This required first removing some confounding variables, notably random gents seemingly disturbed by states of singularity and wanting to know if they could have my mobile number to arrange a visit sometime soon. I appreciate that outliers can make the other dots in a series feel uncomfortable, or apparently get the wrong idea about the availability of the outlier for social outings, but I feel no need to be drawn into anyone else's line of best fit.
After some precision dispersal I found myself unfortunately regressing into a mental foetal position as speaker after speaker spoke about the state of statistics and economic measurement in India. Note that's not discussing the state of poverty in India, but rather how their ability to measure it has gone into decline since the original launch of the book, 'Poverty and Income Distribution in India', in 1974. Detecting a fairly low level of standard deviation in the discussion, someone in the audience finally dared to ask how the eminent scholars (some of the world's finest, at Yale and MIT for example) might actually address poverty in India. The response from the MC was 'that's not the topic of discussion', and we returned to talking about how to measure it instead.
If you've got some of the finest economic minds in the business sitting on the podium surely they could have offered some thought on how to address poverty in India, or the world for that matter, especially now that it's over 40 years since the first publication of the book and in that time India has radically changed, culturally as well as economically, as a result of globalisation and neoliberalism.
To be fair, maybe I'm just too focused on my indeterminate ethnography with its pretty pictures and thematic analysis, underpinned by the certain belief that human beings cannot be reduced in totality to discrete variables, diced into quartiles and measured like so much daal. But I'm happy to work with statisticians and I even got a distinction in statistics in my Psych degree. I know my T-test from my Chi Square. And yes, I get that the underpinning data, government statistics, could be better, and statistical tools, and statisticians, need to be robust.
But dear economists, even with these caveats, in these days of re-writing the economic curriculum take a stand on what this thing called 'poverty' actually means for real people and the human implications of what your measurements are telling you!
I have no review of the actual book. I was going to buy it but by the end of the seminar my eyes had reached peak glaze and the only thing that appealed to me was the cover photo.
