The European skill space: a cross-country analysis of path-dependent capability development

Figures & data

Figure 1. The European skill space and its construction.

Notes: A) The data for the skill space is constructed by matching the ESCO data on skill intensity in occupations (1a) and EU-LFS data on employment shares of occupations (1b), resulting in a skill-complementarity matrix (3) which captures the co-occurrence of skills across occupations. B) The skill space is visualized as a network structure based on the skill complementarity φ using only edge weights, representing skill similarity, greater than 0.4. The visualization reveals two large community clusters of complementary skills. The colouring reflects ESCO level 1 skill categories. Node size represents the skill's centrality, that is the shared similarity of that skill and all other skills.

Figure 2. Different patterns of skill specialization in 2011 and 2018: A) 2011: B) 2018.

Notes: Skill specialization can take three different shapes, with strong specialization in. a. socio-cognitive skills (Belgium, Switzerland, Denmark, Ireland, Iceland, Luxemburg, Norway, The Netherlands, Sweden, UK). b. sensory-physical skills (Czech, Spain, Italy, Croatia, Hungary, Portugal, Romania, Slovakia). c. entire skill space (Austria, Cypress, Germany, Estonia, Finland, France, Greece, Lithuania, Latvia). Filled nodes (black) represent the skills in which the given country has a comparative advantage, that is the skills which are used more than expected on average. Empty nodes (white) are skills in which the given country does not specialize.

Figure 3. Comparison of Czech Republic and Estonia, 2018.

Notes: Filled nodes (black) represent the skills in which the given country has a comparative advantage, that is the skills which are used more than expected on average. Empty nodes (white) are skills in which the given country does not specialize.

Table 1. Regional skill specialization based on the quantity of comparative advantages in 2018.

2018		West		East		Central		North		South
A	communication, collaboration, and creativity	44	(36.1%)	17	(11.6%)	13	(9.6%)	43	(32.8%)	24	(18.2%)
B	information skills	23	(18.9%)	17	(11.6%)	19	(14.1%)	19	(14.5%)	13	(9.8%)
C	assisting and caring	21	(17.2%)	6	(4.1%)	6	(4.4%)	19	(14.5%)	13	(9.8%)
D	management skills	20	(16.4%)	8	(5.4%)	4	(3.0%)	16	(12.2%)	5	(3.8%)
E	working with computers	10	(8.2%)	2	(1.4%)	3	(2.2%)	9	(6.9%)	2	(1.5%)
F	handling and moving	3	(2.5%)	45	(30.6%)	35	(25.9%)	9	(6.9%)	39	(29.5%)
G	constructing	0	(0.0%)	18	(12.2%)	17	(12.6%)	7	(5.3%)	16	(12.1%)
H	working with machinery and specialized equipment	1	(0.8%)	34	(23.1%)	38	(28.1%)	9	(6.9%)	20	(15.2%)
	Number countries	9		5		3		5		5
	Total RCAs	122		147		135		131		132

Figure 4. Average centrality of all skills in which the country has a comparative advantage.

Notes: The bubble size indicates the amount of comparative advantages in skills that a country has.

Figure 5. Probability of diversifying into a new skill at t + 1 based on the skill density at t.

Table 2. Econometric results for the estimation from skill density based on skill space.

Dependent variable $SkillRC{A}_{s,c,t+1}$	OLS (1)	Probit (2)	OLS (3)	Probit (4)	OLS (5)	Probit (6)
$SkillRC{A}_{s,c,t}$	0.818*** (0.003)	2.716*** (0.020)	0.815*** (0.003)	2.695*** (0.020)	0.815*** (0.003)	2.693*** (0.020)
$SkillDensit{y}_{s,c,t}$	0.398*** (0.012)	3.340*** (0.106)	0.424*** (0.013)	3.597*** (0.112)	0.426*** (0.013)	3.623*** (0.112)
ECI			−0.014*** (0.004)	−0.125*** (0.034)	−0.015*** (0.004)	−0.133*** (0.034)
Capital Stock (log)			−0.002 (0.001)	−0.013 (0.010)	−0.002 (0.001)	−0.012 (0.010)
Human capital			−0.002 (0.005)	−0.059 (0.040)	−0.002 (0.005)	−0.153 (0.040)
EduExpenditure			−0.011*** (0.002)	−0.093*** (0.018)	−0.011*** (0.002)	−0.099*** (0.0118)
TertiaryEdu			0.0003** (0.0002)	0.003** (0.001)	0.0004** (0.0002)	0.004*** (0.002)
High-tech share			−0.0002 (0.0002)	0.00002 (0.002)	−0.0002 (0.0002)	0.0001 (0.002)
Manufacturing ratio			−0.0006** (0.0003)	−0.005** (0.003)	−0.0006* (0.0003)	−0.005** (0.003)
R&D share			0.013*** (0.003)	0.121*** (0.021)	0.013*** (0.003)	0.124*** (0.022)
Year dummy	No	No	No	No	Yes	Yes
Constant	−0.102*** (0.005)	−2.978*** (0.047)	−0.029 (0.033)	−2.0331*** (0.283)	−0.031 (0.033)	−2.344*** (0.284)
Observations	52,200	52,200	52,200	52,200	52,200	52,200
R-squared	0.770		0.770		0.770
Log-likelihood		−11609.2		−11576.1		−11570.8

