Ghana was the first country in Sub-Saharan Africa to meet the Millennium Development Goal (MDG1) target of halving extreme poverty by 2015. A share of the population living in poverty decreased from 52% in 1991 to 24% in 2012. Ghana is eager to lead the way in Africa again, but this time to graduate extreme poor households, out of poverty. The current policy debates are around graduating in about three to four years some 8.4 % of households living in extreme poverty. But to what occupations?
Let’s start with the perennial question on whether cash transfers affect work incentives… the answer is yes but not by much. A review by Baird et al shows that programs tend to result in little or no change in adult labor decisions. The exceptions are adults living with seniors receiving pensions and on select refugee programs (although to a limited extent and in risky locations). Check out tables 1 and 2 (p.26-27) for handy summaries of the evidence. Similarly, Daidone et al. found significant impacts of the Zimbabwe Harmonized Social Cash Transfer Program on beneficiary agricultural activities, the share of households owning livestock, and non-farm enterprises.
When people say “evidence-based policymaking” or they talk about the “credibility revolution, they are surely trying to talk about the fact that (a) we have (or trying hard to have) better evidence on impacts of various approaches to solve problems, and (b) we should use that evidence to make better decisions regarding policy and program design. However, the debate about the Haushofer and Shapiro (2018) paper on the three-year effects of GiveDirectly cash transfers in Kenya taught me that how people interpret the evidence is as important as the underlying evidence. The GiveDirectly blog (that I discussed here, and GiveDirectly posted an update here) and Justin Sandefur’s recent post on the CGD blog are two good examples.
From the DIME Analytics Weekly newsletter (which I recommend subscribing to): applyCodebook – One of the biggest time-wasters for research assistants is typing "rename", "recode", "label var", and so on to get a dataset in shape. Even worse is reading through it all later and figuring out what's been done. Freshly released on the World Bank Stata GitHub thanks to the DIME Analytics team is applyCodebook, a utility that reads an .xlsx "codebook" file and applies all the renames, recodes, variable labels, and value labels you need in one go. It takes one line in Stata to use, and all the edits are reviewable variable-by-variable in Excel. If you haven't visited the GitHub repo before, don't forget to browse all the utilities on offer and feel free to fork and submit your own on the dev branch. Happy coding!
Is it possible to speed up a justice system? On the Let's Talk Development blog, Kondylis and Corthay document a reform in Senegal that gave judges tools to speed up decisions, to positive effect. The evaluation then led to further legal reform.
"Reviewing thousands of evaluation studies over the years has also given us a profound appreciation of how challenging it is to find interventions...that produce a real improvement in people’s lives." Over at Straight Talk on Evidence, the team highlights the challenge of finding impacts at scale, nodding to Rossi's iron law of evaluation ("The expected value of any net impact assessment of any large scale social program is zero") and the "stainless steel law of evaluation" ("the more technically rigorous the net impact assessment, the more likely are its results to be zero – or no effect"). They give evidence across fields – business, medicine, education, and training. They offer a proposed solution in another post, and Chris Blattman offers a critique in a Twitter thread.
Kate Cronin-Furman and Milli Lake discuss ethical issues in doing fieldwork in fragile and violent conflicts.
"What’s the latest research on the quality of governance?" Dan Rogger gives a quick round-up of research presented at a recent conference at Stanford University.
In public procurement, lower transaction costs aren't always better. Over at VoxDev, Ferenc Szucs writes about what procurement records in Hungary teach about open auctions versus discretion. In short, discretion means lower transaction costs, more corruption, higher prices, and inefficient allocation.
Justin Sandefur seeks to give a non-technical explanation of the recent discussion of longer term benefits of cash transfers in Kenya (1. Cash transfers cure poverty. 2. Side effects vary. 3. Symptoms may return when treatment stops.) This is at least partially in response to Berk Özler's dual posts, here and here. Özler adds some additional discussion in this Twitter thread.
