Actually it doeas!
I happened to make a presentation of a paper that talks about this topic. They called it DenseNet, which stands for densely connected convolutional networks. Just like in your question, within a denseblock the output of each layer is given as input to all subsequent layers. Put another way, in a normal feed forward neural network the l-th layer is a function of the previous output x_l = H(x_{l-1}), while in the densenet each layer is a function of all the previous outputs x_l = H([x_0, x_1,..., x_{l-1}])

However, since it is a CNN, there is a reduction in the size of the feature maps with each pooling layer, so to keep the dimensions constant, there is an alternation between denseblock and pooling layers.
The results are clear: not only in almost all the tests the accuracy of the densenet is greater to that of the other methods, but they do so using up to 90% fewer parameters, i.e. they have a high efficiency of parameters. Moreover, as suggested by the authors themselves, the improved accuracy can be explained by the shorter connections between the layers, which allow to act during the training phase in a deep supervision fashion, solving the vanishing gradient problem. This is similar to how it was done in other methods, but with a less complicated gradient.
If you're interested you should definitely check out their paper.