This chapter explores possible measures of economic development and how they relate to measures commonly used for studying economic growth. Since potential factors for measuring economic development typically differ greatly over large geographical areas, we will study the indicators on a municipality level to avoid large geographical units of analysis. The data is from 2013 and 2014 and is found in official Swedish data repositories, and include variables such as gross pay per employee, citizen satisfaction with the municipality and Gini-coefficient for Swedish municipals. The results indicate that trying to find one overall measure for economic development is not feasible. Our main conclusions are that indicators for economic growth and economic development in some cases move in opposite directions. There is a need for more direct and harmonized, national and international data to further drive the research in the field of economic development.

This chapter investigates the importance of cross-border activities for border regions in Sweden. We acknowledge the heterogeneity between regions by dividing them into three categories depending on the population density on each side of the national border. A spatial model is estimated using data from 2014 that takes into account geographical proximity and spatial correlation. The model examines the difference in gross pay per inhabitant and employment rate between municipalities belonging to a border region and an average comparable Swedish municipality. The results show that the largest positive effect can be found for municipalities belonging to sparsely populated regions bordering a densely populated region. For municipalities in densely populated regions bordering a densely populated region comparable improvements are revealed, although not of the same magnitude. For municipalities in sparsely populated areas bordering rural areas, no statistically significant differences are discovered between them and the Swedish average.

Accessibility has for many years been a widely used tool in transportation research. Many definitions have been suggested and researchers have constructed numerous mathematical formulations to measure its value in order to be able to evaluate the relationships between the nature of the transport systems and the patterns of land use. Such correlations have been used especially in assessing existing transport systems and forecasting their performance to provide decision-makers with ideas about the need for investments in the transport systems. However, accessibility measures can be regarded as the spatial counterparts of discounting. The measures represent the spatial distribution of economic agents and their activities in a simple way that imposes a very clear structure upon the relationship between these agents and their activities and their environment. Various frictional effects arising from geographical distance between economic agents determine their interaction options, that is, their options to trade, to cooperate, to learn, to commute, and so on. Observing that the time sensitivities of the economic agents vary between different spatial scales (and between different economic activities) we may impose a spatial structure (for example, local, intra-regional, interregional and international) which offers opportunities to define variables in such a way that spatial dependencies can be accommodated. These newly defined variables can then be used in empirical explanations of various spatial phenomena, such as patent output, new firm formation, the emergence of new export products, and economic growth in different spatial units. We will in this chapter against this background show that accessibility is an underused analytical and empirical tool in regional science with an underestimated potential.

Using variable selections methods this chapter examines variables potentially suitable as indicators for different dimensions of economic development. Since potential factors for measuring economic development typically will differ greatly over large geographical areas, the study uses indicators on municipality level to avoid large geographical units of analysis. The analysis is performed on Swedish municipality data from 2015. An extensive search yielded a number of prospective indicators to use even though the official data gathered in Sweden are not particularly suitable for investigating the softer dimensions of economic development. The results of the study suggest that the indicators of economic development can be categorized into five dimensions/indices that represent: “Quality of Living”, “Economic Capacity”, “Wealth and Stability”, “Growing Worries” and “Gross Municipal Product per inhabitant” in the municipality.