Statistical Study on Undergraduate Employment Rate Based on

Regression Analysis

Siyu Liu

Dundee International Institute of Central South University, Hunan Province, 410083, China

Keywords: Undergraduate Employment Rate, Regression Analysis, GDP Growth Rate, Educational Funding Investment,

Proportion of Tertiary Industry.

Abstract: Against the backdrop of higher education universalization and transformative shifts in labor market structures,

undergraduate employment has emerged as a societal concern. This study is based on the employment rate of

Chinese undergraduate students from 2018 to 2022 and related macro data. A multiple linear regression model

is constructed, which includes GDP growth rate, education funding investment, and the proportion of the

tertiary industry. The impact mechanism of each factor on the employment rate is systematically analyzed.

Research has found that the direct driving effect of GDP growth rate on employment rate is most significant

(regression coefficient 0.780), and the synergy effect between education funding investment (0.250) and the

proportion of the tertiary industry (0.410) is formed by improving talent quality and optimizing employment

structure. Model predictions show that the employment rate for undergraduate students will reach 92.500%

by 2025, with an average annual growth rate of 0.800 percentage points. The research results provide a

quantitative basis for optimizing government policies, adjusting university majors, and student career

planning, emphasizing the importance of the synergistic effect of economic growth, education investment,

and industrial upgrading in alleviating employment market contradictions.

1 INTRODUCTION

Under the background of the accelerated

popularization of higher education, the importance of

undergraduate education, as the core way to train

high-quality professionals in China, has become

increasingly prominent. Over the past five years, the

number of undergraduate graduates in China has

increased from about 8.2 million in 2018 to about 9.6

million in 2023, with an average annual growth rate

of 3.9% (Ministry of Education, 2023). Global

economic integration and technological

advancements have precipitated profound

transformations in labor market demands. The rapid

development of emerging industries, such as artificial

intelligence and big data, and the intelligent

transformation of traditional industries have put

forward higher requirements for the knowledge

structure and skill level of undergraduate graduates

(China Artificial Intelligence Industry Development

Report Committee, 2018-2023; The State Council,

2019). In light of this trend, research on the factors

https://orcid.org/0009-0007-9682-6032

that influence and predict undergraduate graduation

rates is particularly critical.

Previous studies have revealed the influencing

mechanism of employment rate from multiple

dimensions. Li and Wang (2020) confirmed through

the VAR model that an increase of 1 percentage point

in GDP growth can drive an increase of 0.8% in

employment rate. Using multi-layer linear regression,

Chen and Zhang (2021) found that the elasticity

coefficient of education expenditure on employment

rate was 0.32. Sun and Huang (2022) calculated this

based on the degree of industrial structure and

showed that when the proportion of tertiary industry

increased by 1%, the demand for high-skilled jobs

increased by 0.45%. It is worth noting that most of the

existing studies have adopted the single-factor linear

analysis framework, and few literatures have built

dynamic prediction models including multi-

dimensional indicators such as macroeconomic

fluctuations, educational resource allocation, and

industrial structure upgrading.

Liu, S.

Statistical Study on Undergraduate Employment Rate Based on Regression Analysis.

DOI: 10.5220/0013813700004708

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy (IAMPA 2025), pages 42-46

ISBN: 978-989-758-774-0

The purpose of this study is to quantify the

combined impact of GDP growth rate, education

funding input, and tertiary industry ratio on the

employment rate of undergraduate students by

constructing a multiple regression model, and to

predict the future employment trend. The results of

this study will provide a scientific basis for the

government to formulate precise employment

policies, universities to optimize their major

offerings, and students to enhance their employment

competitiveness.

2 RESEARCH METHODS

2.1 Data Collection and Processing

The data for this study comes from official data

released by the National Bureau of Statistics, the

Ministry of Education, and various universities,

covering the national undergraduate employment rate

and its related influencing factors (such as GDP

growth rate, education funding investment,

proportion of the tertiary industry, etc.) from 2018 to

2022. To ensure the accuracy and reliability of the

data, all data has been standardized to eliminate

dimensional influences.

