April 2, 2019

Submitted via www.regulations.gov

Office of Regulations and Reports Clearance Social Security Administration 3100 West High Rise Building 6401 Security Boulevard Baltimore, Maryland 21235-6401

RE: Proposed Rule: Dependents RIN: 0960-AH86

Dear Office of Regulations and Reports Clearance:

Thank you for the opportunity to comment on the Social Security Administration (SSA)’s proposed rule, “Removing Inability to Communicate in English as an Education Category.” The undersigned organizations are members of a campaign united to protect and defend access to basic needs programs for immigrants and their .

Under current law, proficiency in English is one of the considerations used by the Social Security Administration when making a disability determination for claimants who don't meet a medical impairment listing. While a limited proficiency in English is not alone sufficient to establish disability, it may be considered in coordination with other factors, including the skill level of the person's previous jobs, whether any of those skills could be transferred to a new job, and the physical residual functional capacity of the claimant.

We are deeply concerned by this proposal which would result in Limited English Proficient (LEP) individuals with disabilities being wrongfully denied access to vital economic support which helps them to live healthy and secure lives in the United States. We submit the following comment to oppose the proposed rule and provide insight on the continuing challenges immigrants and LEP individuals living with disabilities face in our economy.

LEP Individuals with Disabilities Face Unique Workforce Barriers

In the Notice of Proposed Rulemaking (NPRM), SSA states that, “since we adopted these rules, the U.S. workforce has become more linguistically diverse and work opportunities have expanded for individuals who lack English proficiency.” However, the NPRM cites as evidence a Brookings Institution report that examines data about LEP workers generally, not those living with disabilities.1 Findings about employment of LEP individuals more generally do not necessarily apply to LEP individuals with disabilities.

While most Limited English Proficient (LEP) individuals who can engage in employment are already employed, LEP workers with disabilities face unique barriers to employment. Even not factoring in disabilities, individuals with limited English-speaking ability are at a disadvantage in the workplace relative to their peers who speak

1 Jill Wilson, Investing in English Skills: The Limited English Proficient Workforce in U.S. Metropolitan Areas, The Brookings Institution, September 2014, https://www.brookings.edu/research/investing-in-english-skills-the-limited-english-proficient-workforce-in-u-s- metropolitan-areas/; Hamutal Bernstein and Carolyn Vilter, Upskilling the Immigrant Workforce to Meet Employer Demand for Skilled Workers, Urban Institute, July 2018, https://www.urban.org/research/publication/upskilling-immigrant-workforce-meet-employer- demand-skilled-workers. 1

English only. Federal law acknowledges that LEP individuals face discrimination in accessing employment and public services, and language spoken had been upheld by the courts as a proxy for national origin discrimination.2 The NPRM does not address the fact that employers have been found to discriminate against both workers with disabilities and with Limited English Proficiency (LEP) and that this must be taken into account when considering employment prospects for LEP individuals with disabilities. 3

As SSA notes in the NPRM, most LEP workers are concentrated in jobs that do not require English language skills to be successful. However, many of these jobs are in fields like construction that are inaccessible to individuals with significant physical limitations. 4 LEP adults ages 18 and older account for 39 percent of all workers in positions that do not require a postsecondary degree or credential.5 In California and Texas, the two states with the largest LEP populations and largest state economies, more than half of workers employed in positions that do not require postsecondary education have limited English proficiency.6 Compared to their English-proficient counterparts, LEP individuals are more likely to work in construction, service, production, material-moving and maintenance occupations.7 Based on the amount of physical exertion that these jobs require, they are typically categorized in the heavy or medium exertional categories in the Dictionary of Occupational (DOT). If a claimant is only able to physically engage in light or sedentary work, ability to speak English is directly relevant to the claimant’s ability to find employment that accommodates their exertional limitations. Therefore, the current grid rules direct a finding of disabled for claimants over age 45 who are unable to communicate in English and are limited to sedentary work, and for those over age 50 who are limited to light work.8

In the proposed rule, the agency defends its position, saying that “claimants who cannot read, write, or speak English often have a formal education that may provide them with a vocational advantage.” This is directly inconsistent with the explanation provided in the current regulatory text which notes that “[b]ecause English is the dominant language of the country, it may be difficult for someone who doesn't speak and understand English to do a job, regardless of the amount of education the person may have in another language.”9 Furthermore, the reality is that LEP adults are much less educated than their English-proficient peers. As of 2013, 46 percent of all LEP individuals ages 25 and over had no high school diploma compared to 10 percent of their English-proficient counterparts. About 14 percent of LEP adults had a bachelor’s degree or higher, compared to 31 percent of

2 VI of the Civil Rights Act of 1964, 42 U.S.C. §§ 2000d - 2000d-7, and its implementing regulations, 28 C.F.R. Part 42 and 34 C.F.R. Part 100 (Title VI), https://www.govinfo.gov/content/pkg/USCODE-2008-title42/pdf/USCODE-2008-title42-chap21-subchapV.pdf. 3 Megumi Hosoda, Lam T. Nguyen, Eugene F. Stone-Romero, "The Effect of Hispanic Accents on Employment Decisions,” Journal of Managerial Psychology, Vol. 27 Issue: 4, pp. 347-364 (2012), https://psycnet.apa.org/record/2012-10713-003; Sharon Segrest, Pamela L. Perrewe, Treena L. Gillespie, et al., "Implicit Sources of Bias in Employment Interview Judgments and Decisions," Organizational Behavior and Human Decision Processes 101.2, pp. 152-167 (2006), https://www.sciencedirect.com/science/article/pii/S0749597806000690. 4 Ibid., 1. 5 Chhandasi Pandya, “Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities,” Migration Policy Institute, July 2012, https://www.migrationpolicy.org/article/limited-english-proficient-workers-and-workforce-investment-act- challenges-and-opportunities. 6 Ibid. 7 Jie Zong and Jeanne Batalova, “The Limited English Proficient Population in the United States,” Migration Policy Institute, July 2015, https://www.migrationpolicy.org/article/limited-english-proficient-population-united-states#Education%20and%20Employment. 8 20 CFR §§: Part 404, Subpart P, Appendix 2, https://www.govinfo.gov/content/pkg/CFR-2012-title20-vol2/pdf/CFR-2012-title20-vol2- part404-subpartP-app2.pdf. 9 20 CFR § 404.1564, https://www.govinfo.gov/content/pkg/CFR-2014-title20-vol2/pdf/CFR-2014-title20-vol2-sec404-1564.pdf. 2

English-proficient adults.10 For those immigrants who have credentials and job experience in their home countries, transferring those skills and finding employment in the U.S. in their previous profession can be extremely difficult.11

Finally, limited English proficiency is also associated with poor health outcomes, which can limit workforce participation.12 Additionally, people with disabilities earn 37 percent less than other workers, on average, thus LEP individuals with disabilities face a dual penalty.13

LEP individuals with disabilities face barriers to learning English

The assumption underlying this proposed rule is that LEP individuals with disabilities can simply learn English, but the NPRM fails to acknowledge that some cognitive and physical disabilities may interfere with the ability to learn a new language. Moreover, the current supply of English as a Second Language and Vocational English as a Second Language courses is incapable of meeting the demand for English instruction, even in cases when LEP individuals may be able to benefit from English language instruction. The Adult Education and Literacy Act (AEFLA), which funds adult education and English language instruction is, “severely underfunded, serving a small fraction of the nation’s low-skilled adults.” In 2013, AEFLA met only 4 percent of the need for these classes nationally. 14 The structure of AEFLA also discourages the states from providing services to harder-to- serve populations such as LEP individuals with disabilities, as lower success rates will impact performance measures as defined by the law.15 By making it more difficult for LEP individuals with disabilities to obtain economic stability, the proposed rule could leave affected populations with little time or ability to focus on skills development. 16

The Proposed Rule Is Directly Inconsistent with The Administration’s Position in the Recent Public Charge Notice of Proposed Rulemaking

In a Notice of Proposed Rulemaking posted on October 10, 2018, the Department of Homeland Security (DHS) seeks to include limited English proficiency as a negative factor in determining whether an immigrant is admissible under public charge grounds.17 In the preamble to the proposed rule, DHS states that “an inability to speak and understand English may adversely affect whether an [immigrant] can obtain employment.” Therefore,

10 Ibid. 11 Ibid., 1. 12 Gilbert C. Gee and Ninez Ponce, "Associations Between Racial Discrimination, Limited English Proficiency, and Health-related Quality of Life Among 6 Asian Ethnic Groups in California," American Journal of Public Health 100.5, pp. 888-895 (2010), https://ajph.aphapublications.org/doi/10.2105/AJPH.2009.178012. 13 Michelle Yin, An Uneven Playing Field: The Lack of Equal Pay for People With Disabilities, American Institutes for research, December 2014, https://www.air.org/resource/uneven-playing-field-lack-equal-pay-people-disabilities. 14 Margie McHugh and Madeleine Morawski, Immigrants and WIOA Services: Comparison of Sociodemographic Characteristics of Native- and Foreign-Born Adults in New York State,” Migration Policy Institute, December 2015, https://www.migrationpolicy.org/sites/default/files/publications/WIOAFactSheet-New%20York-FINAL.pdf. 15 Ibid. 16 Jennifer Ludden, "Barriers Abound for Immigrants Learning English," National Public Radio, September 2007, https://www.npr.org/templates/story/story.php?storyId=14330106. 17 Proposed Rule: Inadmissibility on Public Charge Grounds, 83 Fed. Reg. 51,114, 51,291 (to be codified in 8 C.F.R. § 212.22(b)(4)(i)(F)(i)).)), Department of Homeland Security, October 2018, https://www.federalregister.gov/documents/2018/10/10/2018-21106/inadmissibility- on-public-charge-grounds. 3

in one proposal, the Administration claims that ability to speak English directly impacts ability to work and now, in another proposal, the Administration claims that ability to speak English has no bearing on ability to find employment. The only common thread between these directly conflicting positions is that they are both used to advance an anti-immigrant agenda. This is consistent with numerous and frequent statements and policy proposals from President Trump and his Administration that explicitly express hostility towards immigrants in the United States.

The undersigned organizations strongly opposed the public charge proposal and firmly believe that immigrants with limited English proficiency can serve important employment roles in the U.S. economy. However, we believe that the law must consider the intersection of disability and English language proficiency. While there are myriad areas where a person who speaks a language other than English can meaningfully contribute to the workforce and to civic society, those opportunities are significantly narrowed for individuals who have limited functional capacity. As a country, we have a long way to go to ensure that immigrants and LEP individuals receive equitable treatment. Limiting access to basic needs programs which help individuals and families thrive is a step in the completely wrong direction.

Instead of continuing to advance contradictory and harmful anti-immigrant policies which undermine the success of immigrants, we encourage the Administration to instead dedicate its efforts to advancing policies that truly support economic security, self-sufficiency, and a stronger future for the United States by promoting the ability of immigrants and their families to thrive.

Conclusion

The proposal to eliminate the inability to communicate in English as an education category in eligibility determinations for people with disabilities is discriminatory and inconsistent with fundamental American principles, including equal opportunity. Most LEP individuals who can participate in the workforce are already employed, and this proposal has not made a case for why the current SSA justifications for including inability to communicate in English in as a vocational factor no longer apply.

The data presented by SSA in the NPRM only points to an increase in low-skill jobs for the LEP population in general, which is not applicable to individuals with disabilities/individuals with significant physical limitations. By failing to acknowledge the barriers that LEP immigrants encounter when finding employment, the proposed policy will only exacerbate racial and ethnic injustice in our county.

We urge you to withdraw the proposed rule change to avoid this needless attack on limited English proficient people with disabilities.

Sincerely,

1. Alabama Arise 2. Alarcon Legal 3. Asian & Pacific Islander American Health Forum 4. Asian American Legal Defense and Education Fund (AALDEF) 5. Asian Counseling and Referral Service 6. Asian Pacific Institute on Gender-Based Violence 4

7. Association of Asian Pacific Community Health Organizations 8. Cabrini Immigrant Services of NYC, Inc. 9. CACF: Coalition for Asian American Children & Families 10. California Food Policy Advocates 11. California League of United Latin American Citizens (LULAC) 12. California Rural Legal Assistance Foundation 13. Catholic Relief Services 14. Center for Health Equity Research 15. Center for Health Progress 16. Center for Medicare Advocacy 17. Center for Law and Social Policy 18. Center for Public Policy Priorities, Texas 19. Centro de Comunidad y Justicia 20. Charlotte Center for Legal Advocacy 21. Children's Defense Fund-New York 22. Children's HealthWatch 23. Chula Vista Partners in Courage 24. City of Cambridge - Commission on Immigrant Rights & Citizenship 25. Claims and Issues Group, Inc. 26. Coalition for Humane Immigrant Rights (CHIRLA) 27. Community Action Marin 28. Community Catalyst 29. Community Healthcare Network 30. Community Legal Services of Philadelphia 31. Comunidades Unidas 32. CT Shoreline Indivisible 33. Doctors for America 34. Don't Separate Families 35. Education and Leadership Foundation 36. El Centro, Inc. 37. End Domestic Abuse WI 38. Equality North Carolina 39. Farmworker's Self-help Inc. 40. First 5 Marin Children and Families Commission 41. Florida Center for Fiscal & Economic Policy 42. Florida Health Justice Project 43. Florida Immigrant Coalition 44. Grow Pittsburgh 45. Health Law Advocates, Inc. 46. Hispanic Federation 47. Housing Action Illinois 48. Houston Immigration Legal Services Collaborative 49. Illinois Coalition for Immigrant and Refugee Rights (ICIRR) 50. Immigrant Legal Advocacy Project (ILAP) 51. Inland Empire Immigrant Youth Collective 52. JGR Educational Associates, LLC 5

53. Korean Resource Center 54. La Union del Pueblo Entero 55. Latino Coalition for Healthy California (LCHC)/ Tides Center 56. Legal Aid Society of San Mateo County 57. Legal Voice 58. Long Island Language Advocates Coalition 59. Make the Road New York 60. March and Rally Los Angeles 61. Massachusetts Immigrant and Refugee Advocacy Coalition (MIRA) 62. Maternal and Child Health Access 63. Mujeres Latinas en Accion 64. Multicultural Alliance of Virginia 65. Muslim Public Affairs Council (MPAC) 66. National Asian Pacific American Families Against Substance Abuse (NAPAFASA) 67. National Center on Domestic Violence, Trauma & Mental Health 68. National Health Law Program 69. National Immigration Law Center 70. National LGBTQ Task Force 71. National Resource Center on Domestic Violence 72. National Women's Law Center 73. NC Child 74. Nebraska Appleseed 75. NETWORK Lobby for Catholic Social Justice 76. New Mexico Voices for Children 77. New York Lawyers for the Public Interest (NYLPI) 78. NM Center on Law and Poverty 79. Northwest Harvest 80. OneAmerica 81. ParentsTogether 82. PathWays PA 83. Pisgah Legal Services 84. Positive Women's Network-USA 85. Project IRENE 86. RESULTS Miami 87. Resurrection House Mission 88. Safe Communities 89. Services, Immigrant Rights & Education Network (SIREN) 90. SOA Watch 91. SOMOS FAMILIA VALLE 92. South Bay People Power 93. Resource Action Center (SEARAC) 94. St. Nicholas Parish Peace & Justice Committee, Evanston, IL 95. Surge Reproductive Justice 96. Tennessee Justice Center 97. The Illinois Hunger Coalition 98. The Legal Aid Society 6

99. The New York Immigration Coalition 100. T'ruah: The Rabbinic Call for Human Rights 101. Ujima Inc: The National Center on Violence Against Women in the Black Community 102. Unity Health Care 103. University YMCA New American Welcome Center 104. URI Feinstein Hunger Center 105. Utahns Against Hunger 106. Virginia Coalition of Latino Organizations 107. Washington State Coalition for Language Access 108. Welcome Project 109. William E. Morris Institute for Justice 110. Wisconsin AAP 111. Wisconsin Faith Voices for Justice

7

APPENDIX

1. Jill Wilson, Investing in English Skills: The Limited English Proficient Workforce in U.S. Metropolitan Areas, The Brookings Institution, September 2014.

2. Hamutal Bernstein and Carolyn Vilter, Upskilling the Immigrant Workforce to Meet Employer Demand for Skilled Workers, Urban Institute, July 2018.

3. Sharon Segrest, Pamela L. Perrewe, Treena L. Gillespie, et al., "Implicit Sources of Bias in Employment Interview Judgments and Decisions," Organizational Behavior and Human Decision Processes 101.2, pp. 152-167 (2006).

4. Chhandasi Pandya, “Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities,” Migration Policy Institute, July 2012.

5. Jie Zong and Jeanne Batalova, “The Limited English Proficient Population in the United States,” Migration Policy Institute, July 2015.

6. Gilbert C. Gee and Ninez Ponce, "Associations Between Racial Discrimination, Limited English Proficiency, and Health-related Quality of Life Among 6 Asian Ethnic Groups in California," American Journal of Public Health 100.5, pp. 888-895 (2010).

7. Michelle Yin, An Uneven Playing Field: The Lack of Equal Pay for People With Disabilities, American Institutes for research, December 2014.

8. Margie McHugh and Madeleine Morawski, Immigrants and WIOA Services: Comparison of Sociodemographic Characteristics of Native- and Foreign-Born Adults in New York State,” Migration Policy Institute, December 2015.

9. Jennifer Ludden, "Barriers Abound for Immigrants Learning English," National Public Radio, September 2007.

Investing in English Skills: The Limited English Proficient Workforce in U.S. Metropolitan Areas

By Jill H. Wilson

Findings An analysis of the labor market characteristics of the working-age limited English proficient (LEP) population in the United States and its largest metropolitan areas reveals that:

n Nearly one in 10 working-age U.S. adults—19.2 million persons aged 16 to 64—is consid- “National, state, ered limited English proficient. Two-thirds of this population speaks Spanish, but speakers of Asian and Pacific Island languages are most likely to be LEP. The vast majority of working-age and regional LEP adults are immigrants, and those who entered the United States more recently are more likely to be LEP. leaders have n Working-age LEP adults earn 25 to 40 percent less than their English proficient counter- an opportunity parts. While less educated overall than English proficient adults, most LEP adults have a high school diploma, and 15 percent hold a college degree. LEP workers concentrate in low-paying to enhance the jobs and different industries than other workers.

human capital n Most LEP adults reside in large metropolitan areas, but their numbers are growing fast- est in smaller metro areas. Eighty-two percent of the working-age LEP population lives in and economic 89 large metropolitan areas, and 10 metro areas account for half of this population. Large immigrant gateways and agricultural/border metro areas in California and Texas have the mobility of their largest LEP shares of their working-age populations. Smaller metro areas such as Cape Coral, Indianapolis, and Omaha experienced the fastest growth in LEP population between 2000 and current and 2012. Los Angeles was the only metro area to experience a decline.

future workforce n Educational attainment and the native languages of LEP adults vary considerably across metro areas. The share who have completed high school ranges from 33 percent in by investing in Bakersfield to 85 percent in Jacksonville. Spanish is the most commonly spoken non-English language among LEP adults in 81 of the 89 large metro areas, but the share varies from a low adult English of 5 percent in Honolulu to 99 percent in McAllen.

instruction.” n Most working-age LEP people are in the labor force. A majority across all 89 large metro areas is working or looking for work, and in 19 metro areas, at least 70 percent are employed. Workers proficient in English earn anywhere from 17 percent to 135 percent more than LEP workers depending on their metro location.

English proficiency is an essential gateway to economic opportunity for immigrant workers in the United States. Yet access to acquiring these skills is persistently limited by a lack of resources and attention. Increasing investment in adult English instruction—through more funding, targeted outreach, and instructional innovations—would enhance the human capital of immigrants that could lead to more productive work and better outcomes for their children. Given the large num- ber of LEP workers in the United States and the fact that virtually all of the growth in the U.S. labor force over the next four decades is projected to come from immigrants and their children, it is in our collective interest to tackle this challenge head on.

BROOKINGS | September 2014 1 Introduction

he United States is a polyglot nation and has been for most of its history.1 More than one in five working-age adults in the United States—some 45 million people—speak a language other than English at home. More than half of them also speak English very well. But 19.2 million are considered limited English proficient (LEP), comprising almost 10 percent of the working- ageT population. English proficiency is a strong predictor of economic standing among immigrants regardless of edu- cational attainment. Numerous studies have shown that immigrants who are proficient in English earn more than those who lack proficiency, with higher skilled immigrants reaping the greatest advantage.2 Conversely, high-skilled immigrants who are not proficient in English are twice as likely to work in “unskilled” jobs (i.e. those requiring low levels of education or training) as those who are proficient in English.3 This underemployment represents a loss of productivity that yields lower wages for individu- als and families and lower tax revenues and consumer spending for local areas. LEP immigrants also have higher rates of unemployment and poverty than their English proficient counterparts.4 Moreover, higher proficiency in English among immigrants is associated with the greater academic and economic success of their children.5 English skills also contribute to immigrants’ civic involvement and social connection to their new home.6 Immigrants who arrive in the United States without knowing English do, by and large, improve their proficiency over time; those who arrive at younger ages learn English faster than those whose age at arrival is higher, and the children of immigrants fare even better.7 But mastering a new language—espe- cially without formal instruction—takes years. Assuming that immigrants will “pick up the language,” while proving true in the long run, is not an efficient strategy for improving labor market outcomes in the shorter term. Rather, increasing the investment in adult English instruction now would enhance the human capital of immigrants that could lead to more productive work, and benefit their children, sooner. Given the high number of LEP workers in the United States and the fact that virtually all of the growth in the U.S. labor force over the next four decades is projected to come from immigrants and their children, it is in our collective interest to tackle this challenge head on.8 National, state, and regional leaders have an opportunity to enhance the human capital and eco- nomic mobility of their current and future workforce by investing in adult English instruction.9 A 2011 report by the McGraw-Hill Research Foundation quantified the return on investment in adult educa- tion for the nation and for some states. It found that not only do adult education and workforce development programs boost human capital and individual employment prospects, but they also reduce spending on healthcare, public assistance, and incarceration.10 Scholars at the Migration Policy Institute point to the potential improvements in labor market outcomes as a result of investments in English instruction for immigrants: higher productivity, earnings, and income tax payments; lower poverty and use of public benefits; and better educational and labor market outcomes for the children of immigrants.11 They also acknowledge the need for improvements in the quality of instruction and programming to enhance outcomes.12 High levels of immigration to the United States during the 1990s and early 2000s boosted the size of the LEP population. Between 1990 and 2000, the number of working-age LEP individuals grew 57 percent, slowing in the 2000s to a growth rate of 20 percent. Because not all immigrants are LEP and because English proficiency often improves over time, the LEP population has not grown as quickly as the overall foreign-born population (Figure 1). In fact, as the share of the working-age population that is foreign-born continued to climb from 1980 (7 percent) to 2012 (16 percent), the LEP share plateaued after 2005, remaining under 10 percent. Nevertheless, the size of the working-age LEP population is more than two-and-a-half times what it was in 1980, and the LEP share of the U.S. working-age popula- tion has increased from 4.8 to 9.3 percent. Infrastructure and public funding for adult English instruction has not kept pace with this growth.13 The Adult Education and Family Literacy Act (AEFLA), enacted as Title II of the Workforce Investment Act (WIA) of 1998, is the primary source of federal funding for adult English for Speakers of Other Languages (ESOL) instruction. Although it technically expired in 2003, Congress continues to appro- priate funds for WIA Title II ($575 million in FY2013 with a $71 million set-aside for English language and civics training).14 Since 2000, funding from the U.S. Department of Education for adult ESOL

2 BROOKINGS | September 2014 instruction has hovered at around $250 million per year, with another $700 million provided by states.15 The number of adults served by programs receiving federal funding dropped from about 1.1 million earlier in the decade to about 700,000 in 2011, a tiny fraction—about one-half of a percent—of the adult LEP population in the United States.16 Meanwhile, states, which have typically contributed about three quarters of the funding for adult ESOL instruction, faced growing deficits after the reces- sion and many slashed adult education and ESOL budgets.17 This decline in funding was not accompanied by a decline in the adult LEP population. It is not surprising, then, that individuals wishing to enroll in English classes face access difficulties. A 2006 study of 187 providers across the country found that 57 percent had waiting lists, with wait times ranging from a few weeks to over three years.18 In 2007, the National Adult Education Professional Development Consortium estimated that nationwide, there were 93,480 people on waiting lists for adult education and literacy classes, including adult ESOL.19 A 2010 survey of 1,368 adult education providers found that 72 percent (in all 50 states) had waiting lists, representing some 160,000 indi- viduals who had a desire to access services but could not. Moreover, wait times had doubled since the survey two years prior.20 Over the past two decades, the growth in the LEP population has been felt most acutely in places without a recent history of receiving newcomers from abroad. The new geography of immigration means that many cities and suburbs across the country are facing the challenges of a sizeable LEP population for the first time, both in their schools and in the workforce. Insufficient funding, com- bined with a lack of infrastructure and experience working with LEP populations in more recent destinations, has resulted in uneven and inadequate access to adult English instruction. Adding to the challenge is the diversity of this population. While 65 percent of the U.S. LEP popula- tion speaks Spanish, this proportion varies greatly in different parts of the country. Likewise, in some regions, a high proportion of LEP speakers have low levels of literacy in any language, whereas in other areas, many have college degrees. Workers without full proficiency in English are found in a variety of occupations and industries, represent a wide swath of origin countries and cultures, and have varying levels of income. These factors shape whether and how LEP workers succeed in increas- ing their English proficiency over time and how this impacts their labor force trajectories. These characteristics should also inform the policies and programs designed to help LEP workers enhance their English skills. This report examines the metropolitan geography of the working-age limited English proficient population and their labor force characteristics. It offers evidence for the economic benefits of investing in adult English instruction and presents data useful for tailoring interventions to the specific characteristics of an area’s LEP population. It concludes with some options for enhancing investment in adult English instruction through increased funding, targeted outreach, and instruc- tional innovations.

Methodology

he main data source for this report is the U.S. Census Bureau’s 2012 American Community Survey (ACS). The ACS is an ongoing survey of approximately 3 million U.S. households. Data are released annually covering demographic, social, economic, and housing topics, including language use and English ability. This analysis makes use of the ACS Public Use MicrodataT Sample (PUMS), accessed via the Integrated Public Use Microdata Series (IPUMS) web- site.21 PUMS data allow for customized cross-tabulations which provide detailed characteristics of the LEP population including country of birth, age, period of entry to the United States (for immigrants), language spoken at home, educational attainment, labor force participation, occupation, industry, and earnings. In addition to the ACS, data from the 1980, 1990, and 2000 censuses are used to exam- ine broad changes over time, including growth in the working-age LEP population. This analysis focuses on the working-age limited English proficient (LEP) population, defined as persons aged 16–64 who speak English less than “very well.”22 (See sidebar, “Defining the LEP Population.”) Of the almost 25 million LEP individuals (aged 5 and older) counted in the United States in 2012, the vast majority—77 percent—were of working age (16–64). Almost 9 percent were children

BROOKINGS | September 2014 3 between the ages of 5 and 15, and 15 percent were 65 and older. Immigrants make up the vast majority (87 percent) of the working-age LEP population, but 13 percent are native born. In this analysis, the terms “immigrant” and “foreign born” are used interchangeably to refer to anyone born outside the United States to non-U.S.-citizen parents. Educational attainment data are for those aged 25-64. The Census Bureau collects data on language spoken at home for the population who do not speak English at home (see sidebar, “Defining the Limited English Proficient Population.”) Respondents write in their home language, and the Census categorizes these responses into 382 single languages or language families.23 (See Sidebar, “Language Classifications.”) Due to small sample size and confi- dentiality concerns, however, Census does not regularly tabulate data for all 382 categories; rather, they collapse them into 39 groups and, more broadly, into four groups (Spanish, other Indo-European languages, Asian and Pacific Island languages, and Other languages). This paper presents data on languages spoken at home using the four broad categories and, in some instances, the most detailed categories, which are available from the PUMS. (See sidebar, “Language Classifications,” for which languages fall into which categories.) This report provides data on industry and occupation for persons aged 16–64 who had worked in the previous five years. Industry data were recoded from the 2007 Census industry classification scheme to closely align with the North American Industry Classification System (NAICS) 2-digit categories. A few exceptions were made to isolate the private households sector (where LEP workers are concen- trated) and to collapse real estate with finance, and management of companies with professional, sci- entific, and technical services (where very few LEP workers concentrate). For the occupation variable, data were recoded into 25 occupational categories following the 2010 ACS classification system. Data on earnings are for persons who worked at least 35 hours/week and at least 50 weeks over the last 12 months, and include wage income and income earned from a person’s own business or farm. This report presents data both at the national level and for 89 metropolitan areas. These 89 metro areas were selected because they were among the 100 most populous metropolitan areas in the United States and had a working-age LEP population sample size of at least 100 in the PUMS.24 The Census Bureau provides summary data at the metropolitan level, but some manipulation is necessary to use microdata for this geography. The lowest level of geography for PUMS data is the Public Use Microdata Area (PUMA) which is built from census tracts and counties and contains roughly 100,000 people. Because PUMAs do not necessarily align precisely with metro area boundaries, this analysis uses PUMA-based metropolitan area definitions for much of the data on characteristics of the LEP population. These definitions were created by allocating a PUMA to a metro area if more than 50 per- cent of its 2010 population fell within the metro-area boundary. Likewise, PUMAs that overlap metro areas but in which less than 50 percent of the population fell within a metropolitan area’s boundaries were not included in the PUMA-based metro area definition. Limited English Proficiency or “LEP” is defined as speaking English less than “very well,” i.e. “well,” “not well,” or “not at all.” “Not LEP” or “English proficient” are those who speak English “very well” or who speak it at home. This definition is not perfect. For one thing, it is subjective and self-reported (or, reported by the head of household for other members of the household). It is also limited to speaking ability and does not address the ability to read, write, or listen in English. A less conservative definition counts those who speak English “well” among the English proficient population, but most researchers and policy- makers follow the Census practice of categorizing the lower three levels as LEP. This practice grew out of a study done by the Census Bureau in 1982 for which respondents’ abilities to read, under- stand, and produce English were tested and compared to their responses on English ability. Those who categorized their ability as less than “very well” had difficulty with the English test while those who responded “very well” performed on par with native English speakers.26 Census tests have also revealed a tendency among respondents to over-report their English ability, so using the lowest three categories of response serves as a more valid estimate of the LEP population.27 Despite the limita- tions, the ACS data remain the chief source of current information about the LEP population in the United States, especially comparable data at the sub-national level.

4 BROOKINGS | September 2014 DEFINING THE LIMITED ENGLISH PROFICIENT POPULATION

The Census Bureau began collecting data on language use in the United States in 1890. Ques- tions about “mother tongue,” language spoken at home, and ability to speak English were asked, in various forms, once a decade until 2000. With the advent of the American Community Survey (ACS) in the mid-2000s, data on language use are now available on an annual basis. These data are used by the government for compliance with the Voting Rights Act of 1965 (providing for bilingual election materials), the allocation of funds to school districts for educating LEP children, the distribution of grant money to states and localities for adult education and job training, and compliance with an executive signed in 2000 for federal agencies to provide language American Communityassistance Survey services.25 (ACS): Questions on the Form and Why We Ask Today, the American Community Survey provides annual data on language spoken at home and the ability to speak English for the population aged 5 and older. Translation assistance is available for those who need help filling out the questionnaire. The questions used to collect this informa- tion have remained the same sinceLanguage 1980:

} Not LEP

} Not LEP

LEP }

Source: U.S. Census Bureau, 2013 AmericanSource: Community ACS Survey -1(2013)KFI

.Why We Ask: We ask these questions to understand how well people in each community speak English, and to analyze and plan programs for adults and children who do not speak English well. Statistics about language spoken are used to ensure that information about public health, voting, and safety

information is communicated in languages that community members understand.

BROOKINGS | September 2014 5 History: The first language question was asked in the Census of 1890. A language question was included when the ACS was implemented nationwide in 2005.

Federal Uses: The U.S. Department of Education uses language data to allocate funds to improve programs for young children learning English, and to provide educational opportunities for adults. Many agencies use language statistics to determine how to deliver important information to those who have difficulty with English. Under the Voting Rights Act, voting materials must be made available in the languages spoken in a community.

State and County Uses: State and local governments use language statistics to provide services and information to people in the community that may not speak English well. For example, educational agencies receive grants for programs to improve educational skills, complete secondary schooling, and provide job training and placement for adults based on language information, and agencies providing health care may provide instructions, information, or assistance in the languages spoken in the community.

Private Sector Uses: Businesses may wish to employ bilingual workers, translate advertisements, or provide translated information in areas where potential customers do not speak English well. Advocacy groups use these statistics to understand and advocate for policies that impact their communities.

All questions on the ACS are required to manage and evaluate a wide range of federal, tribal, state, and local programs, but may also be useful for research, education, journalism, advocacy, business, and many other uses. This series explains the current uses of each question

Language Classifications

For data on language spoken at home, the Census Bureau classifies languages into four major groups:

Spanish includes Spanish, Spanish Creole, and Ladino.

Other Indo-European Languages include most languages of and the Indic languages of India. These include the Germanic languages, such as German, Yiddish, and Dutch; the Scandina- vian languages, such as Swedish and Norwegian; the Romance languages, such as French, Italian, and Portuguese; the Slavic languages, such as Russian, Polish, and Serbo-Croatian; the Indic languages, such as Hindi, Gujarati, Punjabi, and Urdu; Celtic languages; Greek; Baltic languages; and Iranian languages.

Asian and Pacific Island Languages include Chinese; Korean; Japanese; Vietnamese; Hmong; Khmer; Lao; Thai; Tagalog or Pilipino; the Dravidian languages of India, such as Telugu, Tamil, and Malayalam; and other languages of Asia and the Pacific, including the Philippine, Polynesian, and Micronesian languages.

Other languages include Uralic languages, such as Hungarian; the Semitic languages, such as Arabic and Hebrew; languages of Africa; native North American languages, including the Ameri- can Indian and Alaska native languages; and indigenous languages of Central and South America.

Source: Camille Ryan, “Language Use in the United States: 2011” (Washington: U.S. Census Bureau, 2013).

Findings

A. Nearly one in 10 working-age U.S. adults—19.2 million persons aged 16–64—is consid- ered limited English proficient.

Numbers and growth Twenty-two (22) percent of working-age adults in the United States— 45.4 million people—speak a language other than English at home. More than half of them (58 percent) also speak English very well and thus are considered proficient. These individuals have better labor market outcomes than the 19.2 million people of limited English proficiency who comprise 9.3 percent of the working-age population. Between 2000 and 2012, the working-age LEP population increased by 3.2 million, a growth rate of 20 percent. In spite of the larger number of LEP individuals in 2012, their share of the total working- age population did not increase much over the same time period: from 8.7 to 9.3 percent. By compari- son, the foreign-born share of the working-age population increased from 14 percent in 2000 to 16 percent in 2012. Although immigration drives growth in the LEP population, newly arriving immigrants vary in their English skills, and those who have lived in the United States for a number of years often improve their English ability over time. Thus, the growth in the LEP population over the last two decades coincides with—but does not precisely mirror—growth in the foreign-born population (Figure 1). The growth in the LEP population has lagged behind the increase in the foreign-born population, and a smaller share of working-age immigrants in 2012 were LEP (50 percent) than in 2000 (52 percent). Between 2000 and 2012, the nation’s working-age foreign-born population grew 32 percent. Over the same period, the working-age LEP population increased by 20 percent (Figure 1). As the share of the working-age popu- lation that is foreign-born climbed steadily from 1980 (6.7 percent) to 2012 (16 percent), the LEP share rose from 1980 (4.8 percent) through 2005 (9.5 percent), but plateaued after that, remaining under 10 percent (Figure 1).

6 BROOKINGS | September 2014 Fig. 1. Foreign-Born Versus Limited English Proficient Population in the U.S., Ages 16-64, 1980–2012

35 Foreign born

30

25

20 LEP

Millions 15

10

5

0

2012 1980 1980 2000 200520062007200820092010 2011

Source: Author’s analysis of data from the 1980, 1990, and 2000 decennial census PUMS and the 2005 through 2012 ACS PUMS

Home language Spanish is the dominant non-English language spoken in the United States today, but a variety of other languages have significant numbers of speakers residing in this country, and their English skills vary. Two-thirds of the working-age LEP adults in the United States (12.7 million) speak Spanish at home (Table 1). Most of them (9.7 million) were born in Mexico. Additionally, El Salvador, the Dominican Republic, Guatemala, Cuba, and Colombia are the birthplaces of at least 500,000 Spanish-speaking LEP adults each. As a broad linguistic group, speakers of Asian and Pacific Island languages make up 18 percent of the working-age LEP population. The most commonly reported language among this group is Chinese (4.4 percent of the working-age LEP population), with another 1 percent each who reported speaking Mandarin or Cantonese at home.28 Vietnamese (3.4 percent), Korean (2.4) and Filipino/Tagalog (1.9) are the other top Asian/Pacific Island languages spoken among LEP adults (Table 1). Speakers of Indo-European languages (other than Spanish) make up almost 12 percent of the working-age LEP population, including those who speak Russian (1.4 percent), French or Haitian Creole (1.3), Portuguese (1.0), and French (1.0). Arabic is the most commonly spoken language that falls into the catch-all “other” category (which also includes African and Native American languages and together accounts for 3.4 percent of working-age LEP persons). Arabic speakers make up 1.5 percent of working-age LEP adults. Among the four broad language categories, speakers of Asian and Pacific Island languages are most likely to be LEP (47 percent), followed by Spanish speakers (45 percent) (Table 1). The Asian and Pacific Island category includes speakers of languages with some of the lowest rates of English profi- ciency: Uzbek/Uighur, Burmese/ Lisu/Lolo, Trukese, Nepali, and Vietnamese (all with over 60 percent of working-age speakers who are LEP). In fact, 13 of the 15 detailed languages/language groupings with a majority of speakers who are LEP are Asian or Pacific Island languages. Several of these groups have arrived recently under the U.S. refugee resettlement program or come from countries with low

BROOKINGS | August 2014 7 levels of education and have had very limited opportunities to learn English either before or after resettlement in the United States.29 Those whose home language falls into the broad “other” category are less likely to be LEP overall (31 percent), though this group includes the other two detailed language groupings whose speakers are majority LEP: South/Central American Indian languages (70 percent LEP) and Cushite/Beja/Somali (56 percent LEP). Speakers of Indo-European languages (other than Spanish) are least likely to be LEP (30 percent), and no detailed language in this group has a majority of its working-age speakers who are LEP. (See sidebar “Language Classifications.”)

Table 1. Language Spoken at Home among the LEP Population, Ages 16-64, 2012

% of working-age % of speakers who Language # of speakers LEP population are LEP

Spanish 12,705,412 66.3 45.2

Asian and Pacific Island languages 3,524,709 18.4 47.0

Chinese 833,276 4.4 58.1

Vietnamese 651,786 3.4 60.2

Korean 462,168 2.4 53.4

Filipino, Tagalog 366,900 1.9 29.2

Mandarin 196,809 1.0 47.4

Cantonese 184,911 1.0 51.5

Other API 828,859 4.3 39.9

Other Indo-European languages 2,278,667 11.9 29.5

Russian 266,833 1.4 41.5

French or Haitian Creole 247,635 1.3 42.2

Portuguese 190,078 1.0 36.1

French 183,174 1.0 20.6

Other Indo-European 1,390,947 7.3 27.4

Other languages 642,996 3.4 31.1

Arabic 289,393 1.5 38.8

Other “other” 353,603 1.8 26.7

Total 19,151,784 100.0 100.0

Source: Author’s analysis of ACS 2012 PUMS data

Nativity and period of immigration An individual’s place of birth and immigration status is a factor in their access to publicly funded pro- grams aimed at improving English proficiency and employment prospects. The American Community Survey does not inquire about legal status, but does ask about place of birth and citizenship. Inde- pendent estimates of the unauthorized immigrant population estimate that about 55 percent do not have the ability to pass an English test similar to the U.S. citizenship exam.30 Based on that estimate, upward of one-third of the working-age LEP population could be unauthorized. The rest are either native-born residents of the U.S., naturalized citizens, legal permanent residents, or legal temporary residents. Unauthorized immigrants are not barred from accessing services provided through Title II of the Workforce Investment Act, but are not permitted to access WIA Title I funding. While the vast majority of the working-age LEP population is foreign-born, 13 percent is native-born.

8 BROOKINGS | September 2014 Seventy-seven (77) percent of native-born working-age LEP adults speak Spanish, with another 3 percent each who speak French and German. Among these native-born adults, half were born in California, Texas, or Puerto Rico. It is unknown whether they grew up in the United States and attended U.S. schools. Given the high rates of migration between Mexico and the Southwestern U.S., it is likely that some of this population born in Border States spent at least part of their school-age years in Mexico or Central America before returning to the United States. Puerto Rico’s status as a U.S. territory means that people born there are counted as U.S. natives, but Spanish is the language of instruction in public schools and is dominant in all aspects of business and daily life on the island. Accordingly, over 80 percent of residents of Puerto Rico are considered limited English proficient.31 Prior research shows that time spent in the United States is a key determinant of English proficiency among immigrants.32 Among the foreign-born population of working age, those who immigrated recently are more likely to be LEP. Fifty-seven (57) percent of those who came to live in the United States in 2000 or later are LEP, compared to 48 percent and 44 percent of those who entered in the 1990s and 1980s, respectively. Just 28 percent of those who immigrated to the United States before 1980 are LEP. In addition to the fact that those who arrived earlier have had a longer period of time in the United States to learn English, the composition (language and country of origin, educational attainment, age at entry, linguistic isolation, etc.) of different immigrant cohorts also influences their proficiency trajectories.33 Death and outmigration can also change how the proficiency of one cohort stacks up to the others. Regardless of these intervening factors, these data are consistent with others’ findings that immigrants do learn English over time, but not, on the whole, at a fast pace.34 Rather, these data reveal plenty of opportunity for accelerating proficiency gains. Because recent arrivals are less likely to be English proficient and because those who entered after 1999 make up the largest share (36 percent) of working-age immigrants in the United States, newcom- ers also make up the largest share of the working-age LEP population. Among the 17 million working- age LEP immigrants, 7.6 million (44 percent) came to live in the United States in 2000 or later; 29 percent entered in the 1990s, 18 percent in the 1980s, and 9 percent before 1980. Efforts to provide English instruction must take into account that recent arrivals may be less likely than more estab- lished immigrants to know about opportunities for adult English instruction and to afford them. They may also be less likely to have access to transportation and child care, two of the foremost practical barriers for adults learning English.

B. Working-age LEP adults earn 25 to 40 percent less than their English proficient counterparts. In general, LEP individuals experience worse labor market outcomes than those who are proficient in English. While lower educational attainment among LEP adults accounts for some of this difference, English proficiency is correlated with better outcomes at all levels of education.

Educational attainment Most LEP adults (60 percent) are high school graduates, including 15 percent who hold a college degree. While these numbers may be higher than conventional wisdom suggests, working-age LEP adults are significantly less educated than their non-LEP counterparts. Ninety-three percent of the working-age population that is proficient in English has completed high school, including 32 percent who hold a college degree (Figure 2). As noted in the previous finding, time in the United States is correlated with higher English proficiency among immigrants. But educational attainment is a stronger predictor of English skills.35 Working-age adults (regardless of nativity) who have completed high school are much less likely than their less edu- cated counterparts to be LEP. Only 5 percent of college graduates and 7.9 percent of those with a high school diploma or some college are LEP, compared to 40 percent of those without a high school diploma.

BROOKINGS | September 2014 9 Fig. 2a and 2b. Educational Attainment by LEP status, Ages 25–64, 2012

LEP Non-LEP

HS or HS or some college some college 45% 61%

Source: Author’s analysis of ACS 2012 PUMS data

Labor force status and employment Working-age adults who are proficient in English are somewhat more likely to be in the labor force (74 percent) than their LEP counterparts (71 percent), and their employment rates are likewise higher: 67 percent versus 64 percent for the LEP population. However, the difference in employment rates between LEP and non-LEP adults varies by educational attainment. Among those (age 25-64) with a bachelor’s degree or more , LEP individuals are 13 percentage points less likely to be employed than their English proficient counterparts; conversely, those without a high school diploma are 19 percent- age points more likely to be employed than those who are not LEP (Figure 3). A number of factors could contribute to the higher employment rate among the LEP population without a high school diploma compared to their English proficient counterparts. Overall, immigrants have higher employment rates than natives. For many immigrants, especially low-skilled ones, the primary motive for migration to the United States is the need to earn money to support themselves and their families (either in the United States or back home), and they might return home if they are unable to find work. Immigrants are more likely to be of working age, able-bodied, and willing to work in dirty, dangerous, or demeaning occupations for wages that may be low by U.S. standards but higher than what they could earn in their home countries.36 For their part, U.S.-born workers are eligible for more social safety net services than immigrants (especially those who lack legal status), and therefore have more options for income other than employment. Thus, high proficiency in English appears to be helpful for boosting the employability of those with a bachelor’s degree, but not, on average, for those without such a degree. Indeed, a lack of English proficiency does not, by and large, prevent low-skilled workers from obtaining employment. However, for those who are working, the advantage of English proficiency is evident in their income levels.

10 BROOKINGS | September 2014 Fig. 3. Employment-to-Population Ratio by Educational Attainment and English Proficiency, Ages 25–64, 2012

83.2

70.5 68.4 69.4 62.9

43.6 ■ LEP ■ not LEP

Bachelor’s or higher HS diploma or some college Less than HS

Source: Author’s analysis of ACS 2012 PUMS data

Earnings and poverty Among full-time, year-round workers, English proficiency is associated with an earnings advantage at all levels of educational attainment. Proficiency in English makes the greatest percent difference in earnings for those in the middle of the educational attainment range (high school diploma or some college). Their median earnings are 39 percent higher if they are English proficient ($40,000) than not ($28,700). Among those with a bachelor’s degree, non-LEP individuals earn $65,000 annually compared to $50,000 for LEP individuals, a 30 percent difference. English proficiency makes the least difference in earnings for those with the lowest levels of education, a sign of the poor labor market for those who have not completed high school. LEP persons with less than a high school diploma earn 24 percent less ($22,600) than their non-LEP counterparts ($28,000). Higher earnings are evident not just for those who transition from LEP to non-LEP status, but also for each incremental increase in proficiency. That is, median earnings are higher with each level of English proficiency, at all levels of educational attainment (Figure 4). In some cases, better English pro- ficiency is associated with more of a difference in earnings than is higher educational attainment. For example, those without a HS diploma who are not LEP (i.e. speak English very well or only) earn more than those with a high school diploma or some college who don’t speak English well or at all. One anomaly is for those who have a bachelor’s degree or higher and speak English very well (that is, they speak a language other than English at home but are not LEP). These individuals have higher median earnings than college educated workers who speak English at home. This could indicate a wage premium for bilingualism (at least among the college educated).37 It could also be that highly educated bilinguals (mostly immigrants) are in higher paying jobs because they are more likely to work in STEM fields or other higher-paying occupations or to have advanced degrees than those who speak English at home.38

BROOKINGS | September 2014 11 Fig. 4. Median Earnings by English Proficiency and Educational Attainment, Ages 25–64

$80,000

$70,000 Speaks English $60,000 ■ Not at all ■ Not well ■ Well $50,000 ■ Very well ■ At home $40,000

$30,000

$20,000

$10,000

$0 Bachelor’s or HS diploma or Less than higher some college high school

Source: Author’s analysis of ACS 2012 PUMS data; earnings are calculated for those who worked at least 35 hours per week and at least 50 weeks over the previous 12 months

Given the wage premium for English proficiency, it is not surprising that the working-age LEP population is more likely to be poor (25 percent) than those who are not LEP (14 percent). Another 31 percent of LEP persons have incomes that put them between 100 and200 percent of the poverty threshold compared to 16 percent of non-LEP adults. Likewise, LEP individuals are over-represented among the poor, comprising 16 percent of the working-age poor population compared to 9.3 percent of the total working-age population. These are the LEP adults least likely to be able to afford private English instruction and most in need of publically provided services. Governments at all levels spend a lot of money serving low-income populations, and the literacy needs of these LEP adults should be taken into consideration.

Occupation The distribution of LEP workers across occupations and industries is relevant for determining what types of English skills would benefit them most in the workplace and how to engage these workers and their employers in improving their language skills. LEP workers are concentrated in low-paying jobs for which high levels of English are not a require- ment. Seven occupational groups (out of 25) each have over one million LEP workers, and in five of these, more than 10 percent of workers are LEP (Table 2). The largest number work in building and grounds cleaning and maintenance. More than one quarter (26 percent) of workers in this occupa- tional category are LEP, the highest rate with the exception of the much smaller farming, fishing, and forestry category (556,000 workers, 40 percent of whom are LEP). Besides the five large occupational categories in which at least 10 percent of the workforce is LEP, two other categories have high LEP proportions: personal care and service occupations (12 percent) and the aforementioned farming, fish- ing, and forestry occupations (40 percent). All seven of these occupational groups have annual mean wages in the lowest two quintiles of the wage distribution.39

12 BROOKINGS | September 2014 Table 2. Occupations with at Least 1 Million LEP Workers, 2012

Share of LEP Occupation Type # LEP % LEP workers

Building and Grounds Cleaning and Maintenance 1,936,079 26.1 12.8 Production 1,791,108 17.2 11.8 Construction and Extraction 1,598,962 17.6 10.5 Food Preparation and Serving 1,597,171 14.4 10.5 Transportation and Material Moving 1,368,872 12.6 9.0 Sales and Related 1,135,482 6.0 7.5 Office and Administrative Support 1,063,015 4.6 7.0

Source: Author’s analysis of ACS 2012 PUMS data

Industry LEP workers can be found in every industry, but two-thirds of working-age LEP adults are con- centrated in six industry categories (out of 20), each with at least 1 million LEP workers (Figure 5). Manufacturing and accommodations/food services each have just over 2 million LEP workers, accounting for almost 14 percent each of the working-age LEP population. Between 1 million and 2 million LEP workers each are in construction, retail trade, health/social services and administrative/ waste management services. Among the 20 broad industry categories, seven have at least 10 percent of their workforce that is LEP. The private households category has the highest share (33 percent), followed by agriculture (27 percent). Within the broader industry categories, more detailed sectors stand out for their high numbers of LEP workers. In the manufacturing sector, animal slaughtering/processing and cut and sew apparel manufacturing account for the largest number of LEP workers, 8 and 6 percent, respectively, of LEP manufacturing workers. Among all detailed industry categories, cut and sew apparel manufacturing has the highest share of its workforce who are LEP (42 percent). Within the accommodations and food services sector, 81 percent of LEP workers are employed in restaurants and other food services. One quarter of LEP persons working in the retail trade sector work in grocery stores. Within health/ social services sector, 17 percent of LEP workers are employed in child day care services, and another 13 percent in individual and family services. Over seventy percent of LEP workers in the administra- tive/waste management services sector work in janitorial services (37 percent) and landscaping (35 percent). LEP workers tend to concentrate in industries that non-LEP workers do not, and vice versa, suggest- ing complementarity. Figure 6 shows the location quotient (LQ) for each industry (calculated as the share of LEP workers in each industry divided by the share of non-LEP workers in that industry; an LQ greater than one indicates that LEP workers are disproportionately found in that industry). There are two outliers for LEP concentration: private households (with an LQ of more than five, meaning that a LEP worker is five times more likely than a non-LEP worker to work in a private household), and agri- culture, forestry, fishing and hunting (with an LQ of 3.8). A LEP worker is almost twice as likely to work in administrative and waste management industry (which includes landscaping and janitorial services). Construction and accommodation and food services are two other industries in which LEP workers are concentrated. At the other end of the spectrum, non-LEP workers are five times more likely to be in the armed forces than LEP workers, and more than three times as likely to work in utilities or public administration. Transportation and warehousing is closest to balanced (1.0), followed by retail trade, with an LQ of 0.8 (Figure 6).

BROOKINGS | September 2014 13 Fig. 5. LEP Workers by Industry (red bars indicate sectors in which more than 10 percent of workers are LEP)

Thousands 0 500 1,000 1,500 2,000 2,500 Manufacturing Accommodations and Food Services Construction Retail Trade Health and Social Services Admistrative and Waste Management Other Services (Except Private Households) Agriculture, Forestry, Fishing, and Hunting Transportation and Warehousing Educational Services Wholesale Trade Finance, Insurance, Real Estate, and Rental and Leasing Professional, Scientific, and Management Services Private Households Arts, Entertainment, and Recreation Public Administration Information and Communications Mining Utilities Armed Forces

Source: Author’s analysis of ACS 2012 PUMS data

Fig. 6. LEP/Non-LEP Concentration by Industry (LQs)

Armed Forces Utilities Public Administration Educational Services Professional, Scientific, and Management Services Information and Communications Finance, Insurance, Real Estate, and Rental and Leasing Mining Arts, Entertainment, and Recreation Health and Social Services Retail Trade Transportation and Warehousing Wholesale Trade Manufacturing Other Services (Except Private Households) Accommodations and Food Services Construction Administrative and Waste Management Services Agriculture, Forestry, Fishing, and Hunting Private Households

0 1 2 3 4 5 6 Source: Author’s analysis of ACS 2012 PUMS data

These patterns of occupation and industry suggest opportunities to tailor interventions to spe- cific jobs, career paths, and workplaces where LEP workers are employed (SeeDiscussion and Policy Implications, below).

14 BROOKINGS | September 2014 C. Most LEP adults reside in large metropolitan areas, but their numbers are growing fastest in smaller metro areas.

Distribution Mirroring the dispersal of immigrants over the last two decades to newer destinations in cities and suburbs across the country, the LEP population is found in places large and small, in all corners of the country. Like the foreign-born population on the whole, the LEP workforce is over-represented in large urban areas. The 89 large metropolitan areas included in this analysis are home to 64 percent of the total working-age population but 82 percent of those who are LEP. This is not surprising, given that most LEP workers are immigrants and that 84 percent of working-age immigrants in the United States reside in these 89 metro areas. While LEP individuals make up 9.3 percent of the national working-age population, in the 89 metro areas, they comprise a larger share: 12 percent. The largest immigrant gateways are home to the greatest number of working-age LEP residents (Table 3). New York and Los Angeles each account for about 12 percent of this population nation- ally, each with 2.3 million LEP residents of working age. Miami and Chicago have over 800,000 each, followed by Houston, Dallas, San Francisco, Riverside, Washington, and San Diego. Together, these 10 metro areas account for half of the nation’s working-age LEP population. All but one is also among the 10 metro areas with the largest working-age foreign-born populations; San Diego takes the place of Boston, which has more immigrants than San Diego but fewer working-age LEP adults.

Table 3. Top Ten Metro Areas for LEP Population, Ages 16–64, 2012

Metropolitan Area # LEP % LEP

1 New York-Northern New Jersey-Long Island, NY-NJ-PA 2,330,496 18.3 2 Los Angeles-Long Beach-Santa Ana, CA 2,264,513 25.7 3 Miami-Fort Lauderdale-Pompano Beach, FL 865,905 23.2 4 Chicago-Joliet-Naperville, IL-IN-WI 820,012 13.0 5 Houston-Sugar Land-Baytown, TX 721,872 17.8 6 Dallas-Fort Worth-Arlington, TX 640,695 14.7 7 San Francisco-Oakland-Fremont, CA 557,878 18.4 8 Riverside-San Bernardino-Ontario, CA 498,001 17.8 9 Washington-Arlington-Alexandria, DC-VA-MD-WV 456,972 11.9 10 San Diego-Carlsbad-San Marcos, CA 350,998 16.3

Source: Author’s analysis of ACS 2012 PUMS data

Seventeen metro areas have at least 200,000 working-age LEP individuals and thereby account for at least 1 percent of the U.S. LEP population each. These include (in addition to those listed above): Boston, Atlanta, Phoenix, San Jose, Philadelphia, Seattle, and Las Vegas. All of these metro areas are immigrant gateways, with varying histories of receiving large numbers of immigrants over the long or shorter term.40

Concentration Metro areas with high concentrations of immigrants—especially metro areas in California and Texas— dominate the list of places with the highest share of their working-age population that is LEP (Table 4). Among the top 10, Miami is the only metro area not in California or Texas, and among the top 15, 10 are California metro areas. McAllen and El Paso, TX are both on the Mexican border and rank highest for the share of their working-age population that is LEP, almost one third. They each have about 150,000 working-age LEP individuals.

BROOKINGS | September 2014 15 Table 4. Top and Bottom 10 Metro Areas for LEP Percent of Working-Age Population, 2012

Metropolitan Area % LEP

1 McAllen-Edinburg-Mission, TX 32.0 2 El Paso, TX 29.8 3 Los Angeles-Long Beach-Santa Ana, CA 25.7 4 Miami-Fort Lauderdale-Pompano Beach, FL 23.2 5 Fresno, CA 22.8 6 San Jose-Sunnyvale-Santa Clara, CA 22.6 7 Bakersfield-Delano, CA 20.4 8 Stockton, CA 19.3 9 Modesto, CA 18.6 10 San Francisco-Oakland-Fremont, CA 18.4 Metropolitan Area % LEP

80 Louisville/Jefferson County, KY-IN 3.2 81 Charleston-North Charleston-Summerville, SC 2.9 82 Albany-Schenectady-Troy, NY 2.9 83 Columbia, SC 2.9 84 Birmingham-Hoover, AL 2.9 85 Syracuse, NY 2.8 86 St. Louis, MO-IL 2.7 87 Virginia Beach-Norfolk-Newport News, VA-NC 2.5 88 Cincinnati-Middletown, OH-KY-IN 2.5 89 Pittsburgh, PA 1.6

Source: Author’s analysis of ACS 2012 PUMS data

Miami and Los Angeles stand out for both their number and share of population that is LEP (See Tables 3 and 4). Not only do they each have more than 800,000 working-age LEP individuals, but in each metro area, the LEP population represents about a quarter of the working age population (23 percent in Miami, and 26 percent in Los Angeles). San Francisco is the only other metro area that ranks among the top 10 for both the number and percent of its working-age population that is LEP. The next six metro areas in rank for their percent LEP (fifth through 10th) are in California: Fresno, San Jose, Bakersfield, Stockton, Modesto, and San Francisco. New York—where 2.3 million LEP individu- als make up 18 percent of the working-age population—ranks 11th. In seven metro areas, at least one in five working-age adults are LEP, and 27 of the 89 large metro areas have a LEP share of over 10 percent. These are mostly metro areas in border states, especially California, Texas, and Florida, but also include New York, Honolulu, Las Vegas, Bridgeport, Chicago, and Washington, places where at least 20 percent of the working-age population is foreign born. By contrast, in 29 of the 89 large metro areas less than 5 percent of the working-age population is LEP. Not surprisingly, many of these places are former immigrant gateways that attracted large number of immigrants in the early 1900s but have seen relatively little recent immigration (such as Detroit, Cleveland, and Pittsburgh). But they also include metro areas with fast-growing immigrant populations: eight of the nine metro areas whose total foreign-born population doubled between 2000 and 2010 (Table 4).41 In these places, the LEP population, while still relatively small, grew at a faster- than-average rate.

16 BROOKINGS | September 2014 Change since 2000 Between 2000 and 2012, the total growth in the working-age LEP population in the 89 large metro areas matched that for the nation as a whole: almost 20 percent. The average growth rate for these 89 areas, however, was much higher (44 percent) than the total rate because many of the smaller metropolitan areas witnessed very high rates of growth. Cape Coral more than doubled its working- age LEP population between 2000 and 2012, and Lakeland, Indianapolis, and Omaha each saw between 95 and 99 percent growth. While the size of the working-age LEP population remained relatively low in these places (only Indianapolis had over 50,000), it is often the fast pace of change that is felt most acutely on the ground. Among the metro areas with over 100,000 working-age LEP population in 2012, four saw rates of growth above 50 percent: Orlando (71), Las Vegas (61), Bakers- field (59), and Seattle (55) (Map 1). Fifteen metro areas did not experience statistically significant change in their working-age LEP populations between 2000 and 2012. Three metro areas grew at a rate of lower than 10 percent: New York, Chicago, and San Jose. Los Angeles was the only metro area to experience a statistically significant decline in its working-age LEP population, decreasing by some 91,000 people (a 3.9 percent decline) (See Appendix). While the data do not allow a distinction between what proportion of the decline was due to outmigration from the metro area and what is attributable to a change from LEP to non-LEP status (or an aging out of working age), this decrease coincides with a small increase (0.7 percent) in the working-age foreign-born population in Los Angeles over the same time period.

Map 1. Percent Change in the LEP Population, Ages 16-64, 89 Metro Areas, 2000-2012

Percent change -3.9 – 0.0 0.1 – 19.5 89 metros: 19.5 19.6 – 49.1 49.2 – 74.5 74.6 – 116.8 No change*

Source: Author’s analysis of ACS 2012 PUMS data *at the 90 percent confidence level

D. Educational attainment and the native languages of LEP adults vary considerably across metro areas. The linguistic and labor force characteristics of LEP workers vary across the country, and these differ- ences have implications for outreach and service provision.

Home language The mosaic of languages spoken by the LEP population can be quite different from region to region and reflects immigrant origins. The linguistic mix is important for localities to understand as they seek

BROOKINGS | September 2014 17 Map 2. Percent of LEP Population who Map 3. Percent of LEP Population who Speaks Asian Speaks Spanish, and Pacific Island Languages, Ages 16-64, Ages 16-64, 89 Metro Areas, 2012 89 Metro Areas, 2012

4.7 – 35.9 1.0 – 13.9 36.0 – 56.5 14.0 – 25.8 56.6 – 75.0 25.9 – 51.5 75.1 – 99.0 93.9

Source: Author’s analysis of ACS 2012 PUMS data Source: Author’s analysis of ACS 2012 PUMS data

Map 4. Percent of LEP Population who Speaks Other Map 5. Percent of LEP Population who Speaks Other Indo-European Languages, Languages, Ages 16-64, Ages 16-64, 89 Metro Areas, 2012 89 Metro Areas, 2012

0.1 – 5.5 0.2 – 2.5 5.6 – 11.6 2.6 – 5.6 11.7 – 22.4 5.7 – 15.1 22.5 – 35.2 15.2 – 26.5

Source: Author’s analysis of ACS 2012 PUMS data Source: Author’s analysis of ACS 2012 PUMS data

18 BROOKINGS | September 2014 to meet the needs of residents not yet proficient in English.42 A federal executive order from 2000 requires that LEP individuals have meaningful access to federally funded programs and activities. Recipients of federal aid—which includes virtually every locality in the country—are expected to take reasonable steps to provide access to LEP individuals without unduly burdening their mission. To do so, they are expected to take into account the number, proportion, and frequency with which LEP indi- viduals interact with their agency or program and the importance of the program’s mission to people’s lives. This includes translation of vital documents into languages commonly spoken by LEP individuals likely to be served in that location.43 While almost two-thirds (65 percent) of the LEP workforce in the 89 large metropolitan areas overall speaks Spanish, this proportion varies from a low of 4.7 in Honolulu to 99 percent in McAllen. Generally, metro areas in the Southwestern and Western U.S.— destinations closest to Mexico and Central America— have the highest shares of Spanish speakers, but some metro areas in the Southeast whose immigrant populations grew quickly over the past decade also have higher-than-average Spanish shares (Map 2). In 81 of the 89 large metro areas, Spanish is the most commonly spoken lan- guage among the working-age LEP population. Overall, 20 percent of the working-age LEP population in the 89 large metropolitan areas speaks an Asian or Pacific Island (API) language. The share is significantly higher in some West Coast metro areas whose proximity to Asia has facilitated large inflows of migrants from that part of the world (Map 3). Honolulu, the closest metro area to Asia, has a significant number of speakers of Tagalog/ Filipino, Chinese, Japanese, and Native Hawaiian. Thus, it stands out on this measure with 94 per- cent of its working-age LEP population speaking an Asian or Pacific Island language. More than half (52 percent) of San Jose’s working-age LEP population speaks an Asian or Pacific Island language at home, and in San Francisco and Seattle, more than 40 percent do. In San Jose, LEP Vietnamese speak- ers outnumber LEP Chinese speakers, while in Seattle the reverse is true. Most of San Francisco’s LEP speakers of API languages speak Chinese (including Mandarin and Cantonese), with Filipino and Vietnamese speakers making up 15 and 10 percent, respectively, of the metro area’s LEP API speakers. In total, in seven of the 89 largest metro areas, a plurality of the working-age LEP population speaks an Asian or Pacific Island language: Honolulu, San Jose, Seattle, Pittsburgh, Syracuse, Albany, and Buffalo. (See Appendix for data on all 89 metro areas.) In Pittsburgh, a former immigrant gateway that now attracts relatively few immigrants, LEP Chinese speakers account for more than half of LEP API speakers. Refugees from Southeast Asia have boosted the LEP API population in Syracuse, Albany, and Buffalo.44 Indo-European languages (other than Spanish) are spoken by 13 percent of the working-age LEP population in the 89 large metropolitan areas. Seven of the 10 metropolitan areas with the high- est shares of other Indo-European language speakers (between 30 and 35 percent) are located in Pennsylvania (Harrisburg, Lancaster, and Pittsburgh) and upstate New York (Syracuse, Albany, Poughkeepsie, and Buffalo) in metro areas with relatively small immigrant populations. Detroit and Providence also rank among the top 10 metro areas on this measure, and Detroit is the only metro area in which a plurality (33 percent) of its working-age LEP population speaks an Indo-European language (Map 4). The assortment of Indo-European languages across and within these metro areas is very diverse: from Portuguese in Providence to Albanian in Detroit, from Serbo-Croatian in Harrisburg to Russian in Buffalo, from Pennsylvania Dutch in Lancaster to Yiddish in Poughkeepsie, each metro area has its own story to tell.45 Most metro areas have small shares of their working-age LEP population who speak languages not included in the Spanish, Asian and Pacific Island, or Other Indo-European categories (3.3 percent overall). In only six metro areas do LEP speakers of “other” languages make up at least 10 percent of the working-age LEP population, and they are most likely to speak Arabic or African languages.46 In Detroit and Columbus, about one quarter speak an “other” language, and in four more metro areas (Minneapolis, Buffalo, Worcester, and Louisville) between 11 and 15 percent do (Map 5). Arabic speakers account for three quarters of LEP “other” language speakers in Detroit and Buffalo, while those who speak Somali/Cushite/Beja account for the largest shares in Minneapolis and Columbus. In Worcester, where a large number of Liberians have settled, 63 percent speak Kru.

BROOKINGS | September 2014 19 Period of entry As discussed in finding A, immigrants who have been living in the United States for a shorter length of time are more likely to be LEP. Among the 89 large metro areas, 45 percent of the working-age LEP population are immigrants who came to live in the United States in 2000 or later, but this proportion ranges from less than a third in El Paso, Riverside, and Lancaster to over 70 percent in Richmond, Scranton, Pittsburgh, Columbia, and Louisville. The metro areas with the lowest shares of newcomers among their working-age LEP populations tend to be less diverse in terms of the languages spoken, consistent with prior literature demonstrating that in places where a large number of people speak the same non-English language, it is more feasible to remain limited English proficient for a longer period of time than in places with a greater mix of languages.47

Educational attainment When it comes to building English skills, the educational background of the learner is a key factor in designing effective instruction. Those who have completed more years of schooling and can read and write in their own language will benefit from different interventions than those who require basic lit- eracy instruction. Among the LEP population age 25-64 in the 89 large metro areas, 16 percent hold a college degree. In two metropolitan areas, the rate is more than double that. In Pittsburgh and Albany 39 and 33 percent, respectively, of the LEP population have attained a bachelor’s degree or higher. Pittsburgh is the only metro area in which a plurality of LEP adults (39 percent) hold a bachelor’s degree or higher. In 21 metro areas, at least one in five LEP adults holds a college degree. In contrast, there are 16 metro areas in which fewer than one in 10 LEP adults do. (See Appendix for data for 89 metro areas.) In the middle of the educational attainment spectrum, 46 percent of working-age LEP adults hold a high school diploma or have attended some college. In most of the large metro areas (61 of the 89 in this analysis), more working-age LEP adults fall into this middle category than the highest or lowest categories, and in 28 metro areas, a majority of the working-age LEP population is mid-skilled. At the low end of the educational spectrum, 38 percent of the LEP population between the ages of 25 and 64 living in the 89 large metro areas has not completed high school. In nine metro areas, those without a high school diploma make up a majority of the working-age LEP population. These include four California metro areas in which agricultural workers are concentrated (Bakersfield, Modesto, Fresno, and Oxnard), two Texas metro areas (McAllen and Dallas), two metro areas in Oklahoma (Oklahoma City and Tulsa), and Omaha. In 27 metro areas, a plurality of LEP adults has not completed high school. In these and other places where high shares of the LEP population have not graduated from high school, workers may require basic educational and literacy services along with English lan- guage instruction in order to make substantial gains in proficiency.

