Unofficial Economist

Welcome to the Unofficial Economist

The Unofficial Economist name reflects the fact that I want to start working on and practicing economics, thinking deeply about it, but I’m only an aspirant to graduate-level training so far.

Being ~unofficial~ allows me room to experiment, take guesses, and dig into economic ideas that interest me, without too much pressure to get it completely right yet.

The blog is really a conglomeration of everything that’s interested me (and that I’ve found time to write about) since April 2018. I try my hand at interpreting economic papers and development blog posts; I send news / proof of life to my parents via the life updates section; I recommend books and podcasts; and I share other bits of my life, like the Bollywood dances I’ve been practicing.

The menu to the left breaks the posts into four categories: economics, life updates, recommendations, and other. I hope you enjoy!

Do I have to wait for tenure?

Yesterday, I attended the ASSA panel discussion, “How Can Economics Solve Its Race Problem?” with my colleague Soala Ekine.

I was deeply impressed with the leadership by example of the panel members in being open, vulnerable, and deeply conscientious in their discussion.

Throughout the event, the panel called on tenured professors and leaders in the profession (journal editors, leaders in the various professional organizations) to take responsibility for actively questioning and changing the racist, colonialist, and elitist culture in economics.

During audience questions, I asked the panel:

I know someone who is renouncing the label of economist and calling themselves a social scientist because of the culture in the profession. Despite warning signs, many of us still want to enter the profession – in addition to the advice for how leaders can drive change, what advice do you have for those of us coming up in the profession to also be drivers of change throughout our careers?

I think a more precise version of what I was trying to ask is, How and to what extent can non-tenured professors, grad students, and even RAs contribute to cultural changes without sacrificing longterm success in the profession?

Underlying even that question is: Assuming there would be backlash to being outspoken on these issues, would it still be worth it to be very outspoken? In order to establish what you believe is right and wrong, unequivocally, and unapologetically, directly try to make those changes? Or is your overall impact on the profession greater if you use more subtle and incremental techniques to try to make changes over a longer period of time?

There’s an empirical question that needs to be answered in all of this: To what extent does activism within the profession detract from your lifetime effectiveness as a researcher? And is it worth it anyways? (I lean yes, do it anyways, but maybe there’s certain better techniques?)

I’m imagining a situation where the leadership is NOT making changes. The expectation for young researchers is deference to seniors and adaptation to the toxic culture. It can be risky to speak against norms set by the people who you are going to rely on for recommendations in order to take the next step in this career, especially for non-white, non-hetero, non-male researchers.

If all goes perfectly according to plan, I will have tenure at a fancy research university in about 13-15 years. That is a long time to wait to be “allowed” to contribute to culture changes, or to wait to act in order to avoid backlash that could injure my career.

For those of us who are sticking with economics despite the many warnings signs (bullying, racism, sexism, colonialism, mental health challenges), who love the work, who want to be part of a change in these problems with the culture in the profession, there are non-trivial tactical questions of how to be change agents without decreasing our overall impact during our careers.

Unfortunately, we ran out of time for the panel to fully address any specific advice on that front.

I guess my hypothesis is that – assuming that it would indeed be really bad to be too “controversial” or outspoken too early in your career (I’d like to think that the warnings against causing too much trouble before tenure are overblown, but it does seem like there is a strong consensus that speaking up too early will damage your career prospects, thus reducing your overall career effectiveness) – the best way for young researchers to effect change will be through peer relationships, and how they interact with those coming up behind them.

Advocating for each other (especially for peers of color or who are not men), social support and encouragement, and a more collaborative mindset overall are some ways that younger researchers can reinforce better social norms in the profession. Also, we can resist the pervasive idea that the best researchers are the ones who minimize all service work to spend the maximum time possible on research. Service work is important and those willing to do that work can be the ones who set departmental norms to combat discrimination in the profession.

I think another path can be taking the role of student seriously by asking a lot of questions about how things are done, uncovering unspoken rules, asking for greater transparency in how the status quo operates, and trying to find the data to answer these questions. A lot of the panelists are doing amazing work on this front already, and we can all continue this work.

Every generation that we can improve the culture creates more room for the next generation to make progress, too.

This post is missing something else: How do these paths to making change look different for young economists of color? I’m a woman, so I’m seeing through that lens, but my small amount of Indian heritage doesn’t mean much in terms of understanding how the rules (and the urgency of these challenges) are different for economists of color in the U.S. In particular, grappling with these issues is not as much of a choice for economists of color as it is for white economists. Plus, as my colleague Soala pointed out during the Q&A, the rules for success also change for international students of color.

I also feel a bit yucky about the whole question because I don’t think the question of how and whether to speak up against discrimination should depend on the long-term career effects of speaking up. That is likely a case of over-optimization (a classic economist problem), at the expense of living up to my values.

My overall feeling after the session is in line with how many of the panelists wrapped up the discussion: I am cautiously optimistic. About the profession overall and about my own ability to effectively change the culture as I learn more and try to practice what I’m preaching here.

The panel included Randall Akee (UCLA), Cecilia Conrad (Pomona), Trevon Logan (The Ohio State University), Edward Miguel (UC-Berkeley), Marie T. Mora (U Missouri-St. Louis), and Ebonya Washington (Yale). It was chaired/introduced by Janet Yellen. 

Climate change economics?

My father keeps telling me, “Climate change will be the biggest challenge of your generation.”

Over email: “Have you read this article? Uninhabitable Earth seems like a book you should read.”

On a hike: “If you want to do something big, you should study the climate.”

Popping up in the background when I’m FaceTiming my mother: “Did you read that article I sent you?”

Okay, okay, padre. So I’m looking into it.

Some initial notes on one climate change-development paper:

Crick, Eskander, Fankhauser & Diop 2018

  • Small, medium, micro enterprises are especially vulnerable to climate change
  • Semi-arid lands have high exposure to climate stress
  • Africa will face particularly high temperature rises this century
  • Some of the challenges they will face:
    • less predictable seasonal rains
    • increased intensity when it does rain
    • reduction in crop quality & yields
    • decreased live-stock productivity
    • reduced availability of freshwater
  • Sustainable adaptation = maintaining business operations at existing levels
  • Unsustainable adaptation = contraction in business activity (e.g. distress sale of assets)
  • Financial barriers and insufficient market access are associated with business contraction (unsustainable adaptation)
  • Info, gov’t support, adaptation assistance increase prob. of sustainable adaptation
    • Room for policy to increase sustainable adaptation

 

It’s just one small peak into what’s being researched, just a taste of the questions out there, but I already have so many questions:

  1. What are the primary risks from climate change for developing countries? For others as well as specifically farmers?
  2. How aware / conscious are people about climate change and its impacts? How much does that factor into decision making?
  3. Why are women particularly vulnerable to climate change impacts?
  4. What ARE the climate change impacts that we expect to see in the next 10, 20, 50 years? What are we already seeing?
  5. What kinds of adaptations are sustainable vs. unsustainable?
  6. How can policy influence people’s ability to adapt?
  7. What is the ideal outcome / goal of sustainable adaptation? What’s the best possible outcome given the realities of climate change?
  8. Are businesses that adapt to climate change more or less profitable than those that do not? (Is climate change adaptation profitable?)
  9. What local or national practices are best for counteracting the negative impact?

