Do ICT based extension services improve technology adoption and welfare? Empirical evidence from Ghana

ABSTRACT

This paper examines the impact of ICT-based extension services on farmers’ adoption of a new agricultural technology (Bradyrhizobium inoculant), knowledge gain on the new technology, yields and net returns, using recent survey data of 600 soybean farmers from Ghana. We employ a copula functions approach to account for potential selection bias and endogeneity. Standard selectivity correction models often employed in the literature rely on multivariate normality (MVN) assumption, which is easily violated, especially when there is tail dependence in the distribution of the observed data, thus making the distribution non-normal. The copula functions approach allows the modelling of selectivity based on multivariate non-normality to account for this deficit in the data, but retains the MVN as a special case. Our empirical findings reveal that farmers who participated in ICT-based extension obtained 205% knowledge scores, 151% yields and 88% farm net returns, compared to 174% knowledge scores, 148% yields and 86% farm net returns for conventional extension participants. The current study provides evidence that employing ICT-based extension delivery to farmers can help in accelerating progress towards the achievement of the Sustainable Development Goals, particularly goals two and five, which seek to achieve zero hunger and equalaccess to extension services.

KEYWORDS:

• ICT-based extension
• inoculant adoption
• welfare
• copulas functions
• selection bias

JEL CLASSIFICATION:

• C31
• C34
• O33
• Q16
• Q55

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ ICT-based extension channels refer to extension services delivered through information and communication technology (ICT) channels such as radio, television, video, internet, mobile phone, etc. (see World Bank, 2017).

² Mixed-copula as used here refers to the combination of different copulas either from the same family or from different families of copulas, such as combining different Archimedean and/or Elliptical family of copulas to avoid misspecification and improve model fit.

³ Readers interested in the formulation of the copula specifications and the maximum likelihood can refer to Appendices A2 and A3 as well as the relevant literature cited in this paper for further reading.

⁴ We validated our inoculant knowledge test questions with the frontline organization that carry out the dissemination intervention.

⁵ Specific inoculant brands we used for farmer identification are Sarifix, Legumfix, Biofix and Nodumax, the placebo was cow dunk, and a well-known dairy product packaged similarly as the inoculant for the raw and packaged inoculants, respectively.

⁶ Different types of the inoculant are made for different leguminous crops such as soya bean, groundnuts, cowpea, etc.

Additional information

Funding

This work was supported by the Deutscher Akademischer Austauschdienst (DAAD) as part of the [Ghanaian-German Postgraduate Training Programme, 2017 (funding ID no.57344816), 2].