E. Most working-age LEP people are in the labor force.

Labor force status and employment A majority of the working-age LEP population in each of the 89 large metro areas is in the labor force. Overall, labor force participation in these 89 metro areas is 72 percent, but it ranges from 52 percent in Syracuse to 82 percent in Des Moines. Among the working-age LEP population in the 89 large metro areas, 65 percent is employed. In three metro areas, employment rates for the LEP population are 75 percent or higher: Des Moines, Cincinnati, and Palm Bay. In another 16 metro areas, the ratio of employed LEP workers to working-age LEP population reaches at least 70 percent. By contrast, in Syracuse and Buffalo less than half of working-age LEP individuals are employed (See Appendix).

Earnings As described in Finding B, workers proficient in English have substantially higher earnings than those who are LEP. Among full-time, year-round workers in the 89 large metro areas, median earnings for the working-age LEP population range from a low of $19,500 in Columbia to a high of $45,000 in Albany (See Appendix). On average, LEP workers in the 89 metro areas earn 85 percent less than those who are English proficient.

20 BROOKINGS | September 2014 The income premium for English proficiency is higher in some metro areas than in others. In 11 metro areas, median earnings for the English proficient working-age population are at least double that for the LEP population: Bridgeport, Washington, San Jose, Columbia, Bakersfield, Raleigh, New York, Los Angeles, Denver, Charlotte, and Nashville. And in 79 of the 89 large metro areas, median incomes are at least 50 percent higher for English proficient workers than LEP workers.48

Poverty Almost one quarter (24 percent) of working-age LEP adults in the 89 largest metropolitan areas live below the federal poverty line; their English proficient counterparts have a poverty rate of 13 percent. More than one third of the working-age LEP population is poor in 10 metro areas.49 By contrast, the highest non-LEP poverty rate among the 89 metro areas is 24 percent (McAllen). Washington stands out for its low LEP poverty rate— 13 percent—though this is still almost double the rate of the working- age non-LEP population there. In all, 14 metro areas have LEP poverty rates lower than 20 percent, compared to 87 out of 89 metro areas in which non-LEP poverty rates are this low (See Appendix).

Occupation Taken together, LEP workers in the 89 large metro areas have very similar occupational profiles as the nation as a whole with the top five occupations (building and grounds cleaning and maintenance, production, construction, food preparation and serving, and transportation and material moving) each accounting for between nine and 13 percent of LEP workers.

MapMa p6. 6 Top. To pOccupation occupatio nfors fLEPor L EWorkers,P worke rAgess, ag e16-64, 16-64 ,89 89 Metropolitanmetro areas, Areas2012

Bldg & Grounds Clean m Farming, Fishing & Forestry Sales & related &B Maintenanceldg & Grounds Clean & Maint õ Farming,Fishing & Forestry © Sales & related E ComputerCompute &r &Math Math FFoodood PrepPrep Transportation & TMaterialransp & Moving Material Moving 89:} ConstructionConstructio n& &related related A PProductionroduction

Source: Author’s analysis of ACS 2012 PUMS data

Among the 89 metro areas, however, occupational patterns show more variety. In 28 metropolitan areas, production is the largest occupational category, accounting for an average of 20 percent of LEP workers in these metro areas (Map 6 and Appendix). Construction ranks highest in another 19 metro

BROOKINGS | September 2014 21 areas (representing 20 percent of LEP workers on average in these places). In 19 metro areas, building and grounds cleaning and maintenance workers account for the largest share of the LEP population, 18 percent on average. And food preparation and serving workers is the largest category in 11 metro areas, accounting for an average of 16 percent of LEP workers in these metro areas. Only four other occupational categories rank highest in at least one metro area: farming, fishing, forestry, and hunting (Bakersfield, Fresno, Stockton, Boise City, Modesto, and Oxnard); transportation and material moving (Harrisburg, Modesto, Syracuse, and Riverside); sales and related (El Paso and Palm Bay); and com- puter and math (Pittsburgh). The extent to which LEP workers are concentrated in the top occupations versus a wide dispersal across a range of jobs within a metro area also varies. In general, LEP workers are less concentrated occupationally than they are by industry. There are only two metro areas in which more than one third of LEP workers have jobs in the top occupational group: Grand Rapids (39 percent in production) and Bakersfield (38 percent in farming, fishing, forestry, and hunting). At the other end of the spectrum, in 15 metro areas the largest occupational group accounts for less than 15 percent of LEP workers. Metro areas in which the largest number of LEP workers performs farming, fishing, forestry and hunting jobs have the highest concentrations of workers in their top occupational group: 27 percent on average. In metro areas where LEP workers are highly concentrated in similar jobs, it may be easier for work- ers to persist with low levels of English proficiency, especially if their co-workers speak their native language. On the other hand, in such places, there is a clearer target for outreach, especially that involving vocational training.

Industry The same two industry sectors that account for the highest shares of LEP workers nationwide—accom- modations and food services, and manufacturing—also account for the highest shares in the 89 large metropolitan areas: about 14 percent each. Twelve percent of LEP workers in the 89 large metro areas work in construction, with another 11 percent working in retail trade. More of the 89 large metro areas (29) have manufacturing as their top industry for LEP workers than any other industry. In 28 metro areas, accommodations and food services account for the highest share of LEP workers, and construc- tion ranks highest in 18 metro areas (Map 7). Agriculture (including forestry, fishing and hunting), retail trade, administration and waste management, and health and social services employ the most LEP workers in between two and five metro areas each (Map 7 and Appendix). In different metro areas, LEP workers concentrate in different industries, depending on the indus- trial mix of the region. Agricultural workers in Bakersfield, Fresno, Boise City, Oxnard, and Stockton account for the highest share of LEP workers in these metro areas (between 20 and 45 percent). Retail trade ranks highest in Syracuse, Palm Bay, Philadelphia, and Miami, accounting for between 14 and 18 percent of LEP workers in these metro areas. In North Port, Bridgeport, and Phoenix, admin- istrative and waste management services account for the greatest share of LEP workers, between 16 and 19 percent. Only in Tampa and Jacksonville do health and social services account for the highest share of the metro area’s LEP workers, about 13 percent.

22 BROOKINGS | September 2014 MapMa 7.p Top6. T oIndustryp indust rfory f oLEPr LE Workers,P worker sAges, age 16-64, 16-64, 8989 mMetropolitanetro areas, 2Areas012

AccommodationsAccommodation sand an dFood Foo Servicesd Services HealthHealth & & Social Soc SServiceserv l AdministrationAdmin and Wa &s tWastee Mgm Managementt A ManufacturingManufacturing õ Agriculture,Agriculture ,fishing, fishing, forestry,forestry , huntinghunting © RetailReta ilTrade Trade 89:} ConstructionConstruction

Source: Author’s analysis of ACS 2012 PUMS data

Metro areas also differ in the extent to which LEP workers concentrate in the top industries versus dispersing across a broader set of industries. In four metro areas, for example, more than one third of LEP workers are concentrated in one industry: Bakersfield (45 percent in agriculture, forestry, fishing, and hunting), Grand Rapids (43 percent in manufacturing), Fresno (41 percent in agriculture, forestry, fishing, and hunting), and Las Vegas (34 percent in accommodations and food services). At the other end of the spectrum, in six metro areas, the largest industry for LEP workers accounts for a smaller-than-16-percent share: Jacksonville (13 percent in health and social services), El Paso (13 percent in manufacturing), Tampa (13 percent in health and social services), Baltimore (14 percent in accommodations and food services), Miami (14 percent in retail trade), and San Diego (15 percent in accommodations and food services). Like occupational concentration, concentration of LEP workers in an industry may serve as an advantage and a disadvantage for the improvement of English skills. More workers in an industry who speak the same non-English language may encourage LEP workers to stay that way since they are able to communicate with co-workers in their native tongue; yet, having a high concentration of LEP workers in one industry eases the way for targeted outreach to LEP workers, especially via employers.

BROOKINGS | September 2014 23 Discussion and Policy Implications

mmigrants (and their children) have become an increasingly important part of the U.S. labor force and are projected to account for almost all of its growth through 2050.50 Limits to their economic opportunity today threaten collective well-being tomorrow. English proficiency is the most essential means of opening doors to economic opportunity for immigrant workers in the UnitedI States.51 And yet access to acquiring these skills is persistently limited by a lack of resources and attention. This analysis calls attention to the size, scope, and geographical variation in the need for English instruction at the national and metropolitan levels. By providing metro-level characteristics of the working-age LEP population, it provides regional decision makers with data they need to tailor their outreach depending on the languages spoken, educational attainment, employment status, income, and other characteristics of their LEP population. It also provides federal policymakers with a better understanding of the demand for and gaps in adult English instruction. This research can also help prepare for the possibility of federal changes to our immigration system. If an opportunity for either temporary or permanent legal status is made available to those living in the United States without legal status, some level of English proficiency could be a prerequisite. In such a case, the demand for English (and civics) instruction would climb sharply, and state and local institutions would have a key role to play in meeting this educational requirement.52 Furthermore, successful implementation of a legalization program would require federal, state, and local actors to coordinate their efforts well in advance.53 Even at the status quo, the need for English instruction is dwarfed by current efforts to address it, thereby limiting the economic, civic, and social well-being of individuals, families, metropolitan regions, and the nation. Innovation—in funding, outreach, and instructional methods— is necessary in order to bridge the gap. While the data analysis presented in this report does not address many of the policy and programmatic aspects of improving the adult educational system for English learners, it lends support for additional and smarter investments in three arenas.

Funding Practitioners in the adult education and workforce development arena speak of the chronic lack of funding that prevents them from being able to meet the demand for their services.54 This analysis confirms that the need for one type of those services—adult English instruction—is not only large and growing, but also geographically dispersed around the country. While the data presented here support other research demonstrating that immigrants do, by and large, improve their English skills as their time living in the United States increases, this analysis also points to the slow pace of progress. Wait- ing for adult immigrants to “pick up the language” delays their economic (not to mention social and civic) integration. Investment in adult English instruction should be increased to allow more workers to enhance their human capital and boost their productivity more quickly. An increase in funding for adult English instruction could come from a number of sources: ➤➤ A reformed Workforce Investment Act. Title II of the Workforce Investment Act (AEFLA)—the main source of federal funding for adult education including English instruction—is severely underfunded, serving a small fraction of the nation’s low-skilled adults.55 Not only is it important to raise the level of overall funding, but funds should be distributed to better meet the needs of English language learners. LEP adults are eligible for Title II services regardless of their educa- tional attainment, but the current formula for distributing funds to states is based only on the number of adults without a high school diploma. The analysis presented in this report shows that such a formula takes into account only 40 percent of the working-age LEP population. The state distribution formula should be changed to take into account the 60 percent of the LEP popula- tion with a high school diploma.56 In addition to increasing and more equitably distributing Title II funding, WIA Title I fund- ing could be better utilized for LEP adults. The purpose of Title I is to connect job seekers with employment and training services, implemented through a nationwide network of one-stop centers. Title I’s funding level ($2.97 billion in FY2010) is more than four times that of Title II. While Title I funding is sometimes used for connecting LEP adults with language training, its

24 BROOKINGS | September 2014 requirement that individuals progress through other tiers of service before being able to access the training tier delays or prevents access to English instruction for many.57 Moreover, many one-stop centers are not prepared—with bilingual staff, cultural competency, or familiarity with immigrant-serving organizations—to serve a LEP population. In Montgomery County, Maryland, where 15 percent of residents are LEP, the local workforce investment board (MontgomeryWorks) and literacy coalition (Montgomery Coalition for Adult English Literacy) collaborate to help bridge the gap between the ESOL and workforce develop- ment systems. Together they created an employment readiness toolkit for providers working with LEP adults to help increase the number of students being referred between ESOL classes and workforce programs. ➤➤ States and localities. Most public funding for adult education does not come from the federal government. Under AEFLA, states must ensure that at least 25 percent of total adult education funding comes from non-federal sources—state, local, private, etc.—in order to receive a federal grant. In 2010, the average non-federal contribution to adult education was about 73 percent. Some states, like California, Connecticut, and Minnesota, contributed more than average while other states, like Texas, Tennessee, and Nevada, contributed much less than the average.58 However, as state finances suffered in the recession, some states dramatically reduced or even eliminated their adult education contributions. California, typically a leader in adult education funding contributions, adjusted its policy during its state budget crisis. Beginning in 2008-2009, the state reduced adult education funding and allowed school districts to redirect adult educa- tion funding to other programs. As a result, the state’s Legislative Analyst’s Office found that only 40 to 50 percent of adult education funding was actually spent on adult education.59 Conversely, some states with a history of contributing little or nothing to adult education are now adopting new measures to support adult education. Colorado recently enacted legislation that directs state funding to adult education for the first time. Although these state funds will not go toward the 25 percent contribution requirement because they are earmarked for programs not covered under the Workforce Investment Act—direct student services, employment prepara- tion, job placement activities, and skills training—this is a step in the right direction for a state that has historically not allocated funds to adult education.60 Other states including Arizona, Iowa, Minnesota, and New Hampshire have reenacted or increased their funding. This trend is promis- ing, but more will need to be done to meet the high demand for adult English instruction. In addition to state funding, some localities provide money to fund adult education. Montgomery County funds the Montgomery Coalition for Adult English Literacy (MCAEL). MCAEL promotes adult education and English literacy instruction through grants, workshops, instructor training, learning and teaching tools, public outreach, and advocacy. Local funding is an impor- tant part of the adult education equation, as local organizations are better equipped to gauge the needs of the community. ➤➤ Employers (and employees). Employers stand to benefit from workers who have the English skills to perform their work safely and productively. And employees stand to benefit by improv- ing their confidence and access to better pay or working conditions. Therefore, it is reasonable that employers and employees should be called upon to contribute to the investment in English skills. This analysis provides data on the industries and occupations in which LEP workers con- centrate and can be used to enlist employer and employee participation. One example comes from the food services industry where a large number of LEP workers are concentrated (see Finding B). McDonald’s created a program called “English under the Arches” to help shift managers improve their English skills and confidence in working with employees and customers. Using community college ESOL instructors and web-conferencing technology, McDonald’s is able to provide instruction at multiple sites at low cost. Instruction takes place during working hours, allowing students to maintain work and family commitments. Five hundred (500) students participated during the first three years of the program, and the vast majority graduated, received pay increases, and continued working for McDonald’s, which has a history of promoting from within.61 Another promising example comes from King County in Seattle where the local workforce investment board partnered with employers from small and medium-sized businesses where

BROOKINGS | September 2014 25 many immigrants and refugees were employed. In addition to federal (WIA Title I) and founda- tion funding, Literacy Works relied on employers to provide at last half-time pay to employees during instructional time. In turn, employees were expected to commit their time and efforts to succeeding. In its first two years, Literacy Works served 307 employees at 25 companies. A range of industries were represented, but most were manufacturing firms, the sector with the highest number of LEP workers nationally. Employees reported increased confidence and ability to perform their jobs, and employers reported improvements in morale, productivity, absentee- ism, turnover, labor-management relations, and the health and safety records of participants.62 ➤➤ Philanthropy. Improving the adult English instruction services available to the LEP population will require a multifaceted approach in which philanthropic contribution can play a vital role. Foundations and other private donors interested in immigrants, workforce development, and equality of opportunity should consider investing more in the English skills of the LEP workforce. Donors should consider several factors before contributing funds, including community needs, existing resources, promising practices of English instruction and/or naturalization programs, and opportunities for collaboration.63 Considering each of these factors will help determine the specific educational and occupational areas in need of philanthropic support and the most effec- tive strategies for improvement. In some cases, philanthropic funding of adult English education will not involve the estab- lishment of a brand new program. Rather, funding may be more effectively used to expand and improve the capabilities of existing community organizations. In 2013, the Silicon Valley Community Foundation, an organization that manages philanthropic giving in the region, pro- vided over $800,000 in grants to existing adult English instructional programs in Santa Clara and San Mateo counties in California. The programs seeking grants undergo an application and review process, which ensures that philanthropic donations to SVCF are reaching promising programs. In other cases, collaboration between philanthropic foundations, private donors, and govern- ment entities can create new, effective adult English education programs. The Tucson-based Literacy Connects Infusion Project is funded through a partnership of Pima County, the mayor’s office, the Stocker Foundation, the Helios Education Foundation, and individual donors. This program integrates literacy instruction for both children and adults. Parents in the Sunnyside Unified School District in Tucson can receive English, GED and citizenship instruction, while their children receive reading, writing, and homework help. Both of these methods of philanthropic funding rely on community support and engagement to thrive. Oftentimes, regional literacy coalitions serve as facilitators of the relationship between English instruction providers and philanthropic donors. There are dozens of such organizations across the country that link community stakeholders and provide resources to promote English literacy.64 Philanthropic funding from foundations and individual donors can afford literacy coali- tions the opportunity to more effectively assist the LEP population, especially considering that the presence of a literacy council in an area has been found to increase funding for all service providers there.65 ➤➤ Charter schools. A potential source of public funding for adult education often overlooked is charter schools, which typically serve a K-12 population. Charter schools operate independently of public school systems but receive public funding on a per-pupil basis, allowing students to attend tuition-free. The relative stability of charter funding allows adult education providers to build their programmatic and staff capacity to serve more students with high quality offerings in a way that is difficult to achieve with more uncertain or fluctuating revenue sources, such as grants and contracts. State laws on charter schools vary greatly, but almost all states currently prohibit using per-pupil funding for adults in order to avoid taking funding away from K-12 education. The District of Columbia is unique in its approval of public charter funds for adult education, and there are currently eleven such schools in operation there. The oldest and perhaps most well-known adult education program in the District is Carlos Rosario International Public Charter School. Founded 40 years ago, it has received national and international attention as a model in adult education and workforce development and has been able to meet and exceed standards for charter school status. (See “Targeted Outreach” and “Instructional Innovations” below for more

26 BROOKINGS | September 2014 about this model.) Other states should consider revising their charter school statutes to allow funds to be used for adult education.

Targeted Outreach While the need for more funding is clear, existing and future approaches must be wisely targeted to make the most of limited resources. By taking into account the characteristics of an area’s LEP popula- tion, interventions can be tailored to the specific needs and assets of a region’s LEP workforce: ➤➤ Population size, growth, and period of arrival. Metro areas with small numbers of LEP work- ers or without a recent history of receiving immigrants may be less likely to have a robust immi- grant service infrastructure in place to provide adult English instruction and other assistance to newcomers. These places are often the same ones that have experienced the fastest growth rates in their LEP populations and may be struggling to adapt to the demographic changes happening in their regions. Without a large network of more established immigrants, recent arrivals to these places are less likely to have knowledge about and access to ESOL and vocational training. They may also be more likely to need wrap-around services such as transportation and child care to make participating in English instruction practical as they get their feet on the ground in a new place. Even in a metro area with a large and more established immigrant population like Washington, D.C., working-age LEP immigrants can benefit from supportive services. One of the keys to Carlos Rosario School’s success has been their Supportive Services Department which addresses many of the barriers to adult student achievement through bilingual counseling, health and child care referrals, college financial aid assistance, life skills workshops, and career/vocational counseling and job placement. ➤➤ Home languages. Knowing what languages are spoken by the LEP population in any particu- lar metro area is a key first step toward addressing the integration challenges they face. Local governments must have these data in order to provide reasonable access to their services, as outlined in the federal government’s language access executive order.66 Non-profit agencies who work closely with immigrants can also benefit from this information in order to provide transla- tion and interpretation services for their clientele. Oftentimes, these services are provided by someone who speaks a non-English language at home but who has also obtained proficiency in English. Knowing that such a population exists can help service providers tap into these commu- nity assets A preponderance of one foreign language spoken in a metro area, or a sizeable number of speakers of one language, may prompt municipalities, service providers, and businesses to reach out to that particular group. Indianapolis, for example, established an Office of Latino Affairs to help make the city more accessible to its fast-growing Spanish-speaking population by connecting newcomers to English classes, business seminars, and health and education fairs. In Washington, D.C., where a relatively high share of immigrants were born in Africa, the city coun- cil established an Office on African Affairs in 2006, and Amharic (a language of Ethiopia) is one of the six LEP languages covered under the city’s 2004 Language Access Act, based on Census Bureau data similar to that analyzed in this report. Sometimes it makes sense to conduct instruction in learners’ native languages, particularly in situations where workers share the same non-English language. Bilingual vocational training can accelerate student comprehension of difficult concepts and minimize miscommunication.67 In McAllen, where 99 percent of the working-age LEP population speaks Spanish, South Texas College provides dual language instruction whereby contextualized English classes and classes covering occupational vocabulary in Spanish are pursued together for college credit and occupa- tional certificates. Other programs prepare students to take the GED in Spanish, allowing them to qualify for more jobs as well as Pell Grants to further their education. ➤➤ Educational attainment and earnings. The educational attainment of a region’s LEP workforce is perhaps the most important characteristic to take into account when addressing the English instruction and workforce readiness needs of LEP adults. Working-age LEP adults who lack a high school diploma may need basic education services—especially if they are not literate in their native language—in addition to English instruction. Furthermore, this population has the lowest

BROOKINGS | September 2014 27 median earnings and the highest rates of employment, and thus may be more likely to lack the money and time to commit to conventional, sequential models of instruction. Programs that integrate ESOL with jobs skills training provide an alternative to the lengthier traditional process. Washington state’s Integrated Basic Education and Skills Training (I-BEST) is a nationally recognized model that addresses students’ occupational training and basic skills needs simultaneously in order to move them into living wage work faster. Rather than the tradi- tional model of completing a series of basic skills and literacy courses before moving on to job training, I-BEST classes involve two instructors—one for professional content and one for basic skills and ESOL—so that students begin learning job skills immediately. Evaluation of the program has shown that, compared to students in traditional ESOL and basic skills classes, I-BEST stu- dents are more likely to earn college credit, obtain a certificate, and improve their basic skills.68 The model has attracted the attention of the U.S. Department of Labor, which in 2013 embarked on a three-year project to replicate it in Connecticut, Georgia, Maryland, and Texas; many other states and localities are also pursuing this approach to integrated instruction. Accelerate Texas, for example, is a statewide effort to integrate career and technical training with adult education, including ESOL. Students graduate with both a GED and a work training certificate applicable to their regional labor market. According to this analysis, proficiency in English makes the greatest difference in earnings for those in the middle of the educational attainment range (high school diploma or some col- lege), and most working-age LEP adults fall into this category. In addition to the I-BEST model, which is applicable to mid-skilled workers, “career pathway bridges” is a model which has been developed to assist students who lack a college degree to move more quickly through basic skills and vocational training. Building on the career pathways model, “bridges” extend access to less educated workers by providing targeted basic skills or English language instruction.69 One example is found in the Minneapolis-St. Paul metro area, where partnership between St. Paul College, the Ramsey County WIB, the St. Paul school district, healthcare employers, and Goodwill allows students to earn a for-credit Medical Records Clerk certificate. Classes are team taught by adult basic education (ABE) and career and technical education (CTE) instructors, and students in need of ABE/ESOL enroll in a pre-program bridge course which includes computer literacy and prepares them to succeed in the career pathways course.70 The 15 percent of LEP adults nationwide who hold a college degree—a share which more than doubles in some metro areas— typically qualify for higher level English instruction. But in addition to building their English skills, addressing other factors that contribute to their under- employment is important. Frequently, professional licenses and certifications earned abroad do not transfer easily to the U.S. labor market. Based in New York and Toronto, World Education Services is a non-profit organization that works with professionals, students, employers, and licensing boards to evaluate foreign credentials for the U.S. and Canadian markets. Lack of professional English skills and familiarity with the U.S. job market is another source of underem- ployment for the highly educated LEP adult. Programs such as Upwardly Global that assist this population with networking, resume writing, job interview skills, and career planning are essen- tial to helping highly educated immigrants advance.71 ➤➤ Industry and occupation. The data analyzed in this report show that LEP workers are con- centrated in certain industries and occupations that can be targeted for local outreach. In the Washington metropolitan area, Casa de Maryland has partnered with community colleges in Prince George’s and Montgomery counties to offer training programs aimed at the industries and occupations in which LEP workers concentrate or would qualify for with further instruction: child care, construction trades, security guards, landscaping, building maintenance, electrical installa- tion, hospitality, and HVAC. In order to determine which career training pathways to develop, school leaders at the Carlos Rosario School use a sophisticated, best practices approach which includes environmental scans, regional multi-year workforce projections, living wage statistics, and more. Career training classes are currently offered in the fields of healthcare, culinary arts and information technol- ogy. The School recently opened a state-of-the-art facility to provide students with even more hands-on learning opportunities in these three areas. Corporate advisory committees, comprised

28 BROOKINGS | September 2014 of industry professionals, are maintained for each career field to help them focus their training on high-demand fields with upward mobility and family sustaining wages in the Washington, DC region. Local employers from the advisory committees provide up-to-date industry information and expectations so that curriculum can be tailored to the region’s industry needs. Committee members also offer field training opportunities such as internships and shadowing, participate in activities such as mock interviews and resume reviews, and present in-class workshops on relevant industry topics. In San Francisco, the Welcome Back Center connects internationally trained healthcare workers with the need for linguistically and culturally competent health services in underserved communi- ties. Realizing that a lack of English proficiency was one of the biggest barriers to these health- care professionals applying their skills to the U.S. market, the Welcome Back Center designed English Health Train, a highly specialized, health-focused ESOL program.72

Instructional Innovations Many LEP adults want to learn English (as demonstrated by full enrollments and waiting lists), but do not have access to instruction that is affordable, convenient in terms of time and place, and acces- sible. New methods and models are needed to more quickly and effectively improve adults’ English skills. The following innovations show promise for reaching more students with more effective instruction: ➤➤ Worksite. Two major barriers to regular attendance of adult English classes is inconvenience of location and time away from work or family obligations. An effective way to combat these bar- riers is worksite instruction. The worksite location also lends itself to the inclusion of vocational curriculum (Vocational English as a Second Language or VESL), which has well-documented benefits for employees and employers.73 A handful of workplaces already take advantage of this idea, but it would be advantageous for both employers and employees if more workplaces were to develop worksite English programs. Building Skills Partnership (BSP) is a California-based nonprofit collaboration between unions, building owners, employers and community leaders that brings adult English and vocational education to the workplace. Participants, most of whom are janitors, attend class onsite during paid-work time, which bypasses other obstacles such as family obligations, transportation, and child care costs. BSP offers a variety of educational programs, including ADVANCE—an intensive, six-month program that combines VESL and job training. A large majority (80 percent) of initial participants graduates from the program and many consequently receive promotions to more highly-paid positions. Private companies can also effectively organize worksite English instruction by utilizing com- munity resources. For example, Burris Logistics, a food distribution operation with locations along the Eastern seaboard, partnered with Orange County Public Schools in Orlando to provide onsite English instruction to employees. There are clear benefits to both the employee and the employer with programs like Burris Logisitics’—the employee improves English skills which are vital to economic prosperity and the employer improves the ability to communicate with workers. Collaborating with existing community educational resources also eliminates the need to hire and train new instructors. ➤➤ Online. Online instruction has the potential for substantial cost savings over traditional class- room models, and advances in and greater distribution of computer technology in recent years have made this more feasible and affordable. Online instruction has the potential to reach a greater number of students by removing some of the cost and convenience barriers of classroom instruction. Online instruction could be especially helpful in places that have seen rapid recent growth in their LEP populations. Tapping into what others have developed and made available online can fill the gap in places without a well-developed immigrant service infrastructure. For students with lower levels of English proficiency and educational attainment, a purely online, independent program may not be successful. Rather, hybrid models that blend online and face-to-face instruction are more promising. Motivated by the huge gap between demand for and supply of adult ESOL instruction, and with funding from the Gates Foundation, OneAmerica in

BROOKINGS | September 2014 29 Washington state developed a pilot project called English Innovations to teach English to adults using a combination of digital technology and classroom instruction. Students were given their own laptops to use both at home and in the classroom. There were three main components: 1) digital literacy, which included how to fill out a job application online, write an email to a child’s teacher, or find local employment services; 2) learning English using LiveMocha software, which was also available on mobile devices so it could be used anywhere; and 3) English discussion, including conversing with volunteers over Skype. The pilots were conducted at libraries, com- munity centers (where child care was available), and in workplace settings such as hotels and restaurants. Their results were comparable to community college classes, but at a faster pace. Students made significant gains in English, with the byproduct of increased digital literacy. Now they are working to scale up nationally and have developed a Training of Trainers toolkit – an online workshop to train participants to implement the program in their area. A future expansion will include the use of Xenos, a gaming platform aimed at helping adult Spanish speakers learn English and digital literacy. Developed by the Learning Games Network at MIT, with funding from the Gates Foundation, this program has been used successfully by the Boston and San Francisco public libraries. In 2008, with funding from federal and state education departments, the Sacramento County Office of Education developed the USA Learns website with technical assistance from the University of Michigan’s Institute for Social Research. This website allows users to learn begin- ning or intermediate English for free by watching videos and completing educational activities. The site features an introduction in Spanish or English, a picture dictionary, quizzes, immediate feedback on comprehension, and simple navigation. It been used by nearly six million people from around the world since its launch. Due to high demand, a mobile application has been developed (see below). Other popular sites for adults learning English include Dave’s ESLCafe and ManyThings. ➤➤ Mobile. Like online instruction, mobile technology has the advantages of lower cost and higher convenience. The rapid adoption of mobile technology—particularly via smartphones—makes this an attractive means of delivering instruction to a wide audience. A 2008 study by the Parthenon Group found that over 75 percent of those who speak English “not at all” have access to mobile phones.74 Given technological advances and cost reductions since then, it is safe to assume that the current adoption rate among LEP adults is higher. While not directly measuring LEP adults or immigrants, 2014 survey data from the Pew Research Internet Project show that 92 percent of Hispanics own a cell phone and 61 percent own a smartphone, a higher rate than that of whites or blacks.75 For those who have basic cell phones but not smartphones or an internet connection, a new platform called Cell-ED delivers English lessons through a simple phone connection. Users can listen to short audio lessons, read text lessons, text back responses, receive additional help, or continue with the next lesson. Evaluation of a similar Cell-ED program teaching Spanish literacy showed promising results.76 The New York State Office for New Americans plans to provide this service through the “English on the Go!” campaign for LEP adults, beginning in remote areas where immigrants are less likely to have access to classroom instruction or internet. USALearns, the website described in the previous section, recently made available a mobile version of its program: four apps are available for $0.99 each and can be run on all the major mobile platforms. Other programs such as LiveMocha and wlingua (specifically for Spanish speakers) provide a myriad of options for LEP adults to learn English at their own pace, at a place and time that is convenient.

30 BROOKINGS | September 2014 Conclusion

ver the last several decades, the number and share of limited English proficient adults has grown, but efforts to address the need for English instruction have lagged. LEP adults are more likely to be poor, less educated, underemployed, and have lower earnings than adults who are English proficient. These characteristics yield lower wages for individuals and familiesO and lower tax revenues and consumer spending for local areas. English proficiency is the most essential means of opening doors to economic opportunity for immigrant workers in the United States. Relying on LEP adults to “pick up the language” is not an efficient strategy for improving their labor market outcomes in the near term. Rather, increasing the investment in adult English instruction now would enhance the human capital of immigrants and lead to more productive work—benefiting whole families—sooner. National, state, and regional leaders have an opportunity to enhance the human capital and economic mobility of their current and future workforce by investing in adult English instruction through more funding, targeted outreach, and innovative instruction. Given the large number of LEP workers in the United States and the fact that virtually all of the growth in the U.S. labor force over the next four decades is projected to come from immigrants and their children, it is in our collective interest to tackle this challenge.

BROOKINGS | September 2014 31 Appendix. Limited English Proficient Population, Ages 16-64, 89 Metropolitan Areas, 2012

Educational attainment, Language spoken at home (%) age 25-64 (%)

Asian Other and % Indo- Pacific Change, European Island Other % HS or Median 2000- lan- lan- lan- Recent Some % in Labor % Annual Industry Occupational Metropolitan Area LEP % LEP 2012* Spanish guages guages guages Immigrants

Albany-Schenectady-Troy, NY 16,599 2.9 - 32.6 30.4 33.4 - 53.1 30.6 36.0 33.4 57.6 52.0 23.6 $45,000 Manufacturing 22.1 Production 15.9

Albuquerque, NM 46,037 7.9 - 81.3 2.5 11.5 4.7 45.2 43.8 48.3 7.9 65.6 57.7 36.3 $27,000 Construction 22.0 Construction & related 18.2

Allentown-Bethlehem-Easton, PA-NJ 39,721 7.4 83.3 68.6 16.6 10.4 4.4 49.9 26.3 55.4 18.4 72.1 65.2 20.4 $30,000 Manufacturing 21.0 Production 17.1

Atlanta-Sandy Springs-Marietta, GA 306,060 8.4 41.8 60.1 14.1 21.0 4.8 56.1 35.6 47.2 17.3 73.0 66.2 26.8 $24,000 Construction 20.2 Construction & related 18.0

Austin-Round Rock-San Marcos, TX 142,338 11.1 40.6 82.6 5.1 9.8 2.5 50.8 45.9 41.4 12.8 72.9 67.8 26.2 $24,000 Construction 26.2 Construction & related 21.4

Bakersfield-Delano, CA 111,633 20.4 58.6 92.0 2.1 4.8 1.1 33.5 66.5 28.8 4.7 71.1 60.8 30.8 $22,000 Agriculture, Fishing, Forestry, and Hunting 44.9 Farming, fishing, forestry 37.9

Baltimore-Towson, MD 78,676 4.3 44.2 42.4 21.9 29.3 6.4 53.0 24.3 46.4 29.3 75.5 69.6 17.4 $34,000 Accommodations and Food Services 13.9 Bldg & grounds cleaning and maintenance 13.8

Birmingham-Hoover, AL 21,465 2.9 55.2 66.0 13.5 16.9 - 59.1 27.3 55.4 17.3 76.1 72.8 25.6 $22,900 Accommodations and Food Services 21.7 Construction & related 18.3

Boise City-Nampa, ID 21,911 5.2 64.1 73.1 8.5 18.4 - 34.3 49.6 42.0 8.4 74.4 68.5 34.2 $25,000 Agriculture, Farming, Fishing, and Hunting 22.1 Farming, fishing, forestry 19.9

Boston-Cambridge-Quincy, MA-NH 315,770 9.9 26.1 40.6 29.0 25.1 5.4 55.1 29.0 51.4 19.7 74.9 67.0 20.2 $31,200 Accommodations and Food Services 18.3 Bldg & grounds cleaning and maintenance 15.3

Bridgeport-Stamford-Norwalk, CT 86,549 14.3 33.6 63.6 23.0 10.6 2.7 56.5 26.6 53.7 19.7 80.9 71.8 16.8 $29,000 Admin and Waste Mgmt 16.9 Bldg & grounds cleaning and maintenance 24.2

Buffalo-Niagara Falls, NY 26,012 3.5 29.0 24.4 30.1 32.5 12.9 61.8 24.7 46.2 29.1 59.0 49.0 39.7 $25,000 Accommodations and Food Services 17.4 Production 15.4

Cape Coral-Fort Myers, FL 39,838 10.6 116.8 80.1 15.2 4.7 - 52.0 42.9 46.5 10.6 77.7 72.6 27.9 $21,800 Accommodations and Food Services 19.0 Bldg & grounds cleaning and maintenance 20.2

Charleston-North Charleston-Summerville, SC 13,755 2.9 30.6 66.0 6.6 27.4 - 67.8 33.4 49.3 17.3 73.3 70.7 19.5 $22,000 Construction 28.7 Construction & related 27.9

Charlotte-Gastonia-Rock Hill, NC-SC 88,931 7.5 62.7 72.7 9.1 16.1 2.1 61.2 37.6 47.2 15.2 74.5 67.9 24.2 $22,000 Construction 23.5 Construction & related 22.4

Chicago-Joliet-Naperville, IL-IN-WI 820,012 13.0 5.1 65.3 20.2 11.7 2.8 38.5 37.6 47.4 15.0 73.7 66.6 19.6 $27,400 Manufacturing 25.4 Production 20.9

Cincinnati-Middletown, OH-KY-IN 35,430 2.5 55.1 41.9 15.8 35.2 7.2 62.7 25.7 54.3 20.0 76.3 75.3 18.1 $25,000 Accommodations and Food Services 18.8 Food Prep 14.6

Cleveland-Elyria-Mentor, OH 47,434 3.6 - 38.9 32.9 19.5 8.7 45.0 24.0 51.9 24.1 62.9 55.9 26.0 $30,000 Manufacturing 25.2 Production 18.5

Colorado Springs, CO 19,555 4.4 54.8 74.1 8.2 16.0 - 41.1 32.9 52.5 14.6 73.7 70.9 36.4 $25,000 Construction 21.5 Bldg & grounds cleaning and maintenance 22.4

Columbia, SC 15,451 2.9 47.8 68.7 11.6 18.2 - 72.9 37.0 41.8 21.2 73.2 68.8 23.1 $19,500 Construction 25.9 Construction & related 20.5

Columbus, OH 46,116 3.7 36.2 35.0 14.8 25.8 24.4 62.9 26.4 48.1 25.5 68.7 63.5 27.9 $25,000 Accommodations and Food Services 18.5 Food Prep 15.8

Dallas-Fort Worth-Arlington, TX 640,695 14.7 34.2 79.2 4.1 13.9 2.7 46.7 52.1 37.1 10.8 72.6 68.3 23.5 $24,000 Construction 20.7 Construction & related 18.4

Denver-Aurora-Broomfield, CO 163,875 8.9 24.2 70.7 7.0 17.0 5.3 48.7 45.6 41.6 12.8 73.0 68.5 22.7 $25,000 Construction 18.4 Food Prep 15.5

Des Moines-West Des Moines, IA 19,379 4.6 69.4 48.9 22.1 20.4 - 48.2 46.1 35.5 18.4 82.3 76.2 - $26,000 Manufacturing 23.3 Food Prep 21.8

Detroit-Warren-Livonia, MI 128,722 4.7 - 24.0 33.0 16.5 26.5 52.9 30.6 45.6 23.8 60.2 52.4 31.5 $30,600 Manufacturing 24.4 Production 15.5

El Paso, TX 156,506 29.8 17.7 97.1 1.4 1.3 - 26.1 38.7 48.6 12.6 63.0 57.3 33.1 $22,000 Manufacturing 13.2 Sales & related 11.9

Fresno, CA 136,712 22.8 22.3 78.8 6.2 14.4 0.6 33.6 65.9 27.7 6.4 70.0 59.0 36.5 $24,000 Agriculture, Farming, Fishing, and Hunting 40.6 Farming, fishing, forestry 31.0

Grand Rapids-Wyoming, MI 20,687 5.1 - 66.8 11.3 18.4 - 48.6 44.6 42.4 13.0 72.4 61.8 40.1 $23,000 Manufacturing 42.9 Production 35.5

Greensboro-High Point, NC 34,541 6.7 56.1 56.5 9.3 26.9 7.3 63.9 46.7 45.0 8.3 73.0 64.8 31.5 $21,000 Manufacturing 27.4 Production 25.8

Greenville-Mauldin-Easley, SC 20,769 4.4 54.7 76.0 7.5 11.9 - 61.8 46.2 34.0 19.8 77.6 65.9 36.2 $24,000 Accommodations and Food Services 18.5 Construction & related 17.2

Harrisburg-Carlisle, PA 15,412 4.3 66.7 39.1 35.2 18.5 - 54.7 36.6 52.2 11.3 72.2 64.5 23.9 $23,500 Manufacturing 22.5 Transp & Mat Moving 27.4

Hartford-West Hartford-East Hartford, CT 61,152 7.6 10.8 52.6 28.2 15.8 3.4 42.8 25.4 55.4 19.2 68.6 60.8 26.2 $30,000 Manufacturing 24.3 Production 18.1

Honolulu, HI 95,581 14.9 27.8 4.7 1.0 93.9 - 43.4 20.1 62.6 17.3 72.8 68.6 16.3 $32,000 Accommodations and Food Services 26.7 Food Prep 17.5

Houston-Sugar Land-Baytown, TX 721,872 17.8 40.1 82.3 4.6 11.4 1.7 43.6 49.8 39.9 10.3 72.7 67.8 26.6 $25,000 Construction 20.6 Construction & related 17.6

Indianapolis-Carmel, IN 53,140 4.5 98.9 66.4 8.7 17.8 7.0 55.1 38.9 41.6 19.4 78.2 73.9 30.0 $22,400 Accommodations and Food Services 26.5 Food Prep 16.9

Jacksonville, FL 42,003 4.7 84.7 40.0 26.2 28.8 5.0 55.7 14.9 60.8 24.3 66.3 61.8 15.7 $26,000 Health & Soc Serv 13.1 Bldg & grounds cleaning and maintenance 13.1

Kansas City, MO-KS 55,616 3.9 28.4 62.6 8.9 21.5 7.0 55.5 36.1 45.6 18.2 73.6 68.1 20.8 $24,000 Accommodations and Food Services 17.0 Bldg & grounds cleaning and maintenance 16.6

Lakeland-Winter Haven, FL 33,735 9.0 99.2 82.5 9.4 7.4 - 51.4 41.3 43.4 15.3 73.7 61.1 24.1 $30,000 Accommodations and Food Services 15.5 Construction & related 14.4

Lancaster, PA 16,506 5.0 - 41.4 34.6 21.2 - 29.0 29.4 65.1 - 67.2 60.6 25.0 $30,000 Manufacturing 27.0 Production 24.1

32 BROOKINGS | September 2014 Educational attainment, Language spoken at home (%) age 25-64 (%)

Asian Other and % Indo- Pacific Change, European Island Other % HS or Median 2000- lan- lan- lan- Recent Some % in Labor % Annual Industry Occupational Metropolitan Area LEP % LEP 2012* Spanish guages guages guages Immigrants

Albany-Schenectady-Troy, NY 16,599 2.9 - 32.6 30.4 33.4 - 53.1 30.6 36.0 33.4 57.6 52.0 23.6 $45,000 Manufacturing 22.1 Production 15.9

Albuquerque, NM 46,037 7.9 - 81.3 2.5 11.5 4.7 45.2 43.8 48.3 7.9 65.6 57.7 36.3 $27,000 Construction 22.0 Construction & related 18.2

Allentown-Bethlehem-Easton, PA-NJ 39,721 7.4 83.3 68.6 16.6 10.4 4.4 49.9 26.3 55.4 18.4 72.1 65.2 20.4 $30,000 Manufacturing 21.0 Production 17.1

Atlanta-Sandy Springs-Marietta, GA 306,060 8.4 41.8 60.1 14.1 21.0 4.8 56.1 35.6 47.2 17.3 73.0 66.2 26.8 $24,000 Construction 20.2 Construction & related 18.0

Austin-Round Rock-San Marcos, TX 142,338 11.1 40.6 82.6 5.1 9.8 2.5 50.8 45.9 41.4 12.8 72.9 67.8 26.2 $24,000 Construction 26.2 Construction & related 21.4

Bakersfield-Delano, CA 111,633 20.4 58.6 92.0 2.1 4.8 1.1 33.5 66.5 28.8 4.7 71.1 60.8 30.8 $22,000 Agriculture, Fishing, Forestry, and Hunting 44.9 Farming, fishing, forestry 37.9

Baltimore-Towson, MD 78,676 4.3 44.2 42.4 21.9 29.3 6.4 53.0 24.3 46.4 29.3 75.5 69.6 17.4 $34,000 Accommodations and Food Services 13.9 Bldg & grounds cleaning and maintenance 13.8

Birmingham-Hoover, AL 21,465 2.9 55.2 66.0 13.5 16.9 - 59.1 27.3 55.4 17.3 76.1 72.8 25.6 $22,900 Accommodations and Food Services 21.7 Construction & related 18.3

Boise City-Nampa, ID 21,911 5.2 64.1 73.1 8.5 18.4 - 34.3 49.6 42.0 8.4 74.4 68.5 34.2 $25,000 Agriculture, Farming, Fishing, and Hunting 22.1 Farming, fishing, forestry 19.9

Boston-Cambridge-Quincy, MA-NH 315,770 9.9 26.1 40.6 29.0 25.1 5.4 55.1 29.0 51.4 19.7 74.9 67.0 20.2 $31,200 Accommodations and Food Services 18.3 Bldg & grounds cleaning and maintenance 15.3

Bridgeport-Stamford-Norwalk, CT 86,549 14.3 33.6 63.6 23.0 10.6 2.7 56.5 26.6 53.7 19.7 80.9 71.8 16.8 $29,000 Admin and Waste Mgmt 16.9 Bldg & grounds cleaning and maintenance 24.2

Buffalo-Niagara Falls, NY 26,012 3.5 29.0 24.4 30.1 32.5 12.9 61.8 24.7 46.2 29.1 59.0 49.0 39.7 $25,000 Accommodations and Food Services 17.4 Production 15.4

Cape Coral-Fort Myers, FL 39,838 10.6 116.8 80.1 15.2 4.7 - 52.0 42.9 46.5 10.6 77.7 72.6 27.9 $21,800 Accommodations and Food Services 19.0 Bldg & grounds cleaning and maintenance 20.2

Charleston-North Charleston-Summerville, SC 13,755 2.9 30.6 66.0 6.6 27.4 - 67.8 33.4 49.3 17.3 73.3 70.7 19.5 $22,000 Construction 28.7 Construction & related 27.9

Charlotte-Gastonia-Rock Hill, NC-SC 88,931 7.5 62.7 72.7 9.1 16.1 2.1 61.2 37.6 47.2 15.2 74.5 67.9 24.2 $22,000 Construction 23.5 Construction & related 22.4

Chicago-Joliet-Naperville, IL-IN-WI 820,012 13.0 5.1 65.3 20.2 11.7 2.8 38.5 37.6 47.4 15.0 73.7 66.6 19.6 $27,400 Manufacturing 25.4 Production 20.9

Cincinnati-Middletown, OH-KY-IN 35,430 2.5 55.1 41.9 15.8 35.2 7.2 62.7 25.7 54.3 20.0 76.3 75.3 18.1 $25,000 Accommodations and Food Services 18.8 Food Prep 14.6

Cleveland-Elyria-Mentor, OH 47,434 3.6 - 38.9 32.9 19.5 8.7 45.0 24.0 51.9 24.1 62.9 55.9 26.0 $30,000 Manufacturing 25.2 Production 18.5

Colorado Springs, CO 19,555 4.4 54.8 74.1 8.2 16.0 - 41.1 32.9 52.5 14.6 73.7 70.9 36.4 $25,000 Construction 21.5 Bldg & grounds cleaning and maintenance 22.4

Columbia, SC 15,451 2.9 47.8 68.7 11.6 18.2 - 72.9 37.0 41.8 21.2 73.2 68.8 23.1 $19,500 Construction 25.9 Construction & related 20.5

Columbus, OH 46,116 3.7 36.2 35.0 14.8 25.8 24.4 62.9 26.4 48.1 25.5 68.7 63.5 27.9 $25,000 Accommodations and Food Services 18.5 Food Prep 15.8

Dallas-Fort Worth-Arlington, TX 640,695 14.7 34.2 79.2 4.1 13.9 2.7 46.7 52.1 37.1 10.8 72.6 68.3 23.5 $24,000 Construction 20.7 Construction & related 18.4

Denver-Aurora-Broomfield, CO 163,875 8.9 24.2 70.7 7.0 17.0 5.3 48.7 45.6 41.6 12.8 73.0 68.5 22.7 $25,000 Construction 18.4 Food Prep 15.5

Des Moines-West Des Moines, IA 19,379 4.6 69.4 48.9 22.1 20.4 - 48.2 46.1 35.5 18.4 82.3 76.2 - $26,000 Manufacturing 23.3 Food Prep 21.8

Detroit-Warren-Livonia, MI 128,722 4.7 - 24.0 33.0 16.5 26.5 52.9 30.6 45.6 23.8 60.2 52.4 31.5 $30,600 Manufacturing 24.4 Production 15.5

El Paso, TX 156,506 29.8 17.7 97.1 1.4 1.3 - 26.1 38.7 48.6 12.6 63.0 57.3 33.1 $22,000 Manufacturing 13.2 Sales & related 11.9

Fresno, CA 136,712 22.8 22.3 78.8 6.2 14.4 0.6 33.6 65.9 27.7 6.4 70.0 59.0 36.5 $24,000 Agriculture, Farming, Fishing, and Hunting 40.6 Farming, fishing, forestry 31.0

Grand Rapids-Wyoming, MI 20,687 5.1 - 66.8 11.3 18.4 - 48.6 44.6 42.4 13.0 72.4 61.8 40.1 $23,000 Manufacturing 42.9 Production 35.5

Greensboro-High Point, NC 34,541 6.7 56.1 56.5 9.3 26.9 7.3 63.9 46.7 45.0 8.3 73.0 64.8 31.5 $21,000 Manufacturing 27.4 Production 25.8

Greenville-Mauldin-Easley, SC 20,769 4.4 54.7 76.0 7.5 11.9 - 61.8 46.2 34.0 19.8 77.6 65.9 36.2 $24,000 Accommodations and Food Services 18.5 Construction & related 17.2

Harrisburg-Carlisle, PA 15,412 4.3 66.7 39.1 35.2 18.5 - 54.7 36.6 52.2 11.3 72.2 64.5 23.9 $23,500 Manufacturing 22.5 Transp & Mat Moving 27.4

Hartford-West Hartford-East Hartford, CT 61,152 7.6 10.8 52.6 28.2 15.8 3.4 42.8 25.4 55.4 19.2 68.6 60.8 26.2 $30,000 Manufacturing 24.3 Production 18.1

Honolulu, HI 95,581 14.9 27.8 4.7 1.0 93.9 - 43.4 20.1 62.6 17.3 72.8 68.6 16.3 $32,000 Accommodations and Food Services 26.7 Food Prep 17.5

Houston-Sugar Land-Baytown, TX 721,872 17.8 40.1 82.3 4.6 11.4 1.7 43.6 49.8 39.9 10.3 72.7 67.8 26.6 $25,000 Construction 20.6 Construction & related 17.6

Indianapolis-Carmel, IN 53,140 4.5 98.9 66.4 8.7 17.8 7.0 55.1 38.9 41.6 19.4 78.2 73.9 30.0 $22,400 Accommodations and Food Services 26.5 Food Prep 16.9

Jacksonville, FL 42,003 4.7 84.7 40.0 26.2 28.8 5.0 55.7 14.9 60.8 24.3 66.3 61.8 15.7 $26,000 Health & Soc Serv 13.1 Bldg & grounds cleaning and maintenance 13.1

Kansas City, MO-KS 55,616 3.9 28.4 62.6 8.9 21.5 7.0 55.5 36.1 45.6 18.2 73.6 68.1 20.8 $24,000 Accommodations and Food Services 17.0 Bldg & grounds cleaning and maintenance 16.6

Lakeland-Winter Haven, FL 33,735 9.0 99.2 82.5 9.4 7.4 - 51.4 41.3 43.4 15.3 73.7 61.1 24.1 $30,000 Accommodations and Food Services 15.5 Construction & related 14.4

Lancaster, PA 16,506 5.0 - 41.4 34.6 21.2 - 29.0 29.4 65.1 - 67.2 60.6 25.0 $30,000 Manufacturing 27.0 Production 24.1

BROOKINGS | September 2014 33 Appendix. Limited English Proficient Population, Ages 16-64, 89 Metropolitan Areas, 2012 (continued)

Educational attainment, Language spoken at home (%) age 25-64 (%)

Asian Other and % Indo- Pacific Change, European Island Other % HS or Median 2000- lan- lan- lan- Recent Some % in Labor % Annual Industry Occupational Metropolitan Area LEP % LEP 2012* Spanish guages guages guages Immigrants

Las Vegas-Paradise, NV 207,224 15.7 61.2 75.0 4.3 18.3 2.4 46.8 38.4 51.3 10.2 74.4 67.0 23.0 $28,300 Accommodations and Food Services 34.3 Bldg & grounds cleaning and maintenance 23.9

Los Angeles-Long Beach-Santa Ana, CA 2,264,513 25.7 -3.9 69.1 6.4 23.0 1.4 34.4 45.3 41.0 13.7 71.7 64.8 24.1 $25,000 Manufacturing 19.2 Production 14.9

Louisville/Jefferson County, KY-IN 26,210 3.2 61.5 60.4 10.2 19.0 10.5 75.2 30.0 57.6 12.4 71.4 64.1 32.5 $25,000 Accommodations and Food Services 18.6 Production 15.3

McAllen-Edinburg-Mission, TX 154,012 32.0 20.6 99.0 - 1.0 - 33.8 57.5 34.4 8.1 62.7 55.8 43.7 $24,000 Construction 15.2 Construction & related 13.4

Memphis, TN-MS-AR 30,957 3.8 41.3 67.3 5.5 20.5 6.8 54.2 41.8 45.2 12.9 72.6 69.1 29.9 $26,000 Construction 28.8 Construction & related 19.3

Miami-Fort Lauderdale-Pompano Beach, FL 865,905 23.2 21.7 80.8 16.0 2.6 0.5 58.3 22.4 58.6 19.0 75.4 66.1 23.3 $24,000 Retail Trade 14.0 Bldg & grounds cleaning and maintenance 14.0

Milwaukee-Waukesha-West Allis, WI 61,241 6.0 39.9 60.0 18.3 18.4 3.3 43.5 32.9 49.0 18.1 71.6 67.0 21.0 $25,000 Manufacturing 31.6 Production 26.4

Minneapolis-St. Paul-Bloomington, MN-WI 134,927 5.8 45.7 39.5 9.2 36.1 15.1 62.0 35.7 46.1 18.2 76.7 66.1 27.9 $26,000 Manufacturing 20.9 Production 18.4

Modesto, CA 61,935 18.6 36.1 79.3 6.3 10.4 3.9 39.5 58.9 37.5 3.6 72.8 59.0 33.8 $24,000 Manufacturing 20.3 Farming, fishing, forestry 17.5

Nashville-Davidson--Murfreesboro--Franklin, TN 47,890 4.2 54.6 66.8 9.1 16.6 7.5 66.2 43.7 43.3 13.0 71.5 66.6 31.7 $20,000 Construction 23.4 Construction & related 23.0

New Haven-Milford, CT 51,204 9.0 33.5 65.1 18.5 13.3 3.2 49.3 33.5 49.7 16.7 71.5 63.4 21.8 $29,200 Manufacturing 22.4 Production 18.9

New Orleans-Metairie-Kenner, LA 39,818 4.9 21.0 65.9 10.3 22.1 - 55.3 34.2 53.4 12.4 76.0 70.0 29.1 $24,000 Construction 26.5 Construction & related 27.4

New York-Northern New Jersey-Long Island, NY-NJ-PA 2,330,496 18.3 7.8 55.6 21.4 19.7 3.3 46.2 31.6 49.9 18.5 72.0 64.9 23.0 $28,000 Accommodations and Food Services 15.4 Food Prep 11.0

North Port-Bradenton-Sarasota, FL 33,021 8.2 74.5 68.2 17.4 13.4 - 52.4 36.8 42.0 21.2 79.2 69.7 22.8 $23,000 Administration and Waste Management 18.6 Bldg & grounds cleaning and maintenance 26.1

Ogden-Clearfield, UT 16,813 5.0 47.4 80.9 - 16.8 - 53.7 37.7 56.5 5.9 74.7 70.0 22.7 $24,000 Accommodations and Food Services 24.3 Production 22.0

Oklahoma City, OK 58,561 6.6 67.7 79.7 3.1 16.0 - 55.1 54.7 38.9 6.4 76.5 71.3 29.4 $22,000 Construction 25.4 Construction & related 25.1

Omaha-Council Bluffs, NE-IA 38,695 6.1 95.1 65.1 10.2 18.4 6.3 63.3 51.0 34.1 14.9 74.0 70.1 27.9 $23,000 Manufacturing 29.4 Production 26.8

Orlando-Kissimmee-Sanford, FL 176,390 12.0 71.3 72.4 16.3 9.5 1.8 49.7 23.9 60.0 16.1 73.4 63.2 25.7 $22,500 Accommodations and Food Services 19.9 Bldg & grounds cleaning and maintenance 17.0

Oxnard-Thousand Oaks-Ventura, CA 91,231 16.8 - 85.4 3.2 10.4 - 36.8 57.9 33.5 8.6 74.1 67.6 21.3 $28,000 Agriculture, Farming, Fishing, and Hunting 20.6 Farming, fishing, forestry 16.7

Palm Bay-Melbourne-Titusville, FL 11,858 3.5 37.2 68.3 9.8 19.9 - 38.5 21.4 61.9 16.7 78.9 74.5 18.9 $28,700 Retail Trade 15.9 Sales & related 10.6

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 265,797 6.6 40.0 45.1 24.0 26.9 4.0 47.9 30.6 47.8 21.6 70.7 62.3 22.9 $28,000 Retail Trade 15.4 Production 11.5

Phoenix-Mesa-Glendale, AZ 304,190 11.0 14.8 83.1 4.8 9.3 2.8 38.9 47.2 43.2 9.6 65.7 59.5 32.1 $24,000 Admin and Waste Mgmt 15.9 Bldg & grounds cleaning and maintenance 20.1

Pittsburgh, PA 23,875 1.6 - 24.1 29.7 36.8 9.5 71.5 23.3 37.5 39.2 62.9 58.5 20.8 $33,800 Accommodations and Food Services 23.8 Computer & Math 11.6

Portland-Vancouver-Hillsboro, OR-WA 125,611 8.1 22.2 51.1 14.7 30.9 3.3 49.7 35.6 46.6 17.8 73.7 65.8 25.1 $29,000 Manufacturing 21.4 Production 13.4

Poughkeepsie-Newburgh-Middletown, NY 34,785 7.8 47.0 55.0 30.3 11.7 2.9 36.9 29.7 52.6 17.7 68.6 65.1 22.4 $30,000 Accommodations and Food Services 21.4 Food Prep 15.7

Providence-New Bedford-Fall River, RI-MA 90,433 8.4 - 53.6 32.7 10.7 3.0 48.2 42.9 46.5 10.6 70.2 59.8 22.2 $28,000 Manufacturing 29.7 Production 24.6

Provo-Orem, UT 17,161 5.2 47.5 83.7 6.2 10.1 - 62.1 36.4 42.9 20.7 72.9 69.6 32.9 $25,000 Manufacturing 18.6 Food Prep 16.2

Raleigh-Cary, NC 53,099 6.3 59.9 66.9 10.9 16.6 5.6 64.9 42.6 41.5 15.9 73.1 67.7 29.3 $22,800 Accommodations and Food Services 25.2 Food Prep 20.7

Richmond, VA 35,939 4.4 80.6 50.7 14.6 28.2 6.5 71.0 28.2 50.6 21.1 71.2 67.1 16.2 $26,600 Construction 21.2 Construction & related 18.1

Riverside-San Bernardino-Ontario, CA 498,001 17.8 44.9 83.2 3.2 12.3 1.4 26.6 48.1 43.3 8.6 66.8 58.3 24.1 $28,000 Manufacturing 17.4 Transp & Mat Moving 14.0

Rochester, NY 26,374 3.6 - 37.7 24.3 33.3 4.7 65.0 26.3 58.2 15.5 63.4 58.3 31.4 $28,000 Manufacturing 23.3 Production 21.7

Sacramento--Arden-Arcade--Roseville, CA 182,651 12.6 46.9 44.4 22.4 31.5 1.8 44.5 35.5 48.5 16.1 68.5 59.5 28.4 $27,000 Accommodations and Food Services 16.0 Bldg & grounds cleaning and maintenance 13.2

St. Louis, MO-IL 50,663 2.7 33.9 35.9 27.5 32.1 4.5 49.3 26.9 50.0 23.1 68.4 62.8 24.8 $35,000 Accommodations and Food Services 21.7 Production 16.4

Salt Lake City, UT 64,030 8.4 37.1 67.4 9.2 19.2 4.2 45.6 47.2 37.8 14.9 75.0 66.5 24.3 $28,000 Manufacturing 21.1 Production 18.9

San Antonio-New Braunfels, TX 169,292 11.9 12.8 90.5 1.6 7.3 - 35.4 43.4 47.8 8.9 68.4 64.8 25.0 $23,000 Construction 18.3 Construction & related 15.5

San Diego-Carlsbad-San Marcos, CA 350,998 16.3 23.5 68.7 5.5 21.9 3.9 39.2 44.0 41.4 14.7 67.7 61.5 23.2 $24,000 Accommodations and Food Services 14.9 Bldg & grounds cleaning and maintenance 15.6

San Francisco-Oakland-Fremont, CA 557,878 18.4 12.5 46.6 8.2 42.4 2.8 47.1 33.0 46.3 20.7 72.9 66.2 18.1 $27,000 Accommodations and Food Services 18.2 Bldg & grounds cleaning and maintenance 14.1

San Jose-Sunnyvale-Santa Clara, CA 278,214 22.6 3.7 39.8 7.8 51.5 0.9 45.1 28.9 42.3 28.9 70.4 63.6 17.5 $31,100 Manufacturing 22.9 Production 12.1

34 BROOKINGS | September 2014 Educational attainment, Language spoken at home (%) age 25-64 (%)

Asian Other and % Indo- Pacific Change, European Island Other % HS or Median 2000- lan- lan- lan- Recent Some % in Labor % Annual Industry Occupational Metropolitan Area LEP % LEP 2012* Spanish guages guages guages Immigrants

Las Vegas-Paradise, NV 207,224 15.7 61.2 75.0 4.3 18.3 2.4 46.8 38.4 51.3 10.2 74.4 67.0 23.0 $28,300 Accommodations and Food Services 34.3 Bldg & grounds cleaning and maintenance 23.9

Los Angeles-Long Beach-Santa Ana, CA 2,264,513 25.7 -3.9 69.1 6.4 23.0 1.4 34.4 45.3 41.0 13.7 71.7 64.8 24.1 $25,000 Manufacturing 19.2 Production 14.9

Louisville/Jefferson County, KY-IN 26,210 3.2 61.5 60.4 10.2 19.0 10.5 75.2 30.0 57.6 12.4 71.4 64.1 32.5 $25,000 Accommodations and Food Services 18.6 Production 15.3

McAllen-Edinburg-Mission, TX 154,012 32.0 20.6 99.0 - 1.0 - 33.8 57.5 34.4 8.1 62.7 55.8 43.7 $24,000 Construction 15.2 Construction & related 13.4

Memphis, TN-MS-AR 30,957 3.8 41.3 67.3 5.5 20.5 6.8 54.2 41.8 45.2 12.9 72.6 69.1 29.9 $26,000 Construction 28.8 Construction & related 19.3

Miami-Fort Lauderdale-Pompano Beach, FL 865,905 23.2 21.7 80.8 16.0 2.6 0.5 58.3 22.4 58.6 19.0 75.4 66.1 23.3 $24,000 Retail Trade 14.0 Bldg & grounds cleaning and maintenance 14.0

Milwaukee-Waukesha-West Allis, WI 61,241 6.0 39.9 60.0 18.3 18.4 3.3 43.5 32.9 49.0 18.1 71.6 67.0 21.0 $25,000 Manufacturing 31.6 Production 26.4

Minneapolis-St. Paul-Bloomington, MN-WI 134,927 5.8 45.7 39.5 9.2 36.1 15.1 62.0 35.7 46.1 18.2 76.7 66.1 27.9 $26,000 Manufacturing 20.9 Production 18.4

Modesto, CA 61,935 18.6 36.1 79.3 6.3 10.4 3.9 39.5 58.9 37.5 3.6 72.8 59.0 33.8 $24,000 Manufacturing 20.3 Farming, fishing, forestry 17.5

Nashville-Davidson--Murfreesboro--Franklin, TN 47,890 4.2 54.6 66.8 9.1 16.6 7.5 66.2 43.7 43.3 13.0 71.5 66.6 31.7 $20,000 Construction 23.4 Construction & related 23.0

New Haven-Milford, CT 51,204 9.0 33.5 65.1 18.5 13.3 3.2 49.3 33.5 49.7 16.7 71.5 63.4 21.8 $29,200 Manufacturing 22.4 Production 18.9

New Orleans-Metairie-Kenner, LA 39,818 4.9 21.0 65.9 10.3 22.1 - 55.3 34.2 53.4 12.4 76.0 70.0 29.1 $24,000 Construction 26.5 Construction & related 27.4

New York-Northern New Jersey-Long Island, NY-NJ-PA 2,330,496 18.3 7.8 55.6 21.4 19.7 3.3 46.2 31.6 49.9 18.5 72.0 64.9 23.0 $28,000 Accommodations and Food Services 15.4 Food Prep 11.0

North Port-Bradenton-Sarasota, FL 33,021 8.2 74.5 68.2 17.4 13.4 - 52.4 36.8 42.0 21.2 79.2 69.7 22.8 $23,000 Administration and Waste Management 18.6 Bldg & grounds cleaning and maintenance 26.1

Ogden-Clearfield, UT 16,813 5.0 47.4 80.9 - 16.8 - 53.7 37.7 56.5 5.9 74.7 70.0 22.7 $24,000 Accommodations and Food Services 24.3 Production 22.0

Oklahoma City, OK 58,561 6.6 67.7 79.7 3.1 16.0 - 55.1 54.7 38.9 6.4 76.5 71.3 29.4 $22,000 Construction 25.4 Construction & related 25.1

Omaha-Council Bluffs, NE-IA 38,695 6.1 95.1 65.1 10.2 18.4 6.3 63.3 51.0 34.1 14.9 74.0 70.1 27.9 $23,000 Manufacturing 29.4 Production 26.8

Orlando-Kissimmee-Sanford, FL 176,390 12.0 71.3 72.4 16.3 9.5 1.8 49.7 23.9 60.0 16.1 73.4 63.2 25.7 $22,500 Accommodations and Food Services 19.9 Bldg & grounds cleaning and maintenance 17.0

Oxnard-Thousand Oaks-Ventura, CA 91,231 16.8 - 85.4 3.2 10.4 - 36.8 57.9 33.5 8.6 74.1 67.6 21.3 $28,000 Agriculture, Farming, Fishing, and Hunting 20.6 Farming, fishing, forestry 16.7

Palm Bay-Melbourne-Titusville, FL 11,858 3.5 37.2 68.3 9.8 19.9 - 38.5 21.4 61.9 16.7 78.9 74.5 18.9 $28,700 Retail Trade 15.9 Sales & related 10.6

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 265,797 6.6 40.0 45.1 24.0 26.9 4.0 47.9 30.6 47.8 21.6 70.7 62.3 22.9 $28,000 Retail Trade 15.4 Production 11.5

Phoenix-Mesa-Glendale, AZ 304,190 11.0 14.8 83.1 4.8 9.3 2.8 38.9 47.2 43.2 9.6 65.7 59.5 32.1 $24,000 Admin and Waste Mgmt 15.9 Bldg & grounds cleaning and maintenance 20.1

Pittsburgh, PA 23,875 1.6 - 24.1 29.7 36.8 9.5 71.5 23.3 37.5 39.2 62.9 58.5 20.8 $33,800 Accommodations and Food Services 23.8 Computer & Math 11.6

Portland-Vancouver-Hillsboro, OR-WA 125,611 8.1 22.2 51.1 14.7 30.9 3.3 49.7 35.6 46.6 17.8 73.7 65.8 25.1 $29,000 Manufacturing 21.4 Production 13.4

Poughkeepsie-Newburgh-Middletown, NY 34,785 7.8 47.0 55.0 30.3 11.7 2.9 36.9 29.7 52.6 17.7 68.6 65.1 22.4 $30,000 Accommodations and Food Services 21.4 Food Prep 15.7

Providence-New Bedford-Fall River, RI-MA 90,433 8.4 - 53.6 32.7 10.7 3.0 48.2 42.9 46.5 10.6 70.2 59.8 22.2 $28,000 Manufacturing 29.7 Production 24.6

Provo-Orem, UT 17,161 5.2 47.5 83.7 6.2 10.1 - 62.1 36.4 42.9 20.7 72.9 69.6 32.9 $25,000 Manufacturing 18.6 Food Prep 16.2

Raleigh-Cary, NC 53,099 6.3 59.9 66.9 10.9 16.6 5.6 64.9 42.6 41.5 15.9 73.1 67.7 29.3 $22,800 Accommodations and Food Services 25.2 Food Prep 20.7

Richmond, VA 35,939 4.4 80.6 50.7 14.6 28.2 6.5 71.0 28.2 50.6 21.1 71.2 67.1 16.2 $26,600 Construction 21.2 Construction & related 18.1

Riverside-San Bernardino-Ontario, CA 498,001 17.8 44.9 83.2 3.2 12.3 1.4 26.6 48.1 43.3 8.6 66.8 58.3 24.1 $28,000 Manufacturing 17.4 Transp & Mat Moving 14.0