Easterly: Academic publishing standards constrain work on important policy questions

220px-William_Easterly_by_Jerry_Bauer“Academic standards are leading us to concentrate on the less important policies. The worst case scenario is development economists risk becoming irrelevant because [they] concentrate on small issues that policy makers don’t think are important.” – Bill Easterly

 

Summary of interview w/ VoxDev

Trends:

  • Growth response to globalization policies (esp. in Africa, e.g.)
  • BUT Intellectual backlash against globalization

Doubts are legitimate – it’s hard to measure causality of these macro dynamics.

But do have persuasive correlations (high rates of inflation are strongly negatively correlated with growth rates). Linked to worse welfare. But rigorous causality determination is difficult – can’t rule out third factor or reverse causality.

Academics are reluctant to study inflation and growth and globalization; we need to present non-causal correlations if that’s the best we can do. It’s a responsibility of the economist to look at these as honestly as possible, even if isn’t most rigorous type of evidence. It’s what we’ve got!

Not enough research on big-picture policies.

Zimbabwe is relapsing into high inflation – will be very destructive and needs to be studied. Venezuela another example of poor policies around inflation. Not many policy makers / academic economists will think of these as good policies, but they used to be very common in S. America and Africa in 1970s-90s.

Incentives in economic publishing – prioritize rigorous resolution through causality. Young economists: by all means, stick with this! Tenured professors: stylized facts are also useful pieces of evidence. Need to work on these big, non-causal issues, too.

Evidence on small scale programs are less relevant for policy-makers.

Good model of what we should do more of: Acemoglu’s work. Easterly’s own research.

Paradox: development economists really want to talk about these big pictures, but very challenging to publish any research on them b/c of huge prioritization of rigor.

Wish the “brightest young minds in our field” have to do RCTs instead of look at the big questions.

IMF / World Bank are more interested in policy practicalities so they’re not as biased as journals.

(Small) RCTs useful for NGOs / specific aid agency programs.

Governments want institutional reforms or macro policy changes.

 

 

 

 

Extensive vs. Intensive Margins

Finally looked up intensive vs. extensive margins, which I’ve been able to decipher based on context, but for which I haven’t known the formal definitions.

Extensive margin: how many units/resources are used at the margin

Intensive margin: how “hard” (or “intensely”) resources are used at the margin (“how much per unit”)

For example, a policy change to increase teacher salary and set a portion of that increase as pay-for-performance might work a) at the extensive margin to increase the number of teachers (likely in the long-run, as teaching is viewed as a more attractive profession), and b) at the intensive margin, by increasing the effort put in by existing professors to achieve high performance (in this case, more of a short-term effect).

ILR CH1

India’s Long Road by Vijay Joshi is the start of my long road to understanding the Indian economy.

To follow are books on Indian history, the jobs crisis, and public institutions, as well as podcasts from Prof. Muralidharan’s course The Indian Economy. In his course, Prof. Muralidharan recommends noting three things from each class or reading:

  1. Something you learned
  2. Something you have a question about
  3. Something you are curious about

Presumably, I then follow up on the question and follow the thread of my curiosity.

So, India’s Long Road, Chapter 1 – India at the Cusp:

  1. Joshi is defining high quality growth as a) inclusive and b) environmentally friendly. Hooray on both accounts! Also:
    • Adumbrate – to report or represent in an outline
    • venality – openness to bribery or overly motivated by money
  2. What IS the mean world per capita growth rate? Answer: Around 1-3%
  3. Curious to see the practical sides of Joshi’s argument – what are the “radical reform model” components he recommends?

Dev links: Migration & Replication

Migration

No short-term effect of foreign aid on refugee flows

Overview: “We estimate the causal effects of a country’s aid receipts on both total refugee flows to the world and flows to donor countries.”

Data: “Refugee data on 141 origin countries over the 1976–2013 period [combined] with bilateral Official Development Assistance data”

Identification strategy: “The interaction of donor-government fractionalization and a recipient country’s probability of receiving aid provides a powerful and excludable instrumental variable (IV) when we control for country- and time-fixed effects that capture the levels of the interacted variables.”

Findings: “We find no evidence that aid reduces worldwide refugee outflows or flows to donor countries in the short term. However, we observe long-run effects after four three-year periods, which appear to be driven by lagged positive effects of aid on growth.”

Authors: Dreher, Fuchs, & Langlotz

Living abroad doesn’t change individual “commitment to development”

Overview: “Temporary migration to developing countries might play a role in generating individual commitment to development”

Data: “unique survey [of Mormon missionaries] gathered on Facebook”

Identification strategy: “A natural experiment – the assignment of Mormon missionaries to two-year missions in different world regions”

Findings: “Those assigned to the treatment region (Africa, Asia, Latin America) report greater interest in global development and poverty, but no difference in support for government aid or higher immigration, and no difference in personal international donations, volunteering, or other involvement.” (controlling for relevant vars)

Author: Crawfurd

Replication

Lessons from 3ie replications of development impact evaluations

Overview: “focus is internal replication, which uses the original data from a study to address the same question as that study”

Findings: “In all cases the pure replication components of these studies are generally able to reproduce the results published in the original article. Most of the measurement and estimation analyses confirm the robustness of the original articles or call into question just a subset of the original findings.” + some advice info on how to better translate study findings into policy

Authors: Brown & Wood

Practical advice for conducting quality replications 

Overview: The same authors share practical advice address the challenge “to design a replication plan open to both supporting the original findings and uncovering potential problems.”

Contribution:

1. Tips for diagnostic replication exercises in four groups: validity of assumptions, data transformations, estimation methods, and heterogeneous impacts, plus examples and other resources

2. List of don’ts for how to conduct and report replication research

 

 

Building State Capacity: Evidence from Biometric Smartcards in India

Preface: I always say I want to read more papers & summarize them. That can seem like an overwhelmingly massive undertaking. But I am forging ahead! This is the first step of what I hope to be a regular habit of reading and summarizing papers. “Building State Capacity” raised a lot of interesting points – it’s the first paper I’ve read in a while. As I refamiliarize myself with academic writing and various development econ concepts, I hope to become increasingly concise.


Summary

Program: Use of biometric identification system to administer benefits from two large welfare programs

Where: Andhra Pradesh, India

When: 2010 (baseline) – 2012 (endline)

Sample: 157 sub-districts, 19 million people

Identification strategy: RCT

Findings

  1. Payment collection became faster and more predictable
  2. Large reductions in leakage (fraud/corruption)
  3. Increase in program access: Reduction in gov’t officials claiming benefits in others’ names
  4. Little heterogeneity of results: No differences based on village or poverty/vulnerability of HH
  5. Strength of results: “Treatment distributions first-order stochastically dominate control distributions,” which means that “no treatment household was worse off relative to the control household at the same percentile of the outcome distribution”
  6. Drivers of impact? (non-experimental decomposition)
    • For payment process improvement: changed organization responsible for managing fund flow and payments
    • For decrease in fraud: biometric authentication
  7. Cost effective, for state and beneficiaries

Methodology details

Surveys: Baseline and endline household surveys (2 years between)

Randomization: Graduated rollout over 2 years. Treatment subdistricts were first wave, then buffer subdistricts (during survey time), then finally the control subdistricts (note: subdistricts = “mandals” in India)

Stratification: By district and a principal component of socioeconomic characteristics

Analysis: Intent-to-treat (ITT): “estimates the average return to as-is implementation following the ‘intent’ to implement the new system”

