Why Good Policy Requires Responsible Communication and Consumption of Research

Over on the Shared Justice website, I (with Katie Thompson) wrote a piece on responsible communication and consumption of research. We focus this post on the recent dust-up with respect to research into the welfare effects of participation in payday lending. Here is an excerpt:

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My #OxCSAE2019 Recap

Over the past weekend, I was able to attend the 2019 Centre for the Study of African Economies (CSAE) Conference at the University of Oxford. Established in 1096 the University of Oxford is the oldest English-speaking university in the world. Walking around the campus is inspiring, but even more inspiring than that is the work presented by so many on how to improve economic and social outcomes across the continent of Africa (… erm… the world. Evidently, the conference is also open to studies implemented in locations other than Africa.)

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Is there an Identification-Importance Trade-off?

There is a story that floats around my department about an invited seminar speaker a few years back. Someone from the audience asked the speaker how they originally thought of the research idea. The speaker answered, “Well, my co-authors and I found some exogenous variation in our data and so we began to look for a relevant outcome.”

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Five Myths About Research on Violent Conflict

A forthcoming review article in a special issue of the Journal of Development Economics reviews the economics literature on violent conflict since the review of Blattman and Miguel (2010). If you do research in this area or teach development economics, the entire article is worth a read. Of more broad application, however, is the author’s listing of five myths about the microeconomics of violent conflict.

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Summer Update

Blog posts have been fairly sparse lately. A couple major life events have taken place that (to say the least) are way more important than blogging.

First, I got married to the love of my life two weekends ago. It was a wonderful weekend filled with friends and family.

Second, I defended my MS Thesis and completed the requirements for my MS degree from the Department of Agricultural, Food, and Resource Economics at Michigan State University last week (presentation slides here). More posts on my thesis research will (undoubtably) be posted later.

So what is next?

For the next three(ish) months my wife and I will be living in Washington DC. I’ll be spending my time over at USAID in the U.S. Global Development Lab. Officially I’m a HESN Intern in partnership with the Global Center for Food Systems Innovation at Michigan State University. Specifically, I’m a Research Associate with the MERLIN Program (Monitoring, Evaluation, Research and Learning Innovations) housed in the Office of Evaluation and Impact Assessment at USAID.

The MERLIN Program is a fairly new initiative within USAID. It’s task is to improve upon traditional approaches to monitoring and evaluation of development projects – specifically when outputs and outcomes of development projects are not easily identifiable prior to the start of the project. The particular focus of the MERLIN Program is on projects operating in highly complex environments, where the best approach to the development problem is not well recognized, and project managers must adapt the project design over the course of the project.

Many of you who know me will understand why I’m so excited for this opportunity over the next few months. My first “job” out of college was to implement an impact evaluation on a business training program in Western Kenya. The evaluation I helped design and run was adequate but clunky and time consuming. Since then, I’ve learned a lot about data collection, survey design methods, and econometrics. The world is complex and one of the most complex and puzzling problems of our time is poverty and underdevelopment amidst unbelievable technological innovation and economic growth. I think it is through efforts like the MERLIN Program – through adaptation in design and humility about what is known – that complex problems are ultimately solved.

Finally, after the summer months, my wife and I will move to the Twin Cities in Minnesota where I will begin a PhD in the Department of Applied Economics at the University of Minnesota. To fund this educational endeavor, I will work at the Minnesota Population Center (MPC) as a graduate research assistant. The MPC manages, disseminates, and harmonizes administrative and demographic data from both the United States and all over the world. For the nerdy data-savvy readers they are the home of the IPUMS, IDHS, NHGIS, and IHIS datasets.

Many exciting changes, hope to get back to blogging regularly soon!

Mini-Summaries from MIEDC 2016

A recent trend at nerdy development conferences is for someone to round up and post summaries of the selected papers and presentations. (For example, here is David Evans summarizing the latest CSAE conference.) I think this trend generates a tremendous resource for both folks who were unable to attend the conference or folks who did attend but were unable to witness every presentation of interest.