Rochester, NY 26,374 3.6 - 37.7 24.3 33.3 4.7 65.0 26.3 58.2 15.5 63.4 58.3 31.4 $28,000 Manufacturing 23.3 Production 21.7

Sacramento--Arden-Arcade--Roseville, CA 182,651 12.6 46.9 44.4 22.4 31.5 1.8 44.5 35.5 48.5 16.1 68.5 59.5 28.4 $27,000 Accommodations and Food Services 16.0 Bldg & grounds cleaning and maintenance 13.2

St. Louis, MO-IL 50,663 2.7 33.9 35.9 27.5 32.1 4.5 49.3 26.9 50.0 23.1 68.4 62.8 24.8 $35,000 Accommodations and Food Services 21.7 Production 16.4

Salt Lake City, UT 64,030 8.4 37.1 67.4 9.2 19.2 4.2 45.6 47.2 37.8 14.9 75.0 66.5 24.3 $28,000 Manufacturing 21.1 Production 18.9

San Antonio-New Braunfels, TX 169,292 11.9 12.8 90.5 1.6 7.3 - 35.4 43.4 47.8 8.9 68.4 64.8 25.0 $23,000 Construction 18.3 Construction & related 15.5

San Diego-Carlsbad-San Marcos, CA 350,998 16.3 23.5 68.7 5.5 21.9 3.9 39.2 44.0 41.4 14.7 67.7 61.5 23.2 $24,000 Accommodations and Food Services 14.9 Bldg & grounds cleaning and maintenance 15.6

San Francisco-Oakland-Fremont, CA 557,878 18.4 12.5 46.6 8.2 42.4 2.8 47.1 33.0 46.3 20.7 72.9 66.2 18.1 $27,000 Accommodations and Food Services 18.2 Bldg & grounds cleaning and maintenance 14.1

San Jose-Sunnyvale-Santa Clara, CA 278,214 22.6 3.7 39.8 7.8 51.5 0.9 45.1 28.9 42.3 28.9 70.4 63.6 17.5 $31,100 Manufacturing 22.9 Production 12.1

BROOKINGS | September 2014 35 Appendix. Limited English Proficient Population, Ages 16-64, 89 Metropolitan Areas, 2012 (continued)

Educational attainment, Language spoken at home (%) age 25-64 (%)

Asian Other and % Indo- Pacific Change, European Island Other % HS or Median 2000- lan- lan- lan- Recent Some % in Labor % Annual Industry Occupational Metropolitan Area LEP % LEP 2012* Spanish guages guages guages Immigrants

Scranton--Wilkes-Barre, PA 14,723 4.6 - 65.7 23.2 9.3 - 71.1 43.2 42.0 14.8 75.4 68.6 19.2 $40,000 Manufacturing 28.3 Production 25.2

Seattle-Tacoma-Bellevue, WA 238,003 9.8 54.7 31.9 17.2 42.0 8.9 55.8 26.8 52.9 20.3 68.4 63.0 21.6 $24,900 Accommodations and Food Services 17.4 Food Prep 12.2

Springfield, MA 35,481 7.6 - 60.7 24.6 11.8 2.9 60.8 38.2 51.2 10.7 60.6 50.1 35.3 $30,000 Manufacturing 17.5 Production 15.0

Stockton, CA 86,094 19.3 32.3 68.0 7.3 23.1 1.6 34.3 47.7 45.4 6.8 68.9 58.0 22.5 $28,000 Agriculture, Farming, Fishing, and Hunting 19.9 Farming, fishing, forestry 20.5

Syracuse, NY 12,246 2.8 - 29.4 30.5 33.7 6.5 64.1 27.8 48.7 23.5 52.6 47.9 32.0 $23,000 Retail Trade 17.8 Transp & Mat Moving 15.0

Tampa-St. Petersburg-Clearwater, FL 144,106 7.9 49.1 70.0 11.6 14.5 3.9 51.6 27.0 56.7 16.4 70.4 60.1 26.8 $25,000 Health & Soc Serv 13.4 Bldg & grounds cleaning and maintenance 12.8

Tucson, AZ 55,059 8.7 - 85.0 4.2 9.3 1.5 36.4 37.1 51.5 11.4 68.7 60.1 28.3 $25,000 Accommodations and Food Services 17.1 Bldg & grounds cleaning and maintenance 22.0

Tulsa, OK 28,109 5.5 32.4 74.8 4.1 19.4 - 49.0 51.1 39.9 9.0 73.8 70.1 25.9 $24,000 Manufacturing 24.9 Production 25.3

Virginia Beach-Norfolk-Newport News, VA-NC 27,949 2.5 - 45.1 13.1 32.8 9.1 58.3 27.6 49.5 22.9 78.0 73.4 16.4 $24,000 Accommodations and Food Services 22.8 Construction & related 18.5

Washington-Arlington-Alexandria, DC-VA-MD-WV 456,972 11.9 35.4 53.5 13.5 23.6 9.4 57.9 30.7 45.1 24.2 79.8 74.3 13.2 $30,000 Construction 17.5 Bldg & grounds cleaning and maintenance 15.3

Wichita, KS 25,316 6.5 45.4 65.8 - 27.1 - 40.6 43.3 41.9 14.8 69.2 65.7 25.9 $24,000 Accommodations and Food Services 20.5 Bldg & grounds cleaning and maintenance 18.3

Worcester, MA 36,962 6.9 31.6 45.1 23.5 19.7 11.7 48.2 24.6 57.6 17.8 69.2 59.5 22.7 $30,000 Manufacturing 21.0 Production 15.0

89 Metro Total 15,749,108 12.0 19.5 64.6 12.6 19.6 3.3 45.2 38.1 46.0 15.8 71.9 64.9 23.9 $26,000 Accommodations and Food Services 14.6 Bldg & grounds cleaning and maintenance 12.9

US Total 19,151,784 9.3 19.8 66.3 11.9 18.4 3.4 44.0 40.1 45.0 14.9 71.3 64.4 24.6 $26,000 Manufacturing 13.6 Bldg & grounds cleaning and maintenance 12.8

*statistically significant at the 90% confidence level -no statistically reliable data Source: Author’s analysis of ACS 2012 PUMS data

36 BROOKINGS | September 2014 Educational attainment, Language spoken at home (%) age 25-64 (%)

Asian Other and % Indo- Pacific Change, European Island Other % HS or Median 2000- lan- lan- lan- Recent Some % in Labor % Annual Industry Occupational Metropolitan Area LEP % LEP 2012* Spanish guages guages guages Immigrants

Scranton--Wilkes-Barre, PA 14,723 4.6 - 65.7 23.2 9.3 - 71.1 43.2 42.0 14.8 75.4 68.6 19.2 $40,000 Manufacturing 28.3 Production 25.2

Seattle-Tacoma-Bellevue, WA 238,003 9.8 54.7 31.9 17.2 42.0 8.9 55.8 26.8 52.9 20.3 68.4 63.0 21.6 $24,900 Accommodations and Food Services 17.4 Food Prep 12.2

Springfield, MA 35,481 7.6 - 60.7 24.6 11.8 2.9 60.8 38.2 51.2 10.7 60.6 50.1 35.3 $30,000 Manufacturing 17.5 Production 15.0

Stockton, CA 86,094 19.3 32.3 68.0 7.3 23.1 1.6 34.3 47.7 45.4 6.8 68.9 58.0 22.5 $28,000 Agriculture, Farming, Fishing, and Hunting 19.9 Farming, fishing, forestry 20.5

Syracuse, NY 12,246 2.8 - 29.4 30.5 33.7 6.5 64.1 27.8 48.7 23.5 52.6 47.9 32.0 $23,000 Retail Trade 17.8 Transp & Mat Moving 15.0

Tampa-St. Petersburg-Clearwater, FL 144,106 7.9 49.1 70.0 11.6 14.5 3.9 51.6 27.0 56.7 16.4 70.4 60.1 26.8 $25,000 Health & Soc Serv 13.4 Bldg & grounds cleaning and maintenance 12.8

Tucson, AZ 55,059 8.7 - 85.0 4.2 9.3 1.5 36.4 37.1 51.5 11.4 68.7 60.1 28.3 $25,000 Accommodations and Food Services 17.1 Bldg & grounds cleaning and maintenance 22.0

Tulsa, OK 28,109 5.5 32.4 74.8 4.1 19.4 - 49.0 51.1 39.9 9.0 73.8 70.1 25.9 $24,000 Manufacturing 24.9 Production 25.3

Virginia Beach-Norfolk-Newport News, VA-NC 27,949 2.5 - 45.1 13.1 32.8 9.1 58.3 27.6 49.5 22.9 78.0 73.4 16.4 $24,000 Accommodations and Food Services 22.8 Construction & related 18.5

Washington-Arlington-Alexandria, DC-VA-MD-WV 456,972 11.9 35.4 53.5 13.5 23.6 9.4 57.9 30.7 45.1 24.2 79.8 74.3 13.2 $30,000 Construction 17.5 Bldg & grounds cleaning and maintenance 15.3

Wichita, KS 25,316 6.5 45.4 65.8 - 27.1 - 40.6 43.3 41.9 14.8 69.2 65.7 25.9 $24,000 Accommodations and Food Services 20.5 Bldg & grounds cleaning and maintenance 18.3

Worcester, MA 36,962 6.9 31.6 45.1 23.5 19.7 11.7 48.2 24.6 57.6 17.8 69.2 59.5 22.7 $30,000 Manufacturing 21.0 Production 15.0

89 Metro Total 15,749,108 12.0 19.5 64.6 12.6 19.6 3.3 45.2 38.1 46.0 15.8 71.9 64.9 23.9 $26,000 Accommodations and Food Services 14.6 Bldg & grounds cleaning and maintenance 12.9

US Total 19,151,784 9.3 19.8 66.3 11.9 18.4 3.4 44.0 40.1 45.0 14.9 71.3 64.4 24.6 $26,000 Manufacturing 13.6 Bldg & grounds cleaning and maintenance 12.8

*statistically significant at the 90% confidence level -no statistically reliable data Source: Author’s analysis of ACS 2012 PUMS data

BROOKINGS | September 2014 37 Endnotes (Baltimore: The Anne E. Casey Foundation, 2005). 5. Martinez and , “Supporting English 1. Ruben G. Rumbaut, and Douglas S. Massey, Language Acquisition.”; Lennox McLendon, “Immigration and Language Diversity in the Debra Jones, and Mitch Rosin, “The Return on United States,” Daedalus 142 (3) (2013): 141-154 Investment (ROI) from Adult Education and 2. Geoffrey Carliner, “The Wages and Language Training” (New York: McGraw-Hill Research Skills of U.S. Immigrants” (Cambridge: National Foundation, 2011). Bureau of Economic Research, 1996).; Shawn 6. Chiswick and Miller, “International Migration and Fremstad, “Immigrants, Persons with Limited the Economics of Language.” Proficiency in English, and the TANF program: 7. Dowell Myers, and John Pitkin, “Assimilation What Do We Know? “ (Washington: Center on Today: New Evidence Shows the Latest Budget and Policy Priorities, 2003).; Elizabeth Immigrants to America are Following in our Greenberg and others, “English Literacy and History’s Footsteps” (Washington: Center for Language Minorities in the United States” American Progress, 2010).; Dowell Myers, Xin Gao, (Washington: National Center for Education and Amon Emeka, “The Gradient of Immigrant Statistics, 2001).; Margie McHugh, Julia Gelatt, Age-at-Arrival Effects on Socioeconomic and Michael Fix, “Adult English Language Outcomes in the U.S.,” International Migration Instruction in the United States: Determining Review 43 (1) (2009): 205-229; Julie Park, Need and Investing Wisely” (Washington: and Dowell Myers, “Intergenerational Mobility Migration Policy Institute, 2007).; Barry R. in the Post-1965 Immigration Era: Estimates Chiswick, and Paul W. Miller, “Immigrant Earnings: by an Immigrant Generation Cohort Method,” Language Skills, Linguistic Concentrations Demography 47 (2) (2010): 369-392 and the Business Cycle,” Journal of Population 8. Estimates of immigration’s contribution to future Economics 15 (2002): 31-57; Barry R. Chiswick, labor force growth are from Jeffrey S. Passel, and Paul W. Miller, “The Endogeneity between “Demography of Immigrant Youth: Past, Present, Language and Earnings International Analyses,” and Future,” The Future of Children 21 (1) (2011): Journal of Labor Economics 13 (2) (1995): 19-41 246-288; Barry R. Chiswick, and Paul W. Miller, 9. Audrey Singer, “Investing in the Human Capital of “IZA Discussion Paper No. 7880: International Immigrants, Strengthening Regional Economies” Migration and the Economics of Language” (Washington: Brookings Institution, 2012). (Bonn, Germany: Institute for the Study of 10. McLendon, Jones, Rosin, “Return on Investment.” Labor, 2014).; Marie T. Mora, and Alberto 11. McHugh, Gellatt, Fix, “Determining Need and Davila, “Gender, Earnings, and the English Skill Investing Wisely.” Acquisition of Hispanic Workers in the United 12. Ibid. Sates,” Economic Inquiry 36 (4) (1998): 631-644; 13. Marcie Foster and Lennox McLendon. “Sinking or Ross M. Stolzenberg, “Ethnicity, Geography, Swimming: Findings from a Survey of State Adult and Occupational Achievement of Hispanic Men Education Tuition and Financing Policies.” Center in the United States,” American Sociological for Law and Social Policy and the National Council Review 55 (1) (1990): 143-154; Libertad Gonzalez, of State Directors of Adult Education. Washington, “Nonparametric Bounds on the Returns of D.C. 2012. Language Skills,” Journal of Applied Econometrics 14. The Workforce Innovation and Opportunity Act 20 (6) (2004): 771-795 re-authorizing and streamlining the country’s 3. Jeanne Batalova, Michael Fix, and Peter A. workforce investment system was enacted in July, Creticos, “Uneven Progress: The Employment 2014, occurring too late to be fully reflected in Pathways of Skilled Immigrants in the United this paper. States” (Washington: Migration Policy Institute, 15. Department of Education funding was calculated 2008).; Neeta P. Fogg, and Paul E. Harrington, by dividing its annual budget by the share of “Mal-Employment Problems Among College- enrollments that were for Engligh Language/ Educated Immigrants in the U.S.” (Philadelphia: Civics. Data source: http://www2.ed.gov/about/ Drexel University, 2012). overview/budget/history/index.html 4. Tia Elena Martinez, and Ted Wang, “Supporting 16. Enrollment figures come from AEFLA annual English Language Acquisition: Opportunities reports to Congress, available at http://www2. for Foundations to Strengthen the Social and ed.gov/about/offices/list/ovae/resource/index. Economic Well-Being of Immigrant Families” html

38 BROOKINGS | September 2014 17. In order to receive a federal grant, AEFLA requires speak English “well,” “not well,” or “not at all” as that at least 25 percent of a state’s total adult “limited English proficient,” or LEP. See Sidebar education fund come from non-federal sources. “Defining the LEP population.” This means that state, local, or private funds 23. If a respondent writes in more than one language, contributed in cash or in kind all need to sum the first language reported is the only one to at least 25 percent of the total money spent counted by the Census Bureau. Furthermore, in a state on adult education. While about three some respondents report a language grouping quarters of the overall funding (federal plus (e.g. “Chinese”) rather than an individual language state) for adult education has typically come from (e.g. “Mandarin” or “Cantonese”), and the Census state coffers, states vary widely in the extent Bureau tabulates these separately for its detailed to which they match federal funding levels. The data but aggregates them (i.e. “Chinese”) for highest contributors—Connecticut, Minnesota, collapsed tables. and California—funded about 88 percent of their 24. This analysis uses the Office of Management and state’s total adult education budget in 2010. While Budget’s 2009 metropolitan area delineations and the lowest—Nevada, Texas, and Tennessee—only Census 2010 population data to select the 100 funded about 25 percent of their adult education most populous metro areas. Unweighted counts of budgets. California’s budget crisis prompted the working-age LEP population were determined the state legislature to allow school districts to for these metropolitan areas using the ACS 2012 redirect funding from adult education to other 1-year Public Use Microdata Sample. Eleven of the programs, and more than half of adult education 100 most populous metropolitan areas had fewer funding was re-directed in 2011-12, according to than 100 unweighted cases (ranging from 5,700 California’s Legislative Analyst’s Office. See also to 17,400 weighted cases) and were eliminated Nicholas Johnson, Phil Oliff, and Erica Williams, from the analysis: Akron, Augusta, Baton Rouge, “An Update on State Budget Cuts: At Least 46 Chattanooga, Dayton, Knoxville, Jackson, Little States Have Imposed Cuts that Hurt Vulnerable Rock, Madison, Toledo, and Youngstown. Residents and the Economy” (Washington: 25. See https://www.census.gov/acs/www/about_ Center on Budget and Policy Priorities, 2010).; the_survey/questions_and_why_we_ask/ for the Christopher Connell, “Empty Promises: The first three examples and www.lep.gov for the last Unmet Need for English Instruction across Illinois” example. (Chicago: Illinois Coalition for Immigrant and 26. U.S. Department of Commerce Bureau of the Refugee Rights, 2009). Census, “English Language Proficiency Study 18. James Thomas Tucker, “The ESL Logjam: Waiting (ELPS), 1982,” (Inter-university Consortium for Times for Adult ESL Classes and the Impact Political and Social Research 1988). on English Learners.” (Los Angeles: National 27. Paul Siegel, Elizabeth Martin, and Rosalind Bruno, Association of Latino Elected and Appointed “Language Use and Linguistic Isolation: Historical Officials Educational Fund, 2006). Data and Methodological Issues” (Washington: 19. Margie McHugh, Julia Gellatt, and Michael Fix. U.S. Census Bureau, 2001). “Adult English Language Instruction in the United 28. Because home language is a self-reported write-in States: Determining Need and Investing Wisely.” response, the level of linguistic detail varies. Some (Washington: Migration Policy Institute, 2007). respondents report Mandarin or Cantonese as 20. Lennox McLendon, “Adult Student Waiting List their home language, while others use the more Survey” (Washington: National Council of State general “Chinese.” The Census Bureau tabulates Directors of Adult Education, 2010). these separately in the microdata files used in this 21. Steven Ruggles and others, “Integrated Public Use report. Microdata Series: Version 5.0 [Machine-readable 29. Research shows that refugees, compared to database],” (University of Minnesota, 2010). migrants motivated by employment or family 22. The ACS questionnaire asks respondents to reunification, have the lowest levels of destination identify whether or not they speak a language language proficiency. See Barry R. Chiswick, and other than English at home. If they answer “yes,” Paul W. Miller, “Language Skills and Immigrant they are asked what language they speak at Adjustment: The Role of Immigration Policy.” home and how well they speak English (very well, In Deborah A. Cobb-Clark and Siew-Ean Khoo, well, not well, or not at all). This analysis follows eds., Public Policy and Immigrant Settlement. the common practice of categorizing those who (Cheltenham, United Kingdom: Edward Elgar

BROOKINGS | September 2014 39 Publishing, 2006).; Barry R. Chiswick, and Paul W. in the area grew, suggesting that the larger Miller, The Economics of Language: International supply of Spanish-speakers and translators in Analyses (London: Routledge, 2007) those areas decreased the wage premium. Using 30. Marc C. Rosenblum, Randy Capps, and Serena data from 1992, Fry and Lowell found that any Yi-Ying Lin, “Earned Legalization: Effects of wage premium for bilingualism disappeared when Proposed Requirements on Unauthorized Men, educational attainment and visible characteristics Women, and Children” (Washington: Migration of employees were controlled for. See Albert Saiz, Policy Institute, 2011). The authors used Level 4 and Elena Zoido, “Listening to What the World of the National Reporting System (NRS) scale to Says: Bilingualism and Earnings in the United calculate this estimate. While there is no precise States,” Review of Economics and Statistics 87 way to match NRS levels to the self-reported (3) (2005): 523-538 David E. Kalist, “Registered English ability from Census data, it is likely that Nurses and the Value of Bilingualism,” Industrial many individuals who would test at an NRS and Labor Relations Review 59 (1) (2005): 101-118; Level 4 would still be considered LEP by Census Richard Fry, and B. Lindsay Lowell, “The Value of standards. Bilingualism in the U.S. Labor Market,” Industrial 31. The data analyzed in this report are for and Labor Relations Review 57 (1) (2003): 128-140. residents living in the 50 states and the District 38. Vivek Wadhwa and others, “Skilled Immigration of Columbia. Residents of Puerto Rico are not and Economic Growth,” Applied Research in included, but people born in Puerto Rico who now Economic Development 5 (1) (2008): 6-14; Fry and reside in the United States (1.6 million including Lowell, “The Value of Bilingualism.” all ages) are included in this analysis. 39. Mark Glassman, “Correlations: Pay and Payrolls.” 32. Rumbaut and Massey, “Immigration and Bloomberg Businessweek, April 21-27, 2014, 17. Language Diversity in the United States.”; 40. Audrey Singer, “The Rise of Immigrant Gateways” Brigitte S. Waldorf and others, “The Role of (Washington: Brookings Institution, 2004). Human Capital in Language Acquisition among 41. Metro areas whose immigrant population doubled Immigrants in U.S. Metropolitan Areas,” Regional between 2000 and 2010 and had lower than 5 Science Policy & Practice 2 (1) (2010): 39-49 percent LEP in 2012 are Scranton, Little Rock, 33. Myers and Pitkin, “Assimilation Today.” Indianapolis, Birmingham, Nashville, Louisville, 34. Ibid. Knoxville, and Jackson. Cape Coral also doubled 35. Rumbaut and Massey also found that educational its immigrant population in the 2000s but had a attainment is a key determinant of English 2012 LEP concentration of 11 percent. Jackson, proficiency in the U.S. Rumbaut and Massey, Knoxville, and Little Rock are not included in the “Immigration and Language Diversity in the rest of this report because the sample size of United States.” working-age LEP adults there was too small (i.e. 36. Elizabeth Dwoskin, “Why Americans Won’t lower than 100 unweighted cases). For more on Do Dirty Jobs.” Bloomberg Businessweek, foreign-born population change metropolitan 2011.; Michael A. Clemens, and Lant Pritchett, areas, see Audrey Singer and Jill H. Wilson, “Temporary Work Visas: A Four-Way Win for the “Immigrants in 2010Metropolitan America: A Middle Class, Low-Skill Workers, Border Security, Decade of Change.” (Washington: Brookings and Migrants” (Washington: Center for Global Institution, 2011). Development, 2013).; Gopal A. Singh, and Sue C. 42. Ryan Holeywell, “How Language Fits into the Lin, “Marked Ethnic, Nativity, and Socioeconomic Immigration Issue.” Governing, January, 2012. Disparities in Disability and Health Insurance 43. For more information on Executive Order 13166 among U.S. Children and Adults: The 2008–2010 see http://www.lep.gov/faqs/faqs.html. American Community Survey,” BioMed Research 44. See Audrey Singer and Jill H. Wilson, “From International (2013) ‘There’ to ‘Here’: Refugee Resettlement in 37. Saiz and Zoido, for example, report a 2-3% wage Metropolitan America “ (Washington: Brookings premium for college graduates in the U.S. who Institution, 2006). for more about refugee can speak a second language. A 2005 study of resettlement patterns in U.S. metropolitan areas, registered nurses found that Spanish-speaking especially the concentration in smaller metro RNs earned 5% more than their monolingual areas such as those in upstate New York and the colleagues on average, but that the wage Rust Belt. premium fell as the Spanish-speaking population 45. Not only are the languages diverse across metro

40 BROOKINGS | September 2014 areas, but also within metro areas. Providence Legalization Program: Registration as the First and Lancaster are the only metro areas in which Step” (Washington: Migration Policy Institute, one language accounts for a majority of LEP Indo- 2010). European speakers; 69 percent speak Portuguese 54. Foster and McLendon. “Sinking or Swimming.” in Providence, and 51 percent speak Pennsylvania 55. National Skills Coalition, “Workforce Investment Dutch in Lancaster. Act Policy Recommendations” (Washington: 46. Due to small sample sizes, data for LEP speakers 2012). of “other” languages are available for only 62 56. A 2009 Migration Policy Institute report similarly metro areas. recommends that the funding formula to states 47. Lazear (1999) found that the likelihood that an take into account the LEP population with a high immigrant would learn English was inversely school diploma, as well as give greater weight to related to the proportion of the local population the population without a high school diploma who that spoke his or her language. Later studies require both basic education and ESOL services. have resulted in similar findings. Building off That report used 2005-2007 ACS data and found Lazear’s work, a 2010 study of Mexican and that 52 percent of LEP adults age 16 and over Chinese immigrants in U.S. metropolitan areas by (11.2 million people) had a high school diploma. Waldorf and others found that those who settled The numbers in the present analysis differ in that in places with a large number of co-ethnics were the data are from 2012 and are limited to those less likely to be proficient in English. See Edward aged 25-64. See Randy Capps and others, “Taking P. Lazear, “Culture and Language,” Journal of Limited English Proficient Adults into Account in Political Economy 107 (S6) (1999): S95-S126 and the Federal Adult Education Funding Formula” Waldorf and others, “Role of Human Capital.” (Washington: Migration Policy Institute, 2009). See also Kristen E. Espinosa and Douglas S. 57. The Workforce Innovation and Opportunity Massey, “Determinants of English Proficiency Act (WIOA) was enacted in July, 2014; WIOA, a among Mexican Migrants to the United States,” replacement for WIA, requires unified state plans International Migration Review 31 (1) (1997): and consolidates the core and intensive service 28-50 and Raymond J. G. M. Florax, Thomas tiers into one, somewhat easing the way for LEP de Graaff, and Brigitte S. Waldorf, “A Spatial adults to access literacy services, particularly as Economic Perspective on Language Acquisition: they relate to employability. Segregation, Networking, and Assimilation of 58. “State Profiles of the Adult Education Target Immigrants,” Environment and Planning A 37 (10) Population,” available at http://www2.ed.gov/ (2005): 1877-1897 about/offices/list/ovae/pi/AdultEd/state- 48. In two metro areas with relatively small LEP profiles.html (June 2014). populations—Albany and Pittsburgh—there is not 59. Mac Taylor, “Restructuring California’s Adult a statistically significant difference between LEP Education System” (Sacramento: California and non-LEP median earnings. Legislative Analyst’s Office, 2012). 49. This analysis does not include Des Moines as an 60. Colorado has met its 25 percent nonfederal individual metro area due to small sample size, funding contribution requirement through local but includes Des Moines in the 89-metro rate. adult education programs. In Colorado, local 50. Immigration Task Force, “Immigration: America’s programs that receive federal grants are required Demographic Edge” (Washington: Bipartisan to match a portion of the funding with nonfederal Policy Center, 2014). funds. The state then uses these local matches to 51. Heide Spruck Wrigley and others, “The Language fulfill its required contribution for WIA. of Opportunity: Expanding Employment 61. Migration Policy Institute, “Press Release: Prospects for Adults with Limited English McDonald’s Innovative English Under the Arches Skills” (Washington: The Center for Law and Program Honored as an Exceptional Immigrant Social Policy and the National Adult Education Integration Initiative” (Washington: 2010). Professional Development Consortium, 2003). 62. Institute for Work and the Economy and DTI 52. Pew Charitable Trusts, “Immigration and Associates, Inc. “Workplace Education Program Legalization: Roles and Responsibilities of States Profiles in Adult Education” (2003). and Localities” (Washington: 2014). 63. Alice Cottingham and others, “Building Capacity 53. Donald Kerwin, and Laureen Laglagaron, for ESL, Legal Services, and Citizenship: A Guide “Structuring and Implementing an Immigrant for Philanthropic Investment and Partnerships”

BROOKINGS | September 2014 41 (Sebastopol, CA: Grantmakers Concerned with 71. For more examples of programs and partnerships Immigrants and Refugees, 2008). that help immigrants with various levels of 64. “Literacy Coalitions,” available at www. education better connect with and advance in the literacypowerline.com/literacy-coalitions/ U.S. labor market, see Audrey Singer, “Investing in (2014 June). the Human Capital of Immigrants, Strengthening 65. Vicki Clark and Kimberly Scott. 2014. “The Regional Economies” (Washington: Brookings Collaboration Imperative.” Presented during the Institution, 2012). Families Learning Summit & National Conference 72. “About English Health Train,” available at on Family Literacy, edited by Literacy Powerline. welcomebackinitiative.org/englishhealthtrain.org/ Washington. about/ (June 2014). 66. For more information on Executive Order 13166 73. Zorkani, “Charting a Course.”, Alice Cottingham see http://www.lep.gov/faqs/faqs.html. and others, “Building Capacity for ESL, Legal 67. Barbara Tondre-El Zorkani, “Charting a Services, and Citizenship.”, and Nathalie Duval- Course: Responding to the Industry-Related Couetil, and Larry Mikulecky, “Immigrants, Adult Basic Education Needs of the Texas English, and the Workplace: Evaluating Workforce Handbook Two” (Texas Center for the Employer Demand for Language Education in Advancement of Literacy and Learning, 2006). Manufacturing Companies,” Journal of Workplace 68. Matthew Zeidenberg, Sung-Woo Cho, and Learning 23 (3) (2010): 209-223. Davis Jenkins, “CCRC Working Paper No. 20: 74. The Parthenon Group, “Innovation in ESL Washington State’s Integrated Basic Education Technology: Mobile Learning Technology” (The and Skills Training Program (I-BEST): New Gates Foundation, 2008). Evidence of Effectiveness” (New York: Community 75. “Mobile Technology Fact Sheet,” available at College Research Center, Columbia University, www.pewinternet.org/fact-sheets/mobile- 2010). technology-fact-sheet/ (June 2014). 69. Julie Strawn, “Farther, Faster: Six Promising 76. “Teach Literacy by Text Message. Really,” Programs Show How Career Pathway Bridges available at http://blogs.worldbank.org/ Help Basic Skills Students Earn Credentials that impactevaluations/teach-literacy-text- Matter” (Washington: Center for Law and Social message-really (July 2014). Policy, 2011). 70. Ibid.

References

Batalova, Jeanne, Michael Fix, and Peter A. Creticos. 2008. “Uneven Progress: The Employment Path- ways of Skilled Immigrants in the United States.” Washington: Migration Policy Institute.

Capps, Randy and others. 2009. “Taking Limited English Proficient Adults into Account in the Federal Adult Education Funding Formula.” Washington: Migration Policy Institute.

Carliner, Geoffrey. 1996. “The Wages and Language Skills of U.S. Immigrants.” Cambridge: National Bureau of Economic Research.

Chiswick, Barry R., and Paul W. Miller. 1995. “The Endogeneity between Language and Earnings Inter- national Analyses.” Journal of Labor Economics 13 (2): 246-288.

———. 2002. “Immigrant Earnings: Language Skills, Linguistic Concentrations and the Business Cycle.” Journal of Population Economics 15: 31-57.

———. 2006. “Language Skills and Immigrant Adjustment: The Role of Immigration Policy.” In Deborah A. Cobb-Clark and Siew-Ean Khoo, eds., Public Policy and Immigrant Settlement. Cheltenham, United Kingdom: Edward Elgar Publishing.

42 BROOKINGS | September 2014 ———. 2007. The Economics of Language: International Analyses. London: Routledge.

———. 2007. “IZA Discussion Paper No. 2664: Occupational Language Requirements and the Value of English in the U.S. Labor Market.” Bonn, Germany: Institute for the Study of Labor.

———. 2014. “IZA Discussion Paper No. 7880: International Migration and the Economics of Language.” Bonn, Germany: Institute for the Study of Labor.

Clark, Vicki, and Kimberly Scott. 2014. “The Collaboration Imperative.” Presented during the Families Learning Summit & National Conference on Family Literacy, edited by Literacy Powerline. Washington.

Clemens, Michael A., and Lant Pritchett. 2013. “Temporary Work Visas: A Four-Way Win for the Middle Class, Low-Skill Workers, Border Security, and Migrants.” Washington: Center for Global Development.

Connell, Christopher. 2009. “Empty Promises: The Unmet Need for English Instruction across Illinois.” Chicago: Illinois Coalition for Immigrant and Refugee Rights.

Cottingham, Alice and others. 2008. “Building Capacity for ESL, Legal Services, and Citizenship: A Guide for Philanthropic Investment and Partnerships.” Sebastopol, CA: Grantmakers Concerned with Immigrants and Refugees.

Duval-Couetil, Nathalie, and Larry Mikulecky. 2010. “Immigrants, English, and the Workplace: Evaluat- ing Employer Demand for Language Education in Manufacturing Companies.” Journal of Workplace Learning 23 (3): 209-223.

Dwoskin, Elizabeth. 2011. “Why Americans Won’t Do Dirty Jobs.” Bloomberg Businessweek.

English Health Train. “About” (welcomebackinitiative.org/englishhealthtrain.org/about/ [June 2014]).

Espinosa, Kristen E., and Douglas S. Massey. 1997. “Determinants of English Proficiency among Mexi- can Migrants to the United States.” International Migration Review 31 (1): 28-50.

Florax, Raymond J. G. M., Thomas de Graaff, and Brigitte S. Waldorf. 2005. “A Spatial Economic Perspective on Language Acquisition: Segregation, Networking, and Assimilation of Immigrants.” Envi- ronment and Planning A 37 (10):1877-1897.

Fogg, Neeta P., and Paul E. Harrington. 2012. “Mal-Employment Problems Among College-Educated Immigrants in the U.S.” Philadelphia: Drexel University.

Foster, Marcie and Lennox McLendon. “Sinking or Swimming: Findings from a Survey of State Adult Education Tuition and Financing Policies.” Center for Law and Social Policy and the National Council of State Directors of Adult Education. Washington, D.C. 2012.

Fremstad, Shawn. 2003. “Immigrants, Persons with Limited Proficiency in English, and the TANF pro- gram: What Do We Know?” Washington: Center on Budget and Policy Priorities.

Fry, Richard, and B. Lindsay Lowell. 2003. “The Value of Bilingualism in the U.S. Labor Market.” Indus- trial and Labor Relations Review 57 (1): 128-140.

Glassman, Mark. 2014. “Correlations: Pay and Payrolls.” Bloomberg Businessweek, April 21-27, 17.

Gonzalez, Libertad. 2004. “Nonparametric Bounds on the Returns of Language Skills.” Journal of Applied Econometrics 20 (6): 771-795.

BROOKINGS | September 2014 43 Greenberg, Elizabeth and others. 2001. “English Literacy and Language Minorities in the United States.” Washington: National Center for Education Statistics.

Holeywell, Ryan. 2012. “How Language Fits into the Immigration Issue.” Governing, January.

Immigration Task Force. 2014. “Immigration: America’s Demographic Edge.” Washington: Bipartisan Policy Center.

Institute for Work and the Economy and DTI Associates, Inc. 2003. “Workplace Education Program Profiles in Adult Education.”

Johnson, Nicholas, Phil Oliff, and Erica Williams. 2010. “An Update on State Budget Cuts: At Least 46 States Have Imposed Cuts that Hurt Vulnerable Residents and the Economy.” Washington: Center on Budget and Policy Priorities.

Kalist, David E. 2005. “Registered Nurses and the Value of Bilingualism.” Industrial and Labor Rela- tions Review 59 (1): 101-118.

Kerwin, Donald, and Laureen Laglagaron. 2010. “Structuring and Implementing an Immigrant Legaliza- tion Program: Registration as the First Step.” Washington: Migration Policy Institute.

Lazear, Edward P. 1999. “Culture and Language.” Journal of Political Economy 107 (S6): S95-S126.

Literacy Powerline. “Literacy Coalitions” (www.literacypowerline.com/literacy-coalitions/ [2014 June]).

Martinez, Tia Elena, and Ted Wang. 2005. “Supporting English Language Acquisition: Opportunities for Foundations to Strengthen the Social and Economic Well-Being of Immigrant Families.” Baltimore: The Anne E. Casey Foundation.

McHugh, Margie, Julia Gelatt, and Michael Fix. 2007. “Adult English Language Instruction in the United States: Determining Need and Investing Wisely.” Washington: Migration Policy Institute.

McLendon, Lennox. 2010. “Adult Student Waiting List Survey.” Washington: National Council of State Directors of Adult Education.

McLendon, Lennox, Debra Jones, and Mitch Rosin. 2011. “The Return on Investment (ROI) from Adult Education and Training.” New York: McGraw-Hill Research Foundation.

Migration Policy Institute. 2010. “Press Release: McDonald’s Innovative English Under the Arches Pro- gram Honored as an Exceptional Immigrant Integration Initiative.” Washington.

Mora, Marie T., and Alberto Davila. 1998. “Gender, Earnings, and the English Skill Acquisition of His- panic Workers in the United Sates.” Economic Inquiry 36 (4): 631-644.

Myers, Dowell, Xin Gao, and Amon Emeka. 2009. “The Gradient of Immigrant Age-at-Arrival Effects on Socioeconomic Outcomes in the U.S.” International Migration Review 43 (1): 205-229.

Myers, Dowell, and John Pitkin. 2010. “Assimilation Today: New Evidence Shows the Latest Immigrants to America are Following in our History’s Footsteps.” Washington: Center for American Progress.

National Skills Coalition. 2012. “Workforce Investment Act Policy Recommendations.” Washington.

44 BROOKINGS | September 2014 Park, Julie, and Dowell Myers. 2010. “Intergenerational Mobility in the Post-1965 Immigration Era: Esti- mates by an Immigrant Generation Cohort Method.” Demography 47 (2): 369-392.

Parthenon Group. 2008. “Innovation in ESL Technology: Mobile Learning Technology.” The Gates Foundation.

Passel, Jeffrey S. 2011. “Demography of Immigrant Youth: Past, Present, and Future.” The Future of Children 21 (1): 19-41.

Pew Charitable Trusts. 2014. “Immigration and Legalization: Roles and Responsibilities of States and Localities.” Washington.

Pew Research Center. 2014. “Mobile Technology Fact Sheet.” (www.pewinternet.org/fact-sheets/ mobile-technology-fact-sheet/ [June 2014]).

Rosenblum, Marc C., Randy Capps, and Serena Yi-Ying Lin. 2011. “Earned Legalization: Effects of Proposed Requirements on Unauthorized Men, Women, and Children.” Washington: Migration Policy Institute.

Ruggles, Steven and others. 2010. “Integrated Public Use Microdata Series: Version 5.0 [Machine-read- able database].” University of Minnesota. Minneapolis: Minnesota Population Center.

Rumbaut, Ruben G., and Douglas S. Massey. 2013. “Immigration and Language Diversity in the United States.” Daedalus 142 (3): 141-154.

Ryan, Camille. 2013. “Language Use in the United States: 2011.” Washington: U.S. Census Bureau.

Saiz, Albert, and Elena Zoido. 2005. “Listening to What the World Says: Bilingualism and Earnings in the United States.” Review of Economics and Statistics 87 (3): 523-538.

Siegel, Paul, Elizabeth Martin, and Rosalind Bruno. 2001. “Language Use and Linguistic Isolation: His- torical Data and Methodological Issues.” Washington: U.S. Census Bureau.

Singer, Audrey. 2004. “The Rise of New Immigrant Gateways.” Washington: Brookings Institution.

———. 2012. “Investing in the Human Capital of Immigrants, Strengthening Regional Economies.” Wash- ington: Brookings Institution.

Singer, Audrey, and Jill H. Wilson. 2010. “Immigrants in 2010 Metropolitan America: A Decade of Change.” Washington: Brookings Institution.

Singer, Audrey, and Jill H. Wilson. 2006. “From There to Here: Refugee Resettlement in Metropolitan America.” Washington: Brookings Institution.

Singh, Gopal A., and Sue C. Lin. 2013. “Marked Ethnic, Nativity, and Socioeconomic Disparities in Dis- ability and Health Insurance among U.S. Children and Adults: The 2008–2010 American Community Survey.” BioMed Research International.

Stolzenberg, Ross M. 1990. “Ethnicity, Geography, and Occupational Achievement of Hispanic Men in the United States.” American Sociological Review 55 (1): 143-154.

Taylor, Mac. 2012. “Restructuring California’s Adult Education System.” Sacramento: California Legisla- tive Analyst’s Office.

BROOKINGS | September 2014 45 Tondre-El Zorkani, Barbara. 2006. “Charting a Course: Responding to the Industry-Related Adult Basic Education Needs of the Texas Workforce Handbook Two.” Texas Center for the Advancement of Literacy and Learning.

Tucker, James Thomas. 2006. “The ESL Logjam: Waiting Times for Adult ESL Classes and the Impact on English Learners.” Los Angeles: National Association of Latino Elected and Appointed Officials Educational Fund.

U.S. Department of Commerce Bureau of the Census. 1988. “English Language Proficiency Study (ELPS), 1982.” Inter-university Consortium for Political and Social Research.

U.S. Department of Education Office of Career, Technical, and Adult Education. “State Profiles of the Adult Education Target Population” (http://www2.ed.gov/about/offices/list/ovae/pi/AdultEd/state- profiles.html [June 2014]).

Wadhwa, Vivek and others. 2008. “Skilled Immigration and Economic Growth.” Applied Research in Economic Development 5 (1): 6-14.

Waldorf, Brigitte S. and others. 2010. “The Role of Human Capital in Language Acquisition among Immigrants in U.S. Metropolitan Areas.” Regional Science Policy & Practice 2 (1): 39-49.

Wrigley, Heide Spruck and others. 2003. “The Language of Opportunity: Expanding Employment Pros- pects for Adults with Limited English Skills.” Washington: The Center for Law and Social Policy and the National Adult Education Professional Development Consortium.

Zeidenberg, Matthew, Sung-Woo Cho, and Davis Jenkins. 2010. “CCRC Working Paper No. 20: Wash- ington State’s Integrated Basic Education and Skills Training Program (I-BEST): New Evidence of Effectiveness.” New York: Community College Research Center, Columbia University.

46 BROOKINGS | September 2014 Acknowledgments

The author would like to thank individuals who provided advice and suggestions on drafts of this paper: Audrey Singer, Alan Berube, Martha Ross, Jonathan Rothwell, Neil Ruiz, Nicole Prchal Svajlenka, Julia Gelatt, Flavia Jimenez, and Heide Wrigley. Nicole Prchal Svajlenka, Joanne Zalatoris, Hayley Brown, and Kelly Bies supplied important research assistance, and David Jackson edited the manuscript.

The Metropolitan Policy Program would also like to thank the John D. and Catherine T. MacArthur Foundation, the George Gund Foundation, and the Charles Stewart Mott Foundation for general support for the program’s research and policy efforts. We also thank the Metropolitan Leadership Council, a network of individual, corporate, and philanthropic investors that provides us financial support but, more important, a true intellectual and strategic partnership.

For More Information

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BROOKINGS | September 2014 47 About the Metropolitan Policy Program at Brookings Created in 1996, the Brookings Institution’s Metropolitan Policy Program provides decision makers with cutting- edge research and policy ideas for improving the health and prosperity of cities and metropolitan areas including their component cities, suburbs, and rural areas. To learn more visit www.brookings.edu/metro.

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telephone 202.797.6139 fax 202.797.2965 web site www.brookings.edu/metro IMMIGRANTS AND IMMIG RATION

RESEARCH REPORT Upskilling the Immigrant Workforce to Meet Employer Demand for Skilled Workers Hamutal Bernstein Carolyn Vilter July 2018

ABOUT THE URBAN INSTITUTE The nonprofit Urban Institute is a leading research organization dedicated to developing evidence-based insights that improve people’s lives and strengthen communities. For 50 years, Urban has been the trusted source for rigorous analysis of complex social and economic issues; strategic advice to policymakers, philanthropists, and practitioners; and new, promising ideas that expand opportunities for all. Our work inspires effective decisions that advance fairness and enhance the well-being of people and places.

Copyright © July 2018. Urban Institute. Permission is granted for reproduction of this file, with attribution to the Urban Institute. Cover image courtesy of Shutterstock. Contents

Acknowledgments iv

Executive Summary v

Upskilling the Immigrant Workforce to Meet Employer Demand for Skilled Workers 1 Need for This Study 2 Research Approach 4 Basic Demographics of the Immigrant Workforce 8 Perspectives from Three Local Workforce Systems 17 Lessons for Developing Strong Middle-Skill Workforce Strategies 33

Appendix. Assigning Skill Requirements to American Community Survey Occupation Codes 38

Notes 52

References 54

About the Authors 57

Statement of Independence 58

Acknowledgments

This report was funded by a grant from JPMorgan Chase. We are grateful to them and to all our funders, who make it possible for Urban to advance its mission.

The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts. Further information on the Urban Institute’s funding principles is available at urban.org/fundingprinciples.

First, we thank our interviewees, who were willing to take time away from their important work serving their communities to share their perspectives on this topic. We thank those who have provided advice as we developed this report, including Audrey Singer, David Dyssegaard Kallick, Rachel Peric, Erica Bouris, Hadya Abdul Satar, Loh-Sze Leung, and Jeanne Batalova, and our partners at JPMorgan Chase. We sincerely thank our reviewers and counselors Amanda Bergson-Shilcock, Pam Loprest, Marcela Montes, and Shayne Spaulding, for their insights and support along the way. Thanks to Sarah Gault for earlier research assistance with the census data. Thanks go to Elizabeth Forney and Serena Lei for their assistance on copyediting.

IV ACKNOWLEDGMENTS

Executive Summary

In communities across the country, many employers are having trouble finding enough skilled workers. They may be overlooking an untapped resource. A large share of immigrant workers are in lower-skilled jobs, however, with the right access to education and training they need to advance their careers, many have the potential to meet these labor force needs. Workforce development services could help them develop their skills, earn higher wages to support themselves and their families, and meet employer demand.

Immigrants make up one out of six workers in the United States. They are an often overlooked but vital part of local economies and, therefore, should be a part of local workforce development strategies. Middle-skilled jobs are an avenue for many of these workers to get good jobs without needing a four- year degree. And employers have expressed a need for workers with bilingual and cultural skills to serve an increasingly diverse public. Many cities and organizations are engaged in upskilling their immigrant workforce, though it is challenging to serve this population effectively, and there is not much systematic knowledge about the most effective way to address barriers and design training for this group.

In this report, we examine the size and characteristics of the potentially untapped immigrant workforce and the barriers to and opportunities for education, training, and workforce services. We provide national and metropolitan-level statistics to inform decisionmakers. We also explore strategies that organizations in three cities—Dallas, Miami, and Seattle—are using to support immigrant training and advancement and offer practice and policy recommendations for others.

Here, we focus on immigrants currently employed in lower- and middle-skilled occupations and who are authorized to work in the US. Many of the issues discussed in this report also apply to undocumented workers, who could benefit from upskilling efforts, but they are not eligible for certain federal employment programs.

Characteristics of the Immigrant Workforce

Understanding the characteristics of the potential immigrant middle-skilled workforce can inform strategies to support education and training efforts. We looked at the educational attainment, occupations, wages, and English proficiency of immigrant workers. Educational Attainment

As a whole, immigrant workers have lower educational attainment than native-born workers, although a similar share in both groups has a college or advanced degree.

 26 percent of immigrant workers have less than a high school diploma or equivalent, compared with 5 percent of native-born workers

 21 percent of immigrant workers have a high school degree, compared with 26 percent of native-born workers

 21 percent have some postsecondary education but less than a bachelor’s degree, compared with 36 percent of native-born workers

 31 percent have a college degree or above, compared with 33 percent of native-born workers

Occupation

Despite having lower educational attainment, foreign-born workers are just as likely as native-born workers to hold middle-skilled jobs. This suggests that immigrant workers may be gaining some middle- skilled jobs through job experience and on-the-job training rather than entering with specific credentials required for entry into a specific job. About 25 percent of both groups have middle-skilled jobs, though immigrant workers are more likely than native-born workers to have lower-skilled jobs (50 percent compared with 44 percent) and less likely to have high-skilled jobs (25 percent compared with 31 percent).

Among foreign-born workers, the most common lower-skilled occupations are maids and housekeeping cleaners, janitors, construction laborers, cashiers, grounds maintenance workers, retail salespersons, agricultural workers, and waiters. The most common middle-skilled occupations are cooks; drivers; nursing, psychiatric, and home health aides; and first-line supervisors of retail workers. Though native-born workers also hold many of these same lower- and middle-skilled occupations, they are more likely to have secretary and customer service representative positions and much less likely to work as maids, grounds maintenance workers, and agricultural workers.

Wages

Immigrant workers in lower- and middle-skilled jobs earn less than their native-born counterparts. They have a median wage of $21,266 compared with $24,421 among the native born with lower-skilled jobs.

VI EXECUTIVE SUMMARY

And among those with middle-skilled jobs, immigrants have a median wage of $28,905 compared with $36,041 for native-born workers.

Limited English Proficiency

Limited English proficiency is common among immigrant workers, though it is more common for those with lower-skilled jobs (60 percent) compared with those in middle-skilled jobs (52 percent) or in high- skilled jobs (19 percent). Limited English proficiency is correlated with lower wages; in general, the lower-paid occupations are more likely to have workers who are limited English proficient (LEP), and the higher-paid occupations have fewer LEP workers. But there is not a direct relationship between wage and LEP rate. Some higher-paying occupations in construction and related fields have a large majority of LEP workers. There are also LEP workers in some first-line supervisor positions, which tend to be better paid. There are some other well-paying jobs (for the native born) that immigrants are not attaining at comparable rates, which could offer paths to better wages and could be reached with some upskilling in English, career readiness, or technical training.

The results suggest that certain types of training and education are needed to support immigrant upskilling and leverage their unique assets and needs across sectors and occupations. Limited English proficiency is a common issue among immigrants employed in lower- and middle-skilled jobs, suggesting the importance of English language training as part of workforce development strategies for this population.

Barriers to Education and Training and Better Jobs

We talked with service providers and stakeholders in Dallas, Miami, and Seattle to better understand the challenges that immigrants face in pursuing education and training. Many described the following barriers.

 Limited English proficiency. Many service providers said that limited English proficiency is a significant barrier to promotion and higher wages and can make workers vulnerable to abuse in the workplace. In the cities we visited, the demand for English language training is greater than the supply. Service providers said that English as a Second Language (ESL) instruction should be customized for different needs, but existing classes are rarely able to support the wide range of immigrant backgrounds and skill levels. They also noted that employers and policymakers

EXECUTIVE SUMMARY VII

sometimes have unrealistic expectations about how quickly students can advance in their English proficiency.

 Difficulty transferring foreign credentials and overseas job experience to the US job market. Immigrants who have credentials and job experience in their home countries have trouble finding employment in their previous professions. Service providers said that they try to offer alternative training or career paths to immigrants with particular skill sets or try to place them in lower- or middle-skilled jobs where they can get a foot in the door rather than trying to match them with jobs similar to the ones they held in their home countries.

 Low digital literacy and low basic skills. Many service providers described how some of their immigrant clients need training in basic computer skills, which matter for a wide range of jobs and for accessing workforce services and tools. Workers with low digital literacy might also have more fundamental basic skills gaps and low literacy in their native languages.

 High housing costs and lack of transportation and child care. These three barriers are perhaps the greatest challenges immigrant workers—and native-born workers—face for participating in education and training. Low wages and high housing costs drive many immigrants to work multiple jobs, leaving little time for upskilling. Transportation challenges, such as inadequate public transportation and long commute times, can keep workers from pursuing education and training. And finding affordable quality child care is a major hurdle for many low-wage workers, including immigrant workers.

 Financial pressures. Service providers said that many immigrants work in “survival jobs,” which allow them to support themselves but are not likely to lead to advancement. The financial pressure of providing for themselves and their families on low wages is a major barrier to investing time and money in education and training.

In addition, service providers grapple with their own challenges to supporting education, training, and workforce services and supports. These include raising funds to develop and maintain programs, managing complex and sometimes contradictory performance reporting requirements from different funding sources, and working with the federal workforce system structure implemented through the Workforce Innovation and Opportunity Act (WIOA).

VIII EXECUTIVE SUMMARY

Strategies to Support Immigrant Workers and Develop a Strong Middle-Skilled Workforce

Despite these challenges, many organizations are designing workforce development services for immigrant workers and helping them address their barriers to participation. The following strategies include approaches to training that consider workers’ needs and solutions to increase immigrant access to education and training.

 English language skill-building. Many service providers said that vocational English training that integrates workforce content or combines ESL with technical training is more effective and efficient than classic ESL. Offering instruction at the workplace can make training more accessible and can help workers avoid additional transportation and child care costs.

 Training supports. Supportive services, such as help with transportation or child care, can make it easier for workers to enroll and participate in education and training programs.

 Inclusive staffing. To make immigrants feel welcome and to provide the support they need to succeed in training, organizational staff should be multilingual, culturally competent, and available to help participants navigate systems.

 Community outreach. Many service providers said that community outreach is essential for engaging immigrant communities. This may mean advertising on language-specific media, providing recruitment materials in multiple languages, or tapping into community networks.

 Trusted providers. The service providers we interviewed said that a common strategy for outreach is leveraging community organizations and providing services at trusted places, like on location at immigrant-serving community-based organizations.

 Collaboration with other service organizations. Collaboration across sectors allows service providers to leverage resources and make training and supports more accessible to students and their families.

Local Workforce Development Strategy Should Include Immigrant Workers

Immigrant workers play essential roles in local economies but are often not able to access the training that would benefit themselves, their employers, and their communities. Their role in local labor markets

EXECUTIVE SUMMARY IX and their needs should be considered in broader decisionmaking and in every community’s workforce development goals.

Like native-born workers, many foreign-born workers in lower- and middle-skilled jobs are working hard to support their families, handling high costs of living, and struggling to make ends meet with low- paid work. Given the challenges of balancing life and work on a low income, they have limited opportunity to invest in their own training and enhance their prospects for better-paid work and career advancement.

It is important that communities acknowledge and address the often-overlooked immigrant workforce that sustains their local economies and make sure that immigrants and LEP workers are a part of local economic development and workforce development strategies and conversations. There is still too much division between immigrant-serving organizations and ESL providers, the workforce development board and community colleges, and employers who are relying on immigrant talent. All actors would benefit from bridging that gap and coming together to engage immigrants and employers in workforce development strategies and foster immigrant upskilling

Investing in immigrant workers and their upskilling is important for a range of decisionmakers across sectors, who may or may not have immigrant and LEP issues on their radar.

• State and local policymakers making policy and funding decisions can be aware of and sensitive to the key role that immigrant workers are playing in their communities, pushing forward workforce and education issues. Recognizing the contributions of the immigrant community is critical, especially at this time of restrictive immigration policies and toxic national rhetoric. Policymakers can put more resources into English language learning and encourage organizations to serve immigrants and LEP individuals, track information about programs to assess gaps in services, and actively engage immigrant-serving organizations to ensure policy is grounded in community needs.

• Organizations providing workforce development services and education and training can work to improve program accessibility and design to serve immigrant community members. Local workforce development boards, community colleges, and school districts, whose mission is to serve the community, can assess the extent to which they are funding immigrant-serving organizations or serving immigrant and LEP communities. They can work to improve the linguistic and cultural competence and accessibility of programs by hiring multilingual staff and partnering with immigrant-serving organizations to fill in gaps in visibility, language access, and cultural knowledge. Organizations should connect with immigrant-serving organizations to

X EXECUTIVE SUMMARY

leverage WIOA’s opportunity for pressing forward the needs of LEP individuals as a priority of service population, as well as youth in immigrant families.

• Funders interested in supporting equity and economic mobility can consider incorporating immigrant populations and English language learning into their funding priorities. They can work to ensure that these issues are reflected in their awarding of grants—that local grantees are reaching this population through direct services or incorporating an immigrant-inclusive lens in systems-level work. This might mean encouraging collaboration across sectors like connecting immigrant-serving ESL providers to local workforce development or economic development organizations or employers, or conducting needs assessments to inform future investment.

• Employers who want to recruit and retain workers can invest in improving job quality by recognizing barriers like child care and transportation and providing supports or services to help current employees. They can also invest in upskilling their employees through onsite training to foster English language learning and promote advancement. And they can collaborate with immigrant-serving organizations and other training providers to ensure that training programs are informed by current industry need.

This issue is even more important in our current political climate, when immigrant communities are subject to harsh political rhetoric and are suffering the consequences of heightened immigration enforcement and volatile immigrant policy developments. In the face of this painful debate and as many are suffering uncertainty and family separation, each day immigrant workers continue to work to support their families, employers, and communities. States and local areas that are working to support their immigrant residents need to keep training and education on their agenda as they develop proactive policies and continue to support workers.

EXECUTIVE SUMMARY XI

Upskilling the Immigrant Workforce to Meet Employer Demand for Skilled Workers

Immigrants make up one out of six workers in the US, and they support the vitality of local economies across the country. The foreign born are employed at high rates, but a large share have low educational attainment and are limited in their English proficiency. Most immigrant workers with low-paying jobs have limited opportunities to pursue education and training and expand their English and technical skills, steps that might unlock better wages and support career advancement. At the same time, with low rates of unemployment, employers are voicing demand for workers, especially to fill middle-skilled positions that require some postsecondary training but not a four-year college degree. Many employers need skilled workers with bilingual and cultural skills to serve an increasingly diverse public and work in a globalized economy. It has been challenging for many local workforce systems to serve immigrant workers effectively, but many organizations and cities are engaged in upskilling their immigrant workforce to meet this employer demand. “Local workforce systems” here is used broadly to include not just the federally funded public workforce system but the range of organizations across sectors that serve and support individuals and employers,1 including education and training providers, immigrant- serving organizations, and other organizations. This report examines the size and characteristics of the immigrant workforce at the national and metropolitan levels, and explores key strategies that organizations in three cities are using to support training this population for better jobs.

We explore barriers to and opportunities for education, training, and workforce services, focusing specifically on employed immigrants who could help fill employer demand and should be a target of middle-skill workforce development strategies. Our discussion of education and training considers a range of programming that could foster mobility of working immigrants and limited English proficient (LEP) individuals; this includes English as a second language (ESL), general career readiness and workforce training, and technical training for specific occupations. We use recent census data to provide a demographic profile of the immigrant workforce with national- and metropolitan-level statistics for the largest 100 metropolitan regions. We share insights collected through interviews with service providers and stakeholders in Dallas, Miami, and Seattle to better understand the barriers to training for this population and the experiences of organizations serving immigrant communities. The results point to the ways that local communities focused on the issue of middle-skill workforce

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 1 development can account for the important role that immigrants have to play and design appropriate strategies to foster their upskilling. Strategies should address barriers that are preventing many immigrants from advancing out of low-wage jobs, as many immigrant workers are struggling to make ends meet with low-paid lower-skilled or middle-skilled jobs. Limited English proficiency is a major challenge for this group, and English language training must be a component of workforce training for this population. It is also, however, crucial to recognize the linguistic and cultural assets that immigrants can and do contribute, especially in key occupations and sectors that require bilingual skills whether to serve a diverse public or work with a diverse employee base.

We begin with a description of the policy and research context and describe our research design, which draws on quantitative and qualitative data sources. We provide basic statistics on the immigrant workforce and then share perspectives from the field collected through interviews with staff from service provider and stakeholder organizations. We conclude with practice and policy recommendations to inform future decisionmaking to leverage the key role immigrant workers can play for middle-skill workforce strategies.

BOX 1 New Skills at Work

The Urban Institute is collaborating with JPMorgan Chase over five years to inform and assess JPMorgan Chase’s philanthropic investments in key initiatives. One of these is New Skills at Work, a $250 million multiyear workforce development initiative that aims to expand and replicate effective approaches for linking education and training efforts with the skills and competencies employers need. The goals of the collaboration include using data and evidence to inform JPMorgan Chase’s philanthropic investments, assessing whether its programs are achieving desired outcomes, and informing the larger fields of policy, philanthropy, and practice. As one of several resources Urban is developing for the field, this report focuses on immigrant workers and effective strategies for upskilling this key segment of many local workforces.

Need for This Study

There is a broad conversation in the workforce development field and among employers about the “middle-skills gap:” the idea is that employers are in need of skilled workers with specific postsecondary credentials but less than a four-year college degree (Holzer 2015; Kochan, Finegold, and Osterman 2012; Modestino 2016).2 Though not all middle-skilled jobs offer high wages, there is a growing focus

2 UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND on job quality and the importance of prioritizing workforce and training services to prepare and place individuals in “good jobs.” In-demand positions can offer higher wages and desirable work conditions without a lengthy and expensive investment in a four-year degree that may not be in tune with employer demand (Carnevale et al. 2017). The link between the middle-skills gap and the immigrant workforce is rarely voiced explicitly. However, employers in many sectors, such as health care, sales, and customer services, where skilled workers are needed to serve a diverse multilingual public, are articulating the need for a trained immigrant workforce (Callahan and Gándara 2014; Hohn et al. 2016; New American Economy 2017). Immigrant workers offer valuable assets like linguistic and cultural knowledge that are a good match for these needs, but a large share is employed in low-paid lower- skilled work. Alongside the financial and logistical challenges faced by all low-income workers seeking training or employment services, immigrants face additional barriers of limited English language proficiency, lack of knowledge of training and employment options, and challenges in having foreign credentials and experience recognized in the US labor market.

There is not much systematic evidence-based knowledge about the most effective ways to address these barriers and design workforce services and training for low-income immigrant populations, although there are many cases of individual successful projects and best practices in programming and funding (Bergson-Shilcock 2016; Connell 2008; Gershwin et al. 2009; Spence 2010). In fact, much of the discussion on immigrant workforce issues has focused on high-skilled immigrants who have college and advanced degrees and the barriers they face getting recognition of their educational credentials earned abroad and reaching employment aligned with their skills (Batalova, Fix, and Bachmeier 2016; Bergson- Shilcock and Witte 2015; Hall et al. 2011; McHugh and Morawski 2017). Although there has been some research on the middle-skilled part of the labor market and how immigrants fit in (Capps, Fix, and Yi- Ying Lin 2010; National Skills Coalition, n.d. a, n.d. b) and study of key roles that immigrant workers fill in the labor market (Dramski 2017; Espinoza 2017; Paral 2018), there is still a gap in knowledge about effective strategies for upskilling immigrants to reach better jobs.

The division between the immigrant services field and the workforce development field in the US has long been a challenge to developing effective workforce services and systems for this population (Montes and Choitz 2016a, 2016b).3 In the context of a broader conversation around immigrant integration, there has been a shift in the last few years toward better coordination in some communities and more attention paid to refugees’ and other immigrants’ access to workforce development resources (Kallenbach and Nash 2016; US Department of Health and Human Services 2014). But there are significant challenges around immigrants’ and LEP individuals’ access to the federal workforce development system, and many have highlighted the mismatch between immigrants' needs and federal,

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 3 state, and local practices (Hamaji and González-Rivera 2016; McHugh and Morawski 2015). Passage of the Workforce Innovation and Opportunity Act (WIOA) in 2014 has created risks and offered opportunities to help address this gap.4 WIOA updated the structure of the publicly funded workforce system and included requirements that all core programs, including adult basic education and English language literacy programs, track a common set of outcomes. When WIOA was passed, some immigrant advocates and researchers were concerned about the potential negative impacts for serving the immigrant community,5 fearing that the increased focus on employment outcomes in programs designed to improve basic skills would reduce their access to WIOA-funded adult education services. Others highlighted opportunities such as the potential for the involvement of immigrant-serving organizations in local planning and a focus in the legislation on barriers to service and basic skills deficient individuals, which would include LEP individuals (National Skills Coalition 2016).

As WIOA is implemented and communities continue to address skills gaps, cities and organizations across the country are handling immigrant workforce issues in different ways, so it is important that research be grounded in specific local settings. This report was motivated by the need for practitioners and policymakers to have a better understanding of where immigrants are accessing training, the barriers they face, and what funding streams and resources are supporting this population. We analyze the challenges and opportunities around training immigrant workers that might allow them to attain higher-paying, higher-quality jobs, focusing on a specific target population for policymakers: currently employed immigrant workers who hold lower- and middle-skilled jobs.

Research Approach

In this report we explore the following key research questions:

1. How big is the immigrant workforce and what are its key characteristics? How much does educational attainment align with occupational skill requirements? How do immigrant workers differ from native-born workers?

2. What are the key barriers to education, training, and advancement for immigrant workers?

3. How are local workforce systems and stakeholders coping with these barriers? What sources of training and workforce development services are immigrants accessing? What funding sources and strategies are local workforce actors using to support immigrant workers?

4 UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND

4. How can local workforce systems better support immigrant workers to reach better-paid and middle-skilled employment? Have recent changes to WIOA had an impact on strategies on the ground?

To inform middle-skill workforce policy and efforts to train workers for good jobs, we focus on the experiences of immigrants employed in lower- and middle-skilled occupations; this is the potential target population for upskilling efforts to fill employer needs for skilled workers. We use the terms “immigrant” and “foreign born” interchangeably throughout the report. The main focus of this report is on how workforce systems support education and training for immigrant workers and thus it focuses primarily on the experiences of work-authorized immigrants rather than undocumented immigrants. Undocumented immigrants may participate in training and education. Many of the issues we discuss in this report apply to undocumented workers, who could benefit from upskilling efforts, although issues around legal status were not a focus of our work. Other research described above directly addresses the conditions faced by undocumented immigrants, who are ineligible for certain federal employment programs and face barriers to accessing public services for which they are eligible. In the current immigration policy climate, heightened immigration enforcement and political rhetoric have raised the vulnerability and fear across immigrant communities across the US (Cervantes, Ulrich, and Matthews 2018; McHugh 2018). While the immigration policy debates rage on and individuals and communities suffer the consequences of uncertainty and family separation, immigrant workers continue to clock in to support their families, employers, and communities. This report turns a spotlight on the immigrant workers who underlie many local economies in lower- and middle-skilled jobs who should be factored into local workforce development strategies.

Census Analysis

We provide a demographic profile of the immigrant workforce using recent Census bureau data: the five-year American Community Survey (ACS) sample for 2011–15. (See appendix for further details.) We apply Bureau of Labor Statistics (BLS) classifications of skills requirements to occupations as follows:6

 Occupations with limited skills requirements or “lower-skilled jobs:” Require no specific education or work experience and only short- or moderate-term on-the-job training

 Occupations with middle skills requirements or “middle-skilled jobs:” Require a postsecondary nondegree or associate’s degree, some work experience in a related occupation, or long- term on-the-job training or an apprenticeship

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 5

 Occupations with high skills requirements or “high-skilled jobs:” Require a bachelor’s, master’s, doctoral, or professional degree

Each of these broad categories includes a wide range of jobs and occupations that vary greatly in terms of wage level, job quality, and prospects for advancement. Not all middle-skilled jobs are necessarily “good jobs” with high wages, so we focus on both the lower- and middle-skilled occupations and the challenges that workers face to participating in training that would help them advance. Although we use the term "lower-skilled jobs" in this report, we acknowledge that all jobs require certain skills and experience and do not want to diminish the talent and skills involved in all work, including jobs often derided as "low skilled” (Hagan et al. 2015). Though there are limitations with these terms, we use these categories to be able to describe large patterns and to speak to ongoing policy conversations around issues like the middle-skills gap.

One major limitation of the ACS data, similar to the decennial census, is that certain populations, including undocumented immigrants, are difficult to reach and are thus undercounted. The US Office of Immigration Statistics assumes a 10 percent undercount of the undocumented population in the ACS (Baker 2017). This limitation of the ACS data affects the findings of our study since many undocumented immigrants are in occupations with lower- and middle-skills requirements.7

Interviews with Organizations

To complement our quantitative findings that reveal broad patterns in educational attainment and employment, we conducted site visits to three cities, Dallas, Miami, and Seattle, to better understand how local workforce systems and organizations are supporting this population. These cities capture a range of economic and social contexts and are all growing economies with low unemployment rates where immigrants are and will continue to be important for middle-skills workforce strategies. We drew on existing research, consultations with experts, and ACS data to identify cities that had large and diverse immigrant workforces, would be likely to have innovative practices, and had not previously been studied in this context. We looked for variation in terms of geography, mix of industries, local context of immigrant reception, and workforce demographics like skill distribution, English language proficiency, and the ethnic and national background of immigrant workers. See appendix table a.1 for an overview of basic demographic statistics for each metropolitan area.

For each of the cities, we identified organizations and individuals to interview through consultation with experts and internet research, looking for promising practices and evidence of outreach to

6 UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND immigrant or LEP populations. We identified a range of organizations that are involved in workforce development issues and immigrant services:

 education and training providers including community colleges, school districts, and other English as a second language, GED, and technical training providers

 immigrant-serving organizations and community-based organizations

 stakeholders from local government, the local workforce development board, chambers of commerce or other economic development organizations, and philanthropy

This produced 30 semistructured interviews, through which we gathered perspectives on the strategies and programs in place to reach immigrant workers, assets immigrants offer and barriers they face, challenges the service organizations face, sources of funding, local economic and policy context, including the impact of the WIOA, and other issues. We intentionally engaged a wide range of stakeholders and service providers across sectors that normally remain siloed. We were thus able to capture a rarely seen multisector view across immigrant services, adult basic education, higher education, workforce development, and economic development organizations. We believe that the insights are relevant to other cities and metros in the US. Table 1 lists the organizations we interviewed in each city.

