Skill Incompetence, Training and Firm Performance in Transitional Economy Ying Chu NG Department of Economics Hong Kong Baptist University Noel Y. M. SIU Department of Marketing Hong Kong Baptist University
Skill Incompetence, Training and Firm Performance in Transitional Economy
by
Ying Chu NG1 Department of Economics Hong Kong Baptist University
Noel Y. M. SIU Department of Marketing Hong Kong Baptist University
1
The research was supported by funds from the Research Grant Council of the Hong Kong Government (HKBU 2057/99H). All correspondences should be addresssed to Dr. Y. C. Ng, Department of Eocnomics, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, e-mail:
[email protected]. 1
Abstract Using data from a sample of manufacturing enterprises in Shanghai, the authors examine the effect of training on enterprise productivity and the issue of promotion and turnover of the trained workers by type of enterprise ownership. With labor input disaggregated into different skill levels, the production function estimation shows that there is a positive relationship between training expenditures (expenditures on technical training as well as managerial training) and enterprise productivity. Unlike most of previous studies, we find that the provision of technical and managerial training exerts no impact on the promotion or turnover of the trained workers. The results from the factor analysis of training objectives suggest that training could be used as a means of competency improvement and skill improvement in transitional economies such as China. Analyzing the issues by types of enterprise ownership highlights the difference in the effectiveness of the training resources utilized by state-owned enterprises versus non-state-owned enterprises.
2
Introduction For more than 20 years of reform experience, enormous growth in the industrial output as well as in the economy as a whole occurred in China. Such growth has exposed the incompetence in the skill levels of workers as one of China’s major problems (Borgonjon and Vanhonacker 1994; Child 1994; Lu and Bjorkman 1997 and 1998; Nyaw 1995; Tsang 1994; Warner 1988). Becker (1975) has argued that training is important in facilitating the development of a skilled workforce that is necessary for economic growth. This argument is applicable to developed countries such as the US and the UK (Booth 1993; Veum 1995) as well as developing countries such as Peru and China (Arriagada 1990; Bu 1994). While much work has been conducted to understand the incidence and the impacts of training at the firm level in developed countries2, analyzing such training effects on firm performance is rarely found in developing countries. As argued by Xiao and Tsang (1999), there is a need to understand the skill market and training needs, especially employer-sponsored on-the-job training, in developing countries. The information derived from such study will contribute to more efficient investment of scarce resources in training and in targeting human-capital development. Accordingly, the present study attempts to fill the gap by examining the training impact on enterprise (firm) performance, measured in terms of sales, in the context of the business
2
See, for example, Alba-Ramirez (1994), Barrett and O’Connell (2001), Bartel (1994), Black and Lynch (1996),
Lynch and Black (1997), MacDuffie and Kochan (1995) and San (1990). Bishop (1997) also provides a review of the literature on the issue.
3
environment in China. Choosing enterprises from the Chinese business environment as the target of study is of great interest to policy-makers, practitioners, enterprise owners and managers in understanding how the demand for human capital can be met and how much human capital investment should be made in a fast growing economy in transition. The implementation of the economic reforms implies the breakdown of the planned economy and a transition to a more market-oriented economy. This definitely has put extreme pressure on the state-owned enterprises (SOEs) in competing with non-SOEs (joint venture enterprises and wholly foreign-owned enterprises). It is argued that non-SOEs possess the advantages of hiring better qualified workers and having access to advanced production technologies. It follows that the problem of incompetence in the skill levels of SOEs workers becomes even more acute. Analyzing the return to training by enterprise ownership type would provide a good measure for sensible resource allocation in both SOEs and non-SOEs. Intergroup comparisons also serve as guides for relatively low performing groups to address training strategy. Apart from the performance-related issue, training provision is also associated with the existence of an internal labor market. Within a firm’s hierarchy, there is greater opportunity for job mobility and thus the duration of employment is expected to be longer. As a result, the provision of training, in particular, firm-specific training, becomes more profitable. Firms have strong incentives to provide higher levels of training because the longer work tenure of the
4
trained employees allows the firm to recoup the cost of training (Baldwin, Gray and Johnson 1995, Black, Noel and Wang 1999, Idson 1996). On the other hand, firm-specific or skillspecific training implies that it will be difficult for employees to seek employment opportunities outside the firm. For this reason, turnover rates would be lower for firms with higher levels of training. In this regard, the effect of training on the turnover rates of employees, especially trained employees, and that of the upward mobility of the trained employees are of interest to firms operating in a transitional economy. Being unable to retain trained employees results in huge losses in the human capital investment of the firm. This, in turn, has important implications on the firm’s strategy towards training provision and resource allocation.
