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INTERNSHIP  PORTFOLIO    

XIAOYANG  SUN  SPRING  2015  

     

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TABLE  OF  CONTENTS      

Resume……………………………………………………………………………………………………………..3  

Organizational  Analysis………………………………………………………………………………………7  

Data  Cleaning  and  Appending……………………………….…………………………………………….36  

Self  Reflection……………………………………………………………………………………………………43  

Writing  Sample………………………………………………………………………………………………….49  

List  of  References………………………………………………………………………………………………78  

 

 

 

 

 

 

 

 

 

   

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     RÉSUMÉ  

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Xiaoyang Sun

Address: Contact Information: 214 North 10th St. Phone: 310-920-9127 Apt 3A E-mail: tuf15311@temple.edu Philadelphia, PA, 19107 Professional Objective__________________________________________ To obtain experience in the area of applied social science research aimed at improving the educational opportunities for minority and underrepresented groups. Particularly desire to acquire an internship in which I can further develop my knowledge and skills in qualitative and quantitative methods related to my specialization in Sociology of Education. Educational Background________________________________________ M.A. in Sociology (anticipated May 2015) Ph.D. Sociology (anticipated May 2017) Department of Sociology Temple University Philadelphia, PA 19140 B.A. in Sociology (June 2013) Department of Sociology Minzu University of China Beijing, China 100081 Research and Teaching Experience_______________________________ Quantitative Intern Research for Action January 2015 to Present Cleaning and preparing data with Stata for quantitative analysis; Merging data from individual dataset; Generating graphs and figures useful in developing sound research reports; Writing summaries using bullet points to explain the findings of a particular quantitative analysis. Graduate Teaching Assistant Department of Sociology, Temple University, Philadelphia, PA August 2013 to Present Assist faculty with course-related responsibilities, including exam preparation and grading, one-on-one student advising, and work with online course system (Blackboard). Course I work as a TA for including: Sociology of the Self, Introduction of Sociology, Ethnicity and Immigrations in the U.S.

   

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Research Assistant School of Education, Minzu University of China, Beijing September 2012 to June 2013 Assisted a visiting UCLA professor with an ethnographic study of faculty life and organizational change at China’s leading minority university. The study focused on challenges the university faced in maintaining its mission to serve ethnic minorities. Responsibilities including assisting in arranging the interview schedule, transcription and translation, and data coding and analysis. Field Worker Minzu University Rural Development Project February 2011 Conducted fieldwork in a rural region of northern China to evaluate government funded infrastructure projects relating to community improvements; administered surveys and interviewed community members and leaders. Served as lead author of the final report titled, “Research on the Service Condition of Public Infrastructure and Strategic Analysis in Shangdianzicun Beijing” (awarded “Outstanding Achievement Prize for a Team Project” by Minzu University). Field Worker Minzu University Fei Xiaotong Research Project Spring & Summer Semesters 2011 Conducted collaborative research at a Beijing nursing home for senior citizens. Interviewed senior citizens and health care providers about the quality of the nursing facilities and available resources. Developed a final report titled, “Research of Low-Income Senior Citizens’ Living Conditions in a Nursing Home” (awarded “Third Prize in the Fei Xiaotong Minzu University School of Ethnology and Sociology Student Research Competition”) Volunteer Experience___________________________________________ Volunteer Teacher, Beijing Elementary School for Immigrant Workers’ Children July 2010 to June 2011 Provide lessons on moral and civic education to the children of Beijing immigrants from the country-side on a weekly basis. Volunteer Teacher, Minzu University Rural Development Project Winter Break 2011 Taught children in grades 1 through 6 the basics of creative expression through painting. Research Papers and Presentations_______________________________ Rhoads, Robert A., and Xiaoyang Sun. (2014, April). “Ethnic Diversity in China and the Role of Minzu University: Analyzing Organizational Narratives of Change.” Paper

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presented at the Annual Meeting of the American Educational Research Association (AERA), Philadelphia. Sun, Xiaoyang. (2014). “Income Inequality/Disparity among First-Generation Immigrants in the U.S Labor Market: Examining the Effect of Country of Origin with 2012 American Community Survey Data.” Unpublished paper. Academic Awards and Honors___________________________________ ▪ Graduate Teaching Assistantship from the Department of Sociology, Temple University

(August 2013 to present) ▪ Certificate of Completion in Advanced Academic Intensive English Program, UCLA

Extension (Summer 2012) ▪ “Outstanding Academic Performance Scholarship,” Minzu Department of Sociology

(2009-10) ▪ “Academic Excellence Scholarship Award” from the Hong Kong Xin Shan Foundation

(based on grades for the academic year 2010-11) ▪ “Third Place Award in the Fei Xiaotong Research Project” from the Minzu University

School of Ethnology and Sociology (June 2011) ▪ “Outstanding Achievement Prize for a Team Project” by Minzu University (February 2011) ▪ “Third Place Award” (among all Beijing university students) from the National English

Contest for College Students (NECCS) (July 2010) ▪ “Excellent Speaker Award” from Minzu University English Speech Contest (July 2011) ▪ “Outstanding Volunteer Award,” Minzu Department of Sociology (2010-11) Language and Technical Skills___________________________________ ▪ Fluent in Chinese Mandarin and English (TOEFL score of 106) ▪ Skilled with SPSS, STATA, Word, Excel, PowerPoint

   