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 7

TABLE 1 Organizations Interviewed

Dallas Miami Seattle Training providers Dallas County Community Miami Dade College, Port Jobs College District, El Centro Wolfson Campus College Seattle Central College, Miami Dade College, Basic & Transitional Fort Worth Independent REVEST Program Studies School District, Office of Adult Education Miami-Dade County Public Schools, Division of Adult and Workforce Education Immigrant-serving Park Cities Presbyterian ConnectFamilias El Centro de la Raza organizations Church, Northwest Bible Church, For the Nations, Family Action Network Literacy Source New City Fellowship Movement (FANM) Neighborhood House International Rescue Hispanic Unity of Florida Committee in Dallas OneAmerica Sant La Haitian Literacy Instruction for Neighborhood Center Seattle Jobs Initiative Texas (LIFT) Youth Co-Op, Inc. Transformance USA Stakeholders City of Dallas, Office of Miami-Dade Beacon City of Seattle, Office of Welcoming Communities Council Immigrant and Refugee and Immigrant Affairs Affairs South Florida Hispanic United Way of Chamber of Commerce Seattle Metropolitan Metropolitan Dallas Chamber of Commerce United Way of Miami-Dade Workforce Solutions Workforce Development Greater Dallas Council of Seattle-King County

Basic Demographics of the Immigrant Workforce

Immigrants make up 17 percent of the workforce nationally, with much higher shares in some major metropolitan regions, such as Miami or San Jose (48 percent) or New York (37 percent) (see appendix table a.4). To provide background on what the potential immigrant middle-skilled workforce looks like, we developed a basic statistical profile of the immigrant workforce including information on educational attainment, occupation, wages, and limited English proficiency. We highlight differences and similarities between foreign-born and native-born workers, focusing our analysis on foreign-born individuals ages 16 to 64 who are employed in the civilian labor force:

 Immigrant workers have lower educational attainment than native-born workers, although a similar share in both groups has a college or advanced degree.

8 UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND

 Despite lower educational attainment, immigrant workers are as likely as the native born to work in middle-skilled occupations.

 Immigrant workers in lower- and middle-skilled jobs earn less than their native-born counterparts.

 The majority of immigrant workers are LEP, which is correlated with lower wages.

The results suggest that certain types of training and education are needed to support immigrant upskilling and leverage their unique assets and needs across different sectors and occupations. Limited English proficiency is a common issue among immigrants employed in lower- and middle-skilled jobs, suggesting the importance of English language training as part of workforce development strategies for this population.

Characteristics of Immigrant Workers

As a whole, foreign-born workers have lower educational attainment than native-born workers (figure 1): about a quarter have less than a high school diploma or equivalent, compared with 5 percent among the native born. On the other hand, about one-third of foreign-born workers have a college or advanced degree, similar to the share among the native born. This diversity within the immigrant workforce—with high numbers at both ends of the educational spectrum—is important context for our focus in this report on opportunities for immigrants employed in lower- and middle-skilled jobs.

To provide some basic information on who we are talking about, most immigrant workers have been in the US for many years (table 2), with a median of 17 years of residence and a median age of 41. Just under half are LEP; this is most prevalent among workers with lower educational attainment and it is relatively low for those with college or advanced degrees. Their median annual wages are low, $29,407 for all immigrant workers and lowest for those with lower educational attainment. These broad trends suggest the importance of investment in education and training for immigrant workers, who could potentially raise their wages and ability to support their families with greater training and investment in their upskilling.

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 9

FIGURE 1 Educational Attainment

Less than high school High school only Some college College or advanced degree 100%

90% 33% 33% 31% 80%

70%

60% 21%

50% 33% 36%

40% 23% 30%

25% 20% 26% 26% 10% 9% 5% 0% All workers Native-born workers Foreign-born workers

URBAN INSTITUTE Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Note: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration.

TABLE 2 Basic Demographics of Foreign-Born Workers by Educational Attainment

Less than High school Some College or All high school only college advanced degree Median years in country 17 17 17 19 17 Median age 41 42 41 40 41 Share LEP 48% 81% 56% 35% 23% Median annual wage $29,407 $20,351 $24,028 $29,033 $60,000 Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Notes: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration. Median annual wage data are further restricted to those individuals who reported positive wage and salary income during the year before the survey. Individuals are considered limited English proficient if they report not speaking English, speaking English but not well, or speaking English well. LEP = limited English proficient.

Characteristics of the Jobs Immigrant Workers Hold

As mentioned above, we divide jobs into three groups based on the entry-level requirements of occupations. Jobs with higher-skill requirements come with higher median wages for both the foreign-

10 UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND born and the native-born (figure 2). However, foreign-born workers in lower- and middle-skilled jobs earn less than their native-born counterparts. This wage differential is not true for workers in high- skilled jobs.

FIGURE 2 Median Annual Wages by Occupation Skill-Level Requirement

Native-born workers Foreign-born workers

$68,502

$59,017

$36,041

$28,905 $24,421 $21,266

Lower-skilled jobs Middle-skilled jobs High-skilled jobs

URBAN INSTITUTE Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Note: Data refer to individuals ages 18–64, in the civilian labor force, employed during the week before the survey administration, and reporting positive wage and salary income for the previous year.

Across the entire workforce, slightly less than half of all workers hold lower-skilled jobs, one quarter hold middle-skilled jobs, and the remainder hold high-skilled jobs (figure 3). Despite lower educational attainment, foreign-born workers are just as likely to hold middle-skilled jobs as native- born workers. Immigrant workers are more likely than native-born workers to have lower-skilled jobs (50 percent compared with 44 percent for the native born) and less likely than the native born to have high-skilled jobs (25 percent compared with 31 percent).

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 11

FIGURE 3 Occupation Skill-Level Requirements

Lower-skilled jobs Middle-skilled jobs High-skilled jobs 100%

90% 25% 30% 31% 80%

70%

25% 60% 25% 25% 50%

40%

30% 50% 45% 44% 20%

10%

0% All workers Native-born workers Foreign-born workers

URBAN INSTITUTE Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Note: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration.

Figure 4 reinforces what figures 2 and 3 demonstrate—that foreign-born workers are able to attain middle-skilled jobs despite lower educational attainment. Over a quarter (28 percent) of immigrants with middle-skilled jobs have less than a high school credential, compared with only 7 percent among native-born workers with middle-skilled jobs. Secondly, a much smaller share of immigrants in middle- skilled jobs have had some college (26 percent) compared with the native-born (43 percent). There are many possible reasons for these patterns. These figures do not take into account the length of experience a worker has in the occupation, and it may be that workers are moving into some middle- skilled jobs through job experience and on-the-job training rather than entering with specific credentials required for entry into a specific job. This suggests the possibility that immigrant workers with lower educational attainment may be gaining some middle-skilled jobs through additional years of experience.8 Another issue is that these are large categories, which combine many occupations and jobs within an occupation. The average requirements of an occupation used in this analysis can mask substantial variation among jobs in a skill-level category.

12 UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND

FIGURE 4 Educational Attainment by Occupation Skill-Level Requirement

Less than high school High school only Some college College or advanced degree 100% 13% 90% 16% 17% 17%

80% 22% 70% 26% 42% 71% 60% 43% 79%

50% 28% 30% 40%

30% 35% 33% 20% 36% 22% 28% 14% 10% 8% 7% 7% 5% 0% Native-born Foreign-born Native-born Foreign-born Native-born Foreign-born Lower-skilled jobs Middle-skilled jobs High-skilled jobs URBAN INSTITUTE Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Note: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration.

The fact is that within lower- and middle-skilled jobs immigrants are in different occupations than native-born workers (table 3). We combine in these tables workers with lower- and middle-skilled jobs, color coding each group. (For a focus on individuals with only middle-skilled jobs, see appendix table a.2). The top 25 occupations we list here make up around half of workers in lower- and middle-skilled jobs (55 percent of foreign born and 48 percent of native born). We also note occupations that appear in the top 25 for one group but not the other.

Among foreign-born workers, the most common lower-skilled occupations (share above 2 percent) are maids and housekeeping cleaners (with the single largest share at 4.3 percent), janitors, construction laborers, cashiers, grounds maintenance workers, retail salespersons, agricultural workers, and waiters. The most common middle-skilled occupations are cooks; drivers and truck drivers; nursing, psychiatric, and home health aides; and first-line supervisors of retail workers. The native born are likely to hold many of these same lower- and middle-skilled occupations, but there are some major differences. The native born are more likely to have secretary and customer service

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 13 representative positions and are much less likely to work as maids, grounds maintenance workers, and agricultural workers.

The share of LEP workers varies a lot across occupations, as might be expected depending on the skills required. Limited English proficiency is common among immigrant workers, though it is more common for those with lower-skilled jobs (60 percent) compared with those in middle-skilled jobs (52 percent), or in high-skilled jobs (19 percent). Limited-English speakers fill a number of jobs, indicating that one can still get ahead with limited English proficiency in some jobs. To get a sense of the patterns, we look at the rate of limited English proficiency within occupations. (See appendix table a.3 for immigrant-held occupations listed in order of share who are LEP.)

Limited English proficiency is correlated with lower wages. In general, the lower-paid occupations are more likely to have more LEP workers, and the higher-paid occupations are less likely to have many LEP workers. For example, occupations with the very highest LEP rates, which the native born are not likely to hold (e.g., agricultural workers, grounds maintenance workers, and maids and housekeeping cleaners), where 80 percent or more of immigrant workers with that position are LEP, all have low median annual wages (below $20,000). However, there is not a direct relationship between wage and LEP rate:

 First, the very lowest–paying jobs immigrants hold, in customer service, do not have the highest LEP rates. Child care workers, cashiers, and personal care aides, with median annual wages between $13,215 and $15,263, have more moderate LEP rates (between 50 and 60 percent); this makes some sense, since in some places such client-facing jobs would require English and in other cases they would require a foreign language to serve a diverse public. These jobs may also be more likely to be part time, which this analysis does not account for.

 Secondly, there are several higher-paying occupations, in construction and related fields, where a large majority of workers are LEP (over 70 percent). Immigrant construction laborers; painters, construction, and maintenance workers; other production workers; carpenters; and miscellaneous assemblers and fabricators are occupations for which communication in English is likely less central to job function and where LEP workers are earning somewhat higher annual wages (with median annual wages between $23,185 and $25,438).

 There are also many LEP workers in some first-line supervisor positions, which tend to be better paid. Median annual wages are between $42,155 and $49,056 for first-line supervisors of production and operating workers, office and administrative support workers, and nonretail sales positions (see table a.2). Though these positions are less common among the foreign born

14 UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND

than among the native born, they may offer an opportunity for immigrants’ linguistic and cultural skills to be an asset in a multilingual and diverse work setting where other employees speak a foreign language.

There are some additional well-paying jobs (for the native born) that immigrants are not attaining at comparable rates, which seem like they could offer paths to better wages. Positions like first-line supervisors of construction (where native-born workers earn a median annual wage of $55,063) or electricians (native-born median annual wage of $46,553) are potential jobs for immigrant workers engaged in construction and maintenance fields who could attain supervisory positions with some upskilling in English, career readiness, or technical training.

These statistics describe the immigrant workforce at the national scale. For information on the immigrant workforce at the metropolitan level for the 100 largest metropolitan areas, see appendix tables a.4 and a.5. Table a.4 provides statistics on the foreign-born workforce at all skill levels, and table a.5 provides more detailed information on only those who are currently in lower- and middle-skilled jobs. These statistics provide a quick overview of the local immigrant workforce to inform local decisionmakers.

This demographic profile simply describes where immigrants are currently employed. We do not explore the reasons for these outcomes, although we offer some potential explanations that require further research. Many issues influence these occupation and wage features of the immigrant workforce, including employer hiring, employment practices, and institutional and policy conditions in addition to worker characteristics and experiences. Employers’ hiring and training practices, occupational licensing rules and foreign credential recognition, workers’ social networks and access to jobs, patterns of educational attainment, and rates of limited English proficiency all shape these outcomes. These inputs, immigrants’ barriers to entry into occupations, as well as immigrants’ assets in certain sectors and occupations should be taken into account and inform strategies to develop a stronger immigrant middle-skill workforce.

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TABLE 3 Top Lower- and Middle-Skilled Occupations for Foreign-Born and Native-Born Workers

Foreign-Born Workers Native-Born Workers Median Median annual annual Rank Occupation LEP (%) wage Share Occupation wage Share 1 Maids and housekeeping cleanersa 79.5 $16,018 4.3% Secretaries and administrative assistants $30,562 3.7% 2 Cooks 76.3 $18,000 4.0% Retail salespersons $17,807 3.5% 3 Janitors and building cleaners 73.3 $20,000 3.7% Driver/sales workers and truck drivers $36,131 3.3% 4 Construction laborers 76.5 $24,028 3.4% Cashiers $10,175 3.2% 5 Driver/sales workers and truck drivers 59.2 $31,616 3.1% First-line supervisors of retail sales workers $36,000 3.2% 6 Cashiers 52.5 $14,333 3.0% Customer service representatives $25,029 2.8% 7 Nursing, psychiatric, and home health aides 42.8 $22,900 2.7% Waiters and waitresses $12,210 2.2% 8 Grounds maintenance workersa 80.8 $19,098 2.7% Laborers and freight, stock, and material movers, hand $21,266 2.2% 9 Retail salespersons 38.7 $20,000 2.4% Nursing, psychiatric, and home health aides $20,023 2.1% 10 Miscellaneous agricultural workersa 87.9 $17,298 2.3% Janitors and building cleaners $20,351 2.1% 11 First-line supervisors of retail sales workers 35.7 $35,614 2.2% Cooks $12,904 1.8% 12 Waiters and waitresses 52.4 $16,517 2.1% Stock clerks and order fillers $18,112 1.6% 13 Carpenters 71.7 $25,438 1.9% Sales representatives, wholesale and manufacturinga $60,000 1.5% 14 Laborers and freight, stock, and material movers, hand 67.5 $21,525 1.9% First-line supervisors of office and admin. support workersa $44,390 1.5% 15 Personal care aides 56.2 $15,263 1.6% Bookkeeping, accounting, and auditing clerksa $32,037 1.3% 16 Customer service representatives 31.9 $25,029 1.6% Office clerks, generala $26,347 1.3% 17 Other production workers 71.9 $24,982 1.5% Construction laborers $28,491 1.3% 18 Secretaries and administrative assistants 25.1 $30,969 1.5% Receptionists and information clerksa $20,824 1.3% 19 Child care workers 57.0 $13,215 1.4% Child care workers $11,193 1.2% 20 Painters, construction and maintenancea 75.9 $23,185 1.4% First-line supervisors of nonretail salesa $60,000 1.2% 21 Food preparation workersa 72.8 $15,365 1.3% Other production workers $30,526 1.1% 22 Stock clerks and order fillers 57.9 $20,646 1.2% Personal care aides $15,000 1.1% 23 Miscellaneous assemblers and fabricatorsa 71.0 $24,000 1.2% Teacher assistantsa $15,808 1.1% 24 Food service managersa 44.7 $35,614 1.2% Security guards and gaming surveillance officersa $25,029 1.0% 25 Miscellaneous personal appearance workersa 75.1 $15,808 1.1% Carpenters $31,747 1.0% All occupations 57.3 $24,000 100.0% All occupations $28,032 100.0% Lower-skilled occupations Middle-skilled occupations Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Notes: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration. The total weighted foreign-born population meeting these conditions and employed in a lower- or middle-skilled occupation is 17,597,817; the total weighted native-born population meeting these conditions and employed in a lower- or middle-skilled occupation is 78,031,181. Median annual wage data are further restricted to those individuals who reported positive wage and salary income during the year before the survey. Individuals are considered limited English proficient if they report not speaking English, speaking English but not well, or speaking English well. LEP = limited English proficient. a These occupations appear in the top 25 for one group but not the other.

16 UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND

Perspectives from Three Local Workforce Systems

Many immigrants are working in lower-skilled jobs or in middle-skilled jobs that are low paying, and low educational attainment and limited English proficiency limit job and wage opportunities for many individuals. Education, training, and workforce development supports can help workers develop their skills, advance in their jobs, and potentially earn higher wages to support themselves and their families. To address employer demand for skilled workers, local workforce systems and immigrant-serving and training and education providers can help address this skill gap by supporting immigrant upskilling while recognizing the jobs that immigrant workers are filling and the barriers they face to better jobs and higher wages. To better understand strategies to address some of these challenges, we conducted qualitative data collection during site visits to Dallas, Miami, and Seattle. Although the three cities differ in local development trends and experiences of the immigrant workforce, they share many challenges immigrant workers in lower- and middle-skilled jobs face and have organizations that have led similar efforts to facilitate immigrant training and advancement.

Our conversations touched on issues that affect a range of working immigrant populations, from the recently arrived to long-term residents, with a focus on those with lower- or middle-skilled jobs with low wages and limited mobility. This includes the foreign born in general and in some cases specific subpopulations with unique circumstances, like resettled refugees, who are provided government assistance at the time of their arrival in the US to support them in quickly finding employment and transitioning to host communities.

Barriers to Education and Training and Better Jobs

We begin with the perspectives service providers and stakeholders shared on the challenges that immigrants face preventing them from pursuing education or training or succeeding in those efforts. The barriers we discuss also stand in the way of better-paid lower- or middle-skilled jobs.

LIMITED ENGLISH PROFICIENCY AND LANGUAGE LEARNING

The statistics in table 3 suggest, service providers shared that limited English proficiency is a critical barrier that many immigrants face to higher-paid work and career advancement. Although finding initial employment in lower-skilled jobs is often not a challenge for their clients—these are all places with high labor demand—their jobs are limited in wage and quality and often do not provide opportunities for

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 17 advancement. Limited English also makes workers vulnerable to abuse in the workplace. One service provider explained,

Even though they can get jobs without speaking English, they still come back to me and say, “My employer makes me work 80 hours a week,” or “they don’t let me take lunch breaks,” or “if my kid is sick they threaten to fire me and I can’t take off.” Just that inability to speak English means that they have no other options and just take all this abuse.

Immigrants in lower- and middle-skilled positions who have limited English proficiency may not be able to access better paying positions or they make lower wages than their native-born counterparts. Workers with limited English proficiency may be less likely to be promoted out of lower-skilled jobs; several service providers described clients who had worked as janitors or in grocery stores, worked steadily and loyally, but found that their language ability made it unlikely that they would advance to managerial roles.

Though English language training is key for addressing this barrier, demand for ESL is larger than the supply and resources available in the cities we visited.9 We heard from service providers that there were ESL courses available in a range of settings, from community-based organizations (CBOs) to faith- based organizations to community colleges and public schools, but that further work is needed to scale up and improve access to meet the huge ESL need. Discussing the findings of a recent needs assessment that her organization conducted, a service provider said, “A lot [of survey respondents] expressed language as a huge barrier, time and again saying we want more ESL classes in the community available and accessible.”

Making progress in English language learning may take a lot of time and need to be customized for different individuals’ backgrounds. Service providers shared that a one-size-fits-all approach will not work and that available ESL instruction can rarely effectively support the wide range of experiences and skill sets that immigrants arrive with and accommodate how those backgrounds affect their language- learning experience. One service provider reflected on this common trap, saying,

I think oftentimes programs lump [immigrants] together. That looks very different depending on your background. There are immigrants who don’t have English as their first language but have a master’s degree and skilled background in their profession, and there are those who attended zero or two years of school and were a farmer. So, it’s not the same sort of issue with different people, and they’re often lumped together as “it’s all immigrants” or “it’s all English language” instead of addressing language and literacy separately.

Several service providers observed that organizations outside of the ESL field—employers as well as policymakers—may have an unrealistic sense of how quickly English language training and improvement can be accomplished. Service providers noted that one well-known program in Seattle

18 UPSKILLING THE IMMIG RAN T WORKF ORCE TO MEET EMPLOYE R DEM A N D assumed that individuals at basic levels of English could learn English, incorporate work-related content, and leave fully equipped for employment after only one academic quarter of instruction; the service providers expressed that this ambitious timeline does not align with the realities of ESL instruction and the pace at which individuals make progress in English language acquisition.10 Another expressed the importance of setting reasonable expectations for training timelines, especially with the immigrant population:

I think personally allowing time, understanding that it’s not just take a class and [they’re] ready to go.…For those [who] don’t have a long history of education or any education, or other barriers that are in the way, it’s not that simple of just “take this class and you’re ready,” so I think just acknowledging multiple pathways and different lengths of time [is important].

RECOGNITION OF FOREIGN CREDENTIALS AND HOME-COUNTRY JOB EXPERIENCE Immigrants who arrive in the US with job experience and foreign-earned educational credentials face barriers in finding employment in their previous profession. Although most mentions of credentials barriers focused on high-skilled jobs, this is also a relevant issue for middle-skilled positions, where work experience or certificates earned in immigrants’ home countries are not likely to be transferred smoothly into the US labor market or education system. We heard from service providers that immigrant workers face confusing credentialing systems, high fees for credential recognition or requirements for additional courses, and other barriers to accessing the education or training they may need to supplement or adapt their previously earned education to the US context. Rather than trying to replicate the position they occupied in their home country, service providers explained that they often offer alternative training and employment paths to immigrants who arrive with particular skill sets and try to place them in lower- or middle-skilled jobs that are more attainable despite the mismatch with their earlier employment and skills. That means that clients with foreign credentials often turn to finding entry-level work in industries that may not align with their background but where they find lower barriers to entry. Several service providers described clients with backgrounds in nursing and marketing, for example, taking service-sector jobs to support their families with hopes of eventually seeking training and returning to their areas of expertise sometime in the future. One service provider shared the story of an immigrant arriving with an agricultural sciences research background who was placed in a lower-skilled position selling tickets at the local arboretum, where she was able to “get her foot in the door” in hopes of eventually moving up into more skilled opportunities. These challenges around transferring skills and credentials to the US job market are major barriers for immigrant workers across a wide range of occupations.

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In addition to barriers specific to the immigrant population, we also learned in the site visits about fundamental challenges that affect all low-income workers (including immigrants), which we discuss in the next sections. These challenges may be accentuated by immigrant-specific barriers, but they are common barriers for all low-income working individuals who might be part of middle-skill workforce strategies.

LOW DIGITAL LITERACY AND BASIC SKILLS Many service providers emphasized a commonly overlooked area that is critical to education and training: low digital literacy and low basic skills, including native language literacy. Many service providers described how many of their immigrant clients require assistance using the computer and training in general computer fundamentals. This is important both for designing training and for understanding barriers to job opportunities or advancement. Difficulty using computers limits workers’ access to an increasingly wide array of jobs. As one service provider told us, many occupations that were previously tech-free, like janitorial work, now require the use of technology for such basic tasks as checking room assignments and filling out timecards. The same interviewee described seeing many employers, especially hospitals, introducing new digital systems to wider groups of employees without realizing that a large segment of their workforce might not be digitally literate. The need for digital literacy is growing, but employers may not be prepared to help or hire employees who require assistance. Even when digital literacy problems are recognized, employers may not realize that underlying them are more fundamental basic skills gaps and low native language literacy, which must also be considered in developing effective training. This service provider, who has worked with local hospitals to design curricula to address literacy issues, explained,

There’s the assumption that you just need to teach digital skills and not understanding that the reason they don’t have them or can’t learn them easily is because they lack basic literacy skills. They can’t just plug in, log on, and read the directions—it’s not just about the computer but about other skills building up to that.

Outside of the workplace, low digital literacy can also keep individuals from being able to benefit from workforce services and tools developed to assist them. Service providers observed that new online tools, like cost of living calculators, online American Job Center (formerly One-Stop Center) accounts, and online applications for training and jobs, can unintentionally exclude immigrant and other populations who are not well-equipped to handle online activities. One provider explained that on top of this challenge for the target populations, the tools can create significant burden on staff to support individuals who are struggling to interact with online tools and websites. She told us about a new website that requires clients to set up an online profile to determine their eligibility and complete

20 UPSKILLING THE IMMIG RAN T WORKF ORCE TO MEET EMPLOYE R DEM A N D paperwork for receiving WIOA-funded services, sharing, “It might be easy if you speak English and are computer savvy. If not, it can take a long time, and if you don’t use email or use logins it can take a long time and take up a whole lot of staff time.”

THE BIG THREE: HIGH HOUSING COSTS, TRANSPORTATION, AND CHILD CARE As discussed, many immigrant workers are earning low wages. In our site visits, service providers shared that many immigrants are working long hours to support themselves and their families in expensive local contexts. We heard about how low wages and high housing costs drive many immigrants to work multiple jobs, leaving little to no available time to invest in raising their future wages through participation in education and training. When we asked what the greatest challenges were to participation, similar to challenges native-born workers face, we heard that workers have difficulty accessing convenient and affordable housing, transportation, and child care. Service providers and stakeholders consistently cited these three areas as fundamental challenges, reflecting on local trends in economic development and gaps in public transportation that have made life even more expensive and inconvenient for many immigrant workers. Interviewees emphasized Seattle’s unprecedented development boom through which rising housing costs have displaced immigrant workers into adjacent suburban communities. Though the tech economy and high-skilled jobs are more visible there, the immigrant lower- and middle-skilled workforce is also top of mind for local organizations. We heard in Miami’s deeply immigrant-defined community and local economy, where Spanish is ubiquitous and immigrants make up half of all residents, that there are challenges in supporting the evolving immigrant population there; this “gateway city” receives many new arrivals to the US, whose flows vary, and is also home to diverse longer-term residents. Interviewees shared that rising housing costs have imposed additional pressures recently. In Dallas, rapid growth led to increased demand for construction and service jobs, but even in this relatively low-cost housing market, service providers said wages are not keeping up with housing prices.

Transportation was a common barrier discussed; interviewees spoke of inadequate public transportation systems in their cities, and long travel times and walking distances for potential training participants trying to combine work and training. In Seattle, several providers and stakeholders reported that as rising housing prices have pushed low-income residents out of the city, public transportation has become an even greater challenge for residents who face hours-long commutes. Many immigrant workers do not have access to a car, and though some programs offer refurbished cars to low-income individuals, one service provider described a long waiting list for this service. Even with a car, in spread-out cities like Dallas, commutes take a long time, especially to workplaces in the high- demand trade and logistics industry where companies are often located outside of downtown or far

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 21 from affordable housing. These challenges make working more time-consuming and leave less time available to pursue training.

Last, finding child care is a hurdle for many, as it is for all working parents who may want to invest in their own upskilling but face the challenge of balancing work and training with family commitments (Adams, Spaulding, and Heller 2015). We heard some examples of how some community organizations, training programs, and even employers are introducing their own child care programs to meet this need, but service providers emphasized that this continues to be a major barrier.

FINANCIAL IMPERATIVE Because of these financial pressures, service providers shared that many immigrants are working in “survival jobs,” which allow them to financially support themselves and their families but are not likely to lead to advancement and leave little time to invest in further training. The challenge of keeping food on the table is a massive barrier to taking time away from work to invest in training. One service provider described the issue as such:

The reality is that people are fighting to put food on the table, pay their rent, and they’re in survival mode, and I think most of the service organizations are responding to that, rightfully so, but it’s keeping the community and a lot of these families in a cyclical dynamic instead of an opportunity to actually move up and build skills with an investment.

The limitations are both finding time to get to and participate in training, and the costs of training itself. One service provider described the financial pressure and trade-offs that push immigrants with different backgrounds into the job market and discourage them from investing in training and education:

An immigrant, an asylee, a refugee—they come to this country and they have to support themselves. There are bills to pay; Miami rents are really high. They need to learn English, because if they do, they’ll get better jobs. But on the other side, they need to work. Sometimes there’s a contradiction between their jobs and their studies, so they have to make a priority, one or the other.

Even when workers choose to begin training courses, they may not complete programs because of the pressure to work or because other barriers get in the way. Most organizations do not have the resources to provide funded courses so that students do not have to pay training costs, nor can they compensate students for their time, which would ease some of the financial burden. Service providers shared that it is often a financial decision for students, who need to decide whether the opportunity cost of time spent in training is worth it. Although students could potentially benefit from additional study, only a minority of immigrant workers find ways to combine work with training and education opportunities that align with their work schedules and other constraints. The pressure to stop training efforts poses a challenge to the type of sustained engagement that is needed to succeed in formal

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English language programs and the work required to learn and advance through multiple ESL levels. One service provider explained that she sometimes sees students dropping out around the second-to- last level of ESL, at which point they have improved their English skills considerably and may expect to be able to find a better job. Continued investment might unlock even better job opportunities or possibilities for promotion, but workers must balance the competing demands of work and their upskilling efforts.

Strategies to Support Immigrant Workers

Our site visits explored what types of services immigrant workers are accessing to further their training, including services funded through public workforce system funds and other sources. Here, we describe perspectives and lessons shared on how organizations are designing programming to suit immigrants’ needs for training and education, and strategies they are using to better reach and support immigrant workers.

APPROACHES TO TRAINING Varying Approaches to ESL. Given how key limited English proficiency is in shaping job opportunities, learning English is an important component of workforce training for many immigrants. English training that integrates workforce content can help immigrants be more successful in their current workplace, avoid mistreatment on the job, attain a better job or move into a different sector, or prepare to enter other academic or vocational training. We heard about different approaches to ESL provision to benefit workers, ranging from the integration of some work or career content into standard ESL classes to fully integrated courses that combine ESL with technical training to the provision of ESL courses onsite at workplaces.

Vocational English incorporates workforce topics into ESL curricula, including job-specific vocabulary, employability skills, and job search and job readiness skills like preparing applications, writing résumés, and participating in mock job interviews. Workforce-oriented English courses can be sector specific, or they can be more general and provide conversational English and workplace “dos and don’ts” to help students leave more prepared for the workplace environment. Many service providers said that this is more effective and efficient than classic ESL training since students can apply what they learn to their workplace immediately and it is more relevant to their lives than English content divorced from their experience. Many service providers also suggested that it is impractical to focus on ESL alone before any workforce content is presented. One interviewee noted that this is a major gap for services targeted for immigrants, explaining,

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The idea of assuming that immigrants have to learn the language before they get into vocational skills program is crazy, it’d take years to master the language then to enroll in a program. By then, you’re in debt, you have so many issues.…And I make that comment because I know there are best practices across the nation but not enough for the clients we serve in programs that integrate communication and vocational skills. In our community, I don’t know any.

Integrated Education and Training approaches combine vocational training with integrated basic skills training. We learned about successful examples of this approach used through an Integrated Basic Education Skills Training (I-BEST) model in a community college setting, where immigrants with higher level English (ESL level 4 or more) could participate in integrated programs. One community college system offers an array of I-BEST courses in different occupations and sectors, which targets both immigrants needing ESL support and native-born individuals with basic skills gaps. Topics include early childhood education, business technology management, accounting, phlebotomy, automotive maintenance and repair, and welding fabrication. The I-BEST model allows students to start a college professional technical program and still work on basic skills like ESL. Students take a classic vocational course along with native speakers, and the course is co-taught by a content and a basic skills instructor, so that LEP students receive content instruction and English support at the same time.

Offering training at workplaces is another promising strategy for making training more accessible to immigrant workers (Burt and Mathews-Aydinli 2007; Enchautegui 2015). We heard about several examples of vocational ESL classes provided onsite at a workplace, which may help workers avoid some of the scheduling, transportation, and cost barriers described above. Some employers further aid workers by arranging shift schedules to be compatible with course times, providing workers with paid release time to attend training, or creating hybrid (online and in-person) offerings. We heard about several examples of worksite ESL offered through partnerships across organizations. One community college is growing its effort to partner with employers to provide ESL onsite at the workplace; it has customized curriculum to provide ESL for employees of a grocery store chain, a hospital, and other local employers. Another college is providing a worksite-based ESL program with a local grocery store chain, which is part of a larger national effort. Another multisite national program offers an onsite English model with partner employers that integrates digital literacy and community organizing principles.

In addition to ESL, we learned about some workplaces experimenting with technical training courses customized for their immigrant employees: these classes develop job skills in a classroom environment that might have smaller enrollment, move more slowly, or be otherwise tailored to fit the needs of immigrant workers with limited English proficiency. One service provider described partnering with a local hospital to provide support around a cleaning skills certification test for its LEP employees. Hospital employees who complete the training receive a pay raise, but, without assistance, some

24 UPSKILLING THE IMMIG RAN T WORKF ORCE TO MEET EMPLOYE R DEM A N D immigrant workers might not pass the test because of gaps in English, literacy, or digital skills. Her organization is providing hospital staff with a “train the trainers” course to raise their capacity to help immigrant employees succeed. We also learned about a large programming effort based at an airport that provides courses to airport employees, many of whom are immigrants; courses are taught by community college faculty onsite to provide easy access to training for current employees. Implementation of such efforts is complex in the 24-hour work environment. Classes are scheduled during shift changes and altered seasonally to allow maximum access for a wide range of airport employees.

Small Business Training. Business ownership offers an employment path for many immigrants, allowing them an opportunity to apply their skills and, in some cases, leverage their linguistic and cultural knowledge to serve a diverse public. Immigrants are more likely than native-born individuals to own businesses, and immigrant-run small businesses animate many neighborhoods and contribute to local development and economic activity (Dyssegaard Kallick 2015). We heard this story loud and clear in Miami, where stakeholders told us that immigrant entrepreneurs are “the backbone of the local economy,” playing a critical role in creating and running local businesses. As one stakeholder described it, many immigrants will at least supplement their incomes through small business, initially in an informal capacity. But over time, training programs that equip entrepreneurs with business knowledge can support small business strategies by helping immigrants formalize and take fuller advantage of their entrepreneurial activities and advance their careers. One Seattle service provider has developed a business opportunity center, which provides aspiring entrepreneurs with retail space, carts, tables, access to a commissary kitchen, business education, and one-on-one technical assistance. Although small business ownership can offer a particularly important path for immigrant workers given trends in entrepreneurship, entrepreneurship training is rarely well integrated into workforce development services, which focus on filling the needs of existing employers.

SOLUTIONS TO INCREASE IMMIGRANT ACCESS Training Supports. Many service providers talked about how supportive services that address some of the challenges described above can increase program participants’ likelihood of success. By alleviating some of the challenges with support for transportation, child care, or other services, wraparound services help encourage participants to enroll and begin training programs, as well as persist and complete training programs as they balance competing demands (Hamaji and González-Rivera 2016). One challenge around providing supportive services is the wide range of needs and assistance that an individual may need. This means that clients often need to find and go to multiple service providers to fill different needs that are standing in the way of their participation. In the cities we visited, we heard

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 25 about a number of “one-stop” approaches where organizations have grouped supportive services together so that they are readily accessible in one physical space and can address the range of supportive services that may be required for a given client. Examples included a community organization that, in addition to workforce supports, addresses the needs of the whole family through parenting classes, counseling and mental health services, and domestic violence and survivor intervention; foreign-language–specific job clubs that facilitate access to medical services; and a service provider that offers employment services alongside a food pantry, family violence services, mental health services, and children’s services. Providing services in one place has the benefit of convenience— participants do not have to wade through multiple organizations’ processes or travel from organization to organization to access different services. We also heard about organizations partnering with other organizations but not necessarily providing a “one-stop” approach where all services are offered in the same place. One example is a workforce and financial training provider partnering with a veteran’s organization for bus passes, a food pantry for food assistance, and a refugee resettlement affiliate organization for translation services. Another service provider described partnering with a local nonprofit organization to support their ESL students on financial and legal assistance; the service provider suggested that, in their case, they had decided that adding those support services to their ESL program was inefficient and that it made more sense to partner with an outside organization to address students’ support needs. She described how her organization had realized that collaboration with other organizations was a better strategy than pivoting toward providing additional services:

A few years ago, [we considered supplementing our ESL services with other programs], but it wasn’t our area of expertise. It watered down our three primary programs and kept pulling our attention from those. So finally, as an organization, we decided not to do all these areas since other organizations are already doing it. [We decided it was best to] let [other organizations] do it well and we can partner.

Inclusive Staffing. To make immigrants feel welcome and provide the support they need to succeed in training, we heard again and again that organizational staff should be multilingual, culturally competent, and available to work one-on-one with participants. Having multilingual staff allows organizations to be accessible to clients with a variety of language backgrounds. As clients advance on their training path, multilingual staff can support and retain clients by offering courses and trainings in multiple languages (like Spanish-language GED), or provide services in more than one language (like job circles in different languages). One service provider explained the advantage of bilingual staff:

Having bilingual staff ability allows us more flexibility in being able to work with people where they’re at. We have a range of language capability that makes us able to work with more people right then and there, if they need to make progress on their English proficiency before their next step in their career plans.

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Service providers also emphasized that organizational staff needs to be culturally competent to effectively serve immigrants. This means being sensitive to specific preferences communities may have around food and religious observance, child care, and other issues. To provide this cultural competence and encourage immigrant participation, several service providers talked about how important it is to have immigrants themselves in service roles. One service provider said how valuable it is for immigrant students to see other immigrants in staff roles who can be role models for their peers. Stakeholders emphasized their multilingual staff and expressed the importance of having staff reflect the demographics of clients, so that “people…see themselves when they come in our centers.”

In addition to linguistic capacity and cultural competence, staff said it is important to take time to help clients navigate complex systems like the community college enrollment process. One service provider described the positive impact of having staff walk students through bureaucratic details like entrance requirements, applications, orientations, schedules, course levels, and credit-bearing versus noncredit-bearing classes and explain to students the potential impact of different training options on their future job opportunities. She described how challenging the setting of the community college can be for a new student, with even higher barriers for immigrants who may have even less familiarity with such educational providers:

Anybody—native speaker or immigrant—walks in, and you often get sent to five different places. It can be very frustrating. To be an immigrant on top of that when you already don’t understand the system, language might be a barrier—it just becomes easier to walk back out the door instead of continuing on.

Community Outreach. Many service providers said that community outreach is essential for reaching immigrant communities. This may mean advertising on language-specific media, like Telemundo, and putting out recruitment materials in multiple languages or with simpler English. One service provider advertises technical training programs at school youth fairs, in bus advertisements, on Facebook, TV, and radio, including interviews on Haitian Creole and Spanish stations. Even with all these efforts, this provider still found that their best source of referrals is word of mouth. These outreach strategies may be particularly important for immigrants. One service provider explained how challenging it can be to get newly arrived immigrants to come to unfamiliar places and the crucial issue of accessibility in immigrant-dense neighborhoods:

Especially with this population it’s important to go out to community members, not rely on them coming.…Especially the immigrant population that may have recently arrived, they don’t know what’s available to them when they first get here, so really going out there and doing that specific outreach, letting them know what’s available to them is important.

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This speaks to the importance of another form of community outreach—personally tapping into community networks. Training providers described forming partnerships with other community-based organizations and conducting outreach about new programs to community stakeholders, like church leaders and community elders in immigrant communities. One service provider explained that “with any population, especially an underserved one, there’s a trust issue,” and securing support through trusted organizations is an effective strategy. Another service provider shared the same insight and described how outreach efforts to create one-on-one relationships between the program provider and leaders in the immigrant community were important for building trust and building some buy-in; their efforts to attend a monthly “chat and chew” and engage with “churches and elders in the community” were beginning to pay off for their recruitment. We also heard how important it is to engage intentionally with different communities and customize efforts to align with the vast cultural and linguistic diversity across immigrant groups. One service provider explained how challenging it is for organizations to handle this diversity within the immigrant community:

The communities are so insular; it is so hard to have a generic approach to get into any community. What works great for Somali, Ethiopian, Eritrean [individuals] actually doesn’t even necessarily work between those three, and forget about trying to treat [other national] populations the same.…I see very few efforts that are actually able to create that universal umbrella with differentiated approach to gather that all together and create something that could cut across [immigrant communities].

Trusted Providers. Given the importance of outreach to specific communities and the inability of service providers to always be located in immigrant-dense neighborhoods, a common strategy is leveraging community organizations and providing services at trusted places, such as at immigrant-serving CBOs. Service providers shared that though environments like community colleges or American Job Centers can be intimidating, CBOs, religious organizations, or local public schools are more familiar to immigrant communities. We heard about one example of a workforce development board implementing a “connection site” model, which provides basic workforce services through staff on site at locations, such as CBOs, community colleges, housing authorities, public libraries, and other community sites, that are more accessible or approachable than American Job Centers. This approach responded to changing demographics and feedback from their own staff. Describing the program, one stakeholder discussed this change coming from the client-facing staff, who saw a gap between who they were serving and the demographics of the communities they served:

I think for a long time the public workforce system hasn’t thought about outreach.…The staff came up with community outreach, because they were the ones in the offices realizing there are some folks that aren’t going to walk through the door.

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One service provider discussed a creative partnership that built on the trust that they had developed with their immigrant clients; they worked with a community college, which provided technical training and was “willing to let [clients] start here [onsite at the CBO] for the first class or two to gain confidence and then transition [to the campus].…At least it helps them to start where they feel at home.” Another immigrant service provider made the same point about the importance of trusted providers having better access to the target community and the importance of providing workforce development services through these trusted channels, saying, “[workforce services should] use the CBOs as a point of reference. [Immigrants] come here anyway.”

Collaboration with Other Service Organizations. Service providers shared that coordination between organizations across sectors is important for leveraging resources and making training accessible. They described how coordination helps service providers focus on providing specific services well, avoid overextending staff, and avoid duplication across multiple organizations. One service provider also made the point that a focus on coordination is a common theme in national funding conversations as well as within local provider communities: “They’re all talking about collaboration and avoiding overlapping of services.” Across our three site visits, we saw a variety of collaborations between different types of organizations, with services going both directions to share expertise and add value. We learned, on the one hand, about CBOs staffing community college basic education programs with career navigators and benefits counselors. On the other hand, we learned of community colleges lending CBOs technical advice for new trainings or, as mentioned above, providing their technical and basic skills training on site at CBOs. Service providers and stakeholders also described efforts to share resources, lessons, and best practices between organizations. One service provider described collaboration between agencies with a common funder that has encouraged a small group of related grantees to meet regularly, receive trainings, and share best practices and tools.

KEY ISSUES FOR SERVICE PROVIDER ORGANIZATIONS Service organizations face a number of key issues that limit and enable their efforts to support immigrant education, training, and workforce supports. Many of the issues they discussed are true for all organizations, immigrant specific or not, but here we highlight the perspectives we heard in our interviews.

Funding. Funding is a huge challenge for organizations working to develop and maintain training programs for immigrant workers. We heard a lot from organizations about the pressures of fundraising and appealing to the changing priorities of funders. One service provider said that she had seen a shift toward middle-skill strategies from funders. She noted that in the past her organization had been

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 29 funded to provide basic employment assistance services for entry-level jobs, but recently they were being encouraged to provide sector-specific programs focused on jobs that require some technical training. Another service provider also observed a shift in local funding priorities from basic skills to employment outcomes and articulated that they are now needing to pitch their ESL services in terms of workforce outcomes. Many organizations shared the perspective that in their experience, immigrant- serving organizations are less likely to receive funds from the local workforce development board; they suggested that adult basic education funds tend to go to community colleges rather than immigrant- serving CBOs in their communities. One shared this point on funding:

The biggest challenge is just ongoing funding; so much is coming from workforce development councils to the community colleges.…It’s hard to get a foothold in the adult basic [education] English language world when so much of the funding and airspace is dominated by community colleges.

Similar to other organizations, many of the larger service providers that do receive public workforce system funds described combining or “braiding” multiple sources of funding, which they said allows them to keep their programs stable and more flexibly serve participants. A service provider explained that more sources of funding allow more flexibility, both for the programs and for participants. However, managing braided funds is taxing. The same service provider handles 12 different grants just for the workforce development branch of their services, which requires a great deal of time and energy to manage and budget.

Performance Reporting. Braiding funding sources can exacerbate another common challenge that service providers emphasized—the difficulty of handling complex and contradictory performance reporting requirements around client eligibility, timing of services, and completion. Performance metrics can vary slightly or significantly from grant to grant, making it challenging to effectively combine funding sources. One service provider explained that many funding sources impose rigid program structure and eligibility requirements that limit the participants a program can accept and how they can be served. Though some degree of selectivity makes sense for a program, a service provider suggested that slightly broader eligibility criteria could help them support clients more effectively:

If you have to drive a client through a tiny hole of eligibility, the amount of energy it takes to do that, versus if we had just a little bit more flexibility—some funding sources actually give that.…But for some sources, they’re very rigid about the requirements.

We heard that short time limits are a major challenge. Funding sources often stipulate short windows during which a client can be served or build in incentives to exit clients from training programs quickly. The result is that programs have to keep their caseloads small and cannot provide meaningful services to people no longer on their active caseload. Service providers expressed that this short

30 UPSKILLING THE IMMIG RAN T WORKF ORCE TO MEET EMPLOYE R DEM A N D window of service limits participants’ possibilities for meaningful upskilling or movement into better jobs. As one provider explained, funding that gives programs multiple years to work with people opens the doors to “huge, lifetime transformations” like becoming a registered nurse; more common, short- term programs do not make that possible. Service providers said that this affects immigrant students differently because of challenges of limited English proficiency and some students’ difficulty completing courses on very rapid timelines; although nearly all workforce development efforts face these challenges of showing results on short timelines, and this is not specific to immigrant students. We were told by several service providers, however, that some funding sources incentivize programs not to serve immigrants. One described the link with performance metrics explicitly, pointing out the mismatch between the rapid progress some funding sources demand and the realities of the timeline for English language training:

Because of the absolute requirements on metrics…that motivates high-volume, light-touch work; ESL with immigrant populations is slow, it takes a long time, and so that’s who’s not going to get attention, based on the sheer weight of having small numbers, [but the pressure to show] high- volume results.

WIOA and Local Implementation. When we raised the issue of WIOA, the federal workforce funds legislation, and its impact for serving immigrant clients, interviewees focused on its increased emphasis on employment-related outcomes for basic skills training programs, namely ESL. Integrated models that bring a workforce focus into basic skills training models are not new to many organizations, but the new requirements have encouraged many to adjust their programming. We heard many references to the direct and indirect effects of these changes on community-based literacy providers, community colleges and school districts, and funders of literacy services. In addition to CBOs, we heard many examples of community colleges and K–12 literacy providers experiencing this shift to a workforce focus. One service provider described a technical college that used to have family- and citizenship-oriented “English for the sake of English” offerings but had moved to a model with a focus on job training. Her view was that this was leaving behind immigrants in need:

I think contextualized language learning is important, but there’s a whole community of folks who aren’t being served as a result. I know that previously there was a lot more funding for ESL for citizenship; now a lot of the priority is only to workforce and job placement.

Echoing the changes in funder priorities mentioned above, we heard that the move toward a workforce focus in public-sector funding has had some spillover into private philanthropy; one funder, for example, reframed its adult literacy grantmaking to fall under its “income” grantmaking rather than where it sat previously under its “education” portfolio. A stakeholder confirmed this trend and described their effort

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 31 to encourage and support workforce outcomes from their ESL provider grantees, who in some cases do not agree with the new focus:

We’re trying to figure out how to encourage [literacy provider] grantees to think more in terms of employment outcomes, but some feel that their bread and butter is helping people learn English; they don’t feel that their mission should be limited to equipping clients for good jobs.

We also heard about other impacts of WIOA on coordination around training for immigrant communities. One stakeholder observed that WIOA’s focus on community partnerships can be used to foster community outreach and partnership, which can benefit immigrants if such partnerships include immigrant-serving CBOs and literacy and basic-skills providers. Several interviewees described seeing more partnership in the workforce development community as a result of these aspects of WIOA. This push for partnership was highlighted by one service provider as positive in fostering better coordination between adult basic-skills organizations and workforce development organizations, which for too long have been disconnected:

I think the workforce development system and basic skills system are only really very recently talking to each other. Nationally that’s been a gap. Clearly those two entities should be talking to each other, more of that is happening, probably because of WIOA: it’s required now and there are mandates around that.

We also discussed with service providers how effectively their local workforce development boards and job centers were filling the needs of local LEP and immigrant workers. Service providers and stakeholders shared that though there were some positive measures and, in some cases, organizations with experience serving immigrant populations were receiving local workforce funds, there were still major service gaps in effectively reaching immigrant communities. One stakeholder described struggling to find materials put out by the local workforce board that would be appropriate for the immigrant community. As one service provider put it, her immigrant clients do not have the language or technical skills or the ability to navigate the local job centers and the job opportunities they point clients to:

The [workforce development board] model, based on my understanding at this point, is a model created for individuals who are transitioning from a place of work and will need to know what to do…[where they can learn] which industries are growing and in-demand. The model is not for my typical client who comes in and doesn’t understand a thing and could not navigate the process by themselves.…If we had a client [who] could walk into a [workforce development board] center and navigate that process by themselves, that’s perfect, but I haven’t seen that. It’s language, it’s understanding what’s being offered and being able to assess where they fit in.

Other Policy Issues. A range of other policy areas affect the work of organizations engaged in training for immigrants. We learned of a recent example where one state’s implementation of new occupational

32 UPSKILLING THE IMMIG RAN T WORKF ORCE TO MEET EMPLOYE R DEM A N D license standards for early childhood educators affected immigrant women who provide early childhood education in their homes. Although this initially disrupted many child care providers, advocacy organizations were able to add some flexibility to new requirements, convincing the state legislature to accept a certificate in lieu of a high school diploma as the requirement for early child care workers. This policy change and the work to support child care providers catalyzed new coordination between organizations. In fact, the requirement for the new certificate led to the development of relevant programming to fill in this gap at the community colleges. One service provider noted, “This is a place where we’ve seen really positive partnership with the community colleges—some have started I- BEST programs or different integrated programs specific to this [and have offered some in Somali and Spanish].”

City- and county-level policies, both general and targeted for immigrants, also play a role in shaping immigrant workers’ prospects for training and advancement. Many cities have established local offices devoted to supporting welcoming activities and supporting immigrant communities. Although it was a new development in Dallas, where the Mayor’s Office of Welcoming Communities and Immigrant Affairs had been recently established at the time of our visit, many stakeholders suggested that this type of institution could have a positive impact. Seattle’s parallel office of Immigrant and Refugee Affairs has engaged in research and programming on immigrant workforce topics, alongside other initiatives to promote immigrant integration and well-being in the city. In Seattle, we also heard about the salience of local legislation targeted at the entire low-income population as it applied to immigrant workers. Several stakeholders cited as relevant to the immigrant workforce the recent changes to local labor rights regulations, including the establishment of a $15 minimum wage and investment in local resources to implement new labor rights requirements. This example highlighted the importance of practices and policies that affect the entire workforce and public and how they need to be considered in addition to immigrant-specific measures. Examples gleaned from the site visits suggested that a wide range of different policy levers can affect the immigrant workforce and be used as part of a larger local workforce development strategy.

Lessons for Developing Strong Middle-Skill Workforce Strategies

Immigrants are an important source of local talent who can meet the demands of employers in need of skilled workers. This report suggests that strategies to develop a middle-skilled workforce should consider how to best support immigrants and leverage their unique assets to fill jobs across different

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 33 sectors and occupations. Like native-born workers, many foreign-born workers in lower- and middle- skilled jobs are working hard to support their families, handling high costs of living, and struggling to make ends meet with low-paid work. Given the challenges of balancing life and work on a low income, they have limited opportunity to invest in their own training and enhance their prospects for better paid work and career advancement. At the same time, employers are demanding skilled workers to fill their evolving needs, and in many areas the assets that immigrant workers offer are important for keeping up with demographic change, serving a diverse public, and working in a globalized economy. The demographic profile above showed that a large share of immigrant workers are in lower-skilled jobs and a quarter have middle-skilled jobs, similar to the native-born workers, despite lower educational attainment and limited English proficiency. Immigrants work in different occupations than native-born individuals. Immigrants with limited English proficiency work in many occupations, but their wages and job quality may be lower than for native-born workers, they can be easily exploited, and they have fewer prospects for advancement in many jobs.

Given these challenges, service providers and stakeholders emphasized the role that English learning can play in supporting immigrants’ mobility. They shared that even small improvements and gaining very basic English skills can lead to better experiences on the job and protect workers from workplace abuses. This reinforces the importance of English language training for supporting immigrant training and advancement. Our findings suggest a couple of key features for providing effective English language training to maximize the likelihood of students’ participation and retention, as well as translation to better jobs:

 Customized content: English language training should integrate workforce and career content and avoid a “one size fits all” approach, so that instruction effectively supports students with different educational backgrounds and employment experiences

 Accessible setting: Programs should be as accessible as possible for individuals who have limited time outside of their working hours. Classes can be provided at the worksite through engagement with employers and coordination with education providers or in trusted spaces, like CBOs that are frequented by immigrant communities and by organizations with inclusive staffing that has linguistic and cultural competence relevant to specific immigrant communities

 Coordination across sectors: Coordination between employers, education providers, and community-serving organizations will help ensure that training is accessible and successful for students and addresses any specific barriers that individuals might face and the range of supports needed to complement training coursework and help students be successful

34 UPSKILLING THE IMMIG RAN T WORKF ORCE TO MEET EMPLOYE R DEM A N D

Local workforce development and training efforts should include programming like ESL to address limited English proficiency among some immigrant workers and be sensitive to the variety of experiences across the immigrant community. It is, however, also important that immigrant workers and their key role in local labor markets be recognized in broader decisionmaking and strategy on local workforce development. There is some tension between the idea of developing customized immigrant- specific initiatives and “mainstreaming” immigrants or LEP individuals into services and conversations about public services and well-being.11 The experiences of immigrant-serving organizations and the context that immigrants face are naturally similar to the experiences of organizations serving the general public, and native-born workers. Immigrants contend with all the same barriers other workers face, like challenges of access, affordability, and balance with life demands. In addition, they face issues like limited English proficiency, recognition of their previous experience and educational background earned in their home countries, and social networks that limit their knowledge of or access to certain job or education opportunities. Local systems and organizations need to balance the specific needs of different immigrant groups with the universal needs of low-income groups as well as other key populations of concern.

It is important that communities acknowledge and address the often-overlooked immigrant workforce that sustains their local economies and make sure that immigrants and LEP workers are a part of local economic development and workforce development strategies and conversations. There is still too much division between immigrant-serving organizations and ESL providers, the workforce development board and community colleges, and employers who are relying on immigrant talent. All actors would benefit from bridging that gap and coming together to engage immigrants and their employers in workforce development strategies and foster immigrant upskilling. Awareness of how services can be modified or made more accessible to immigrant workers is an area that requires further attention and investment and should engage the employers who are relying on immigrants in a wide range of positions. Immigrant-serving organizations need to be at the table with other key organizations in the workforce and economic development field, helping to inform and make relevant to their communities the range of policy developments in workforce, education, and other fields that affect their constituents, both good and bad.

Investing in immigrant workers and their upskilling is important for a range of decisionmakers across sectors, who may or may not have immigrant and LEP issues on their radar.

• State and local policymakers making policy and funding decisions can be aware of and sensitive to the key role that immigrant workers are playing in their communities, pushing forward workforce and education issues. Recognizing the contributions of the immigrant community is

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 35

critical, especially at this time of restrictive immigration policies and toxic national rhetoric. Policymakers can put more resources into English language learning and encourage organizations to serve immigrants and LEP individuals, track information about programs to assess gaps in services, and actively engage immigrant-serving organizations to ensure policy is grounded in community needs.

• Organizations providing workforce development services and education and training can work to improve program accessibility and design to serve immigrant community members. Local workforce development boards, community colleges, and school districts, whose mission is to serve the community, can assess the extent to which they are funding immigrant-serving organizations or serving immigrant and LEP communities. They can work to improve the linguistic and cultural competence and accessibility of programs by hiring multilingual staff and partnering with immigrant-serving organizations to fill in gaps in visibility, language access, and cultural knowledge. Organizations should connect with immigrant-serving organizations to leverage WIOA’s opportunity for pressing forward the needs of LEP individuals as a priority of service population, as well as youth in immigrant families.

• Funders interested in supporting equity and economic mobility can consider incorporating immigrant populations and English language learning into their funding priorities. They can work to ensure that these issues are reflected in their awarding of grants—that local grantees are reaching this population through direct services or incorporating an immigrant-inclusive lens in systems-level work. This might mean encouraging collaboration across sectors like connecting immigrant-serving ESL providers to local workforce development or economic development organizations or employers, or conducting needs assessments to inform future investment.

• Employers who want to recruit and retain workers can invest in improving job quality by recognizing barriers like child care and transportation and providing supports or services to help current employees. They can also invest in upskilling their employees through onsite training to foster English language learning and promote advancement. And they can collaborate with immigrant-serving organizations and other training providers to ensure that training programs are informed by current industry need.

This issue is even more important in our current political climate, when immigrant communities are subject to harsh political rhetoric and communities are suffering the consequences of heightened immigration enforcement and slowed immigration and citizenship processing. Uncertainty around Deferred Action for Childhood Arrivals (also known as DACA), temporary protected status, interior and

36 UPSKILLING THE IMMIG RAN T WORKF ORCE TO MEET EMPLOYE R DEM A N D border enforcement, the immigration court system, and disincentives to receive essential public assistance because of public charge regulation developments are among the gamut of immigration policies that are causing uncertainty and hardship for communities across the US. With this onslaught of federal policy change, policies and practices at the state and local level are becoming even more crucial to protecting and supporting the immigrant workers and families who are part of neighborhoods, work places, schools, and communities across the country. States and local areas that are taking action to support their immigrant residents need to keep training and education on their agenda as they develop proactive policies and continue to support workers who are playing essential roles in our economy but are often not able to access the training that would benefit both them and employers.

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 37

Appendix. Assigning Skill Requirements to American Community Survey Occupation Codes

As described above, we used Bureau of Labor Statistics (BLS) occupation skill information to assign lower-, middle-, and high-skilled categories to each of the occupation codes in the American Community Survey (ACS). We chose to use BLS occupation skill information over comparable ONET data because of the closer alignment of ACS codes to BLS codes and following the precedent set by Capps, Fix, and Yi-Ying Lin (2010).

Following Capps, Fix, and Yi-Ying Lin (2010), we categorized each Standard Occupational Classification (SOC) code in the BLS data with a skill requirement level (either lower-, middle-, or high-skilled). First, occupations were labeled lower skilled if short- or moderate-term on-the-job training is typically needed to attain competency in that occupation. Occupations were then labeled middle skilled if they typically require long-term on-the-job training or an apprenticeship to achieve competency, if they require any work experience in a related occupation (“less than five years” or “five years or more,” but not “none”), or if they typically require a postsecondary nondegree award or associate’s degree for entry. Next, occupations were labeled high skilled if they require a bachelor’s, master’s, doctoral, or professional degree. Finally, jobs that were not captured by any of these criteria (i.e., were not associated with any of the listed training, experience, or education requirements) were labeled lower skilled.

Having assigned a skill level to each SOC code in the BLS data, we then used an ACS/BLS crosswalk to match each ACS code with an SOC code and corresponding skill level assignment.12 Because the SOC occupation codes are finer grained and more numerous than the ACS occupation codes, some ACS codes matched with multiple SOC codes. In these “1 to many” merge cases, we took the average of the skill levels assigned to each matched SOC code, weighted by the number of individuals employed in each SOC code, and rounded the result. We then assigned the resulting skill level to the ACS occupation code in question.

38 UPSKILLING THE IMMIG RAN T WORKF ORCE TO MEET EMPLOYE R DEM A N D

TABLE A.1 Workforce Statistics of Metropolitan Areas Chosen for Site Visits

Dallas-Fort Worth- Miami-Fort Lauderdale- Seattle-Tacoma- Arlington West Palm Beach Bellevue Unemployment rate 3.2% 4.0% 4.3% Population in the civilian labor force 3,333,906 2,813,144 1,857,302 Foreign-born workers 26.4% 51.7% 20.5% Foreign-born individuals Region of birth Latin America 62.1% 86.3% 20.0% Asia 25.7% 5.3% 50.7% Europe 4.4% 5.8% 16.1% Africa 6.2% 0.9% 7.2% 1.2% 1.6% 4.1% 0.3% 0.1% 1.8% Foreign-born employed 744,558 1,211,939 360,931 Educational attainment College or advanced degree 25.3% 28.4% 41.1% Some college 16.9% 27.3% 24.7% High school only 20.7% 29.6% 18.7% Less than high school 37.2% 14.7% 15.5% Occupation High-skilled 21.5% 22.0% 34.1% Middle-skilled 26.7% 27.3% 22.1% Lower-skilled 51.9% 50.7% 43.8% Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Unemployment rates in November 2017 from Bureau of Labor Statistics; national average of 4.1 percent. Notes: Data refer to individuals ages 18–64 and in the civilian labor force. “Employed” individuals are those employed during the week before the survey administration. Median annual wage data are further restricted to those individuals who reported positive wage and salary income during the year before the survey.

UPSKILLING THE IMMIG RANT WORKFORCE TO ME ET EMPLOYER DEMAND 39

TABLE A.2 Top Middle-Skilled Occupations for Foreign-Born and Native-Born Workers

Foreign-Born Workers Native-Born Workers Median Median annual annual Rank Occupation LEP (%) wage Share Occupation wage Share 1 Cooks 76.3 $18,000 12.1% Driver/sales workers and truck drivers $36,131 9.1% 2 Driver/sales workers and truck drivers 59.2 $31,616 9.4% First-line supervisors of retail sales workers $36,000 8.9% 3 Nursing, psychiatric, and home health aides 42.8 $22,900 8.3% Nursing, psychiatric, and home health aides $20,023 5.9% 4 First-line supervisors of retail sales workers 35.7 $35,614 6.8% Cooks $12,904 5.1% First-line supervisors of office and administrative support 5 Carpenters 71.7 $25,438 5.7% workers $44,390 4.1% 6 Food service managers 44.7 $35,614 3.6% First-line supervisors of nonretail sales workers $60,000 3.3% 7 Miscellaneous personal appearance workersa 75.1 $15,808 3.2% Carpenters $31,747 2.8% 8 Automotive service technicians and mechanics 59.5 $29,508 2.9% First-line supervisors of production and operating workers $51,640 2.6% 9 First-line supervisors of non-retail sales workers 35.7 $49,056 2.8% Food service managers $31,616 2.5% 10 Chefs and head cooksa 65.9 $25,000 2.4% Hairdressers, hairstylists, and cosmetologists $18,316 2.4% First-line supervisors of office and administrative support 11 workers 25.2 $42,736 2.3% Automotive service technicians and mechanics $35,000 2.3% First-line supervisors of construction trades and 12 Hairdressers, hairstylists, and cosmetologists 54.1 $17,343 2.3% extraction workers $55,063 2.3% First-line supervisors of production and operating 13 workers 50.8 $42,155 2.1% Electricians $46,553 2.2% First-line supervisors of construction trades and 14 extraction workers 49.6 $47,054 1.7% Licensed practical and licensed vocational nurses $34,039 2.2% 15 Licensed practical and licensed vocational nurses 27.2 $38,000 1.7% Preschool and kindergarten teachersa $19,820 1.8% 16 Electricians 51.8 $36,131 1.6% Farmers, ranchers, and other agricultural managersa $36,631 1.7% First-line supervisors of food preparation and serving 17 Pipelayers, plumbers, pipefitters, and steamfitters 63.3 $31,616 1.5% workers $20,023 1.6% First-line supervisors of food preparation and serving Property, real estate, and community association 18 workers 44.7 $24,428 1.4% managersa $45,052 1.5% 19 Maintenance and repair workers, general 58.3 $34,066 1.4% Maintenance and repair workers, general $40,047 1.5% 20 Bakersa 72.5 $20,023 1.2% Pipelayers, plumbers, pipefitters, and steamfitters $42,155 1.5%

All occupations 52.3 $28,905 100.0% All occupations $36,041 100.0% Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Notes: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration. The total weighted foreign-born population meeting these conditions and employed in a middle-skilled occupation is 5,783,853; the total weighted native-born population meeting these conditions and employed in a middle-skilled occupation is 27,860,273. Median wage data are further restricted to those individuals who reported positive wage and salary income during the year before the survey. Individuals are considered limited English proficient if they report not speaking English, speaking English but not well, or speaking English well. LEP = limited English proficient. a This occupations appears in the top 20 for one group but not the other.

40 APPENDIX

TABLE A.3 Top Lower- and Middle-Skilled Occupations for Foreign-Born Workers Sorted by rate of limited English proficiency Median annual Rank Occupation LEP (%) wage Share 10 Miscellaneous agricultural workersa 87.9 $17,298 2.3% 8 Grounds maintenance workersa 80.8 $19,098 2.7% 1 Maids and housekeeping cleanersa 79.5 $16,018 4.3% 4 Construction laborers 76.5 $24,028 3.4% 2 Cooks 76.3 $18,000 4.0% 20 Painters, construction, and maintenancea 75.9 $23,185 1.4% 25 Miscellaneous personal appearance workersa 75.1 $15,808 1.1% 3 Janitors and building cleaners 73.3 $20,000 3.7% 21 Food preparation workersa 72.8 $15,365 1.3% 17 Other production workers 71.9 $24,982 1.5% 13 Carpenters 71.7 $25,438 1.9% 23 Miscellaneous assemblers and fabricatorsa 71.0 $24,000 1.2% 14 Laborers and freight, stock, and material movers, hand 67.5 $21,525 1.9% 5 Driver/sales workers and truck drivers 59.2 $31,616 3.1% 22 Stock clerks and order fillers 57.9 $20,646 1.2% 19 Childcare workers 57.0 $13,215 1.4% 15 Personal care aides 56.2 $15,263 1.6% 6 Cashiers 52.5 $14,333 3.0% 12 Waiters and waitresses 52.4 $16,517 2.1% 24 Food service managersa 44.7 $35,614 1.2% 7 Nursing, psychiatric, and home health aides 42.8 $22,900 2.7% 9 Retail Salespersons 38.7 $20,000 2.4% 11 First-line supervisors of retail sales workers 35.7 $35,614 2.2% 16 Customer service representatives 31.9 $25,029 1.6% 18 Secretaries and administrative assistants 25.1 $30,969 1.5% All occupations 57.3 $24,000 100.0% Lower-skilled occupations Middle-skilled occupations Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Notes: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration. Median wage data are further restricted to those individuals who reported positive wage and salary income during the year before the survey. Individuals are considered limited English proficient if they report not speaking English, speaking English but not well, or speaking English well. LEP = limited English proficient. a This occupations appears in the top 25 for one group but not the other.

