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Abstract
This study compares the growth in salaries across three spatial regions in China during the period 1993–1998, when economic reforms were implemented nationwide. 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, and adult education paid for by the employees themselves. We used a three‐level hierarchical linear 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, and two in the western part). We found that: (1) regional disparities have a paramount impact on differences in salary; (2) individual characteristics defined by firm as well as firm characteristics are significantly related to salary decisions; (3) returns to formal schooling increase significantly in more market‐based regions; and (4) employees also benefit by receiving on‐the‐job‐training and by participating in adult education programs outside their firm.
Acknowledgements
Both authors contributed equally to this work and their names are stated in alphabetical order. This paper is part of a research project entitled ‘Education and Work: The Efficacy of Schooling in Human Resource Development in Three Regions in China’, which is partially funded by the Research Grants Council of Hong Kong (CUHK 4379/00H). Jin Xiao is the principal investigator for this research project.
Notes
1. The classification of provinces is in accordance with Hu et al. (Citation1995, pp. 18–50).
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 the West.
3. Another indicator also shows clear regional inequality in China. In 1998, per‐capita foreign direct investment in Guangdong was RMB 168, and was RMB 92 in Jiangsu, RMB 22 in Hebei, RMB 16 in Hubei, RMB 4 in Yunnan, and RMB 8 in Shaanxi (National Bureau of Statistics of China, Citation1999).
4. Please see Xiao (Citation2004) 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 by Hu et al. (Citation1995), using major economic indicators (see discussion about labor market in the second section). They are: Guangdong and Jiangsu in the East, Hebei and Hubei in the Central, and Yunnan and Shaanxi in the West. Second, two counties with a medium level of economic development in each of the six provinces were selected. They were Jiangmen and Heshan in Guangdong province, Wuxi and Jiangyin in Jiangsu province, Handan and Cixian in Hebei province, Yichang and Zhijiang in Hubei province, Mile and Luxi in Yunnan province, and Baoji and Fengxiang in Shaanxi 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 intact workgroups or production lines on the floor were selected to obtain a sample, which consisted of all 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 a 92.0% response rate. To analyze the three‐level model, 16 485 individual cases and 365 firms were used, after deleting cases that contained missing values in any of the variables. Some firms did not want 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 underestimate 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 egalitarian planned economy and at the beginning of the economic reforms in 1993. As part of the economic reforms, increases in salary came to be determined according to proficiency of performance and ability. This was a big event for everyone in China, as such an approach was novel. Therefore, we assumed that our sampled employees would best be able to recall their salary in 1993, the baseline point, and in 1995 when they had their first raise. We also compared our data with the national census data of 1995 as used by Li (Citation2003) and Li and Ding (Citation2003). Our data are very much along the same pattern.
7. The MEE (Mincer, Citation1974) is: .
8. In the West, the variance within the firm was smaller (46.9%) than the variance (53.1%) among firms for salary growth rate.
9. A study on school culture in the United States showed that between‐school variance for leadership accounted for 23% of the total variance (Rowan et al., Citation1991, p. 254).
10. The coefficients β02j for initial‐point salary in 1993 accompanied with one more year of FS are 0.0165, 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 avoid multicollinearity.
12. Xiao and Tsang (Citation2004) 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 learning effectively improves individual productivity.
13. In our trial analysis, we put types of OJT as predictors in models, but the coefficients had the similar results.
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 West was less than 56% (see Table ).
15. β00j = γ000 + γ001(Region) j + γ002(Ownership) j + γ003(Section) j + γ004(Size) j + γ005(Av_edu) j + u 00j ; β10j = γ100 + γ101(Region) j + γ102(Ownership) j + γ103(Section) j + γ104(Size) j + γ105(Trextent) j + u 10j .
16. Conventional characteristics refer to those characteristics that are usually used to describe a firm’s features, such as geographic location, ownership, size, and sector (Naderi and Mace, 2003).
17. Of the three firm characteristics—namely, ownership, sector, and size—ownership is the most powerful explanatory variable (see columns 3‐1b, 3‐1c and 3‐1d). 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 population continue, because moving across regions to the east will automatically result in at least a 50% increase in salary, according to Table . Education might have another screening function as a ticket 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 the authors.