Gaussian Markov Chain Deep Neural Network Investigation for College Graduates' Initial Employment and Long-Term Career Development from an Economic Perspective
Keywords:
Initial Employment, Deep Neural Network, Gaussian Model, Hidden Chain, Markov Chain, ClassificationAbstract
The initial employment and long-term career development of college graduates are critical topics from an economic perspective, with implications for both individuals and society as a whole. Examining graduates' entry into the labor market provides insights into broader economic trends, such as job availability, wage levels, and skill demands. Several issues affect the initial employment and long-term career development of college graduates. These include mismatched skills and job requirements, resulting in underemployment or unemployment among graduates. This study examines the initial employment and long-term career development of college graduates from an economic perspective, employing the Gaussian Markov Chain Deep Neural Network (GMC-DNN) for analysis. By integrating economic theory with advanced machine learning techniques, the research aims to elucidate the complex dynamics underlying graduates' labor market outcomes and career trajectories. Through the GMC-DNN model, which combines the capabilities of Gaussian Markov Chains for time series analysis and Deep Neural Networks for nonlinear pattern recognition, the study explores factors influencing graduates' employment transitions, wage growth, and career advancement prospects over time. Additionally, the model provides insights into the impact of economic factors, such as GDP growth, industry trends, and labor market conditions, on graduates' career trajectories. Simulation results demonstrated that the average starting salary for college graduates is found to be $50,000, with variations across fields of study and geographic regions. Furthermore, the GMC-DNN model predicts a median wage growth rate of 3% per year for the first five years of employment, with graduates in STEM fields experiencing higher wage growth rates compared to those in the humanities. Additionally, the simulation reveals that economic recessions lead to temporary setbacks in wage growth, with an average decrease of 2% observed during recessionary periods.
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