Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis

Abraham Amoussouga Gero

doi:doi:10.11648/j.ijae.20200503.14

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Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis

Abraham Amoussouga Gero

Published in International Journal of Agricultural Economics (Volume 5, Issue 3)

Received: 26 May 2020 Accepted: 9 June 2020 Published: 20 June 2020

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Abstract

Agriculture plays an important role in the African continent’s growth. However, regions’ characteristics differences explain different types of production technologies use leading to a technological gap which delays these regions’ economic convergence. This article uses the stochastic metafrontier analysis based on a new approach for Technical Efficiency’s (TE) estimation and the technological gap ratios (TGR) of the agricultural production of the five African regions from 1980 to 2012. The results reveal a very high average TE score of 92.73% of the five regions whereas a low TGR score of 35.63% is noticed. The EAST region is the closest one to the best technology available with a 68.73% score. Besides, these results also show the existence of a catch-up phenomenon between low TGR level countries and those with higher TGR level. Zimbabwe has the highest catch-up score with a yearly average of 3%. Considering the agricultural sector's importance in Africa's national production, the results suggest increasing investments in Research and Development, popularizing services, and a policy of larger expansion of the technologies applied by the regions close to the optimal technology in order to facilitate new agricultural production techniques’ adoption and development. Agriculture plays an important role in the growth of the African continent. However, regions diversity of characteristics explains the use of different types of production technologies, resulting in a technology gap that delays the economic convergence of these regions.

Published in	International Journal of Agricultural Economics (Volume 5, Issue 3)
DOI	10.11648/j.ijae.20200503.14
Page(s)	80-88
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Metafrontier, Technical Efficiency, Technological gap, Agricultural, Africa

References

[1]	Ng’ombe JN. Technical efficiency of smallholder maize production in Zambia: a stochastic meta-frontier approach. Agrekon 2017; 56: 347–365.
[2]	Alem H, Lien G, Hardaker JB, and al. Regional differences in technical efficiency and technological gap of Norwegian dairy farms: a stochastic meta-frontier model. Applied Economics 2019; 51: 409–421.
[3]	Churchill SA, Inekwe J, Ivanovski K. Convergence of R&D intensity in OECD countries: evidence since 1870. Empirical Economics. Epub ahead of print 2019. DOI: https://doi.org/10.1007/s00181-019-01628-1.
[4]	Fare R, Grosskopf S, Lovell CAK. Production Frontiers. Cambridge University Press, 1994.
[5]	O’Donnell CJ, Rao DSP, Battese GE. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics 2008; 34: 231–255.
[6]	Jovanovic B, Rousseau PL. General Purpose Technologies. In: Handbook of Economic Growth. Elsevier, pp. 1181–1224.
[7]	Hayami Y, Ruttan WV. Induced innovation in agricultural development. Staff Papers Series 1971; 71: 47.
[8]	Binswanger HP, Ruttan Vernon. Induced innovation : technology, institutions, and development, https://trove.nla.gov.au/version/13094960 (1978, accessed 11 May 2020).
[9]	Battese GE, Rao DSP. Technology Gap, Efficiency, and a Stochastic Metafrontier Function. International Journal of Business and Economics 2002; 1: 87–93.
[10]	Battese GE, Rao DSP, O’Donnell CJ. A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies. Journal of Productivity Analysis 2004; 21: 91–103.
[11]	Chang B-G, Huang T-H, Kuo C-Y. A comparison of the technical efficiency of accounting firms among the US, China, and Taiwan under the framework of a stochastic metafrontier production function. Journal of Productivity Analysis 2015; 44: 337–349.
[12]	Huang CJ, Huang T-H, Liu N-H. A new approach to estimating the metafrontier production function based on a stochastic frontier framework. Journal of Productivity Analysis 2014; 42: 241–254.
[13]	Khanal U, Wilson C, Shankar S, and al. Farm performance analysis: Technical efficiencies and technology gaps of Nepalese farmers in different agro-ecological regions. Land Use Policy 2018; 76: 645–653.
[14]	Villano R, Bravo-Ureta B, Solís D, and al. Modern Rice Technologies and Productivity in the Philippines: Disentangling Technology from Managerial Gaps. Journal of Agricultural Economics 2015; 66: 129–154.
[15]	Moreira VH, Bravo-Ureta BE. Technical efficiency and metatechnology ratios for dairy farms in three southern cone countries: a stochastic meta-frontier model. Journal of Productivity Analysis 2010; 33: 33–45.
[16]	Liu J, Li H, Sriboonchitta S, and al. Technical Efficiency Analysis of Top Agriculture Producing Countries in Asia: Zero Inefficiency Meta-Frontier Approach. In: Kreinovich V, Sriboonchitta S (eds) Structural Changes and their Econometric Modeling. Cham: Springer International Publishing, pp. 702–723.
[17]	Nkamleu G Blaise, Nyemeck J, Sanogo D. Metafrontier Analysis of Technology Gap and Productivity Difference in African Agriculture. Journal of Agriculture and Food Economics 2006; 1: 111–120.
[18]	World Development Indicators, Washington DC, http://data.worldbank.org/data-catalog/world-development-indicators. (2020).
[19]	Statistic of Food and Agriculture Organization of the United Nations, http://www.fao.org/faostat/en/#data. (2020).
[20]	Battese GE, Coelli TJ. Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India. Journal of Productivity Analysis 1992; 3: 153–169.
[21]	Battese GE, Rao DSP, Walujadi D. Technical Efficiency and Productivity Potential of Firms Using a Stochastic Metaproduction Frontier. Efficiency Series Paper 2001; 21.
[22]	Mugera A, Ojede A. TECHNICAL EFFICIENCY IN AFRICAN AGRICULTURE: IS IT CATCHING UP OR LAGGING BEHIND?: Technical Efficiency in African Agriculture. Journal of International Development 2014; 26: 779–795.
[23]	Tian X, Yu X. Crop yield gap and yield convergence in African countries. Food Security. Epub ahead of print 3 September 2019. DOI: 10.1007/s12571-019-00972-5.

