Impact of Public Transportation on European Countries’ Development: a Spatial Perspective
| 22 lis 2023
O artykule
Data publikacji: 22 lis 2023
Zakres stron: 403 - 413
DOI: https://doi.org/10.2478/ceej-2023-0023
Słowa kluczowe
© 2023 Andreea Matyas, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2.
Figure 3.
Appendix 1.
Appendix 2.
Multiple OLS Regression Between Public Transport and Development
| Transport | 0.0731 |
| (2.39) | |
| C | 7.6408 |
| (11.58) | |
| Greenhouse | 0.1220 |
| (3.4) | |
| Education | 0.116 |
| (2.03) | |
| Attractiveness | 0.2936 |
| (1.27) | |
| Log Roads | 0.1747 |
| (1.93) | |
| Adjusted R-Squared | 0.43 |
| F-Statistic | 4.98 |
| Multicollinearity | 17.99 |
| N | 27 |
Diagnostics for Spatial Dependence
| Moran's I (errors) | 1.3554 |
| Prob | (0.17) |
| Lagrange Multiplier (lag) | 1.5050 |
| Prob | (0.21) |
| Robust LM (lag) | 6.3663 |
| Prob | (0.11) |
| Lagrange Multiplier (errors) | 0.1421 |
| Prob | (0.71) |
| Robust LM (errors) | 5.0035 |
| Prob | (0.02) |
| Lagrange Multiplier (SARMA) | 6.5084 |
| Prob | (0.03) |
Descriptive Statistics
| 4.41 | 13.13 | 7.92 | 57.24 | 0.18 | 1.97 | |
| 4.37 | 12.9 | 7.3 | 53.6 | 0 | 1.98 | |
| 0.05 | 0.71 | 0.51 | 3.41 | 0.08 | 0.13 | |
| 0.26 | 3.68 | 2.64 | 17.7 | 0.39 | 0.67 | |
| 0.41 | 0.34 | 1.62 | 0.56 | 1.72 | −0.61 | |
| −0.33 | 0.17 | 4.17 | 0.96 | 1.02 | 0.24 | |
| 27 | 27 | 27 | 27 | 27 | 27 |
SARMA Regression Model Between Public Transport and Development
| Transport | 0.0087 |
| (3.21) | |
| C | −4.5844 |
| (−1.12) | |
| Greenhouse | 0.0925 |
| (2.85) | |
| Attractiveness | 0.1129 |
| (0.67) | |
| Education | 0.0011 |
| (0.17) | |
| Log Roads | 0.1809 |
| (2.21) | |
| Weighted Dependent Var. | 1.2699 |
| (2.73) | |
| Lambda | −1.0000 |
| (−0.75) | |
| Pseudo R-Squared | 0.58 |
| N | 27 |
Variables Description
| Log GDP/Cap | Development | Economic development is the endogenous variable of the study, and it's represented by a country's logged GDP/cap value |
| Transport | Public Transportation | Public transportation represents the exogenous variable of the study and was calculated as total volume of km travelled by road and rail transportation by the average citizen of the country in the year of reference |
| Greenhouse | Sustainability | Used as a control variable for sustainability, the greenhouse variable represents the total CO2 emissions per capita |
| Education | Education | A proxy variable was used to represent education, namely the graduates in tertiary education by age groups per 1000 of population between the ages of 20 and 29 |
| Attractiveness | Country Attractivity | Attractivity of the country is a dummy variable that takes the value 1 for the 5 most attractive countries in the EU in terms of investments |
| Log Roads | Infrastructure | As a proxy to represent a country's infrastructure, the logged value of the total km of roads was used |
Correlogram
| 1 | 0.265306 | 0.678325 | 0.050464 | 0.345262 | −0.11932 | |
| 0.265306 | 1 | 0.215899 | −0.44597 | 0.054979 | −0.75423 | |
| 0.678325 | 0.215899 | 1 | −0.17053 | 0.246438 | −0.23339 | |
| 0.050464 | −0.44597 | −0.17053 | 1 | −0.1022 | 0.437435 | |
| 0.345262 | 0.054979 | 0.246438 | −0.1022 | 1 | 0.005346 | |
| −0.11932 | −0.75423 | −0.23339 | 0.437435 | 0.005346 | 1 |
Spatial Weight Matrix Discrimination
| W1010km | 0.318 | 0.004 |
| W1250km | 0.209 | 0.006 |
| W2nearest | 0.699 | 0.001 |
| W4nearest | 0.536 | 0.001 |
| W5nearest | 0.487 | 0.001 |