Economic modelling to evaluate Smart Specialisation: an analysis of research and innovation targets in Southern Europe

Figures & data

Table 1. Research and development (R&D) personnel targets and estimated total factor productivity (TFP) shocks.

Region	Baseline R&D personnel	Target R&D personnel	Target % increase
North Aegean (EL41)	743	750	0.94%
Western Macedonia (EL53)	473	578	22.15%
Central Greece (EL64)	1030	1132	9.86%
Piemonte (ITC1)	28,247	35,309	25.00%
Liguria (ITC3)	7411	7890	6.47%
Abruzzo (ITF1)	2920	4884	67.26%
Campania (ITF3)	14,692	16,009	8.97%
Calabria (ITF6)	1895	2694	42.17%
Sardinia Sardegna (ITG2)	3747	4223	12.70%
Emilia-Romagna (ITH5)	24,576	41,894	70.47%
Toscana (ITI1)	15,136	17,550	15.95%
Norte (PT11)	14,913	16,020	7.42%
Algarve (PT15)	762	830	8.87%
Centro (PT16)	9192	13,024	41.69%
Área Metropolitana de Lisboa (PT17)	20,158	21,260	5.47%
Alentejo (PT18)	1028	1248	21.37%
Principado de Asturias (ES12)	3372	3570	5.88%
Aragón (ES24)	5534	7137	28.97%
Castilla-La Mancha (ES42)	2777	3204	15.38%
Extremadura (ES43)	2126	4252	100.00%
Illes Balears (ES53)	1956	2290	17.07%
Región de Murcia (ES62)	5290	6677	26.21%
Canarias (ES70)	3308	4153	25.53%

Source: Eurostat (baseline R&D personnel) and authors’ own calculations based on Operational Programmes’ (OPs) targets.

Table 2. Stochastic frontier regression results.

	(I)	(II)	(III)	(IV)
Frontier – equation (1)
Log of capital	0.748**	0.804**	0.745**	0.685**
	(32.09)	(37.73)	(26.44)	(31.94)
Log of labour	0.293**	0.212**	0.266**	0.459**
	(12.25)	(9.34)	(5.11)	(11.17)
Intercept	0.477**	0.375**	0.824*	0.0975
	(4.44)	(4.08)	(2.18)	(0.33)
Inefficiency: $\ln {\sigma }_u^2$ – equation (2)
Log of R&D personnel		−0.571**		−2.995**
		(−9.36)		(−10.79)
Intercept	−3.209**	0.903*	−4.393**	25.500**
	(−19.47)	(2.18)	(−40.58)	(9.26)
Error: $\ln {\sigma }_v^2$
Intercept	−4.204**	−4.254**	−6.366**	−7.346**
	(−30.02)	(−45.33)	(−34.36)	(−61.28)
Fix effects	No	No	Yes	Yes
Observations	960	954	960	954
Log-likelihood	338.34	396.16	1112.76	1449.26

Note: t-statistics are shown in parenthesis. **, *Statistical significance at p < 0.001 and < 0.05 levels, respectively. In columns (II) and (IV), six observations are lost due to the unavailability of data for the research and development (R&D) personnel variable for the autonomous cities of Ceuta and Melilla (ES63 and ES64) between 2000 and 2002.

Table 3. Estimated total factor productivity (TFP) shocks.

Region	Cumulative TFP shock (%)	Region	Cumulative TFP shock (%)
North Aegean (EL41)	0.05	Algarve (PT15)	0.05
Western Macedonia (EL53)	0.57	Centro (PT16)	0.41
Central Greece (EL64)	0.07	Área Metropolitana de Lisboa (PT17)	0.02
Piemonte (ITC1)	0.29	Alentejo (PT18)	0.11
Liguria (ITC3)	0.18	Principado de Asturias (ES12)	0.13
Abruzzo (ITF1)	0.35	Aragón (ES24)	0.10
Campania (ITF3)	0.11	Castilla-La Mancha (ES42)	0.12
Calabria (ITF6)	0.56	Extremadura (ES43)	1.48
Sardinia Sardegna (ITG2)	0.12	Illes Balears (ES53)	0.27
Emilia-Romagna (ITH5)	1.02	Región de Murcia (ES62)	0.29
Toscana (ITI1)	0.22	Canarias (ES70)	0.02
Norte (PT11)	0.07

Source: Authors’ own estimates.

Figure 1. Macroeconomic impacts of Thematic Objective 1 (TO1) targets’ achievement in the 23 analysed regions, percentage deviations from the baseline.

Source: Computer simulations with the RHOMOLO model.

Table 4. Macroeconomic impacts of Thematic Objective 1 (TO1) targets’ achievement in the 23 analysed regions, percentage deviations from the baseline projections (cumulative changes 2014–23).

Region	TFP shock	GDP	Household consumption	CPI	Employment	Exports/imports
North Aegean (EL41)	0.05	0.03	0.01	−0.01	0.01	0.04
Western Macedonia (EL53)	0.57	0.39	0.22	−0.05	0.24	0.11
Central Greece (EL64)	0.07	0.05	0.04	−0.01	0.03	0.01
Principado de Asturias (ES12)	0.13	0.10	0.06	−0.03	0.05	0.07
Aragón (ES24)	0.10	0.08	0.05	−0.03	0.04	0.05
Castilla-La Mancha (ES42)	0.12	0.09	0.06	−0.03	0.05	0.08
Extremadura (ES43)	1.48	1.06	0.72	−0.26	0.62	1.18
Illes Balears (ES53)	0.27	0.20	0.13	−0.05	0.12	0.13
Región de Murcia (ES62)	0.29	0.22	0.15	−0.06	0.13	0.13
Canarias (ES70)	0.02	0.01	< 0.01	−0.02	0.00	0.01
Piemonte (ITC1)	0.29	0.16	0.05	−0.10	0.02	0.44
Liguria (ITC3)	0.18	0.07	−0.01	−0.07	−0.01	0.31
Abruzzo (ITF1)	0.35	0.17	0.02	−0.10	<−0.01	0.53
Campania (ITF3)	0.11	0.03	−0.03	−0.06	−0.02	0.19
Calabria (ITF6)	0.56	0.28	0.10	−0.10	0.03	0.53
Sardinia Sardegna (ITG2)	0.12	0.03	−0.03	−0.06	−0.02	0.21
Emilia-Romagna (ITH5)	1.02	0.56	0.31	−0.11	0.14	0.15
Toscana (ITI1)	0.22	0.12	0.07	−0.05	0.04	< 0.01
Norte (PT11)	0.07	0.05	0.03	−0.02	0.02	0.03
Algarve (PT15)	0.05	0.04	0.03	−0.03	0.02	0.01
Centro (PT16)	0.41	0.27	0.14	−0.06	0.11	0.16
Área Metropol. Lisboa (PT17)	0.02	0.02	0.01	−0.02	0.01	0.01
Alentejo (PT18)	0.11	0.08	0.05	−0.03	0.04	0.05

Note: CPI; consumer price index; GDP, gross domestic product; TFP, total factor productivity.

Figure 2. Gross domestic product (GDP) decomposition by demand components in the 23 analysed regions, percentage deviations from the baseline.

Source: Computer simulations with the RHOMOLO model.

Figure 3. Sectoral impacts of TO1 targets’ achievement in the 23 analysed regions, percentage deviations from the baseline.

Source: Computer simulations with RHOMOLO model.