Research on Supply and Demand Matching Model and Strategy in the Job Market of College Graduates

In recent years, with the increasingly severe employment situation of graduates, the issue of matching the employment demand of graduates with the supply and demand of the labor market has attracted much attention. Matching supply and demand refers to the degree of match between the demand for talents on the demand side and the abilities and specialties of the talents provided by the supply side [1]. In the employment market of graduates, from the perspective of matching supply and demand, the main problems are asymmetric employment information, lack of career guidance system for graduates, lack of professional career counselors, etc. The importance of matching supply and demand cannot be ignored. The importance of matching supply and demand cannot be ignored. If the supply and demand match is not reasonable, it will lead to both idle talents and insufficient talents of employers [2]. First of all, matching supply and demand for graduate employment means accurate positioning of the demand for talent, according to the needs of employers, targeted training of talents, so that talents have professional skills and lifelong skills, which helps to improve the employment rate of graduates [3-5]. Secondly, matching supply and demand helps to adjust the employment structure and avoid the problem of labor surplus [6-7]. The rationality of matching the supply and demand of talents can promote the coordinated development of economy [8].

Now the unemployment problem is serious, both laid-off workers re-employment problem, and a large number of labor force transfer problem, at the same time, the epidemic trauma and reduce employment opportunities, the total supply of labor force is far greater than the demand [9-12]. At the same time, the slowdown in economic growth has made the structural contradiction between supply and demand in the job market more prominent, and the mismatch between supply and demand of graduates’ employment has seriously restricted the improvement of employment quality [13-15]. The diversification and individualization of employment demand put forward new challenges to the supply structure of the labor market, and the imbalance of supply and demand matching leads to the phenomenon of difficult employment of graduates [16-18]. Unemployment of graduates will bring considerable negative impacts on social development, such as exacerbating the gap between the rich and the poor, affecting social security and stability, causing a waste of human capital, causing greater losses to the national economy, and negative effects on the development of education, etc., which is not conducive to the construction of a harmonious society [19-22]. Therefore, in-depth analysis of matching supply and demand in the graduate employment market is of great significance in solving the problem of difficult employment of graduates.

This paper selects the employment market of a region from 2010 to 2023 as the research object and quantitatively analyzes the supply and demand matching of employment talents in the city. Taking colleges and universities as the talent supply side and enterprises as the talent demand side, the number of college graduates and the number of employed talents needed by enterprises in the next five years are predicted through the GM(1,1) model, and the results of their supply and demand matching are analyzed. Subsequently, the evaluation indexes of employability are selected from the aspects of vocational ability and professionalism, and after collecting data by questionnaire, the difference between the perceived employability of college graduates and employers’ expectations is quantitatively analyzed by using the gray-target transformation method. Finally, the supply and demand matching analysis of graduates’ employment talents and employability is comprehensively analyzed, and the enhancement strategy of matching supply and demand in the employment market is explored from the dimensions of university talent cultivation and industrial structure adjustment.

2

Analysis of supply and demand for graduates’ employment

Difficulty in employment of college students has always been a problem of social development, and the idea of matching supply and demand can point out the direction for the development of talent training in colleges and universities. This chapter is aimed at matching supply and demand of graduates in the employment market, using the GM(1,1) model to predict and analyze the supply and demand of graduates.

2.1

The GM(1,1) model

The ultimate goal of this paper is to forecast the supply and market demand of graduates in a certain region, and the commonly used forecasting methods in general are gray forecasting, exponential smoothing, autoregression, moving average, etc., among which the gray forecasting model has a greater advantage in dealing with small-scale, non-stationary, non-conventional time-series data, and the model does not need to be trained with a large amount of historical data, and the computational efficiency is relatively high. Because the data set used in this paper is small in size, the gray GM(1,1) prediction model can also show better prediction results with less data, and it is easy to understand and implement, with wide adaptability, so this paper chooses the GM(1,1) model to predict the supply of graduates and market demand.

