This essay series is primarily focused on the dynamics between urban politics and powerful companies.
Is San Jose a particularly cyberpunk city? Not really, especially not compared to the metropolises of Tokyo or Hong Kong. If I were to call an American city cyberpunk, San Francisco is closer to the mark, visually. But beyond aesthetics, I find San Jose is worth our attention: a city larger than neighboring SF, home to some of the wealthiest people in the country, and one of the world’s foremost tech capitals.
Part 3 explores the effect of San Jose’s high-tech industry on socioeconomic diversity, in partial response to what scholarly literature predicts about the subject. You can read Part 4 here.
*Note: I understand very well that socioeconomic diversity includes more than ethnicity and economic status, but to keep the article streamlined we focused on those two metrics. You can view more data yourself at the US Census site, though you may have to make some charts on your own.
In recent decades, American suburbs have become increasingly relevant to urban life. Much is made of suburbs, from scholars and residents alike, but all can agree on their newfound importance. However, there is one area of the country full of small, sleepy suburbs hosting industry giants and tremendous economic innovation. San Jose and its satellite cities form the chunk of Silicon Valley, a region with the economic characteristics of a big city put into a series of tiny towns. The scholarly socioeconomic descriptions of suburbs mostly match this area, but the concentration of industry seems to be something not expounded upon enough in the books.
A useful scholarly context has been established by several authors, who feature in a collection of works by Paul Kantor and Dennis R. Judd. Specifically, authors Rosalyn Baxandall and Elizabeth Ewen (with a work on Long Island suburbs) and Eric Avila (writing on Los Angeles suburbs) discuss the shape modern suburbs are taking. Baxandall and Ewen describe an influx of immigrants from a variety of economic backgrounds populating and shaping suburbs of Long Island. Eric Avila discusses the socioeconomic diversification of the Los Angeles suburbs: “the demographic transformation….poses a powerful challenge to the regional hegemony of suburban whiteness.” He documents the ethnic diversification of the region and how residential areas once seen as the domain of white, middle class-ness have begun to change.
It is from these two works and an additional excerpt about “boomburbs” that Kantor and Judd draw conclusions about the features of 21st century suburbs: they are “multiracial and multiethnic…the suburbs seem to be fragmenting into enclaves that work to separate suburban residents on the basis of class, race, and ethnicity.”
San Jose presents an interesting scenario, one in which the aforementioned academic context is mostly correct but faces new dynamics. For one, though a couple suburbs of San Jose are large, with over 100,000 residents, the central city’s population is still slightly larger than all its surrounding suburbs’ populations put together. However, the suburbs of Santa Clara county, despite coming in a variety of sizes, for the most part host at least one major company and in many cases, multiple. Most of Silicon Valley’s household names are located in (and dominate) sleepy suburbs outside of San Jose.
Santa Clara County is an area of diversity and wealth, and it has seen much rapid demographic change of the sort described by Kantor and Judd’s authors. As the general economic trend of late has been increasing levels of wealth inequality, and as San Jose’s industry has been a key growth sector, it seemed likely the racial demographic changes described by scholars would hold true, but the economic demographics would be more nuanced.
With that in mind, I came to the following guiding question: to what extent does an industry leading city’s industry affect the socioeconomic diversity of its suburbs? I anticipated finding, as mentioned, racial diversity and a lack of economic diversity. This was mostly confirmed by the evidence, but a lack of available information prevents us from definitively understanding industry as the main cause of recent changes in socioeconomic diversity.
I based most of the research on publicly available demographic data from recent years (and when possible, from recent decades) for Santa Clara County. If there seemed to be very little demographic change, then of course there would already be an answer to the question; however, if there was significant demographic change, industry could have played a role that would need to be investigated further. San Jose, the central city of Santa Clara County, has seen demographic changes almost identical to the county itself, and the county changes have been mostly consistent among the suburbs with little variation. It is for this reason I measured the county all together: it is more efficient and should still provide a statistically accurate portrait of San Jose’s suburbs as a whole. The census data shows an explosion in racial diversity that is still limited to certain groups, and a decrease in economic diversity.
Demographic data and analysis
The most immediately clear change to Santa Clara county’s demographics is in pure population: the 2010 estimate for the county was 1,781,672 people, which had grown to 1,919,402 by 2016. In fact, Santa Clara county was the fastest growing California county in 2013 and 7th fastest in 2014-2015. Since 1970, Santa Clara county’s population has nearly doubled in size.
These high growth rates have been characterized by increasing racial and ethnic diversity.
Aside from racial or ethnic grouping, a majority of the county is closely tied to immigration: 60% of Santa Clara County’s population are first or second generation immigrants. All in all, the scholarly descriptions of the racial diversity of suburbia hold true for Silicon Valley, though it is important to note it is a limited diversity. If one assesses diversity by the amount of non-white residents, the area is certainly one of the most diverse in the country. However, if one measures diversity by a more even proportion of racial groups, Santa Clara County can only be described as limited, particularly for the groups mentioned by Avila, Baxandall, and Ewen (African American and Latino people, but especially African Americans).
While the racial diversity of Santa Clara County has very obviously spiked, the economic diversity of the suburbs has been shrinking.
