Digital transformation as a means to attract innovative establishments to municipalities

Figures & data

Figure 1. Conceptualisation of research question. Notes: The arrows indicate expected causality. Source: Own illustration.

Figure 2. Declining municipalities in Sweden. Notes: There are 290 municipalities in Sweden. Approximately one out of five experience a decline in population during the years 2010–2021 (darker shade). Source: Statistics Sweden.

Table 1. Development of number of innovative establishments.

	ICT services		R&D		High technology manufacturing
	Mean	Total	Mean	Total	Mean	Total
All municipalities
2010	141.1	40,905	11.2	3,241	5.9	1,707
2011	168.3	48,801	13.1	3,799	6.4	1,852
2012	170.4	49,402	13.5	3,913	6.2	1,805
2013	169.9	49,275	13.7	3,982	6.0	1,745
2014	176.1	51,083	14.1	4,091	6.1	1,766
2015	180.7	52,402	14.4	4,184	6.2	1,795
2016	186.1	53,982	14.6	4,231	6.3	1,824
2017	190.8	55,334	14.9	4,312	6.1	1,760
2018	197.8	57,355	15.1	4,373	6.0	1,738
2019	204.1	59,182	15.2	4,398	5.9	1,724
2020	209.9	60,861	15.8	4,576	5.9	1,709
2021	217.8	63,159	16.2	4,688	5.9	1,722
Annual growth (mean), per cent		4.0		3.4		0.1
Average size, employees		3		7		23
Establishments, size-class 1–9 employees
2010	35.3	10,232	3.1	901	2.0	571
2011	40.0	11,603	3.2	925	2.0	584
2012	43.6	12,653	3.3	946	2.0	582
2013	45.3	13,145	3.3	956	2.0	576
2014	48.4	14,044	3.5	1,001	2.0	581
2015	50.8	14,742	3.6	1,033	2.0	570
2016	52.5	15,217	3.6	1,039	1.9	561
2017	57.9	16,785	3.8	1,090	2.0	568
2018	63.6	18,443	3.9	1,138	1.9	554
2019	68.6	19,900	4.0	1,157	1.9	561
2020	62.1	17,998	3.4	977	1.6	478
2021	66.0	19,143	3.5	1,018	1.6	476
Annual growth (mean), per cent		5.9		1.1		−1.6
Establishments in 62 declining municipalities
2010	16.8	1,040	1.2	75	1.4	88
2011	19.0	1,180	1.5	96	1.6	99
2012	19.0	1,177	1.5	96	1.6	98
2013	18.8	1,167	1.5	90	1.6	97
2014	19.0	1,179	1.5	90	1.5	96
2015	19.0	1,180	1.5	94	1.5	94
2016	19.3	1,199	1.5	91	1.5	94
2017	19.5	1,210	1.5	91	1.5	91
2018	20.1	1,245	1.5	92	1.5	95
2019	20.6	1,279	1.5	95	1.5	92
2020	21.4	1,326	1.6	97	1.4	84
2020	21.4	1,326	1.6	97	1.4	84
2021	21.8	1,353	1.7	104	1.3	84
Annual growth (mean), per cent		2.4		3.0		−0.4

Notes: ICT refers to NACE Rev. 2 industries 59-63, R&D to industry 72 and high technology manufacturing to industries 21, 26 and 30.3.

Source: Swedish Agency for economic and regional growth and Statistics Sweden.

Figure 3. Proportion of establishments with high-speed broadband access nearby (per cent). Notes: The evolution is measured as unweighted means across municipalities. Source: Swedish Post and Telecommunication Agency.

Table 2. Development of independent variables across municipalities, unweighted averages.

	Proportion of establishments with high-speed broadband access nearby, per cent		Proportion of highly skilled inhabitants, per cent		Public investment, SEK million (logarithm)
	All	Declining	All	Declining	All	Declining
2010	17.1	13.6	13.6	9.7	4.6	3.5
2011	21.9	19.6	14.0	10.0	4.7	3.6
2012	25.6	22.7	14.3	10.3	4.7	3.5
2013	29.4	25.5	14.7	10.6	4.7	3.5
2014	34.8	30.9	15.1	10.9	4.8	3.6
2015	40.5	33.7	15.5	11.1	4.9	3.6
2016	47.1	38.2	15.9	11.3	5.1	3.9
2017	56.3	47.3	16.2	11.5	5.2	4.0
2018	63.5	54.8	16.6	11.8	5.3	4.0
2019	69.2	60.4	17.0	12.1	5.3	4.1
2020	72.6	63.2	17.4	12.5	5.3	4.0
2021	76.7	68.7

Source: Kommuninvest, Statistics Sweden, Swedish Agency for Economic and Regional Growth and Swedish Post and Telecommunication Agency.

Table 3. Summary statistics estimation dataset.

