As high-tech hubs like San Francisco become increasingly unaffordable, we need to be asking the right questions.
The San Francisco Bay Area has been the epicenter for technological revolutions for the better part of the past half-century, the launching pad for world-shaping startups from Intel and Apple to Google, Facebook, and Twitter. But Silicon Valley is also home to “The Jungle,” a 68-acre homeless camp in South San Jose, considered the largest such settlement in the United States. And as tech firms have shifted from suburban nerdistans to more urban locations, longtime Bay Area residents have raged against the Google buses, which shuttle tech workers between their city digs and offices and have become a symbol of the gap between the metro’s rich and poor. There is increasing concern over skyrocketing rising housing costs and rents (which are among the very highest in the nation), growing displacement of long-term residents, and the widening economic wedge between wealthy techies and everybody else.
All of this suggests that "startup" urbanism—not just the winner-take-all mentality of the one percent—is at a minimum connected to growing inequality and increasing unaffordability of America’s leading knowledge cities. But to what extent?
With the help of my Martin Prosperity Institute (MPI) colleague Charlotta Mellander, I decided to take a closer look at high-tech startups and these two key urban problems: increasing economic inequality and worsening housing affordability. Mellander ran a basic correlation analysis between several measures of high-tech startups (including the concentration of high-tech firms, the level of innovation measured by patents per capita, and the level of venture capital startups and investment) and income inequality and wage inequality. Mellander’s analysis of high-tech firms and innovation covers all of the nation’s 350-plus metros, and her analysis of venture capital investment covers the roughly 130 U.S. metros that received venture capital investment. (The usual caveats about correlation not equaling causation and pointing only to associations between variables apply.)
Let’s start with housing costs and affordability. Have high-tech and venture capital-funded startups helped push up housing prices in metro areas? Mellander’s data suggests the phenomena are associated.
Her analysis found median monthly housing costs to be closely associated with high levels of innovation (.49), high concentrations of high-tech industry (.58) and venture capital startups (.60), and investment (.56).
The following graphic, which illustrates her findings, is interactive, so you can hover over the dots to see levels of venture investment and housing costs. Notice the extreme outliers at the top right, like San Jose, San Francisco, and Washington, D.C., which combine high housing costs and high levels of venture capital investment. Other tech hubs, like Boston, New York, and Seattle are much closer to the trend line, though still above it, indicating that their high housing costs more neatly line up with levels of venture capital investment.
The reality is that techies and knowledge workers have high salaries. So it’s no wonder that many blame them for bidding up the cost of housing, which leads to gentrification and rising costs of living for all in the neighborhood. In fact, salaries and wages are so high among these tech and knowledge workers in hubs like San Francisco, Silicon Valley, and Boston that residents of these cities are among those with the largest sums of money left over after paying for housing. But the real problem here is that the burden of rising housing prices falls unevenly on lower-skilled, lower-paid service and blue collar workers, who have considerably less money to spend on other cost of living expenses after paying for housing in these leading tech hubs.
When it comes to inequality, the picture is less clear. The associations between high-tech factors and our two measures of inequality (wage and income inequality) are decidedly mixed, according to Mellander’s analysis. On one hand, she found close correlations between wage inequality and innovation (.44), the concentration of venture capital startups (.60), and the amount of venture capital investment (.55). The scatterplot below illustrates the close connection between wage inequality and venture capital investment (again as a logged value). The upward sloping line again illustrates a strong connection between the two. Again, tech hubs like San Jose and Boulder are outliers, illustrating the close connection between venture investment and wage inequality in these places. Interestingly, San Francisco is just below the trend line on the far right, indicating that its wage inequality is not quite as dramatic as its level of venture capital investment might suggest. Boston, New York, Seattle, and Austin are also closer to the trend line: Boston and Seattle below, New York and Austin above.
But the relationship is not as strong when it comes to the broader measures of income inequality based on the Gini Coefficient. Mellander’s analysis found this measure of income inequality to be weakly related to high-tech industry (.16), venture capital startups (.24), and investment (.22), and to have a statistically insignificant association with innovation as measured by patents per capita. The scatterplot below illustrates the weak connection between venture capital investment and income inequality. This fitted line is basically flat, indicating the weak connection between venture capital investment and income inequality.
These two divergent patterns reflect the underlying differences between wage and income inequality. As I previously noted on this site and in a paper with Mellander published in the journal Regional Studies [PDF], wage inequality is highly correlated with the concentration of high-tech industry, the share of college grads, and of the creative class. But the same factors explain just 15 percent of the geographic variation in income inequality. Income inequality instead reflects the staggering inequities of poverty and race at the very bottom of our socioeconomic order, as well as de-unionization and low rates of taxation. Income inequality at the metro level, then, is the localized expression of the unraveling of the post-war social compact between capital and labor.
With income inequality surging and housing becoming all but unaffordable for the working and middle classes in urban tech hubs, one can understand where the anger projected at techies comes from. That said, it may be a huge mistake to blame technology, innovation, and startups—the very things that drive productivity and job creation—for today’s urban problems. As Tyler Cowen recently noted, while technology may increase inequality in the short run, it may also play a key role in reducing it in the long term. Certainly, the rise of urban tech creates new sources of jobs, money, and municipal revenue that cities can use to address mounting inequities through initiatives that raise the minimum wage, upgrade low-wage service jobs, and build more affordable housing.
It’s time to stop pointing fingers and get on with the far more important task of harnessing the urban tech revolution to create a new urban middle class and a more inclusive urbanism—one in which many more workers and residents can participate, and one from which many more can benefit.