By Anita Kumar, Centre for Development Finance

According to a Monitor Group report there is a ‘vibrant housing market in urban India’ seen from the spectacular growth in housing finance at 36% CAGR for more than a decade. However, formal housing finance has not reached lower income groups in India, even in urban areas. This is because of the high perception of risks in lending to this income group, the difficulties of verifying incomes in the informal sector, and the high transaction costs of collecting repayment relative to the size of the loans. Despite the very limited availability of formal housing finance, families living in informal housing settlements have clearly been able to build homes, and to make regular incremental improvements to their homes.

A survey done in 2009 in Hyderabad showed that incremental housing happens over multiple stages as and when the need arises (e.g. adding a tiled/concrete roof, adding a room, laying some pipes etc.). In fact, incremental building accounts for 50-90% of residential construction in all developing countries. However, there is very little research available on how communities actually access the finance needed for these incremental improvements. The dearth of such information prevents the government and the private sector from crafting financial instruments that effectively address the needs of low-income residents.

Transparent Chennai, a project housed at Centre for Development Finance, IFMR Research, will be conducting a pilot research study with the support of Department of International Development, UK (DFID) that can help to fill this gap in existing research. We will be conducting a survey in two low-income areas in Chennai to identify sources, methods of access, and amounts of housing finance, types of incremental housing improvements carried out, rates of interest borrowers are paying, and whether sources of finance have any relationship to the security of tenure, in order to better understand residents’ needs and behaviors. Such research can help create financial products and policies that complement and strengthen the use of personal savings and extended family contributions towards housing investments, but which provide an alternative to high-cost borrowing, such as those from moneylenders. Such research will be important in ensuring that new programs such as the Rajiv Awas Yojana, which has a housing finance component, do not close off existing avenues of access to finance among residents. Over the next few weeks, as the project progresses, we will keep you updated on interesting data and insights from the field.

This is the fourth in the series of posts under the topic “Understanding the KGFS Customer”. The authors, Sowmya Vedula and Shilpa Sathe of IFMR Rural Finance, present data regarding occupations of KGFS customer. The information is as declared by the customer at the time of enrolment or at the time of any periodic updating of data.

Notes:

• This blog post displays data of all enrolled members of the three KGFSs, Pudhuaaru (Tamil Nadu), Dhanei (Orissa) and Sahastradhara (Uttarakhand), obtained from the datasets of the Customer Management System (CMS)
• Data considered for this post is as of November 15, 2011
• Pudhuaaru has 1,29,380 enrolled customers, Dhanei has 25,654 and Sahastradhara has 20,253 which brings the number to a total of 1,75,287
• This post also uses data of family members of enrolled customers from our customer management database. The total number of individuals considered for the analysis is thus 3,35,627 for Pudhuaaru, 88,452 for Dhanei and 85,316 for Sahastradhara
• Customers can enrol at any time throughout the year and hence the data collected is at different points in time
• Categories of occupations in the database are –Agriculture and allied activities, labour, migrant labour, student, business, working abroad, salaried, unemployed, housewife, and retired/pensioner. Agriculture and allied activities include people who own land and cultivate it, people engaged in fishing and dairy, and traders of crops and agricultural products. ‘Others’ includes income from self-employment in activities other than those listed above.

Overall Occupation Distribution

Chart 1 below shows the overall distribution of occupations by categories. Students are the largest in number (26.58%) followed by labourers (22.4%) and people pursuing agriculture and allied activities (13.6%). The number of unemployed people is also significant and amounts to 13.27%.

Chart 2 shows the distribution of occupations by KGFS entity. In Pudhuaaru and Dhanei, students and labourers form majority of the population. The unemployment rate is higher in these two geographies (14%-16%). The proportion of labourers migrating within India is the highest in Dhanei as compared to the other two geographies while the number of international migrants is higher in Pudhuaaru. In Sahastradhara, about one third of the population is students followed by agriculture and allied activities (25.4%) and salaried people (13%).

Most Common Combinations of Occupations

There are 15,648 individuals (5.77% of the total earning population) who are involved in more than one occupation overall. Chart 3 shows the most common combinations of occupations followed by these individuals. For example, there are 6771 labourers who also cultivate their own land, 2037 businessmen who are involved in dairy and 1175 labourers who are also involved in dairy activities.