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Professional Affiliations & Conferences___________________________ ▪ Attended the 2014 Annual Meeting of the American Educational Research Association (AERA), Philadelphia, Pennsylvania. ▪ Attended the 2014 Annual Meeting of the Association for the Study of Higher Education (ASHE), Washington D.C. References____________________________________________________ Available upon request.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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 ORGANIZATIONAL  

ANALYSIS    

 

 

 

 

 

 

 

 

 

 

 

 

   

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Organizational  Analysis  of  Research  for  Action  

 

1. Clients and Customers

Research for Action (RFA) is a non-profit research organization that does applied

research to help address educational inequality and improve teaching quality and thus

better students’ outcomes in the very disadvantage and segregated neighborhoods and

communities. RFA also does evaluation projects for schools, districts, educational

organizations and communities, providing research-based advice for educational policy

making locally and national wide. The projects are typically longer-term research, during

which multiple publications are produced. Normally publications are individual

documents – research reports or evaluation studies – that typically fall under a project.

With in mind the kind of research RFA does, it is not hard to imagine the potential clients

and customers they have. RFA accepts funding from both individual clients and

organizational clients. Some of their funders are American Association of University

Women, Bread Loaf Rural Teachers’ Network, New York City Mathematics Project, The

Social Impact of the Arts Project (SIAP) (UPenn), City University of New York, and

Educating Children for Parenting (now Educating Communities for Parenting) etc. The

above examples of RFA’s founders reveal the needs and interests of their customers and

clients. The founders of RFA are mainly educational agencies and institutions that

concern themselves with the educational opportunities of women, children, the wellbeing

of rural teachers etc. They also devote to research on the external factors that may have

impacts on people’s educational outcomes such as extracurricular math tutoring, artistic

and sports activities.

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A larger portion of RFA’s work is supported by the Philadelphia institutions such

as The Philadelphia Foundation, and Philadelphia Education Fund etc., therefore the

research and studies RFA generate are also mainly within the Philadelphia area. The

economically deprived neighborhoods and communities (mainly in the northern part of

Philadelphia) are all aware of the fact that educational institutions, such as some of the

clients of RFA, devote time and money into researching educational inequality and racial

segregation in the very disadvantaged areas in Philadelphia. And those educational

institutions also invest a lot of money on providing all kinds of programs that aim at

improving academic performance and enriching the out-school lives of the kids in those

neighborhoods. At the same time, those educational institutions also found research

organizations such as RFA to do evaluation research on the efficiency of these special

programs. Thus local people in the poor neighborhoods have very high expectations of

the clients of RFA, they have the assumption that the effort these local institutions put has

the potential to alleviate unequal opportunity and access to educational resource and to

help more lower class kids to realize upward mobility through education, and the

communities in the areas of Philadelphia encourage their kids to attend these programs

with great enthusiasm.

The relationship between RFA and its clients are mutual. The educational

institutions that are eager to act on the inequality in education in Philadelphia trust the

ability of RFA to conduct thorough and rigorous research in order to provide information

and feedbacks of their programs and projects helping the poor. At the same time, RFA is

also able to trust the will and determination of their clients and founders that they would

only want to conduct just and unbiased research and implement effective and fair

   

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programs. I did a small interview with one research associate at RFA about what they

expect from their clients and founders, he told me that before they take the case and

accept the funding, they will have to make sure the study their clients want them to do

does not contain any politically extreme elements or would mislead the public on certain

sensitive issues. RFA also makes sure that what their founders want to publish based on

the research does not violate the interest of the vulnerable population such as the poor

and people of color, and RFA promises the research will protect the privacy of the

respondents. Thus RFA ceases serving their clients when RFA believes what their clients

ask for is not in line with the value of RFA which is to address educational inequality and

help the vulnerable population.

2. Community

Although Research for Action (RFA) has built its reputation through the nation in

terms of doing rigorous educational studies and evaluation, it is a Philadelphia-based

research institution which means most of its main projects and research revolves around

the areas of Philadelphia. Philadelphia is the 5th largest city in the United States and has

about 1,553,165 people by 2014, it was the first capital city of the nation, and it was

ranked number 4 in the list of “You must go to” places in the world in 2014. Having all

the nice thing people say about Philadelphia in mind, we must admit Philadelphia as a

major city with large urban areas has its own problems that must be addressed in order to

accomplish sustainable growth and development, including one of the country’s highest

poverty rates, high crime rates, a declining real estate market and an unemployment rate

above the nation’s average etc. And there are many other issues that come with high

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poverty rates, limited educational resources and unequal educational opportunities is one

of them, and this is what RFA intends to address and help with.

Due to the terrible economic conditions of Philadelphia, there are many low-

income communities, especially in the northern part of Philadelphia. In those poor

communities where the schools are under-funded have been failing minorities and

contributing to social reproduction and cycles of high school dropouts, drug addiction,

crime, etc. One recently published book by Alice Goffman titled On the Run: Fugitive

Life in an American City gives us a good glimpse of the poor situation in these

neighborhoods. This ethnographic study is done in one of the poorest racially segregated

neighborhood in Philadelphia, it gives rich and in-depth description of how desperate the

young black males in this neighborhood is. Due to the deep rooted structural and

institutional racist that permeates our society, it is hard for those young black males to

enter the mainstream world and benefit from public institutions such as schools, hospitals

etc. And it seems like that the only way to make a living is to break rules and violate the

laws by selling drugs etc. Thus very few of the young black males can stay completely

free from jail or poison which makes them live fugitive lives.

