This is the third part in a series on the Universal Basic Income. Read part one Explained: What is Universal Basic Income?, and part two UBI explained: Is a minimum wage programme better than Universal Basic Income?
In our previous discussions, we saw how UBI is a useful tool in the battle against poverty in Part 1, and how it is a better solution than the current schemes including Minimum Wages Act and NREGA in Part 2. We will now look at some practical questions about how a radical scheme like UBI can be implemented in a country like India.
Across the world, multiple experiments, pilot programmes, and studies have been conducted with various forms of UBI. Most recently, Finland released the results of a two-year UBI experiment. The world has also witnessed successful cash transfer schemes in recent decades.
In an ideal world, we would have simply scrapped all our existing welfare programmes and replaced them with one UBI. We would have had robust tax compliance coverage, and would have used the money from withdrawing these schemes to fund the UBI. Since this is an ideal situation we are talking about, we would have targeted the absolutely deserving recipients of this scheme with 100% efficiency. However, we do not live in an ideal world, and we must recognise and operate within the constraints of our nation
Here are a few questions that any UBI scheme must consider to develop an effective strategy in India:
- What do the UBI experiments from around the world teach us about implementation?
- Will a universal basic income be disbursed to the rich and the super-rich as well? If yes, won’t that be unfair to the poorer sections? If no, then which sections would be targeted?
- Would it be an all out implementation, or would it be staggered across time and populations?
- How will we finance this scheme? Do we have enough money to do so?
UBI experiments from around the world
One of the most famous UBI experiments concluded in Finland last year. As part of the experiment, 2,000 unemployed citizens received €560 (~Rs 44,700) every month. Although the extensive results are due to come out in 2020, a small finding was released recently. It said that although the recipients reported an increase in happiness levels, it did not increase the employment rate.
One can argue that the experiment was flawed to begin with. The experiment was designed to give money to the unemployed who were already receiving unemployment subsidies from the state. Moreover, the government had designed the policy while the country was in the middle of a recession. This meant that the primary goal of the experiment shifted from reducing inequality or liberating the poor to compelling people into accepting low-paying and low-productivity jobs.
For India, where implementing a UBI is now part of political discourse, it would be of utmost importance to define relevant goals of engendering equality and tracking progress towards them accordingly. It is important to select a sample set comprising not only the unemployed, but also the ones currently engaged in low-paying jobs.
GiveDirectly, aUS-based social enterprise, is currently conducting a 12-year study of UBI in Kenya. Each of the 12,000 recipients are receiving at least 2,000 Kenyan shillings (~Rs 8440) every month in one form of disbursement or the other. Although the final results of this experiment will be available only in the next decade, there are some encouraging interim results. The experiment has so far resulted in a significantly higher food security index, investment in durables such as furniture and metal roofs, increase in psychological well-being, and an increase in female empowerment with largely beneficial tangential effects.
India must look to replicate the temporal scale of this project. A pilot run over a time frame of 12 years can give excellent insights into children’s development, food security and long-term happiness of families.
An experiment famously called Mincome was implemented in a small town in Canada between 1974 and 1979. Each family received between $5,000 (~Rs 39,000) to $12000 (~Rs 93,600) depending on their current income. The experiment noted an 8.5% reduction in hospitalisation rates and an increase in one year of schooling for teenagers. This is another good example of a long-term UBI pilot.
Apart from the usual indicators of personal income and happiness, India must also look to track hospitalisation rates in its pilot to gauge the health of its sample populations.
After the Mincome trials, a 3-year experiment commenced in April 2017 wherein 4,000 families would have receives minimum annual income of CAD17,000 (~Rs 9,07,500)). However, this project was scrapped in late 2018 because the government said it was “not the answer for Ontario families”. No data was released to substantiate this statement. Hence, this withdrawal cannot be attributed to a failure of the initiative itself.
Medium of transfers
UBI would best work in an environment where the recipients receive their payouts directly from the government in their bank accounts. Historically, India has had issues of inefficiency in systems where the recipient has had to go to a physical centre and complete certain formalities to avail the benefits, either in terms of cash or kind. The inefficiencies of NREGA and the Minimum Wage Act were a function of this ecosystem. This system of electronic cash disbursement can be buttressed by the trinity of Jan Dhan bank accounts, Aadhaar linking and mobile penetration in India as per the Economic Survey 2016-2017. A direct transfer into bank accounts eliminates the need for recipients to jump through hoops to receive their benefits, and also eliminates the inefficiencies resulting from corruption and bribery.