Training and Firm Performance It is argued that “truly” successful firms will invest aggressively in human resources (Von Glinow 1992; Ulrich, Yeng and Brockbank 1991). This is evidenced by the findings of previous studies. Using the 1986 Columbia Business School Survey, Bartel (1994) analyzed the training program effect on the logarithm of net sales of manufacturing firms. Only new training programs, but not formal training, exerts a positive effect on firm sales. Of 595 medium- and large-sized Spanish firms, those that training is provided are found to have a higher levels of sales per employee or higher value-added per employee according to the 1988 Collective Bargaining in Large Firms study conducted by the Spanish Ministry of Economics
5
and Finance (Alba-Ramirez 1994). If the training measure is replaced by the percentage of employees trained, only the percentage of senior employees trained has a positive relationship with firm performance. Based on manufacturing and non-manufacturing firms in the Educational Quality of the Workforce National Employers Survey (EQW-NES) administered by the US Bureau of Census, Lynch and Black (1995, 1996) found that the sales of the firm do not depend on how many employees are trained but on the percentage of off-the-job formal training received by employees in the manufacturing sector. Similar insights are found in examining 215 Irish firms in the manufacturing, construction and private services industries in 1993 and 1995 (Barrett and O’Connell 2001). Bartel (2000) reviewed the literature on the econometric analysis of the training effect at firm level for studies with large samples of firms. Tan and Batra (1995) provided estimates of the effect of training provisions on the value-added of the firm in five developing countries. Findings from Tan and Batra (1995) indicate that training increases the value-added by 0.028%, 0.711%, 0.266%, 0.282% and 0.444% for firms in Taiwan, Indonesia, Columbia, Malaysia and Mexico, respectively.
Estimation Framework For the examination of enterprise performance (productivity) with respect to training, the present study adopts, as with Alba-Ramirez (1994), Barrett and O’Connell (2001), Bartel
6
(1994), Black and Lynch (1996), Lynch and Black (1995), Tan and Batra (1995), an augmented Cobb-Donglas production function as follows:
3
7
14
i =1
i =5
i =8
Ln Sales = α o + ∑ α i Ln Li + α 4 Ln K + ∑ α i Branchi + ∑ α i INDi + α 15 EXPORT + α 16 Ln TechT + α 17 Ln MgntT + µ ,
where Li represents the total number of workers on the production line, in technical positions, and in managerial positions; K is measured as the value of net fixed assets; Branchi are dichotomous variables for different types of multi-establishment enterprises with singleestablishment as the reference group; EXPORT measures the percentage of the output intended for export; INDi are the industrial dummy variables with other manufacturing industries as the omitted category; Ln TechT and Ln MgntT are the total enterprise expenditures on technical training and managerial training in logarithmic form. The inclusion of Branchi and EXPORT accounts for differences in the nature of the business (Alba-Ramirez 1994; San 1990). Given the fact that China is in a transitional stage, any skill incompetence in the workforce could be raised from the technical aspect, the managerial aspect, or both. Addressing the different types of training is of great interest to enterprises in guiding investment decisions on various types of training. Unlike most studies, the present study measures the effect of training by the total expenditures on training in logarithmic form. In other words, α16 and α17 give the elasticity of firm performance (measured in sales of the 7
enterprise) with respect to technical and managerial training, respectively. Turning to the issue of turnover rate and promotion of trained workers, a two-limit Tobit model is used with the percentage of trained workers being promoted and the turnover rate of trained workers as dependent variables. In addition to INDi , EXPORT , Branchi , Ln TechT and Ln MgntT as defined earlier, an extra independent variable, Ln Sale , is included. This variable captures any upswing or downsizing effect of enterprises in handling promotions and staffing practices. That is,
Turnover Rate of Trained Workers = β o + β 1 Ln Sale + β 2 Ln TechT + β 3 Ln MgntT + 6
13
i=4
i=7
∑ β i Branchi + ∑ β i INDi + β14 EXPORT + ε .
