The Gig Economy and Youth Unemployment: a Cross-Country Panel Analysis

Joseph Jeyaraj, Justina (2025) The Gig Economy and Youth Unemployment: a Cross-Country Panel Analysis. Doctoral thesis, Tunku Abdul Rahman University of Management and Technology.

Text 23 Justina ap Joseph Jeyaraj.pdf Restricted to Registered users only Download (1MB)

Abstract

Youth unemployment remains a global challenge. The 2030 Agenda for Sustainable Development considers youth across all Goals and Targets, signifying their prominence in today’s world for sustainable development. Addressing youth unemployment is essential as its impact extends to individuals, society and the economy. Despite the economic crisis in 2020, the digital economy, particularly the gig economy, has rapidly expanded since this period. The general objective of this study is to assess whether the gig economy can significantly reduce youth unemployment, utilising a panel of 79 countries across different income categories in the period 2017 to 2021. In particular, the gig economy is measured using the Online Labour Index, which is the first economic indicator that provides a gig economy measurement equivalent to conventional labour market statistics. The first specific objective is to determine the gig economy's impact on youth unemployment using the two-step System GMM. The analysis reveals a statistically significant negative relationship between the gig economy and youth unemployment, where a 1 per cent increase in the gig economy reduces youth unemployment by 1.875 per cent. The second specific objective is to evaluate how a country's income influences the gig economy's ability to address youth unemployment, given that income, which serves as a proxy for economic development, affects access to technology and participation in the gig economy. System GMM and the average marginal effects are utilised to achieve this objective. While the gig economy does reduce youth unemployment, the analysis revealed that the effect varies by income level. The gig economy is effective in reducing youth unemployment in low- to middle-income countries, whereas in high-income countries, the gig economy may increase youth unemployment instead. The third specific objective is identifying the threshold level of labour freedom necessary for the gig economy to reduce youth unemployment using the Dynamic Panel Threshold Model effectively. The study finds that a labour freedom threshold of 53.5596 is needed to maximise the positive impact of the gig economy on youth unemployment. This study has significant implications in two key areas: advancing knowledge and implications for policymakers and gig economy stakeholders. This study has added to the accumulation of knowledge in the gig economy and youth unemployment, as there are currently limited studies on how the gig economy affects youth unemployment. For policymakers, the gig economy signifies a major work shift, requiring comprehensive policies for sustainable growth and economic integration. Explicit policies to support gig economy expansion are essential, such as financial incentives for firms to hire gig workers. Another crucial aspect is redesigning the education framework to teach essential digital and financial skills. Additionally, labour market policies must protect gig workers' interests by defining their rights and responsibilities and ensuring social protections. Overall, the gig economy requires a multifaceted policy approach to create a thriving ecosystem for the gig economy.