The challenge of gentrification

Richard Florida (study here) adds clustering of talent based on the level of occupational creativity (knowledge-based Vs routine) with Michael Porter's theory of tradeable and locally-oriented industrial clusters (to create four distinct industrial-occupational categories), for over 260 metro areas making up three-quarters of US population, and comes up with very interesting findings,

Creative-in-traded employment is a key driver of both innovation and economic growth, according to our analysis. It is positively associated with higher levels of innovation (with a correlation of .61), higher levels of economic output per capita (.53), and higher wages (.6). (As usual, we note that correlation does not equal causation, but simply points to associations between variables). That said, creative occupations are more closely associated with innovation and economic growth (with correlations of .52 to economic output, .52 to patents, and .66 to wages) than traded industries (with correlations of .38 to economic output, .35 to patents, and .25 to wages). Furthermore, creative-in-local jobs are modestly associated with wages (.34) and economic output (.17), but not with innovation. On the other hand, both routine-in-traded and routine-in-local jobs are negatively correlated to wages and innovation, while only routine-in-local jobs are negatively associated with economic output per capita.

Further, creative occupations, tradeable and local, make up just 39% of the workforce, while routine-local occupations make up nearly 45%. The average salary in creative-traded occupations is 31% more than those of creative-local workers, 117% more than toutine-traded workers, and 182% more than routine-local workers. And these trends have been widening over the years.

Pointing to gentrification effects, it finds higher concentration of creative-traded occupations associated with greater inequality, and the same being magnified by housing costs, 

The share of creative-in-traded jobs is positively associated with income inequality (.31)... The share of routine-in-traded jobs (largely in manufacturing), for instance, is negatively associated with inequality (-.20). This divide is magnified by housing costs, which are higher in more knowledge-based metros... All four categories of workers have higher wages in more creative-in-traded metros. But, when we take housing costs into account, we find distinct winners and losers in these more advanced, knowledge-based metros. On the one hand, the two groups of creative workers end up being better off after paying for housing, with positive correlations for both creative-in-traded (.33) and creative-in-local (.36) workers. For both categories, their wages rise enough on average to more than cover the increased costs of housing in these more expensive metros. On the other hand... our analysis found a statistically insignificant correlation for routine workers in traded clusters, and routine workers in local industries are significantly worse off (with a negative correlation of -.43).

And its consequences are stark,

Higher wages in metros with larger creative-in-traded employment create greater incentives for more skilled and advantaged workers to migrate to these metros. As housing costs rise, routine workers—especially those in routine-in-local jobs—are shunted off to less expensive metros which, by definition, have smaller concentrations of higher-paying creative-in-traded jobs. This creates a vicious cycle in which the advantaged become more advantaged over time, while the disadvantaged sink further into poverty.

This challenge is most unlikely to be resolved anytime in the foreseeable future and the problem will keep getting worse. Cities in developing countries like India are witnessing such trends playing out on a much faster scale. Housing within a reasonable distance of the city center in large cities is out of reach for all but those at the top 1% of the income ladder. Government officials, with their quarters, and the small sliver of senior level private employees belong to this category. The booming suburban clusters and towns, into where private economic activity, especially in knowledge-based services, has been displaced, too are soon likely to experience similar gentrification effects, and so on. 

Standard public policy approaches, including those aimed at education and skilling, are unlikely to make any dent on the problem. Robust social safety nets (eg access to quality healthcare), massive affordable housing programs, re-skilling programs for dis-employed workers especially those engaged in routine-traded sectors, and equal access to inter-generational mobility opportunities (eg. top-quality higher education) have to be essential components of any public policy intervention to mitigate the consequences of this trend. It has to be complemented with far higher levels of taxation at the top of the income ladder to generate the additional resources required to support such policy interventions.