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This paper mainly uses the gray relational analysis method to analyze the effect of the upgrading of industrial structure and the internal structure of the three industries on the employment of labor force in Guangdong Province. Based on the empirical analysis, this paper puts forward the promotion of the present situation in Guangdong Province suggestions for healthy development of employment.

Since the reform and opening up, Guangdong Province has made full use of policy advantages and regional advantages, with low labor cost “comparative ad- vantage” to participate in the international division of labor, through the develop ment of labor-intensive industries to achieve rapid economic development, and long-term economic dominance. After the financial crisis, Guangdong export-oriented economy is facing difficulties, labor-intensive industries unsustainable, and Guangdong industrial structure is facing a new round of transformation and upgrading [

This paper is divided into three parts. The first part mainly analyzes the evolution of industrial structure and employment structure in Guangdong Province. The second part uses the gray correlation analysis method to analyze the relationship between industrial structure and employment structure in Guangdong Province. The third part is based on the empirical analysis, combined with the development of Guangdong Province, and puts forward feasible policy recommendations.

Since the reform and opening up, the economic development of Guangdong Province has made remarkable achievements. The GDP of the region has increased from RMB 18.585 billion in 1978 to RMB 2,728,155 million in 2015. At the same time, the industrial structure of Guangdong Province has evolved. Between 1978 and 2015, the proportion of output value of primary industry showed a downward trend, the proportion of the output value of the secondary industry remained basically fluctuated between 40% - 50%, and the proportion of output value of tertiary industry showed a continuous growth trend. In 1985, the proportion of the output value of the tertiary industry in Guangdong Province exceeded that of the first industry for the first time. The three industrial structures changed from 29.8:46.6:23.6 in 1978 to 29.8:39.8:30.4 in 1985, and the industrial structure evolved into “two, three, one” from the original “two, one, three”. By the year 2000, the proportion of the three industrial structures has evolved to 9.2:46.5:44.3, and “two, three, one” structure is further strengthened. In year 2001, the proportion of the tertiary industry for the first time more than the secondary industry, and form a short-term “three, two, one” industrial structure. Since 2003, due to the proportion of the primary industry output value and the proportion of the tertiary industry decreased, and back to the “two, three, one” industrial structure; by 2013, three industrial structure adjustment of 4.9: 47.3:47.8, the tertiary industry with a slight advantage over the secondary industry, so that the evolution of the three industrial structure upgrades to “three, two, one” structure, industrial structure once again ushers in an important conversion upgrade. It can be seen from

Since the reform and opening up more than 30 years, with the rapid economic development of Guangdong Province, China’s largest labor into the province. In 1978, the proportion of employment in the first industry in Guangdong Province remained at a high level of 73.6%. The proportion of employment in the secondary and tertiary industries was 13.8% and 12.6% respectively. The gap between the primary industry and the secondary and tertiary industries was significant , Since the beginning of 1978, with the Guangdong industrial structure of the continuous adjustment and upgrading, the flow of workers also continue to optimize the upgrade;

Year | Number of employed persons/ Million people | The proportion of employment in the primary industry | The proportion of employment in the secondary industry | The proportion of employment in the tertiary industry |
---|---|---|---|---|

1978 | 2275.95 | 73.70 | 13.70 | 12.60 |

1980 | 2367.78 | 70.70 | 17.10 | 12.20 |

1985 | 2731.11 | 60.30 | 22.50 | 17.20 |

1990 | 3118.10 | 53.00 | 27.20 | 19.80 |

1995 | 3551.20 | 41.50 | 33.80 | 24.70 |

2000 | 3989.32 | 39.90 | 28.00 | 32.10 |

2005 | 5022.97 | 32.10 | 38.10 | 29.80 |

2006 | 5177.02 | 30.20 | 38.90 | 30.90 |

2007 | 5341.50 | 29.20 | 39.40 | 31.40 |

2008 | 5471.72 | 27.90 | 39.70 | 32.40 |

2009 | 5688.62 | 26.60 | 40.30 | 33.10 |

2010 | 5870.48 | 24.40 | 42.40 | 33.20 |

2011 | 5960.74 | 23.90 | 42.40 | 33.70 |

2012 | 5965.95 | 23.80 | 42.00 | 34.20 |

2013 | 6117.68 | 23.00 | 41.90 | 35.10 |

2014 | 6183.23 | 22.40 | 41.40 | 36.20 |

2015 | 6219.31 | 22.10 | 41.00 | 36.90 |

On the whole, Guangdong Province, the adjustment of modern industrial structure optimization, coupled with the capital knowledge and technology and other elements continue to increase, resulting in the development of the first industry in Guangdong Province not only did not increase employment, but led to labor savings. The economic growth of the secondary industry has a strong ability to absorb the labor force, which reflects the characteristics of employment structure in Guangdong Province. The economic growth of the tertiary industry is relatively stable and the absorptive capacity of the labor force is not prominent. In summary, the employment structure in Guangdong Province has maintained a 2311 employment structure [

