Introduction

Regions

What region are you looking for?

Industry composition

Industry space

Industry comparison

Smart specialisation

Source: Balland et al. (2019), p. 1259.

Occupation composition

Occupation space

Occupation comparison

Career potential

Data

For the construction of this online resource we rely on matched employer-employee data from Swedish registers collected by Statistics Sweden. In each case the explorables display aggregated data to prevent the identification of individuals.

Functional labour market regions

This online resource uses functional labour market regions (FA-regions) as its spatial unit of analysis. The 2015 version of this classification, developed by Tillväxtanalys and intended for use for about 10 years, sorts the 290 municipalities of Sweden into 60 FA-regions based on commuting patterns across municipality borders. See more information about this classification here.

Industries and occupations

In this online resource an industry means one of 265 3-digit SNI07 industry codes, corresponding to the NACE Rev. 2 classification system, while an occupation means one of 144 3-digit SSYK2012 occupation codes, corresponding to the ISCO08 classification system.

The relative specialisation of regions

In order to explore the economic structure of regions, we need to identify which regions are specialised in which economic activity (e.g., industries and occupations). To do so, we first rely on the common measure of location quotient, which tells us how over- or underrepresented the employment of economic activity $i$ is in a region $r$, compared with the activity’s employment share in the national economy of Sweden:

$$LQ_{i,r} = \frac{emp_{i,r} / emp_r}{emp_i / emp}$$

Skill-relatedness network

There are number of ways to reveal how related economic activities are to one another. For instance, a skill-relatedness network reveals the similarity between economic activities in terms of the workers’ skills they rely on, as workers are more likely to move between activities where they can still benefit from their accumulated skills and expertise.

Following the scientific literature (e.g., Neffke et al. 2017), we consider two economic activities (industry or occupation) as skill-related, if the observed labour flow between them ( $F_{ij}$ ) exceeds what we would expect based on the propensity of these activities to experience labour flows ( $(F_{i.} F_{.j}) ⁄ F_{..}$ ):

$$SR_{ij} = \frac{F_{ij}}{(F_{i.} F_{.j}) ⁄ F_{..}}$$

Here, $F_{i.}$ is the total outflow of workers from activity $i$, $F_{.j}$ is the total inflow to activity $j$, and $F_{..}$ is the total flow of workers in the Swedish economy. Labour flows are considered across 2011-2018.

To arrive at the final measure of skill-relatedness, we consider the average of $SR_{ij}$ and $SR_{ji}$ to receive a symmetric measure; we normalize the measure to have its range between -1 and +1 ( $\tilde{SR}_{ij} = \frac{SR_{ij} - 1}{SR_{ij} + 1}$ ); and keep only those links that are above 0, corresponding to above expected labour flows.

These networks tend to be too dense for visual interpretation. For example, the Swedish skill-relatedness network is made up of 9849 ties between 265 industries. Hence, to produce a visual representation the industry and occupation spaces, following Hidalgo et al. (2007), we first derive a meaningful backbone of this network by taking the maximum spanning tree of the skill-relatedness networks. In these subnetworks, every economic activity is connected to every other through exactly one path, such that the total strength of ties kept is as high as possible. To this skeleton, we then add the strongest 5% of ties in the case of industries and 15% in the case of occupations. The resulting network visualisation can be considered the collection of the strongest similarities of skill requirements across industries or occupations, while leaving no activity isolated either. This approach is only used for visualising the industry and occupation space, while for all the analysis provided on the site, including the ego-networks of occupations, we consider all identified ties of these networks.

Local capability base - relatedness density

To measure the degree to which an economic activity is related to the economic portfolio of the region, we measure its relatedness density. For an economic activity $i$ in region $r$, relatedness density $RD_{i,r}$ shows what percent of the activity’s potential skill-relatedness ties connects it with activities that a given region shows relative specialisation in:

$$RD_{i,r} = \frac{\sum_{i} SR_{ij}I(LQ_{j,r})}{\sum_{i}SR_{ij}}$$

Here, $I(LQ_{j,r})$ is an indicator variable, showing whether region r has relative specialisation in industry $j \neq i$ (taking the value of 1), or not (taking the value of 0).

