Clusters in UK Self-Employment

UK Self-employment has soared in recent years. With existing labour market policy designed to cater for conventional employee relationships, policymakers in this field are increasingly seeking to better understand these workers' characteristics in order to ensure that new labour market regulations are designed appropriately, and are targeted towards the groups that require social assistance. In this paper I apply a machine learning method to ask whether there exist distinct 'clusters' of workers within self-employment, corresponding to groups with similar observable characteristics. My analysis first uncovers a two-group typology, with a distinct divide between a low-educated male group and a high-educated female group. While groups differ on characteristics, drawing on new survey data I find that both are similarly satisfied with self-employment. I also uncover a six-group typology. This detailed clustering reveals a sub-group of low-educated young men who are dissatisfied with self-employment and are most likely to report self-employment as their only employment option, many of whom can be broadly classified as 'gig economy' workers.