2.2 Regression Model Construction

A multiple linear regression model was constructed to

predict the undergraduate employment rate. The basic

form of the model is

𝑌 = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽3𝑋3 + 𝜖 (1)

Among them, Y is the employment rate of

undergraduate students; 𝑋1, 𝑋2, and 𝑋3 respectively

represent the GDP growth rate, education funding

investment, and the proportion of the tertiary

industry; 𝛽0 is a constant term; 𝛽1 , 𝛽2 , 𝛽3 are

regression coefficients; 𝜖 is the random error term.

By using stepwise regression to screen variables, the

optimal model is ultimately determined. The

goodness of fit of the model is evaluated by the

coefficient of determination (R²) and adjusted R², and

significance is verified by F-test and t-test (Wang

&Chen, 2020; Zhang &Li, 2021; Wu &Li, 2019).

2.3 Model Verification

In this paper, the variance inflation factor (VIF) was

calculated to evaluate the correlation between

variables. The judging criteria were: VIF < 5: no

severe collinearity; 5 ≤ VIF < 10: caution should be

interpreted; VIF ≥ 10: need to be treated. After

constructing a multiple linear regression model, this

study systematically tested the residuals to ensure the

validity of the model. First, the Shapiro-Wilk test

(p>0.05) was used to verify whether the residuals

were consistent with the normal distribution

hypothesis so as to ensure that the regression

coefficient estimates were not affected by non-

normality. Secondly, the Durbin-Watson statistic is

calculated to check whether there is an

autocorrelation between the residuals. If the Durbin-

Watson value is close to 2, it indicates that there is no

autocorrelation between the residuals, which

conforms to the independence assumption of the

regression model. Through these testing steps, the

assumptions of the multiple linear regression model

can be verified so as to ensure the validity and

reliability of the model results.

3 RESEARCH RESULTS

3.1 Regression Analysis Results

The final regression equation is:

𝑌 = 85.320 + 0.780𝑋1 + 0.250𝑋2 + 0.410𝑋3(2)

The model yielded a coefficient of determination

(R² = 0.923) and adjusted R² (0.915), evincing robust

explanatory power. The regression coefficients and

significance of each variable are shown in Table 1.

Table 1: Regression coefficients and significance test results

variable re

ression coefficient standard erro

t-values

-value VIF

Constant ter

85.320 1.230 69.370 0.000 -

GDP growth rate (

1) 0.780 0.080 9.750 0.000 2.340

Education funding investment (

2) 0.250 0.050 5.000 0.002 1.870

Proportion of tertiary industry (

3) 0.410 0.060 6.830 0.000 2.010

Table 1 shows the parameter estimation and

statistical test results of the multiple linear regression

model. The constant term is 85.320%, which

represents the benchmark level of employment rate

when all independent variables are zero. For every 1

percentage point increase in GDP growth rate, the

Statistical Study on Undergraduate Employment Rate Based on Regression Analysis

average employment rate increases by 0.780

percentage points, with the highest regression

coefficient among all variables, indicating that

economic growth has the most significant promoting

effect on the employment rate. The regression

coefficient of education funding investment is 0.250,

reflecting that education investment indirectly

promotes employment through optimizing talent

cultivation. The coefficient of the proportion of the

tertiary industry is 0.410, which confirms the driving

effect of industrial structure upgrading on

employment.

3.2 Model Validation Results

This study constructed a multiple linear regression

model and systematically tested the residuals to

ensure the effectiveness of the model. The Shapiro

Wilk test (p>0.05) confirmed that the data conforms

to the assumption of normal distribution, indicating

that the regression coefficient estimation is not

affected by nonnormality interference. The

independence test observed sequence correlation by

drawing residual time series graphs and calculated the

Durbin-Watson statistic as 1.98 (1.5<DW<2.5),

indicating that there is no significant autocorrelation

problem between residuals.

