Evaluation of location’s attractiveness for business growth in smart development

Figures & data

Figure 1. The model of factors determining the location’s attractiveness for business (LAB) development in the context of smart growth.

Table 1. Quantitative characteristics of factors determining the location’s attractiveness for business development in the context of smart growth.

Attractiveness in regard to the intelligence of the location	Quantitative indicators characterizing attractiveness factors (variable abbreviation)
1.1. Local and international image of a location 1.2. Location development strategy 1.3. The friendliness of the local authority 1.4. The competence of the local government's strategic insight 1.5. Location management efficiency 1.6. The attractiveness of business environment 1.7. Tax policy 1.8. Level of corruption	1.1.1. GDP per capita (GDPCAP) 1.6.1. Foreign direct investment per capita (FDICAP) 1.6.2. Tangible investments per capita (matinv1gyv)
Attractiveness in regard to networking and infrastructure of the location	Quantitative indicators characterizing attractiveness factors
2.1. Accessibility and speed of the Information and Communication Technology network 2.2. The convenience of availability at national and international level 2.3. The efficiency of energy supply 2.4. Functioning of cooperation and competence networks between government and individual groups of society	2.2.1. The length of car roads (national and local) (autokelilg)
Attractiveness in regard to the coherence of the location	Quantitative indicators characterizing attractiveness factors
3.1. Implementation of the principles of sustainable development 3.2. Pollution 3.3. Implementation of social responsibility principles	3.2.1. Emission of pollutants into the atmosphere from stationary sources of pollution in 1 sq. km. (tersismet) 3.2.2. The environmental pollution taxes per capita are paid and counted into municipal budgets (mokaplinka)
Attractiveness in regard to the digitalisation of the location	Quantitative indicators characterizing attractiveness factors
4.1. High level of computer literacy and ICT usage 4.2. Accessibility of intellectual electronic services 4.3. Electronic terrain	4.1.1. Households that have internet access (namukinternet) 4.1.2. Households that have a personal computer (namukkomp) 4.1.3. Part of people who used internet every day throughout the last three months (asminternet) 4.1.4. Part of companies, operating in the field of information and communication activities (imIRV)
Attractiveness of the location in regard to learning	Quantitative indicators characterizing attractiveness factors
5.1. The systems of science and education, lifelong learning and lifelong development are developed 5.2. The image of the learner and knowledge centre is formed 5.3. High-skilled workers 5.4. Adequacy of the workforce that meets business needs	5.1.1. Number of people pursuing higher education (university and college students) per 1000 inhabitants (aukstissilavstudent) 5.2.1. Part of population who have completed tertiary education (aukstissilav) 5.2.2. Average consumption expenditure per household member per month for education (vidislsviet) 5.4.1. Part of the working age population (darbingyv) 5.4.2. The population's domestic and international migration balance per 1000 inhabitants (migrac saldo) 5.4.3. Average gross monthly earnings (vidmenbruto) 5.4.4. Unemployment rate (nedarblyg)
Attractiveness in regard to the mobility of the location	Quantitative indicators characterizing attractiveness factors
6.1. The expedition of location management 6.2. The financial stability and capacity of public authorities 6.3. The purchasing power of the location and the level of material well-being 6.4. Openness and integrity of the location's economy in the national and international economy 6.5. High level of entrepreneurship	6.2.1. The taxes per capita are paid and counted into municipal budgets (mokesc1gyv) 6.2.2. Municipal budget expenditure and income ratio (islpajsantyk) 6.2.3. General government gross debt compared to GDP of the city (skola) 6.2.4. Part of the population living in households experiencing economic difficulties (eksunkum) 6.2.5. Average annual ratio of recipients of social benefits and residents (socpasalposgavej) 6.2.6. The number of social risk families per 1000 people (socrizseimos) 6.2.7. Pension recipients per 1000 people of working age (pensijasgaunanciudalis) 6.3.1. The ratio of income gap if compared to the capital (pajskirtsostine) 6.4.1. Share of export of domestic goods in GDP (export) 6.4.2. Revenue from export per capita (pajexport1gyv) 6.5.1. Number of economic entities operating per 1000 inhabitants (veikianciosim) 6.5.2. The number of companies undertaking bankruptcy per 1000 operating companies (bankrutim)
Attractiveness in regard to the innovativeness of the location	Quantitative indicators characterizing attractiveness factors
7.1. Environment that encourages innovation 7.2. The abundance of innovative companies in the location 7.3. Investments that public authorities make in research and innovation	7.1.1. Expenditure R&D ratio to GDP (islMTEP) 7.2.1. The part of companies that have implemented innovations (iminovac) 7.2.2. The part of companies that have implemented technological innovations (imtechnologinov) 7.3.1. Expenditure R&D in higher education sectors (islMTEPaukstmoksl) 7.3.2. Expenditure R&D in government sectors (islMTEPvaldzia)
Attractiveness in regard to how much the location is based on knowledge	Quantitative indicators characterizing attractiveness factors
8.1. Active cooperation between science, business and government 8.2. Productivity in the private sector	8.1.1. Employees, involved in the R&D higher education and government sectors (darbuotMTEP) 8.2.1. Added value, created during an hour actually worked (pridetvert1val)

