I spend some of my time (technically half my time, but #gradstudentlife) working as a Graduate Research Assistant at the Minnesota Population Center primarily with the Integrated Public Use Microdata (IPUMS) Project. Most of my work is with the User Support Team helping users figure out how to make use of IPUMS data and fixing strange quirks in the data. After over a year of this job, I began to notice some frequently asked questions. In a new blog series on the Minnesota Population Center blog “Use It For Good“, I answer some of these FAQs.
Super cool mapping of the world’s immigration flows, country-by-country.
This map shows the estimated net immigration (inflows minus outflows) by origin and destination country between 2010 and 2015. Blue circles = positive net migration (more inflows). Red circles = negative net migration (more outflows). Each yellow dot represents 1,000 people. On original website, you can zoom in and click on individual countries.
Is globalization bad for the global poor? A nice write-up on Dercon and Blattman’s “randomizing sweatshops” study in Ethiopia.
Why is it so hard for academics and NGOs to work together? (and what can be done about it)
Who is the worst culprit of using international development jargon in their reports? (and why we all should use less of it)
This second post highlights the Lab’s commitment to impact and evidence. I’ve written before that the lack of a reliable and rigorous evidence base is largely to blame for the observation that spending on aid and development haven’t done much in terms of aiding and developing. When I joined the Lab, I was pleased to meet an office full of people who not only shared this perspective, but were also actively working to correct it. The Lab is joining the “credibility revolution” in a number of ways, I’ll highlight three.
First, I wrote in part 1 about how the Lab aims to produce development innovations. The first requirement for the Lab to fund any idea is that the idea must have rigorous evidence behind it. This has two key implications, from what I have seen.
- The Lab has a lot of scientists working in it; in fact it has the most scientists per capita of any bureau in the entire government. Here the term ‘scientist’ generally refers to people who hold an advanced degree (often a PhD) in a scientific field. These scientists come from a diverse set of fields (not just the usual development-ty fields… erm… economics) such as biology, chemistry, forestry, etc. Many of these scientists are hired through the AAAS Fellowship program and support the Lab’s capacity to understand and interpret cutting edge scientific research and apply it to the Lab’s global development objectives.
- A lot of development organizations often suggest something along the lines of, “Yeah, rigorous evidence is nice, but we are not a research institution.” At the Lab, the “we are not a research institution” excuse is not a valid argument. Although, it is rare that the Lab actually produces its own research (it’s often contracted out), they work hard to identify gaps in the evidence base and are relentlessly uncomfortable when USAID is funding programs that are not supported by rigorous evidence.
Second, the Lab is partnering with Google and the NGO Give Directly to perform ‘cash benchmarking’ studies in a variety of sectors in a variety of contexts. These studies seek to understand what would happen if we just gave all the money needed to run a USAID program directly to the end beneficiaries?
I want you to stop and pause for a second… USAID is studying whether or not their programs, their work, their bureaucracy is better than simply liquefying these programs and simply giving people around the world the money… I think this is incredible and deserves much appreciation!
This is pretty much as good as it gets from an evidence perspective. Often times monitoring and evaluation (M&E) activates perform studies that aim to understand if some program “worked”. The definition of “worked” could range from “providing positive benefits” to “providing benefits that outweigh the costs”. These cash benchmarking studies take this one step further by taking into account the opportunity cost of just giving all the operating expenses of some program directly to the end beneficiaries. (For frequent readers: This is basically the index funds for development idea, I’ve written about before.)
Third, the Lab is working to improve the way USAID (and other funders) both implement and use evidence within their work. Traditionally M&E within implementing and funding development agencies has aimed to improve accountability of aid projects and programs. The role data and evaluation plays is in ensuring that public funds have been used in the intended manner, as described by some sort of contract or scope of work.
Perhaps this is obvious, but this is a rather rigid structure for M&E. It almost entirely prohibits the ability of program administrators and development practitioners to adapt or make corrections mid-program cycle. This is again perhaps obvious, but poverty is an unsolved problem. This means we don’t know how to solve it. This being the case, we need to be learning as much as we can about what works, what doesn’t, and why.
The MERLIN (Monitoring, Evaluation, Research, and Learning Innovations) Program (the program I am working most closely with during my time at the Lab this summer) is made up of a mix of organizations each with their own specialty and strength spanning from randomized control trials and complex systems modeling to social network analyses. They have developed five “innovative” (to the USAID context at least) M&E activities that allow USAID programs to engage with complex systems more effectively and be more adaptive in their management and programing.
The third and final post of this series will focus on the Lab’s organizational structure and their commitment to collaboration.
