# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 4 Results and Discussion
### Panel ARDL Elasticities of Impacts of Digital Tools on Agricultural Production  
The Hausman test, which is a test for model misspecification helps detect endogenous variables in a model. With regards to the panel ARDL estimations, the pooled mean group (PMG) and dynamic fixed effect (DFE) estimators were employed. Since the aforementioned estimators' function under different assumptions, it is prudent to employ the Hausman test to determine the most efficient estimator (i.e., among PMG and DFE) for the analysis. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{\(H_{0}\) = All Panels Contain Unit Roots} \\  & \multicolumn{4}{c}{\(H_{0}\) = Series Have a Unit Roots} \\ \hline  & \multicolumn{4}{c}{**[PERSON]’s CADF Test**} \\ \hline  & \multicolumn{4}{c}{**At First**} \\  & \multicolumn{4}{c}{**Difference I(1)**} \\ \hline
**Variable** & \(t\)**-Statistic** & \(t\)**-Statistic** & **Decision: \(H_{0}\)** & **Result** \\ \hline lnY1 & \(-2.255\)*** & \(-3.021\)*** & Reject & I(0) at 1\% \\ lnY2 & \(-2.035\)** & \(-2.503\)** & Reject & I(0) at 5\% \\ lnX1 & \(-2.014\) & \(-2.102\)** & Reject & I(1) at 5\% \\ lnX2 & \(-2.563\)*** & \(-2.956\)*** & Reject & I(0) at 1\% \\ lnX3 & \(-1.795\) & \(-2.333\)*** & Reject & I(1) at 1\% \\ lnX4 & \(-1.668\) & \(-2.740\)*** & Reject & I(1) at 1\% \\ lnX5 & \(-2.185\)*** & \(-2.608\)*** & Reject & I(0) at 1\% \\ lnX6 & \(-2.327\)*** & \(-3.399\)*** & Reject & I(0) at 1\% \\ \hline  & \multicolumn{4}{c}{**Im-Pesaran–Shin unit-root test**} \\ \hline  & \multicolumn{4}{c}{**At First**} \\  & \multicolumn{4}{c}{**Difference I(1)**} \\ \hline
**Variable** & \(t\)**-Statistic** & \(t\)**-Statistic** & **Decision: \(H_{0}\)** & **Result** \\ \hline lnY1 & \(-0.8175\) & \(-8.6710\)*** & Reject & I(1) at 1\% \\ lnY2 & \(0.2771\) & \(-5.1729\)*** & Reject & I(1) at 1\% \\ lnX1 & \(0.2727\) & \(-3.3191\)*** & Reject & I(1) at 1\% \\ lnX2 & \(-2.3181\)** & \(-3.5185\)*** & Reject & I(0) at 5\% \\ lnX3 & \(2.0290\) & \(-3.9593\)*** & Reject & I(1) at 1\% \\ lnX4 & \(-0.1403\) & \(-5.9441\)*** & Reject & I(1) at 1\% \\ lnX5 & \(-0.2970\) & \(-3.9756\)*** & Reject & I(1) at 1\% \\ lnX6 & \(1.000\) & \(-13.347\)*** & Reject & I(1) at 1\% \\ \hline \hline \end{tabular} Note. ** and ** indicate significance at 5% and 1% levels, respectively. Variable list: Y1 = crop production index; Y2 = livestock production index; 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).**  
\end{table} 
Table 6: Unit root test. results garnered after performing the Hausman test on the two estimators indicate that the PMG estimator is the preferred estimator between the two estimators (Table 8). Thus, the PMG estimator was preferred for both the crop production category and livestock category. Henceforth, the PMG estimator is the most efficient estimator for the analysis as compared to the DFE estimator. Since the PMG estimator was the most efficient estimator among the three estimators, the study reported the PMG results. The results of the panel ARDL (using the PMG estimation technique) on the relationship between digital technologies and agricultural production (proxied by crop production and livestock production) in Africa can also be found in Table 8. Considering the long-run relationship, the results indicate that internet usage is statistically significant at 1% for crop production. Notice that the coefficient for internet usage recorded under the crop production is positive (0.071). Thus, the usage of internet positively influences crop production in these African countries listed in Table 1. All other things being equal, a percentage increase in the patronage of internet in these 32 African countries will increase their crop production index by 0.071%.