In the promotion equation, the dependent variable is replaced by percentage of trained workers being promoted such that:
Percentage of Trained Workers Promoted = γ o + γ 1 Ln Sale + γ 2 Ln TechT +
γ 3 Ln MgntT + 13
∑γ i =7
i
6
∑γ i=4
i
Branch i +
IND i + γ 14 EXPORT + ν .
The estimated β2 and β3 (γ2 and γ3) represent the percentage of expenditures on technical and managerial training in affecting the turnover rate (percentage of trained workers being 8
promoted), respectively. To highlight the possible differences in the analysis by ownership type, both the production function and the two-limit Tobit model are estimated separately for SOEs and non-SOEs. For the case of non-SOEs, additional dummy variables classifying different nonSOEs are included as regressors. The reference group for this set of variables is wholly foreign-owned enterprises. Data To address the training impact on enterprise productivity and the turnover rate of trained workers and to test the existence of an internal labor market in the Chinese business environment, a total of 576 questionnaires were randomly distributed to enterprises belonging to the manufacturing sector in Shanghai. Shanghai has been the fastest growing business center and city in China in recent years. Although the concentration on one locality will bring into question the generalizability of the empirical results, insights obtained from the study will still provide valuable information on training to the rest of China as well as to developing countries undergoing rapid economic transformation. A total of 515 (237 SOEs and 278 non-SOEs) usable questionnaires were received for analysis. Among the non-SOEs, half were joint ventures with non-Hongkong/Macau/Taiwan partners.
Empirical Findings
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As shown in Table 1, there is a remarkable difference in enterprise characteristics between SOEs and non-SOEs. In terms of the scale of production (measured by sales) and the endowment of enterprises (both physical capital and human capital endowments), SOEs are, on average, on the large scale when compared to non-SOEs. In terms of the nature of the businesses, SOEs are tied strongly to the local market whereas non-SOEs, by the nature of their origin, have overseas branches and international connections. It was, thus, not surprising to find that, on average, nearly one-fourth of the output produced by non-SOEs was intended for export as compared to the output of SOEs (13.38%) (Table 1). Most enterprises, be they SOEs or non-SOEs, are involved in the machinery, electronics and telecommunications equipment manufacturing industries (35% of the surveyed SOEs and 26% of the sampled non-SOEs). Relatively more non-SOEs were found in the food, beverage and tobacco, textile, garments and leather, petroleum processing, chemicals, plastics, minerals and medicine and metal industries when compared to the industrial sectors of SOEs. In spite of the substantial physical capital endowment, enormous firm size (measured in terms of the number of workers) and the larger scale of production of SOEs, the resources devoted to employee training were far lower than that of non-SOEs. On average, the total expenditures on technical training by SOEs was slightly over two-thirds of that by non-SOEs. The amount of money spent on managerial training by SOEs was about 63% of that spent by non-SOEs. Without regard to the type of training received, trained workers in non-SOEs had a
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relatively higher chance to gain promotions. Within each enterprise subgroup, workers receiving managerial training were more likely to be promoted than those who received technical training. At the same time, trained workers in non-SOEs were also more likely to leave as compared to those of SOEs. There is not much difference in the turnover rate of trained workers between types of training for SOEs and non-SOEs. Table 2 presents the estimation results of the production function by ownership type. As expected, capital input contributed positively to the productivity of enterprises. Among various types of human capital, only technicians induced a higher level of sales in SOEs with an elasticity of 0.42. On the contrary, higher productivity, in terms of sales, was found in nonSOEs in which a higher percentage of workers were in managerial positions. This observation highlights the difference in the competitive edge of human resources utilization between SOEs and non-SOEs. Regarding the training provision by enterprise, both SOEs and non-SOEs obtained a positive return from devoting resources to managerial training. A one percent increase in managerial training induced a 0.35% (0.14%) increase in sales for SOEs (non-SOEs) (Table 2). These estimates fall within those found by Tan and Barta (1995) in their investigation of five developing countries. Unlike SOEs, the return to technical training not only was positive but also higher than that to managerial training in non-SOEs. Combining the estimated result of the return to labor inputs, we can conclude that workers in SOEs are relatively less competent in
