Exploration of Determinants of Occupational Mobility

The Importance of Country/Context Specific Conditions in the Occupational Mobility of Immigrants

Dissertation Proposal

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Maria Mercedes Sanchez, MBA

Education and Human Ecology Graduate

The Ohio State University

2011

Dissertation Committee:

Dr. Joshua Hawley, Advisor

Dr. David Stein

Dr. Jeffrey H. Cohen

Dr. Rebecca Heildt

Copyrighted by Maria Mercedes Sanchez 2011

Abstract

Using an immigrant assimilation framework, this dissertation builds upon

Chiswick’s (1977) human capital model, and attempts to develop a model of occupational mobility of immigrants that takes into consideration the effect of country/context specific conditions. This study tests the fitness of Chiswick’s (1977) against a proposed final model and poses questions regarding the determinants of changes in occupational status of college-educated immigrants from their country of origin to subsequent jobs in the

U.S. Using Multinomial Logistic Regression and data from the 2003 New Immigrant

Survey, this study found that the final model including structural predictors proved to enhance the prediction of changes in job quality over the model with only human capital factors. The following results are uncovered in the analysis: First, a large portion of foreign-educated immigrants experience a sharp decline in occupational status when they first move to the U.S. followed by a rise in job quality with time spent in the country, however, most are not able to recover all the status they lost initially. Overall, college educated immigrants had varying outcomes in terms of occupational mobility. These outcomes depended on immigrants’ gender, origin, English proficiency, time spent in the

United States, place of education and work experience, type of immigration visa, receiving a job offer prior to migration, and occupational licensing requirements.

Dedicated to my beloved daughter Sofia

iii

VITA

April 6, 1963 … …………………… Born – Madrid, Spain

1998 ……………………………….. B.S. Management, The Ohio State University

1998 – 2003 ……………………….. Manager, International Trade, Columbus Chamber of Commerce

2000 – 2005...... Director, International Trade Assistance Center Columbus Chamber of Commerce Columbus State Community College

2001 ……...... M.B.A., Capital University

2005- 2009...... Director, Latino Small Business Development Center – Ohio Business Connection

2010-2011 …..……………………... Associate Researcher, Ohio Board of Regents

2009-Present...... Consultant – Business and Workforce Development Specialist Medical Interpreter

PUBLICATIONS

Peer Reviewed Journals

 Sanchez, M. (2009). Review of ideologies in ESL curriculum development and instruction. Thresholds in Education, 35 (4)

Articles Published

 Sanchez, M. (2009). The need for a Latino research initiative at The Ohio State University. Que Pasa, OSU? Winter 2009 http://quepasa.osu.edu/issues/wi09/w18.html

 Sanchez, M. (2009). “Reflections on Our Community”. Que Pasa, OSU?Spring 2009. http://quepasa.osu.edu/issues/sp09/sp6.html

 Sanchez, M. (2009). “Reflections on Our Community: Educational Attainment of Hispanics/Latinos. Que Pasa, OSU? Summer 2009. http://quepasa.osu.edu/issues/su09/s14.html

 Sanchez, M. (2009). “Latinos buy more than they take: An exploration of the economic contributions of the Hispanic population to the development of the United States.”Que Pasa, OSU? Winter 2010 http://quepasa.osu.edu/issues/wi10/article14.html

CONFERENCES

 2010 Ohio Hispanic Leadership Summit (OHLS, 2010). The Ohio State University. Presented “The economic contributions of the Hispanic population”. Participated as Panelist in the section “Hispanic/Latino Businesses: A History of Success and Commitment”.

 Sanchez, M.(2009). “The Influence of Education on Labor Outcomes of Immigrant Workers”. OSU Literacy Conference for Graduate Students, “Expanding Literacy Studies”.

 Sanchez, M. (2009). “The Influence of Education on Labor Outcomes of Immigrant Workers”. AHRD International Research Conference in the Americas.

 Sanchez, M.(2008).“Can English Language and Job Training Services Impact Labor Market Success? The Effect of Language Acquisition, Workplace Literacy and Educational Attainment on Employment Outcomes.” Midwest Conference Research to Practice. Western Kentuky University, Bowling Green, KY. http://www.wku.edu/aded/MWR2P/MWR2P%20Proceedings2008.pdf

AWARDS

 2006 and 1996 Critical Difference for Women Scholarship ($1,000), The Ohio State University.

 2008 Cooperative System Fellowship ($3,000), 1-week training and technical assistance. Using the National Assessment of Adult Literacy (NAAL) for Research and Policy Discussion, National Center for Education, Institute of Education Science, U.S. Department of Education.

 2000 ITAC Director of the Year, The Ohio Department of Development. v

 1999 ITAC Counselor of the Year, The Ohio Department of Development

FIELDS OF STUDY

Major Field: Education

Workforce Development and Educational Policy Joshua D. Hawley, Ed.D. Adult Education David Stein, Ph.D. Research Methods and Statistics Richard Lomax, Ph.D. Literacy David Bloom, Ph.D

Table of Contents

Abstract ...... ii

Dedication….…...…………………………………………………………………….. …iii

Vita………………………………………………………………………………………. iv

List of tables ...... xii

List of figures ...... xiv

Chapter 1: Introduction ...... 1

Statement of the problem ...... 7

Main contribution of the study…………………………………………………… 7

Research questions………………………….……………………………………. 9

Significance of the study ………………….……………………………………... 9

Assumptions ……………………………………………………………………. 10

Limitations and delimitations of the study ……………………………..………. 11

Definitions …………………………………………………………………….…13

Chapter 2: Literature review ……………………………………………………………16

Theories of international migration……………………………………..……….16

Push and pull factors of migration ……………………………………………....16

Migration theories focused on micro-level decisions processes ………………...17

Migration theories focused on forces operating at a macro-level of aggregation..18 vii

Theoretical perspectives on immigrant assimilation..……………………………19

Human capital theory…………………………………………………… 20

Transferability of human capital ……………………………….. 20

The effect of cultural similarity …………………………………24

English language skills…………………………………………. 25

Source of human capital …………………………………………26

Time of residence in the new country …………………………...29

Labor market segmentation and structural explanations ………………..29

Labor market segmentation ……………………………………..30

Job mobility ……………………………………………………. 30

Occupational licensing requirements …………………………...31

Immigration policy …………………………………………….. 32

Labor market inequalities ……………………………………… 33

Social capital theory …………………………………………………… 33

Assistance to obtain employment …………………………….. 34

Homophily and cultural reproduction……………………...…….34

Labor market discrimination theories ………………………...…36

Conceptual model ……………………………………………………………… 40

Chapter 3: Methodology ………………………………………………………………. 43

Research type ………………………………………………………………….. 43

Research setting ……………………………………………………………….. 44

Data ……………………………………………………………………. 44

viii

Sample limitations ……………………………………………………... 45

Sample size justification ………………………………………………...46

Operationalization of Variables …………………………………………………47

Occupational mobility as change in occupational status ………………..48

Dependent variables ……………………………………………………. 49

Independent variables …………………………………………………...51

Categorical variables …………………………………………….52

Continuous variables …………………………………………… 58

Multinomial Logistic Regression ………………………………………………..62

General purpose and description ………………………………………………...62

Measures of effect size and power in MLR ……………………………..64

Data analysis …………………………………………………………………….65

Model fit …………………………………………………………………67

Model comparison ………………………………………………………68

Interpretation of significant effects of individual predictors ……………69

Limitation of MLR ………………………………………………………71

Ratio of cases to variables and missing data …………………….71

Adequacy of expected frequencies and power…………..……….71

Multicollinearity .…………………………………...…………...72

Linearity in the logit ………………………………………..……72

Absence of outliers in the solution ………………………..……..73

Independence of errors ……………………………………….….73

Statistical procedures to answer research questions …………………………..73

Research question #1 …………………………………………………...73

Research question #2………………………………………………….…74

Chapter 4: Results ……………………………………………………………………….76

Descriptive statistics …………………………………………………………….76

Results for question #1 ………………………………………………………… 78

Model fit …………………………………………………………………79

Model comparison ……………………………………………………....82

Results for the first stage regression …………………………………….82

Results for the second stage regression …………………………………83

Results for question #2 …………………………………………………………125

Determinants of upward occupational mobility ……………………….125

Determinant of downward occupational mobility ……………………. 126

Chapter 5: Summary, discussion and implications …………………..………………...128

Summary of results ……………………………...……………………………. 128

Discussion………………………………………………………………………136

Determinants of upward mobility …………..………………………………….133

Determinants of downward mobility ………………………………………..…134

Control variables ……………………………………………………….140

Human capital variables ………………………………………………..141

Structural variables …………………………………………………….141

Implications ……………………………….……………………………………142

Revised conceptual model …………………………………………..…143

The micro-level ……………………………………………...…144

The meso-level …………………………………………………146

The macro-level ………………………………………………..147

Variables in revised conceptual model ……………………...…149

Recommendation for future research…………………………………………...151

Analysis of trends of occupational mobility of immigrants ……………151

Immigration policy …………………………………………………..…151

Recommendations for workforce development policy ……………...... …152

Recognition of foreign credentials …………………………………..…152

References ...... 154

Appendixes:

Appendix A: Histograms of continuous variables ...... 164

Appendix B: Correlations of continuous variables……………………………………..165

Appendix C: Calculation and Recoding of Variables………….……………………….166

List of Tables

Table 3.1 Descriptive statistics of difference in occupational status ………………50

Table 3.2 Frequency Distributions of Difference in Occupational Scores …….…..51

Table 3.3 Frequency Distributions of Discrete Variables Recoded ………………. 52

Table 3.4 Descriptive Statistics of Variables in Continuous Form ………………...59

Table 4.1 Initial and Long Term Occupational Trajectory of in origin and in the U.S. …………………………………………………………….....77

Table 4.2 Model Fitting Information First Stage Regression ……………………...80

Table 4.3 Model Fitting Information Second Stage Regression …………………...80

Table 4.4 Model Goodness-of-Fit First Stage Regression …………………………81

Table 4.5 Model Goodness of Fit Second Stage Regression ………………………82

Table 4.6 Pseudo R-Square Values ………………………………………………...84

Table 4.7 Classification First Stage Regression ……………………………………85

Table 4.8 Classification Second Stage Regression ………………………………...86

Table 4.9 Log Likelihoods in First Stage Regression - Contribution of Individual Predictors to the Full Model …………………………………………….87

Table 4.10 Log Likelihoods in Second Stage Regression - Contribution of Individual Predictors to the Full Model …………………………………88

Table 4.11 Parameter Estimates of Significant Predictors in First Stage Regression .90

Table 4.12 Group Differences in discrete variables for males – First stage regression………………………………………………………………..93

xii

Table 4.13 Mean of Continuous Variables by Outcome for Males – First Stage from Country of Origin to First US Job ……..……………...95

Table 4.14 Parameter Estimates of Significant Predictors in Second Stage Regression ……………………………………………………………….96

Table 4.15 Occupational mobility of males in the U.S. – Group differences...…….100

Table 4.16 Mean of Continuous Variables by Outcome for Males – Second Stage Regression from First US Job to Current US Job ..……………………..102

Table 4.17 Parameter Estimates of Significant Predictors in First Stage Regression…...………………………………………………………… 104

Table 4.18 Group differences in discrete variables for females – First stage Regression…...………………………………………………………… 107

Table 4.19 Mean of Continuous Variables by Outcome for Females – First Stage Occupational trajectory from Country of Origin to First US Job ……...109

Table 4.20 Parameter Estimates of Significant Predictors in Second Stage Regression ……………………………………………………………...110

Table 4.21 Group Differences for females– Second stage regression……….……..114

Table 4.22 Mean of Continuous Variables by Outcome for Females – Second Stage Occupational trajectory from First US Job to Current US Job …..115

Table 4.23 Occupational status of immigrants in their country of origin and the U.S. measured using the Nam Powers Boyd scale ………………...…..119

xiii

List of Figures

Figure 2.1 Conceptual model of occupational mobility of immigrants ………….....42

Figure 4.1 Occupational status of immigrants at origin and the U.S. by gender.…...77

Figure 4.2 Effect of human capital variables in the occupational status of immigrants at origin and the U.S. ……………………………………...116

Figure 4.3 Occupational status of male immigrants in their origin country and the U.S. ………………………………………………………………... 121

Figure 4.4 Occupational status of female immigrants in their origin country and the U.S. ………………………………………………………………... 122

Figure 5.1 Revised conceptual model of occupational mobility of immigrants….. 148

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CHAPTER 1

INTRODUCTION

Since the Immigration and Nationality Act of 1965 (also known as the Hart-Cellar

Act) abolished previous restrictions by national origin, the labor market adjustment of immigrants has been of great interest among researchers. The most dramatic effect of the act was to shift immigration from Europe to developing countries. For decades, the U.S. experienced a huge increase in the inflow of immigrants with little formal education and that differed culturally from the bulk of the established population. Nevertheless, over the last two decades, immigration policy in this country increased the emphasis on skill based selection criteria through the Immigration Act of 1990 (Mattoo et al., 2010). As a result, immigrants who arrived recently to the U.S. tend to have a higher level of education and language skills than previous cohorts (Jasso and Rosenzweig, 1998).

Currently, there is a growing interest on learning about the ability of college- educated immigrants to translate pre-immigration labor market experiences into labor market success in this country. However, little attention has been paid to the occupational mobility of high-skilled labor experienced when migrating from one country to another.

Occupational mobility is defined here as the extent to which immigrants workers are able

1 to move within the occupational structure ladder. It is called horizontal mobility if it does not result in a change in status, and vertical (upward or downward) mobility if it does

(Gabriel, 2003). Occupational status is one component of socioeconomic status summarizing the power, income and educational requirements associated with a particular job. The literature uses the term occupational status and occupational prestige interchangeably. Occupational mobility was chosen as the prefer measure because, in addition to other work structures, is considered an important determinant of individuals’ market positions, life-chances, access to valued goods, job autonomy, and social prestige in the U.S. market economy (Weeden and Grusky, 2005).

Most of the existing analysis of labor market integration compares native and immigrant workers’ occupational attainment in terms of earnings (Borjas, 1985, 1995), educational returns (Friedberg, 2000), or visa admission class (Jasso & Rosenzweig,

1995). More recent studies have focused on the over-education1 and the international transferability of human capital skills of immigrants in OECD2 countries (Mattoo, et. al.

2010; Batalova, Fix & Creticos, 2008; Bohen, 2005 Chiswick & Miller, 2002, 2007a;

2007b; Nielsen 2007; Sanromá, et al. 2008; Gächter & Smoliner, 2010 ).

Studies on occupational mobility were conducted to assess the costs and benefits of the skill-based immigration policy implemented in most OECD countries. Researchers

1 The literature uses the term "over-education" or "skill underutilization" to describe the waste of skills and knowledge of higher educated individuals who end up employed in occupations for which they are overqualified. Skill underutilization can occur in different forms; it may be the downward mobility of skills, i.e., 'de-skilling'. Another kind is 'brain waste,' because the quality and the skills of people are not properly utilized (Bauder, 2003). Further, this situation is expressed as 'skill loss,' because immigrants work in occupations that are lower in skill (and status) than their pre-migration employment (Williams et al., 1997).

2 Organization for Economic Co-operation and Development. 2 in the United States have made efforts to evaluate if the family unification criteria, followed since the enactment of the Hart-Cellar Act, should be continued or replaced by an employment-based selection criteria such as that of Canada and Australia. Analysis of occupational trajectories of immigrants found that employer sponsored immigrants had better occupational attainment at time of migration, however, their trajectory deteriorated with time of residence in the U.S. The opposite was true for those entering with family visas which after facing initial disadvantages were able to improve their occupational attainment with longer time of residence. Consequently, researchers concluded that there was no evidence suggesting that a change in immigration policy should be currently addressed in the U.S. (Akresh, 2007, 2008; Yap Co, 2007).

Nevertheless, these studies raised concerns among policy makers by evidencing that a large number of newly arrived immigrants experience a decline in occupational status compared to the working status they formerly held in their country of origin

(Akresh, 2007, 2008; Batalova, et al. 2008; Chiswick and Miller, 2002, 2007; Yap Co,

2007). As a result of this decline in occupational status, many college-educated immigrants end up employed in occupations for which they are overqualified (Batalova, et al. 2008; Lewin-Epstein, Semyonov, and Kogan, 2003).

According to data from the U.S. Census Bureau's 2007 American Community

Survey (ACS) 21.9% (or 1,412,484) of the 6.1 million college-educated immigrants in this country were underutilized -working in occupations that do not match their skill level, compared to 16.3% (or 5,704,985) natives (Batalanova et at., 2008). These

3 numbers suggest that high skilled immigrants experience limited occupational mobility in this country.

We know little about how and why some high skilled immigrants are integrated successfully into the workplace while others end up in occupations for which they are overqualified. Usually, upward occupational mobility generally results from the acquisition of additional human capital. However, the literature suggests that, depending on their birth place, education and experience may be assessed differently among immigrants and lead to different probabilities of getting into a high skilled occupation.

Understanding the determinants of immigrants’ occupational mobility is important because downward mobility has negative outcomes.

The loss of immigrant occupational status represents a waste of valuable human capital, which leads to a loss in worker productivity to the national economy, and is a detriment to the well-being of immigrant workers and their families (Batalova, et al.,

2008). Equity issues may also arise if immigrants are unable to find skilled employment or earn lower wages for similar jobs in comparison to native workers (Hersh, 2008a;

2008b; 2008c; 2009).

Historically, the processes for effective immigrant integration are, for the most part, taken for granted in the United States (Creticos, 2007).While immigrant over- education has become a policy priority in OECD countries, the U.S. lags behind other host countries in establishing policies and measures to prevent the underutilization of their skills. Evidence exists demonstrating that growth in the workforce of scientists, engineers and other professions requiring high level of technical specialization depends

4 substantially on migrations of foreign workers, therefore, their successful integration in the labor market is of great relevance for the economy of this country (Creticos, et al.,

2006; 2007).

Researchers have identified several contributing determinants of downward mobility including limited host country language skills, lack of connections in the job market, poor familiarity with the job market in the receiving country, limited country of destination-related work or academic experience, lack of recognition for foreign academic credentials; and lack of value placed by employers on their foreign acquired skills and experience (Akresh, 2004; Basilio& Bauer, 2010; Batalova et al. 2008; Bohen,

2005; Chiswick& Miller, 2007; Gächter & Smoliner, 2010; Mattoo, et. al. 2010;

Messinis 2008; Nielsen, 2007; Sanromá, Ramos, & Simón, 2008).

Chiswick’s (1977) model of occupational mobility, tested in this dissertation, predicts that those who are a better cultural fit for the U.S. labor market achieve higher occupational success. He later acknowledged that the labor market attainment of immigrants increases with duration of residence in the destination country and acquisition of country specific human capital skills (Chiswick, 2002). Alternative explanations of immigrants’ occupational attainment were offered by other economic researchers and include lower quality of human capital (Borjas 1994, 1995), differences in school quality across continents of origin (Gächter & Smoliner, 2010; Mattoo, et. al. 2010), and the lack of use of English as a medium of instruction (Mattoo, et. al. 2010). Sociologists also offered alternative explanations to differentials in immigrants’ occupational attainment such as employment discrimination practices and exclusionary behavior towards

5 immigrants (Bohen, 2004, Gächter & Smoliner, 2010), ability to form quality social networks to obtain employment (Ellitot; 2001, Gächter & Smoliner, 2010; Stanek &

Veira, 2009; Vallat 2010), and labor market inequalities that decrease immigrant’s ability to obtain high paying positions (Stanek & Veira, 2009).

Previous research has been limited by the lack of availability of information on immigrant’s legal status, year of arrival, pre-immigration work experience, and source of education, among other factors. These studies were unable to evaluate significant contributing factors to labor market outcomes, such as immigration status, years of education and time of residence in the new country (Akresh, 2007; Hersh, 2008; Yap Co,

2007), skin color gradient (Hersh, 2008a; 2008b; 2008c; 2009), locally-specific country conditions such as immigration policy (Akresh, 2007), or regulatory requirements in the occupational system.

In addition, past studies relied on aggregate information to calculate effects at the individual level and have been limited by the cross-sectional, and therefore, multiple cohort nature of the data (Akresh, 2008; Yap Co, 2007). Chiswick (1980, 2002) identified that the use of cross-sectional data can provide biased estimates of the longitudinal effect of time of residence in the destination country on labor market outcomes at the individual level. This study can overcome data limitations with the use of the 2003 New Immigrant

Survey data. The NIS collects information of a single cohort of legal immigrants into the

U.S. labor market including individual’s pre and post occupational data, years of education in the U.S., skin color gradient, source of employment assistance, and visa admission class, along with other variables influencing occupational mobility.

Statement of the Problem

For the last decade, the number of new legal immigrants with higher education and English language skills has increased significantly compared with previous cohorts

(Jasso & Rosenzweig, 1998). Despite the growth in the high skilled immigrant population, many college-educated immigrants end up employed in occupations for which they are overqualified. We know little about how and why some high skilled immigrants are integrated successfully into the workplace while others end up in occupations for which they are overqualified. Understanding the determinants of immigrants’ occupational mobility is important because downward mobility leads to a loss in worker productivity to the national economy, and is a detriment to the well-being of immigrant workers and their families (Batalova, et al., 2008). Equity issues may also arise if immigrants are unable to find skilled employment or earn lower wages for similar jobs in comparison to native workers (Hersh, 2008a; 2008b; 2008c; 2009).

The objective of this study is to extend the discussion about the determinants of immigrant’s occupational mobility by incorporating new elements into the analysis. In particular, the study evaluates the effect of country specific conditions such as occupational regulatory requirements, immigration policy and source of assistance to find employment by incorporating social and structural factors into Chiswick’s (1997) human capital model.

Main contribution of the study

Previous studies (Akresh, 2007; 2008; Batalova, et al. 2008; Chiswick & Miller,

2009; Yap Co, 2007) revealed the importance of the effects of certain country/context specific conditions on the occupational mobility of immigrants such as immigration policy. Although a few studies (Akresh, 2007, 2008; Batalova, et al. 2008; Chiswick and

Miller, 2007; Yap Co, 2007) attempted to incorporate factors related to the occupational structure, such as level of skills and schooling required to perform certain professions, they failed to provide an operational factor that will allow for the calculation of the effect of occupational regulatory requirements in the labor market assimilation of immigrants.

Regulatory requirements affecting certain high skilled occupations are a barrier to enter professions requiring country specific licenses and certifications, such as most medical and teaching occupations. Although these regulatory requirements affect both native and foreign workers, they are particularly challenging to immigrants who already face a list of additional burdens that negatively affect their occupational mobility.

The analysis in the study identifies the occupations that require the acquisition of specific licenses or certifications in the U.S. in addition to a college degree. The study offers a comparison of the occupational mobility of immigrants who were employed in non-regulated occupations in their countries of origin, versus those who were employed in regulated occupations that require licenses or certifications in the U.S.

In addition to other factors that derived from country/context specific conditions, such as immigration visa type, this study also takes into account immigrant’s region of birth, gender, years of education and time of residence in the new country, pre-

8 immigration work experience and years of schooling, source of education, and being offered a job prior to migration.

Research Questions

Research Question #1

The guiding research question in this dissertation is:

a) Do country/context specific conditions increase the explanatory power of

Chiswick’s model of occupational mobility of immigrants?

Research Question #2

b) What are the determinants of vertical occupational mobility of immigrants’ at

time of arrival in the country? Do these determinants change with extended time

of residence in the U.S.?

Significance of the Study

Globalization has fostered the flow of capital and trade as well as labor across national borders, making the issue of portability of human capital of growing importance for economic development. Furthermore, technological changes have affected the demand for labor with high level of skills, heightening the international competition for talented scientists, engineers and other highly specialized personnel. Government policies are critical to minimize the brain waste of highly skilled immigrants who come to support the knowledge-based economy and global competitiveness of the U.S. Skill utilization represents a loss in worker productivity to the national economy and the well-being of

9 immigrant workers and their families (Reitz, 1995 in Batalova et al., 2008). Immigrants’ occupational downgrading could lead to dependence on public assistance and affect remittance behavior and/or the circulation of knowledge and expertise (Akresh, 2007).

Immigrants’ success in obtaining similar or better occupations in the U.S. is crucial as

88% of the adult in the 2003 NIS database reported intending to live in the United States for the rest of their lives (Yap Co, 2007).

While the brain waste of college-educated immigrants has become a policy priority in other immigrant-receiving countries such as Australia, Canada and the

European Union, the U.S. lags behind other host countries in establishing policies and measures to prevent the underutilization of the college-educated. Other countries are making highly skilled migration an essential component of national economic development and competitiveness and have enacted policies and invested in programs to ensure that future opportunities for economic success are not jeopardized by a lack of needed skills, nor limited by the inability of highly educated individuals to put their skills to use.

It is clear that for the U.S. economy to be efficient and competitive in the global market, the development of government policy for human capital transferability and the understanding of the factors affecting immigrants’ loss of occupational status have become issues of considerable relevance.

Assumptions

It is assumed that national origin provides useful information in terms of the education and training of immigrants; cultural and linguistic similarities relative to the

U.S.; socioeconomic and linguistic constraints and opportunities at home; the home country educational systems’ quality and comparability with that of the United States; and different modes of admission and climates of reception in the United States for newcomers from different regions of the world (Batalanova et al., 2008).

