Dvara Research BlogDvara Research Blog
Dvara Research Blog
Doorway to Financial Access
  • Home
  • Our Work
  • Themes
  • Subscribe
    • Email Subscription
    • Feed
  • Contact Us
Menu back  

Leveraging Mapping for the Rural Economy – Part 1

November 25, 20134 CommentsHousehold Research, Technology Viewed : 5849

By Balajee GE, with inputs from Shilpa Bhaskar & Gayathri V, IFMR Rural Finance

How do spatial parameters like distance and accessibility impact financial inclusion? How can an organisation striving to achieve financial inclusion locate itself strategically such that it becomes truly inclusive in every sense of the word?

When Shilpa Bhaskar and team visited a KGFS branch on an assignment, they noticed that the product renewals by customers who were in villages around 4 or more kilometres away was comparatively lower than by the customers who were close by. This was despite the fact that enrolment penetration was high across these areas.

This might not appear unusual at first look, as it is understandable that frequency of visit thins out as one goes further away from the office of the service provider. However, in this case, the said village was specifically mapped to the KGFS branch. This means that the service area of the branch was fixed and the branch was dedicated to serve customers from these areas and yet customers did not make full use of it. The team went back and decided to look into this a bit deeper.

They then learnt that, the existing method of fixing a branch service area as an approximate five-kilometre radius purely based on political map boundaries and relying on secondary data sources and manual recces could be made more efficient. Though the distance may be optimal, there could be other factors in play. For example, though the distance to the branch was less, the customers might not have access to bus services and might have to cross flooded fields to reach the KGFS branch. Or the commercial centre of the said village could be on the other side and villagers hardly found any reason to travel in the direction of the branch.

They also learnt that purely relying on secondary data for details such as population and occupation was not enough. Usually, before optimally locating a KGFS branch, demographic details were collected from the Village Administrative Officer and the Panchayat. Branch locations were then chosen based on this data. But unfortunately, one is a revenue office and the other is a development node. There were clear differences in the way these two offices looked at the same data. There had to be a way to triangulate and arrive at the right way of looking at this data. There seemed to be no better way than doing it first-hand.

When IFMR Rural Channels, the current licensee of the KGFS model, decided to open a new KGFS entity in Krishnagiri, the team decided to map the entire service area from scratch.

What followed next was an intense mapping exercise (the details of which will be explained in Part 2) of the proposed branch locations of the new KGFS entity. The exercise involved manually geo-tagging all important locations in the village along with photographs on Google Earth.

While the exercise was initiated to just map the service area and help identify the ideal branch locations that would help achieve its mission, the same has now grown in size and the spin-offs could lead to interesting and impactful developments.

Along with mapping every household in the village, the team decided to geo-tag details of infrastructure and public utilities. A team of surveyors has mapped out every single bank branch, school, telephone exchange, medical facilities, and bus stops along with details of economic activities at the village level, block level and at the district level.

The result is a detailed yet simple and intuitive map visually representing every important economic detail in the village.

Mapping_KGFS
(Map view of Krishnagiri district – Bus stops, financial institutions, Medical facilities, schools have been manually geo-tagged.)

The KGFS entity can now use this data and map to plan its annual operations and deploy a very detailed and tactical plan to ensure financial services delivery reaches the remotest of the locations.

(In Part 2 we discuss in detail the mapping methodology and the possible uses of the outcome of the exercise.)

Share Via :Tweet about this on Twitter
Twitter
Share on Facebook
Facebook
Share on LinkedIn
Linkedin
Email this to someone
email
KGFSmappingrural finance
4 Comments
  1. Reply
    November 26, 2013 at 1:06 pm
    balasekar

    IS THIS MAPPING BASED ON LOCATION OF POSSIBLE CLIENTS OR PREFERENCE OF ESTABLISHING AN OFFICE ???????????

  2. Reply
    November 26, 2013 at 1:33 pm
    Shilpa Bhaskar

    The mapping process helps identify locations of potential customer agglomerations and hence, set up our network of branches based on that. So it helps achieve both.

