<![CDATA[Unemployment remains a major economic issue in Indonesia, particularly in Banten Province, which has the highest open unemployment rate. Traditional models struggle to capture the zero inflation characteristics in labor force data, where most individuals are employed. This study applies the Zero-Inflated Ordered Logit (ZIOL) model to better analyze labor force status in Banten by distinguishing between genuinely unemployed individuals and those appearing unemployed due to external factors.Using data from the National Labor Force Survey (SAKERNAS) 2023, this study examines the impact of gender, education, residence, job training access, and work experience on employment. The results show that women, individuals with lower education, and those lacking work experience are more likely to be unemployed or underemployed. ZIOL outperforms traditional ordinal logit models in capturing these dynamics.The findings provide insights for policymakers to design more effective employment strategies, particularly in regions facing high unemployment.]]>
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