From creative cities to creative towns: an investigation into remote workers attraction incentives programs

During the COVID-19 pandemic, many U.S. small towns and secondary cities started offering cash and in-kind incentives to attract remote workers eager to escape overcrowded coastal cities. These new incentive schemes, known in economic literature as Remote Work Attraction Incentives Programs (see Strauss and Jow, 2022), were highly debated on the pages of mainstream news websites (e.g., ForbesFortuneBloomberg). Someone even created a website (MakeMyMove) to help remote workers find their next location.

In this research, recently published in the International Journal of Communication, I analyze the websites of 15 Remote Work Attraction Incentives Programs to understand how U.S. second-tier cities, rural regions, metropolitan areas, and entire states are reshaping their public images in an attempt to rebrand themselves as ideal places for remote workers. The findings show how Remote Work Attraction Incentives Programs counter canonical models for talent attraction (like Richard Florida’s infamous theory of the creative class) by promoting their respective locations as places of production rather than places of consumption and by playing on the contrast between the lifestyles remote workers can afford in the promoted locations and the issues affecting the quality of life in major U.S. cities (housing crisis, criminality, etc.).

This paper is the culmination of 18 months of work. I am grateful for all the feedback I received at the Communicative Cities Research Network Symposium 2022 at the London School of Economics and the 2022 Canadian Communication Association conference. You can download the open-access paper from the IJOC website.

Below, instead, is a visualization I created in Tableau using the data I collected for the research. Hover over a marker to see a summary of the main themes employed by each relocation program, and click to see a detailed breakdown of each theme into its respective subthemes.

You can also download the dataset and the coding protocol from my OSF repository. As usual, all materials are released under a Creative Commons license (CC-By Attribution 4.0 International).

Photo credit: Sergio Souza.

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