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!

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