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Abraham Amoussouga Gero. (2020). Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis. International Journal of Agricultural Economics, 5(3), 80-88. https://doi.org/10.11648/j.ijae.20200503.14

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ACS Style

Abraham Amoussouga Gero. Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis. Int. J. Agric. Econ. 2020, 5(3), 80-88. doi: 10.11648/j.ijae.20200503.14

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AMA Style

Abraham Amoussouga Gero. Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis. Int J Agric Econ. 2020;5(3):80-88. doi: 10.11648/j.ijae.20200503.14

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@article{10.11648/j.ijae.20200503.14,
  author = {Abraham Amoussouga Gero},
  title = {Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis},
  journal = {International Journal of Agricultural Economics},
  volume = {5},
  number = {3},
  pages = {80-88},
  doi = {10.11648/j.ijae.20200503.14},
  url = {https://doi.org/10.11648/j.ijae.20200503.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijae.20200503.14},
  abstract = {Agriculture plays an important role in the African continent’s growth. However, regions’ characteristics differences explain different types of production technologies use leading to a technological gap which delays these regions’ economic convergence. This article uses the stochastic metafrontier analysis based on a new approach for Technical Efficiency’s (TE) estimation and the technological gap ratios (TGR) of the agricultural production of the five African regions from 1980 to 2012. The results reveal a very high average TE score of 92.73% of the five regions whereas a low TGR score of 35.63% is noticed. The EAST region is the closest one to the best technology available with a 68.73% score. Besides, these results also show the existence of a catch-up phenomenon between low TGR level countries and those with higher TGR level. Zimbabwe has the highest catch-up score with a yearly average of 3%. Considering the agricultural sector's importance in Africa's national production, the results suggest increasing investments in Research and Development, popularizing services, and a policy of larger expansion of the technologies applied by the regions close to the optimal technology in order to facilitate new agricultural production techniques’ adoption and development. Agriculture plays an important role in the growth of the African continent. However, regions diversity of characteristics explains the use of different types of production technologies, resulting in a technology gap that delays the economic convergence of these regions.},
 year = {2020}
}

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TY  - JOUR
T1  - Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis
AU  - Abraham Amoussouga Gero
Y1  - 2020/06/20
PY  - 2020
N1  - https://doi.org/10.11648/j.ijae.20200503.14
DO  - 10.11648/j.ijae.20200503.14
T2  - International Journal of Agricultural Economics
JF  - International Journal of Agricultural Economics
JO  - International Journal of Agricultural Economics
SP  - 80
EP  - 88
PB  - Science Publishing Group
SN  - 2575-3843
UR  - https://doi.org/10.11648/j.ijae.20200503.14
AB  - Agriculture plays an important role in the African continent’s growth. However, regions’ characteristics differences explain different types of production technologies use leading to a technological gap which delays these regions’ economic convergence. This article uses the stochastic metafrontier analysis based on a new approach for Technical Efficiency’s (TE) estimation and the technological gap ratios (TGR) of the agricultural production of the five African regions from 1980 to 2012. The results reveal a very high average TE score of 92.73% of the five regions whereas a low TGR score of 35.63% is noticed. The EAST region is the closest one to the best technology available with a 68.73% score. Besides, these results also show the existence of a catch-up phenomenon between low TGR level countries and those with higher TGR level. Zimbabwe has the highest catch-up score with a yearly average of 3%. Considering the agricultural sector's importance in Africa's national production, the results suggest increasing investments in Research and Development, popularizing services, and a policy of larger expansion of the technologies applied by the regions close to the optimal technology in order to facilitate new agricultural production techniques’ adoption and development. Agriculture plays an important role in the growth of the African continent. However, regions diversity of characteristics explains the use of different types of production technologies, resulting in a technology gap that delays the economic convergence of these regions.
VL  - 5
IS  - 3
ER  -