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managerial skills while non-SOEs have relative disadvantage in aquiring technical skills. There is no difference in terms of productivity among different types of SOEs (i.e., single-establishment or multiple-establishment). However, non-SOEs with branches else where in China indicated a much higher level of sales when compared with solitary non-SOE establishments. This probably reflects the notion that only firms with the ability to penetrate the local market can increase their market share. Across various manufacturing industries, SOEs in the food, beverage, tobacco industry were found to be more productive. SOEs (nonSOEs) in the machinery, electronics and telecommunications equipment industry (in the paper making, printing, stationary industry) were found to have lower productivity. At least in our survey sample, there is no difference in productivity among the three types of non-SOEs. According to Table 3, technical training predicts a higher turnover rate for those trained in SOEs. There is, however, no effect on the turnover rate of trained workers having received managerial training by SOEs and non-SOEs. One explanation for the insignificant training effect on the turnover rate of trained workers is that the training provided is firm specific or skill specific. This is particularly the case in non-SOEs (none of the training variables is statistically significant). With relatively strong commitment and attachment of workers, it is probably worthwhile to devote more resources to managerial training. As expected, an increase in sales induces enterprises, both SOEs and non-SOEs, to promote workers with managerial training. In terms of the effect of training on the promotion
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of trained workers, it was found that such an effect was dependent upon the types of training provided. For SOEs, a higher rate of promotion for workers receiving technical training was expected while no such relation was found in non-SOEs (Table 4). Interestingly, a higher level of managerial training did not imply a higher percentage of trained workers to be promoted in both SOEs and non-SOEs. These results imply that an internal labor market does not exist in China or that the emphasis of the training was on skills improvement. To have a more thorough understanding of the unexpected results of the training effect on the promotion of workers (the test of the existence of an internal labor market), enterprises were surveyed on what extent their training objectives had been achieved. A total of thirteen objectives (items) were adopted to measure the perceived assessment of training objectives being achieved. For each objective, enterprises were asked to rate the achievement level, if there was such an objective, on a 5-point Likert scale with “1” being “Not at all” and “5” being “To a very great extent”. A rating of “3” meant that the objective had been achieved to a reasonable extent. Part of the items (training objectives) was adapted from Von Glinow (1993). Two new items were added to the scale. They were “remedy the lower education level of employees”, and “remedy the inadequate supply of workers with appropriate skills”. They were regarded as having local bearing on local market conditions. To provide a meaningful interpretation of these training objectives (items), a principal components factor analysis with varimax rotation was performed on the thirteen items. The
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factor analysis addresses the problem of analyzing the structure of the interrelationships among a large number of variables, such as the scale for measuring training objectives in the present case, by defining a set of common underlying dimensions (Hair, Anderson, Tatham and Black 1998). The statistical test result (KMO = 0.880, Bartlett’s Test of Sphericity = 3249.987, Significance = 0.000) indicated that the factor analysis method was appropriate. The thirteen items were reduced to two factors with eigenvalues greater than 1.0. Of the thirteen items analyzed, five were found to be loaded on one factor and four on the other factor with factor loadings greater than 0.5. Four items were eliminated since they were insignificant in the factor analysis and were cross-loaded. The resulting factor structure explained 73.855 per cent of the total variance, which is widely regarded as acceptable. Cronbach’s alpha coefficient was computed to assess the reliability of the scale. The overall reliability of the construct was satisfactory (Cronbach’s coefficient alpha = 0.9138). The reliability coefficients for the two factors were 0.9126 and 0.8753, respectively, indicating a strong internal consistency among the items on each dimension. The results of the factor analysis are shown in Table 5. Factor 1, which was labelled as competency improvement, was composed of five items and accounted for 59.406 per cent of the variance. In general, this factor is similar to Becker’s notion of general training (Becker, 1975) in the sense that the emphasis is on broad skills and knowledge that could be used outside the current firm (Barrett and O’Connell, 2001).