Plenty of vibrant discussions on the role of cash transfers in the ‘graduation’ agenda…
Banerjee et al are back with a new NBER paper on the classic graduation model (a package of assets, training, coaching, and savings). They explore two variants: whether the transfer of assets only would generate similar impacts, and whether access to a savings account and a deposit collection service would generate comparable impacts. Neither outperforms the holistic package. Similarly, a CSAE paper by Sedlmayr et al assesses graduation variants in Uganda--the full package of transfers and training, only the transfers, transfers with only a light-touch training and just attempting to boost savings. They find that cash only was less effective than the more integrated interventions.
Can cash transfers increase trust that citizens bestow upon their government… and even help it work a little better? Yes they can, according to a new paper (and accompanying blog) by Evans, Kosec and Holtemeyer. In 2010, Tanzania launched a pilot conditional cash transfer program, with a randomized roll-out in half of a set of 80 villages. After 2.5 years of transfers, beneficiaries – relative to potential beneficiaries in the waitlisted villages – report a stronger belief that their elected village leaders can be trusted in general, but not their appointed bureaucrats. Beneficiaries are more likely to report that local government leaders take citizens’ concerns into account, and that their honesty has improved over time. Notably, this increased trust does not translate into political activity. Beneficiary households are no more likely to vote in Village Council elections, or attend more Village Council meetings. The research even suggests that the program improves record keeping in the government, but only in sectors linked to transfers (education and health).
Cash transfers seem to be everywhere. A recent statistic suggests that 130 low- and middle-income countries have an unconditional cash transfer program, and 63 have a conditional cash transfer program. We know that cash transfers do good things: the children of beneficiaries have better access to health and education services (and in some cases, better outcomes), and there is some evidence of positive longer run impacts. (There is also some evidence that long-term impacts are quite modest, and even mixed evidence within one study, so the jury’s still out on that one.)
In our conversations with government about cash transfers, one of the concerns that arose was how they would affect the social fabric. Might cash transfers negatively affect how citizens interact with each other, or with their government? In our new paper, “Cash Transfers Increase Trust in Local Government” (can you guess the finding from the title?) – which we authored together with Brian Holtemeyer – we provide evidence from Tanzania that cash transfers increase the trust that citizens have in government. They may even help governments work a little bit better.
A central question in development economics is how to fund public goods. Informal taxation, whereby households make direct contributions to local public goods (such as water resources, roads and schools) outside of the formal tax system, is an important source of funding for public goods in many low-income countries, especially Kenya (Olken and Singhal 2011, Ngau 1987, Barkan and Holmquist 1986). Informal taxes are coordinated and collected by local leaders and enforced via social sanctions rather than the state. In a formal tax system, legal statutes dictate how taxes change with household income. But how does informal taxation respond to changes in household income?
My job market paper first quantifies informal taxation in Kenya. Using household panel data, I estimate informal tax schedules over the income distribution and test whether informal taxes respond to changes in earned income. Second, I estimate how informal taxation and public goods respond to a large, one-time increase in income from a randomized unconditional cash transfer program targeting poor households.
On my first project visit since joining the World Bank, I had a chance to accompany the Productive Social Safety Nets project team across the country to the Fouta Djallon region, in the northern part of Guinea, for the launch of their Labor Intensive Public Works (THIMO) activities. This trip allowed me to see firsthand what extreme poverty is. You hear and read about it, but I had the opportunity to meet people who experience it every day. I say opportunity, because going through this further humbled me, gave me more determination, and added purpose to the need to tell their stories—stories of their struggles and their achievements.
Poverty affected about 55% of Guinea’s population in 2012, but this percentage is likely to have increased as a result of the Ebola crisis and economic stagnation in 2014 and 2015. Poverty in Guinea is highly concentrated in the rural areas, where the poverty headcount rate remains far higher (65% in 2012) than in urban centers (35%). The lack of infrastructure, and limited economic opportunities and access to education all create a major development issue for these areas.
India’s state of Chhattisgarh faced a daunting challenge in the mid-2000s. About half of its public food distribution was leaked, meaning that it never reached the intended beneficiaries. By 2012, however, Chhattisgarh had nearly eliminated leakages, doubled the coverage of the scheme, and reduced exclusion errors to low single digits.
How did they do it?