is not due to formal education, on-the-job training, or adult education per se. EDUCATION (γ 410 ), CHANGE (γ 420 ), TRAINING AMOUNT (γ 423 ), and ADULT EDUCATION (γ 440 ) were used to predict PERF GAIN (π 1ij ). First, formal education prior to the job (γ 410 ) offered marginal or no effect on gains in firm recognized proficiency level (γ 410 =.00655, p =.056). The amount of change experienced in the job (γ 420 =.0122, p =.007) showed a positive and significant association with a gain in proficiency level, about a 1.2 percent increase in salary for every one more change experience in the job. This indicates that change promotes performance improvement through learning by doing. The amount of OJT (γ 430 =.013, p =.005) also showed a positive and significant association with a gain in proficiency level, thus associated with increased salary. With each OJT received in the workplace, employees are likely to obtain some gain in job s technical proficiency level, in association with 1.3 percent salary increase. AET received outside the firm showed no association with job performance. AET chosen by employees outside the firm may largely suit employees individual needs, or needs perceived by employees themselves. Such needs may not be relevant to specific job requirements, though AET can be a means to change jobs. Thus, those who received AET showed no gain in firm recognized technical proficiency level, thus no salary increase. With CHANGE and TRAINING AMOUNT as predicting variables, the PERF GAIN slope now becomes non-significant (γ 400 = , t = , p =.260), which means that statistically, CHANGE and TRAINING AMOUNT have explained all the variations of PERF GAIN among individuals within firms. CHANGE and TRAINING AMOUNT are powerful predictors and have contributed to explain a gain in firm recognized technical proficiency in association with salary increase in the positive direction. OJT and 14

16 experience of change in the workplace are associated with increased salary through improved performance, as recognized by firms with a gain in proficiency level. In short, formal education and job position largely determine the initial salary. Sex discrimination exists; females receive 5.9 percent less initial salary than their male counterparts. Annual salary growth is substantial and also due to the effect of position; formal education had no effect on annual salary increases. A gain in proficiency is strongly associated with increased salary, but such gains are associated with an employee s experience of change in their firms, a type of learning-by-doing in technical changes in the job, and with receiving OJT. It is interesting to note that formal pre-job education has a positive effect on initial salary, but no positive effect on annual salary increase, nor on improved job performance. This supports the Spence s (1973) argument that in hiring, formal education provides a signal in the labor market that employees with more education might have higher productivity. With respect to productivity, more educated employees can be trained at a lower cost (Thurow, 1975). Therefore, only the initial salary is in accord with formal education. (Insert Table 6 about here) For random effects testing, Table 6 shows that the residual variance among employees (R 0ij ) is now reduced from in the baseline model to for the average initial salary, and from to for annual growth (R 1ij ). About 11 percent of the variance in initial salary and 4.7 percent of the variance in annual salary growth among employees within firms were explained by predictors in the L2 model. The residual variance is (U 00j ) for the average initial salary between firms, (U 10j ) for annual growth, and (U 40j ) for PERF GAIN in the baseline model. With the individual predictors included in the L2 model, about 31 percent, percent, and 44.2 percent of variance at the firm was explained in association with the personal characteristics of employees. For the variance in mean growth (U 10j ) across firms, that the variance among individuals within firms (R 1ij ) was reduced made the variance between firms appear to be larger. The variance due to firm impact, which was confused, is decomposed by the individual variables. It is interesting to note that the individual characteristics of employees explain more for the firmlevel difference than for the individual-level difference. This indicates that individual variables affect salary due to the employees being nested in a firm and due to firm decisions about individuals. Thus, productivity is largely a workplace characteristic; and individual characteristics promote productivity in a collective manner according to how production is organized in the firm context rather than in an individual manner. Nevertheless, the corresponding χ2 statistics accompanying these variance components remain significant, except variance for PERF GAIN (U 40j ). This indicates that there is still significant variation (R 0ij and R 1ij ) among employees within firms in terms of mean initial salary and mean annual growth as well 15

17 as significant variance (U 00j, U 10j ) between firms for mean initial salary, and mean annual salary increase. Individual predictors in the L2 model explain all the variance of PERF GAIN (U 40j ). 5.3 Conditional L3 model with firm characteristics as predictors The L1 model and L2 model being the same, the L3 model will use firm characteristic variables to predict six Level-2 parameters and to present their variability between firms. In the following equations, the firms characteristic variables serve as explanatory variables to define the association of employees performance in the firm context: β 00j = γ γ 001 (SIZE) j + u 00 j (3.00c) β 01j = γ γ 011 (OWNERSHIP) j (3.01c) β 02j = γ γ 021 (SECTOR) j (3.02c) β 03j = γ 030 (3.03c) β 04 j = γ 040 (3.04c) β 05 j = γ 050 (3.05c) β 10j = γ γ 101 (TRAINING EXTENT) j + γ 102 (OWNERSHIP) j + u 10 j (3.10c) β 11j = γ 110 (3.11c) β 12 j = γ 120 (3.12c) β 13j = γ 130 (3.13c) β 14j = γ 140 (3.14c) β 15j = γ 150 (3.15c) β 20j = γ 200 (3.20c) β 30j = γ 300 (3.30c) β 40j = γ u 40 j (3.40c) β 41j = γ 410 (3.41c) β 42j = γ γ 421 (SIZE) j + γ 422 (LOCATION) j + γ 423 (SECTOR) j + γ 424 (OWNERSHIP) j (3.42c) β 43j = γ γ 431 (SIZE) j + γ 432 (LOCATION) j + γ 433 (SECTION) j + γ 434 (OWNERSHIP) j (3.43c) β 44j = γ 440 (3.44c). parameters (β pqj ). It is hypothesized these firm level predictors (W sj ) have effect on their corresponding level 2 (Insert Table 7 about here) 16