  Linguistic disparity in the NLP world is a problem that has been widely
acknowledged recently. However, different facets of this problem, or the
reasons behind this disparity are seldom discussed within the NLP community.
This paper provides a comprehensive analysis of the disparity that exists
within the languages of the world. We show that simply categorising languages
considering data availability may not be always correct. Using an existing
language categorisation based on speaker population and vitality, we analyse
the distribution of language data resources, amount of NLP/CL research,
inclusion in multilingual web-based platforms and the inclusion in pre-trained
multilingual models. We show that many languages do not get covered in these
resources or platforms, and even within the languages belonging to the same
language group, there is wide disparity. We analyse the impact of family,
geographical location, GDP and the speaker population of languages and provide
possible reasons for this disparity, along with some suggestions to overcome
the same.
