# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 3 Methodology
### Analytical Technique  
The paper applied the panel autoregressive distributed lag (ARDL) framework to estimate the relationship between information and communication technologies and agricultural production (proxied by crop production index and livestock production index) in Africa and used STATA 17 software for the analysis, as applied by [PERSON] et al. [74] and [PERSON] et al. [75] in their studies in Africa. One important feature of the panel ARDL framework is ability to determine long-run relationships between the dependent and independent variables and being free from the endogenous problem [76]. The Pesaran Cross-sectional Augmented Dickey-Fuller (CADF) and Im-Pesaran-Shin unit-root test were used to determine the existence of unit roots among the variables, while the [PERSON] test and Westerlund test were applied to test whether the variables are cointegrated. The panel autoregressive distributed lag was used to compute the long- and short-run impacts of the independent variables on the dependent variable. The implicit model of our panel autoregressive distributed lag framework is stated as follows:  
\[Yk=f(X1,X2,X3,X4,X5,X6,e) \tag{1}\]  
where:  
\(Yk=\) agricultural production;  
\(X1=\) individuals using the internet (% of population);  
\(X2=\) mobile cellular subscriptions (number of persons);  
\(X3=\) fixed telephone subscriptions (number of persons);  
\(X4=\) adjusted net national income per capita (current USD)  
\(X5=\) domestic credit to private sector by banks (% of GDP);  
\(X6=\) rural population (% of total population);  
\(e=\) error term;  
\(k=\) 1 (crop production index), 2 (livestock production index). To control for possible heteroskedasticity in our dataset, we converted the real values of the variables to their logarithmic values. The panel autoregressive distributed lag model with the logarithms is presented thus:  
\[\ln Y_{it}=\beta_{0}+\beta_{1}\ln X1_{it}+\beta_{2}\ln X2_{it}+\beta_{3}\ln X 3_{it}+\beta_{4}\ln X4_{it}+\beta_{5}\ln X5_{it}+\beta_{6}\ln X6_{it}+\varepsilon _{it} \tag{2}\]  
where \(i\): 1, 2, 3,..., 32 countries; \(t\): 2004, 2005, 2006,..., 2019 year; In denotes natural logarithm; and \(\varepsilon\) is the error term. Furthermore, \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\), \(\beta_{4}\), \(\beta_{5}\), and \(\beta_{6}\) define the estimated percentage change in agricultural production (crop production index and livestock production index) caused by a one percent change in internet use, mobile cellular subscriptions, fixed telephone subscriptions, net national income, credit to the private sector, and rural population, respectively, while all other factors are constant. Standard panel regressions as the expressed model in Equation (2) can be used to estimate efficiently and consistently if all the variables are stationary at levels. In a situation \begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Source** \\ \hline Crop production index (2014–2016 = 100) & World Development Indicators of World Bank \\ Fixed telephone subscriptions (number of persons) & World Development Indicators of World Bank \\ Livestock production index (2014–2016 = 100) & World Development Indicators of World Bank \\ Individuals using the internet (\% of population) & World Development Indicators of World Bank \\ Mobile cellular subscriptions (number of persons) & World Development Indicators of World Bank \\ Adjusted net national income per capita (current US5) & World Development Indicators of World Bank \\ Domestic credit to private sector by banks (\% of GDP) & World Development Indicators of World Bank \\ Rural population (\% of population) & World Development Indicators of World Bank \\ \hline \hline \end{tabular}  
Link to the data: [[https://databark.worldbank.org/source/world-development-indicators](https://databark.worldbank.org/source/world-development-indicators)]([https://databark.worldbank.org/source/world-development-indicators](https://databark.worldbank.org/source/world-development-indicators)). Accessed 22 October 2022.  
\end{table}