Does tertiary education promote technological innovation sustainability? The role of national intellectual capital. An empirical evidence

Figures & data

Figure 1. Gross enrollment ratio in tertiary education, 2014.

Source: Unesco publication (2020).

Figure 2. Worldwide school enrollment, tertiary (% gross) from 1970 to 2020.

Figure 3. The framework of tertiary education and innovation generations and development.

Table 1. Variables, sources, and descriptive statistics.

Variables	Definition and sources	Mean	S. D	Median
Patents	‘The total number of patents applied by country residents.’ Sources: WIPO Statistics database (2017).	9.19	1.878	8.732
Trademarks	‘The total number of trademarks applied by country residents.’ Sources: WIPO Statistics database (2017).	10.154	1.304	9.893
Education	‘Tertiary school enrollment (% gross enrollment).’ Sources: WDI (2017).	4.019	0.468	4.089
Capital	‘Mobile cellular subscriptions per 100 people’: Sources: WDI (2017).	0.713	0.128	0.728
Finance	‘Index of financial development.’ IMF (2017)	13.546	1.301	12.921
Population	‘The total number of populations residing in a particular country.’	4.163	0.96	4.521
Income	‘Real GDP at constant 2011 national prices (in mil. 2011 US$).’	3.885	0.095	3.888
Efficiency	‘Government effectiveness (ranges from 0 to 100).’ Source: WGI 2017	3.06	1.634	2.174
Labour	‘Labour force participation rate (% of the total population).’ WDI (2017).	4.504	0.138	4.541

The study utilised data from WIPO – ‘World Intellectual Property Organisation’ (WIPO Report, 2020), WDI – ‘World Development Indicator’ (World Bank, 2020), IMF – ‘International Monetary Fund’ (IMF, 2020), WGI – ‘World Governance Indicator’ (World Bank, 2020), PWT – ‘Penn World Table’ (GGDC, 2018). The data window is from 1995 to 2017 because of data disposal for the core variables.

Sources: Authors conceptualization.

Table 2. Pesaran (2015) test for cross-sectional dependence.

Variables	Test values
Patent	5.489***
	(0.000)
Trademark	2.975***
	(0.003)
Education	24.586***
	(0.000)
Finance	26.756***
	(0.000)
Income	44.221***
	(0.000)
Capital	44.653***
	(0.000)
Labour	17.992***
	(0.000)
Population	36.269***
	(0.000)
Efficiency	2.845***
	(0.000)

Note: H0: Errors are weakly cross-sectional dependent. *** represents statistical significance at 1%.

Sources: Authors conceptualization.

Table 3. Instrumental variable regression for the influence of national intellectual capital on technological innovation.

Variables	Patents		Trademarks
	(1)	(2)	(3)	(4)
Capital	0.808***	0.721***	0.047	0.258***
	(0.067)	(0.107)	(0.048)	(0.082)
Finance	−2.167***	−2.578***	−0.852***	0.296
	(0.200)	(0.549)	(0.294)	(0.478)
Income	1.479***	0.611	0.965***	0.718***
	(0.027)	(0.431)	(0.046)	(0.098)
Labour	1.295***	1.958***	1.803***	1.817***
	(0.381)	(0.501)	(0.326)	(0.324)
Population		0.893**		0.187**
		(0.440)		(0.091)
Efficiency		3.870**		1.280*
		(1.544)		(0.745)
Constant	−18.47***	−28.68***	−9.758***	−12.463***
	(2.088)	(5.007)	(1.861)	(1.861)
F – statistics	3638.75	6514.08	427.01	115.31
P value	0.000	0.000	0.000	0.000
R-square	0.8763	0.8884	0.9231	0.9343
Observation, Country	308,79	266,79	305,79	263,79

1. Driscoll-Kraay robust standard errors reported in parenthesis account for cross-sectional dependency.

2. * p < 0.1, ** p < 0.05, *** p < 0.01.

Sources: Authors conceptualization.

