The missing bridge: How immigrant networks keep Americans out of dirty jobs

Immigrants are over half of the new entrants in occupations in southwestern labor markets that range from farmworker to janitor to engineering professor. This paper explains how the availability of immigrant workers changes employment practices in ways that push and pull Americans out of jobs which remain "dirty" or unattractive to U.S. workers. An immigrant network that eventually takes over an enterprise's jobs can begin by accident or design. Accidental immigrant takeovers begin with pioneer workers who persist in high turnover jobs such as seasonal farmworker. These workers offer to bring their friends and relatives to fill vacancies as they occur. Takeovers by design sometimes follow a strike in which immigrants are recruited to be strikebreakers. After immigrants dominate the workforce, the language and culture of the workplace change in ways that make American workers feel out-of-place. Employers feel under no compulsion to upgrade dirty jobs as long as immigrant workers are available, so the technology and productivity of immigrant-enclave jobs tend to be frozen, guaranteeing that dirty jobs get less and less attractive to Americans. Indeed, industries dependent on immigrant workers often turn protectionist when they realize that even low immigrant wages are not sufficient to ensure their survival in the global marketplace. In this way, Americans lose out in several ways: They must compete directly with immigrant workers in the labor market, and then pay higher prices for the goods produced by immigrant workers because the industries preserved by them demand protection from imports. This paper will explore the manner in which immigrant networks displace Americans from dirty jobs and how the availability of immigrant workers can preserve sunset industries which turn protectionist. Examples from fruit and vegetable agriculture, garments, and shoes will be discussed.     

An extension of ebloc to valued graphs

In this paper we show how the algorithm EBLOC can be extended to deal with valued data directly. The technique is then applied to Sampson's (1969) monastery data and comparisons are made with the original EBLOC algorithm.     

Building a Values‐Informed Mental Model for New Orleans Climate Risk Management

Individuals use values to frame their beliefs and simplify their understanding when confronted with complex and uncertain situations. The high complexity and deep uncertainty involved in climate risk management (CRM) lead to individuals’ values likely being coupled to and contributing to their understanding of specific climate risk factors and management strategies. Most mental model approaches, however, which are commonly used to inform our understanding of people's beliefs, ignore values. In response, we developed a “Values‐informed Mental Model” research approach, or ViMM, to elicit individuals’ values alongside their beliefs and determine which values people use to understand and assess specific climate risk factors and CRM strategies. Our results show that participants consistently used one of three values to frame their understanding of risk factors and CRM strategies in New Orleans: (1) fostering a healthy economy, wealth, and job creation, (2) protecting and promoting healthy ecosystems and biodiversity, and (3) preserving New Orleans’ unique culture, traditions, and historically significant neighborhoods. While the first value frame is common in analyses of CRM strategies, the latter two are often ignored, despite their mirroring commonly accepted pillars of sustainability. Other values like distributive justice and fairness were prioritized differently depending on the risk factor or strategy being discussed. These results suggest that the ViMM method could be a critical first step in CRM decision‐support processes and may encourage adoption of CRM strategies more in line with stakeholders’ values.