APPEN DIX 41

TABLE A.4 Characteristics of All Employed Foreign-Born Workers by Metropolitan Area Occupation Skill-Level Requirements Educational Attainment College Less or N of all Foreign- Lower- Middle- High- than HS Some advanced Metropolitan statistical area workers born skilled skilled skilled HS only college degree National 137,074,610 17% 50% 25% 25% 26% 23% 21% 31% Akron, OH 317,769 5% 39% 19% 41% 11% 18% 18% 53% Albany-Schenectady-Troy, NY 397,710 8% 38% 23% 40% 12% 18% 22% 48% Albuquerque, NM 369,721 13% 49% 31% 20% 33% 27% 19% 22% Allentown-Bethlehem-Easton, PA-NJ 371,433 10% 52% 22% 26% 19% 25% 24% 32% Atlanta-Sandy Springs-Roswell, GA 2,460,430 18% 47% 25% 28% 24% 22% 20% 34% Austin-Round Rock, TX 947,700 18% 48% 24% 27% 29% 22% 17% 32% Bakersfield, CA 314,354 30% 65% 25% 10% 49% 24% 16% 11% Baltimore-Columbia-Towson, MD 1,263,768 13% 37% 25% 39% 13% 19% 20% 48% Baton Rouge, LA 365,312 5% 40% 28% 32% 22% 23% 17% 38% Birmingham-Hoover, AL 468,932 6% 46% 30% 24% 28% 26% 18% 28% Boise City, ID 286,041 9% 59% 19% 21% 32% 26% 18% 24% Boston-Cambridge-Newton, MA-NH 2,328,957 21% 43% 23% 35% 15% 23% 20% 41% Bridgeport-Stamford-Norwalk, CT 428,098 28% 48% 24% 28% 20% 25% 20% 35% Buffalo-Cheektowaga-Niagara Falls, NY 504,482 6% 44% 18% 38% 13% 19% 23% 45% Cape Coral-Fort Myers, FL 241,097 22% 59% 26% 15% 29% 30% 23% 18% Charleston-North Charleston, SC 319,806 7% 45% 30% 25% 26% 24% 22% 29% Charlotte-Concord-Gastonia, NC-SC 1,055,154 13% 51% 24% 25% 28% 22% 21% 30% Chattanooga, TN-GA 221,692 6% 48% 25% 27% 33% 18% 17% 32% Chicago-Naperville-Elgin, IL-IN-WI 4,294,773 23% 52% 25% 24% 24% 25% 20% 31% Cincinnati, OH-KY-IN 941,446 5% 40% 21% 39% 13% 21% 20% 46% Cleveland-Elyria, OH 898,554 6% 37% 24% 39% 11% 20% 23% 46%

42 APPENDIX

Occupation Skill-Level Requirements Educational Attainment College Less or N of all Foreign- Lower- Middle- High- than HS Some advanced Metropolitan statistical area workers born skilled skilled skilled HS only college degree Colorado Springs, CO 282,477 9% 45% 30% 25% 23% 24% 26% 27% Columbia, SC 279,210 7% 43% 29% 28% 30% 20% 17% 33% Columbus, OH 886,581 10% 41% 23% 35% 14% 21% 20% 45% Dallas-Fort Worth-Arlington, TX 3,113,487 24% 52% 27% 22% 37% 21% 17% 25% Dayton, OH 336,241 4% 41% 21% 38% 17% 17% 24% 43% Deltona-Daytona Beach-Ormond Beach, FL 222,539 9% 45% 30% 25% 16% 28% 31% 25% Denver-Aurora-Lakewood, CO 1,369,559 15% 52% 26% 22% 30% 22% 20% 28% Des Moines-West Des Moines, IA 262,005 9% 54% 24% 22% 22% 23% 22% 33% Detroit-Warren-Dearborn, MI 1,763,843 11% 39% 22% 40% 13% 18% 22% 47% El Paso, TX 313,191 31% 51% 30% 19% 30% 26% 24% 20% Fresno, CA 356,627 30% 65% 24% 12% 48% 20% 18% 14% Grand Rapids-Wyoming, MI 418,038 9% 61% 18% 20% 30% 23% 21% 26% Greensboro-High Point, NC 338,050 11% 56% 26% 17% 34% 23% 20% 23% Greenville-Anderson-Mauldin, SC 386,694 8% 52% 24% 25% 27% 24% 19% 29% Harrisburg-Carlisle, PA 257,081 7% 43% 26% 32% 18% 23% 20% 39% Hartford-West Hartford-East Hartford, CT 566,874 16% 42% 26% 33% 12% 23% 26% 39% Houston-The Woodlands-Sugar Land, TX 2,831,820 30% 50% 27% 23% 35% 21% 18% 27% Indianapolis-Carmel-Anderson, IN 880,366 8% 53% 21% 26% 26% 24% 18% 32% Jackson, MS 249,878 3% 43% 17% 40% 22% 16% 15% 47% Jacksonville, FL 587,074 11% 44% 26% 30% 12% 24% 29% 36% Kansas City, MO-KS 958,378 8% 51% 23% 26% 28% 20% 21% 32% Knoxville, TN 382,210 5% 49% 24% 27% 26% 23% 19% 33% Lafayette, LA 221,624 4% 57% 28% 16% 36% 27% 18% 20% Lakeland-Winter Haven, FL 230,349 14% 61% 23% 16% 33% 27% 21% 19% A PPEN DIX 43

Occupation Skill-Level Requirements Educational Attainment College Less or N of all Foreign- Lower- Middle- High- than HS Some advanced Metropolitan statistical area workers born skilled skilled skilled HS only college degree Lancaster, PA 236,308 5% 51% 26% 23% 19% 31% 21% 29% Las Vegas-Henderson-Paradise, NV 878,784 29% 62% 25% 12% 27% 28% 26% 19% Little Rock-North Little Rock-Conway, AR 303,969 5% 46% 23% 31% 25% 27% 17% 32% Los Angeles-Long Beach-Anaheim, CA 5,847,570 43% 54% 24% 22% 32% 21% 21% 27% Louisville/Jefferson County, KY-IN 552,352 7% 52% 23% 25% 21% 27% 20% 32% Mcallen-Edinburg-Mission, TX 284,422 38% 55% 32% 13% 48% 21% 16% 15% Memphis, TN-MS-AR 533,051 8% 53% 23% 24% 31% 21% 17% 31% Miami-Fort Lauderdale-West Palm Beach, 48% 51% 27% 22% 15% 30% 27% 28% FL 2,538,152 Milwaukee-Waukesha-West Allis, WI 726,941 9% 51% 21% 28% 24% 24% 19% 33% Minneapolis-St. Paul-Bloomington, MN-WI 1,739,049 12% 48% 21% 31% 19% 20% 24% 37% Nashville-Davidson-Murfreesboro-Franklin, 10% 53% 26% 21% 28% 26% 18% 27% TN 866,108 New Haven-Milford, CT 391,807 15% 45% 26% 30% 16% 26% 21% 37% New Orleans-Metairie, LA 543,213 10% 54% 26% 20% 25% 28% 23% 25% New York-Newark-Jersey City, NY-NJ-PA 8,952,613 37% 48% 26% 27% 19% 25% 21% 35% North Port-Sarasota-Bradenton, FL 266,873 16% 56% 24% 20% 24% 30% 23% 24% Ogden-Clearfield, UT 242,025 9% 54% 29% 17% 28% 28% 25% 19% Oklahoma City, OK 613,429 11% 55% 27% 18% 38% 24% 16% 21% Omaha-Council Bluffs, NE-IA 475,474 8% 58% 21% 21% 40% 19% 17% 24% Orlando-Kissimmee-Sanford, FL 1,008,438 21% 53% 26% 22% 16% 26% 30% 28% Oxnard-Thousand Oaks-Ventura, CA 377,626 30% 58% 21% 20% 39% 18% 20% 23% Philadelphia-Camden-Wilmington, PA-NJ- 13% 43% 24% 34% 17% 22% 19% 43% DE 2,682,015 Phoenix-Mesa-Scottsdale, AZ 1,849,108 18% 54% 25% 21% 33% 23% 21% 23%

44 APPENDIX

Occupation Skill-Level Requirements Educational Attainment College Less or N of all Foreign- Lower- Middle- High- than HS Some advanced Metropolitan statistical area workers born skilled skilled skilled HS only college degree Pittsburgh, PA 1,039,229 4% 29% 19% 52% 8% 13% 18% 61% Portland-South Portland, ME 257,703 5% 52% 21% 27% 13% 21% 30% 35% Portland-Vancouver-Hillsboro, OR-WA 1,082,365 16% 51% 24% 25% 24% 22% 25% 30% Providence-Warwick, RI-MA 743,571 15% 51% 27% 22% 25% 28% 24% 24% Provo-Orem, UT 229,149 10% 56% 22% 22% 23% 23% 32% 22% Raleigh, NC 597,728 15% 44% 23% 33% 26% 18% 17% 38% Richmond, VA 560,817 10% 44% 24% 33% 22% 21% 19% 39% Riverside-San Bernardino-Ontario, CA 1,675,394 30% 55% 28% 17% 35% 23% 22% 19% Rochester, NY 494,789 7% 44% 20% 36% 13% 19% 26% 41% Sacramento-Roseville-Arden-Arcade, CA 918,995 23% 50% 24% 26% 21% 22% 28% 30% St. Louis, MO-IL 1,269,158 6% 39% 23% 38% 14% 20% 21% 45% Salt Lake City, UT 543,243 15% 56% 26% 18% 30% 25% 22% 23% San Antonio-New Braunfels, TX 966,557 15% 51% 30% 19% 32% 24% 22% 22% San Diego-Carlsbad, CA 1,393,639 29% 50% 24% 26% 27% 19% 23% 32% San Francisco-Oakland-Hayward, CA 2,161,847 37% 44% 21% 34% 19% 18% 21% 43% San Jose-Sunnyvale-Santa Clara, CA 877,691 48% 36% 17% 46% 16% 14% 18% 53% Santa Rosa, CA 223,133 22% 60% 25% 15% 36% 22% 25% 17% Scranton-Wilkes-Barre-Hazleton, PA 223,607 7% 68% 20% 13% 30% 30% 18% 22% Seattle-Tacoma-Bellevue, WA 1,728,111 21% 44% 22% 34% 16% 19% 25% 41% Spokane-Spokane Valley, WA 228,292 7% 51% 27% 22% 18% 28% 29% 26% Springfield, MA 252,533 10% 46% 26% 28% 14% 25% 30% 31% Stockton-Lodi, CA 266,331 33% 58% 27% 15% 35% 24% 23% 18% Syracuse, NY 289,246 6% 40% 22% 39% 14% 19% 25% 43% Tampa-St. Petersburg-Clearwater, FL 1,199,812 16% 48% 27% 26% 17% 27% 26% 31% A PPEN DIX 45

Occupation Skill-Level Requirements Educational Attainment College Less or N of all Foreign- Lower- Middle- High- than HS Some advanced Metropolitan statistical area workers born skilled skilled skilled HS only college degree Toledo, OH 277,764 4% 39% 20% 41% 14% 20% 19% 48% Tucson, AZ 396,871 16% 53% 25% 23% 27% 25% 24% 24% Urban Honolulu, HI 425,080 23% 53% 26% 21% 14% 26% 32% 28% Virginia Beach-Norfolk-Newport News, VA- 8% 44% 28% 28% 13% 23% 29% 36% NC 709,368 Washington-Arlington-Alexandria, DC-VA- 29% 43% 24% 34% 19% 19% 20% 42% MD 2,919,062 Wichita, KS 266,160 10% 52% 29% 19% 34% 24% 18% 24% Winston-Salem, NC 264,301 11% 63% 21% 17% 43% 24% 13% 19% Worcester, MA-CT 420,167 13% 42% 25% 33% 12% 27% 24% 38% Youngstown-Warren-Boardman, OH-PA 224,231 2% 41% 21% 39% 10% 23% 20% 47% Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Notes: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration. HS = high school.

46 APPENDIX

TABLE A.5 Characteristics of Foreign-Born Workers Employed in Lower- and Middle-Skilled Jobs by Metropolitan Area

Median Annual Educational Attainment Wages College Less or than HS Some advanced Foreign- Native- Metropolitan statistical area N LEP HS only college degree born born National 17,597,817 57% 33% 29% 24% 15% $24,000 $28,032 Akron, OH 8,400 47% 18% 30% 26% 26% $26,840 $26,531 Albany-Schenectady-Troy, NY 18,434 32% 20% 29% 31% 20% $26,558 $30,969 Albuquerque, NM 38,523 60% 39% 32% 20% 9% $20,023 $25,808 Allentown-Bethlehem-Easton, PA-NJ 28,147 48% 25% 32% 27% 15% $25,000 $30,000 Atlanta-Sandy Springs-Roswell, GA 325,351 54% 32% 29% 23% 17% $22,000 $29,192 Austin-Round Rock, TX 125,813 63% 40% 29% 19% 13% $22,025 $29,000 Bakersfield, CA 83,743 69% 54% 25% 16% 5% $20,351 $27,031 Baltimore-Columbia-Towson, MD 99,633 42% 20% 28% 28% 24% $26,347 $33,034 Baton Rouge, LA 13,096 59% 31% 33% 21% 15% $22,025 $28,455 Birmingham-Hoover, AL 19,770 61% 35% 33% 19% 13% $18,800 $27,473 Boise City, ID 19,501 56% 41% 30% 18% 11% $21,077 $25,438 Boston-Cambridge-Newton, MA-NH 319,314 53% 22% 34% 26% 18% $26,347 $33,034 Bridgeport-Stamford-Norwalk, CT 86,277 58% 27% 32% 24% 17% $26,030 $34,066 Buffalo-Cheektowaga-Niagara Falls, NY 18,041 40% 20% 29% 31% 20% $21,921 $28,833 Cape Coral-Fort Myers, FL 45,457 57% 33% 33% 23% 11% $21,472 $25,808 Charleston-North Charleston, SC 15,476 49% 33% 28% 24% 16% $21,600 $27,873 Charlotte-Concord-Gastonia, NC-SC 100,917 59% 36% 27% 24% 13% $21,077 $28,032 Chattanooga, TN-GA 8,887 60% 45% 21% 17% 17% $20,351 $25,830 Chicago-Naperville-Elgin, IL-IN-WI 752,716 60% 31% 31% 22% 15% $25,029 $30,526

A PPEN DIX 47

Median Annual Educational Attainment Wages College Less or than HS Some advanced Foreign- Native- Metropolitan statistical area N LEP HS only college degree born born Cincinnati, OH-KY-IN 31,200 53% 22% 32% 26% 20% $23,185 $28,032 Cleveland-Elyria, OH 34,707 47% 18% 31% 30% 20% $23,185 $28,032 Colorado Springs, CO 18,744 50% 30% 29% 28% 13% $20,646 $26,347 Columbia, SC 13,278 55% 41% 27% 18% 14% $20,000 $25,808 Columbus, OH 54,466 46% 22% 30% 27% 22% $20,824 $28,982 Dallas-Fort Worth-Arlington, TX 584,821 66% 46% 25% 18% 11% $22,386 $30,526 Dayton, OH 9,002 44% 27% 26% 30% 18% $21,077 $25,029 Deltona-Daytona Beach-Ormond Beach, FL 15,490 42% 19% 34% 34% 13% $22,025 $25,029 Denver-Aurora-Lakewood, CO 156,780 58% 38% 27% 22% 14% $24,776 $31,405 Des Moines-West Des Moines, IA 18,966 55% 28% 29% 27% 17% $25,000 $30,969 Detroit-Warren-Dearborn, MI 118,511 45% 21% 29% 30% 21% $23,403 $27,473 El Paso, TX 79,218 67% 37% 30% 25% 9% $18,582 $21,921 Fresno, CA 95,050 70% 54% 22% 18% 7% $18,970 $24,421 Grand Rapids-Wyoming, MI 28,712 57% 37% 28% 23% 12% $22,025 $26,347 Greensboro-High Point, NC 31,312 53% 41% 27% 21% 12% $21,024 $26,840 Greenville-Anderson-Mauldin, SC 22,228 60% 35% 31% 22% 13% $20,023 $26,030 Harrisburg-Carlisle, PA 12,731 49% 26% 32% 24% 18% $25,000 $30,035 Hartford-West Hartford-East Hartford, CT 59,730 40% 17% 33% 32% 18% $30,035 $33,000 Houston-The Woodlands-Sugar Land, TX 662,968 66% 44% 26% 19% 11% $23,000 $30,526 Indianapolis-Carmel-Anderson, IN 53,260 59% 35% 30% 20% 15% $20,351 $29,000 Jackson, MS 4,572 58% 35% 27% 19% 19% $15,017 $25,438 Jacksonville, FL 45,688 44% 16% 32% 33% 19% $24,000 $27,150

48 APPENDIX

Median Annual Educational Attainment Wages College Less or than HS Some advanced Foreign- Native- Metropolitan statistical area N LEP HS only college degree born born Kansas City, MO-KS 58,672 57% 37% 26% 23% 15% $21,679 $30,035 Knoxville, TN 14,054 49% 34% 31% 22% 13% $17,916 $25,705 Lafayette, LA 7,869 70% 42% 31% 18% 9% $21,165 $26,347 Lakeland-Winter Haven, FL 26,919 57% 39% 31% 21% 9% $20,351 $25,808 Lancaster, PA 9,725 53% 24% 38% 24% 14% $25,029 $29,508 Las Vegas-Henderson-Paradise, NV 225,644 55% 31% 31% 26% 12% $26,840 $30,000 Little Rock-North Little Rock-Conway, AR 11,448 54% 35% 37% 17% 11% $20,351 $26,347 Los Angeles-Long Beach-Anaheim, CA 1,926,580 65% 40% 25% 22% 14% $22,895 $27,774 Louisville/Jefferson County, KY-IN 26,806 52% 27% 33% 23% 17% $23,026 $28,032 Mcallen-Edinburg-Mission, TX 94,679 71% 54% 22% 16% 8% $17,298 $20,351 Memphis, TN-MS-AR 30,590 58% 40% 26% 20% 14% $20,351 $26,427 Miami-Fort Lauderdale-West Palm Beach, FL 945,454 54% 18% 36% 29% 17% $23,026 $27,473 Milwaukee-Waukesha-West Allis, WI 44,295 57% 33% 32% 23% 13% $22,711 $29,508 Minneapolis-St. Paul-Bloomington, MN-WI 142,965 52% 26% 27% 30% 17% $24,000 $32,670 Nashville-Davidson-Murfreesboro-Franklin, TN 66,676 56% 35% 31% 20% 14% $20,646 $28,000 New Haven-Milford, CT 41,245 51% 23% 35% 26% 16% $25,000 $32,561 New Orleans-Metairie, LA 42,124 59% 30% 33% 25% 12% $23,000 $27,473 New York-Newark-Jersey City, NY-NJ-PA 2,400,571 53% 25% 33% 24% 18% $26,000 $34,066 North Port-Sarasota-Bradenton, FL 34,129 51% 28% 34% 24% 14% $21,470 $26,831 Ogden-Clearfield, UT 17,126 51% 33% 32% 25% 11% $22,826 $26,347 Oklahoma City, OK 54,777 65% 46% 29% 17% 9% $20,824 $27,401 Omaha-Council Bluffs, NE-IA 29,996 63% 49% 23% 18% 11% $22,711 $30,000 A PPEN DIX 49

Median Annual Educational Attainment Wages College Less or than HS Some advanced Foreign- Native- Metropolitan statistical area N LEP HS only college degree born born Orlando-Kissimmee-Sanford, FL 167,849 44% 20% 32% 33% 16% $21,077 $25,029 Oxnard-Thousand Oaks-Ventura, CA 90,395 66% 49% 21% 21% 9% $21,368 $30,000 Philadelphia-Camden-Wilmington, PA-NJ- DE 229,306 53% 25% 31% 23% 21% $24,569 $31,616 Phoenix-Mesa-Scottsdale, AZ 266,004 57% 41% 27% 22% 10% $22,711 $30,000 Pittsburgh, PA 20,610 41% 15% 26% 31% 28% $24,028 $29,610 Portland-South Portland, ME 8,852 39% 18% 28% 35% 20% $21,077 $28,455 Portland-Vancouver-Hillsboro, OR-WA 130,322 58% 31% 27% 28% 14% $23,743 $29,614 Providence-Warwick, RI-MA 86,762 49% 31% 33% 25% 11% $26,347 $30,526 Provo-Orem, UT 18,249 54% 28% 28% 32% 12% $23,185 $20,023 Raleigh, NC 59,960 55% 38% 25% 21% 16% $19,614 $29,627 Richmond, VA 37,699 55% 32% 29% 22% 17% $22,600 $ 29,731 Riverside-San Bernardino-Ontario, CA 413,671 59% 41% 27% 22% 9% $25,029 $26,347 Rochester, NY 22,244 42% 20% 29% 34% 17% $22,626 $27,031 Sacramento-Roseville-Arden-Arcade, CA 157,251 58% 28% 28% 32% 13% $24,239 $30,000 St. Louis, MO-IL 43,033 46% 22% 31% 29% 18% $23,026 $28,595 Salt Lake City, UT 68,490 54% 36% 29% 23% 12% $23,403 $27,401 San Antonio-New Braunfels, TX 117,783 62% 39% 29% 22% 10% $22,025 $25,293 San Diego-Carlsbad, CA 300,714 56% 35% 24% 26% 15% $24,000 $28,800 San Francisco-Oakland-Hayward, CA 519,760 58% 28% 26% 26% 21% $28,000 $35,541 San Jose-Sunnyvale-Santa Clara, CA 223,890 60% 28% 25% 26% 21% $29,000 $31,900 Santa Rosa, CA 40,534 61% 42% 25% 26% 8% $25,947 $30,969 Scranton-Wilkes-Barre-Hazleton, PA 12,957 65% 34% 34% 18% 14% $21,921 $28,032 50 APPENDIX

Median Annual Educational Attainment Wages College Less or than HS Some advanced Foreign- Native- Metropolitan statistical area N LEP HS only college degree born born Seattle-Tacoma-Bellevue, WA 237,707 50% 23% 27% 31% 19% $26,347 $34,139 Spokane-Spokane Valley, WA 12,546 50% 22% 33% 33% 12% $22,426 $26,840 Springfield, MA 18,165 44% 18% 32% 35% 15% $25,808 $29,421 Stockton-Lodi, CA 74,349 63% 41% 27% 23% 9% $24,776 $28,905 Syracuse, NY 10,795 38% 22% 29% 29% 20% $22,300 $29,000 Tampa-St. Petersburg-Clearwater, FL 145,188 48% 22% 34% 29% 16% $23,227 $27,131 Toledo, OH 6,426 48% 23% 31% 26% 19% $20,646 $25,808 Tucson, AZ 50,364 54% 34% 30% 26% 10% $20,646 $25,000 Urban Honolulu, HI 75,396 59% 17% 32% 35% 17% $27,331 $31,616 Virginia Beach-Norfolk-Newport News, VA- NC 41,803 41% 17% 31% 33% 20% $25,000 $28,000 Washington-Arlington-Alexandria, DC-VA- MD 562,778 48% 28% 27% 25% 21% $26,631 $36,131 Wichita, KS 21,681 60% 41% 29% 18% 12% $24,776 $27,401 Winston-Salem, NC 23,751 63% 51% 28% 13% 8% $20,000 $27,000 Worcester, MA-CT 37,219 47% 17% 37% 29% 17% $26,430 $31,616 Youngstown-Warren-Boardman, OH-PA 2,508 32% 14% 31% 29% 27% $24,421 $25,000 Source: Five-year American Community Survey sample, 2011–15 collected from IPUMS. Notes: Data refer to individuals ages 18–64, in the civilian labor force, and employed during the week before the survey administration. Median wage data are further restricted to those individuals who reported positive wage and salary income during year before the survey.

A PPEN DIX 51

Notes

1 For more information on the organizations and activities that make up the workforce system, see Eyster et al. 2016. 2 Many contest the notion of a skills gap and, rather than blaming workers for lacking skills, they put the onus on employers and labor-market intermediaries to invest in skill development through employer-provided training or coordination with education providers. See Andrew Weaver, “The Myth of the Skills Gap,” MIT Technology Review, August 25, 2017, https://www.technologyreview.com/s/608707/the-myth-of-the-skills-gap/. 3 “Becoming Bilingual in Immigration & Workforce: What Philanthropic Leaders Need to Know,” YouTube video, 1:29:25, Grantmakers Concerned with Immigrants and Refugees, August 2, 2016, https://www.youtube.com/watch?v=s-ls2DBwP6k&feature=youtu.be.

4 “Ensuring Immigrants’ Access to WIOA: Data and Advocacy Tools for Adult Educators.” YouTube video, 1:03:02, Coalition on Adult Basic Education, February 22, 2017, https://www.youtube.com/watch?v=qd1MOrfeBo4. 5 Advocates were concerned in particular about two changes: A new set of six common performance measures emphasizing employment outcomes that apply to Title I workforce services and Title II adult education services alike; and a reconstitution of the former English Language/Civics funding as the new Integrated English Literacy and Civics Education with a greater emphasis on workforce outcomes compared with citizenship outcomes. Though WIOA Title I requires participants to have legal authorization to work in the US, Title II is silent on immigration status. For information on WIOA and its impact for immigrants, see National Skills Coalition, 2016. 6 The BLS system reports the “1) typical education needed for entry, 2) commonly required work experience in a related occupation, and 3) typical on-the-job training needed to obtain competency in the [each] occupation.” 7 As a point of comparison, recent research finds 7.6 million unauthorized individuals in the labor force, making up 4.7 percent of the total US workforce of 161 million (Paral 2018). That study found that 62 percent of foreign- born workers were employed in lower-skilled jobs (requiring no formal educational credential, or a high school diploma or equivalent) and 12 percent had middle-skilled jobs (requiring some college, a postsecondary nondegree award, or an associate’s degree), yet our analysis finds a share of 50 percent in lower-skilled jobs and 25 percent in middle-skilled jobs following BLS classifications. The difference may be because of differences in job classifications, Paral’s correction for the undercount of undocumented immigrants (which he addresses using a secondary dataset), and differences in definition of the workforce; ours is limited to 18- to 64-year-olds in the civilian labor force and Paral’s includes a broader group (16+ in civilian and military positions). 8 Immigrants in middle-skilled and high-skilled positions have had more years of residence in the US, on average, than immigrants in lower-skilled occupations, although the difference is not large (median of 18 years compared with 16 years). 9 For information documenting the insufficient English language training opportunities in another local area, see Soricone et al. 2011. The broader national challenge of limited ESL-training supply to meet demand is discussed in McHugh, Gelatt, and Fix, 2007. 10 Soricone et al. (2011) noted that the research suggests that considerable time is required for language acquisition; one paper estimated an immigrant with native language literacy would need 500 to 1,000 instructional hours to reach a minimal functional level of English and cited another study that suggested 150 hours were needed to achieve one level gain in ESL. 11 This issue of “mainstreaming” has been discussed in the European context, where immigrant integration policies including workforce training have treated immigrants’ needs explicitly and “developed group-targeted policies,”

52 NOTES

that are now increasingly being transformed to incorporate immigrants into broader service systems (Benton, McCarthy, and Collett 2015). 12 “Employment Projections,” US Bureau of Labor Statistics, accessed May 25, 2018, https://www.bls.gov/emp/ep_crosswalks.htm.

NOTES 53

References

Adams, Gina, Shayne Spaulding, and Caroline Heller. 2015. Bridging the Gap: Exploring the Intersection of Workforce Development and Child Care. Washington, DC: Urban Institute. Baker, Bryan. 2017. “Estimates of the Unauthorized Immigrant Population Residing in the United States: January 2014.” Washington, DC: US Department of Homeland Security. Office of Immigration Statistics. Batalova, Jeanne, Michael Fix, and James D. Bachmeier. 2016. Untapped Talent: The Costs of Brain Waste among Highly Skilled Immigrants in the United States. Washington, DC: Migration Policy Institute. Benton, Meghan, Helen McCarthy, and Elizabeth Collett. 2015. Into the Mainstream: Rethinking Public Services for Diverse and Mobile Populations. Washington, DC: Migration Policy Institute. Bergson-Shilcock, Amanda. 2016. Upskilling the New American Workforce: Demand-Driven Programs that Foster Immigrant Worker Success & Policies that Can Take them to Scale. Washington, DC: National Skills Coalition. Bergson-Shilcock, Amanda, and James Witte. 2015. Steps to Success: Integrating Immigrant Professionals in the U.S. New York: World Education Service. Burt, Miriam, and Julie Mathews-Aydinli. 2007. “Workplace Instruction and Workforce Preparation for Adult Immigrants.” Center for Adult English Language Acquisition, Center for Applied Linguistics. Callahan, Rebecca M., and Patricia C. Gándara, eds. 2014. The Bilingual Advantage: Language, Literacy, and the US Labor Market. Buffalo, NY: Multilingual Matters. Capps, Randy, Michael Fix, and Serena Yi-Ying Lin. 2010. Still an Hourglass? Immigrant Workers in Middle-Skilled Jobs. Washington, DC: Migration Policy Institute, National Center on Immigrant Integration Policy. Carnevale, Anthony P., Jeff Strohl, Ban Cheah, and Neil Ridley. 2017. Good Jobs That Pay without a BA. Washington, DC: Georgetown University, McCourt School of Public Policy, Center on Education and the Workforce.

Cervantes, Wendy, Rebecca Ulrich, and Hannah Matthews. 2018. Our Children’s Fear: Immigration Policy’s Effects on Young Children. Washington, DC: CLASP. Connell, Christopher. 2008. “The Vital Role of Community Colleges in the Education and Integration of Immigrants.” Sebastopol, CA: Grantmakers Concerned with Immigrants and Refugees. Dramski, Pavel. 2017. On the Clock: How Immigrants Fill Gaps in the Labor Market by Working Nontraditional Hours. New American Economy. Dyssegaard Kallick, David. 2015. Bringing Vitality to Main Street: How Immigrant Small Businesses Help Local Economies Grow. New York: Americas Society/Council of The Americas and the Fiscal Policy Institute.

Enchautegui, Maria. 2015. Engaging Employers in Immigrant Integration. Washington, DC: Urban Institute. Espinoza, Robert. 2017. “Immigrants and the Direct Care Workforce.” Bronx, NY: PHI. Eyster, Lauren, Christin Durham, Michelle Van Noy, and Neil Damron. 2016. “Understanding Local Workforce Systems.” Washington, DC: Urban Institute. Gershwin, Mary, Tammy Coxen, Brian Kelley, and Gary Yakimov. 2009. Building Tomorrow’s Workforce: Promoting the Education & Advancement of Hispanic Immigrant Workers in America. Ann Arbor, MI: Corporation for a Skilled Workforce. Hagan, Jacqueline Maria, Rubén Hernandez-León, and Jean Luc Demonsant. 2015. Skills of the “Unskilled”: Work and Mobility among Mexican Migrants. Berkeley: University of California Press. Hall, Matthew, Audrey Singer, Gordon F. De Jong, and Deborah Roempke Graefe. 2011. “The Geography of Immigrant Skills: Educational Profiles of Metropolitan Areas.” Washington, DC: Brookings Institution.

54 REFERENCES

Hamaji, Kate, and Christian González-Rivera. 2016. A City of Immigrant Workers: Building a Workforce Strategy to Support All New Yorkers. New York: Center for an Urban Future and the Center for Popular Democracy. Hohn, Marcia D., Justin P. Lowry, James C. Witte, and José Ramón Fernández-Pena. 2016. Immigrants in Health Care: Keeping Americans Healthy through Care and Innovation. Fairfax, VA: George Mason University Institute for Immigration Research and the Immigrant Learner Center. Holzer, Harry. 2015. “Job Market Polarization and U.S. Worker Skills: A Tale of Two Middles.” Washington, DC: Brookings Institution. Kallenbach, Silja, and Andy Nash. 2016. Adult Education and Immigrant Integration: Lessons Learned from the Networks for Integrating New Americans Initiative. Boston: World Education. Kochan, Thomas, David Finegold, and Paul Osterman. 2012. “Who Can Fix the ‘Middle Skills’ Gap? Companies Should Take the Lead in Creating Collaborative Programs to Train Workers.” Watertown, MA: Harvard Business Review.

McHugh, Margie. 2018. In the Age of Trump: Populist Backlash and Progressive Resistance Create Divergent State Immigrant Integration Contexts. Washington, DC: Migration Policy Institute. McHugh, Margie, Julia Gelatt, and Michael Fix. 2007. Adult English Language Instruction in the United States: Determining Need and Investing Wisely. Washington, DC: Migration Policy Institute. McHugh, Margie, and Madeleine Morawski. 2015. “Immigrants and WIOA Services: Comparison of Sociodemographic Characteristics of Native- and Foreign-Born Adults in the United States.” Washington, DC: Migration Policy Institute, National Center on Immigrant Integration Policy. ———. 2017. “Unlocking Skills: Successful Initiatives for Integrating Foreign-Trained Immigrant Professionals.” Washington, DC: Migration Policy Institute. Modestino, Alicia S. 2016. “The Importance of Middle-Skill Jobs.” Issues in Science and Technology 33 (1). Montes, Marcela, and Vickie Choitz. 2016a. Improving Immigrant Access to Workforce Services: Partnerships, Practices & Policies. Washington, DC: Aspen Institute, Workforce Strategies Initiative.

———. 2016b. Working Together to Strengthen America’s Immigrant Workforce: Partnerships Between Community Colleges and Immigrant-Serving Organizations. Washington, DC: Aspen Institute, Workforce Strategies Initiative. New American Economy. 2017. “Not Lost in Translation: The Growing Importance of Foreign Language Skills in the U.S. Job Market.” New American Economy. National Skills Coalition. n.d. a. “Middle-Skill Credentials and Immigrant Workers: Arizona’s Untapped Assets.” Washington, DC: National Skills Coalition. ———. n.d. b. “Middle-Skill Credentials and Immigrant Workers: California’s Untapped Assets.” Washington, DC: National Skills Coalition. ———. 2016. “WIOA Final Regulations and Immigrants.” Webinar slides, August 23. Washington, DC: National Skills Coalition. Paral, Rob. 2018. Ready to Work: Understanding Immigrant Skills in the United States to Build a Competitive Labor Force. Chicago Council on Global Affairs. Soricone, Lisa, Navjeet Singh, Cristine Smith, John Comings, Katherine Shields, Lynne Sacks, and Andrea Tull. 2011. Breaking the Language Barrier: A Report on English Language Services in Greater Boston. Boston Foundation and Commonwealth Corporation. Spence, Robin. 2010. Sound Investments: Building Immigrants’ Skills to Fuel Economic Growth. New York: Economic Mobility Corporation.

REFERENCES 55

US Department of Health and Human Services. 2014. Models of Collaboration between Workforce Investment and Refugee Resettlement Stakeholders. Washington, DC: Administration for Children and Families, Office of Refugee Resettlement.

56 REFERENCES

About the Authors

Hamutal Bernstein is a senior research associate in the Income and Benefits Policy Center at the Urban Institute, where her research focuses on immigration and integration, workforce development and education, and program evaluation. She has wide expertise in mixed-methods research, including original survey development, secondary data analysis, and qualitative data collection and analysis. She is a principal investigator on the Annual Survey of Refugees and Survey Redesign for the US Department of Health and Human Services. Her other areas of research focus on immigrant upskilling, immigrant inclusion at the local level, and other human services topics. Before joining Urban, Bernstein was a program officer at the German Marshall Fund of the United States, managing public opinion survey research in the United States and Europe. This position followed her work on global and US migration research as a research associate at the Institute for the Study of International Migration at Georgetown University and as a migration consultant to international organizations. She has conducted fieldwork in English and Spanish in the United States and abroad, and she speaks fluent French. Bernstein received her BA in international relations from Brown University and her PhD in government from Georgetown University.

Carolyn Vilter is a research assistant in the Income and Benefits Policy Center at the Urban Institute, where she primarily works on workforce development and immigration-related issues. Before joining Urban, she interned with the US Department of State in Tijuana, Mexico, and the US House of Representatives. She holds a BA in political economy from Georgetown University.

ABOUT THE AUTHORS 57

STATEMENT OF INDEPENDENCE

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2006 Implicit sources of bias in employment interview judgments and decisions. Sharon Segrest [email protected]

Pamela L. Perrewe

Treena L. Gillespie

Bronston T. Mayes

Gerald R. Ferris

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Recommended Citation Segrest Purkiss, S. L., Perrewé, P. L., Gillespie, T. L., Mayes, B. T., & Ferris, G. R. (2006). Implicit sources of bias in employment interview judgments and decisions. Organizational Behavior and Human Decision Processes, 101(2), 152-167. doi:10.1016/ j.obhdp.2006.06.005

This Article is brought to you for free and open access by the Scholarly Works at Digital USFSP. It has been accepted for inclusion in Faculty Publications by an authorized administrator of Digital USFSP. Bias in the Interview 1

NOTICE: this is the author’s version of a work that was accepted for publication in Organizational Behavior and Human Decision Processes. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Organizational Behavior and Human Decision Processes, 2006, 101(2), 152-167. doi:10.1016/j.obhdp.2006.06.005

Running head: BIAS IN THE INTERVIEW

Implicit Sources of Bias in Employment Interview Judgments and Decisions*

Sharon L. Segrest Purkiss

California State University, Fullerton

Pamela L. Perrewé

Florida State University

Treena L. Gillespie and Bronston T. Mayes

California State University, Fullerton

Gerald R. Ferris

Florida State University

*David Harrison provided unusual and outstanding guidance and support throughout the review and revision process. The authors feel that his insight made an important contribution to the quality of this paper and we would like to express our gratitude to him. Correspondence should be directed to: Pamela L. Perrewé, Department of Management, College of Business, Florida State University, Tallahassee, Florida 32306-1110, Ph: (850) 644-7848, Fax: (850) 644-7843, E-mail: [email protected] Bias in the Interview 2

Abstract

This study empirically examined implicit sources of bias in employment interview judgments and decisions. We examined two ethnic cues, accent and , as sources of bias that may trigger prejudicial attitudes and decisions. As predicted, there was an interaction between the applicant name and accent that affected participants’ favorable judgments of applicant characteristics. The applicant with the ethnic name, speaking with an accent, was viewed less positively by interviewers than the ethnic named applicant without an accent and non-ethnic named applicants with and without an accent. Furthermore, modern ethnicity bias had a negative association with the favorable judgments of the applicants, which, in turn, affected hiring decisions.

Implications of the results, limitations of the study, and directions for future research are discussed.

Bias in the Interview 3

Implicit Sources of Bias in Employment Interview Judgments and Decisions

The employment interview is an important source for information and remains, by far, the most

frequently used employment selection and decision-making device in organizations (e.g., Eder & Harris, 1999;

Posthuma, Morgeson, & Campion, 2002). Unfortunately, this reality stands in stark contrast to the continued questions about interview validity and the persistence of biases in the interview process, suggesting that more research in this area is needed (Pingitore, Dugoni, Tindale, & Spring, 1994; Roehling, Campion, & Arvey,

1999). In particular, the effects of various interviewer and applicant characteristics on the interview process and

outcomes deserve additional exploration (Dipboye, 1992). Although some research has addressed these issues,

most of the studies of interviewer biases and stereotypes have focused on non-subtle, demographic effects on

interviewers’ judgments and decisions.

In a review of the interview literature, Posthuma et al. (2002) suggested that researchers redirect

attention from examining simple demographic effects and consider these as potential cues for other causal

factors, particularly attitudes and values. The present study addresses this appeal, and it extends previous work

on applicant characteristics by focusing on the effects of implicit or subtle cues on interview outcomes within

the framework of modern racism or modern ethnicity bias. Specifically, the purpose of the present study is to

investigate the extent to which ethnic name and accent serve as cues that trigger biased interviewer judgments

and decisions in the employment interview process.

Employment Interview

As a traditional component of most organizations’ human resource management selection systems,

research has been conducted on the employment interview for nearly a century (e.g., Eder & Harris, 1999).

Interview scholars have been interested in a broad range of topics over the years, including psychometric

properties of the interview as a measurement device, interview format and type (e.g., structured, unstructured

and situational), notions of fit (e.g., person-job and person-organization), and interviewer cognition and Bias in the Interview 4 decision-making processes. However, we need more employment interview research examining the effects of applicant demographic characteristics as cues affecting interviewer judgments and decisions.

Research needs to probe beyond simple demographic category effects to investigate potential underlying reasons for what are observed as judgment and decision biases. With increased interest in person-organization fit in the interview, there has come a realization that the homogenization effects from such assessments, which drive employment decisions, potentially could account for discrimination effects (e.g., Judge & Ferris, 1992).

However, we still need to know much more about the perceptual cues associated with applicants of different races and ethnicities that might be driving these assessments, judgments, and employment decisions.

The Psychological Processes of Prejudice and Stereotyping

Prejudicial attitudes, as well as other interviewer characteristics, such as race and personality, affect interviewer perceptions of applicants (Dipboye, 1992). Prejudice and ethnicity stereotypes tend to be positively related to each other in both the historical and current views (Dovidio, Brigham, Johnson, & Gaertner, 1996), and some researchers have agreed that the positive relationship is due to stereotypes being the cognitive component of racial attitudes or prejudice (Jones, 1986). Stereotypes are particular types of knowledge structures or cognitive schema that link group membership to certain traits (Ford & Stangor, 1992; Nesdale &

Rooney, 1990), and which have been found to influence the interpretation of others’ behavior (Duncan, 1976), the memory of others (Stangor & McMillan, 1992), and behavior toward others (Snyder, Tanke, & Berscheid,

1977).

Research has suggested that prejudice tends to evoke negative stereotypes. Participants with high levels of prejudice are more likely to use cultural stereotypes, and high levels of prejudice correspond with more negative stereotypes (Kawakami, Dion, & Dovidio, 1998). These stereotypes could have been elicited through the use of a cue, and in the Kawakami et al. (1998) study, the category label, Black, was purported to activate Bias in the Interview 5 the stereotype. This type of cue likely activates judgments of a specific group without the awareness of the perceiver, consistent with an implicit form of racism, called modern racism (McConahay, 1986).

We would expect that prejudice against a specific ethnic group (e.g., Hispanics) would affect judgments about that group, but would not necessarily influence judgments about a different group (e.g., non-Hispanics). It is important to note that prejudicial attitudes and stereotypes about race and ethnicity may be generated by multiple cues. We argue that examining multiple cues, such as ethnic accent and name, is key to understanding how prejudicial attitudes and stereotypes are triggered. Work in these areas is examined next.

Ethnic Speech Accent and Name

Subtle cues may play a role in triggering implicit discriminatory responses. One possible cue may be applicant accent. Whereas other fields, such as linguistics and communication, have recognized the important role of accent in the perception of individual characteristics, organizational research has neglected this area.

Accent can initiate perceptions regarding intelligence and kindness, as well as status, solidarity, economic class, national origin, or ethnicity (Lippi-Green, 1994; Nesdale & Rooney, 1990).

For example, in the U.S., French accents often are associated with sophistication, Asian accents tend to be linked with high economic and educational attainments (Cargile, 2000; Lippi-Green, 1997), and in England, the Liverpool accent is considered less cultured than accents associated with Oxford and Cambridge (Lippi-

Green, 1997). Due to the verbal nature of the employment interview process, and the potential for triggering biased judgments, accent may prove to be a particularly important factor affecting interview decisions.

Although it may be subtle, accent has been demonstrated to be easily perceptible. Research has demonstrated that even linguistically naïve individuals can make basic distinctions among differing accents

(Cargile, 2000; Giles, Williams, Mackie, & Rosselli, 1995; Podberesky, Deluty, & Feldstein, 1990). However, this recognition of accent distinctiveness seems to apply only to a certain degree. Specifically, when presented with four varieties of Spanish-accented English (i.e., Cuban, Costa Rican, Argentinean, and Puerto Rican), and Bias in the Interview 6 four varieties of Asian-accented English, most American listeners could not distinguish between the different

varieties of Spanish- and Asian-accented English speech (Podberesky et al., 1990). It appears that a general

Spanish accent is recognized by most listeners, and often evokes similar reactions, regardless of the specific variety of Spanish spoken.

Accents associated with countries of lower socio-economic status and darker skin colors frequently are denigrated (Lippi-Green, 1997). However, some regional accents are looked upon less favorably, even when skin color is not an issue. For example, in the U.S., “Appalachian English” is downgraded (Atkins, 1993). In

general, the accent of the dominant or majority group in a society is evaluated most positively (Nesdale &

Rooney, 1990). Interestingly, the dominant accent often is judged more positively not only by the dominant

group, which is Anglo Americans in the U.S., but also by minority groups, such as African Americans and

Hispanics (Brennan & Brennan, 1981; DeShields, Kara, & Kaynak, 1996). Therefore, one would expect that

interviewers would evaluate applicants more positively if their accent matched that of the majority group,

regardless of whether the interviewers were minority group members.

Another problem occurs when the limited selection research examining minorities fails to distinguish

between race and ethnicity. Although African Americans and Hispanics both share the distinction of being minorities in the United States, one difference needs to be clarified. The terms race and ethnicity often elicit confusion. Race has been defined as a social grouping based on visible physical characteristics, such as skin color, and on supposed common ancestral origins, whereas ethnicity has been defined as a group’s cultural and social heritage that has been transferred through generations of group members (Bolaffi, Braham, Gindro,

Tentori, & Bracalenti, 2003; Singer & Eder, 1989; Slavin, Rainer, McCreary, & Gowda, 1991). For example, one study selected research participants on the basis of their appearance, speech, and name being indicative of

Hispanic descent (Kenney & Wissoker, 1994). The study, designed to differentiate between the success of an

Anglo and a Hispanic job candidate, revealed that an Anglo candidate was more likely to be successful than the Bias in the Interview 7

Hispanic counterpart at filing an application, obtaining an interview, and receiving a job offer. However, these results may have been confounded by the failure to control for race and/or accent, exemplifying the difficulties

of research in this area.

Similarly, a field study found significant same-race bias between the interviewer and applicants for

custodial jobs. It was acknowledged that, whereas their Black/White and Black/Hispanic comparisons examined

racial differences, the White/Hispanic comparisons examined differences in ethnic background (Lin et al.,

1992). Even though it was stated that race similarity effects were examined, it appeared that no data were gathered regarding the actual race of the Hispanic applicants, or other potentially confounding factors, such as the degree of accent.

Singer and Eder (1989) separated the effects of ethnicity and accent cues in the selection process, and found negligible effects for accent, but significant effects for ethnicity. In contrast to the statistical results, participants in the role of interviewer perceived and rated applicant accent as moderate in importance and applicant ethnicity as low in importance in their selection decision (Singer & Eder, 1989). This suggests that interviewers may rely on applicant accent as a more concrete, legitimate justification for ethnicity discrimination. Considering applicant accent also could reflect some implicit theory on the part of the interviewer that an applicant should not have an accent, because having an accent might affect job performance negatively. Of course, it also might be that accent is, in fact, job-related, and reflects an important requirement in job applicants.

An ethnic cue (e.g., accent) that is paired with another minority ethnic group cue (e.g., name) may evoke a consistent stereotype, resulting in a negative evaluation of an applicant. Consistent with the premise of modern racism, these negative judgments are likely made automatically, not consciously. However, when accent alone or ethnicity alone is perceived, a single cue may not be enough to trigger modern racism. Because research inconsistently has demonstrated lower evaluations of minority applicants (Lewis & Sherman, 2003; Bias in the Interview 8

Mullins, 1982; Vrij & Winkel, 1994), it may be a combination of cues that elicit modern racism. In other words, when two minority ethnic cues are paired together, the ethnicity of the target person is clearer, evoking

automatic negative stereotypes. However, if only one cue is present, the ethnicity is less clear, which may

trigger a more conscious process of evaluation.

Modern Racism and the Complex Effects of Ethnic Cues

Racial and ethnic demographic classifications may solicit categorical reactions and decisions that really

mask underlying subtle cue effects related to individuals who are members of those categories. Modern racism

could be a potential explanation for such effects. Racial prejudice is defined as “an unfair negative attitude

toward a social group or a person perceived to be a member of that group” (Dovidio, 2001, p. 329). Prior to the

Civil Rights era, prejudice was viewed as a psychopathology, with those perpetuating prejudice as individuals

in need of reform (Dovidio, 2001). Racial prejudice was defined as blatant and overt. However, this “old

fashioned” racism soon melted into a more implicit form of racism, or “modern racism” (McConahay, 1986).

This more subtle racial prejudice was recognized as a normal process that emerged from, and was perpetuated

by, socialization and social norms (Dovidio, 2001).

As unintentional and subtle, individuals who are high in modern racism may denounce racism, but still

act in ways that discriminate against others without consciously doing so (Dovidio, 2001). Modern racists

espouse egalitarianism, so they do not openly discriminate. However, their underlying feelings may lead them

to engage in unintentional discrimination when another factor exists to sway their decision (Dovidio, 2001). For

example, Dovidio and Gaertner (2000) discovered that when applicants had marginally acceptable

qualifications, Black candidates were less often selected than White candidates, although their qualifications

were identical. However, when qualifications were low (i.e., a clear need to reject the candidate) or

qualifications were high (i.e., a clear decision to select the candidate), bias was not evident. Bias in the Interview 9

Modern racism, hereafter, referred to as modern ethnicity bias, offers researchers the context for less detectable discrimination in the workplace. Whereas blatant or “old-fashioned” racism is unacceptable and illegal as a means for making selection decisions, subtle cues may be triggering unconscious or implicit forms of ethnicity bias in judgments and decisions.

A combination of ethnic minority cues (i.e., as opposed to a single cue) may be more likely to trigger an unconscious and automatic negative reaction because of the salience of the cues and the ease in which one is more confident about placing someone in a class or category; essentially, stereotyping. Further, “when one’s attention is differentially directed to one portion of the environment rather than to others, the information contained in that portion will receive disproportionate weighting in subsequent judgments” (Taylor &

Thompson, 1982, p. 175). Thus, observing a combination of two or more ethnic cues might lead to an unconscious, automatic, negative labeling of an individual. However, a single ethnic cue is less likely to trigger an automatic stereotype. In this case, a single cue might trigger a more conscious process of labeling an individual. When an individual is conscious of placing another into a class or category, stereotyping due to modern racism is less likely to occur.

Hypotheses

Based on the previous discussion, we argue that the interaction of ethnic cues (i.e., ethnic name and accent) is more likely to elicit ethnic stereotypes and negative appraisals than a single cue. Thus, the following hypothesis is proposed:

Hypothesis 1: Ethnic name and ethnic accent will interact to predict unfavorable judgments about the applicant and a reduced likelihood of deciding to hire the applicant. The synergistic combination of two ethnic cues (i.e., when both ethnic name and ethnic accent are present), will lead to the most negative judgments of applicants.

Bias in the Interview 10

Within the context of modern ethnicity bias, individuals may unconsciously attend to the ethnicity

without recognizing its impact on their decisions. We expect that those who have an ethnicity bias will react more negatively to ethnic cues and will be more likely to hold unfavorable judgments about ethnic minorities than those who do not have an ethnicity bias. Further, these judgments will likely affect interviewers’ decisions to hire ethnic minorities. In accordance with the previous discussion of ethnicity bias, it would be expected that ethnic applicants also would be judged less favorably in an interview context by both minority and non-minority interviewers. In light of the relationship between prejudicial attitudes, stereotypes, and perceptions of ethnic group members, the following relationship is hypothesized:

Hypothesis 2: The ethnicity of the applicant will interact with modern ethnicity bias such that the negative relationship between the ethnicity of the applicant and judgments and decisions to hire the applicant will be exacerbated when modern ethnicity bias is high.

Method

Participants

Two hundred and twelve students enrolled in basic management courses at a large southeastern university voluntarily participated in this study in exchange for extra course credit. The mean age of the participants was 22 with a range from 18 to 47 years. The average total work experience was 2.7 years. The ethnicity composition of the sample was as follows: 66% Caucasian (not of Hispanic origin); 18% African

American; 11% Hispanic; 4% Asian / Pacific Islander; and 1% Other. For data analysis purposes, the following three classifications were used for participants’ ethnicity: 0 = Caucasian / White (not of Hispanic origin); 1 =

Other Minorities; 2 = Hispanic. Fifty-six percent of the participants were male.

Procedure

Although previous employment interview research has examined ethnicity cues on interviewer decisions, the study of Hispanic ethnicity has been neglected relative to other minority applicants (Lin, Dobbins, Bias in the Interview 11

& Farh, 1992). This dearth in research persists even though the Hispanic segment of the population is growing rapidly in the United States (Grow, 2004; Mosisa, 2002; Sanchez & Brock, 1996), and despite evidence that biases against Hispanic employees exist (Kenney & Wissoker, 1994; Sanchez & Brock, 1996). Thus, we chose to examine sources of bias toward Hispanics.

In order to reduce the potential of experimenter bias due to differences in sex or ethnicity, White (non-

Hispanic), male doctoral students were selected and trained to administer the surveys. The administrators were

personally trained by one of the researchers, and given specific written instructions to follow.

Two large entry-level management classes (N = 115, N = 150) were selected for the study. One week

prior to data collection, the instructor informed these students of an extra credit opportunity that would take

place the following week during the scheduled class time. The participants reported to their regular classroom

where they were randomly assigned to one of four separate, prearranged rooms. All participants were told they

would serve as employment interviewers, and they were asked to watch a video of a job applicant participating

in an interview. However, the video in each of the rooms differed on the basis of applicant name and applicant

accent. Participants were exposed to one of four conditions: a Hispanic accent with a Hispanic name, a Hispanic

accent with a non-Hispanic name, a standard American-English accent with a Hispanic name, or a standard

American-English accent with a non-Hispanic name.

After the participants reported to their assigned room, they were seated so that they could clearly view

and hear the videotaped interview. They signed an informed consent form. The participants were instructed to

imagine that they were hiring for the Human Resources Manager position. The general procedure was then

explained.

The participants were given the job description and the resume with the appropriate name manipulation

(Michael Fredrickson/ Miguel Fernandez) to review. The job description for the Human Resource Manager was

adapted from the Dictionary of Occupational Titles (U.S. Department of Labor, 1991). The resume informed Bias in the Interview 12 the participants that the applicant had the following qualifications: an MBA with a concentration in Human

Resource Management from a large state university (3.7 Grade Point Average - GPA); a B.S. in Business

Administration from a large state university (3.5 GPA); and internship experience with two major corporations,

performing duties such as designing training programs, updating job position descriptions, and working with

salary surveys.

Finally, the participants watched the ten-minute videotaped job interview that included the accent

manipulation and name manipulation (i.e., ethnicity cue), and then answered a two-part anonymous survey

related to the interview. When participants finished with the first part of the survey, they turned it in to the

administrator and received the second matched part of the survey. The first part of the survey contained

questions related to the following: the applicant’s perceived characteristics, the interviewer’s attitude toward

hiring the applicant, decision to hire, hire decision, participant demographics, and perceptions of the videotaped

job applicant’s demographics.

Precautions were taken to conceal the true nature of the study. Items tapping individuals’ perceptions of

accent and ethnicity were embedded among many other demographic-type items. The second part of the survey

included the ethnicity bias scale questions. This section of the survey was given separately in order to prevent

the participants’ answers on the first part of the survey from being primed by the modern ethnicity bias scale

questions.

Experimental Manipulations

Early linguistics researchers often used a matched-guise technique in an experimental situation in order

to control for extraneous factors. The present study utilized this approach to examine the influence of ethnicity

cue (i.e., name) and accent in the interview process by having the same actor perform identical interview scripts

while the accent and ethnicity cues of the actor were manipulated. With this technique, factors such as Bias in the Interview 13 appearance and voice tone were held constant in order to focus on the variables of interest (i.e., accent and ethnicity cue).

When creating the matched-guise videotapes for the accent and ethnicity cue manipulations, details were

thoroughly considered to insure quality manipulations. An experienced videographer donated her time and equipment to the project, including a professional video camera, lights, and microphones. An interview script, adapted from research by Howard and Ferris (1996), was used to ensure the same information was communicated in all conditions. The interview script combined with the applicant’s resume showed that the applicant was articulate, enthusiastic and motivated, as well as highly qualified for the position with the relevant university degrees and work experience.

Accent. Auditions were held in order to find an actor for the applicant role who had the ability to speak

with a standard American-English accent and a Hispanic accent. The chosen actor was a White male who had

experience with theater and with national commercials in both Spanish and English. Three linguistic experts independently verified the realism and the understandability of the Hispanic accent. The actor was instructed to keep body movements, facial expression, and posture as similar as possible in both the accented and non- accented conditions. Because the applicant was the same person in both videos, factors such as applicant attractiveness, tone of voice, and other mannerisms were virtually identical. Finally, the actor wore the same conservative business suit and tie in both conditions.

The second actor, a male with a standard American-English accent, assumed the role of the interviewer and was not shown in the video to prevent interference with the manipulations. Both actors had microphones, with the applicant’s microphone hidden under his clothing to prevent interference with realism. Participants were either exposed to the Hispanic-accented applicant (coded 1) or to the standard American-English accent

(coded 0). Bias in the Interview 14

Name. For the name manipulation, two identical resumes were constructed, with the only difference

being the name. Miguel Fernandez was used for the Hispanic ethnic cue, and Michael Fredrickson was used for

the non-Hispanic ethnic cue. Thus, participants encountered one of the following four conditions: Miguel with a standard American-English accent; Miguel with a Hispanic accent; Michael with a standard American-English accent; or Michael with a Hispanic accent. Additionally, the videotapes were professionally edited to reinforce the name manipulation by inserting the title “Human Resource Manager Applicant: Michael Fredrickson

(Miguel Fernandez)” into the introduction. At the beginning of the videotaped interviews, the applicant name was displayed for approximately seven seconds. Editing also was used to insert the beginning segment of dialogue in which both the interviewer and the applicant use the appropriate applicant name for added emphasis. The participants were asked to write the applicant name on the survey to check that they were aware of the name manipulation. All of the participants correctly recorded the applicant’s name.

The main actor (Miguel/ Michael) was a White male. Care was taken to choose an individual with physical characteristics such as white skin, brown eyes, and brown hair that could typically be considered either

Anglo American or Hispanic American. This race/gender mix was chosen in an effort to control for the potentially negative effect of other races and sex, because white males are still predominant in high-status positions in U.S. organizations (Ely, 1995). Controlling the race of the individual was imperative; as previously

mentioned, past studies examining biases against Hispanics have failed to control for this potentially important

factor. The participants were exposed to either Michael (non-Hispanic, coded 0) or to Miguel (Hispanic, coded

1).

Model Variables

Interviewer perceptions of applicant accent. Perceptions of accent were measured with an item used in

previous linguistics research (Ryan, Carranza, & Moffie, 1977). This item was embedded with other items that

measured the applicant’s perceived characteristics in an effort to conceal the fact that accent was a main Bias in the Interview 15 variable of interest in the study. Participants rated the applicant on a seven-point scale ranging from ethnic accented speech to Standard American accented speech (e.g., television/radio accent). Higher scores indicate perceptions of ethnic accented speech.

Interviewer perceptions of applicant ethnicity. The participants indicated which of the following categories they believed applied to the video applicant: Caucasian (0); African American (1); Hispanic (2);

Native American (3); Asian/ Pacific Islander (4); and Other (5). These categories for race/ethnicity were based on EEOC guidelines. Perceived ethnicity was coded “0” for Caucasian, “1” for Other Minority, and “2” for

Hispanic.

Modern ethnicity bias. Currently, no published modern ethnicity bias scale exists in the research literature that focuses specifically on Hispanics. Therefore, a scale was derived from McConahay’s (1986)

Modern Racism Scale in order to specifically assess the degree of the participants’ biases against Hispanics.

The Modern Racism Scale was originally designed to inconspicuously measure prejudice against

African Americans (McConahay, 1986). In our scale, all occurrences of the word “African American/s” were changed to “Hispanic/s”. In addition to the word adaptations, five items were added to the scale based on research examining controversial issues related to Hispanics, such as Spanish language usage in the United

States, border crossing issues, affirmative action, and treatment of migrant farm workers. One item that related to segregation issues appeared to be irrelevant to Hispanics, so it was adapted to reflect issues related to

Hispanics and school language issues.

All of the items were significantly correlated, and the Cronbach alpha reliability estimate was .79 in this study, and .85 in an earlier pilot study. Evidence of the construct validity (Nunnally, 1978) of our measure is demonstrated in the present study by virtue of its significant negative correlation with perceptions of Hispanics

(r = -.42, p < .001). Participants who have higher scores on the modern ethnicity bias scale tend to describe

Hispanics in more negative terms than those with lower scores on the scale. Please see the Appendix for all of Bias in the Interview 16 the items included in the modern ethnicity bias scale. Participants responded to a 7-point scale (7 = strongly agree and 1 = strongly disagree) with higher numbers indicating greater levels of prejudice.

This scale was designed to be less vulnerable to social desirability effects due to the type of questions used. The items used for this scale dealt with issues that are political in nature (e.g., Discrimination against

Hispanics is no longer a problem in the United States), instead of directly asking the respondent about their prejudice (e.g., Do you believe that Hispanics are less industrious than non-Hispanics). Therefore, it assessed prejudice in a less conspicuous manner than previous scales.

Interviewer judgments of applicant characteristics. Participants’ judgments of the applicant’s characteristics were assessed by asking the participants to rate the applicant on twenty-six bipolar pairs of adjectives using a seven-point scale, with 7 as the anchor for favorable traits and 1 as the anchor for unfavorable traits ( = .87). The adjective pairs were adapted from previous research focusing on characteristics of the ideal employee, effective top managers, and motivated workers (Larkin & Pines, 1979), and from research concentrating on Hispanics and accent discrimination by employment recruiters (Brennan & Brennan, 1981).

The following are examples of the adjective pairs used: unsuccessful-successful, lazy-industrious, unstable- stable, and tardy-prompt.

Interviewer decision to hire. Three statements, coded 1-7 (1 = strongly disagree, 7 = strongly agree;  =

.94), measured interviewers’ decision to hire the applicant. The scale items were: “I will probably NOT hire the applicant for the Human Resource Manager position” (reverse-coded); “It is likely that I WILL hire the applicant for the Human Resource Manager position”; and “I plan to hire the applicant for the Human Resource

Manager position.” Higher scores indicate a stronger decision to hire the applicant.

Control Variables

Social desirability. Because past research has indicated that social desirability among raters may affect the results of ethnicity-oriented studies (Mullins, 1982), an abbreviated 10-item form of the social desirability Bias in the Interview 17 scale was used (Strahan & Gerbasi, 1972) (coded 1-7, with 1 = strongly disagree, 7 = strongly agree;  = .70) as a control variable. Higher scores indicate a tendency to give socially desirable responses.

Accent understandability. Due to concerns that negative evaluations might be due to the applicant not

being understandable, and not due to the ethnic cues of accent and name, we controlled for the understandability

of the applicant. We asked participants to indicate their ability to understand the applicant’s speech on a one-

item, seven-point scale that ranged from “not understandable accent” to “understandable accent”. Higher scores

indicate a higher degree of understandability.

Participants’ demographic features. Self-reported demographic information on participants’

race/ethnicity, gender, GPA, and work experience were collected and used as control variables, based on previous research suggesting these variables may bias the results (Kenney & Wissoker, 1994; Vrij & Winkel,

1994).

Results

Table 1 presents the means, standard deviations, and the zero-order correlations among study variables.

After list-wise deletion of cases with missing data, 200 participants were included in the analyses. Diagonal entries indicate the internal consistency reliability estimates (i.e., Coefficients alpha). As indicated in the table, understandability and social desirability among participants had a greater number of significant correlations with the study variables than the other control variables (i.e., work experience, gender, GPA, and participant ethnicity). Following are more details regarding the manipulation checks, as well as the analytical results relating to the hypotheses.

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Insert Table 1 about here

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Bias in the Interview 18

Manipulation Checks

For the manipulation checks, there were two primary considerations: the name and the accent manipulations. Among the four experimental conditions, the strongest ethnicity manipulation was when accent and Hispanic name were combined. In this case, 100% of the participants identified the applicant as Hispanic or

Other Minority. When Miguel had no accent, 85% of the participants identified the applicant as Minority, and

when Michael had an accent, 96% of the participants identified him as Minority. In the condition where there were no Hispanic ethnicity cues, 32% of the participants identified the applicant as Hispanic.

We conclude that the combination of Hispanic name and accent is a strong cue to the ethnicity of the applicant. However, it is also clear that only one ethnic cue is needed to trigger the identification of the applicant as Hispanic or Other Minority. Interestingly, 32% of the participants identified the applicant with no

Hispanic ethnicity cues as Hispanic or Other Minority. Because all of the participants were from management classes, perhaps topics such as diversity in organizations were salient to them, which may have impacted their perception. Unfortunately, because the surveys were anonymous, no follow-up interviews were possible.

The correlation between perceived accent and the manipulated applicant accent was .79 (p < .001),

which demonstrates that the accent manipulation was effective. We also asked the participants to indicate their

ability to understand the applicant’s speech. A mean score of 5.95 (SD = 1.47), on a seven-point scale, was

obtained for this measure, which indicates that the applicant was generally well understood. The mean score for

understandability in the Ethnic Accent condition was 5.64, and for the Standard Accent condition the mean

score was 6.19 (t = 2.63, df = 188; p < .01). These results suggest that somewhat lower understandability ratings

were provided when a Hispanic accent was present, but in both conditions understandability was close to 6 on a

7-point scale.

Bias in the Interview 19

Tests of Hypotheses

We tested Hypotheses 1 and 2 using hierarchical regression, and Tables 2 and 3 provide the results of the data analyses for both hypotheses. To test Hypothesis 1, we entered the control variables, social desirability, modern ethnicity bias, work experience, GPA, participant ethnicity, perceived accent understandability, and participant gender in the first step of the regression analysis predicting the favorability of judgments of the applicant. At step two, we entered the name cue manipulation and accent cue manipulation variables, and in step three, we entered the interaction of the name cue and accent cue manipulations. Step three produced a

2 significant interaction effect (R =.021, F 1,189 = 5.157, p < .05) for the prediction of interviewers’ judgments

of the applicant. These results are presented in Table 2, indicating that Hypothesis 1 was partially supported.

Namely, applicant name and accent interacted to predict interviewers’ favorable judgments of the applicant. The

name and accent interaction was not significantly related to decision to hire.

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Insert Table 2 about here

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Procedures outlined by Cohen and Cohen (1983) were used to compute regression equations showing

the relationship between accent and favorable judgments of the applicant for the Hispanic and non-Hispanic

name conditions. A graphic representation of these equations is shown in Figure 1.

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Insert Figure 1 about here

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Figure 1 demonstrates that perceptions of the Hispanic named applicant became more negative when

the Hispanic named applicant also had an accent. However, this effect did not occur for the applicant with an

Anglo name, which corresponds to research that suggests accents associated with countries of lower socio- Bias in the Interview 20 economic status or darker skinned people are often viewed negatively (Ryan & Carranza, 1975; Callan, Gallois,

& Forbes, 1983). Factors such as the applicant qualifications, clothing, physical attractiveness, and age were

held constant by utilizing the same person and identical resume content. Only accent and name varied, yet

perceptions of the applicant changed. Moreover, in this study, the accented applicant was fluent in English,

used correct grammar, and had an understandable accent. Therefore, any accent discrimination against this

individual could not be justified as a legitimate communication issue.

As previously mentioned, it appears that only one ethnic cue is needed to trigger interviewers to identify

applicants as Hispanic. However, as expected, one ethnic cue did not result in as negative of a judgment as the

synergistic effect of two cues. Interestingly, the most favorable judgment was triggered by applicants with a

Hispanic name and with no accent.

Hypothesis 2 stated that participants’ modern ethnicity bias will interact with the ethnicity of the

applicant such that the negative relationship between ethnicity and judgments and decisions to hire will be

stronger when modern ethnicity bias is high. Hierarchical regression was used to test this hypothesis, with

modern ethnicity bias, perceived applicant ethnicity, and their interaction as predictors of favorable judgments

of applicant characteristics and decision to hire. The results of this analysis for favorable judgments are

presented in Table 3.

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Insert Table 3 about here

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The interaction term introduced at step 3 was not significant, which indicates that participant modern

ethnicity bias was not selectively associated with judgments about only the Hispanic applicant. However, step 2

2 was significant (R =.059; F 2,191 =7.226, p < .001). The standardized beta weight for perceived accent understandability (β = .286, t = 4.428, p < .001) shows that the applicant was judged more favorably when his Bias in the Interview 21 accent was perceived higher in understandability. The beta weight for participant GPA (β = -.188, t = -2.851, p

< .01) and gender (β = .147, t = 2.200, p < .05) were also significant indicating that male participants and those

with higher GPA’s tended to judge the applicant less favorably than female participants and those with lower

GPA’s. The beta for modern ethnicity bias (β = -.183, t = -2.627, p < .01) shows that this variable was

negatively related to favorable judgments of applicant characteristics.

In regard to decision to hire, the interaction term introduced at step 3 was not significant, which

indicates that participant modern ethnicity bias was not selectively associated with the decision to (or not to)

hire the Hispanic applicant. However, modern ethnicity bias was also negatively related to decision to hire (β =

-.261, t = -3.564, p < .01).

Path Analysis Results

Although the hypothesized name and accent cue interaction was related to favorable judgments of the

applicant, but not significantly related to the decision to hire, modern ethnicity bias was significantly related to

these variables. To further understand the nature of the relationship among ethnic cues, modern ethnicity bias,

judgments of the applicant, and decision to hire, we performed a path analysis using these variables. Figure 2

shows the results of the path analysis.

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Insert Figure 2 about here

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Figure 2 demonstrates that these causal paths explain significant amounts of variance in decision to hire

2 (R = .23; F(5,206) = 12.456; p < .001). As one would expect, a significant positive path was demonstrated

between favorable judgments of the applicant and decision to hire ( = .42; t= 6.562; p < .001). A significant

negative path to decision to hire was also obtained for modern ethnicity bias ( = -.16; t= -2.582; p < .01). Bias in the Interview 22

Significant negative paths to favorable judgments of the applicant were obtained for modern ethnicity bias ( =

-.24; t = -3.703; p < .001), and the name/accent cue interaction ( = -.31; t = -2.934; p < .01)

Table 4 shows the decomposition of causal effects through the path model (Alwin & Hauser, 1975).

The path analysis demonstrates that modern ethnicity bias indirectly affects the decision to hire through the

intervening variable, favorable judgments of the applicant, but it also exerts a significant negative direct effect

as well. The ethnicity cue interaction seems to affect decision to hire through its negative indirect effect on favorable judgments about the applicant, but its direct effect is not significant.

