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?
How long do the effects of cash transfers last? A paper by Blattman et al found that after nine years from inception, cash grants for young-adults in Uganda had lasting impacts on assets and skilled work, but had little eﬀect on mortality, fertility, health or education. See Ozler’s nice blog dissecting the study. A paper by Barham et al found that, after 10 years from inception, conditional cash transfers in Nicaragua did not lead to long-term impacts in learning, but did yield significant impacts on nutrition (body mass index), fertility, and subsequent labor market outcomes and income.
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.
Let’s start with social protection in Africa. A new paper by Kagin et al. estimates that in Malawi, each Malawi Kwacha (MK) transferred through the Social Cash Transfer Program generates 1.88 MK, while multipliers of public works are between 2.9-3.24 MK. In the same country, the Malawi Economic Monitor by Kandoole et al. has a very crisp, insightful edition discussing safety nets, e.g., spending is only 0.6% of GDP compared to 2% of input subsidies, and almost 6% on humanitarian aid.
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.