Notes: Standard errors are shown in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

Table 3. Econometric results for the estimation from skill density based on skill space, by skill groups.

	Socio-cognitive Skills (A, B, C, D)		Sensory-physical Skills (E, F, G, H)
Dependent variable $SkillRC{A}_{s,c,t+1}$	OLS (1)	Probit (2)	OLS (3)	Probit (4)
$SkillRC{A}_{s,c,t}$	0.824*** (0.004)	2.697*** (0.027)	0.796*** (0.004)	2.633*** (0.031)
$CogSkillDensit{y}_{s,c,t}$	0.499*** (0.020)	4.479*** (0.184)	0.224*** (0.027)	1.458*** (0.225)
$PhySkillDensit{y}_{s,c,t}$	0.004 (0.030)	0.188 (0.259)	0.480*** (0.021)	4.163*** (0.188)
ECI	−0.016*** (0.005)	−0.137*** (0.045)	−0.021*** (0.006)	−0.199*** (0.054)
Capital Stock (log)	−0.007*** (0.002)	−0.058*** (0.014)	−0.002 (0.002)	−0.021 (0.017)
Human capital	−0.011* (0.006)	−0.141*** (0.054)	−0.005 (0.007)	−0.117* (0.064)
EduExpenditure	−0.022*** (0.003)	−0.196*** (0.024)	−0.009*** (0.003)	−0.088*** (0.029)
TertiaryEdu	−0.0006** (0.0003)	−0.004* (0.002)	−0.001*** (0.0003)	−0.010*** (0.002)
High-tech share	−0.0001 (0.0003)	−0.001 (0.002)	−0.001*** (0.0003)	−0.010*** (0.003)
Manufacturing ratio	−0.0007* (0.0004)	−0.010*** (0.003)	−0.001** (0.0005)	−0.010** (0.004)
R&D share	0.022*** (0.003)	0.213*** (0.029)	0.002 (0.004)	0.019 (0.035)
Year dummy	Yes	Yes	Yes	Yes
Constant	0.118** (0.044)	−1.155*** (0.375)	0.031 (0.051)	−1.646*** (0.468)
Observations	29,520	29,520	22,680	22,680
R-squared	0.771		0.774
Log-likelihood		−6443.0		−4871.4

Notes: Standard errors are shown in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

Table 4. Econometric results for the relationship between economic complexity and skill specialization.

Dependent variable ECI	OLS (1)	F.E. (2)	OLS (3)	F.E. (4)
HHI	0.029*** (0.007)	0.013** (0.007)	0.028*** (0.007)	0.014** (0.007)
Capital Stock (log)	0.116*** (0.020)	0.706*** (0.230)	0.116*** (0.020)	0.834*** (0.252)
Human capital	0.664*** (0.077)	−0.329 (0.211)	0.667*** (0.078)	−0.293 (0.249)
EduExpenditure	0.042 (0.039)	−0.013 (0.028)	0.034 (0.040)	−0.022 (0.028)
TertiaryEdu	−0.006** (0.003)	−0.003 (0.004)	−0.005 (0.003)	−0.001 (0.005)
High-tech share	−0.005 (0.004)	0.006** (0.003)	−0.005 (0.004)	0.006** (0.003)
Manufacturing ratio	0.041*** (0.005)	−0.005 (0.005)	0.041*** (0.005)	−0.006 (0.005)
R&D share	0.217*** (0.042)	0.039 (0.042)	0.217*** (0.042)	0.053 (0.043)
Year dummy	No	No	Yes	Yes
Constant	−6.716*** (1.042)	−9.280*** (3.157)	−6.584 (1.068)	−11.571*** (3.755)
Observations	210	210	210	210
R-squared	0.750	0.211	0.752	0.236