It is also assumed that neighboring countries that are close geographically and those which share colonial links or speak the same language share values, norms, and social practices and possibly other factors such as histories, climates, socio- political structures and institutions, creating cultural similarities between countries and geographical regions (Hofstede, 1999, 2006; Inglehart, 2003, 2006; Schwartz, 2009).

It is further assumed that immigrants who had been living in the U.S. for a longer period of time had greater chances to develop resources for economic mobility, such as professional networks, gaining more US work experience, improving English fluency, obtaining a U.S. education, and/or changing their profession altogether. Longer duration in this country can also reflect a deepening retreat in the face of U.S. labor-market realities preventing occupational success, which could have resulted in partial or permanent withdrawal from the labor market and/or long-term underemployment

(Batalanova & Fix, 2006).

Limitations and Delimitations of the Study

My analysis is restricted to the study of the changes in occupational prestige of foreign college-educated males and females with at least a bachelor’s degree, who were

11 between 18-63 years old at the time they were surveyed (2003), had work experience prior to immigrating, and found employment sometime after entering the U.S.

The author acknowledges the existence of additional extraneous and intervening variables that could confound the results of this analysis. The literature has identified that factors that affect immigrant’s hiring processes could cause a change in immigrant’s occupational status. This include household wealth, marriage status, parents’ education level and variables related to social capital formation, such as the support of family and social networks (Elliot, 2001; Valat, 2010), climate of receptivity towards immigrants by geographical region within the U.S. (Elliot, 2001), economic conditions, and processes of cultural reproduction creating inequalities in the labor market (Rivera, 2009) that will not be accounted for in the quantitative analysis.

In regards to limitations in the data, the 2003 NIS does not include information about the country where immigrants received their education, which is relevant to my analysis. To overcome this limitation, this study will use the proxy of “foreign-educated” immigrants, which will be defined as immigrants with at least a bachelor’s degree and who were educated abroad and may or may not completed education in the United States at an age older than 23. “US-educated” immigrants will be defined as immigrants who completed schooling in the US. There will be divided in two groups, those who completed education at age 23 or younger, and those who completed it later.

The measure of English skills also presents limitations. It is not clear that an immigrant’s English speaking ability at the time of the interviews at the same level as when he obtained his first U.S. job (Yap Co, 2007. p.10).

Definitions

The following terms will be defined: culture, cultural similarities, homophily, human capital, occupational status, occupational structure, regulated occupations, skill underutilization, and social capital.

Cultural similarities - Neighboring countries that are close geographically and those which share colonial links or speak the same language share values, norms, and social practices and possibly other factors such as histories, climates, socio- political structures and institutions, creating cultural similarities between countries and geographical regions (Hofstede, 1999, 2006; Inglehart, 2003, 2006; Schwartz, 2009).

According to the cultural similarity hypothesis (Inglehart, 2006; Hoffestede, 2006;

Lazear, 1999; Zolberg, 2006), the differences between the home and host cultures for immigrants will relate directly to their economic assimilation and difficulties in adapting to the host culture.

Homophily - (i.e., love of the same) is the tendency of individuals to associate and bond with similar others. In this paper, it refers to the preference for others with similar culture.

The presence of homophily has been discovered in a vast array of studies of social capital and it is believed to influence labor market outcomes.

Human capital – The term human capital refers to peoples' skills and abilities as used in employment and its contribution to the economy. Economists define human capital as the productive investment in humans, including their skills and health, which are the outcomes of education, health care, on-the-job training, work experience, and which

13 increase that employee's value in the marketplace. Many early economic theories refer to it simply as labor, one of three factors of production, and consider it to be a homogeneous and easily interchangeable commodity. This theoretical concept will be further discussed in chapter two of this dissertation.

Occupational status - Occupational status is one component of socioeconomic status

(SES), summarizing the power, income and educational requirements associated with various positions in the occupational structure. Occupational status indexes reflect the consensual rating a job based on the collective belief of its worthiness. The literature uses the term occupational status and occupational prestige interchangeably.

Occupational structure - Occupational structure refers to the aggregate distribution of job occupations in society, classified according to skill level, economic function, or social status (Marshall, 1998). As a general rule, high-ranked jobs in the occupational structure ladder are well-paying and scarce, while low-ranked jobs are common and, often, poorly paid (Bohon, 2005). Changes in occupational structure are indicators of human capital transferability. According to Marshall (1998), the factors that determine occupational structure are the structure of the economy, the labor market (which determines wages and conditions attached to occupations), and occupational attainment which is influenced by labor market inequalities, life-style, and social values.

Regulated occupations - A "regulated" occupation is one that is controlled by local, state and federal regulatory agencies and/or professional associations and has very specific requirements regarding the credentials necessary to practice certain occupations, such as licenses, certificates, or registration. Examples of regulated occupations are physicians,

14 nursing, engineering, and teaching. A non-regulated occupation is a profession/trade for which there is no legal requirement or restriction on practice with regard to licenses, certificates, or registration. Examples of non-regulated occupations that require years of education and training are computer analysts or biologist.

Skill underutilization - Skill underutilization can occur in different forms, it may be the downward mobility of skills, i.e. 'de-skilling' (Bauder and Cameron, 2002). Another kind is 'brain waste', because the quality and the skills of people are not properly utilized

(Bauder, 2003), and therefore, there is waste of skills and knowledge (Ozden and Schiff,

2006). Further, this situation is expressed as 'skill loss', because immigrants work in occupations that are lower in skill (and status) than their pre-migration employment

(Williams et al., 1997).

Social capital - The term “social capital” was first used by Loury (1977) to communicate the idea that social relationships constitute a resource for individuals, akin to physical or human capital. Putnam (1995) defines social capital as “features of social life – networks, norms and trust – that enable participants to act together more effectively to pursue shared interests” (pp. 664-65). Portes (1998) defined social capital as the ability of individuals and groups to secure certain resources through the membership in interpersonal networks and social institutions.

CHAPTER 2

LITERATURE REVIEW

This chapter provides the theoretical foundations of international migration as well as a review of the literature on immigrant assimilation informing the hypothesized model tested in this dissertation. It concludes with a proposed conceptual framework based on the literature review.

Theories of international migration

This section discusses a number of theories that explain international migration and employ different frames of reference, concepts and assumptions.

Push and pull factors of migration

Lee's (1966) theory of migration divides factors causing migrations into two groups: push and pull factors. Push factors are things that are unfavorable about the area that one lives in and refer to conditions that drive people to leave their countries of origin.

Pull factors refer to conditions that attract people to a new area. Among the most important push factors of labor migration are differentials in wage rates, the possibility of enjoying higher standards of living and escape from poverty. An individual may choose to migrate if the value of wages in the new country surpasses the value of wages in their

16 native country, as long as the opportunity costs3 are not too high. The cost of migrating, which includes both explicit costs, such as travel expenses, and implicit costs, such as lost work time and loss of community ties, plays a major role pulling immigrants away from their native country (Lee, 1966). The possibility of enjoying higher standards of living in developed countries is an important motivator to migrate, especially for those with scarce economic resources and low career prospects in less developed countries.

Among important pull factors are low opportunity costs to migrate and high demand for labor in the new country. According to Lee (1966), the demand for labor tends to be greater in periods of economic expansion. Additionally, immigration rates tend to be higher when the opportunity cost to migrate is lower or there is greater availability of jobs in the new country.

Massey et al. (1993) discussed the most relevant migration theories and organized them according to their micro- or macro- level scope of focus. The following section summarizes their review of international migration theories.

Migration theories focused on micro-level decision processes

Neoclassical economics focuses on wages differentials between countries, employment conditions, and migration costs. According to neoclassical economic theory, immigration is the result of an individual decision for income maximization; hence, the main reason for labor migration is wage difference between two geographic locations.

These wage differences are linked to geographic labor demand and supply. Labor tends to

3 Opportunity cost is defined as the cost of output foregone such as monetary or financial costs, lost time, pleasure or any other benefit that provides utility. 17 flow from low-wage countries to high-wage countries. Often, this flow of labor produces changes in the economy of the sending as well as the receiving country.

The theory of new economics of migration states that migration flows and patterns cannot be explained solely at the level of individual workers and their economic incentives, and posits that wider social entities and conditions in a variety of markets, not just labor markets, must be considered as well. One such social entity is the household.

According to this theory, migration can be viewed as a household decision taken to minimize risks to family income or to overcome financial constraints. The household, in this case, is in need of extra capital that can be achieved through remittances sent back by family members who participate in migrant labor abroad. The capital sent in these remittances can also have a broader effect on the economy of the receiving country as a whole.

Migration theories focused on forces operating at a macro level of aggregation

Dual labor market theory links immigration to the structural requirements of modem industrial economies. This theory provides an explanation of the inequality that exists in the labor market and is based on Doeringer & Piore’s (1971, as cited in Stanek

& Veira, 2009) labor market segmentation theory. Dual labor market theory states that immigration is mainly caused by pull factors in more developed countries. This theory assumes that the labor markets in these developed countries consist of two segments based on the quality of the jobs: the primary labor market segment, which requires high- skilled labor, and the secondary, which is very labor-intensive but requires low-skilled workers. This theory assumes that migration from less developed countries into more

18 developed countries is a result of a pull created by a need for labor in the developed countries in their secondary market. Migrant workers are needed to fill the lowest ranking positions of the labor market because the native laborers do not want to do these jobs.

The initial shortage in available labor pushes wages up, making migration even more enticing.

World systems theory sees immigration as a natural consequence of economic globalization and market penetration across national boundaries. It explains that interaction between different societies can be an important factor of social change. It argues that international trade causes economic decline in another, may create incentive to migrate to a country with a more vibrant economy. According to this theory, when developed countries import labor-intensive goods from less developed countries, it causes an increase in employment of unskilled workers in the less developed countries, thus, decreasing the outflow of migrant workers. Likewise, the export of capital-intensive goods from developed countries to less developed countries equalizes income and employment conditions, slowing migration as well.

Theoretical perspectives on immigrant assimilation

Since the literature on immigrant assimilation presents a large number of alternative explanations for their occupational mobility, some complementary, other contradictory, various theoretical perspectives were examined to formulate the hypothesized model used in this dissertation. Within these theories, several concepts were chosen and operationalized to form part of the conceptualized model.

Human capital theory

Becker’s (1966) human capital theory has been one of the most influential frameworks for explanations for immigrants’ integration and occupational mobility.

Economists define human capital as the productive investment in developing and preparing workforce knowledge and skills of humans through education, on-the-job training and labor market experience. One major premise of human capital theory is that investments made in educating the labor force to develop their skills, in addition to providing nourishment and health care, will pay off by contributing to an increase in production. In general, immigrants acquire productive capacity in their origin country, but only part of this human capital can be transferred to the labor market at their destination. Therefore, depending on a number of factors, a large number of immigrants experience an occupational downgrading or recession after arrival in the host country.

Transferability of human capital

The predominant theoretical framework of immigrant adjustment in the labor market of the receiving country is based on Chiswick’s (1978, 1979, 1986) notion of international transferability of human capital and his model of occupational mobility of immigrants. According to Chiswick (1978), the stock human capital that immigrants obtained abroad may not be fully transferable to the requirements of the host country’s labor market. According to his model, foreign workers tend to experience an occupational trajectory that follows a U-shaped pattern. Initially, immigrants experience a decline in their occupational status relative to their pre-migration occupational status. Chiswick

(1978) proposed that immigrants from countries that are culturally similar (e.g., in quality

20 of education, closeness of language spoken, and parity in economic development level) are likely to have a shallow U-shaped occupational status curve, since their pre-migration skills are more transferable.

This model predicts that the lower the international transferability of human capital, the sharper will be the decline in occupational status and the higher the earnings disadvantage of the immigrants at the time of migration.

Chiswick (1978) posits that with increased time of residence in the host country, immigrants are able to invest in the country-specific human capital of the receiving country, as well as to adapt their stock of foreign human capital to the new labor market.

The additional investments in human capital help improve immigrants’ position on the occupational ladder, as well as increasing their earnings when compared to natives. The subsequent increase in occupational status and earning will be faster for those who initially had the lowest degree of human capital transferability (Bauer & Zimmermann,

1999). In other words, the steeper the initial decline in occupational status, the steeper will be the subsequent increase for a highly-skilled immigrant from a country of origin that is dissimilar (e.g. in language) to the host country (Chiswick, 1978). The extent of human capital transferability between two countries depends on the type of individuals’ skills, and the similarity of the sending and receiving country with regard to language, culture, labor market structure and institutional settings (Chiswick, 1978, 1986).

Bauer and Zimmermann (1999) noted that the value of pre-immigration human capital investments and their effects on post-immigration occupational status and earnings is inversely related to the degree of human capital transferability. Highly

21 educated immigrants have a higher degree of occupational specialization, and therefore, a higher degree of country-specific human capital. Then, according to Chiswick’s model, more educated immigrants should experience a relatively higher loss of occupational status. However, immigrants with lower degrees of human capital transferability should feel a higher incentive to invest in country-specific human capital of the receiving country and the modification of previous investments, which might lead to higher assimilation rates for this group of migrants.

Duleep and Regets (1997 in Bauer & Zimmermann, 1999) identified three reasons for the faster assimilation of migrants with a lower degree of human capital transferability. First, the opportunity costs of additional human capital investments at the time of immigration are smaller for those with low human capital transferability. Second, the returns to additional investments are greater for those immigrants with a lower degree of transferability due to complementarities between the acquisition of new country- specific skills human capital and un-transferred foreign human capital. Third, the optimal level of investment in human capital is increased by the lower opportunity costs of and higher returns to additional human capital investments. To summarize, according to the model of occupational mobility of immigrants, those with a low degree of human capital transferability will experience a high deterioration in their labor market position at the time of immigration, but also a faster improvement with time of residence in the receiving country (Bauer & Zimmermann, 1999).

Chiswick (2007) acknowledged alternative explanations for immigrants’ loss of occupational status or their over-education. One of them is based on the effects of

22 technological change in the labor market. Kiker et al.’s (2000) Technological Change

Theory argues that the rapid pace of technological change may require human capital skills higher than those possessed by currently employed workers. This theory points to the existing need to improve school-provided skills to keep up with the pace of any technological change in a country’s labor market. Those who acquired new technological academic skills will be considered more educated than those who are currently employed in the labor force. However, employers will not be able to replace their existing workforces by new, more educated workers in the short term due to adjustment costs.

Hence, the presence of undereducated workers will create disequilibrium in the labor force. Nevertheless, as firms gradually employ higher educated workers, these new workers will be considered overeducated as compared to those who entered the workforce earlier with less technological academic education.

According to Chiswick (2007), Kiker et al.’s (2000) Technological Change

Theory implies that the incidence of loss of occupational status or over-education among immigrants will be related to the levels of development of the origin and destination countries. He argues that it is likely that the schooling of immigrants from many Western countries has a technology base that is comparable to that of native-born workers in the highly developed destination countries. However, it is more likely that the schooling of immigrants from less-developed countries has a technology component that is less relevant to the labor market in the highly developed destination country. Therefore, according to this theory, for a given level of education, immigrants from less-developed countries are more likely to experience a higher loss of occupational status than

23 immigrants from advanced Western countries, as the immigrants from less-developed countries will find that they have fewer destination-relevant skills (Chiswick, 2007).

The effect of cultural similarity

Cultural similarity is a measure of the level of shared values, language distance, and differences in quality of education and standards of living between immigrants’ country of birth and the United States. According to the cultural similarity hypothesis

(Inglehart, 2006; Hoffestede, 2006; Lazear,1999; Zolberg, 2006), differences between the home and host cultures for immigrants will relate directly to their economic assimilation and difficulties adapting to the host culture. Chiswick’s (1978) model of occupational mobility predicts that the acquisition of country relevant human capital skills is easier for those from similar cultures than for immigrants coming from countries with greater cultural distance from the U.S. This model predicts that those who are a better cultural fit for the U.S. labor market achieve higher occupational success.

The results of the analysis performed by Akresh (2008) and Yap Co (2007) of the

2003 New Immigrant Survey seem to corroborate this theory and suggest that the possibility of an occupational match or upgrade is greater for immigrants from English speaking countries, which have similar levels of economic development with the U.S., and lower for developing countries and those with more distant cultures. The analysis of

Mattoo (2010) evidenced that in the U.S., even with a master’s degree, a Mexican still has only a 43% probability of obtaining a skilled job, while the probability for an Indian is 80%. The study of Mattoo et al. (2010) found that with some exceptions, educated immigrants from Latin American and Eastern European countries, are more likely to end

24 up in unskilled jobs in the U.S. than immigrants from Asia and industrial countries

(Mattoo et. al., 2010). Additional studies from other OECD countries showed that immigrants from less developed countries and more distant cultures initially face higher unemployment rates and a higher incidence of over-education compared to natives

(Nielsen 2007).

English Language Skills

Several studies identified the impact of language dissimilarities4, and highlighted the importance of host-country language proficiency for occupational success since language conditions skill utilization and further acquisition of labor market skills upon migration. Chiswick et al. (2003) and Chiswick and Miller (2007a; 2007b) posit that proficiency in the host language facilitates immigrants’ cultural integration and the transfer and adjustment of other human capital skills. Several studies have evidenced that

English proficiency is one of the main factors that prevent or soften occupational downgrading in the initial stage of immigrants’ career in the U.S. after arrival. According to Borjas (1995) and Chiswick and Miller (2007a; 2007b), the lack of language capital is the most important explanatory factor of why immigrants with a foreign education tend to have lower employment and labor market participation rates compared to immigrants who have completed their education in the U.S. Chiswick’s (1978) model suggests that the lower the language distance, the easier will be for immigrants to learn the language of the new country. Mattoo et al. (2010) added that the lack of use of English as a medium

4 Hofestede’s (1999, 2006) research provided evidence that linguistic difference is highly correlated to cultural distance between countries and regions of the globe. 25 of instruction, which denotes English proficiency, can also explain variations in occupational attainment of immigrants in the U.S.

Source of human capital

In the last few years, a number of studies evidenced that employers treat schooling from certain countries of origin differently from the way they treat schooling from natives. Gächter and Smoliner (2010) noted that while workers in their home country all have similar occupational returns to education, immigrant’s returns lag considerably behind, and suggested that many employers do not recognize foreign credentials.

Ferrer et al. (2006) explained that the “skills generated through education or work experience in the source country cannot directly be transferred to the host country, resulting in apparently well qualified immigrants holding low-paying jobs” (pp. 380-

381). Borjas (1985, 1995) found that the foreign-acquired human capital of immigrants in the U.S., measured in education and experience completed abroad, is significantly less valued than human capital of natives. In a later study, he suggested that those who downgrade might have received a lower quality of formal education in their home countries (Borjas, 2006). Sanroma et al.’s (2008) also noted that the low value placed on immigrants’ human capital is attributed to differentials in the quality of educational systems between immigrant-sending and -receiving countries. Using the Technological

Change Theory, Kiker et al. (2000) suggested that immigrants from less-developed countries are more likely to have a technology component to their schooling that is less relevant to the labor market in the advanced destinations. Difficulties in obtaining

26 recognition for foreign credential and mismatches in technological requirements can mean that education and experience obtained in most other countries are not as productive in the U.S. as education and experience acquired here.

The literature appears to confirm that differences in school quality between immigrant-sending and -receiving countries affect immigrants’ labor outcomes. However, this argument is highly debatable since different returns for education have also been found in the native population of immigrant-receiving countries by race. Studies by

Ashraf (1994) and Chang (1991) showed that the returns to education for ethnic minorities (both native and immigrants) in the U.S. lag behind the returns for the white population. Lindley (2009) also found that despite receiving the same quality of education, non-white natives in the UK are more likely to be overeducated than white natives.

In a study of immigration to Israel, Friedberg (2000) found that the portability of education varies significantly with its level: Elementary school education is perfectly transferable no matter where it was completed, but the return on postsecondary schooling varies greatly with the place of origin (Friedberg, 2000). Studies from Batalova et al.,

(2008) noted that middle and higher education in particular are not equally rewarded in the U.S. if acquired abroad, proving further evidence that the source of higher education matters. In addition, they found that the returns to education are higher for immigrants from Europe and the Western Hemisphere than for immigrants from Asia and Africa

(Batalova, et al., 2008). Furthermore, several studies found that the education and working experience acquired in the U.S. are significantly more important for the recovery

27 of occupational status and improvement of economic returns for immigrants (Arkesh,

2008; Batalova, et al., 2008; Chiswick & Miller, 2007a; 2007b; Yap Co, 2007).

The notion of the transferability of human capital can explain why the initial loss in occupational status is particularly pronounced for college-educated immigrants who previously occupied highly ranked positions. Besides the fact that there is more room to drop down the occupational ladder in the case of highly skilled workers compared to less- skilled workers, those immigrants who had higher positions in the occupational structure in their country of origin are more likely to find that at least some of their skills are of less value or less compatible with the skills required in the new country. However, although college educated immigrants might experience a sharper decline in occupational status, they will also experience a faster recovery. Stanek and Veira (2009) found evidence that professional immigrants educated in occupations with the highest status tend also to improve their situation in the host country more rapidly due to a major propensity to invest in their human capital. Nevertheless, researchers have identified that inequalities caused by the existing fragmentation in labor markets erode the effect of investments in education and time of residence in occupational mobility of immigrants

(Stanek & Veira, 2009).

Other human capital models focus on deficits of immigrants’ skills, experiences and capacities as a main cause of the lost in job quality post-migration. Human capital theorists assume that foreign workers with a higher quality of human capital are more successful when entering the host country’s labor market (Borjas, 1995). Hence, according to this theory, immigrants who have lost status tend to have human capital

“deficiencies” and are less experienced and have less job training. Any “excess” human capital (skill surpluses or over-education) from schooling is in fact compensating deficiencies in other substitutable human capital forms (Borjas, 1995). In the case of immigrants in this country, human capital “deficiencies” might relate to lack of U.S. specific labor market experience and low levels of English proficiency (Borjas, 1995,

Chriswick & Miller, 2007b). Sociologists warn that models based on immigrant

“deficiencies” could be discriminatory (Bohon, 2005; Gächter & Smoliner, 2010) and they are difficult to apply to the highly skilled immigrant population (Batalanova et al.,

2008).

Time of residence in the new country

Chiswick et al. (2003) noted that the acquisition of host country relevant skills increases return from the human capital obtained previously in the country of origin. As the period of residence in the host country lengthens, they tend to improve their language proficiency, acquire additional skills, as well as familiarity with the new labor market, and country specific labor market experience, which allows them to improve their labor market position compared to the initial one (Basilio & Bauer, 2010; Akresh Redstone

2008; Stanek & Veira, 2009). Hence, the human capital model sees occupational recession as a temporary disequilibrium phenomenon (Chiswick et al., 2003).

Accordingly, one may expect that immigrants will initially experience a loss in occupational status that will increase over time.

Labor market segmentation and structural explanations

The vast majority of economic studies of immigrant labor market integration have overlooked the effect of host country conditions such as job mobility, occupational requirements, immigration policy and labor market inequalities as barriers to entry in specific professional occupations. This could partially be due to the lack of available data and/or proper understanding of the occupational system. This study attempts to operationalize some of these barriers to provide a measure of their influence in occupational mobility.

Labor market segmentation

Doeringer and Piore’s (1971 as cited in Stanek & Veira, 2009) labor market segmentation theory referenced above suggests that the disadvantages faced by immigrants are the result of a fragmented labor market characterized by differentials in systems, institutions, working conditions, job rewards and career opportunities.

According to segmentation theory, inequalities will persist because of the continued existence of segmented markets where people are allocated according to different group characteristics. The primary market is characterized by high wages, stable working conditions, and opportunities for advancement. On the other side of the spectrum, the secondary market is characterized by low skilled jobs and high underemployment.

Job mobility

Job mobility between the primary and the secondary market is highly restricted and only possible if post-secondary credentials are attained. However, as connections between primary and secondary segments are structurally limited or not available for most immigrants, the length of time of residence in the U.S. and investments in education

30 have relatively little influence on their occupational mobility (Stanek & Veira, 2009).

Historically, immigrants have had more limited career opportunities than natives.

Research has evidenced that immigrants around the world, regardless of their skills level and previous work experience, tend to be channeled into low paid jobs in specific labor intensive economic sectors (i.e. domestic work, construction, agriculture) and specific low-paid and low-prestigious positions which explain their initial loss of occupational status (Stanek & Veira, 2009).

Some authors have observed that immigrants create their own mobility opportunities by opening their own business, often in their ethnic enclaves, to avoid barriers to occupational success (Bohon, 2004; Ellis, 2001; Portes 1980).

Occupational licensing requirements

Occupational requirements and professional standards affect the transferability of immigrant skills and pose important barriers to entry that have been overlooked by the sociologists. The most prestigious positions in the occupational system require higher education that may or may not be recognized in the host country depending of its source.

Immigrants seeking to enter or who wish to maintain membership in an occupation must offer proof that their academic or technical training credentials meet the minimum standards for the occupation.

In general, when professional standards are established and recognized by international governing bodies, as in the case of engineers and technicians, there is greater transferability of skills. However, these professions may have some local-specific requirements such as licenses and permits that could require additional post-secondary

31 education in the host country. Professions requiring local licenses and credentials are categorized as “regulated” and are controlled by local governing bodies. Generally, the skills and knowledge required by regulated occupations are less transferable.

Some regulated occupations have high locally-specific contexts, such as the legal system, which varies widely from one country to another. Several professions may follow international standards in their practice, such as medical doctors and architects, and still require specific local licenses and permits that are not recognized internationally. In contrast, non-regulated occupations such as computer analysts or biologists have no legal requirements or restrictions on practice with regard to licenses, certificates, or registration. Normally, the skills required by non-regulated occupations are more transferable, since the decision to recognize educational credentials in non-regulated occupations is left to employers (Creticos, 2007).

