Tradeable mobility credits

The original dataset contained sensitive personal information and could therefore not be included here. The socio-demographic data is therefore agregated to minimise the chance of reidentification.

The dataset pertains to a stated preference (SP) choice study, analysing the preferences and potential of tradeable mobility credits among the urban Dutch population. The dataset was filetered based on a minimal and maximum response time, completeness of the survey and straightlining behaviour.

This README file explains the coding of the dataset in more detail. In addition to the main dataset, the following files are also included:
design.csv		--> SP design of the choice tasks
survey_transcript.docx	--> transcript of the full survey in English and Dutch


For the main dataset, the columns refer to the following:
Duration	--> time between starting and completing the survey [in seconds]. Note that respondents may have left and came back several hours or days later.
consent		--> consent questions if respondents agreed to taking part in the survey and their data being stored

set1_buy	--> the number of credits bought in choice set 1
set1_sell	--> the number of credits sold in choice set 1
set1_mode	--> the travel mode chosen in choice set 1
rate1		--> the exchange rate to buy or sell credits in choice set 1
credits_start	--> the number of credits the respondent was starting with
exchange_block	--> which block of exchange rates (low, medium, high) the respodent was randomly allocated to
budget_block	--> which block of starting values (150, 250, 350) the respondent was randomly allocated to

finance_1-12	--> attitudinal statements related to financial behaviour

trip_type	--> what trip the respondent pictured when making choices

mode_work	--> the most frequently chosen mode when travelling to/from work or education
mode_social	--> the most frequently chosen mode when travelling to/from social gatherings (with friends, family,...)
mode_recreation	--> the most frequently chosen mode when travelling to/from sports or recreation
mode_shopping	--> the most frequently chosen mode when travelling to/from shopping
mode_frequency	--> how frequently a mode is used 
n_cars		--> the number of cars in the household
car_license	--> if the respondent has a drivers license

age		--> age group of the respondent
gender		--> gender of the respondent
hh_income	--> household income group of the respondent
education	--> education level group of the respondent
employment	--> employment status of the respondent
hh_size		--> household size of the respondent