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.

Are we murderers for not donating our organs? [repost]

Zell Kravinsky risked his life to donate his healthy kidney to a complete stranger. Would you do the same?

Kravinsky is a radical altruist. He believes in giving away as much as possible to others, including his nearly $45 million fortune and his own body parts. Most people would consider donating a kidney as going above and beyond, but Kravinsky told the New Yorker in 2004 that he considers anyone who doesn’t donate their extra kidney a murderer.

We probably don’t, as individuals, have a moral responsibility to donate our organs, but maybe we do have a societal responsibility to find a system by which we can match kidney donors and recipients so that no one has to die just because there isn’t a transplant available. In 2012, there were 95,000 Americans on the wait list for a life-saving kidney, according to economists Gary Becker and Julio Elias. The average wait time for a kidney in 2012 was over four years.

Becker and Elias are proponents of creating a formal, legal market for organs to eliminate long wait times and better match recipients with donors. Right now, it is illegal to sell your organs in most of the world, including in the U.S.

The main risks of monetary compensation for organ donations are the coercion of unwilling donors, the potentially unequal distribution of donors — poor people would be more likely to become donors, and the moral question of whether or not it is okay to sell body parts, even if they are our own.

Purely moral arguments aside for a moment, there are ways to alleviate the risks of a market for organs. Waiting periods between registration and donation, psychiatric evaluation ahead of registration as an organ donor, and strict identification requirements or even background checks can all combat coercion in the market for organs, while saving the lives of the many Americans who die on an organ waitlist. Becker and Elias also point to the fact that people in lower income brackets are disproportionately affected by long waitlists: the wealthy can fly abroad to obtain a healthy organ or manipulate the current waitlist system in their favor, while poorer Americans face longer wait times. While donors may be disproportionately poor, which raises concerns of implicit economic coercion, the lower income brackets also benefit disproportionately from the policy.

Even more powerful than a legal market alone would be a combination of a legal market for organs and an implied consent law, which would mean people would have to opt out of being an organ donor, rather than the U.S. standard of opting into being a donor. A 2006 study by economists Alberto Abadie and Sebastien Gay found that implied consent laws have a positive impact on organ donations. Under a combination of these two initiatives, essentially all organ donor needs might be met, and a person’s will might come to include provisions for their organs to be harvested and family members to be compensated.

While Kravinsky donated his kidney for free, he once offered a journalist $10,000 to donate a kidney to a stranger, according to Philadelphia Magazine. But the journalist backed out of the deal he struck with Kravinsky after his wife and friends convinced him not to go through with it. They convinced him that the risk of surgery, though relatively minor, was not worth saving a life. But if a safe, legal market for organ sales is established, perhaps the establishment of a market price for organ donation and a normalization of the procedure will allow Americans to save lives and make money, without requiring Kravinsky’s extreme, and perhaps aggressive, sort of altruism.

Originally written for my Economics of Sin senior seminar, spring 2017; previously published at the Unofficial Economist on Medium.