Toolkit for measuring quality of life under spatial frictions

(c) Gabriel M. Ahlfeldt, Fabian Bald, Duncan Roth, Tobias Seidel Version 1.0.0, 2024-10

General instructions

This toolkit implements a numerical solution algorithm to invert a quality of life (QoL) from observed data in various programming languages. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024): Measuring quality of life under spatial frictions. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictions—generated by idiosyncratic tastes and local ties—and trade frictions—generated by trade costs and non-tradable services, thereby reducing non-classical measurement error.

Notice that quality of life is identified up to a constant. Therefore, the inverted QoL measures measure has a relative interpretation only. We normalize the QoL relative to the first observation in the data set. It is straightforward to rescale the QoL measure to any other location or any other value (such as the mean or median in the distribution of QoL across locations).

When using this toolkit in your work, please cite Gabriel M. Ahlfeldt, Fabian Bald, Duncan Roth, Tobias Seidel (forthcoming): Measuring quality of life under spatial frictions.

Getting started

1. Navigate to the subfolder of your preferred language (MATLAB, Python, R, Stata)
2. Follow installation / setup instructions from README.md of the respective folder
3. Consult Example script in respective folder to get familiar with the toolkit
4. Apply the toolkit on your data to invert the quality of life measure

Repository contents

Folders

Name | Description | |:---------------------------------------------|:-------------------------------------------------------------------------| | DATA | Folder containing data a set on which ABRSQOL can be tested. | | MATLAB | Folder containing a MATLAB function along with instructions. It does not contain the variables used in the paper. | | Python | Link to repository containing a Python package along with instructions. | | R | Folder containing an R package along with instructions. | | Stata | Folder containing a Stata ado file along with instructions. |

Files

Folder | Name | Description | |:-------------------|:-------------------------------------|:-------------------------------------------------------------------------| | - | ABRSQOL-Codebook.pdf | Codebook introducing variables and laying out the structure of the solver in pseduo code | | DATA | ABRSQOL-testdata.csv | Test data set in comma separated format. Please note that this is a test data set, and it is not identical to the data used in the paper. The data set includes average disposable household income as a measure of wage, the local labour market house price index from Ahlfeldt, Heblich, Seidel (2023), the 2015 census population as a measure of residence population and hte 1985 census population as measure of hometown population. Tradable goods price and local services price indices are uniformly set to one. | | DATA | ABRSQOL-testdata.dta | Stata version of the test data set. Please note that this is a test data set, and it is not identical to the data used in the paper. | | MATLAB | Example.m | Illustrative script that reads the test data set and calls the ABRSQOL funcation with an exemplary syntax. Your journey starts here. Just open the script file in MATLAB after you have copied it from the MATLAB folder and take it from there. You may also just copy the code to your MATLAB script editor and run it as a script (do not just copy the code to the command window). | | MATLAB | abrsqol.m | MATLAB function. It needs to be copied to yourworking directory file folder. This is done by the Examlple.m file. | | R | Example.do | Illustrative script that reads the test data set and calls the ABRSQOL programme with an exemplary syntax. Your journey starts here. Just open the do file in R and take it from there. You may also just copy the code to your R script editor and run it (do not just copy the code to the command window). | | Python | Example.py, Example.ipynb | Illustrative script that reads the test data set and calls the ABRSQOL programme with an exemplary syntax. Your journey starts here. Just open the file in an editor and take it from there. You may also just copy the code into your editor (e.g. Jupyter Notebook) and run it. | | Stata | Example.do | Illustrative script that reads the test data set and calls the ABRSQOL programme with an exemplary syntax. Your journey starts here. Just open the do file in Stata after you have installed the ABRSQOL-toolkit and take it from there. You may also just copy the code to your Stata do file editor and run it as a script (do not just copy the code to the command window). | | Stata | abrsqol.ado | Stata ado file. It needs to be copied to your personal ado file folder. This is done by the Examlple.do file. You can also copy it manually. To locate the path, type adopath in Stata. Alternatively, you can type ssc install ABRSQOL in Stata. | | Stata | abrsqol.sthlp | Stata help file. It needs to be copied to your personal ado file folder. This is done by the Examlple.do file. You can also copy it manually. Once copied, you can call it by typing help ABRSQOL in Stata.

Further resources:

Gabriel M. Ahlfeldt, Fabian Bald, Duncan Roth, Tobias Seidel (forthcoming): Measuring quality of life under spatial frictions.

Acknowledgements

We thank Max von Mylius for implementing the algorithm in Python and R.