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Examples include the improvement of interpersonal abilities, the enhancement of the work performance of employees and a remedy to employees’ past poor performance. However, this factor also emphasizes human resource planning and strategies, such as a remedy for the lower education level of employees as well as for the inadequate supply of workers. Factor 2 was labelled as skills improvement and accounted for 14.449 per cent of the variance. This factor is analogous with what Becker describes as specific training. The objectives centered on improving employees’ specific skills so that they could take up new assignments or adopt new technologies. As shown in Table 5, more than 80 percent of the surveyed enterprises treated training as a source of skills improvement (Factor 2 according to the factor analysis). This is particularly common in non-SOEs. A higher percentage of non-SOEs report having skills improvement objectives as compared to SOEs (89 to 95 percent versus 83 to 90 percent). In reference to skills improvement as a goal of training provision, a relatively higher percentage of non-SOEs claimed that such a goal had been achieved to a reasonable extent or above, ranging from 45 to 78 percent, depending on specific objective items. A lower percentage of SOEs were found to have competency improvement (Factor 1) as an objective in providing training (about 58 percent to 80 percent depending on the objective items), while over three-quarters of non-SOEs treated training as a way of upgrading workers to a competency level. Similar to the achievement level of Factor 2, non-SOEs were able to
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utilize training more effectively as a remedy for the incompetent skill levels of workers. Over 40 percent of non-SOEs with the goal of competency improvement were able to fulfill this goal to a reasonable extent or above. SOEs, on the other hand, had achievement levels to a lesser extent. In summary, there is strong evidence that training, particularly in our sampled enterprises, is used as a channel for upgrading skills and/or improving the competency of skill levels of workers. A marked difference in achieving these objectives through the provision of training between types of enterprise ownership is found.
Conclusions and Discussion In economies undergoing rapid transformation, any skill incompetences in the workforce can be remedied through training. The intensity or volume of training heavily depends on the return to this form of human capital investment. Training also impacts firm-level strategic choices about technical and human capabilities within the production system (McDuffie and Kochan 1995). Using a sample of enterprises operating in the manufacturing sector in Shanghai, we found a positive relationship between training, particularly managerial training, and firm productivity. The evidence of the insignificant effect of training on turnover rates of trained workers implies a possible non-negative return to training investment (through the retention of trained workers) by enterprises. Commitment to the workplace and longer work
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tenure for recouping the costs of training investment could be secured (Cappelli 1995; Frazis, Gittleman and Joyce 2000; Osterman 1994). In this regard, non-SOEs may find it useful to allocate resources continuously to both managerial and technical training. For SOEs, it seems that the provision of managerial training has the competitive edge over that of technical training. The general insignificant or negative impact of training provision on trained worker promotion shed lights on the need for skills improvement and, to a lesser extent, for competency improvement of the workforce in transitional economy such as China. This is particularly obvious in the context of managerial skills and SOEs. Unable to hire workers of relevant skill level or requiring more skilled workers due to the adoption of more advanced production technologies, enterprises can offer training to workers as an alternative means to tackle the issue of the “low” skill level of their workers. This is consistent with the notion that enterprises with workers lacking expected levels of education or skills are more likely to establish remedial training (Scott and Meyer 1991). Although SOEs seem to be in a better position in acquiring workers of appropriate skill level (a lower percentage of SOEs see skills improvement as a goal in providing training), they are found to be less effective in utilizing training. Among the nine training objectives, the level of achievement within SOEs was much lower than that of non-SOEs. Of the two factors, competency improvement and skill improvement, the achievement level of skill improvement
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is much higher in the case of SOEs. These results are consistent with findings on the impact of training on enterprise productivity (results of Table 2). The more effective utilization of training resources in skill improvement and competency improvement allows non-SOEs to increase enterprise productivity through the provision of both technical and managerial training. This is only the case for SOEs in terms of technical training provision (the significant and positive estimated coefficient for the expenditures on technical training in the production function estimation) which is more skill specific. Accordingly, training provision and its contribution to enterprises varies between types of training, namely managerial and technical training, and between types of ownership (SOEs vs. non-SOEs). This issue is of particular importance to enterprises operating in a transitional economy.
