The Rajasthan Map of Financial Inclusion visualizes several facets of financial inclusion at the district level within Rajasthan. Eight different layers track various indicators covering banking correspondents (BCs), self help groups (SHGs) and microfinance institutions (MFIs). These datasets draw on the State Level Banker’ Committees Financial Inclusion Plans (FIPs), NABARD, and MIX’s collected data tracking the quarterly progress of MFIs currently operating within each district. By uniting these three important datasets against a backdrop of demographic and development data for the first time, it becomes possible to gain insights on the geographic relationships of three key channels in the financial inclusion landscape. This will allow the user to gain a more comprehensive view into the access point and supply of Rajasthan’s financial services to low income households and their businesses.
The purpose of Rajasthan’s State Level Banker’s Committee (SLBC) is to act as the main coordination body for financial institutions within Rajasthan. This committee, convened and housed by a designated Lead Bank, is also responsible for authoring certain development plans for Rajasthan. One such plan, the Financial Inclusion Plan (FIP), outlines the location and number of banking correspondents (BCs) to be set up in each district within villages of more than 2000 persons, as mandated by the government. The data layer that you see on this map was collected from Rajasthan’s SLBC website, where their FIP is made public. The raw dataset itself is included in both a spreadsheet and a PDF available for download here. When hovering over each district on this data layer, an interactive pop-up box provides users with supplemental information indicating the name of the district and state, the total number of planned BCs and the bank set to host the majority of BCs within that district. If two or more such banks tied hosting the majority of BCs, we used both (or all) of their names as this value.
Four layers within this map offer insight into the number of points of service (defined as branch locations) for each MFI as of June 30, 2012 and then the number of MFIs operating in each district, the total number of outstanding loans per district and the total amount of loan portfolio in each district for the fiscal quarter ending on March 31, 2013. Please note again that if two or more MFIs tied for having the most branches in a district or state, we used both (or all) of their names as the value of “MFI with Most Branches” within the points of service layer. These data are sourced directly from the respective MFIs, which have been gathered together by our field unit in India. MIX works in partnership with the Small Industries Bank of India (SIDBI) to collect these layers of data.
The map shown on the initial load of this page shows the combined MFI points of service and the SLBC layers to allow for a more immediate comparison of the two datasets. Please note that a value of “No Data” has been given to districts with no SLBC data, as the data do not clearly indicate whether there are no BCs in these districts or that there is a discrepancy between what the state and what MIX’s shapefile consider to be its districts. This point is elaborated further in the shapefiles and boundaries section.
The map displays two important layers of SHG data: Number of SHGs, and outstanding Bank Loans (in Lakhs Rupees) to SHGs. These data are made publicly available through the National Bank for Agriculture and Rural Development (NABARD). NABARD is a government affiliated apex development bank which is responsible for a wide variety of rural development initiatives, credit and otherwise. NABARD is notably renowned for the Self Help Group Linkage program, which allows SHGs across India to loans from formal institutions. This data is published each year, for select states, in NABARD’s State Focus Papers.
Percentage of population living in poverty is perhaps the most insightful base layer. Rural and urban poverty percentages wer taken from a study published in Economic and Political Weekly in February 2009 entitled, “Levels of Living and Poverty Patterns: A District-Wise Analysis for India”, by Siladitya Chaudhuri and Nivedita Gupta. These percentages wer calculated through various modeling based on the National Sample Survey Organization’s (NSSO) Consumer Expenditure Survey. The article, including a detailed description of the methodology used in the study, can be found here. The poverty percentage for the entire population was calculated using these figures combined with the urban and rural populations from the 2011 Indian Census.
All the colors displayed in this map are scaled based on the 25th, 50th, 75th and 90th percentiles of the respective dataset they represent. The circles are scaled based on these and the 10th percentile, additionally. This guarantees a view into the relative extremes of each dataset, rather than employing an absolute scale. The 25th, 50th, 75th and 90th percentiles for the shaded contextual data are displayed in the legend of each layer.
The population, population density, urban population and literacy contextual layers are sourced from the 2011 Indian Census. These data were aggregated from the state specific entries posted on this site and are available for download as an aggregate whole here. Included in the download are the following indicators:
Please note that all Indian census figures are preliminary.
The HDI and Average Income per Capita contextual layers stem from a 2008 report conducted by the Institute of Development Studies based out of Jaipur, Rajasthan. This research was supported by both the Government of India and the UNDP.
UIDsare a 12 didgit unique number given to each individual as proof of identity. It is required in order to access a variety of services, such as government payments or private sector bank accounts. The data here are sourced from the Rajasthan UID Project.
All of these contextual layers are sourced from the [District Level Household and Facility Survey] (http://www.rchiips.org/index.html), specifically the 2007-8 survey, which was conducted by the International Institute for Population Sciences and funded by the Indian Ministry of Health and Family Welfare. These four layers, then, represent survey estimates of district values.
The map file representing the state and district boundaries is sourced largely from Global Administrative Areas (GADM), an international database of geographic files (shapefiles) covering every country in the world. The data are maintained by contributors working at the International Rice Research Institute and the University of California, Berkeley’s Museum of Vertebrate Biology. The shapefiles are drawn primarily from the Centers for Disease Control and the UN Geographic Information Working Group’s “Second Administrative Level Boundaries” project. Some additional boundaries were manually added on the part of MIX to include the district of Pratapgarh, which recently became a district in 2008. It is based on this map image from the government of Rajasthan’s website.