Since the experiences of the housing boom and bust in the first decade of the 21st century, there has been growing interest in studying the connections between housing markets and labor market activity. Notably, a number of theoretical works have attempted to understand how housing tenure affects labor market outcomes. Interestingly, despite the inherent appeal of the logic that homeownership reduces worker mobility, much of this research does not predict that homeownership is associated with inferior outcomes when compared to renting. Thus, it is important to also examine the implications of homeownership empirically. Although initially focused on macroeconomic studies looking at owner-occupation rates and unemployment across countries, the empirical literature expanded by introducing microeconometric research that examines an individual’s tenure status and labor market results. To begin, it appears that unemployed homeowners may not necessarily suffer from longer unemployment durations than other workers. Further, they may also be less likely to become unemployed; however, homeownership might be associated with lower wages because homeowners are limited in their job searches. In particular, homeowners suffering from negative equity seem to approach search efforts and job acceptance rates differently from other workers. Yet, such individuals are unlikely to default on their mortgages unless they experience adverse labor market shocks.
Robert R. Reed III
Gilles Duranton and Anthony J. Venables
Urbanization is a central challenge of our times. At its core, it is an urban development challenge that requires addressing transportation and housing in cities. Transport improvements can reduce travel times and improve the spatial reach of urban dwellers. But these improvements may be crowded out by latent demand for travel and may lead to worse congestion, pollution, and other negative externalities associated with urban traffic. To evaluate the effects of transport improvements, direct travel effects must be measured. Then, an improvement in traffic conditions somewhere may spill over to other areas. Firms and residents may also relocate, so economic growth close to a transport improvement may just result from a displacement of economic activity from other areas. Conversely, better accessibility is expected to foster agglomeration effects and increase productivity. Valuing these changes is difficult, as it requires being able to quantify many externalities such as congestion delays, scheduling gains, and greater job accessibility. Housing policies present different challenges. More fundamental policies seek to enable housing construction by offering more secure property rights, up-to-date land registries, and competent land-use planning—all complex endeavors and all necessary. Other housing policies rely on heavy government interventions to provide housing directly to large segments of the urban population. These policies often flop because governments fail to link housing provision with job accessibility and appropriate land-use planning. Housing is also an expensive asset that requires significant initial funding, while credit constraints abound in the urbanizing world. Policymakers also need to choose between small improvements to extremely low-quality informal housing, retrofitting modern housing in already-built urban areas, or urban expansion. All these options involve sharp trade-offs, subtle induced effects, and complex interactions with transport. All these effects are difficult to measure and challenging to value.
Economic and social activities in different locations interact through systematic connections, which can be modeled as network structures. For example, production processes combine various inputs, tasks, and intermediate products that are spread over space; laborers transmit knowledge and skills along networks of work relations; products are delivered through transportation networks; and local public goods have external effects that spill over into the network of neighborhoods. Such networks bring benefits to connected nodes in the form of externalities. Approaches adopted for modeling networks of locations or that are applicable to spatial economics can be placed into two major categories. First, networks can be formed endogenously when nodes choose links strategically. Thus, networks are outcomes that emerge from strategic equilibria. This approach anylyzes the patterns of networks that are in equilibrium and patterns that are efficient. Second, networks can be a background structure with fixed existing links. In this approach, centrality measures are designed to indicate the importance of a node in the network. In many contexts, these measures determine the equilibrium and efficient behavior of nodes. Networks can be applied to broad issues in urban, regional, and location economics, such as neighborhood interactions, transportation, local public goods, trade, industrial sites, business operations. The strategic connection approach models the network as a strategic game. Both cooperative and noncooperative equilibrium concepts have been adopted in the literature. A link may form cooperatively when both nodes are better off, or one node may force a link noncooperatively onto another. The structure of intracity and intercity networks can be investigated using this framework: In a city, neighborhoods are networks of blocks, which are connected by streets and sidewalks; external benefits spill over into connected blocks; locally integrated neighborhoods emerge in equilibrium; and cities are connected by intercity transportation networks. In such models, the core–periphery patterns of cities are found to emerge in the equilibrium. The structural approach treats network structures as exogenously fixed, and links are not subject to change. In such settings, centrality measures, which indicate how centrally connected the position of a node is in the network, determine the behaviors of nodes. For example, when there are widespread externalities so that payoffs of nodes are determined by efforts of all connected nodes, the equilibrium effort of a node is proportional to its Bonacich centrality measure. Centrality measures determine equilibrium and efficient outcomes in other network settings as well. Examples of such are how conformity in peer networks affects criminal behaviors, how nodes choose security investments against the spread of infection in the network, how intercity transportation networks determine the distribution of city size, and how community residents choose the number of visits to an urban center. Futher findings include, for example, in an economy-wide trade network of intermediate inputs, local economic shocks can cause aggregate production fluctuations; in a network of neighboring jurisdictions, voluntary contributions to local public goods are neutral to income transfers; in a geographical trade network, a firm that already exports to a location will have a higher probability of exporting to a second location if the two locations have a larger volume of trade; and firms spread adverse impacts from a local economic shock through their internal networks across regions.