Labor market institution governing many regulated occupations have established explicit and sometimes confusing methods for recognizing the educational credentials of foreign educated immigrants. The educational requirements for non-regulated occupations are much less formal and, as mentioned above, the process of interpreting and accepting these credentials is left to employers. Researchers recommend increasing the efforts toward the recognition of foreign qualifications and credentials to alleviate the under-placement of educated migrants (Batalanova, 2008; Creticos, 2007).

Immigration Policy

Researchers have identified that some characteristics of a segmented economy overlap with immigration policies and divide immigrants into several categories such as

32 legal – illegal, or by type of visa admission class (family sponsored – employment sponsored – refugee – diversity, U.S. citizens – third country citizens). Each category of immigrant has unequal access to certain sectors and occupations, and very different opportunities for occupational mobility (Yap Co, 2007). This phenomenon was illustrated by studies from Akresh (2007, 2008), Bohon (2005) and Toussaint-Comeau (2006) evidencing that immigrant trajectories toward labor market success vary systematically by visa admission class and immigration legal status.

Labor Market Inequalities

It has also been observed that upward mobility between occupational sectors is influenced by some demographic characteristics such as gender, race and ethnic origin which suggests that direct or indirect forms of discrimination take place in the labor market (Akresh, 2008; Bohon, 2005; Gächter & Smoliner, 2010; Hersh, 2008a; 2008b;

2008c; 2009; Toussaint-Comeau, 2006; Yap Co, 2007).

Social capital theory

Social capital is a critical resource for immigrants since it can reduce costs of labor market entry as it provides information, housing and financial assistance among other supports. Social capital can influence career success since it facilitates access to information that may lead workers to find jobs. Social capital theory has gained importance in explaining differentials in labor market performance among immigrant groups admitted through different visa class (Akresh, 2007; Yap Co, 2007).

The effects of social capital on occupational success depend on the size, density, diversity of the backgrounds and social situations of the network members. Class and

33 ethnic diversification has been identified as a crucial factor in the access to better jobs and occupational positions (Stanek & Veira, 2009).

Assistance to Obtain Employment

Research has evidenced that friends and relatives assist migrants in their job search by providing them with useful labor market information such as how and where to look for jobs, how to behave in job interviews, what wages to ask for, and which sorts of jobs were best to avoid (Gächter & Smoliner, 2010). Employment sponsored immigrants may have been offered a job position prior to migrating and facilitated important links with professional networks. On the other hand, refugees or immigrants who entered through the diversity programs are less likely to have prior connection with employers or already established friends and relatives living in the U.S. (Vallat, 2010). In addition, there is evidence that the social network of immigrants helps them to invest in host- country specific human capital (Gächter & Smoliner, 2010).

Homophily and cultural reproduction

Homophily – defined here as the preference for others with similar culture – is a contributing factor to the occupational outcomes of highly educated immigrants. As

McPherson, Smith-Lovin, and Cook (2001) explain, homophily limits people’s social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Recent studies have established that homophilic processes affect both the demand and supply side of the labor market. In a study of who gets hired in high paying elite job positions, Rivera (2009) evidenced that

34 homophilic processes contributed to a cultural reproduction of the labor force by affecting employers’ hiring decisions and impacting social network formation.

The presence of homophily has been discovered in a vast array of studies of social capital. In a review of the literature on networks, sociologist McPherson, Smith-Lovin and Cook (2001) cite over a hundred of studies that have observed homophily in some form or another. These include culture, age, gender, class, organizational role, and so forth. Lazarsfeld and Merton (1954, as cited in McPherson, Smith-Lovin and Cook 2001) were first to formulate the concept and they distinguished between status homophily and value homophily. Status homophily means that individuals with similar social status are more likely to associate with each other. By contrast, value homophily refers to a tendency to associate with others who think in similar ways, regardless of differences in status.

The literature identifies differentials in the ability and quality of social capital formation that may explain much of the variation in labor market performance of immigrant males and females and between different immigrant groups (Stanek & Veira,

2009). In general, females tend to form social networks with little diversification and which offer fewer career opportunities to access to better jobs and occupational positions

(Stanek & Veira, 2009). Lazear (1999) noted that immigrants who have a larger inclination to cluster together have a larger inclination to stick to their own social and cultural norms, including their own language, hindering their ability to integrate in the native culture. Homophily practices, such as clustering with one’s own national group in immigrant enclaves, are more frequent among refugees and immigrants from developing

35 countries who entered through the diversity program. Recent research differentiated between ethnic and native based social capital and indicated that ethnic ties may be less valuable than ties with natives, as ethnic peers may be less informed about the situation on the labor market as well as on specific job openings of the host country (Ellis, 2001).

Ellis (2001) posit that if immigrants rely greatly on homogeneous personal ties, especially in groups with few economic resources and human capital, this could result in clustering in occupational niches and obstruct socioeconomic mobility.

Labor market discrimination theories

Some researchers have used discrimination theories as an alternative to the theory of transferability of human capital to explain labor market outcomes of immigrants and ethnic minorities. Nielsen (2007) used theories of discrimination to explain immigrants’ over-education, Bohon (2005) to explicate earnings differentials among immigrants and natives, and Gächter and Smoliner (2010) to address lack of educational portability.

Labor market discrimination can exist at the individual, group and organizational levels (Hayes, 2000 as cited in Gächter & Smoliner, 2010). At the individual level, discrimination results from personal prejudice. At a group level, discrimination can result from social closure. At the organizational level, unconscious institutionalized practices and structural barriers are typical for this kind of discrimination. Precursors of current labor market discrimination theories include the taste-based theory by Gary Becker

(1957) and the theory on statistical discrimination by Arrow (1973). The taste-based theory by Gary Becker assumes that native employers, employees or customers have distaste for working together and/or communicating with certain ethnic immigrant

36 groups. This “taste-model” tastes for or against members of disadvantaged groups are treated the same like preferences or antipathies for certain goods and services.

Arrow’s (1973) theory of statistical discrimination assumes that employers have incomplete information about the skills and productivity levels of job applicants in the hiring process. In order to minimize the costs of acquiring information, employers screen job applicants by indicators that they believe to be associated with productivity such as ethnicity, gender, accent, name or educational credentials. Thus, individuals from different groups may have different occupational success despite being observably similar, equally talented and productive (Gächter & Smoliner, 2010). “The employer’s assessment of a worker’s skills depends on his/her perception of the average qualifications in the group to which the worker belongs and his perception of the reliability of the indicator for the members of the concerned group” (Jakobsen 2004, p. 4 as cited in Nielsen, 2007). Studies of the 2003 New Immigrant Survey conducted by

Hersh (2008a; 2008b; 2008c) showed that immigrants with darker skin color have lower wages than comparable immigrants with lighter gradient, even after controlling for

English language proficiency, education, occupation before migrating to the United

States, and family background. These results suggest that U.S. employers may prefer immigrants with lighter skin tones. Nielsen (2007) proposed that when immigrants cannot find employment due to discrimination, they are more likely to accept a job that does not match their qualifications. Consequently, in the presence of discrimination, one might expect a higher share of immigrants to be overeducated compared to natives.

Coate and Loury (1993 as cited in Gächter and Smoliner, 2010) extended the statistical theory of discrimination to include aspects of human capital theory. They found that the skill gap that exists between African-American and Caucasian workers is the major cause of discrimination and that skill gaps are socially and culturally constructed.

Family background and community background are critical variables in determining future occupational outcomes. Therefore, differentials in occupational outcomes steam from discrimination that exists outside the labor market (Coate and Loury, 1993 as cited in Gächter and Smoliner, 2010).

Queuing Theory has commonly been used to explain racial and gender discrimination in hiring and promotion (Bohon, 2005; Ritz, 2005). As Bohon (2005) explains, queuing is a theoretical framework based on the idea that workers stand in an imaginary line for good jobs. Employers rank jobs and employees according to some standard of desirability. Workers place in line is determined by these standards. At the top of the line are the most desired workers and at the end of the line are the least desired.

Despite having similar job skills, the least desired workers face a disadvantage from the get go. When the time comes to compete for the same jobs, workers who belong to the more desirable group have an advantage to access those jobs (Bohon, 2005).

The framework of Queuing theory is suitable to explain the labor market performance of college-educated immigrants. The literature has documented that among immigrants, queue placement usually also depends on linguistic and cultural proximity, which is a factor of country of origin. Employers in the U.S. prefer native workers or immigrants from certain countries over others in hiring and pay. Within the immigrant

38 population, workers from highly developed countries or that have a similar culture or language have preference over workers from developing countries and those with more distant cultures (Aaditya et. al., 2010; Sanromá et. al. 2008). This is illustrated in a study from Bohon (2005) which found that, even with a masters degree, a Mexican engineer still has only a 43% probability of obtaining a job that matches his skills, while the probability for an Indian engineer is 80%.

Gächter and Smoliner (2010) attempted to measure the impact of discrimination in the labor market outcomes of immigrants in Germany. Their findings suggested that employers tend to place a low value on foreign education and it varies according to perceptions of quality of an immigrant’s education by national origin. Nevertheless, they explain that it is very difficult to provide empirical evidence for the existence of discrimination, since employers are reluctant to publicly express their opinion about the work skills of people according to their racial and ethnic background (Gächter &

Smoliner, 2010).

Nielsen (2007) and Messinis (2008) advise caution in determining which effects of immigrant under-placement are related to discrimination per se since employers do not have perfect knowledge about the content or quality of a given education obtained in a foreign country. Faced with a choice between imperfect knowledge about the skills of an individual educated abroad and better knowledge about the average quality of the skills of an individual educated in the U.S., it is feasible that an employer would make a “safer” choice by employing workers with a U.S. education. Hiring decisions may not be due directly to discrimination, but rather to problems of imperfect information.

Conceptual Model

The proposed conceptual model considers four types of independent variables to reflect the following attributes and factors: control variables, individual human capital, social capital and structural factors from the theoretical concepts discussed above.

Control variables include the demographic variables gender and region of origin.

Gender will be used to identify differentials in occupational trajectories of males and females. Region of origin will account for linguistic and cultural similarities between immigrants’ country of origin and the U.S.

Human capital is measured by a combination of English proficiency, years of education and work experience and it takes into consideration the source where human capital was acquired (i.e. foreign or U.S.). Since employment experience is accumulated with time of residence in the U.S. and older arrivals tend to have longer foreign work experience, declared age at the moment of arrival and time of residence in the U.S. were included as a measure of human capital (Chiswick et al. 2005). Immigrants who were younger than 23 years of age at the time of entry most likely had acquired part or all their post-secondary education in the U.S. Therefore, U.S. schooling was entered at the time it was acquired and accumulated (before the first U.S. job and/or between the first job and the current job at time of interview).

In addition to control and individual human capital variables, the model includes measures of social capital and structural factors to account for country/context specific conditions. First, I included measures related to social capital providing information on immigrants’ job contract status at the moment of arrival in U.S. (i.e., if he or she was

40 offered a verbal/formal work contract by an employer) (Staneck & Veira, 2009). Another social capital variable that I attempted to include was the participation of social network members, specifically family members, in obtaining the first job in U.S. (Yap Co, 2007;

Akresh, 2008). Unfortunately, lack of sufficient data precludes the use of this measure.

Finally, structural conditions affecting the labor market are recorded based on occupational requirements and immigration policy. Professions that have U.S. local- specific requirements in the form of licenses and permits have been identified. (i.e. nurses, doctors, teachers, etc). In addition, immigration policy was found to affect the access to certain sectors and occupation in previous studies of immigrant occupational mobility (Yap Co, 2007; Akresh, 2008). Therefore, type of immigrant visa can be considered to represent the labor market segmentation effect.

An attempt was made to include skin color to study labor market inequalities by race. However, lack of sufficient data precludes the use of this measure. Instead, separate regression analysis by gender will be used to reveal potential labor market inequalities denoted by differentials in the occupational trajectory of female and male college- educated immigrants.

Following is a graphical representation of the research design.

Human Capital: Foreign Education Foreign Work Experience Human Capital: English Proficiency Time of Residence Age at entry U.S. Education (2) U.S. Education (1) U.S. Work Experience

Control Variables: Pre-immigration Initial post-migration Occupational Gender Occupational Occupational Status status at time of

Country of Origin Status (T1) (T2) interview (T3)

Country/Context Conditions Social Capital: Offered a job prior to migration Structural Factors: Immigration Visa Type Working in U.S. Regulated Occupation

*Occupational mobility is defined here as the extent to which immigrants workers are able to move within the occupational structure ladder from their countries of origin to subsequent occupations in the U.S. Immigrants will experience horizontal mobility (occupational match) if the move does not result in a change in status, and vertical (upward or downward) mobility if it does U.S. Education (1) = Those who complete education in the U.S. at or before age 23. U.S. Education (2) = Those who acquired both U.S. after age 2

Figure 2.1 Conceptual Model of Occupational Mobility* of Immigrants 35

CHAPTER 3

METHODOLOGY

This chapter describes the research methodology used to answer the research questions of the study. The first section describes the research type. The second section discusses the research setting. The third section explains the operationalization of variables. The fourth section describes the statistical method and statistical procedures followed to answer specific research questions.

Research Type

This research used descriptive and multinomial logistic regression to answer the research questions. The study is ex post facto in nature. Given the broad nature of the grouping variables in my study, the design will consists of non-equivalent groups with great variation in characteristics of subjects within groups as well as between groups. Ex post facto studies start with groups that are already different with regard to certain characteristics and then proceed to search in retrospect for the factors that brought those differences (Cohen, 2007). Instead of comparing different treatments as in

43 experimental design and exploring causal relationships between independent and dependent variables to determine direct cause and effect relationships, ex post facto research compares the differences in variables and examine whether they differ relative to predictors (Light, Singer, & Willett, 1990).

Research Setting

This section is organized into two parts. The first part describes the population of interest and selected data. The third part outlines the sample limitations.

Data

This study uses cohort data from the 2003 New Immigrant Survey consisting of

U.S. immigrants admitted to legal permanent residence between May and November of

20035. This study analyzes the transferability of human capital and occupational mobility of college-educated immigrants. Hence, the population of interest is formed by foreign males and females with at least a bachelor’s degree, who had working experience prior to migration and found employment sometime after entering the U.S. The survey was designed to evaluate if the family unification criteria, followed since the enactment of the

Immigration and Nationality Act of 1965, should be continued or replaced by skill- and employment-based selection criteria followed by other OCED countries such as Canada and Australia.

Adult immigrants in the NIS were stratified in four types: spouses of U.S. citizens

(16.5%), employment principals (16.5%), diversity principals (13.5%), and other

5 The adult sample (18 years old or older) has 8,573 completed interviews conducted between June 2003 and June 2004. 44 immigrants (53.5%). Immigrants were admitted through employment sponsorship, family reunification, diversity and refugee visas. Immigrants who were admitted through employment and diversity visas were oversampled two to three times their actual shares in the 1996-2000 period because there is much interest in finding information about the assimilation experience of these immigrants, while spouses of U.S. citizens were under- sampled at half their actual share of 27.2%-33.3% in the 1996-2000 period. Round 2 interview of this cohort was scheduled for 2007 (Jasso, et al., 2004).

The survey enables study of whether immigrants’ economic experience in the

United States varies according to their country of origin, admission class, English language skills, whether the source of education is foreign or U.S. and/or their pre- and post-migration labor market experiences.

Key to occupational success is immigrants’ chances in obtaining U.S. occupations that are comparable (i.e., same or better) to their last foreign occupations; and, whether the probability of occupational success improves with time in the United States. These questions are relevant as 88% of the adult NIS respondents reported that they intend to live in this country for the rest of their lives.

Sample Limitations

Out of 8,573 cases, 3075 individuals in the NIS are reported to have at least a bachelor’s degree. To study the determinants of an occupational match and mobility between an immigrant’s last foreign occupation, first U.S. occupation and current U.S. occupation, all respondents with non-missing occupational codes for the three jobs are collected. Only 1374 cases contained information on three outcomes of interest. After

45 removing nine identified outliers and those in the military or working in unclassifiable occupations (2002 Census occupational codes greater than 9750), for which a prestige score in the Nam-Power-Boyd (NPB) scale used in this study did not exist, the sample size was reduced to 1342 cases. Once the sampling weight was introduced in the analysis, the population of interest was reduced to 986 cases. This is due to SPSS rounding up of weighted frequencies when using proportional weights found in stratified databases such as the 2003 NIS.

After careful comparison and analysis of unweight and weighted frequencies, the decision was made to report weighted frequencies in this study. The larger amount of immigrants with employment visas in the un-weighted sample population create bias estimates in group comparisons, lowering the proportion of immigrants who lost status or found similar occupations. This is due to the fact that those with employment visas had the best occupational trajectories and tended to gain status or find occupations with similar job quality. Weighted frequencies were considered to present a more accurate reflection of the population of immigrants for statistical analysis and were almost identical to the results presented in the study of Akresh (2008) using the same survey data. A SPSS tutorial on weighting recommended using original sampling weights to run regression analysis, obtain mean averages, and inserting a warning in frequency tables.

Sample Size Justification

The substantial loss of sample size is due to the stringent data requirements to study occupational mobility, necessitating both that the respondent reported a last job abroad and was in the U.S. labor force at the time of the interview and reported an

46 occupation. Akresh (2008) noted that imputation of missing data on occupational prestige for the last job abroad is precluded for several reasons, one of which is the inability to ascertain who was in the labor force (and therefore eligible for imputation) prior to their arrival in the United States. Further, since the covariates are measured at t3, corresponding to current occupation at time of interview, there is no reliable way to impute occupational prestige at t1 (last job abroad prior to arrival to U.S.) or t2 (first job in the U.S.) that would distinguish it from the more reliable t3 imputation (Akresh, 2008).

The criteria for inclusion in the sample suggest that the results of this analysis may be generalizable only to the occupation-reporting working population.

After reviewing the literature using the 2003 NIS to study occupational mobility I could not find any indications that the omission of last occupation abroad was non- random. Some studies point to the fact that the NIS contains a group of immigrants who were granted amnesty through the 1996 Immigration Reform and Control Act (IRCA).

Prior to this act, this group was not legally authorized to work. It is very likely that these individuals may have avoided answering questions regarding employment prior to their legalization. However, these cases cannot be clearly identified in the public version of the

NIS data (Yap Co, 2007).

Operationalization of Variables

This section describes the constructs identified in the conceptual model for the proposed data analysis and creation of variables: dependent variable -change in occupational status, and independent variables -control and human capital variables;

47 social and structural factors. The syntax of instructions performed in SPSS is presented as an attachment to this study in Appendix B.

Occupational Mobility as Change in Occupational Status

The dependent variable measures occupational mobility to provide a sense of the occupational trajectory of immigrants in their countries of origin and the U.S.

Occupational mobility is measured as the change in occupational status of the jobs legal immigrants held at three points of time. In order to assess the quantitative meaning of the categorical occupations, this study makes use of an index of socioeconomic status score, the 2000 Nam-Power-Boyd (NPB) scale, based on 2000 census coding. NPB scores range from 0 to 100 and represent the socioeconomic standing of a particular occupation in the universe of detailed occupations of all individuals in the labor force (Nam & Boyd,

2004). The NPB scores were calculated using regression analysis of education and income as a mean to capture the consensual rating a job based on the collective belief of its worthiness (Toussaint-Comeau, 2006). This scale has previously been used by several studies of occupational attainment and mobility because its occupational scores are able to strongly differentiate among social groups (Bohon, 2004; Toussaint-Comeau, 2006).

The NPB occupational scores for 2000 census occupational codes and titles are provided as an attachment to this study.

The data on occupation in the 2003 NIS are coded according to the 2002 Census

Occupational Classification System (OCS), which has 509 separate categories arranged into the 23 major occupational groups (U.S. Bureau of Labor Statistics). Assignment of

NPB scores to the 2002 occupation codes was straightforward. The 2002 OCS consists of

48 four digits ending in 0, and the 2000 OCS only has three digits. The first three of the

2002 coding are identical to the three digits of the 2000 OCS (U.S. Census Bureau,

Division of Industry and Occupation, 2011).

Dependent variables

Once the NPB code was assigned to the 2002 OCS codes, two dependent variables were constructed as follows: First, two continuous variables (DV1 and DV2) were created as an intermediate step. These variables were useful to run meaningful descriptive statistics.

Intermediate constructs DV1 and DV2 are the difference in NPB occupational status scores between occupations. DV1 is the difference between the occupational status of the first job in the U.S. and the job in the country of origin. It provides a measure of the magnitude of initial mobility at migration. DV2 is the difference between the occupational status of the current job in the U.S. and the first job in the in the U.S. It provides a measure of the long term occupational trajectory after a period of residence in the new country. Computation formulas are as follows:

DV1= S2 – S1 DV2= S3- S2

where S1= status score at t1; S2 = status score at t2; and S3 = status score at t3 t1= Year a college-educated immigrant started working in the last job at origin; t2= Year started to work in the first job in the U.S. after migration; and t3= Year started to work in the current position at time of survey interview.

Table 3.1 Descriptive statistics of difference in occupational status (Weighted N=995)

Standard Mean Deviation Minimum Maximum

DV1 -18.00 34.627 -96.00 87.00

DV2 5.80 22.410 -75.00 95.00

Since the method used for analysis, Multinomial Logistic Regression, requires the dependent variable to be categorical, the values of continuous variables DV1 and DV2 were subsequently coded into two discrete variables (Dependent1 and Dependent2) with three alternative categorical outcomes as follows:

Occupational upgrade: If value of S2>S1 (or S3>S2), then S2 -S1> 0; the difference between the two status scores is positive and is coded with value 1. It represents occupational recovery and improvement in status.

Occupational downgrade: If value of S2

Occupational match: If a person reported the same status score from t1 to t2 (or from t2 to t3), then S2-S1=0 or S3-S2= 0; the difference of scores has a value of 0 and is coded as 3. It represents landing an occupation with the similar status. All regression analyses use this category as the reference.

Results in Table 3.2 indicates that the majority of immigrants (57.6%) lost occupational status when they first came to this country, a portion were able to retain their status in similar occupations (21.1%) and only a minority were able to gain status

(21.3%). Results measuring changes in status from the first U.S. job to the current U.S job indicate that the occupational status of most college-educated immigrants (49.1%) remained unchanged after residing for a period of time in the U.S. In this second stage, a larger portion (31.1%) were able to improve labor market outcomes, but a portion

(19.8%) experienced a further loss of status. Figure 3.1 provides a visual representation of proportion of cases by category of outcome.

Table 3.2 Frequency Distributions of Difference in Occupational Scores Recoded Warning: SPSS rounded weighted frequencies. Confidence interval 93%

Dependent1 Frequency Percent Dependent 2 Frequency Percent

Upgraded 210 21.3 Upgraded 306 31.1

Downgraded 568 57.6 Downgraded 195 19.8

Match 208 21.1 Match 484 49.1

Total 986 100.0 Total 986 100.0

Independent Variables

The independent variables of this study have either a categorical or continuous form. Meaningful descriptive statistics are different for each type of variable: therefore,

51 each will be listed according to its form. Appendices with frequency distributions are attached as needed.

Categorical Variables

Based on the theoretical foundations and empirical evidence found in the literature review, the following control and structural categorical variables were selected as potential determinants of occupational mobility: Gender, geographical region of origin,

English proficiency, visa type, having a job offered prior to moving to the U.S., and working in a regulated occupation that required licenses to practice in the U.S. Coding of dichotomous categorical variables with response type yes/no follows SPSS LOGIT jargon: the positive response category was coded 1 (“yes”) indicating the presence of the predictor, and the negative response category was coded 0 (“no”), indicating its absence.

SPSS uses the last category as the reference group, therefore the base group will have the value 1. Following is a table of discrete variables showing proportion of cases by group.

Table 3.3 Frequency Distributions of Discrete Variables Recoded. Warning: SPSS rounded up weighted frequencies Discrete Variable Frequency Percent

Gender

Male 584 59.3

Female 402 40.7

Total 986 100.0

(Continued)

Table 3.3 (Continued) Discrete Variable Frequency Percent

Region of Origin

Valid Asia 268 27.2

India 140 14.2

Europe, Canada and Oceania 220 22.3

East Europe 109 11.1

Latin America & the Caribe 109 11.0

Africa & Middle East 137 13.9

Total 983 99.7

Missing System 3 .3

Total 986 100.0

Visa Type

Spouses of U.S. Citizens 345 35.0

Employment Principals 244 24.7

Diversity 80 8.1

Other 318 32.2

Total 986 100.0

English Proficiency

Proficient in English 773 78.4

Not Proficient in English 213 21.6

Total 986 100.0

(Continued)

Table 3. 3 (Continued)

Discrete Variable Frequency Percent

Worked in Regulated Occ. Abroad

Licensed occupations 324 32.8

Not-Licensed occupations 662 67.2

Total 986 100.0

Worked in Regulated Occ. First U.S. Job

Licensed occupations 202 20.4

Not-Licensed occupations 784 79.6

Total 986 100.0

Worked in Regulated Occ. Current U.S. Job

Licensed occupations 217 22.0

Not-Licensed occupations 769 78.0

Total 986 100.0

Job Offered job prior to moving to U.S.

Valid Job was offered 151 15.3

Job was not offered 747 75.8

Total 898 91.1

Missing System 88 8.9

Total 986 100.0

Following is a description of discrete variables in the study:

Gender

Gender was coded 1-male; 0-female. Since the literature has made evident that the employment trajectories of men and women are significantly different, analysis will be conducted separately for males and females in this study (Yap Co, 2007).