Gray correlation analysis is a multi-factor statistical analysis method created by Professor Deng Julong in 1982, that is, through the quantitative analysis and comparative analysis of the geometric relations of the statistical data of the developmental change system, and then determine the superiority of the system bad factors, this method is not required on the number of samples, the calculation is small, do not need a typical distribution. The core is to calculate the correlation coefficient. In general, the calculation of the degree of correlation first to the original data processing, and then calculate the correlation coefficient, which calculated the degree of relevance, discharge the order [

The first step, establish the reference series and comparison series. Establish the dependent variable reference sequence (the parent sequence)

The second step, we can do the number of non-dimensional processing. We can use the initialization method to eliminate the effects of various series of different dimensions. Then we can get a new series. This is

The third step, calculate the correlation coefficient. The relevance is essentially a difference in the geometric properties of the curve, so the degree of correlation between the curves can be measured. The eliminated dimension series X_{0}(t), X_{i}(t), If the two sequences are at the same time k, the values are respectively {X_{0}(t), X_{i}(t)}, which is

Remember_{min}. The maximum is recorded as Δ_{max}.

The correlation coefficient is calculated as follows:

In the Equation (I): Δ_{i}(k) is the absolute difference between two comparison sequences at time k; ρ is the resolution coefficient and 0 ≤ ρ ≤ 1. In general, the value of ρ is between 0.1 and 0.5.

For a reference sequence X_{0}, there are several comparison sequences X_{1}, X_{2}, ・・・, X_{N}, the difference between the comparison curve and the reference curve can be expressed by the following relation [

In the Equation (II),_{i}(k) is the correlation coefficient of X_{i} to X_{0} at time k.

The fourth step, calculate the degree of correlation.

The fifth step, do the relevance ranking. Sort the size of R_{i} to distinguish the size of their association. The larger the value of R_{i}, the greater the degree of association; the smaller the value of R_{i}, the smaller the degree of association.

Select the time series data from 2005 to 2015 as the analysis object. The variables used in the article have industrial structure coefficient (X_{0}), Total number of employees (Y), First Industry Employment (X_{1}),Secondary industry employment (X_{2}), Employment of tertiary industry (X_{3}), in which the industrial structure coefficient = (second industry output value + tertiary industry output value)/province GDP. That is, we put the industrial structure coefficient (X_{0}) as a reference series, other indicators as a comparison sequence. The following

In this paper, the original data is transformed by the initial value method. The results of the initial transformation are shown in the following

According to the calculation formula of the correlation coefficient. In order to eliminate the effect that the value of Δ_{max} is too large and the value of the correlation coefficient r is distorted, we choose ρ = 0.5, the sequence of the correlation coefficients of each comparison sequence is shown in the

The correlation between the factors used in the mean method is shown in the following _{i} represents the degree of correlation of the curve X_{i} to the reference curve X_{0}.

