- • Stage 4: running the IPF procedure, first for mid-2017 then for March 2020. • Stage 5: performing quality assurance tests on the reweighted input datasets. Stage 1: preparing the external population control totals relating to demographic profile This step consisted of extracting the relevant population estimates from the file produced by Statistics South Africa and structuring the data into separate Excel worksheets for the relevant years. The population estimates produced by Statistics South Africa consist of estimated counts by age, sex, and population group. There are 34 separate quinary age/sex groups for each of the four population groups, resulting in 136 discrete demographic categories (i.e. 34 × 4 = 136). Population estimates are available for all 136 demographic categories at the mid-point of each year.
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Bassier, I., J. Budlender, R. Zizzamia, M. Leibbrandt, and V. Ranchhod (2021). ‘Locked Down and Locked Out: Repurposing Social Assistance as Emergency Relief to Informal Workers’. World Development, 139: 105271. https://doi.org/10.1016/j.worlddev.2020.105271 Branson, N., and M. Wittenberg (2019) ‘Longitudinal and Cross-Sectional Weights in the NIDS Data 1–5’.
- Department of Employment and Labour (2020). ‘Unemployment and Labour on UIF Coronavirus COVID-19 TERS Payments’. Media Statement, 27 October. Available at: www.gov.za/speeches/unemployment-and-labour-uif-coronavirus-covid-19-payment-funds-exceedr51 -billion-27-oct# (accessed 8 April 2021).
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Dolls, M., C. Fuest, and A. Peichl (2012). ‘Automatic Stabilizers and Economic Crisis: US vs. Europe’.
- dwt_2020q1p: reweighted to 2020 Q1 external controls for population estimates (‘p’) only. dwt_2020q1l: reweighted to 2020 Q1 external controls for labour market (‘l’) only.
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- Figure B4 shows the effects of moving from the rebased mid-2017 timepoint to the ‘pre-crisis’ timepoint of March 2020. It is evident that the magnitude of difference between the rebased mid2017 weights and the ‘pre-crisis’ March 2020 weights is much less than the magnitude of difference between the original survey weights and the rebased mid-2017 weights. 23 Figure B1: Reweighting from original dwt to rebased dwt_2017q2pl Source: authors’ construction.
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- Following the NIDS weight calibration approach adopted by Branson and Wittenberg (2019), prior to implementing the IPF procedure the three most elderly Indian/Asian age groups (70–74, 75–79, and 80+) were collapsed into a combined ‘aged 70+’ category for males of the Indian/Asian population group and also separately for females ‘aged 70+’ of the Indian/Asian population group.
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- Given the structure of this data and its similarity to that used in the UK, this methodology draws principally from that of Brewer and Tasseva (2020) in terms of regression design and dependent variables. Principally, this means that we model probabilities of the employed transitioning into different states, rather than probabilities of being employed or not in the COVID era.
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Helsinki: UNU-WIDER. https://doi.org/10.35188/UNU-WIDER/2021/938-9 Ranchhod, V., and R. Daniels (2020). ‘Labour Market Dynamics in South Africa in the Time of COVID19: Evidence from Wave 1 of the NIDS-CRAM Survey’. NIDS-CRAM Paper. Cape Town, Stellenbosch, and Johannesburg: University of Cape Town, University of Stellenbosch, and University of the Witwatersrand. Available at: https://cramsurvey.org/wpcontent /uploads/2020/07/Ranchhod-Labour-market-dynamics-in-the-time-of-COVID-19..pdf (accessed 8 April 2021).
- In line with the UKMOD (Brewer and Tasseva 2020) and ECUAMOD (Jara et al. 2021) studies, this methodology focuses on estimating employment shocks (and enabling poverty and inequality estimates) at the peak of the crisis (i.e. in April when the level 5 lockdown was in place), leaving estimates of further developments in June and beyond to future work. C.1 Modelling the employment shock using NIDS-CRAM For February employed with positive earnings,19 a multinomial logit model was run,20 with the dependent variable being April employment outcome with four possible values: (1) employed with no drop in earnings; (2) employed with decreased earnings; (3) furloughed; and (4) not employed. Because of a lack of information with which to characterize the kind of work people were doing in the pre-COVID scenario, a single model was run for all employed rather than separate models for different kinds of workers (e.g. for the employed and the self-employed).
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Jain, R., J. Budlender, R. Zizzamia, and I. Bassier (2020). ‘The Labour Market and Poverty Impacts of COVID-19 in South Africa’. NIDS-CRAM Paper. Cape Town, Stellenbosch, and Johannesburg: University of Cape Town, University of Stellenbosch, and University of the Witwatersrand. Available at: https://cramsurvey.org/wp-content/uploads/2020/07/Jain-The-labour-market-and-povertyimpacts.
