This study follows the Lordan and Neumark (2018) analysis for the US, and examines whether minimum wage increases affect employment opportunities in automatable jobs in the UK for low-skilled low-wage workers. Overall, I find that increasing the minimum wage decreases the share of automatable employment held by low-skilled low-wage workers, and increases the likelihood that workers in automatable jobs become dis-employed. On aggregate the effect size is modest, but I also provide evidence that these effects are larger in more recent years. The study also highlights significant heterogeneity by Overall, I find that increasing the minimum wage decreases the share of automatable employment held by low-skilled low-wage workers, and increases the likelihood that workers in automatable jobs become dis-employed. On aggregate the effect size is modest, but I also provide evidence that these effects are larger in more recent years. The study also highlights significant heterogeneity by industry and demographic group, including more substantive adverse effects for older low-skilled workers in manufacturing, as well as effects at the intensive margin. This underlines the importance of considering differential effects at the individual level to understand completely who the winners and losers are. Specifically, those that lose are part-time women [ 3 – 4 ], part-time workers in general [ 3 ], service industry employees [ 5 ] and care home workers [ 6 ]. In addition, there is evidence of a significant amount of job switching for low-paid workers after minimum wage increases, which has associated costs [ 7 ]. Outside employment effects employers can respond in a number of other ways to increases in the minimum wage. For example, they may alter job amenities [ 8 ] or compress wages [ 9 – 10 ]. A recent paper by Lordan and Neumark [ 11 ] explores whether minimum wage affects the employment possibilities for workers relying on automatable employment indirectly by considering if individuals in automatable jobs are more likely to lose their jobs, following minimum wage increases in the US. The authors note that the adoption of new technology should also create jobs within firms as they require different types of workers to maintain their new technologies, however these roles will be of higher skill than the ones the technology replaced. A related analysis [ 12 ] analysed the susceptibility of low-wage employment to technological substitution. 2) lag of Industry level unemployment rate. 3) lagged area level demographics that vary over time: average age, education, gender. 4) occupation demographics measured at the area/industry/year level. Specifically, these are average age, education, gender. The estimates do not change if lags are replaced with contemporaneous values. I am specifically interested in b 1 , where a negative and significant estimate implies that the share of automatable employment is declining in response to minimum wage increases. Individual-level analysis for automatable employment I also estimate regressions using individual-level data on low-skilled low-wage individuals. Specifically, I estimate: (5) E M P j i a t = b 1 ( R S H j i a t L o g M W a t ) + b 2 R S H j i a t + T i S s γ + I i φ + ε j i a t In Eq ( 5 ) Emp is the probability that the j th person is employed in industry i , area a , at time t+ 1. It is assigned zero if a person was unemployed in t +1. The sample consists of those low-skill low-wage workers employed in period t , and either employed or dis-employed in period t +1. In this analysis the first period is quarter 1 and the second is quarter 5. Therefore, I calculate the probability of still being employed one year later, and the probability of still holding the same occupation with a one-year lag. Minimum wage is then defined at the level that was in effect when the person was interviewed in 2015 prices. Analysis of hours I also consider whether minimum wage increases cause changes to hours in automatable employment like Lordan and Neumark [ 1 ] by re-estimating Eq ( 3 ) and relating the log of minimum wage to the share of automatable hours. To consider whether a higher minimum wage actually increases dis-employment among low-skilled low-wage workers who were in automatable employment relative to workers in non-automatable work, Table 5 reports estimates of Eq ( 5 ), which models the effects of the minimum wage on the probability a particular individual who holds an automatable is still employed, as opposed to being dis-employed, compared to those in non-automatable work. Given the effect is identified from the interaction between minimum wages and being in automatable work, the baseline minimum wage effect is then not identified because of the inclusion of year*area fixed effects. For example, a 10% increase in the minimum wage reduces by 0.08 percentage points the overall probability of remaining employed in the next period for White low skilled low wage workers in automatable employment. The effects for Black and Asian workers are noisy, but do suggest significant declines in banking and finance for Asian workers (0.04 percentage point decline). Hours effects It is also possible that firms substitute with technology and decrease the hours of their employees, rather than culling their jobs I consider this explicitly by re-estimating Eq ( 4 ) and relating minimum wage variation to an alternate dependent variable. Drawing on the QLFS from 1994–2017 and classifying each occupation as either automatable or non-automatable I consistently highlight that minimum wage increases significantly decrease the shares of automatable employment available to low-skilled low-wage workers following a minimum wage increase. However, these effects are modest in size. For example, an increase of 10% in the minimum wage implies a 0.11 percentage point decrease in the shares of automatable employment. This effect is about one third the size found by Lordan and Neumark [ 1 ]