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Tan, Hong W., and Geeta Batra. 1995. “Enterprise Training in Developing Countries: Incidence, Productivity Effects and Policy Implication.” Unpublished Paper, The World Bank. Tsang, Eric W. K. 1994. “Human Resource Management Problems in Sino-foreign Joint Ventures.” International Journal of Manpower, Vol. 15, No. 9, pp. 4-21. Veum, Jonathan R. 1995. “Sources of Training and their Impact on Wage.” Industrial and Labor Relations Review, Vol. 48, No. 4, pp. 812-26. Von Glinow, Mary Ann. 1993. “Diagnosing ‘Best Practice’ in Human Resources Management Practices.” In James B. Shaw, Paul S. Kirkbride, and Kendrith M. Rowland, Suppl. 3, eds., Research in Personnel and Human Resources Management. Greenwich: JAI Press Inc, pp. 95-112. Warner, Malcolm. 1988. “China’s Management Training at the Crossroads.” Journal of General Management, Vol. 14, No. 1, pp. 78-91. Xiao, Jin, and Mun C. Tsang. 1999. “Human Capital Development in an Emerging Economy: The Experience of Shenzhen, China.” The China Quarterly, Vol.157 (Mar), pp. 72-114.
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Table 1: Sample Statistics by Ownership
Sales (in 10 thousand RMB) of 1999 Net fixed assets (in 10 thousand RMB) of 1999 Total number of workers in 1999 Total number of production workers in 1999 Total number of technicians in 1999 Total number of managerial workers in 1999 Percentage of enterprise with local branches only Percentage of enterprise with overseas branches only Percentage of enterprise with both local and overseas branches Percentage of enterprise in food, beverage, tobacco industry Percentage of enterprise in textile, garments, leather industry Percentage of enterprise in furniture, timber processing industry Percentage of enterprise in paper making, printing, stationary industry Percentage of enterprise in petroleum processing, chemicals, plastic, minerals, medicine industry Percentage of enterprise in metals industry Percentage of enterprise in machinery, electronics and telecommunications equipment industry Percentage of output for exports Percentage of enterprise are of wholly foreign-owned Percentage of enterprise are of joint ventures Percentage of enterprise are of joint ventures (Hong Kong, Macau and Taiwan) Total expenditures on technical training in 1998 (in 10 thousand RMB) Total expenditures on managerial training in 1998 (in 10 thousand RMB) Percentage of trained workers being promoted in 1999 -- technical training -- managerial training Turnover rate among trained workers in 1999 -- technical training -- managerial training Sample Size Note: Standard deviations are in parentheses.
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SOEs
Non-SOEs
41979.15 (450231.21) 29612.15 (270081.45) 592.10 (861.00) 420.57 (636.07) 93.10 (184.91) 74.49 (109.24) 29.96 0.42 0.42 13.92 5.91 2.95 4.22
13808.09 (33769.61) 4627.11 (9925.74) 334.86 (364.32) 233.14 (273.94) 54.36 (68.34) 44.56 (51.40) 14.03 6.47 8.27 16.55 9.71 1.44 3.24
10.55
17.27
3.80 35.02
6.12 25.90
13.38 (16.79)
24.99 (26.04) 15.47 52.52 32.01
8.86 (20.67) 9.28 (12.78)
13.10 (19.80) 14.71 (36.73)
3.54 (7.54) 5.01 (9.41)
5.62 (6.12) 8.00 (9.57)
3.12 (3.40) 3.31 (3.94) 237
3.82 (5.75) 3.64 (5.57) 278
Table 2: Empirical Results for Enterprise Productivity by Ownership