  3. Reply
    November 26, 2013 at 1:38 pm
    Pras

    Good point. Distance = time taken plus cost, not the terrain length. In fact in public services delivery too, its not about size and location – if a fire truck gets there in 10 mins is more relevant than if its 1 km or 5 km away.

    It may be handy to apply the same technique that a Domino’s outlet takes to its service area – delivery in 30 mins being the criterion. It charts out what’s possible for its bike crew to get there. So on a spatial map, distances look unequal, but it keeps its service promise.

  4. Reply
    November 26, 2013 at 10:28 pm
    Dr S Santhanam PhD(Eco), CAIIB

    Very interesting inference from the study that geographical location of the proposed branch should not be simply based on standard KM range normally being followed by bankers. One way of searching and locating a branch could be by using Bayesian theory (BT). Of course, BT is generally used to precisely to search and locate the lost object. It may need to be tweaked to suit the needs of the organisation which wants to locate its branch that would give most optimum business. This would mean developing set of hypotheses which among other things include the infrastructure development, connectivity, education level of people, appreciation of financial management etc.

Leave Comment

Cancel reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>

three × 2 =

clear formSubmit

Related posts
Blog Competition on Suitable Finance for Agricultural Households
October 4, 2019
Bridging Gaps in Household Finance Through Research Evidence and Data
July 15, 2019
The Dvara Open Online Repository for Household Finance
March 30, 2019
A 100 Papers, 200 + Points of Evidence, 1 Financial Well-being Evidence Gap Map
March 18, 2019
Process innovations in Microfinance aimed at avoiding over-indebtedness
January 8, 2019
Household Over-indebtedness in Europe: Definitions, Indicators and Influencing factors
January 2, 2019
Search
Recent Comments
  • Prasanna Srinivasan on Care through competition: The case of the Netherlands: “This made interesting and informative reading. Thank you. Inevitably, the mind ran a comparison with the Indian context even while…”
  • Misha Sharma on Direct Benefit Transfers in Assam, Chhattisgarh, and Andhra Pradesh: Introducing the Dvara-Haqdarshak Study on Exclusion in Government to Person Payments: “Great post, Aarushi. It will also be interesting to document the challenges faced in accessing these transfers and experiences with…”
  • Misha Sharma on What is Social Protection?: “Thanks for writing this, Anupama. A much needed piece and looking forward to the second post in this series. It…”
Subscribe and Follow Us

Popular Post

Popular Post
  • ‘Buy Now, Pay Later’: What is it, and how does it affect customer protection?
    May 5, 2022
  • Call for Papers: Field Workshop on Household Finance 25th June, 2022
    May 4, 2022
  • Care through competition: The case of the Netherlands
    April 28, 2022

Categories

Categories
  • Channels(88)
  • Consumer Protection(33)
  • Events(30)
  • Featured(42)
  • Field Reports(6)
  • From the field(9)
  • General(22)
  • Guest(30)
  • Household Research(75)
  • Long Term Debt Markets(9)
  • News(45)
  • Origination(30)
  • Products(42)
  • Regulation(112)
  • Research(254)
  • Risk Aggregation(26)
  • Risk transmission(63)
  • Small Cities(21)
  • Technology(25)
  • Uncategorized(105)
  • Unemployment Support(5)

