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Is India’s Poverty & Illiteracy Driving Down Prudential Financial Decision?

Do you know that only 5% of an Indian household savings is in financial assets and unsecured debt constitutes more than 50% of the liabilities? These and many facts were highlighted by the report of a RBI appointed committee. For a comparison, consider the facts that China is at 9%, 23% and US at 30%,12% respectively. Is this dismal state a grim reminder of the level of (lack of) holistic development of the country? Possibly YES. The report does suspect that but what are the data points to compare and how does that comparison show up?

Country Comparison

We will take 2 critical metrics to reflect the level of development of a country; ‘GDP per capita’ and the ‘Rural population as %age of total country population’. Same has been mapped against the ‘Financial asset %age of total HH savings’ and the ‘Unsecured debt as %age of HH Liabilities’

The GDP and the rural %age has been taken from World Bank Data. Both the data are as of ‘Year 2016’. The Household (HH) data is an estimation from the RBI appointed committee report charts (REPORT OF THE HOUSEHOLD FINANCE COMMITTEE, July 2017, Figure 1.1).

A close look at the graphs throws up a few interesting points:

  1. A close relationship between the ‘Financial Asset %age of HH Savings’ and the ‘GDP per capita’. The financial asset %age nearly mimics the growth curve of GDP per capita. At the higher range of the comparison, the relationship is less clear. A closer look at the RBI report shows a significantly higher contribution of ‘Durable goods’ for UK, USA compared to Germany. Such durable goods may include valuables e.g. arts, carpets etc again a reflection of choices exhibited by rich.
  2. An inverse relationship between ‘Unsecured debt %age’ and the ‘GDP per capita’. As we move from ‘low GDP per capita’ to ‘higher GDP per capita’, the ‘unsecured debt %age’ falls steeply and except for a blip at Germany, the symmetry holds true. The blip for Germany can perhaps be explained by lower mortgage debt (rental is very strong there) and state sponsored pension. So, it is again an exhibition of choice.
  3. A similar close relationship can be seen between ‘rural %age of population’ and the ‘unsecured debt %age’. The 2 lines nearly follows each other and reflects the fact that rural people typically would have much lesser access to financial instruments and lack of education can also be an added factor.

Similarly, it can be assumed that the ‘unsecured debt %age’ will also be driven by ‘financial market depth of the country’. To test this, we looked at the World Bank data on ‘financial market depth’. The WB has many factors/ metrics for the same. We just picked up one for which data is available in 2015 for all the countries and is a decent indicator in general sense for measuring ‘availability factor’. The metric we picked up is ‘account at a formal financial institution (%age 15+)’. We expected an inverse relationship, implying that higher availability of the later will drive down the ‘unsecured debt %age’.

  1. If we leave aside Germany as an aberration, the hypothesis holds strongly for the rest of the countries.
  2. India with a low ‘account concentration’ exhibits higher ‘unsecured debt %age’ and then slowly as ‘account at a formal financial institution (%age 15+)’ increases, there is a consequential drop in ‘unsecured debt %age’


India; State Level

While at macro level, our hypothesis seems to hold true (from graphical sense and not really driven by regression correlation coefficients/ statistical analysis), will it hold at a more micro level? So, we take our analysis to states of India to see whether similar graphical relationship symmetry is being exhibited based on the different levels ‘Literacy’ and ‘Urbanization’.

The state wise HH data is from the RBI committee report while the state wise Literacy & Urbanization data has been taken from last Census data (2011) directly from the census site.

The first chart maps the ‘concentration of non-financial assets in HH Savings’ against the ‘Literacy level’ of the state.

  1. On a broad scale, the symmetry is quite evident (the inverse relationship: higher literacy will mean higher %age of financial assets and hence lower %age of non-financial assets). Example, observe the trend from Bihar > Rajasthan > Nagaland > Manipur > UP & so on.
  2. There are aberrations, example Kerala with very high literacy but comparatively lower %age of financial assets. This can be explained (the RBI report also alludes to it) by individual cultural aspects of the state. Kerala & Southern states have high affinity of Gold which is not only used as an asset but also leveraged significantly for big weddings etc. A few N-E states also exhibits similar trend and this needs to be studied further to understand the socio-cultural nuances that may be driving this trend

The next chart is that of ‘concentration of non-financial assets in HH Savings’ compared against the ‘States Urbanization’/ ‘Rural dominance’. The hypothesis is that higher the Rural Dominance, higher will be the contribution of non-financial assets in HH Savings.

  1. Observe the trend from Bihar > Rajasthan > Nagaland > Manipur > UP & so on, it exhibits a proportional trend between the two. Compare the slight dip in Gujarat’s non-financial asset %age with the dip in ‘Rural %age’
  2. While overall the graph shows that relationship exists, the degree of influence on each state has significant variation while proves the fact that multiple reasons factors are influencing the ‘financial assets %age in HH Savings’ including literacy & poverty level of each state.

Next, let us shift our focus on the liabilities side. One of the key discussion point/ highlights of the RBI report has been the apparent disconnect between the high ‘%age of real estate in HH Savings’ and the very low ‘mortgage debt in the HH Liabilities’. The report does talks about possible quality of such real estate and as well as purpose of the same (it speaks of the huge difference in value of that asset between India & developed countries). Our intent was to extend that reasoning to a micro level, do such relationship is also exhibited across the states? For this we mapped the state-wise ‘Mortgage Debt %age of HH Liabilities’ against the ‘Own House in Good Condition %age’. The later was calculated by multiplying 2 data from the Census 2011, ‘House Assets Data’ (the 2 data are ‘Owned House %age’ and ‘Condition of house %age’). The hypothesis is that the multiplied data will give us ‘%age of owned house in good condition’ which is prerequisite to enable ‘mortgage’ to happen in general financial intelligence.

  1. The chart below shows that there exists a direct relationship between the two though there are a few states. AP & West Bengal and a few others throws up opposing trend.
  2. In general, trough & crests matches; for example, see how Assam > HP > Arunachal Pradesh > Sikkim. The degree of impact differs across the states That again proves that there are multiple influencing factors.

Quite predictably, states with higher %age of rural population should also see higher %age of non-institutional debt. This is directly correlated with the lack of financial inclusion in rural & has been a topic of heated discussion and multiple initiatives (including Jan Dhan Yogna, priority sector banking, MFI) etc. but success has been elusive. The below chart exposes that relationship though a couple of states seems to buck the trend (e.g. Orissa, a few N-E states) which may be due to higher success of financial inclusion or other contributing factors.



While it is for statisticians & economists to dug up more in terms of mathematical/ statistical correlation between these different factors, it is quite clear that social-financial-economic development of the masses are very closely interrelated. While poverty, illiteracy drags the financial development; urbanization, better living conditions and a more mature financial market can bring in more choices to general masses which can lead to smarter & efficient investments & returns and thus improve living conditions further. Impact of a strong economy whether it is China or the US or the European countries is well exhibited. The current economic stress/ GDP slump down notwithstanding, it is high time India works towards a more holistic growth both in terms of demographic coverage and coverage of different parameters of growth. An all inclusive growth is the need of the hour.


  2. World Bank Data (GDP Per capita, Rural Urban Classification, Financial Depth)
  3. India Census 2011, http://census2011.co.in
  4. Featured image taken from ‘http://www.indiaspend.com/sectors/can-womens-education-arrest-indias-declining-sex-ratio-9743’ (Aug 26, 2013). We have taken the liberty to reuse the image. If there is an objection, we will take it off.
  5. Calculations, graphs are works of Randomwalks.

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