“Up-take”: 50% of payments transferred to electronic in 2 years

Main controls: district FEs, “the first principal component of a vector of mandal characteristics used to stratify,” baseline outcome levels where possible

Standard errors: clustered at mandal level (Lowest level of stratification)

Robustness checks:

  1. No differential misreporting: not driving results either due to collusion btwn officials and respondents or to inadvertent recall problems
  2. No spillovers: no evidence of either strategic spillovers (officials diverting funds to control mandals they can more easily steal from) or spatial spillovers (from neighbor gram panchayats – village counsels)
  3. No effects of survey timing relative to payment time
  4. No Hawthorne effects

Thoughts & Questions

  1. “Evaluated at full scale by government”: This minimizes risks around external validity that are often an issue for studies on NGO-operated programs at a smaller scale. Vivalt (2019) found that programs implemented by governments had smaller effect sizes than NGO/academic implemented programs, controlling for sample size; Muralidharan and Niehaus (2017) and others have discussed how results of small pilot RCTs often do not scale to larger populations.
  2. Love that they remind you of ITT definition in the text – makes it more readable. Also that they justify why ITT is the policy-relevant parameter (“are net of all the logistical and political economy challenges that accompany such a project in practice”)
  3. Again, authors define “first-order stochastically dominate” in the text, which I was wondering about from the abstract. Generally, well-written and easy to understand after a while not reading academic papers all the time!
  4. What does “non-experimental decomposition” mean? (This is describing how the authors identified drivers of treatment effects)
  5. Is it particularly strong evidence that treatment distribution was first-order stochastically dominant over control distribution? How do we interpret this statistically? Logically, if treatment was better for all HHs, relative to the closest comparison HH, that’s a good sign. But what if your results were not stat sig but WERE first-order stochastically dominant? What would that mean for interpreting the results?
  6. What is the difference between uptake and compliance? Uptake = whether treatment HHs take up the intervention/treatment. Compliance = whether the HH complies with its assigned status in the experimental design (applies to both treat and control households). Is that right?
  7. What does “first stage” mean? In this paper, it seems to be asking, How did treat and control units comply with the evaluation design, and what is the % uptake? (Basically, did randomization meaningfully work?) Is this always what first stage means for RCTs? How does its meaning differ for other identification strategies?
  8. Reminder: Hawthorne effects = when awareness of being observed alters study participant behavior
  9. What does “principal component” mean? Is it like an index?
  10. Authors note that the political case for investment in capacity depend on a) magnitude and b) immediacy of returns -> Does that mean policy makers are consistently biased toward policies w/ short-term pay-offs? (If yes, would expect there to be a drop-off for policies that have pay-offs on longer timeline than election cycle… or maybe policy just not data driven enough to see that effect?) Also, would this lead to fewer studies on long-term effects of programs/interventions with strong short-term payoffs because little policy appetite for longterm results?
  11. Challenges of working in policy space: program was almost ended b/c of negative feedback from local leaders (whose rents were being decreased!), but evidence from study, including positive beneficiary feedback, helped state gov’t stay the course! Crazy!
  12. Reference to “classic political economy problem of how concentrated costs and diffuse benefits may prevent the adoption of social-welfare improving reforms” In future, look up reference: Olson 1965
  13. Type I and II errors being referenced in a new (to me) way: Type I as exclusion, Type II as inclusion errors … I know these errors in statistical terms as Type I = false positive (reject true null) and Type II = false negative (fail to reject false null). In the line following the initial reference, the authors seem to refer to exclusion errors as exclusion of intended recipients, so not sure if these are different types of errors or I’m not understanding yet. To be explored further in future.

Muralidharan, K., Niehaus, P., & Sukhtankar, S. (2016). Building state capacity: Evidence from biometric smartcards in India. American Economic Review106(10), 2895-2929.

All the countries I have visited

1994 Canada – born!

1995 US – moved to NY then NC

2007 UK (England) – visited Mom’s family

2007 Kenya – visited close family friends

2015 Rwanda – summer internship

2015 Uganda – vacation from summer internship

2015 India – study abroad

2016 France – visiting friend studying abroad

2016 Italy – visiting friend studying abroad

2016 Netherlands – vacation with friends studying abroad in Europe

2017 Zambia – on-boarding to new job

2018 Liberia – work trip

2018 Zimbabwe – Victoria Falls half-marathon with boyfriend

2018 South Africa – work trip

2018 Spain – visiting brother Max studying abroad

2018 Germany – random 12-hour layover

Plan for 2019: Mexico

 

Weekly Development Links #8

Brought to you by #NEUDC2018! Check out mini summaries of the many awesome papers featured at this conference here,  and download papers here. These are three that really struck me.

1. Psychological trainings increase chlorination rates
Haushofer, John, and Orkin 2018: (RCT in Kenya) “One group received a two-session executive function intervention that aimed to improve planning and execution of plans; a second received a two-session time preference intervention aimed at reducing present bias and impatience. A third group receives only information about the benefits of chlorination, and a pure control group received no intervention.” Executive function and time preference trainings led to stat sig increases in chlorination and stat sig decreases in diarrhea rates.

2. Conditional cash transfers reduce suicides!
Christian, Hensel, and Roth 2018: (RCT in Indonesia) This paper is so cool! One mechanism is by mitigating the negative impact of bad agricultural shocks and decreasing depression. “We examine how income shocks affect the suicide rate in Indonesia. We use both a randomized conditional cash transfer experiment, and a difference-in-differences approach exploiting the cash transfer’s nation-wide roll-out. We find that the cash transfer reduced yearly suicides by 0.36 per 100,000 people, corresponding to an 18 percent decrease. Agricultural productivity shocks also causally affect suicide rates. Moreover, the cash transfer program reduces the causal impact of the agricultural productivity shocks, suggesting an important role for policy interventions. Finally, we provide evidence for a psychological mechanism by showing that agricultural productivity shocks affect depression.”

3. Women police stations increased reporting of crimes against women
Amaral, Bhalotra, and Prakash 2018: (in India) “Using an identification strategy that exploits the staggered implementation of women police stations across cities and nationally representative data on various measures of crime and deterrence, we find that the opening of police stations increased reported crime against women by 22 percent. This is due to increases in reports of female kidnappings and domestic violence. In contrast, reports of gender specific mortality, self-reported intimate-partner violence and other non-gender specific crimes remain unchanged.”

BONUS: Amazing 3-D map of world populations
(The Pudding has so many other really interesting and informative graphics, too!)

Country 15: Spain, con Max

I just got back from visiting my brother in Spain. He’s studying abroad in Granada, the beautiful city I never wanted to leave.

***

I’ve never been a fan of window seats – I get claustrophobic stuck in the corner, and I always drink a lot of water and then have to pee which means everyone in the row has to get up twice – but I’m glad I forgot to pre-check and choose an aisle seat.

When I landed, I had to take a bus from Málaga to Granada. I spent the two hours staring out the window. Somehow I’d thought Spain was more lush, less dry.

The first night, Max introduced me to tapas, which is the reason I want to live in Granada forever. You hardly need to eat dinner, just go out for drinks and every drink gets you a snack. One place we went gave us mini enchiladas.

I posted up in different cafes to work Monday – Wednesday, and Maxie came and did work with me in between classes.