Geographical Region

The information on country of origin appeared modified by groups in the NIS.

See Appendix A for frequency distributions of this variable. The decision was made to group immigrants into five distinctive geographical cultural regions to allow for general cross-cultural comparisons.

Region of origin is recoded as follows: 1- Asia (China, Korea, Philippines,

Vietnam, East Asia, South Asia & The Pacific); 2- India; 3- Europe, Canada, and Oceania

(Canada, United Kingdom, Europe & Central Asia); 4-East Europe (Poland, Russia and

Ukraine); 5-Latin America (Colombia, Cuba, Dominican Republic, El Salvador,

Guatemala, Haiti, Jamaica, Mexico, Peru, Latin America & The Caribbean); 6- Africa and the Middle East (Ethiopia, Nigeria, African Sub-Saharan, Middle East & North

Africa). Frequency distributions of the recoded variable appear in Table 3.3. Note that the proportion of the group from Asia was largest (27.2%).

Visa Admission class

The U.S. grants immigrant visas to individuals who meet the eligibility criteria set forth for the various classes of admission; such immigrants are called principals in the

NIS. Examples include the spouses of U.S. citizens, refugees, workers of several kinds, and winners of the diversity visa lottery. The spouses and minor children of employment

55 principals, sibling principals, refugees; and spouses and parents of U.S. citizens accompanying, or following to join principals in certain classes of admission are also granted immigrant visas. Immigrants who were admitted through employment and diversity visas were oversampled two to three times their actual shares while spouses of

U.S. citizens were under-sampled at half their actual share.

Frequency distributions for the original adult sample (N=8,573) and the college- educated immigrants who reported an occupation at the three times of interest in this analysis (N=1,342) are offered in Appendix A. Note that there are substantial differences in the amount of employment sponsored immigrants with college education in the population of interest of this study in comparison to the general population in the NIS.

Coding for this variable is: 1- Family Sponsored (Spouses of U.S. citizens only),

2- Employment Principals, 3- Diversity and 4- Other type of visa includes refugees, those who were legalized and individuals sponsored by relatives, other than spouses of U.S. citizens. The last group is the reference category.

Level of English proficiency

The measurement of English proficiency in the NIS presented challenges, a problem that has been acknowledged by other researchers (Akresh, 2008; Chiswick &

Miller, 2007a; 2007b; Yap Co, 2007). For this analysis, the values of the variables self- reported speaking ability (very well, well, not well, not at all) and interviewer’s rating of respondent’s English level (very good, good, fair, poor, no English) were compared. The total number of responses in categories ”not well” and ”not at all” in the self-reported assessment were the same as the total number in categories ”poor” and ”no English” in

56 the interviewer’s rating. Since the self-reported variable contained 120 missing cases and the level of non-proficiency was the same for the two variables, the interviewer’s rating on the English speaking ability of the interviewee was used to measure language skills.

Please see Attachment A for frequency distribution of this variable.

A dichotomous variable was created and recoded. Value 1 represented the presence of the predictor, that is, the subject was proficient in English and included values very well, well, and fair. Value 0 represented the absence of the predictor, that is, the subject was not proficient in English and included values poor and no English.

Results in Table 3.3 indicate that most college-educated immigrants are proficient in

English (78.4%). The group proficient in English is the reference group.

Job Offered Prior to Moving to U.S.

The literature review established that employment-sponsored immigrants often enter as expatriates who were offered a job position prior to moving to U.S. Table 3.3 indicates that this was the case of 15.3% of college-educated immigrants. A dichotomous variable was created and recoded. Value 1 represented the presence of the predictor, that is, the subject received a job offer prior to moving to the U.S. value 0 represented its absence, that is the subject did not receive a job offer. The group receiving a job offer is the reference group.

Working in Regulated or Unregulated occupations

Using the 2000 Census occupational codes, three new variables were created to study the effect of credentialing barriers in occupational mobility of foreign educated immigrants as follows: The three occupations studied in this dissertation (last occupation

57 abroad, first occupation in the U.S., and current occupation in U.S. at time of interview) were assessed to check if they were regulated, that is, if they required licenses or certificates in the U.S. (such as those in the health care field or engineering), or not (such as those in the information technology field or business management). Each regulated occupation was identified and labeled. Three dichotomous variables with values 0 and

1were included to detect the presence of licensing requirements. Value 1 represented the presence of the predictor, that is, the subject was working in an occupation requiring a license. Value 0 represented the absence of the predictor, that is, the subject was working in an occupation that did not required licenses. The group requiring licenses is used as the reference group.

Frequency distributions in Table 3.3 indicated that the majority of immigrants worked in occupations that were not regulated in the three times an occupation was reported. Nearly 33% of immigrants worked abroad in professions that are regulated in the U.S., but only 20% were able to work in a regulated occupation in their first U.S. jobs. A small improvement is shown in the results of current U.S. job at time of interview indicating that 22% were working in regulated occupations. See Appendix A for breakdown of frequency distributions of regulated occupations that immigrants held in country of origin and job positions in the U.S.

Continuous variables

The literature review identified the following determinants of occupational mobility: age at entry into U.S., years of foreign education, years of U.S. education, years of foreign work experience, years of U.S. work experience, and time of residence in the

U.S. Calculation and coding of variables was a very complex and lengthy process. The syntax of instructions performed in SPSS is presented as an attachment to this study in

Appendix B. Following is a table with descriptive statistics. Histograms of these variables are also offered in Appendix A, although normality is not required in the analytical method used in this study.

Table 3.4 Descriptive Statistics of Variables in Continuous Form

N Minimum Maximum Mean Std. Deviation

Age at entry in the U.S. 940 0 76 31.69 9.522

Years Foreign Education 985 0 34 16.23 2.833

Years worked at origin 955 0 51 7.64 7.304

Years US edu age <=23 77 1 14 4.62 2.867

Years US edu age>=23 150 1.00 11.00 2.8840 1.752

Years worked in U.S. 984 0 31 4.09 4.509

Time of Residence 940 0 37 5.58 5.704

Valid N (listwise) 909

Age at Entry

Age was created by subtracting the year reported as birth date from the year they reported moving into the U.S. for the first time.

Years of foreign work experience

The survey provides information on two possible pre-immigration work experiences since individuals started to work after their 16th birthday. A new variable was computed by adding the years they worked at each different job they reported working before they came to live to the U.S. Please see the attached calculation and coding instructions.

Years of work experience in the U.S.

The NIS data allowed calculating the amount of years that immigrants worked on their first job after coming to the U.S. and if they changed jobs, the amount of years they were working in their current position they held at the time of interview. Many immigrants reported that they started working in the same year they were interviewed, corresponding to a value 0 in this variable. Please see the attached calculation and coding instructions.

Years of U.S. schooling

This information was found in a variable in the survey with the same label. The decision was made to split this variable in two groups to study the occupational trajectories of those who completed U.S. education by age. The cut off used was age 23 since by this age most immigrants might have completed at least a bachelor degree. Two

60 new variables accounted for those who completed education at or before age 23 and those who completed education after 23.

Year of foreign education

The NIS survey included information on total years of education immigrants had completed as well as how many of these years were completed in the U.S. The difference between these two variables was calculated to create a new variable reflecting years of foreign schooling.

Time of residence in US

The calculation of total time of residence in the U.S. posed many challenges. The

NIS recorded the entry and exit of immigrants from the U.S. in 40 iterations. Immigrants were asked in what year they left their country of birth to live in another country for at least 60 days and they country to which they moved at that time. A review of the data evidenced that a large number of immigrants had migrated to another country prior to the coming to the U.S. or travel in and out of the country frequently. Following the study from Yap Co (2007) only the first year of move to US was recorded. If subject left the

U.S. and came back it was not taken into account. Time of residence was calculated by subtracting the year that subjects first moved to U.S. from the year the survey interview was completed (2003 or 2004). For detailed information, please refer to attached information on variable calculations and coding.

Multinomial Logistic Regression

This section provides a revision of the statistical method used to conduct the data analysis. The first section provides the general purpose and description of Multinomial

Logistic Regression. The second section discusses the data analyses of model comparison and the effect of individual significant predictors. The third section addresses limitations of this method. The section concludes with a general description of the statistical procedures followed to answer each research question.

General purpose and Description

MLR is a form of logistic analysis that allows one to predict a discrete outcome, such as group membership, from a set of variables in mixed form. This type of discrete choice model is suitable to handle the categorical dependent in this study, which has three alternative outcomes, and the mixed forms of predictors, which are continuous, categorical or dichotomous. The goal of the analysis is to correctly predict the category of outcome for individual cases. The dependent variables in this study have three alternative outcome categories: Upgrade in occupational status, downgrade, or match. Match is the reference category.

This study used directed (standard) MLR to assess first if the outcome, occupational mobility, can be predicted from the set of proposed human capital variables, and then to evaluate if prediction rates improve with the addition of three structural variables. In logistic regression jargon this is called model comparison. In direct MLR all the variables in a statistical model are entered simultaneously, and the set of variables is

62 evaluated as a whole to check on the goodness of fit of the model to predict the observed data. The effect of individual predictors within the model is also assessed.

Like other forms of regression, MLR generated B-weights (or slope) and a constant. The logistic coefficient B was used to calculate the logit or natural logarithm of odds for alternative categories of the dependent, rather than scores. The logistic coefficient B represents the expected amount of change in the logit for each one unit change in the predictor. The estimation of coefficients is done through maximum likelihood, which finds the best linear combination of predictors to maximize the likelihood of obtaining the observed outcome frequencies (Tabachnick & Fidel, 2007).

Maximum likelihood is an iterative process in which coefficients and residuals for the predicted model are tested until the coefficients change very little and convergence is reached.

Maximum likelihood estimation maximizes the log likelihood (probability) that observed independent variables can estimate the log of the dependent variable. “In effect, maximum likelihood estimates are those parameter estimates that maximize the probability of finding the sample data that actually have been found (Hox, 2002, as cited in Tabcknich and Fidell, 2007).”

The multinomial logistic equation predicts the log odds of a particular value of y, the dependent variable occupational mobility. The natural log of the odds of an outcome equals the natural log of the probability of the outcome occurring divided by the probability of the outcome not occurring:

Ln(odds(outcome)) = ln(prob(outcome)/prob(non-outcome))

Logistic analysis can be extended to multinomial dependents by modeling a series of binary comparisons: the lowest value of the dependent (in our case, 1- upgrade) is compared to a reference category which by default it will be the highest category (3- match), the next-lowest value (2-downgrade) compared to the reference category (3- match), and so on, creating k - 1 binary model equations for the k values of the multinomial dependent variable.

MLR analyses are used to predict group membership or outcome, to evaluate the direction of the effect (increase/decrease) and importance of predictors (magnitude of change), classification rates of models, model fit to correctly classify outcome and to compare fitness of different models.

Measures of Effect Size and Power in MLR

An effect size is a measure of the strength of the relationship between two variables in a statistical population. The power of a statistical test is the probability that the test will reject the null hypothesis when the null hypothesis is actually false (i.e. the probability of not committing a Type II error). Type II error is made when accepting a null hypothesis that is actually false. As the power increases, the chances of a Type II error occurring decreases.

In MLR we can assess effect size and power by looking at the Pseudo R-Square tables (Tabachnick & Fidell, 2007, p. 460). The Pseudo R-Square table displays three metrics which have been developed to provide a number familiar to those who have used traditional, standard multiple regression. They are treated as measures of effect size, similar to how R² is treated in standard multiple regression. Higher values indicate better

64 model fit. McFadden's Pseudo R-Square compares the variability in the outcome variable explained by the fitted model to that explained by the null or intercept model. It also explains improvement from null model to fitted model. Cox and Snell Pseudo R-Squared explains improvement from null model to fitted model. The ratio of the likelihoods reflects the improvement of the full model over the intercept model (the smaller the ratio, the greater the improvement). It has a maximum value that is less than one since the model which could predict the outcome perfectly has a likelihood of 1. Nagelkerke adjusts Cox and Snell's so that the range of possible values extends to 1. Another measure of effect size is the odds ratio. The closer the odds ratio is to 1, the smaller the effect size.

Data Analysis

Direct (standard) multinomial logistic regression (MLR) analyses were performed using SPSS NOMREG to assess prediction of membership in one of three categories of outcome (upgrade, downgrade or match) measuring occupational mobility (change in occupational status) from the last occupation held abroad to the first occupation held in the U.S. (first stage regression) and from the first occupation held in the U.S. to the current occupation held at time of interview (second stage regression).

The sample population consisted of males and females with college education in the 2003 NIS who reported information on pre and post-immigration employment, and who worked on occupations with a score in the NPB scale ranging from 1 to 99. After deleting 10 outliers corresponding to reporting errors, 875 males and 467 females were

65 available for analysis. After sample weights were applied, sample population size was reduced to 584 males and 402 females.

Separate first and second stage multinomial regression analysis of males and females were conducted since the literature had established that they have very different occupational trajectories. The significance of the Wald test for the variable gender confirmed that this decision was appropriate. A total of eight direct (standard) multinomial logistic regression (MLR) analyses were performed.

Following the conceptual model presented in chapter two, predictors in the human capital (HC) model for the first stage regression consisted of the demographic predictor region of origin and predictors of foreign human capital “age at Entry into US”, “English proficiency”, “years of foreign education”, and “years of working experience abroad”.

The human capital variable “years of education completed in US at age 23 or younger” was added to account for those who completed most of their post-secondary education in the U.S. To form the second model, which was denominated Full model, the structural variables “visa type”, “offered a job prior to migration”, and “working in a regulated occupation abroad” were added to the initial set of predictors.

In the second stage regression, the human capital model reflected the acquisition of U.S. specific human capital that occurs with increased time of residence in the country.

Hence, apart from “time of residence”, continuous variables “years of working experience in U.S.” and “years of education completed after age 23” were added to the

HC model.

The assessment of change in occupational status was done first on the basis of control (demographic) and human capital predictors, and then, after addition of three structural predictors. The two models were compared to evaluate if the model with structural predictors increases the prediction of occupational mobility of immigrants compare to the model that includes only human capital variables. Statistical analyses included assessment of each model. Model comparison included several tests. Finally, the importance of individual significant predictors in the best fitted model was evaluated.

Model Fit

Model log-likelhoods were calculated to assess goodness-of fit, that is, the fit of expected frequencies to observed frequencies. The Model Fitting Information table provides information of a constant- (intercept) only model that includes no predictors, the human capital model that includes the constant plus some predictors, or the full model that includes the constant plus all predictors tested in this study.

The presence of a relationship between the dependent variable and combination of independent variables is based on the statistical significance of the final model chi- square. To be clear, we want the p-value to be less than the established cutoff at α6= 0.05

(p<.05) to indicate good fit. If the model chi-square is statistically significant, then, we can reject the null hypothesis that there was no difference between the model (s) without independent variables and the model (s) with independent variables.

The Goodness-of-Fit tables containing Pearson and Deviance statistics provides additional information on model fit. Both the Pearson and Deviance statistics are also chi-

6 Type I error occurs when one rejects the null hypothesis when it is true. At 5% possibility of being wrong if we reject the null hypothesis. 67 square based methods and subject to inflation with large samples. Here, we interpret lack of significance as indicating good fit. To be clear, we want the p-value to be greater than the established cutoff at α= 0.05 (p<.05) to indicate good fit. We can further evaluate model fit, effect size and power by looking at the Pseudo R- Square tables. In general, the higher the value, the better the fit.

Model Comparison

Model comparison in MLR allow us to evaluate how all the variables, as a set, might improve model fit or ability to predict the outcome. In order to be compared, models must be nested, meaning that all the components of the smaller model must also be in the bigger model. The full (bigger) model is the one to which predictors have been added to the smaller model (Tabachnick & Fidell, 2007). In this study, the smaller model is the HC model, and the bigger model is the Full model with three additional structural predictors.

Model comparison is done using several tests and procedures including calculating the difference in -2 Log-likelihoods, the difference in chi-squares in the

Likelihood Ratio test, comparison of Pseudo R-Square statistics and correct classification rates.

The Model Fitting Information table provides the model -2 log-likelihood and

Likelihood Ratio Tests statistics (χ², df and p-value). The log likelihood of the model is the value that is maximized by the process that computes the maximum likelihood value for the Bi parameters. Model comparison is done by evaluating if the log-likelihood decrease/increase significantly with the addition/deletion of predictor(s).

Two models are compared by computing the difference in their log-likelihoods

(times -2= -2LL) and using chi-square. The -2 LL is the deviance and represents the unexplained variance in the outcome variable. Therefore, the smaller the value of -2 LL, the better the fit (Tabachnick & Fidell, 2007, p. 445). The calculation of the difference of log-likelihoods of both models uses the formula:

χ² = ((-2*(log-likelihood in smaller model) – (-2*log-likelihood in bigger model)).

The Likelihood Ratio chi-square test is alternative test of goodness-of-fit. In this test, the difference of models’ chi-square is calculated. Degrees of freedom for both tests above are the difference between degrees of freedom for the bigger and smaller models.

The resulting chi-squares are assessed according to the Table of Critical Values of Chi-

Square (χ²), p<.05. If it they are significant, we conclude that the Full model improves the predictability of occupational mobility over the HC model.

Additionally, the model with higher values of Pseudo R-Square and predictive success rate in the classification tables will be considered to have a better fit. As a general rule, the benchmark used by researchers to characterize a multinomial logistic regression model as useful is a 25% improvement over the rate of accuracy achievable by chance alone. If the bigger model has a larger percentage of prediction success, we will consider that it improved correct classification rates over the smaller model.

Interpretation of significant effects of individual predictors

MLR allows answering questions regarding if a particular predictor increases or decreases the probability of an outcome or if it has no effect on it. Relevant information

69 to assess the importance of predictors is found in the Likelihood Ratios Tests table, and the Parameter Estimates table.

The Likelihood Ratios Tests table provides statistics (-2LL, χ², df and p-value) to evaluate the statistical significance of the contribution of the individual predictors to the model. Hosmer and Lemeshow (2000) recommended a criterion for inclusion of a variable that is less stringent than α= .05, suggesting something in the range of α= .15 or

.20 to ensure entry of variables with coefficients different from zero. Following former studies of occupational mobility using the same sample data (Yap Co, 2007), this study uses a criterion for inclusion of a variable between α= .05 and .10 .

Parameter Estimates statistics allow us to assess the impact of predictor variables within the model, as part of a set. Statistics include regression coefficients and chi- squared test of them as well as odds ratios and the 95% confidence intervals around them.

The impact of predictor variables is explained in terms of odds ratios. The odds ratio is the change in odds (or likelihood) of being in one of the categories of outcome when the value of a predictor increases by one unit. The statistically significant predictors that change the odds of the outcome the most are interpreted as the most important. That is, the farther the odds ratio (Exp (B)) from 1, the more influential the predictor.

Tabachnick and Fidell, (2007) recommend the use of descriptive statistics to further interpret significant effects of categorical and continuous predictors in individual form, not as part of a set. For categorical predictors, group differences are observed in proportion of cases in each category of predictor for each category of outcome. For continuous predictors, interpretation is based on mean differences for significant

70 predictors for each category of outcome. This type descriptive statistics can be used to draw tables and line charts that provide accurate and clear graphic displays of the effect of predictors as determinants of occupational status gains and losses.

Limitations of MLR

Unlike OLS regression, logistic regression does not require multivariate normality, linear relationships among dependents and independents, homoelasticity, or equal variance-covariance matrices. However, MLR has some assumptions and limitations offered bellow that needed to be taken into consideration:

Ratio of cases to variables and missing data

If the data has too many variables relative to the few cases in outcome, logistic regression may produce extremely large parameter estimates and standard errors, and possibly failure to convergence when combination of discrete variables result in too many cells with no cases. Descriptive statistics showed acceptable ratio of cases to variables and missing data in the sample population and there were no convergence problems in the analyses.

Adequacy of Expected Frequencies and Power

The goodness-of-fit test of a model compares observed and expected frequencies in cells formed by combinations of discrete variables. This test will have little power if expected frequencies are too small. Cross-tabulation of discrete predictors with the dependent variables showed that the data met the criteria for adequacy of expected frequencies and power that all expected frequencies were greater than one, and no more than 20% of the cells have frequencies less than five (Tabachnick and Fidell, 2007, p.

442). Therefore, there was no restriction on the goodness-of-fit criteria used to evaluate the model.

Multicollinearity

Multicollinearity is a statistical phenomenon in which two or more continuous predictor variables in a multivariate regression model are highly correlated. In this situation the coefficient estimates (B) may change erratically in response to small changes in the model or the data. Multicollinearity does not reduce the predictive power or reliability of the model as a whole but it affects calculations regarding individual predictors. A standard error larger than 2.0 may indicate multicollinearity among the independent variables (Tabachnick & Fidell, 2007, p. 443). Standard errors for the b coefficients were examined. None of the coefficient estimates of predictors in the MLR analyses in this study had a standard error larger than 2.0., ruling out mulitcolinearity among continuous predictors.

Linearity in the Logit

In logistic regression, there should be a linear relationship between continuous predictors and the logit transform of the dependent variable. Violation of this assumption can be found by testing the significance of interactions between each continuous predictor and its natural log. If a predictor offenders this assumption, it should be transformed. In the first stage regression, four interactions between continuous variables and their natural logarithms were formed as LIN_Variable=Variable*LN(Variable). In the second stage regression, seven interactions were tested. Likelihood Ratio Test of continuous variables in the first and second stage MLR and their natural logs were

72 conducted (Tabachnick & Fidell, 2007, p. 474). These tests did not reveal any violations of the assumption of linearity in the logit.

Absence of Outliers in the Solution

Interpretation and analysis of residuals was conducted using binomial logistic regression to assist in the evaluation of the fit of the model to each case. Analysis of residuals followed Schwab (2005). No violations of this assumption were detected.

Independence of Errors

In logistic regression, responses of different cases should be independent of each other. Each response should come from a different, unrelated case. The effect of non- independence is over-dispersion, that is, the variability in cell frequencies is greater than expected in the underlying model. Over-dispersion is indicated by large discrepancies between the Pearson and the deviance test statistics (Tabachnick and Fidell, 2007, pp.

444-445). No violations of this assumption were detected in the data analysis.

Statistical Procedures to Answer Research Questions

Research Question #1

The guiding research question in this dissertation is: Do country/context specific conditions increase the explanatory power of Chiswick’s model of occupational mobility of immigrants?

In order to answer this question, a total of eight models for males and females respectively were formed following the conceptualized model in this dissertation. In the first stage regression, a smaller model containing demographic and foreign human capital

73 predictors was created to represent Chiswick’s model and was denominated Human

Capital (HC) model; a bigger model was formed by adding the three structural predictors to the smaller model, and was denominated Full model. In the second state regression, the smaller model was formed by adding three predictors of U.S. specific human capital to the initial HC model. The bigger (Full) model was once again formed by adding the three structural predictors to the latest HC model.

Model fit statistics were assessed and model comparisons were performed in the first and second stage regressions to assess if the Full model improve prediction rates of the outcome, change in occupational status, over the HC model. Several procedures were used to evaluate model goodness of fit by comparing observed with expected frequencies to find differences among the models: Differences of model -2LL (log likelihoods) and likelihood ratio tests were performed. Differences in Pseudo R-square and classification rates were assessed.

Research Question #2

What are the determinants of vertical occupational mobility of immigrants’ at time of arrival in the country? Do these determinants change with extended time of residence in the U.S.?

The effects of significant individual predictors were evaluated. Information found in the first stage regression was used to assess initial occupational attainment.

Information found in the second stage regression was used to assess long term occupational trajectory. Procedures include evaluation of parameter estimates of significant individual predictors within the model, as part of a set. Additionally,

74 descriptive statistics were used to create tables and figures displaying group differences in categorical predictors and mean differences of continuous predictors. Statistic results were interpreted to identify among predictors, the determinants related to gaining or losing status from the country of origin to the first U.S. job and from the first U.S. job to the current position at time of interview.

CHAPTER 4

RESULTS

This chapter presents the results of the study. The first section presents descriptive statistics of immigrants’ initial and long term occupational trajectory. The rest of the chapter is organized by research questions. The second section reports the results of the

Multinomial Logistic Regression used to answer the first question, including individual model fit, model comparison and interpretation of significant predictors of the best fitted model. The final section interprets individual parameter estimates and descriptive statistics to find determinants of vertical occupational mobility.

Descriptive Statistics

Descriptive statistics of immigrant’s occupational trajectory provide a sense of the magnitude of immigrant’s gains and losses in status. Figure 4.1 provides a visual image of the average Nam Power Boyd occupational status score college-educated immigrants attained in the last job held in the country of origin, the first job attained at arrival, and the job they held at the time they were interviewed in the survey. It displays a U-shaped curve of occupational status of immigrants in their country of origin and the U.S. by gender, providing evidence of differences in occupational trajectories for males and

76 females suggesting that separate regressions for male and females were necessary. The initial and long term occupational trajectory of college-educated immigrants can further be appreciated in Table 4.1 which displays the proportion of cases by alternative of

outcome (upgrading, downgrading and

match) reflecting first the occupational

mobility between their country of origin

and their first job in the U.S. (first

stage), and then, from their first job in

the U.S. to the job they held at the time

of interview (second stage).