Since the late 1990s, spatial models have become a growing addition to econometric research. They are characterized by attention paid to the location of observations (i.e., ordered spatial locations) and the interaction among them. Specifically, spatial models formally express spatial interaction by including variables observed at other locations into the regression specification. This can take different forms, mostly based on an averaging of values at neighboring locations through a so-called spatially lagged variable, or spatial lag. The spatial lag can be applied to the dependent variable, to explanatory variables, and/or to the error terms. This yields a range of specifications for cross-sectional dependence, as well as for static and dynamic spatial panels. A critical element in the spatially lagged variable is the definition of neighbor relations in a so-called spatial weights matrix. Historically, the spatial weights matrix has been taken to be given and exogenous, but this has evolved into research focused on estimating the weights from the data and on accounting for potential endogeneity in the weights. Due to the uneven spacing of observations and the complex way in which asymptotic properties are obtained, results from time series analysis are not applicable, and specialized laws of large numbers and central limit theorems need to be developed. This requirement has yielded an active body of research into the asymptotics of spatial models.
Uschi Backes-Gellner and Patrick Lehnert
Despite the common view that innovation requires academically educated workers, some countries that strongly emphasize vocational education and training (VET) in their education systems—such as Switzerland and Germany—are highly competitive internationally in terms of innovation. These countries have dual VET programs, that is, upper-secondary-level apprenticeship programs, that combine about three quarters of workplace training with about one quarter of vocational schooling, and design them in such a way that their graduates (i.e., dual apprenticeship-graduates) play crucial roles in innovation processes. Regular updates of VET curricula incorporate the latest technological developments into these curricula, thereby ensuring that dual apprenticeship-graduates possess up-to-date, high-level skills in their chosen occupation. This process allows these graduates to contribute to innovation in firms. Moreover, these graduates acquire broad sets of technical and soft skills that enhance their job mobility and flexibility. Therefore, conventional wisdom notwithstanding, dual apprenticeship-graduates in such countries not only have broad skill sets that accelerate innovation in firms, but also willingly participate in innovation because of their high flexibility and employability. Moreover, Switzerland and Germany have tertiary-level VET institutions that foster innovation. These are universities of applied sciences (UASs), which teach and conduct applied research, thereby helping build a bridge between different types of knowledge (vocational and academic). UAS students have prior vocational knowledge through their dual apprenticeship and acquire applied research skills from UAS professors who usually have both work experience and a doctoral degree from an academic university. Thus UAS graduates combine sound occupational knowledge with applied research knowledge inspired by input from the academic research frontier and from practical research and development (R & D) in firms. Firms employ UAS graduates with their knowledge combination as an important input for R & D. Consequently, regions with a UAS have higher levels of innovation than regions without one. This effect is particularly strong for regions outside major innovation centers and for regions with larger percentages of smaller firms.
Ching-mu Chen and Shin-Kun Peng
For research attempting to investigate why economic activities are distributed unevenly across geographic space, new economic geography (NEG) provides a general equilibrium-based and microfounded approach to modeling a spatial economy characterized by a large variety of economic agglomerations. NEG emphasizes how agglomeration (centripetal) and dispersion (centrifugal) forces interact to generate observed spatial configurations and uneven distributions of economic activity. However, numerous economic geographers prefer to refer to the term new economic geographies as vigorous and diversified academic outputs that are inspired by the institutional-cultural turn of economic geography. Accordingly, the term geographical economics has been suggested as an alternative to NEG. Approaches for modeling a spatial economy through the use of a general equilibrium framework have not only rendered existing concepts amenable to empirical scrutiny and policy analysis but also drawn economic geography and location theories from the periphery to the center of mainstream economic theory. Reduced-form empirical studies have attempted to test certain implications of NEG. However, due to NEG’s simplified geographic settings, the developed NEG models cannot be easily applied to observed data. The recent development of quantitative spatial models based on the mechanisms formalized by previous NEG theories has been a breakthrough in building an empirically relevant framework for implementing counterfactual policy exercises. If quantitative spatial models can connect with observed data in an empirically meaningful manner, they can enable the decomposition of key theoretical mechanisms and afford specificity in the evaluation of the general equilibrium effects of policy interventions in particular settings. Several decades since its proposal, NEG has been criticized for its parsimonious assumptions about the economy across space and time. Therefore, existing challenges still require theoretical and quantitative models on new microfoundations pertaining to the interactions between economic agents across geographical space and the relationship between geography and economic development.