2.1.1

Forecasting steps

GM(1,1) model is a basic model in gray system theory, which represents a first-order univariate gray model. The core idea of the model is to “gray out” the information in the uncertain system, so that the original uncertain information gradually tends to be certain, and the number of those who don’t know its exact value is also called the gray number. The specific steps for prediction in the model are as follows:

Step 1, so that the original data sequence is $x^{(0)} = {x^{(0)} (1), x^{(0)} (2), \dots x^{(0)} (n)}$ , and a one-time accumulation of x⁽⁰⁾ generates a one-time accumulation data sequence $x^{(1)} = {x^{(1)} (1), x^{(1)} (2), \dots x^{(1)} (n)}$ . The accumulation process reduces the randomness of the original data. (1) $x^{(1)} (m) = \sum_{i = 1}^{m} x^{(0)} (i)$

Eq. i = 1, 2, …n.

In step 2, assume that x⁽¹⁾ can satisfy the first order differential equation: (2) $\frac{d x^{(1)}}{d t} + a x^{(1)} = b$

where a is the development coefficient and b is the gray role quantity.

Step 3, let M be the parameter vector to be estimated, i.e., M = (a, b)^T. According to the least squares method, the parameters in the gray differential equation are estimated: (3) $M = {(N^{T} N)}^{- 1} N^{T} Y$

Among them: (4) $N = [\begin{matrix} - Z^{(1)} (2) & 1 \\ - Z^{(1)} (3) & 1 \\ \dots & \dots \\ - Z^{(1)} (n) & 1 \end{matrix}], Y = [\begin{matrix} x^{(0)} (2) \\ x^{(0)} (3) \\ \dots \\ x^{(0)} (n) \end{matrix}]$

As above, there are:

The solution of the first order differential equation is also called the time response function: (5) ${\hat{x}}^{(1)} (m + 1) = (x^{(0)} (1) - \frac{a}{b}) e^{- a m} + \frac{b}{a}$

Eq. m = 1, 2, …, n.

The gray differential equation corresponding to the time response sequence is: (6) ${\hat{x}}^{(1)} (m + 1) = [x^{(1)} (0) - \frac{b}{a}] e^{- a m} + \frac{b}{a}$

where m = 1, 2, …, n.

Step 4, take x⁽¹⁾(0) = x⁽⁰⁾(1), then: (7) ${\hat{x}}^{(1)} (m + 1) = [x^{(0)} (1) - \frac{b}{a}] e^{- a m} + \frac{b}{a}$

Eventually, the reduced form of the original data series, i.e., the prediction equation, is obtained: (8) ${\hat{x}}^{(0)} (m + 1) = {\hat{x}}^{(1)} (m + 1) - {\hat{x}}^{(1)} (m)$

In the formula, m = 1, 2, …, n.

2.1.2

Model testing

$G M (1, 1)$ After the model is built, a model accuracy test is needed to determine whether the model can be used for prediction. The specific test steps are as follows: 1)

Calculate the model accuracy: (9) $E (a v g) = \frac{1}{n - 1} \sum_{i = 2}^{n} | ε (m) |$ (10) $P = (1 - E (a v g)) \times 100 %$

where ε(m) is the relative residual, E(avg) is the average relative error, and P is the precision value. 2)

Calculate the a posteriori difference ratio

First, the standard deviation of the original data series is calculated: (11) $S_{1} = \sqrt{\frac{\sum_{i = 1}^{n} {[x^{(0)} (m) - {\bar{x}}^{(0)}]}^{2}}{n - 1}}$

where ${\bar{x}}^{(0)}$ is the mean of the original data series.

Next, the standard deviation of the absolute error series is calculated: (12) $S_{2} = \sqrt{\frac{\sum_{i = 1}^{n} {[E^{(0)} (m) - {\bar{E}}^{(0}]}^{2}}{n - 1}}$

where ${\bar{E}}^{(0)}$ is the mean of the absolute error data series.

Finally, the a posteriori difference ratio expression is: (13) $C = \frac{s_{2}}{s_{1}}$

2.2

Supply Forecast Analysis of Graduates

In order to facilitate analysis and easy calculation, this paper does not consider non-quantifiable factors such as government policy factors, but only quantifiable factors and the most important ones. This paper adopts the number of graduates of general colleges and universities in the statistical yearbook data of a city from 2010 to 2023 as the basic data for analysis, and uses the gray GM(1,1) model to predict the number of supply of graduates in a city in the next few years.