Table 4. 2SLS (Instrumental variable regression) for the influence of tertiary education on technological innovation and the role of national intellectual capital.

Variables	Patents		Trademarks
	(1)	(2)	(3)	(4)
Education	0.655**	1.211***	0.526***	0.023
	(0.240)	(0.322)	(0.129)	(0.249)
Finance	−2.724***	−1.376**	−0.636**	0.266
	(0.352)	(0.616)	(0.277)	(0.496)
Income	1.522***	0.258	0.969***	1.013***
	(0.048)	(0.394)	(0.036)	(0.087)
Capital	0.523***	4.430***	0.376***	4.614***
	(0.121)	(1.144)	(0.104)	(0.378)
Labour	1.752***	2.840***	1.326***	1.508***
	(0.395)	(0.564)	(0.348)	(0.284)
Population		1.251***		−0.122
		(0.385)		(0.084)
Efficiency		−0.920		0.119
		(1.487)		(0.866)
Education.Capital		4.753***		4.125***
		(1.051)		(0.344)
Constant	−21.36***	−9.483	−7.174***	−5.154***
	(2.512)	(5.075)	(1.622)	(1.132)
F–statistics	1611.15	13024.00	508.64	144.26
P value	0.000	0.000	0.000	0.000
R-square	0.8795	0.9036	0.9313	0.9478
Observations, Countries	272,79	240,79	269,79	237,79

1. Driscoll-Kraay robust standard errors reported in parenthesis account for cross-sectional dependency.

2. * p < 0.1, ** p < 0.05, *** p < 0.01.

Sources: Authors conceptualization.

Table 5. Instrumental variable fixed effect regressions for the influence of tertiary education on technological innovation and the role of national intellectual capital.

Variables	Patents		Trademarks
	(1)	(2)	(3)	(4)
Education	0.732***	0.267*	0.205**	0.276***
	(0.166)	(0.163)	(0.096)	(0.104)
Finance	2.123***	2.056***	0.985**	0.690*
	(0.586)	(0.553)	(0.403)	(0.367)
Income	0.487	0.437	0.976***	1.000**
	(0.413)	(0.479)	(0.249)	(0.409)
Capital	0.642***	2.784***	0.341***	1.738*
	(0.124)	(0.328)	(0.093)	(0.916)
Labour	−0.515	−0.935	−0.218	0.332
	(0.955)	(1.062)	(1.241)	(0.828)
Population		0.648		−0.343
		(0.563)		(0.660)
Efficiency		1.488		1.503**
		(1.521)		(0.646)
Education.Capital		3.312***		1.403*
		(0.342)		(0.859)
R-squared Within	0.7517	0.7327	0.5407	0.5224
P value	0.000	0.000	0.000	0.000
F-statistics	7292.53	42941.97	3671.25	3325.66
Observations, Countries	272,79	240,79	269,79	237,79

1. Driscoll-Kraay robust standard errors reported in parenthesis account for cross-sectional dependency.

2. * p < 0.1, ** p < 0.05, *** p < 0.01.

Sources: Authors conceptualization.

Table 6. Robustness–Instrumental variable quantile regression for the impact of tertiary education on technological innovation (Patents).

Variable	Q (0.10)	Q (0.25)	Q (0.50)	Q (0.75)	Q (0.90)
	(1)	(2)	(3)	(4)	(5)
Education	0.320	0.402*	0.532**	0.612**	0.665***
	(0.607)	(0.231)	(0.254)	(0.299)	(0.286)
Finance	2.018	1.972**	1.898***	1.853***	1.823**
	(1.369)	(0.973)	(0.571)	(0.675)	(0.870)
Income	0.261	0.427	0.688*	0.850*	0.956
	(0.959)	(0.682)	(0.404)	(0.473)	(0.610)
Capital	0.700**	0.650***	0.571***	0.521***	0.489**
	(0.343)	(0.244)	(0.144)	(0.169)	(0.218)
Labour	0.356	0.0127	−0.560	−0.907	−1.136
	(2.210)	(1.570)	(0.927)	(1.089)	(1.405)
Population	0.704	0.311	−0.306	−0.689	−0.942
	(2.244)	(1.595)	(0.945)	(1.106)	(1.427)
Efficiency	2.666	2.083	1.166	0.597	0.221
	(2.221)	(1.576)	(0.941)	(1.094)	(1.415)
Observation, Countries	240,79	240,79	240,79	240,79	240,79