***

On Friday, Max, his friend Silas, and I went to Albaicín, a neighborhood on a hill that was the original walled city when it was under Arab rule.

We wandered so many good alleys in search of this one lookout Silas remembered. Along the way, we were propositioned to buy weed by this kid who looked 16 and we bought our lunches at a little grocery store.

The lookout had a view of Alhambra, the awesome fort that overlooks the city from a hill.

We wanted to find a quieter lunch spot so we walked all over Albaicín until we found this awesome wall that overlooked caves where people were living and, apparently, farming, as well as the rest of the city.

After a delicious sandwich for lunch (baguette is good in Spain, too), we climbed down the other side of the wall and walked by some of the cave homes on our way down to the river that would lead us back into town.

I would absolutely move to Granada – I would just need a job and also to learn Spanish. (I was totally useless, sometimes forgetting to say “si” instead of “oui” but it was fun to see Max in action with the Spanish.)

The hills were awesome, and from some vantage points you could see the white caps of mountains not too far away. I love the white buildings with the red tile roofs, and the cobblestone streets, and all the bread and cheese. Not sure I could get used to a giant lunch and nothing being open from 2-5 pm, but for the scenery, I would certainly try.

***

Max had been injured while climbing a mountain (he had to be helicoptered out!) before I got there and had just gotten his stitches out. So while I was there, we went on a run together with Silas and his other friend Ivy for his first run back. He was like, great, running with y’all will be so chill, and THEN WE RAN FOR TWO HOURS.

Still chill for him, not so much for me! It was the longest I’ve run since the half marathon in July.

The run was beautiful, though, we ran up into the hills behind Alhambra before turning down into the valley and making our way back along a winding country road. 

I ran on my own on Monday and followed the same trail but not as far. It was so dang beautiful.

I’m trying to run consistently again (Max recommended a training plan), and it would be so easy if I could run on those trails all the time!

My Monday run was the first of the training week and I felt pretty awesome about meeting my goal of sub-9’30” average pace (9’23” in the end). It had a rough start because the first two miles were basically straight uphill past Alhambra. (And miles 6 and 7 were back down the same hills.)

When Max was helping me with a training schedule, he recommended training for 5K instead of the half marathon, since it’s easy to run a lot of 5Ks and see your progress. Plus I’m more of a sprintery type. We’re going to get the whole fam to run a 5K in December when I’m home for the holidays.

I was guessing I’d run that 5K at about 25 minutes and then try to improve all the way down to sub-20 (which is SO FAST – you have to run 6’25” pace!). The only 5K I’ve ever run before (just after volleyball season 3-4 years ago), I did in about 26 mins. But now I’m hoping to get more like 23 or 24 mins, since the last 5K of my 8 mile run was in 25:56. We’ll see!

***

On the last day of my visit, Max took me to see his school. This is the view from the rooftop study area, which is ridiculous:

10/10 would go to Spain again.

Weekly Development Links #7

1. 11 years later: Experimental evidence on scaling up education reforms in Kenya (TL;DR gov’t didn’t adopt well)

(This paper was published in Journal of Public Econ 11 years after the project started and 5 years after the first submission!) “New teachers offered a fixed-term contract by an international NGO significantly raised student test scores, while teachers offered identical contracts by the Kenyan government produced zero impact. Observable differences in teacher characteristics explain little of this gap. Instead, data suggests that bureaucratic and political opposition to the contract reform led to implementation delays and a differential interpretation of identical contract terms. Additionally, contract features that produced larger learning gains in both the NGO and government treatment arms were not adopted by the government outside of the experimental sample.”

2. Argument for reporting the “total causal effect”

  • Total causal effect (TCE) = weighted average of the intent to treat effect (ITT) and the spillover effect on the non-treated (SNT)
  • Importance: “RCTs that fail to account for spillovers can produce biased estimates of intention-to-treat effects, while finding meaningful treatment effects but failing to observe deleterious spillovers can lead to misconstrued policy conclusions. Therefore, reporting the TCE is as important as the ITT, if not more important in many cases: if the program caused a bunch of people to escape poverty while others to fall into it, leaving the overall poverty rate unchanged (TCE=0), you’d have to argue much harder to convince your audience that your program is a success because the ITT is large and positive.”
  • Context: Zeitlin and McIntosh recent paper comparing cash and a USAID health + nutrition program in Rwanda. From their blog post: “In our own work the point estimates on village-level impacts are consistent with negative spillovers of the large transfer on some outcomes (they are also consistent with Gikuriro’s village-level health and nutrition trainings having improved health knowledge in the overall population). Cash may look less good as one thinks of welfare impacts on a more broadly defined population. Donors weighing cash-vs-kind decisions will need to decide how much weight to put on non-targeted populations, and to consider the accumulated evidence on external consequences.”

3. Why don’t people work less when you give them cash?

Excellent post by authors of new paper on VoxDev, listing many different mechanisms and also looks at how this changes by type of transfer (e.g. gov’t conditional and unconditional, remittances, etc.)

BONUS: More gender equality = greater differences in preferences on values like altruism, patience or trust (ft. interesting map)

Falk & Hermle 2018

Causal Inference: The Mixtape

“Identifying causal effects involves assumptions, but it also requires a particular kind of belief about the work of scientists. Credible and valuable research requires that we believe that it is more important to do our work correctly than to try and achieve a certain outcome (e.g., confirmation bias, statistical significance, stars). The foundations of scientific knowledge are scientific methodologies. Science does not collect evidence in order to prove what we want to be true or what people want others to believe. That is a form of propaganda, not science. Rather, scientific methodologies are devices for forming a particular kind of belief. Scientific methodologies allow us to accept unexpected, and sometimes, undesirable answers. They are process oriented, not outcome oriented. And without these values, causal methodologies are also not credible.”

Causal Inference: The Mixtape by Scott Cunningham, associate professor of economics at Baylor University (oh and there’s an accompanying Spotify playlist)

Weekly Development Links #4 – #6

Dev links coming to you weekly from now on!

Week #6: Oct 17

1. Cash transfers increase trust in local gov’t

“How does a locally-managed conditional cash transfer program impact trust in government?”

  • Cash transfers increased trust in leaders and perceptions of leaders’ responsiveness and honesty
  • Beneficiaries reported higher trust in elected leaders but not in appointed bureaucrats
  • Government record-keeping on health and education improved in treatment communities

2. Kinda random: sand dams

Read a WB blogpost on sand dams as a method for increasing water sustainability in arid regions … but that did not explain how the heck you store water in sand, so watched this cool video from Excellent Development, a non-profit that works on sand dam projects.

3. USAID increasingly using “geospatial impact evaluations” ft. MAPS!

Outlines example of a GIE on USAID West Bank/Gaza’s recent $900 million investment in rural infrastructure

Ariel BenYishay, Rachel Trichler, Dan Runfola, and Seth Goodman at Brookings

BONUS: In other geospatial news
LSE blog post on the work of ground-truthing spatial data in Kenya

Week #5: Oct 10

Health Round-Up Edition

1. Dashboards for decisions: Immunization in Nigeria

A new dashboard is being used to improve data on routine immunizations … but doesn’t look like the underlying data quality has been improved. Is this just better access to bad data?