------

Insert Table 4 about here

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Discussion

Inappropriate, inaccurate, and even illegal decisions can occur regardless of the type of human resource management selection device utilized. However, the employment interview should be a prime target for research in this area, because it is the most frequently used tool for making employment decisions, and because more than other selection devices, the interview presents considerable opportunity for the influence of subtle cues and perceptual and judgmental biases to affect decisions. The present study examined the effects of ethnic cues on interviewers’ favorable judgments and their decision to hire applicants. We hypothesized that the synergistic effect of ethnic cues (i.e., ethnic name and ethnic accent), were more likely to trigger negative interviewer reactions toward an applicant than one ethnic cue or no ethnic cues. Even after controlling for participant modern ethnicity bias, support was found for the effect of an accent x ethnicity cue interaction on the favorable judgments of applicants’ characteristics.

As hypothesized, the most unfavorable judgments of the applicant were triggered by the combination of ethnic name and accent. Interestingly, the most favorable judgments were triggered when the applicant did not Bias in the Interview 23 have an accent, but had an ethnic name. These findings can be partially explained by the expectancy-violation

theory (Jussim, Coleman, & Lerch, 1978), which suggests that there often are lower expectations for minorities,

and when these expectations are violated in a positive direction (i.e., no accent), evaluations will be in the

direction of the violation. In other words, the Hispanic named applicant might have been viewed positively

because he spoke without an accent. This theory is similar to the accommodation hypothesis reported in the

linguistics field by Giles and Bourhis (1976), which states that efforts by ethnic minorities to increase

similarities between themselves and the majority group are associated with more favorable evaluations. To the

extent that speech contains prejudicial triggers, it can be altered to a style that is deemed more socially

acceptable. Accommodating their speech allows minority members to potentially reduce social costs and

increases the likelihood of social approval. Consistent with the accommodation hypothesis, participants may

have perceived the candidates to be more similar to the majority group, thus rating them more positively.

Modern ethnicity bias toward Hispanics was predicted to relate negatively to favorable judgments of the

Hispanic applicant, and not relate to judgments of the non-Hispanic applicant. We examined modern ethnicity

bias, perceived applicant ethnicity, and their interaction as predictors of favorable judgments of applicant

characteristics, and found that the interaction was not significant. This indicates that modern ethnicity bias was

not selectively associated with judgments about only the Hispanic applicant. Based on this finding, we then

examined the main effects of modern ethnicity bias on judgments of the applicant, attitudes about hiring, and

decision to hire the applicant.

Modern ethnicity bias demonstrated a negative relationship with favorable judgments of the applicant,

and these judgments of the applicant showed a positive association with the decision to hire. Further, modern

ethnicity bias had a direct negative relationship with the decision to hire. Together, these results indicate that

modern ethnicity bias seems to have a negative association with the favorable judgments of, and decisions to

hire all applicants, not just Hispanic applicants. Perhaps interviewer ethnic biases trigger a skeptical and Bias in the Interview 24 guarded view of others, which is translated into more negative perceptions of applicants in general. Additional research on the effects of modern ethnicity biases is needed. Finally, having favorable judgments of applicants is associated with the decision to hire the applicant.

Limitations of the Study

Some researchers may consider generalizability a problem when using students as interviewers.

However, when examining ethnicity issues, the deviations between students and actual employees or managers may be minimal (Barr & Hitt, 1986). This may be due to the fact that students, like managers, have been exposed to similar ethnic stereotypes through the media and society in general. If stereotypes are less prevalent among students, due to more progressive ideas among the youth of society, then the evidence of ethnicity bias among students found in this study may be a conservative estimate of the ethnicity bias of practicing managers.

Alternatively, it may be that practicing managers are more experienced with, and aware of, discrimination issues, and therefore would be less likely to respond in a biased manner. In order to investigate these potential differences, examining practicing professionals should help to extend this research.

A related concern is the potential lack of realism in the situation. As with most laboratory experimental situations, some realism is lost, but control is gained, by allowing for more precise manipulation of the variables of interest. Posthuma et al. (2002), in a review of the research pertaining to interviews, suggested that having participants view an interview without actively participating could lead to lack of involvement for participants, thus alleviating the responsibility that organizational members may feel in a real interview situation.

Presumably, this lack of accountability could lessen participants’ attention to the task at hand.

In the present study, the procedure was designed in order to increase participant involvement.

Interviewers were instructed to examine the applicant’s resume, to watch the interview carefully and imagine that they were actually interviewing the applicant, to rate the applicant on various aspects, and to make a hiring decision. Additionally, the use of a matched-guise video provided an opportunity to control verbal and Bias in the Interview 25 nonverbal cues (Posthuma, et al., 2002), which allowed accent to be teased apart and isolated from other cues in the environment.

In this study, the job description used for the applicant was for the Human Resource Manager position.

The choice of this particular job description may have affected the results if participants perceived that this job was associated with a certain degree of status. Previous research by Kalin and Rayko (1978) documented varying effects due to differing degrees of job status. Specifically, they found that foreign accented applicants were given lower evaluations for high status jobs and higher evaluations for low status jobs. Therefore, different job descriptions of varying degrees of status should be examined in future research.

Further, the qualifications of the applicant for the Human Resource Manager position in this study were high, indicating the applicant was clearly qualified for the position. Dovidio and Gaertner (2000) found that ethnic bias was most likely to occur when applicants had only reasonable qualifications and least likely to occur when applicants had qualifications that were either low (i.e., clear decision not to hire) or high (i.e., clear decision to hire). This study found ethnic biasing effects even when the applicant was clearly qualified. In the workplace, it is likely that applicants will have some good qualities as well as some less attractive qualities and that interviewers are normally dealing with individuals who do not have such high qualifications that the decision to hire is clear. Thus, the results of this study may actually underestimate the degree to which ethnic biases affect the judgments and decisions of interviewers.

The results of this research also may vary depending on where the study is conducted, and on the composition of the sample. In other parts of the country, Hispanic ethnicity cues may be perceived more or less readily, and Hispanics may face more or less discrimination. For example, in the Miami, Florida, area where there is a large Cuban-American population, is there more or less accent and general ethnicity discrimination against Hispanics than in an area of the country where there is very little exposure to Hispanics? Research has found some support for the contention that ethnic minorities, like members of the majority ethnic group, tend to Bias in the Interview 26 have negative perceptions of ethnic accents and positive perceptions of standard accents (Ryan & Carranza,

1975; Callan et al., 1983).

Other research has suggested that individuals are more likely to gravitate toward others who are similar.

For example, in a study of workgroup preferences, individuals demonstrated a clear desire for working with

others who were racially similar (e.g., Hinds, Carley, Krackhardt, & Wholey, 2000). More research is needed to

validate these results, and to examine various populations of Hispanics (e.g., Mexican Americans, Puerto

Ricans, Cuban Americans) to observe whether there are any subculture differences in responses.

Implications and Future Research Directions

A key contribution of the present study is that it allowed a closer look at the potential underlying triggers

of bias in the employment interview process by examining cues associated with ethnicity. Building on this

study, there are some important directions for future research. One area that needs attention is to investigate the

influence of interview structure on ethnic/racial cue effects on interviewer decisions. The present study used a

standard stimulus (i.e., videotaped interview) in presentation of applicant cues to an interviewer. This most

closely resembles a structured interview format, where it would be argued that biases might be less observable

because attention and focus is maintained on job-related content issues. Indeed, the unstructured interview tends

to be where job-irrelevant information tends to emerge to influence interviewer decisions (e.g., Dipboye, 1994).

It would be interesting to compare structured to unstructured interviews to see if the observed effects from this

study regarding ethnic cues differ by interview format.

There has been growing research interest in recent years in the use of applicant impression management

tactics, and their effects on interviewer ratings (e.g., Gilmore, Stevens, Harrell-Cook, & Ferris, 1999). Indeed,

Gilmore et al. proposed an adaptation of the Ferris and Judge (1991) framework, which shows applicant

impression management tactics affect interviewer decisions and actions through the potential mediating

variables of liking, perceived similarity, or perceived competence. It would be interesting to investigate whether Bias in the Interview 27 applicants’ impression management tactics overshadowed, and thus neutralized, their race or ethnicity in affecting interviewers’ judgments and decisions. It might be the case that minority job applicant self-promotion tactics are successful in elevating their competence in the eyes of the interviewer to a level that eliminates any effects of ethnicity bias. This would be a new area of research because virtually no work has been done relating ethnicity to social influence (Ferris, Hochwarter, Douglas, Blass, Kolodinsky, & Treadway, 2002).

Besides interview format and impression management, future research should examine other factors that might constrain or magnify the effects of applicant ethnic/racial cues on interviewer judgments and decisions. It would be interesting to investigate the ethnicity of both applicant and interviewer in employment interviews to see if there are rating effects for ethnicity similarity. Effects of ethnicity or racial similarity on interview ratings have been reported for both African Americans (McFarland, Sacco, Ryan, & Kriska, 2000; Prewett-Livingston,

Feild, Veres, & Lewis, 1996), and for Hispanics (Lin et al., 1992). However, all three of these studies used panel interviews as opposed to the more conventional one-on-one interviews. Because one-on-one interviews would seem even more likely to produce ethnic/racial similarity effects (Sacco, Scheu, Ryan, & Schmitt, 2003), interview scholars should proceed in this direction.

Additionally, we would suggest that future research investigate the area of person -organization fit as it relates to interviewer decision making regarding ethnic/racial cues. Some recent work has proposed social- cognitive theoretical underpinnings for an integrative theory of multidimensional fit that focuses on a prototype- matching approach (Wheeler, Buckley, Halbesleben, Brouer, & Ferris, 2005). Most fit research in the employment interview has investigated supplementary fit, which considers how applicants seek to match particular characteristics they possess to the employing organization’s environment. The investigation of ethnic/racial cues in the area of person-organization fit would be applicable to the concept of complimentary fit, whereby applicants’ personal attributes and characteristics are viewed as adding something new that is not presently found in the organizational environment. Bias in the Interview 28

Future research is needed to determine if the results found in this study replicate to other scenarios or samples. One area for further exploration is investigate whether name and accent cues trigger interviewer perceptions based on country of origin as well as race. For instance, is a Caucasian applicant with a Russian accent and Russian name perceived more negatively or positively than when the applicant has a Russian name and no accent, or a Russian accent and non-Russian name? Furthermore, future research may address the extent to which these effects occur in decision-making processes with internal applicants (e.g., promotions, opportunities for training). Perhaps these efforts will help delineate the relative strength of name and accent cues in different samples, as well as identify situations in which the effects generalize.

The results of the present study suggest the need for continued efforts to increase the effectiveness of

interviewer judgment and decision making. As demonstrated in this study, interviewers are vulnerable to

making biased judgments about applicants. Potential solutions to reducing interviewer biases include training

interviewers, structuring the rating procedures, using multiple interviewers, using videotaped interviews, and

selecting effective interviewers. In particular, future research focused on interviewer training is needed.

Although there is evidence that trained interviewers may be able to make more objective hiring decisions, most

interviewers still do not receive much training, if any at all, before being allowed to conduct employment

interviews (Howard & Ferris, 1996; Kennedy, 1994). More research is needed that explores the effectiveness of

interviewer-training methods in reducing biases.

Interviewer characteristics, besides a bias toward ethnic minorities, also should be examined in future

research. For example, international experience may be correlated with more positive perceptions of applicants

with diverse characteristics, such as non-standard accents. Personality differences among interviewers also may

be important in this research. Perhaps interviewers that rate high on the “openness to experience” dimension of

the Five-Factor Model of personality are less likely to be prejudiced toward ethnic minorities, or less likely to Bias in the Interview 29 apply these prejudices toward particular job applicants. Openness to experience or extraversion of the trainees also may be important individual difference variables related to the effectiveness of interviewer training.

Conclusion

This study offers several notable contributions to the research literature. First, because it allows for excellent control in experimental conditions, the matched-guise technique was employed in the present study.

Although this technique has not been widely used in management research to date, perhaps the present study will encourage researchers to consider using this type of methodological approach in studying organizational phenomena. Second, this research separates the effects of accent from the effects of ethnicity cues. Previous ethnicity research generally has failed to separate the confounding factors of accent and ethnicity, factors that appear to have interactive effects. The results of the present study indicate that, to a degree, interviewers are still allowing illegal and often irrelevant factors, such as the combined effects of ethnicity and accent, to affect judgments and decisions about job applicants, instead of focusing only on job-related qualifications. In essence, we are still judging the book by the cover rather than solely by the contents. Bias in the Interview 30

References

Alwin, D. F., & Hauser, R. M. (1975). The decomposition of effects in path analysis. American Sociological

Review, 40, 37-47.,

Atkins, C. P. (1993). Do employment recruiters discriminate on the basis of nonstandard dialect? Journal of

Employment Counseling, 30, 108-118.

Barr, S. H., & Hitt, M. A. (1986). A comparison of selection decision models in manager versus student

samples. Personnel Psychology, 39, 599-617.

Bolaffi, G., Braham, P., Gindro, S., Tentori, T., & Bracalenti, R. (2003). Dictionary of Race, Ethnicity &

Culture. Thousand Oaks, CA: Sage Publications.

Brennan, E. M., & Brennan, J. S. (1981). Accent scaling and language attitudes: Reactions to Mexian American

English speech. Language and Speech, 24, 207-221.

Callan, V. J., Gallois, C., & Forbes, P. A. (1983). Ethnic stereotypes: Australian and Southern European youth.

Journal of Social Psychology, 119, 287-288.

Cargile, A. C. (2000). Evaluations of employment suitability: Does accent always matter? Journal of

Employment Counseling, 37, 165.

Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences

(Second edition). Hillsdale, NJ: Lawrence Erlbaum.

DeShields, O. W., Kara, A., & Kaynak, E. (1996). Source effects in purchase decisions: The impact of physical

attractiveness and accent of salesperson. International Journal of Research in Marketing, 13, 89-101.

Dipboye, R.L. (1994). Structured and unstructured selection interviews: Beyond the job-fit model. In G.R.

Ferris (Ed.), Research in personnel and human resources management (Vol. 12, pp. 79-123).

Greenwich, CT: JAI Press.

Dipboye, R. L. (1992). Selection interviews: Process perspectives. Cincinnati, OH: South-Western. Bias in the Interview 31

Dovidio, J. F. (2001). On the nature of contemporary prejudice: The third wave. Journal of Social Issues, 57,

829-849.

Dovidio, J. F., Brigham, J. C., Johnson, B. T., & Gaertner, S. L. (1996). Stereotyping, prejudice, and

discrimination: Another look. In C. N. Macrae, C. Stangor, & M. Hewstone (Eds.), Stereotypes and

stereotyping (pp. 276-319). New York: The Guilford Press.

Dovidio, J. F., & Gaertner, S. L. (2000). Aversive racism and selection decisions: 1989 and 1999. Psychological

Science, 11, 319-323.

Duncan, B. L. (1976). Differential social perception and attribution of intergroup violence: Testing the lower

limits of stereotyping of blacks. Journal of Personality and Social Psychology, 34, 590-598.

Eder, R.W., & Harris, M.M. (1999). Employment interview research: Historical update and introduction. In

R.W. Eder & M.M. Harris (Eds.), The employment interview handbook (pp. 1-27). Thousand Oaks, CA:

Sage Publications.

Ely, R. J. (1995). The power in demography: Women’s social constructions of gender identity at work.

Academy of Management Journal, 38, 589-703.

Fairchild, H. H., & Cozens, J. A. (1981). Chicano, Hispanic or Mexican American: What’s in a name.

Hispanic Journal of Behavioral Sciences 3, 191-198.

Ferris, G.R., & Judge, T.A. (1991). Personnel/human resources management: A political influence perspective.

Journal of Management, 17, 447-488.

Ferris, G.R., Hochwarter, W.A., Douglas, C., Blass, F.R., Kolodinsky, R., & Treadway, D.C. (2002). Social

influence processes in organizations and human resources systems. In G.R. Ferris & J.J. Martocchio

(Eds.), Research in personnel and human resources management (Vol. 21, pp. 65-127). Oxford, UK:

JAI Press/Elsevier Science. Bias in the Interview 32

Ford, T. E., & Stangor, C. (1992). The role of diagnosticity in stereotype formation: Perceiving group means

and variances. Journal of Personality and Social Psychology, 63, 356-367.

Giles, H., & Bourhis, R. Y. (1976). Methodological issues in dialect perception: A social psychological

perspective. Anthropological Linguistics, 19, 294-304.

Giles, H., Williams, A., Mackie, D. M., & Rosselli, F. (1995). Reactions to Anglo- and Hispanic-American-

accented speakers: Affect, identity, persuasion, and the English-only controversy. Language and

Communication, 15, 107.

Gilmore, D.C., Stevens, C.K., Harrell-Cook, G., & Ferris, G.R. (1999). Impression management tactics. In R.W.

Eder & M.M. Harris (Eds.), The employment interview handbook (pp. 321-336). Thousand Oaks, CA:

Sage Publications.

Grow, B. (March 15, 2004). Hispanic nation: Is America ready? Business Week, 59-70.

Hinds, P. J., Carley, K. M., Krackhardt, D., & Wholey, D. (2000). Choosing work group members: Balancing

similarity, competence, and familiarity. Organizational Behavior and Human Decision Processes, 81,

226-251.

Howard, J. L., & Ferris, G. R. (1996). The employment interview context: Social and situational influences on

interviewer decisions. Journal of Applied Social Psychology, 26, 112-136.

Jones, J. M. (1986). Racism: A cultural analysis of the problem. In J. F. Dovidio & S. L. Gaertner (Eds,),

Prejudice, discrimination, and racism (pp. 279-314). Orlando, FL: Academic Press.

Judge, T.A., & Ferris, G.R. (1992). The elusive criterion of fit in human resources staffing decisions. Human

Resource Planning, 15, 47-67.

Jussim, L., Coleman, L.M., & Lerch, L. (1987). The nature of stereotypes: A comparison and integration of

three theories. Journal of Personality and Social Psychology, 52, 536-546. Bias in the Interview 33

Kalin, R., & Rayko, D. S. (1978). Discrimination in evaluative judgments against foreign-accented job

candidates. Psychological Reports 43, 1203-1209.

Kawakami, K., Dion, K. L., & Dovidio, J. F. (1998). Racial prejudice and stereotype activation. Personality &

Social Psychology Bulletin, 24, 407-416.

Kennedy, R. B. (1994). The employment interview. Journal of Employment Counseling, 31, 110-114.

Kenney, G. M., & Wissoker, D. A. (1994). An analysis of the correlates of discrimination facing young

Hispanic job-seekers. American Economic Review, 84, 674-683.

Larkin, J. C., & Pines, H. A. (1979). No fat persons need apply: Experimental studies of the overweight

stereotype and hiring preference. Sociology of Work and Occupations, 60, 312-327.

Lewis, A. C., & Sherman, S. J. (2003). Hiring you makes me look bad: Social-identity based reversals of the in-

group favoritism effect. Organizational Behavior and Human Decision Processes, 90, 262-276..

Lin, T. R., Dobbins, G. H., & Farh, J. L. (1992). A field study of race and age similarity effects on interview

ratings in conventional and situational interviews. Journal of Applied Psychology, 77, 363-371.

Lippi-Green, R. (1994). Accent, standard language ideology, and discriminatory pretext in the courts.

Language in Society, 23, 163-198.

Lippi-Green, R. (1997). English with an accent: Language, ideology, and discrimination in the United States.

London: Routledge.

Marin, G. (1984). Stereotyping Hispanics: The differential effect of research method, label, and degree of

contact. International Journal of Intercultural Relations, 8, 17-27.

Markus, H. & Zajonc, R.B. (1985). The cognitive perspective in social psychology. In G. L.indzey & E.

Aronson (Eds.), Handbook of social psychology (Vol. 1, pp. 137-230). New York: Random House.

McConahay, J. B. (1986). Modern racism, ambivalence, and the Modern Racism Scale. In J. F. Dovidio & S.

L. Gaertner (Eds.), Prejudice, discrimination, and racism (pp. 91-125). Orlando: Academic Press. Bias in the Interview 34

McFarland, L.A., Sacco, J.M., Ryan, A.M., & Kriska, S.D. (2000). Racial similarity and composition effects on

structured panel interview ratings. Paper presented at the 15th Annual Conference of the Society for

Industrial and Organizational Psychology, New Orleans.

Mosisa, A. T. (2002). The role of foreign-born workers in the U.S. economy. Monthly Labor Review, 125, 3-14.

Mullins, T. W. (1982). Interviewer decisions as a function of applicant race, applicant quality and interviewer

prejudice. Personnel Psychology, 35, 163-174.

Nesdale, A. R., & Rooney, R. (1990). Evaluations and stereotyping. Australian Journal of Psychology, 42, 309-

319.

Nunnally, J. C. (1978). Psychometric theory (Second edition). New York: McGraw-Hill.

Pingitore R., Dugoni, B. L. Tindale, R. S., & Spring, B. (1994). Bias against overweight job applicants in

simulated employment interview. Journal of Applied Psychology, 79, 909-917.

Podberesky, R., Deluty, R. H., & Feldstein, S. (1990). Evaluations of Spanish- and Oriental-accented English

speakers. Social Behavior and Personality, 18, 53-63.

Posthuma, R. A., Morgeson, F. P., & Campion, M. A. (2002). Beyond employment interview validity: A

comprehensive narrative review of recent research and trends over time. Personnel Psychology, 55, 1-

81.

Prewett-Livingston, A.J., Feild, H.S., Veres, J.G. & Lewis, P.M. (1996). Effects of race on interviewer ratings

in a situational panel interview. Journal of Applied Psychology, 81: 178-186.

Roehling, M.V., Campion, J.E., & Arvey, R.D. (1999). Unfair discrimination issues. In R.W. Eder & M.M.

Harris (Eds.), The employment interview handbook (pp. 49-67). Thousand Oaks, CA: Sage Publications.

Ryan, E. B., & Carranza, M. A. (1975). Evaluative reactions of adolescents toward speakers of standard English

and Mexican-American accented English. Journal of Personality and Social Psychology, 31, 855-863. Bias in the Interview 35

Ryan, E. B., Carranza, M. A., & Moffie, R. W. (1977). Reactions toward varying degrees of accentedness in

the speech of Spanish-English bilinguals. Language and Speech, 20, 267-273.

Sacco, J.M., Scheu, C.R., Ryan, A.M., & Schmitt, N. (2003). An investigation of race and sex similarity effects

in interviews: A multilevel approach to relational demography. Journal of Applied Psychology, 88, 852-

865.

Sanchez, J. I., & Brock, P. (1996). Outcomes of perceived discrimination among Hispanic employees: Is

diversity management a luxury or a necessity? Academy of Management Journal, 39, 704-719.

Sigelman, L., Shockey, J. W., & Sigelman, C. K. (1993). Ethnic stereotyping: A Black-White comparison. In

P. M. Sniderman, P. E. Tetlock, & E. G. Carmines (Eds.), Prejudice, politics, and the American

dilemma (pp. 104-126). Stanford, CA: Stanford University Press.

Simmons, O.G. (1961). The mutual images and expectations of Anglo Americans and Mexican Americans.

Daedalus, 90, 286-299.

Singer M., & Eder, G. S. (1989). Effects of ethnicity, accent, and job status on selection decisions.

International Journal of Psychology, 24, 13-34.

Slavin, L. A., Rainer, K. L., McCreary, M. L., & Gowda, K. K. (1991). Toward a multicultural model of the

stress process. Journal of Counseling & Development, 70, 156-163.

Snyder, M., Tanke, E. D., & Berscheid, E. (1977). Social perception and interpersonal behavior: On the self-

fulfilling nature of social stereotypes. Journal of Personality and Social Psychology, 35, 656-666.

Stangor, C., & McMillan, D. (1992). Memory for expectancy-congruent and expectancy-incongruent

information: A review of the social and social developmental literatures. Psychological Bulletin, 111,

42-61.

Strahan, R., & Gerbasi, K. (1972). Short, homogeneous versions of the Crowne-Marlowe Social Desirability

Scale. Journal of Clinical Psychology, 28, 191-193. Bias in the Interview 36

Taylor, S.E. & Fiske, S.T. (1978). Salience, attention, and attribution: Top of the head phenomena. In L.

Berkowitz (Ed.), Advances in experimental social psychology (Vol. 11). New York: Academic Press.

Taylor, S.E. & Thompson, S.C. (1982). Stalking the elusive “vividness” effect. Psychological Review, 89, 155-

181.

U. S. Department of Labor, Employment and Training Administration, U. S. Employment Service, (1991).

Dictionary of occupational titles (4th edition revised). Washington D.C., U. S. Government Printing

Office.

Vrij, A., & Winkel, F. W. (1994). Perceptual distortions in cross-cultural interrogations: The impact of skin

color, accent, speech style, and spoken fluency on impression formation. Journal of Cross-Cultural

Psychology, 25, 284-295.

Wheeler, A.R., Buckley, M.R., Halbesleben, J.R., Brouer, R.L., & Ferris, G.R. (2005). “The elusive criterion of

fit” revisited: Toward an integrative theory of multidimensional fit. In J.J. Martocchio (Ed.), Research in

personnel and human resources management (Vol. 24, pp. 265-304). Oxford, UK: JAI Press/Elsevier

Science.

Wilson, C. C. II, & Gutierrez, F. (1995). Race, multiculturalism, and the media: From mass to class

communication (Second edition). Thousand Oaks, CA: Sage Publications. Bias in the Interview 37

Figure Captions

Figure 1

Graphic Representation of Name Cue x Accent Cue Interaction on Interviewer Judgment of

Applicant

Figure 2

Path Analysis of the Effects of Modern Ethnicity Bias, Name Cue and Accent Cue on Interviewer

Judgment of Applicant and Interviewer Decision to Hire Bias in the Interview 38

Figure 1

For Hispanic Name: Interviewer Judgment of Applicant = -.237 x Accent + 5.411

For Anglo Name: Interviewer Judgment of Applicant= .088 x Accent + 5.254

5.45 5.4 5.35 5.3 Anglo Name Cue 5.25 Hispanic 5.2 Name Cue 5.15 5.1 5.05 No Accent Accent

Bias in the Interview 39

Figure 2

-.16** Modern Ethnicity Bias

-.07 -.24***

Name .01 Cue .14

Favorable Decision to Judgment of -.13* .42*** Hire Applicant R2=.23*** -.04 R2 =.10*** .10 Accent .50*** Cue

-.31**

.54***

.08 Name x Accent Interaction

Numbers on paths are r’s (i.e., double arrows) and ’s (i.e., single arrows).

* p < .05 ** p < .01 *** p < .001

Bias in the Interview 40

Table 1 Means, Standard Deviations, and Correlations among Study Variables (Diagonal Values Are Reliabilities) (N=200)

M SD. 1 2 3 4 5 6 7 8 9 10 11 12 13 1 Applicant Namea 0.52 0.50 -

2 Applicant Accentb 0.46 0.50 -.118* -

3 Perceived Accent 3.70 1.97 -.122* .792*** -

4 Perceived Accent 5.95 1.47 .114 -.198** -.155* - Understandability 5 Perceived Applicant 1.53 0.80 .312*** .380*** .376*** -.014 - Ethnicityc 6 Modern Ethnicity Bias 3.18 0.95 - .064 - .017 .060 - .086 -.065 .84

7 Interviewer Favorable 5.09 0.54 .032 -.094 -.146* .292*** .172** -.223*** .87 Judgment of Applicant 8 Interviewer Decision to 5.10 1.60 .035 -.002 -.039 .172** .116* -.286*** .94 Hire .447*** 9 Social Desirability 4.36 0.87 -.028 .036 .020 .071 -.053 -.192** .151* .160* .70

10 Work Experience 5.05 3.65 .061 -.123* -.032 .019 -.075 -.145* -.076 -.071 .160* - (Months) 11 Grade Point Average 3.00 0.43 .013 .034 -.039 .001 .025 .009 -.148* -.058 -.042 -.052 -

12 Participant Ethnicityd 0.47 0.70 -.027 .096 .156* .005 .037 -.294*** .035 .083 -.001 -.053 -.131* -

13 Participant Gendere 0.46 0.50 -.078 .093 -.014 -.089 .040 -.105 .118* .061 -.028 -.140* .186** .082 -

a 0= Anglo name cue; 1= Hispanic name cue b 0= North American English accent; 1= Hispanic accent c 0= Caucasian; 1= Other Minority; 2= Hispanic d 0= Caucasian; 1= Other Minority; 2= Hispanic e 0= Male; 1= Female

One-tail significance * p < .05 ** p < .01 *** p < .001 Bias in the Interview 41 Table 2 Hierarchical Regression of Interviewer Favorable Judgment of Applicant on Name Cue, Accent Cue, and Their Interaction (N =200) Step 1 Step 2 Step 3 Independent Variables B  t B  t B  t Control Variables Accent Understandability .104 .283 4.331*** .099 .270 4.017*** .089 .242 3.584*** Social Desirability .068 .109 1.632 .114 .071 1.694 .071 .115 1.723 Work Experience -.018 -.120 -1.790 -.019 -.128 -1.889 -.018 -.120 -1.787 GPA -.234 -.185 -2.768** -.231 -.183 -2.726** -.225 -.179 -2.682** Participant Ethnicitya -.052 -.067 -0.971 -.047 -.061 -0.878 - .036 - .047 -0.683 Participant Genderb .161 .149 2.204* .165 .152 2.240* .161 .149 2.213* Modern Ethnicity Bias -.112 -.197 -2.790** -.112 -.197 - 2.776** -.109 -.192 -2.739** Predictor IV’s Name Cue Manipulationc .005 .005 0.074 .157 .145 1.613 Accent Cue Manipulationd -.071 -.065 -0.965 .088 .081 0.872 Name-Accent Product -.325 -.243 -2.271* Intercept 5.275 13.349***5.314 13.262***5.254 13.223*** Regression Statistics R .441 .446 .469 F (df) Regression 6.639 (7,192)*** 5.242 (9,190)*** 5.337 (10,189)*** Adj. R2 .166 .161 .179 R2 .195 .004 .021 F (df) of R2 6.639 (7,192)*** .478 (2,190) 5.157 (1,189)* a 0= Caucasian; 1= Other Minority; 2= Hispanic b 0= Male; 1= Female c 0= Anglo name cue; 1= Hispanic name cue d 0= North American English accent; 1= Hispanic accent Two-tail Significance: * p < .05; ** p < .01; *** p < .001 Bias in the Interview 42 Table 3 Hierarchical Regression of Interviewer Favorable Judgment of Applicant on Modern Ethnicity Bias, Perceived Applicant Ethnicity, and Their Interaction (N =200)

Step 1 Step 2 Step 3 Independent Variables B  t B  t B  t Control Variables Accent Understandability .110 .299 4.508*** .105 .286 4.428*** .105 .285 4.390*** Social Desirability .088 .142 2.119* .073 .118 1.787 .073 .117 1.774 Work Experience -.013 -.091 -1.345 -.016 -.108 -1.627 -.016 -.108 -1.625 GPA -.228 -.180 -2.652** -.238 -.188 -2.851** -.237 -.187 -2.826** Participant Ethnicitya -.007 -.009 -0.130 -.053 -.069 -1.010 -.052 -.067 -0.978 Participant Genderb .185 .170 2.496* .159 .147 2.200* .159 .147 2.199* Predictor IV’s Perceived Ethnicityc .110 .163 2.539* .083 .123 0.506 Modern Ethnicity Bias -.104 -.183 - 2.627** -.117 -.206 -1.378 Product .008 .046 0.171 Intercept 4.724 13.561*** 5.057 12.674*** 5.099 10.879*** Regression Statistics R .403 .470 .470 F (df) Regression 6.229 (6,193)*** 6.780(8,191)*** 5.999(9,190)*** Adj. R2 .136 .189 .184 R2 .162 .059 .000 F (df) of R2 6.229 (6,193)*** 7.226 (2,191)*** .029(1,190) a 0= Caucasian; 1= Other Minority; 2= Hispanic b 0= Male; 1= Female c 0= Caucasian; 1= Other Minority; 2= Hispanic Two-tail Significance: * p < .05; ** p < .01; *** p < .001 Bias in the Interview 43

Table 4

Decomposition of Direct and Indirect Effects for Modern Ethnicity Bias and Name/Accent Cue Interaction on Decision to Hire

Coefficients ()

Dependent Variables Independent Variables Favorable Decision to Decision Judgment of Hire to Hire Applicant Step 1 Step 2

Modern Ethnicity Bias -.244*** -.267*** -.163* Name Cue .144 .018 -.043 Accent Cue .101 .039 -.004 Name x accent -.314** -.028 .105 Favorable Judgment of .423*** Applicant

Dependent Independent Total Effect Indirect Direct Effect Variables Variables Effect via Favorable Judgment of Applicant Favorable Modern -.244 ---- -.244 Judgment of Ethnicity Bias Applicant Name Cue .144 .144 Accent Cue .101 .101 Name x Accent -.314 -.314 ---- Decision to Modern -.265 -.103 -.162 Hire Ethnicity Bias Name Cue .061 .061 .000 Accent Cue .042 .042 .000 Name x Accent -.051 -.132 .081 Favorable .421 .421 Judgment of Applicant Bias in the Interview 44

Appendix

Please indicate the degree to which you disagree or agree with each of the following statements by circling the appropriate number.

1. Over the past few years, the government and news media have shown more respect to Hispanics than they deserve. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 2. It is easy to understand the frustration of Hispanics in America. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 3. Discrimination against Hispanics is no longer a problem in the United States. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 4. Over the past few years, Hispanics have gotten more economically than they deserve. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 5. Hispanics have more influence upon school language issues than they ought to have. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 6. Hispanics are getting too demanding in their push for the usage of the Spanish language. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 7. Hispanics should not push themselves where they are not wanted. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 8. Hispanics are taking advantage of their minority status. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 9. Hispanics are taking too many jobs from non-minorities. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 10. Migrant farm-workers have been treated poorly in many instances. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 11. Hispanics often intentionally exclude non-Spanish speakers in their conversations. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree 12. Mexicans crossing the US border are often dealt with too harshly. Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree

Modern Ethnicity Bias Scale (Adapted from McConahay’s Modern Racism Scale, 1986). Items 1 through 7 are adapted from the original scale and items 6 through12 are additions. Questions 2, 10, and 12 are reverse-coded. 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities

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Home > Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities

Limited English Procient Workers and the Workforce Investment Act: Challenges and Opportunities

JULY 19, 2012 FEATURE By Chhandasi Pandya

As the U.S. Congress considers reauthorization of the Workforce Investment Act (WIA) of 1998, policymakers and immigrant advocates are pushing for a variety of changes meant to better help Limited English Proficient (LEP) immigrants access the employment and training services necessary to enter into or advance within the U.S. labor market. With a number of proposals floated to rethink the WIA-funded workforce and adult education systems, the debate highlights the challenges LEP workers face in earning family-sustaining wages and effectively participating in a changing labor market.

Though perhaps little recognized as such, WIA represents one of the most important immigrant integration programs at the federal level both in size and scope, and its reauthorization has key implications for the integration of immigrant workers into the U.S. economy and society more broadly.

Reauthorization — which is likely to fall flat in the current Congress (as it has a number of times since the law technically expired in 2003) but may be taken up by future Congresses — has general support from both sides of the aisle. Stark disagreement remains on how to revamp the system, however, chiefly for reasons having nothing to do with immigrant workers. In the meantime, the employment and training needs of foreign-born workers, which are often different than those for the native born, are quickly changing and states face significant limitations in serving this population through federally funded programs.

There were 23 million immigrants in the U.S. civilian labor force in 2010, accounting for one in six workers ages 16 and older — a figure that has roughly doubled since 1990. Within the immigrant labor force, nearly half speak English less than "very well" (as defined by the U.S. Census Bureau) and are classified as "Limited English Proficient," a language barrier that affects their employability and wage-earning potential. Though the WIA system is the core source of federal support for workforce training, English as a Second Language (ESL) instruction, and other adult education programs, the current design has created barriers for LEP workers seeking workforce services. In particular, misalignment in goals, program services, and performance https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 1/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities accountability mechanisms across WIA-funded programs has made it difficult for these workers to obtain the language and work training services they need to advance in the workforce. At the same time, the LEP population continues to grow. In 2000, about 17.8 million U.S. adults, or 8.5 percent of the population, had limited English proficiency. Today, there are roughly 22.5 million LEP adults, accounting for 10 percent of the U.S. adult population and 9 percent of the labor force.

At the time of WIA's passage in 1998, policymakers were focused on "work-first" policies popularized under welfare reform and so emphasized rapid re-employment and the acquisition of short-term skills as core training approaches. Since then, labor force demand for middle-skilled jobs — which require longer-term education and training — has grown. The Bureau of Labor Statistics (BLS) reports that one-third of all job openings and nearly half of all new jobs created between 2008 and 2018 will require a postsecondary degree or credential. Nationally, immigrants accounted for 49 percent of workers without a high school degree and 15 percent of college- educated workers ages 25 and older. (Just 11 percent of all U.S.-born adults lack a high school diploma or GED, compared to 32 percent of immigrant adults.) California, for example, ranked first for the highest share of immigrants among low-educated workers (80 percent) and the highest share of immigrants in the college- educated workforce (30 percent). LEP adults ages 18 and older account for 39 percent of all low-skilled workers. In California and Texas, the two states with the largest LEP populations and largest state economies, more than half of low-skilled workers have limited English proficiency (66 percent in California, 53 percent in Texas). Furthermore, 47 percent of LEP adults lack a high school diploma.

Demographic and economic changes have created pressure for states and localities to develop training and education programs to allow workers to upgrade their skills and obtain credentials to fill jobs in high-growth sectors such as health care and information technology. For these opportunities to become available to a significant number of LEP workers, the number of WIA-funded training programs that provide integrated workforce training and English-language instruction would need to be greatly expanded.

This article provides an overview of WIA's structure relating to employment and training services for LEP workers, examines Department of Labor (DOL) data showing the extent to which LEP workers have accessed the system since its inception, and reviews key themes of relevance in the reauthorization debate.

Challenges to the Workforce Investment Act (WIA) of 1998

WIA's Title I and Title II provide funding for different aspects of workforce training and education. Title I funds employment and training initiatives, and Title II funds adult education and ESL instruction.

Congress has continued to appropriate funds for WIA despite its expiry in 2003. Funding has been on a general decline, however, amid failed attempts to reauthorize the law and growing bipartisan criticism of WIA's structure and implementation. Between fiscal years (FY) 2001 and 2010, funding for employment and training initiatives (Title I) declined 30 percent, according to inflation-adjusted calculations by the National Skills Coalition. In FY 2010 — the latest year for which data are presented below — Congress appropriated $2.97 billion for workforce training programs, which decreased to $2.62 billion in FY 2012. Between FY 2001-10, federal funding for adult education (Title II) declined by about 16 percent, with Congress appropriating $628.2 million for adult education and ESL instruction in FY 2010 and $606.3 million in FY 2012. For LEP workers who https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 2/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities require services under both titles to become job ready, the decline in funding has presented challenges to obtaining basic ESL courses, which are often oversubscribed, as well as appropriate training opportunities.

Title I's employment and training system provides funding to states for workforce services for three key populations: adults, youth, and dislocated workers (individuals who have been laid off, fired, or were self- employed but can no longer work because of economic conditions). Dislocated workers are treated as a separate population under Title I because of their more immediate need for employment, compared with the general adult population, which may seek employment and training services to upgrade their skills while current employed. The law allows some flexibility for states and localities to devise strategies to serve these populations, although they must adhere to the same performance accountability and programmatic standards, and funding guidance regardless of their approach. Authority for implementation flows down from DOL's Employment and Training Administration, through governors and state-level workforce investment boards, to local workforce boards, and finally to One Stop Centers that offer services.

State workforce boards are tasked with developing statewide strategic plans for meeting labor market needs for a diversity of workers, while local workforce boards execute these plans at the community area. These local boards select One Stop operators and eligible training providers, coordinate linkages with local economic training and employer organizations, and promote employer involvement in all areas of the local system. Finally, One Stop Centers are the place where workers can receive services to help them upgrade their skills or find employment.

One Stop Center services for adults and dislocated workers are divided into three categories: core, intensive, and training services. Core services are available to all individuals and include self-directed job searching or assistance with filing unemployment claims. Intensive services (as well as training services) require registration and are available to legal residents and include activities such as a skills assessment, career counseling, and short-term pre-vocational services. And training services include occupational skills and on-the-job training, and sessions offered through institutions of higher education and community-based organizations and apprenticeships. Individuals deemed eligible by One Stop Center staff are given training accounts to seek services from local training providers.

Separately, Title II is administered by the Department of Education's Office of Vocational and Adult Education and provides funds for adult education, literacy, and ESL classes, targeting individuals 16 years or older without a high school diploma or basic workplace literacy skills. Organizations providing such activities include community colleges, public schools, and community-based organizations.

There is broad consensus among analysts, academics, and policymakers that the legal and administrative structure of the WIA system has created service access hurdles for LEP workers and others with training and education needs. In particular, the mandate that One Stop Centers serve as the single point of access for different federally funded employment, training, and adult education programs has not been met. This is largely due to disincentives One Stop Center staff — and state and local workforce boards trying to show positive performance outcomes — face when deciding what services to refer LEP workers.

https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 3/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities Titles I and II have separate performance accountability goals, despite the fact that LEP workers and others require services under both to become job-ready. Performance accountability measures are not aligned across titles, which, critics argue, has created disincentives to serving LEP workers. States are required to negotiate performance levels for Titles I and II with the Departments of Labor and Education separately, despite both sets of services being mandated under WIA to be available through the One Stop system. Critics argue that pressures to meet performance levels enter into states' determinations on how to serve LEP workers. States and in turn their contracted service providers may be sanctioned for not meeting negotiated performance levels, thus creating incentives to direct Title I funds to serve less needy populations — a practice known as "creaming" — at the expense of difficult-to-serve populations, such as LEP workers.

Title I's performance measures emphasize rapid re-employment and improvements in earning — measures of performance difficult for LEP individuals to quickly achieve. Specifically, LEP individuals receiving training may not be able to acquire English skills fast enough to keep pace with the training and get hired for new work, and may also have difficulty retaining employment long enough to be counted as a positive performance outcome. Further, studies have found that One Stop Center staff cognizant of the difficulty LEP individuals have in meeting performance targets may intentionally avoid placing eligible individuals into training as these LEP individuals may not successfully complete the training and thus not be counted as a positive performance outcome. A 2002 U.S. Government Accountability Office (GAO) report found that "states reported that the need to meet these performance levels may lead local staff to focus WIA-funded services on job seekers who are most likely to succeed in their job search…"

Meanwhile, Title II's performance accountability measures emphasize improvements in literacy, including English-language acquisition, as well as placement or retention in postsecondary education, training, or employment. Given the overlapping but separate performance measures in Titles I and II, studies of One Stop Centers have noted confusion about whether to place LEP individuals in Title I or II programs, with the tacit acknowledgement that meeting some Title II goals may be easier than Title I goals. The result is that immigrants may be placed in English language courses without a workforce training component, limiting the potential success in the transition to employment.

LEP Workers' Access to WIA Programs

Data from DOL on enrollment in Title I programs show that the share of LEP workers among all workers receiving training is on the decline. Meanwhile, co-enrollment in Title II programs for LEP adults and dislocated workers has increased in recent years. (The data only highlight the extent to which co-enrollment in ESL or other adult basic education course is occurring for LEP workers.)

Most adults or dislocated workers who access WIA Title I employment and training services only use "core" services, which are typically self-directed and do not require registration. For example, in program year (PY) 2010, about 803,000 adults accessed Title I core services, while 497,000 received intensive or training services. Of all individuals receiving intensive or training services, LEP adults account for a small share despite their over-representation among the low-skilled labor force. LEP adults represented 2 percent of all adults receiving intensive or training services in PY 2010, compared to 9.5 percent in PY 2000 (see Figure 1).

https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 4/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities The percent of LEP adults served under Title I was highest in PY 2002, when 14,668 accessed intensive or training services (see Table 1). This population reached a low in numbers served in PY 2007, when 8,400 LEP adults accessed intensive or training services.

Although the scale of the decline has been less dramatic than for LEP adults, dislocated workers with limited English proficiency also have seen a decline in their representation among those receiving intensive or training services.

Figure 1. Share of LEP Population Served in WIA Title 1 Adult or Dislocated Worker Programs with Intensive or Training Services, (%) PY 2000-10

Notes: PY 2010 is the latest available and includes data from April 1, 2010 to March 31, 2011. Source: MPI analysis of WIA Standardized Record Data. Available online.

Table 1. Number of LEP Adults Accessing WIA Title I Adult or Dislocated Worker Programs with Intensive or Training Services, PY 2000-10

PY00 PY01 PY02 PY03 PY04 PY05 PY06 PY07 PY08 PY09 PY10 Adult Number of LEP Exiters N/A N/A 14,668 10,836 8,985 7,728 9,498 8,400 8,420 9,266 9,958 Number of Total Exiters N/A 135,448 196,719 176,192 177,280 178,965 196,290 223,336 309,073 428,522 497,454 Dislocated Worker Number of LEP N/A N/A 9,546 9,239 8,828 7,101 7,090 7,939 6,155 6,858 7,248 Number of Total Exiters N/A 112,192 158,231 164,038 152,902 167,715 148,329 132,571 139,204 237,393 352,699 Source: MPI analysis of WIA SRD data.

The share of LEP youth served has changed over time as well. In PY 2000, 16.9 percent of all youth served had limited English proficiency. By comparison in PY 2010, 8.2 percent were LEP. Compared to the adult and dislocated worker programs, the WIA youth program has served relatively high shares of LEP youth over time; https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 5/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities however, the number of LEP youth receiving intensive or training services has declined dramatically — from a high of 16,369 in PY 2002 to a low of 8,615 in PY 2005 (see Table 2).

Figure 2. Share of LEP Youth Accessing WIA Title I Programs with Intensive or Training Service, (%), PY 2000-10

Notes: PY 2010 is the latest available and includes data from April 1, 2010 to March 31, 2011. Source: MPI analysis of WIA SRD data.

Table 2: Number and Share of LEP Youth Accessing WIA Title I Programs with Intensive or Training Service, Program Years 2000-10

PY00 PY01 PY02 PY03 PY04 PY05 PY06 PY07 PY08 PY09 PY10 Youth Number of LEP Youth Exiters NA NA 16,369 14,087 13,800 8,615 9,551 8,762 14,555 11,997 10,661 Number of Total Youth Exiters 115,797 126,348 164,266 166,831 149,597 145,263 116,598 108,418 115,083 119,969 129,505 Source: MPI analysis of WIA SRD data.

Data on co-enrollment with Title II programs (as measured by LEP adults and dislocated workers receiving adult education or ESL classes in combination with Title I training services) indicates that even as the share of LEP individuals receiving Title I services has declined over time, they are increasingly receiving Title I services in concert with adult education or ESL courses. While the quality of this combined training and education is not examined — or whether the two are integrated in a work environment, as many analysts agree is most effective — Figure 3 shows an increasing trend toward co-enrollment. Given existing evidence of the insufficient capacity of the Title II adult education and ESL system to meet instructional demand from LEP individuals, the increase in co-enrollment shows a growing awareness among One Stop Center staff — and the workforce boards that https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 6/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities guide them — about the dual employment and language instruction needs of LEP workers. In recent years almost half of all individuals served under Title II programs were enrolled in ESL programs and many such classes in states with high concentrations of immigrant populations consistently reach full registration.

Figure 3. Share of LEP Adults and Dislocated Workers Co-Enrolled in Title II Programs,* (%), PY 2005-10

Notes: Data refer to LEP adults and dislocated workers who received training, and not intensive services, and who received Adult Basic Education or ESL programs in combination with training services. Similar data are not available for PY 2000—04. Publicly available data do not provide the numbers of individuals with limited English prociency co-enrolled — only the percent. Source: MPI analysis of WIA SRD data.

Current Debate Explored

While WIA reauthorization proposals circulating in Congress differ, they share three common themes and represent some consensus about how to address the systems' challenges: consolidate or better align programs and program funding and goals; establish common performance measures and strategic planning across Titles I and II; and change the role and representation of state and local workforce boards.

Within these themes, the role of LEP individuals and others with multiple barriers to employment (such as low- income individuals) has been hotly contested by public and private stakeholders. The following are changes within the existing proposals before Congress.

Consolidate or better align workforce programs, program funding, and goals. Citing a 2011 GAO report that identified 47 job training programs offered through nine federal agencies, several members of Congress are seeking to streamline WIA to eliminate redundancies. Proposals range from consolidation of programs and funding into a single block grant to give states flexibility to determine how to meet local training, education, and labor market needs, to multiple workforce investment funds being targeted to particular populations. Proponents of consolidation note that fewer funding streams that target broad populations would allow state and local governments to develop innovative approaches to serving their local labor market needs with less https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 7/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities administrative burden. Others have argued for better alignment across WIA's Titles I and II to ease training access for different subpopulations (LEP workers, low-income workers, etc.) within adults, dislocated workers, and youth. Lifting current fund restrictions and tying workforce development to educational attainment are among the proposals being suggested to counterbalance calls for consolidation of the system as well as barriers to access under the existing system.

Establish common performance measures and strategic planning. Broad consensus exists about the need to align performance measures across Titles I and II. Calls for unified state plans would jointly report the goals and program approaches of the workforce training and adult education systems. Advocates for improving access for hard-to-serve populations have called for improvements to existing state planning proposals that would require states to detail how specific populations, such as LEP workers, will be served. Many states already include information about how they provide language access to LEP individuals through the One Stop system, but do not provide strategies for effective training and education services. Similarly, while there is consensus about the need to create common performance measures across both programs to eliminate disincentives to serving hard- to-serve populations, disagreement exists about what measures would be effective in demonstrating skills improvements. For example, some proposals include credential attainment within a specific time period as a common performance measure, reflecting the importance of certifications and postsecondary education to job success in the economy. While many welcome this clarification, others argue that interim gains in improved English proficiency or movement toward credential attainment are an important barometer of growth for those who need extra time to improve their skills. While there have also been calls to weight the performance of LEP individuals and other special populations differently, little evidence exists of what statistical models would be appropriate to capture relative performance.

Change the role and representation of state and local workforce boards. State and local workforce boards are comprised of members of the business community, mandatory state agency partners, labor unions, and community organizations. Advocates for greater inclusion of hard-to-serve populations argue that those groups' competing interests may sidetrack WIA's core goal to retrain workers to fill business demand and boost local economies. They point out that empowering the business community to shape local training and education systems will lead to the increased availability of sector partnerships and industry-led collaborations for targeted training. As a result, many proposals call for workforce board membership to be heavily weighted toward the business community. However, community-based organizations and immigrant advocates note that LEP individuals and others with multiple barriers to employment will be left out unless their needs are heard. They note that workforce boards' selection of training providers offered through the One Stop system should include organizations that understand the needs and availability of locally provided training services appropriate for LEP individuals. As well, many have called for increasing flexibility for local boards to award contracts if the board determines that a community or private organization has demonstrated effectiveness in serving individuals with barriers to employment.

Next Steps

As immigrant populations continue to spread to new destinations less equipped than the traditional destination states to provide services for the LEP population — and amid continued uncertainty over when federal and state budgets will rebound — the need to have a more responsive WIA system is likely to become more visible. https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 8/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities The nature of the current WIA reauthorization debate — in addition to the failures of previous attempts at reauthorization — highlights the complications and challenges of revamping the workforce training and adult education systems. While the debate has implications for many groups, LEP individuals — caught between the overlapping, but separate, missions of Titles I and II — stand to gain or lose in particular. Existing structural, funding, and political constraints continue to limit the ability of the system to meet the needs of LEP individuals.

Along the way, states and localities with rich nonprofit and community-based organization networks serving LEP populations have filled in service gaps left by Title I and Title II programs. The inability of the system to scale up to the need of diverse populations will have significant implications for not only these immigrant individuals and their families, but for the economy in general.

Sources

Capps, Randy, Michael Fix, and Serena Yi-Ying Lin. 2010. Still an Hourglass? Immigrant Workers in Middle- Skilled Jobs. Washington, DC: Migration Policy Institute. Available online.

Capps, Randy, Michael Fix, Margie McHugh, and Serena Yi-Ying Lin. 2009. Taking Limited English Proficient Adults into Account in the Federal Adult Education Funding Formula. Washington, DC: Migration Policy Institute. Available online.

Center for Asian American Advocacy. 2005. Bridging the Language Gap: An Overview of Workforce Development Issues Facing Limited English Proficient Workers and Strategies to Advocate for More Effective Training Programs. Washington, DC: Center for Asian American Advocacy. Available online.

National Council of State Directors of Adult Education. 2010. Adult Student Waiting List Survey. Washington, DC. Available online.

National Skills Coalition. 2011. Training in Policy Brief: Workforce Investment Act, Title I. Washington, DC. Available online.

Terrazas, Aaron. 2011. The Economic Integration of Immigrants in the United States: Long-and Short-Term Perspectives. Washington, DC: Migration Policy Institute. Available online.

U.S. Census Bureau. 2006 and 2010 American Community Surveys. Accessed from Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2010. Available online.

U.S. Department of Labor. Social Policy Research Associates data, 2001-10. Available online.

U.S. Government Accountability Office. 2009. Workforce Investment Act: Labor Has Made Progress in Addressing Areas of Concern, but More Focus Needed on Understanding What Works and What Doesn't. Washington, DC. Available online. https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 9/10 4/2/2019 Limited English Proficient Workers and the Workforce Investment Act: Challenges and Opportunities

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https://www.migrationpolicy.org/print/4252#.XKOhdJhKg2w 10/10 4/2/2019 The Limited English Proficient Population in the United States

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Home > The Limited English Proficient Population in the United States

The Limited English Procient Population in the United States

JULY 8, 2015 SPOTLIGHT By Jie Zong and Jeanne Batalova

The United States attracts immigrants from across the globe, who speak a diverse array of languages. In 2013, approximately 61.6 million individuals, foreign and U.S. born, spoke a language other than English at home. While the majority of these individuals also spoke English with native fluency or very well, about 41 percent (25.1 million) were considered Limited English Proficient (LEP). Limited English proficiency refers to anyone above the age of 5 who reported speaking English less than “very well,” as classified by the U.S. Census Bureau. Though most LEP individuals are immigrants, nearly 19 percent (4.7 million) were born in the United States, most to immigrant parents. Overall, the LEP population represented 8 percent of the total U.S. population ages 5 and older.

Between 1990 and 2013, the LEP population grew 80 percent from nearly 14 million to 25.1 million (see Figure 1). The growth of the LEP population during this period came largely from increases in the immigrant LEP population. The most dramatic increase occurred during the 1990s as the LEP population increased 52 percent. The growth rate then slowed in the 2000s and the size of the LEP population has since stabilized. Over the past two decades, the LEP share of the total U.S. population has increased from about 6 percent in 1990 to 8.5 percent in 2013.

Figure 1. LEP Population by Nativity, 1990-2013

https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 1/10 4/2/2019 The Limited English Proficient Population in the United States

Source: Migration Policy Institute (MPI) tabulations from the U.S. Census Bureau’s 1990 and 2000 Decennial Censuses and 2010 and 2013 American Community Surveys (ACS).

Immigrants to the United States come from many different language backgrounds and may be in various stages of English proficiency. Of Definitions the total immigrant population of 41.3 million in 2013, about half was LEP. Limited English Proficient The term Limited English Proficient (LEP) refers to any person age 5 and older who Compared to the English-proficient population, the LEP population reported speaking English less than "very well" as classified by the U.S. Census was less educated and more likely to live in poverty. Employed LEP Bureau. The term English proficient refers to men in 2013 were more likely to work in construction, natural people who reported speaking English only or "very well." resources, and maintenance occupations than English-proficient Foreign Born men, while LEP women were much more likely to be employed in The terms foreign born and immigrant are used interchangeably and refer to individuals service and personal-care occupations than English-proficient who had no U.S. citizenship at birth. The women. foreign-born population includes naturalized citizens, lawful permanent residents, refugees and asylees, authorized nonimmigrants Using data from the U.S. Census Bureau (the most recent 2013 (including those on student, work, or other temporary visas), and persons residing in the American Community Survey [ACS] and the 1990 and 2000 Decennial country without authorization. Census), this Spotlight provides a demographic and socioeconomic profile of LEP individuals (ages 5 and older) residing in the United States, focusing on the size, geographic distribution, and socioeconomic characteristics of this population.

Distribution by State and Key Cities https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 2/10 4/2/2019 The Limited English Proficient Population in the United States Nativity Methodology Language Diversity In the U.S. Census Bureau’s American Age, Race, and Ethnicity Community Survey, respondents who reported that they spoke a language other Education and Employment than English at home were asked to self- Poverty assess their English-speaking abilities with the options of "not at all," "not well," or "well," LEP Children and English Language Learners and "very well." The results are thus subjective and not based on English language tests. Distribution by State and Key Cities Only individuals ages 5 and older were surveyed about their self-reported English As of 2013, the highest concentrations of LEP individuals were found proficiency. Therefore, information about individuals who are LEP is restricted to the in the six traditional immigrant-destination states—California (6.8 population ages 5 and older. While most million, or 27 percent of the total LEP population), Texas (3.4 million, Spotlights focus uniquely on the foreign-born population, this article examines English 14 percent), New York (2.5 million, 10 percent), Florida (2.1 million, 8 proficiency for both immigrants and the U.S. percent), Illinois (1.1 million, 4 percent), and New Jersey (1 million, 4 born. percent). Together, the top six states accounted for approximately two-thirds of the LEP population.

Eleven states had a higher share of LEP residents than the nationwide proportion of 8 percent. California had the highest share, with LEP individuals accounting for 19 percent of the state population, followed by Texas (14 percent), and New York and Hawaii (13 percent each) (see Figure 2).

Figure 2. States with Highest Share of LEP Residents, (%), 2013

https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 3/10 4/2/2019 The Limited English Proficient Population in the United States Source: MPI tabulation of data from the U.S. Census Bureau 2013 ACS.

Click here for data on the LEP population by state from 1990-2013.

In the 2009-13 period, the top five counties with the largest LEP populations were Los Angeles County in California (10 percent of the LEP population), Miami County in Florida (3 percent), Harris County in Texas (3 percent), Cook County in Illinois (3 percent), and Queens County in New York (2 percent). Together, these five counties represented 21 percent of the total LEP population.

LEP residents accounted for more than one-third of the total population ages 5 and over in nine counties. Of these counties, seven were located in Texas, one in Alaska, and one in Florida. The counties with the highest share of LEP residents included Starr County, Texas (28,000 LEP residents, or 50 percent of total county population); Aleutians East Borough, Alaska (2,000, 49 percent); Maverick County, Texas (24,000, 47 percent); and Webb County, Texas (108,000, 47 percent). While most of these areas did not have large populations of LEP individuals in absolute terms relative to other counties, the share of LEP residents was significantly higher.

Click here for data on the linguistic diversity of LEP individuals at the state and county level.

Nativity

The foreign-born population was much more likely to have limited English proficiency than the native-born population (see Table 1). In 2013, about 50 percent of immigrants (20.4 million) were LEP, compared to 2 percent of the U.S.-born population.

Table 1. LEP Individuals (Ages 5 and Over) in the United States by Nativity, 2013

Source: MPI tabulation of data from the U.S. Census Bureau 2013 ACS.

https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 4/10 4/2/2019 The Limited English Proficient Population in the United States In 2013, 81 percent of LEP individuals were immigrants. Of the total foreign-born LEP population, 39 percent were born in Mexico, followed by China (6 percent), El Salvador (4 percent), Vietnam (4 percent), Cuba (3 percent), and the Dominican Republic (3 percent). Foreign-born LEP individuals were less likely than the overall immigrant population to be naturalized citizens (36 percent versus 47 percent, respectively).

Of native-born LEP individuals, 14 percent were born in Puerto Rico and less than 2 percent were born in Mexico to at least one U.S.-citizen parent. Three percent were born abroad elsewhere to at least one U.S.-citizen parent, with the remaining 82 percent born in one of the 50 U.S. states or the District of Columbia.

Click here for data on LEP adults (ages 18 and over) by U.S. citizenship status and by state.

Language Diversity

Spanish was the predominant language spoken by both immigrant and U.S.-born LEP individuals. About 64 percent (16.2 million) of the total LEP population spoke Spanish, followed by Chinese (1.6 million, or 6 percent), Vietnamese (847,000, 3 percent), Korean (599,000, 2 percent), and Tagalog (509,000, 2 percent). Close to 80 percent of the LEP population spoke one of these five languages.

There were marked differences, however, in the top languages spoken by LEP persons by nativity. As shown in Figure 3, 77 percent (3.6 million) of the U.S.-born LEP population spoke Spanish, followed by German (140,000, or 3 percent), Chinese (116,000, 2 percent), French (82,000, 2 percent), and Vietnamese (80,000, 2 percent). Spanish was also the predominant language, spoken by about 62 percent (12.5 million) of immigrant LEP individuals. However, Asian languages were more likely to be spoken by the foreign-born LEP population, including Chinese (1.5 million, or 7 percent), Vietnamese (767,000, 4 percent), Korean (564,000, 3 percent), and Tagalog (484,000, 2 percent).

Figure 3. Top Ten Languages Spoken by Native- and Foreign-Born LEP Individuals, 2013

https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 5/10 4/2/2019 The Limited English Proficient Population in the United States Notes: Chinese includes Chinese, Mandarin, and Cantonese; Creole includes French and Haitian Creole; and Hmong includes Miao-Yao, Mien, and Miao. Source: MPI tabulation of data from the U.S. Census Bureau 2013 ACS.

The linguistic diversity of the LEP population varied across states and counties due to differing immigration trends and population composition. For instance, in the 2009-13 period, the top three languages spoken by LEP residents in New York were Spanish, Chinese, and Russian. In contrast, the top three in Maine were French, Spanish, and African languages, and in Hawaii, miscellaneous Pacific Island languages, Tagalog, and Japanese.

In addition, the share of LEP persons among speakers of particular languages varied widely. In the 2009-13 period, Vietnamese speakers had the highest share of LEP individuals (60 percent), compared to 55 percent of Chinese speakers, 44 percent of Spanish speakers, 20 percent of French speakers, and 16 percent of German speakers.

Click here for an interactive chart showing the top ten languages spoken and LEP share by language.

Age, Race, and Ethnicity

Compared to their English-proficient counterparts, LEP individuals were much less likely to be of school age and much more likely to be of working age (see Table 2). In 2013, 10 percent of LEP individuals were children between the ages 5 and 17, versus 19 percent of the English-proficient population. Seventy-five percent were between ages 18 and 64 compared to 66 percent of English-proficient individuals. About 15 percent of both the LEP and English-proficient populations were seniors.

Table 2. Age Distribution by English Proficiency, 2013

Source: MPI tabulation of data from the U.S. Census Bureau 2013 ACS.

https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 6/10 4/2/2019 The Limited English Proficient Population in the United States LEP individuals were much more likely to be Latino or Asian than their English-proficient counterparts (see Figure 4). While Latinos comprised 63 percent of the LEP population, they accounted for only 12 percent of the English-proficient population. Likewise, 21 percent of LEP individuals were Asian compared to only 4 percent of English-proficient individuals.

Figure 4. Race and Ethnicity of LEP and English-Proficient Populations, 2013

Source: MPI tabulation of data from the U.S. Census Bureau 2013 ACS.

Education and Employment

In general, LEP adults were much less educated than their English-proficient peers. As of 2013, 46 percent of all LEP individuals ages 25 and over had no high school diploma compared to 10 percent of their English- proficient counterparts. About 14 percent of LEP adults had a bachelor’s degree or higher, compared to 31 percent of English-proficient adults.

LEP individuals age 16 and over participated in the civilian labor force at a slightly lower rate than English- proficient individuals (61 percent versus 64 percent), largely because of greater gender differences in employment outcomes within the LEP population. Among English-proficient individuals, men participated in the civilian labor force at a slightly higher rate than women (68 percent versus 60 percent), while the gender gap was much larger for LEP individuals (75 percent versus 49 percent).

https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 7/10 4/2/2019 The Limited English Proficient Population in the United States

Figure 5. Employed Workers in the Civilian Labor Force (Ages 16 and Older) by Occupation, English Proficiency, and Gender, 2013

Source: MPI tabulation of data from the U.S. Census Bureau 2013 ACS.

Compared to their English-proficient counterparts, LEP men were more likely to work in natural resources, construction, and maintenance occupations (15 percent versus 28 percent), service occupations (14 percent versus 25 percent), and production, transportation, and material-moving occupations (17 percent versus 24 percent) (see Figure 5). LEP women were significantly more likely to work in service occupations (45 percent versus 20 percent), as well as production, transportation, and material-moving occupations (16 percent versus 5 percent) than their English-proficient counterparts.

Poverty

LEP individuals were more likely to live in poverty than English-proficient individuals. In 2013, about 25 percent of LEP individuals lived in households with an annual income below the official federal poverty line— nearly twice as high as the share of English-proficient persons (14 percent).

LEP Children and English Language Learners

Note: This section refers to children ages 5 to 17 residing with at least one parent. https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 8/10 4/2/2019 The Limited English Proficient Population in the United States In 2013, of the 51.3 million children ages 5 to 17 in the United States, approximately 8 million (16 percent) lived with at least one LEP parent.

Among the 2.3 million children who were themselves LEP, 23 percent were foreign born. The remaining 77 percent (1.8 million) were U.S. born, with 77 percent (1.4 million) having at least one immigrant parent.

The majority of LEP children enrolled in school, also known as English Language Learners (ELLs), were U.S. born: 85 percent of pre-kindergarten to 5th grade ELL students and 62 percent of 6th to 12th grade ELL students (see Figure 6). ELL and English Learner (EL) are used interchangeably with LEP to describe school-age children who are learning English and who may receive English language instruction support at school.

Figure 6. ELL Students by Grade and Nativity, 2013

Note: The figure refers to English Language Learner (ELL) students, ages 5 to 17, enrolled in school by grade. Source: MPI tabulation of data from the U.S. Census Bureau 2013 ACS.

Click here to learn more about the geographic distribution and here for languages spoken by English Language Learners in the United States.

Sources

U.S. Census Bureau. 2010. 2013 American Community Survey. Accessed from Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 9/10 4/2/2019 The Limited English Proficient Population in the United States Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota. Available Online.

U.S. Census Bureau 1990 Decennial Census Summary Tape File 3. 2013. Accessed from the Minnesota Population Center, University of Minnesota. National Historical Geographic Information System: Version 2.0. Minneapolis: University of Minnesota. Available Online.

U.S. Census Bureau 2000 Decennial Census Summary File 4. 2013. Accessed from the Minnesota Population Center, University of Minnesota. National Historical Geographic Information System: Version 2.0. Minneapolis: University of Minnesota. Available Online.

