The willingness to pay for urban amenities

Last registered on April 08, 2020

View Trial History

Pre-Trial

Trial Information

General Information

Title

The willingness to pay for urban amenities

RCT ID

AEARCTR-0005574

Initial registration date

April 08, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

April 08, 2020, 11:51 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

Germany

Region

Primary Investigator

Name

Eduard Brüll

Affiliation

ZEW Mannheim

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Cäcilia Lipowski

PI Affiliation

ZEW Mannheim

Contact Investigator

PI Name

Melanie Arntz

PI Affiliation

ZEW Mannheim, University of Heidelberg

Contact Investigator

Additional Trial Information

Status

In development

Start date

2020-04-10

End date

2020-04-17

Keywords

Labor, Other

Additional Keywords

Urban, Regional

JEL code(s)

R12, R22, R58

Secondary IDs

Abstract

Attracting high-skilled individuals is one of the most important aspects of cities' regional development strategies. While some policies aim at attracting firms to increase the local demand for high-skilled labour, others aim to directly raise high-skilled labour supply by attracting high skilled individuals with better local amenities. Empirically, cities with better cultural offerings or greater perceived openness indeed tend to have a higher share of high-skilled people. However, the effectiveness of these policies that are focused on amenities are hotly debated, as it is not clear whether people follow amenities or amenities follow people.
Therefore we use a stated-preference choice experiment to study how individuals value local amenities in their relocation decisions between different cities. In this experiment, we pose a variety of randomized choices between job offers in two hypothetical cities to a representative sample of the German working population. Each of the choice scenario consists of two alternatives specifying a combination of earnings and the quality of different amenities. Our main focus in the analysis of these choices will be on the respondents' willingness to forego higher wages for better local amenities: Which amenities are more relevant for the relocation choice? Does the measured implicit willingness to pay for different types of urban amenities vary between different groups of respondents? Are preferences different between high-skilled and less-skilled individuals?

External Link(s)

Registration Citation

Citation

Arntz, Melanie, Eduard Brüll and Cäcilia Lipowski. 2020. "The willingness to pay for urban amenities." AEA RCT Registry. April 08. https://doi.org/10.1257/rct.5574-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2020-04-10

Intervention End Date

2020-04-17

Primary Outcomes

Primary Outcomes (end points)

Preferences, measured as the willingness to forgo earnings for six different urban amenities. The amenities presented in the choice scenarios are 6 ratings for different aspects of the quality of live in cities. These 6 categories are cultural offerings, social diversity , ecological quality, quality of the infrastructure, economic dynamism of the city and family friendliness.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Our experiment is a discrete choice experiment in a representative offline sampled online panel of the German population (Payback Panel). Each survey participant is presented with seven randomly selected scenarios from a series of 6435 possible scenarios, where he or she has to choose from two job offers in hypothetical cities. These scenarios differ in 7 attributes that can have three possible levels each. The first attribute is the hypothetical income of the respondents, which can be either the last monthly wage or an increase of 5% or 10%, depending on the scenario. The other six attributes are measures for the quality of different urban amenities and can take three different levels ("low quality", "medium quality" and "high quality"). These individual amenities are cultural offerings, social diversity, ecological quality, quality of the infrastructure, economic dynamism and family friendliness of a city.
Our design of the choice scenarios is based on a resolution 4 orthogonal array (L243.3.20) of the possible attribute combinations, which contains 243 alternatives. We choose this design array as a starting point so that we can estimate the effect of two-level interactions of attributes. If all possible unique combinations of the 243 alternatives are considered, there would be 29,403 possible decision scenarios.
However we impose three different restrictions on the design. First, given that 6 of our attributes are either the respondents wage or a public amenity that has a clear quality ordering, we exclude strictly dominated choices from the set of decision scenarios. Second, for scenarios, where the wage is equal in both alternatives, we only keep the ones where this is the last monthly wage. Third, to reduce the cognitive load of the scenarios we only retain choices where a maximum of 3-4 factors are different in the two alternatives.

Experimental Design Details

Randomization Method

Uniform randomization of choice scenario from the candidate array based on the software of a professional survey firm.

Randomization Unit

Randomization is at the individual choice scenario level

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Number of individuals in our surveys

Sample size: planned number of observations

13000 choices across 1900 individuals

Sample size (or number of clusters) by treatment arms

Uniform randomization of 6435 candidate choice scenarios across 13000 choices (7 choices per individual). The sample is stratified to oversample university educated individuals. 50% of repsondents will have a tertiary education degree . When computing preference paramters for the overall population, we will use apropriate weights.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB