Privacy on the Line: What do Indians think about privacy & data protection?
By Surabhi Mall, IFMR Rural Finance
In the previous blog post of the KGFS Model Incubation series, we drew out the implications of mapping the GDP of a branch’s service area on strategic decisions related to district selection, branch potential, product suitability and customer centricity. In this context, some of the pertinent follow-up questions that arise are – How much will this activity cost? Should this capability be harnessed indigenously or simply outsourced? What is a statistically approved research design to follow? This post essentially focusses on understanding the ABC of executing this study with relevant elaborations and learning from the past.
The GDP exercise can serve different purposes and based on the objective use and nature of requirement the scope of the study can be defined. The following two studies conducted in two different districts in Tamil Nadu attempt to illustrate this.
The objective of the first study was to compare the gross potential of a branch in the Krishnagiri district, Tamil Nadu, to that of a model KGFS branch[1] wherein ‘comparable’ was defined as a range with an acceptable degree of error from the estimates of the model branch. In this case, focus of the study was to get the aggregate number and not necessarily its constituents. Simply put, this means that while sector-wise distribution of the branch economy could be insightful, it was not the focus of the study per se. Resources used as part of this included a full-time staff that spent one day in assimilating secondary data, three working days on the field to collect primary data as well as to validate the secondary data. The staff was actively supported by a Wealth Manager at the branch location.
The second study at Ellakuruchi village in Ariyalur district, Tamil Nadu, was done with an objective of profiling the district as well as the branch service area. District profiling required a thorough review of the district’s demography, geography, economic status, main crops, enterprises and occupations. Profiling the branch service area required field insights on aspects such as different occupations that thrive in the area so as to map each economic activity with its volume; cash-flows associated with the occupation so as to map business potential; formal and informal financial providers so as to understand current and potential gaps in the financial landscape among others. This objective required one data analyst responsible for secondary data collection, methodology design, primary data collection, data collation and presentation of the findings. Primary data collection was actively supported by 1-2 Wealth Managers in the field for 6-8 working days. The entire exercise was executed in one month. In order to add greater rigour and sanctity to the estimates, a similar study was executed in another branch in the district.
Below are pie-charts depicting the findings from both GDP studies in Krishnagiri and Ariyalur districts respectively.
One of the big challenges in initiating such a study is that the data records of this kind are not methodical, very contextual and mainly absent from conventional databases for any triangulation. The other concern may be related to the design of the research methodology for the intended purpose of the study. Often, simple doubts such as the size of a representative sample directly impact the resource requirements and rigour of the study. To address these issues simple project management tools such as defining the objective, scope and research design a priori through a clear project plan will be quintessential. In the first study, since the objective was clearly identified as “compare the gross potential of the branch to that of a model KGFS branch,” an exhaustive sector-distribution map of the branch’s economy was not required. Conversely, in the second study, to “profile the district and branch service area”, there was need for profound understanding of the demographic and economic constitution of the area. This in turn required information about the share of each activity in the total economic pie.
In cases wherein the objective of the exercise is to design a customer engagement strategy or an optimal capacity plan for the branch by projecting lean and peak cash requirement periods, activity mapping will need to be extremely exhaustive. This would imply that aspects such as the ‘crop net income’ component of the GDP pie be further broken down to list the main crops, their seasonality, and cash flow projections related to each crop.
It is also important to acknowledge the homogeneity in variables during the study. For example, if a service area constitutes of four villages of 300 households each[2], the economic map of one village can be multiplied to give the macroeconomic map of the branch. This adds to operational efficiency in the execution whilst minimizing scope for error.
Since the KGFS model is designed to entrench itself in the community it serves by developing a deep understanding of the geography, the local culture, the economic activities and dominant customer segments, the GDP exercise is perfectly tailored for the KGFS model. Understanding its benefits as a principal and inaugural step in model incubation and thereby budgeting for the costs involved will lead towards deepening the very foundation of the model.
The next blog post in the series will discuss the heuristics of site selection in a rural village context. By illustrating the KGFS’ experience across diverse geographies, it will attempt to showcase the various components that may play a foundational role in the science of site selection.
[1] The model KGFS branch is conceived and defined based on the KGFS mission, the business requirements and past learning experience.
[2] Unit of aggregation may vary from a household, a village, a panchayat or a block.
This is a good exercise to develop a guide to the banking potential / credit in the service area at any given point of time. A couple of suggestions on this, in case the detailed study didn’t cover these aspects:
i) It may be useful to identify the wages, income segments relating to the domestic industry, and those outside. For example, if labour income is primarily agri derived, then a larger share of risk is associated with agri produce.
ii) It may also be useful to segregate the income stream that flows into the service area. For example, crops sold or factory output sold outside the service area. Incomes accruing to services/retailers for products sold into the area. As a guide, the externally driven economy may have potential for sharp growth that a normal risk assessment of credit potential may not support. So setting up a garmenting unit, may entail say Rs x crore of credit that may be a multiple of what local economic size my warrant – except it can be a viable credit option based on external factors.
These may be doable given the relatively smaller footprint in terms of households, at these branches. ANd the fact that bank officers are in close touch with them.
I may also suggest, that estimates needn’t be 100% accurate – but within a range of certainty. As this is meant to guide the bank’s credit/financial product decisions. 100% accuracy up front may become overkill.
Dear Pras, really appreciate your insightful comments on this.
The suggestion to segregate the income stream that flow into the services area and thereby assess the risk from external factors is a great thought. The risk can be built into the design features of the credit product for financing the specific activity. Another extension of this understanding on economic dependence can be to gauge fund-flow between rural and urban areas as well as the credit absorption capacity.
I also fully agree that 100% accuracy in this exercise will be an overkill and the idea is to use this as a guiding indicator on the economic spread and credit potential.
Look forward to receiving your thoughts throughout this series!