## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**(c)**} \\ \hline \multirow{2}{*}{**Variable**} & **EGLS** & **EGLS** & **EGLS** & \multirow{2}{*}{**EGLS Period**} & \multirow{2}{*}{**EGLS Period**} & \multirow{2}{*}{**EGLS Period**} \\  & **Cross-Section** & **Cross-Section** & & & & \\  & **Coefficient** & **Std. Error** & \(p\)**-Value** & & & \\ \hline LOGATM & 0.133626 & 0.030709 & 0.0000 & 0.188631 & 0.046479 & 0.0001 \\ LOGBANKEMPL & \(-\)0.209911 & 0.04237 & 0.0000 & \(-\)0.32377 & 0.056521 & 0.0000 \\ LOGDESI\_HC & 1.096892 & 0.142832 & 0.0000 & 0.926947 & 0.202242 & 0.0000 \\ LOGDESI\_IDT & \(-\)1.16967 & 0.042611 & 0.0000 & \(-\)0.921624 & 0.096607 & 0.0000 \\ LOGDIGINCL & 2.026711 & 0.133127 & 0.0000 & 2.296045 & 0.371943 & 0.0000 \\ LOGEBANK & \(-\)0.249761 & 0.038688 & 0.0000 & \(-\)0.289611 & 0.065868 & 0.0000 \\ LOGDDP & \(-\)0.258883 & 0.064009 & 0.0001 & \(-\)0.281414 & 0.099263 & 0.0053 \\ RD & 0.273297 & 0.012905 & 0.0000 & 0.24581 & 0.035356 & 0.0000 \\ MOMENT1 & 0.650968 & 0.015505 & 0.0000 & 0.41884 & 0.048837 & 0.0000 \\ MOMENT2 & 0.391258 & 0.005623 & 0.0000 & 0.428785 & 0.023229 & 0.0000 \\ C & \(-\)2.864215 & 0.610652 & 0.0000 & \(-\)3.436383 & 1.451358 & 0.0193 \\ \hline \hline \end{tabular} Source: Author’s own calculation.  
\end{table} 
Table 7: _Cont._The results from the _panel unit root tests_ (Table 8) provide important insights into the stationarity properties of the examined time series, which is fundamental for ensuring robust econometric modelling and inference. The application of multiple tests, including [PERSON], Lin & Chu, Im, Pesaran & Shin, and Fisher-type ADF and PP tests, allows for a comprehensive assessment under different assumptions regarding the underlying data-generating processes. At the level, the findings consistently indicate the presence of a unit root across most series. The \(p\)-values for [PERSON], [PERSON], & Chu [123], [PERSON], Pesaran, & Shin [118], and the Fisher tests [124] remain significantly above conventional significance thresholds, suggesting that these series exhibit non-stationary behavior. For instance, the variable LOGATM demonstrates a \(p\)-value of 1.000 across most tests, reinforcing the hypothesis that it follows a unit root process. Similarly, LOGBANKEMPL, LOGEDSI_HC, and LOGEDSI_IDT exhibit \(p\)-values close to one, confirming the inability to reject the null hypothesis of a unit root. However, there are exceptions where certain tests provide mixed evidence. For example, LOGDIGINCL and LOGEBANK show \(p\)-values below 0.05 under [PERSON], [PERSON], & [PERSON], indicating stationarity under the assumption of a common unit root process, whereas alternative tests such as those of [PERSON], [PERSON], & [PERSON] and Fisher-type methods suggest non-stationarity. This discrepancy underscores the importance of considering multiple tests, as differences in test power and assumptions may yield divergent conclusions. Upon taking the first difference, the stationarity properties of the variables undergo a substantial transformation. Across all the examined series, the \(p\)-values for the [PERSON], [PERSON], & Chu, [PERSON], Pesaran, & Shin, and Fisher tests decline sharply, overwhelmingly rejecting the null hypothesis of a unit root. For example, D(LOGATM) exhibits a \(p\)-value of 0.0001 under [PERSON], [PERSON], & [PERSON], while the [PERSON], Pesaran, & Shin test confirms stationarity with a \(p\)-value of 0.0096. Similar results emerge for other transformed variables such as D(LOGBANKEMPL), D(LOGESI_HC), and D(LOGEDSI_IDT), all of which display highly significant test statis