“Employee Turnover in the RMG Sector in Bangladesh: Causes and HR Strategies to Minimize it”

Abstract

The RMG (Ready-Made Garments) industry is a cornerstone of Bangladesh's national economy, accounting for over 81% of exports and employing more than 4 million workers. However, this industry faces a persistent challenge: high employee turnover, ranging from 10% to 30% annually. This study aims to explore the main causes of employee turnover and propose human resource strategies to reduce it and improve organizational stability and efficiency. A mixed-methods approach was used, collecting data from 45 employees across three medium-sized companies (Fortune Zipper Bangladesh, Doreen Garments Ltd., and Brannerson Apparel’s Ltd.) using a structured questionnaire. Purposive sampling was employed to ensure sample diversity in terms of job title, gender, and seniority. Analysis methods included descriptive statistics, visualizations using bar and pie charts, and theme coding. Key findings support Hypothesis 1 by showing that non-financial variables like poor working conditions and limited professional growth, as well as financial factors like inadequate salary, are the primary causes of employee turnover. High levels of recognition and transparency are linked to lower employee turnover, while the efficacy of human resource management and communication systems vary (Hypothesis 2). staff retention is positively impacted by incentive systems and staff development initiatives, especially training and advancement (Hypothesis 3). Setting yearly compensation goals, enhancing the workplace, creating institutionalized recognition systems, boosting professional growth path transparency, enhancing communication, and assessing the return on training expenditure are some of the recommendations. Finally, developing a human resource strategy that considers both financial and nonfinancial needs is important for reducing employee turnover, improving employee resilience, and maintaining competitiveness in the garment industry. Future research should explore long-term impacts and expand the sample size to gain a deeper understanding.