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https://www.migrationpolicy.org/print/15316#.XKOhpZhKg2w 10/10 RESEARCH AND PRACTICE

Associations Between Racial Discrimination, Limited English Proficiency, and Health-Related Quality of Life Among 6 Asian Ethnic Groups in California

Gilbert C. Gee, PhD, and Ninez Ponce, PhD

Despite their image as a successful ethnic minority, Asian Americans experience consid- Objectives. We examined the association of racial discrimination and limited erable barriers to their well-being.1 Two major English proficiency with health-related quality of life among Asian Americans in California. barriers are racial discrimination and limited Methods. We studied Chinese (n=2576), Filipino (n=1426), Japanese (n=833), English proficiency. Both are risk factors for 2–4 Korean (n=1128), South Asian (n=822), and Vietnamese (n=938) respondents to illness and diminished quality of life. We the California Health Interview Survey in 2003 and 2005. We assessed health- evaluated how these 2 factors were associated related quality of life with the Centers for Disease Control and Prevention’s with health-related quality of life among 6 Asian measures of self-rated health, activity limitation days, and unhealthy days. ethnic groups. Results. Overall, Asians who reported racial discrimination or who had limited Discrimination is experience with racial bias English proficiency were more likely to have poor quality of life, after adjustment enacted by others, and limited English pro- for demographic characteristics. South Asian participants who reported dis- ficiency refers to an individual’s skill with crimination had an estimated 14.4 more activity limitation days annually than a specific language. Hence, discrimination rep- South Asians who did not report discrimination. Results were similar among resents exposure to external risk factors, and other groups. We observed similar but less consistent associations for limited English proficiency. English proficiency is related to personal abil- Conclusions. Racial discrimination, and to a lesser extent limited English ity. Despite these differences, both can com- proficiency, appear to be key correlates of quality of life among Asian ethnic promise quality of care, for example, by groups. (AmJPublicHealth.2010;100:888–895. doi:10.2105/AJPH.2009. damaging the trust between patient and 178012) provider.5–8 Both may be associated with more fundamental determinants of health, such as restricted employment opportunities.9–11 Further, state of complete physical, emotional and social and dispel stereotypes, and hence, encounter less individuals who face discrimination or who have well-being, and not merely the absence of disease discrimination than newer cohorts. Yet another language barriers may encounter stress, which or infirmity.’’18(p1) We hypothesized that reports perspective suggests that ‘‘all Asians look alike’’ to then may contribute to allostatic load and di- of racial discrimination and limited English pro- non-Asians, and for this reason, levels of dis- minished well-being.4,12 Moreover, limited En- ficiency would be associated with decreased criminationshouldbefairlysimilaracrossall glish proficiency and discrimination may be quality of life among 6 Asian ethnic groups. groups.2,20 interrelated. Limitations with English or a notice- We included individuals identifying as sin- Limited English proficiency should be high- able accent may trigger discrimination.13,14 Lack gle-race Chinese, Filipino, Japanese, Korean, est among groups who are the most recent of familiarity with English may prevent recogni- Vietnamese, or South Asian. Although there is immigrants (e.g., Vietnamese and Koreans) and tion of discrimination when it occurs. much interest in disaggregating Asian ethnic lower among earlier immigrants (e.g., Japanese We investigated health-related quality of life groups, there is surprisingly little guidance on and Chinese). This language barrier should because discrimination and limited English how these groups might differ. be lowest among immigrants from countries proficiency may influence a variety of other We expect that groups may differ in the where English is an official language (e.g., risk factors, such as stress, health care access, reporting of discrimination. Scholars have sug- and South Asians). and socioeconomic position.10,15,16 For a prob- gested that darker-skinned individuals experi- Our study had 3 features that distinguished lem of this range and complexity, a focus on ence more discrimination.19 This implies that it from previous research. First, we obtained narrow outcomes would likely understate any darker-skinned South Asians and Filipinos might large, population-based, representative samples associations of discrimination and limited English report more discrimination than members of of several ethnic groups. Most previous studies proficiency with well-being, so we investigated other Asian groups. Alternatively, older cohorts of Asian Americans analyzed small conve- the broader issue of quality of life.17 Further, the (e.g., Chinese, Japanese, and Filipino) may have nience samples or included fewer ethnic World Health Organization defines health as ‘‘a had more time to integrate into American society groups. For example, the National Latino and

888 | Research and Practice | Peer Reviewed | Gee and Ponce American Journal of Public Health | May 2010, Vol 100, No. 5 RESEARCH AND PRACTICE

Asian American Study, one of the largest say that in general your health is [excellent/very indicates greater wealth). We also included an studies to date, disaggregated only Chinese, good/good/fair/poor]?’’ Following previous indicator of the year of the survey to account for Filipino, and Vietnamese.21 Second, we included studies, we dichotomized the response categories secular changes between 2003 and 2005. South Asians. Although there have been several as excellent/very good/good or fair/poor.27,31 studies of discrimination among South Asians in The second question asked about activity limi- Analyses the United Kingdom,22–24 we are aware of no tation days: ‘‘During the past 30 days, for about Our analyses began with simple descriptives population-based studies of discrimination and how many days did poor physical or mental and bivariate associations. For multivariate health among this population in the United health keep you from doing your usual activities, analyses, we used logistic regression for self- States. Similarly, few studies have examined such as self-care, work, or recreation?’’ Responses rated health and negative binomial regression limited English proficiency among South ranged from 0 to 30. for activity limitation days and unhealthy days Asians.25 Third, we used measures of health- We derived our unhealthy days measure by (we did not use Poisson because of overdis- related quality of life developed by the Centers combining the final 2 questions: ‘‘Now thinking persion). for Disease Control and Prevention (CDC).18,26,27 about your physical health, which includes All analyses were weighted to account for Discrimination is associated with poor scores physical illness and injury, for how many days the sampling design across years and to allow on these CDC measures among Whites, Blacks, during the past 30 days was your physical estimates to be representative of the target and Hispanics.28 However, these associations health not good?’’ and ‘‘Now thinking about population in California.36,37 We used the SVY have not been investigated among Asian Americans. your mental health, which includes stress, de- suite of commands with Stata version 10.1 pression, and problems with emotions, for how (StataCorp LP, College Station, TX). METHODS many days during the past 30 days was your mental health not good?’’ Following the CDC’s RESULTS Data come from the California Health Inter- recommendation, we summed responses to view Survey (CHIS), a random-digit-dialed, pop- the 2 questions, but capped the total at 30 On average, Vietnamese Americans ulation-based telephone survey administered days.18,26,27 reported the lowest quality of life and South every other year since 2001.29,30 We aggregated We assessed racial discrimination with 1 Asians the highest (Table 1). More Vietnamese data from 2003 and 2005 to increase the item: ‘‘Thinking about your race or ethnicity, (43%) reported fair or poor health, followed by stability of our estimates for the Asian ethnic how often have you felt treated badly or Koreans (26%), Chinese (22%), Filipinos groups. Other years (2001 and 2007) were unfairly because of your race or ethnicity?’’ (16%), Japanese (12%), and South Asians (6%). excluded because the measures of discrimination This question is similar to one used in previous The most unhealthy days were reported by were not comparable in those years. The overall studies.32 We dichotomized the responses into Vietnamese and Koreans (7.3 days), followed adult response rates for 2003 and 2005 were never/rarely or sometimes/often/all of the time by Filipinos (5.3 days), Japanese (5.2 days), 33.5% and 26.9%, respectively. These rates because of some skewness with the distribution. Chinese (5.0 days), and South Asians (4.6 were comparable to the 2003 and 2005 Cal- We assessed limited English proficiency with days). The most activity limitation days were ifornia Behavioral Risk Factor Surveillance Sys- a question adapted from the 2000 Census: reported by Vietnamese (1.8 days), followed by tem rates of 39% and 29.2%, respectively. ‘‘Would you say you speak English [very well/ Japanese (1.7 days), Filipinos and South Asians We analyzed responses of adult Asian re- well/not well/not at all]?’’ Individuals were (1.5 days), Koreans (1.4 days), and Chinese spondents who were interviewed in Cantonese, classified as having limited proficiency if their (1.0 days). English, Korean, Mandarin, Spanish, or Viet- response was not well or not at all. Individuals We found a wide variation in language namese. We excluded interviews conducted by were classified as not having limited profi- barriers. Only 3% of South Asians, 5% of proxy (n=312), because proxy reports of dis- ciency if they reported that they spoke English Filipinos, and 6% of Japanese reported limited crimination are likely to be unreliable. We also well or very well. English proficiency. By contrast, 52% of Viet- excluded other Asians (n=293) and individ- Covariates were age, gender, percentage of namese, 47% of Koreans, and 34% of Chinese uals self-identifying with multiple races life spent in the United States, marital status, reported limited English proficiency. Reports of (n=276), because their samples were too small employment, and education. We selected these discrimination varied less. Filipinos were the to be statistically reliable. Our final sample variables because the literature suggested that most likely to have reported discrimination comprised 2576 Chinese, 1426 Filipino, 833 they are associated with quality of life, dis- (33%), followed by Japanese (28%), Chinese Japanese, 1128 Korean, 822 South Asian, and crimination, and language proficiency among (28%), Koreans (25%), South Asians (23%), 938 Vietnamese respondents. Asian Americans.8,33 As an additional control and Vietnamese (23%). for socioeconomic resources, we included the Japanese and South Asians differed the most Measures income–poverty ratio, which can be interpreted in demographic characteristics. On average, Our dependent variables were derived from as the ratio of income to the federal poverty Japanese participants were oldest (52 years), the CDC’s measure of health-related quality of level.34,35 For example, a person with an in- spent the highest percentage of their life in life, which has 4 questions.18,26,27 The first come–poverty ratio of 3 has an income that is 3 the United States, were the least likely to question assessed self-rated health: ‘‘Would you times the poverty level (hence a larger number be currently employed, were more likely to be

May 2010, Vol 100, No. 5 | American Journal of Public Health Gee and Ponce | Peer Reviewed | Research and Practice | 889 RESEARCH AND PRACTICE

TABLE 1—Characteristics of Asian Ethnic Groups: California Health Interview Survey, 2003–2005

Chinese (n=2576), Filipino (n=1426), Japanese (n=833), Korean (n =1128), South Asian (n=822), Vietnamese (n =938), mean (SE) or % mean (SE) or % mean (SE) or % mean (SE) or % mean (SE) or % mean (SE) or %

Activity limitation days 1.0 (0.1) 1.5 (0.1) 1.7 (0.2) 1.4 (0.1) 1.5 (0.3) 1.8 (0.2) Unhealthy days 5.0 (0.2) 5.3 (0.3) 5.2 (0.4) 7.3 (0.4) 4.6 (0.4) 7.3 (0.5) Fair/poor health 22.1 16.3 12.2 26.3 6.3 43.3 Reported racial discrimination 27.9 33.5 28.4 24.9 23.3 22.3 Had limited English proficiency 33.8 4.6 6.5 46.9 3.1 51.8 Women 55.3 53.0 59.1 57.1 43.8 50.6 Married 63.9 57.0 54.9 64.3 73.8 60.1 % life lived in United States 0–20 20.2 15.1 3.6 22.2 32.6 23.2 21–40 26.1 17.5 4.8 26.1 29.0 27.4 41–60 19.6 23.8 6.8 25.5 18.8 28.6 61–80 9.5 9.6 7.1 8.3 6.6 9.5 81–100 24.5 34.0 77.8 18.0 13.1 11.3 Currently employed 62.8 71.4 52.2 58.3 69.2 54.5 Education

Note. Estimates were weighted to account for the sampling design. aAs determined by the US Department of Health and Human Services federal poverty level for 2003 and 2005. female (59%), and were least likely to be self-rated health for Chinese, Koreans, and characteristic for categorical variables (i.e., married (55%). South Asians were the most Vietnamese but not for the other groups. they reflect a respondent who was female, likely to be married (74%), had the youngest Table 3 displays the multivariate analyses employed, a college graduate, married, and mean age, spent the lowest proportion of their for activity limitation days and unhealthy days. proficient in English) and centered at the life in the United States, had the lowest pro- Reports of discrimination were associated with ethnic-group mean for continuous variables. portion of women (44%), had the most edu- more activity limitation days among Chinese, The mode for gender among South Asians was cation, and had the highest income–poverty Japanese, South Asians, and Vietnamese. Lim- male, and the mode for education among ratio (i.e., more economic resources). Filipinos ited English proficiency, however, was not Vietnamese was high school, but we used had the highest rates of employment, and associated with activity limitation days for any female and bachelor’s degree across all groups Vietnamese participants had the least educa- group. Reports of discrimination were associ- to facilitate comparison. tion and lowest income–poverty ratio. ated with more unhealthy days for all groups Figure 1a shows the predicted probability of After adjustment for covariates, reports of except Filipinos. Limited English proficiency fair or poor health by discrimination and discrimination were significantly associated was associated with more unhealthy days only ethnicity. Chinese, Koreans, and Vietnamese with increased odds of poor self-rated health among Japanese participants. who reported discrimination were also more for Chinese, Koreans, and Vietnamese but not In Figure 1 we plotted the predicted esti- likely to report fair or poor health than were for Japanese, Filipinos, or South Asians (Table mates for discrimination from Tables 2 and 3. others of their ethnic origin. For example, the 2). Similarly, limited English proficiency was Estimates reflect the values for a typical re- probability of reporting fair or poor health was associated with increased odds for poor spondent, defined as possessing the modal 0.11among Vietnamese who did not report

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TABLE 2—Associations Between Self-Reported Discrimination, Limited English Proficiency, and Poor Self-Rated Health, by Asian Ethnic Group: California Health Interview Survey, 2003–2005

Chinese (n=2576), Filipino (n=1426), Japanese (n =833), Korean (n=1138), South Asian (n =822), Vietnamese (n=938), OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

Reported racial discrimination No (Ref) 1.00 1.00 1.00 1.00 1.00 1.00 Yes 1.52 (1.09, 2.09) 1.14 (0.75, 1.72) 0.88 (0.38, 2.01) 1.64 (1.03, 2.61) 0.93 (0.46, 1.87) 2.21 (1.33, 3.65) English proficiency Not limited (Ref) 1.00 1.00 1.00 1.00 1.00 1.00 Limited 2.46 (1.72, 3.49) 1.47 (0.63, 3.39) 1.71 (0.47, 6.06) 2.43 (1.44, 4.10) 2.12 (0.31, 14.37) 1.98 (1.23, 3.15)

Note. OR=odds ratio; CI=confidence interval. All estimates were adjusted for age, gender, employment, education, income–poverty ratio, marital status, survey year, and percentage of life spent in the United States. Estimates were weighted to account for the sampling design. Models used logistic regression. discrimination and 0.22 among those who did We found a similar, but markedly greater, activity limitation days measures as dichoto- experience discrimination. pattern of effects on unhealthy days (Figure 1c). mous variables (<14 unhealthy days versus These predicted estimates were particularly Reports of discrimination were associated with ‡14 days). Our findings were substantively meaningful for the activity limitation days and more unhealthy days among Chinese (1.6 similar across these different modeling as- unhealthy days measures. Figure 1b shows days), Japanese (2 days), Koreans (2.7 days), sumptions. We found no statistically significant that South Asians who did not report discrim- Vietnamese (3.6), and South Asians (4.4 days). interactions between discrimination and lim- ination had a predicted average of 0.7 activity We performed additional analyses to verify ited English proficiency, education, or poverty. limitation days per month; for those who our findings (data not shown) by (1) respecify- reported discrimination the average was 1.9 ing our measure of discrimination as a contin- DISCUSSION days, a difference of 1.2 days. We observed uous variable, (2) running separate models for similar differences for Japanese (1.2 days), the mental and physical unhealthy days ques- Our analysis of Asian Americans in Califor- Vietnamese (1 day), and Chinese (0.3 days). tions, and (3) modeling our unhealthy days and nia yielded 2 major findings: (1) a substantial

TABLE 3—Associations Between Self-Reported Discrimination, Limited English Proficiency, and Activity Limitation and Unhealthy Days, by Asian Ethnic Group: California Health Interview Survey, 2003–2005

Chinese Filipino Japanese Korean South Asian Vietnamese (n =2576), b (SE) (n=1426), b (SE) (n=833), b (SE) (n=1138), b (SE) (n =822), b (SE) (n=938), b (SE)

Activity limitation days Reported racial discrimination No (Ref) 1.00 1.00 1.00 1.00 1.00 1.00 Yes 0.54** (0.18) 0.19 (0.19) 0.73* (0.36) 0.01 (0.26) 1.03* (0.42) 0.77** (0.24) Limited English proficiency No (Ref) 1.00 1.00 1.00 1.00 1.00 1.00 Yes 0.03 (0.28) 0.46 (0.49) 0.35 (0.59) 0.18 (0.28) 1.20 (0.77) 0.42 (0.28) Unhealthy days Reported racial discrimination No (Ref) 1.00 1.00 1.00 1.00 1.00 1.00 Yes 0.32*** (0.08) 0.11 (0.1) 0.38* (0.16) 0.38** (0.12) 0.60** (0.2) 0.61*** (0.14) Limited English proficiency No (Ref) 1.00 1.00 1.00 1.00 1.00 1.00 Yes 0.14 (0.11) –0.44 (0.24) 0.73* (0.34) 0.21 (0.15) 0.56 (0.31) 0.30 (0.17)

Note. Models use negative binomial regression. All estimates were adjusted for age, gender, employment, education, income–poverty ratio, marital status, survey year, and percentage of life spent in the United States. Estimates were weighted to account for the sampling design. *P£.05; **P£.01; ***P£.001.

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number of Asian Americans reported encoun- tering racial discrimination and possessing limited English proficiency, and (2) both dis- crimination and limited English proficiency were related to decreased quality of life, al- though discrimination was a more consistent correlate of quality of life than was English proficiency. Although Filipinos were the most likely (1 in 3 respondents) and Vietnamese were the least likely (1 in 4 respondents) to report discrimi- nation, these between-group differences were not large. More than a quarter of Asian Amer- icans in California said they were at least sometimes treated badly because of their race or ethnicity. Single-item measures, however, can underestimate the full range of discrimi- nation.33,38 More comprehensive scales may increase the range in reporting of discrimination by capturing multiple dimensions (e.g., subtle versus blatant discrimination) or types of cir- cumstances (e.g., when shopping). Further, mul- tiple items may prompt the reporting of specific experiences of discrimination (e.g., discrimination at school) that might be missed by single, global questions. Hence, it is possible that our estimates of discrimination would have been higher had we used full questionnaires.39–41 Nonetheless, it is striking that racial discrimination was so commonly reported in a state where Asians are a substantial proportion of the population and atatimewhensomehavedescribedAsiansas ‘‘honorary Whites.’’42,43 By contrast to the findings on discrimination, our data revealed considerable variation in English proficiency across groups. Only 1 in 32 South Asians, but 1 in 2 Vietnamese, reported having limited English proficiency. This varia- tion does not appear to be due to time in the United States, as South Asians are more likely to be recent immigrants than are the Viet- namese. Some of this variation might be due to economic differences, but a more important Note. Estimates reflect the mode for categorical variables and the mean for continuous variables. reason may be sociohistorical context. English a Statistically significant difference between discrimination and no discrimination, adjusted for covariates. is commonly spoken throughout as FIGURE 1—Association between racial discrimination and health-related quality of life, by a consequence of British colonialism, but En- Asian subgroup for (a) probability of fair or poor health, (b) predicted number of activity glish is not so widely disseminated in Vietnam limited days, and (c) predicted number of unhealthy days: California Health Interview Survey, because it does not have the same historical 2003–2005. legacy. These 2 measures provide a key lesson. The issue of heterogeneity among Asian Americans is critically important, but Asian Americans are not necessarily heterogeneous in all

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characteristics. Perhaps heterogeneity is most their services to clients with limited English concerns over response bias, but the caveats relevant for measures that are strongly related proficiency. These policies include the Califor- nonetheless remain. to cultural and historical context, such as nia Language Assistance Program (Senate Bill It is unclear how generalizable our results language. Discrimination, however, is not in- 853), effective in 2009, which requires in- are outside of California. It is possible that herently related to Asian culture. A recurrent surers to provide interpreters and translated levels of limited English proficiency are higher theme in Asian American scholarship is that materials to patients with limited English pro- in California because of the large population of many perpetrators of racial bias do not distin- ficiency.56 Our analyses support those continued immigrants. Hence our study should be repli- guish between Asian ethnic groups.44,45 A efforts. Indeed, Chinese, Koreans, and Vietnam- cated elsewhere. notable example came from the murder of ese with limited English proficiency had more Our study also had several strengths. We Vincent Chin, a Chinese American, who was than twice the odds of reporting poor health as analyzed a probability sample of adults across called a ‘‘Jap’’ by his assailants.46 Hence the small did others of the same ethnic origin who did not the entire state of California, which was not between-group variation in reporting of racial have limited English proficiency. Further, Japa- restricted to specific populations (e.g., college discrimination may indicate that discrimination is nese with limited English proficiency had a pre- students). Our large sample allowed us to a shared experience across many Asian Ameri- dicted 47 more unhealthy days over the year disaggregate our analyses to examine 6 Asian can ethnic groups. than Japanese without language barriers. How- ethnic groups. A key addition was inclusion of Consistent with the literature, both discrim- ever, limited English proficiency was a less con- South Asians, because most previous studies of ination and limited English proficiency were sistent correlate of quality of life than was this population were conducted in the United associated with decreased health-related qual- discrimination. Some of this inconsistent effect Kingdom.22–24 Finally, our use of the standard ity of life.4,47–50 These associations remained may have arisen because the CHIS was not CDC measures allowed for comparisons with when we adjusted discrimination and limited available in Japanese, Tagalog, or South Asian other studies.18 English proficiency for one another and for other languages (e.g., Urdu, Hindi). CHIS does not demographic characteristics. provide questionnaires in these languages be- Conclusions Several studies have found that self-reported cause, according to the US Census, few California We found that reports of discrimination and discrimination is related to poor self-rated residents with these ethnic origins have limited limited English proficiency were associated health and quality of life.2,24,51 However, the English proficiency. Hence estimates of limited with decreased health-related quality of life CDC unhealthy days measure puts some of these EnglishproficiencyinCHISarenotlikelyto among Asian Americans in California. The associations in a new perspective. For instance, be dramatically different from population esti- widest gaps we observed in unhealthy days we estimated that over1year, the typical Chinese mates. were between South Asians who reported respondent who reported discrimination would discrimination and those who did not. Future have 19 more unhealthy days than a counterpart Limitations and Strengths studies should disaggregate South Asian sub- who did not report discrimination. More strik- Our data were cross sectional, and we groups and investigate other ethnic popula- ingly, among respondents who reported dis- were unable to evaluate causal associations. tions. crimination, we projected that Japanese would Several prospective studies have found that Research has shown that limited English have an additional 24 unhealthy days; Koreans, reports of discrimination predict subsequent proficiency is strongly related to health care 33 days; Vietnamese, 42 days; and South Asians, illness.57–60 Nonetheless, our findings need to be access and utilization.8,62,63 Although still pre- 51days,comparedwithmembersofthese verified with longitudinal data. Our data were liminary, our findings suggest that although ethnic groups who did not report discrimination. derived from self-report, which may introduce limited English proficiency may not be a strong One finding was unexpected. Discrimination response biases (e.g., faulty recall), and CHIS has risk factor for decreased quality of life itself, it was not associated with any of the quality-of- a low response rate. This rate is comparable to may still be a barrier to health communication life measures for Filipino Americans. This the response rates of other telephone surveys, and access to services. We hope that our study finding contrasts with several other studies that such as the California Behavioral Risk Factor will be used as a baseline by local organiza- reported that discrimination is associated with Surveillance System. The major problem arising tions to conduct their own studies of health, increased illness among this population.52–55 from a low response rate is potential sampling language proficiency, and racial discrimina- The reason for our null finding is unclear, but it bias. That is, nonresponders may differ in key tion. j might be related to our measures, which have not ways from responders. CHIS respondents, how- been validated for Filipinos. There might have ever, match the demographic characteristics of been unobserved factors that resulted in our California reported by the census. Our health About the Authors variables not operating similarly among Filipinos measures were also comparable to other surveys. The authors are with the School of Public Health, University of California, Los Angeles. and other Asians. It is also possible that the null For example, our analysis of data from the 2005 Correspondence should be sent to Gilbert C. Gee, PhD, findings resulted from chance alone. California Behavioral Risk Factor Surveillance 650 Charles E. Young Drive South, Los Angeles, CA In California, much attention has focused on System found that Asians in California reported 90095-1772 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the ‘‘Reprints/ language access, and several policies have a mean 4.9 unhealthy days, which is similar to Eprints’’ link. been instituted to require providers to improve our estimates.61 These external data temper This article was accepted November 3, 2009.

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Contributors 15. Ponce NA, Ku L, Cunningham WE, Brown ER. 31. Manor O, Matthews S, Power C. Dichotomous or G. C. Gee conceptualized the study and led the writing. N. Language barriers to health care access among Medicare categorical response? Analysing self-rated health and Ponce led the analysis and assisted with the writing. beneficiaries. Inquiry. 2006;43(1):66–76. lifetime social class. Int J Epidemiol. 2000;29(1):149– 16. Williams DR. Race, socioeconomic status, and 157. Acknowledgments health. The added effects of racism and discrimination. 32. Jackson JS, Brown TN, Williams DR, Torres M, Ann N Y Acad Sci. 1999;896:173–188. This project was partially supported by the National Sellers SL, Brown K. Racism and the physical and mental Cancer Institute (grant K07 CA100097). 17. Moriarty DG, Zack MM, Kobau R. The Centers for health status of African Americans: a thirteen year Disease Control and Prevention’s healthy days measures— national panel study. Ethn Dis. 1996;6(1–2):132–147. population tracking of perceived physical and mental Human Participant Protection 33. Gee GC. A multilevel analysis of the relationship health over time. Health Qual Life Outcomes. 2003;1:37. between institutional and individual racial discrimination This research used data from secondary sources available Available at: http://www.hqlo.com/content/1/1/37. and health status. Am J Public Health. 2002;92(4):615– in the public domain and was declared exempt from Accessed January 28, 2010. 623. University of California, Los Angeles institutional review 18. Preamble to the Constitution of the World Health board review per protocol #08-329. 34. US Census Bureau. Current Population Survey Organization as Adopted by the International Health 2004 Annual Social and Economic Supplement. Poverty Conference, New York, June 19–22, 1946; signed on 22 Thresholds 2003. Available at: http://www.census.gov/ July 1946 by the representatives of 61 States (Official References hhes/www/poverty/threshld/thresh03.html. Accessed Records of the World Health Organization, no. 2, p. 100) 1. Zhou M, Gatewood JV. Mapping the terrain: Asian March 5, 2010. American diversity and the challenges of the twenty-first and entered into force on 7 April 1948. Geneva, century. Asian Am Policy Rev. 2000;IX:5–29. Switzerland: World Health Organization; 1948. 35. US Census Bureau. Poverty Thresholds 2005. Available at: http://www.census.gov/hhes/www/ 2. Gee GC, Ro A, Shariff-Marco S, Chae D. Racial 19. Klonoff EA, Landrine H. Is skin color a marker poverty/threshld/thresh05.html. Accessed November discrimination and health among Asian Americans: evi- for racial discrimination? Explaining the skin color– 1, 2009. dence, assessment, and directions for future research. hypertension relationship. J Behav Med. 2000;23(4): Epidemiol Rev. 2009;31:130–151. 329–338. 36. Weighting and Variance Estimation. Report 5, California Health Interview Survey 2005 Methodology 3. Sun A, Wong-Kim E, Stearman S, Chow EA. Quality 20. Kitano HL. Asian-Americans: The Chinese, Japa- Series. Los Angeles: University of California, Los Angeles of life in Chinese patients with breast cancer. Cancer. nese, Koreans, Pilipinos, and Southeast Asians. Ann Am Center for Health Policy Research; 2007. 2005;104(suppl 12):2952–2954. Acad Pol Soc Sci. 1981;454(1):125–138. 37. Weighting and Variance Estimation. Report 5, 4. Williams DR, Mohammed SA. Discrimination and 21. Takeuchi DT, Zane N, Hong S, et al. Immigration- California Health Interview Survey 2003 Methodology racial disparities in health: evidence and needed research. related factors and mental disorders among Asian Series. Los Angeles: University of California, Los Angeles J Behav Med. 2009;32(1):20–47. Americans. Am J Public Health. 2007;97(1):84–90. Center for Health Policy Research; 2005. 5. LaVeist TA, Nickerson KJ, Bowie JV. Attitudes 22. Nazroo JY. The structuring of ethnic inequalities in 38. Landrine H, Klonoff E, Corral I, Fernandez S, about racism, medical mistrust, and satisfaction with health: economic position, racial discrimination, and Roesch S. Conceptualizing and measuring ethnic dis- care among African American and White cardiac racism. Am J Public Health. 2003;93(2):277–284. crimination in health research. J Behav Med. 2006; patients. Med Care Res Rev. 2000;27(suppl 1):146– 23. Karlsen S, Nazroo JY. Relation between racial 29(1):79–94. 161. discrimination, social class, and health among ethnic 39. Kressin NR, Raymond KL, Manze M. Perceptions of 6. LaVeist TA, Rolley NC, Daila C. Prevalence and minority groups. Am J Public Health. 2002;92(4): race/ethnicity-based discrimination: a review of mea- patterns of discrimination among U.S. health care con- 624–631. sumers. Int J Health Serv. 2003;33(2):331–344. 24. Bhui K, S, McKenzie K, Karlsen S, Nazroo J, sures and evaluation of their usefulness for the health Weich S. Racial/ethnic discrimination and common care setting. J Health Care Poor Underserved. 2008;19(3): 7. Ponce NA, Chawla N, Babey SH, et al. Is there 697–730. a language divide in pap test use? Med Care. 2006; mental disorders among workers: Findings from the EMPIRIC Study of Ethnic Minority Groups in the United 44(11):998–1004. 40. Liang CTH, Li LC, Kim BSK. The Asian American Kingdom. Am J Public Health. 2005;95(3):496–501. Racism-Related Stress Inventory: development, factor 8. Ponce NA, Hays RD, Cunningham WE. Linguistic 25. Tang H, Shimizu R, Chen MS. English language analysis, reliability, and validity. J Couns Psychol. 2004; disparities in health care access and health status among proficiency and smoking prevalence among California’s 51(1):103–114. older adults. J Gen Intern Med. 2006;21(7):786–791. Asian Americans. Cancer. 2009;104(suppl 12):2982– 41. Williams DR, Yu Y, Jackson JS, Anderson NB. Racial 9. Gee GC, Walsemann KM, Takeuchi DT. English 2988. differences in physical and mental health: socio-economic proficiency and language preference: testing the equiva- 26. Hennessy CH, Moriarty DG, Zack MM, Scherr PA, status, stress and discrimination. J Health Psychol. 1997; lence of two measures. Am J Public Health. 2010; Brackbill R. Measuring health-related quality of life for 2(3):335–351. 100(3):563–569. public health surveillance. Public Health Rep. 1994; 42. Bonilla-Silva E. From bi-racial to tri-racial: towards 10. McManus W, Gould W, Welch F. Earnings of 109(5):665–672. a new system of racial stratification in the USA. Ethn Hispanic men: the role of English language proficiency. 27. Zahran HS, Kobau R, Moriarty DG, Zack MM, Holt J, Racial Stud. 2004;27:931–950. J Labor Econ. 1983;1(2):101–130. Donehoo R. Health-related quality of life surveillance— 43. Zhou M. Are Asian Americans becoming ‘‘White’’? 11. Krieger N. Does racism harm health? Did child United States, 1993–2002. MMWR Surveill Summ. Contexts. 2004;3(1):29–37. abuse exist before 1962? On explicit questions, critical 2005;54(4):1–35. science, and current controversies: an ecosocial perspec- 44. Takaki R. Strangers From a Distant Shore: A History 28. Fujishiro K. Is perceived racial privilege associated tive. Am J Public Health. 2003;93(2):194–199. of Asian Americans. New York, NY: Little, Brown and with health? Findings from the Behavioral Risk Factor Company; 1989. 12. Thomas TN. Acculturative stress in the adjustment Surveillance System. Soc Sci Med. 2009;68(5):840–844. of immigrant families. J Soc Distress Homeless. 1995; 45. Chou RS, Feagin JR. The Myth of the Model Minority. 29. Brown ER, Holtby S, Zahnd E, Abbott GB. Com- 4(2):131–142. Boulder, CO: Paradigm Publishers; 2008. munity-based participatory research in the California 13. Spencer MS, Chen J. Effect of discrimination on Health Interview Survey. Prev Chronic Dis. 2005;2(4): 46. Zia H. Asian American Dreams: The Emergence of an mental health service utilization among Chinese Ameri- 1–8. American People. New York, NY: Farrar, Straus and Giroux; 2000. cans. Am J Public Health. 2004;94(5):809–814. 30. Ponce NA, Lavarreda SA, Yen W, Brown ER, 14. Yoo HC, Gee GC, Takeuchi D. Discrimination and DiSogra C, Satter DE. The California Health Interview 47. Gee GC, Ro A. Racism and discrimination. In: Trinh- health among Asian American immigrants: disentangling Survey 2001: translation of a major survey for Califor- Shevrin C, Islam NS, Rey MJ, eds. Asian American racial from language discrimination. Soc Sci Med. 2009; nia’s multiethnic population. Public Health Rep. 2004; Communities and Health: Context, Research, Policy and 68(4):726–732. 119(4):388–395. Action. San Francisco, CA: Jossey-Bass; 2009:364–402.

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48. Paradies Y. A systematic review of empirical re- search on self-reported racism and health. Int J Epidemiol. 2006;35(4):888–901. 49. Chan C. The quality of life of women of Chinese origin. Health Soc Care. 2001;8(3):212–222. 50. Mui AC, Kang SY, Kang D, Domanski MD. English language proficiency and health-related quality of life among Chinese and Korean immigrant elders. Health Soc Work. 2007;32(2):119–127. 51. Noh S, Beiser M, Kaspar V, Hou F, Rummens J. Perceived racial discrimination, depression, and coping: a study of Southeast Asian refugees in Canada. J Health Soc Behav. 1999;40(3):193–207. 52. de Castro AB, Gee GC, Takeuchi DT. Workplace discrimination and health among Filipinos in the United States. Am J Public Health. 2007;98(3):520–526. 53. Gee GC, Chen J, Spencer MS, et al. Social support as a buffer for perceived unfair treatment among Filipino Americans: differences between San Francisco and Honolulu. Am J Public Health. 2006;96(4):677–684. 54. Gee GC, Spencer MS, Chen J, Takeuchi D. A nationwide study of discrimination and chronic health conditions among Asian Americans. Am J Public Health. 2007;97(7):1275–1282. 55. Mossakowski KN. Coping with perceived discrimi- nation: does ethnic identity protect mental health? J Health Soc Behav. 2003;44(3):318–331. 56. Cal Health & Safety Code 1367.04, 1367.07, 10133.8, 10133.9 (Escutia and Perata, 2003). 57. Pavalko EK, Mossakowski KN, Hamilton VJ. Does perceived discrimination affect health? Longitudinal re- lationships between work discrimination and women’s physical and emotional health. J Health Soc Behav. 2003; 44(1):18–34. 58. Lewis TT, Everson-Rose SA, Powell LH, et al. Chronic exposure to everyday discrimination and coro- nary artery calcification in African-American women: the SWAN Heart Study. Psychosom Med. 2006;68(3):362– 368. 59. Schulz AJ, Gravlee CC, Williams DR, Israel BA, Mentz G, Rowe Z. Discrimination, symptoms of depres- sion, and self-rated health among African American women in Detroit: results from a longitudinal analysis. Am J Public Health. 2006;96(7):1265–1270. 60. Gee GC, Walsemann KM. Does health predict the reporting of racial discrimination or do reports of dis- crimination predict health? Findings from the National Longitudinal Study of Youth. Soc Sci Med. 2009;68(9): 1676–1684. 61. Centers for Disease Control and Prevention, Na- tional Center for Chronic Disease Prevention and Health, Behavioral Risk Factor Surveillance System. BRFSS annual survey data. Available at: http://www.cdc.gov/ brfss/technical_infodata/surveydata/2005.htm. Accessed February 3, 2010. 62. Jacobs E, Chen AH, Karliner LS, Agger-Gupta N, Mutha S. The need for more research on language barriers in health care: a proposed research agenda. Milbank Q. 2006;84(1):111–133. 63. Jacobs EA, Karavolos K, Rathouz PJ, Ferris TG, Powell LH. Limited English proficiency and breast and cervical cancer screening in a multiethnic population. Am J Public Health. 2005;95(8):1410–1416.

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An Uneven Playing Field: The Lack of Equal Pay for People With Disabilities

Michelle Yin Senior Researcher AIR

Dahlia Shaewitz Principal Researcher AIR

Mahlet Megra Research Assistant AIR

December 2014

Introduction

For people with disabilities, does attaining educational success equal to that of their non-disabled peers ensure opportunities for financial independence and success? The existing disability literature compares earnings and income between people with disabilities and those without, examines employment rates among people with different types of disabilities, and identifies labor market outcomes for specific populations with disabilities (e.g., male heads of households and post-high school young adults). However, the current research does not describe the income difference between people with disabilities and their non-disabled counterparts in full-time employment by educational level. Nor does it describe the subsequent economic impact on individuals, states, or the nation. To address this gap in research, our study focuses on two pertinent questions: 1. Do earnings differ between these two groups after accounting for educational attainment and workforce participation? 2. If so, how great is that discrepancy and what is its economic impact?

What we found is alarming. Despite educational attainment, earnings inequalities certainly exist between the two groups, and surprisingly the gap actually widens as educational attainment increases. In fact, . The greatest earnings inequalities occur among those with a master’s degrees or higher; . The U.S. economy would have received an additional $141 billion in 2011—roughly 1% of the gross domestic product (GDP) if people with disabilities were paid comparably as those without, and . The earnings difference from people with disabilities would have translated into another $25 billion in federal taxes and $6.5 billion in state taxes.

The Challenge in Context

As the national interest in preparing adults for college and career readiness grows, a similar push is being made to move people with disabilities into postsecondary education, training, and work. But statistics and studies show that people of working age with disabilities (men and women, ages 16–64) have historically fared poorly in both education and the labor market relative to people without disabilities. In 2011, about 10 percent of working-age adults with disabilities1 in the United States had a bachelor’s degree or higher, compared with more than 25% of working-age adults without disabilities.

1 “The current American Community Survey (ACS) covers six disability types (and their PUMS variable):Hearing difficulty deaf or having serious difficulty hearing (DEAR).Vision difficulty blind or having serious difficulty seeing, even when wearing glasses (DEYE).Cognitive difficulty Because of a physical, mental, or emotional problem, having difficulty remembering, concentrating, or making decisions (DREM).Ambulatory difficulty Having serious difficulty walking or climbing stairs (DPHY).Self-care difficulty Having difficulty bathing or dressing (DDRS). Independent living difficulty because of a physical, mental, or emotional problem, having difficulty doing errands alone such as visiting a doctor’s office or shopping (DOUT). Respondents who report anyone of the six disability types are considered to have a disability.” Definition retrieved from https://www.census.gov/people/disability/methodology/acs.html.

American Institutes for Research Equal Pay for People with Disabilities—1 The employment rate was only 27% for people with disabilities but more than 65% for those without disabilities.2

Lower rates of employment and educational attainment directly influence earnings for people with and without disabilities, but poverty looms more heavily over the disabled population. In 2011, nearly 28% of non-institutionalized people with disabilities in the United States, 21–64 years of age, lived below the poverty line, compared with 12% of people without disabilities (Cornell University, 2013). Of all working-age adults in poverty, 47% reported having a disability; that number rose to 50–60% for those in long-term poverty (She & Livermore, 2009). These numbers are driving current policy and practice efforts that will, as President Obama stated after passing the 2014 Workforce Innovation and Opportunity Act (WIOA), “help workers, including workers with disabilities, access employment, education, job-driven training, and support services that give them the chance to advance their careers and secure the good jobs of the future” (Hoff, 2014).

And yet, even when people with disabilities find jobs, their hourly, weekly, and monthly earnings are lower than those of workers without disabilities. People with disabilities earn on average only 64% as much as those without disabilities. Among those working full time, people with disabilities still make only 86% as much (LaPlante, Kennedy, Kaye, & Wenger, 1996). The difference is even greater for minority populations (Mwachofi, Broyles, & Khaliq, 2009; Ozawa & Yeo, 2006; She & Livermore, 2009). In 2012, the Annual Disability Status report (Cornell University, 2013) found that full-time working-age people with disabilities made $6,000 less on average annually than those without disabilities, and that annual household income for people with disabilities was $23,300 less than that for people without disabilities.

Compared with people without disabilities, the earnings of people with disabilities tend to be lower because of their work limitations and because this population as a whole tends to be older, have higher numbers of minorities, and have lower levels of education. Even so, we would expect that full-time workers with disabilities who have the same amount of education as their non-disabled peers would have, on average, equal earnings. Indeed, recent federal legislation through WIOA opens the door for people with disabilities to enhance their career opportunities on the grounds that education leads to greater employment opportunities and financial independence. However, our examination of the data shows that people with disabilities remain at a disadvantage despite educational accomplishments and participation in competitive employment.

Data and Methods To carry out this analysis, we used data from the U.S. Census Bureau’s 2011 American Community Survey to estimate the percentage of people with disabilities in each state at five educational attainment levels: high school graduate, some college, associate’s degree, bachelor’s degree, and master’s or higher). As shown in Figure 1 and Table 1, people with disabilities tend to have lower educational attainment than their counterparts. For example, less than 7% of people with disabilities had bachelor’s degrees or higher, compared with 13% for people without disabilities and only 3% of

2 According to authors’ calculations using data from the 2011 ACS.

American Institutes for Research Equal Pay for People with Disabilities—2 people with disabilities had master’s degrees or higher, compared with nearly 8% of people without disabilities.3

Next, we estimated earnings for people with and without disabilities by educational attainment level for each state and at the national level. In this study, we opted to use the average earnings that people receive primarily from their full-time jobs. In other words, we did not consider other forms of income, such as income from Social Security payments, pensions, child support, public assistance, or annuities. This allowed us to directly compare income due to employment between people with and without disabilities. Results showed (Figure 2 and Table 1) that earnings for people with disabilities were much lower than those of their counterparts at all levels of educational attainment. Using the disability and income estimates, we next quantified the income inequalities by state and calculated corresponding lost federal and state income taxes. To do so, we used the federal income tax rate and individual state income tax schedules4 from 2011 to first estimate the lost income taxes at the individual level. Using the state’s population and disability statistics, we estimated the lost income taxes for each state and at the national level as shown in tables 2 and 3.5,6

Finally, we tested whether people with disabilities face greater economic discrimination than those without disabilities in a regression framework, after controlling for certain demographic and labor market supply characteristics. The regression framework eased concerns about the sample size for this study. We examined the relationships between a set of individual characteristics, including disability status and earnings (and log earnings), using an Ordinary Least Square model. Results in Table 4 showed that people with disabilities face a level of economic discrimination similar to that of female employees—that is, 37% lower pay for people with disabilities compared to 35% lower pay for women.

Individual, State, and Federal Losses

Among working-age people with disabilities, 33% had no degree, compared with 35% of people without disabilities. Nearly 31% had a high school diploma, compared with almost 20% of people without disabilities. As for those with some college, the percentages begin to even out for people with (20%) and without (18%) disabilities. For both groups, approximately 6% attain an associate’s degree. However, as levels of education increase so does the gap in attainment between the two

3 In some states, the sample size for people with disabilities with different educational attainment was small and might not have provided an accurate estimate of the average earnings. However, the American Community Survey offered the most current data available for the study (see notes in Appendix, Table A-1 for the list of states). 4 Federal and state tax rates were calculated using rates that were obtained from http://taxfoundation.org/data. Rates for single individuals were used to estimate the average federal and state tax burden. While calculating the state tax rates, we included as many exemptions as possible. However, we did not include Social Security and disability exemptions because we were looking at tax paid on employment-related income and we could not differentiate total and partial disability. Social Security benefits were allotted for only those who had total disability. 5 The number of observations decreases while moving up the educational attainment ladder. This might cause over or under estimation of the earnings difference in states with small populations. Nonetheless, data from the American Community Survey remain the best available source for this type analysis, but further studies will be needed to validate these findings. 6 Full tables of average earnings of people with and without disabilities, and the corresponding federal and state income taxes for each state can be found in appendix.

American Institutes for Research Equal Pay for People with Disabilities—3 groups. Less than 7% of people with disabilities earn bachelor’s degrees, compared with about 13% of those without disabilities, and slightly more than 3% of people with disabilities obtain master’s degrees or higher, compared with 8% of those without disabilities.

Despite the lower percentage of people with disabilities with advanced degrees, we still expected to find people with equivalent educational attainment to earn equal pay. Instead, the research reveals that gaps in earned income grew as levels of educational achievement increased. Specifically, people with disabilities earned on average $6,505 less than their working-age colleagues without disabilities with a high school or equivalent degree, and that difference in earnings reached $20,871 on average among those with master’s degrees or higher (see Table 1).

What do pay differentials like this mean for the nation? In the working-age population as a whole, people with disabilities are paid nearly 37% (or $10,700) less than people without disabilities, even after controlling for labor supply and certain demographic and labor market characteristics (Table 4). In 2011, the additional earnings of people with disabilities would have produced an additional $141 billion for the U.S. economy, representing approximately 1% of the GDP.

The loss in earnings lead to additional state and national losses in tax revenue as shown in tables 2 and 3. The gap in federal income tax steadily increased from a difference of $976 per person with a high school diploma to $5,218 per person for people with master’s degrees. In 2011, the total approximate loss of tax income was more than $25 billion at the federal level and more than $6.5 billion at the state level.

For individual states, income inequality varied by educational attainment, a pattern similar to the national trend. For people with and without disabilities who have a high school diploma or equivalent, the highest average income inequalities were observed in Vermont ($12,700), Connecticut ($12,000), and Iowa ($10,000). For those with bachelor’s degrees, the greatest inequality was observed in Washington, DC ($20,000), followed by Minnesota ($18,000) and the State of Washington ($17,000). At the master’s degree and higher levels, income inequalities were highest in Nevada ($38,700), Connecticut ($35,500), and Hawaii ($33,800).

As with earnings, the difference in lost tax revenue varied by state and level of educational attainment. For example, Connecticut reported the highest state tax difference for people with a high school diploma ($600), California for people with some college or associate’s degrees ($700), and Idaho ($2,000) and New Jersey ($3,000) for those with bachelor’s or master’s degrees or higher, respectively.

In some states, these trends are reversed, but only in specific educational categories (Table 5). For example, people with disabilities earn more than their counterparts across certain educational levels in the following states: . Some level of college education: North Dakota ($9,000), Mississippi ($2,000), Rhode Island ($2,000), and Washington, DC ($500) . Associate’s degrees: Utah ($6,500), Wyoming ($1,500), New Jersey ($1,000), Delaware ($500), and Washington, DC ($50) . Bachelor’s degrees: Wyoming ($6,000), New Hampshire ($4,500), and Alaska ($2,500)

American Institutes for Research Equal Pay for People with Disabilities—4 . Master’s degrees or higher: South Dakota ($16,000), Utah ($15,000), Alaska ($14,000), and Kentucky ($2,000).

More analysis of these states is needed to identify the education policies and labor market forces that may lead to differential economic opportunities for people with disabilities.

How Can the Pay Gap Be Closed and Losses Stemmed? Earnings inequalities for people with disabilities translate into lower pay, reduced federal and state tax income, and higher poverty rates. The U.S. economy as a whole suffers from the unequal pay between people with disabilities and their equally educated peers without disabilities. Because nearly 10% of working-age adults have a disability, education and job attainment for people with disabilities should be a priority. But education and employment are not enough—equal earnings is key to ensuring that people with disabilities remain economically independent and out of poverty.

Americans view education as an equalizer, offering opportunities for any person to attain a career and a higher standard of living. People with disabilities who have the same education or training as those without disabilities should be prepared for and able to perform the same job for the same pay. But for people with disabilities, particularly those with severe disabilities, the cost of living often includes additional expenditures on health care, personal services, accommodations in the home, and transportation. For these workers, lower earnings presents an even greater challenge.

Despite the difference in earnings between people with disabilities and those without disabilities, higher education and full-time competitive employment remain the best options to live independently and escape poverty. Therefore, the challenge to addressing the disparities between earnings of people with disabilities and their non-disabled peers requires a look at policies and practices that support fair wages for all workers in the public and private sectors. Although equal pay and non-discriminatory compensation are protected through the Civil Rights Act, the Americans with Disabilities Act, and the Rehabilitation Act, the results of this study show that earnings inequalities widen as educational attainment increases between people with and without disabilities in almost every state.

What factors account for this widespread disparity? Are people with disabilities trapped in low- paying positions because employers do not promote them? Are people with disabilities accepting lower wage jobs that offer better benefit packages, including health care and other supports? More research is needed to examine the demographics of people with disabilities in employment, their employment settings and types of employers, the effects of years worked on their wages, and the impact of full-time versus part-time status.

Also, given the wage gaps characterized here, the federal government and state legislators may want to consider additional policies and practices that support fair and equal pay for people with disabilities. For example, federal policies that support lower wages for people with disabilities7 create a belief that those people do not deserve equal pay, even when they have equal levels of educational attainment and postsecondary training. New WIOA legislation decreases reliance on sheltered

7 Fair Labor Standards Act (FLSA) passed in 1938.

American Institutes for Research Equal Pay for People with Disabilities—5 workshops and promotes plans that transition youth into competitive employment and higher education. Still, concerns remain about placing people with disabilities in non-stereotypical work environments and true career pathways (Hoff, 2014; Jones, 2014)—witness the current debate over subminimum wage jobs (Bagenstos, n.d.; Preedy, 2014).

Additionally, perceived stigma of people with disabilities in the workplace may also affect performance evaluations, which affect earnings over time (McLaughlin, Bell, & Stringer, 20014). As noted by Mwachofi and colleagues (2009, p. 175), “Policies or educational programs [need to be] designed to improve the productivity or the market worth of recipients.” Businesses must view people with disabilities as valuable employees. Employers that fail to recognize and reward employees with disabilities lose an opportunity to engage a highly productive workforce.

Employers also need to meet the challenge of providing equal pay for all employees. Bartolotta, Skaff, and Klayman’s (2014) white paper on engaging employers to hire people with disabilities describes specific activities that help vocational rehabilitation and outreach service providers to directly target “the creation of counter-stereotypes” (p. 10) in the workplace. For their part, people with disabilities themselves need to be aware of this income disparity, know their worth, and advocate for themselves—service providers can assist through assertiveness training and skills development for salary negotiations. Further analysis is needed to examine more closely the number and rate of promotion of people with disabilities and to determine the barriers that educated, full- time employees face on the job.

Evidence on wage gaps between people with and without disabilities is scarce. This study contributes to a much needed and growing body of evidence that can provide a richer picture of the challenges faced by people with disabilities. The time is ripe for creating an even playing field for the employment and advancement of people with disabilities in education and the workforce.

American Institutes for Research Equal Pay for People with Disabilities—6 Tables and Figures

Figure 1. Educational Attainment, by Disability Status

40.0% 35.3% 35.0% 33.4% 30.9% 30.0%

25.0% 19.5% 20.2% 20.0% 18.2%

15.0% 13.4%

10.0% 7.6% 6.0% 5.8% 6.6% 5.0% 3.2%

0.0% No degree High school or Some college Associate's Bachelor's Master's equal degree degree degree or higher

Without a Disability With a Disability

Figure 2. Earnings and Earnings Differences, by Disability Status and Educational Attainment

Without a Disability With a Disability Income Difference

87,771

66,899 58,822 46,103 39,968 31,104 32,768 29,471 26,489 22,966

(6,505) (4,615) (7,199) (12,719) (20,871)

High school or equal Some college Associate's degree Bachelor's degree Master's degree or higher

American Institutes for Research Equal Pay for People with Disabilities—7 Table 1. Earnings Differences Between People With and Without Disabilities and Percent 2011 U.S. Gross Domestic Product, by Educational Attainment

Percent Without a With a Per Person of 2011 Educational Attainment Disability Disability Difference Total U.S. GDP High school or equal 29,471 22,966 (6,505) (58,914,709,302) -0.41% Some college 31,104 26,489 (4,615) (26,972,672,886) -0.19% Associate's degree 39,968 32,768 (7,199) (12,335,481,087) -0.09% Bachelor's degree 58,822 46,103 (12,719) (23,941,509,797) -0.17% Master's degree or higher 87,771 66,899 (20,871) (19,325,754,193) -0.13% Total (141,490,127,265) -0.98%

Table 2. Lost Federal Taxes Due to Income Inequalities Between People With and Without Disabilities and Percent of 2011 U.S. Gross Domestic Product, by Educational Attainment

Percent of Without a With a Per person 2011 Educational Attainment Disability Disability Difference Total U.S. GDP High school or equal 2,571 1,595 (976) (8,835,930,480) -0.06% Some college 2,816 2,123 (692) (4,045,900,933) -0.03% Associate's degree 4,145 3,065 (1,080) (1,867,216,827) -0.01% Bachelor's degree 8,456 5,276 (3,180) (5,617,028,454) -0.04% Master's degree or higher 15,693 10,475 (5,218) (4,868,605,097) -0.03% Total (25,234,681,791) -0.18%

Table 3. Lost State Taxes Due to Income Inequalities and Percent of 2011 U.S. Gross Domestic Product, by Educational Attainment

Educational Attainment Total Percent of 2011 U.S. GDP High school or equal (2,280,765,384) -0.02% Some college (1,384,632,223) -0.01% Associate's degree (679,763,797) 0.00% Bachelor's degree (1,176,906,992) -0.01% Master's degree or higher (979,852,089) -0.01% Total (6,501,920,485) -0.05%

American Institutes for Research Equal Pay for People with Disabilities—8 Table 4. Income Inequalities After Controlling for Specific Characteristics

(1) (2) Characteristics Actual Earning Log Earning Disabled -10,759*** -0.465*** (330.2) (0.00948) Female -18,628*** -0.422*** (425.6) (0.0102) Age 3,531*** 0.215*** (107.9) (0.00309) Age square -34.20*** -0.00225*** (1.120) (3.33e-05) High school degree 5,608*** 0.436*** (807.9) (0.00664) Some college 11,624*** 0.538*** (1,056) (0.0109) Associate’s degree 16,238*** 0.774*** (1,063) (0.00825) Bachelor’s degree 34,153*** 1.070*** (1,577) (0.0124) Master’s degree or higher 60,118*** 1.388*** (2,170) (0.0136) African American -6,590*** -0.164*** (485.9) (0.0176) American Indian -5,836*** -0.152*** (571.7) (0.0195) Alaska Native -5,652*** -0.301*** (459.8) (0.0181) American Indian and Alaska Native -6,767*** -0.184*** (813.7) (0.0288) Asian -1,357** -0.0428*** (567.1) (0.0134) Native Hawaiian or Other Pacific Islander -2,478*** 0.0268 (609.0) (0.0223) Other race -5,609*** -0.00324 (386.7) (0.0118) Two or more races -3,440*** -0.123*** (397.1) (0.0124) Constant -47,993*** 4.968*** (2,248) (0.0626) Observations 1,454,003 1,454,003 R-squared 0.248 0.362 Note. Robust standard errors are presented in parentheses. **p < 0.05, ***p < 0.01.

American Institutes for Research Equal Pay for People with Disabilities—9

Table 5. States With Higher Earnings Among People With and Without Disabilities, by Educational Attainment

Earnings Earnings (Without (With Per Person Educational Attainment State Disabilities) Disabilities) Difference Some college Washington, DC 28,279.50 28,744.09 464.59 Some college Rhode Island 28,614.82 30,693.02 2,078.20 Some college Mississippi 26,705.46 28,830.55 2,125.09 Some college North Dakota 32,524.41 41,708.47 9,184.06

Associate’s degree Wyoming 36,703.23 38,353.33 1,650.10 Associate’s degree Washington, DC 47,374.66 47,414.29 39.63 Associate’s degree Delaware 41,959.78 42,388.89 429.11 Associate’s degree New Jersey 46,642.93 47,720.33 1,077.40 Associate’s degree Utah 33,454.14 40,223.73 6,769.59

Bachelor’s degree Wyoming 47,928.62 53,846.15 5,917.53 Bachelor’s degree New Hampshire 57,655.32 62,143.75 4,488.43 Bachelor’s degree Alaska 54,680.17 57,235.71 2,555.54

Master’s degree or higher Alaska 80,950.89 95,230.00 14,279.11 Master’s degree or higher South Dakota 63,450.38 79,840.00 16,389.62 Master’s degree or higher Kentucky 68,794.17 70,841.52 2,047.35 Master’s degree or higher Utah 85,831.72 101,655.20 15,823.48

American Institutes for Research Equal Pay for People with Disabilities—10

References

Bagenstos, S. R. (n.d.). The case against the Section 14(c) Subminimum Wage Program. Retrieved from https://nfb.org/images/nfb/documents/word/14c_report_sam_bagenstos.doc

Bartolotta, R., Skaff, L., & Klayman, D. (2014). Employer engagement strategy: Workforce inclusion. Gaithersburg, MD: Social Dynamics, LLC.

Cornell University. (2013). Disability statistics: Online resource for U.S. disability statistics. http://www.disabilitystatistics.org/

Hoff, D. (2014). WIA is now WIOA: What the new bill means for people with disabilities. The Institute Brief, (31), 1–4.

Jones, A. (2014). Workforce bill provides new supports for people with disabilities. Retrieved from http://disabilityrightsgalaxy.com/workforce-bill-provides-new-supports-for-people-with- disabilities/

LaPlante, M. P., Kennedy, J., Kaye, S. H., & Wenger, B. L. (1996). Disability and employment. Disability Statistics Abstract, 11.

McLaughlin, M. E., Bell, M. P., & Stringer D. Y. (2004). Stigma and acceptance of persons with disabilities: Understudied aspects of workforce diversity. Group & Organization Management, 29(3), 302–333.

Mwachofi, A. K., Broyles, R. & Khaliq, A. (2009). Factors affecting vocational rehabilitation intervention outcomes: The case for minorities with disabilities. Journal of Disability Policy Studies, 20(3), 170–177.

Ozawa, M. N., & Yeo, Y. H. (2006). Work status and work performance of people with disabilities. Journal of Disability Policy Studies, 17(3), 180–190.

Preedy, M. (2014). Subminimum or subpar? A note in favor of repealing the Fair Labor Standards Act's Subminimum Wage Program. Seattle University Law Review, 37(3), 1097–1125.

She, P., & Livermore, G. A (2009). Long-term poverty and disability among working-age adults. Journal of Disability Policy Studies, 19(4), 244–256.

U.S. Census Bureau. (2011). American Community Survey. http://www.census.gov/acs/www/

American Institutes for Research Equal Pay for People with Disabilities—11 Appendix: State Tables

Table A-1. Disability Prevalence and Educational Attainment for People With Disabilities, by State

Percent Percent Percent Percent Percent Percent Disabled, Disabled, Disabled, Disabled Disabled, Disabled, Adults State Percent Adults With Adults Adults Adults With State Age With Population Disabled High School With With Master's Younger Some or Equal Associate’s Bachelor's Degrees or Than 65 College Diploma Degrees Degrees Higher Degree Alabama 4,802,740 20% 14% 31% 20% 5% 5% 3% Alaska* 722,718 12% 8% 32% 29% 5% 6% 2% Arizona 6,482,505 14% 9% 26% 26% 6% 7% 4% Arkansas 2,937,979 21% 15% 36% 19% 4% 4% 2% California 37,691,912 12% 8% 23% 23% 6% 7% 4% Colorado 5,116,796 13% 9% 28% 23% 7% 10% 5% Connecticut 3,580,709 13% 8% 30% 18% 5% 9% 5% Delaware* 907,135 14% 9% 32% 19% 4% 8% 3% DC 617,996 13% 9% 26% 18% 4% 8% 6% Florida 19,057,542 15% 10% 30% 20% 7% 7% 4% Georgia 9,815,210 15% 11% 32% 19% 5% 6% 3% Hawaii* 1,374,810 12% 7% 29% 20% 9% 9% 4% Idaho* 1,584,985 15% 10% 31% 25% 8% 7% 3% Illinois 12,869,257 14% 9% 31% 21% 6% 7% 3% Indiana 6,516,922 16% 11% 36% 19% 5% 4% 2% Iowa 3,062,309 15% 9% 34% 22% 7% 6% 3% Kansas 2,871,238 15% 10% 34% 23% 6% 7% 3% Kentucky 4,369,356 19% 14% 36% 17% 5% 4% 2% Louisiana 4,574,836 19% 13% 33% 18% 4% 6% 2% Maine* 1,328,188 18% 13% 38% 17% 6% 6% 3% Maryland 5,828,289 13% 8% 31% 20% 4% 8% 6% Massachusetts 6,587,536 13% 8% 28% 18% 6% 8% 5% Michigan 9,876,187 16% 11% 33% 23% 6% 6% 2% Minnesota 5,344,861 13% 8% 33% 22% 7% 7% 2% Mississippi 2,978,512 20% 14% 28% 18% 4% 5% 2% Missouri 6,010,688 17% 12% 34% 21% 5% 5% 3% Montana* 998,199 16% 10% 35% 24% 7% 8% 3% Nebraska 1,842,641 14% 8% 31% 24% 8% 9% 4% Nevada 2,723,322 14% 9% 29% 24% 7% 7% 3% New Hampshire* 1,318,194 13% 8% 34% 16% 6% 9% 5% New Jersey 8,821,155 12% 7% 30% 18% 5% 9% 5% New Mexico 2,082,224 16% 10% 28% 23% 6% 7% 5% New York 19,465,197 14% 9% 29% 17% 6% 7% 4% North Carolina 9,656,401 16% 11% 30% 21% 6% 6% 2% North Dakota* 683,932 13% 7% 32% 25% 11% 8% 4% Ohio 11,544,951 16% 11% 36% 18% 5% 5% 2% Oklahoma 3,791,508 19% 13% 34% 21% 6% 6% 3% Oregon 3,871,859 16% 11% 29% 26% 7% 7% 3% Pennsylvania 12,742,886 15% 10% 39% 16% 5% 6% 3% Rhode Island* 1,051,302 15% 9% 28% 16% 6% 8% 4% South Carolina 4,679,230 17% 12% 31% 18% 6% 6% 3% South Dakota* 824,082 15% 9% 35% 21% 6% 7% 3% Tennessee 6,403,353 18% 13% 36% 18% 4% 5% 2% Texas 25,674,681 15% 10% 28% 21% 5% 6% 3% Utah 2,817,222 11% 7% 28% 24% 6% 9% 3% Vermont* 626,431 15% 10% 30% 20% 4% 8% 6% Virginia 8,096,604 13% 9% 30% 19% 5% 7% 4% Washington 6,830,038 15% 10% 28% 25% 8% 8% 4% West Virginia 1,855,364 22% 16% 41% 16% 5% 4% 2% Wisconsin 5,711,767 13% 8% 35% 20% 6% 6% 3% Wyoming* 568,158 13% 9% 33% 27% 7% 5% 4% TOTAL 311,591,917 15% 10% 31% 20% 6% 7% 3% Note. Asterisk (*) indicates states with sample size smaller than 50 in the category of individuals with M.A. degrees.

