## 5 Conclusions and Policy Implications  
This study investigated the relationship among credit access, output, and productivity in the agricultural sector for a sample of 114 countries analyzed from 2000 to 2012. The empirical strategy used an ANNs experiment in ML. The time-series results, confirmed by the ANNs process and the Confusion Matrix tests, show that the productivity is stimulated by credit access and the latter is facilitated by higher levels of agricultural output. Put differently, higher levels of output tend to stimulate the economic development in the agricultural sector, via higher productivity and, more importantly, by improving credit access. These results, specific for the agricultural sector, are in line with the arguments supported by [6, 7] on the positive relationship between credit markets development and economic growth, and the role of productivity growth. Moreover, the discovered relationships between credit, output, and productivity are in line with earlier and recent studies [21, 69]. It is interesting to note that [PERSON]'s law [70] is confirmed in all our estimates: in fact, output significantly influences productivity in all tested samples. Notably, the fundamentals of the agricultural economy follow different mechanisms across countries: the relationships among the three variables are tighter (and of longer impact) for OECD countries, where the credit stimulates both productivity and output. On the other hand, these relationships are loose (and of shorter term) in developing countries, where the stimulus of credit is only beneficial to agricultural output, and not to the productivity. A plausible explanation of our findings is provided by the well-established literature on the role of technological innovations in agriculture [71, 72]). The role of credit is more important for developed economies, and advanced agricultural sectors \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline
**Actual Positive** & 19,852 & 319 \\
**Actual Negative** & 712 & 19,451 \\ \hline
**Accuracy** & 0.974 & \\
**Precision** & 0.956 & \\
**Sensitivity** & 0.984 & \\
**SP** & 0.964 & \\
**FPR** & 0.035 & \\ \hline \hline \end{tabular} Sources: our elaborations in Oryx 2.0.8.  
\end{table} 
Table 4: Confusion Matrix for developing countries. Figure 3: ANNs model (developing countries). Sources: our elaborations in Oryx 2.0.8.  
where agricultural firms may easily exploit, through credit, the advantages of technological innovations. Differently, providing credit to firms located in developing countries is only able to boost production, exactly because technologies spread slowly though learning-by-doing and learning-from-others mechanisms, and the gains from advanced technologies cannot be exploited. Our results favor the motivations for intervention in credit markets as a strategy to promote economic development. Following the argument of [73], who state that \"agricultural credit was conceptualized as factor of production, [... ] an increase in supply of credit would lead to an increase in production and income\", we conclude that policies facilitating credit access leverage output. In addition, the evidence on the developed economies suggests that such policies may have impacts both on production and on productivity. A direct implication of our analysis is that while it is true that credit constraints tend to limit growth [1], the higher the economic development, the more agricultural development is hampered by lack of credit access. Put differently, pro-growth policy interventions in the least developed countries may not necessarily require the development of credit markets, whereas the opposite would be true in developed economies. A limitation of our analysis is that we are not able to disentangle the mechanisms that trigger the reciprocal causality between credit and productivity.