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!

Dev links: Migration & Replication


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


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.”


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.


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


  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)