Technically, and I emphasize the technically, the set of function represented by a neural network require only one layer. However, there is little guarantee that you can feasibly find the proper configuration or train the network accurately.
By adding another layer, you can reduce the training burden by spreading it across layers. The extra dropout also allows more regularization.
This is the part of deep learning where it's less science and more, "eh, sounds like it works."