The core problem I want to solve is to give a lever to the labor force that is earning less than the country’s per capita income. Further I want to restrict the market to only blue collar jobs as these are the ones which have lesser income growth compared to white collar. Which means their might be many white collar workers below the country’s per capita income, we will not focus on those as my assumption is that necessary levers for them already exist and it might be a skill or information problem.
Hence the term ‘Gig Workers’ for us here means, individuals working on blue collar jobs like construction labour, hawker, domestic help, driver, delivery agents, or other forms of middlemen/agents. Considering a certain sector of blue collars might as well be above per capita income like certain agricultural families - we will consider a factor that removes this segment from our market.
That gives us our vision, “Provide leverages that improve incomes for blue collar workforce with a goal of rallying the country’s per capita income on an upward trend”.
Based on the vision whatever the product we are looking here to build is primarily for Emerging Economies - as the growth of PCI will be substantial here. Depending on the size of markets this might also be implemented to develop lower strata of developed economies.
The next question is, What type of solution would provide such great leverages? The north star metric has to be User Earnings. Now these user earnings can be improved might Increasing Productivity or by Maximising Value Add.
For instance, for a driver, they can either early more by maximising number of trips or completing trips of bigger values. The same will apply in any gig work because the fundamental equation to earnings will be Quantity x Value. Time is constant and hence not a factor, it also makes it a constraint. Quantity and Value are not constants and vary based on supply and demand. The users which will have best information of supply on demand would be able to decide whether to optimize quantity or value and hence perform better.
So the solutions should help with,
- Getting them more customers
- Getting them higher valued customers
- Helping them optimise for the above two through information
Hence fundamental products would be like - mechanisms to get year round work, mechanisms to get higher valued work (maybe through bids), dashboards and tools to help them select the optimum, easy credit for them to start work (travel to location, temporary shelter, etc).
Blue Collar Workforce earning below per capita sectoral split
The table guesstimates the number of blue collar workforce earning below the country’s per capita income. The sectoral split approximates the field of work with highest workforce that fit our category.
Note: India does not have Agriculture as a major split because we are hypothesising that a majority of this workforce would be families working on their own farms, which means work is guaranteed and revenues are predictable (even if lower than per capita) - the pain points of this group might be very different from our gig worker category.
| Rank | Country | Estimate (low — mid — high) | Sectoral split (mid estimate %) |
|---|---|---|---|
| 1 | China | 120M — 180M — 260M | Agriculture / seasonal 30%, Construction 20%, Trade/vendors 15%, Manufacturing/artisans 15%, Transport/drivers 10%, Domestic/other 10%. |
| 2 | India | 90M — 115M — 140M (from earlier) | Construction 25%, Trade/vendors 20%, Transport/drivers 15%, Manufacturing/artisans 15%, Domestic/other 15%, Other 10%. (International Labour Organization) |
| 3 | Nigeria | 30M — 50M — 80M (from earlier) | Trade/vendors 35%, Agriculture 25%, Construction 15%, Transport 10%, Other 15%. (Reuters) |
| 4 | Indonesia | 25M — 40M — 60M (from earlier) | Agriculture 30%, Trade/vendors 20%, Construction 15%, Manufacturing 15%, Transport 10%, Other 10%. (ILOSTAT) |
| 5 | Bangladesh | 12M — 25M — 40M | Garment/manufacturing day workers 25%, Trade/vendors 25%, Agriculture/seasonal 20%, Construction 15%, Transport/domestic 15%. |
| 6 | Brazil | 12M — 20M — 32M (from earlier) | Trade/vendors 30%, Transport/delivery 20%, Construction 20%, Manufacturing 10%, Domestic/other 20%. (Agência de Notícias - IBGE) |
| 7 | Philippines | 8M — 15M — 25M (from earlier) | Trade/vendors 30%, Domestic/household 15%, Transport/drivers 15%, Construction 15%, Manufacturing/artisans 10%, Other 15%. |
| 8 | Vietnam | 6M — 12M — 18M | Agriculture/seasonal 30%, Trade/vendors 25%, Manufacturing/artisans 20%, Construction 10%, Transport/domestic 15%. (International Labour Organization) |
| 9 | Kenya | 6M — 12M — 20M (from earlier) | Trade/vendors 40%, Agriculture 20%, Construction 15%, Transport 10%, Other 15%. (WIEGO) |
| 10 | Thailand | 5M — 10M — 16M | Trade/vendors/tourism casuals 30%, Agriculture 20%, Construction 20%, Manufacturing 15%, Transport/other 15%. (International Labour Organization) |
| 11 | Malaysia | 2.5M — 6M — 10M | Construction/plant 30%, Transport/drivers 20%, Trade/vendors 15%, Manufacturing 20%, Domestic/other 15%. |
| 12 | South Africa | 3M — 7M — 12M (from earlier) | Trade/vendors 30–35%, Construction 20%, Domestic 15%, Transport 10%, Manufacturing/other rest. |
Sector based TAM
If we are building a digitally product and starting with India, some good options as per below table would be,
- Trade / hawkers / vendors / tourism casuals: Good TAM and urban reachable. Kirana startups prove there’s a market ready to adopt tech.
