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Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania
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Agricultural training is a potentially effective method to diffuse relevant new technologies to increase productivity and alleviate rural poverty in Sub-Saharan Africa (SSA). However, since it is prohibitively expensive to provide direct training to all the farmers in SSA, it is critically important to examine the extent to which technologies taught to a small number of farmers disseminate to non-trained farmers. This paper investigates the technology dissemination pathways among smallholder rice producers within a rural irrigation scheme in Tanzania. As an innovative feature, we compare the performance of three categories of farmers: key farmers, who receive intensive pre-season training at a local training center; intermediate farmers, who are trained by the key farmers; and other ordinary farmers. By collecting and analyzing a unique five-year household-level panel data set, we estimate difference-in-differences models to assess how the gap in performance evolve as the technologies spill over from the trained farmers to the ordinary farmers. To disentangle the technology spillover process, we also examine the extent to which social and geographical network with the key and intermediate farmers influences the adoption of technologies by the ordinary farmers, by incorporating social relationship variables into spatial econometric models. We found that the ordinary farmers who were a relative or residential neighbor of a key or intermediate farmer were more likely to adopt new technologies than those who were not. As a result, while the key farmers’ technology adoption rates rose immediately after the training, those of the non-trained ordinary farmers caught up belatedly. As the technologies disseminated, the paddy yield of the key farmers increased from 3.1 to 5.3 tons per hectare, while the yield of the ordinary farmers increased from 2.6 to 3.7 tons per hectare. Our results suggest the effectiveness and practical potential of farmer-to-farmer extension programs for smallholders in SSA as a cost effective alternative to the conventional farmer training approach.
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Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
How Much Should We Trust Differences-in-Differences Estimates?
Bertrand, M., Duflo, E., & Mullainathan, S. (2004) · The Quarterly Journal of Economics · doi:10.1162/003355304772839588 · Cited by 10,641
SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING
Caliendo, M., & Kopeinig, S. (2008) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.2007.00527.x · Cited by 6,238
Identification of Endogenous Social Effects: The Reflection Problem
Manski, C. F. (1993) · The Review of Economic Studies · doi:10.2307/2298123 · Cited by 6,209
Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme
Heckman, J. J., Ichimura, H., & Todd, P. E. (1997) · The Review of Economic Studies · doi:10.2307/2971733 · Cited by 5,486
Introduction to Spatial Econometrics
LeSage, J., & Pace, R. K. (2009) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420064254 · Cited by 5,420
Adoption of Agricultural Innovations in Developing Countries: A Survey
Feder, G., Just, R. E., & Zilberman, D. (1985) · Economic Development and Cultural Change · doi:10.1086/451461 · Cited by 3,482
An Introduction to Spatial Econometrics
LeSage, J. P. (2008) · Revue d'économie industrielle · doi:10.4000/rei.3887 · Cited by 2,998
Rosenbaum, P. R. (2002) · Springer Series in Statistics · doi:10.1007/978-1-4757-3692-2 · Cited by 2,394
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
Hirano, K., Imbens, G. W., & Ridder, G. (2003) · Econometrica · doi:10.1111/1468-0262.00442 · Cited by 2,355
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
Learning by doing and learning from others: Human capital and technical change in agriculture
Foster, A. D., & Rosenzweig, M. R. (1995) · Journal of Political Economy · doi:10.1086/601447 · Cited by 2,038
Kelejian, H. H., & Prucha, I. R. (1998) · The Journal of Real Estate Finance and Economics · doi:10.1023/a:1007707430416 · Cited by 1,976
Natural and Quasi- Experiments in Economics
Meyer, B. (1994) · Journal of Business and Economic Statistics · doi:10.2307/1392369 · Cited by 1,792
A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model
Kelejian, H. H., & Prucha, I. R. (1999) · International Economic Review · doi:10.1111/1468-2354.00027 · Cited by 1,476
Social Networks and Technology Adoption in Northern Mozambique
Bandiera, O., & Rasul, I. (2006) · The Economic Journal · doi:10.1111/j.1468-0297.2006.01115.x · Cited by 1,361
Identification of peer effects through social networks
Bramoullé, Y., Djebbari, H., & Fortin, B. (2009) · Journal of Econometrics · doi:10.1016/j.jeconom.2008.12.021 · Cited by 1,234
Pagan, A., & Ullah, A. (1999) · Cited by 1,108
Natural and Quasi-Experiments in Economics
Meyer, B. D. (1995) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1995.10524589 · Cited by 1,100
Lagrange Multiplier Test Diagnostics for Spatial Dependence and Spatial Heterogeneity
Anselin, L. (1988) · Geographical Analysis · doi:10.1111/j.1538-4632.1988.tb00159.x · Cited by 942
The Estimation of Causal Effects by Difference-in-Difference Methods
Lechner, M. (2011) · Foundations and Trends® in Econometrics · doi:10.1561/0800000014 · Cited by 935 · cited 1×
“Second, DID estimation requires the common trends assumption (Lechner, 2010), which essentially asserts that the group participating in the program would have experienced the same change in the outcome variable between the pre-program and the post-program periods as those not participating.”