Hence, this measure shows how well a given economic activity fits a region’s existing industrial portfolio in terms of similar skill-requirements. It is well established in the scientific literature, that industries with high relatedness density are more likely to enter regions and less likely to exit them (e.g., Neffke et al. 2011), that firms recruiting employees from related industries outperform other similar firms (Boschma et al. 2009) which translates to regional growth (Boschma et al. 2014), that a high degree of embeddedness in related activities facilitates structural change in relation to firm-closures and the phasing out of entire sectors (Eriksson et al. 2016), and that embeddedness facilitates upward wage mobility (Elekes et al. 2023).

Economic complexity - industries

Without going into too much detail, economic complexity has been shown to predict the economic growth rate of places (Hidalgo & Hausmann 2009), while more complex economic activities tend to cluster in large cities (Balland et al. 2020). Following this stream of studies, we consider an industry complex, if there are only a few places that show relative specialisation in it, but those places tend to house a diverse set of industries. Conversely, regions are deemed more complex if they have a diverse set of specialisations, including rare specialisations. Using the “method of reflections” introduced by Hidalgo & Hausmann (2009), each industry is assigned a number between 0 and 1, where a higher value indicates a more complex, and arguably a more economically valuable activity. As we rely on only Swedish data, the complexity measure will indicate the complexity of activities relative to others within the Swedish context as discussed in detail by Elekes & Eriksson (2021) for industries and Hane-Weijman et al. (2022) for occupations.

Smart specialisation matrix - industries

Equipped with the measures of relatedness density and industry complexity, we can organise the existing specialisations and diversification options for each region into a two-by-two matrix, where prime candidates for diversification can be found closer to the upper-right corner (Balland et al. 2019). Such industries first have relatively high relatedness density, which indicates that the region already has many related skills in the local workforce. Second, these activities are relatively complex, and therefore can be considered valuable to diversify into. We highlight in green the top 10 such candidates among industries that a region does not currently have specialisation in, but that are in the upper-right quadrant of this matrix. The base for identifying the top 10 is the vector distance of these missing industries from the origin, meaning that they either have high complexity, high relatedness density, or a balance between the two. We further provide these measures for all industries in regions in the interactive table. This table includes a ranking variable, arranging the most promising diversification options in the upper left quadrant of the smart specialisation matrix. Ranking beyond these depends on preference towards related or complex activities and so the ranking variable stays the same for industries beyond the most promising set.

Variables

Regions tab:

Population density: the number of inhabitants per square kilometre.
Regional employment: the number of workers aged 18-64 in the region with main income from work.
Number of industry specialisations: the number of 3-digit industries in which the region exhibits relative specialisation in.
Median age of workers: the median age of workers (aged 18-64) with income from work in the region.
Median income of workers: the median income of workers (aged 18-64) in the region with income from work.
Share of higher-educated workers: the share of workers (aged 18-64) in the region with income from work, having at least a Bachelor’s degree.

Industries tab:

Number of workers: the number of workers (aged 18-64) in the region with income from work, working in the specific industry.
Median age of workers: the median age of workers (aged 18-64) in the region with income from work, working in the specific industry.
Median income of workers: the median income of workers (aged 18-64) in the region with income from work, working in the specific industry.
Share of higher-educated workers: the share of workers (aged 18-64) in the region with income from work, having at least a Bachelor’s degree, working in the specific industry.
Share of female workers: the share of female workers (aged 18-64) in the region with income from work, working in the specific industry.
Share of workers with immigrant background: the share of workers (aged 18-64) with immigrant background in the region with income from work, working in the specific industry.
Relatedness density: the degree of regional concentration of industries that are skill-related to the focal one.
Industry complexity: the economic complexity index of an industry, as measured by the Method of Reflections.

Occupations tab:

Number of workers: the number of workers (aged 18-64) in the region with income from work, working in the specific occupation.
Median age of workers: the median age of workers (aged 18-64) in the region with income from work, working in the specific occupation.
Median income of workers: the median income of workers (aged 18-64) in the region with income from work, working in the specific occupation.
Share of higher-educated workers: the share of workers (aged 18-64) in the region with income from work, having at least a Bachelor’s degree, working in the specific occupation.
Share of female workers: the share of female workers (aged 18-64) in the region with income from work, working in the specific occupation.
Share of workers with immigrant background: the share of workers (aged 18-64) with immigrant background in the region with income from work, working in the specific occupation.
Relatedness density: the degree of regional concentration of occupations that are skill-related to the focal one.
Mode education: the most frequent education code(s) among workers of a specific occupation.