Gender Wise Distribution of Occupations

Chart 4, 5 and 6 show the distribution of occupations by gender across the three KGFS entities. Noticeably, it’s a good sign to see almost equal number of male and female students across the three geographies. Wage labour is the most common activity taken-up by women in Pudhuaaru (25.89%) and Dhanei (19.37%) whereas in Sahastradhara majority of women are involved in agriculture and allied activities (43.70%). Migration among women is not a common trend and only 0.24% of women in all three geographies migrate for work within the country and abroad. The percentages of women who are unemployed are as follows: Pudhuaaru – 18.78%, Dhanei – 21.16% and Sahastradhara – 17.35%.

Among men, the distribution of occupations varies widely among categories in the three geographies. In Pudhuaaru, majority of men are either labourers (30.30%) or are involved in agriculture and allied activities (14.88%) whereas in Dhanei they are more or less equally distributed among agriculture & allied activities (13.94%), business (15.20%), labour work (15.40%) and domestic migration (13.80%). In Sahastradhara however, majority of the men are salaried (22.03%), followed by business (14.91%) and agriculture & allied activities (10.93%).

Distribution of Occupations by Age-Group

Charts 7, 8 and 9 represent the distribution of occupations by age-group. In all three geographies majority of individuals between the age of 1 and 20 are students. In the age group of 20 to 60, which is the productive age, majority of individuals are labourers (25.44%) in Pudhuaaru, while in Dhanei the majority is labourers (14.32%) and businessmen (9.48%). In Sahastradhara, most people are involved in agriculture (22.73%) and salaried employment (12.71%). Overall, domestic and international migration is observed mainly within the age group of 20 and 40 (73.33% of international migrants and 65.65% of domestic migrants). In the age group beyond 60, there is a significant difference in occupations among Pudhuaaru, Dhanei and Sahastradhara. In Pudhuaaru and Dhanei most individuals in this category (39.68% and 49.50% respectively) are unemployed while in Sahastradhara, this number is less than 19%.

Distribution of Occupations by Level of Education

Next, we look at the distribution of occupations by the highest level of education completed. Table 1 shows the overall distribution, while charts 10, 11 and 12 show the KGFS-wise distribution. Overall, 37.5% of the population has an education between 6th and 12th standard. Only 6% of the population has pursued graduate, post-graduate or vocational courses and the majority of these individuals are salaried employees, businessmen or are working abroad. Around 50% of the unemployed individuals cannot read and write.

In all three geographies, majority of the people who have pursued graduation, post-graduation and vocational courses have a salaried job. However, in Pudhuaaru alone, about 20% of people in these categories are unemployed. Among people who have studied till the 10th standard, majority of them are labourers In Pudhuaaru, and labourers or business people in Dhanei. In Sahastradhara, majority of them are involved in agriculture & allied activities. Pudhuaaru has a large chunk (44%) of unemployed people who cannot read and write, followed by Dhanei (36%) and Sahastradhara (30%).

Our next blog post will talk in detail about the distribution of incomes for all the above parameters.

By Shilpa Sathe and Amit Shah, IFMR Rural Finance

In this third post under the series “Understanding the KGFS Customer” we present data regarding expenditure patterns of rural households enrolled with KGFS. We also try to understand what the Planning Commission’s recently proposed poverty line cut-off (Rs.26 per person per day expenditure in rural areas) means for the average KGFS customer.

Notes:

• This blog post displays data of all enrolled households of the three KGFSs, Pudhuaaru (Tamil Nadu), Dhanei (Orissa) and Sahastradhara (Uttarakhand), obtained from the datasets of the Customer Management System (CMS).
• Data considered for this post is as of October 5th, 2011. The information is as declared by the customer at the time of enrolment or at the time of any periodic updating of data.
• Customers can enrol at any time during the year and hence the data collected is at different points in time.
• Data for expenses is captured household-wise (as against individual customer-wise). Pudhuaaru has 79,120 enrolled households, Dhanei has 18,216 and Sahastradhara has 17,513 which brings the number to a total of 114,849 households. There may be more than one customer per household; hence the total number of customers is much higher.
• With respect to household expenses, the CMS database contains data on expenses on food, clothing, health, travel, electricity, telephone and festivals. Expenses in the ‘others’ category includes rent, cable connection charges, and miscellaneous cash outflows as declared by the customer.
• It is important to note that recall periods vary for each category. The CMS database includes options for daily, weekly, monthly, quarterly and annual frequencies for every expenditure category and the frequency is chosen based on one at which the customer recalls expenditure accurately.
• Throughout the post we have used the term Monthly Per capita Consumption Expenditure (MPCE) which is the household expenditure per month divided by number of members in the household.

