The CCFS Report has laid out a vision for Sufficient Access to Affordable Formal Credit that places a goal of achieving a Credit to GDP ratio for each district of atleast 10% by January 1, 2016, and to cross 50% by January 1, 2020. Presently, based on estimates used in the CCFS Report, 94% of all districts’ urban areas and only 30% of all districts’ rural areas have achieved the 10% target, while only 18% of all districts’ urban areas and 2% of all districts’ rural areas have achieved the 50% target.
Notwithstanding the difficulty in obtaining reliable data1 for measuring Credit to GDP for all districts, we estimate2 and display below the district level variations in Credit to GDP for two States, namely Maharashtra and Bihar. While these two States are comparable in terms of size and population, there exists vast disparities between their financial depth ratios – while Maharashtra has a Credit to GDP ratio of 116%, Bihar has a very low ratio of 16%3. Below are the District-level interactive maps for the two States, displaying both the Rural and Urban Credit to GDP ratios.
Maharashtra (35 Districts) – Please click on the map to swap between Urban & Rural data. To know the value of a particular district, hover your mouse over it to get specific details.
Bihar (38 Districts) – Please click on the map to swap between Urban & Rural data. To know the value of a particular district, hover your mouse over it to get specific details.
While Maharashtra is known to have the highest financial depth among Indian States, a deeper look at the variation across its districts reveals that the districts of Mumbai and Mumbai Suburban have more than 500% Credit to GDP. While this can be attributed to large corporate loans that get booked in the metropolitan city and that get deployed elsewhere across India, the fact remains that in the context of both rural and urban credit, all other districts lag behind tremendously and are in fact at par with the extent of financial depth for the districts of Bihar.
India has a modest overall bank Credit to GDP ratio of around 70%4. Attempting to estimate this metric for all districts reveals regional variations across States and districts and is evidence of poor credit outreach by the formal financial system. The districts in the North-Eastern States have a median Credit to GDP ratio of 6% (for rural) and 18% (for urban) with the lowest values of 0.40% (for rural) and 1.15% (for urban) respectively.
All India map – Please click on the map to swap between Urban & Rural data. To know the value of a particular district, hover your mouse over it to get specific details.
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- Latest GDP data is not available for districts. Also, while credit through the Urban Cooperative Banking channel is significant in Maharashtra, district level data for the same is not available. Data in these maps pertains to Scheduled Commercial Banks’ rural and urban credit data made available on the RBI website.
- Refer Footnote 30 of the CCFS Report for Estimation methodology. Additionally, these maps have been prepared for the Census 2011 districts.
- http://www.rbi.org.in/scripts/bs_viewcontent.aspx?Id=2598
- http://data.worldbank.org/indicator/FS.AST.DOMS.GD.ZS/countries/IN-CN-XM-XP-XD?display=graph
Note: Credit to Avinash of Data Stories fame for technical inputs on the Maps visualisation.
3 Responses
The credit to GDP ratio in aggregate sums up regional and sub-regional disparities. However, from an access to finance point of view it may be worthwhile examining this a little differently.
i) Personal credit as distinguished from corporate credit. The latter, as pointed out in the post, skews numbers as loans are booked at Corporate office locations for use around the country
ii) As an access issue, the quantum of total credit is less significant than the spread of use of credit. The spread with respect to personal credit reflects induction into formal channels. To illustrate:
a… Personal credit will be for housing, credit card / HH goods spending, one-offs such as marriage, health care and educational loans. housing and credit card will always be higher in urban locations – opportunities to spend plus payments infrastructure plus comfort of consumers. HOusing tends to be far more expensive in urban areas with each loan sufficient for 10-15 less expensive homes for rural areas.
b. Personal credit could include loans for self-employed, or home based livelihoods. Roughly – most of these enterprises aren’t “limited companies” and hence the distinction between self and business isn’t there. With 90% classified as unorganized and informal, this is a metric of using credit for personal income opportunities.
c. At present the means of the credit for a. above is also informal lending. So lending under these heads would qualify as formalization of lending.
d. The number of loans, and coverage of loans (% of population) in rural areas could be compared with urban areas. This indexes the basic frame that can expand with economic and livelihood opportunities, very rapidly. It will also provide valuable info on two account households (husband and wife earnings) etc as an indicator of social issues.
Quantum comparisons will just serve to illustrate what we already know, viz., some regions are more prosperous and less banked. A few big loans will skew data, whereas the govt and banking objective is to get greater spread of account users.
Sir, thank you for your valuable comments. The CCFS report uses two metrics with respect to credit delivery – credit to GDP, and credit access points per grouping of 10000 eligible persons. While the former seems to be a quantum metric, the latter is an access metric and indicates the presence of the supply point near the customer, and little else. However, when seen together, they can indicate lack of access even in cases where access points may be present (the lack of access also in terms of poor infrastructure, lack of political will, supply side distortions and so on. Also, the quantum of credit is not seen here as a % of accounts or population but in relation to the GDP of the region, and this therefore prevents a situation where credit gets pushed to individuals who may not have a need for it.
You make a distinction between retail ‘consumption’ credit and retail and corporate ‘production’ credit. While such a distinction would be valuable at a more granular level, there is a case for using the pure quantum of credit in relation to the district’s GDP, tracked at a national level, to be used for target-setting for policy purposes, as has been done in the CCFS report. Whether credit has been deployed for consumption or for investment is perhaps not as important, as long as the financial system is able to offer credit to meet the purpose for which it is sought. There would definitely be value to segregating corporate loans such as those booked in Mumbai, and to track their actual deployment in other regions. Your point on the need to look at urban and rural financial depth with a different lens is well-taken. To make a broader point, financial depth of the size of the intermediary sector has been found to have a statistically significant and economically large relationship with long-run real per capita growth, capital accumulation and productivity growth (King and Levine, 1993).
Thanks for responding in detail. My comments were more a file & use if valid! Worth thinking about it looking at quantum as a metric for undeveloped markets at first stage – not that its irrelevant. But most markets, companies would look to “seed” conditions prior to rapid growth. I feel depth in quality banking is no different. The overemphasis on targets in quantum terms ab initio leads to pressures on bank staff that distort how the market develops and leads to edgy practices. Much like appropriate land preparation (2-3 years sometimes), enhances long term crop productivity! Short term emphasis leads to excessive fertilizer use and kills soil quality!