	Observations	Mean	Std. dev.	Min	Max
Number of establishments, ICT services, total	3,190	188.3	925.4	0.0	17039.0
Number of establishments, R&D services, total	3,190	14.6	62.4	0.0	928.0
Number of establishments, high technology manufacturing, total	3,190	6.1	17.6	0.0	265.0
Number of establishments, ICT services, 1–9 employees	3,190	54.4	286.7	0.0	5838.0
Number of establishments, R&D services, 1–9 employees	3,190	3.5	15.7	0.0	240.0
Number of establishments, high technology manufacturing, 1–9 employees	3,190	1.9	6.1	0.0	98.0
Proportion of establishments with high-speed broadband access nearby of minimum 100Mbits, per cent	3,190	0.49	0.26	0.00	0.99
Proportion of households with high-speed broadband access nearby of minimum 100Mbits, per cent	3,190	0.55	0.26	0.00	1.00
Highly skilled inhabitants (night population tertiary degree or more, per cent)	3,190	0.15	0.07	0.07	0.50
ln local public investment (t-1) (SEK)	3,190	4.96	1.36	−0.38	9.90

Note: Establishment is equivalent to workplace.

Source: Kommuninvest, Statistics Sweden, Swedish Agency for Economic and Regional Growth, Swedish Post and Telecommunication Agency and own calculations.

Table 4. Importance of high-speed broadband access for innovative establishments across municipalities, Fixed Effects Poisson regressions.

	Knowledge intensive services						High technology
	ICT			R&D			manufacturing
	i. All establishments
	Coeff.		z-stat	Coeff.		z-stat	Coeff.		z-stat
High-speed broadband access (t-1), per cent	0.060	***	2.58	0.028		0.42	−0.064		−0.62
Highly skilled inhabitants (t-1), per cent	3.984	***	6.82	4.921	***	3.71	2.502		1.29
ln local public investment (t-1)	0.009		1.57	−0.057	***	−2.79	−0.005		−0.30
Year dummy variables	yes			yes			yes
Log likelihood	−8720			−3907			−3933
Number of observations	3190			2959			2970
Number of municipalities	290			269			270
	ii. Establishments size class 1–9 employees
	Coeff.		z-stat	Coeff.		z-stat	Coeff.		z-stat
High-speed broadband access (t-1), per cent	0.148	***	3.58	0.028		0.17	−0.110		−0.55
Highly skilled inhabitants (t-1), per cent	7.018	***	5.75	2.749		1.03	0.592		0.21
ln local public investment (t-1)	0.006		0.62	−0.077	**	−2.09	−0.036		−1.00
Year dummy variables	yes			yes			yes
Log likelihood	−6670			−2454			−2300
Number of observations	3190			2299			2288
Number of municipalities	290			209			208
	iii. All establishments with interaction terms
	Coeff.		z-stat	Coeff.		z-stat	Coeff.		z-stat
High-speed broadband access (t-1), per cent	0.074	**	1.99	−0.083		−0.61	−0.362	***	−2.69
Highly skilled inhabitants (t-1), per cent	4.145	***	5.70	4.059	**	2.27	−1.306		−0.65
High-speed broadband access (t-1), per cent X Highly skilled inhabitants (t-1), per cent	−0.074		−0.49	0.527		0.94	1.609	***	3.00
ln local public investment (t-1)	0.009		1.52	−0.052	***	−2.65	0.001		0.04
Year dummy variables	yes			yes			yes
Log likelihood	−8720			−3907			−3933
Number of observations	3190			2959			2970
Number of municipalities	290			269			270
	iv. Marginal effects of broadband given the highly skilled share
Highly skilled inhabitants (t-1), per cent	dy/dx		z-stat	dy/dx		z-stat	dy/dx		z-stat
0	−0.36	***	−2.69	−0.08		−0.61	0.07	**	1.99
0.1	−0.20	*	−1.88	−0.03		−0.32	0.07	**	2.46
0.2	−0.04		−0.39	0.02		0.32	0.06	***	2.56
0.3	0.12		0.97	0.08		0.87	0.05	*	1.84
0.4	0.28	*	1.74	0.13		1.00	0.04		1.14
0.5	0.44	**	2.14	0.18		1.02	0.04		0.72

Notes: Asterisks ***, ** and * denote significance at the 1, 5 and 10 per cent levels, respectively. The Conditional Fixed Effects Poisson regression employs cluster-adjusted standard errors at the municipality level. dy/dx denotes the marginal effects that are calculated using the delta method.

Source: Kommuninvest, Statistics Sweden, Swedish Agency for Economic and Regional Growth, Swedish Post and Telecommunication Agency and own calculations.

Table 5. Importance of high-speed broadband access for innovative establishments in declining municipalities, Fixed Effects-Poisson regressions.

