Abstract
Farm-level decision-making by resource-constrained smallholder farmers, such as investment in improved farm management practices and technologies may considerably be influenced by the age of farm operators. However, evidence of the effect of farm operators’ age on farm efficiency and technological endowment, and consequently on agricultural productivity in sub-Saharan Africa has been inexact. To contribute to an improved understanding of the age-efficiency-productivity nexus, this study investigates the impacts of farm operators’ age on agricultural productivity by evaluating the managerial performance and technological endowment of the operators, disaggregated across three age cohorts, viz. the youth, middle-aged and the aged. We fit the meta-stochastic frontier statistical framework to a country-wide sample of 24,596 farm households, spanning three decades of data collection in Ghana. The results show that relative to the potentials of each age cohort, more output can be generated using currently allocated inputs, but under improved farm management practices. Whereas we did not find evidence for possible age-related technological differences in agricultural production in Ghana, we did find strong support for possible age-induced managerial differences in farm production, with youth operators being more efficient than their middle-aged and aged peers. Consequently, the age of farm operators significantly affects agricultural productivity in Ghana through their efficiency of resource allocation. We find these results relevant for policy attention, in terms of the targeting of support to farm operators in the various age cohorts and in the country’s quest to achieve greater agricultural productivity.
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Data availability
Replication materials are available in GitHub at https://github.com/ftsiboe/Agricultural-Productivity-in-Ghana.
Notes
Given our objective of estimating elasticities at the individual level, our choice of functional form was limited to the Generalized Production Function (GPF), Transcendental Production Function (TPF), and the Translog Production Function (TL). The GPF and TPF failed to converge across most of the survey rounds while the TL converged for all. Between the TL and CD, our test results indicate that about 80% of the estimated models of the TL and CD are statistically different. Given our objective of going after the individual elasticities and scores, we chose the TL.
Bellemare and Wichman (2020) note that inverse hyperbolic sine function transformed models understate the correct percentage effect, thus the elasticities presented in this study are likely a lower bound of the true estimates.
Table 3 in the appendix shows the sample distribution of the pooled data by survey and cereal. A substantial proportion (88.95%) of the harmonized data was derived from the GLSS. The GSPS constituted only 11.05% of the sample and only 7.10% of the sample was panel. We therefore considered the pooled sample as a cross-sectional sample of the population of Ghanaian crop farmers at 5-year intervals.
In the estimation of Eq. (6) we account for the two-stage sampling design of the surveys. We set the sampling weights to 1 for each farm as the original weights were set at the household level and are not entirely useful for our setting where we used subsamples.
All results can be generated using replication materials available at https://github.com/ftsiboe/Agricultural-Productivity-in-Ghana.
For the likelihood ratio test, the log-likelihood value for the restricted model was derived from the national frontier, and that of the unrestricted model by summing up the log-likelihood values of the age cohort frontiers. The test was done separately for each survey-wave.
We were not able to account for the violations in the regularity conditions due to the non-convergence of our models when these conditions were imposed.
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Acknowledgements
The authors gratefully acknowledge the Ghana Statistical Service, Institute of Statistical Social and Economic Research (ISSER), Economic Growth Center, and the International Food Policy Research Institute (IFPRI) for making the dataset available for the study. All errors are the responsibility of the authors. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy. This research was supported in part by the U.S. Department of Agriculture, Economic Research Service. JA acknowledges funding from the Academy for International Agricultural Research (ACINAR). ACINAR, commissioned by the German Federal Ministry for Economic Cooperation and Development (BMZ), is being carried out by ATSAF (Council for Tropical and Subtropical Agricultural Research) e.V. on behalf of the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH.
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Asravor, J., Tsiboe, F., Asravor, R.K. et al. Technology and managerial performance of farm operators by age in Ghana. J Prod Anal (2023). https://doi.org/10.1007/s11123-023-00679-y
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DOI: https://doi.org/10.1007/s11123-023-00679-y
Keywords
- Agricultural productivity
- Farmer age
- Managerial performance
- Stochastic frontier framework
- Technological performance
- Ghana