For constructing text embeddings, mean pooling, which averages token embeddings, is the standard approach.This paper examines whether mean pooling actually works well in real text encoders.First, we note that mean pooling can collapse information beyond the first-order statistics of the token embeddings, such as second-order statistics that capture their spatial structure, potentially mapping distinct token embedding distributions to similar text embeddings.Motivated by this concern, we propose a simple metric to quantify such a collapse induced by mean pooling.Then, using this metric, we empirically measure how often this collapse arises in actual models and texts, and find that mean pooling works well in modern text encoders.In particular, this collapse is less likely to arise in contrastive fine-tuned text encoders than in their pretrained backbone models.We also find that the robustness of these text encoders to collapse stems from the concentration of token embeddings within each text.In addition, we find that robustness to this collapse, as quantified by our proposed metric, correlates with downstream task performance.Overall, our findings help explain why modern text encoders remain effective despite relying on seemingly coarse mean pooling.