THE PRODUCTIVE VALUE OF EDUCATION IN AGRICULTURAL DEVELOPMENT
Open accessAbstract
The purpose of this paper is to present some empirical evidence from a developing agriculture in northwestern India, evidence which shows that schooling of the farm people contributes to their useful productive abilities.
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Several papers show a positive relationship between education and agricultural technology adoption.
“Several papers show a positive relationship between education and agricultural technology adoption.”
From Displacement and development: Long term impacts of population transfer in India (2019) · cites this paper as Sidhu (1976)
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Displacement and development: Long term impacts of population transfer in India
Bharadwaj, P., & Ali Mirza, R. (2019) · Explorations in Economic History · doi:10.1016/j.eeh.2019.05.001 · Cited by 29 · see citing works
- Authors
- Sidhu, S. S., & Sidhu, S. S.
- Venue
- Unknown
- Publisher
- Unknown
- Published
- 1976
- OpenAlex
- W191116642
- Cited by
- 3
- References
- 0
- Citations by year
- 2019: 1 · 2018: 1
- Type
- posted content
- Language
- en
- Open access
- green
- ageconsearch.umn.edu
- Licence
- cc-by-nc-nd
- Locations
- RePEc: Research Papers in Economics (submittedVersion)
- AgEcon Search (University of Minnesota, USA) (submittedVersion, cc-by-nc-nd)
- AgEcon Search (University of Minnesota, USA)
- Topics
- 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
- Social Sciences › Sociology and Political Science › Social and Economic Development in India
- Keywords
- returns to education, agricultural development, farm laborers, school education, human capital, agricultural productivity, northwest India
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.
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