Constant Logarithm of total number of production workers Logarithm of total number of technicians Logarithm of total number of managerial workers Logarithm of net fixed assets Have local branches only Have overseas branches only Have both local and overseas branches Enterprise of food, beverage, tobacco industry Enterprise of textile, garments, leather industry Enterprise of furniture, timber processing industry Enterprise of paper making, printing, stationary industry Enterprise of petroleum processing, chemical, plastic, minerals, medicine Enterprise of metals industry Enterprise of machinery, electronics and telecommunications equipment industry Percentage of output for exports Logarithm of total technical training expenditures Logarithm of total managerial training expenditures
SOEs
Non-SOEs
1.8210* (0.3408) -0.0571 (0.1038) 0.4198* (0.1004) -0.1674 (0.1007) 0.6744* (0.0367) 0.0526 (0.1206) 0.7332 (0.7922) -0.1799 (0.7774) 0.4142* (0.1783) 0.0928 (0.2373) -0.3853 (0.3105) 0.1495 (0.2695) 0.1262 (0.1922) 0.0830 (0.2764) -0.2701* (0.1363) 0.0037 (0.0032) -0.0197 (0.0557) 0.3503* (0.0710)
0.7872
2.1650* (0.3196) -0.0169 (0.0571) 0.0845 (0.0762) 0.3000* (0.0788) 0.5899* (0.0414) 0.3200* (0.1139) 0.2797 (0.1721) 0.2629 (0.1503) -0.0948 (0.1317) -0.1492 (0.1507) -0.2960 (0.3343) -0.4819* (0.2355) -0.0423 (0.1275) -0.3122 (0.1785) -0.2074 (0.1175) 0.0004 (0.0016) 0.1501* (0.0583) 0.1359* (0.0609) 0.1208 (0.1177) -0.0195 (0.1263) 0.7661
237
278
Enterprise are of joint ventures Enterprise are of joint ventures (Hong Kong, Macau & Taiwan) R-Square Sample Size
Note: Standard errors are in parentheses. * indicate significant level at 5% or less
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Table 3: Regression Results for the Turnover Rate of Trained Workers by Types of Training and by Ownership
Constant Logarithm of sales Logarithm of total expenditures on technical training Logarithm of total expenditures on managerial training Having local branches only Having overseas branches only Having both local and overseas branches Enterprise of food, beverage, tobacco industry Enterprise of textile, garments, leather industry Enterprise of furniture, timber processing industry Enterprise of paper making, printing, stationary industry Enterprise of petroleum processing, chemicals, plastic, minerals, medicine industry Enterprise of metals industry Enterprise of machinery, electronics and telecommunications equipment industry Percentage of output for exports
SOEs Technical Managerial Training Training 1.6346 1.9110 (1.5244) (1.8854) -0.1256 0.1639 (0.1961) (0.2460) 1.4734* (0.2755) -0.4138 (0.4187) -1.3828* -0.8985 (0.6508) (0.8070) 1.6194 1.0338 (4.0712) (5.1365) 1.2748 7.2569 (4.0534) (5.1152) -1.6385 -1.7118 (0.9465) (1.1831) -1.2930 -1.9692 (1.3003) (1.6595) -0.1717 -1.0280 (1.7059) (2.1352) 1.4563 1.3394 (1.4318) (1.8019) 2.1454* 0.8514 (1.0213) (1.3096)
Non-SOEs Technical Managerial Training Training 2.9504 -0.3574 (4.0060) (4.0200) -0.2870 0.1840 (0.4969) (0.4998) 0.0822 (0.6080) -0.7525 (0.6432) -1.1536 1.6088 (1.4611) (1.4341) 1.5038 -1.0526 (2.1613) (2.3048) 0.6037 -0.0532 (1.9962) (2.0319) 0.3494 1.0078 (1.6622) (1.6874) -3.2792 -1.9698 (1.9953) (2.0011) -8.5303 -6.6192 (5.2333) (5.0998) -5.5603 -1.1301 (3.3791) (3.1773) 0.9988 0.5307 (1.5974) (1.6253)
-0.0216 (1.5470) -0.9820 (0.7355) 0.0199 (0.0173)
1.2392 (1.9199) -0.8558 (0.9238) 0.0365 (0.0218)
-523.7115
-559.9174
1.4940 (2.2493) -0.0449 (1.4887) 0.0183 (0.0201) 0.4721 (1.5518) 2.2969 (1.6435) -700.9489
0.4374 (2.2842) -0.2664 (1.5167) 0.0406* (0.0205) 0.2896 (1.5670) 1.9626 (1.6588) -681.0248
237
237
278
278
Enterprise are of joint ventures Enterprise are of joint ventures (Hong Kong, Macau and Taiwan) Log-likelihood Sample Size
Note: Standard errors are in parentheses. * indicates significant level at 5% or less.
