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A statistics analysis on contributions of talent-index-based human capital structure in the central and western regions of china to economic

Bian Jun-jie School of Economics and Management

JiangXi University of Science and Technology Ganzhou, P.R.China 76060742@qq.com

Zong Xue-ping School of Economics and Management

JiangXi University of Science and Technology Ganzhou, P.R.China

Zxp_snow007@163.com

Abstract—According to the theory of talent education index, The higher the index, the higher the educational level of talent, the larger numbers of the highly-educated talent. The analysis of empirical study through the talent education index suggests that those problems which coupled with economic growth, such as brain drain, talent inefficiency and lack of sustainability are common and must be handled seriously both in central and western regions of China. It’s necessary to transfer the mode of economic growth, improve the proportion of highly-educated talent, optimize the talent structure, and develop the new economic growth mode in the way of self-innovation-based connotation to increase modern economy and reduce the regional economic disparities. Meanwhile, there is a more efficient employment of highly-educated talent in central regions than in western regions, which indicates different talent development strategies taken in the central and western regions.

Keywords- talent index; human capital structure;,economic growth

I. INTRODUCTION Nowadays, In terms of human capital structure, most

foreign scholars focused mainly on the equal extent of education distribution, while domestic scholars have not formed a unified theoretical framework until now. However, some scholars do have sought to clarify this issue, it’s proposed by Yang Xianming and Tao Xiaolong (2009) that the human capital structure is a ratio, a effect, and an organization of various components of human capital structure. From a macro perspective, the human capital structure includes the stock structure and flow structure. Stock structure is embodied in many aspects which includes the spatial distribution of human capital, human capital level of structure, energy level of human capital, while the flow structure is specifically expressed in human capital investment and the structure of capital flows. This paper divided human capital variable into four: technical (high)school, technical college, undergraduate and postgraduate, in accordance with the educational backgrounds, using the concept of the application and the hierarchy of talent structure, by which the marginal contribution rate of talent capital structure to economic growth can be studied from .

II. THE CURRENT STATUS OF HUMAN RESOURCE STRUCTURE IN CENTRAL AND WESTERN

REGIONS OF CHINA For the sake of accuracy, we’d better define something

before the analysis. First of all, the central and western regions studied in this paper refers to the geographic concept of 6 provinces in central China, and 11 Western provinces (municipalities)1.The data collection in this paper is based on China Statistical Yearbook (1995-2009), China Labor Statistical Yearbook (1995-2008), as well as the talent classification, which illustrate the current status of human resources structure in central and western regions of China. More details are supplied in Table 1, Table 2, and Table 3.

TABLE 1 HUMAN RESOURCES STRUCTURE IN THE 6 CENTRAL PROVINCES (10 BILLION YUAN / 10 THOUSAND PERSONS)

year GDP Secondary school& technical school

technical college

Under graduate

Post-graduate

1994 7261.998 1521.4078 307.5866 122.1589 3.3886 1995 10037.05 1719.4613 392.4811 146.6445 4.8910 1996 12937.6 1821.8864 419.9022 160.8651 5.9086 1997 15666.51 2066.2875 500.5911 174.5606 8.0222 1998 17329.01 1995.8581 481.4806 168.3459 9.0325 1999 18285.94 2000.789 545.6006 182.1195 10.4847 2000 19712.1 2145.565 610.2445 208.8345 12.4186 2001 21345.75 2325.234 721.0233 238.4618 16.421 2002 23696.41 2130.4529 654.7833 221.126 17.8434 2003 26036.03 2436.2106 803.6118 229.4449 9.4703 2004 28665.67 2435.3906 854.0418 279.2728 13.1012 2005 31646.9 2132.318 740.5067 298.7597 16.8599 2006 35317.94 1987.7469 667.7439 283.9777 33.574 2007 39520.77 2200.6872 754.9979 287.4915 31.4196 2008 43077.64 2282.9482 865.0373 329.9061 40.8733

The figures in table 1 shows that, there is a great fluctuation of this trend since 2000, technical school people in

1 It should be noted that Chongqing City was separated into municipality management from Sichuan Province of the western provinces in 1996, and therefore from the perspective of the Statistical Yearbook of employees and the total of capital formation, the index data in Sichuan Province greatly reduced since 1996. In order to ensure the stability of the data, this study will take the data of Chongqing into Sichuan account.

