Counting South African Womens Work Morn Oosthuizen Development - PowerPoint PPT Presentation

. . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion Counting South African Women’s Work Morné Oosthuizen Development Policy Research Unit, University of Cape Town, South Africa . . . . . . . . . . . . . . . . . . . . . . . . . NTA X, Peking University, Beijing, 13 November 2014

. . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion Acknowledgements This research is made possible by the Counting Women’s Work project, sponsored by the International Development Research Centre (IDRC), Canada, and the William and Flora Hewlett . . . . . . . . . . . . . . . . . . . . . . . . . Foundation

. Data and Methodology . . . . . . . . . . Introduction Results . Conclusion Outline Introduction Data and Methodology Data Methodology Results “What do people do all day?” Household Production Valued Labour Income by Gender Combining Market and Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion

. . . . . . . . . . . . Introduction . Data and Methodology Results Conclusion Why consider gender? Standard National Transfer Accounts (NTA): timing of labour market entry; likelihood of fjnding employment; ‘quality’ of employment for extended periods of time women often specialise tasks constrains labour market engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . production ▶ Obscure inter-group inequalities ▶ Males and females may difger in access to education/health; ▶ Child-rearing may keep many women out of the labour force ▶ Potentially significant differences i n resources in old age ▶ Sufger from the same problems as national accounts ▶ SNA excludes non-market household production in which ▶ Female specialisation in time-infmexible, non-discretionary ▶ Strong lifecycle dimension to non-market household

. . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion Why consider gender in South Africa? Consistent high-level ‘commitment’ to gender equality… but… . . . . . . . . . . . . . . . . . . . . . . . . . non-market household production ▶ Traditional views of “women’s work” deeply rooted ▶ Despite rapid increase in female labour force participation ▶ Perhaps compounded by migrant labour system? ▶ Strong gender difgerences in the labour market ▶ Participation, unemployment, job quality, wages etc. ▶ Weaker outcomes linked to women’s obligations in

. . . . . . . . . . . . Introduction . Data and Methodology Results Conclusion Data Time-Use Survey 2010 of 2010 household at 4am consecutive); NO primary/secondary distinction for ‘waiting’) and specifjc prompting at the end of survey . . . . . . . . . . . . . . . . . . . . . . for omitted childcare . . . . . ▶ Nationally representative; collected during fourth quarter ▶ Up to two (randomly selected) respondents aged 10+ per ▶ 24 hour diary for the day preceding the interview, starting ▶ Slots of 30 minutes; up to three activities (simultaneous or ▶ ICATUS classifjcation, with modifjcations (includes code

. . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion Data Income and Expenditure Survey 2010/11 to August 2011 according to purpose”) classifjcation Labour Market Dynamics Survey 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . National Accounts, administrative data ▶ Nationally representative; collected from September 2010 ▶ Combination of diary (two weeks) and recall methods ▶ COICOP (“Classifjcation of individual consumption ▶ ‘Stacked’ Quarterly Labour Force Surveys, incl. wages

. . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion Methodology 1. Disaggregation of market NTAs by gender 2. Construction of National Time Transfer Accounts by gender production, by gender transfers production . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Combine NTA and NTTA ▶ From TUS, calculate time spent in unpaid household ▶ Allocation of production to consumption gives rise to ▶ Choice of appropriate wage to value unpaid household ▶ Valuation of time production, consumption and transfers

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . “What do people do all day?”

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . Males typically work more hours in the market…

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . …but women spend more time in household production

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . Overall, women spend more time in productive activities

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . Gender specialisation in time

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . Time Production and Consumption

. . . . . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion . . . . . . . . . . . . . . . . . . . . . . . Valuing household production

. . . . . . . . . . . . . . Introduction Data and Methodology Results Conclusion Valuing household production D omestic worker wage 15.3 million employed (6.4 percent) employment; bulk of unemployed are relatively unskilled most, if not all, household production activities the distribution . . . . . . . . . . . . . . . . . . . . . . . . . . ▶ Opportunity cost vs replacement cost ▶ Specialist vs generalist replacement ▶ For now, generalist replacement across all activities: ▶ Domestic work one of SA’s largest ‘employers’: 970k out of ▶ Unskilled workers account for 27.8 percent of total ▶ Domestic workers are commonly employed to undertake ▶ Imputed wages for bracket responses; trimmed top 0.2% of ▶ National mean hourly wage: R 32.43 (USD 2.95) ▶ Mean hourly wage: R 13.75 (USD 1.25) ▶ Median hourly wage: R 8.65 (USD 0.79) ▶ Offjcial minimum wage: R 6.44–9.12 (USD 0.59–0.83)

. R 104 billion R 327 billion Household production 18.4% 10.1% R 268 billion Female 7.2% 3.9% Male 22.4% 25.5% 14.0% R 372 billion NNTA work R 1 457 billion R 2 659 billion Value Labour Income 12.3% Male MEDIAN WAGE Male For females, care work is 13.9% of NTTA work 2.5% 1.4% R 37 billion Female 0.6% 0.3% R 8 billion 3.1% R 96 billion 1.7% R 45 billion Care work 15.8% 8.7% R 230 billion Female 6.6% 3.6% GDP Valuing household production . . . . . . . . . . . . . . . . . . . . . . Conclusion . Results Data and Methodology Introduction . . . . . . . . . . . . . . . . For males, it is 7.8%