6.5 VALUING UNPAID CARE WORK TIME
6.5.5 Comparing results from the various methods
Table 6.17: Earnings across methods (Rands)
Method Approach Median
hourly earnings
Mean hourly
earnings Median earnings as
% of mean earnings
Female average earnings 4.58 8.89 51.5
Av earnings
(emp) Male average earnings 7.92 12.95 61.2
Female average earnings 3.43 6.14 55.9
Av earnings
(self-emp) Male average earnings 6.10 12.73 47.9
Male metro – no schooling 3.05 7.60 40.1
Male metro – primary/inc primary 9.15 10.60 86.3
Male metro – inc secondary 8.47 10.25 82.6
Male metro – matric plus 15.26 18.03 84.6
Male non-metro – no schooling 3.62 6.37 56.8
Male non-metro – primary/inc primary 4.09 7.34 55.7
Male non-metro – inc secondary 6.10 9.15 66.7
Male non-metro – matric plus 11.44 16.99 67.3
Female metro – no schooling 4.13 4.22 97.9
Female metro – primary/inc primary 3.81 4.89 77.9
Female metro – inc secondary 4.16 6.11 68.1
Female metro – matric plus 8.24 12.04 68.4
Female non-metro – no schooling 3.05 3.86 79.0
Female non-metro – prim/inc primary 3.24 4.24 76.4
Female non-metro – inc secondary 3.81 5.93 64.2
Opportunity cost - education
Female non-metro – matric plus 10.30 15.72 65.5
Female – inst. based pers. care workers 18.12 13.49 134.3
Female - traditional med. practitioners 3.81 4.74 80.4
Female – salespersons 1.83 1.66 110.2
Opportu- nity cost – work
Female - farm-hands & labourers 3.18 3.51 90.6
Generalist Proportionate-dom-+ nursing-type work 3.43+7.63 3.66+11.44 93.7+66.7
Nursing 26.70 24.56 108.7
Buying 14.24 11.98 118.9
Cooking 5.72 6.64 86.1
Laundering 6.57 9.50 69.2
Specialist
Messenging 5.40 11.35 47.6
Table 6.17 shows the mean and median earnings rates across the approaches for all the methods.
The results are likely to differ from Budlender’s (2008) findings because this small localised sample is not nationally representative. However, the results are compared to Budlender’s (2002) recommendations on what the lowest, highest and widest range of estimates should be.
The lowest earnings rates, using both the mean and median, are for a female salesperson, using the opportunity cost method that uses employment information. This finding is in contrast to Budlender’s (2002) forecast that the generalist method will produce the lowest values of all the methods. While domestic workers may be at the low end in terms of earnings, there were caregivers in the qualitative study who were earning even less informally, hence the difference from Budlender’s recommendation.
The highest earnings rates using both mean and median are for nurses using the specialist
method. Since the bulk of caregivers’ activities consist of nursing type work and the earnings rate for nurses is the highest for all the occupations considered for the specialist method, the value of unpaid care work is very high using this method. Part of the reason why this profession has the highest earning rates is because the earnings for nursing and midwifery professionals was used and not the earnings of nursing assistants. This is because there were no nursing assistants in the September 2004 LFS. If there had been such an occupational code it would have reduced the earnings rates substantially. This finding is in contrast to Budlender’s description of the
opportunity cost method as the one that produces the highest values, but this could be attributed to the occupational code selected, as described above.
The widest range of estimates, using both the median and mean earnings rates, is produced by the specialist method, reflecting the wide array of professions cited. This is in contrast to Budlender’s prediction that the opportunity cost method gives the widest range of estimates. However she qualifies this by stating that it depends on the skills and the opportunity wage of the individual performing it. Budlender’s recommendation would have been true for this study too, if the nursing and midwifery professionals code had not been used in the specialist method, since the opportunity cost method that uses education information produces the ‘next’ widest range of
estimates. A lot therefore depends on what occupational codes are available to be used, and therefore much depends on what the limitations of the data set are from which earnings rates are imputed. Clearly the limitations of the September 2004 LFS data for KwaZulu-Natal have steered the findings in a certain direction, accounting in part for the differences from Budlender’s (2002) description of what the findings are likely to be.
What explains the difference between the methods? The two average earnings methods both result in a single earnings rate per sex, and for the remainder of the methods there are an array of earnings rates determined by level of education or the selected occupational codes. The
occupational codes are determined to some extent by the occurrence of cases in the LFS data.
Which methods are appropriate to the limitations of the LFS data? For the opportunity cost approach that uses employment information and the specialist method, the sample sizes were very small, so small as to be considered unreliable, which brings into question the use of these earnings rates. Nevertheless, these approaches were still applied to the study data for the sake of being comprehensive. The findings from these approaches should however be treated with caution, particularly with regard to the particular professions that had very low sample sizes in the LFS, and this limits the usefulness of these approaches using the LFS data. The two average earnings methods, the opportunity cost method that uses education information and the selected generalist method all seem robust when using the LFS data.
6.5.6 The method and earnings rates most appropriate for the African poor in