Targeted transfers may be more effective than Universal Basic Income3 min read . Updated: 30 Aug 2018, 10:13 AM IST
The data from Peru and Indonesia show targeted schemes can meet welfare goals much more effectively compared with Universal Basic Income
New Delhi: Over the past few years, the idea of a Universal Basic Income for all citizens has been gaining currency, in the developed as well as the developing world. In the developed world, the threat of automation and job losses has led to an intense debate on Universal Basic Income. In developing countries such as India, the leakages associated with traditional welfare schemes have prompted a discussion on the Universal Basic Income, with former chief economic adviser, Arvind Subramanian, pushing its case most vigorously in the Economic Survey of 2016-17.
But while Universal Basic Income may seem a more attractive alternative to targeted transfers on paper, it may not always be so in reality, a new research paper by Rema Hanna of the Kennedy School of Government at Harvard University and Benjamin Olken of the Massachusetts Institute of Technology (MIT) suggests. Hanna and Olken use data from Peru and Indonesia to show that targeted schemes can meet welfare goals such as poverty reduction much more effectively compared with Universal Basic Income for a given programme cost.
As the Universal Basic Income programme would depend on contributions from a small tax-paying minority in a developing country, this may impose a very heavy tax burden on them. And the welfare gains may not be worth the cost. Since the poorest households (say at the 10th percentile) would receive the same transfer payment as the moderately better off households (say, those at the 75th percentile), the impact on poverty reduction is likely to be muted, Hanna and Olken argue. “Our evidence from Indonesia and Peru shows that existing targeting methods in developing countries, while imperfect, appear to deliver substantial improvements in welfare compared with universal programs, because they can transfer much more on a per-beneficiary basis to the poor as compared with universal programs," the authors write. “The primary downside of these programmes is horizontal equity—because targeting is imperfect, there will be a substantial number of poor households who slip through cracks and are excluded. Nevertheless, for many developing countries, our simulations suggest the welfare gains from targeting may be substantial."
Hanna and Olken argue that improving the method of targeting may be more effective in providing assistance to the poor compared to a universal assistance programme. They cite India’s Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) as one such example, where there is no explicit screening but the imperative of having to queue for work under the hot sun is enough to dissuade the affluent. The MGNREGS is also much less fiscally demanding at less than 0.5% of India’s GDP, whereas even a quasi-universal Universal Basic Income such as the one proposed by the Economic Survey of 2016-17 would amount to roughly 5% of India’s GDP. Whether or not such schemes with implicit screening are superior to a Universal Basic Income depends on whether the costs to beneficiaries are outweighed by the cost savings from better targeting, Hanna and Olken point out.
The gains or leakages from welfare schemes ultimately depend on the precision of the targeting mechanism. Regardless of whether India moves towards a quasi-universal Universal Basic Income or continues with targeted programs, better targeting can certainly sharpen the impact of government expenditures on welfare programmes. Most developing countries in the world rely on proxy-means tests—based on directly verifiable and observable information on household assets or amenities (such as roof and wall material) rather than on self-reported incomes—to classify and target households. But as Hanna and Olken point out, India is one of the few developing countries which implements targeting without a proxy-means test. Maybe the next economic survey should focus on a proxy-means test for India.