From the statistical test results, it can be seen that

the standard errors of each variable are relatively

small (such as the standard error of GDP growth rate

being 0.080), indicating that the estimation accuracy

of the regression coefficients is high. The t-value is

much greater than the critical value (such as the t-

value of GDP growth rate being 9.750), and the p-

values of all variables are less than 0.050 (partially

approaching 0), further verifying the statistical

significance of the variable's influence. In addition,

the variance inflation factor (VIF) is all below 5

(maximum value is 2.34), indicating that the model

does not have serious multicollinearity problems and

the independence between variables is good. Overall,

the model has high explanatory power (R ²=0.923)

and can effectively quantify the impact of various

factors on the employment rate.

The employment rate forecast is based on the

predicted values of economic indicators for the next

three years (data source: National Bureau of Statistics

2023 Economic Outlook Report), and the model

forecast results are shown in Table 2. It is expected

that the employment rate will rise to 93.500% by

2025, with an average annual growth rate of about

0.800 percentage points.

Table 2: Forecast of Undergraduate Employment Rate

from 2023 to 2025

year GDP

growt

h rate

(

)

Education

funding

investmen

(

)

Proportion

of tertiary

industry

(

)

Predicted

employme

nt rate (%)

202

5.500 4.200 54.500 90.300

202

5.800 4.100 55.000 91.100

202

6.000 4.000 55.500 92.500

Table 2 predicts the employment rate of

undergraduate students in the next three years based

on the regression model, and the values of the core

independent variables refer to the National Bureau of

Statistics 2023 Economic Outlook report and related

policy trends. The GDP growth rate is assumed to

gradually increase from 5.500% in 2023 to 6.000% in

2025, reflecting the expectation of a steady economic

recovery; Education expenditure as a share of GDP

rises from 4.200% to 4.000%, reflecting policy

continuity; The proportion of tertiary industry

increased from 54.500% to 55.500% at an average

annual rate of 0.5 percentage points, in line with the

long-term trend of industrial structure optimization

and upgrading. By plugging the above predicted

values into the regression equation (2), the

undergraduate employment rate for 2023 to 2025 is

calculated to be 90.300%, 91.100%, and 92.500%,

respectively. Taking 2023 as an example, the specific

calculation process is as follows:

𝑌 = 85.320 + 0.780 × 5.500 + 0.250 ×

4.200 + 0.410 × 54.500 = 90.300% (3)

The forecast results show that the employment

rate will maintain an average annual growth rate of

1.1 percentage points and is expected to reach

92.500% by 2025. Among them, the contribution of

GDP growth rate exceeds 60%, highlighting the core

driving role of economic development in

employment.

It should be pointed out that this forecast is based on

existing policy and economic environment

assumptions and does not take into account potential

external shocks (such as economic crises or major

policy adjustments), regional differences,

professional structures, and other influencing factors,

which may limit the accuracy of the forecast.

Nevertheless, the model results provide an important

reference for forward-looking decision-making by

the government, universities, and students.

IAMPA 2025 - The International Conference on Innovations in Applied Mathematics, Physics, and Astronomy

4 DISCUSSION

4.1 Result Analysis

The economic development level (GDP growth rate)

has the most significant direct effect on the increase

of the undergraduate employment rate. This indicates

that the expansion of the economic scale can create

more jobs, especially in the context of the rapid

development of technology-driven industries.

economic growth has a positive interaction with the

demand for high-quality employment. Findings align

with Li and Wang's (2020) conclusions, further

underscoring that in the context of industrial structure

upgrading, the driving role of GDP growth on

employment may be further amplified by the

enhanced absorption capacity of emerging industries.