Continuing this trend, several colleagues and I set out to summarize every presentation at the 2016 Midwest International Economic Development Conference (MIEDC) held last weekend in Minneapolis, Minnesota. Although we ultimately were unable to attend every session, we were able to witness most of them. Miniature summaries of the presentations were collected and have been posted on the Economics That Really Matters blog.

MIEDC 2016: Recap

This (probably) goes without saying, but if you are at all interested in the complexities of development economics, poverty reduction, international agricultural development, or applied microeconomics then you should probably be following the Economics That Really Matters blog.

Bad Research is Bad Research.

I want to highlight a recent post by AidLeap on Why Programme Monitoring is so Bad. It brings up an important point and sheds light on an issue I’ve personally experienced.

A manager in a field programme that I evaluated recently showed me the glowing findings from his latest monitoring trip – based on a total sample size of two farmers. When I queried the small sample size, he looked shocked that I was asking. “It’s OK”, he explained, “We’re not aiming for scientific rigour in our monitoring.”

I regularly hear variants of this phrase, ranging from the whiny (“We’re not trying to prove anything”), to the pseudo-scientific (“We don’t need to achieve 95% confidence level!”) It’s typically used as an excuse for poor monitoring practices; justifying anything from miniscule samples, to biased questions, to only interviewing male community leaders.

I’ll second hearing the statement “We don’t need to achieve 95% confidence level”. I’ve also heard “Well, when we really want to do a deeper evaluation we’ll use a comparison group”.

There seems to be a sort of bias with folks who work for organizations and agencies that actually do stuff against spending resources (money and time mostly) finding out if their work actually works. In principle this has changed as even the smallest of organizations have a “Data Analysis Intern”or a “M&E Fellow”. In practice, however this monitoring and evaluation is typically pretty terrible.

This is likely due to a lack of rigorous (usually double-blind) peer-review that is the norm in academia. As well as the standards and goals of the various institutions. But that doesn’t change the reality.

This is unfortunate because if we think the work of these organizations is worth doing, then it is certainly worth doing well. Most of the time we just think the work is worth doing… and then stop there. Nobody tests whether the reality actually matches the theory.

Evaluating the effectiveness of something, especially when it comes to human livelihoods is important, no matter who you are. As such, performing evaluations as a veteran practitioner, a data analysis intern, or an M&E fellow is no different than performing evaluations as a professional scientist (economist, sociologist, anthropologist, agronomist, ect.) or tenured university professor. Bad research is just bad research. Full stop.

[If you’ve read this far, and haven’t already please read the original AidLeap blog post.]

The Economics of Hope

Two years ago I was a senior in college and sitting in a professor’s office discussing several topics I could focus on for a senior thesis. At the time the economics of happiness was gaining a lot of momentum as a research topic. I asked my professor if I could think about the concept of hope from an economic perspective. We did some searching for relevant literature and didn’t really find much. I moved on to a different topic.

Fast forward to now. The economics of hope has a growing and promising literature. I have plans to travel to Myanmar to try and collect data to better understand this topic. As I begin to dig into the literature, I thought it would be nice to record a roadmap of sorts.

Two summary resources provide a great starting point, and much of the insights in this blog post:

What does hope have to do with economics?

At first it may seem like there is not much connecting a light and fluffy topic like hope with cold and calculating economics. Generally economists have a lot to say about a lot of things. But hope is a topic that has historically belonged to theologians, philosophers, poets, and signer-song-writers. At second thought, however, hope is fundamental to any economic activity. Consider the words of Martin Luther:

Everything that is done in the world is done by hope. No husbandman would sow one grain of corn, if he hoped not it would grow up and become seed; no bachelor would marry a wife, if he hoped not to have children; no merchant or tradesman would set himself to work, if he did not hope to reap benefit thereby. How much more, then, does hope urge us on to everlasting life and salvation?

And John Stuart Mill:

A hopeful disposition gives a spur to the faculties and keeps all the working energies in good working order.