Previous experiments of directly crediting bank accounts have not been successful because of a weak digital infrastructure. A Direct Bank Transfer (DBT) programme replaced the Public Distribution System (PDS) in Puducherry. However, it was rolled back because citizens found it much more difficult to access the fewer number of bank branches than they did ration shops. Another huge issue was the misallocation of funds to incorrect accounts.
A successful introduction of UBI is heavily dependent on a robust digital infrastructure.
Should UBI replace existing social welfare schemes?
The failure in Puducherry does not indicate the failure of UBI as a concept. However, it does bring to light the inefficiencies of the current infrastructure, upon which PDS and NREGA also rely. These schemes, however, haven’t been able to benefit the intended recipients as visible in the figure below.
An MIT study reported that replacing PDS with UBI would result in expenditure savings of 36% and also bring down the share of losers in the poorer sections down from one-half to one-third. Many conservative and libertarian advocates of a basic income in advanced economies have long maintained that a UBI must replace centrally administered, safety net programmes for it to be affordable and to significantly improve social welfare resource allocation
How do you select UBI beneficiaries?
There are two broad schools of thought when it comes to coverage of UBI—it can either be truly universal by covering all sections and demographics of the population, or it can focus on a smaller subset which would be identified by either its current income level, means of employment or a demographic factor like age or gender. The primary benefit of a targeted approach is a lower burden on the exchequer’s budget
With any social welfare scheme, there are always two major problems when it comes to targeting—inclusion errors and exclusion errors. Inclusion errors mean the accidental inclusion of the undeserving sections either because of poor implementation or because of false data. In this case, it would mean the accidental inclusion of the rich when the scheme was supposed to cover only the poor. Exclusion errors, on the other hand, are those errors in which the deserving do not get the benefits of the scheme because of systemic inefficiencies. For instance, a daily wage worker below the poverty line may not receive their monthly income because the data miscategorised the worker as affluent. Whenever a scheme is not universal and is intended for only a subset of the population, these errors creep in.
India can potentially follow one of these four methods for a targeted UBI implementation:
- Individual targeting: It be done by either visiting individual households to gather information on income, or gauging economic status through proxy means such as ownership of assets like cars, refrigerators, etc.
- Categorical targeting: This approach involves using categories like gender, ethnicity, age, or geographical area to determine eligibility.
- Community-based targeting: This method relies on the local knowledge of community groups like village elders or school councils to identify the poorest households.
- Self-targeting: In this approach, each beneficiary is required to overcome a small entry barrier either in terms of registration, enrolment, or work requirement. This would dissuade richer sections from enrolling because the benefits would be of a lower value than the effort for these sections.
Targeting within populations also brings about with it certain costs such as:
- Administrative costs: The costs borne by the administrative bodies implementing the targeting system.
- Private costs: The costs borne by all beneficiaries while applying for or participating in welfare programmes. These range from the cost of obtaining relevant applications and personal information to the opportunity cost of time and wages foregone in queuing, travelling, and even paying bribes.
- Social costs: These are the costs borne by the community when finer targeting cuts through neighborhoods and villages and divides a population into beneficiaries and non-beneficiaries. This includes the stigma of being branded as the poor (or incorrectly recognised as the non-poor), which can hurt participants’ self-image and self-esteem and reduce community cohesion.
- Incentive costs: When families modify their lives to fit a strict targeting criteria such as work effort, family size or migration decisions.
- Political costs: When finer targeting leads to political disapproval from some sections of society. For instance, exclusion of the middle class from UBI could potentially lead to political lack of support from the middle class for the government that implements UBI.One of the biggest concerns of the critics of UBI is that it wouldn’t be fair to the poorest sections of society if the rich also received the same monthly income. This inclusion could happen either through an inclusion error in a targeted UBI implementation, or would anyway be a default result of a truly “universal” UBI.
Is a truly “universal” programme the solution?
In a 2013 study, the authors found that expanding the eligibility criteria increased the likelihood of manipulation by corruptible officials for allocating BPL cards to the poor in rural Karnataka. They estimated the exclusion error to be 13%, and that pro-poor administrators would prefer a universal scheme to a targeted one. An independent study carried out almost two decades ago in developed countries cited a “paradox of redistribution” and concluded that “The more we target benefits at the poor only and the more concerned we are with creating equality via equal public transfers to all, the less likely we are to reduce poverty and inequality”.