The gray GM(1,1) model is used to predict the gray demand for graduates in the sample area, which is divided into master’s degree graduates, bachelor’s degree graduates, and specialist graduates. Based on the 2010-2023 statistical yearbook of a city, the predicted supply of graduate talent in the sample area, as well as predicting the supply of graduate talent in the sample area at each level of education.

The results of the graduate talent supply forecast are shown in Figure 1. With the increase of years, the talent supply of graduates from colleges and universities in a city rises continuously, from 280,300 in 2024 to 437,100 in 2028, of which undergraduate graduates account for the largest proportion of the talent supply, with a proportion between 48.98% and 51.58%.

2.3

Forecast analysis of demand for graduates

In this paper, on the basis of considering the current situation of talents and industrial policies, the forecasting method of the demand for graduates’ talents in a certain city focuses on quantitative analysis, and on the basis of the existing data, the time series method, mainly GM (1,1) model, is used to analyze and process the relevant historical data available in the sample area, and to obtain the forecasting results.

In order to facilitate the demand forecast of graduate talent, this paper makes several basic assumptions: first, it is assumed that there will be no major changes in the current policies on various types of college graduates’ talent; second, among the many factors affecting the demand for college graduates’ talent, the non-quantifiable factors are not considered in this paper, and the quantifiable factors are only selected as the most important factors in this paper.

The forecast results of talent demand of college graduates are shown in Figure 2.The total talent demand of college graduates in the sample area from 2024 to 2028 is 295,400, 329,100, 379,900, 403,700 and 450,600 respectively, showing a rising trend year by year.

2.4

Matching Analysis of Supply and Demand of Graduates

Through the previous prediction of the supply and demand of college graduate talent in a city, it is possible to derive the prediction of the gap of its college graduate talent in the next few years. For the prediction of the supply and demand of college graduate talents by academic level, the gap between the supply and demand of graduate talents predicted by academic level can be derived, and the results of the supply and demand matching prediction of college graduates are shown in Figure 3. It can be seen that the results of matching the supply and demand of college graduates from 2024 to 2028 are all negative, and the supply and demand gap of graduates ranges from -148,300 to -43,300, and the supply and demand gap of talents with bachelor’s degree is larger, accounting for about more than 50% of both, followed by the supply and demand gap of master’s degree graduates. In the next five years, the city’s supply and demand gap of college graduates’ talents will become bigger and bigger. The city faces a huge gap in the supply and demand of college graduates in the future, and it is not possible to achieve a balance between the supply and demand of college graduates by relying only on the strength of local colleges and universities, and it is necessary to take measures to increase the supply of talents and to introduce talents from the outside.

3

Supply and demand analysis of employability of graduates

After analyzing the supply and demand of college graduates’ talents in the job market, this chapter starts from the perspective of graduates’ employability, constructs the index system of college graduates’ employability, and explores the matching of supply and demand between college graduates’ employability and enterprises’ expectations.

3.1

Selection of indicators

This paper analyzes employers’ demand for college graduates’ employability by mining online recruitment information. Through the steps of data cleaning and keyword extraction, 21 competency indicators with a frequency of more than 10% were finally obtained.

According to the nature, the employment ability is divided into two categories: vocational ability and professionalism. Occupational ability includes professional and technical ability X1, ability to identify and analyze problems X2, ability to solve practical problems X3, learning ability X4, communication, collaboration and social ability X5, innovation ability X6, writing and language expression ability X7, information technology ability X8, and managerial ability X9. Occupational literacy includes executive ability X10, initiative work X11, independent work X12, adaptability X13, psychological quality X14 and achievement motivation X15.

After calculating using Hosty’s formula for percentage of consistency, R = 2M/(N1 + N2), the reliability of the data obtained was 88.54%, which meets the reliability requirements.

3.2

Methods of analysis

This study designs a questionnaire on college students’ employability based on the employability index system to investigate students’ perceived employability. Employers’ expectations of employability are analyzed through online recruitment information, and quantitative difference analysis is carried out between college students’ perceived employability and employers’ expectations of employability by using the gray target transformation method.