X_{0} | Y | X_{1} | X_{2} | X_{3} |
---|---|---|---|---|

0.936682778 | 5022.97 | 1609.89 | 1916.16 | 1496.92 |

0.942373107 | 5177.02 | 1562.17 | 2015.88 | 1598.97 |

0.946641613 | 5341.50 | 1562.19 | 2102.28 | 1677.04 |

0.946379717 | 5471.72 | 1526.66 | 2172.93 | 1772.13 |

0.949097449 | 5688.62 | 1514.04 | 2292.05 | 1882.53 |

0.950322192 | 5870.48 | 1435.17 | 2487.25 | 1948.06 |

0.949945705 | 5960.74 | 1427.34 | 2526.48 | 2006.92 |

0.950177041 | 5965.95 | 1418.38 | 2509.69 | 2037.88 |

0.952346699 | 6117.68 | 1405.06 | 2563.50 | 2149.12 |

0.953298525 | 6183.23 | 1382.41 | 2560.65 | 2240.16 |

0.954052701 | 6219.31 | 1375.15 | 2546.57 | 2297.58 |

Year | X_{0} | Y | X_{1} | X_{2} | X_{3} |
---|---|---|---|---|---|

2005 | 1 | 1 | 1 | 1 | 1 |

2006 | 1.006075 | 1.030669 | 0.970358 | 1.052042 | 1.068173 |

2007 | 1.010632 | 1.063415 | 0.970371 | 1.097132 | 1.120327 |

2008 | 1.010352 | 1.089340 | 0.948301 | 1.134002 | 1.183851 |

2009 | 1.013254 | 1.132521 | 0.940462 | 1.196168 | 1.257602 |

2010 | 1.014561 | 1.168727 | 0.891471 | 1.298039 | 1.301379 |

2011 | 1.014159 | 1.186696 | 0.886607 | 1.318512 | 1.340700 |

2012 | 1.014406 | 1.187734 | 0.881042 | 1.309750 | 1.361382 |

2013 | 1.016723 | 1.217941 | 0.872768 | 1.337832 | 1.435695 |

2014 | 1.017739 | 1.230991 | 0.858698 | 1.336345 | 1.496513 |

2015 | 1.018544 | 1.238174 | 0.854189 | 1.328997 | 1.534872 |

The correlation coefficient of the total amount of employees | The correlation coefficient of the number of employed persons in the primary industry | The correlation coefficient of the number of employed persons in the secondary industry | The correlation coefficient of the number of employed persons in the third industry |
---|---|---|---|

1.000000 | 1.000000 | 1.000000 | 1.000000 |

0.912982 | 0.878489 | 0.848734 | 0.806059 |

0.830170 | 0.864946 | 0.748984 | 0.701740 |

0.765648 | 0.806059 | 0.676185 | 0.598007 |

0.683890 | 0.779994 | 0.585261 | 0.513734 |

0.626000 | 0.677072 | 0.476551 | 0.473665 |

0.599396 | 0.669173 | 0.458844 | 0.441498 |

0.598285 | 0.659259 | 0.466390 | 0.426541 |

0.561942 | 0.641880 | 0.445615 | 0.381184 |

0.547518 | 0.618796 | 0.447546 | 0.350251 |

0.540297 | 0.610888 | 0.453922 | 0.333290 |

relevance degree in First industry | R_{1} | R_{1} (X_{0}, X_{1}) | 0.746051 |
---|---|---|---|

relevance degree in Second industry | R_{2} | R_{2} (X_{0}, X_{2}) | 0.600730 |

relevance degree in Third industry | R_{3} | R_{3} (X_{0}, X_{3}) | 0.547816 |

From the above table can be drawn, the degree of correlation ranked R_{3} < R_{2}_{ }< R_{1}, indicating that the proportion of tertiary industry in Guangdong Province and the proportion of labor force employment is the most closely related to the primary industry, secondary industry second, the tertiary industry is the last one in Guangdong Province, and the current industrial structure is not entirely consistent, and the two are not coordinated, so there is still much room for improvement [

First of all, we should continue to accelerate the process of urbanization, and continue to strengthen the social division of labor [

Second, we have to strengthen the quality of education and training of workers, to make them can adapt to changes in industrial structure needs [

Third, continue to optimize the internal structure of the three industries [

Finally, we should to establish the correct employment concept of college students, to solve the problem of college students’ employment. College students should pay attention to the cultivation of their own skills in practice during school. According to the above analysis, we can see that the employment direction of college students should be biased towards the tertiary industry service industry. From the structural deviation point of view, the tertiary industry still exists a lot of employment space, the labor force also has a strong absorption. Choosing the tertiary industry can help solve some of the difficulties of employment of college students. At the same time, they should strengthen their own quality training and practical ability to improve their own employment competitiveness, for the transformation and upgrading of employment structure to contribute their own strength.

This paper mainly through the Guangdong employment structure and industrial structure of the discussion and data analysis to find the relationship between the two. And on the basis we have found that the employment structure lags behind the industrial structure, and we have also found that the relationship between the employment structure and industrial structure is uncoordinated. These problems have a very negative effect on the upgrade and transformation of the industrial structure and employment structure in Guangdong Province. There- fore, we must continue to accelerate the process of urbanization, strengthen the social division of labor, followed by strengthening the quality of education and training of workers, the third continue to optimize the internal structure of the three industries, and finally establish the correct employment concept of college students. From these aspects to coordinate the relationship between the two, the two promote each other, and jointly promote economic development.

Wu, B. (2017) Empirical Analysis of Gray Relational Analysis of Employment Structure and Industrial Structure in Guangdong Province. Journal of Human Resource and Sustainability Studies, 5, 47-56. https://doi.org/10.4236/jhrss.2017.51005