Another concern is that political support for targeted programmes depends largely on the priorities of powerful constituencies because the poor, by themselves, may be unable to generate widespread political consensus. The degree of political support can also be influenced by concerns around fairness of distribution and targeting or preferential treatment, the extent of ethnic or religious divisions, and an undeserving large middle class capturing the welfare benefits.
Is a targeted programme then better than a “universal” one?
A study reported that means testing is expensive and yields high errors of inclusion and exclusion, while proxy means tests vary depending on how well the indicators used correlate with income or consumption. Categorical targeting does well at identifying and reaching the eligible population, but does worse at identifying the poor if they do not fall into defined categories. Geographical targeting is efficient only if poverty is spatially concentrated. Self-targeting may be rendered inadequate by high demand and fall prey to high exclusion errors.
Even NREGA allocates 40% of its budget towards administrative costs. In India, it is next to impossible to verify incomes given the country’s pervasive informal economic sector and large poor population.
Experts’ opinion on UBI targeting and implementation
A 2018 paper for Carnegie India lists down many proposed methods by leading economists for implementing UBI which attempt to resolve basic issues such as population coverage, annual UBI amount, cost of proposed scheme, and the financial sources to be tapped. Four of these proposals are:
- Pranab Bardhan (2016)
- Population Coverage: All
- Annual transfer amount: Rs. 10,000
- Cost as % of GDP: 10%
- Financial mechanism: Roll-back non-merit subsidies such as those on fuel, and electricity that disproportionately benefit the well-off Indians.
- Abhijit Banerjee (2016)
- Population Coverage: All
- Annual transfer amount: Rs. 13,000
- Cost as % of GDP: 11%
- Financial mechanism: Replace welfare schemes like the PDS and MGNREGA
- Reetika Khera (2016)
- Population Coverage: All elderly, widows, disabled persons, and pregnant women
- Annual transfer amount: Rs.12,000 for pensions and Rs.6,000 for maternity entitlements per child
- Cost as % of GDP: 1.5%
- Financial mechanism: No replacement of existing schemes
- Economic Survey 2016-2017
- Population Coverage: All
- Annual transfer amount: Bottom 75% of the income distribution
- Cost as % of GDP: Rs.7620
- Financial mechanism: Roll-back social sector programs, middle class subsidies, centrally sponsored schemes
Separate studies carried out by the Planning Commission and by the Economic Survey have shown allocation of funds towards schemes and subsidies that do not benefit the most vulnerable. For example, the share of non-merit (goods and services that do not need subsidies) subsidy is Rs 60,100 crore in a total of Rs 79,828 crore. This amounts to approximately 75 per cent of the total subsidies. This is yet another data point that shows how much funds can be freed up from existing schemes to fund the UBI.
Is phased implementation the solution?
The Economic Survey proposes an approach in which we could gradually phase in UBI coverage by targeting the more vulnerable sections first. These could include women, widows, pensioners and agriculture labourers. Noted economist and social scientist Reetika Khare espouses this idea by also citing one of the bigger reasons of the failure of demonetisation: “The experience with demonetisation allows us to appreciate the insensitive manner of policy implementation. The government has acknowledged poor implementation too late and even then, there has been little by way of policy response to the havoc it has created.”
The political and administrative realities notwithstanding, a phased approach would ensure that the most vulnerable groups get benefited first, thereby reinforcing the value of UBI. It would also serve the government well by achieving validation for the gradual spend with proven results at every step.
In a big bang form of implementation, we would lose the ability to correct course.
Is India in a position to implement UBI right now?
Not right away. As of now, we have sparse data to efficiently implement UBI at a national scale. A few experiments in the past, like the one in Madhya Pradesh, have been encouraging but have not had large enough sample sets to be deemed representative of the country. We will need to run an extensive pilot that spans a few years and covers a larger population to be able to get closer to the hallowed answer of the perfect UBI. In the recent past, Jammu and Kashmir, and Telangana have shown interest in rolling out their own experiments of UBI.
The need of the hour for pro-UBI voices is to conduct large scale experiments and pilots in different states, and systematically arrive at the best parameters of transfer amount, targeted population, and financing mechanisms.
Aditya Mani is a writing analyst at Qrius.
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