Table 2. Descriptive general statistics of the data used.

Criterions	FDICAP	PAJSKIRTSOSTINE	MIGRACSALDO	PRIDETVERT1VAL	SOCPASALPOSGAVEJ	VEIKIANCIOSIM	NEDARBLYG	VIDMENBRUTO	GDPCAP
Mean	2261	80	−2995	10	5	8325	11	521	85
Median	842	78	−2217	10	4	4710	11	528	77
Maximum	11830	100	1931	19	11	35018	24	804	187
Minimum	63	45	−17612	6	1	1600	2	271	31
Std. Dev.	2914	11	2796	3	3	8345	5	107	30
Skewness	1.82	−0.12	−2.16	1.13	0.47	1.70	0.32	−0.14	1.22
Kurtosis	5	3	10	4	2	5	2	3	4
Jarque-Bera	85	0	339	27	10	70	4	0	37
Probability	0.0000	0.7966	0.0000	0.0000	0.0064	0.0000	0.1176	0.8328	0.0000
Sum	248746	8807	−329471	1144	518	915696	1219	57262	9325
Sum Sq. Dev.	9.25E + 08	1.38E + 04	8.52E + 08	7.48E + 02	1.13E + 03	7.59E + 09	3.12E + 03	1.25E + 06	9.53E + 04
Observations	110	110	110	110	110	110	110	110	110

Table 3. Dependent Variable: GDPCAP. Example of procedure of independent variables selection.

	Independent variables
Criterions	FDICAP	FDICAP(-1)	FDICAP(-2)	FDICAP(-3)	PAJSKIRTSOSTINE	PAJSKIRTSOSTINE(-1)	PAJSKIRTSOSTINE(-2)	PAJSKIRTSOSTINE(-3)
C-Coefficient	85.07349	87.27307	87.38923	88.39376	90.09455	85.36460	80.81417	79.66703
C-Std. Error	0.716782	0.611943	0.643353	0.697415	4.289146	3.626976	3.845363	4.150377
C-t-Statistic	118.6881	142.6164	135.8341	126.7448	21.00524	23.53602	21.01600	19.19513
C-Prob	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Coefficient	−0.000135	0.000234	0.000375	0.000227	−0.066524	0.030470	0.093246	0.116049
Std. Error	0.000198	0.000170	0.000175	0.000187	0.053117	0.044976	0.047808	0.051628
t-Statistic	−0.682159	1.376159	2.149189	1.219130	−1.252409	0.677468	1.950427	2.247798
Prob	0.4969	0.1726	0.0350	0.2274	0.2137	0.5001	0.0551	0.0282
R-squared	0.967811	0.977921	0.978332	0.977518	0.968204	0.977527	0.978096	0.978714
Adjusted R-squared	0.960578	0.972677	0.972839	0.971354	0.961058	0.972190	0.972543	0.972877
S.E. of regression	5.872073	4.773468	4.780347	4.906746	5.836200	4.815843	4.806312	4.774476
Sum squared resid	3068.830	1822.880	1622.472	1492.722	3031.450	1855.388	1640.145	1413.328
Log likelihood	−339.1547	−287.0440	−257.8398	−230.5683	−338.4806	−287.9278	−258.3273	−228.3821
F-statistic	133.7980	186.4899	178.0967	158.5739	135.5027	183.1487	176.1351	167.6866
Prob(F-statistic)	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Mean dependent var	84.76818	87.80000	88.24889	88.91875	84.76818	87.80000	88.24889	88.91875
S.D. dependent var	29.57486	28.87813	29.00584	28.99065	29.57486	28.87813	29.00584	28.99065
Akaike info criterion	6.548267	6.140880	6.151996	6.214207	6.536011	6.158556	6.162830	6.159553
Schwarz criterion	7.063813	6.661914	6.679733	6.750163	7.051557	6.679590	6.690567	6.695509
Hannan-Quinn criter.	6.757375	6.351751	6.364810	6.429087	6.745119	6.369428	6.375645	6.374433
Durbin-Watson stat	0.788439	0.667566	0.802348	0.697179	0.785277	0.620551	0.750807	0.720329

Table 4. Statistics of selected independent variables. Dependent Variable: GDPCAP.

