Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification

Figures & data

Figure 1. European knowledge space.

Figure 2. Branching opportunities of European regions.

Table 1. Entry models – full sample.

	Dependent variable: Entry (= 1) | 1990–2009
	Baseline	Complexity	Controls	Full model	Full model (FE)
	(1)	(2)	(3)	(4)	(5)
Constant	0.1632872*** (0.0005543)	0.1632945*** (0.0005543)	0.1498963*** (0.0005242)	0.1639320*** (0.0005722)	–0.0117608 (0.0255653)
Relatedness Density	0.0042477*** (0.0000388)	0.0042494*** (0.0000388)		0.0041635*** (0.0000419)	0.0037696*** (0.0000449)
Knowledge Complexity		0.0000459* (0.0000199)		0.0000354 (0.0000211)	–0.0000575** (0.0000215)
Population (log)			0.0322163*** (0.0008129)	0.0172538*** (0.0008150)	–0.1155466*** (0.0148724)
GDP per capita			0.0000020*** (0.0000001)	0.0000005*** (0.0000001)	0.0000017*** (0.0000003)
Population Density			–0.0000090*** (0.0000007)	–0.0000030*** (0.0000007)	0.0000198 (0.0000122)
Technological stock			–0.0000022*** (0.0000001)	–0.0000022*** (0.0000001)	–0.0000023*** (0.0000002)
Technological size			0.0000004** (0.0000002)	0.00000005 (0.0000002)	0.0000013*** (0.0000002)
Region fixed effects	No	No	No	No	Yes
Time fixed effects	No	No	No	No	Yes
Observations	498,785	498,785	466,814	466,814	466,814
R^2	0.0303005	0.0303106	0.0040004	0.0306804	0.0371538
Adjusted R^2	0.0302985	0.0303068	0.0039897	0.0306659	0.0366399

Notes: The dependent variable entry equals 1 if a region r gains a new relative technological advantage (RTA) in a given technology i during the corresponding five-year window; and 0 otherwise. All independent variables are mean centred and lagged by one period. Heteroskedasticity-robust standard errors (clustered at the region and technology level) are shown in parentheses.

Coefficients are statistically significant at the *p < 0.05, **p < 0.01 and ***p < 0.001 level.

Table 2. Entry models by level of relatedness.

	Dependent variable: Entry (= 1) | 1990–2009
	High relatedness	Low relatedness	High relatedness	Low relatedness	High relatedness	Low relatedness
	(1)	(2)	(3)	(4)	(5)	(6)
Constant	0.3669312*** (0.0023488)	0.0309562*** (0.0006430)	0.3614363*** (0.0026666)	0.0405249*** (0.0009141)	0.2306594 (0.1847726)	0.0903739** (0.0327663)
Knowledge Complexity	0.0004628*** (0.0001007)	–0.0000389 (0.0000272)	0.0002671* (0.0001127)	–0.0000062 (0.0000395)	0.0002526* (0.0001124)	–0.0000359 (0.0000419)
Population (log)			0.0433384*** (0.0044990)	0.0224518*** (0.0014247)	–0.0657516 (0.0934813)	0.0488210* (0.0200723)
GDP per capita			0.0000004 (0.0000004)	0.0000015*** (0.0000001)	0.0000016 (0.0000016)	0.0000002 (0.0000005)
Population Density			0.0000016 (0.0000034)	–0.0000057*** (0.0000015)	0.0000252 (0.0000569)	–0.0000202 (0.0000281)
Technological stock			–0.0000026*** (0.0000004)	0.0000002 (0.0000002)	–0.0000036*** (0.0000007)	0.0000003 (0.0000004)
Technological size			0.0000088*** (0.0000012)	0.0000021** (0.0000007)	0.0000139*** (0.0000013)	0.0000018* (0.0000007)
Region fixed effects	No	No	No	No	Yes	Yes
Time fixed effects	No	No	No	No	Yes	Yes
Observations	42,164	72,557	34,309	47,029	34,309	47,029
R^2	0.0005119	0.0000281	0.0053447	0.0127176	0.0584039	0.0334063
Adjusted R^2	0.0004882	0.0000143	0.0051707	0.0125916	0.0515479	0.0282820

Notes: High relatedness models only include the top 10% region–technology observations in terms of relatedness density. Low relatedness models only include the bottom 10% region–technology observations in terms of relatedness density. The dependent variable entry equals 1 if a region r gains a new relative technological advantage (RTA) in a given technology i during the corresponding five-year window; and 0 otherwise. All independent variables are mean centred and lagged by one period. Heteroskedasticity-robust standard errors (clustered at the region and technology level) are shown in parentheses.

Coefficients are statistically significant at the *p < 0.05, **p < 0.01 and ***p < 0.001 level.

Table 3. Growth models – full sample.

