Projects
Research → Action
Most academics study problems. I also build solutions.
These projects translate my research on platform capitalism, labour process, and digital alternatives into working systems that people can actually use.
🍽️ OpenCoop / OpenFood
A cooperative food delivery platform where everyone benefits: workers, restaurants, and customers.
The Problem
Platform capitalism extracts 25-35% commission from restaurants, suppresses worker earnings through algorithmic coercion, and hides money flows behind opaque systems.
My PhD research documented these mechanisms in detail. This project is the alternative.
What It Does
OpenCoop is a cooperative food delivery platform with:
- 0% restaurant commission — restaurants receive 100% of menu price
- Workers keep 80%+ of delivery fees + 100% of tips
- Daily minimum wage guarantee (₹600, governed by cooperative vote)
- Open job board — workers see all jobs, no algorithmic penalties
- Transparent fees — every charge breakdown visible
- Cooperative governance — one member, one vote
Why It Matters
This project demonstrates that cooperative platform models are technically and economically viable. It translates my PhD research on platform capitalism into a working alternative.
Technical Details
Modular monolith designed for pilot scale (~100 restaurants, ~300 workers):
- Backend: Node.js/TypeScript, Express.js, PostgreSQL 16, Redis 7
- Frontend: Next.js 14, Tailwind CSS
- Key features:
- Event-sourced architecture with append-only audit log
- Redis job board (workers choose jobs, not algorithmic assignment)
- SHA-256 hash chain for tamper-evident records
- Governance-by-vote (proposal → vote → auto-execute)
- Escrow via Stripe Connect with worker guarantee pool
Status & Links
- Status: Active development, pre-pilot
- GitHub: github.com/abhinavjnu/opencoop
- License: Open source (commons-owned)
- Tech stack: Node.js, TypeScript, PostgreSQL, Redis, Next.js, Docker
- Documentation: Full architecture docs in repo
📊 IndiaData-colab
Automated analysis pipeline for Indian government survey data, helping researchers with limited compute bypass the need for massive infrastructure.
The Problem
Large-scale Indian survey datasets (PLFS, NSS, HCES, ASI) are:
- Massive (hundreds of thousands of records)
- Complex (require specialized knowledge of survey weights, strata, clusters)
- Difficult to analyze without expensive local compute or R/Python expertise
- Prone to errors if multipliers and sampling weights aren’t handled correctly
This creates a barrier for researchers, especially those without institutional resources or technical training.
The Solution
Zero-installation analysis:
- Google Colab notebook — click badge, upload CSV, run → get results
- No local R setup needed — runs entirely in browser
- Validated against real data — 100% exact match to official MoSPI calculations (< 0.01pp)
Official MoSPI methodologies:
- Principal + Subsidiary Status (PS+SS) — official “Usual Status” approach
- Current Weekly Status (CWS) — short-term labour status
- Principal Status (PS) — strict majority-time status
- Automatic weight computation — handles NO_QTR, combined subsamples, Calendar Year datasets
Key features:
- Labour indicators: LFPR, WPR, UR (overall + disaggregated)
- Intelligent survey design with correct weights, strata, clusters
- Export to Word (.docx) and LaTeX
- Memory-efficient Parquet export for large datasets
- API integration with microdata.gov.in for data discovery/download
What This Demonstrates
- Public good tool for Indian labour market research community
- Lowers barriers to rigorous survey data analysis
- Transparent, open-source alternative to proprietary statistical software
- Enables replication and verification of published studies
Validation
Tested on real MoSPI PLFS Calendar Year 2024 data (415,000+ records):
| Indicator | Manual | Pipeline | Diff |
|---|---|---|---|
| LFPR (CWS) | 55.2% | 55.2% | 0.01pp |
| WPR (CWS) | 52.4% | 52.4% | 0.01pp |
| UR (CWS) | 5.0% | 5.0% | 0.00pp |
| LFPR (PS+SS) | 59.6% | 59.6% | 0.01pp |
| WPR (PS+SS) | 57.7% | 57.7% | 0.01pp |
| UR (PS+SS) | 3.2% | 3.2% | 0.00pp |
✅ ALL INDICATORS MATCH MANUAL GROUND TRUTH (< 1pp)
Status & Links
- Status: Active development, pre-pilot
- GitHub: github.com/abhinavjnu/IndiaData-colab
- Colab Notebook:
- Tech stack: R, Python, Google Colab, data.table, survey, srvyr
- License: Open source
- Datasets supported: PLFS, NSS, HCES, ASI (any microdata.gov.in dataset)
Contact
Interested in collaborating on these projects? Reach out:
Email: maurya.abhinava@gmail.com