6.5 VALUING UNPAID CARE WORK TIME
6.5.6 The method and earnings rates most appropriate for the African poor in KwaZulu-Natal
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
generalist and specialist methods estimate the value of care provision if the care service were to be purchased.
The various methods present particular challenges. Earnings rates for the average earnings method (using earnings of the employed and the self-employed) are much higher for males than females because of the marked earnings differentials by sex across occupations, which are partly caused by men and women tending to be in different occupations and sectors. However, because of the minimal time spent in providing unpaid care by males, and because females spend so much time providing unpaid care, the value of the work of males is much lower than that of females using the average earnings method. Despite this, the average earnings method – especially the approach that uses the earnings of the self-employed – tends to undervalue women’s unpaid care of ill people. Nevertheless, the average earnings method using the earnings of the self-employed represents an important innovation for the South African context.
With regard to the opportunity cost method that uses education information, having a matric and over as the highest education level has a large impact on hourly earnings but very few of the household caregivers in the study have this level of education. Since most of the household caregivers have very low levels of education, this method results in a relatively low value for the work of most caregivers.
Only a few caregivers in the qualitative study were in employment at the start of the illness period, and therefore the opportunity cost findings based on employment information do not give an idea of the value of unpaid care work for all caregivers. Apart from the institutional-based personal care worker, the caregivers in employment would not have earned very much had they been working. As already noted, earnings rates based on occupation are not always higher than earnings rates estimated using education levels. This means that using the education approach, the value of the time spent in unpaid care work by an unemployed person could be higher than the value of the same time spent by an employed person using the employment approach. The opportunity cost method is therefore problematic in the South African context where very high rates of unemployment prevail.
A further difficulty of the opportunity cost method that uses employment information is that it is not likely that the very long hours spent each day in unpaid care work would have been spent on the relevant occupation. For eight of the ill people more than nine hours were spent per day providing unpaid care, longer than the maximum length of a work day of nine hours (according to the Basic Conditions of Employment Act). Therefore the calculated values of time spent in
unpaid care work do not accurately represent the value of time in employment that has been lost.
Changing focus to the two methods that attempt to estimate the value of unpaid care work if the household bought in the care, the generalist method that uses a proportionate approach puts the value closer to that of a personal care worker than a domestic worker, which is appropriate considering that about three-quarters of the work of caregivers consists of nursing type work.
This approach therefore takes account of the time weighting of work instead of finding an average of domestic worker and personal care worker wages and assigning it to the entire time spent in unpaid care provision.
The specialist method is not as appropriate as the generalist method which uses the proportionate approach. While the specialist method would be the best method to estimate the value of the unpaid care work for a caregiver who is a nurse, most of those undertaking unpaid care work do not have the knowledge nor the skills of professionally qualified nurses, and many of the tasks undertaken by the caregivers are basic domestic tasks, and this would therefore not seem to be an appropriate way to value their work.
With regard to mean and median earnings rates, it has been noted (see Budlender, 2008) that using the median to estimate earnings is better than using the mean – since earnings tend to be skewed toward the lower end, because the mean tends to over-state the true ‘middle’, and because using the median avoids the difficulty of having to deal with outliers. In Table 6.17 the median earnings rate is estimated as a percentage of the mean earnings rate in order to get an idea of how far apart the two estimations lie. This is not a perfect approach but simply one which helps to understand the difference between the two. Smaller percentages indicate that the distance
between the two measures is larger, while larger percentages indicate that the difference between the two is relatively small.
In 87 percent of cases the median earnings rate is lower than the mean earnings rate. On average, for all approaches for which mean and median earnings are estimated within the various methods (except the proportionate approach, for which it is difficult to do so) the median earnings rate is 76 percent of the mean earnings rate. It is only with regard to some of the approaches that the difference between the two earnings rates is great. For instance, the median earnings for ‘male metro – no schooling’ using the opportunity cost approach that uses education information is 40 percent of mean earnings. This indicates that the mean is overstating the true middle by just under half. In this case it would be safer to take the median earnings rate as a reference point. On the other hand, the median earnings for ‘female metro – no schooling’ using the same approach is 98 percent of mean earnings, indicating that the two earnings rates fall very close together and there is not much difference between them. It also means that the mean earnings lie very close to the midpoint. In this case it would be relatively safe to use the mean earnings rate in addition to the median earnings as the reference point. However, in 13 percent of cases the median earnings rate exceeds the mean earnings rate. For instance, for female institutional based personal care workers (using the opportunity cost method that uses employment information) the median earnings rate is 134 percent of the mean earnings. Here the mean earnings rate is lower than the midpoint.
Therefore the mean is understating the true middle, and here again the median earnings rate is preferred. Finally though, it is important to note that with some of these categories, for instance
‘messengers’ using the specialist method, there are so few observations that neither the mean nor the median is reliable.
Based on these study findings, conclusions can be drawn relating to the method for valuing unpaid care work most appropriate to the African poor in KwaZulu-Natal. To begin, the methods that value unpaid care work by asking what it would cost to buy in the care are preferable to the methods that seek to find out what the value of caregivers’ time would be if they were not providing unpaid care. This is simply because of the very high unemployment levels that prevail in South Africa, and because of the low chance of employability of most of the caregivers. In
other words, it is unlikely that many of the caregivers who are unemployed would find work easily, especially considering their low levels of education.
With regard to the approach that seeks to find out what it would cost to buy in the care, the specialist approach would seem inappropriate in the South African context, not least because of the high unemployment levels already referred to. It is highly unlikely that these caregivers would be remunerated at such high rates of pay for the different activities they undertake – the market would simply not see this through. The generalist method is a more appropriate method to adopt, since the work of personal care workers and domestic workers comprises most of the activities that they undertake, and because the earnings rates estimated for this work are, for the most part, in line with market-related wages for this type of work. Of all the approaches that fall within the generalist method, the proportionate approach – which is essentially a simple specialist approach – seems most appropriate and accurate in terms of valuing this work among the African poor in KwaZulu-Natal. Unpaid care work consists not simply of domestic work, nor is it entirely the work of personal care workers. It is made up of both types of work and it therefore seems reasonable to value it proportionately according to how these different types of work are
weighted. In other words, this approach represents well the hours spent on the different activities that comprise unpaid care work. It neither values unpaid care work too high nor too low – and it therefore seems to be an appropriate input-related method to choose for the valuation of the work that goes into caring for ill people in the home.
It also seems that the median earnings rate is the preferable rate to use when valuing the time spent in unpaid care work for ill people within the home. Whether the mean earnings rate falls above or below the median earnings rate (whether it over- or under-states the true middle), it is the latter that represents the true middle. Unlike the mean, the median is not affected by outliers.
For the types of employment for which earnings are estimated in this study, earnings are very much skewed towards the lower end, and the mean is therefore inappropriate to use. These are the reasons Budlender (2008) gives for choosing the median and these are the reasons that apply in this study as well. Therefore in section 6.8 in which costed unpaid care provision is estimated per method, only median earnings are applied.