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Housing Booms and Segregation
Last registered on September 06, 2019

Pre-Trial

Trial Information
General Information
Title
Housing Booms and Segregation
RCT ID
AEARCTR-0004665
Initial registration date
September 05, 2019
Last updated
September 06, 2019 1:50 PM EDT
Location(s)

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Primary Investigator
Affiliation
University of Cologne
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2019-10-01
End date
2019-12-31
Secondary IDs
Abstract
A growing literature in economics highlights the importance of cities and neighborhoods for employment outcomes, social mobility, and individual well-being (Bayer et al., 2008; Chetty et al., 2014; Chetty et al., 2016). The importance of specific locations suggests that spatial segregation according to socioeconomic status is likely to be important for various aspects of inequality and welfare in a society. However, little is known about the drivers of spatial segregation, its evolution over time, and the implications for employment outcomes, individual welfare or mobility. Rising rents and house prices in large and growing cities and metropolitan areas are among the most pressing policy issues in many countries around the world. Ample anecdotal evidence from the media suggests that increasing rent levels lead to the displacement of current residents, changing neighborhood composition, and thereby rising segregation within and across cities. In this research project, we study the interplay of booms in local housing prices and socio-economic segregation across space and aim at quantifying the associated welfare consequences.
External Link(s)
Registration Citation
Citation
Löffler, Max. 2019. "Housing Booms and Segregation." AEA RCT Registry. September 06. https://doi.org/10.1257/rct.4665-1.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-10-01
Intervention End Date
2019-12-31
Primary Outcomes
Primary Outcomes (end points)
Several measures of economic and employment status, market income, and welfare benefits. Household location and moving/relocation decisions. Household composition in terms of socio-economic status.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will combine novel geo-referenced individual data from social security records with localized housing market data to answer the following questions:
- How has spatial segregation evolved over the past decades?
- What are the driving forces behind this development? Have rising housing costs led to the displacement of current residents and fostered spatial segregation of individuals by income and status? Or do housing booms lead to lock-in effects in affected neighborhoods?
- Who are the winners and losers of this process, which groups were forced to move/stay? Where did they move to? Which workers benefit from new jobs in boom areas?
- What are the consequences for employment outcomes, disposable income, and individual well-being? (How) should governments (optimally) react to these developments?
- How do experienced local segregation and inequality affect political preferences?

We plan to proceed in four steps. The first part is a largely descriptive study of the evolution and patterns of segregation over the last two decades. The second part of the project is devoted to a causal analysis of the effects of housing booms. The focus will be on identifying the importance of housing market developments on the location of workers, i.e., on studying displacement/lock-in effects and the implications for neighborhood composition and socio-economic segregation. The third part of the proposed project is concerned with the labor market and welfare implications of these changes in spatial segregation and its drivers, and to develop optimal policy reactions for (local) governments and employment agencies. The final step of our analysis is to explore the importance of experienced segregation and economic inequality for the formation of political preferences.
Experimental Design Details
Not available
Randomization Method
Quasi-experimental variation in housing booms across neighborhoods or grid cells
Randomization Unit
Grid cells or local neighborhoods in Berlin, grid cells or cities in surrounding municipalities
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
Approximately 1000 grid cells or 450 neighborhoods in Berlin and 100 surrounding municipalities in the metropolitan area
Sample size: planned number of observations
Universe of local working age population registered at social security registers in this area (roughly three million individuals)
Sample size (or number of clusters) by treatment arms
In principle, each neighborhood is affected (potentially) differently by local housing booms. However, the timing and the intensity of treatment may be equal or similar across neighborhoods.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number