PDS Survey for AI and Blockchain based Digital Platform for Offering Public Distribution System [PDS] Services through General Trade Outlets in India
Introduction
In the previous post we, AsterQuanta [AQ] introduced the public distribution system [PDS] in India and also captured how we have obtained the permissions from the government.
In this post we present insights from the pre-deployment solution development and survey that we have conducted.
Need for Survey
Generally we discover a problem, have helpful constructs and metrics define the problem and work towards designing a solution. Discovering the problem and designing a solution are generally tangible activities. What is interesting is the hypothesis construction for problem definition that is generally abstract in nature. Good product designers’ work towards making the abstract things become tangible as much as possible.
At AQ, we adopt multiple mechanisms and channels to work towards problem construction and hypothesis validation. In the PDS scenario, our hypothesis is that a single window solution for decentralizing public distribution services, by leveraging the large number of general trade (Kirana) outlets in India, would help in reducing waiting times and providing easier access to the benefits closer to home. This decentralization is enabled by using frontier technologies such as AI & blockchain, which also enhances accountable transparent and equitable distribution.
Since there are multiple stakeholders in the PDS scenario, accordingly our hypothesis had multiple dimensions. A survey is a prerequisite to validate our hypothesis and understand what the end users, stakeholders look for in any solution that is designed for them. As a starting point, we conducted a survey with the beneficiaries (end-users) and the stakeholders (retailers, government officials, VLE’s).
Survey Design
We, with the assistance of our partner Sampoorna Swaraj Foundation [SSF] undertook a survey of 391 beneficiaries in 5 different Gram Panchayats in Chikkaballapur District in Karnataka State. The main objective of the study is to carry out a beneficiary-level evaluation along with system evaluation.
Beneficiary-level evaluation across different beneficiary categories (Below Poverty Line/Above Poverty Line/Priority Card Holders)
o Average quantity received relative to entitlement and price paid for the purchases by the beneficiaries.
o Regularity/predictability in receiving PDS items including aspects like timely opening of FPS, availability of ration, etc.
o Access to FPS including distance travelled, transaction time, dealers’ attitude, etc.
o Quality of food grains received from FPS.
o Poor people always receive the full quota of food grain from PDS.
o Overall assessment of beneficiary satisfaction with the PDS including reasons for dissatisfaction.
System evaluation of the public distribution mechanism by measuring
o Targeting errors, i.e., inclusion/exclusion errors.
o Extent of distribution in food grains across beneficiary categories, i.e., APL/BPL/AAY
o For understanding the operational challenges that we might face from the beneficiaries’ point of view if a proxy distribution system and platform were to exist.
o For exploring potential opportunities for AQ to build a business case
We also undertook a survey in 37 general trade outlets in the same Gram Panchayats with the following objectives:
System evaluation of retailers for the public distribution mechanism
o For understanding the retailers’ current business & operations model and his/her interests & challenges.
o For making the retailer understand the potential opportunities available in the retail ecosystem.
o For understanding the operational challenges that we might face from the retailers’ point of view if such a platform were to exist.
o For exploring potential opportunities for AQ to build a business case.
Survey Findings
a) We surveyed a spread of 391 Beneficiaries and categorized the type of beneficiary based on their ration card type [below poverty level, above poverty level, priority holder (AAY)].
98% of the beneficiaries surveyed hold below poverty line ration cards.
b) The window of opportunity to avail ration by beneficiaries was captured through the number of days that fair-price outlet was closed/not-available for distribution.
Nearly 80% of the time, the window of opportunity to avail ration is less than 10 days. I.e. usually the PDS benefits are distributed between the dates of 11th-20th of a calendar month. And in these days it is generally between 8AM-12PM and 4PM to 7PM that the benefits are distributed. People generally have to avail their benefits with these windows of opportunity either by taking a break from their daily work schedules or managing their work schedules. Also, the window of opportunity is not a fixed window and the date and time information is generally obtained through neighborhood word of mouth in those villages/areas.
c) We captured the total time spent by beneficiaries to avail ration, through the time spent in line and time spent in travel to avail ration by beneficiaries.
65% of the beneficiaries spend more than 1-hour to avail ration. 30% of them spend more than 2-hours i.e. nearly 25% of earning/wage time has to be allocated.