	Knowledge intensive services						High technology
	ICT			R&D			manufacturing
	Coeff.		z-stat	Coeff.		z-stat	Coeff.		z-stat
High-speed broadband access (t-1), per cent	0.242	**	2.30	−0.158		−0.50	0.212		0.71
Highly skilled inhabitants (t-1), per cent	6.681		1.46	21.041	***	2.77	−4.045		−0.35
ln local public investment (t-1)	0.004		0.24	−0.012		−0.30	0.046		0.83
Year dummy variables	Yes			yes			yes
Log likelihood	−1464			−551			−574
Number of observations	682			561			583
Number of municipalities	62			51			53

Notes: Asterisks ***, ** and * denote significance at the 1, 5 and 10 per cent levels, respectively. The Conditional Fixed Effects Poisson regression employs cluster-adjusted standard errors at the municipality level.

Source: Kommuninvest, Statistics Sweden, Swedish Agency for Economic and Regional Growth, Swedish Post and Telecommunication Agency and own calculations.

Table 6. Importance of high-speed broadband access for innovative establishments including spatial dependencies across municipalities, Spatial Fixed Effects-Poisson regressions.

	Knowledge intensive services						High technology
	ICT			R&D			manufacturing
	Coeff.		z-stat	Coeff.		z-stat	Coeff.		z-stat
High-speed broadband access (t-1), per cent	0.061	**	2.30	0.071		0.87	−0.130		−1.34
W High-speed broadband access (t-1), per cent	0.045	*	1.76	0.063		0.61	0.323	***	3.00
Highly skilled (HS) inhabitants (t-1), per cent	3.332	***	4.95	2.702	*	1.87	−0.059		−0.02
W Highly skilled inhabitants (t-1), per cent	1.143	**	2.22	4.667	***	2.75	4.618	*	1.93
ln local public investment (t-1)	0.010	*	1.75	−0.048	**	−2.39	0.000		0.00
Year dummy variables	yes			yes			yes
Log likelihood	−8714			−4392			−3933
Wald test HS and W HS = 0 (p-value)	0.00			0.00			0.02
Wald test BB and W BB = 0 (p-value)	0.00			0.37			0.00
Number of observations	3190			2959			2970
Number of municipalities	290			269			270

Notes: Asterisks ***, ** and * denote significance at the 1, 5 and 10 per cent levels, respectively. The Conditional Spatial Fixed effects Poisson regression employs cluster-adjusted standard errors at the municipality level. Source: Kommuninvest, Statistics Sweden, Swedish Agency for Economic and Regional Growth, Swedish Post and Telecommunication Agency and own calculations.

Figure 4. Estimates of quadratic form of high-speed broadband access on ICT establishments. Notes: Calculated using the delta method based on specification in Table 2 upper panel (Specifications i and ii) and augmented by a quadratic term for broadband access. Source: Kommuninvest, Statistics Sweden, Swedish Agency for Economic and Regional Growth, Swedish Post and Telecommunication Agency and own calculations.

Table 7. Importance of high-speed broadband access for the number of ICT establishments across municipalities, controlling for endogeneity.

				(ii) Size class 1–9 employees			(iii) Interaction term
	(i) All						declining regions
	Coeff.		z-stat	Coeff.		z-stat	Coeff.		z-stat
High-speed broadband access (t-1), per cent	0.053	*	1.72	0.137	***	2.67	0.043		1.56
Highly skilled inhabitants (t-1), per cent	3.928	***	6.95	6.934	***	5.94	3.787	***	6.22
ln local public investment (t-1)	0.009		1.56	0.006		0.52	0.009		1.54
High-speed broadband access (t-1) X declining regions, per cent							0.188	*	1.75
Highly skilled inhabitants (t-1) X declining regions, per cent							2.941		0.64
ln local public investment (t-1) X declining regions							−0.006		−0.29
Year dummy variables	yes			yes			yes
Year dummy variables X declining regions							yes
Fitted residual from first stage	0.048		0.62	0.079		0.52	0.059		0.86
Wald test broadband and interaction term, p-value							0.032
Log likelihood	−8716			−6670			−8717
Number of observations	3190			3190			3190
Number of municipalities	290			290			290

Notes: Asterisks ***, ** and * denote significance at the 1, 5 and 10 per cent levels, respectively. The significance of the fitted residual of the first stage regression of the proportion of establishments with high-speed broadband access as a function of the similar proportion of households is the test for the endogeneity of high-speed broadband. If by rejecting the null hypothesis that the coefficient of the fitted residual is zero, endogeneity cannot be rejected. The standard errors are bootstrapped to account for the inclusion of the predicted regressor (control function adjusted residuals from the first stage) in the second stage regression.

Source: Kommuninvest, Statistics Sweden, Swedish Agency for Economic and Regional Growth, Swedish Post and Telecommunication Agency and own calculations.

Data availability statement:

Data on the number of establishments is confidential and cannot be shared.