978-1-4244-8694-6/11/$26.00 ©2011 IEEE

2003 reached its summit 24360 thousand, then declined slowly in recent years, while the rest 3 types of talent keep increasing gradually. Besides, there is a greater volatility in some years(eg.2002-2003) that a substantial decline in postgraduate academic qualifications, plummeted from 170 thousand to 90 thousand people, which indicates a serious loss of top talent. TABLE 2 HUMAN RESOURCES STRUCTURE IN THE 11 WESTERN

PROVINCES (10 BILLION YUAN / 10 THOUSAND PERSONS) year GDP secondary

school& technical school

technical college

Unde- rgraduate

Post- graduate

1994 6759.017 1411.8686 295.6571 120.7272 4.1847 1995 8924.799 1545.8582 372.8047 133.2134 4.6806 1996 11354.11 1712.7179 485.2031 146.6377 5.3930 1997 13274.28 1726.5554 490.1169 160.3361 7.0954 1998 14765.57 1682.9142 539.2353 156.2028 8.8773 1999 15572.07 1759.4198 548.8878 186.1500 10.9616 2000 16588.31 1879.0887 558.4261 207.6690 13.0608 2001 18121.72 1917.4376 538.7031 219.4621 13.1488 2002 20309.03 1929.3282 655.9983 208.6216 15.1408 2003 22682.48 1993.0752 735.5654 309.5191 17.8898 2004 22909.30 2041.0421 864.8389 323.1141 13.4250 2005 25200.23 1730.4989 758.4044 323.5948 16.8149 2006 28123.47 1715.9168 570.6522 224.9634 7.7928 2007 31470.16 2109.4025 871.7214 347.1317 11.9694 2008 34302.48 2406.1703 1004.7849 423.5586 16.3028

Compared with the growing trend of all kinds of talent, it can be found that there is a difference between central regions and western regions. That is, although the trend of all kinds of talent has been increased from 1994 to 2003, the growth rate is slow, then in the year of 2004-2006, talent in the western regions declined sharply: technical school people in 2003 reached its highest point 20410 thousand, and down to 17150 thousand in 2006, undergraduate from the 3230 thousand down to 2240 thousand, postgraduates declined significantly, from 170 thousand dropped to 77 thousand, which indicates a serious loss of senior talent. Fortunately the decline has been eased since 2007.

TABLE 3 GDP AND HUMAN RESOURCE STRUCTURE OF 17 PROVINCES IN THE CENTRAL AND WESTERN REGIONS

(10 BILLION YUAN /10 THOUSAND PERSONS) year GDP secondary

school& technical school

technical college

Under graduate

Post- graduate

1994 14021.015 2933.276 603.2438 242.8862 7.5733 1995 18961.849 3265.32 765.2859 279.8579 9.5716 1996 24291.71 3534.604 905.1053 307.5029 11.3016 1997 28940.79 3792.843 990.708 334.8968 15.1177 1998 32094.58 3678.772 1020.716 324.5488 17.9098 1999 33858.01 3760.209 1094.488 368.2696 21.4463 2000 36300.41 4024.654 1168.671 416.5035 25.4794 2001 39467.47 4242.672 1259.726 457.9239 29.5698 2002 44005.44 4059.781 1310.782 429.7476 32.9842 2003 48718.51 4429.286 1539.177 538.964 27.3601 2004 51574.97 4476.433 1718.881 602.3869 26.52625 2005 56847.13 3862.817 1498.911 622.3545 33.67488 2006 63441.41 3703.664 1238.396 508.9411 41.36681 2007 70990.93 4310.09 1626.719 634.6233 43.38902 2008 77380.12 4689.119 1869.822 753.4647 57.17616

It’s shown in table 3 that as far as the numbers of total human resources are concerned, all kinds of talent have an increasing trend, but the growth rate keeps slow. What’s more, those who have got college degree or higher degree take only

a little proportion of total employment, which indicates the insufficiency of high-educated talent in the central and western regions of China.