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Author Information

Abraham Amoussouga Gero

Department of Agribusiness and Agricultural Policies, National University of Agriculture, Porto-Novo, Benin

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APA Style

Abraham Amoussouga Gero. (2020). Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis. International Journal of Agricultural Economics, 5(3), 80-88. https://doi.org/10.11648/j.ijae.20200503.14

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ACS Style

Abraham Amoussouga Gero. Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis. Int. J. Agric. Econ. 2020, 5(3), 80-88. doi: 10.11648/j.ijae.20200503.14

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AMA Style

Abraham Amoussouga Gero. Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis. Int J Agric Econ. 2020;5(3):80-88. doi: 10.11648/j.ijae.20200503.14

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@article{10.11648/j.ijae.20200503.14,
  author = {Abraham Amoussouga Gero},
  title = {Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis},
  journal = {International Journal of Agricultural Economics},
  volume = {5},
  number = {3},
  pages = {80-88},
  doi = {10.11648/j.ijae.20200503.14},
  url = {https://doi.org/10.11648/j.ijae.20200503.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijae.20200503.14},
  abstract = {Agriculture plays an important role in the African continent’s growth. However, regions’ characteristics differences explain different types of production technologies use leading to a technological gap which delays these regions’ economic convergence. This article uses the stochastic metafrontier analysis based on a new approach for Technical Efficiency’s (TE) estimation and the technological gap ratios (TGR) of the agricultural production of the five African regions from 1980 to 2012. The results reveal a very high average TE score of 92.73% of the five regions whereas a low TGR score of 35.63% is noticed. The EAST region is the closest one to the best technology available with a 68.73% score. Besides, these results also show the existence of a catch-up phenomenon between low TGR level countries and those with higher TGR level. Zimbabwe has the highest catch-up score with a yearly average of 3%. Considering the agricultural sector's importance in Africa's national production, the results suggest increasing investments in Research and Development, popularizing services, and a policy of larger expansion of the technologies applied by the regions close to the optimal technology in order to facilitate new agricultural production techniques’ adoption and development. Agriculture plays an important role in the growth of the African continent. However, regions diversity of characteristics explains the use of different types of production technologies, resulting in a technology gap that delays the economic convergence of these regions.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Regional Differences in Technology Gap Ratio and Efficiency in African Agriculture: A Stochastic Metafrontier Analysis
AU  - Abraham Amoussouga Gero
Y1  - 2020/06/20
PY  - 2020
N1  - https://doi.org/10.11648/j.ijae.20200503.14
DO  - 10.11648/j.ijae.20200503.14
T2  - International Journal of Agricultural Economics
JF  - International Journal of Agricultural Economics
JO  - International Journal of Agricultural Economics
SP  - 80
EP  - 88
PB  - Science Publishing Group
SN  - 2575-3843
UR  - https://doi.org/10.11648/j.ijae.20200503.14
AB  - Agriculture plays an important role in the African continent’s growth. However, regions’ characteristics differences explain different types of production technologies use leading to a technological gap which delays these regions’ economic convergence. This article uses the stochastic metafrontier analysis based on a new approach for Technical Efficiency’s (TE) estimation and the technological gap ratios (TGR) of the agricultural production of the five African regions from 1980 to 2012. The results reveal a very high average TE score of 92.73% of the five regions whereas a low TGR score of 35.63% is noticed. The EAST region is the closest one to the best technology available with a 68.73% score. Besides, these results also show the existence of a catch-up phenomenon between low TGR level countries and those with higher TGR level. Zimbabwe has the highest catch-up score with a yearly average of 3%. Considering the agricultural sector's importance in Africa's national production, the results suggest increasing investments in Research and Development, popularizing services, and a policy of larger expansion of the technologies applied by the regions close to the optimal technology in order to facilitate new agricultural production techniques’ adoption and development. Agriculture plays an important role in the growth of the African continent. However, regions diversity of characteristics explains the use of different types of production technologies, resulting in a technology gap that delays the economic convergence of these regions.
VL  - 5
IS  - 3
ER  -

Copy | Download