And it is in this kind of community context--further damaged by withdraws of

federal support for schools and social programs under neoliberal economic regimes--that

RFA seeks to operate and have an impact. RFA intend to alleviate the terrible situation in

these communities by conducting educational research that fully explores the

complexities of educational inequality in Philadelphia, interpreting research for multiple

audiences, and also providing recommendations on education reform for educational

   

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“policy organizations and foundations, communities and school districts, and city- and

state-level policymakers”1.

The work RFA does has profound meanings to the residents in those communities

because they stick to the goal of “better helping their clients and their community

understand the current state of knowledge on key issues”. And a lot of people in the poor

communities have very high expectation and hope towards RFA’s work in addressing and

alleviating educational inequality as their work is straightforward and friendly to all

public reader with a variety of educational levels. According to one of the research

associates at RFA, a lot of residents, schools and companies etc. in the areas of

Philadelphia are very cooperative when collecting data as part of their own effort to help

alleviate inequality together with RFA. And in these processes, RFA has been getting

more and more support and founding from both personal and institutional levels and

continuing to “draw national attention as an organization that exemplifies the value of

being a locally-focused, applied research organization”.  

3. Careers

Given the nature of Research for Action (RFA) is a research organization that

concerns their research effort to addressing inequality and other issues in the field of

education, most of RFA’s staff are professionals who have academic background in

social science and educational study. In order to give an even clearer glimpse of what

RFA’s staff composition is, I browsed through most of their staff’s bios on their website.

                                                                                                                                       1  http://www.researchforaction.org/menu/about-us/

 

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Based on what I have observed, many of RFA’s staff is trained in universities in the area

of Philadelphia such as Temple University, University of Pennsylvania, Drexel

University, and there is also some staff from Colombia University, University of Georgia,

and Florida State University etc. The kind of degree RFA’s staff hold ranges from

Bachelor’s degree in Sociology to M.S. in public policy and finally to Ph.D. in Urban

Education etc. Therefore we can say that RFA’s staff composition is quite diverse.

In terms of career path and choice of RFA’s staff, I found the short conversation I

had with my adviser at RFA who is the project director of 21 Century convincing. I asked

him why he did not want to become a professor working at a good university, he briefly

told me why he chose his current career path at RFA instead of being a faculty and

teaching at universities and colleges. My adviser Jina Gao is originally from China and

he came to the United States to pursue his Ph.D. in Foundations of Education at Florida

State University. He was an excellent student at school and especially good at

quantitative research skills due to his statistical background in economics, thus he was

chosen to work for the local government where his university was in Florida, even when

he was still finishing up his doctoral study. And from then on he was set on the track of

working as a researcher for governmental office and research institutions. Therefore his

suggestion for is that, once I decide what to choose between working for research

institutions and teaching at universities, it is not easy to switch because these two tracks

could be quite different in many ways. For example, the style and quality of publications

is very different at research institutions whose goal is to write and present the research

results in a concise and straightforward way so it could be open to public audiences.

Whereas for faculties at universities, it is more important to generate new theories, find

   

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new areas to explore and publish articles in very good journals in the field. Thus the

nature of the first permanent job to a great extent decides the lifelong career path. I found

that conversation very enlightening since what I am doing now at RFA is a great

opportunity for me to find out which one of these two tracks appeals to me more, so I can

make better career choice in the future.

Due to the nature of social science, the methodological training needed in RFA is

qualitative method, quantitative method and also mixed method. And RFA has gained

profound recognition in their methodological strengths. They provide methodological

training to every new staff, even to their interns, based on their preference. With

qualitative training, RFA lays emphasis on the careful analysis of interviews, documents

and other format of qualitative data; with quantitative training, RFA not only provides

numerous opportunities for their staff to get hands-on experience with real data, ranging

from basic data cleaning and synthesizing to advanced quantitative analysis, RFA also

teaches their new staff how to design quantitative studies and carry out the study with

efficiency by both traditional paper-pencil based questionnaires and internet based

surveys. Normally, RFA “begins every project by identifying the most pressing research

questions, and employing the methodological approach that will yield the most robust

and useful results”.

The field RFA’s in is called “applied research and policy making” and people

with skills in such areas can work in other industries besides educational research centers.

For example, public health research, public policy research, governmental offices that do

policy research, foundations such as the Pew Foundation (in Philadelphia) do policy

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research. Education is not the only field that intersects with RFA, but public policy and

applied research is the bigger field.

4. Safety

Organizational risk can contain a wide array of risks, including budgetary risk,

investment risk, program management risk, safety risk, inventory risk, legal liability risk

and the risk from information systems etc. What the major and crucial risks are for

organizations, to a great extent, depends on the nature of the organization and the way the

organization is structured. With the case of Research for Action (RFA), because it is a

non-profit research organization, the biggest risk faced by RFA is its funding structure,

given that the organization largely operates on the basis of grant money (soft money) and

does not have a huge source of steady and predictable income. Although the Executive

Director, Kathleen Shaw who is a former Temple University professor has been very

successful at raising revenue for RFA, especially in light if its significant contributions to

improving education, she still must depend on wealthy donors and grant opportunities.

Should such revenue decrease significantly over the course of time, then RFA could

become quite vulnerable.