American Institutes for Research Equal Pay for People with Disabilities—12 Table A-2. Estimated Lost Income, Federal Tax, and State Tax for People With Disabilities With a High School or Equal Diploma, by State

Estimated Estimated Estimated Estimated Earnings Earnings Per Per Per Federal Federal State Tax State Tax State (No (With Person Subtotal Person Subtotal Person Subtotal Tax (No Tax (With (No (With Disability) Disability) Difference Difference Difference Disability) Disability) Disability) Disability) Alabama 27,725 24,624 (3,100) (631,746,509) 2,309 1,844 (465) (94,761,976) 1,056 924 (132) (26,849,227) Alaska 30,318 24,372 (5,946) (111,794,786) 2,698 1,806 (892) (16,769,218) - - - - Arizona 27,545 21,926 (5,619) (854,789,361) 2,282 1,439 (843) (128,218,404) 569 407 (162) (24,617,934) Arkansas 27,213 22,167 (5,047) (793,590,267) 2,232 1,475 (757) (119,038,540) 1,242 939 (303) (47,615,416) California 29,531 24,586 (4,945) (3,352,894,896) 2,580 1,838 (742) (502,934,234) 796 598 (198) (134,115,796) Colorado 29,808 24,831 (4,977) (611,623,141) 2,621 1,875 (747) (91,743,471) 940 710 (230) (28,318,151) Connecticut 35,677 23,490 (12,187) (1,074,980,191) 3,502 1,673 (1,828) (161,247,029) 934 324 (609) (53,749,010) Delaware 33,613 24,339 (9,274) (258,481,073) 3,192 1,801 (1,391) (38,772,161) 1,708 1,127 (581) (16,193,905) DC 30,756 26,777 (3,978) (61,478,734) 2,763 2,167 (597) (9,221,810) 1,425 1,186 (239) (3,688,724) Florida 27,690 22,092 (5,598) (3,100,521,449) 2,304 1,464 (840) (465,078,217) - - - - Georgia 27,244 21,627 (5,617) (1,863,170,494) 2,237 1,394 (843) (279,475,574) 1,145 808 (337) (111,790,230) Hawaii 28,803 27,155 (1,648) (44,920,676) 2,470 2,223 (247) (6,738,101) 1,488 1,362 (125) (3,413,971) Idaho 26,128 19,822 (6,306) (303,931,294) 2,069 1,123 (946) (45,589,694) 1,206 822 (385) (18,539,809) Illinois 30,155 23,394 (6,761) (2,321,631,700) 2,673 1,659 (1,014) (348,244,755) 1,408 1,070 (338) (116,081,585) Indiana 28,680 23,087 (5,593) (1,442,209,729) 2,452 1,613 (839) (216,331,459) 907 717 (190) (49,035,131) Iowa 31,353 21,145 (10,209) (968,505,472) 2,853 1,322 (1,531) (145,275,821) 1,265 715 (549) (52,104,818) Kansas 28,501 23,404 (5,097) (484,019,282) 2,425 1,661 (765) (72,602,892) 1,041 722 (319) (30,251,205) Kentucky 27,794 22,061 (5,733) (1,255,721,135) 2,319 1,459 (860) (188,358,170) 1,419 1,086 (333) (72,831,826) Louisiana 31,894 27,395 (4,498) (866,358,151) 2,934 2,259 (675) (129,953,723) 728 575 (153) (29,456,177) Maine 27,061 18,870 (8,191) (527,846,340) 2,209 981 (1,229) (79,176,951) 1,040 467 (573) (36,949,244) Maryland 34,395 28,224 (6,171) (882,940,546) 3,309 2,384 (926) (132,441,082) 1,334 1,041 (293) (41,939,676) Massachusetts 31,689 24,402 (7,288) (1,139,876,546) 2,903 1,810 (1,093) (170,981,482) 1,446 1,060 (386) (60,413,457) Michigan 26,878 21,008 (5,870) (2,147,953,216) 2,182 1,301 (881) (322,192,982) 1,013 757 (255) (93,435,965) Minnesota 31,847 22,532 (9,315) (1,299,805,052) 2,927 1,530 (1,397) (194,970,758) 1,196 697 (498) (69,539,570) Mississippi 27,069 20,989 (6,079) (704,756,134) 2,210 1,298 (912) (105,713,420) 838 534 (304) (35,237,807) Missouri 28,350 21,742 (6,608) (1,558,581,949) 2,403 1,411 (991) (233,787,292) 864 527 (337) (79,487,679) Montana 27,758 19,134 (8,623) (291,553,421) 2,314 1,020 (1,293) (43,733,013) 837 365 (472) (15,966,682) Nebraska 29,302 22,515 (6,788) (318,632,202) 2,545 1,527 (1,018) (47,794,830) 931 572 (359) (16,867,214) Nevada 30,515 22,603 (7,912) (581,099,936) 2,727 1,540 (1,187) (87,164,990) - - - - New Hampshire 33,066 30,120 (2,946) (106,106,560) 3,110 2,668 (442) (15,915,984) - - - - New Jersey 35,671 26,114 (9,556) (1,814,021,743) 3,501 2,067 (1,433) (272,103,262) 537 370 (167) (31,745,381) New Mexico 27,088 21,172 (5,917) (356,443,892) 2,213 1,326 (888) (53,466,584) 590 308 (282) (16,962,326) New York 31,042 24,041 (7,002) (3,497,777,999) 2,806 1,756 (1,050) (524,666,700) 1,216 769 (447) (223,181,133) North Carolina 26,649 21,249 (5,400) (1,727,040,434) 2,147 1,337 (810) (259,056,065) 1,608 1,230 (378) (120,892,830) North Dakota 33,832 25,429 (8,403) (126,305,171) 3,225 1,964 (1,260) (18,945,776) 448 293 (155) (2,324,015) Ohio 28,642 20,787 (7,855) (3,607,050,168) 2,446 1,268 (1,178) (541,057,525) 602 330 (272) (124,950,266) Oklahoma 28,240 24,120 (4,120) (717,772,916) 2,386 1,768 (618) (107,665,937) 1,032 806 (227) (39,477,510) Oregon 27,188 19,347 (7,841) (945,280,692) 2,228 1,052 (1,176) (141,792,104) 1,838 1,238 (600) (72,313,973) Pennsylvania 30,206 22,867 (7,339) (3,726,156,560) 2,681 1,580 (1,101) (558,923,484) 927 702 (225) (114,393,006) Rhode Island 30,292 23,412 (6,880) (187,049,293) 2,694 1,662 (1,032) (28,057,394) 723 465 (258) (7,014,349) South Carolina 26,193 23,988 (2,204) (383,406,035) 2,079 1,748 (331) (57,510,905) 1,030 876 (154) (26,838,422) South Dakota 30,962 23,726 (7,236) (181,070,592) 2,794 1,709 (1,085) (27,160,589) - - - - Tennessee 27,135 20,806 (6,330) (1,888,332,138) 2,220 1,271 (949) (283,249,821) - - - - Texas 29,366 23,595 (5,771) (4,191,441,484) 2,555 1,689 (866) (628,716,223) - - - - Utah 29,205 22,414 (6,792) (380,216,726) 2,531 1,512 (1,019) (57,032,509) 1,329 901 (428) (23,953,654) Vermont 29,401 16,610 (12,791) (234,804,668) 2,560 711 (1,849) (33,944,784) 706 252 (454) (8,335,566) Virginia 29,588 22,644 (6,945) (1,470,753,723) 2,588 1,547 (1,042) (220,613,058) 1,348 949 (399) (84,568,339) Washington 31,029 22,392 (8,637) (1,611,976,129) 2,804 1,509 (1,296) (241,796,419) - - - - West Virginia 28,324 23,855 (4,469) (537,806,440) 2,399 1,728 (670) (80,670,966) 1,060 774 (285) (34,343,948) Wisconsin 30,001 22,157 (7,844) (1,316,755,423) 2,650 1,474 (1,177) (197,513,314) 1,065 583 (482) (80,980,459) Wyoming 36,166 32,957 (3,209) (51,726,835) 3,575 3,094 (481) (7,759,025) - - - - TOTAL 29,471 22,966 (6,505) (58,914,709,302) 2,571 1,595 (976) (8,835,930,480) - - - (2,280,765,384)

American Institutes for Research An Uneven Playing Field—13 Table A-3. Estimated Lost Income, Federal Tax, and State Tax for People With Disabilities With Some College Degree, by State

Estimated Estimated Estimated Estimated Earnings Earnings Per Per Per Federal Federal State Tax State Tax State (No (With Person Subtotal Person Subtotal Person Subtotal Tax (No Tax (With (No (With Disability) Disability) Difference Difference Difference Disability) Disability) Disability) Disability) Alabama 28,653 23,690 (4,963) (659,578,533) 2,448 1,703 (744) (98,936,780) 1,095 884 (211) (28,032,088) Alaska 37,389 34,502 (2,887) (49,075,512) 3,758 3,325 (433) (7,361,327) - - - - Arizona 31,440 26,175 (5,265) (812,302,106) 2,866 2,076 (790) (121,845,316) 681 530 (152) (23,394,301) Arkansas 26,721 23,165 (3,557) (299,897,040) 2,158 1,625 (533) (44,984,556) 1,212 999 (213) (17,993,822) California 35,068 28,432 (6,636) (4,439,633,780) 3,410 2,415 (995) (665,945,067) 1,450 752 (698) (467,028,835) Colorado 31,628 26,769 (4,859) (482,296,287) 2,894 2,165 (729) (72,344,443) 1,025 800 (225) (22,330,318) Connecticut 33,981 28,406 (5,575) (298,235,720) 3,247 2,411 (836) (44,735,358) 849 570 (279) (14,911,786) Delaware 34,428 29,237 (5,192) (84,247,181) 3,314 2,536 (779) (12,637,077) 1,753 1,465 (288) (4,675,719) DC 28,280 28,744 465 4,982,586 2,392 2,462 70 747,388 1,276 1,304 28 298,955 Florida 31,664 27,815 (3,849) (1,445,889,304) 2,900 2,322 (577) (216,883,396) - - - - Georgia 28,966 25,899 (3,066) (603,049,993) 2,495 2,035 (460) (90,457,499) 1,248 1,064 (184) (36,183,000) Hawaii 33,048 26,304 (6,744) (127,555,582) 3,107 2,096 (1,012) (19,133,337) 1,810 1,301 (510) (9,638,561) Idaho 27,417 23,728 (3,689) (142,774,176) 2,263 1,709 (553) (21,416,126) 1,285 1,060 (225) (8,709,225) Illinois 31,496 27,057 (4,440) (1,011,328,794) 2,874 2,208 (666) (151,699,319) 1,475 1,253 (222) (50,566,440) Indiana 27,679 23,206 (4,473) (617,715,330) 2,302 1,631 (671) (92,657,300) 873 721 (152) (21,002,321) Iowa 27,536 20,227 (7,308) (441,385,101) 2,280 1,184 (1,096) (66,207,765) 1,054 668 (387) (23,352,284) Kansas 29,718 22,445 (7,273) (464,779,281) 2,608 1,517 (1,091) (69,716,892) 1,117 662 (455) (29,048,705) Kentucky 27,513 25,912 (1,602) (169,493,164) 2,277 2,037 (240) (25,423,975) 1,402 1,310 (93) (9,830,604) Louisiana 31,540 30,114 (1,425) (150,380,066) 2,881 2,667 (214) (22,557,010) 716 668 (48) (5,112,922) Maine 26,093 19,317 (6,776) (193,264,854) 2,064 1,048 (1,016) (28,989,728) 972 498 (474) (13,528,540) Maryland 37,712 31,831 (5,881) (554,404,012) 3,807 2,925 (882) (83,160,602) 1,492 1,212 (279) (26,334,191) Massachusetts 29,676 22,760 (6,916) (687,119,085) 2,601 1,564 (1,037) (103,067,863) 1,340 973 (367) (36,417,312) Michigan 27,863 23,087 (4,775) (1,212,853,788) 2,329 1,613 (716) (181,928,068) 1,055 848 (208) (52,759,140) Minnesota 31,669 26,668 (5,002) (453,547,401) 2,900 2,150 (750) (68,032,110) 1,186 918 (268) (24,264,786) Mississippi 26,705 28,831 2,125 160,364,132 2,156 2,475 319 24,054,620 820 927 106 8,018,207 Missouri 27,538 26,061 (1,477) (217,289,891) 2,281 2,059 (221) (32,593,484) 822 747 (75) (11,081,784) Montana 28,708 23,666 (5,042) (120,505,474) 2,456 1,700 (756) (18,075,821) 893 597 (296) (7,067,646) Nebraska 27,734 20,263 (7,471) (267,753,047) 2,310 1,189 (1,121) (40,162,957) 851 491 (359) (12,880,721) Nevada 34,713 28,953 (5,760) (353,848,749) 3,357 2,493 (864) (53,077,312) - - - - New Hampshire 30,776 23,508 (7,267) (124,910,009) 2,766 1,676 (1,090) (18,736,501) - - - - New Jersey 39,612 33,047 (6,565) (729,086,821) 4,092 3,107 (985) (109,363,023) 739 491 (248) (27,553,425) New Mexico 28,100 26,570 (1,530) (76,207,524) 2,365 2,135 (229) (11,431,129) 639 564 (75) (3,734,169) New York 32,123 26,531 (5,592) (1,587,086,803) 2,968 2,130 (839) (238,063,020) 1,290 916 (374) (106,102,505) North Carolina 27,883 22,321 (5,562) (1,271,751,112) 2,332 1,498 (834) (190,762,667) 1,695 1,305 (389) (89,022,578) North Dakota 32,524 41,708 9,184 107,990,743 3,029 4,406 1,378 16,198,611 424 593 169 1,987,030 Ohio 28,266 25,512 (2,754) (649,751,794) 2,390 1,977 (413) (97,462,769) 589 492 (97) (22,877,761) Oklahoma 29,835 28,753 (1,082) (112,215,608) 2,625 2,463 (162) (16,832,341) 1,120 1,061 (60) (6,171,858) Oregon 29,070 23,604 (5,466) (603,747,755) 2,510 1,691 (820) (90,562,163) 1,982 1,564 (418) (46,186,703) Pennsylvania 27,799 24,110 (3,689) (747,819,907) 2,320 1,767 (553) (112,172,986) 853 740 (113) (22,958,071) Rhode Island 28,615 30,693 2,078 31,335,040 2,442 2,754 312 4,700,256 661 738 78 1,175,064 South Carolina 27,054 23,670 (3,384) (341,442,218) 2,208 1,701 (508) (51,216,333) 1,091 854 (237) (23,900,955) South Dakota 26,329 21,903 (4,426) (65,907,682) 2,099 1,435 (664) (9,886,152) - - - - Tennessee 28,575 24,427 (4,148) (603,006,623) 2,436 1,814 (622) (90,450,993) - - - - Texas 32,688 29,582 (3,106) (1,631,369,379) 3,053 2,587 (466) (244,705,407) - - - - Utah 29,250 24,253 (4,997) (237,743,369) 2,537 1,788 (750) (35,661,505) 1,332 1,017 (315) (14,977,832) Vermont 25,789 18,327 (7,463) (91,325,903) 2,018 899 (1,119) (13,698,886) 578 313 (265) (3,242,070) Virginia 32,334 28,909 (3,425) (476,956,501) 3,000 2,486 (514) (71,543,475) 1,506 1,309 (197) (27,424,999) Washington 34,368 30,086 (4,283) (713,377,463) 3,305 2,663 (642) (107,006,619) - - - - West Virginia 26,489 22,936 (3,553) (169,420,204) 2,123 1,590 (533) (25,413,031) 880 737 (142) (6,776,808) Wisconsin 28,801 22,157 (6,644) (634,743,013) 2,470 1,474 (997) (95,211,452) 992 583 (409) (39,036,695) Wyoming 36,241 32,480 (3,761) (49,272,444) 3,586 3,022 (564) (7,390,867) - - - - TOTAL 31,104 26,489 (4,615) (26,972,672,886) 2,816 2,123 (692) (4,045,900,933) - - - (1,384,632,223)

American Institutes for Research Equal Pay for People with Disabilities—14 Table A-4. Estimated Lost Income, Federal Tax, and State Tax for People With Disabilities With Associate’s Degrees, by State

Estimated Estimated Estimated Estimated Earnings Earnings Per Per Per Federal Federal State Tax State Tax State (No (With Person Subtotal Person Subtotal Person Subtotal Tax (No Tax (With (No (With Disability) Disability) Difference Difference Difference Disability) Disability) Disability) Disability) Alabama 37,068 32,878 (4,190) (149,003,746) 3,710 3,082 (629) (22,350,562) 1,453 1,275 (178) (6,332,659) Alaska 45,144 40,075 (5,069) (14,837,346) 5,036 4,161 (875) (2,560,348) - - - - Arizona 37,477 36,320 (1,157) (42,706,058) 3,772 3,598 (174) (6,405,909) 912 873 (39) (1,434,924) Arkansas 35,491 26,459 (9,032) (145,377,094) 3,474 2,119 (1,355) (21,806,564) 2,041 1,197 (845) (13,598,394) California 44,281 34,763 (9,518) (1,807,145,808) 4,820 3,364 (1,456) (276,401,660) 2,608 1,431 (1,176) (223,351,333) Colorado 38,437 31,211 (7,225) (235,925,765) 3,916 2,832 (1,084) (35,388,865) 1,340 1,005 (335) (10,923,363) Connecticut 46,634 31,998 (14,637) (224,446,263) 5,409 2,950 (2,459) (37,706,417) 1,482 750 (732) (11,222,313) Delaware 41,960 42,389 429 1,647,833 4,444 4,508 64 247,175 2,171 2,195 24 91,455 DC 47,375 47,414 40 82,262 5,594 5,604 10 20,565 2,514 2,518 3 6,992 Florida 38,011 32,495 (5,516) (696,448,194) 3,852 3,024 (827) (104,467,229) - - - - Georgia 37,098 30,975 (6,123) (297,303,611) 3,715 2,796 (918) (44,595,542) 1,736 1,368 (367) (17,838,217) Hawaii 40,998 33,075 (7,924) (65,157,232) 4,300 3,111 (1,189) (9,773,585) 2,420 1,812 (608) (5,000,255) Idaho 35,141 24,804 (10,337) (126,732,352) 3,421 1,871 (1,551) (19,009,853) 1,756 1,126 (631) (7,730,674) Illinois 39,075 31,264 (7,810) (522,236,435) 4,011 2,840 (1,172) (78,335,465) 1,854 1,463 (391) (26,111,822) Indiana 38,259 32,830 (5,429) (214,138,318) 3,889 3,074 (814) (32,120,748) 1,233 1,048 (185) (7,280,703) Iowa 36,874 24,704 (12,170) (239,129,119) 3,681 1,856 (1,825) (35,869,368) 1,577 901 (676) (13,288,176) Kansas 35,329 32,078 (3,252) (54,999,017) 3,449 2,962 (488) (8,249,852) 1,468 1,264 (203) (3,440,126) Kentucky 36,697 25,825 (10,872) (312,936,523) 3,655 2,024 (1,631) (46,940,478) 1,935 1,305 (631) (18,150,318) Louisiana 40,707 32,042 (8,665) (210,223,136) 4,256 2,956 (1,300) (31,533,470) 1,028 733 (295) (7,147,587) Maine 37,389 28,340 (9,049) (96,643,825) 3,758 2,401 (1,357) (14,496,574) 2,144 1,130 (1,014) (10,829,720) Maryland 46,240 40,489 (5,751) (115,689,124) 5,310 4,223 (1,087) (21,858,817) 1,897 1,624 (273) (5,495,233) Massachusetts 45,802 32,248 (13,555) (431,798,541) 5,201 2,987 (2,213) (70,511,339) 2,194 1,476 (718) (22,885,323) Michigan 36,809 32,516 (4,293) (265,479,895) 3,671 3,027 (644) (39,821,984) 1,445 1,258 (187) (11,548,375) Minnesota 40,401 29,622 (10,779) (305,126,355) 4,210 2,593 (1,617) (45,768,953) 1,786 1,077 (709) (20,078,089) Mississippi 33,769 25,552 (8,217) (144,784,783) 3,215 1,983 (1,233) (21,717,717) 1,173 763 (411) (7,239,239) Missouri 35,602 26,160 (9,441) (343,186,869) 3,490 2,074 (1,416) (51,478,030) 1,269 752 (517) (18,789,839) Montana 36,618 25,270 (11,348) (78,699,856) 3,643 1,941 (1,702) (11,804,978) 1,410 691 (719) (4,985,512) Nebraska 36,793 33,308 (3,485) (40,375,031) 3,669 3,146 (523) (6,056,255) 1,654 1,416 (238) (2,761,652) Nevada 41,300 34,139 (7,161) (120,962,357) 4,345 3,271 (1,074) (18,144,354) - - - - New Hampshire 44,352 34,896 (9,456) (64,123,036) 4,838 3,384 (1,453) (9,856,922) - - - - New Jersey 46,643 47,720 1,077 32,865,956 5,411 5,680 269 8,216,489 1,712 1,771 60 1,815,844 New Mexico 39,391 37,652 (1,740) (22,774,067) 4,059 3,798 (261) (3,416,110) 1,193 1,107 (85) (1,115,929) New York 42,065 31,326 (10,739) (1,154,041,259) 4,460 2,849 (1,611) (173,106,189) 1,971 1,235 (736) (79,051,826) North Carolina 37,165 30,041 (7,124) (482,339,026) 3,725 2,656 (1,069) (72,350,854) 2,345 1,846 (499) (33,763,732) North Dakota 43,639 40,296 (3,342) (17,016,677) 4,696 4,194 (501) (2,552,501) 630 567 (64) (324,407) Ohio 38,146 32,179 (5,967) (407,085,398) 3,872 2,977 (895) (61,062,810) 937 726 (210) (14,333,477) Oklahoma 37,810 33,275 (4,535) (128,750,497) 3,822 3,141 (680) (19,312,575) 1,559 1,309 (249) (7,081,277) Oregon 39,395 31,406 (7,988) (244,143,084) 4,059 2,861 (1,198) (36,621,463) 2,772 2,160 (611) (18,676,946) Pennsylvania 38,885 31,237 (7,649) (523,598,355) 3,983 2,835 (1,147) (78,539,753) 1,194 959 (235) (16,074,469) Rhode Island 39,478 35,392 (4,087) (23,288,287) 4,072 3,459 (613) (3,493,243) 1,068 915 (153) (873,311) South Carolina 36,336 31,554 (4,782) (164,270,390) 3,600 2,883 (717) (24,640,558) 1,741 1,406 (335) (11,498,927) South Dakota 39,173 22,986 (16,187) (73,108,270) 4,026 1,598 (2,428) (10,966,240) - - - - Tennessee 39,456 31,252 (8,204) (262,028,966) 4,068 2,838 (1,231) (39,304,345) - - - - Texas 42,132 34,970 (7,162) (928,109,830) 4,470 3,396 (1,074) (139,216,475) - - - - Utah 33,454 40,224 6,770 87,337,013 3,168 4,184 1,015 13,100,552 1,597 2,023 426 5,502,232 Vermont 41,029 38,654 (2,375) (6,033,014) 4,304 3,948 (356) (904,952) 1,119 1,035 (84) (214,172) Virginia 41,310 35,557 (5,752) (222,850,440) 4,346 3,484 (863) (33,427,566) 2,022 1,691 (331) (12,813,900) Washington 40,591 36,358 (4,233) (220,092,283) 4,239 3,604 (635) (33,013,842) - - - - West Virginia 35,968 35,036 (932) (12,579,090) 3,545 3,405 (140) (1,886,864) 1,404 1,362 (42) (566,059) Wisconsin 38,550 31,711 (6,839) (205,046,774) 3,932 2,907 (1,026) (30,757,016) 1,777 1,333 (445) (13,328,040) Wyoming 36,703 38,353 1,650 5,357,244 3,655 3,903 248 803,587 - - - - TOTAL 39,968 32,768 (7,199) (12,335,481,087) 4,145 3,065 (1,080) (1,867,216,827) - - - (679,763,797)

American Institutes for Research An Uneven Playing Field—15 Table A-5. Estimated Lost Income, Federal Tax, and State Tax for People With Disabilities With Bachelor’s Degrees, by State

Estimated Estimated Estimated Estimated Earnings Earnings Per Per Per Federal Federal State Tax State Tax State (No (With Person Subtotal Person Subtotal Person Subtotal Tax (No Tax (With (No (With Disability) Disability) Difference Difference Difference Disability) Disability) Disability) Disability) Alabama 52,910 49,873 (3,037) (101,368,466) 6,978 6,218 (759) (25,342,116) 2,082 1,968 (114) (3,801,317) Alaska 54,680 57,236 2,556 9,782,797 7,420 8,059 639 2,445,699 - - - - Arizona 55,451 40,260 (15,191) (641,169,160) 7,613 4,189 (3,424) (144,508,529) 1,515 1,005 (510) (21,543,284) Arkansas 50,111 38,276 (11,835) (212,489,213) 6,278 3,891 (2,386) (42,844,997) 3,065 2,236 (828) (14,874,245) California 65,185 53,273 (11,912) (2,622,290,117) 10,046 7,068 (2,978) (655,572,529) 5,212 4,104 (1,108) (243,872,981) Colorado 58,446 42,573 (15,873) (693,579,227) 8,361 4,536 (3,826) (167,157,733) 2,266 1,531 (735) (32,112,718) Connecticut 75,442 68,973 (6,469) (166,106,301) 12,610 10,993 (1,617) (41,526,575) 2,922 2,599 (323) (8,305,315) Delaware 60,323 52,815 (7,507) (49,288,887) 8,831 6,954 (1,877) (12,322,222) 3,190 2,773 (417) (2,735,533) DC 70,025 48,096 (21,929) (106,209,832) 11,256 5,774 (5,482) (26,552,458) 4,440 2,576 (1,864) (9,027,836) Florida 56,596 47,809 (8,787) (1,164,916,778) 7,899 5,702 (2,197) (291,229,194) - - - - Georgia 56,829 42,815 (14,014) (897,022,025) 7,957 4,572 (3,385) (216,669,631) 2,920 2,079 (841) (53,821,322) Hawaii 51,056 45,757 (5,299) (47,930,875) 6,514 5,189 (1,325) (11,982,719) 3,215 2,796 (419) (3,787,049) Idaho 45,934 33,578 (12,356) (142,572,099) 5,233 3,187 (2,047) (23,617,091) 3,636 1,661 (1,976) (22,794,841) Illinois 59,680 43,542 (16,138) (1,201,938,198) 8,670 4,681 (3,989) (297,074,957) 2,884 2,077 (807) (60,096,910) Indiana 50,603 44,170 (6,433) (208,776,644) 6,401 4,792 (1,608) (52,194,161) 1,652 1,434 (219) (7,098,406) Iowa 48,074 32,391 (15,683) (266,571,386) 5,769 3,009 (2,760) (46,911,158) 2,196 1,322 (875) (14,867,963) Kansas 50,779 38,878 (11,901) (227,159,041) 6,445 3,982 (2,463) (47,012,568) 2,464 1,696 (768) (14,651,758) Kentucky 53,348 42,177 (11,170) (278,830,069) 7,087 4,477 (2,610) (65,157,413) 2,901 2,253 (648) (16,172,144) Louisiana 52,276 44,329 (7,947) (263,013,638) 6,819 4,832 (1,987) (65,753,410) 1,388 1,150 (238) (7,890,409) Maine 46,646 39,833 (6,813) (68,632,003) 5,412 4,125 (1,287) (12,960,395) 2,930 2,351 (579) (5,833,720) Maryland 67,218 59,247 (7,971) (309,297,004) 10,554 8,562 (1,993) (77,324,251) 2,893 2,515 (379) (14,691,608) Massachusetts 64,859 48,933 (15,926) (733,616,415) 9,965 5,983 (3,981) (183,404,104) 3,204 2,360 (844) (38,881,670) Michigan 53,215 38,118 (15,097) (995,623,800) 7,054 3,868 (3,186) (210,114,730) 2,158 1,502 (657) (43,309,635) Minnesota 55,664 37,164 (18,500) (534,550,383) 7,666 3,725 (3,941) (113,884,828) 2,862 1,558 (1,304) (37,685,802) Mississippi 46,629 39,027 (7,601) (168,110,736) 5,407 4,004 (1,403) (31,030,177) 1,816 1,436 (380) (8,405,537) Missouri 51,197 37,129 (14,067) (473,287,740) 6,549 3,719 (2,830) (95,206,523) 2,205 1,361 (844) (28,397,264) Montana 41,092 38,515 (2,577) (21,064,061) 4,314 3,927 (387) (3,159,609) 1,719 1,541 (178) (1,453,420) Nebraska 47,656 42,914 (4,741) (66,941,118) 5,664 4,587 (1,077) (15,202,594) 2,397 2,073 (324) (4,578,772) Nevada 56,407 42,761 (13,645) (245,859,429) 7,852 4,564 (3,287) (59,233,311) - - - - New Hampshire 57,655 62,144 4,488 45,133,007 8,164 9,286 1,122 11,283,252 - - - - New Jersey 72,936 61,598 (11,338) (656,578,116) 11,984 9,149 (2,834) (164,144,529) 3,164 2,538 (626) (36,275,941) New Mexico 50,593 41,900 (8,693) (128,167,351) 6,398 4,435 (1,963) (28,946,002) 1,741 1,315 (426) (6,280,200) New York 64,355 48,028 (16,327) (2,008,575,673) 9,839 5,757 (4,082) (502,143,918) 3,498 2,379 (1,118) (137,587,434) North Carolina 53,861 38,860 (15,002) (986,565,846) 7,215 3,979 (3,236) (212,836,354) 3,513 2,463 (1,050) (69,059,609) North Dakota 48,826 35,394 (13,432) (52,103,313) 5,956 3,459 (2,497) (9,687,515) 809 476 (332) (1,289,257) Ohio 54,403 42,433 (11,969) (765,528,731) 7,351 4,515 (2,836) (181,360,642) 1,585 1,093 (492) (31,455,576) Oklahoma 50,524 38,577 (11,948) (350,839,262) 6,381 3,936 (2,445) (71,784,466) 2,258 1,601 (657) (19,296,159) Oregon 50,401 40,029 (10,372) (324,472,665) 6,350 4,154 (2,196) (68,694,754) 3,799 2,820 (979) (30,622,483) Pennsylvania 53,970 37,151 (16,819) (1,303,931,919) 7,242 3,723 (3,520) (272,883,833) 1,657 1,141 (516) (40,030,710) Rhode Island 57,236 45,159 (12,077) (87,462,149) 8,059 5,040 (3,019) (21,865,537) 1,734 1,281 (453) (3,279,831) South Carolina 49,700 39,971 (9,728) (300,311,031) 6,175 4,146 (2,029) (62,641,345) 2,676 1,995 (681) (21,021,772) South Dakota 46,216 34,516 (11,700) (58,556,907) 5,304 3,327 (1,977) (9,892,743) - - - - Tennessee 52,216 43,404 (8,812) (387,125,943) 6,804 4,661 (2,143) (94,163,656) - - - - Texas 62,131 51,464 (10,667) (1,741,678,619) 9,283 6,616 (2,667) (435,419,655) - - - - Utah 50,814 50,361 (453) (8,342,577) 6,453 6,340 (113) (2,085,644) 2,690 2,662 (29) (525,582) Vermont 44,484 28,433 (16,051) (74,122,691) 4,871 2,415 (2,456) (11,341,744) 1,258 672 (586) (2,703,941) Virginia 63,192 53,852 (9,340) (497,560,446) 9,548 7,213 (2,335) (124,390,112) 3,280 2,743 (537) (28,609,726) Washington 61,522 44,351 (17,170) (920,704,361) 9,130 4,838 (4,293) (230,176,090) - - - - West Virginia 49,717 33,343 (16,374) (202,438,055) 6,179 3,151 (3,028) (37,434,205) 2,263 1,285 (978) (12,086,283) Wisconsin 50,852 42,530 (8,322) (247,554,277) 6,463 4,530 (1,933) (57,516,925) 2,577 2,036 (541) (16,091,028) Wyoming 47,929 53,846 5,918 14,408,977 5,732 7,212 1,479 3,602,244 - - - - TOTAL 58,822 46,103 (12,719) (23,941,509,797) 8,456 5,276 (3,180) (5,617,028,454) - - - (1,176,906,992)

American Institutes for Research Equal Pay for People with Disabilities—16 Table A-6. Estimated Lost Income, Federal Tax, and State Tax for People With Disabilities With Master’s Degrees, by State

Estimated Estimated Estimated Estimated Earnings Earnings Per Person Federal Federal Per Person State Tax State Tax Per Person State (No (With Subtotal Subtotal Subtotal Difference Tax (No Tax (With Difference (No (With Difference Disability) Disability) Disability) Disability) Disability) Disability) Alabama 75,366 48,735 (26,631) (452,500,818) 12,592 5,934 (6,658) (113,125,205) 2,924 1,925 (999) (16,968,781) Alaska 80,951 95,230 14,279 19,292,298 13,988 17,621 3,634 4,909,408 - - - - Arizona 77,198 55,758 (21,440) (456,337,342) 13,050 7,689 (5,360) (114,084,336) 2,546 1,526 (1,020) (21,711,932) Arkansas 68,558 68,098 (460) (3,760,377) 10,889 10,774 (115) (940,094) 4,356 4,324 (32) (263,226) California 99,505 74,884 (24,622) (2,733,752,397) 18,819 12,471 (6,348) (704,774,005) 8,404 6,114 (2,290) (254,238,973) Colorado 79,673 63,537 (16,136) (331,443,766) 13,668 9,634 (4,034) (82,860,942) 3,249 2,502 (747) (15,345,846) Connecticut 109,104 73,583 (35,521) (481,362,208) 21,506 12,146 (9,360) (126,846,771) 4,605 2,829 (1,776) (24,068,110) Delaware 81,837 48,485 (33,352) (86,760,790) 14,209 5,871 (8,338) (21,690,198) 4,648 2,533 (2,115) (5,502,174) DC 99,981 72,204 (27,777) (92,892,120) 18,952 11,801 (7,151) (23,913,359) 6,986 4,625 (2,361) (7,895,830) Florida 85,675 64,247 (21,428) (1,464,967,899) 15,169 9,812 (5,357) (366,241,975) - - - - Georgia 79,317 55,388 (23,929) (730,609,054) 13,579 7,597 (5,982) (182,652,263) 4,269 2,833 (1,436) (43,836,543) Hawaii 72,518 38,647 (33,871) (131,306,212) 11,879 3,947 (7,932) (30,751,302) 4,986 2,236 (2,750) (10,660,035) Idaho 70,672 54,442 (16,231) (78,032,424) 11,418 7,360 (4,058) (19,508,106) 5,145 4,155 (990) (4,759,978) Illinois 90,839 71,331 (19,508) (641,578,576) 16,460 11,583 (4,877) (160,394,644) 4,442 3,467 (975) (32,078,929) Indiana 77,100 58,856 (18,244) (293,881,631) 13,025 8,464 (4,561) (73,470,408) 2,553 1,933 (620) (9,991,975) Iowa 74,604 59,578 (15,027) (112,311,633) 12,401 8,644 (3,757) (28,077,908) 3,742 2,849 (893) (6,671,311) Kansas 74,598 57,394 (17,204) (130,936,415) 12,400 8,099 (4,301) (32,734,104) 4,000 2,891 (1,110) (8,445,399) Kentucky 68,794 70,842 2,047 27,631,056 10,949 11,460 512 6,907,764 3,797 3,915 119 1,602,601 Louisiana 76,742 68,466 (8,276) (114,294,712) 12,935 10,866 (2,069) (28,573,678) 2,308 1,936 (372) (5,143,262) Maine 66,392 39,977 (26,415) (147,465,620) 10,348 4,147 (6,201) (34,620,648) 4,609 2,364 (2,245) (12,534,578) Maryland 97,786 79,604 (18,182) (480,457,291) 18,337 13,651 (4,686) (123,829,241) 4,345 3,482 (864) (22,821,721) Massachusetts 92,218 78,099 (14,119) (357,546,977) 16,805 13,275 (3,530) (89,386,744) 4,654 3,906 (748) (18,949,990) Michigan 81,790 66,041 (15,749) (407,827,482) 14,198 10,260 (3,937) (101,956,870) 3,401 2,716 (685) (17,740,495) Minnesota 82,353 56,334 (26,019) (259,772,677) 14,338 7,834 (6,505) (64,943,169) 4,743 2,909 (1,834) (18,313,974) Mississippi 68,433 62,460 (5,972) (52,980,283) 10,858 9,365 (1,493) (13,245,071) 2,907 2,608 (299) (2,649,014) Missouri 75,013 52,590 (22,424) (422,058,393) 12,503 6,897 (5,606) (105,514,598) 3,634 2,288 (1,345) (25,323,504) Montana 61,977 38,580 (23,397) (75,337,111) 9,244 3,937 (5,307) (17,089,037) 3,160 1,545 (1,614) (5,198,261) Nebraska 73,198 58,327 (14,871) (78,957,656) 12,049 8,332 (3,718) (19,739,414) 4,144 3,127 (1,017) (5,400,704) Nevada 81,592 42,878 (38,714) (290,638,676) 14,148 4,582 (9,566) (71,817,464) - - - - New Hampshire 79,952 64,097 (15,855) (92,690,671) 13,738 9,774 (3,964) (23,172,668) - - - - New Jersey 105,463 71,883 (33,579) (1,024,334,588) 20,487 11,721 (8,766) (267,397,317) 6,021 3,106 (2,914) (88,897,930) New Mexico 78,538 67,011 (11,528) (112,813,807) 13,385 10,503 (2,882) (28,203,452) 3,111 2,546 (565) (5,527,877) New York 92,116 71,533 (20,583) (1,571,594,726) 16,779 11,633 (5,146) (392,898,681) 5,399 3,989 (1,410) (107,654,239) North Carolina 83,651 59,435 (24,217) (641,731,692) 14,663 8,609 (6,054) (160,432,923) 5,928 3,938 (1,990) (52,732,235) North Dakota 69,773 52,756 (17,018) (30,943,530) 11,193 6,939 (4,254) (7,735,882) 1,529 944 (585) (1,064,457) Ohio 78,138 67,478 (10,660) (280,294,371) 13,285 10,619 (2,665) (70,073,593) 2,560 2,122 (438) (11,517,296) Oklahoma 76,565 55,053 (21,512) (288,324,113) 12,891 7,513 (5,378) (72,081,028) 3,690 2,507 (1,183) (15,857,826) Oregon 76,022 53,291 (22,732) (284,456,869) 12,756 7,073 (5,683) (71,114,217) 6,105 4,059 (2,046) (25,601,118) Pennsylvania 84,531 69,833 (14,698) (549,311,261) 14,883 11,208 (3,675) (137,327,815) 2,595 2,144 (451) (16,863,856) Rhode Island 83,166 75,353 (7,813) (32,464,840) 14,541 12,588 (1,953) (8,116,210) 2,878 2,507 (371) (1,542,080) South Carolina 74,804 67,842 (6,962) (100,348,475) 12,451 10,711 (1,740) (25,087,119) 4,433 3,946 (487) (7,024,393) South Dakota 63,450 79,840 16,390 30,009,753 9,613 13,710 4,097 7,502,438 - - - - Tennessee 78,696 62,341 (16,355) (288,194,716) 13,424 9,335 (4,089) (72,048,679) - - - - Texas 91,732 71,411 (20,320) (1,499,034,221) 16,683 11,603 (5,080) (374,758,555) - - - - Utah 85,832 101,655 15,823 83,844,986 15,208 19,420 4,213 22,321,206 4,896 5,893 997 5,282,234 Vermont 61,537 52,471 (9,065) (32,443,718) 9,134 6,868 (2,266) (8,110,930) 2,417 1,801 (616) (2,206,173) Virginia 96,654 84,810 (11,844) (358,120,532) 18,020 14,952 (3,068) (92,753,620) 5,204 4,523 (681) (20,591,931) Washington 84,680 53,948 (30,732) (874,113,807) 14,920 7,237 (7,683) (218,528,452) - - - - West Virginia 70,427 45,835 (24,592) (145,812,057) 11,357 5,209 (6,148) (36,453,014) 4,148 2,030 (2,118) (12,556,129) Wisconsin 78,659 52,303 (26,356) (316,689,871) 13,415 6,826 (6,589) (79,172,468) 4,384 2,671 (1,713) (20,584,842) Wyoming 65,783 37,191 (28,592) (53,043,879) 10,196 3,729 (6,467) (11,997,738) - - - - TOTAL 87,771 66,899 (20,871) (19,325,754,193) 15,693 10,475 (5,218) (4,868,605,097) - - - (979,852,089)

American Institutes for Research An Uneven Playing Field—17

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Fact Sheet

December 2015

Immigrants and WIOA Services Comparison of Sociodemographic Characteristics of Native- and Foreign-Born Adults in New York State

By Margie McHugh and Madeleine Morawski

This fact sheet provides a profile of key characteristics of foreign-born and native-born resi- dents of the state of New York that are relevant to understanding needs for adult education and workforce training services. It is part of a larger series of state and county fact sheets produced by the Migration Policy Institute’s (MPI) National Center on Immigrant Integration Policy to support equitable implementation of the Workforce Innovation and Opportunity Act (WIOA), as well as consideration of other policy and funding initiatives to promote the successful linguistic, economic, and civic integration of immigrants and refugees who have settled in the United States.

The estimates provided are based on MPI analysis of U.S. Census Bureau American Commu- nity Survey (ACS) data pooled over the 2009-13 period in order to provide the most detailed sociodemographic portrait possible of residents’ characteristics. Mirroring the design of federal adult education and workforce training program rules, data are provided for those ages 16 and over. a c t s F

n 1) Nativity, Age, and Origin of New York Residents o As of 2009-13, New York State was home to more than 15.7 million residents ages 16 and i older; approximately 4.1 million of whom, or 26 percent, were foreign born. Relatively fewer of the state’s foreign-born individuals are ages 16-18 or ages 19-24 as compared to its native-born residents; rather, they are more likely to be in their prime working years, with 68 r a t percent falling in the 25-to-44 and 45-to-59 age bands, as compared to 57 percent of native- g born residents. Of particular note are the almost 40 percent of foreign-born individuals who i are 25 to 44 years old, a group that will continue to play a key role in the state’s labor force for several decades to come. Shares of foreign- and native-born New Yorkers over 60 years m m old are roughly the same at 23 and 24 percent respectively. I Half of the state’s immigrant residents ages 16 and over hail from Latin America, as compared to 53 percent nationwide; 26 percent are of Asian origin—similar to the 28 percent share nationally; and 19 percent are European, significantly higher than the national share of 13 percent.

Relevance for WIOA Implementation: Provisions of WIOA’s Title I address the country’s three primary workforce training programs (youth, adult, and dislocated worker), target subpopulations within them (e.g. out-of-school youth ages 16 to 24), and the nature of services to be provided through them. Title II of the law—Adult Education and Literacy (commonly referred to as the Adult Education and Family Literacy Act, or AEFLA)—provides the national framework for services designed to build the basic skills of adults who lack a Table 1. Age, Gender, and Origin of the New York Population (ages 16 and older), by Nativity, 2009-13 Total Native Born Foreign Born Number Number Percent Number Percent Total population ages 16 and 15,742,000 11,593,000 100% 4,149,000 100% over Age Groups 16 to 18 799,000 714,000 6% 85,000 2% 19 to 24 1,698,000 1,411,000 12% 288,000 7% 25 to 44 5,308,000 3,683,000 32% 1,625,000 39% 45 to 59 4,137,000 2,960,000 26% 1,177,000 28% 60 and over 3,800,000 2,826,000 24% 974,000 23% Gender Female 8,211,000 6,018,000 52% 2,193,000 53% Regions of Birth (excluding birth at sea and unspecified countries) Africa X X X 157,000 4% Asia X X X 1,070,000 26% Europe X X X 790,000 19% Latin America X X X 2,067,000 50% Northern America X X X 54,000 1% Oceania X X X 10,000 0% Notes: Latin America includes South America, Central America, Mexico, and the Caribbean; Northern America includes Canada, Bermuda, Greenland, and St. Pierre and Miquelon. All numbers are rounded to the nearest thousand; calcula- tions in the text use absolute numbers. Source: Migration Policy Institute (MPI) analysis of U.S. Census Bureau data from the pooled 2009-13 American Com- munity Survey (ACS). high school diploma or equivalent or who are 2) Educational Attainment Limited English Proficient (LEP). States and localities must ensure that eligible popula- Foreign-born young adults represent only tions are given equitable access to informa- about 11 percent of the state’s 16-to-18- tion and services provided under the law in year-old population; however, they comprise order not to run afoul of federal civil-rights nearly 20 percent of the state’s out-of-school and antidiscrimination provisions. This youth in this age range, and are almost twice includes, for example, ensuring that language as likely to lack a high school diploma or barriers do not impede access to informa- equivalent (HSD/E) and not be enrolled in tion and services provided by American Job school as their native-born peers. Similarly, Centers (formerly known as One-Stop Career foreign-born young adults are 17 percent Centers) through which states and locali- of the state’s 19-to-24-year-olds but are ties organize local access to WIOA-funded almost twice as likely as native-born peers services. Given the highly diverse nature of to lack a HSD/E, comprising 28 percent of its foreign-born population (and their range state residents in this age range who have of educational backgrounds and levels of not obtained a HSD/E. Further, foreign- English proficiency—as described below), born young adults who lack a HSD/E are those engaged in implementing WIOA in significantly less likely than their native-born New York State face complex challenges in peers to be enrolled in school (17 percent ensuring that the state’s large and diverse versus 23 percent). Finally, among those not immigrant population has equitable access to enrolled in school, foreign-born young adults services provided under the law. are far more likely than the native-born to be

2 Immigrants and WIOA Services: New York Fact Sheet

working (52 percent versus 25 percent). Title I workforce services, and adults who lack a HSD/E are targets for both Title I and Title II Foreign-born individuals 25 and older account services. Given that foreign-born individuals for 29 percent of New York residents ages 25 are significantly over-represented among those and older; they are almost three times as likely with no HSD/E in the three age bands, services as native-born peers to lack a HSD/E, accounting created with these funds should be targeted in for just over half of adults in this age group who equitable measure to meet their needs. This will have not completed high school. While similar represent a shift for systems that heretofore shares of native-born and foreign-born individu- have not prioritized those with basic skills needs als have completed a HSD/E (28 percent versus (whether native- or foreign born) for workforce 26 percent), a far higher share of the native- training services, and/or whose service design born has completed some college or an associ- is largely sequential—i.e. expecting adults ate’s degree compared to the foreign born (27 to complete basic skills requirements before percent versus 19 percent). Similarly, 35 percent gaining access to workforce training programs. of the native born have completed a bachelor’s At the same time, provisions in the law that degree or higher, as compared to 29 percent of promote the use of career pathway service the foreign born. designs for serving WIOA clients pose significant capacity-building challenges for the state, given Relevance for WIOA Implementation: Out- the difficulties many such pathway programs of-school youth are a primary focus of WIOA’s face in equitably serving adults with basic skills

Table 2. Educational Attainment of New York Residents (ages 16 and older), by Nativity, 2009-13 Total Native Born Foreign Born Educational Attainment Number Number Percent Number Percent Population ages 16 to 18 799,000 714,000 100% 85,000 100% Not enrolled and no high school 31,000 25,000 4% 6,000 7% diploma or equivalent Population ages 19 to 24 1,698,000 1,411,000 100% 288,000 100% With at least high school diploma 1,508,000 1,274,000 90% 234,000 81% or equivalent Without high school diploma or 190,000 136,000 10% 54,000 19% equivalent Enrolled in school 41,000 32,000 23% 9,000 17% Not enrolled in school and not 87,000 70,000 52% 16,000 30% employed Not enrolled in school and 63,000 34,000 25% 28,000 52% employed Population ages 25 and older 13,245,000 9,469,000 100% 3,776,000 100% Less than high school diploma or 1,984,000 963,000 10% 1,021,000 27% equivalent High school diploma or equivalent 3,606,000 2,633,000 28% 973,000 26% Some college or associate's 3,273,000 2,570,000 27% 703,000 19% degree Bachelor's, graduate, or 4,382,000 3,303,000 35% 1,079,000 29% professional degree Foreign college-educated X X X 567,000 53% Note: All numbers are rounded to the nearest thousand; calculations in the text use absolute numbers. Source: MPI analysis of pooled 2009-13 ACS.

3 Migration Policy Institute needs.1 Integrated education and training account for most of New York’s LEP residents models must also comply with immigration (87 percent), with the 13 percent of native- status restrictions placed on Title I-funded born LEP individuals comprised largely of programs.2 However, while those who lack Spanish speakers.6 Quite remarkably, however, work authorization are not eligible for WIOA- the state’s total number of LEP residents funded workforce services, all refugees and (2,277,000) is greater than its total number the majority of New York’s immigrants legally of low-educated individuals ages 19 and older reside in the United States and are therefore (2,174,000). However, only adults with less eligible for Title I as well as Title II services, than a high school education are counted in which are not subject to immigration status the formula used by the federal government to restrictions.3 provide adult education funds to states.7

Finally, the analysis also shows that immigrants Among all LEP individuals ages 19 to 24 and under age 25 who lack a HSD/E are far more ages 25 and older, 919,000 lack a HSD/E, likely than their native-born counterparts indicating that 42 percent of the state’s low- to be employed and not enrolled in school. educated adults in this age range are also This points to a need for education and train- LEP. Significant numbers of LEP individuals ing services designed for “nontraditional” also have high levels of underlying education, students—i.e., in addition to using appropri- including 564,000 of those 25 and over who ate instructional designs, programs should have earned a high school diploma or equiva- anticipate the needs of part-time students, the lent, and an additional 644,000 who have demands of their work schedules, and trans- either completed some college or an associate’s portation issues or other constraints they may degree or who have earned a bachelor’s degree face in attending and completing more tradi- or higher. tionally structured programs. Relevance for WIOA Implementation: New York’s large number of LEP individuals surpasses its number of those who are low- 3) Limited English Proficiency educated; each group is eligible for AEFLA services, which in recent years met only about and Educational Attainment 4 percent of need nationally.8 The state faces complex challenges in equitably reflecting Estimates of limited English proficiency among the significant and wide range of LEP learner New York residents are provided below given needs and goals in its Combined State Plan that the relevance of LEP status4 for access to will govern WIOA service provision in New WIOA-funded services—e.g., English Language York and that the U.S. Secretaries of Labor and Acquisition services (formerly known as Education must ultimately approve. For one, English-as-a-Second-Language or ESL) are a the state’s LEP residents include those who key element of AEFLA services, while adult need AEFLA services but may not seek the English learners meet the “priority” defini- employment or postsecondary transition and tion for adult workforce services.5 Table 3 also completion goals that are the primary focus of provides individuals’ LEP status crossed with the law’s narrow accountability measures—for levels of educational attainment, in order to example, immigrant mothers of young children inform the efforts of state and local planners seeking literacy and other programming that to provide education and training services that will help them support their children’s kinder- equitably meet the needs of LEP individuals garten readiness, or those seeking citizenship with different levels of formal education. preparation services.

Not surprisingly, foreign-born individuals In addition, the law’s significant new emphasis

4 Immigrants and WIOA Services: New York Fact Sheet

on postsecondary training is likely to pose I-funded intensive or training services.9 major challenges for local systems that in the past provided ESL and workforce training Stakeholders in WIOA’s implementation services separately and/or served few low- therefore face challenges in ensuring that local skilled or LEP individuals in Title I programs. service plans and the state’s Combined Plan While new provisions in WIOA do target provide both the range of AEFLA services envi- workforce services to these basic skills-defi- sioned under the law, and equitable access to cient individuals, the record of career pathway Title I-funded services for low-educated and/ models and other training programs in provid- or LEP individuals who are work authorized. ing equitable access to individuals who are Significant policy, planning, and capacity- low-educated and/or LEP is very weak. For building efforts will be needed as the state example, nationally in the past five years LEP and its localities take steps to address their individuals have consistently comprised less obligation to provide equitable access to Title than 2 percent of individuals receiving Title I-funded programs for those who are LEP and

Table 3. Limited English Proficiency and Educational Attainment of New York Residents (ages 16 and older), by Nativity, 2009-13 Total Native Born Foreign Born Percent Percent LEP Population by Educational Number Number Native Number Foreign Attainment Born Born Total LEP population 2,277,000 307,000 13% 1,970,000 87% Number Number Percent Number Percent LEP population ages 16 to 18 48,000 22,000 100% 26,000 30% Not enrolled and no high school 6,000 2,000 8% 4,000 64% diploma or equivalent LEP population ages 19 to 24 149,000 37,000 100% 111,000 100% With at least high school 101,000 28,000 75% 73,000 66% diploma or equivalent Without high school diploma or 48,000 9,000 25% 38,000 34% equivalent Enrolled in school 6,000 2,000 22% 4,000 11% Not enrolled in school and not 17,000 5,000 52% 12,000 31% employed Not enrolled in school and 24,000 2,000 26% 22,000 58% employed LEP population ages 25 and older 2,081,000 248,000 100% 1,833,000 100% Less than high school diploma 872,000 114,000 46% 758,000 41% or equivalent High school diploma or 564,000 65,000 26% 499,000 27% equivalent Some college or associate's 305,000 38,000 15% 267,000 15% degree Bachelor's, graduate, or 340,000 31,000 12% 309,000 17% professional degree Notes: Limited English Proficient (LEP) refers to any person age 5 and older who reported speaking English less than "very well" as classified by the U.S. Census Bureau. All numbers are rounded to the nearest thousand; calculations in the text use absolute numbers. Source: MPI analysis of pooled 2009-13 ACS. 5 Migration Policy Institute Table 4. Brain Waste among New York Residents (ages 25 and older), by Nativity, 2009-13 Native Born Foreign Born Brain Waste Number Percent Number Percent Total civilian, college-educated labor force 2,580,000 100% 824,000 100% Underutilized (i.e., in low-skilled jobs or 452,000 18% 209,000 25% unemployed) Note: All numbers are rounded to the nearest thousand; calculations in the text use absolute numbers. Source: MPI analysis of pooled 2009-13 ACS. lack a HSD/E, as well as the significant number academic or professional qualifications obtained of LEP individuals who already possess a high abroad, difficulties in filling gaps in education or school diploma or higher, and are therefore gaining U.S. work experience, steep and expen- positioned to directly access postsecondary level sive barriers to gaining professional licenses, training programs. and/or poor understanding of U.S. job search norms. Table provides estimates of brain waste among native-born and foreign-born residents of New4 York, showing nearly one-fifth 4) Brain Waste of all highly educated workers in the state are affected, with the high levels of education of the “Brain waste”—the phrase used to describe foreign born more likely to be underutilized (25 when individuals with four-year college percent versus 18 percent). degrees or higher work in low-skilled jobs or are unemployed—is a particular concern for Relevance for WIOA Implementation: Highly foreign-educated immigrants given the unique educated individuals who are LEP fall into the barriers they often face in attempting to transfer “basic skills deficient” service priority category their education, training, and work experience for Title I adult workforce services and also to the U.S. labor market.10 Fifty-three percent qualify for AEFLA-funded services. Many of of foreign-born individuals in New York who these individuals have degrees in the health- possess a college degree or higher were educat- care, STEM, and education fields where their ed abroad (see Table 2), indicating a significant skills can be applied in high-demand occupa- share of the state’s highly educated immigrants tions. Nimble workforce and adult education and refugees is at risk for brain waste. programs can help address the particular needs of these individuals by braiding funds across Data provided in Table point to one of the titles—or using strictly Title I funds—to help most significant factors responsible for brain them return to jobs in their profession or a waste—limited English3 proficiency. Among related field that will leverage the significant foreign-born LEP individuals ages 25 and older, investments they have already made in their 309,000 (17 percent) have completed a bach- education and training. elor’s degree or higher. Few adult education programs currently provide instruction that can help these individuals acquire the academic or professional-level English that will allow them 5) Parents of Young Children to fully apply their education and training in the U.S. labor market. Parents of young children have long been a population of special focus for adult education In addition to difficulties accessing profes- and training programs due to the powerful sional-level English classes, other factors that role education and skills play in helping them can contribute to brain waste include lack of provide economic stability for their family, and recognition by employers or licensing bodies of the predictive role of parental education—

6 Immigrants and WIOA Services: New York Fact Sheet

Table 5. Family Structure and Young-Child Parental Status for New York Residents (ages 16 and older), by Nativity, 2009-13 Total Native Born Foreign Born Parental Status Number Number Percent Number Percent Reside with at least one child 3,679,000 2,492,000 100% 1,188,000 100% under age 18 Single mother 710,000 503,000 20% 207,000 17% Single father 190,000 133,000 5% 57,000 5% Two parents 2,780,000 1,856,000 74% 924,000 78% Reside with at least one child ages 2,134,000 1,430,000 100% 703,000 100% 0-8 Limited English Proficient (LEP) 364,000 34,000 2% 330,000 47% Low-educated 271,000 110,000 8% 161,000 23% Low-income (below 200% of 748,000 418,000 29% 330,000 47% FPL) FPL = Federal poverty level. Notes: Limited English Proficient (LEP) refers to any person age 5 and older who reported speaking English less than "very well" as classified by the U.S. Census Bureau. The federal poverty level (FPL), calculated based on total family income before taxes (excluding capital gains and noncash benefits such as food stamps), was $23,834 for a family of four in 2013. All numbers are rounded to the nearest thousand; calculations in the text use absolute numbers. Source: MPI analysis of pooled 2009-13 ACS. particularly the mother’s—for the future likely to have low incomes—47 percent versus education success of their children. This focus 29 percent of the native born. Not surprisingly, is especially pertinent now, with policymakers foreign-born parents account for the vast major- at all levels of government engaged in inten- ity of the state’s LEP parents of young children sive efforts to scale quality early childhood (91 percent). programs that will close gaps in school readi- ness that could otherwise threaten children’s Relevance for WIOA Implementation: Though lifelong education and career prospects. As their WIOA’s Title II provisions speak of services that children’s first and most important teachers, “enable parents or family members to support parents are universally acknowledged as critical their children’s learning needs” and provide to the success of these efforts. “training for parents or family members regard- ing how to be … full partners in the education of While 26 percent of the state’s overall popula- their children,” the law’s performance measures tion ages 16 and older, immigrants and refugees leave little room for states to serve parents who in New York account for 32 percent of parents are arguably most in need of these services. residing with at least one child under age 18, Many low-educated and/or LEP parents who and 33 percent of those with at least one child seek such programs do not have learning ages 0 to 8. Among parents residing with a child goals that align with the law’s primary perfor- under age 18, single-mother or single-father mance measures—particularly those focused households are slightly less common among the on employment, earnings, and secondary/ foreign born (22 percent versus 26 percent for postsecondary degree and credential attain- the native born). Most strikingly, immigrants ment.11 With all WIOA-funded programs judged and refugees comprise nearly 60 percent of the according to these measures and with states state’s low-educated parents of young children, facing financial penalties should they not meet being almost three times more likely than their performance targets, many states and localities native-born counterparts to lack a high school may be reluctant to provide AEFLA services to diploma or equivalent. Foreign-born parents low-educated and LEP parents whose primary of young children are also significantly more concerns are basic literacy and supporting their

7 Migration Policy Institute Table 6. Poverty and Health Insurance for New York Residents (ages 16 and older), by Nativity, 2009-13 Total Native Born Foreign Born Poverty Number Number Percent Number Percent Population (for whom poverty 15,287,000 11,190,000 100% 4,097,000 100% status is determined) Below 100% of FPL 2,091,000 1,381,000 12% 710,000 17% 100-199% of FPL 2,470,000 1,581,000 14% 889,000 22% At or above 200% of FPL 10,726,000 8,228,000 74% 2,498,000 61% Health Insurance Coverage Total population 15,742,000 11,593,000 100% 4,149,000 100% No health insurance coverage 2,075,000 1,086,000 9% 989,000 24% FPL = Federal poverty level. Notes: The federal poverty level (FPL), calculated based on total family income before taxes (excluding capital gains and noncash benefits such as food stamps), was $23,834 for a family of four in 2013. All numbers are rounded to the nearest thousand; calcula- tions in the text use absolute numbers. Source: MPI analysis of pooled 2009-13 ACS. children’s kindergarten readiness and future percent falling below the 200 percent threshold educational success. Should New York State as compared to 26 percent for those who are choose to maintain parent-focused programs for native born. Looking to an additional indicator this population it would likely need to negotiate of economic vulnerability, the state’s foreign- lower performance targets for these programs born adults are more than 2.5 times as likely to on the law’s six accountability measures, and lack health insurance coverage as those who are presumably judge their performance against native born. state measures that better reflect expected outcomes of parent-focused programs. Alterna- Relevance for WIOA Implementation: WIOA’s tively, the state or its localities may simply avoid broad architecture as well as many of its serving many parents of its most at-risk young specific provisions place a tight focus on direct- children with AEFLA funds—even though failing ing services to low-income individuals, with to address their needs could undermine the the goal of helping them attain the education, success of investments being made by all levels degrees, and credentials they need to ensure of government in early childhood education and a lifetime of improved earnings and economic care services. stability. The disproportionate representation of foreign-born individuals among New York resi- dents living in or near poverty provide impor- tant measures against which the adequacy of 6) Poverty and Health Insurance state and local service designs and equity in distribution of services can be gauged. WIOA’s investments are intended to help meet local needs for skilled workers while also reducing welfare dependency and supporting workers in attaining education and skills that 7) U.S. Citizenship and will allow them to earn a family-sustaining wage. While many of New York’s immigrants Immigration Status enjoy high levels of education and earnings, Table 6 data indicate that the state’s foreign- Publicly available data from the U.S. Census born residents are significantly more likely to Bureau’s American Community Survey (ACS) earn below either 100 percent or 200 percent are the basis for all figures provided in the of the federal poverty level (FPL),12 with 39 preceding sections of this profile. However,

8 Immigrants and WIOA Services: New York Fact Sheet

Table 7. U.S. Citizenship Status of Foreign-Born Residents (ages 16 and older) in New York, 2009-13 U.S. Citizenship Status Number Percent Foreign born 4,504,000 100% Naturalized citizens 2,230,000 50% Noncitizens 2,274,000 50% Legal permanent residents 1,290,000 57% Legal nonimmigrants 170,000 7% Unauthorized immigrants 814,000 36% DACA immediately eligible (2012) 72,000 9% DACA eligible but for education (2012) 21,000 3% Note: All numbers are rounded to the nearest thousand; calculations in the text use absolute numbers. Sources: MPI analysis of pooled 2009-13 ACS, and the 2008 Survey of Income and Program Participation (SIPP) by James D. Bachmeier and Colin Hammar of Temple University and Jennifer Van Hook of The Pennsylvania State University, Population Research Institute. immigration status affects eligibility for certain launched in 2012, with thousands more aging WIOA services, and the ACS does not collect into eligibility since that time. Many have come detailed information on respondents’ immigra- forward to obtain these protections—according tion status. To better assist stakeholders in to U.S. Citizenship and Immigration Services, considering the interplay of immigration status (USCIS), 34,710 New York residents had with WIOA implementation efforts, Table 7 received DACA status as of June 2015.15 provides estimates of the shares of foreign-born New York residents in key immigration-status Relevance for WIOA Implementation: categories. The MPI estimates are based on a Immigration status is relevant to a variety of methodology that imputes immigration status WIOA programs beyond the broad provisions from two Census Bureau surveys—the ACS and described earlier that restrict unauthorized the Survey of Income and Program Participa- immigrants from accessing Title I services and tion (SIPP).13 In part because this methodology the absence of status restrictions placed on Title involves inflating ACS figures in order to account II services. For example, under Title II a primary for presumed undercounting of noncitizens, purpose of the Integrated English Literacy especially those who are unauthorized, the and Civics Education program is to support figures are not directly comparable to the esti- immigrants in preparing for citizenship and full mates used in the earlier portions of this profile. participation in the civic life of their commu- nity.16 And while all immigrants—regardless Using this methodology, MPI estimates that of immigration status—are eligible for AEFLA among New York’s foreign-born residents ages services, states that choose to braid Title I and 16 and older, half were naturalized citizens. Of II funds to provide integrated education and the approximately 2.3 million noncitizens, 57 training services may inadvertently place Title percent were lawful permanent residents (LPRs) II funds beyond the reach of unauthorized and 36 percent were unauthorized.14 Within the immigrants and/or create the need to imple- unauthorized population, 11 percent—93,000 ment complex new administrative procedures individuals—were potentially eligible to apply to assess the immigration status of recipients of for protection from deportation and work adult education services. authorization under the Deferred Action for Childhood Arrivals (DACA) program when it first

9 Migration Policy Institute Endnotes 1 Richards and Kristen Kulongoski, Contextualized College Transition Strategies for Adult Basic Skills Students:See, for example, Learning John from Wachen, Washington Davis State’s Jenkins, I-BEST Clive Program Belfield, Modeland Michelle (New York: Van Noy The withCommunity Amanda College Research Center, Teacher’s College, Columbia University, 2012), 21-22, www.sbctc.ctc.edu/college/ abepds/ibest_ccrc_report_december2012.pdf. 2 - sues. See the final section of this fact sheet for additional data and information on immigration status is 3 In addition, many unauthorized young adults are eligible for protection under the Deferred Action for Childhood Arrivals (DACA) program; DACA approval would allow them to qualify for WIOA Title I services, as opposed to strictly Adult Education and Family Literacy Act -funded services. 4

Limited English Proficient (LEP) refers to any person age 5 and older who reported speaking English 5 Individuals considered a priority for Title I adult employment and training services are “recipients of less than “very well” as classified by the U.S. Census Bureau. Workforce Investment and Opportunity Act, Public Law 113–128, U.S. Statutes at Large 128 (2014) 1425,public Title assistance, I Sec. 134 other (c)(3)(E), low-income www.congress.gov/113/bills/hr803/BILLS-113hr803enr.pdf individuals, and individuals who are basic skills deficient.”. See 6 American Fact Finder, “Nativity by Language Spoken at Home by Ability to Speak English for the Population 5 Years and Over,” 2011-2013 American Community Survey 3-Year Estimates, accessed November 23, 2015, xhtml?pid=ACS_13_3YR_B16005&prodType=table. www.factfinder.census.gov/faces/tableservices/jsf/pages/productview. 7 See, for example, Randy Capps, Michael Fix, Margie McHugh, and Serena Yi-Ying Lin, Taking Limited English Proficient Adults into Account in the Federal Adult Education Funding Formula (Washington, DC: Migration Policy Institute, 2009), www.migrationpolicy.org/research/taking-limited-english-pro- . 8 For example, in 2013 Title II adult education programs served about 1.6 million people while MPI ficient-adults-account-federal-adult-education-funding-formula estimates that among adults 19 and over, approximately 43 million were either low-educated or LEP.

Adult education, National Reporting System, “State Enrollment by Program Type (ABE, ASE, ESL): All States,”For adult program education year enrollment 2013, data, see U.S. Department of Education, Office of Career,. Technical and 9 Social Policy Research Associates, Program Year 2013 WIASRD Data Book (Washington, DC: U.S. De- www.wdcrobcolp01.ed.gov/CFAPPS/OVAE/NRS/reports/ 2015), www.doleta.gov/performance/results/pdf/PY_2013_WIASRD_Data_Book.pdf. partment of Labor, Employment and Training Administration, Office of Performance and Technology, 10 Jeanne Batalova, Michael Fix, and Peter A. Creticos, Uneven Progress: The Employment Pathways of Skilled Immigrants in the United States (Washington, DC: Migration Policy Institute, 2008), www. migrationpolicy.org/research/uneven-progress-employment-pathways-skilled-immigrants-united- states. 11 See Workforce Investment and Opportunity Act, Title I Sec. 116(b)(2)(A)(i) for a description of the law’s six accountability measures. 12 The federal poverty level (FPL), calculated based on total family income before taxes (excluding

more information, see U.S. Census Bureau, “How the Bureau Measures Poverty,” accessed November 23,capital 2015, gains www.census.gov/hhes/www/poverty/about/overview/measure.html and noncash benefits such as food stamps), was $23,834 for a family. of four in 2013. For 13 James D. Bachmeier, A Demographic, Socioeconomic, and Health Coverage Profile of Unauthorized Im- For a detailed discussion of this methodology, see Randy Capps, Michael Fix, Jennifer Van Hook, and

10 Immigrants and WIOA Services: New York Fact Sheet

migrants in the United States (Washington, DC: Migration Policy Institute, 2013), www.migrationpolicy.

united-states. org/research/demographic-socioeconomic-and-health-coverage-profile-unauthorized-immigrants- 14 For more detailed MPI estimates of the unauthorized population in New York prepared using this methodology at national, state, and top county levels, see MPI Data Hub, “Unauthorized Immigrant www.migrationpolicy.org/programs/us-immigra- . Population Profiles,” accessed November 23, 2015, 15 U.S. Citizenship and Immigration Services (USCIS), “Number of 1-821D, Consideration of Deferred Ac- tion-policy-program-data-hub/unauthorized-immigrant-population-profiles tion for Childhood Arrivals by Fiscal Year, Quarter, Intake, Biometrics and Case Status: 2012-2015 (June 30),” accessed November 20, 2015, - data_fy2015_qtr3.pdf. www.uscis.gov/sites/default/files/USCIS/Resources/Reports%20 and%20Studies/Immigration%20Forms%20Data/All%20Form%20Types/DACA/I821d_performance 16 Workforce Innovation and Opportunity Act, Title II Sec. 203 (12).

About the Authors

Margie McHugh is Director of the Migration Policy Institute’s National Center on Immigrant Integration Policy. Her work focuses on education quality and access issues for immigrants and their children from early childhood through K-12 and adult, postsecondary and workforce skills programs. She also leads the Center’s work seeking a more coordinated federal response to immigrant integration needs and impacts, and more workable systems for recognition of the education and work experience immigrants bring with them to the United States.

Prior to joining MPI, Ms. McHugh served for 15 years as Executive Director of The New York Immigration Coalition, an umbrella organization for over 150 groups in New York that uses research, policy development, and community mobilization efforts to achieve landmark integra- tion policy and program initiatives. Prior to joining NYIC, Ms. McHugh served as Deputy Director of New York City’s 1990 Census Project and as Executive Assistant to New York Mayor Ed Koch’s chief of staff.

Ms. McHugh is a graduate of Harvard and Radcliffe Colleges.

Madeleine Morawski is an Associate Policy Analyst at MPI, where she provides program support for the National Center on Immigrant Integration Policy and works on issues such as adult education, early childhood educa- tion, and language access.

Previously, Ms. Morawski worked as a Research Assistant at the Kalmanovitz Initiative for Labor and the Working Poor and as an intern with the U.S. Com- mittee for Refugees and Immigrants.

She has a bachelor’s of science in foreign service from Georgetown University, where she ma-

jored in international politics and completed a certificate in international development. 11 Migration Policy Institute Acknowledgments The authors are grateful to MPI colleagues Jie Zong and Jeanne Batalova for their compilation of U.S. Census Bureau data used throughout this fact sheet.

For more information please contact Margie McHugh, Director of MPI’s National Center on Immi- grant Integration Policy ([email protected]), or Associate Policy Analyst Madeleine Morawski ([email protected]).

© 2015 Migration Policy Institute. All Rights Reserved.

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September 11, 2007 · 4:00 PM ET Heard on All Things Considered

JENNIFER LUDDEN

Immigrants' English skills are often part of the U.S. debate over foreign workers. Demand for English classes far outstrips supply, even as work, family duties and other obstacles stand in the way of efforts to master a new language.

MICHELE NORRIS, host:

It's a common thread in the debate on immigration. Americans ask, why don't immigrants learn English? When we have stories featuring Spanish-speaking immigrants, we often get e-mails to that effect.

Well, NPR's Jennifer Ludden set out to find answers and she has this report.

JENNIFER LUDDEN: Maria Morales(ph) is a hotel housekeeper in Maryland. While most of her colleagues are Hispanic, her supervisors only speak English, so Morales tries her best like when she asks for supplies.

Ms. MARIA MORALES (Hotel Housekeeper, Maryland): I need the towels. I need washcloths. I need sheets.

LUDDEN: Sheets? https://www.npr.org/templates/story/story.php?storyId=14330106 1/11 4/2/2019 Barriers Abound for Immigrants Learning English : NPR Ms. MORALES: Sheets, yes.

LUDDEN: She hesitates to say the word sheets, knowing it can be a big gaff if she mispronounces it. It may sound like Morales recently arrived from her native El Salvador. In fact, she's been in the U.S. 13 years, and has been studying English since 2001.

Unidentified Woman #2: Now, everybody. Excuse me.

Unidentified Group: Excuse me.

Unidentified Woman #2: What time is it?

Unidentified Group: What time is it?

LUDDEN: Morales learns in these classroom trailers of Prince George's Community College in Maryland. And just getting here is a triumph of sorts.

Unidentified Woman #2: Excuse me.

Unidentified Group: Excuse me.

Unidentified Woman #2: Oh, you can do better than that. Excuse me.

LUDDEN: Educators say a lack of transportation and childcare are common barriers. Despite that, there are many more immigrants who want to sign up than slots available. A study last year by the National Association of Latino Elected and Appointed Officials found that over half of publicly funded classes had waitlists, some up to three years. Then, once in the program, Prince George's Adult Education director Barbara Denman says keeping immigrants at class can be a problem. Take landscapers.

Ms. BARBARA DENMAN (Director, Prince George's County Adult Education Program): In the spring semester when the schedules change, particularly after daylight savings time kicks in, we'll lose those students because their employment requires them now to work much longer hours, and they can be very long hours.

https://www.npr.org/templates/story/story.php?storyId=14330106 2/11 4/2/2019 Barriers Abound for Immigrants Learning English : NPR LUDDEN: Housekeeper Maria Morales admits she hasn't had much time to study these past six years. She's been holding down two jobs. Another student, 38-year-old Adolfo Anton(ph) said he found it impossible to take English while he was working two shifts at a cement block factory from 12 noon until 3:00 in the morning. When he got a new schedule a year ago, Anton signed up for English two mornings a week.

Mr. ADOLFO ANTON (Factory Worker): Because it's important in my job. My supervisor no speak Spanish, and it's difficult for me understand English.

LUDDEN: A few years ago, the Department of Homeland Security created an office of citizenship with one of its goals to better promote English, but it suffered from lack of funding.

Mr. MICHAEL FIX (Vice President and Director of Studies, Migration Policy Institute): The policy continues to lack demography.

LUDDEN: Michael Fix is with the Migration Policy Institute. He says the U.S. invests far less in language courses for immigrants than some other developed nations, and services are spotty. For every federal dollar they get, states like California and Florida spend eight dollars of their own money.

Mr. FIX: If you'll look at some other states, say, Kansas, Nebraska or Texas, all now important immigrant-receiving states, they spend 30 cents for every dollar that they receive from the federal government. This is a kind of discrepancy state-by-state that you don't see in many other grants, programs.

LUDDEN: Some argue the current influx of Spanish-speaking immigrants poses a unique threat to English. Even some Latinos say it's just too easy to live in a Spanish bubble especially if you work in a field like construction or meatpacking. Yet others see little that's new.

History professor Alan Kraut of American University says a century and more ago, first generation immigrants also were slow to learn English.

Dr. ALAN KRAUT (Professor of History, American University): In places like Iowa and other areas of the Midwest with high concentrations of Germans, German was the https://www.npr.org/templates/story/story.php?storyId=14330106 3/11 4/2/2019 Barriers Abound for Immigrants Learning English : NPR language of everyday life and German was also the language of the public schools. People paid taxes, schools were financed, and classes taught in German.

LUDDEN: Back at Prince George's, factory worker Adolfo Anton says he loves his English classes and plans to keep them up as long as he can. By the way, as with a lot of people you may hear on the radio, I asked Anton to switch to Spanish so he can express himself more fully.

Mr. ANTON: (Spanish spoken)

LUDDEN: To learn English, he says, means you can communicate if there's an emergency, or you can get a better job. Anton says he hopes with better English, he can eventually become a supervisor.

Mr. ANTON: (Spanish spoken)

LUDDEN: Prince George's program director Barbara Denman says it takes years to master a foreign language, and the key part is gaining the courage to make mistakes. If Americans want immigrants to improve their English, Denman says there's a way everyone can help them, be patient and listen.

Jennifer Ludden, NPR News.

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