Figure 4.1 Occupational status of immigrants at origin and the U.S. by gender

Table 4.1 Initial and Long Term Occupational Trajectory of in origin and in the U.S. Warning: SPSS rounded up weighted frequencies Occupational Trajectories Between Occupational Trajectories Between Country of Origin and First US Job First US Job and Current US Job Upgrading Downgrading Match Upgrading Downgrading Match (Recovery) (Regression) (Similar) (Recovery) (Regression) (Similar) Total 21.3% 57.6% 21.1% 31.1% 19.8% 49.1%

Males 22.6% 53.8% 23.6% 33.0% 19.2% 47.8%

Females 19.5% 63.1% 17.4% 28.2% 20.7% 51.1%

Results in table 4.1 indicate that near 60% if immigrants downgraded in occupational status, losing 18 points in NPB score at arrival in the U.S. A small percentage of them were able to find similar positions that they held in their countries or improve their occupational status, 20% respectively. The breakdown by gender indicates that 54% of males lost 16 points in status score between the occupations they held in their countries (with avg. score= 72) to the first job they obtained once they arrived in the U.S.

(avg. score= 56). The impact of migration on women’s occupational attainment was even more negative. Results show that 63% of them lost an average of 21 points in status at arrival, from an average score of 69 down to 49. Results of average scores reported in the current U.S. job, indicate that both male and female college-educated immigrants experienced some gains in status after some time residing in the U.S., although the average gain of 6 points was not enough to recover the status initially lost.

The attached Nam Power Boyd scale of occupational status contains census occupational codes and titles corresponding to average scores above. Among occupations with scores between 63 and 73 are managerial, financial specialists, business, teaching, engineering and some health care practitioners, among others. Occupations with scores between 46 and 56 are office and administrative support staff, healthcare technicians and teaching assistants, among others (Nam Power Boyd, 2004).

Results for Question #1

The first question assessed if the addition of country/context specific conditions increased the explanatory power of Chiswick’s model of occupational mobility. Model

78 comparisons in the first and second stage Multinomial Logistic Regressions were conducted to answer this question. The results of the first stage regression reflect the occupational mobility of immigrants from the job at country of origin to the first US job., and the second stage regression reflect the occupational mobility from the first US job to the current US job. Separate regressions were conducted by gender.

A total of four models for males and four models for females were formed following the conceptualized model discussed in chapter two of this dissertation. At each stage, a smaller model containing demographic and foreign human capital predictors was created to represent Chiswick’s model and was denominated Human Capital (HC) model; a bigger model was formed by adding the three structural predictors to the smaller model, and was denominated Full model. In the second state regression, the smaller model was formed by adding three predictors of U.S. specific human capital to the initial HC model.

The bigger (Full) model was once again formed by adding the three structural predictors to the latest HC model.

Model Fit

Tables 4.2 and 4.3 provide model fitting information to assess the fit of each model. For all eight models, p-values shown in these tables is less than the established cutoff at α= 0.05 (p<.05), indicating a good fit of expected frequencies to observed frequencies. The null hypothesis that there was no difference between the model (s) without independent variables and the model (s) with independent variables was rejected in all cases.

Table 4.2 Model Fitting Information First Stage Regression Model Model Fitting Likelihood Ratio Fitting Likelihood Ratio Criteria Tests Criteria Tests Males -2 LL χ² Df Sig. Females -2 LL χ² df Sig.

HC Model HC Model

Intercept 1.054E3 Intercept 685.339

Final 914.910 139.53 20 .000 Final 625.746 59.593 20 .000

Full Model Full Model

Intercept 950.322 Intercept 652.979

Final 767.750 182.573 30 .000 Final 521.581 131.398 30 .000

Table 4.3 Model Fitting Information Second Stage Regression Model Model Fitting Likelihood Ratio Fitting Likelihood Ratio Criteria Tests Criteria Tests Males -2 LL χ² Df Sig. Females -2 LL χ² df Sig.

HC Model HC Model

Intercept 1.095E3 Intercept 768.207

Final 981.602 113.457 26 .000 Final 690.799 77.409 26 .000

Full Model Full Model

Intercept 967.100 Intercept 722.894

Final 848.336 118.765 36 .000 Final 616.217 106.676 36 .000

Tables 4.4 and 4.5 display Pearson and Deviance statistics of goodness of fit.

Results indicate that they were not significant, with p-values greater than the established cutoff α= 0.05 (p<.05) providing further evidence of the good fit for all models as well.

Table 4.4 displays statistics for the first regressions of male and females. Statistics for the

HC model in males are Chi-Square (df 1410)= 891.445, p=1.00 and in females Chi-

Square (df 806, N= 584)= 618.264, p=1.00, using the deviance criterion of goodness-of- fit. There was a good model fit for both genders on the basis of the human capital predictors alone. After addition of the three structural predictors, statistics for the full model in males are Chi-Square (df 1410, N= 584)= 760.033, p=1.00, and in females Chi-

Square (df 776, N= 584)= 518.202, p=1.00.

Table 4.4 Model Goddness-of-Fit First Stage Regression Males χ² Df Sig. Females χ² df Sig.

HC Model HC Model

Pearson 1093.318 1472 1.000 Pearson 740.849 806 .951

Deviance 891.445 1472 1.000 Deviance 618.264 806 1.000

Full Model Full Model

Pearson 912.678 1410 1.000 Pearson 694.176 776 .984

Deviance 760.033 1410 1.000 Deviance 518.202 776 1.000

Table 4.5 Model Goodness of Fit Second Stage Regression Males χ² Df Sig. Females χ² df Sig.

HC Model HC Model

Pearson 1070.816 1548 1.000 Pearson 785.739 832 .873

Deviance 975.658 1548 1.000 Deviance 690.174 832 1.000

Full Model Full Model

Pearson 1010.841 1434 1.000 Pearson 740.109 780 .844

Deviance 846.842 1434 1.000 Deviance 615.593 780 1.000

Model Comparison

Differences of model -2LL (log likelihoods) and likelihood ratio tests were performed to compare models. Deviance values (-2 Log-likelihoods) were obtained from the Model Fitting Information tables 4.2 and 4.3 above.

Results for the first stage regression: a) Differences of model -2LL (log likelihoods)

χ²males (df10) = 914.910-767.750 = 146.25

χ²females (df10) = 652.979-625.746= 27.233 b) Likelihood ratio tests

χ²males (df10) = 182.573 - 139.530 = 43.043

χ²females (df10) = 131.398- 59.593= 71.805

According to the Table of Critical Values of Chi-Square (χ²), male and female results for the test of differences of model -2LL (log likelihoods) and likelihood ratio tests are significant at α=.05, which further indicates that the full model with structural predictor variables predicts significantly better, or more accurately, than the human capital model for both males and females. We conclude that there was a statistically significant improvement in the first stage model with the addition of structural predictors.

Results for the second stage regression: a) Differences of model -2LL (log likelihoods)

χ²males (df10) = 981.602-848.336= 133.266

χ²females (df10) = 690.799-616.217= 74.582 b) Likelihood ratio tests

χ²males (df10) = 118.765- 113.457 = 5.308

χ²females (df10) = 106.676- 77.409= 29.267

Critical Values of Chi-Square (χ²) at α=.05 indicate the results of the likelihood ratio test are only significant for females. The full model with structural predictors improved the prediction over the HC model in both cases, but this improvement was statistically significant only for females in the second stage regression.

Differences in Pseudo R-square and classification rates were also assessed to compare model goodness of fit. The three measures of Pseudo R-Square in table 4.6 below are higher for the full model both for male and females in the first and second stage regressions, indicating that the model with structural predictors improves upon the prediction of the HC model. Pseudo R- Square statistics were used as well to evaluate

83 effect size and power. Cox and Snell, Nagelkerke and McFadden values indicated acceptable levels of effect size and power.

Table 4.6 Pseudo R-Square Values First Stage Regression Males Pseudo R-Square Females Pseudo R-Square

HC Model HC Model

Cox and Snell .231 Cox and Snell .147

Nagelkerke .267 Nagelkerke .174

McFadden .130 McFadden .086

Full Model Full Model

Cox and Snell .320 Cox and Snell .309

Nagelkerke .369 Nagelkerke .367 Pseudo R-Square Values Second Stage Regression MalesMcFadn7 Pseudo.191 R-Square FemalesMcFadden Pseudo R-.200Square

HC Model HC Model

Cox and Snell .192 Cox and Snell .186

Nagelkerke .220 Nagelkerke .214

McFadden .103 McFadden .101

Full Model Full Model

Cox and Snell .222 Cox and Snell .259

Nagelkerke .255 Nagelkerke .298

McFadden .123 McFadden .148

Classification table 4.7 shows how well each model correctly classifies cases in the first stage regression. Classification results indicate that on the basis of human capital variables alone, correct classification rates for this model was 62.8.3% for males and

62.0% for females. Overall success rate improved to 66% for males and 68.0% for females with the addition of three structural predictor variables in the Full model. Results above indicate that the models classify better the cases of the largest group - those who lost status (Tabachnick and Fidell, 2007, p. 497).

Table 4.7 Classification First Stage Regression Males Females Predicted Predicted HC Model Percent Correct HC Model Percent Correct

Upgraded 21.2% Upgraded 9.2%

Downgraded 86.6% Downgraded 96.2%

Match 47.6% Match .0%

Overall Percentage 62.8% Overall Percentage 62.0%

Full Model Full Model

Upgraded 30.1% Upgraded 40.3%

Downgraded 84.6% Downgraded 85.8%

Match 55.8% Match 36.4%

Overall Percentage 66.0% Overall Percentage 68.0%

Classification results for the second stage regression in table 4.8 indicate that correct classification rates for the HC model was 59.3% for males and 57.3% for females.

Overall success rate improved to 61.3% for males and 64.2% for females in the Full model. Results above indicate that the models classify better cases in the largest group, corresponding to those who found positions with similar status (Tabachnick and Fidell,

2007, p. 497).

Table 4.8 Classification Second Stage Regression Males Females Predicted Predicted HC Model Percent Correct HC Model Percent Correct

Upgraded 51.6% Upgraded 37.3%

Downgraded 7.0% Downgraded 1.8%

Match 85.4% Match 90.6%

Overall Percentage 59.3% Overall Percentage 57.3%

Full Model Full Model

Upgraded 52.4% Upgraded 49.1%

Downgraded 4.7% Downgraded 16.3%

Match 87.9% Match 91.1%

Overall Percentage 61.3% Overall Percentage 64.2%

Table 4.9 Log Likelihoods in First Stage Regression - Contribution of Individual Predictors to the Full Model Males χ² Df Females χ² Df

Control and HC var. Control and HC var.

Age at entry .065 2 Age at entry .105** 2

Work experience abroad 6.469* 2 Work experience abroad 1.156 2

Foreign education 1.753 2 Foreign education .317 2

US education <=23 16.870* 2 US education <=23 .667 2

Region of Origin 30.354* 10 Region of Origin 25.919* 10

English Proficiency 1.740 2 English Proficiency 2.021 2

Structural Variables Structural Variables

Visa type 25.606* 6 Visa type 17.752* 6

Job offer prior migration 6.712* 2 Job offer prior migration 5.857* 2

Licensed occ. abroad 7.192* 2 Licensed occ. abroad 29.962* 2

*p <0.05, **p<0.10

Table 4.9 displays the log likelihoods (chi-square statistics) of individual variables in the first stage regression indicating the contribution of individual predictors to the best fitted Full model. For males, years of working experience abroad, years of US education at age 23 or before, region of origin, visa type, offered a job prior to migration, and working in a regulated occupation abroad, are statistically significant at α= .05. The

87 results for females varied from males and show age at entry, region of origin and the three structural predictors as significant at α= .05 or α= .10.

Table 4.10 Log Likelihoods in Second Stage Regression - Contribution of Individual Predictors to the Full Model Males χ² df Females χ² df

Control and HC var. Control and HC var.

Age at entry .710 2 Age at entry 8.786* 2

Work experience abroad 2.893 2 Work experience abroad 2.856 2

Foreign education 2.543 2 Foreign education .601 2

Work experience US 34.364** 2 Work experience US 32.735* 2

US education <=23 5.117* 2 US education <=23 5.423** 2

US education >=23 2.505 2 US education >=23 .065 2

Time of residence 9.241** 2 Time of residence 10.678* 2

Region of origin 34.678** 10 Region of origin 16.022** 10

English proficiency .798 2 English proficiency 2.009 2

Structural Variables Structural Variables

Visa type 3.147 6 Visa type 11.34** 6

Job offer prior migration 4.254 2 Job offer prior migration 1.790 2

Licensed occ. 1st US job 6.465* 2 Licensed occ. 1st US job 12.313* 2

*p <0.05, **p<0.1

Table 4.10 indicates that in the second stage regression that for males, years of working experience in the U.S., years of US education at age 23 or before, time of residence and region of origin and working in licensed occupation in the first US job are significant at α= .05 or α= .10. Females show once again different results than males.

Age at entry, years of working experience in the U.S., years of US education at age 23 or before, time of residence and region of origin and two structural predictors, visa type and working in a licensed occupation in the first US job, are significant at α= .05 or α= .10.

SPSS tables 4.11 and 4.12 of parameter estimates show regression coefficients of significant predictor, odds ratios (Exp(B)), and 95% confidence interval around them by alternative outcome. Match is the reference category, therefore, MLR displayed only results for the alternative outcomes for occupational upgrade (or gain in status) and downgrade (loss in status). The presence of a predictor (value 1) is reference category for dichotomous variables.

Results from table 4.11 indicates that those who worked abroad in an occupation not requiring a license in the U.S. and those who attended US school at or before age 23 were more likely to gain status than finding a similar occupation; with odds ratios of 2.6 and 1.8 respectively. The European group (Europe, Canada and Oceania) was more likely to obtain similar occupations than gain status (odds ratio 1- .03 = 0.7) in relation to

Africans and Middle Easterners, the reference category. The fact that those who did not work in a licensed occupation abroad is also a significant predictor of the alternative outcome downgrade (odds ratio = 1.2) indicates that this group has difficulties finding similar occupations, and either gain or lose status.

Table 4.11 Parameter Estimates of Significant Predictors in First Stage Regression Males – Dependent1a 95% Confidence Interval Exp(B)

Upgraded B Exp(B) Lower Bound Lower Bound

Intercept .639

Age at entry .005 1.006 .961 1.052

Work exp. Abroad .033 1.033 .973 1.097

Foreign education -.087 .917 .806 1.044

US education <=23 .584 1.792 1.227 2.619

Asia .341 1.406 .501 3.947

India -.833 .435 .149 1.271

Europe -1.132* .322 .113 .919

East Europe .379 1.460 .335 6.365

Latin America & Caribe .120 1.127 .194 6.560

Africa & Middle East 0b . . .

[English Proficient=0] -.104 .901 .330 2.457

[English Proficient=1] 0b . . .

Spouses of US citizens -.291 .748 .289 1.936

Employment visa -.583 .558 .233 1.339

Diversity -.589 .555 .122 2.528

Other type of visa 0b . . .

[Job offer prior migration=0] .044 1.045 .499 2.190

[Job offer prior migration=1] 0b . . .

(Continued)

Table 4.11 (Continued) Males – Dependent1a 95% Confidence Interval Exp(B)

Upgraded B Exp(B) Lower Bound Lower Bound

[licensed occ. abroad=0] .960* 2.611 1.212 5.626

[licensed occ. abroad =1] 0b . . .

Downgraded

Intercept 1.011

Age at entry .002 1.002 .962 1.043

Work exp. Abroad .064* 1.066 1.012 1.123

Foreign education -.035 .966 .866 1.077

US education <=23 .276 1.317 .894 1.941

Asia -.317 .729 .302 1.759

India -1.074* .342 .137 .850

Europe -1.255* .285 .123 .659

East Europe .169 1.185 .336 4.180

Latin America & Caribe .872 2.392 .569 10.057

Africa & Middle East 0b . . .

[English Proficient=0] .360 1.433 .622 3.301

[English Proficient=1] 0b . . .

Spouses of US citizens -.445 .641 .293 1.404

Employment visa -1.457* .233 .109 .498

Diversity .456 1.578 .528 4.718

(Continued)

Table 4.11 (Continued) Males – Dependent1a 95% Confidence Interval Exp(B)

Downgraded Exp(B) Lower Bound Lower Bound

Other type of visa 0b . . .

[Job offer prior migration=0] .854* 2.349 1.158 4.765

[Job offer prior migration=1] 0b . . .

[licensed occ. abroad=0] .189* 1.208 .665 2.192

[licensed occ. abroad =1] 0b . . .

*p<0.05 a. The reference category is: Match b. This parameter is set to zero. [Value= 1 ] is reference category

Positive effects that increased the likelihood of losing status were found also for found for those who were not offered a job prior to migration (odds ratio= 2.35), indicating that they were more prompt to downgrade that those who were offered a job.

Years of work experience abroad is also significant with a positive coefficient and odds ratio of 1.07.Negative effects that decreased the likelihood of downgrading were found for Europeans (odds ratio -71.5%), and Indians (odds ratio -65%), indicating that these groups tended to obtain similar occupations and were less likely to downgrade than

Africans and Middle Easterners, the reference category.

Interpretation of parameter estimates in MLR can be confusing. Further understanding of individual effects can be obtained by observing group differences in discrete variables and by observing mean differences in continuous variables by each alternative outcome in the initial and long terms occupational trajectory.

Table 4.12 Group Differences in discrete variables for males – First stage regression Warning: SPSS rounded up weighted frequencies From Country of Origin to First US Job Upgraded Downgraded Match Asia 31.1% 50.8% 18.2%

India 18.8% 37.6% 43.6%

Europe, Canada and Oceania 29.8% 37.4% 32.8%

East Europe 19.0% 65.5% 15.5%

Latin America & the Caribe 7.5% 86.8% 5.7%

Africa & Middle East 16.0% 70.8% 13.2%

Total by Origin 22.5% 53.9% 23.6%

Proficient in English 24.5% 48.5% 27.1%

Not Proficient in English 15.6% 73.8% 10.7%

Total by English Proficiency 22.6% 53.8% 23.6%

Spouse of US Citizen 27.9% 53.3% 18.8%

Employment Principal 28.0% 27.4% 44.6%

Diversity 7.1% 80.4% 12.5%

Other 17.5% 69.6% 12.9%

Total by Visa Type 22.5% 53.9% 23.7%

Job Offered pre- migration 26.4% 25.3% 48.4%

Not Job Offered 20.4% 60.2% 19.4%

Total by Job Offered 21.4% 54.1% 24.5%

(Continued)

Table 4.12 (Continued) From Country of Origin to First US Job Upgraded Downgraded Match Licensed occupation at origin 11.6% 62.6% 25.9%

Not Licensed occ. abroad 26.3% 50.8% 22.9%

Total Licensed occ. at origin 22.6% 53.8% 23.6%

Cross tabulations tables of discrete predictors with the outcome of the first stage regression show that males from Latin America, Africa and East Europe downgrade in larger proportions (86.8%, 70.8% and 65.5%, respectively) than males from India,

Europe and Asia (37%, 37% and 50% respectively). Great disparities in occupational mobility also exist according to the visa type immigrants entered the country with. Those sponsored by employers are more prompt to find similar occupation (44.6%) or gain status (28.0%) in greater proportions, followed by spouses of US citizens (18.8% and

27.9% respectively). The worst occupational results are shown for immigrants entering through diversity visa and other visa types (84.4% and 69.6% downgraded). A small proportion in these groups upgraded or found a match. Those who were offered a job prior to moving to the US had greater chances of finding similar occupations (48.4%), while those who were not offered a position were more prompt to lose status (60%).

Immigrants working in regulated occupations abroad were more likely to find similar occupations (25.9%) or lose status (62.6%) compared to those working in non regulated occupations (22.9% and 50.8%, respectively), while those working in non-regulated occupations had greater chances of gaining status (26.3%) than those working in licensed

94 occupations (11.6%). This explains why we find the variable “working in non-regulated occupations having a positive coefficient in both alternatives “upgrade” and “downgrade” in table 12 of parameter estimates.

Mean difference of continuous variables indicate that on average, male immigrants who improved their labor market outcomes and gained status at migration were younger (mean=30), and had more years of US education completed before age 23

(mean=1.17). On average, those who lost status tended to be older (mean=33.54), and have more years of work experience abroad (mean=8.80). Average years of foreign education were very similar for the three alternatives of outcome, indicating that it does not affect labor market outcomes once in the U.S.

Table 4.13 Mean of Continuous Variables by Outcome for Males – First Stage from Country of Origin to First US Job Upgrade Downgrade Match

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Age at entry 29.64 9.116 33.54 10.193 30.96 8.441

Work exp. Abroad 7.00 6.830 8.80 7.397 6.59 7.288

Foreign Education 15.61 2.890 16.28 2.460 16.62 3.130

US education <=23 1.17 2.489 .13 .753 .13 .644

Results for the second stage regression in males are shown in table 4.14. Positive effects increasing the likelihood of recovering status versus obtaining a similar

95 occupation are shown for those who were not offered a job prior to migration (odds ratio=2.175) those who worked in occupations not requiring a license in the first US job

(odds ratio 2.5), working experience in the U.S. (odds ratio 1.34) and U.S. education completed at or before age 23 (odds ratio 1.27). Negative effects that decrease the likelihood of upgrading versus obtaining a match by region of origin India (odds ratio

.432), Asia (odds ratio .513), and time of residence (odds ratio .901). Note that none of the parameters for alternative downgrade for males in the second stage regression are significant.

Table 4.14 Parameter Estimates of Significant Predictors in Second Stage Regression Males – Dependent2a 95% Confidence Interval Exp(B)

Upgraded B Exp(B) Lower Bound Lower Bound

Intercept -1.755

Age at entry .000 1.000 .961 1.040

Work exp. Abroad .035 1.035 .990 1.083

Foreign education -.051 .950 .856 1.055

Work experience in US .295* 1.344 1.173 1.539

US education <=23 .241* 1.344 1.173 1.539

US education >23 .142 1.272 1.011 1.602

Time of Residence -.104** 1.153 .940 1.415

Asia -.667** .901 .800 1.015

(Continued)

Table 4.14 (Continued) Males – Dependent2a 95% Confidence Interval Exp(B)

Upgraded B Exp(B) Lower Bound Lower Bound

India -.840** .513 .235 1.123

Europe -.540 .432 .186 1.002

East Europe .071 .583 .272 1.248

Latin America & Caribe .796 1.074 .413 2.795

Africa & Middle East 0b 2.217 .839 5.859

[English Proficient=0] -.325 . . .

[English Proficient=1] 0b .723 .365 1.429

Spouses of US citizens -.208 . . .

Employment visa -.083 .812 .409 1.611

Diversity -.072 .921 .442 1.918

Other type of visa 0b .931 .383 2.262

[Job offer prior migration=0] .777** 2.175 .998 4.739

[Job offer prior migration=1] 0b . . .

[licensed occ. abroad=0] .898* 2.454 1.151 5.231

[licensed occ. abroad =1] 0b . . .

Downgraded

Intercept -1.209

Age at entry .000 1.000 .961 1.042

(Continued)

Table 4.14 (Continued) Males – Dependent2a 95% Confidence Interval Exp(B)

Downgraded B Exp(B) Lower Bound Lower Bound

Work exp. Abroad .002 1.002 .954 1.052

Foreign education -.074 .928 .826 1.043

Work experience in US .040 1.041 .935 1.159

US education <=23 .008 1.009 .784 1.297

US education >23 -.038 .962 .754 1.229

Time of Residence .043 1.044 .966 1.129

Asia .556 1.743 .738 4.121

India .296 1.344 .520 3.473

Europe -.107 .898 .368 2.195

East Europe .264 1.302 .394 4.302

Latin America & Caribe .420 1.522 .409 5.670

Africa & Middle East 0b . . .

[English Proficient=0] -.231 .793 .370 1.704

[English Proficient=1] 0b . . .

Spouses of US citizens .485 1.623 .739 3.565

Employment visa .388 1.474 .648 3.352

Diversity .577 1.780 .656 4.834

Other type of visa 0b . . .

(Continued)

Table 4.14 (Continued) Males – Dependent2a 95% Confidence Interval Exp(B)

Downgraded B Exp(B) Lower Bound Lower Bound [Job offer prior migration=1] 0b . . .

[licensed occ. abroad=0] .574 1.776 .810 3.891

[licensed occ. abroad =1] 0b . . .

*p<0.05; **p<0.10 a. The reference category is: Match b. This parameter is set to zero. [Value= 1 ] is reference category

Table 4.15 show that a whopping 64.2% of immigrants from Latin America are able to recover status in the second stage, followed by Africans (39.6%) and East

Europeans (36.2%). Indians (23.3%) and Asians (22.7%) downgraded this time in the highest proportions, and by contrast, Latin Americans downgraded in the lowest proportion (11.3%). Asian (53%), Indians (52.4%) and Europeans (50.4%) found similar occupations in greater proportions. More than half of immigrants who were not proficient in English were able to find similar occupations and about a third was able to upgrade.

The position of male immigrants who entered the country with an employment visa deteriorated in the largest proportion (23.7%) this time and spouses of U.S. citizens recovered status in largest proportions (35.2%). Almost 23% of those holding diversity were able to upgrade. Those who were offered a job prior to migrating found similar positions in greater proportions (60.4%), although some of them lost status (22.0%) and a few were able to upgrade (17.6%). Those who did not have a job offer were able to improve their occupational trajectory, 35.4% of them were able to upgrade and 47.1%

99 were able to find similar occupations. Most immigrants who worked abroad in occupations that required a license in the US found similar occupations this time (51.4%), a third of them upgraded and the rest lost status. Results for those working in non- regulated occupations are very similar (46.7%, 34.1% and 19.2% respectively). A larger number of immigrants working in occupations regulated in the U.S. in their first job after migrating found occupational matches (57.6%), the rest upgraded or downgraded in similar proportions (22.4% and 20.0%). Those who worked in non-regulated occupations upgraded in the largest proportion (34.9%), while 46.1% of them found similar positions and 19% lost status.