2. Norway vs. Thailand vs. US

A comparative study of health services for undocumented migrants

3. Traditional Midwives in Guatemala

Aljazeera on the complicated relationship between traditional midwives providing missing services and the gov’t trying to provide those services in health centers

BONUS: Visualizing fires + “good”

  • Satellite imagery of crop burning in India in 2017 vs 2018
  • How good is good? 6.92/10. The YouGov visualization on how people rate different descriptors on a 0-10 scale is really interesting if you look at the distributions – lots of agreement on appalling, average (you’d hope there would be clustering around 5!), and perfect. Then, pretty wide variance for quite bad, pretty bad, somewhat bad, great, really good, and very good. Shows how you should cut out generic good/bad descriptions in your writing and use words like appalling or abysmal that are more universally evocative.

Week #4: Oct 3

1. Tanzania outlaws critiques of their data!?

“Consider a simple policy rule: if a government’s statistics cannot be questioned, they shouldn’t be trusted. By that rule, the Bank and Fund would not report Tanzania’s numbers or accept them in determining creditworthiness—and they would immediately withdraw the offer of foreign aid to help Tanzania produce statistics its citizens cannot criticize.”

2. 12 Things We Can Agree On About Global Poverty?

In August, a CGDev post proposed 12 universally agreed-upon truths about global poverty. Do you agree? Are there other truths we should all agree on?

3. Food for thought on two relevant method issues

  • Peter Hull released a two-page brief on controlling for propensity scores instead of using them to match or weight observations
  • Spillover and estimands: “The key issue is that the assumption of no spillovers runs so deep that it is often invoked even prior to the definition of estimands. If you write the “average treatment effect” estimand using potential outcomes notation, as E(Y(1)−Y(0))E(Y(1)−Y(0)), you are already assuming that a unit’s outcomes depend only on its own assignment to treatment and not on how other units are assigned to treatment. The definition of the estimand leaves no space to even describe spillovers.”

BONUS: New head of IMF
Dr. Gita Gopinath takes over.

Weekly Development Links #3

My final week of taking over IDinsight’s internal development links.

1. Development myths: debunked

Rachel Glennerster asked for examples of development myths, resulting in a list development myths along with debunking sources / evidence against. Some of the myths shared, with accompanying evidence:

2. Traditional local governance systems (autocratic) underutilize local human capital

A new paper by Katherine Casey, Rachel Glennerster, Ted Miguel, and Maarten Voors. “We experimentally evaluate two solutions to these problems [autocratic local rule by old, uneducated men] in rural Sierra Leone: an expensive long-term intervention to make local institutions more inclusive; and a low-cost test to rapidly identify skilled technocrats and delegate project management to them. In a real-world competition for local infrastructure grants, we find that technocratic selection dominates both the status quo of chiefly control and the institutional reform intervention, leading to an average gain of one standard deviation unit in competition outcomes. The results uncover a broader failure of traditional autocratic institutions to fully exploit the human capital present in their communities.“

3. Aggressive U.S. recruitment of nurses from Philippines did not result in brain drain / negative health impacts

A new paper by Paolo Abarcar and Caroline Theoharides. “For each new nurse that moved abroad, approximately two more individuals with nursing degrees graduated. The supply of nursing programs increased to accommodate this. New nurses appear to have switched from other degree types. Nurse migration had no impact on either infant or maternal mortality.”

BONUS. Data viz: Poverty persists in Africa, falls in other regions

Justin Sandefur shared that the Economist much improved a World Bank graphic to more clearly visualize how the number of people living in poverty has risen slightly in Africa while other regions have seen sharp decreases in # of people in poverty over time. (Wonder how the graphic would like stacked Africa, South Asia, then East Asia & Pacific? Less dramatic contrast between Africa and the other regions? Number of poor in South Asia hasn’t decreased as dramatically as East Asia, would look more similar to Africa trend than East Asia trend until about 2010 I think.)

Weekly Development Links #2

This is part 2 of me taking over IDinsight’s internal development link round-up.

1. This week in gender & econ

2. Two papers on p-hacking or bad reporting in econ papers

3. Mapping trade routes Tilman Graff shared some really cool visualizations of trade routes, aid, and infrastructure in several Africa countries. They were created as part of his MPhil thesis.

Footbridges for higher wages

Lant Pritchett and other researchers often argue that development economists are too focused on one-off, micro interventions and fail to see the big picture. They are highly critical of the hype that develops around specific interventions following the release of studies using RCTs or other quasi-experimental methods to measure the impact of a specific program – microfinance, for example, had a big moment and, more recently, cash transfers have dominated many discussions of economic development.

Pritchett’s scorecard comparing first generation RCT practice to the approach of the non-RCT crowd is an especially brutal assessment of the micro development literature (second table in the link). He writes, “National Development leads to better well being. National development is ontologically a social process (markets, politics, organizations, institutions). RCTs have focused on topics that account for roughly zero of the observed variation in human development outcomes.”

There’s a lot that’s valid about this line of critique, although I think it’s more a call to be sure to contextualize learnings, ideally with qualitative research to investigate the how and why of a quantitative claim, rather than motivation to throw out the micro development approach altogether.

Besides, there is something so satisfying about how a small intervention can have a big impact.

Small bridges, big deal

Brooks and Donovan’s recent paper (full PDF here) found that building footbridges in Northern Nicaragua protected local workers from the typical wage loss seen during flooding, when travel routes are cut off, and even led to increased profits of local farmers.

Their primary finding is best seen through two graphics from the paper. The first shows the distribution of wage earnings before footbridge construction, and you can clearly see a massive disadvantage to those experiencing flooding. In the second, the gap has disappeared.

Figures 1 & 2: Distribution of wage earnings BEFORE footbridge construction

Figure 2: AFTER

They also find positive spillover effects. First, rural villagers were able to take higher paying jobs in nearby towns, increasing their wages and increasing the wages of those left behind, who faced less competition in the local labor market. (A similar mechanism to that found in the No Lean Season research, which offered select villagers incentives to migrate to cities for work and found positive income effects for those households and neighboring non-study households.)

Second, farmer profits increased. Not because of lower trade costs that allowed farmers to buy cheaper inputs, but because they were able to access new purchasing markets for their goods and diversify their income sources.

This paper is amazing because the data viz communicates clearly, the findings are meaningful and positive, and the idea for the research design had to have come from an intimate knowledge of the challenges facing rural citizens of Northern Nicaragua.

A national and local development tool

Infrastructure studies connect easily to those big questions about national development that anti-randomistas would prefer to focus on.While it won’t be footbridges in every location, there are lots of countries where road and transport infrastructure solutions are needed to promote both local and national development.

Papers like this one show how connectivity and access can be an important determinant of economic welfare via multiple mechanisms. Besides income effects like those measured in the Brooks and Donovan paper, there are possible effects for access to credit, healthcare, or other public services that isolated communities would otherwise miss out on.

Gaining entitlements with infrastructure and cash

There’s a seriously inspiring narrative in there – a simple change that leads to more options, more opportunities, more connectivity. As my colleague Sindy was discussing today, there is a pattern that interventions about increasing options and expanding opportunity, such as infrastructure improvements or cash transfers, seem more powerful to affect broad change than interventions targeting very narrow and specific goals.

Although, there is probably a gain in using both types of interventions at different times, or concurrently.

McIntosh and Zeitlin’s new paper compares a cash transfer program directly with a child nutrition program.The final line of their abstract made me think about paternalism and beneficiary preferences: “The results indicate that programs targeted towards driving specific outcomes can do so at lower cost than cash, but large cash transfers drive substantial benefits across a wide range of impacts, including many of those targeted by the more tailored program.”

