نوع مقاله : مقاله پژوهشی
نویسنده
دانشیار اقتصاد، گروه آموزشی مدیریت و اقتصاد، دانشکده علوم انسانی واجتماعی، دانشگاه گلستان، گرگان
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسنده [English]
The gig economy, as one of the structural manifestations of the digital economy, presents an emerging challenge in designing social welfare policies and labor market regulations. This study employs an agent-based simulation model to evaluate the impacts of four policy scenarios—"social insurance," "unemployment insurance," "skills training programs," and "simultaneous policy combinations"—within the context of Iran's economy. Simulation results reveal that policy implementation can yield contradictory effects on core welfare and inequality metrics. Isolated insurance policies, when decoupled from enabling measures, demonstrate limited efficacy in enhancing welfare and may even increase unemployment or reduce labor market participation in some cases. Conversely, while skills training policies positively influence economic growth and employment, they generate unequal benefit distribution, exacerbating poverty and Gini indices. Sensitivity analyses indicate that social welfare maximization requires simultaneous optimization of insurance rates, unemployment benefit levels, training quality, and platform wages. Key findings demonstrate that integrating social insurance with skills training yields the most significant positive impact on social welfare and unemployment reduction. Additionally, calibrating wages at an optimal threshold (1.37 times the minimum wage) and capping gig employment at 10-20% of total labor participation improves market equilibrium. The study concludes by emphasizing the necessity for integrated, multi-tiered, and interaction-sensitive policy frameworks to steer gig economy development toward both social equity and economic efficiency. This approach addresses the inherent tensions between labor market flexibility and worker protection in digital platform ecosystems.
کلیدواژهها [English]
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