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Hackernoon logoIdentifying The Poor in India: A Data Driven Analysis by@anish-malpani

Identifying The Poor in India: A Data Driven Analysis

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@anish-malpaniAnish Malpani

I fancy data, spoken-word, Arsenal. Poverty is multidimensional. SocEnt is hard.

Ever since I quit the corporate world, the story I have been telling myself is that I want to work on uplifting the poorest. It sounds romantic at the onset but like most things, is a lot more complicated when you get down into the weeds.

I am data-man, or so I would like to think. So, it is essential for me to understand the size and depth of this problem of poverty. And how do you even define poverty? Let alone quantify it.

Over the past three years, I believe I have earned a decent understanding of it. This is an attempt at formalizing that understanding.

First, what is poverty?

Believe it or not, this isn’t straightforward. There are four different ways to look at it:

Absolute Poverty: This draws an income line across the world and everyone under it is poor and everyone above it is not poor. There are different lines; the most popular being the “extreme poverty” line which is $1.90 / day adjusted for purchasing power parity. Which basically means let’s keep things apples to apples, not apples to moons. Pros and cons here and you can think about that all day.

Relative Poverty: This arranges people within a certain population according to income, and those that fall in the bottom tranche are considered poor. A good way of understanding this is that while there is no absolute extreme poverty in the U.S., there’s a ton of relative poverty. Relative poverty will almost always exist. Absolute poverty doesn’t need to.

Multidimensional Poverty: This is my preferred definition. It is based on the tenet that poverty is more than just a lack of money — it is a lack of access to healthcare, education and a decent standard of living. It just falls short of including dignity, but dignity is something even the Gods won’t be able to quantify. Intuitively, this increases the total number of poor because it increases that base standard we aspire to for humanity.

Hunger: If you’re absolutely poor, you’re probably hungry. So, sometimes, looking at the hunger index is also a good way of gauging pain. Sounds morbid, but it is easier to quantify.

And these definitions are just skimming the surface of the ocean that is measuring injustice. There are also recall methods, survey nuances, time-sensitivity issues, and other permutations that give data-abusers enough ammo to craft any story that fits their narrative.

I’m going to avoid doing that.

Okay enough jargon, so how many people in India are poor then?

You guessed right; there are many different estimates for this as well. But I promise I’ll end this section with somewhat of a reasonable number for us to anchor to.

Meanwhile, here is a bunch of poverty estimates for India that you’ll see flying around in the public:

Image by Author, Sources:2012 Extreme Poverty, 2020 Extreme Poverty, Multidimensional Poverty

It’s important to consider what the numbers were during the survey year because that’s when the data is the cleanest. Estimates based off that add more assumptions to an assumption-laden world, which only contaminates the truth.

For instance, I don’t fully believe World Poverty Clock’s assumption that India has only 40 million extremely poor people right now. Either way, I don’t think looking at extreme poverty is the best way of looking at specific country's poverty — it’s singular, and poverty is not singular. So, let’s drop that and progress.

Multidimensional poverty on the other hand, is hands-down the best way to look at poverty based on what’s out there. It was developed by Oxford University in 2010 so there’s some credibility for you. This metric is hard to calculate but not impossible. Enter UNDP. They have been nice enough to calculate and aggregate this for us.

Ahh, I’m finally getting to the point.

In India, in 2017, 28% or 374 million were multidimensionally poor. I did my own math from an incredible human development dataset from 2012 using a similar thought process, and my estimate was ~34% for back then.

So, taking all that into consideration, and the fact that India has been growing economically (at least pre-COVID), I think it is fair to assume that today, India has a multidimensional poverty rate of 20%-25%. Extrapolating that to current population estimates (2020), that’s somewhere between 274 million and 342 million people.

To make it a little easier, let’s settle for a number somewhere in the middle which conveniently comes to 300 million people that makes up ~22% of India’s population.

There, as I promised, a reasonable number we can anchor ourselves to.

Fine, so who are the poor?

It’s now time to get to the meat of things. If you’re familiar with the social impact space, none of the above should have been bombastically revealing. The following though? Maybe.

The question that was grappling my mind (and my heart) is who exactly are the poor? And not just from a meta, philosophical perspective, but from an actionable, data-driven perspective.

Farmers, construction workers, waste-pickers, Dalits — that’s what comes to mind right off the bat, but how many of them? By how much? Who else? From what perspective are they poor — health, education, income? And what about security guards and dhobis and coolies? Are they poor? Not all of them, obviously, but how many?

