Over the last two decades, the sociology of entrepreneurship has exploded as an area of academic inquiry. Most of this research has been focused on understanding the environmental conditions that promote entrepreneurship and processes related to the initial formation of an organization. Despite this surge in activity, many important questions remain open. Only more recently have scholars begun to turn their attention to what happens to organizations, and the people connected to them, as they mature and move through the life cycle of entrepreneurship. These open questions, moreover, connect to many classic themes in the literature on careers, organizational sociology, stratification, and work and occupations. Using a framework that focuses on three phases of the entrepreneurial life cycle—pre-entry, entry, and post-entry—we summarize sociological research on entrepreneurship and highlight opportunities for future research.
Annual Review of Sociology, 50: 341-364 (OPEN ACCESS)
]]>How do high-growth startups influence the neighborhoods in which they locate? Using data from the greater London area, we show a positive relationship between entrepreneurship and the subsequent growth of residential real estate prices in a neighborhood. These effects appear concentrated in places that had been cheaper prior to the entry of the entrepreneurs. The demographic composition of these communities also changes in a classic pattern of gentrification, with older, less educated residents being replaced by younger, more educated ones.
]]>How does expansion in the high-tech sector influence the broader economy of a region? We demonstrate that an infusion of venture capital in a region leads to: (i) declines in the number of establishments and in employment in non–high-tech industries in the tradable sector; (ii) increases in entry and in employment in the non-tradable sector; and (iii) a rise in income inequality in the non-tradable sector. Expansion in the high-tech sector therefore leads to a less diverse tradable sector and to increasing inequality in the region.
Entrepreneurship Theory & Practice, 47(2): 344-368.
]]>We argue that the actions for which actors receive recognition vary as they move up the hierarchy. When actors first enter a community, the community rewards them for their easier-to-evaluate contributions to the community. Eventually, however, as these actors rise in status, further increases in stature come increasingly from engaging in actions that are more difficult to evaluate or even impossible to judge. These dynamics produce a positive feedback loop, in which those who have already been accorded some stature garner even greater status through quality-ambiguous actions. We present evidence from Stack Overflow, an online community, and from two online experiments consistent with these expected patterns.
Organization Science, 33(6): 2519-2540 (OPEN ACCESS)
]]>Evaluating the attractiveness of startup employment requires an understanding of both what startups pay and the implications of these jobs for earnings trajectories. Analyzing Danish registry data, we find that employees hired by startups earn roughly 17% less over the next ten years than those hired by large, established firms. About half of this earnings differential stems from sorting—from the fact that startup employees have less human capital. Long-term earnings also vary depending on when individuals are hired. While the earliest employees of startups suffer an earnings penalty, those hired by already-successful startups earn a small premium. Two factors appear to account for the earnings penalties for the early employees: Startups fail at high rates, creating costly spells of unemployment for their (former) employees. Job mobility patterns also diverge: After being employed by a small startup, individuals rarely return to the large employers that pay more.
Organization Science, 32 (3): 587-604 (OPEN ACCESS)
]]>We analyzed Danish registry data from 1991 to 2006 to determine how firm age and size influence wages. Unadjusted statistics suggest that smaller firms paid less than larger ones and that firm age had little or no bearing on wages. After adjusting for differences in the characteristics of employees hired by these firms, however, we observed both firm age and firm size effects. We found that larger firms paid more than smaller firms for observationally-equivalent individuals but, contrary to conventional wisdom, that younger firms paid more than older firms. The size effect, however, dominates the age effect. Thus, while the typical startup – being both young and small – paid less than a more established employer, the largest ones paid a wage premium.
]]>We examine the relationship between income inequality and corporate demography in regional labor markets and specify two mechanisms through which the number and diversity of employers in a labor market affect wage dispersion. Vertical differentiation, or variation in the ability of organizations of a particular kind to benefit from labor inputs, amplifies inequality through quality sorting, as the most productive employees in a particular domain pair with the most productive employers. Increasing horizontal differentiation—variation in the kinds of organizations—reduces inequality as individuals can more easily find firms interested in their distinctive attributes and talents. Our analysis of Danish census data provides support for each thesis. Increased numbers of organizations operating within an industry in a region, a proxy for vertical differentiation, increases wage dispersion in that industry-region. Variation in wages, however, declines with increased horizontal differentiation among employers; this is measured by the diversity of industries offering employment within a region and the variance in firm sizes in an industry-region.
]]>