Other risks faced by RFA relate to competitions for research funding and

opportunities. And such competitions could come from research centers at the nearby

universities and colleges such as University of Pennsylvania, Temple University or

Drexel University, or in terms of national projects from universities and research centers

around the country. Other than the formal educational institutions, RFA also needs to

compete for funding, clients and customers against non-profit research agencies and

organizations that provide similar service and conduct similar kind of social science

   

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research. Such organizations come to mind are Equal Measure (OMG Center for

Collaborative Learning which also provide research-based evaluation and philanthropic

services), Research for Better Schools (RBS which is “a private nonprofit educational

R&D firm also located in Philadelphia”) etc. 2With the fierce competition from other

similar research organizations in mind, there is also potential risk of topic researchers at

RFA being recruited elsewhere in those research organizations mentioned above;

therefore RFA will also need to work on building a staff-friendly corporate culture that

ensures staff loyalty.

And then there are also risks for RFA including researchers and field workers

working in the context of applied settings such as schools and the potentials for mistakes

or ethical lapses to occur. As we all know, it is very important for researchers to keep in

mind their goal of conducting just research and sticking to ethical principles when doing

research. I can recall seeing a truck running through the city of Philadelphia last semester

carrying a big sign with eye-catching notes on it: tell Temple University to stick to higher

ethical standard. After doing little search online, I found it was about “an ethics

investigation by Temple University into two of its professors and their research in favor

of – and funded by – the private prison industry”. 3In this case, the two professors were

charged of not sticking to the disclosure standards for working papers and opinion and

the publication of their final work spurred some public attention to this issue. Thus RFA

as a research organization also faces the risk of being charged with these accusations if

they don’t pay attention to the ethical principles when carrying out research of their own.

                                                                                                                                       2  http://www.rbs.org/About-RBS/History/289/  

3  https://www.insidehighered.com/news/2014/06/11/temple-­‐u-­‐professors-­‐accused-­‐not-­‐  

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Last but not least, there is also the risk of danger for RFA’s researchers working

in the field, especially in high-crime or run-down areas in the city of Philadelphia or other

urban areas where often times schools face the biggest challenges.

5. Power

Power is a crucial component within any social organization. Where there is

hierarchy, there is power structure and power stratification. Therefore I would think about

the power within Research for Action (RFA) in two ways--internal power dynamics

among actors within RFA and then also RFA as an organizational player in a broader

context. Issues of power at RFA may be considered in terms of the internal organizational

structure and any hierarchy that may exist as well as the overall role and influence of

RFA as a player in the larger educational research and policy making arena.

In terms of internal power relations, one might consider the nature of the

employees and volunteers comprising the organizations staffing. For example, the most

powerful actor at RFA is the Executive Director, Kathleen Shaw. She also has a high

standing beyond RFA given that her work has recognized by the Association for the

Study of Higher Education (ASHE) in terms of giving her the first ever "Excellence in

Public Policy in Higher Education" award in November 2013. This award recognized

Shaw as an individual who contributes excellent work at the nexus of academic

scholarship and policy practice in the field of public policy and higher education. Her

status no doubt brings greater power and influence to the overall organization with the

broader policy arena.

   

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What is more, in terms of the internal dynamics of power, there are many other

roles and working arrangements that must be analyzed. Within the job structure at RFA,

there are four levels of jobs: administrators, research associates, data analysts and interns,

from higher positions to lower ones in the hierarchy of power. The administrators at

higher level are in charge of keeping RFA running by raising funding, attracting

customers, clients, and promoting RFA in the market. Once staff at administration level

succeeds in bringing in money and projects, the next level research associates would be

in charge of designing the research, building research agenda, generating different levels

of task for people at lower levels to do. Then the next level of data analysts would be the

people doing the real physical working of carrying out the research, including collecting

data, entering data, cleaning datasets for statistical analysis, and doing some basic level of

data analysis. Then on the bottom of the power structure within RFA would be interns

like me. The interns normally are undergraduate or graduate students from universities in

Philadelphia. Most of the times, interns work for free at RFA without getting paid, but

RFA does provide 10 dollars traffic stipend if interns manage to work 10 hours per week.

Although interns have some discretion in terms of their work schedule at RFA (RFA

really is relaxed and flexible in terms of the working schedule for interns, as long as the

interns can fulfil 10 hours a week, it does not matter on what days the interns have to

work at RFA), they have no control over what projects they would be working on. The

research associates and data analysts would supervise the interns and tell them what to do

which further will determines what kind of training the interns get. Because the interns

are at the bottom of the power stratification, they may not always get the exact kind of

training and working experience they intended to gain.

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In terms of RFA’s relationships with other organizations, including the city of

Philadelphia, the State of Pennsylvania, and other entities, its power and influence is

largely tied to its success in doing meaningful policy-oriented research. When the

organization is successful, its power expands and its ability to generate grants and

additional funding increases. This further strengthens the cycle of power. But on the other

hand, when the organization fails to achieve its research goals for a variety of complex

reasons, its overall power and influence within local community, the state, and the whole

nation may suffer.

6. Identity and Diversity

Identity and diversity are crucial dimensions when looking at organizations in

society. Society is comprised of mass of heterogeneous individuals therefore people are

sorted along the line of gender, race, class, and sexual orientation etc. At Research for

Action (RFA), I clearly observed how gender, race and sexual orientation shapes the

organization and its relationship to its staff, clients, funding base and even to the larger

community.