Jara, H.X., L. Montesdeoca, and I. Tasseva (2021). ‘The Role of Automatic Stabilizers and Emergency TaxBenefit Policies During the COVID-19 Pandemic in Ecuador’. WIDER Working Paper 4/2021.
- Journal of Public Economics, 96(3–4): 279–294. https://doi.org/10.1016/j.jpubeco.2011.11.001 Ingle, K., T. Brophy, and R.C. Daniels (2020). National Income Dynamics Study: Coronavirus Rapid Mobile Survey (NIDS-CRAM) Panel User Manual. Version 2. Cape Town: Southern Africa Labour and Development Research Unit.
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- NIDS-CRAM is a broadly representative individual-level survey implemented using CATI and focusing on adult individuals’ responses to the COVID-19 pandemic and national lockdown (Ingle et al. 2020). Conducted in May, respondents were asked retrospectively about their employment in April (after the imposition of the level 5 lockdown) and in February (pre-lockdown).
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- Republic of South Africa (2020). Government Gazette Vol. 657, 26 March. Available at: www.gpwonline.co.za/gazettes/gazettes/43161_26-3_labour.pdf (accessed 8 April 2021).
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- Rogan, M., and C. Skinner (2020). ‘The Covid-19 Crisis and the South African Informal Economy: “Locked Out” of Livelihoods and Employment.’ NIDS-CRAM Paper. Cape Town, Stellenbosch, and Johannesburg: University of Cape Town, University of Stellenbosch, and University of the Witwatersrand. Available at: https://cramsurvey.org/wp-content/uploads/2020/07/Rogan-Covidcrisis -and-the-South-African-informal-economy.pdf (accessed 8 April 2021).
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- SALDRU (Southern Africa Labour and Development Research Unit) (2017). National Income Dynamics Study: Wave 5 [dataset]. Version 1.0.0. Cape Town: SALDRU. https://doi.org/10.25828/fw3h-v708 Statistics South Africa (2020). ‘29 September Quarterly Labour Force Survey (QLFS) – Q2: 2020’. Press Statement. Available at www.statssa.gov.za/?p=13652 (accessed 8 April 2021).
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- Second, the reweighting was performed again, this time controlling to demographic and labour market profiles for March 2020. The advantage of adopting this two-stage approach is that it enables an assessment of the magnitude of weight changes at each stage. The demographic profiles were derived from Statistics South Africa’s population estimates by population group, age, and sex. The labour market profiles were derived from Statistics South Africa’s QLFS. The reweighting process was undertaken using the technique of iterative proportional fitting (IPF; also referred to as ‘raking’). The Stata .ado file ‘ipfraking’ was utilized for this purpose. The reweighting procedure consisted of five main stages: • Stage 1: preparing the external population control totals relating to demographic profile. • Stage 2: preparing the external population control totals relating to labour market profile. • Stage 3: preparing the SAMOD input dataset prior to running IPF.
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- Source: authors’ construction. 0 10,000 20,000 30,000 40,000 dwt_2017q2pl 0 10,000 20,000 30,000 40,000 dwt x=y South Africa: dwt_2017q2pl-vs- dwt 0 10,000 20,000 30,000 40,000 dwt_2017q2p 0 10,000 20,000 30,000 40,000 dwt x=y South Africa: dwt_2017q2p-vs- dwt 0 10,000 20,000 30,000 40,000 dwt_2017q2l 0 10,000 20,000 30,000 40,000 dwt x=y South Africa: dwt_2017q2l-vs- dwt 25 Figure B4: Effects of reweighting from rebased 2017 weight (dwt_2017q2pl) to match the population estimate for end of March 2020 and the QLFS labour market profile for 2017 Q2 Panel A Panel B Panel C Note: dwt_2017q2pl: reweighted to 2017 Q2 external controls for population estimates (‘p’) and labour market (‘l’). dwt_2020q1pl: reweighted to 2020 Q1 external controls for population estimates (‘p’) and labour market (‘l’).
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- Source: authors’ construction. 0 10,000 20,000 30,000 40,000 dwt_2020q1pl 0 10,000 20,000 30,000 40,000 dwt_2017q2pl x=y South Africa: dwt_2020q1pl-vs- dwt_2017q2pl 0 10,000 20,000 30,000 40,000 dwt_2020q1p 0 10,000 20,000 30,000 40,000 dwt_2017q2pl x=y South Africa: dwt_2020q1p-vs- dwt_2017q2pl 0 10,000 20,000 30,000 40,000 dwt_2020q1l 0 10,000 20,000 30,000 40,000 dwt_2017q2pl x=y South Africa: dwt_2020q1l-vs- dwt_2017q2pl 26 Appendix C: Modelling labour market transitions on the basis of NIDS-CRAM This appendix describes the simulation of the COVID-19 employment shock and the accompanying lockdowns in the input data (after updating to March 2020 pre-lockdown levels). The employment shock is estimated using NIDS-CRAM Wave 1 (referred to as the ‘shock data’).