People spend their money with different priorities than programs dictate and seem to get more out of it. That suggests to me that cash transfers (or infrastructure improvements) are a way to improve this baseline ability to provide for your household (“entitlements” à la Amartya Sen), while specific health or education interventions are more useful as public service-style campaigns to promote undervalued goods, such as immunizations.

A final thought

I’m generally curious how often Sen’s entitlements approach is explicitly applied to non-famine topics in development research. I’m guessing often. (A two-minute google led me to a PhD thesis called “Poverty as entitlement failures” that sounds interesting.)

Weekly Development Links #1

Each Wednesday at IDinsight, one of our tech team members, Akib Khan, posts a few links (mostly from Twitter!) to what he’s been reading in development that week. For the next three weeks, he’s on leave and I am taking over! Thought I should cross-post my selections (also mostly curated from #EconTwitter):

Cash Transfer Bonanza: The details matter
Blattman et al. just released a paper following up on previous 4-year results from a one-time cash transfer of $400, now reporting 9-year results (see first 3 links). To liven up the internal discussion, I’m adding critiques by Ashu Handa (UNC Transfer Project / UNICEF-Innocenti economist and old family friend), who has cautioned against lack of nuance in interpretation of CT study results, esp. around program implementation details like who is distributing grants, the size of the grants, and how frequently they are given – he studies social protection programs giving repeat cash transfers.

Diff-in-diff treatment timing paper… with GIFs!
Andrew Goodman-Bacon (what a name!) has a new paper that all of #EconTwitter is going crazy over. It deals with some methodological issues using diff-in-diff when treatment turns on at different times for different groups, and other scenarios where timing becomes important. Real paper not for the faint hearted, but the Twitter thread has some great GIFs!

African debt to China: reality doesn’t match the hype

Bonus link: Eritrea & Ethiopia border opening party

Thesis revamp: All hail Ted Miguel, PhD, god of economic writing!

      Ted Miguel, god of economic writing

In order to have a high-quality writing sample for the RA jobs I’m applying to this fall, I am revamping my thesis! Joy of joys!

I thought about doing this earlier in the year and even created a whole plan to do it, but ended up deciding to work on this blog, learning to code, and other, less horrifying professional development activities.

I say horrifying because the thesis I submitted was HORRIBLY WRITTEN. So so so bad. I cringe every time I look back over it. I had tackled a 6-year project (the length of time it took to write the paper I was basing my thesis on, I later found out) in four months time. Too little of the critical thinking I had done on how to handle the piles and piles of data I needed to answer my research question actually ended up in writing.

I thought it would be a drag to fix up the paper. I didn’t expect to still be as intrigued by my research topic (democracy and health in sub-Saharan Africa!) or to be as enthusiastic about practicing my economic writing. I’m taking the unexpected enjoyment as a positive sign that life as a researcher will be awesome.

I’ve been thinking critically about the question of democracy and health and how they’re interrelated and how economic development ties into each. I’ve read (skimmed) a few additional sources that I didn’t even think to look for last time and I already have some good ideas for a new framing of why this research is interesting and important. The first time around, I focused a lot on the cool methodology (spatial regression discontinuity design) because that’s what I spent most of my time working on.

My perspective on the research question has been massively refreshed by time apart from my thesis, new on-the-ground development experience, and the papers I’ve read in the interim.

My first tasks have been to re-read the thesis (yuck), and then gather the resources I need to re-write at least the introduction. I am focusing on the abstract and introduction as the first order of business because some of the writing samples I will need to submit will be or can be shorter and the introduction is as far as most people would get anyways.

To improve my writing and the structure of my introduction, my thesis advisor – who I can now call Erick instead of Professor Gong – recommended reading some of Ted Miguel’s introductions. I printed three and all were well-written and informative in terms of structure; one of them (with Pascaline Dupas) even helped me rethink the context around my research question and link it more solidly to the development economics literature.

The next move is to outline the introduction by writing the topic sentence of each paragraph (a tip taken from my current manager at IDinsight, Ignacio, who is very into policy memo-style writing) using a Miguel-type structure. I’ll edit that structure a bit, then add the text of the paragraphs.

Noble work: Anand Giridharadas on the EKS

There was a recent discussion on the IDinsight #philosophy Slack channel about a recent Ezra Klein Show (EKS on this blog from now on, since I talk about it all the time) podcast with Anand Giridharadas. My contribution built off someone else’s notes that Giridharadas is spot on about how companies (also IDinsight in some ways) sell working for them as an extension of the camaraderie and culture of a college campus, how he doesn’t offer concrete solutions and that’s very annoying, and some reflections on transitioning from private sector consulting to IDinsight’s social sector, non-profit consulting model. I related more to the moral arguments in the podcast, and this is what I shared:

I connected most with his argument about how the overall negative impact of many big for-profit companies on worldwide well-being vastly outweighs any individual good you can do with the money you earn. One of EA’s recommended pathways to change is making a ton of money and giving it to effective charities, but if you do that by working for an exploitative company, then you’re really contributing to the maintenance of inequality and of the status quo racist, sexist, oppressive system.

My dad was always talking about having a “noble” profession when I was growing up (he’s a teacher and my mom’s a geriatric physical therapist) and even though “noble” is a strange way to put it, I think it is really important to (as much as possible) only be party to organizations and companies that are doing good or at least not doing active harm.

That being said, there are more reasons for going into the private sector and aiming to make money than are really dealt with in the podcast. For example, a few people we’ve talked to in South Africa have mentioned that many highly skilled South Africans are responsible for the education costs for all siblings/cousins and that is a strong motivator to take a higher paying salary.

It becomes very related to the debate about how much development or social sector workers should get paid, relative to competitive private sector jobs. I think IDinsight does a pretty good job of being in the middle for US associates anyways – paying enough that you can even save some, which is more than a lot of non-profits provide, but not necessarily trying to compete with private sector jobs because our model relies a lot on hiring people who are in it to serve, not for the money. Something for us to continue thinking about is how this might exclude candidates who have other financial responsibilities and how we should respond to this issue in how we hire and set salaries.

It’s so frustrating when people identify a problem without offering solutions. The closest he comes to offering solutions is to have organizations stop lobbying for massive tax breaks or in other ways deprioritize the bottom line of profitability. Sounded to me like his vision involves a lot more socialist ideas: the full solutions to these issues would involve massive-scale reorganizing of the existing economic system… although maybe we are heading in that direction with more co-op style companies and triple bottom line for-profit social enterprises? (Don’t know a ton about this co-op stuff – mostly from another Ezra Klein show episode probably, but it sounds cool!) …Maybe his next book will try to map out solutions, though?

Feminism contains multitudes: Annotated critique of WSJ op-ed on day care in Sweden

My annotated critique of “The Human Cost of Sweden’s Welfare State” – a poorly argued op-ed in the WSJ by psychoanalyst Erica Komisar.

Follow up research

  • Andersson, 1989: “Children with early day care (entrance before the age of 1) were generally rated more favorably and performed better than children with late entrance or home care.”
  • OECD report, 1999, p. 60: Acknowledges that children with poor immune systems or who are not good in group settings, could fare better at home or in home-style day cares (similar language to what the op-ed author uses). But points out that the increased feasibility of mothers staying home with young children for longer alleviates some concerns about mother-child early separation, giving parents flexibility to choose what option works best.
  • Another poorly supported op-ed from the Irish Times, 2011:

    “Working as a management consultant, Himmelstrand heard from women how sad they were about leaving their one-year-olds in daycare. He began to notice there were no children in the playgrounds during the day. If you walked down the street with a three-year-old toddler, people were amazed and disapproving the child was not in daycare.

