Impact of Public Transportation on European Countries’ Development: a Spatial Perspective

Figure 1.

Figure 2.

Figure 3.

Appendix 1.

Appendix 2.

Multiple OLS Regression Between Public Transport and Development

Variables	Log GDP/Cap
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

Diagnostics for Spatial Dependence	Log GDP/Cap
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

Variables	GDP/Cap	Transport	Sustainability	Education	Attractiveness	Log Roads
Mean	4.41	13.13	7.92	57.24	0.18	1.97
Median	4.37	12.9	7.3	53.6	0	1.98
Standard Error	0.05	0.71	0.51	3.41	0.08	0.13
Standard Deviation	0.26	3.68	2.64	17.7	0.39	0.67
Skewness	0.41	0.34	1.62	0.56	1.72	−0.61
Kurtosis	−0.33	0.17	4.17	0.96	1.02	0.24
N	27	27	27	27	27	27

SARMA Regression Model Between Public Transport and Development

Variables	Log GDP/Cap
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

Variable Alias	Variable Name	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

Variables	GDP/Cap	Transport	Sustainability	Education	Attractiveness	Log Roads
GDP/Cap	1	0.265306	0.678325	0.050464	0.345262	−0.11932
Transport	0.265306	1	0.215899	−0.44597	0.054979	−0.75423
Sustainability	0.678325	0.215899	1	−0.17053	0.246438	−0.23339
Education	0.050464	−0.44597	−0.17053	1	−0.1022	0.437435
Attractiveness	0.345262	0.054979	0.246438	−0.1022	1	0.005346
Log Roads	−0.11932	−0.75423	−0.23339	0.437435	0.005346	1

Spatial Weight Matrix Discrimination

Weight Matrix type	Moran's I	Pseudo P-Value Moran
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