What we talk about when we talk about hope

There are a couple ways the word hope is used in english language and the difference between the two is subtle. Consider the difference between two sentences: “I hope it is sunny tomorrow.” and “I hope to go for a run tomorrow.” Both use the term hope but in different ways. Both terms indicate some sort of uncertainty but the second usage implies human agency. I may hope it is sunny tomorrow, but there is nothing I can do to make it sunny. I also may hope to go for a run tomorrow and I certainly can do things to make that happen. Lybbert and Wydick create a helpful figure to represent the differences between “Hope 1”, “Hope 2”, “Hopeless 1”, and “Hopeless 2”.

Screen shot 2015-04-10 at 8.56.51 PMHopelessness 1 is experienced by someone with both low agency over the future and low optimism about the future. This is a person who is feeling both hopeless and helpless. For example a victim of a famine who has no food availability in the future and no way to get it either. Hopelessness 2 is experienced by someone with high agency over the future but low optimism about the future. This is a person who is feeling hopeless but not helpless. For example someone who works very hard to survive but doesn’t see a future of any other way of life. Hope 1 is experienced by someone with low agency over the future but high optimism about the future. This is someone who hopes it will be sunny tomorrow. Hope 2 is experience by someone with both high agency over the future and high optimism about the future. This is someone who hopes to go for a run tomorrow.

As Lybbert and Wydick explain:

Distinguishing between these types of hope is useful, but individuals often experience hope as a combination of Hope 1 and Hope 2. Both types of hope, for example, are manifest in the case of a famine victim, or someone who is trapped, lost, or stranded, where a person may have to take painful but proactive steps to survive (internal agency) while awaiting relief or rescue (external to agency). Consider similarly the plight of someone suffering from a potentially terminal disease, in which there is some probability that a breakthrough in treating the disease may occur in the future. Survival thus depends on two events: (i) that the breakthrough occurs by time t; and (ii) that the patient is able to survive until time t. Hope for the patient thus consists of Hope 1 (hope that the breakthrough will occur) and Hope 2 (hoping to remain as healthy as is possible until the breakthrough arrives), which implies some degree of agency that may involve costs. (We might call this type of hope “Hope 1.5.”) In contrast, a person beset by hopelessness has concluded that the joint probability of these events is sufficiently dwarfed by the agency costs of survival, ensuring the unfortunate outcome.

 

Hope seems to matter (some evidence) 

Abhijit Banerjee, Esther Duflo, Raghabendra Chattopadhyay, and Jeremy Shapiro have a (2011) working paper entitled Targeting the Hard-Core Poor: An Impact Assessment. In it they evaluate a program designed to provide development services to people who don’t (for whatever reason) take up microfinance when it is offered to them. The program transferred assets (cow, goat, chickens) worth about $100 to the ultra-poor in Murshidabad, India. The results of the program were huge! 21% increase in earned income. 15% increase in consumption. An hour more work per day. Large psychological health effects. These effects are surprising given the amount of the asset transfer. What could be happening? Why are the benefits of giving an extremely poor person in India $100 WAY more than $100? What is making this return so large? Several things could be happening:

Perhaps the asset freed up a “nutrition based poverty trap”. In such a trap wages are so low that by working all day an you would only make, say, 800 calories, far lower than the necessary 1200, or so, calories needed per day. In this type of situation you will not be able to work or work very little and you will be very unproductive and stay very poor. So perhaps an asset transfer allows you to earn a small return on the asset (i.e. the cow gives milk, the chickens lay eggs etc.) and this pushes you above the necessary 1200 calories per day. Now the return on a $100 asset transfer is magnified by the workings of the labor market that you are now able to take advantage of because you are now able to put in a full day of work. Even if the labor market wages are still very low the return from the ultra-poor asset transfer will be quite large.

