Monthly Archives: July 2014

Gender Ratios: Rural vs Urban India

India has an ugly problem with regard to gender ratio in its population. This problem is of recent vintage. Until 1971, the gap seems to have been negligible; in the past generation however, a gap has opened up and is worsening exponentially with every decade.



The state-wise distribution, especially among large states, tells a story. Most of India, except Kerala, Tamil Nadu and Chattisgargh, have a sex ratio that’s worrying. Some rich states in North India, such as Punjab and Haryana, are far worse than poor ones like Rajasthan and Uttar Pradesh.



But what stands out is that Delhi in this list is the absolute worst. Chandigargh as Union Territory is also close behind Delhi. A hypothesis one is therefore tempted to arrive at is: Urban India, especially in northern parts of the Country, is where men from the country side congregate to find work. Possibly skewing an already bad gender ratio to make it even worse.


But as the data suggests, only Delhi and Punjab have this skew. Most other states, despite their overall bad numbers, don’t have urban migration of men skewing the numbers much further. Sikkim and Jammu & Kashmir can possibly be excluded from this analysis owing to the Indian Army being made up mostly of men.

The other question one’d then ask is: how does the ratio vary within the various sized villages in that particular state? Do smaller villages have better gender ratios compared to larger ones? Broadly, that seems to be a reasonable assumption;  the largest state of Uttar Pradesh and the worst offending states of Punjab and Haryana seem to fit the pattern.





The above charts, as the X axis indicates, measure the M/F ratio for villages of various sizes [1]. Starting will villages under 200 people and going upto villages over 10,000 people. It appears the most skew in gender ratio is possibly not in India’s cities and towns but in its larger villages.

[1] – Apologies on the confused ranges of the X axis. The actual ranges are, < 200, 200-499, 500-999, 100-1999, 2000-4999, 5000-9999 and >10000.

[2] All data is from