Table 4.15 Occupational mobility of males in the U.S. – Group differences Warning: SPSS rounded up weighted frequencies From First US Job and Current US Job Upgraded Downgraded Match Asia 24.2% 22.7% 53.0%

India 24.3% 23.3% 52.4%

Europe, Canada and Oceania 29.8% 19.8% 50.4%

East Europe 36.2% 19.0% 44.8%

Latin America & the Caribe 64.2% 11.3% 24.5%

Africa & Middle East 39.6% 14.2% 46.2%

Total by Origin 33.1% 19.2% 47.7%

(Continued)

100

Table 4.15 (Continued) From First US Job and Current US Job

Upgraded Downgraded Match

Proficient in English 34.0% 19.7% 46.3%

Not Proficient in English 29.5% 17.2% 53.3%

Total by English Proficiency 33.0% 19.2% 47.8%

Spouse of US Citizen 35.2% 20.6% 44.2%

Employment Principal 26.6% 23.7% 49.7%

Diversity 23.2% 17.9% 58.9%

Other 40.0% 14.4% 45.6%

Total by Visa Type 22.5% 53.9% 23.7%

Job Offered pre- migration 17.6% 22.0% 60.4%

Not Job Offered 35.4% 17.6% 47.1%

Total by Job Offered 32.2% 18.3% 49.4%

Licensed occupation at origin 29.7% 18.9% 51.4%

Not Licensed occ. abroad 34.1% 19.2% 46.7%

Total Licensed occ. at origin 33.0% 19.1% 47.9%

Mean group differences for significant continuous predictors indicate that on average, males who recovered status had more years of working experience in the US

(mean= 6.74) compared to those who downgraded (mean= 4.33) and those who obtained

101 similar occupations (mean= 3.33). Average time of residence was also largest for the group who upgraded (mean= 7.78) compared to those who lost status (6.83) or found an occupational match (4.80). Average years of education in the U.S. (both completed before age 23 or beyond) was also largest for those who recovered status, although in all groups the mean was lower than one.

Table 4.16 Mean of Continuous Variables by Outcome for Males – Second Stage Regression from First US Job to Current US Job Upgrade Downgrade Match

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Age at entry 31.72 9.178 30.42 10.377 32.95 9.707

Work exp. Abroad 7.93 6.890 7.33 7.492 8.04 7.523

Work exp. US 6.74 5.758 4.33 4.260 3.33 3.559

Foreign Education 15.90 2.716 15.90 3.051 16.55 2.611

US education <= 23 .58 1.833 .42 1.421 .20 .983

US education > 23 .74 1.703 .37 1.130 .34 1.093

Time of Residence 7.78 6.197 6.83 7.317 4.80 4.879

Following are SPSS results for parameter estimates for females providing information on the contribution of individual predictors to influence occupational mobility in their country of origin and the U.S.

102

Results in table 4.17 indicate that females who did not work in a regulated occupation in their country of origin were almost 10 times more likely to experience gains in status than to find a similar occupation. Except for Asian who have a negative coefficient and odds ratio lower close to 1, the rest of groups by region of origin have positive coefficients with odds ranging from 2.4 to 4.2, although none of the origin variables are significant. Odds ratios show that East European women are 4.2 times more likely to upgrade than to find a match in relation to African and Middle Eastern women, the reference category. Working in a non-regulated occupation abroad also shows a positive effect to increase the likelihood of downgrading versus obtaining a match with a smaller odd ratio of 1.8. As in the case of males, the fact that this variable affects both alternative outcomes upgrade and downgrade in females indicates that those working in an occupation abroad not requiring a license in the U.S. have less chances to obtain similar occupations in the U.S. and either gain or lose status. This can be appreciated in table 4.19. Latin American women are also 4.3 times more prompt to downgrade than to obtain a match. The only negative effect that decreases the likelihood of downgrading versus obtaining a match is shown by employment visa type. Those who were sponsored by an employer are almost 3 times less likely to downgrade than to find a match.

103

Table 4.17 Parameter Estimates of Significant Predictors in First Stage Regression Females – Dependent1a 95% Confidence Interval Exp(B)

Upgraded B Exp(B) Lower Bound Lower Bound

Intercept -.711

Age at entry -.003 .997 .935 1.063

Work exp. Abroad -.070 .932 .851 1.020

Foreign education -.004 .996 .836 1.186

US education <=23 .062 1.064 .803 1.410

Asia -.102 .903 .170 4.783

India 1.036 2.819 .415 19.129

Europe .885 2.423 .476 12.342

East Europe 1.436 4.205 .513 34.492

Latin America & Caribe .973 2.645 .287 24.373

Africa & Middle East 0b . . .

[English Proficient=0] .678 1.971 .551 7.053

[English Proficient=1] 0b . . .

Spouses of US citizens -.214 .807 .256 2.544

Employment visa -.483 .617 .168 2.262

Diversity -.730 .482 .057 4.081

Other type of visa 0b . . .

[Job offer prior migration=0] -.602 .548 .186 1.618

[Job offer prior migration=1] 0b . . .

(Continued)

104

Table 4.17 (Continued) Females – Dependent1a 95% Confidence Interval Exp(B)

Upgraded B Exp(B) Lower Bound Lower Bound

[licensed occ. abroad=0] 2.274* 9.719 3.883 24.323

[licensed occ. abroad =1] 0b . . .

Downgraded

Intercept .099

Age at entry .014 1.014 .963 1.067

Work exp. Abroad .001 1.001 .939 1.066

Foreign education .017 1.017 .886 1.169

US education <=23 -.014 .986 .775 1.255

Asia .214 1.239 .353 4.352

India .244 1.276 .287 5.674

Europe -.146 .864 .241 3.096

East Europe .484 1.623 .291 9.059

Latin America & Caribe 1.468** 4.340 .767 24.541

Africa & Middle East 0b . . .

[English Proficient=0] .241 1.272 .449 3.607

[English Proficient=1] 0b . . .

Spouses of US citizens -.465 .628 .255 1.550

Employment visa -1.836* .159 .055 .460

Diversity .048 1.050 .202 5.464

(Continued)

105

Table 4.17 (Continued) Females – Dependent1a 95% Confidence Interval Exp(B)

Downgraded Exp(B) Lower Bound Lower Bound

Other type of visa 0b . . .

[Job offer prior migration=0] .596 1.816 .744 4.433

[Job offer prior migration=1] 0b . . .

[licensed occ. abroad=0] 586** 1.797 .898 3.596

[licensed occ. abroad =1] 0b . . .

*p<0.05; **p<0.10 a. The reference category is: Match b. This parameter is set to zero. [Value= 1 ] is reference category

Group differences in table 4.18 indicates that females who were sponsored by employers showed the best results (25.3% upgraded, 30.7% downgraded and 44.0% found similar occupations). Large numbers of spouses of US citizens lost status (65.7%) but a portion of them were also able to upgrade (22.1%). Those entering through the diversity program had the worst employment outcomes. Those working abroad in non US regulated occupations had larger rates of upgrading (29.3%). Latin American and Asian women lost status in larger proportions (85.7%, 60.0%, respectively). Asians had the best rate for matches (25.9%) and Europeans for upgrading (32.6%). Those who were not proficient in English had worst results and lost status in large proportions (76.9%), although some managed to upgrade (15%). Those who were offered a job prior to

106 migration had better chances of upgrading or finding a similar occupation (26.7% and

41.7%, respectively), although near one third of them lost status.

Table 4.18 Group differences in discrete variables for females – First stage regression Warning: SPSS rounded up weighted frequencies From Country of Origin to First US Job Upgraded Downgraded Match Asia 14.1% 60.0% 25.9%

India 21.6% 59.5% 18.9%

Europe, Canada and Oceania 32.6% 49.4% 18.0%

East Europe 32.6% 49.4% 18.0%

Latin America & the Caribe 8.9% 85.7% 5.4%

Africa & Middle East 16.1% 71.0% 12.9%

Total by Origin 19.3% 63.3% 17.3%

Proficient in English 20.6% 59.0% 20.3%

Not Proficient in English 15.4% 76.9% 7.7%

Total by English Proficiency 19.5% 63.1% 17.5%

Spouse of US Citizen 22.1% 65.7% 12.2%

Employment Principal 25.3% 30.7% 44.0%

Diversity 13.0% 78.3% 8.7%

Other 13.0% 76.4% 10.6%

Total by Visa Type 19.4% 63.2% 17.4%

(Continued)

107

Table 4.18 (Continued) From Country of Origin to First US Job

Upgraded Downgraded Match

Job Offered pre- migration 26.7% 31.7% 41.7%

Not Job Offered 17.8% 68.4% 13.8%

Total by Job Offered 19.2% 62.6% 18.2%

Licensed occupation at origin 7.3% 67.2% 25.4%

Not Licensed occ. abroad 29.3% 59.6% 11.1%

Total Licensed occ. at origin 19.7% 62.9% 17.4%

Although none of the continuous predictors were significant, mean differences by each category of outcome in table 4.19 below revealed that those who lost status were, by average, older, had more years of working experience in their country of origin and more years of foreign education (means=32.51%, 8.12% and 16.34%, respectively).

108

Table 4.19 Mean of Continuous Variables by Outcome for Females – First Stage Occupational trajectory from Country of Origin to First US Job

Upgrade Downgrade Match

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Age at entry 27.62 6.837 32.51 10.119 30.26 6.981

Work exp. abroad 5.15 5.464 8.12 7.956 6.77 6.097

Foreign Education 16.15 2.580 16.34 2.945 16.16 3.389

US education <=23 .63 1.757 .30 1.447 .23 1.766

Following are the results of parameter estimates of individual predictors indicating their contribution to the model, as well as group and mean differences indicating the individual contribution of predictors in the second stage regression measuring occupational mobility of female college educated immigrants after a period of residence in the U.S.

109

Table 4.20 Parameter Estimates of Significant Predictors in Second Stage Regression Females – Dependent2a 95% Confidence Interval Exp(B)

Upgraded B Exp(B) Lower Bound Lower Bound

Intercept 1.209

Age at entry -.090 .914 .857 .975

Work exp. Abroad .050 1.051 .982 1.125

Foreign education -.014 .986 .864 1.125

Work experience in US .357 1.429 1.232 1.657

US education <=23 .401 1.493 1.008 2.211

US education >23 -.004 .996 .750 1.322

Time of Residence -.172 .842 .744 .954

Asia -.556 .574 .191 1.727

India .032 1.033 .272 3.918

Europe -.792 .453 .145 1.419

East Europe 1.051 2.862 .723 11.330

Latin America & Caribe -.156 .855 .236 3.104

Africa & Middle East 0b . . .

[English Proficient=0] -.535 .586 .242 1.415

[English Proficient=1] 0b . . .

Spouses of US citizens .003 1.003 .460 2.186

Employment visa -.293 .746 .266 2.093

Diversity -.828 .437 .106 1.802

(Continued)

110

Table 4.20 (Continued) Females – Dependent2a 95% Confidence Interval Exp(B)

Upgraded B Exp(B) Lower Bound Lower Bound

Other type of visa 0b . . .

[Job offer prior migration=0] -.178 .837 .294 2.381

[Job offer prior migration=1] 0b . . .

[licensed occ. abroad=0] 1.261 3.530 1.659 7.512

[licensed occ. abroad =1] 0b . . .

Downgraded

Intercept 1.209

Age at entry -.090 .914 .857 .975

Work exp. Abroad .050 1.051 .982 1.125

Foreign education -.014 .986 .864 1.125

Work experience in US .357 1.429 1.232 1.657

US education <=23 .401 1.493 1.008 2.211

US education >23 -.004 .996 .750 1.322

Time of Residence -.172 .842 .744 .954

Asia -.556 .574 .191 1.727

India .032 1.033 .272 3.918

Europe -.792 .453 .145 1.419

East Europe 1.051 2.862 .723 11.330

Latin America & Caribe -.156 .855 .236 3.104

(Continued)

111

Table 4.20 (Continued) Females – Dependent2a 95% Confidence Interval Exp(B)

Downgraded Exp(B) Lower Bound Lower Bound

Africa & Middle East 0b . . .

[English Proficient=0] -.535 .586 .242 1.415

[English Proficient=1] 0b . . .

Spouses of US citizens .003 1.003 .460 2.186

Employment visa -.293 .746 .266 2.093

Diversity -.828 .437 .106 1.802

Other type of visa 0b . . .

[Job offer prior migration=0] -.178 .837 .294 2.381

[Job offer prior migration=1] 0b . . .

[licensed occ. abroad=0] 1.261 3.530 1.659 7.512

[licensed occ. abroad =1] 0b . . .

*p<0.05; **p<0.10 a. The reference category is: Match b. This parameter is set to zero. [Value= 1 ] is reference category

Table 14.20 displays results for females in the second stage regression. Positive effects that increase the likelihood of upgrading versus obtaining a match are as follows: years of working experience in the U.S., (odds ratio= 1.4); completing U.S. education before age 23, (odds ratio= 1.5); and working in a non-regulated occupation in the first job after migration (odds ratio= 3.5). Negative effects that decrease the likelihood of

112 upgrading versus obtaining a match were found for age at entry, and time of residence, which suggests that older women and those residing more years in the U.S. are less likely to upgrade than to find similar occupations.

Positive effects that increase the likelihood of downgrading versus obtaining a similar position are also shown for years of working experience in the U.S and completing U.S. education before age 23 with odds ratio 1.2 and 1.4 respectively, which indicates that the likelihood of downgrading versus obtaining a match is greater for some groups of women with more years of human capital acquired in the U.S. Females from

India are almost three times more likely to downgrade. Positive effects increasing the likelihood of downgrading are also shown for those not proficient in English and those working in a non-regulated occupation in the U.S. The only significant negative effect that decreases the likelihood of downgrading versus obtaining a similar position is shown by time of residence, with an odds ratio of -10%. Negative effects are also shown for those who were not offered a job prior migration, as well as those entering with diversity and employment visas.

Results in table 4.21 show that East European and African women have the highest rates for recovering status (54% and 35%); European and Indian women lost status this time at the highest rates (26% and 24%).Those who were proficient in English show higher rates for upgrading (30%). Espouses of U.S. citizens recovered status in greater proportion (33.3%). Those who were not offered a job prior to migration showed higher rates for recovering status (30%). Those working in occupations that did not required a licensed upgraded at higher rates (35%).

113

Table 4.21 Group Differences for females– Second stage regression From First US Job and Current US Job Upgraded Downgraded Match Asia 22.8% 20.6% 56.6%

India 24.3% 24.3% 51.4%

Europe, Canada and Oceania 20.5% 26.1% 53.4%

East Europe 54.0% 8.0% 38.0%

Latin America & the Caribe 27.3% 23.6% 49.1%

Africa & Middle East 35.5% 16.1% 48.4%

Total by Origin 28.0% 20.7% 51.4%

Proficient in English 25.0% 22.8% 52.2%

Not Proficient in English 25.0% 22.8% 52.2%

Total by English Proficiency 28.1% 20.6% 51.2%

Spouse of US Citizen 33.3% 26.1% 40.6%

Employment Principal 23.0% 14.9% 62.2%

Diversity 21.7% 8.7% 69.6%

Other 25.0% 18.5% 56.5%

Total by Visa Type 28.2% 20.7% 51.1%

Job Offered pre- migration 16.4% 23.0% 60.7%

Not Job Offered 30.0% 20.0% 50.0%

Total by Job Offered 27.8% 20.5% 51.7%

Licensed occupation at origin 35.1% 20.0% 44.9%

Not Licensed occ. abroad 35.1% 20.0% 44.9%

Total Licensed occ. at origin 28.4% 20.6% 51.0%

114

Mean differences of continuous variables by each alternative outcome in table

4.22 show that average means of variables associated with the acquisition of U.S. specific human capital skills time of residence, years of work experience in the U.S. and both measures of US education are highest for those upgrade (means=6.18, 5.23, .98 and .49, respectively). Mean difference of foreign human capital variables age at entry, years of work abroad, and years of foreign education are lowest for those who upgrade (means=

28.93, 6.45, 15.59, respectively) but largest for those who obtain an occupational match

(means= 32.43, 7.64, 16.65, respectively). Results in tables 4.19 and 4.22 were graphically in Figure 4.2 to provide a clear image of the effect of human capital variables in the occupational status of immigrants at origin and the U.S. by gender.

Table 4.22 Mean of Continuous Variables by Outcome for Females – Second Stage Occupational trajectory from First US Job to Current US Job Upgrade Downgrade Match

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Age at entry 28.93 8.294 31.01 8.404 32.43 9.865

Work exp. abroad 6.45 6.782 7.55 7.219 7.64 7.589

Work exp. US 5.23 4.762 3.41 3.881 2.14 3.003

Foreign Education 15.59 2.662 16.27 3.479 16.65 2.823

US education <= 23 .96 2.328 .27 1.815 .05 .487

US education > 23 .4850 1.18611 .4068 1.34006 .3227 .87903

Time of Residence 6.18 4.922 4.56 4.528 4.09 5.419

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Figure 4.2 Effect of human capital variables in the occupational status of immigrants at origin and the U.S

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Occupational Mobility Second Stage

Figure 4.2 (Continued)

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Table 4.23 and Figures 4.1, and 4.3 contain information on the occupational status of immigrants in their origin countries and in the U.S. using mean scores of the Nam

Power Boyd scale. The information is presented separately for males and females in the sample and is broken down by region of origin and the additional categorical factors in the conceptualized model. The use of the NPB occupational status scale allows quantification of the magnitude of the occupational trajectories and thus an accurate and clear image of the U-shape of the effect of individual predictors and differences by group category. This type of descriptive evidence of immigrants’ occupational mobility has been previously used in the studies of Akresh (2008), Batalanova, et al. (2008) and

Simon, et al. (2011).

At a first glance, these results show that immigrants experience a severe loss of occupational status when they enter the U.S labor market. Thus, while the average occupational status in their countries of origin is 72 points for males and 69 points for females in the NPB scale, the status of the first job in the U.S is substantially smaller, showing 56 points for males and 48 points for females. Graphical displays in Figure 4.3 of mean occupational status score shows a rising trend over the years of residence in the

U.S. With increased stay in the U.S., immigrants are able to recover part of their occupational status. Mean average scores for the current job in U.S. is 62 points for males and 54 points for females. The improvement of job quality is limited and clearly lower than the initial loss of status, which is the main reason why the occupational status of immigrant workers in the U.S. is substantially worse than the one they had in their countries of origin.

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Table 4.23 Occupational status of immigrants in their country of origin and the U.S. measured using the Nam Powers Boyd scale County Current job Origin First Job US US Male 72.16 56.25 61.90

Females 68.91 48.49 54.17

Results for males

Asia 69.40 58.95 60.09

India 81.89 72.50 76.62

Europe, Canada and Oceania 68.45 65.28 67.74

East Europe 70.67 46.13 49.96

Latin America & the Caribe 69.86 33.62 54.29

Africa & Middle East 72.63 43.14 53.32

Proficient in English 73.60 61.16 67.16

Not Proficient in English 66.72 37.61 41.92

Spouse of US Citizen 68.58 56.44 61.82

Employment Principal 81.66 81.48 84.41

Diversity 69.58 35.48 37.19

Other 67.68 40.14 49.53

Job Offered pre- migration 79.50 81.15 81.48

Not Job Offered 70.58 50.72 57.75

Licensed occupation at origin 90.56 60.25 65.66

Not Licensed occ. abroad 65.97 54.90 60.63

(Continued) 119

Table 4.23 (Continued) C. Origin First Job US Current job Licensed occupation 1st US job 78.85 82.66 80.8

Not Licensed occ. 1st US job 71.02 51.75 58.66

Results for females

Asia 70.67 51.44 55.10

India 76.90 60.95 65.34

Europe, Canada and Oceania 62.91 55.58 57.23

East Europe 64.54 36.62 52.58

Latin America & the Caribe 76.69 36.48 40.05

Africa & Middle East 62.36 41.47 56.28

Proficient in English 68.01 51.83 58.63

Not Proficient in English 71.97 37.18 39.05

Spouse of US Citizen 63.91 47.25 54.72

Employment Principal 76.35 72.71 75.67

Diversity 63.40 32.82 36.89

Other 72.76 38.61 43.62

Job Offered pre- migration 75.87 74.66 75.47

Not Job Offered 67.42 44.15 50.57

Licensed occupation at origin 83.87 51.28 51.14

Not Licensed occ. abroad 57.18 46.30 56.54

st Licensed occupation 1 US job 75.64 68.71 68.29

st Not Licensed occ. 1 US job 66.16 40.23 48.41

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Figure 4.3 Occupational status of male immigrants in their origin country and the U.S. y-axis is mean Nam Power Boyd occupational status score.

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Figure 4.4 Occupational status of female immigrants in their origin country and the U.S. y-axis is mean Nam Power Boyd occupational status score in each job.

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The U-shaped patterns of the evolution of the occupational status of immigrants from their country of origin to the U.S., characterized by an intense loss of job quality on arrival the United States and a limited recovery of status after a period of residence, follow the predictions of the model by Chiswick and Miller (2003) and Duleep and

Regets (1997 as cited in Bauer & Zimmermann, 1999). However, it does so only partially, since the initial loss of status is more intense than the subsequent recovery. This can be appreciated by the asymmetry between the first stage occupational trajectory, from country of origin to the first job in U.S., and second stage occupational trajectory from the first job in the U.S. to the current position at the time of interview.

The described U-shaped pattern is observed, in general, for all types of immigrants except for females from Latin America and the Caribe, who display an L- shaped curve since they were never able to recover the status lost initially. The theoretical predictions regarding the varying depth (flat, shallow or steep) of the U for the different groups analyzed are also confirmed, except, once again, for the group of females from

Latin America and the Caribe.

Figure 4.3 and 4.4 show marked differences related to gender, with males outperforming females, starting and ending in lower ranking positions except for the category “working in licensed occupations abroad”. Overall result for males show that they started with an average of 72 point of NPB status score, lose 16 points at arrival and recover 6 points to end up at 62 points. Females start at an average of 69 points, lose 21 points at arrival and recover also 6 points to end up at 54 points.

Both male and females show marked differences according to region of origin. In general, the groups from India, Asia and Europe were best performing, although females

123 lost more in status than males. Indians started and ended higher than any group. The

European groups showed flat curves. Worst performing group with a steep U-shape curve were African males. Although they started higher than Europeans and Asians, lost 20 points and ended the lowest at 53 points. Females also lost 20 points but ended a couple of points higher than males. For females, the worst performing group with an L-shape curve was the one from Latin America and the Caribe, who started highest at 77, lost 40 points and was able to gain only 3 points and ended at 40. Their male counterparts started lower at 70 points lost 36 points but were able to recover 20 and end up at 54.

Deep U-shape curves are observed for females who were not proficient in

English, who lost 35 points, while their male counterparts lost 29 points. Although those who were proficient in English also experienced declines in status, both male and females started and ended up in higher ranking positions than those who did not speak English.

Groups by visa type showed similar results for both male and females, with flat curves for those with employment visas. Spouses of U.S. citizens lost 12 points but recovered 5 points. Worst performing group with deep U-shapes for both was the diversity groups, specifically for males who started at a high 70 points, lost 34 points and after recovering only 2 points ended up at 37 points.

Males who were offered job contracts prior migration showed flat curves, even ended up 2 points higher than they started. Those who were not offered job contracts showed steep curves, males lost 20 points while females lost 23 points. Recovery was similar for both groups. Those not working in regulated occupations in their countries of origin or in the first job in U.S. started and in lower ranking positions than those who worked in occupations required licenses and had deep U-shaped curves of mobility.

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Females working in regulated occupations in their countries of origin started with an average NPB score of 84 points and ended with 51, losing 33 points total. Males started at 91, lost 30 points and ended at 66 points. Males working in licensed occupations in the first job in U.S. actually gained 4 points at arrival, and show a very flat curve. Females lost seven points and had shallow curves.

Results for Question #2

Following is a summary of results by type of predictor variable indicating the importance of these factors as determinants of upward or downward mobility.

Determinants of Upward Occupational Mobility

Descriptive statistics7 in table 4.1 shows that about 21% of immigrants in the sample gained status when they started their first US job. Results varied by gender (23% males and 20% females). A total of 31.6% were able to recover status after time spent in the U.S. while moving from their first job in the US job to the position they held at time of interview. Once again, results varied by gender (33% males and 28% females).

Initially, the only true determinants of upward occupational mobility were entering the country with an employment visa and been offered a job prior to migration.

Figures 4.3 and 4.4 show ascending and flat curves of occupational mobility for these variables. The group from Europe, Canada and Oceania exhibited a shallower curve, although best results were achieved by Indians followed by Asians. Results vary by gender. All groups were able to recover part of the status lost with time in the U.S.

7 Warning: SPSS rounded up weighted frequencies. Confidence interval is 93%

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However, recovery in the second stage was very poor for women from Latin America and the Caribe, who started highest and ended in the lowest job quality rankings.

