Does a Country/Region’s Economic Status Affect Its Universities’ Presence in International Rankings?

Esteban Fernández Tuesta; Carlos Garcia-Zorita; Rosario Romera Ayllon; Elías Sanz-Casado

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Does a Country/Region’s Economic Status Affect Its Universities’ Presence in International Rankings?

Esteban Fernández Tuesta

Carlos Garcia-Zorita

Rosario Romera Ayllon

and

Elías Sanz-Casado

| Jun 07, 2019

Journal of Data and Information Science

Volume 4 (2019): Issue 2 (May 2019)

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Cite

Published Online: Jun 07, 2019

Page range: 56 - 78

Received: Feb 05, 2019

Accepted: Mar 25, 2019

DOI: https://doi.org/10.2478/jdis-2019-0009

Keywords
Academic Ranking of World Universities, Socio-economic indicators, Regression analysis

© 2019 Esteban Fernández Tuesta, Carlos Garcia-Zorita, Rosario Romera Ayllon, Elías Sanz-Casado, publsihed by Sceindo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Outliers detected using Cook’s distance.

Actual vs MLR model-estimated number of universities on the ARWU (excluded influence observations).

Actual vs robust regression model-estimated number of universities on the ARWU (excluding observations with high influence)

Model-estimated(M-E) number of universities (n.u) on ARWU before vs after raising HDI by 10%.

Model-estimated (M-E) number of universities(n.u) on ARWU before vs after raising GDP per capita by 10%.

Model-estimated (M-E) number of universities (n.u) on ARWU before vs after raising GDP per capita and HDI by 10%.

Point graph showing the differences between the number of universities on the ARWU when score 1 is modified.

Values for each indicator by component (after excluding outliers).

Principal Component Analysis

Indicator	Population	GDP	GDP PC	HDI
First component	-0.377	0.119	0.970	0.946
Second component	-0.922	-0.992	0.011	0.153
% of explained variance	0.993	0.999	0.942	0.918

Effect of GDP per capita and HDI on countries/regions’ presence in the ARWU top 100 (dummy variables).

		No universities	Total	p-value
GDP PC > median(GDP PC)	14	5	19	0,000*
GDP PC ≤ median(GDP PC)	2	18	20
Very high HDI	15	17	32	0,093
Other	1	6	7

Relationship between number of universities per million inhabitants and country/region presence in the ARWU top 100 (dummy variables).

		Presence in the ARWU top 100

No. univ. per 1 M inhabit.	At least 1 univ.	No univ.	Total	p-value
more than 2	16	12	28	0.000*
2 or fewer	0	11	11

Total	16	23	39

Values for each indicator by component (before excluding outliers).

Principal Component Analysis

Indicator	Population	GDP	GDP PC	HDI
First component	-0.499	0.041	0.979	0.964
Second component	0.864	0.998	-0.099	-0.153
% of explained variance	0.996	0.998	0.967	0.952

Correlations and partial correlations

	CORRELATIONS AND P-VALUES

	HDI	GDP PC	GDP	POP
NU	0,434	0,488	0,685	0,290
	(0,006)	(0,002)	(0,000)	(0,074)
IDH		0,918	-0,122	-0,599
		(0,000)	(0,459)	(0,000)
GDP PC			-0,051	-0,586
			(0,760)	(0,000)
GDP				0,839
				(0,000)

	PARTIAL CORRELATIONS AND P-VALUES
Control Variable (IDH)

	GDP_PC	GDP	POP

NU	0,251	0,825	0,762
	(0,128)	(0,000)	(0,000)

Control Variable (GDP_PC)

	IDH	GDP	POP

NU	-0,041	0,814	0,814
	(0,805)	(0,000)	(0,000)

Control Variable (GDP)

	IDH	GDP_PC	POP

NU	0,716	0,718	-0,719
	(0,000)	(0,000)	(0,000)

Control Variable(POP)

	IDH	GDP_PC	GDP

NU	0,793	0,848	0,848

	(0,000)	(0,000)	(0,000)

Values of indicators studied (2012).

Country/region	GDP per capita	HDI 2012	GDP Mill ($)	Population 2012	N.U. (500)	500%	Median_ NU	Global share of GDP	N.U. (100)
United States (USA)	51,748.6	0.94	16,244,600	313,914,040	150	30.00	153.20	22.64	53
United Kingdom (GBR)	39,093.5	0.88	2,471,784	63,227,526	38	7.60	39.00	3.44	9
Germany (DEU)	41,862.7	0.92	3,428,131	81,889,839	37	7.40	39.00	4.78	4
Chinese mainland* (CHN)	6,091.0	0.70	8,227,103	1,350,695,000	28	5.60	21.80	11.47	0
Canada (CAN)	52,219.0	0.91	1,821,424	34,880,491	22	4.40	22.00	2.54	4
Japan (JPN)	46,720.4	0.91	5,959,718	127,561,489	21	4.20	26.20	8.31	4
France (FRA)	39,771.8	0.89	2,612,878	65,696,689	20	4.00	21.80	3.64	3
Italy (ITA)	33,071.8	0.88	2,014,670	60,917,978	20	4.00	21.40	2.81	0
Australia (AUS)	67,555.8	0.94	1,532,408	22,683,600	19	3.80	17.40	2.14	5
Netherlands (NLD)	45,954.7	0.92	770,555	16,767,705	13	2.60	12.40	1.07	2
Spain (ESP)	28,624.5	0.89	1,322,965	46,217,961	11	2.20	10.40	1.84	0
Sweden (SWE)	55,041.2	0.92	523,806	9,516,617	11	2.20	11.00	0.73	3
Korea (KOR)	22,590.2	0.91	1,129,598	50,004,441	10	2.00	9.60	1.57	0
Chinese Taiwan (TWN)	20,335.9	0.91	4,741,490	23,315,822	9	1.80	7.40	1.00	0
Austria (AUT)	46,642.3	0.90	394,708	8,462,446	7	1.40	7.00	0.55	0
Belgium (BEL)	43,372.4	0.90	483,262	11,142,157	7	1.40	7.00	0.67	1
Switzerland (CHE)	78,924.7	0.91	631,173	7,997,152	7	1.40	7.40	0.88	4
Brazil (BRA)	11,339.5	0.73	2,252,664	198,656,019	6	1.20	6.20	3.14	0
Israel (ISR)	30,413.3	0.90	240,505	7,907,900	6	1.20	6.60	0.36	3
Finland (FIN)	45,720.8	0.89	247,546	5,414,293	5	1.00	5.40	0.34	1
Chinese Hong Kong (HKG)	36,795.8	0.91	263,259	7,154,600	5	1.00	5.00	0.37	0