The distribution of Monthly Per capita Consumption Expenditure (MPCE) for an average household for the three geographies is shown in the graph below. Pudhuaaru has the highest average MPCE at Rs.837, followed by Dhanei (Rs.625) and Sahastradhara (Rs.618). We can observe that the overall expenditure is highest on food lowest on telephone across all three geographies.

Expenses on education and festivals are almost double (Rs.79 and Rs.68) in Pudhuaaru compared to that of Dhanei (Rs.31 and Rs.36) and Sahastradhara (Rs.32 and Rs.35). In rural areas of Thanjavur where Pudhuaaru KGFS operates, expenses on travel are higher as compared to rural Ganjam (Dhanei) and rural Garhwal (Sahastradhara). Electricity and ‘others’ are the only two categories where MPCE is higher for Dhanei and Sahastradhara as compared to Pudhuaaru.

The break-up of the average MPCE on food and non-food items is given in graph below. It is interesting to note that in absolute terms, the MPCE on food items is highest in Pudhuaaru, followed by Sahastradhara and Dhanei. However, non-food items have a greater share in the average household’s overall MPCE for Pudhuaaru as compared to Dhanei and Sahastradhara.

The 3 graphs below show the percentage-wise distribution of MPCE figures for KGFS customers. Overall, 62% of the enrolled households have a per capita daily expenditure of less than or equal to Rs.26 (monthly Rs. 780) which is the proposed poverty line definition of the Planning Commission. In Pudhuaaru, approximately 58% of the households have an overall MPCE of less than or equal to Rs.780. This number increases to 75% for Dhanei and 78% for Sahastradhara.

For food items alone in the below graph, 91% of the households in Pudhuaaru have an MPCE of less than or equal to Rs.780. This number increases only slightly to 92% for Dhanei and 93% for Sahastradhara.

The graphs below show the distribution of MPCE by income quartiles and by geography. Overall, MPCE increases with increase in income and the difference is the greatest between the third and the fourth quartile.

However, as we can see in the below graphs, MPCE on food items increases at a slower rate than non-food items as income increases. In other words, richer customers are spending a lesser proportion of their overall expenditure on food as compared to non-food items.

This is the second in the series of posts under the topic “Understanding the KGFS Customer”. The author, Sowmya Vedula, of IFMR Rural Finance, presents data regarding KYC documents (both ID and address proof documents) furnished by potential customers when they enrol with KGFS. The author also presents data of existing financial services that customers were already availing at the time of visiting KGFS. The information is as declared by the customer at the time of enrolment or at the time of any periodic data updation.

This blog post displays data of all enrolled members of the three KGFSs, Pudhuaaru (Tamil Nadu), Dhanei (Orissa) and Sahastradhara (Uttarakhand), obtained from the datasets of the Customer Management System (CMS).

Note:
•    Data considered for this post is as of September 20th 2011
•    The enrolment statistics for the three KGFSs is: Pudhuaaru – 126,082; Dhanei – 24,793; Sahastradhara – 18,404; Total=169,279

ID Proof Documents

ID proof documents are collected from customers during enrolment with KGFS. Voter ID is the most commonly submitted ID proof document across the three geographies. The major document in the ‘Others’ category for Pudhuaaru and Dhanei are driving licence, bank passbook, PAN card and passport, while for Sahastradhara it is driving licence, PAN Card and passport.

The ‘Panchayat Certificate’ category comprises of letters obtained from a gazetted officer or the Panchayat Head or the Village Admin officer (VAO). Slightly more than 10% of all enrolments in Pudhuaaru and Dhanei used this category of ID proof. In Pudhuaaru, 72% of these enrolments were by females while in Dhanei, females in this category formed only 41%. A major portion of these enrolled customers were labourers or housewives.

PAN Card

PAN card (Permanent Account Number issued by the Income Tax Department) applications had been facilitated by the KGFS branches for its customers up to March 2010.

Address Proof Documents

The break-up of the document types collected as address proof are given above. The major document types in the ‘Others’ category for Pudhuaaru and Dhanei are driving licence, bank passbook and passport, while for Sahastradhara it is driving licence and passport.