III. THE PLANNING OF TALENT EDUCATION INDEX

Talent education index is a comprehensive dynamic relative number describing the level of talent’s educational background. The higher the talent education index, the higher the educational level of talent, the larger numbers of the high-educated talent, and vice versa. Therefore, talent education index can be obtained by means of division, that is, the number which the proportion of all educational levels multiply the number of weight given to all academic levels in the report period ,is divided by the one assigned to all academic levels in the base period.

For the convenience to study that issue, the educational structure of talent in this paper is based on the following 4 levels: technical school, technical college, undergraduate, and postgraduate. In general, weight number of all educational levels can be determined in various ways. With study time and educational fund increasing, talent begin to acquire much more knowledge and ability. So the higher the educational degree is, the greater the weight number will be. Now suppose the weight number to different educational level of talent are as follows: 1 for technical school students, 2 for technical college students, 3 for undergraduate, 4 for postgraduate and higher education. Thus, talent education index could be calculated as:

Talent Education Index RCXLreporting period =

dbasisperio

dbasisperiodbasisperiodbasisperiodbasisperio

eriodreportingp

eriodreportingperiodreportingperiodreportingperiodreportingp

X4X3X2X1X

X4X3X2X1X

4321

4321

Σ×+×+×+×

Σ×+×+×+×

Where: RCXL stands for talent education index; X1 stands for the number of technical school education, X2 the number of technical college education, X3 the number of undergraduate , X4 the number of postgraduate.According to the data of Table 1 table2 and Table 3, the talent education index of central and western regions can be obtained in accordance with the above formula , as it showed in table 4:

TABLE 4 TALENT EDUCATION INDEX OF THE CENTRAL AND WESTERN REGIONS

Year RCXL(central regions)

RCXL(western regions)

RCXL(Midwest regions0

1994 100 100 100 1995 101.7 101.3 101.2 1996 102.2 102.9 102.7 1997 102.2 103.7 103.6 1998 102.4 105.2 104.9 1999 104.4 106.2 106.2 2000 105.4 106.2 106.4 2001 107.0 106.0 106.3 2002 107.2 107.6 107.5 2003 106.5 112.4 113.1 2004 109.1 113.7 114.3 2005 111.5 116.5 117.4 2006 112.5 108.8 109.3 2007 111.4 113.8 114.5 2008 114.0 114.9 115.7

IV. THE ANALYSIS ON MARGINAL CONTRIBUTION RATE OF ECONOMIC GROWTH 1. The basic assumption and specific establishment of the measurement model

(1) Taking no other factors into account, the economic growth is the function of education index in the given regions, and a linear relationship between the two.

(2) Whether the region's economy is able to keep growing continuously in self-reliance way is based on a reasonable talent structure with different educational levels in given regions. According to the Lucas’ endogenous growth theory, talent with high degrees will have strong external effects and will be more endogenous. Owing to the increase of highly-educated talent, the technology of material capital will be improved continuously and the optimal and inter-perfect combination of elements come into being, which thereby affect the persistence of regional economic growth and play an important role in getting rid of poverty.

(3) Regional economic growth is a dynamic, so economic growth in a specific period depends on the number of human capital in this period, as well as the accumulation effect of human capital in the region. In other words, if we assumed the first t years of a region's economic growth rate as A, the power source of its growth comes from not only human capital of the first t years, but also from the first t-1 years, t-2 ... ... t-n years. Some reasons account for it: Firstly, human capital needs the accumulation because it takes time to play the function of economic growth. Secondly, human capital has strong externalities, once the talent resources formed, the role of the region's economic growth will be sustained during the next few years in the given region. Last but not least, from the perspective of experience, talent resources, talent management, talent incentive system are rather poor in central and western regions, which affected the work of different levels of talent and made a lower efficient employment of talent. Thus, it reaches the point that the economic growth of central and western regions in a certain year is affected by both RCXL and its lagged value during the same period. Based on the assumptions above, this part of paper tries to transform some data with the Almon polynomial and apply the dynamic variable -distributed lag model to examine the relationship between GDP and RCXL. Using this model has the following advantages: On one hand, this method can overcome the persistent problem of insufficient degree of freedom, on the other hand it can solve those linearly-related and highly self-related issues. The process of setting model and analysis is as follows: Assume the order of distributed lag model first, then set a specific model according to it, and the order is set to 2 or 3 bands. 2Now take an example of lag period III to illustrate the process of Almon polynomial transformation.

