Chongqing, China, is set to witness significant changes in its rural labor dynamics over the next few years, according to new research deploying an innovative forecast model for estimating the number of rural migrant workers. The study, published recently, unveils the optimized grey N_Verhulst model, which predicts the workforce is expected to grow from 2.41 million in 2023 to approximately 2.85 million by 2028, marking an 18.26% increase with an average annual growth rate of 3.41%.
The research is especially relevant as it follows the shift from poverty alleviation to rural revitalization, underlining the pressing need to accurately assess and manage the talent required for rural transformation. Inevitably, imbalances between the demand and supply of qualified individuals hinder progress. Therefore, proactive strategies based on reliable forecasts can aid the government and related departments to formulate timely policies, allocate resources efficiently, and address the glaring talent shortage problem.
The optimized grey N_Verhulst model enhances prediction accuracy significantly by employing fractional-order operators, extending traditional accumulating orders to all real numbers. This innovation strengthens the model’s ability to analyze S-shaped time-series datasets, which typify the population growth patterns observed. Notably, the model exhibited remarkable improvements, lowering the mean relative simulation percentage error from 3.66% to 2.93%, alongside decreasing forecast errors from 8.02% to 2.18%.
The research identifies three distinct growth phases of migrant workers during recent years. Firstly, from 2013 to 2015, the numbers remained stable. A subsequent rapid growth phase lasted until 2018, after which growth slowed considerably, presenting the challenges and opportunities faced by local economies.
Key to the study’s findings is the acknowledgment of multiple influencing factors, from political and economic to social dimensions, which can fluctuate the patterns of migration. Silence on these variables often leads to incomplete predictions; hence, the grey system theory was adopted, allowing more sophisticated handling of uncertain information inherent to small data samples.
The predictive modeling extends beyond mere statistics; its applications can inform comprehensive rural revitalization strategies aimed at attracting skilled labor back to the countryside. The ability of the optimized N_Verhulst model to reflect subtle changes requires policymakers to adjust strategies according to forecasted trends, particularly focused on employment training, social security, and local entrepreneurship.
By 2028, the projected increases are not just numbers; they represent dynamic shifts within the labor market as Chongqing positions itself as a potential key player for rural development and talent attraction.
With targeted recommendations, including well-structured incentive policies for local employment, the research asserts the importance of enhancing rural appeal to migrant workers and ensuring sustainable livelihood opportunities. The array of strategic countermeasures proposed will bolster local industrial integration, making rural jobs more attractive and sustainable.
The comprehensive nature of this model could be instrumental for other regions undergoing similar transitions, embodying practical strategies rooted firmly on scientific foresight and rigorous analysis.
Conclusively, this study reaffirms the principle of data-driven decision-making, offering grounded insights for stakeholders involved in crafting future rural policies, all aimed at fulfilling the specific needs of rural areas. Accurate numerical forecasts empower stakeholders with the knowledge necessary to navigate the complex interplay of rural labor dynamics and manage resources effectively.