Is There Trickle-Down from Tech? Poverty, Employment, and the High-Technology Multiplier in U.S. Cities

Abstract

High-technology industries are seen as important in helping urban economies thrive, but at the same time they are often considered potential drivers of relative poverty and social exclusion. Little research, however, has assessed how high-tech affects urban poverty and the wages of workers with little formal education. This article addresses this gap in the literature and investigates the relationships among employment in high-tech industries, poverty, and the labor market for non-degree-educated workers using a panel of 295 Metropolitan Statistical Areas (MSAs) in the United States between 2005 and 2011. The results show no real impact of the presence of high-technology industries on poverty and, especially, extreme poverty. Yet there is strong evidence that tech employment increases wages for non-degree-educated workers and, to a lesser extent, employment for those without degrees. These findings suggest that although tech employment has some role in improving welfare for non-degree-educated workers, tech employment alone is not enough to reduce poverty.

高科技产业被视为协助城市经济繁盛的重要产业, 但它们却同时经常被认为是相对贫穷与社会排除的潜在驱力。然而鲜少有研究评价高科技产业如何影响城市贫穷以及正式教育水平不高的受薪劳工之工资。本文处理既有文献中的此一阙如, 并运用美国自 2005 年至 2011 年间的两百九十五个大都会统计区 (MSAs) 的面板, 探讨高科技产业就业、贫穷和接受非学位教育的劳工的劳动市场之间的关联性。研究结果显示, 高科技产业的存在, 对于贫穷并没有实际的影响, 特别是对极端贫穷而言。但却有强而有力的证据证实, 科技就业增加了接受非学位教育的工人薪资, 并在较小的幅度上, 增进了无学位者者的就业。这些发现指出, 尽管科技就业在提升接受非学位教育的劳工福祉中扮演了若干角色, 但科技就业本身并不足以降低贫穷。

Las industrias de alta tecnología son vistas como importantes para contribuir a que las economías urbanas prosperen, pero al mismo tiempo con frecuencia se las considera causantes de pobreza relativa y exclusión social. Muy poca investigación, sin embargo, ha evaluado la manera como la alta tecnología afecta la pobreza urbana y los salarios de trabajadores con poca educación formal. Este artículo se ocupa de esta laguna en la literatura e investiga la relación entre empleo y las industrias de alta tecnología, pobreza y el mercado laboral para trabajadores educados sin título, usando un panel de 295 Áreas Estadísticas Metropolitanas (MSA, por la sigla en inglés) en los Estados Unidos entre 2005 y 2011. Los resultados no muestran un impacto real de la presencia de industrias de alta tecnología sobre la pobreza y, especialmente, sobre la pobreza extrema. No obstante, hay fuerte evidencia de que el empleo en tecnología incrementa los salarios de trabajadores con educación sin título e incrementa, en menor grado, el empleo para quienes carecen de título. Estos descubrimientos sugieren que, aunque el empleo tecnológico tiene algún papel en mejorar el bienestar de los trabajadores con poca educación, el empleo tecnológico por sí solo no es suficiente para reducir la pobreza.

Key Words:

• employment
• high-technology industries
• metropolitan areas
• poverty
• wages

关键词：:

• 就业, 高科技产业, 大都会地区, 贫穷, 工资。

Palabras clave:

• empleo
• industrias de alta tecnología
• áreas metropolitanas
• pobreza
• salarios

Acknowledgments

We are very grateful to Professor Richard Wright and three anonymous referees for their diligent and constructive comments on the article. Alix Cocude provided excellent research assistance on this project, and Callum Wilkie and Viola von Berlepsch helped with the maps. The article benefited from comments received at the Global Conference in Economic Geography 2015 and an RTPI seminar. Data were accessed through the Integrated Public Use Microdata (IPUMS) Service at the University of Minnesota.

Funding

The authors appreciate the financial support of the European Research Council under the European Union's Seventh Framework Program (FP7/2007–2013)/ERC grant agreement No. 269868 and the Economic and Social Research Council ES/M007111/1.

Notes

1. Route 128 was structured around a few large firms, whereas Silicon Valley was based on a model of networked firms, with fewer large firms dominating (Saxenian 1994).

2. Unfortunately, lack of adequate data at the MSA level prevents us from introducing a variable containing the skill composition of migrants. We are therefore compelled to resort to the percentage of foreign-born in a given MSA as a proxy for all types of migrants. Although, according to Achen (2005), this amounts to treating “the independent variable list as a garbage can” (329) and does not really successfully control for the effect of migration, we prefer to follow the tradition in the literature of including this or similar variables as a proxy for migration, rather than omitting migration altogether from the model.

3. There might also be nonlinearity in the relationship between tech and poverty. To investigate this possibility, we experiment with a variety of model specifications but find no evidence of such a relationship. A second challenge is that year-on-year variation might not be sufficient for any relationship to be apparent. This can be addressed by estimating the regressions using only the end years of the period of analysis. The results of this estimation—available on request—do not differ significantly from those of the panel data analysis reported in the article.

4. We focus on those qualified below associate's degree level.

Additional information

Notes on contributors

Neil Lee

NEIL LEE is an Assistant Professor in the Department of Geography & Environment at the London School of Economics, Houghton Street, London WC2A 2AE, UK. E-mail: [email protected]. His research interests include economic development, urban areas, and poverty.

Andrés Rodríguez-Pose

ANDRÉS RODRÍGUEZ-POSE is a Professor in the Department of Geography & Environment at the London School of Economics, Houghton Street, London WC2A 2AE, UK. E-mail: [email protected]. He is currently president of the Regional Science Association International. His research has focused on issues related to regional and urban development, institutions, and public policy.