Time Spent in Line : 50% of the beneficiaries spend more than 30-minutes in line to avail ration.
Time Spent in Travel : 20% spend more than 30-minutes in travel to avail ration.
d) We captured the Opportunity Cost for availing ration benefits — this basically captures the amount of money that beneficiaries are spending or losing (like wages, transportation cost) to avail ration.
In our preparation for the survey and findings from the same, we realized that there is not much research or studies that were conducted from this point of view i.e. an “opportunity cost for the beneficiary”.
71% of the beneficiaries spend more than INR 100 to avail ration. 50% of beneficiaries spend more than INR 200 — which is more than half-a-day’s minimum wage. The MNREGA daily wage ( a minimum guaranteed wage from the government) is a little less than INR 300.
80% incur one form of cost or the other to avail ration. Only 20% avail it through walk.
Story of a Beneficiary —
Nagaraju (61) is working as a daily wage laborer. There are 4 members in his house. Wife (56) and first son (26) are casual labourers while the second son (22) is studying. He has a BPL card and shared his difficulties to get ration at a fair price shop. They have to leave the wages of Rs 300 to Rs 400/day and go to the fair price shop in Darbur village, about 5km away. The more difficult thing is to give a fingerprint for getting the ration. For this they have to travel 2kms to Jedamakalakahalli and travel another 3 kms to Darbur to get their rations. Ironically, though there is a fair price shop in Darbur, because of the server problem the beneficiaries should come to Jedamakkalahall for fingerprints alone.
Also, the beneficiaries from other 5–6 villages come to Jedamakalalahalli for fingerprints. Due to server problems they have to stand in a queue for 2 -3 days to give finger print and then take an auto or own vehicle to carry ration from Darbur Fair price shop.
e) We captured the non-uniformity in product availment i.e. the chance of getting a particular set of benefits (rice/wheat/pulses) based on the fair price outlet that is visited.
Nearly 98% of the beneficiaries hold the same type of card, and all of them are entitled for the same set of items and quantity. But based on the availability of stock in each of the fair price outlets, the distribution can vary almost with some getting either no wheat or no pulses. For example, in our survey we find that 42% of beneficiaries get Rice and Pulses but no wheat. While 38% beneficiaries get all the three items i.e. Rice, Wheat and Pulses.
f) We captured the feedback on what beneficiaries would like to get addressed for an enhanced experience– one of four options in reduced waiting time, reduced distance, choice of benefits, and treatment by outlet associates
63% of the beneficiaries want distribution to happen near their home and 37% want reduced waiting times (line waiting time).
g) We captured the feedback on our proposal and our solution — about proxy pickup through a new process. We have walked the beneficiaries through the process, their role and solution wireframes.
Our hypothesis of the process and the solution is more or less validated as a concept. We have an outstanding response in terms of willingness (98% +) to enroll.
h) We captured the willingness from retailers to be a PDS node — about enabling proxy pickup through a new process. We have walked the retailers through the process, their role and mobile application solution wireframes.
Our hypothesis of the process and the solution is more or less validated as a concept. We have an outstanding response in terms of willingness to enroll by the retailers.
i) Expectation from retailers to be a PDS node — how does being a PDS node help the retailer? why would he/she be interested in being a PDS node?
Retailers expect to be compensated through direct or indirect means. Direct meaning a payout to them on a monthly fixed basis or variable transaction value basis. They are also interested in exploring how the increased foot-fall can increase their net revenue.
Learnings
Hiring and Training of Operators to conduct the survey
We were able to hire and train VLE’s to kick start our phase-1, with the help of our NGO partner SSF. We designed and developed wireframes, mobile applications and training modules to enable the survey.
SSF conducted multiple training sessions in Chikkaballapur district with the VLE’s to enable the survey process. SSF were able to hire operators who had prior experience in such surveys and operations.
One of the key learnings during the process of hiring and training is that it helps to hire people who are local to those areas where the survey and subsequent deployment of the solution would be conducted. These operators need to be well known and well acquainted with the local population to make introducing the solution and process simple and easy. Also, trust factor plays a big role. The operators being from the same local areas helps in developing a trust factor along with accountability to the beneficiaries and retailers.