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Table 4: Regression Results for the Promotion of Trained Workers by Types of Training and by Ownership SOEs Technical Managerial Training Training Constant -1.3240 -13.4532* (3.7950) (3.8082) Logarithm of sales 0.0647 2.8958* (0.4820) (0.4908) Logarithm of total expenditures on 2.1989* technical training (0.6812) Logarithm of total expenditures on -3.2279* managerial training (0.8241) Having local branches only -2.0031 -1.5467 (1.5933) (1.6076) Having overseas branches only 1.1404 -7.4518 (9.8806) (10.3289) Having both local and overseas branches -60.2001 -65.6428 (3060.8453) (3206.6890) Enterprise of food, beverage, tobacco -2.5138 -3.0634 industry (2.3066) (2.3421) Enterprise of textile, garments, leather -3.2238 0.3565 industry (3.2689) (3.2924) Enterprise of furniture, timber processing -2.1908 0.2213 industry (4.1987) (4.2084) Enterprise of paper making, printing, -2.4621 -0.1781 stationary industry (3.5487) (3.5474) Enterprise of petroleum processing, 1.6406 2.5087 chemicals, plastic, minerals, medicine (2.5062) (2.5860) industry Enterprise of metals industry -2.5668 -2.7387 (3.8737) (3.9012) Enterprise of machinery, electronics and -1.5421 -0.1322 telecommunications equipment industry (1.8116) (1.8539) Percentage of output for exports 0.0274 0.0165 (0.0426) (0.0437) Enterprise are of joint ventures Enterprise are of joint ventures (Hong Kong, Macau and Taiwan) Log-likelihood Sample Size
Non-SOEs Technical Managerial Training Training 1.1397 1.4737 (3.5737) (4.8588) 0.7671 2.0621* (0.4418) (0.6046) -0.6187 (0.5441) -3.7018* (0.7645) -2.5295 -3.0019 (1.3030) (1.7949) -1.7120 -1.0989 (1.9364) (2.6801) -0.3887 5.5728* (1.7254) (2.3839) -1.9695 -0.5701 (1.5004) (2.0695) 0.8664 3.2069 (1.7215) (2.3851) -0.4899 -4.0969 (3.8077) (5.3093) -0.2330 -3.8079 (2.6802) (3.9348) 0.3215 0.3245 (1.4357) (1.9946)
-640.6923
-716.8580
0.4802 (2.0066) 1.3935 (1.3234) -0.0153 (0.0179) -1.0133 (1.3317) -0.8946 (1.4272) -805.8226
237
237
278
Note: Standard errors are in parentheses. * indicates significant level at 5% or less.
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-1.3715 (2.8034) 0.8665 (1.8351) -0.0340 (0.0248) -3.2805 (1.8500) -4.5582* (1.9728) -918.2000 278
Table 5: Training Achievement by Objective
Factor 1: Competency Improvement
No Such Objective Objective Being Factor Cronabch’s Achieved to Loading Alpha Reasonable Extent or Above SOEs NonSOEs NonSOEs SOEs 0.9126
1. Remedy the lower education level of employees
36.7%
20.1%
28.3%
44.2%
0.825
2. Enhance the work performance of employees
19.4%
6.5%
38.0%
63.3%
0.841
3. Improve employees interpersonal abilities
33.3%
13.3%
29.5%
59.0%
0.814
4. Remedy the inadequate supply 42.6% of workers with appropriate skills
23.0%
27.4%
41.7%
0.787
5. Remedy employees past poor performance
19.4%
32.1%
56.8%
0.811
40.5%
Factor 2: Skills Improvement
0.8753
1. Upgrade employee skill for new technologies adopted
17.7%
10.8%
49.8%
62.6%
0.837
2. Provide substantial training when employees first start working in the company
8.9%*
5.0%*
60.3%
77.7%
0.835
3. Enhance different skill aspects of the employees so that they are able to take up different types of task
9.7%*
11.5%* 39.2%* 44.6%*
0.800
4. Prepare employees to take up 10.5% new job assignment
5.0%
52.3%
71.2%
0.754
Note: Overall Reliability Coefficient Alpha = 0.9138. The two factors accounted for 73.855% of the variance (Factor 1 = 59.406%; Factor 2 = 14.449%). Each item is evaluated by a 5-point Likert scale. Enterprises rate with 3 point or above is said to have the objective achieved to reasonable extent or above. * indicates an insignificant t-test on the sample mean difference by ownership at conventional level (5%). 26
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