Thilo R. Huning and Fabian Wahl
The study of the Holy Roman Empire, a medieval state on the territory of modern-day Germany and Central Europe, has attracted generations of qualitative economic historians and quantitative scholars from various fields. Its bordering position between Roman and Germanic legacies, its Carolingian inheritance, and the numerous small states emerging from 1150 onward, on the one hand, are suspected to have hindered market integration, and on the other, allowed states to compete. This has inspired many research questions around differences and communalities in culture, the origin of the state, the integration of good and financial markets, and technology inventions, such the printing press. While little is still known about the economy of the rural population, cities and their economic conditions have been extensively studied from the angles of economic geography, institutionalism, and for their influence on early human capital accumulation. The literature has stressed that Germany at this time cannot be seen as a closed economy, but only in the context of Europe and the wider world. Global events, such as the Black Death, and European particularities, such as the Catholic Church, never stopped at countries’ borders. As such, the literature provides an understanding for the prelude to radical changes, such as the Lutheran Reformation, religious wars, and the coming of the modern age with its economic innovations.
Pascal Mossay and Pierre M. Picard
New Economic Geography (NEG) provides microeconomic foundations for explaining the spatial concentration of economic activities across regions, cities, and urban areas. The origins of the NEG literature trace back to trade, location, and urbans economics theories. In NEG, agglomeration and dispersion forces explain the existence of spatial agglomerations. A NEG model usually incorporates a combination of such forces. In particular, firm proximity to large markets and the importance of linkages along a supply chain are typical agglomeration forces. Equilibria properties derived from NEG models are very specific to NEG as they involve multiple equilibria and have a very high dependence on changes in parameters. This phenomenon has important implications for the emergence of nations, regions, and cities. In particular, high transport costs imply the dispersion of economic activities, while low transport costs lead to their spatial concentration. The same forces that shape inequalities and disparities between regions also shape the internal structure of cities. Firms concentrate in urban centers to gain greater access to larger demand. The empirical literature has developed several approaches that shed light on spatial agglomeration and estimate the role and impact of transport costs on market access. A key empirical research question is whether observed patterns could be explained by location amenities or agglomeration forces as put forward by NEG. Quasi-experimental methodology is frequently used for such a purpose. NEG theory is supported by empirical evidence, demonstrating the role of market access.
Hites Ahir and Prakash Loungani
On average across countries, house prices have been on an upward trend over the past 50 years, following a 100-year period over which there was no long-term increase. The rising trend in prices reflects a demand boost due to greater availability of housing finance running up against supply constraints, as land has increasingly become a fixed factor for many reasons. The entire 150-year period has been marked by boom and bust cycles around the trend. These also reflect episodes of demand momentum—due to cheap finance or reasonable or unreasonable expectations of higher incomes—meeting a sluggish supply response. Policy options to manage boom–bust cycles, given the significant costs to the economy from house price busts, are discussed.
Many large cities are found at locations with certain geographic and historical advantages, or the first nature advantages. Yet those exogenous locational features may not be the most potent forces governing the spatial pattern and the size variation of cities. In particular, population size, spacing, and industrial composition of cities exhibit simple, persistent, and monotonic relationships that are often approximated by power laws. The extant theories of economic agglomeration explain some aspects of this regularity as a consequence of interactions between endogenous agglomeration and dispersion forces, or the second nature advantages. To obtain results about explicit spatial patterns of cities, a model needs to depart from the most popular two-region and systems-of-cities frameworks in urban and regional economics in which the variation in interregional distance is assumed away in order to secure analytical tractability of the models. This is one of the major reasons that only few formal models have been proposed in this literature. To draw implications about the spatial patterns and sizes of cities from the extant theories, the behavior of the many-region extension of the existing two-region models is discussed in depth. The mechanisms that link the spatial pattern of cities and the diversity in size as well as the diversity in industrial composition among cities are also discussed in detail, thought the relevant theories are much less available. For each aspect of the interdependence among spatial patterns, size distribution and industrial composition of cities, the concrete facts are drawn from Japanese data to guide the discussion.