The basic idea of gray target theory is: according to the polarity of each index, the best model relative to the evaluation model is given as the bull’s-eye, and the gray correlation between each model in the gray correlation difference information space and the bull’s-eye is called the bull’s-eye proximity (referred to as the bull’s-eye degree), and the gray-target transforming method makes the calculated results clearer and the difference between the merits and demerits of evaluated objects more obvious.

3.2.1

Constructing a matrix of evaluation indicators

When carrying out gray correlation analysis of employability of college graduates, first select evaluation indicators, organize students’ scoring, construct evaluation indicator matrix, assuming that there is m college graduate to be evaluated, and there are n evaluation factors to form the evaluation indicators of employability, then m × n-order evaluation indicator matrix can be constructed $X = {(x_{i j})}_{m \times n}$ .

3.2.2

Establishment of standardized models

The standard model, which is the optimal effect vector of the matrix of evaluation indicators, is denoted by X₀, i.e., the standard model is: (14) $X_{0} = (X_{01}, X_{02}, X_{03}, \dots, X_{0 n})$

where $X_{o j} = \max {x_{i j} | 1 \leq i \leq m}$ , j = 1, 2, ⋯, n.

Adding vector X₀ to the above matrix of evaluation indicators, the constructed (m + 1) × nth order matrix of evaluation indicators is: (15) $\bar{X} = {(x_{i j})}_{(m + 1) \times n}$

3.2.3

Normalization

Due to the large differences between the indicators of graduates’ employability, they must be normalized to make each indicator comparable, and the model adopts the gray target transformation method: (16) $t_{i j} = (x_{i j} - \min_{j} x_{i j}) / (\max_{j} x_{i j} - \min_{j} x_{i j})$

where 1 ≤ i ≤ m + 1.

After normalization, the original evaluation index matrix $\bar{X} = {(x_{i j})}_{(m + 1) \times n}$ is transformed into $T = {(t_{i j})}_{(m + 1) \times n}$ by Equation (14).

The standard model X₀ is transformed into the standard bullseye X₀ = (1, 1, 1, ⋯, 1).

3.2.4

Determination of bipolar differences

Compare each indicator vector with the optimal effect vector, calculate the difference information space, obtain the bipolar difference, and for the gray-target transformed matrix $T = {(t_{i j})}_{(m + 1) \times n}$ , use Eq: (17) $Δ_{i j} = | 1 - t_{i j} |$

A new (m + 1) × n st order matrix, the difference information matrix, is obtained: (18) $R_{i \times j} = {(Δ_{i j})}_{(m + 1) \times n}$

Gray correlation analysis of differences in graduate employability information space is denoted as: (19) $Δ_{G R} = (Δ, ρ, Δ \max, Δ \min)$

Where Δ is the information of all differences, i.e., Δ_ij. ρ is the discrimination coefficient, ρ ∈ (0, 1) and among all the elements of matrix R_i×j, the maximum value is called the bipolar maximum difference Δmax and the minimum value is called the bipolar minimum difference Δmin.

3.2.5

Calculating the center of gravity

Calculate the bullseye degree for employability evaluation: (20) $y_{a} = (ξ_{a} (1) + ξ_{a} (2) + \dots + ξ_{a} (n)) / n$

Where n is the number of evaluation indicators.

Finally, each evaluated graduate’s employability is ranked according to the size of the bull’s-eye degree, the larger the bull’s-eye degree, the stronger the employability.

3.3

Empirical analysis

An electronic questionnaire was used to survey the graduating year students of a local university, four in the east, four in the center and four in the west, totaling 12 universities. 2057 questionnaires were recovered, of which 1836 were valid, with an effective rate of 89.26%. The match and difference between employers and students were analyzed in terms of specific employability indicators.

3.3.1

Results of supply and demand assessments

Using the gray target transformation method to analyze the self-perception of college graduates’ employability and employers’ expectations, we obtain the assessment results of both college graduates and enterprises on the evaluation or demand of employability, and the results of the assessment of supply and demand of employability are shown in Figure 4.

In terms of specific competency indicators, college graduates have higher self-perceived mean values in professional and technical competency X1, practical problem solving competency X3, learning competency X4, communication, collaboration and socialization competency X5, writing and verbal expression competency X7, executive power X10, and initiative work X11, all with evaluation mean values above 6, and weaker competencies in managerial competency X9 and achievement motivation X15, with mean values below 5. Compared with the employer mean, college graduates exceeded employer expectations in Learning Ability X4 and Writing and Verbal Expression Ability X7, while the other 13 ability indicators were below employer expectations.