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- Sutherland, H., and F. Figari (2013). ‘EUROMOD: The European Union Tax–Benefit Microsimulation Model’. International Journal of Microsimulation, 6(1): 4–26. https://doi.org/10.34196/ijm.00075 University of Essex (2019). EUROMOD software v3.1.8. Colchester: University of Essex. 16 Wilkinson, K. (2009). ‘Adapting EUROMOD for Use in a Developing Country: The Case of South Africa and SAMOD’. EUROMOD Working Paper EM5/09. Colchester: University of Essex. Available at: www.microsimulation.ac.uk/publications/publication-512343 (accessed 8 April 2021).
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- The model uses the EUROMOD software developed by Professor Holly Sutherland and colleagues at the University of Essex to simulate policies for the countries in the EU (Sutherland and Figari 2013; University of Essex 2019). The EUROMOD software was designed to enable analysis across countries using harmonized concepts and methodology, and is sufficiently flexible to be applicable to countries outside the EU, with South Africa being the first developing country to use the software (Wilkinson 2009).
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- The objective of the reweighting procedure was to recalibrate the survey weights in the SAMOD input dataset so that the weighted population totals corresponded to estimated demographic profiles and labour market profiles for March 2020. This was achieved through a two-stage process. First, the SAMOD input dataset was reweighted to match external demographic and labour market profiles for the timepoint at which NIDS Wave 5 was enumerated (mid-2017).
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- The objective was to generate an input dataset that would reflect the situation in South Africa immediately prior to the pandemic. This was achieved by reweighting the SAMOD dataset to a ‘pre-COVD’ timepoint of March 2020.14 Selected diagnostic outputs are also included in this appendix to illustrate the impact of the reweighting exercise on the distribution of survey weights.
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- The regressors for the multinomial logit were age (in ten-year brackets), a female dummy, race,21 education dummies, an urban dummy, occupation, baseline earnings quintile, and interactions between the female dummy and the race, education, and income quintile variables. These particular interactions are included because of exploratory findings for an interaction between gender and these factors in the early part of the lockdown (as found in NIDS-CRAM; Casale and Posel 2020).
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- These regressors largely match those used by Jara et al. (2021) with the addition of race, occupation, and baseline earnings quintile.22 Occupation was not asked for February employment in NIDSCRAM, so April occupation is used for those who remained employed in April while last/usual occupation is used for those who were no longer employed in April. In NIDS-CRAM, respondents could respond to earnings questions using bracket responses.
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- This resulted in a final age/sex/population group classification consisting of 132 discrete categories derived from the Statistics South Africa population estimates. 14 The pandemic was declared a national disaster on 15 March 2020 and a national lockdown was announced on 23 March 2020. 19 To derive population estimates for the March 2020 timepoint, it was necessary to interpolate between the population estimates for mid-2019 and mid-2020. A simple linear interpolation approach was used to derive the estimates for March 2020. Stage 2: preparing the external population control totals relating to labour market profile This step entailed the derivation of labour market profiles from specific waves of the QLFS.
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- Weighted population shares by labour market category were calculated for two separate classifications: (1) ‘current economic status’ (relating to the ‘les’ variable in the SAMOD input dataset); and (2) ‘occupation type’ (relating to the ‘loc’ variable in the SAMOD input dataset). A composite classification, named ‘les_loc’, was then derived by disaggregating the ‘self-employed’ and ‘employees’ according to their occupation type. These external statistics were calculated using QLFS waves 2017 Q2 and 2020 Q1.
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- With regards to the demographic controls for mid-2017, it is evident from Panel B of Figure B3 that the original survey weights were typically increased through the rebasing procedure. This was a necessary step to account for the calibration approach adopted in the original NIDS data, whereby individual-level non-response cases (within enumerated households) were allocated a survey weight, despite no information being captured in the survey concerning these ‘nonresponse ’ individuals’ incomes or labour market status. As SAMOD must be based on an underpinning dataset with no missing values, any ‘non-response’ individuals had to be excluded from the dataset. The weight rebasing procedure therefore adjusted the weights to ensure that the weighted total of enumerated cases in the NIDS survey equated to the total population estimate from Statistics South Africa.
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- With regards to the labour market controls for mid-2017, there were differences in the labour market profiles enumerated by the QLFS compared to NIDS. As one of the purposes of this reweighting procedure was to use the QLFS to account for labour market change between mid2017 and March 2020, it was first necessary to reweight the NIDS data to reflect the QLFS labour market profile in mid-2017. Panel C of Figure B3 shows the effects of reweighting the NIDS data so that the weighted labour market totals equated to the QLFS external statistics.
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