MethodPagan, A., & Ullah, A. (1999) · Cambridge University Press eBooks · doi:10.1017/cbo9780511612503 · Cited by 935
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
Thirty years of spatial econometrics
Anselin, L. (2010) · Papers in Regional Science · doi:10.1111/j.1435-5957.2010.00279.x · Cited by 740
DiPrete, T. A., & Gangl, M. (2004) · Sociological Methodology · doi:10.1111/j.0081-1750.2004.00154.x · Cited by 732
Sensitivity analysis for average treatment effects
Becker, S. O., & Caliendo, M. (2007) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0700700104 · Cited by 620
Impact of farmer field schools on agricultural productivity and poverty in East Africa
Davis, K., Nkonya, E., Kato, E., Mekonnen, D. A., Odendo, M., Miiro, R., & Nkuba, J. (2012) · World Development · doi:10.1016/j.worlddev.2011.05.019 · Cited by 561
The Estimation of Causal Effects by Difference-in-Difference MethodsPreprint
Lechner, M. (2011) · RePEc: Research Papers in Economics · Cited by 552
Bias Reduction Using Mahalanobis-Metric Matching
Rubin, D. B. (1980) · Biometrics · doi:10.2307/2529981 · Cited by 530
Advances in Spatial Econometrics: Methodology, Tools and Applications
(2004) · Advances in Spatial Science · doi:10.1007/978-3-662-05617-2 · Cited by 450
Neighborhood influence and technological change
Case, A. (1992) · Regional Science and Urban Economics · doi:10.1016/0166-0462(92)90041-x · Cited by 381
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
Neighbors and Extension Agents in Ethiopia: Who Matters More for Technology Adoption?
Krishnan, P., & Patnam, M. (2014) · American Journal of Agricultural Economics · doi:10.1093/ajae/aat017 · Cited by 370
The complex dynamics of smallholder technology adoption: the case of SRI in Madagascar
Moser, C. M., & Barrett, C. B. (2006) · Agricultural Economics · doi:10.1111/j.1574-0862.2006.00169.x · Cited by 361
Feder, G., Murgai, R., & Quizon, J. B. (2004) · Journal of Agricultural Economics · doi:10.1111/j.1477-9552.2004.tb00094.x · Cited by 311
Nonparametric Density and Regression Estimation
DiNardo, J., & Tobias, J. L. (2001) · Journal of Economic Perspectives · doi:10.1257/jep.15.4.11 · Cited by 257
Chapter 44 Agricultural Extension
(2007) · Handbook of Agricultural Economics · doi:10.1016/s1574-0072(06)03044-1 · Cited by 241
Müller, D. (2002) · Agricultural Economics · doi:10.1016/s0169-5150(02)00073-7 · Cited by 216
Lambrecht, I., Vanlauwe, B., Merckx, R., & Maertens, M. (2014) · World Development · doi:10.1016/j.worlddev.2014.01.024 · Cited by 211
What should we expect from farmer field schools? A Sri Lanka case study
Tripp, R., Wijeratne, M., & Piyadasa, V. H. (2005) · World Development · doi:10.1016/j.worlddev.2005.04.012 · Cited by 210
Drukker, D. M., Prucha, I. R., & Raciborski, R. (2013) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x1301300201 · Cited by 199
The Reliability of Recall Data: a Literature Review
Dex, S. (1995) · Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique · doi:10.1177/075910639504900105 · Cited by 196
Rising trends and variability of rice prices: Threats and opportunities for sub-Saharan Africa