So yes, if work is the center of sustenance of life, I was curious what type of workers comprised the landscape of the poor in India.

I happened upon an incredible human development survey from 2012 that had data at an individual and a household level for over 40,000 households and over 200,000 individuals. It was representative and has been used by many impact strongholds including the International Labour Organization as recently as 2018. Credibility.

I isolated the 50,000 individuals who were mostly working, cleaned up some of the occupation data, mapped it, calculated a reasonable version of multidimensional poverty based on the data that was available, and voila! Here is how I answered those grappling questions of the head (and the heart).

Image by Author, Based on 2012 India Human Development Survey (IHDS)

No surprises here. ~79% of all poor workers in India (62 million) work either in agriculture or construction. Low skilled factory workers come third at 3%, which is substantially lower. The rest are 2% or less. The small revelation for me was the tobacco product makers (beedi-makers) at 2.2% or almost 2 million.

They earn the lowest on average and almost a third of all of them are multidimensionally poor. Interesting cognitive dissonance here: tobacco is bad for you but what happens to the millions of people who make a (poor) living out of making tobacco products?

Image by Author, Based on 2012 India Human Development Survey (IHDS)

We now know that most of the poor are farmers and construction workers. But they also constitute the largest proportion of all workers in general. So what percentage of farmers, and construction workers and drivers are poor?

Image by Author, Based on 2012 India Human Development Survey (IHDS)

Almost a third of all agricultural workers are poor. Construction workers are also above the average, and so are tobacco product makers, household help and stone cutters. And remember, these folks are not just poor from an economic perspective, they are also less educated and much more at risk with their health.

One final bit before I get all personal with you.

Inspired by UNDP’s Human Development Index (HDI), I created a morphed / modified version of it for the various types of workers of India. Why morphed? Well because I was limited by the information available by worker in the data set I was using. Enough excuses, enough disclaimers, it is still helpful, albeit not telling us anything spectacularly new.

Image by Author, Based on 2012 India Human Development Survey (IHDS)

Similar story — farmers, tobacco makers, construction workers, maids and stone cutters live a disdainful life.

One of the morphs I sizzled into this modified HDI was the inclusion of a social status index. This is a tad bit controversial and it’s pretty sad that I even have to include something like this, but in India, it’s just not advantageous being born into a lower caste or a marginalized minority.

Adding that dimension into the mix pulls the occupation group of sweepers lower down. Most sweepers and sanitation workers (~60%) are dalits (low caste folks). Most stone cutters are also dalits and many are adivasis (protected tribes). This shouldn’t make a difference, but it does, and not for the right reasons.

Where I’m coming from around all this

I have been on a nervy adventure of starting a social enterprise focused on multidimensional poverty here in India. And I have been fortunate to have the resources to really understand the space and be as research-driven as I can. Hence this poor little post. To be honest, I have written this more for myself than for anyone else. There is this crystallization of thought that occurs when you formally pen something down.

So if I was to follow my own research, it’s pretty clear. Focus on farmers. And if I want an urban life, focus on construction workers. Those are the biggest pieces of the poverty pie and they need the most amount of attention.

But that’s not what has been on my mind. 

I have been drawn to the waste management space (specifically solid waste) in India for a couple of reasons. One because it employs some of the poorest— waste pickers, scavengers, rag pickers. And two because there is value in waste than can not only fuel financial sustainability, but also make mother earth better.

With regards to the first reason, there isn’t a whole lot of data available that isolates the poorest in the waste management space. Case in point: the data set I am using in this analysis. Out of the 50,000+ entries, I found maybe ten or twelve entries that had their job description as “waste picker” or a Hindi-equivalent.

Some were grouped under agriculture, some under construction, some under coolies, some under sweepers, but to cut a long story short, this is not a category that’s looked at closely from a macro research perspective. There is probably some overlap with the “sweepers” category and there might be some noise is the “sanitation workers” category, but it is not explicit. And that matters because being a municipal sanitation worker versus a rag picker is significantly different.

