The rationale for the design can be found in the original design , but an issue that occasionally comes up is the quality of the hash function, so that demands some more attention.
Many hash functions strive to have little correlation between the input and output values. They attempt to uniformally distribute the output values for very similar inputs. This hash function makes no such attempt. In fact, for integers, the result of the hash function is often just the input value. So similar but different input values will often result in similar but different output values.
This means that it is not appropriate as a general hash function. For example, a hash table may discard bits from the hash function resulting in likely collisions, or might have poor collision resolution when hash values are clustered together. In such cases this hash function will preform poorly.
So why not implement a higher quality hash function? Well, the standard makes no such guarantee, it just requires that the hashes of two different values are unlikely to collide. Containers or algorithms designed to work with the standard hash function will have to be implemented to work well when the hash function's output is correlated to its input. Since they are paying that cost a higher quality hash function would be wasteful.
For other use cases, if you do need a higher quality hash function, there are several options available. One is to use a second hash on the output of this hash function, such as Thomas Wang's hash function. This this may not work as well as a hash algorithm tailored for the input.
For strings that are several fast, high quality hash functions available (for example MurmurHash3 and Google's CityHash), although they tend to be more machine specific. These may also be appropriate for hashing a binary representation of your data - providing that all equal values have an equal representation, which is not always the case (e.g. for floating point values).