Over the last year or so, we’ve done several separate posts on different types of deprivation (or access) using the Census 2011 data. We’ve looked at everything from phones to toilets, to drinking water to electricity. It’s time to put these together in one chart, and try and link these diverse measures together. If a district has high ownership of TV sets for example, thus implying a certain standard of living among households there, how likely is it that those households will also have access to the basics – say water or electricity?
The chart below may look a confusing mass of different coloured lines, but it’s quite simple to interpret. Each line represents one district. Each state is allocated a different colour, and states are ranked from bottom to top (red to green) according to their per capita income.
There are a bunch of different axes from left to right, measuring different parameters – phones, electricity, TV sets etc (also note the ‘rich’ and ‘poor’ measures which I’ll come to in a moment). Tracing the points at which each line (a district), hits an axis, tells us how that district scores on each parameter. So for instance, Paschim Champaran, in Bihar, which is the first entry in the table below the chart, has 52% of households with a phone, 9% with a TV set, 52% with access to water and so on. Hover your mouse over a row in the table and the corresponding district in the graphic gets highlighted.
You can go deeper. Click and drag on any axis to filter only those districts whose lines pass through that part. Now you can move the filter up and down to update the graph. The data table is updated automatically as well, enabling you to get at the actual numbers. You can put in multiple filters on different axes as the same time. Simply click on the ‘reset filters’ button to get back to the original view.
If for instance, we highlight the state of Delhi at the top left, we can see that Delhi’s districts are clustered fairly close together on all measures. Now move the filter down, and we see that other high income states such as Maharashtra, Haryana and Gujarat, are actually quite diverse at the district level, with some districts scoring high, and others scoring low. A state like Kerala may rank lower in the scale of per capita income, but on most measures, Kerala’s districts are tightly clustered together (like Delhi), showing that district-level inequality is lower in that state, than in other, richer, states.
A word about the ‘rich’ and ‘poor’ measures. ‘Rich(a)’ and ‘poor(a)’ are measures of access to a basket of consumption goods, rather than just one. ‘Rich(a)’ for each district, indicates the percentage of households who have a TV, computer, phone and a motorised vehicle (2 or 4-wheeler). ‘Poor(a)’ indicates the percentage of households in a district who have none of the consumption goods covered in the census – not even a phone.
Similarly, ‘rich(b)’ and ‘poor(b)’ measure access to a basket of household amenities like drinking water, toilets, and electricity simultaneously. ‘Rich(b)’ measures those households who have electricity, and access to drinking water and a toilet at home. ‘Poor (b)’ measures those households who have no electricity, and no toilet at home, and have to travel more than 0.5 km for drinking water. Note that unlike the other measures, the lower you are on the ‘poor’ measures, the better off you are.
The table displays data for only ten districts at a time.
As always, all data is from the census 2011.
Last week I mapped the proportion of households who had to travel more than half a km in rural areas (or 100m in urban areas) to find drinking water. The share of such households in the overall population had actually increased between 2001 and 2011.
The map below shows the other part of the story. It maps the proportion of households who had access to drinking water at home, both in rural and urban areas. Interestingly, the proportion of such households too, actually increased between 2001 and 2011 – by about 7.6 percentage points.
But it’s the regional differences which are interesting…compare the map below with the previous one to see what I mean.
(This post may take a while to load on a slow connection, and/or if you are using a tablet or phone. Click or tap on the map to switch between 2001 and 2011. Hover your mouse over a tehsil/subdistrict to see its details. The greyed-out regions are those for whom data couldn’t be compiled, or those which are not relevant e.g. PoK)
In a swathe of central and eastern India in the previous map, access had actually worsened – more households were having to travel a greater distance to find drinking water. In the map above we see that in Northern, Western, North-Eastern and Southern India, and parts of UP and Bihar, access seems to have actually improved – more households have access to drinking water at home in 2011 than a decade earlier.
But we have to be careful in interpreting what ‘improved’ access actually means here – other studies and data show drops in the water table in many of the areas where the census data shows better access. As more households use tubewells / borewells at home, there’s greater pressure on ground water resources.
So on closer examination, even the good news may not be that great…
Please see the previous post.
The Census splits households into three categories based on their access to drinking water. The first one is those households who have a source of drinking water ‘on premises’. A second category covers households whose source of drinking water is ‘near’ the premises i.e. within half a km in rural areas, or 100m in urban areas. The last category (and presumably the worst off) are those households who have to travel more than half a km in rural areas, or more 100m in urban areas, to get drinking water. Below, I’ve mapped this last category of households by subdistricts across the country. The darker the colour of an area, the greater the share of such households in any given tehsil/subdistrict. Click or tap on the map to switch from 2001 to 2011 and back again.
It was Varsha Joshi who made the point (in a larger context of how Census 2011 reveals sharp gender disparities) that the percentage of households in rural areas who have to travel a distance to find drinking water has actually increased between 2001 and 2011 from 19% to 22%. In urban areas, there was an improvement, but a relatively weak one. In net terms, the percentage of households who had to travel ‘away’ from home to find drinking water, increased by almost two percentage points.
(Apologies but this post may take a while to load on a slow connection, and/or if you are using a tablet or phone. Click or tap on the map to switch between 2001 and 2011. Hover your mouse over a tehsil/subdistrict to see its details. The greyed-out regions are those for whom data couldn’t be compiled, or those which are not relevant e.g. PoK)
The worst impact is visible in a belt stretching across Central India – from large parts of Madhya Pradesh, to Odisha, Jharkhand and West Bengal. Parts of Chattisgarh, Assam and Karnataka have also seen a decrease in access to drinking water sources.
But there’s another side to this story and it’s important to point that out. Between 2001 and 2011, the share of households for whom access to water was ‘near’ the premises, actually fell by about 8.5 percentage points. Offsetting this, the share of households who had access to water at home increased by about 7.6 percentage points (rural and urban households together). As did the third category of households, mapped above. Put simply, the middle category of households saw a drop while the two opposite ends of the spectrum (the least privileged and the most privileged), saw their numbers increase. I’ve mapped one part of this story here – the other side is next.
The census data on drinking water is even richer than this – it actually breaks the data down further by source of drinking water – taps, tubewells and so forth. It’s all well worth checking out.
The map is based on the 2001 tehsil boundaries so I had to map the 2011 data to those boundaries.
The mapping across the census years is imperfect since I only did it with the data available at the sub-district level. Real accuracy would require mapping at the village level and then aggregating upwards but that’s really difficult and time-consuming.
For the map, I converted the shapefiles which come with the DevInfo software into SVG format for easier handling.
The drinking water data is from the Census 2001 and Census 2011 tables.