Here’s an excerpt from Assa Doran and Robin Jeffrey’s excellent book, Waste Of A Nation that throws light on this darkness:

On the frontline of rubbish recovery are the people who collect waste. Scavengers, waste-pickers, ragpickers — by whatever name they are called, they carry a burden of poverty and prejudice. They are commonly regarded as dirty people, dislocated migrants, indifferent to basic hygiene. Their scavenging of open dumps is taken as an affront to social order and urban sanitation. And the fact that they work in places that were once regarded as no one’s land, or the commons, but now are often claimed by the state or private owners makes them ready targets for police harassment. Little is mentioned about the effects of their work in reducing the amount of rubbish destined for landfills. 
The most vulnerable scavengers work in grim conditions on mountainous landfills, such as Deonar in Mumbai, Okhla in Delhi, Dhapa in Kolkata, Kodungaiyur in Chennai, and less prominent dumps like Belgachia at Howrah in West Bengal. Estimates put scavengers’ life expectancy at thirty-nine years. In their search for defecation space and salvageable materials, adults and children have learned to tread lightly. At Deonar “there are cracks and crevasses” that can trip, and even swallow, waste-pickers, Doron was told when he visited the smoldering mountain, “and kids inhale the toxic fumes” spewed by the mountain. In 2017, a landslide at another site, East Delhi’s giant Ghazipur dump, killed two people.
The usual competition on open dumpsites comes from rats, dogs, pigs, monkeys, and birds — all thriving on mixed rubbish. For ragpickers, sporadic fires generate an acrid haze that makes breathing difficult and presents the greatest health risk. Waste workers register high levels of tuberculosis.

Doron, Assa. Waste of a Nation (pp. 211–212). Harvard University Press. Kindle Edition.

Also, Kaveri Gill wrote an entire book on poverty and waste-pickers based on her research in Delhi. It’s a little dated (2007–10), but she does have estimates for amounts they earn. When adjusted for inflation, waste-pickers make about 30% less than urban construction workers and low-skilled factory workers.

High-level estimates peg the the number of waste pickers in urban India between 3 and 5 million. Another excerpt from Doran and Jeffrey on how elusive a real number really is:

“The census does not have an occupational category for ragpickers or waste-pickers. In New Delhi, a common estimate was that between 200,000 and 350,000 people worked as waste-pickers in an urban area of 16 million people in 2011. Rough calculations suggest that India’s 53 cities with populations of more than 1 million support close to 2 million waste-pickers, and its 465 cities with populations between 100,000 and a million sustain a further 1.5 million. At that rate, urban India in 2011 had at least 3.5 million people handling waste every day, and these calculations do not include the manual scavengers who clean the dry latrines described in Chapter 3.”

Doron, Assa. Waste of a Nation (p. 189). Harvard University Press. Kindle Edition.

For the record, there are about 2.5 million “manual scavengers” who jump into sewers and cesspits, and I am not even looking to focus on them for now.

How do you quantify this sort of poverty? Of a group that barely gets isolated from a research perspective. Of a group whose life expectancy seems so absurdly low that it’s hard to believe. Of a group that competes with rats, dogs, pigs, monkeys and birds.

Of a group comprising low caste folks and stigmatized minorities that either way get treated like shit. What statistical weight do I put on what factors to spit out an index that quantifies inhumanity? 

Look, this does not mean that being a poor farmer or a poor construction worker or a poor tobacco product maker isn’t as bad. Those lives need uplifting as well, and in some dimensions, even more than waste-pickers. For instance, urban tobacco product makers earn almost half of what urban waste-pickers earn.

And when it comes to poor farmers and poor construction workers, we have already seen that in absolutes, they encompass countries worth of people. But where do waste-pickers lie in a world where even holistic data evades them?

Even if there was, it’s not just as simple as what the data says. There are other philosophical factors at play here that are almost impossible to program in while contemplating starting a social enterprise.

Such as the importance of a viable starting point, the future of work, the probability of success (where the metric is the # of people lifted out of multidimensional poverty) and the scalability potential of a model with high, untapped intrinsic value.

Each of these factors could probably do with a novel worth of explanations, but I’ll spare you the additional dramatic justifications, at least for now.

Or maybe I am overthinking it. Rationalizing my gut in true Haidt-ian fashion. Maybe it is as simple as what the data says and I just focus on farmers and construction workers and tobacco product makers?

This back and forth isn’t startlingly novel, so it’s not like Jesus has returned and I need to reevaluate everything. It has all been hovering around for a while. The only difference now is that it is time to make a decision.

A note on the sources for this post:

The base for the majority of this analysis is the micro-data of the India Human Development Survey: Desai, Sonalde, Reeve Vanneman and National Council of Applied Economic Research, New Delhi. India Human Development Survey-II (IHDS-II), 2011–12. ICPSR36151-v2. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2015–07–31.

I created and cleaned some of the data to make it more sensible.

I have cited sources for all one-off data where I have used them. 

Any questions / concerns, please do not hesitate to reach out. Am a believer in full transparency.


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