Dataset.

While growing bodies of scholars have examined the separate effects of extreme heat and COVID-19 on migrant farmworkers in the United States, we are unaware of any study examining their potential combined impact on agricultural labor supply. We analyzed the combined effect of extreme heat and COVID-19 on farmworkers’ decisions to work and the number of hours they work. We collected survey data from 280 randomly selected migrant farmworkers in three communities in California in 2020. We employed Heckman´s selection model to control for self-selection to participate in the agricultural labor market, despite the dual burden of the presence of COVID-19 and heat waves, which may be associated with unobservable factors such as different power standings between employers and farmworkers. We selected the variables used in our models using the Least Absolute Shrinkage and Selection Operator (LASSO), which is a Machine Learning technique to build simpler models by filtering out unimportant predictors. We found that extreme heat alone and the combination of heat and COVID-19 increase the probability of workers participating in the agricultural labor market, while COVID-19 alone does not. Our findings also show that, once workers enter the agricultural labor market under extreme conditions, the number of hours they work decreases in response to extreme heat, COVID-19, and the combined impact of both.