Seck, P. A., Tollens, E., Wopereis, M. C. S., Diagne, A., & Bamba, I. (2010) · Food Policy · doi:10.1016/j.foodpol.2010.05.003 · Cited by 171
The effect of information sources on technology adoption and modification decisions
Adegbola, P., & Gardebroek, C. (2007) · Agricultural Economics · doi:10.1111/j.1574-0862.2007.00222.x · Cited by 162
From the Help Desk: Bootstrapped Standard Errors
Guan, W. (2003) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0300300105 · Cited by 121
An African green revolution : finding ways to boost productivity on small farms
大塚, 啓., & Larson, D. F. (2013) · Springer eBooks · Cited by 92
Ward, P. S., & Pede, V. O. (2014) · Australian Journal of Agricultural and Resource Economics · doi:10.1111/1467-8489.12058 · Cited by 88
Self-citationEfficiency and Equity Effects of Land MarketsPreprint
Otsuka, K. (2007) · RePEc: Research Papers in Economics · Cited by 85
The sports and exercise life-course: A survival analysis of recall data from Ireland
Lunn, P. (2009) · Social Science & Medicine · doi:10.1016/j.socscimed.2009.11.006 · Cited by 71
Todo, Y., & Takahashi, R. (2011) · Journal of International Development · doi:10.1002/jid.1819 · Cited by 53
Tsusaka, T. W., Kajisa, K., Pede, V. O., & Aoyagi, K. (2015) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2015.04.022 · Cited by 48
Self-citationCongruence Between Panel and Recall Data in Longitudinal Research
Powers, E. A., Goudy, W. J., & Keith, P. M. (1978) · Public Opinion Quarterly · doi:10.1086/268461 · Cited by 44
The Possibility of a Green Revolution in Sub-Saharan Africa: Evidence from Kenya
Otsuka, K., & Yamano, T. (2005) · RePEc: Research Papers in Economics · doi:10.22004/ag.econ.110141 · Cited by 31
What Do We Learn About Social Networks When We Only Sample Individuals? Not MuchPreprint
Santos, P., & Barrett, C. B. (2008) · SSRN Electronic Journal · doi:10.2139/ssrn.1141838 · Cited by 26
Tsusaka, T. W., Velasco, M. L., Yamano, T., & Pandey, S. (2015) · Asian Journal of Agriculture and Development · doi:10.37801/ajad2015.12.1.2 · Cited by 18
Self-citationAn Exploration of a Green Revolution in Sub-Saharan Africa
Otsuka, K., & KALIRAJAN, K. P. (2005) · Cited by 14
Nakano, Y., Kajisa, K., & Otsuka, K. (2015) · Springer eBooks · doi:10.1007/978-4-431-55693-0_3 · Cited by 9
Self-citation
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Jiménez, D., Delerce, S., Dorado, H., Cock, J., Muñoz, L. A., Agamez, A., & Jarvis, A. (2019) · Global Food Security · doi:10.1016/j.gfs.2019.08.004 · Cited by 47
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Lasdun, V., Harou, A. P., Magomba, C., & Guereña, D. (2025) · Journal of Development Economics · doi:10.1016/j.jdeveco.2025.103496 · Cited by 23
Metadata
- Authors
- Nakano, Y., Tsusaka, T. W., Aida, T., & Pede, V. O.
- Venue
- World Development
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 105
- Pages
- 336-351
- ISSN
- 0305-750X (ISSN-L)
- OpenAlex
- W2781601264
- Cited by
- 305
- References
- 57
- FWCI
- 50.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
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