NREGA financial performance

The Mahatma Gandhi National Rural Employment Guarantee Act funds rural employment. Their website provides reports regarding NREGA’s financial performance. Below is a visualisation of their performance for the year 2009-10 (as of March 2010).

Summary of funds and spending


About 60% of the Rs 46,000 crores available was funded by the Centre this year, with the States contributing only 12%. The bulk of the rest are from the previously available balance. This 46K crores translates roughly to Rs 600 per person in the rural areas (based on the rural population of 74 cr in the 2011 census).

The majority of the spend goes towards unskilled labour, followed by materials. The total spending so far accounts for 83% of the available funds. Based on current estimates, the actual spend will be 18% in excess of the available funds.

Visualisation of the State-wise data

The Visualization chart below shows the spending by state, sorted by available funds. The first column shows the % Spend as a ratio of available funds. Other than Karnataka, all other states have stayed within their funding limits.


The next column is a chart that shows a number of things, as given in the legend below, which helps with the way to read the actual chart.


You can see, based on this, that

  • Karnataka and Orissa were funded entirely by State funds rather than Central funds, while most other states were funded predominantly by Central funds.
  • Most states have spent more than the funds released in the current year, and are eating into their balance.
  • Some states are in danger of of exceeding the available funds, and quite significantly. Rajasthan, Madhya Pradesh, Andhra Pradesh, Bihar and Jharkard in particular.

The spend per capita column shows the amount spent per person in the rural areas, and the estimated spend per person. The rural population for the state is based on the 2011 census data.


The North-Eastern states of Tripura, Nagaland, Manipur and Mizoram are the ones that receive the highest assistance per-capita. Outside of these, Rajasthan and Himachal Pradesh too have a fairly high level of per-capita assistance.

The last column shows the mix of spending. Most states spend on unskilled labour and materials, and very little else. The only exceptions are:

  • Jammu and Kashmir, which has a reasonable expenditure on skilled labour, followed by Maharashtra and Nagaland
  • Goa, which has a fairly large component of recurring administrative expenses
  • Tamil Nadu, which seems to be spending almost nothing on materials, and focuses entirely on unskilled labour

For the raw data, please visit the NREGA website.

Colouring the calendar

Sometimes, just viewing a time series as a simple graph isn’t enough.

The graph below shows the daily visitors to a leading Indian website in 2011. The overall trends are apparent. There was a dip in Mar-Apr, and again in Oct, followed by a steady rise in November.


But what’s also apparent is a weekly cyclicality: the steady pattern of rises and falls several times a month, that disturbs this trend.

Yet, there’s considerable insight within that cyclicality, that a calendar heatmap can bring out. Here is the same data on a calendar heatmap. This is simply a calendar on which the values are plotted as a range of colours: red for fewer visitors, green for more visitors.


analytics-octoberThose dips you saw on the line graph? Those were Sundays, when browsing activity dives down consistently. However, as you can see from above, not all Sundays are equal. July 31st and August 7th, though they were Sundays, had considerable traffic. Similarly, weekdays can also experience dips. Jun 23rd is an example of a somewhat unusual dip, and so is Oct 26th – Diwali.

Calendar heatmaps provide a way of exploring information at a far richer level of detail than traditional line graphs or bar graphs do.

For example, they focus on weekly trends. In businesses where there is a weekly cyclicality, it becomes much easier to spot an unusual weekday. In the month of August (see below), it’s fairly obvious from both graphs that August 14th had a bad dip. But what becomes clearer from the calendar map (but not the line graph) is that August 13th was a relatively bad Saturday, and August 16th was a relatively bad Tuesday.


analytics-octoberSecondly, they focus on individual days. Its a lot easier to see the exact date on which an event occurred. For example, in the graph alongside, there has been a big dip in October. The most significant has been in the last week, specifically on October 26th. Once you know the date, it’s easy to associate the change in behaviour with Diwali as its cause.

On the line graph below, you can see the major dip in October. However, mapping this specifically to Diwali is a far tougher task.


Below is another calendar heatmap – this time, showing the percentage of visitors from New Delhi. Consider the month of August. We saw from the earlier calendar map that there was a decline in traffic between August 13 – 16. If that decrease was uniform across cities, the colours below would be uniform too. However, New Delhi’s percentage share declines as well on these days.


Apparently, the people at New Delhi are more likely to spend the day outside on Independence Day than most other cities! In fact, they seem to spend the whole of August avoiding browsing. However, the same cannot be during of Diwali. Delhi-ites are as likely / unlikely to be browsing during Diwali as any denizens of any other city.

The next time you look at data with weekly patterns, where you need to figure out quickly when exactly the numbers rose or fell, do try out a calendar heatmap.

Data visualisation course at IIIT

We are offering Data Visualisation course at IIIT Hyderabad and JNTU Hyderabad as part of the Master of Science in Information Technology (MSIT) outreach programme. This programme is offered by a consortium of universities in collaboration with Carnegie Mellon with the support of State government of Andhra Pradesh.

Through this partnership, Gramener is collaborating to create course content, design curriculum as per industry standards and also have joint partnership to execute projects on predictive analytics and data visualisation.

The course has 5 modules:

  1. Handling big data
    • How to scrape data from external sources
    • How to parse and transform it into a format you need
  2. Analysis
    • Segmentation
    • Predictive analytics
  3. Vector graphics
    • Drawing graphs using SVG
    • Tools to manipulate SVG
  4. Templates
    • Programmatically creating graphs using templates
    • Using data to drive the templates
  5. Gramener visualisation server
    • Using libraries to create visualisations

The course is also available online to those who are interested. You may email us at to access the content, exercises and videos.