The World Bank and the World Health Organization take on depression and anxiety as global development priorities:
Today – March 15, 2016 – Myanmar (formally Burma) elected their new president. This event is significant and interesting for several reasons.
- This is Myanmar’s first president without a military background since 1962.
- Nobel Peace Prize Laureate, and leader of the majority political party (the NLD), Aung San Suu Kyi is constitutionally unable to become president due to a rule that was passed into law explicitly to prevent her from becoming president.
- The rule prevents anyone who has foreign relatives from holding presidential office. Suu Kyi’s two sons are British, as was her late husband.
- In weeks leading up to the election there was wild speculation that a deal would be struck that would allow Suu Kyi to become Myanmar’s next president.
- Alas, no such deal was struck.
- President elect Htin Kyaw is a long-time friend of Suu Kyi and it is rumored that Suu Kyi will hold just as much power with him as president compared to if she was able to be elected.
- The current president, Thein Sein, is peacefully and willingly transferring power to Htin Kyaw.
- This is a big day for democracy, peace, and freedom in Myanmar.
Another year has gone by and I’m still blogging. Here are a list of the top posts from the past year, listed in order of popularity.
Via the Development Research Institute at New York University, “Happy Holidays to Ordinary People”:
Cyrenius, the governor of Syria, was excited about the new regional development plan he had prepared for the area covering Syria, Galilee, and Judea. Cyrenius had already ascended rapidly through the Roman bureaucracy, but he expected this new program meeting the Roman Development Goals to make his name. Caesar Augustus had even agreed to raise taxes to pay for the development plan.
Everyone went to their places of birth to register and pay taxes. Mary was a peasant espoused to a carpenter named Joseph from Nazareth, even though she was already pregnant. Mary and Joseph went to their birthplace of Bethlehem. The innkeeper took one look at the rough carpenter and the very pregnant Mary and told them there were no rooms left. He reluctantly agreed to let them sleep in the stable with the animals, where Mary went into labor.
Cyrenius’ name did end up in one of history’s most read books, but not for the reason he expected. The newborn baby was going to be a little more famous than Cyrenius, even more than Caesar Augustus.
Regardless of religious beliefs, many would agree it is a great story. Perhaps rulers and elites at the top should not take themselves so seriously, because some of the biggest changes in history can come from what appear to be just really ordinary people.
That’s an okay explanation, but I think there is an even more profound lesson about economics woven into this classic movie. It’s a topic I interact with regularly and one that is often misunderstood.
In the movie, “It’s a Wonderful Life” George Bailey gets the outrageous opportunity to see what the world would be like if he had never been born. He’s able to see the impact he’s made on the people around him. What a gift!
Notice how I use the word “impact” in the last paragraph. It is used correctly under the definition that impact is the outcomes of something minus the outcomes if that something did not happen.
Economists try to measure the impact of all sorts of things: from government policies and social programs to business innovations and market forces. When undertaking this endeavor we are presented with the same problem Clarence, George’s guardian angel, faces. We need to show impact. In Clarence’s case he needed to prove to George that he had a meaningful and positive impact on the people around him and that the world was a better place because he was born. Economists generally need to measure the effectiveness (or non-effectiveness) of a specific policy, program, or innovation.
The problem economists have is we aren’t angels and can’t go back in time and re-do the world in the absence of the thing we want to evaluate. Economists encounter what is called the “problem of the counterfactual”. We don’t have an alternate universe in which to compare hypothetical outcomes.
We have to come up with work-arounds – creating proxies of the counterfactual – to solve this problem.
One way is to randomly assign receipt from the program, policy, or innovation. With a large enough sample we would expect that the group of people assigned to receive the program, policy, or innovation would be exactly equal to the group of people who did not receive the program, policy, or innovation. Impact would then be measured by subtracting the outcomes from the group that didn’t receive the program, policy, or innovation from the group that had. This is the randomized experimental method and if it is done correctly it allows economists to show the exact same thing that Clarence showed George. Pause and reflect on that for a moment.
Sometimes (most of the time) a randomized experiment is not feasible or ethical, in which case the experimental ideal (described above) needs to be approximated. There are many (in fact, seemingly infinite) ways to do this, each has it’s own strength and is best suited for a particular situation. Going into the specifics of these is beyond the scope of this blog. (Much more can be learned by reading the excellent Mastering Metrics: The Path From Cause to Effect)
Suffice to say, “It’s a Wonderful Life” has a lot to teach us about economics, particularly the problem with the counterfactual and the power of the randomized experiment.