	Independent variables
Criterions	FDICAP(-2)	PAJSKIRTSOSTINE(-3)	MIGRACSALDO(-1)	PRIDETVERT1VAL(-3)	SOCPASALPOSGAVEJ(-3)	VEIKIANCIOSIM(-3)	NEDARBLYG(-2)	VIDMENBRUTO(-2)
C-Coefficient	87.38923	79.66703	89.11084	82.57206	91.37346	87.31737	89.38319	73.21752
C-Std. Error	0.643353	4.150377	0.799369	2.427619	1.782590	0.738433	2.142937	4.413361
C-t-Statistic	135.8341	19.19513	111.4764	34.01360	51.25881	118.2468	41.71060	16.58997
C-Prob	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Coefficient	0.000375	0.116049	0.000437	0.615337	−0.540126	0.000193	−0.101701	0.029121
Std. Error	0.000175	0.051628	0.000215	0.229785	0.373383	6.33E-05	0.186415	0.008499
t-Statistic	2.149189	2.247798	2.029553	2.677878	−1.446574	3.041910	−0.545559	3.426349
Prob	0.0350	0.0282	0.0457	0.0095	0.1531	0.0034	0.5871	0.0010
R-squared	0.978332	0.978714	0.978505	0.979366	0.977731	0.979969	0.977019	0.980197
Adjusted R-squared	0.972839	0.972877	0.973400	0.973708	0.971625	0.974476	0.971193	0.975176
S.E. of regression	4.780347	4.774476	4.709906	4.700796	4.883482	4.631602	4.923085	4.570013
Sum squared resid	1622.472	1413.328	1774.657	1370.044	1478.601	1330.008	1720.810	1482.837
Log likelihood	−257.8398	−228.3821	−285.7035	−227.1379	−230.1881	−225.9516	−260.4878	−253.7901
F-statistic	178.0967	167.6866	191.6717	173.0996	160.1231	178.4201	167.6936	195.2390
Prob(F-statistic)	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Mean dependent var	88.24889	88.91875	87.80000	88.91875	88.91875	88.91875	88.24889	88.24889
S.D. dependent var	29.00584	28.99065	28.87813	28.99065	28.99065	28.99065	29.00584	29.00584
Akaike info criterion	6.151996	6.159553	6.114070	6.128448	6.204702	6.098790	6.210840	6.062002
Schwarz criterion	6.679733	6.695509	6.635104	6.664404	6.740658	6.634746	6.738578	6.589739
Hannan-Quinn criter.	6.364810	6.374433	6.324941	6.343328	6.419582	6.313671	6.423655	6.274817
Durbin-Watson stat	0.802348	0.720329	0.658301	0.787431	0.694680	0.769456	0.643451	0.928695

Table 5. Panel estimation of selected independent variables relation to GDP per capita.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	69.53114	12.11060	5.741348	0.0000
FDICAP(-2)	−0.000111	0.000315	−0.354366	0.7244
NEDARBLYG(-2)	−0.363834	0.216739	−1.678677	0.0989
PAJSKIRTSOSTINE(-3)	−0.035541	0.078535	−0.452545	0.6527
PRIDETVERT1VAL(-3)	0.165256	0.406777	0.406256	0.6861
SOCPASALPOSGAVEJ(-3)	1.505215	0.657582	2.289014	0.0259
VEIKIANCIOSIM(-3)	0.000330	0.000174	1.896886	0.0631
VIDMENBRUTO(-2)	0.031350	0.016248	1.929483	0.0588
MIGRACSALDO(-1)	0.000295	0.000248	1.191062	0.2387
R-squared	0.984036	Mean dependent var		88.91875
Adjusted R-squared	0.977070	S.D. dependent var		28.99065
S.E. of regression	4.389911	Akaike info criterion		6.046802
Sum squared resid	1059.923	Schwarz criterion		6.791185
Log likelihood	−216.8721	Hannan-Quinn criter.		6.345246
F-statistic	141.2640	Durbin-Watson stat		1.051979
Prob(F-statistic)	0.000000

Figure 2. Panel estimation of selected independent variables relation to GDP per capita: actual, fitted and residual.