    He also found educational standards were slipping in Sweden, and rates of psychological distress and psychosomatic illnesses among teens had gone up dramatically, not to mention disruptive behaviour in schools.”

  • Institute of Marriage and Family in Canada, 2015 blog post by Himmelstrand from a site with the tag line “Latest developments in family friendly research”: Not much research has been done on the Sweden day care system since the 70s and 80s. Highlights some staffing issues I saw mentioned elsewhere, as well, and again mentions the shaming of parents who don’t put their kids in pre-schools. Actually has citations, but most in Swedish and couldn’t follow-up on them.
  • Another Himmelbrand op-ed, 2013: “A study done a few years ago showed that today even socially stable middle class families have problems with their children.” Okay… that’s literally always true of any family. What kinds of problems qualify here? He doesn’t elaborate, but uses this as supposed evidence of poor parenting skills. Research in Swedish, can’t follow-up.
  • Perusing various chat boards and blogs: There does seem to be a general consensus that there’s pressure to fit in and do what other parents are doing across the board in Sweden that stands out to foreign and Swedish parents alike. And a few different posters mentioned pressure to put kids in day care, but never before age 1 unless you’re a crazy foreigner. BUT, there may be a correlation between those who post online about child care and those who feel alienated by the mainstream thought on it. So hard to judge whether the pressure is meaningful, and also whether it’s gov’t promoted or peer-enforced if it is a big deal.

Look out for: Market impacts of cash transfers

Forthcoming – “General Equilibrium Effects of Cash Transfers,” from Paul NiehausJohannes HaushoferTed Miguel and Michael Walker, answering the question: What are the market impacts of an inflow of ~15% of local GDP?

I have to say I barely understand what’s going on in a “market” … my economic background is very individual- and household-focused.

But understanding the effect of an intervention on a community as a whole, not just on those treated, seems really important. Partially, this is why we look at and consider spillover effects – the effect of an intervention on the neighbors of the treated, who didn’t receive the program themselves.

General equilibrium or market effects investigate a level up from spillover effects and treatment effects – they look at the cumulative impact of the program to the way the economy operates.

I couldn’t explain how one studies a specific market, or what counts as being part of the market, in any clear terms, but I’m still excited for this upcoming paper on the market-level effects of cash transfers, a point that has been debated recently, after evidence of potential negative spillover effects came out.

 

I’m pretty sure I just solved life

Disclaimer: I was a little drunk on power (calculations) when I wrote this, but it’s me figuring out that econometrics is something I might want to specialize in!

I think I just figured out what I want to do with the rest of my career.

I want to contribute to how people actually practice data analysis in the development sector from the technical side.

I want to write about study design and the technical issues that go into running a really good evaluation, and I want to produce open source resources to help people understand and implement the best technical practices.

This is always something that makes me really excited. I don’t think I have a natural/intuitive understanding of some of the technical work, but I really enjoy figuring it out.

And I love writing about/explaining technical topics when I feel like I really “get” a concept.

This is the part of my current job that I’m most in love with. Right now, for example, I’m working on a technical resource to help IDinsight do power calculations better. And I can’t wait to go to work tomorrow and get back into it.

I’ve also been into meta-analysis papers that bring multiple studies together. In general, the meta-practices, including ethical considerations, of development economics are what I want to spend my time working on.

I’ve had this thought before, but I haven’t really had a concept of making that my actual career until now. But I guess I’ve gotten enough context now that it seems plausible.

I definitely geek out the most about these technical questions, and I really admire people who are putting out resources so that other people can geek out and actually run better studies.

I can explore the topics I’m interested in, talk to people who are doing cool work, create practical tools, and link these things that excite me intellectually to having a positive impact in people’s lives.

My mind is already racing with cool things to do in this field. Ultimately, a website that is essentially an encyclopedia of development economics best practices would be so cool. A way to link all open source tools and datasets and papers, etc.

But top of my list for now is doing a good job with and enjoy this power calculations project at work. If it’s as much fun as it was today, I will be in job heaven.

Continue reading I’m pretty sure I just solved life

Recommendations of the Week: June 18-24

Blog

Goddess-Economist Seema Jayachandran wrote about economists’ gendered view of their own discipline back in March. Dr. Jayachandran and PhD student co-author Jamie Daubenspeck investigate:

  1. Percent of woman authors on different development topics: Drawing on all empirical development papers from 2007-2017, they find, out of all papers, “51% were written by all men, and 15% by all women. The average female share of authors was 28% (weighting each paper equally).” Gender, health, trade, migration, education, poverty and conflict are the development topics with a greater than average number of woman authors.
  2. Economists’ perspectives on under-researched topics: They show that there is a negative correlation between a topic’s % of woman authors and perceptions the topic is under-researched, a finding they call “a bit depressing.” Same. (They also write that “whether a topic is under-researched are not significantly correlated with the actual number of articles on the topic published in the JDE over our sample period.” So what do these economists even know?)

I love their thoughtful outline of the methodology they used for this little investigation. Describing the world with data is awesome.

Awesome Humans

I ended up hearing about/reading about several amazing humans this week:

Dr. Nneka Jones Tapia – the clinical psychologist running Cook County Jail – had amazing things to say on the Ezra Klein Show last year in July. She is powerful and thoughtful and doing amazing things to improve prisons in the US.

New Zealand PM Jacinda Ardern gave birth on the 21st. She’s only the second world leader to give birth in office, after Pakistan’s Benazir Bhutto. The best part is that she is 100% unapologetic about being a mother in office, even while she acknowledges the challenges she will personally face in balancing a new baby and work.

These two leaders are just out there in the world leading noble, thoughtful, innovative lives. In love.

And then there’s MJ Hegar, who’s running for Congress against a tea partier in Texas. Her amazingly directed ad shows how enduring her dedication to service has been throughout her life:

Life Skill

My best friend Riley and I made a pact to meditate daily for ten days, starting on Monday. I have done it each day this week and my week has felt fuller and more focused than ever. Not willing to attribute full causality to the meditation, but it definitely has been a tool to start my day well and a reminder throughout the day that I can and want to stay focused and in the moment.

Podcast

The Ezra Klein Show interviews are always on point, and “The Green Pill” episode featuring Dr. Melanie Joy was no exception. The June 11 show discussed “carnism” – the unspoken ideology that tells us eating animals, wearing animals, and otherwise instrumentalizing them is good.

I’ve been mulling it over for a while now, but the episode’s frank conversation about why veganism is so hard to talk about pleasantly – and why it’s so hard for people to shift from a carnal mindset – motivated me to head back down the vegetarian path.

I was vegetarian for a year or so in college, but now I’m aiming for veganism, or something close. I’m not eating meat and am not actively purchasing or eating eggs or milk. At this point, I’ll eat eggs or milk or other animal products that are already baked into something – a slice of cake, for example. Eventually, I want to phase out pretty much all animal products. But I’m giving myself some space to adjust and dial back the carnism bit by bit. The incremental approach should let me stick to it better.