Notes: Standard errors are shown in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

Fixed effect has been used following the result of the Hausman Test

Table

Group	ESCO level 1 classification	Examples (Narrower ESCO concepts)	Percentage in skill space
A	communication, collaboration and creativity	advising and consulting; communication, collaboration and creativity; creating artistic, visual or instructive materials; designing systems and products; liaising and networking; negotiating; obtaining information verbally; performing and entertaining; presenting information; promoting, selling and purchasing; solving problems; teaching and training; using more than one language; working with others; writing and composing	22.97
B	information skills	analysing and evaluating information and data; calculating and estimating; conducting studies, investigations and examinations; documenting and recording information; information skills; managing information; measuring physical properties; monitoring developments in area of expertise; monitoring, inspecting and testing; processing information	14.86
C	assisting and caring	assisting and caring; counselling; preparing and serving food and drinks; protecting and enforcing; providing general personal care; providing health care or medical treatments; providing information and support to the public and clients	10.81
D	management skills	allocating and controlling resources; building and developing teams; developing objectives and strategies; leading and motivating; making decisions; management skills; organizing, planning and scheduling work and activities; performing administrative activities; recruiting and hiring; supervising people	9.8
E	working with computers	accessing and analysing digital data; programming computer systems; setting up and protecting computer system;s using digital tools for collaboration, content creation and problem solving; using digital tools to control machinery; working with computers	4.73
F	handling and moving	assembling and fabricating products; cleaning handling and disposing of waste and hazardous materials; handling and moving; handling animals; making moulds, casts, models and patterns; moving and lifting; positioning materials, tools or equipment; sorting and packaging goods and materials; tending plants and crops; transforming and blending materials; using hand tools; washing and maintaining textiles and clothing	17.23
G	constructing	building and repairing structures; constructing; finishing interior or exterior of structures; installing interior or exterior infrastructure	5.74
H	working with machinery and specialized equipment	driving vehicles; installing, maintaining and repairing electrical, electronic and precision equipment; installing, maintaining and repairing mechanical equipment; operating aircraft; operating machinery for the extraction and processing of raw materials; operating machinery for the manufacture of products; operating mobile plant; operating watercraft; using precision instrumentation and equipment working; with machinery and specialized equipment	13.85

Source: Esco Classification – Skills and competences

Table

Top 10 skills in the densest part of the skill space
Code	Skill	Group	Paths
84	measuring dimensions and related properties	B	0.2021762
283	using precision measuring equipment	H	0.2016886
83	measuring physical properties	B	0.1978871
98	monitoring, inspecting, and testing	B	0.1958799
89	estimating resource needs	B	0.1954115
70	interpreting technical documentation and diagrams	B	0.1927094
164	making decisions	D	0.1906456
286	maintaining mechanical equipment	H	0.1882897
146	developing contingency and emergency response plans	D	0.1882621
96	evaluating systems, programmes, equipment and products	B	0.1874835
** B – information skills, D – management skills, H – working with machinery and specialized equipment

Table

Top 10 skills in the sparsest part of the skill space
Code	Skill	Group	Paths
130	preparing and serving food and drinks	C	0.0132432
173	using digital tools for collaboration, content creation, problem solving	E	0.0132618
2	negotiating	A	0.0194425
107	counseling	C	0.0230058
225	handling animals	F	0.0240738
154	performing administrative activities	D	0.0283299
180	sorting and packaging goods and materials	F	0.033479
200	tending plants and crops	F	0.0346276
212	fabricating tobacco products	F	0.0385133
63	solving problems	A	0.0400621

** A – communication, collaboration and creativity, C – assisting and caring, E-working with computers, F – handling and moving

