We present examples to show that people and economic activities are unevenly distributed across space. The variations in density result in strong agglomeration in some important centres. We briefly analyse urban development and illustrate ‘spikiness’ at different spatial scales (global regions, countries, provinces, and counties). We also show that the distribution across space is not random but often displays a remarkably stable and uniform pattern across time and for various levels of geographical aggregation. These observations suggest that similar spatial economic forces are relevant for explaining agglomeration and the regularities of distribution and interaction across space.

The organization of space within cities is mainly driven by spatial equilibrium forces. This chapter argues that a person who is indifferent about living in two locations within the city must derive the same net utility from these locations. This allows us to explain how rent costs decline away from the city centre as people need to be adequately compensated for higher transportation costs. We can extend the basic framework to incorporate the choice of different transport methods, changes in the slope of the rent gradient, the choice of the amount of land to use (and thus population density), building height, individual heterogeneity, the role of amenities, and observed segregation within the city.

Power laws (and the special case of Zipf’s law) allow us to characterize cities on a map with a single number, namely the slope of a rank-size curve. These rank-size curves show the rank of a city as a function of its size: the biggest city gets rank 1, the second largest city rank 2, and so on. The slope tells us whether cities are relatively similar in size (small slope) or unevenly sized (steep slope). But what explains the existence of different sized cities? This chapter introduces some urban theories that explain the existence of different sized cities; a graphical representation of the Henderson model and a graphical representation of a state-of-the-art model of differentiated cities as developed by Davis and Dingle.

We introduce three alternative models that extend the core model of Chapter 7. First, the intermediate goods model includes intermediate production and inter-sectoral labour mobility, instead of inter-regional labour mobility. In some cases this leads to a bell-shaped curve instead of the tomahawk diagram, implying that agglomeration gradually rises and falls as transport costs decline. Second, the generalized model incorporates both the core model and the intermediate goods model in one framework. Third, the solvable model introduces a second production factor (such as human capital) in the manufacturing sector. This model leads to explicit solutions for factor rewards. We conclude with an empirical evaluation of urban power laws and show that adding congestion to the core model allows us to better understand the influence of parameter changes on estimated power law exponents.

First, we show that simple geographic forces play a role in understanding differences in current prosperity and income. This can be partially traced back to deep roots, such as the Agricultural Revolution, which allowed for a transition from a hunter-gatherer society to a farmer society and enabled the building of institutions (this gave the Eurasian continent a head start). Second, we analyse the importance of geo-human interaction for explaining current prosperity levels. There is special attention for the role of embodied institional knowledge incorporated in international migration flows for helping us understand the ancestry-adjusted impact of bio-geographic and institutional factors (which helps explain the reversal of fortune hypothesis). Eventually, bio-geographic factors are thus important for economic development levels, either directly or indirectly through geo-human interaction.

In this chapter, we analyse the rationale of regional policies, discuss the evidence, and highlight some methodological problems. In doing so, we also take a closer look at policy consequences that follow from or can be linked to the models discussed throughout the book. In some of our thought-experiments, we take the models of the book literally and ask, what would be the lessons for policy makers? When we discuss actual cluster/regional policy thinking, but also when we discuss hypothetical policies by sticking as closely as possible to our models, there are always three main elements to take into account. First, whether it is a people- or place-based policy (or a mix of these two). Second, to go beyond the observation that a policy can change spatial economic outcomes by showing that a policy is somehow welfare improving. Third, to take into consideration the important macro/micro distinction in terms of regional policy effectiveness: a ‘good’ regional policy at the city or regional level does not necessarily imply that it is also beneficial for the economy as whole. The main message of the chapter is that regional policies should be handled with care.

Economic activity is unevenly distributed across space, or spiky. Measuring this spikiness is not trivial. A good measure is comparable across space, comparable across sectors, unbiased regarding spatial and sector classification, and should provide a measure of significance. No measure fulfils all these criteria, but some are better than others. Once spikiness is identified the next question is ‘so what?’ Does spatial agglomeration stimulate productivity? Econometric methods to deal with this question and tackle the problem of reverse causality are: difference-in-differences, natural experiments, and regression discontinuity design. These techniques are introduced in this chapter.

The possible sources and mechanisms underlying urban agglomeration economies cannot be tested directly, which implies that empirical research uses approximations for agglomeration economies, like city size, density, economic specialization, or human capital. Since these are all endogenous variables, any effect on city wages or productivity cannot be taken as direct evidence of the relevance of agglomeration economies. This chapter discusses modern studies that use micro-data in combination with instrumental variables or fixed effects to assess the relevance of agglomeration economies. Apart from the use of micro-data and clever empirical estimation strategies, we also discuss several alternative strategies for urban economics to further increase our knowledge of the location choices made by people as well as firms between cities. Among these alternative strategies are the quasi-experimental research design, the integration of the two main building blocks (spatial equilibrium and agglomeration economies) in urban economics, a shift of attention from agglomeration benefits to costs, and looking outside the field of economics.

This chapter focuses on the empirics of geographical economics. In the first part of the chapter we look into (i) the existence of the home market effect and (ii) a spatial wage structure. The home market effect implies that if a region displays a relatively large demand for a certain good, this increased demand will lead to a more-than-proportional increase in the region’s production of that good. The spatial wage structure implies that wages will be higher in or near economic centres. These studies, however, do not offer a wholly convincing test of geographical economics models. A real test should also look at the empirical validity of this approach when the spatial distribution of economic activity is not fixed but subject to change. This is the topic of the second part of the chapter, where we test for the impact of shocks on the spatial allocation of economic activity and for the relationship between transport costs and agglomeration. The chapter also discusses why and how modern empirical research in geographical economics has gradually become part of a broader empirical research agenda in spatial economics with a focus on micro-data and new empirical methods.