2 Note: In fact, there is a certain degree of difficulty to determine the lag order, a common determination is on the basis of test algorithm. After a simple linear regression analysis and test algorithm, we find that central China's human capital on economic growth lag is 2, the Midwest and the western lag is 3.

Distributed lag model is set between GDP and RCXL in given regions (central, western, central and western):

μββββα +++++= −−− 3322110ˆˆˆˆ

ttttt RCXLRCXLRCXLRCXLGDP Change Almon polynomial transformation and obtain the

following equation: μαααα ++++= ttt ZZZGDP 221100

Where: iα i=0-2 is the parameters to be estimated, and:

3212

3211

3210

9432

−−−

−−−

−−−

++=++=

+++=

tttt

tttt

ttttt

RCXLRCXLRCXLZRCXLRCXLRCXLZ

RCXLRCXLRCXLRCXLZ

Change the originaltRCXL into

tZ by means of above formula,and make use of the relationship between

tGDP and tZ ,as well as OLS regression method to obtain

estimated parameters )20( −=iiα . Thus get the following formula )30( −=iiβ estimates:

00ˆ αβ = 2101

ˆ αααβ ++= 2102 42ˆ αααβ ++=

2103 93ˆ αααβ ++= 2. Model estimation and testing

(1) Estimation for the relationship between GDP and RCXL in central regions Establish a 2-order distributed lag model:

μβββα ++++= −− 22110ˆˆˆ

tttt RCXLRCXLRCXLGDP After transforming RCXL data of central regions with

Almon polynomial, we apply Eviews software to OLS regression, and the results are as follows (see table 5):

TABLE 5 THE RESULTS OF REGRESSION ABOUT CORRELATION BETWEEN GDP AND RCXL IN CENTRAL REGIONS

Variable Coefficient Std. Error t-Statistic Prob. C -375257.2 39564.55 -9.484683 0.0000

Z0t 517.5428 926.7996 0.558419 0.5888 Z1t 754.4298 932.7177 0.808851 0.4374

Adjusted R-squared 0.895489 F-statistic 52.41014 Durbin-Watson stat 0.664689 Prob(F-statistic) 0.000005

The results showed that about 25% or so confidence level, Z0t barely significant, Z1t significant, the adjusted R2 was 0.895, statistic of F significant. Perfect fitting by testing, which suggest that RCXL has some impacts on the central regions’ GDP growth. Thus, the model can be written as:

ttt ZZGDP 10 4298.7545428.5172.375257 ++−= The estimated value of

iβ can be obtained from that of

iα Result in

197.127154.5172.375257 −++−= ttt RCXLRCXLGDP (2) The estimation on relationship between GDP and RCXL in western regions

Establish the distribution lag 3 model:

μβα ++= −==

)ˆ(3

0,0it

ijjt RCXLGDP

Transform data of the western region RCXL with Almon polynomial, and apply Eviews software to OLS regression, the results are as follows in table 6:

TABLE 6 THE RESULTS OF REGRESSION ABOUT CORRELATION BETWEEN GDP AND RCXL IN WESTERN REGIONS

Variable Coefficient Std. Error t-Statistic Prob. C -290042.2 33812.98 -8.577834 0.0000

Z0t 721.0972 431.0969 1.672704 0.1329 Z1t -1984.396 896.5074 -2.213475 0.0578 Z2t 855.7548 297.7972 2.873616 0.0207

Adjusted R-squared 0.918169 Prob(F-statistic) 0.000030 Durbin-Watson stat 0.774614 F-statistic 42.14105