Although the degree of influence of the

investment in education (β=0.250) and the proportion

of the tertiary industry (β=0.410) is relatively small,

their mechanisms are complementary. Education

investment can indirectly enhance employment

suitability by improving the quality of talents. The

increase in the proportion of the tertiary industry

directly optimizes the employment structure, which

jointly alleviates the structural contradictions in the

job market. The results also show that the marginal

effect of industrial structure adjustment on

employment is more prominent in the current stage.

Through the panel data analysis of 287 cities, Sun and

Huang (2022) found that when the proportion of

tertiary industry increased by 1 percentage point, the

demand for jobs with college degree or above

increased by 0.64 percentage points, among which the

growth rate of information technology service jobs

was as high as 0.89%. It highlights the significant

marginal effect of industrial structure adjustment on

employment.

The results of the model show that the policies

promoted by the government in recent years, such as

the proportion of education expenditure to GDP to

stabilize at more than 4% and the average annual

growth of the tertiary industry of 0.500 percentage

points, have been quantified through the variable

coefficient. For example, an increase of 1 percentage

point in education funding can boost the employment

rate by 0.250%, which verifies the actual effect of

policy investment. However, the effect of the policy

has a lag, and its cumulative effect needs to be further

evaluated based on long-term data (Lin and Zheng,

2021).

4.2 Robustness and Potential

Challenges of Future Employment

Trends

The model predicts that the employment rate will

reach 93.500% by 2025, with an average annual

growth rate of 0.800 percentage points, but this trend

is highly dependent on the stability of the economic

environment. If the GDP growth rate in the next three

years is lower than expected (such as due to global

economic fluctuations), the increase in the

employment rate may be less. In addition, the model

does not cover micro factors such as regional

differences and professional alignment. For example,

popular majors such as artificial intelligence may

have significantly higher employment rates than

traditional disciplines, while the limited capacity of

the employment market in the central and western

regions may weaken the universality of macro

predictions.

The government needs to guide enterprises to

increase research and development investment

through tax incentives and establish regional

employment subsidy funds, with a focus on

supporting the construction of emerging industrial

clusters in the central and western regions.

Universities should establish a dynamic docking

mechanism between majors and industries, such as

adding the "AI+Manufacturing" training direction to

computer science and technology majors, to improve

the professional alignment rate of graduates. Students

need to master hard skills such as Python and data

analysis and accumulate project experience through

school enterprise joint training programs to enhance

their employment competitiveness.

5 CONCLUSION

Based on the employment and macroeconomic data

of Chinese undergraduate students from 2018 to 2022,

this study constructed a multiple regression model

analysis and found that GDP growth rate, education

funding investment, and the proportion of the tertiary

industry all have a significant positive impact on the

employment rate. Among them, the regression

coefficient of the GDP growth rate is the highest

(0.780), indicating that economic growth is the core

factor driving employment. The synergistic effect of

education funding (0.250) and the proportion of the

tertiary industry (0.410) in improving talent quality

and optimizing employment structure has been

verified, demonstrating the dual role of education

Statistical Study on Undergraduate Employment Rate Based on Regression Analysis

investment and industrial upgrading in the job market.

Model predictions show that by 2025, the

employment rate for undergraduate students will

reach 92.500%, with an average annual growth rate

of 0.800 percentage points, and the contribution of

economic growth to employment growth will exceed

60%.

The research results reveal the structural

characteristics of the current job market: Economic

expansion directly creates job demand, while

education investment and industrial restructuring

indirectly promote employment by enhancing talent

adaptability. However, the forecast results depend on

the assumption of a stable economic environment and

policy continuity and do not take into account micro

factors such as regional differences and professional

structure, which may affect the universality of the

forecast. Future research needs to further explore the

mechanisms of variables such as industry

segmentation and regional economic disparities. This

study provides a quantitative basis for the government

to formulate employment policies, universities to

optimize major settings, and students' career planning,

emphasizing the importance of the coordinated

promotion of economic growth, education investment,

and industrial upgrading in alleviating the

contradictions in the job market.

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