Accounting for India’s ‘Missing’ Women
It’s been well over twenty years since Amartya Sen first coined the term ‘missing women’ to describe the highly-skewed sex ratios across a number of countries, including India. As of the latest census, India had a sex ratio of 940 women for every thousand men. Assuming an ideal sex ratio of 1057 women to thousand men (UN figures for developed regions), that gives us a ‘shortage’ of around 117 women, or roughly 73 million ‘missing’ women as of 2011.
But how do India’s missing women break down by age group? It was long assumed that the main cause of India’s missing women was either female foeticide or infanticide, or worse care in younger age-groups than male peers, leading to higher mortality rates for girls.
But a new paper published last December in the Economic and Political Weekly by Siwan Anderson and Debraj Ray throws entirely new light on that question. Their methodology is slightly different and they estimate ‘missing women’ per year, but it’s entirely consistent with the approach above. Here’s their conclusion:
We estimate that a total of more than two million women in India are missing in a given year. Our age decomposition of this total yields some striking findings. First, the majority of missing women, in India die in adulthood. Our estimates demonstrate that roughly 12% of missing women are found at birth, 25% die in childhood, 18% at the reproductive ages, and 45% die at older ages.
Only in Punjab are the majority of missing women to be found at birth. There are just two states in which the majority of missing women are either never born or die in childhood (i e, before age 15), and these are Haryana and Rajasthan. Moreover, the missing women in these three states add up to well under 15% of the total missing women in India.
Here’s a simplified example to show how they calculate the number of ‘missing’ women : Assume that in the west, four of every thousand women in the 30-35 year age group die every year, compared with five of every thousand men. That relationship – four female deaths to every five male deaths, should ideally hold here as well. If instead, India sees six female deaths to every five male deaths in the 30-35 year age group, that difference – two ‘extra’ female deaths a year – is the measure of missing women for that year (again, please don’t get confused between missing women at any point in time – 73 million in 2011 – and missing women per year which is 2 million).
You can perform a similar exercise for all age groups, as well as a separate calculation for the sex-ratio at birth, which is the figure that is most talked about in the ‘missing women’ debate.
The paper calculated the numbers for 2003. I’ve redone the numbers to calculate excess female deaths per thousand across age-groups for India, China, and a number of Indian states for 2010. Each bar shows excess mortality per 1000 women in that age-group only.
I deliberately didn’t go the final step and actually calculate the number of ‘missing women’ in 2010, because the data should ideally be averaged over several years before you do that (the paper does this). So I left it at excess deaths per thousand.
The final point, and I’ll quote from the paper again, is the following. It’s not exactly earth-shattering but I guess it’s worth emphasizing:
what is most clear from our exercise is that the plight of adult women in India is as serious a problem as that of young girls who were either never born or die prematurely in childhood.
Another key point to note is that the total number of missing women per year in absolute terms, is heavily dependent on the structure of the population i.e. how the female and male populations are distributed across different age-groups. I’ll leave you with a question: What does this research mean, in the context of the changing age structure of the Indian population? I’ll probably look at this topic in a future blog post.
My benchmark to calculate missing women is the UN data for what they call ‘more developed regions’. The data for India is from the Sample Registration System on the census website. UN link here, and SRS link here.
For some reason my web hosting provider doesn’t allow me to upload excel files. If you want worksheets where I calculated the data, please email me at email@example.com
Please note that India, China, UP and Haryana are scaled in a different way to the other states (i.e. the y axis numbers change). That makes comparisons across states difficult I know, but it was the only way to fit in such a wide range of data.
Perhaps the biggest problem with my calculations is that it pertains to one year only – 2010 – when the numbers should ideally be averaged out over a few years at least. Not doing that is probably why, for some age-groups for some states, I actually end up with a surplus, not a deficit. While this is plausible for states like Kerala, it’s less so for states like Haryana, and I can only attribute this to problems with the ‘noisiness’ of the data. In all such cases, I’ve simply put the excess deaths number to zero.
April 21, 2013