Introduction

More than 50% of the world’s population lives in cities. In developed countries, the share is significantly higher, while in developing countries, it is rapidly rising. Productivity advantages and correspondingly higher wages have been identified as potential drivers of urbanization since at least Marshall’s seminal work in 1890. There is abundant evidence confirming that productivity in cities is higher, making them attractive places to work. Cities may also be appealing as places to live due to their urban amenities, such as ethnic restaurants, music venues, or art galleries. In contrast, rural areas may be attractive due to high environmental quality (clean air) or natural amenities (forests and lakes). Economists refer to the joint effect of all such amenities on the perceived attractiveness of a location as quality of life (QoL). While there is a rich economics literature on measuring QoL, we know very little about whether cities tend to have higher QoL than rural areas, i.e. whether there is a positive urban QoL premium. In our recent paper, we argue that the lack of evidence for a positive urban QoL premium may be due to measurement error.  

Summary

Empirically, it is challenging to measure QoL, as many contributing amenities are unobservable or difficult to quantify. Therefore, economists use spatial equilibrium models to uncover unobserved QoL from observed wages and living costs. Intuitively, the canonical framework assumes that all non-housing goods are freely tradable and that workers are homogeneous and perfectly mobile. Under these assumptions, workers will relocate, causing adjustments to wages and house prices, until any difference in QoL between two locations is offset by differences in wages and/or housing costs.

The main limitation of the canonical approach is that it fails to account for different forms of spatial frictions. For example, if there are trade frictions, then non-housing prices will differ between locations for reasons unrelated to QoL. Alternatively, if there are mobility frictions, such as idiosyncratic preferences for specific locations or local ties due to family or friends, small differences in wages generally do not suffice to influence many workers’ location decisions.

We argue that by accounting for these spatial frictions, quantitative spatial models reduce measurement error. Our theoretical analysis indeed confirms that the difference in QoL between locations tends to be underestimated in the canonical model. The extent of measurement error tends to be most pronounced in large cities. Our analysis also provides insights into the relative importance of different spatial frictions for the measurement error. The results from a decomposition show that measurement error to a greater extent due to mobility frictions than trade frictions. These results are highly robust to the choice of parameter values in the models, making it a general finding that likely holds in many countries around the world.

To produce the first theory-based QoL ranking accounting for spatial frictions, we apply the model to rich data from Germany (Immoscout24, the Federal Employment Agency and the Federal Statistical Office). This application illustrates how our approach leads to greater variation in QoL across regions and significant changes in rankings compared to measurement within the standard framework. For example, comparing our approach with the canonical approach for the year 2015, Hamburg is ahead of Munich as the city with the highest QoL. Frankfurt climbs one place to fourth, while Düsseldorf rises seven places to fifth. In contrast, Chemnitz climbs 62 places to 39th, while Lörrach falls 49 places to 86th. Only Berlin (3rd) and Höxter (131st) remain unchanged. On average, the absolute rank change is 17. During the last years, Munich and Hamburg have been battling for #1 in our ranking, switching positions in from 2007 (#1 M) to 2011 (#1 HH) and then again from 2015 (#1 HH) to 2019 (#M). At the same time, Berlin has been catching up. It went from #4 in 2011 to #3 in 2015 and is getting closer and closer to M and HH. We provide an interactive webtool, where users can explore QoL rankings over time for any pair of German cities.

Our QoL measure also reveals a sizable urban QoL premium in Germany. On average, doubling the population of a a region is associated with a 20-percent increase in QoL. For comparison, the same increase in the size of the region is associated with no more than a 5-percent increase in wages.

Discussion

Our findings suggest that QoL is a much more significant determinant of regional success than previously thought. This has profound implications for policymakers. Efforts to make struggling regions more productive are important, but equally critical is ensuring a high quality of life. Strategies could include investing in cultural and recreational amenities, reducing pollution and crime, or improving the urban built environment.

As a tangible contribution to the applied literature, we provide an accessible GitHub toolkit with parsimonious data requirements that solves for a new QoL. Our fully theory-consistent measure of QoL is somewhat data-intensive, we also provide a crude data version based on population statistics which still significantly reduces measurement error relative to the canonical measure. This tool should help policymakers to identify areas with objectively low QoL, allowing for a better understanding of the factors that are beneficial or detrimental to QoL and, ultimately, economic prosperity.

This study is published as a Berlin School of Economics Discussion Paper:

Ahlfeldt, G. M., Bald, Fabian, Roth, D., Seidel, T. (2024). Measuring Quality of Life Under Spatial Frictions. (Berlin School of Economics Discussion No. 57), Berlin School of Economics. (https://doi.org/10.48462/opus4-5676)

INSIGHTS Interview - Measuring Quality of Life Under Spatial Frictions: Insights from Germany