Poorer countries are less effective in reaching their poor. For example, the following graph gives the coverage of social safety nets across countries, from poorest to richest, based on household surveys that identified direct beneficiaries for each of over 100 countries spanning 1998-2012. (Safety net spending includes social insurance and social assistance, including workfare programs.) Richer countries tend to be markedly better at covering their poor, although the bulk of this is explained by differences in the overall coverage rate.
Social safety net coverage rates for poorest quintile (poorest 20% ranked by household income per person) from the World Bank’s ASPIRE site. The data are available for 109 countries; the latest available year is used when more than one survey is available. GDP from World Development Indicators.
This page provides some extra material and links on some of the largest direct interventions against poverty found in the developing world.
An evaluation of India’s National Rural Employment Guarantee Act (NREGA): Right to Work?
Above is a screen shot from NREGA: The Movie, a randomized information intervention that was used to teach poor people in Bihar their rights under the law that created the scheme and the administrative process for acting upon those rights. If you would like to see the full (20 min) movie, in Hindi, you will find it on the sub-page: NREGA: The Movie. For an analysis of the impact of the movie see Ravallion et al., “Empowering Poor People through Public Information.“
This type of information intervention—“edutainment” as Eliana La Ferrara dubs it in her paper “Mass Media and Social Change”—can also be revealing about the the processes of knowledge diffusion, as demonstrated in “Social Frictions to Knowledge Sharing” by Alik-Lagrange and Ravallion. They find that the knowledge diffusion process is far weaker for disadvantaged groups, defined in terms of caste, landholding, literacy, or consumption poverty.
China’s urban Di Bao program: Is it really a poverty trap? On paper, “yes.” But not in practice thanks to local implementation. Benefit incidence Di Bao
The Southwest China Poverty Reduction Project: Still one of the few (only?) long-term evaluations of the impact of a poor-area development project in a developing country: Lasting impacts of aid to poor areas
Plastic mulch being used to conserve water and suppress weeds for enhancing crop yields in poor areas of SW China as part of the SWPRP.
Proxy-means testing has emerged as a popular method of poverty targeting with imperfect information. In a now widely-used version, a regression for log consumption calibrates a proxy-means test score based on chosen covariates, which is then implemented for targeting out-of-sample.
A new paper, poor-means-test, studies the performance of various proxy-means testing methods using data for nine African countries. Standard proxy-means testing helps filter out the nonpoor, but excludes many poor people, thus diminishing the impact on poverty. Some methodological changes perform better, with a poverty-quantile method dominating in most cases. Even so, either a basic-income scheme or transfers using a simple demographic scorecard are found to do as well, or almost as well, in reducing poverty.
However, even with a budget sufficient to eliminate poverty with full information, none of these targeting methods brings the poverty rate below about three-quarters of its initial value. The prevailing methods are particularly deficient in reaching the poorest. We should strive for something better.
In a VOX-EU article I argue that there are straw men galore in the debate on a basic income guarantee (BIG) versus targeting. I point to five straw men:
Straw man 1: A BIG is too expensive. Critics of the BIG idea argue that it would cost far too much to be seriously considered. Yes, some BIG proposals have had scary price tags. But that misses the point. Most countries (including many poor countries) are already spending public money on poverty reduction. If the same resources are better spent fighting poverty using a BIG then that should be done. We can ask that question at any given level of the basic income.
Straw man 2: We don’t need a BIG because we can eliminate poverty at a much lower cost. The favorite calculation here is deceptively simple: just measure each poor person’s poverty gap—the distances below the poverty line for all the poor—and make handouts accordingly. Voila, no more poverty! A budget to cover the aggregate poverty gap will almost always be way less than the cost of a BIG.
This calculation is deceptive for three main reasons. The first it ignores incentives; such a policy can create a poverty trap due to its (implicitly) high marginal tax rates on poor people. Second , it ignores the information constraints on targeting, which can be severe, especially in poor countries with weak administrative capacities. Third, when the budget is free to vary, finer targeting may well undermine the public support (notably from the middle class) for antipoverty policies; the poor end up with a larger share of a smaller pie, with ambiguous gains.
Straw man 3: Targeting in practice may not be perfect but it is good enough. A simple method assigns transfers based on observed categories, such as location, household size and/or landholding (in rural areas). More sophisticated versions use a “proxy means test” (PMT) in which a statistical model is calibrated to a limited set of readily observed household characteristics in a sample survey, which is then used to predict who is poor in the population as a whole using those characteristics. However, these methods have often proved disappointing in practice. Even with a budget sufficient to eliminate poverty with perfect information, the bulk of the poverty will probably remain in practice.
Experience and some research have also pointed to the horizontal inequities of targeted social policies in practice. To many eyes at community level these policies appear opaque and even pretty close to random. On the ground, people see plainly that equally poor families are being treated very differently by these targeting tools. Some get help but others do not. The obvious unfairness of this situation can generate social conflict and undermine well-intentioned social policies.
Whether the information actually available to policy makers is both reliable and sufficient for the task of targeting is an open question. It should be looked at carefully in each setting, taking account of the potential social tensions that can be generated by getting it wrong. But it should not be presumed that there are large gains for poor people from exploiting the information actually available in practice.
Straw man 4: A BIG destroys incentives to work. This one is surprising since a true BIG is probably about the most non-distortionary policy imaginable. Nobody can do anything to change how much they get. Granted there will probably be a positive income effect on demand for leisure. However (like all transfers), one must also consider impacts on other relevant constraints facing poor families, including uninsured risk and credit constraints.
Incentive effects should not be ignored. High marginal tax rates on poor people can be generated by some targeted policies and that is a bad idea. But it is also evident that the incentive concerns are often exaggerated, and do not seem so serious in practice.
Straw man 5: By focusing on cash a BIG diverts attention from health and education. The tradeoffs are undeniable. However, when thinking about a BIG we can define “income” quite broadly—not just cash income. A “full income” concept is appropriate, including imputed values for services in kind, such as publicly-provided health insurance and schooling. A BIG discussion can thus emphasize these “non-cash” dimensions of welfare.
The composition of the basic (full) income package is then a matter of policy choice. That choice is important and it will never be easy. There are real concerns about paternalism (overriding the preferences of poor people). But the key point here is that non-cash benefits should be factored into any characterization of what exactly is being guaranteed. So that is not a valid case against a BIG.