1. Driscoll-Kraay robust standard errors reported in parenthesis account for cross-sectional dependency.

2. * p < 0.1, ** p < 0.05, *** p < 0.01.

Sources: Authors conceptualization.

Figure 4. Framework on how national intellectual capital reinforces the impact of tertiary education on innovation generations.

Figure A1. Quantiles of patent applications. Source: Author’s computations.

Figure A2. Quantiles of trademark applications. Source: Author’s computations.

Table A1. Summary statistics.

	N	Std. Dev.	Mean	Median	min	max	var	skew	kurto	p25	p75
Patent	322	1.878	9.19	8.732	5.595	14.078	3.526	0.658	2.575	7.685	10.47
Trademark	319	1.304	10.154	9.893	8.126	14.557	1.701	0.752	3.384	9.111	11.164
Education	286	0.468	4.019	4.089	1.479	4.553	0.219	−3.011	14.377	3.925	4.288
Finance	308	0.128	0.713	0.728	0.392	0.989	0.016	−0.232	2.811	0.635	0.793
Income	322	1.301	13.546	12.921	11.543	16.759	1.693	0.501	1.995	12.44	14.684
Capital	322	0.96	4.163	4.521	−1.229	5.148	0.923	−2.27	9.301	3.958	4.774
Labour	322	0.095	3.885	3.888	3.607	4.066	0.009	−0.194	2.552	3.829	3.945
Population	322	1.634	3.06	2.174	1.295	7.251	2.669	1.056	3.341	1.701	4.175
Efficiency	266	0.138	4.504	4.541	3.765	4.605	0.019	−2.931	11.618	4.492	4.58

Sources: Authors conceptualization.

Table A2. Correlation coefficient.

	Patent	Trademark	Education	Finance	Income	Capital	Labour	Population	Efficiency
Patent	1
Trademark	0.882	1
Education	0.439	0.621	1
Finance	−0.043	−0.003	0.320	1
Income	0.898	0.945	−0.519	0.097	1
Capital	0.237	0.307	0.691	0.429	−0.237	1
Labour	0.267	0.375	−0.344	0.098	0.282	−0.017	1
Population	0.884	0.938	−0.677	−0.110	0.955	−0.368	0.261	1
Efficiency	−0.515	−0.633	0.852	0.539	−0.554	0.498	−0.248	−0.739	1

Sources: Authors conceptualization.

Table A3. List of countries.

Country	Country	Country
Albania	Greece	Pakistan
Argentina	Guatemala	Peru
Armenia	Hungary	Philippines
Australia	Iceland	Poland
Austria	India	Portugal
Azerbaijan	Indonesia	Romania
Bangladesh	Ireland	Russian Federation
Belarus	Israel	Serbia
Belgium	Italy	Slovakia Republic
Bosnia and Herzegovina	Japan	Slovenia
Brazil	Jordan	South Africa
Bulgaria	Kazakhstan	Spain
Canada	Kenya	Sri Lanka
Chile	Kyrgyzstan	Sweden
China	Latvia	Switzerland
Colombia	Lithuania	Tajikistan
Croatia	Madagascar	Thailand
Cyprus	Malaysia	Tunisia
Czech Republic	Malta	Turkey
Denmark	Mexico	Turkmenistan
Ecuador	Moldova	Ukraine
Egypt	Mongolia	United Kingdom
Estonia	Montenegro	Uruguay
Finland	Morocco	Uzbekistan
France	Mozambique	Zambia
Georgia	North Macedonia
Germany	Norway

Sources: Authors conceptualization.

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