Table

Dependent variable $SkillRC{A}_{s,c,t+1}$	Lag 1 (1)	Lag 1 (2)	Lag 1 (3)	Lag 2 (4)	Lag 2 (5)	Lag 3 (6)	Lag 3 (7)
$SkillRC{A}_{s,c,t}$	0.254*** (0.019)	0.281*** (0.019)	0.244*** (0.018)	0.347*** (0.019)	0.304*** (0.021)	0.368*** (0.025)	0.318*** (0.027)
$SkillRC{A}_{s,c,t-1}$				0.105*** (0.015)	0.078*** (0.016)	0.135*** (0.017)	0.105*** (0.018)
$SkillRC{A}_{s,c,t-2}$						0.027* (0.015)	0.016 (0.015)
$SkillDensit{y}_{s,c,t}$	2.017*** (0.056)	2.017*** (0.059)	2.150*** (0.057)	1.555*** (0.088)	1.780*** (0.098)	1.339*** (0.127)	1.617*** (0.141)
ECI		−0.046*** (0.011)	−0.058*** (0.011)	−0.041*** (0.008)	−0.050*** (0.009)	−0.041*** (0.008)	−0.054*** (0.009)
Capital Stock (log)		−0.006* (0.003)	−0.006* (0.003)	−0.006** (0.003)	−0.006** (0.003)	−0.005** (0.003)	−0.005* (0.003)
Human capital		−0.008 (0.013)	0.002 (0.013)	−0.003 (0.010)	−0.002 (0.011)	−0.003 (0.010)	0.011 (0.011)
EduExpenditure		−0.037*** (0.005)	−0.052*** (0.006)	−0.042*** (0.005)	−0.051*** (0.005)	−0.039*** (0.005)	−0.049*** (0.006)
TertiaryEdu		0.001 (0.001)	0.002*** (0.001)	0.002*** (0.0003)	0.002*** (0.0004)	0.001*** (0.0003)	0.002*** (0.0004)
High-tech share		0.0003 (0.0006)	−0.0001 (0.0006)	0.0001 (0.0004)	−0.0001 (0.0005)	−0.00003 (0.0004)	−0.0002
							(0.0005)
Manufacturing ratio		−0.005*** (0.001)	−0.005*** (0.001)	−0.004*** (0.001)	−0.005*** (0.001)	−0.004*** (0.001)	−0.004*** (0.001)
R&D share		0.050*** (0.007)	0.058*** (0.007)	0.047*** (0.006)	0.055*** (0.006)	0.044*** (0.006)	0.054*** (0.007)
Year dummy	No	No	Yes	No	Yes	No	Yes
Constant	−0.602*** (0.019)	−0.296*** (0.090)	−0.350*** (0.091)	−0.189*** (0.071)	−0.255*** (0.074)	−0.173** (0.071)	−0.262*** (0.077)
Observations	52,200	52,200	52,200	46,660	46,660	37,120	37,120
AR(1) p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AR(2) p-value	0.000	0.000	0.000	0.160	0.008	0.341	0.845

Notes: Standard errors are shown in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

AR(#) gives the p-value of the Arellano-Bond test for AR(#) in first differences

Table

Dependent variable $WRC{A}_{s,c,t+1}$	OLS (1)	Arellano-Bond (2)	OLS (3)	Arellano-Bond (4)
$WRC{A}_{s,c,t}$	0.971*** (0.001)	0.749*** (0.032)	0.971*** (0.001)	0.749*** (0.032)
$SkillDensit{y}_{s,c,t}$	0.029*** (0.003)	0.356*** (0.046)	0.032*** (0.003)	0.382*** (0.050)
ECI			−0.001 (0.001)	−0.013*** (0.005)
Capital Stock (log)			−0.0001 (0.0003)	−0.001 (0.001)
Human capital			0.0002 (0.001)	0.002 (0.003)
EduExpenditure			−0.001 (0.001)	−0.011*** (0.002)
TertiaryEdu			0.00005 (0.00005)	0.001*** (0.0001)
High-tech share			−0.0001 (0.0001)	−0.0001 (0.0001)
Manufacturing ratio			−0.0001 (0.0001)	−0.001*** (0.0003)
R&D share			0.001 (0.001)	0.012*** (0.002)
Year dummy	No	No	Yes	Yes
Constant	0.015*** (0.001)	0.086*** (0.011)	0.020** (0.009)	0.131** (0.026)
Observations	52,200	52,200	52,200	52,200
R-squared	0.961		0.961
AR(1) p-value		0.000		0.000
AR(2) p-value		0.296		0.291

Notes: Weighted RCA of a country c for skill s among all n( = 290) skills is calculated as $WRC{A}_{s,c}=RC{A}_{s,c}/{\displaystyle \frac{1}{n}\sum _{s}RC{A}_{s,c}}$

Standard errors are shown in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

AR(#) gives the p-value of the Arellano-Bond test for AR(#) in first differences

Data availability statements

The skill data that support the findings of this study are available from European Skills, Competences, Qualifications and Occupations (ESCO) database, https://ec.europa.eu/esco/portal/download, and EU Labour Force Survey (LFS) database https://ec.europa.eu/eurostat/web/lfs. Country-specific control variables are from Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), ‘The Next Generation of the Penn World Table’ American Economic Review, 105(10), 3150–3182, available for download at www.ggdc.net/pwt.