Blow fiber, not tumbleweeds.
Fiber-to-the-home (FTTH) systems drive growth in cities by a measurable amount, according to a recent study in Sweden. The analysis was done by Ziyi Xiong, a graduate student at the KTH Royal Institute of Technology in Stockholm.
She crunched demographic and network data from 290 Swedish municipalities, factored out other possible influences, such as the degree of urbanization, and found that increasing fiber availability at workplaces by 10% results in population growth of nearly two-tenths of a percent (.17%). Building residential FTTH systems also leads to population growth, but it takes a 13% increase in fiber penetration to get the same result.
The implication is that going from zero to 100% FTTH availability in a community, rural or urban, would lead to an increase of more than 1% in population. It’s a significant economic development factor for rural communities in Sweden, and elsewhere, that are fighting population decline.
Sweden is the most heavily fibered country in Europe, and near the top of world rankings. That gave Xiong a broader range of data to analyze; FTTH is found in all kinds of communities, not just exceptional ones.
About two-thirds of the population increase comes from people moving into fibered communities, the remainder is due to a higher birthrate. The implication is that better broadband creates jobs that particularly attract younger people who are starting families. Confirmation (or not) of that hypothesis, though, will have to wait. This study did not correlate fiber penetration with employment growth or changes in the mix of jobs available.
Most of the FTTH systems were municipally owned and open access, meaning competitive providers can offer service to residents via the same network. The study did not look at municipal FTTH financial results or try to quantify any economic development benefits beyond population growth. Its limited scope and analytical rigor adds credibility and value to the results, though. Too many FTTH studies try to draw too many conclusions from too little data. Xiong has created a model that, hopefully, will be adopted by future researchers.