Archives

Archives
  • May 2022 (2)
  • April 2022 (4)
  • March 2022 (2)
  • February 2022 (3)
  • January 2022 (3)
  • December 2021 (4)
  • November 2021 (6)
  • October 2021 (4)
  • September 2021 (4)
  • August 2021 (6)
  • July 2021 (6)
  • June 2021 (10)
  • May 2021 (7)
  • April 2021 (9)
  • March 2021 (10)
  • February 2021 (8)
  • January 2021 (4)
  • December 2020 (7)
  • November 2020 (7)
  • October 2020 (11)
  • September 2020 (10)
  • August 2020 (12)
  • July 2020 (3)
  • June 2020 (5)
  • May 2020 (8)
  • April 2020 (4)
  • March 2020 (8)
  • February 2020 (3)
  • January 2020 (9)
  • December 2019 (4)
  • November 2019 (3)
  • October 2019 (7)
  • September 2019 (3)
  • August 2019 (2)
  • July 2019 (4)
  • June 2019 (4)
  • May 2019 (4)
  • April 2019 (7)
  • March 2019 (2)
  • February 2019 (3)
  • January 2019 (3)
  • December 2018 (5)
  • November 2018 (2)
  • October 2018 (5)
  • September 2018 (2)
  • August 2018 (2)
  • July 2018 (2)
  • June 2018 (2)
  • May 2018 (1)
  • April 2018 (1)
  • March 2018 (5)
  • February 2018 (2)
  • January 2018 (2)
  • December 2017 (5)
  • November 2017 (4)
  • October 2017 (3)
  • September 2017 (1)
  • August 2017 (3)
  • July 2017 (1)
  • June 2017 (3)
  • May 2017 (4)
  • April 2017 (3)
  • March 2017 (4)
  • February 2017 (3)
  • January 2017 (6)
  • December 2016 (5)
  • November 2016 (2)
  • October 2016 (3)
  • September 2016 (5)
  • August 2016 (4)
  • July 2016 (4)
  • June 2016 (8)
  • May 2016 (4)
  • April 2016 (5)
  • March 2016 (4)
  • February 2016 (3)
  • January 2016 (3)
  • December 2015 (3)
  • November 2015 (1)
  • October 2015 (2)
  • September 2015 (3)
  • August 2015 (5)
  • July 2015 (3)
  • June 2015 (3)
  • May 2015 (3)
  • April 2015 (2)
  • March 2015 (3)
  • February 2015 (1)
  • January 2015 (1)
  • December 2014 (5)
  • November 2014 (4)
  • October 2014 (3)
  • September 2014 (4)
  • August 2014 (4)
  • July 2014 (4)
  • June 2014 (8)
  • May 2014 (1)
  • April 2014 (4)
  • March 2014 (5)
  • February 2014 (6)
  • January 2014 (8)
  • December 2013 (7)
  • November 2013 (8)
  • October 2013 (7)
  • September 2013 (7)
  • August 2013 (5)
  • July 2013 (6)
  • June 2013 (7)
  • May 2013 (6)
  • April 2013 (8)
  • March 2013 (9)
  • February 2013 (6)
  • January 2013 (9)
  • December 2012 (8)
  • November 2012 (7)
  • October 2012 (5)
  • September 2012 (5)
  • August 2012 (5)
  • July 2012 (7)
  • June 2012 (4)
  • May 2012 (6)
  • April 2012 (4)
  • March 2012 (7)
  • February 2012 (6)
  • January 2012 (8)
  • December 2011 (8)
  • November 2011 (7)
  • October 2011 (8)
  • September 2011 (7)
  • August 2011 (3)
  • July 2011 (6)
  • June 2011 (11)
  • May 2011 (8)
  • April 2011 (9)
  • March 2011 (13)
  • February 2011 (10)
  • January 2011 (8)
  • December 2010 (10)
  • November 2010 (10)
  • October 2010 (10)
  • September 2010 (7)
  • August 2010 (13)
  • July 2010 (10)
  • June 2010 (6)
  • May 2010 (13)
  • April 2010 (7)
  • March 2010 (10)
  • February 2010 (5)
  • January 2010 (4)
  • December 2009 (3)
  • November 2009 (1)
  • October 2009 (6)
  • August 2009 (1)
  • July 2009 (2)
  • June 2009 (1)
  • May 2009 (1)
  • April 2009 (1)
  • March 2009 (1)
Share Via :Tweet about this on Twitter
Twitter
Share on Facebook
Facebook
Share on LinkedIn
Linkedin
Email this to someone
email
Site Map

www.dvara.com