Examining the research RFA does, I don’t think sexual orientation play much of a

role in shaping RFA’s research agenda and policy impact. However, the organization is

guided primarily by concerns linked to gender, race/ethnicity and social class relative to

educational equity. Nearly all the research RFA does address these issues. For example,

RFA’s current work in Rutgers University’s RU STEPped Up for Success Initiative

program is concerned with the evaluation of an NSF-funded program aimed at increasing

recruitment and retention of underrepresented minorities and women in college STEM

majors. The evaluation of this project has great potential in implicating policy making

   

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processes and promoting the opportunities for women and minorities to purse college

majors and careers in the STEM field. Currently, a big chunk of RFA’s effort is devoted

to the 21st Century Community Learning Centers which is the project on am working on

as an intern there. 21st Century Community Learning Center is a project that is funded

through a federal grant to create OST activities in six school districts and communities in

the areas of Philadelphia, and all these six school districts and communities have very

high poverty rate and manly comprised of African American residents. And what RFA

researchers do is to conduct a mixed-methods evaluation of each of these six school

districts and communities to determine the impact of program activities on students’

educational outcomes. Therefor the research results shed light on how governmental

grants and community efforts to provide out-school-tutoring will improve the teaching

quality of some of the very poor and racially segregated neighborhoods.

The way RFA’s research agenda and policy impact are shaped by gender,

race/ethnicity and social class could also be revealed by its founders and customers.

Browsing through the founders’ and clients of RFA, I saw institutions and organizations

such as College Access Foundation, New York City Mathematics Project, Lehman

College, City University of New York, Ms. Foundation for Women and Congreso de

Latinos Unidos all of which indicate their concerns related to gender, race/ethnicity, and

social class. Thus based on examining the kind of research RFA does and the customer

and founding source, I argue RFA’s research agenda and policy impact are clearly shaped

by gender, race/ethnicity and social class.

One could also look at the internal dynamics of RFA and see how identities such

as gender, sexuality, race/ethnicity etc. shape the organizational structure within it. I

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argue RFA seems to be an empowering space for women, especially given that the

Executive Director Kate Shaw is a woman. And by examining the staff directory, I found

that out 25 of RFA’s full time employees, 16 of them are women. Thus RFA is a place

where women have great chance to purse professional careers and a place where women

get autonomous and independence. Race and ethnicity are also active in the internal

dynamics of RFA. Among the same 25 staff of RFA, 2 of them are African American,

and among those two African American employees, the African American woman works

in a key position as the director of finance. Also as I know, 1 of the 25 employees is

originally from China, one originally from India. Given sexuality is not the kind of

identity a lot people are open about, nor is it something once can visually identify, so it is

not easy to argue sexuality plays an important role at RFA. However, I do know as a fact

that the office assistance (the main secretary) is gay and he is apparently comfortable

coming out at RFA. Because the office assistance is one of the people who interviewed

me in the beginning, so we had some casual conversation where he openly talked about

his partner and their plane of adopting a child. Therefore I would assume RFA is also

highly supportive of LBGT people, again given the progressive/liberal orientation of

RFA's leadership.

7. History, mission, values, goals

Research for Action (RFA) is a non-profit research organization in Philadelphia,

center city. Founded in 1992, RFA works with public school districts, postsecondary

institutions, and educational and community organizations to improve the educational

opportunities for those traditionally disadvantaged students. Engaged mainly in

educational research, program evaluation, and strategic advice etc., RFA aims at applying

   

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academic research to promoting equal educational opportunities and increasing

educational quality in the areas of Philadelphia as well as throughout the nation. RFA has

achieved its aim and goal by doing quantitative, qualitative and mixed method research

and presenting these research results in a straightforward manner that is reader friendly to

public audiences.

The organizational mission and goal of RFA is to use research as the basis for the

improvement of educational opportunities and outcomes for traditionally underserved

students. Their work is designed to strengthen public schools and postsecondary

institutions; provide research-based recommendations to policymakers, practitioners and

the public at the local, state and national levels; and enriches the civic and community

dialogue about public education. Take the project I am currently working on at RFA for

example. The 21st century project is an evaluation project on 12 community learning

center providers in the area of Philadelphia. 21st Century community learning center

program is founded by the U.S. Department of Education and its goal is to provide

extracurricular tutoring and activities and enrich student’s school life to students in high

poverty communities. So what we do here is basically collecting questionnaires and

attendance records of students from the 12 providers, and then collecting academic

performance records from the schools those students attend. Finally, we compare the

students’ academic performance at school before and after they attend the community

learning center tutoring and other activities. With our report on the student outcomes after

attending community learning centers, we give overall evaluations of the program in

terms of its efficiency in improving students’ academic performance at school. At the

same time, we also report problems and issues that stood out in the process of

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implementation of the program and provide corresponding suggestions and advice to the

local level staff and institutions, as well as to the federal level institutions. By doing

research and generating evaluation reports like this, RFA helps improving the school life

and learning experience of disadvantaged students in high poverty neighborhoods and

communities thus fulfilling its mission and achieving its goal.

RFA has applied its value of addressing educational inequality and promoting

educational quality to all of their work over the years since it was founded. Education is

becoming an increasingly important mean of realizing upward social mobility in modern

world. Acquiring basic knowledge of the world as well as gaining professional skills, to a

great extent, determines where one will land in the social strata today. Therefore it is

urgent and significant to be aware of the fact that a large portion of school age population

in our society is still living in poverty and does not have equal opportunities to pursue

education. Sociology of education is where my own research interest lies, making the

intern experience at RFA a perfect fit in terms of developing my own research interest

and skills. The overarching value of addressing and alleviating educational inequality

throughout all RFA’s work corresponds to mine own and inspires me from the beginning

of my academic life. It is also the reason why I stick to my academic life—doing

something in the real world, knowing what I do will actually make changes in people’s

life, and hopefully make the world a better place for all of us.

