Collaborative Research: Diversifying the STEM Labor Force: Are Women and the Foreign-Born Complementary or Additive?

合作研究：STEM 劳动力多元化：女性和外国出生的人是互补还是相加？

• 批准号: 1538721
• 负责人: Jennifer Glass
• 金额: $ 6.75万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1538721&HistoricalAwards=false
• 关键词: Collaborative; Research; Diversifying; STEM; Labor

The need for STEM workers is expected to grow at or above the national growth rate over the next decade (U.S. Department of Commerce, 2012). In recent decades the U.S. government has focused a good deal of attention on increasing the presence of women and underrepresented minorities in STEM fields. The science and technology labor force has already diversified in important ways over the past few decades. Women's representation in science and technology education and employment has increased significantly, though their representation remains considerably smaller in fields such as engineering and computer science. This research explores whether the particular components of the STEM labor force - such as the representation of women in particular fields, or the share of coworkers of a particularly nativity or race - are associated with retention in the STEM work force or the gender wage gap. In fields where women account for a larger representation of STEM workers, for example, is the gender wage gap narrower? How is that relationship influenced by the group size of foreign-born workers, and does it matter whether these workers received their education and training in the United States or abroad?There are several social scientific views that can be evoked in seeking to account for the dynamics involved in occupational sex composition and occupational wage rates. These perspectives are not necessarily mutually exclusive, but each focuses on distinct ways in which employers may exhibit preferences by gender or ethnicity. Our project will utilize Queuing theory and Devaluation theory to explore how the changing composition of the STEM labor force promotes (or retards) the retention and earnings of women in STEM. Such an approach will enable us to move beyond a simple gender dichotomy (men/women) to assess variation within gender (native-born/foreign-born). Data come from pooling six waves of the National Science Foundation's (NSF) Scientists and Engineers Statistical Data System (SESTAT), covering years from 1995 through 2008. SESTAT is comprised of three ongoing surveys designed to create a nationally representative sample of science and engineering college degree holders (Kannankutty & Wilkinson, 1999). The restricted SESTAT data include detailed information regarding labor force participation, occupation categories, educational attainment, and demographic characteristics. The proposed project will include three sets of analyses, including descriptive analyses establishing trends in STEM employment by gender and nativity, as well a time since degree; multivariate logistic regression models incorporating predictors of employment in a STEM profession, by sex, and how group representation of the foreign-born moderates these associations; and OLS regression of women's and men's logged wages on observable characteristics.

预计未来十年对STEM工作者的需求将达到或超过全国增长率（美国商务部，2012年）。近几十年来，美国政府一直关注增加妇女和代表性不足的少数民族在STEM领域的存在。在过去几十年中，科学和技术劳动力已经以重要的方式多样化。 妇女在科学技术教育和就业方面的代表性显著增加，但在工程和计算机科学等领域的代表性仍然相当小。这项研究探讨了STEM劳动力的特定组成部分-例如女性在特定领域的代表性，或者特定出生或种族的同事的份额-是否与STEM劳动力的保留或性别工资差距有关。 例如，在女性在STEM工作者中占较大比例的领域，性别工资差距是否会缩小？ 这种关系如何受到外国出生工人群体规模的影响？这些工人是在美国还是在国外接受教育和培训是否重要？在试图解释职业性别构成和职业工资率的动态变化时，可以提出几种社会科学观点。 这些观点不一定相互排斥，但每一种观点都侧重于雇主可能表现出性别或种族偏好的不同方式。 我们的项目将利用价值理论和贬值理论来探讨STEM劳动力组成的变化如何促进（或阻碍）STEM女性的保留和收入。 这种方法将使我们能够超越简单的性别二分法（男子/妇女），评估性别内的差异（本地出生/外国出生）。 这些数据来自美国国家科学基金会（NSF）科学家和工程师统计数据系统（SESTAT）的六波数据，涵盖1995年至2008年。SESTAT由三项正在进行的调查组成，旨在建立一个具有全国代表性的科学和工程学院学位持有者样本（Kannankutty威尔金森，1999年）。 SESTAT数据包括劳动力参与、职业类别、教育程度和人口特征等详细信息。 拟议的项目将包括三组分析，包括按性别和出生地以及自学位以来的时间确定STEM就业趋势的描述性分析;多变量逻辑回归模型，其中包括按性别划分的STEM职业就业预测因素，以及外国出生的群体代表性如何调节这些关联;以及OLS回归妇女和男子的工资记录可观察到的特征。