This paper estimates the causal effect of Venezuelan migration on the wage distribution of low-wage Colombian workers following the 2016 border reopening. Combining a machine-learning-based exposure measure with a grouped difference-in-differences design, I find that the shock increases the probability of being at the bottom of the wage distribution by 3.1 percentage points, alongside a corresponding decline of 3.1 percentage points just below the minimum wage; both effects are statistically significant at the 1 percent level. The effect is concentrated in industries where the minimum wage binds tightly and informality is high. A placebo group with low predicted exposure, but subject to the same local labor market shocks, exhibits no changes across wage bins, and a battery of sensitivity checks rules out selective worker sorting across cities, differential minimum-wage bindingness, and dependence on the training year of the classifier. The shock does not raise unemployment but increases labor-force participation by 3.5 percentage points among the most exposed workers.