8. Bureaucracy

Bureaucracy is an important concept in sociology, especially when it comes to the

sociological study in the field of organization and politics. Many significant sociology

figures have discussed about this concept, including Karl Marx, Max Weber and Peter

   

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Blau. In the discussion of Weber, bureaucracy is an efficient mechanical system that

operates mainly based on the principle of rationalization. In modern times, bureaucracy is

the administrative system governing any formal institution and organization.

Research for Action (RFA) as a non-profit research organization constitutes its

own bureaucracy. The bureaucracy within RFA helps the running and maintenance of the

organization with administrative, research-related, customer funding-related polices and

rules.

Rules and policies regarding the administrative system suggest the power

distribution within RFA. I would argue staffs on the administrative level have more

power, so to speak, in terms of grasping the general direction of RFA. Especially the

executive director Kate Shaw, she is in the highest position in the organization where she

could convoke meetings with people on the administrative level to solicit opinions and

feedbacks on the current administrative rules and policies thus they can make adjustments

and improvement. The administrative rules and policies make it clear to all the people

within the bureaucracy what their roles and responsibilities are thus assured the

functioning of the whole organization.

Rules and policies regarding research and searchers help clarify the principles on

research related ethics including how to do research and how to treat their respondents

when conducting research. For example, in the research-related policies at RFA require

their research associates and data analysts to stick to the principles of confidentiality

which means they would not give away the information or data they collected during

work to anyone for any purposes.

  26  

People can find these rules and policies in the personnel brochure, or you can talk

to the human resource manager and the main secretary at RFA regarding any specific

policies and rules that are not covered in the personnel brochure. Because I am either a

full time employee or a paid intern, I don’t have to sign any formal legal contract with

RFA. Therefore I had to do a little interview with the human resource manager to find out

where I could find the information. And the human resource manager, in the

conversation, told me that once new staff (either a full time employee or a paid intern)

signs the contract with RFA, they will provide the new staff with several hiring material,

including the personnel brochure that contains clear rules and policies of the

organization. And most of the time, people can find very detailed information the history,

mission, staff directory, clients and founder etc. of the organization on RFA’s website.

Unfortunately, RFA does not have its rules and policies online, as the human resource

manager told me, that information is more of internal documents they share with their

staff. And the administrative level staffs such as human resource manager, main

secretary, and the executive director are the key to the implementation of administrative,

research-related, customer funding-related polices and rules that I have discussed above.

Staffs at administrative level will hold meetings on regular basis to discuss the problems

and issues that stand out and make sure the people in the corresponding position will take

care of them on time so the implementation of the rules and policies will be assured and

supervised.

9. Laws and ethics

Research for Action (RFA) mainly depends on establishing and maintaining high

levels of public trust given that their funding and support mostly comes through grants

   

27    

and foundations. This means they must pay great attention to tightly following all

relevant legal and ethical considerations. Especially as a research organization whose

product is very much likely to influence public opinions and add to public knowledge,

RFA needs to be very cautious with lawful and ethical issues within their process of

conducting research to assure the objectivity and fairness of their final research findings.

Legally speaking, RFA is a non-profit organization thus they must be careful not

to take partisan positions lest they lose their standing as a non-profit organization. But

because they are also advocates for education and for addressing educational inequality,

they must walk a fine line in terms of the political positions they adopt with regard to

supporting more equitable schools, well avoiding any appearance of

partisanship. Therefore, this requires RFA to see a ethical balance here so they will not

take extreme partisan positions with any side but, at the meantime, are able to speak for

the disadvantage and marginalized population such as people of color, lower class people

and sexual minorities. One important way to avoid ethical issues within the organization

that comes to mind is that RFA should always keep transparent and clear record of their

funding budge. So if anyone doubts on the objectivity and unbiasedness of their research

results based on the source of money that founds the study, they would have everything

on record and have nothing to hide from the public.

Another aspect to look at the issue in terms of ethics in the field is that RFA must

be very careful in conducting the research they do in schools, universities and lower class

neighborhoods, given that most human subjects in those settings are very likely to be

vulnerable population in our society, for example, school children are under 18 years of

age and hence many research projects must include parental approval. Thus RFA has to

  28  

stick to the ASA Code of Ethics in mind all the time when they go about collecting data

from human subjects in order to protect these vulnerable people from getting hurt and

being exploited. And this way of avoiding ethical issues probably means building close

ties to families and communities in which they conduct their research.

Furthermore, RFA benefits from its many research grants and projects and thus

has an ethical responsibility to fully give back to the communities and school districts in

which they conduct their work. It is very important and necessary to think about what

they can do in return for their respondents who participated in the research project and

contributed to RFA’s work. For example, maybe RFA can, by their publication, bring

public attention to the very school district that is in great needs for money, investment

and social support; maybe RFA can foster the public awareness of the severity of

educational inequality in urban areas to promote and solicit policy reforms from the level

of local, state and federal government.

Based on what I have argued above, in order to ensure that RFA field workers and

researchers engage in ethical practices, the organization should always pay close attention

to the essential ethical obligations of researchers to avoid missteps that would induce

ethical issues. Therefore training new employees at RFA should is always a crucial

process through which ethical considerations would be stressed and emphasized.

10. Money

Nonprofit organizations (NPOs), from a legal perspective, are organizations that

use their revenues to better and further accomplish its organizational missions or goals

that embody their ultimate value instead of distributing its revenues to any benefit seeker

   

29    

within or out of the organization. In a common sense perspective, nonprofit organizations

(NPOs) are organizations that do not see making profit as the main organization goal.

