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Institute
Spatial and social mobility
(2018)
This paper analyzes the relationship between spatial mobility and social mobility. It develops a two-skill-type spatial equilibrium model of two regions with location preferences where each region consists of an urban area that is home to workplaces and residences and an exclusively residential suburban area. The paper demonstrates that relative regional social mobility is negatively correlated with segregation and inequality. In the model, segregation, income inequality, and social mobility are driven by differences between urban and residential areas in commuting cost differences between high-skilled and low-skilled workers, and also by the magnitude of taste heterogeneity.
We use panel data from Germany to analyze the effect of population density on urban air pollution (nitrogen oxides, particulate matter, ozone, and an aggregate index for bad air quality [AQI]). To address unobserved heterogeneity and omitted variables, we present long difference/fixed effects estimates and instrumental variables estimates, using historical population and soil quality as instruments. Using our preferred estimates, we find that the concentration increases with density for NO2 with an elasticity of 0.25 and particulate matter with elasticity of 0.08. The O-3 concentration decreases with density with an elasticity of -0.14. The AQI increases with density, with an elasticity of 0.11-0.13. We also present a variety of robustness tests. Overall, the paper shows that higher population density worsens local air quality.
We use panel data from Germany to analyze the effect of population density on urban air pollution (nitrogen oxides, particulate matter and ozone). To address unobserved heterogeneity and omitted variables, we present long difference/fixed effects estimates and instrumental variables estimates, using historical population and soil quality as instruments. Our preferred estimates imply that a one-standard deviation increase in population density increases air pollution by 3-12%.
We use a quantitative spatial equilibrium model to evaluate the distributional and welfare impacts of a recent temporary rent control policy in Berlin, Germany. We calibrate the model to key features of Berlin’s housing market, in particular the recent gentrification of inner city locations. As expected, gentrification benefits rich homeowners, while poor renter households lose. Our counterfactual analysis mimicks the rent control policy. We find that this policy reduces welfare for rich and poor households and in fact, the percentage change in welfare is largest for the poorest households. We also study alternative affordable housing policies such as subsidies and re-zoning policies, which are better suited to address the adverse consequences of gentrification.
Property tax competition
(2022)
We develop a model of property taxation and characterize equilibria under three alternative taxa-tion regimes often used in the public finance literature: decentralized taxation, centralized taxation, and “rent seeking” regimes. We show that decentralized taxation results in inefficiently high tax rates, whereas centralized taxation yields a common optimal tax rate, and tax rates in the rent-seeking regime can be either inefficiently high or low. We quantify the effects of switching from the observed tax system to the three regimes for Japan and Germany. The decentralized or rent-seeking regime best describes the Japanese tax system, whereas the centralized regime does so for Germany. We also quantify the welfare effects of regime changes.
Urban pollution
(2022)
We use worldwide satellite data to analyse how population size and density affect urban pollution. We find that density significantly increases pollution exposure. Looking only at urban areas, we find that population size affects exposure more than density. Moreover, the effect is driven mostly by population commuting to core cities rather than the core city population itself. We analyse heterogeneity by geography and income levels. By and large, the influence of population on pollution is greatest in Asia and middle-income countries. A counterfactual simulation shows that PM2.5 exposure would fall by up to 36% and NO2 exposure up to 53% if within countries population size were equalized across all cities.
Steigende Mieten?
(2022)
Vor dem Hintergrund rasant steigender Mieten in deutschen Großstädten untersuchen wir in einer neuen Studie die Auswirkungen von Gentrifizierung sowie von politischen Gegenmaßnahmen auf unterschiedliche Einkommensgruppen anhand eines quantitativen Modells für Berlin. Wir finden, dass eine Mietpreisbindung (wie der „Mietendeckel“) allen Haushalten, vor allem aber den ärmeren Haushalten, schadet. Andere Maßnahmen wie Neubau oder direkte Subventionen schneiden besser ab.
We study the effect of energy and transport policies on pollution in two developing country cities. We use a quantitative equilibrium model with choice of housing, energy use, residential location, transport mode, and energy technology. Pollution comes from commuting and residential energy use. The model parameters are calibrated to replicate key variables for two developing country cities, Maputo, Mozambique, and Yogyakarta, Indonesia. In the counterfactual simulations, we study how various transport and energy policies affect equilibrium pollution. Policies may be induce rebound effects from increasing residential energy use or switching to high emission modes or locations. In general, these rebound effects tend to be largest for subsidies to public transport or modern residential energy technology.
This chapter reviews the interplay of agglomeration and pollution as well as the effect of energy policies on pollution in an urban context. It starts by describing the effect of agglomeration on pollution. While this effect is theoretically ambiguous, empirical research tends to find that larger cities are more polluted, but per capita emissions fall with city size. The chapter discusses the implications for optimal city size. Conversely, urban pollution tends to discourage agglomeration if larger cities are more exposed to pollution. The chapter then considers various energy policies and their effect on urban pollution. Specifically, it looks at the effects of energy and transport policies as well as urban policies such as zoning.
We use worldwide gridded satellite data to analyse how population size and density affect urban PM 2.5 pollution. We find that more populated and denser grid cells are more exposed to pollution. However, across urban areas, exposure increases with cities’ population size but decreases with density. Moreover, the population effect is driven mostly by population commuting to core cities rather than the core city population itself. We analyse heterogeneity by geography and income levels. A counterfactual simulation shows that exposure could fall by up to 40% if population size were equalized across all cities within countries, but the relocation of population from large to small cities that maximizes welfare would be small.
We study the effect of energy and transport policies on pollution in two developing country cities. We use a quantitative equilibrium model with choice of housing, energy use, residential location, transport mode, and energy technology. Pollution comes from commuting and residential energy use. The model parameters are calibrated to replicate key variables for two developing country cities, Maputo, Mozambique, and Yogyakarta, Indonesia. In the counterfactual simulations, we study how various transport and energy policies affect equilibrium pollution. Policies may induce rebound effects from increasing residential energy use or switching to high emission modes or locations. In general, these rebound effects tend to be largest for subsidies to public transport or modern residential energy technology.