This nutrition based poverty trap isn’t what seems to be happening in Murshidabad. If the people were so poor that they didn’t have enough food to eat to work for enough hours every day then by giving an asset (such as a cow), all of the extra consumption should be in the form of food. Because feeding yourself adequately is the most productive thing to do. But in this impact assessment the authors find similar increases in overall consumption (15%) and food consumption (17%). People seem to be increasing expenditures in everything, not just food. Moreover, within food consumption the ultra-poor seem to be substituting for higher priced food that is not necessarily the most calories. For example less grains, more meat. Basically if these people were starving they would have maximized the calories available with their resources.

Another possibility is a so-called “credit trap”. In other words the ultra-poor do not have the ability to gain credit (due to a lack of durable assets to use as collateral, or lack of access to a provider, etc.). This again doesn’t seem to be the case because the program in Murshidabad, India was implemented by a microcredit organization explicitly targeting these people because they couldn’t get them to borrow money. So there was a organization in the area providing credit without a restriction of having collateral.

Still another possibility is mental health or psychological health. The beneficiaries of the program recognized fewer symptoms of depression, fewer symptoms of stress, and feeling much happier. This perhaps (as hypothesized by Duflo) could be the mechanism that leads to the large returns on the $100 asset transfer to the ultra poor. The question is whether there is such thing as a “hopelessness trap” (or as Dalton, Ghosal, and Mani (2013) call an “aspirations failure”). Said differently the expectation of future poverty exacerbates current poverty.

Take for example a seamstress. There is a huge difference between the productivity between sewing by hand and having a sewing machine. Additionally there is a big difference between having a mechanical sewing machine and a manual sewing machine. Additionally, there is a difference between having one mechanical sewing machine and two mechanical sewing machines. And so on and so on. In economist speak, the production function for a seamstress has discrete steps. An investment has a threshold before it becomes profitable. The problem is you can’t buy one tenth of a machine. You have to buy the whole thing. If someone is so poor and “hopeless” that they think they will never be able to cross the critical threshold for profitability, there is little incentive to be as productive and rational as possible. Perhaps you should spend more time buying toys for your child rather than save for a sewing machine if you never think you’ll be able to save enough for a sewing machine.

Hope then is a capability (a la Amartya Sen). Hope is a fuel that makes us capable of achieving things. And it also provides motivation to invest in business, education, health, etc.

So, how do we make people hopeful?

There could be many ways, several that have been recorded in the literature so far include:

For a long time those working in development have been focusing on external constraints to economic outcomes. We always think of the obvious things like credit, or agricultural inputs, or business skills training, or health, or nutrition, etc. Perhaps it is time to think about the internal constraints to economic outcomes. Things like aspirations, beliefs, or attitudes. All things that make up what we call hope.

(There is also a theological perspective of all this. Undoubtably, there will be more on that later.)

Links I Like [11.14]

1. Should Political Science Research Influence Politics? Chris Blattman on the ethics of doing research that actually tries to makes things better in the real world

2. Always Regulated, Never Protected: How Markets Work
Money quote: “At the same time, the argument that what conflict-affected countries need is an increase in good jobs – for stronger growth and safer societies – is compelling yet reductive. For one thing, the data required to support the idea that unemployment breeds violence simply do not exist. For another, the mainstream economic policy lens renders most women’s work invisible, consigning participation in the reproductive economy to the margins. And finally, the evidence we do have actually suggests that the main problem tends not to be that people aren’t working, but that they are forced and locked into forms of economic activity that are exploitative and which fail to produce much in the way of decent returns. In many developing countries, underemployment is a far greater problem. People are working, but the labour market is not working for them. Millions end up in forms of self-employment, arguably not because they are ‘born entrepreneurs’ but rather out of a lack of viable alternatives.”

3. How Can Faith Based Groups Get Better at Campaigning for Climate Change? A Biblical climate change policy proposal

4. Private Sector Development Policy Innovation Lab – Call for for Innovative Ideas on SME Growth and Entrepreneurship

5. Health Tip: Find Purpose in Life The science behind theology

6. The Hipster Effect: When Anticonformists All Look the Same The applied math behind why people who can be described as being different from everyone, always look the same