# A Better Integration of Industrial Robots in Romanian Enterprises and the Labour Market
## 3 Modelling Data Regarding Enterprises' Use of Industrial Robots: Key Findings and Main Results
### Data Collection
In our model, we can see that in the principal component model without rotation, the first factor's eigenvalue equals 4.170 and represents 52.122% of the total variability, while the cumulative variance of the first three factors accounts for 91.752% of the total variability; with the second factor displayed being accountable for 26.174% of the variance and the third for 13.456%. Using [PERSON]'s principle for choosing the main components of our model, which states that the most relevant factors are those that register eigenvalues >1 and considering Figure 12 below where we represented the scree plot of all eigenvalues of the factors, listed in decreasing order of value, we retained the factors above the inflexion point and considered the first three factors as principal components for our further analysis: use of industrial robots in enterprises, GDP per capita and employees number. We can notice that the first two components are strongly determined by the share of enterprises using industrial robots and the third one by the employee number. Wages and salaries, apparent labor productivity, gross value added per employee, and turnover written show low and even negative weights with all the three components, while the growth rate of employment shows a slightly higher positive relationship with the second component. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \({}^{\circ}\) & **F1** & **F2** & **F3** & **F4** & **F5** & **F6** & **F7** & **F8** \\ \hline Eigenvalue & 4.170 & 2.094 & 1.077 & 0.563 & 0.069 & 0.016 & 0.010 & 0.002 \\ Variability (\%) & 52.122 & 26.174 & 13.456 & 7.041 & 0.860 & 0.204 & 0.119 & 0.024 \\ Cumulative \% & 52.122 & 78.296 & 91.752 & 98.793 & 99.652 & 99.856 & 99.976 & 100.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated eigenvalues of each variable. Source: Author’s contribution based on data retrieved from Eurostat. Annual enterprise statistics for special aggregates of activities. [[https://appsso.eurostat.ec.europa.eu/uui/show.do?dataset=sbs_na_sca_r2&lang=en](https://appsso.eurostat.ec.europa.eu/uui/show.do?dataset=sbs_na_sca_r2&lang=en)]([https://appsso.eurostat.ec.europa.eu/uui/show.do?dataset=sbs_na_sca_r2&lang=en](https://appsso.eurostat.ec.europa.eu/uui/show.do?dataset=sbs_na_sca_r2&lang=en)), accessed on 9 January 2022. Figure 11: Correlation matrix. Source: Author’s contribution based on data retrieved from Eurostat. Annual enterprise statistics for special aggregates of activities. [[https://appsso.eurostat.ec.europa.eu/uui/show.do?dataset=sbs_na_sca_r2&lang=en](https://appsso.eurostat.ec.europa.eu/uui/show.do?dataset=sbs_na_sca_r2&lang=en)]([https://appsso.eurostat.ec.europa.eu/uui/show.do?dataset=sbs_na_sca_r2&lang=en](https://appsso.eurostat.ec.europa.eu/uui/show.do?dataset=sbs_na_sca_r2&lang=en)), accessed on 9 January 2022. Table 3 presents the factor matrix after Varimax rotation and represents an important outcome for our analysis, revealing that our initial eight-variable model could be reduced to three different variables describing the status of enterprise and economic health: one component provides the measure of robotization, while the other two are strongly correlated with economic results and employment. The result of this analysis suggests that our model has three independent variables (share of enterprises using industrial robots, GDP per capita, and employee number), and five dependent ones, which are the growth rate of employment, wages and salaries, apparent labor productivity, gross value added per employee, and turnover or gross premium written.