This paper studies the impact of economic crisis caused by the COVID on the Indian labour market using the Periodic Labour Force Survey (PLFS). The unique dataset offers the opportunity to analyse sectoral transition and mobility of workers in response to a crisis due to its rotational panel framework. We employ transition matrices, non-parametric cumulative distribution functions, and machine learning techniques to identify the impact of COVID shock on formal and informal sector workers and whether this impact was heterogeneous. We find that labour market outcomes, both in terms of employment status and income, became even more divergent between the formal and informal sectors during the first wave of pandemic and remained divergent in the recovery phase. The classification analysis highlights that the sector in which the worker was employed (formal or informal sector), was an important predictor of income loss during the first wave
