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).
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:
Something you learned
Something you have a question about
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:
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
What IS the mean world per capita growth rate? Answer: Around 1-3%
Curious to see the practical sides of Joshi’s argument – what are the “radical reform model” components he recommends?
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.”
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)
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
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.”
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.”
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)
“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.”
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.
“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.”