Cheese will probably be my “barrier food” – apparently this is so common, there’s a webpage that specifically teaches how to overcome the cheese block. (hehe)

They recommend slowly replacing cheese with guac or hummus, and taking a large break from any cheese before trying vegan cheese. (Which won’t be a problem since I doubt there’s any vegan cheese in Kenya to begin with!)

Cover Image

Fruit

It is not mango season in Kenya, but I had the best mango this week. Maybe because I cut it myself for the first time, making an absolute mess. Or maybe because it was the key ingredient to the first lettuce-containing salad I’ve ever made myself at home. But there’s a lot to be said for a fruit that encourages you to embrace your messy nature.

Why you should convert categorical variables into multiple binary variables

Take the example of a variable reporting if someone is judged to be very poor, poor, moderately rich, or rich. This could be the outcome of a participatory wealth ranking (PWR) exercise like that used by Village Enterprise.

In a PWR exercise, local community leaders can identify households that are most vulnerable. These rankings can then be used to target a development program (like VE’s graduation-out-of-poverty program that combines cash transfers with business training) to the community members that are most in need.

Let’s say that you want to include the PWR results in a regression analysis as a covariate. You have a dataset of all the relevant variables for each household, including a variable that records whether the household was ranked in the PWR exercise as very poor, poor, moderately rich, or rich.

You need to convert this string variable (text) into a numeric value. You could assign each option a value from 1 to 4, with 1 being “very poor” and 4 meaning “rich” … but you shouldn’t use this directly in your regression.

If you have a variable that moves from 1 to 2 to 3 to 4, you’re implying that there is a linear pattern between each of those values. You’re saying that the effect on your outcome variable of going from being very poor (1) to poor (2) is the same as the effect of going from poor (2) to moderately rich (3). But you don’t know what the real relationship is between the different PWR levels, since the data isn’t that granular. You can’t make the linear assumption.

So instead, you should use four different binary variables in your regression: Ranked “very poor” or not? “Poor” or not? “Moderately rich” or not? “Rich” or not?

This Stata support page does a great job of summarizing how to apply this in your regression code or create binary variables from categorical using easy shortcuts. I like:

reg y x i.pwr

But how do you interpret the results?

When you create dummies (binary variables) out of a categorical variable, you use one of the group dummies as the reference group and don’t actually include it in the regression.

By default, the reference group is usually the smallest/lowest group. In this case, that means “very poor.” So in the regression, you’ll have three dummies, not four. Being “very poor” is the base condition against which to compare the other rankings.

Let’s say there is a statistically significant, positive coefficient on the “moderately rich” dummy in your regression results. That means that, compared to the base condition of being very poor, being moderately rich has a positive effect on your outcome variable.

Why is everything called Vox?

My favorite podcast right now is the Ezra Klein Show from Vox Media, the news-explaining organization founded by the podcast’s eponymous host. I am also in love with Vox’s Today, Explained and occasionally enjoy The Weeds and Impact.

Just now, I was looking up humanitarian economics and ended up at voxeu.org – a website of the Centre for Economic Policy Research.

Vox is also an amplifier manufacturer, a vodka, an anime character, and a TV network in Germany, Quebec, and Norway.

“Why is everything called Vox?” The google search answered my query on the fourth try, with a link to the Wikipedia entry for “vox.” Below the link, in the page preview, it said simply “Vox is Latin for voice.” Well, then.

I love when you think there must be a reason for something and then there is, in fact, a perfectly good explanation.

New insights on the development vs. humanitarian sectors

When I was at Middlebury, I took classes like Famine & Food Security and Economics of Global Health, learning more and more about humanitarian aid and international development. It didn’t really sink in that these were two different sectors until today.

I had a chance to talk to someone who worked for REACH – an organization that tries to collect the most accurate data possible from war zones/humanitarian emergency areas to inform policy. Seem like pretty important work.

Our conversation solidified to me that the humanitarian sector is different from the development sector. The humanitarian sector has a totally different set of actors (dominated by the UN) and missions, although the ultimate mission of a better world is the same.

Development is about the ongoing improvement of individuals living in a comparatively stable system; humanitarian aid is about maintaining human rights and dignities when all those systems break down.

There’s some overlap, of course – regions experiencing ongoing war and violence may be targeted by development and humanitarian programs alike, for example. I also think the vocabulary blurs a bit when discussing funding for development and humanitarian aid.

Development isn’t quite sure how it feels about human rights, though. Rights are good when they lead to economic development, which is equivalent to most development work.

I’d say that my definition of what I want to do in the development sector bleeds over into the human rights and humanitarian arenas. (I’m sure there’s also an important distinction between human rights sector and humanitarian sector – probably that the humanitarian sector is more about meeting people’s basest needs in crisis, although human rights workers also deal with abuses during crises.)

My interest in humanitarian work has been piqued by this conversation today, though. It was also piqued by my former roommate’s description of her work with Doctors without Borders. The idea of going on an intense mission trip for a period of time, being all-in, then taking a break is kind of appealing. Although REACH itself wasn’t described as a great work experience. Really long hours, but fairly repetitive work.

Maybe I should read more about the economics/humanitarian aid/data overlap.

Meditation Pact

My best friend Riley and I agreed to meditate every day for the next 10 days.

I got up early this morning for the first day – it’s starting to be winter in Nairobi so I was snug in warm clothes when I did my 10 minutes on the balcony.

The part I love most about the Headspace meditation is when you let go of all thoughts and let your brain wander, then center back into your body and physical sensations. Always makes me feel light but grounded.

Ӧzler: Decrease power to detect only a meaningful effect

Photo by Val Vesa on Unsplash

Reading about power, I found an old World Bank Impact Evaluations blog post by Berk Ӧzler on the perils of basing your power calcs in standard deviations without relating those SDs back to the real life context.

Ӧzler summarizes his main points quite succinctly himself:

“Takeaways:

  • Think about the meaningful effect size in your context and given program costs and aims.
  • Power your study for large effects, which are less likely to disappear in the longer run.
  • Try to use all the tricks in the book to improve power and squeeze more out of every dollar you’re spending.”

He gives a nice, clear example to demonstrate: a 0.3 SD detectable effect size sounds impressive, but for some datasets, this would really only mean a 5% improvement which might not be meaningful in context:

“If, in the absence of the program, you would have made $1,000 per month, now you’re making $1,050. Is that a large increase? I guess, we could debate this, but I don’t think so: many safety net cash transfer programs in developing countries are much more generous than that. So, we could have just given that money away in a palliative program – but I’d want much more from my productive inclusion program with all its bells and whistles.”

Usually (in an academic setting), your goal is to have the power to detect a really small effect size so you can get a significant result. But Ӧzler makes the opposite point: that it can be advantageous to only power yourself to detect what is a meaningful effect size, decreasing both power and cost.

He also advises, like the article I posted about yesterday, that piloting could help improve power calculations via better ICC estimates: “Furthermore, try to get a good estimate of the ICC – perhaps during the pilot phase by using a few clusters rather than just one: it may cost a little more at that time, but could save a lot more during the regular survey phase.”

My only issue with Ӧzler’s post is his chart, which shows the tradeoffs between effect size and the number of clusters. His horizontal axis is labeled “Total number of clusters” – per arm or in total, Bert?!? It’s per arm, not total across all arms. There should be more standardized and intuitive language for describing sample size in power calcs.