The total score of self-perception of employability of college graduates is 85.96, with a mean value of 5.73, indicating that students’ self-perception of employability is good. The total score of employers’ expectations is 90.49, and the mean value is 6.03, indicating that employers have high expectations of students’ employability. From the mean value, the difference between students and employers is not obvious, and the overall self-perceived ability of college graduates is slightly lower than the demand of the employment market.

The results of the sorting of the employability indicators are shown in Figure 5. The difference between employers’ expectations and college graduates’ self-perceptions can be intuitively found in the ranking of ability indicators. The top three indicators with the highest mean value of college graduates’ self-perception are professional and technical ability X1, learning ability X4, and communication, collaboration and socialization ability X5, while the three indicators with the highest mean value of employers’ expectation are professional and technical ability X1, practical problem solving ability X3, and learning ability X4, which are valued by both graduates and the job market.

3.3.2

Ranking analysis of variances

Figure 6 shows the distance between the ranked differences between the perceived employability competencies of college graduates and employers’ expectations. 15 employability competency indicators have ranked difference values of 4 or less, with writing and verbal ability X7 having the largest ranked difference. There are 8 ability indicators with difference values between 1 and 3, namely, problem identification and analysis ability X2, practical problem solving ability X3, learning ability X4, communication, collaboration and socialization ability X5, innovation ability X6, active work X11, independent work X12, and adaptability ability X13. There are 6 ability indicators with a difference value of 0, namely, professional and technical ability X1, information technology ability X8, management the above data fully indicates that there are some differences between employers’ expectations and college graduates’ self-perceptions, but the differences are not significant.

4

Strategies to enhance the matching of supply and demand in the job market

The slowdown in economic growth in recent years, the uneven development of industries and changes in industrial structure have led to the coexistence of insufficient demand and structural imbalance in the labor market, and the employment situation of college graduates is very serious. Guaranteeing the employment of college graduates and optimizing the employment structure of graduates are key concerns of higher education during the popularization stage. Therefore, based on analyzing the match between supply and demand of college graduates, this chapter proposes an enhancement strategy to match supply and demand in the job market. Figure 7 illustrates the enhancement strategy that matches supply and demand in the job market and is discussed in two dimensions: strengthening higher education reform and deepening industrial restructuring.

4.1

Strengthening the reform of higher education

As a gathering place for talent training, colleges and universities should guide the optimization and upgrading of the industrial structure through reform of the education system, conform to the global economic situation and the development of information technology, and promote institutional innovation oriented to market demand.

4.1.1

Optimization of the disciplinary structure

Colleges and universities need to regularly conduct in-depth analysis of the industrial structure and labor market demand, and make appropriate adjustments to the existing professional enrollment, and try to realize the adjustment of the professional structure of colleges and universities in accordance with the current situation and development trend of the industrial structure, so as to ensure the employment quality of graduates. Optimizing the professional structure of colleges and universities can be achieved through the following ways: first, enhance the autonomy of professional settings. Second, optimize the professional structure oriented by market demand. Third, establishing a dynamic mechanism for adjusting specialties.

4.1.2

Strengthening of the education management platform

First, the construction of infrastructure platforms should be strengthened. Colleges and universities should focus on the theme of enhancing the employability of college students and develop a series of tools and models for the cultivation of employability, such as standards for the employability of college students and methods for evaluating the employability of college students, by combining with the surveys on the actual needs of employers, so as to provide educational reform support for higher education.

Secondly, clarify the characteristic cultivation mechanism of colleges and universities. Colleges and universities should first position themselves and distinguish between the actual functions and cultivation mechanisms of research universities and teaching universities. Teaching universities should pay more attention to the cultivation of students’ practical skills, pay more attention to the combination of theory and practice, and pay attention to the development and training of skills. Research universities, in addition to focusing on training work skills, should mainly provide students with training in research capacity, focusing on improving the overall quality of students.

