Paul Niehaus

Department of Economics
University of California, San Diego
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La Jolla, CA 92093
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Fax: 858/534-7040

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NBER Program Affiliations: DEV
NBER Affiliation: Research Associate

NBER Working Papers and Publications

October 2017Experimentation at Scale
with Karthik Muralidharan: w23957
This paper makes the case for greater use of randomized experiments “at scale.” We review various critiques of experimental program evaluation in developing countries, and discuss how experimenting at scale along three specific dimensions – the size of the sampling frame, the number of units treated, and the size of the unit of randomization – can help alleviate them. We find that program evaluation randomized controlled trials published in top journals over the last 15 years have typically been “small” in these senses, but also identify a number of examples – including from our own work – demonstrating that experimentation at much larger scales is both feasible and valuable.

Published: Karthik Muralidharan & Paul Niehaus, 2017. "Experimentation at Scale," Journal of Economic Perspectives, vol 31(4), pages 103-124. citation courtesy of

September 2017General Equilibrium Effects of (Improving) Public Employment Programs: Experimental Evidence from India
with Karthik Muralidharan, Sandip Sukhtankar: w23838
A public employment program's effect on poverty depends on both program earnings and market impacts. We estimate this composite effect, exploiting a large-scale randomized experiment across 157 sub-districts and 19 million people that improved the implementation of India's employment guarantee. Without changing government expenditure, this reform raised low-income households' earnings by 13%, driven primarily by market earnings. Real wages rose 6% while days without paid work fell 7%. Effects spilled over across sub-district boundaries, and adjusting for these spillovers substantially raises point estimates. The results highlight the importance and feasibility of accounting for general equilibrium effects in program evaluation.
March 2014Building State Capacity: Evidence from Biometric Smartcards in India
with Karthik Muralidharan, Sandip Sukhtankar: w19999
Anti-poverty programs in developing countries are often difficult to implement; in particular, many governments lack the capacity to deliver payments securely to targeted beneficiaries. We evaluate the impact of biometrically-authenticated payments infrastructure ("Smartcards") on beneficiaries of employment (NREGS) and pension (SSP) programs in the Indian state of Andhra Pradesh, using a large-scale experiment that randomized the rollout of Smartcards over 158 sub- districts and 19 million people. We find that, while incompletely implemented, the new system delivered a faster, more predictable, and less corrupt NREGS payments process without adversely affecting program access. For each of these outcomes, treatment group distributions first-order stochastically dominated those of the contr...

Published: Karthik Muralidharan & Paul Niehaus & Sandip Sukhtankar, 2016. "Building State Capacity: Evidence from Biometric Smartcards in India," American Economic Review, vol 106(10), pages 2895-2929. citation courtesy of

May 2011Managing Self-Confidence: Theory and Experimental Evidence
with Markus M. Mobius, Muriel Niederle, Tanya S. Rosenblat: w17014
Evidence from social psychology suggests that agents process information about their own ability in a biased manner. This evidence has motivated exciting research in behavioral economics, but has also garnered critics who point out that it is potentially consistent with standard Bayesian updating. We implement a direct experimental test. We study a large sample of 656 undergraduate students, tracking the evolution of their beliefs about their own relative performance on an IQ test as they receive noisy feedback from a known data-generating process. Our design lets us repeatedly measure the complete relevant belief distribution incentive-compatibly. We find that subjects (1) place approximately full weight on their priors, but (2) are asymmetric, over-weighting positive feedback relative to...
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