Examining informal food traders' livelihood status and factors affecting the adoption of their livelihood strategies posti-COVID-19 Pandemic in South Africa : a case study of Polokwane Local Municipality

Abstract

This study aimed to examine the livelihood status of informal food traders and the factors affecting their adoption of livelihood strategies post-COVID-19 in selected areas of Polokwane Local Municipality. To accomplish this, a structured questionnaire was used to conduct in person interviews with 120 informal traders selected through purposive sampling. Descriptive statistics were utilised to address the first objective, which is to describe the socio-economic characteristics of informal food traders. The Livelihood Assessment Index (LAI) tool was used to address the second objective, which is to examine the livelihood status of informal food traders post-COVID-19 pandemic. Further, the Multinomial Logistic (MNL) model was utilised to achieve the third objective of the study, which is to examine factors affecting the adoption of livelihood strategies by informal traders. The LAI reveal that informal food traders had limited access to financial, human, physical, social, and natural capitals, which are essential for recovery and resilience post-COVID-19, resulting in an overall LAI value of 0.44. Therefore, policy interventions should prioritise strengthening human capitals, improving access to financial capitals, enhancing the use of natural capitals, expanding physical capitals, and boosting social capitals. The results for livelihood strategies indicate that most traders maintained a "food trading only" strategy, suggesting that many faced barriers or lacked incentives to diversify into other revenue-generating activities. The MNL results indicate that significant factors reducing the likelihood of adopting multiple livelihood strategies included age (p < 0.05), gender (p < 0.01), trading hours (p < 0.05), trading experience (p < 0.10), trading license (p < 0.01), and shelter (p < 0.05). Conversely, marital status (p < 0.05), educational level (p < 0.01), monthly household income (p < 0.10), trading area (p < 0.05), and access to transport (p < 0.01) significantly increased the likelihood of diversification..Therefore, policy interventions should focus on addressing inhibiting factors and promoting enabling ones to facilitate greater diversification and build resilience in the aftermath of COVID-19 pandemic.