	Dependent variable: Technological growth | 1990–2009
	Baseline	Complexity	Controls	Full model	Full model (FE)
	(1)	(2)	(3)	(4)	(5)
Constant	13.7038900*** (0.1707395)	13.7207500*** (0.1707130)	13.5505400*** (0.1773364)	13.5218000*** (0.1767446)	73.6810700*** (7.4333840)
Relatedness Density	0.4642356*** (0.0101046)	0.4650504*** (0.0101016)		0.3519811*** (0.0113171)	0.2038730*** (0.0119662)
Knowledge Complexity		0.2083142*** (0.0079042)		0.1811793*** (0.0082521)	0.1236107*** (0.0079222)
Population (log)			15.6830000*** (0.2933130)	13.9970400*** (0.2957986)	57.4033100*** (4.4582030)
GDP per capita			0.0004739*** (0.0000201)	0.0003251*** (0.0000205)	0.0000061 (0.0000892)
Population Density			–0.0039671*** (0.0002246)	–0.0033924*** (0.0002242)	–0.0031405 (0.0032979)
Technological stock			–0.0004838*** (0.0000325)	–0.0005061*** (0.0000326)	–0.0051211*** (0.0001068)
Technological size			0.0010760*** (0.0000560)	0.0007250*** (0.0000565)	0.0016523*** (0.0000584)
Region fixed effects	No	No	No	No	Yes
Time fixed effects	No	No	No	No	Yes
Observations	556,721	556,721	521,175	521,175	521,175
R^2	0.0039793	0.0055811	0.0072392	0.0103975	0.0671133
Adjusted R^2	0.0039775	0.0055776	0.0072297	0.0103842	0.0666674

Notes: The dependent variable growth corresponds to the rate of technological growth (growth in the number of claims) of a technology i in a region r from period t to period t + 1. All independent variables are mean centred and lagged by one period. Heteroskedasticity-robust standard errors (clustered at the region and technology level) are shown in parentheses.

Coefficients are statistically significant at the *p < 0.05, **p < 0.01 and ***p < 0.001 level.

Table 4. Growth models by level of relatedness.

	Dependent variable: Technological growth | 1990–2009
	High relatedness	Low relatedness	High relatedness	Low relatedness	High relatedness	Low relatedness
	(1)	(2)	(3)	(4)	(5)	(6)
Constant	53.7695700*** (0.7609844)	–6.0870230*** (0.1377738)	49.6868700*** (0.8003883)	–8.4233190*** (0.2020142)	–31.0214900 (60.1171500)	–10.3398200 (5.9970820)
Knowledge Complexity	0.3256727*** (0.0340018)	–0.0096395 (0.0069824)	0.2581051*** (0.0361123)	–0.0107926 (0.0104075)	0.2276855*** (0.0344880)	–0.0305587** (0.0101187)
Population (log)			28.4596000*** (1.4859160)	–3.3740520*** (0.3181761)	–26.7761900 (31.0434900)	–2.6496500 (4.0115730)
GDP per capita			0.0001111 (0.0001073)	–0.0003621*** (0.0000220)	0.0037211*** (0.0005146)	0.0001280 (0.0000970)
Population Density			–0.0048569*** (0.0009146)	0.0006342* (0.0002874)	–0.0729367*** (0.0144477)	–0.0052360 (0.0046896)
Technological stock			–0.0020091*** (0.0001021)	–0.0001944* (0.0000909)	–0.0080889*** (0.0003040)	–0.0015154*** (0.0002594)
Technological size			–0.0012314*** (0.0001257)	–0.0013100*** (0.0002712)	0.0001989 (0.0001165)	–0.0011047*** (0.0002697)
Region fixed effects	No	No	No	No	Yes	Yes
Time fixed effects	No	No	No	No	Yes	Yes
Observations	63,797	74,199	54,992	48,659	54,992	48,659
R^2	0.0017529	0.0000365	0.0115695	0.0155464	0.1329042	0.0509171
Adjusted R^2	0.0017372	0.0000230	0.0114617	0.0154250	0.1289760	0.0460551

Notes: High relatedness models only include the top 10% region–technology observations in terms of relatedness density. Low relatedness models only include the bottom 10% region–technology observations in terms of relatedness density. The dependent variable growth corresponds to the rate of technological growth (growth in the number of claims) of a technology i in a region r from period t to period t + 1. All independent variables are mean centred and lagged by one period. Heteroskedasticity-robust standard errors (clustered at the region and technology level) are shown in parentheses.

Coefficients are statistically significant at the *p < 0.05, **p < 0.01 and ***p < 0.001 level.

Figure 3. Framework for smart specialization.

Figure 4. Application of the framework: (a) Île-de-France (France, FR10); (b) Noord-Brabant (the Netherlands, NL41); (c) Lancashire (UK, UKD4); and (d) Extremadura (Spain, ES43).