4.2

Deepening industrial restructuring

Industrial structure is an important indicator of the degree of modernization of a country’s or region’s economic development, and the irrationality of industrial structure will lead to the lagging of economic development and directly lead to the irrationality of the demand of the labor market, which will have a serious impact on the employment of college students. Therefore, it is necessary to make more efforts to deepen the adjustment of industrial structure, so as to provide more reasonable demand for graduate labor on the labor market.

4.2.1

Promoting technological progress

Increase policy guidance for the agricultural and traditional manufacturing industries, and through the introduction of high-tech technologies, promote the informatization, modernization, industrialization and scaling up of the agricultural and traditional manufacturing industries, so as to realize the transition and transformation from traditional agriculture and traditional industry to modern agriculture and modern industry. At the same time, for enterprises with a certain scale of production, through government support and other means, to encourage the establishment of technological innovation research and development centers, especially for the traditional manufacturing industry, and strive to promote the development process from manufacturing and processing to independent innovation, so as to improve the modernization and vitality of enterprises. In this way, the demand for high-quality talents, especially college students, will be strengthened in all sectors of the industry.

4.2.2

Strengthening the tertiary sector

The potential of each industry should be brought into full play in accordance with the actual characteristics of each industry, so as to realize the supportive role that the tertiary industry needs to provide for the employment of university students. At the same time, the association between the tertiary industry and the primary and secondary industries should be strengthened to deepen the development mechanism of mutual promotion among the industries, promote the rational layout of industrial structure, and improve the overall development efficiency of the national economy, so as to provide favorable demand support for college students’ employment.

4.2.3

Adjustment of industrial investment structure

In the face of the current irrational industrial structure and employment structure, the government should appropriately reduce its investment in the infrastructure sector and increase its investment in the tertiary industry, especially in industries with high employment elasticity but small employment scale, especially accelerating the expansion of scientific research and cultural industries, so as to make these industries create more demand for high-caliber talents, and thus give full play to their absorptive capacity for college students. At the same time, the state should pay attention to the proportion of investment in scientific research personnel and funds to encourage more college students to encounter the scientific research team.

5

Conclusion

The essence of graduate employment is the process of matching the talent cultivation of colleges and universities with social demand. On the one hand, this topic uses gray GM(1,1) prediction model to analyze the matching of supply and demand of college graduates’ employment talents. On the other hand, it constructs an evaluation index system for the employment ability of college graduates to explore the difference between their employment ability and market demand. And then put forward the enhancement strategy of matching supply and demand in the employment market.

The supply and demand of employment talents for college graduates in the sample region in the next five years are both on the rise, with the supply ranging from 283,300 to 437,100 people and the demand ranging from 295,400 to 450,600 people. There is still a large gap between supply and demand of employed talents in the region in the next five years, with the number of shortfalls ranging from 43,300 to 148,300, and the demand for middle- and high-end talents is particularly strong, with the shortfalls of master’s degree graduates and bachelor’s degree graduates both accounting for more than 90%.

The mean values of college graduates’ self-perceived employability and employers’ expectations are 5.73 and 6.03 respectively, and 86.67% of the employability indicators are lower than employers’ expectations, indicating that the supply and demand match of college graduates’ employability is not enough. However, in terms of the difference in the ranking of employability, the difference between students’ perception and enterprises’ demand is not much different, and both of them attach more importance to professional and technical ability and learning ability.

Colleges and universities should adjust their disciplinary structure in accordance with the needs of the employment market in a timely manner, so as to make the objectives of talent training more in line with the needs of the market and enterprises, and in particular to emphasize the cultivation of middle- and high-end talents. At the same time, the relevant institutions should make more efforts to deepen the industrial structure adjustment, promote the technological progress and the development of tertiary industry, and adjust the industrial investment structure, so as to provide favorable demand support for the employment of college students.

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Research on Supply and Demand Matching Model and Strategy in the Job Market of College Graduates

Yanjun Hou

Publié en ligne: 24 mars 2025

Reçu: 21 nov. 2024

Accepté: 27 févr. 2025

DOI: https://doi.org/10.2478/amns-2025-0789

Mots clésGM(1,1), Gray target transformation, Evaluation index system, Supply and demand matching, College graduates

© 2025 Yanjun Hou, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
GM(1,1), Gray target transformation, Evaluation index system, Supply and demand matching, College graduates