Other financial services

The above charts give information about other financial services being availed by the KGFS Customer – bank account and other loans. The breakup of the source of the loans is given in the panel on the right.

By Deepti Kc, Amulya Champatiray, Researchers, IFMR-CMF

The Andhra Pradesh government while promulgating the MFI Ordinance also stated that it has a mandate to disburse INR 100,000 crores bank loans to SHG women members by 2014 to bring 10 million families out of poverty indicating government’s strong belief in serving the needs of the poor through Self Help Group (SHG) model.

The SHG model has been regarded as an instrument for the empowerment of poor and marginalized sectors as SHGs enable women to their savings and to access the credit which banks are willing to lend. Andhra Pradesh has been proactive about taking an initiative of total financial inclusion through SHGs. Recently, the groups had been given large loans up to INR 0.75 million in order to help them discharge all their other loan liabilities and replace it with the low cost loans from the banking system. Study found that even though SHG members discharged other loans taken at higher rates of interest immediately, however, after 9-15 months, these members had incurred new debts outside the SHGs. The more alarming concern has been the increased levels of default. SOS 2010 report indicates that AP banks typically mentioned recovery levels of between 80-85 percent of loans. On an outstanding loan level of more than Rs 100 billion in the state of AP, a 15% default is highly significant.

While there has been an extensive discussion on the Ordinance and its impact on the Microfinance Industry itself on this blog and on the India Development Blog, we thought it would be interesting to observe if the SHG bank linkage programme has actually kept pace with increased after the MFI industry came to a grinding halt in the state.

The Centre of Microfinance at IFMR Research conducted a study under the Microfinance Researchers Alliance Program (MRAP) to understand how SHG Bank Linkage Programme is performing in Andhra Pradesh and to understand if the SHG model is meeting the demand of the clients especially after AP crisis.

We present below some of our findings from the study.

This study involves interviews with 20 bank branch managers (both commercial and regional rural banks) in 3 randomly-chosen districts of AP (Medak, Mahabubnagar, and Kadapa) with the client base of not less than 50 SHG groups.  We have directly visited and collected data from 20 bank branches of the following 8 banks.

Data collection

We collected data from the abovementioned 20 branches focusing on the following topics:

1. Monthly SHG loan outstanding and the number of SHGs in FY 2010-2011
2. Annual SHG savings and the number of SHGs in FY 2009-2010 and FY 2010-2011
3. Total loan outstanding and number of SHGs as of March 2010
4. Total loan outstanding and number of SHGs as of March 2011
5. Irregular balance (Reschedule and NPA) and number of SHGs as of March 2010
6. Irregular balance (Reschedule and NPA) and number of SHGs as of March 2011

The biggest limitation of this study is not able to collect data on all of the abovementioned topics from 20 bank branches due to the difficulties reaching some data at some bank branches.  Hence, some of the sections in the report are analyzed based on data collected from less than 20 bank branches.

We also collected data from 59 branches of Andhra Pragathi Grameena Bank (APGB) in Kadapa district. For this report, we analyzed that data seperately to understand how this particular Regional Rural Bank is performing in Kadapa district.

Data Findings

Savings

We collected annual two years (FY 2009-10 and FY 2010-11 ) data on total number of SHGs that are saving with the bank branches and their total savings amount from 13 branches of five banks (State Bank of Hyderabad-2 branches, Andhra Pradesh Grameen Vikas Bank-4 branches, Andhra Bank-2 branches, Canara Bank-1 branch, Andhra Pragati Grameen Bank-4 branches.  Out of 13 bank branches, 8 are Regional Rural Banks (RRBs) and 5 are Commercial Banks.

Figure 1 below shows that compared to the FY 2009-10, there is a moderate increase in both SHG savings amount and the number of SHG saving accounts in FY 2010-11.  Even though there is an increase of 7.6% in the total number of SHG accounts in FY 2010-11, when it comes to savings amount, there is an insignificant increase of 0.3% only.

The data also shows that the average savings amount per group has improved for Commercial Banks compared to Regional Rural Banks.

From annual savings data of 59 bank branches of a Regional Rural Bank in Kadapa district, we found that there is an increase of 34% in savings amount even though the number of SHG account holders has decreased by 10% in FY 2010-11.  The average savings amount per group has increased by almost by 49%.