The strongest predictors of upgrading from the first U.S. position to the current

U.S. job are the variables of U.S. specific human capital. Please see Figure 4.2. Mean

NPB status score was highest for those who entered the country at a younger age, had more years of work experience in the U.S. and therefore had spent more time in the country. Having U.S. education provided a boost both in terms of gaining status and securing a job that matched one’s original status. Differences with those who did not complete U.S. education were more pronounced in females if the U.S. schooling was acquired at or before age 23.

Recovery was also higher for those who did not work in licensed occupations abroad, who ended up in jobs with higher quality than those who worked in licensed occupations. Spouses of U.S. citizens and those who entered without a job offer were able to gain most of the lost status with time spent in the U.S. The occupational attainment of those who worked in licensed occupations in the U.S. was much better than those who worked in occupations that were not regulated.

Determinant of Downward Occupational Mobility

About8 57.6% of the migrants included in our sample experienced downward occupational mobility when securing their first job in the U.S. As shown in table 4.2, results varied by gender with 53% of males and 63% of females losing status. With time residing in the U.S., most immigrants remained in similar occupations and a smaller

8 Warning: SPSS rounded up weighted frequencies. Confidence interval is 93%

126 percentage (20%) continued losing status when moving to the current US job at the time of interview. Results by gender were 19% males and 21% females.

On average, women lost more status than males and were not able to recover the job quality they had in their countries of origin. Females and males from Latin American and the Caribe were by far the ones experiencing the deepest declines in status, and while males where able to recover job quality in large proportions, females immigrants remained at the lowest ranking positions. African and East European groups also experienced deep declines in status although they were able to partially recover and obtain jobs of higher quality.

Those who were not proficient in English started in lower ranking positions and were more prompt to lose status and remain in positions with lower status. Steep declines in status were also experienced by those not receiving a job offer prior to migration, diversity visas and those working in licensed occupations in their countries of origin abroad. The last condition was especially harsh for women who were unable to recover the status initially lost and ended in lower rankings than those working in occupations that were not licensed. Those who did not worked in licensed occupations in the first U.S. job had much steeper declines starting and ending in lower ranking occupations.

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CHAPTER 5

SUMMARY, DISCUSSION, AND IMPLICATIONS

This chapter consists of three sections. The first section summarizes the results of the data analysis and describes the findings of the two research questions. The second section discusses the results. The last section proposes a revised conceptual framework and provides implications for workforce development policy makers, practitioners and researchers regarding the occupational mobility of immigrants.

Summary of Results

. The purpose of this study was to evaluate the effect on the occupational mobility of immigrants of specific country/context conditions and develop a model of occupational mobility that takes into consideration s occupational regulatory requirements, immigration policy and contractual employment prior to migration. This study tests the fitness of a smaller model with only human capital variables against a proposed final model and poses questions regarding the determinants of changes in occupational status of college-educated immigrants from their country of origin to subsequent jobs in the U.S. Separate analyses are conducted by gender since it was

128 evidenced that males and females had different occupational trajectories. This study is based on data from the 2003 New Immigrant Survey.

A first stage regression measured change in status from the country of origin to the first job in the U.S., and a second stage regression measured change in status from the first job in the U.S. to the current position in the U.S. at time of interview. The following paragraphs provide a detailed summary of the results.

Almost 60% of college-educated immigrants experience a sharp decline in occupational status, losing 18 points in Nam Powers Boyd score when they first move to the U.S. followed by a rise in job quality with time spent in the country. However, most high skilled immigrants are not able to recover all the status they lost initially. The breakdown by gender indicates that 54% of males lost 16 points in status score between the occupations they held in their countries (with avg. score= 72) to the first job they obtained once they arrived in the U.S. (avg. score= 56). The impact of migration on women’s occupational attainment was even more negative. Results show that 63% of them lost an average of 21 points in status at arrival, from an average score of 69 down to

49 points. Results of average scores reported in the current U.S. job indicate that both male and female college-educated immigrants experienced some gains in status after some time residing in the U.S., although the average gain of 6 points was not enough to recover the status initially lost.

Overall, college educated immigrants had varying outcomes in terms of occupational mobility. These outcomes depended on immigrants’ gender, origin, English ability, time spent in the United States, place of education, type of immigration visa used, employment sponsorship, and occupational licensing requirements.

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1) Do country/context specific conditions increase the explanatory power of Chiswick’s

(1977) model of occupational mobility of immigrants?

Statistics of model comparison, model fit, and classification tables of prediction rates (Tables 4.2 to 4.8) indicate that the full model containing the three additional structural predictors proved to have higher level of prediction capability of occupational mobility of the sample population over the model with only human capital variables in first and second stage for females and in the first stage for males. Statistics of the second stage regression for males, indicate that the full model improved prediction capabilities, but results were not significant.

Model fitting information in tables 4.2 and 4.3 show significant p-values (p <.05 at α= 0.05), indicating a good fit of expected frequencies to observed frequencies. The null hypothesis that there was no difference between the model (s) without independent variables and the model (s) with independent variables was rejected in all cases. Pearson and Deviance statistics provide further evidence of good fit for all models.

According to the Table of Critical Values of Chi-Square (χ²), male and female results for the test of differences of model -2LL (log likelihoods) and likelihood ratio tests are significant at α=.05, in first and second stage for females and in the first stage for males, which further indicates that the full model with structural predictor variables predicts significantly better, or more accurately, than the human capital model for both males and females. Statistics in the second stage for males indicate improved prediction, but results were not significant.

Classification results in table 4.7 for the first stage regression indicate that overall prediction success rate improved 3.2% for males (from 62.8 % to 66%) and 6% for

130 females (from 62 to 68%) with the addition of three structural predictor variables in the full model. Result in table 4.8 for the second stage regression indicate that overall prediction success rate improved a modest 2% for males (from 59.3% to 61.3%) and

6.9% (from 57.3% to 64.2%) for females in the full model.

Log likelihoods (chi-square statistics) of individual predictors in tables 4.9 and

4.10 indicate that the following factors are significant predictors of occupational mobility in the first and/or second regression by gender:

First Stage Regression Second Stage Regression

Males Females

Work experience abroad Age at entry

US education <=23 Visa type

Females Males and females

Age at entry Work experience in the U.S.

Males and females US education <=23

Region of origin Time of residence

Visa type Region of origin

Job offer prior migration Licensed occ. 1st U.S. job

Licensed occ. abroad

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Parameter estimate tables in 4.11; 4.14; 4.17 and 4.20 identify significant predictors within the model, that is, as part of a set. These statistics show how predictors increase or decrease the odds of gaining or losing status – upgrade or downgrade – versus obtaining an occupation with similar status – match – which is the reference category in the MLR analysis. It is important to highlight the absence of foreign education and

English proficiency as significant predictors of occupational mobility outcomes for both males and females. This means that these variables did not make a difference in gaining or losing status or obtaining similar occupations. Most immigrants lost status at arrival to the U.S. regardless of their level of foreign education and English language skills, and recovered some of the status lost. Parameter estimates cannot easily be interpreted as individual effects of predictors using Multinomial Logistic Regression. This is illustrated by the fact that some predictors, such as year of work experience abroad, were significant to increase or decrease the odds of both gaining and losing status, versus the probability of obtaining similar occupations in the U.S. The identification of individual effect of predictors, according to Tabachnick and Fidell (2007), is better achieved through descriptive statistics which were offered to answer the second question in this study.

2) What are the determinants of vertical occupational mobility of immigrants’ at time of

arrival in the country? Do these determinants change with extended time of residence

in the U.S.?

To identify determinants of occupational status gains and losses, this study followed used descriptive evidence. Group differences in proportions of cases by outcome

132 alternative were calculated for categorical predictors. Proportions gave an idea of the amount of people who gain, lost or found similar positions. In the first stage, most people lost status, and in the second stage, the majority of immigrants remained in positions with similar status or were able to recover some of the status lost. In addition, mean differences of continuous predictors were graphically displayed to show patterns of the effect of individual human capital predictors and U-shape curves of occupational mobility of categorical predictors using their mean NPB occupational status score.

Ascending lines indicate gain in status, flat or shallow curves are indicative of smooth occupational trajectories with no or little changes of status, and steep curves indicate sharp loses of occupational status. Following are a summary of the results.

Determinants of upward mobility

Initially, the only true determinants of upward occupational mobility were predictors “employment visa” and “been offered a job prior to migration”. This is evidenced by ascending and flat curves of occupational mobility in figures 4.3 and 4.4 and tables of group differences in tables 4.11, 4.15, 4.18 and 4.22. The occupational mobility curves for men and women entering with employment visas or receiving a job offer prior to migration were flat and job quality was highest overall.

Displays of differences by region of origin indicated that results varied by gender and that the group from Europe, Canada and Oceania exhibited shallow curves of occupational trajectory for both male and females. Best results by region of origin were achieved by Indian and Asians.

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The strongest predictors of upgrading in the second stage from the first U.S. position to the current U.S. job are the variables of U.S. specific human capital. In figure 4.2 we can appreciate that means for these variables are highest for those who entered the country at a younger age, had more years of work experience in the U.S. and therefore had spent more time in the country. Having U.S. education provided a boost both in terms of gaining status and securing a job that matched one’s original status. Differences with those who did not complete U.S. education were more pronounced in females if the U.S. schooling was acquired at or before age 23.

In regards to the effects of structural factors, recovery of status was higher for those who did not work in licensed occupations abroad, spouses of U.S. citizens and for those entering without a job offer, who were able to gain most of the status initially lost as they stay longer in the U.S. Those working in occupations in their countries that did not required licenses to practice in the U.S. had shallower U-shaped curves of occupational mobility, although they started and ended in occupations with lower job quality, more so for females. Males who worked in licensed occupations in their first U.S. job recovered the status originally lost, but females continue experiencing declines with time spent in the U.S.

Determinants of downward mobility

African males were the worst performing male group exhibiting a steep U-shape curve indicating loss of status at migration. Recovery in the second stage was extremely poor for women from Latin America and the Caribe, who after starting highest at 77 NPB score points, lost 40 points and was only able to recover 3 to end in the lowest job quality

134 rankings at 40 points. Although their male counterparts lost 36 points, they had started lower at 70 points but were able to recover 20 points and ended up at 54.

Those who lost status were, by average, older and had more years of working experience abroad, indicating that human capital acquired abroad was not relevant to improve occupational outcomes once in the U.S. This effect was also illustrated by flat or shallow curves of mobility. Mean years of working experience abroad was lowest for those who gained status and highest for those who obtained similar positions. In addition, foreign education was found to be irrelevant to the occupational success of male and female college-educated immigrants in the U.S. This was evidenced by the flat curve of occupational mobility and the lack of significance of this variable in the MLR analysis. In addition, mean averages of NPB status score were similar for those who gained or lost status or found similar occupations in the U.S., further evidencing that it did not make a difference in obtaining one outcome or another.

There were not significant differences in the occupational trajectories with those who did not speak English, as evidenced by results in the MLR indicating that this predictor was not significant of occupational outcomes. However the lower means of NBP scores of those who were not proficient in English suggested that this group started and ended in positions of lower quality. This is further evidenced by the graphical displays in figure

4.2.

Except for employment visas who had a flat curve of occupational mobility, for other visa admission types, there was a decline at migration and a subsequent improvement in job quality, but the rise was not enough to recover the status they had in their countries of

135 origin. The U-shape curve was deepest for those entering with diversity visas, which experienced declines in job quality in greater proportions as well.

Figure 4.2 shows that immigrants who did not receive a job offer started in lower ranking positions, experienced a steep decline in job quality and were not able to recover their original status with time spent in the U.S.

Group differences and mean occupational scores indicate that occupational regulatory requirements had a negative effect on college educated immigrants, impeding their ability to gain status or find similar positions. This was the case more so for women, who experienced a sharper decline in status than males and ended up in lower ranking occupations as well. Females who worked in licensed occupations in their country of origin and their first U.S. job continued experiencing declines with time spent in the U.S.

In contrast, males were able to recover a some of the status lost.

Discussion

This section discusses some possible interpretations of the results and presents an explanation of the findings.

Results of the Multinomial Logistic Regression analysis supported the conceptual framework and are consistent with the existing literature. This section discusses some possible interpretations of the results and presents an explanation of the findings.

The full model containing the three additional structural predictors proved to have higher level of prediction capability of occupational mobility of the sample population over the model with human capital variables only. This is not a surprise since the human capital model addresses only factors at the individual level and the full model offer a

136 larger scope of explanations by including environmental factors affecting labor market outcomes such as immigration policy, occupational regulatory requirements and obtaining a job contract prior to migration. However, MLR classification tables indicated that the full model provided only partial predictability and was not capable of explaining why some immigrants continued losing occupational status or remained in occupations of lower quality with time of residence in the U.S. This is an indication that the conceptual model needs to be revised to increase the explanatory capacity of the occupational mobility phenomena and include additional variables at the individual and environmental levels such as the economic and labor market conditions that were taking place at the time the survey was conducted.

The international literature studying immigrant’s occupational attainment has mostly focused on structural barriers in the U.S. labor market. Factors previously discussed by other studies include acquiring U.S. specific human capital skills, proficiency in English, time of residence in the U.S., personal choices and family demands, the degree of cross-country transferability of one’s skills and profession, access to professional networks with high quality social capital, non-recognition of foreign academic or professional credentials, discrimination, and legal status.

These factors are acknowledged as important determinants of immigrant occupational mobility and will be included in the revised model. Some of these factors are discussed below. In addition, this study proposes that the macro-economic environment and climate of immigrant receptivity in the U.S. at the time the 2003 NIS survey was conducted affected immigrants’ occupational mobility. These conditions have

137 been widely overlooked to explain results in this particular database, and were not properly addressed by previous studies.

During the 1990s the United States experienced the longest postwar economic expansion, attracting large numbers of highly skilled immigrants (Center for Immigrant

Studies, 2011). The beginning of the new millennium started off on a high note as the bubble in the information technology sector that began in the 1990s continued to elevate the overall economy. However, this economic expansion ended in 2001, as the U.S. economy entered a severe national recession following two dramatic events, the dot.com bubble burst in Mach 2001 and the September 11, 2001 terrorist attacks (BLS, 2001;

2002).

The 9/11 attacks on the World Trade Center and the Pentagon had both psychological and economic effects. The destruction of the World Trade Center's Twin

Towers accelerated the stock market crash of 2000-2002, devastating both labor and financial markets (Bureau of Labor Statistics, 2011). Rising unemployment coupled with the psychological and economic effects of the tragic events of September 11 depressed consumer confidence. The U.S. economy experienced dramatic swings, alternating between expansion and contraction (New York State Department of Labor, 2011). The deterioration of the labor market continued during the 2000-2010 as most employers remained reluctant to hire amid the uncertainties in the U.S. economy.

In times of labor market fluctuations, minorities and special populations tend to deteriorate their employment conditions more severely than the main stream population

(Economic Policy Institute, 2010). Adverse employment and economic conditions may

138 have influenced immigrant occupational trajectories. In addition, after the 9/11 attacks, researchers reported that anti-immigrant sentiment reached new heights that may have spurred labor market discrimination practices, affecting some ethnic groups in particular

(Bohon, 2005). Researchers evidenced that Muslims, Arabs and south Asians were subjected to ethnic profiling, and that a strong anti-Hispanic sentiment has long persisted in the United States (Bohon, 2005).

The discussion on the conditions in the economic and social environments above may have helped explain intervening factors at the meso and macro level affecting the deterioration of the labor market performance of the overall immigrant population.

Following is a discussion on how the findings in this study confirm existing theories and former studies cited in the literature review about the determinants of occupational mobility.

The U-shaped patterns of the occupational trajectory of immigrants from their country of origin to the U.S. in this study is characterized by an intense loss of job quality on arrival the United States and recovery of status after a period of residence, and it followed the predictions of the model by Chiswick et al. (2003) and Duleep and Regets

(1999, as cited in Bauer & Zimmermann, 1999). However, it does so only partially, since the initial loss of status is more intense than the subsequent recovery. This is evidenced by the lack of symmetry of the U-shaped curve in figure 4.1. According to this study, the occupational recession is not a temporary disequilibrium phenomenon as predicted by the human capital model (Chiswick et al., 2003). Following is a discussion on the results of the study by type of predictor.

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Control factors

Differences found in the occupational trajectories of college-educated immigrants by gender in the 2003 NIS were previously identified Arkersh (2008) and Yap Co (2008) and were expected in this study. The literature has evidenced that men and women experience differential success in the labor market. This variation may be associated with differences in responsibilities for housework and childcare, lifetime work participation, and gender discrimination in the labor market. The evidence suggests that women often chose to work in occupations and types of jobs of lower quality but that allow greater flexibility to attend family responsibilities, or chose to stay home while child rearing, thus reducing their total years of work experience. However, evidence indicates that sex discrimination remains a possible explanation of the unexplained gender occupational gap (O’Neill and O’Neill 2005).

The results of the study confirm Chiswick’s theory of transferability of human capital predicting that that those who are a better cultural fit for the U.S. labor market achieve higher occupational success. Accordingly, immigrants from countries that are less developed and have less cultural and linguistic similarities with the U.S. will experience a high deterioration in their labor market position at the time of migration.

The MLR analysis in this study suggest that the possibility of an occupational match or upgrade is greater for immigrants from English speaking countries, which have similar levels of economic development with the U.S., and lower for developing countries and those with more distant cultures.

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Indeed, immigrants from Latin America and the Caribe, Africa and East Europe ended up losing more status and in greater proportions than those from more developed

English speaking countries. However, this theory predicted a faster recovery with time of residence in the receiving country for this group (Bauer & Zimmermann, 1999), but the results in the study indicate that the gain in status is pretty limited, and in the case of females from Latin America and the Caribe, there is no recovery at all. Therefore, there is only partial agreement with the predictions of the theory of transferability of human capital.

Human capital factors

As predicted by Chiswick et al. (2003) and Chiswick and Miller (2007a; 2007b), descriptive statistics indicate that immigrants who are proficient in English experience better occupational outcomes than those who are not proficient in English.

The lack of significance of foreign education in the MLR analysis seems to confirm the observations that employers do not recognize foreign credentials (Creticos, 2007; Gächter and Smoliner, 2010). Results for foreign human capital variables suggest that skills developed through education or work experience in the country of origin do not directly transfer to the U.S. labor market as it was predicted in Ferrer et al., (2006).

As expected, my results agree with former studies in that education and working experience acquired in the U.S. are significant contributors to the recovery of occupational status with time of residence in the U.S. (Arkesh, 2008; Batalova, et al.,

2008; Chiswick & Miller, 2007a; 2007b; Yap Co, 2007).,

Structural factors

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The results in this study agree with former studies that found that immigrants entering with employment visas (Arkesh, 2008; Batalova, et al., 2008; Yap Co, 2007) and job contracts prior to migration (Arkesh, 2008) experience more a successful and smoother transition into the U.S. labor market. The poor occupational success those entering through diversity visas was also expected and agrees with existing literature

(Arkesh, 2008; Batalova, et al., 2008; Yap Co, 2007).

Batalnova et al., (2008) and Creticos (2007) had suggested that the lack of transferability of educational credentials coupled with licensing requirements in the U.S. negatively affected immigrants’ ability to make a smooth transition into the U.S. labor.

The operationalization of licensing requirements in this study allowed me to measure the effect of this this variable and demonstrate statistically that their predictions were correct.

Both male and females working in licensed occupations abroad experienced deep declines in status. While males were able to partially recover job quality, females who worked in licensed occupations in their country of origin and their first U.S. job continued experiencing declines with time spent in the U.S.

Implications

The first part of this section presents a revised conceptual framework and implications for future research. The second part identifies areas for future research and workforce development policy.

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Revised Conceptual Framework

This study tests the fitness of a human capital model based on Chiswick’s (1977) against a proposed final model that includes structural factors to account for country/context specific conditions. In addition, the study poses questions regarding the determinants of changes in occupational status of college-educated immigrants from their country of origin to subsequent jobs in the U.S. Using Multinomial Logistic Regression and data from the 2003 New Immigrant Survey.

The results of the study demonstrated that the full model including structural predictors had improved prediction capability of occupational mobility over the human capital model. However, it was apparent that level of prediction of the full model was still only partial. In addition, it was obvious that the conceptual model was not capable of explaining why after time of residence in the U.S., only a third of immigrants were able to improve their occupational status and twenty percent continued losing status and the rest remained in occupations of lower quality. These facts suggested that the conceptual model needed to be revised to account for additional individual and environmental factors affecting the occupational mobility of immigrants.

The following section attempts to provide a comprehensive theoretical framework that will help understand the successful transition of skilled immigrants to the U.S. labor market. In the revised conceptual model, the determinants that affect the occupational trajectory of immigrants have been organized at the macro, meso, and micro levels. The new conceptual model takes into account the dynamic nature of engagement between immigrants and the environment. Personal factors and environmental determinants all

143 interact together affecting immigrants’ occupational trajectory. Determinants of individuals are defined here as socio-economic and psychological factors and the determinants of the environment are defined as the characteristics of the labor market and country institutions, occupational regulatory requirements, the process of obtaining employment, social capital networks, immigration, economic, employment and workforce educational policy and other dominating system characteristics in the U.S.

The Micro level

The micro level takes into account the experiences and perspectives of college- educated immigrants. There are two groups of dimensions at the micro level, the socioeconomic dimension and the psychological dimension.

1) The socio-economic dimension takes into account that people cannot be seen as independent from their social context. The socio-economic dimension is related to the social, cultural and economic backgrounds of college-educated immigrants and include control factors such as race, class, ethnicity, type of occupation, gender, household information, culture and language distance, country of origin, as well as human capital factors such as education, English proficiency, working experience and time of residence in the new country. Personal and environmental factors interact together affecting immigrants’ occupational mobility. Socio-economic factors will influence processes of cultural reproduction (Bourdieu 1973; Rivera, 2009) and determine receptivity by employers and society or discriminatory behaviors and exclusionary employment practices (Gächter & Smoliner, 2010; Hersh, 2008a; 2008b; 2008c; 2009), as well as

144 immigrants’ ability to acculturate and integrate successfully into the new environment

(Stanek & Veira, 2009).

2) The psychological dimension relates to the pull and push factors of migration mentioned in the literature review (Lee, 1966). This psychological dimension affects the motivation to migrate, the perceptions about the new culture, attitudes such as willingness to acculturate and acquire country specific human capital skills, and the intentions stay in the new country,

Apart from demographic and human capital factors already accounted for in the conceptual model tested in this study, the literature review identified several factors affecting the migratory experience and employment at time of interview that could be included at the micro level. These factors are household information including marital status, information on the number of children in the household, region of residence in the

U.S, and a reliable measure of household wealth, race and ethnicity and factors affecting the migratory experience (Massey, 1993). Foreign education should not be included since the literature review established that foreign education is not portable (Gächter &

Smoliner, 2010) and is not recognized in the U.S. (Creticos, 2007). The results of this study corroborated the fact that foreign education does not have any effect on employment outcomes in the U.S.

Among the factors affecting the migratory experience are the reasons to migrate, country of origin GDP per capita, existence of military conflict, and intentions to stay in the U.S., previous residence in a developed country, and adjustment of visa status from temporary to legal permanent resident (Mattoo, 2008).

145

In addition to years of residence in U.S., information on present employment situation useful to the model could be the number of changes of the city and state of residence and unemployment periods longer than one month (Simon, 2011).

The Meso level

At the meso level, the literature has identified that country/context conditions relative to processes of social capital formation (Ellis, 2001; Vallat 2010), job search

(Akresh, 2008), as well as characteristics of the labor market and economic environments and the climate of immigrant receptivity by employers and society at large affect immigrant’s occupational mobility (Gächter & Smoliner, 2010).

Factors involved in the job search process include job search method, time to find the first job and receiving assistance to obtain employment (Akresh, 2008; Simon, 2011); information on support networks (heterogeneous – native and immigrant networks; or homogeneous –only with same kin in immigrant enclaves) (Ellis, 2001; Vallat 2010). The climate of immigrant receptivity to detect exclusionary behaviors towards immigrants is reflected in the openness of US immigration policies to residents of a given country

(Mattoo, 2010). Discriminatory and exclusionary behaviors of employers towards certain types of immigrants can be detected by number of court cases filed by immigrant employees and complaints of abusive behavior of businesses with state attorney general office. The effect of business cycles and economic conditions affecting the labor market at a particular time, such as economic growth and unemployment rates and labor surpluses or shortages by industrial sectors, should also be included in the model. Further investigation is required to find a way to operationalize these conditions in the model.

146

The Macro level

The macro level includes country/context conditions relative to the broader institutional and policy context (immigration, occupational requirements) and the economic and labor market systems. These are key factors that stimulate or hinder the successful occupational trajectory of skilled labor in the new country (Batalanova, 2008;

Creticos, 2007). Structural factors such as visa type , occupational licensing requirements, policies of immigrant integration, economic development policies, taxation and workforce development and education policy belong to this level. Additional structural factors affecting the occupational mobility of immigrants can be added as they are identified in future research efforts.

The resulting conceptualized model can be applied to study the occupational mobility of the immigrant population of any country. Results should vary according to country/specific conditions at the meso and macro levels.

A graphical representation of the research design is offered below followed by a list of control, human capital, environmental and structural variables represented in the diagrams.