Between 2010 and 2015, there was almost no discernible change in the proportions of households by income distributions, save one category: households earning $200,000 or more jumped from accounting for 13.5% of households to 18.2%. In total, as of 2015, just under half of households (48.5%) earned six figures or more, with the rest of the households being split among a variation of incomes below that.
Relative to national demographics of income distribution, Santa Clara county is notably more inequitable. A 2016 Pew Research study found 51% of American adults were middle-income, 29% were lower-income, and 20% were upper-income, but 31% of Santa Clara household incomes were upper-income, far beyond the national average and one of the highest percentages nationwide.
Another notable change in socioeconomic demographics lies not just in an increase in and concentration of the wealthy, but an increase in poverty.
Through the decades of the late 20th century, the percentage of residents living below the poverty line in the county was mostly consistent. The dawn of the 21st century ushered in a sharp spike in poverty levels to unprecedented amounts. Notably, this trend continued both before and well after the 2008-2009 financial crisis.
Taken together, San Jose and its suburbs demonstrate a growing wealth inequality that outmatches most other metropolitan areas. It is here we seem some departure from the literature—the economic diversity of the suburbs in this industry-leading city is smaller than most other places and is steadily shrinking, rather than representing a strong multitude of class.
Although the available data largely confirmed the situation described by the hypothesis, the key component is determining the extent to which industry has caused or affected these changes. To this end, the answer is more nuanced. The main difference between Silicon Valley and any other metropolitan area is its concentration of hi-tech companies and thriving tech sector. There are not concrete studies confirming the involvement of the technology sector in causing socioeconomic diversity in San Jose’s suburbs. One would think there would be a wealth of such hard evidence, but it turns out most of it is anecdotal (i.e., an interview with an immigrant talking about why they joined Silicon Valley) or based on a common-knowledge understanding of the situation (i.e., “this is basic economics, we see this all the time”). One can infer from common knowledge or look at the near-minimum wages payed by top regional employers to lower-end employees to find a causal relationship, but there are no conclusive studies to confirm this, or how the mechanics of the causal relationship work if one exists at all.
It is almost certain industry has caused at least some of these socioeconomic changes in the suburbs and in all likelihood it has contributed to most of them; however, because of a lack of hard data, I cannot say there is conclusive proof of a strong causal relationship. A correlation can at least be said to exist simply because of the basic facts of the situation—a thriving tech sector began ramping up around the same time socioeconomic demographics began to change, and the constant change has accompanied constant tech sector growth. However, these same basic facts do not prove causation and there is little else available to conclusively do so either.
Available data, most of it from the US Census, shows a strong change in socioeconomic diversity, largely where ethnic diversity is concerned. The image painted by academia of suburbs as increasingly racially diverse and immigrant-populated is absolutely correct in San Jose. However, claims of economic diversity fall short as wealth inequality in the suburbs increases. The lack of large, conclusive studies prevents us from drawing strongly-backed conclusions, but it is still safe to say some connection exists between industry and the socioeconomic diversity of the suburbs.
To the extent that it at least correlates with and most likely (but not definitively) causes on some level a limited racial diversity and increased wealth inequity, an industry-leading city’s industry affects the socioeconomic diversity of the suburbs.
 Paul Kantor and Dennis R. Jude, American Urban Politics in a Global Age (Pearson Education Inc, 2006), 177.
 Ibid, 194.
 Boomburbs are suburbs defined by: having over 100,000 residents, not being the core city in the region, and having double-digit population growth rates each census since 1970. (Ibid, 186). There is only one suburb of San Jose that comes close to meeting the definition of a Boomburb, so I did not find the literature relevant as I wanted to study the suburbs as a whole.
 Ibid, 175.
 Though socioeconomic can of course be a broad descriptor, I am focusing on just race and wealth in this paper.
 “Community Facts,” American Fact Finder, accessed November 2017. https://factfinder.census.gov/faces/nav/jsf/pages/community_facts.html
 Unless otherwise noted, information in this section was obtained from available US census data.
 Silicon Valley Institute for Regional Studies, “Research Brief: Population Growth in Silicon Valley,” Joint Venture Silicon Valley, May 2015. https://jointventure.org/images/stories/pdf/population-brief-2015-05.pdf
 In the study, middle income was defined as a household income is 2/3rds to double the national median. Middle income ranged depending on the amount of members of the household, but the overall national range was $42,000 to $125,000 for a household of three. More detailed methodology is available on footnote #12’s link.
 Richard Fry and Rakesh Kocchar, “Are you in the American middle class? Find out with our income calculator,” Pew Research, May 11th, 2016. http://www.pewresearch.org/fact-tank/2016/05/11/are-you-in-the-american-middle-class/
 “America’s Shrinking Middle Class: A Close Look at Changes Within Metropolitan Areas,” Pew Research Center, May 11th, 2016. http://www.pewsocialtrends.org/2016/05/11/americas-shrinking-middle-class-a-close-look-at-changes-within-metropolitan-areas/
 Unlike drawing lines between household incomes, which can be messy in determining economic status, poverty levels are well-defined and widely used in census data. A more detailed explanation on how individuals and households are classified as being in poverty by the Census can be found here: https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html