They are instead more concerned with, most of the times, addressing social inequality

and solving social problems. And due to the nature of nonprofit organizations (NPOs),

people sometimes are very likely to associate nonprofit organizations with charitable

organizations. Although they are two types of organizations, there are some organizations

that are both charitable and nonprofit at the same time, such as Lucile Packard

Foundation for Children's Health in Palo Alto, California, Alexander City Kiwanis

Foundation in Alex City, Alabama, and Vietnam Veterans Workshop New England

Shelter for Homeless Veterans in Boston, Massachusetts etc.

Therefore Research for Action (RFA) as a non-profit organization, it first of all,

meet some of the state’s educational institutions evaluation requirements and thus gets

some funding from state and local governments. Federal, state, and local government

grants fund many programs provided by nonprofits, especially in areas such as urban

human service, higher education and public education etc. As far as I know, the 21st

century project is one of the projects that are founded by the federal government. Other

than getting grant money from governments, RFA also dependents on research grants and

foundation’s philanthropic support to get money. And those research grants and

foundations could be both corporate and individual, but most of them are philanthropic in

nature. Such founders of RFA , for example, includes William Penn Foundation which is

a charitable foundation that is “dedicated to improving the quality of life through efforts

that close the achievement gap for low-income children, ensure a sustainable

environment, foster creativity that enhances civic life, and advance philanthropy in the

  30  

Philadelphia region”. 4And the money source and the nature of founders definitely have

significant power in shaping the mission and purpose of the organization. The kind of

research RFA does reflects the main concern and focus of their clients and founder. The

research of RFA will have to address the issues such as income inequality their founders

like William Penn Foundation would care.

RFA as a nonprofit research organization also gets money and funding from self-

generated fees for research related services they provide. For example, for a lot of

evaluation project RFA does, they get income for doing that kind of work for their

clients. In another word, their clients who come up with certain programs and projects

would buy service from RFA to evaluate the implementation and efficiency of their

programs and projects. Last but not least, RFA also benefits from volunteer assistance

(such as unpaid interns like me) which is in essence a form of income source in that labor

is a cost. RFA every year would need about 5 to 8 interns, depending on the workload of

the specific year. Normally the interns from universities in Philadelphia don’t get paid

doing intern work at RFA. However, the interns do get involved with the actual projects

RFA work on and will do the literal work that contribute to the real projects. Therefore I

would argue getting unpaid interns every year also counts as one source of income.

Again, I suggest you ask for a copy of the budget or at least get some info. However, I

have to say that the above arguments are merely based on summarizing RFA’s present

research grants listed on their website which is available to public. For more detailed

                                                                                                                                       4  http://www.researchforaction.org/

 

   

31    

income source, I probably have to result to budget information of RFA which is not

available to everyone.

11. Web and social media presence

In the era of internet, social media have become an increasingly popular and

important way for organizations and institutions to promote themselves. Through social

media such as Google, YouTube, LinkedIn, Facebook, Twitter etc., organizations build

their public image and convey their organizational missions and goals by posting pictures

related to some of their activities or texts that report what has been going on in with them;

they also utilize social media as a way to promote their influence and attract potential

customers and clients by posting significant work and project online to make them

available to public.

With Research for Action (RFA), they have their own website

http://www.researchforaction.org/. This is RFA’s official website where they introduce

themselves to public by describing who they are, what they do, what their strength are,

who their customers and founders are, and what they have published in their field etc.

The official website of RFA is a straightforward and user friendly one compared with a

lot other official website of other organizations. It is first of all, visually unique with

RFA’s pinwheel logo on the upper left corner of the webpage, and then the whole

webpage is comprised of dark blue, dark orange and light orange which are also the main

color tone of RFA’s business card. From this point of view, RFA has done a good job in

building their image on social media by creating this unique visual representation of their

website and their business card. Second, the webpage has a place where you can put the

key word and search the specific topic related to the key word, then anything on their

  32  

webpage that contains that key word would come out automatically. This is a very user

friendly design that could save the users the trouble of going through massive

information on webpage and immediately direct people to the things they need. What’s

more, RFA has also put their Tag Cloud on the right side of the webpage. With the Tag

Cloud, users can easily identify what are the most frequently used words in their recent

work, what they are most concerned with in their recent projects, and what are the aspects

their recent work revolves around. Again, this is a place where internet users could get a

basic sense of the nature of RFA, and it is also a place where potential customers or

clients could easily identify whether RFA is a good match to their purpose. Another very

useful feather of RFA’s webpage is that they have a button called “Donate” by which one

can click and donate any amount of money to RFA. I think that is a practical function the

webpage given the way RFA functions financially.

I argue the main goals on social media for RFA is to call attention to their work in

disadvantaged communities, especially in terms of strengthening K-12 education for low-

income populations. And I also think that their goals on social media should be to

promote the organization in manner that can be used to encourage funders to support the

organization--given that RFA is a non-profit and depends on such support. That way they

would have more resource to do meaningful research that helps the disadvantaged

students in lower class neighborhoods.

One way I have in mind in terms of improving their web and social media

presence they should offer more talks and presentations related to their findings--so that

regular people and scholars as well will become familiar with the public policy role RFA

   

33    

plays. Their goals are really worthwhile and their research and findings should be shared

more widely.

