It’s hard to argue against the idea that giving cash to someone in need is the best you can do for that person in most circumstances: money maximizes your choice set and any conditions, strings attached, etc. makes that set smaller. With the advance of mobile technologies and better, bigger data, you can now send someone anywhere in the world money and make that person’s life instantly better – at least in the short run. But, what if I told you that with every dollar you send to one poor person, you’re taking away food from a few other people? How should we evaluate the impact of your transfer then?
This is a guest post by Craig McIntosh and Andrew Zeitlin.
We are grateful to have this chance to speak about our experiences with USAID's pilot of benchmarking its traditional development assistance using unconditional cash transfers. Along with the companion benchmarking study that is still in the field (that one comparing a youth workforce readiness to cash) we have spent the past two and a half years working to design these head-to-head studies, and are glad to have a chance to reflect on the process. These are complex studies with many stakeholders and lots of collective agreements over communications, and our report to USAID, released yesterday, reflects that. Here, we convey our personal impressions as researchers involved in the studies.
Blattman, Fiala, and Martinez (2018), which examines the nine-year effects of a group-based cash grant program for unemployed youth to start individual enterprises in skilled trades in Northern Uganda, was released today. Those of you well versed in the topic will remember Blattman et al. (2014), which summarized the impacts from the four-year follow-up. That paper found large earnings gains and capital stock increases among those young, unemployed individuals, who formed groups, proposed to form enterprises in skilled trades, and were selected to receive the approximately $400/per person lump-sum grants (in 2008 USD using market exchange rates) on offer from the Northern Uganda Social Action Funds (NUSAF). I figured that a summary of the paper that goes into some minutiae might be helpful for those of you who will not read it carefully – despite your best intentions. I had an early look at the paper because the authors kindly sent it to me for comments.
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.
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.
Cash transfers are great – lots of people are telling you that on a continuous basis. However, it is an open question as to whether such programs can improve the wellbeing of their beneficiaries well after the cessation of support. As cash transfer programs continue to grow as major vehicles for social protection, it is increasingly important to understand if these programs break the cycle of intergenerational poverty, or whether the benefits simply evaporate when the money runs out…
A whirlwind, surely incomplete tour of cash transfer impacts on health
Your run-of-the-mill conditional cash transfer (CCT) program has significant impacts on health-seeking behavior. Specifically, there are conditions (or co-responsibilities, if you prefer) that children get to school and/or that they get vaccinated or have some wellness visits. While the school enrollment effects are well established, the effects on both health seeking behavior and on health outcomes have been much more mixed. CCTs have led to better child nutritional status and improved child cognitive development in Nicaragua, better nutritional outcomes for a subset of children in Colombia, and had no impacts for child health in studies on Brazil and Honduras. CCTs conditioned only on school enrollment did not lower HIV infections among adolescent girls in South Africa; and in Indonesia CCTs increased health visits but did not translate into measurably improved health. Unconditional cash transfer programs have also had mixed results on health, with better mental health and food consumption in Kenya, better anthropometric outcomes for girls (not boys) in South Africa, no average impacts (although some for the poorest quarter) on child outcomes in Ecuador, and no average impacts on maternal health care utilization in Zambia (albeit yes effects for women with better access to such services).