Assessing Wellbeing in the Indian Leather Industry: Evidence from Kanpur's Factory Workers

Abstract

The wellbeing of workers in the labour-intensive industries like leather manufacturing is vital for their economic stability. This study investigates the multidimensional wellbeing of workers engaged in the Kanpur's leather cluster, an important hub of India's export economy. The study uses primary data collected from randomly selected 103 workers. The survey conducted on factory workers captured the demographics and the five key dimensions of wellbeing: personal wellbeing, health and material wellbeing, workplace wellbeing, and relational wellbeing. Descriptive statistics and stepwise regression analysis were conducted on the survey data to identify the influence of socio-economic, workplace, and demographic factors on different dimensions and the overall wellbeing of the workers engaged in the leather cluster. The descriptive statistics show stark differences in the education levels and skill type amongst the respondents. Results of the stepwise regression analysis reveals gender, job security, job duration, age, and education as the critical determinants of overall wellbeing of the workers. In addition, the workers' skill type, weekly working hours, wages, and housing status also significantly influence different wellbeing dimensions. Importantly, the wage inequality and gender disparities exacerbate challenges, particularly in respect of women and unskilled workers. Thus, the findings underscore the negative impacts of informal employment and highlight the importance of supportive workplace environments. Policy interventions should, therefore, include promoting formal employment, strengthening health and safety measures, addressing wage inequality, and providing targeted supports. Incorporating wellbeing metrics into the industry evaluation mechanism can potentially ensure holistic labour assessments. Nevertheless, future research should explore these aspects in other clusters with more qualitative perspectives for deeper understanding of the underlying dynamics.