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.

The core model of geographical economics takes the spatial equilibrium concept a step further. In contrast to urban economics, spatial interactions between different locations or regions are essential. Regions are connected by introducing transportation costs between different locations. Real wage differences between regions determine migration flows; these flows continue until real wages between regions are the same and a long-run equilibrium is established. The model is an extension of the well-known monopolistic competition model that is popular in international trade theory. In contrast to trade theory, the geographical economics model involves labour mobility between regions. The model is characterized by multiple equilibria that can either be stable or unstable.

For most of human experience, for most people, land transportation meant walking. For raiders like the Mongols and Comanches, it meant horses. In some societies, the better off rode in carriages. With the coming of the steam locomotive in the 1800s, people could ride a train. However, the real agent of deliverance has been the motor car. Steam engines needed a firebox, boiler, and large cylinders and this made them too heavy for a car. The problem was solved by the invention of engines where the fuel burned inside the cylinders. These engines are called internal-combustion engines to distinguish them from the external-combustion steam engines. The most important car engine has been the Otto-cycle, 4-stroke engine invented by Nikolaus Otto in 1876 in Cologne, Germany. In Otto’s engine, fuel mixed with air is first drawn into the cylinder by the motion of the piston. Next the piston compresses the fuel mixture, which is then lit by a flame.

The earth loses heat through infrared radiation to space. This heat loss balances the energy absorbed from the sun. Some atmospheric gases, like water vapor, carbon dioxide, and methane absorb in the infrared. Because the temperature of the atmosphere falls with altitude the atmospheric gases radiate less to space than the surface of the earth would in their absence. The result of this shift in the radiation balance is a warmer earth. The Swedish chemist and Nobel Laureate Svante Arrhenius was the first to quantify the effect in his 1896 paper, “On the influence of Carbonic Acid in the Air Upon the Temperature of the Ground.” He wrote “Now it will be shown in the sequel that a variation of the carbonic acid [carbon dioxide] of the atmosphere in the same proportion produces nearly the same thermal effect independently of its absolute magnitude.”

Models can help us understand what has happened in the past and help us make statements about what might happen in the future. As examples, the growth-rate plot for world population in Section 1.5.3 gave us a projection for the world population peak, and the broken-line electricity model in Section 1.6.2 established a strong relationship between electricity generation and income. Our emphasis will be on linearized models. These models start with a transformation of the data. If the model is a good one, the plot will look like a straight line. These models take advantage of the fact that one’s eyes can judge well whether data are linear. In addition, a shock like the Iranian Revolution can often be characterized in a simple way as a change in slope. For linearized models, the model parameters can be determined by Excel’s SLOPE and INTERCEPT functions.

This chapter starts with a look at empirical evidence on economic growth and development as summarized in four stylized facts: (i) rising income levels, (ii) lasting differences in growth rates, (iii) periods of stagnation followed by rapid growth, and (iv) leap-frogging. We argue that the endogenous growth literature can help us understand the first two facts, while geographical economics can help us understand the last two facts. To this end, we introduce a model that is a merger of the geographical economics model with an endogenous growth model. This model gives us new insights, for example on the importance of international knowledge spillovers. But, as is to be expected from any model, this model also has its shortcomings. We zoom in on the argument that models like this still focus only on the proximate causes of growth at the neglect of the so-called deep determinants of economic development: institutions and geography. Finally, using the case of China’s economic geography as example, we illustrate how the class of geographical economics models can be used to answer what-if questions pertaining to the possible impact of structural changes on a country’s economic growth and development.

In the epilogue, we reflect on some of the major themes linked to the questions driving research in second language acquisition and return to the question that started the field in the 1970s: Are first and second language acquisition similar or different?

This chapter centers on the major descriptive findings of second language research, focusing on ordered and systematic development. We review and discuss such things as morpheme orders, developmental stages/sequences, unmarked before marked, U-shaped development, among others. We also review the evidence for L1 influence on ordered development. We touch on the nature of internal (e.g., Universal Grammar, general learning mechanisms) and external constraints (e.g., quantity and quality of input and interaction with that input, frequency) as underlying factors in ordered development. We also briefly discuss variability during staged development.

In this chapter we discuss the qualitative difference between explicit knowledge and implicit knowledge (underlying mental representation). The chapter focuses on whether instruction affects the latter. We review the accepted finding that instruction does not affect ordered development. We also review the issue of whether instruction affects rate of development and ultimate attainment. We look at important variables in the research on instructed acquisition, including type of knowledge measured, the nature of assessments used in the research, and short-term vs. long-term studies, among others.