- Construction / handymen / labourers: Likely the most impact generating as this group might have the least per capita income.
- Transport / drivers / delivery: Probably the most tech savy and adaptable urban group. They already have digital tools like Uber or Swiggy that help them find work. However, they may not have the means to optimise earnings.
| Sector | Approx TAM (mid, M) | Main contributors | Notes |
|---|---|---|---|
| Trade / hawkers / vendors / tourism casuals | 95M | India, Nigeria, China, Indonesia, Philippines | Street vending, small shops, tourism stalls = huge TAM, relatively “urban & reachable.” |
| Construction / handymen / labourers | 80M | India, China, Bangladesh, Nigeria, Indonesia | One of the largest day-wage gig-like groups, very fragmented but concentrated around cities. |
| Agriculture / seasonal hired labour | 70M | China, Indonesia, Vietnam, Bangladesh, Nigeria | Large absolute numbers, but harder to reach digitally; also less “gig-style” in India as explained. |
| Manufacturing / artisans (small workshops, garment, piece-rate) | 50M | China, Bangladesh, Vietnam, India | Stable large TAM in garment/textiles and small factories; concentrated in industrial zones. |
| Transport / drivers / delivery | 40M | India, China, Brazil, Philippines, Malaysia | This overlaps the “usual gig economy” category; smaller in number but higher per-worker spend. |
| Domestic & other services | 30M | India, Brazil, Philippines, South Africa | Domestic maids, helpers, cleaners, other small tasks; fragmented, usually women-dominated. |
Viable Products
If I consider the Drivers and Delivery sector, only 10% of this sector work as partners for companies like Uber, Ola, Zomato, etc — the rest are on contractual basis. Additionally neither of these companies have public APIs, hence building something over the existing apps is not reliable. The TAM is too low for the uncertainty.
Supplychain workforce like truck and tempo drivers, and non app based passenger transport would be the biggest sectors to target (23M) within the category. Blackbuck and Porter together might have 1.5 M partners.
| Category | Estimate (M) | Notes |
|---|---|---|
| Freight & Logistics drivers (truck, tempo) | 15–20 | Largest share; backbone of India’s supply chain |
| Passenger transport (autos, jeeps, buses) | 8–10 | Mostly informal, stand/union based |
| Courier & postal delivery | 2–3 | Formalized, linked to courier companies & India Post |
| Non-food daily delivery (milk, LPG, etc.) | 2–3 | Subscription/hyperlocal, rarely tech-enabled |
| Support roles (loaders, helpers, mechanics) | 3–5 | Indirect but essential workforce |
| Subtotal | 30–41M | Matches the gap left after app-based cab/delivery partners |
| Given the stats uptil now, I believe the most sensible way to approach this market is to improve productivity of already existing behaviour in the market. In urban centres most of the house help, handyman, and other blue collar work is received through word of mouth or referrals. The second aspect of this is that all decisions happen over a phone call. |
I am thinking of a Phone App that aims to get you work and improve productivity. Today’s Phone Apps only focus as a directory of contacts, however they can totally work as a marketplace to find work or get referrals through mutual contacts. A scheduling tool or calendar integrated within this phone app might help book all the available slots for a blue collar worker.