In general, the operators team in the field were able to collect valuable information from the beneficiaries and the retailers which is helping us in refining our solution. Some of the key learnings —
Engaging with end users & key stake-holders early is critical.
a) We designed the mobile app with certain features and work-flow based on a few assumptions. Some of these assumptions turned out to be wrong. For example, while we had made provision for Localization of the App, we had assumed that VLEs will be comfortable with data in English. This turned out to be false and we had to add the ability to store and serve the data in English and local language much later in the cycle leading to increased effort and regression.
b) We received feedback from the local government officials to include fair price shop owners into the workflow. This wasn’t anticipated during the initial app design. If only we had received this feedback earlier, we could have saved much time and effort.
c) Likewise, we had assumed rations would not have be collected in full, every time, but we learnt that partial delivery of rations was not allowed, we’ve had to remove this option from the app.
Administration Panel
We had assumed that the Django framework provided Admin panel should be good enough for all the data-entry / reporting needs. Given some of the complex data types (JSON) we have used, this turned out to be a challenge to customize the admin screens. It would have been better to have a separate webapp for all data-entry and reporting/audit needs.
Application Usability Challenges
Given that the App is going to be used by lay people with limited or no tech exposure, it would have benefited immensely by having more end users try out the app earlier so we could have addressed some of potential usability issues before the launch. We did share the wireframes though with almost all the end users to minimize the usability issues.
Short feedback Loops with the Government Officials
We were in constant touch with the local government officials updating them on our work and progress. We made it clear to the local government officials that their help and support is critical to the success of this project and we were lucky to run into very proactive and technology savvy officials who were interested in this pilot.
We were able to conduct a workshop to review the findings, survey data, the solution workflows and also the mobile application with the local government officials for their feedback. Through that workshop, we added some additional features in our solution for greater visibility to the fair price shop associates. We are also adding messaging features in our solution for beneficiaries to have real time tracking of their orders.
Although there were quite a few issues and challenges that the beneficiaries were facing in the current system, they were a little hesitant to give qualitative feedback for open ended questions like their overall PDS experience etc. The hesitancy is arising out of apprehension that such qualitative answers may directly or indirectly affect their benefits and entitlements. This is a sensitive issue and we need to respect their apprehension.
Various governments at both the state (including Karnataka) and central level are proactively trying to enhance the experience of beneficiaries through direct to home deliveries. The optimized solution for such deliveries is the one that would scale in the long run. Our experiments and learnings are directed to reach the optimal solution. An example for state implementation in Andhra Pradesh can be found in this report from the ground on South Indian news site The News Minute. The article highlights some of the associated cost and implementation challenges of home deliveries.
Next Steps
There is a lot of excitement from all the stakeholders on how the actual deliveries are going to happen and the subsequent benefits to each of the stakeholders (beneficiaries, government officials and retailers).
We are targeting to deliver the technology & operations process, workflows for enabling proxy distribution. Our next sprint will cover findings of the actual delivery process using 2 different decentralized distribution models and the operational challenges associated with the same.
It is going to be challenging but exciting.
Appendix
Survey Findings
a) Spread of 391 Beneficiaries — type of beneficiary based on the ration card type [below poverty level, above poverty level, priority holder (AAY)]
b) Number of days that the fair-price outlet was closed/not-available — this basically captures window of opportunity to avail ration by beneficiaries.
c) Total time spent by beneficiaries to avail ration — this basically captures the time spent in line and time spent in travel to avail ration by beneficiaries.
d) Opportunity Cost for availing ration benefits — this basically captures the amount of money that beneficiaries are spending or loosing (like wages, transportation cost) to avail ration.
e) Non-uniformity in product availment — this basically captures the chance of getting a particular set of benefits (rice/wheat/pulses) based on the fair price outlet that is visited.
f) Feedback on what beneficiaries would like to get addressed for an enhanced experience– one of four options chosen from reduced waiting time, reduced distance, choice of benefits, treatment by outlet associates
g) Feedback on our proposal and our solution — about proxy pickup through a new process. We have walked the beneficiaries through the process, their role and solution wireframes.
h) Willingness from retailers to be a PDS node — about enabling proxy pickup through a new process. We have walked the retailers through the process, their role and mobile application solution wireframes.
i) Expectation from retailers to be a PDS node — how does being a PDS node help the retailer? why would he/she be interested in being a PDS node?
