This paper conducts an analysis of labour market dynamics in South Africa using all five waves of NIDS-CRAM, as well as five waves of Statistics South Africa’s Quarterly Labour Force Survey
(QLFS) 2020-Q1 to 2021-Q1. We find that much of the differences in estimates of labour force states including employment, unemployment and not economically active, are due to different reference
periods between the two surveys. NIDS-CRAM asks about labour force activity during a reference month, whereas the QLFS utilises a reference week at the time of interview. This leads to higher
estimates of employment in NIDS-CRAM compared to the QLFS for both a pre-pandemic baseline and over the entire period investigated (February 2020 to March 2021). The reference month allows
NIDS-CRAM to very accurately identify labour force states during particular Lockdown levels. The QLFS, on the other hand, is not able to identify labour force states in Lockdown levels because no
Lockdown level has been in effect for an entire quarter of the year. Instead, the QLFS is better suited to estimating the cross-sectional labour force states over each quarter and annually. This implies that the instruments are complementary, not contradictory, improving our understanding of the impact of Covid-19 on the labour force when taken together. Results using NIDS-CRAM show that the labour market in South Africa is very responsive to Lockdown regulations, fluctuating dramatically when bans or restrictions on trading hours and curfews are put in place, while recovering relatively quickly when they are relaxed. The QLFS shows that employment rates in South Africa have still not recovered to their pre-pandemic levels, remaining at approximately 10 percent below pre-pandemic levels for the population as a whole. This points to the fact that lockdown restrictions can have tremendously negative economic impacts that are difficult to recover from over the course of a year, and that wherever possible, all efforts should be made to geographically differentiate Lockdown levels to minimize their national impact, while maximizing the potential for localized economic recoveries.