Credit Linkage

In order to understand the growth in SHG credit linkage from FY 2009-10 to FY 2010-11, we collected data on loans outstanding and the number of SHGs provided with loans from 16 branches out of which 6 are Commercial Banks (Canara Bank, Syndicate Bank, State Bank of Hyderabad and Andhra Bank) and 10 are Regional Rural Banks (Andhra Pradesh Grameen Vikas Bank and Andhra Pragati Grameen Bank).

When we analyzed data from 6 Commercial Bank branches and 16 Regional Rural Bank branches separately, we found that even though the margin of increase in the number of SHG accounts is similar for both types of bank branches, there is a huge difference in the margin of increase in SHG loan outstanding between commercial and Regional Rural Bank branches. When there is an increase of 10% in SHG loan outstanding for Commercial Bank branches, Regional Rural Bank branches have substantially increased its loan disbursement as there is an increase of 35%.

Similar analysis of 59 bank branches of a Regional Rural Bank in Kadapa district shows that even though the cumulative number of SHGs linked with the branches of this particular Regional Rural Bank has decreased by 10%, the total outstanding loan has increased by 16% in FY 2010-1. The average loan size has also increased from Rs. 81,085 to Rs 103,832 in FY 2010-11.

 

Seasonal Difference in Loan Disbursement

We collected month wise loan disbursement data from 20 bank branches out of which 12 are Regional Rural Bank branches (Andhra Pradesh Grameen Vikas Bank, Andhra Pragati Grameena Bank) and 8 are Commercial Bank branches (State Bank of Hyderabad,  State Bank of India, Andhra Bank, Canara Bank, Syndicate Bank, Central Bank of India).

Figure 5 shows the seasonal trend in loan disbursement from 20 bank branches.  The spike in March might explain that in order to receive priority sector lending commitment, banks increase the loan disbursement towards the end of the year to meet the specific target. The other spike can be observed in the period between July and September.  Most of agricultural activities seem to flourish during monsoon season, which is usually from July to September. This might show that SHG loans from these bank branches might be helping farmers meet the seasonal difference in their demand for finance.

Irregular Balance Data

13 bank branches provided data on irregular balance (both Reschedule and Non Performing Asset (NPA)) data for the last two fiscal years. Out of these bank branches, 9 are Regional Rural Bank branches and 4 are Commercial Bank branches. For this report, we first analysed Rescheduled and Non Performing Asset (NPA) data separately, and then we combined data and compared the performance of the branches in the last two years.

Reschedule Data:

As seen in Figure 6, in the FY 2009-10, 8.9% of the total SHG loan outstanding amount was rescheduled. This payment was delayed by 8.2% of the total SHG borrowers. The data shows that the situation improved in FY 2010-11 as only 5.3% of total loan outstanding was delayed in payment by 7.6% of the total number of SHG borrowers.

NPA Data:

As seen in Figure 7, in FY 2009-10, 4.7% of the total loan outstanding was considered Non Performing Asset and almost 3.7% of the total number of SHG borrowers defaulted this amount. Interestingly, the margin on the total number of SHG defaulters increased from 3.7% to 4.4% in FY 2010-11, even though the total loan outstanding amount drastically decreased from 4.7% to 3.1%

Reschedule and NPA Data together:

While combining both NPA and reschedule data together, we found that even though there is a slight increase from 11.9% to 12% of the total SHG borrowers that have been defaulting or delaying the payment from FY 2009-2010 to FY 2010-11, there is a significant drop in irregular balance amount from 13.6% to 8.3%.

When we looked at the irregular balance data of 59 bank branches of APGB , we found that the overall the irregular balance has increased this year from 5.6% of the total SHG loan outstanding in  FY 2009-10 to 5.9% of the total SHG loan outstanding in FY 2010-11 as can be seen in Figure 9.

 The interesting fact is that even though there is an increase in irregular balance, the total number of SHGs that have delayed or defaulted has drastically gone down from 7.1% to 5.8%. When we looked at NPA and reschedule data separately, we found that the situation has slightly worsen in FY 2010-11 compared to FY 2009-10  when it comes to NPA as the percentages of both the SHG defaulters and the total amount have slightly increased as seen in Figure 10.

 When it comes to reschedule amount, even though the percentage of the total number of SHG has increased, the percentage of the total rescheduled amount has decreased as seen in Figure 11.