Alex Wood – April 27th 2021 10:00 GMT
The gig economy is now a profound part of our economy and society, and goes hand-in-hand with the casualisation of labour, especially as the digital economy has become more advanced. In this seminar, Alex Wood will introduce us to his research on the gig economy, based on empirical work with gig workers from six countries across Sub-Saharan Africa and Southeast Asia. Despite varying country contexts and types of work, Alex shows that algorithmic control is central to the operation of online labour platforms. Algorithmic management techniques tend to offer workers high levels of flexibility, autonomy, task variety and complexity. However, these mechanisms of control can also result in low pay, social isolation, working unsocial and irregular hours, overwork, sleep deprivation and exhaustion.
Each seminar was recorded and featured one or more readings, please find these below.
Click here to watch the recording.
This morning Alex Wood spoke to us about his 2019 paper published in Work Employment and Society about job quality in the gig economy.
Some examples of gig work that Alex presented to us. It’s more widespread than many people think and stretches far beyond Uber or Amazon Flex.
Much of the work is based on writing. Writing fake reviews for books, holidays, travel and so on. Many of those involved in this have ambitions to be writers and find the work very stimulating. Alex gave us a fairly comprehensive (though still incomplete!) list:
This paper and some of Alex’s other work is based on empirical work done in SE Asia and sub-Saharan Africa. Both face-to-face interviews and a survey with almost 700 participants.
One of the most interesting main findings that Alex was keen to talk about was the autonomous and joyful nature of the work. While the hours are long and the work can be difficult, many gig workers told Alex and his colleagues that they very much enjoyed their work. It allowed them to pick their own working hours, they enjoy a huge amount of flexibility in how the work is done, they get to be their own bosses, pick their clients, negotiate pay and so on. Of course, the workers also said that they felt some anxiety at being at the mercy of the algorithm, where a single bad review can end their ability to work on a specific platform. Alex also emphasised that he wished to repudiate the belief that the gig economy was Taylorist in character. Under Taylorism there is a high level of monitoring and control of the workers throughout the labour process. However – the algorithm only monitors the result of the labour. It does not track or monitor labour as it is being done, but instead ‘sums up’ the labour after it has been done by giving it a score or a rating. Workers stated that this gave them considerable autonomy, and actually made them feel less (rather than more) monitored. The gig economy then involves considerable elements of give and take.
There are differences between groups of workers, but also quite significantly there are huge differences WITHIN groups. Even for workers in the lower earning percentile, a worker at the top of the lower band makes 19 times more money than a worker at the bottom of the lower band.
Alex concluded his talk by talking to us about the give and take of gig work. It is not simply the case that gig workers are living in a Panopticon / dystopia. Rather they have a powerful sense of control and autonomy and attachment to their work. On the other hand it is not all positive – gig workers live at the mercy of the algorithm, if a customer gives them a bad rating then all of a sudden they will be unable to find any work. Gig workers feel a powerful need to perform, and so they work long hours, insist on perfection and often work nights. There is also a profoundly gendered element to this, women tend to do more routine tasks, but are less likely to lose sleep. Whereas men do less routine tasks, but work at more unusual hours.
Previous seminar: Arpad Szakolczai – Fairground Capitalism.
Next seminar: Angus Bancroft – Rationalities and Moral Economies in Illicit Drug Markets.