New Zealand (NZL)	37,749.4	0.92	167,347	4,433,100	5	1.00	5.00	0.23	0
Denmark (DNK)	56,325.7	0.90	314,887	5,590,478	4	0.80	4.00	0.44	2
Norway (NOR)	99,557.7	0.96	499,667	5,018,869	4	0.80	4.00	0.70	1
Ireland (IRL)	45,931.7	0.92	210,771	4,588,798	3	0.60	3.00	0.29	0
Portugal (PRT)	20,165.3	0.82	212,274	10,526,703	3	0.60	2.20	0.30	0
South Africa (ZAF)	7,507.7	0.63	384,313	51,189,306	3	0.60	3.00	0.54	0
Chile (CHL)	15,452.2	0.82	269,869	17,464,814	2	0.40	2.00	0.38	0
Greece (GRC)	22,082.9	0.86	249,099	11,280,167	2	0.40	2.00	0.35	0
Hungary (HUN)	12,530.5	0.83	124,600	9,943,755	2	0.40	2.00	0.17	0
Poland (POL)	12,707.9	0.82	489,795	38,542,737	2	0.40	2.00	0.68	0
Russia (RUS)	14,037.0	0.79	2,014,775	143,533,000	2	0.40	2.00	2.81	1
Singapore (SGP)	51,709.5	0.90	274,701	5,312,400	2	0.40	2.00	0.38	0
Argentina (ARG)	11,573.1	0.81	475,502	41,086,927	1	0.20	1.00	0.66	0
Czech Republic (CZE)	18,682.8	0.87	196,446	10,514,810	1	0.20	1.00	0.27	0
India (IND)	1,489.2	0.55	1,841,710	1,236,686,732	1	0.20	1.60	2.57	0
Mexico (MEX)	9,748.9	0.78	1,178,126	120,847,477	1	0.20	1.00	1.64	0
Slovenia (SVN)	22,000.1	0.89	45,279	2,058,152	1	0.20	1.00	0.06	0
Turkey (TUR)	10,666.1	0.72	789,257	73,997,128	1	0.20	1.00	1.10	0

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Does a Country/Region’s Economic Status Affect Its Universities’ Presence in International Rankings?

Article Category: Research Paper

Published Online: Jun 07, 2019

Page range: 56 - 78

Received: Feb 05, 2019

Accepted: Mar 25, 2019

DOI: https://doi.org/10.2478/jdis-2019-0009

Keywords
Academic Ranking of World Universities, Socio-economic indicators, Regression analysis

© 2019 Esteban Fernández Tuesta, Carlos Garcia-Zorita, Rosario Romera Ayllon, Elías Sanz-Casado, publsihed by Sceindo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Values for each indicator by component (after excluding outliers).

Effect of GDP per capita and HDI on countries/regions’ presence in the ARWU top 100 (dummy variables).

Relationship between number of universities per million inhabitants and country/region presence in the ARWU top 100 (dummy variables).

Values for each indicator by component (before excluding outliers).

Correlations and partial correlations

Values of indicators studied (2012).

Does a Country/Region’s Economic Status Affect Its Universities’ Presence in International Rankings?

Article Category: Research Paper

Published Online: Jun 07, 2019

Page range: 56 - 78

Received: Feb 05, 2019

Accepted: Mar 25, 2019

DOI: https://doi.org/10.2478/jdis-2019-0009

KeywordsAcademic Ranking of World Universities, Socio-economic indicators, Regression analysis

© 2019 Esteban Fernández Tuesta, Carlos Garcia-Zorita, Rosario Romera Ayllon, Elías Sanz-Casado, publsihed by Sceindo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Values for each indicator by component (after excluding outliers).

Effect of GDP per capita and HDI on countries/regions’ presence in the ARWU top 100 (dummy variables).

Relationship between number of universities per million inhabitants and country/region presence in the ARWU top 100 (dummy variables).

Values for each indicator by component (before excluding outliers).

Correlations and partial correlations

Values of indicators studied (2012).

Keywords
Academic Ranking of World Universities, Socio-economic indicators, Regression analysis