# 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
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Country** & **Class** & **Year** & **Individual Robots** & **Capiti** & **Employes-Number** & **Casouth Rate** & **Wages** & **Apparent** & **Cross Value Added** & **Timower of Census** \\ \hline Austria & 1 & 2018 & 4.0\% & 57/305.3 & 2,606,757 & 2.7 & 96,271.3 & 68.9 & 77.3 & 778.701 \\ Bulgaria & 1 & 2018 & 3.0\% & 29,211.30 & 1,758,308 & 0.9 & 12,626.4 & 14.5 & 16.7 & 141,132 \\ Cyprus & 1 & 2018 & 1.0\% & 40,763.9 & 255,030 & 8.4 & 4623.6 & 35.3 & 37.2 & 337.08 \\ Germany & 1 & 2018 & 3.0\% & 54,954.58 & 28,972.02 & 4.5 & 966,079.3 & 99.7 & 65.4 & 63,801 \\ Hungary & 1 & 2018 & 3.0\% & 33,813.98 & 2,430,651 & 4.3 & 28,533.8 & 25.2 & 29.4 & 324,310 \\ Estonia & 1 & 2018 & 3.0\% & 36,236.20 & 406,062 & 1 & 5806.6 & 31 & 33.4 & 64,882 \\ Lithuan & 1 & 2018 & 2.0\% & 36,364.40 & 808,691 & 1.8 & 9010.1 & 21.3 & 23.6 & 91,926 \\ Mads & 1 & 2018 & 3.0\% & 44,482.24 & 133,531.1 & 6.5 & 2709.2 & 42 & 49.6 & 24,671 \\ Slovakia & 1 & 2018 & 4.0\% & 31,050.66 & 1,265,837 & 2.8 & 165,479.7 & 24.3 & 31.7 & 22,237 \\ Romania & 1 & 2018 & 2.0\% & 20,288.81 & 3,944,656 & 1 & 38,575.2 & 18.4 & 18.9 & 313,933 \\ Czechia & 2 & 2018 & 6.0\% & 413,249.4 & 2,971,724 & 1.5 & 4371.12 & 29.7 & 38.5 & 514,087 \\ Denmark & 2 & 2018 & 7.0\% & 57,428.72 & 1,706,138 & 3.4 & 28,813.0 & 92.8 & 98.4 & 545,569 \\ Finland & 2 & 2018 & 8.0\% & 49,753.14 & 1,403,385 & 1.9 & 52,513.6 & 68.9 & 74.3 & 402,031 \\ France & 2 & 2018 & 6.0\% & 45,609.02 & 14,610,106 & 0.5 & 52,758.78 & 63.7 & 68.9 & 330,309 \\ Italy & 2 & 2018 & 6.0\% & 43,213.61 & 109,795.75 & 1.5 & 259,549.6 & 49.8 & 68.5 & 3,033,601 \\ Netherlands & 2 & 2018 & 7.0\% & 57,901.01 & 5,194,758 & 3.6 & 168,480.0 & 64.8 & 74.9 & 1,644,839 \\ Poland & 2 & 2018 & 5.0\% & 31,978.83 & 803,591 & 6.4 & 99,294.1 & 25.2 & 30.8 & 1,124,830 \\ Portugal & 2 & 2018 & 6.0\% & 34,913.78 & 2,804,551 & 4.5 & 40,277.2 & 26 & 31.8 & 375,645 \\ Slovenia & 2 & 2018 & 6.0\% & 38,915.64 & 50,865 & 4.2 & 12,147.8 & 36.8 & 43 & 102,384 \\ Spain & 2 & 2018 & 8.0\% & 44,720.19 & 10,48,323 & 3.9 & 252,581.7 & 42.2 & 50.8 & 2,055,805 \\ Sweden & 2 & 2018 & 6.0\% & 53,333.31 & 2,820,065 & 2.4 & 106,463.3 & 70.7 & 84.8 & 842,303 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Annual enterprise statistics for special aggregates of activities NACE Rev2. bn_s95_v_k. Source: Author’s contribution based on data retrieved from Eurostat. Annual enterprise statistics for special aggregates of activities. [[https://appsso.eurostat.ec.europa.eu/mi/show/d?datasest=bs_na_sca_r2&lang=en](https://appsso.eurostat.ec.europa.eu/mi/show/d?datasest=bs_na_sca_r2&lang=en)]([https://appsso.eurostat.ec.europa.eu/mi/show/d?datasest=bs_na_sca_r2&lang=en](https://appsso.eurostat.ec.europa.eu/mi/show/d?datasest=bs_na_sca_r2&lang=en)), accessed on 9 January 2022. The correlation matrix presented below in Figure 11 shows us that generally there was a low-to-moderate correlation between the structural business variables included, the enterprises' use of industrial robots, and GDP per capita. Nevertheless, the model shows that the share of enterprises using industrial robots had moderate positive correlations with the GDP per capita, apparent labor productivity, and gross value added per employee, and a low positive correlation with enterprises' wages and salaries, and gross premiums written. It is important to mention that these calculations are different from 0, with a significance level of alpha = 0.05, meaning they are significant at a 95% confidence level. Furthermore, the principal component analysis revealed how these initially correlated variables may be reduced to fewer components in our model. The next table (Table 2) presents the eigenvalues of each dimension, which reflect the quality of the projection from the eight-dimensional initial table to a lower number of dimensions.