The results showed that about 5% or so confidence level, Z0t, Z1t, Z2t significant, the adjusted R2 was 0.918, statistic of F significant, perfect fitting by testing. Therefore, the model can be written as:

tttt ZZZGDP 210 8.8554.19841.7212.290042 +−+−= The estimated value of iβ can be obtained from that of

iα Result in

32

1

7.246932.17554.40710.7212.290042

−−

−

++−+−=

tt

tt

RCXLRCXLRCXLRCXLGDP

(3) The estimation on relationship between GDP and RCXL in central and western regions

Establish the distribution lag 3 model:

μβα ++= −==

)ˆ(3

0,0it

ijjt RCXLGDP

Transform data of RCXL in central and western regions with Almon polynomial, and apply Eviews software to OLS regression, the results are as follows in table 7:

TABLE 5 THE RESULTS OF REGRESSION ABOUT CORRELATION BETWEEN GDP AND RCXL IN CENTRAL AND WESTERN REGIONS

Variable Coefficient Std. Error t-Statistic Prob. C -673349.5 96468.64 -6.979983 0.0001

Z0t 911.6811 948.6536 0.961026 0.3647 Z1t -2362.618 2003.778 -1.179082 0.2722 Z2t 1240.555 719.2046 1.724898 0.1228

Adjusted R-squared 0.887841 Prob(F-statistic) 0.000105 Durbin-Watson stat 0.750785 F-statistic 30.02494

The results showed that about 15% or so confidence level , Z0t, Z1t, Z2t significant, the adjusted R2 was 0.888, statistic of F significant, perfect fitting by testing. Therefore, the model can be written as:

tttt ZZZDP 210 6.12406.23627.9115.673349G +−+−= The estimated value of iβ can be obtained from that of

iα ,Result in

32

1

82.498876.114838.21068.9115.673349

−−

−

++−+−=

tt

tt

RCXLRCXLRCXLRCXLGDP

V. CONCLUSIONS AND ISSUES THAT NEED FURTHER STUDY

1.The common features of the marginal contribution which talent education index contributes to GDP in central regions

It can be concluded from the discussion above that the marginal contribution which talent education index contributes to GDP in central regions is 1789.51, and 2958.58 in western regions, while the overall RCXL marginal contribution to GDP in central and western regions is 7049.17. The more high-level educated people, the greater the impact on GDP, which suggest that high-level talent plays a more significant role in economic growth. Given the sheer human resources, in order to develop modern economy and reduce regional economic disparities, we must rely largely on transforming the economic growth mode, increasing the proportion of highly-educated people, optimizing the talent structure, and developing the new economic growth mode in the way of self-innovation-based connotation. Meanwhile, the results of model above indicate the variation of coefficient in different lag periods makes the marginal contribution to GDP up and down. Moreover, the marginal contribution of RCXL during lag t-1period are negative both in table 6 and table 7, and with the lag time prolonged, the contribution to GDP become smaller, indicating some problems occurred in central and western regions such as brain drain, talent inefficiency and lack of sustainability, which emphasize on the importance of talent development strategy in process of narrowing China's regional economic disparities on the other side.

2. The difference of the marginal contribution which talent structure contributes to GDP in central and western regions

There is a certain difference between the impacts that the talent structure in central and western regions give to GDP. According to previous assumptions, the talent index of central region to GDP lag is 2, the one in western region is 3, if the first t period was merged with t-1 period as a short term, we may find that the marginal contribution which RCXL contributes to GDP in central regions is 1789.51, compared with 313.56 in western regions, which shows a more efficient employment of highly- educated people in central region than in western regions. Based on human capital theory and job search theory: either from management or the incentive system, talent environment in central regions is better than western regions, so does the problem of brain drain, which indicate that talent environment in western regions is relatively worse than in central regions. To some extent, central regions has more advantages on talent than western regions. Fortunately, the analysis results tell us this gap will become increasingly smaller with the time goes by.

REFERENCES [1] Yang Xian-ming, Tao Xiao-long. Human Capital

Structure and Economic Growth in New Development, Management and Social Sciences Review. 2009 492-98.

[2] Ye Ren-sun, Hu Xue-mei, Jiang Xiao-guang. The rising problem of talent in Jiangxi, China Talent Press ,2005