Learning by doing and learning from others: Human capital and technical change in agriculture
Abstract
University of PennsylvaniaHousehold-level panel data from a nationally representative sample of rural Indian households describing the adoption and profitability of high-yielding seed varieties (HYVs) associated with the Green Revolution are used to test the implications of a model incorporating learning-by-doing and learning spillovers. The estimates indicate that: (i) imperfect knowledge about the management of the new seeds was a significant barrier to adoption; (ii) this barrier diminished as farmer experience with the new technologies increased; (iii) own experience and neighbors ' experience with HYV significantly increased HYV profitability; (iv) farmers do not fully incorporate the village returns to learning in making adoption decisions 1 I.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The instrumental variable (Early Copilot Adopter Exposure) satisfies the relevance condition.
“We believe the first component satisfies the relevance condition because prior research shows that individuals learn from peers through social interactions and shared experience (Foster and Rosenzweig 1995, Conley and Udry 2010, Bollinger and Gillingham 2012, Bailey et al. 2022).”
From The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot  (2024) · cites this paper as Foster and Rosenzweig 1995
Cited by
Sorted by citations
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003 · see citing works
Social norms and energy conservation
Allcott, H. (2011) · Journal of Public Economics · doi:10.1016/j.jpubeco.2011.03.003 · Cited by 2,715 · see citing works
“[43] Foster, Andrew, and Mark Rosenzweig (1995).”
Using Online Conversations to Study Word-of-Mouth Communication
Godes, D., & Mayzlin, D. (2004) · Marketing Science · doi:10.1287/mksc.1040.0071 · Cited by 2,601 · see citing works
Learning about a New Technology: Pineapple in Ghana
Conley, T. G., & Udry, C. R. (2010) · American Economic Review · doi:10.1257/aer.100.1.35 · Cited by 2,180 · see citing works
Mobile Phones and Economic Development in Africa
Aker, J. C., & Mbiti, I. M. (2010) · Journal of Economic Perspectives · doi:10.1257/jep.24.3.207 · Cited by 2,216 · see citing works
Microeconomics of technology adoption
Foster, A. D., & Rosenzweig, M. R. (2010) · Annual Review of Economics · doi:10.1146/annurev.economics.102308.124433 · Cited by 1,055 · see citing works
Self-citationFirm-Created Word-of-Mouth Communication: Evidence from a Field Test
Godes, D., & Mayzlin, D. (2009) · Marketing Science · doi:10.1287/mksc.1080.0444 · Cited by 837 · see citing works
Social learning in a heterogeneous population: technology diffusion in the Indian green revolution
Munshi, K. (2004) · Journal of Development Economics · doi:10.1016/j.jdeveco.2003.03.003 · Cited by 798 · see citing works
Rodrik, D. (2008) · doi:10.1596/28009 · Cited by 471 · see citing works
Measuring Social Networks' Effects on Agricultural Technology Adoption
Maertens, A., & Barrett, C. B. (2013) · American Journal of Agricultural Economics · doi:10.1093/ajae/aas049 · Cited by 370 · see citing works
Social Learning and Incentives for Experimentation and Communication
BenYishay, A., & Mobarak, A. M. (2019) · The Review of Economic Studies · doi:10.1093/restud/rdy039 · Cited by 328 · see citing works
Information, Technology, and Information Worker Productivity
Aral, S., Brynjolfsson, E., & Van Alstyne, M. (2012) · Information Systems Research · doi:10.1287/isre.1110.0408 · Cited by 256 · see citing works
DETERMINANTS OF TECHNOLOGY ADOPTION: PEER EFFECTS IN MENSTRUAL CUP TAKE-UP
Oster, E., & Thornton, R. (2012) · Journal of the European Economic Association · doi:10.1111/j.1542-4774.2012.01090.x · Cited by 233 · see citing works
Seeing is believing? Evidence from an extension network experiment
Kondylis, F., Mueller, V., & Zhu, J. (2017) · Journal of Development Economics · doi:10.1016/j.jdeveco.2016.10.004 · Cited by 191 · see citing works
Can Entrepreneurial Activity be Taught? Quasi-Experimental Evidence from Central America
Klinger, B., & Schündeln, M. (2011) · World Development · doi:10.1016/j.worlddev.2011.04.021 · Cited by 171 · see citing works
The Growing Importance of Decision-Making on the Job
Deming, D. (2021) · doi:10.3386/w28733 · Cited by 52 · see citing works
How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program
Choi, S., Kim, N., Kim, J., & Kang, H. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3893835 · Cited by 30 · see citing works
Peer learning and technology adoption in a digital farmer-to-farmer network
Lasdun, V., Harou, A. P., Magomba, C., & Guereña, D. (2025) · Journal of Development Economics · doi:10.1016/j.jdeveco.2025.103496 · Cited by 23 · see citing works
Generative AI in Higher Education: Evidence from an Elite College
Contractor, Z., & Reyes, G. (2025) · doi:10.2139/ssrn.5392712 · Cited by 19 · see citing works
Song, F., Agarwal, A., & Wen, W. (2024) · SSRN Electronic Journal (preprint) · doi:10.2139/ssrn.4856935 · Cited by 30 · see citing works
- Authors
- Foster, A. D., & Rosenzweig, M. R.
- Venue
- Journal of Political Economy
- Publisher
- University of Chicago Press
- Published
- 1995-12
- Volume
- 103
- Issue
- 6
- Pages
- 1176-1209
- ISSN
- 0022-3808 (ISSN-L)
- 1537-534X
- OpenAlex
- W1987832557
- Cited by
- 2,038
- References
- 0
- Citations by year
- 2026: 79 · 2025: 78 · 2024: 72 · 2023: 101 · 2022: 88 · 2021: 100 · 2020: 90 · 2019: 97 · 2018: 108 · 2017: 77 · 2016: 80 · 2015: 98 · 2014: 117 · 2013: 112 · 2012: 101
- FWCI
- 107.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Political Economy (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Innovations and Practices
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Economics and Practices
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Development and Management
- Keywords
- hands-on learning, learning spillovers, human capital, technological change, agricultural technology adoption, Green Revolution, farming experience, knowledge barriers, neighborhood effects
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