147

Macro-level Country/Context Conditions Institutional context Immigration policy Occupational regulatory requirements Economic system

Labor market system

Micro-level Micro-level Human Capital (1) Human Capital (2)

148 Micro-level Pre-immigration Initial post-migration Occupational Control Variables: Occupational Occupational Status status at time of Individual & household Status (T1) (T2) interview (T3) Migratory experience

42 Meso-level Country/Context Conditions Micro-level Labor market environment Present employment info Economic environments Social Capital Networks Job Search Process Climate of immigrant receptivity

Figure 5.1 Revised Conceptual Model of Occupational Mobility of Immigrants

148

Variables in revised conceptual model

Micro-level

Control Variables:

Entered in first stage regression

Individual factors: race, class, ethnicity, type of occupation, gender, country of origin, culture and language distance

Household factors: marital status, information on the number of children in the household, region of residence in the U.S, and household wealth

Migratory experience: reasons to migrate, country of origin GDP per capita, military conflict in home country, intentions to stay in the U.S., previous residence in a developed country, and adjustment of visa status from temporary to LPR

Entered in second stage regression

Present employment information: number of changes of the city and state of residence, and unemployment periods longer than one month

Human Capital Variables:

Entered in first stage regression

Human Capital (1) variables: Foreign Work Experience, English proficiency, age at entry, U.S. Education completed at or before age 23- Note: do not include Foreign

Education.

Entered in second stage regression

149

Human Capital (2) variables: Time of Residence, U.S. Work Experience, U.S.

Education after age 23

Meso-level Country/Context Conditions

Environmental variables

Labor market environment: unemployment rate, labor surplus or shortages by industrial sector

Economic environment: economic growth rate (expansion or recession)

Social Capital Networks: heterogeneous or homogeneous;

Job Search Process: job search method, time to find the first job and receiving assistance to obtain employment (by friends and relatives; institutions, or employment offered prior migration)

Climate of immigrant receptivity: number of court cases filed by immigrant employees or complaints of abusive behavior of businesses with state attorney general office

Macro-level Country/Context Conditions

Structural variables:

Institutional context: policies of immigrant integration

Immigration policy: visa type

Occupational regulatory requirements: licensed occupations

Economic system: economic development policies, taxation policies

Labor market system: workforce development and education policy

150

Recommendations for future research

Analysis of trends of occupational mobility

There is a vacuum of research and well-coordinated U.S. policies that address the under-placement of the highly skilled labor force. First, it will advisable to analyze the data in the 2007 round of interviews of the New Immigrant Survey. The new version may allow for the analysis of trends over time bringing further understanding about what drives group differences in labor-market performance of immigrants (Akresh, 2008).

Although the uncertainties of the labor market during the last decade and the effects of the economic recession may hinder any hopes for improved employment outcomes among the highly educated immigrant population.

Immigration Policy

Improvements in the occupational trajectory of spouses of U.S. citizens suggest that a change in immigration policy from a family based to an employer based selection criteria may not be appropriate at this time until further investigation of longitudinal data.

The analysis of trends over time is necessary before coming to firmer conclusions about group differences in labor-market performance and what drives the differences. Akresh

(2008) found that while employment-based immigrants initially obtained higher-status occupations and higher earnings than family immigrants, the two groups’ labor-market outcomes converged over time.

151

Recommendations for Workforce Development Policy

Recognition of Foreign Credentials

The lack of recognition of foreign education and professional credentials has been recognized as a serious problem by policymakers who have pushed for changes in credentialing and immigrant admission systems. Countries with effective credential- recognition systems such as Australia, Canada and the U.K. are more attractive to prospective high skilled immigrants, and they make better use of their skills (Batalanova et al., 2008). In contrast, the U.S. takes a hands-off approach leaving the recognition of credentials and foreign education to individual states and professional associations.

There are a number of paths that policy makers might take when it comes to credentialing. The ideal path would be to promote international accreditation that would streamline the cross-country transferability; however this solution is not realistically attainable. Another approach would be to make the assessment of foreign credentials part of the pre-immigration admission requirements (Batalanova et al., 2008). Creticos (2007) proposed a more realistic approach and suggested the creation of a central information resource for state occupational licensing information, along with expanding international examination opportunities for certain licensed professions and the establishment of credential bridging programs

Credential bridging programs in partnership with foundations and community- based organizations may provide either accelerated education opportunities leading to a degree at an accredited professional education program or specific courses aimed at filling a gap in the educational credentials of a prospective licensee who was educated at

152

a foreign institution. These initiatives may include English language training, development of alternative career paths in related occupations and credential evaluation, authentication and gap analysis.

It will be advisable to develop and share a catalog of best practices of “what works” to deal with immigrant skill underutilization and recognition of foreign credentials (Creticos, 2007). For example, the state of Maryland created a centralized agency to deal with all licensing requirements and provided mentoring and dedicated advice to immigrants seeking credential recognition. Several states also implemented

“welcome back initiatives” to help foreign health care professionals to enter their occupational fields in the U.S. (International Health Worker Assistance Center, 2011).

Federal, state and local government agencies should take a primary role in supporting the full integration of foreign-educated workers in key occupations facing labor shortages. College-educated immigrants are currently facing both structural and social barriers to full employment. The structural barriers can be resolved by programs and initiatives to provide information on licensing requirements and career pathways. The social barriers can be mitigated by joint collaboration of the business community, public agencies and community organizations.

153

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Appendix A

HISTOGRAMS OF CONTINUOUS VARIABLES

164

Appendix B

CORRELATIONS OF CONTINUOUS VARIABLES

Age_at_entry Workexabr WorkexUS Foreign Edu US edu<=23 USedu > 23 Time_res

Age_at_entry 1 .651** -.189** .213** -.329** -.140** -.337**

Workexabr .651** 1 -.146** .111** -.153** -.136** -.094*

Work_exp_US -.189** -.146** 1 -.136** .132** .331** .784**

Foreign Edu .213** .111** -.136** 1 -.339** -.075 -.162**

US edu<=23 -.329** -.153** .132** -.339** 1 -.093* .261** 165

USedu > 23 -.140** -.136** .331** -.075 -.093* 1 .310**

Time_res -.337** -.094* .784** -.162** .261** .310** 1

*p< 0.05 level (2-tailed); **p<0.10 level (2-tailed)

102

Appendix C

CALCULATION AND RECODING OF VARIABLES

*Calculation of time1, time2, time3

*Time1 (year of employment in last occupation abroad) is B26 (year first employment) if

B50=2 (did not change jobs). If subject changed jobs B50= 1, Then Time 1 is B51 (yr start other job before US)

IF (B50 = 1) Time1=B51.

EXECUTE.

IF (B50 = 2) Time1=B26.

EXECUTE.

*Time2 (year of employment of first occupation in US) is B75 (year first work after moving to US)

COMPUTE Time2=B75.

EXECUTE.

*Time 3 if still working at main US job B98=Yes (1), then time 3 is B75 (year first occ)

(no occupational status change) If changed jobs, B98 = NO(2), then time 3 is C20A_1

(Year started working on current job),

IF (B98=1) Time3=B75.

166

EXECUTE.

IF (B98 = 2) Time3=C20A_1.

EXECUTE.

*Calculate Total Years of foreign employment.

*If the subject changed jobs (B50=1), then total years of foreign employment will be B49

(last year employed at first job) minus B26 (year started first job) plus (+) B73 (year worked at this last job) minus (-) B51 (year started other job)

16 cases in B49 reported 9995 IF STILL WORKING AT THIS JOB in the United States.

In 11 cases when B49= 9995, B50 was 2 (did not changed jobs), so last year of employment abroad was considered first year of employment in US.

IF (B49= 9995 & B50=2) NewB49=B75.

EXECUTE.

In 5 cases when B49= 9995, B50 was 1 (changed jobs). Upon visual observation I noticed that in two cases were B49 was 9995, B73 had also a value of 9995, and B51 had the same value as B75.

IF (B49= 9995 & B50=1 & B73=9995) NewB49=B75.

EXECUTE.

*For the three cases for which B49= 9995 and B50=1 but B73 was not 9995, last year of first job was considered first year of starting second job abroad (B51)

IF (B49= 9995 & B50=1 & B73 ~= 9995) NewB49=B51.

EXECUTE.

167

*For the rest of cases in which B49 was not 9995, I kept the same values of B49 reported as last year of first position

IF (B49 ~= 9995 ) NewB49=B49.

EXECUTE

*To address cases in B73 (last year worked other job abroad) that are coded 9995 because subject did not change companies, I created a new variable in which the last year of working abroad is the first year they started working in the US (B75), if B73 was not

9995 I left the reported year value.

IF (B73~= 9995 ) NewB73=B73.

EXECUTE.

IF (B73= 9995 ) NewB73=B75.

EXECUTE.

* Define Variable Properties.

*NewB73.

FORMATS NewB73(F8.0).

EXECUTE.

*Total years of working experience abroad for those who did not change jobs (B50=2) is

NewB49-B26 (last year in this position – year started working in this position).

IF (B50= 2 ) workexpabroad=NewB49-B26.

EXECUTE.

* Define Variable Properties.

*workexpabroad.

168

FORMATS workexpabroad(F8.0).

EXECUTE.

*There are 18 missing cases, four negative values (one -6, three -1), clear reporting errors of start and finish years and 30 cases started and finish in same year, so resulting value is zero.

*Total years of working experience abroad for those who changed jobs (B50=1) is

NewB49-B26 (last year in this first position – year started working in this first position) plus NewB73-B51 (last year in second position – year started working in second position).

IF (B50= 1) workexpabroad=(NewB49-B26) + (NewB73-B51).

EXECUTE.

*There are 22 missing cases, , four negative values (two -1, one -2, one -9 and one -13), clear reporting errors of start and finish years and 26 cases started and finish in same year, so resulting value is zero.

*To calculate years of working experience in the US

If people changed jobs from the first position in the US to the current position at time of interview (B98=2, not working at same job), then total work experience in US will be the sum of times working at each job.

(B99 (last year employment 1st position in US) – B75 (first year employment in US))

+(year of interview (2003 or 2004) -(C20A_1(year started working on current job)

* Define Variable Properties.

*STRTYR.

169

ALTER TYPE STRTYR(F4.0).

*STRTYR.

VARIABLE LEVEL STRTYR(ORDINAL).

FORMATS STRTYR(F4.0).

EXECUTE.

IF (B98= 2) workexpabroad=(B99-B75) + (STRTYR- C20A_1).

EXECUTE.

If subject did not change main jobs (B98=1, working at the same company), then total work experience is time of interview – B75 (first year employment in US)

IF (B98= 1) workexpabroad= (STRTYR-B75).

EXECUTE.

*Please note that subject could have changed occupational status from their first US position to the current position at time of interview even if they reported being at the same main job in the US.

*To calculate years of foreign education, I subtracted the years of schooling in US from the total years of education (A20-A21)

*Case number 798010 is a clear outlier, the value of total years of schooling is 86 years deleted.

* 320 subjects had completed from 1 to 14 years of US education. If subject had US schooling (USedu>0), age they started studying in US was equal or less than 23

(Batalanova, 2008), it was considered that subject had completed postsecondary education in the US only (USeducation1) and would affect their first job position in the

170

US. This variable will also be entered in the second stage since it could affect current position at time of entry.

All others were considered to have completed both US and foreign postsecondary education (USeducation2). This variable was entered only in the second stage regression as it was considered to affect

*Re-coding variables

R1 Num 8 RESPONDENT ENGLISH. How good was the Respondent's English?

1 Very Good

2 Good

3 Fair

4 Poor

5 N/A, (Interview not conducted in English)

Values 1, 2, 3 recoded to Yes, values 4 and 5 No

RECODE English_Proficient (1=1) (2=1) (3=1) (4=2) (5=2) (MISSING=SYSMIS).

EXECUTE.

* Define Variable Properties.

*English_Proficient.

VALUE LABELS English_Proficient

1 'Proficient in English'

2 'Not Proficient in English'.

EXECUTE.

*Label Visa Type

171

1=spcitz, 2=empprin, 3=divprin, 4=other

* Define Variable Properties.

*Visatype.

VALUE LABELS Visatype

1 'Spouse US Citizen'

2 'Employment principal'

3 'Diversity'

4 'Other'.

EXECUTE.

*Label Offered Job prior to moving to US

* Define Variable Properties.

*C25_1offjob.

VALUE LABELS C25_1offjob

1 'Yes'

2 'No'.

EXECUTE.

*Label country born

* Define Variable Properties.

*A9Amocountry.

VALUE LABELS A9Amocountry

-1 'Missing'

172

38 'Canada'

44 'China'

47 'Colombia'

55 'Cuba'

62 'Dominican Republic'

65 'El Salvador'

69 'Ethiopia'

88 'Guatemala'

92 'Haiti'

98 'India'

105 'Jamaica'

111 'Korea'

135 'Mexico'

152 'Nigeria'

163 'Peru'

164 'Philippines'

166 'Poland'

172 'Russia'

215 'Ukrain'

217 'United Kingdom'

218 'United States'

224 'Vietnam'

173

301 'Europe & Central Asia'

302 'East Asia, South Asia & The Pacific'

304 'Other North America'

305 'Latin America & The Caribbean'

306 'African Sub-Saharan'

307 'Middle East & North Africa'

308 'Oceania'

310 'Artic Region'.

EXECUTE.

*Recode country born into region of origin

RECODE A9Amocountry (38=3) (44=1) (47=5) (55=5) (62=5) (65=5) (69=6) (88=5)

(92=5) (98=2) (105=5)

(111=1) (135=5) (152=6) (163=5) (164=1) (166=4) (172=4) (215=4) (217=3) (224=1)

(301=3) (302=1) (305=4) (306=6) (307=6) (308=3) (MISSING=SYSMIS) INTO

Region_Origin.

EXECUTE.

* Define Variable Properties.

*Region_Origin.

VALUE LABELS Region_Origin

1 'Asia'

2 'India'

3 'Europe, Canada and Oceania'

174

4 'East Europe'

5 'Latin America & the Caribe'

6 'Africa & Middle East'.

EXECUTE.

*Label Highest Degree Received

* Define Variable Properties.

*Highest_degree.

VALUE LABELS Highest_degree

5 'Bachelors'

6 'Masters'

7 'Doctorate'

8 'JD/Md'.

EXECUTE.

*Recode GENDER

MALE...... 1

FEMALE...... 2

* Define Variable Properties.

*Gender.

VALUE LABELS Gender

1 'Male'

2 'Female'.

EXECUTE.

175

*Label regulated occupations

* Define Variable Properties.

*Lastocabroad.

VALUE LABELS Lastocabroad

230 'Education Administration'

300 'Engineering Managers'

350 'Medical & Health Care Mgrs'

500 'Agents Athletes & Artists'

800 'Accountants & Auditors'

1300 'Architect'

1310 'Surveyors'

1320 'Aerospace Engineers'

1330 'Agricultural Engineers'

1340 'Biomedical Engineers'

1350 'Chemical Engineers'

1360 'Civil Engineers'

1400 'Comp. Hardware Engineers'

1410 'Electrical Engineers'

1430 'Industrial Engineer'

1460 'Mechanical Engineers'

1530 'Engineers, all others'

2010 'Social Workers'

176

2100 'Lawyers'

2200 'Post Sec. Teachers'

2300 'Preschool & kinder teachers'

2310 'Elem & Mid School teachers'

2320 'Secondary School Teachers'

2330 'Special Education Teachers'

2340 'Other teachers'

3010 'Dentists'

3050 'Pharmacists'

3060 'Physicians & Surgeons'

3130 'Registered Nurses'

3150 'Occupational Therapists'

3160 'Physical Therapists'

3230 'Speech-Language Pathologist'

3240 'Therapists, all other'

3300 'Clinical Lab Technician'

3410 'Health Supp. Technician'

3500 'License Practical Nurses'

3530 'Misc Health technicians'

3600 'Nursing Aide'

3630 'Massage Therapists'

3640 'Dental Assistants'

177

3650 'Medical Assistants'

6350 'Electricians'

9030 'Aircraft Pilots/flight engineers'

9040 'Air Traffic controlers'

9840 'Military'

9950 'Unclassified occ st zero'

9990 'Unclassifiable'.

*FirstjUS.

VALUE LABELS FirstjUS

230 'Education Administration'

300 'Engineering Managers'

350 'Medical & Health Care Mgrs'

500 'Agents Athletes & Artists'

800 'Accountants & Auditors'

1300 'Architect'

1310 'Surveyors'

1320 'Aerospace Engineers'

1330 'Agricultural Engineers'

1340 'Biomedical Engineers'

1350 'Chemical Engineers'

1360 'Civil Engineers'

178

1400 'Comp. Hardware Engineers'

1410 'Electrical Engineers'

1430 'Industrial Engineer'

1460 'Mechanical Engineers'

1530 'Engineers, all others'

2010 'Social Workers'

2100 'Lawyers'

2200 'Post Sec. Teachers'

2300 'Preschool & kinder teachers'

2310 'Elem & Mid School teachers'

2320 'Secondary School Teachers'

2330 'Special Education Teachers'

2340 'Other teachers'

3010 'Dentists'

3050 'Pharmacists'

3060 'Physicians & Surgeons'

3130 'Registered Nurses'

3150 'Occupational Therapists'

3160 'Physical Therapists'

3230 'Speech-Language Pathologist'

3240 'Therapists, all other'

3300 'Clinical Lab Technician'

179

3410 'Health Supp. Technician'

3500 'License Practical Nurses'

3530 'Misc Health technicians'

3600 'Nursing Aide'

3630 'Massage Therapists'

3640 'Dental Assistants'

3650 'Medical Assistants'

6350 'Electricians'

9030 'Aircraft Pilots/flight engineers'

9040 'Air Traffic controlers'

9840 'Military'

9950 'Unclassified occ st zero'

9990 'Unclassifiable'.

*CurrjUS.

VALUE LABELS CurrjUS

230 'Education Administration'

300 'Engineering Managers'

350 'Medical & Health Care Mgrs'

500 'Agents Athletes & Artists'

800 'Accountants & Auditors'

1300 'Architect'

1310 'Surveyors'

180

1320 'Aerospace Engineers'

1330 'Agricultural Engineers'

1340 'Biomedical Engineers'

1350 'Chemical Engineers'

1360 'Civil Engineers'

1400 'Comp. Hardware Engineers'

1410 'Electrical Engineers'

1430 'Industrial Engineer'

1460 'Mechanical Engineers'

1530 'Engineers, all others'

2010 'Social Workers'

2100 'Lawyers'

2200 'Post Sec. Teachers'

2300 'Preschool & kinder teachers'

2310 'Elem & Mid School teachers'

2320 'Secondary School Teachers'

2330 'Special Education Teachers'

2340 'Other teachers'

3010 'Dentists'

3050 'Pharmacists'

3060 'Physicians & Surgeons'

3130 'Registered Nurses'

181

3150 'Occupational Therapists'

3160 'Physical Therapists'

3230 'Speech-Language Pathologist'

3240 'Therapists, all other'

3300 'Clinical Lab Technician'

3410 'Health Supp. Technician'

3500 'License Practical Nurses'

3530 'Misc Health technicians'

3600 'Nursing Aide'

3630 'Massage Therapists'

3640 'Dental Assistants'

3650 'Medical Assistants'

6350 'Electricians'

9030 'Aircraft Pilots/flight engineers'

9040 'Air Traffic controlers'

9840 'Military'

9950 'Unclassified occ st zero'

9990 'Unclassifiable'.

EXECUTE.

VARIABLE LABELS reglastab 'reg last job abroad' / regfirstus 'reg first job US' / regcurrus 'reg curr job US'

EXECUTE.

182

* Define Variable Properties.

* reglastab.

VALUE LABELS reglastab

0 'Not regulated occupation'

1 'Regulated occupation'.

* regfirstus.

VALUE LABELS regfirstus

0 'Not regulated occupation'

1 'Regulated occupation'.

* regcurrus.

VALUE LABELS regcurrus

0 'Not regulated occupation'

1 'Regulated occupation'.

EXECUTE.

*Recode census classification into NPB scale occupational scores in the three occupation

RECODE FirstjUS CurrjUS Lastocabroad (0010=93) (0020=86) (0050=90) (0060=89)

(0100=82) (0110=93) (0120=86) (0130=82) (0140=84) (0150=86) (0160=70) (0200=49)

(0210=31) (0220=77) (0230=92) (0300=96) (0310=52) (0340=63) (0350=85) (0400=76)

(0410=67) (0420=78) (0430=86) (0500=70) (0510=57)(0520=62) (0530=74) (0540=

73)(0560=80) (0600=76) (0620=77) (0700=83) (0710=92) (0730=69) (0800=85)

183

(0810=81) (0840=94) (0850=92) (0910=76) (0950= 73)(1000=89) (1010=90) (1020=94)

(1040=76) (1060=89) (1100=83) (1110=84) (1200=96) (1220=90) (1230=90)

(1240=91) (1300=92) (1310=84) (1320=95) (1340=91) (1350=95) (1360=94) (1400=92)

(1410=94) (1430=90) (1460=93) (1530=94) (1540=69) (1550=72) (1600=83)

(1610=88) (1640= 88) (1650=93) (1700=99) (1710=94) (1720=91) (1740= 93)

(1760=91) (1800=98) (1810=87) (1820=93) (1830= 92) (1840=96) (1860=82)

(1910=67) (1920=71) (1960=53) (2000=75) (2010=77) (2020=68) (2040=75)

(2050=63) (2060=50) (2100=99) (2140=71) (2150=64)(2200=86) (2300=45) (2310=83)

(2320=86) (2330=80) (2340=45) (2430=82) (2440=22) (2540=32) (2550=88) (2540=32)

(2600=56) (2630=67)(2700= 55) (2710=86) (2720=41) (2740= 32) (2750=51) (2760=37)

(2800=55) (2810=78) (2820=79) (2830=79) (2840=89) (2850=76) (2860=57) (2900=66)

(2910=55) (2920=73) (3010=100) (3050=97) (3060=100) (3130=83) (3150=88)

(3160=90) (3230=87) (3240=74) (3300=73) (3320=72) (3410=49) (3500=57) (3510=45)

(3530=60) (3600=28) (3610=62) (3630=48) (3640=45) (3650=42) (3700=72)

(3730=67) (3740=77) (3820=87) (3850=79) (3920=36) (4000=39) (4010=33) (4020=8)

(4030=3) (4040=30) (4050=4) (4060= 1)(4110=20) (4120=16) (4130=1) (4140=1)

(4150=4) (4200= 37) (4210=52) (4220=17) (4230=7) (4240= 44) (4250=11) (4300=62)

(4320=54) (4510=31) (4530=36) (4540=32) (4550=62) (4600=21) (4610=19) (4620=37)

(4640=36) (4700=60) (4710=76) (4720=11) (4740=17)(4750=42) (4760=32) (4800=73)

(4810=74) (4820=87) (4830=56) (4840=74) (4850=79) (4900=21) (4920=70) (4940=20)

(4950=21) (4960=61) (5000=66) (5020=39) (5100=49) (5110=47) (5120=48) (5140=55)

(5150=63) (5160=36) (5230= 54) (5240=48) (5250=68)(5260=29) (5300=33) (5310=38)

184

(5320=38) (5330=59) (5340=58) (5400=34) (5410=53) (5420=49) (5500=55) (5510= 37)

(5540=69) (5550=69) (5600=66) (5610=33) (5620=24) (5630=36) (5700=54) (5800=58)

(5810=41) (5820=45) (5840=56) (5850=32) (5860=40) (5900=36) (5920=60) (5930=60)

(6000=33) (6050=6) (6200=60) (6230=35) (6240=29) (6260=21) (6320=40)

(6350=58) (6360=41) (6400=30) (6420=23) (6440=47) (6510= 18)(6600=12) (6760=33)

(7010=66) (7020=70) (7040=53) (7140=72) (7200=37) (7210=48) (7220=51) (7240=32)

(7330=56) (7340=47) (7410=64) (7430=67) (7540=46) (7620=43) (7700=60) (7720=28)

(7750=29) (7800=22) (7810= 22) (7840=25) (7850=11)(7900=54) (7950=33) (8000=30)

(8030= 52) (8100=38) (8140=39) (8220=34) (8250=46) (8260=45) (8300=13) (8310=9)

(8320=11) (8350=20) (8500=37) (8550=32) (8600=73) (8610=63) (8640=57) (8710=21)

(8740=45) (8750=34) (8760=47) (8800=18) (8810=30) (8830=33) (8850=23) (8920=38)

(8950=17) (8960=30) (9000=60) (9030=92) (9040=84) (9130=41) (9140=31) (9240=68)

(9350=20) (9360=11) (940=30) (9410=67) (9600=31) (9610=8) (9620=20) (9640=12)

INTO status2 status3 status1.

VARIABLE LABELS status2 'Occ st firstUS' /status3 'occ status cur US' /status1 'occ status abroad'.

EXECUTE

*To find out difference in status from one occupation to another we compute

COMPUTE DV1=status2 - status1.

VARIABLE LABELS DV1 'DV1'.

EXECUTE.

COMPUTE DV2=status3 - status2.

185

VARIABLE LABELS DV2 'DV2'.

EXECUTE.

DESCRIPTIVES VARIABLES=DV1 DV2

/STATISTICS=MEAN STDDEV MIN MAX.

RECODE DV1 DV2 (MISSING=SYSMIS) (0=3) (1 thru 95=1) (-96 thru -1=2) INTO

Dependent1 Dependent2.

VARIABLE LABELS Dependent1 'Dependent1' /Dependent2 'Dependent2'.

EXECUTE.

* Define Variable Properties.

*Dependent1.

FORMATS Dependent1(F8.0).

VALUE LABELS Dependent1

1 'Upgraded'

2 'Downgraded'

3 'Match'.

*Dependent2.

FORMATS Dependent2(F8.0).

VALUE LABELS Dependent2

1 'Upgraded'

2 'Downgraded'

3 'Match'.

EXECUTE.

186