McCormick+Schmitz Handbook for value chain research on - PACA

gendered <strong>chain</strong> map looks like. Its main limitation is that some of the most significant
variations can be found within the broad categories used in this map. For example, in the
sphere of production you may want to distinguish between the 98% female work<strong>for</strong>ce in
sewing operations and the 90% male work<strong>for</strong>ce in the cutting room. These or other more
detailed distinctions are best brought out in sub-maps concerned with particular parts of a
<strong>chain</strong>.
Earnings
In order to work out the average earnings in different <strong>chain</strong>s and different stages in the
<strong>chain</strong>s, you will need to draw on a range of sources. The wage in garment retailing in the
US or European countries can be calculated from the official statistics of the
Government’s Labour Office, <strong>for</strong> example, the US Bureau of Labour Statistics. Average
earnings in garment production may also be available from government sources but check
them against other sources. Labour unions are the obvious source. Some labour unions
have their own <strong>research</strong> wing; <strong>for</strong> example, the South African Clothing Federation
collects and publishes wage data. The problem with these sources is that they probably do
not provide data <strong>for</strong> the specific location and <strong>chain</strong> in which you are interested. For such
disaggregated data you will probably need to enlist the help of the local labour union and
corroborate their data with those from a few enterprises in the <strong>chain</strong>s concerned.
In the case of homeworkers, you need their help to work out the average earnings. Do so
with different homeworkers, like Agnes and Maria. You will need the piece rate of a few
typical products in order to work out their earnings. A critical assumption concerns the
number of hours worked. Since you want comparable data, the earnings presented in the
map should be based on the number of hours typical of full-time factory workers. Or you
can, as shown in Figure 9.1, show the hourly earnings. This would then need to be
complemented with data on the fluctuations in working hours.
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