A Social Algorithm Tool Gives Covid-19 Relief To India’s Informal Sector
As the Covid-19 induced lockdowns force crippling effects on workers globally, the most vulnerable constituency have been those in the informal-sector. This includes those who often earn daily without safety nets, perks or health benefits apart from the wage. Their employers are often loosely regulated, making accountability for worker safety and welfare tough. This challenge has become more pronounced in developing nations like India where the informal workers comprise the major share of labour, with the loss of incomes and livelihoods carrying a ripple-effect across the economy.
ILO’s data on the share of informal employment to total gives an indication of their sheer numbers across most developing nations. This was 88% in India in 2018, higher than Indonesia’s 86%, Pakistan’s 82%, Vietnam’s 76% and Sri Lanka’s 70%. China, Mexico and Brazil ranked lower. Bangladesh, Nigeria, Tanzania and Nepal stood north of India with shares ranging from 89% to 94%. These numbers worsen for all the nations if one looks only at the rural areas, where India’s share at 93% was still higher than Pakistan’s 92%, Indonesia’s 91%, Vietnam’s 85% and China’s 82%. Sri Lanka, Brazil and Mexico ranged between 51% to 73% while Bangladesh, Nigeria, Tanzania and Nepal out ranked India with shares between 93% to 97%.
With the current pandemic creating a period of social stress, many countries are assisting informal workers through emergency measures like cash-transfers so that they have the disposable income to buy staples. Universal cash-transfers are fiscally impossible given budgetary limits. Hence, it is imperative these measures reach those who need it the most and weed out those who siphon it off by taking advantage of systemic inefficiencies. But that is easier said than done. Peers like Vietnam are utilizing tax and utility bills for a cash transfer programme to informal sector families. However, this presupposes such workers have tax and utility documents in their names, which may not often be the case. Indonesia has increased utility subsidies for the poor, but that cannot check if the beneficiaries are indeed the most vulnerable. Thailand is using a digital payment platform for cash transfers. While this makes the flow transparent, it again cannot tell if the beneficiaries were indeed the most vulnerable.
This is where India offers a pragmatic example by combining machine and human intelligence for Covid-19 emergency relief. Grameen Foundation India is a social impact organisation that catalyses double bottom-line approaches to serve low-income communities. As a Covid-19 intervention, it has set a goal to raise crowdsourced funds and provide cash transfers to 6,000 poor families from the informal sector, i.e. ~30,000 beneficiaries in all. For this, it has developed the Grameen4Giving application. This tool assists its field workers identify beneficiaries and disburse grants. The logic in this algorithm identifies the most vulnerable by leveraging a mix of quantifiable indicators and community-based participatory wealth ranking to run instant eligibility tests. Once identified, the tool sends triggers to the project committee for immediate review for onboarding. Following the approval by the beneficiary selection committee, the direct cash transfer to the onboarded beneficiary is made.
Where this digital tool combines with the humane element is in the process to identify the vulnerable. The indicators and ranking encompass multi-dimensional layers including a person’s poverty and asset status, if they are disabled or widowed, food security position, apart from his income, to arrive at a household vulnerability index. Apart from this, the field workers can also use their experience to recommend a rejected case for a second round. If the committee is satisfied, it may override the application’s index by approving the case. This not only ensures an element of humane empathy in an automated tool, but also helps address any initial exclusion errors or bias it may show. Those can be rectified by upgrading the algorithm continuously with intelligent logic as more and more observations are collected from the field.
The Covid-19 crisis has exposed the headwinds our informal workers face like none other. An agile social protection programme which leverages technology with empathy can reach the most people effectively and efficiently. The Grameen4Giving application is proving to be just that. This tool also complements the robust digital payments infrastructure in India to effect real time cash transfers to the beneficiaries, making this intervention very scalable. Grameen has so far raised funds to support 200 families over the next 3 months, and the funds have started to go out to the identified families.
Social algorithm tools like this can ensure the emergency relief reaches those in the informal sector who are the most affected by the pandemic, instead of using proxies like utility and tax bills which can be misused. Peer nations should learn and collaborate with this pioneering example from India to create meaningful benefits during these times of social stress!