12. Performance

A good way to evaluate an organization’s performance is to assess whether the

organizational goals and missions are successfully fulfilled or not. As a non-profit

research organization engaged mainly in educational research, program evaluation, and

strategic advice etc., Research for Action’s (RFA) mission is to use research as the basis

for the improvement of educational opportunities and outcomes for traditionally

underserved students. Their work is designed to strengthen public schools and

postsecondary institutions; provide research-based recommendations to policymakers,

practitioners and the public at the local, state and national levels; and enriches the civic

and community dialogue about public education.

Therefore based on their mission, RFA’s performance can be assessed by its

ability to acquire grant and funding support, as well as its efforts to produce policy

oriented research findings. On one hand, from the founder/client information I can collect

from RFA’s website, it is clear RFA has been getting continuous funding from clients

and founders who provides RFA the ability to fulfill and deliver their mission. Thus from

this regard, RFA as a non-profit research organization has been performing efficiently in

terms of striving for grant and funding which is the premise of delivering its ultimate

organization mission and goal—doing research to influence the real world. On the other

hand, RFA has also been doing a good job in generating research based findings and

recommendations to influence the public policy making process. For example, in 21st

Century Project, RFA’s researchers produce evaluation report that examines the

efficiency and outcomes of the project through rigorous research. Based on the research

results, RFA also indicates the strengths and challenges of OST (out school tutoring)

  34  

programs in Philadelphia, and provides corresponding recommendations and suggestions

for program providers at local scale as well as OST System at city wide scale. And a lot

of the recommendations aiming at strengthening individual programs and improving the

capacity of the City’s OST system are extremely practical which, to a great extent,

guarantees the implementations of these recommendations in the real world thus assures

the delivering of RFA’s mission.

Another key area for assessing RFA’s performance is more difficult to accurately

gauge, and that involves evaluating their actual impact on the kinds of educational

policies and practices they hope to impact. This means actually examining the schools

and educational institutions they work with to determine if changes related to

improvement and increased equity have taken place or not. For the purpose of the  

organizational analysis memos in this class, I have limited resource and access to literally

go to these places to collect data, however, it is still ultimately the best way to assess the

organization’s performance.

As to improving RFA’s performance, I would, first of all, suggest RFA extend

their publicity by taking advantage of the internet era and building a health and positive

public image on multiple social media sites such as LinkedIn, Facebook, Twitter, and

YouTube etc. so that they can attract more founders and clients to bring more financial

guarantee for their ability to continue their research. At the meanwhile, it could also help

RFA broadly and further distribute their research findings and the recommendations

based on the research results to draw public attention and thus increase efficiency in

delivering these recommendations in reality. Last but not least, I would suggest RFA

build more partnerships with educational institutions or other research organizations like

   

35    

themselves so they can collaborate on more significant transformative tasks and projects;

in this way RFA and its partners together can have more meaningful discussions of

educational policies and problems and how to impact them.

 

  36  

   

 

 

 

 

DATA  CLEANSING  &  

APPENDING    

 

 

 

 

 

 

 

 

 

 

 

 

   

37    

 

 

Site:  Research  for  Action  

Date: 03/17/2015

Having dealt with raw data about 8 weeks at RFA, I intend to test my ability of

dealing with data by taking on a data cleaning and appending task independently.

Although the task might not sound too exciting, I still find it challenging and important.

My daily routine at RFA normally starts with my supervisor assigning a task from his

project, and he will tell me what to do and how he wants the task to be done. If I have any

other questions in the process, I would go to my supervisor for further help anytime.

Therefore, at my intern at RFA, I always get detailed guidance and instruction from

people on how to write certain command and how to deal with the unexpected situation

when running command. This time with this independent task, I want to get a project that

is similar to what I normally do at RFA but use the task as a test to see if I can complete

the data cleaning process from the beginning to end all on my own without asking any

question and seeking any help. Although logging raw data and cleansing dataset seem to

be a tedious job, it is still the very crucial part of quantitative study because it constitutes

the premise of accurate statistical analysis. Without accurate and precise data, no matter

how complicated the statistical procedures are, the quantitative results could be

misleading and even meaningless. What is more, my main purpose of doing internship at

RFA is to get plenty hands on experience on dealing with massive data and preparing

data, including logging, cleansing, and synthesize dataset so that I can become adept

taking care of data within Stata, thus this challenge task is a perfect way to evaluate my

goal at RFA.

  38  

As I have stated above, I intend to challenge myself with the task of logging raw

data from Excel into Stata, cleansing and preparing the data ready for further statistical

analysis. Unlike what I usually do, I would not ask for any help from anyone when

completing this task and will deal with any unexpected situation with data cleansing on

my own. The project with which I will challenge myself is Elev8. Basically, Elev8 is a

community schools efforts in four sites across the country – Baltimore, Chicago, New

Mexico and Oakland initiated by The Atlantic Philanthropies (Atlantic). Elev8 mainly

aims at improving students outcomes by providing OST (out-school-time) programs in

disadvantage neighborhoods and communities. The evaluation work RFA does is by

analyzing the data from the community schools that participated in the Elev8 program.

These local schools will provide raw data in Excel files.

In order to take on this challenge, I talked to my supervisor first, explained my

intention of challenging myself, and asked him if he has any projects he works on that

need some data cleaning job. He told me he could use some help with Elev8 project

which needs someone to log the data from Excel and then clean it up within Stata. The

Elev8 sounds exactly perfect for my purpose, so we had a short discussion of the task

where he first showed me what the raw data looked like. Within the Excel file, it has

several variables we need for analysis. However, the variable that matter to us the most is

the year variable. Because we have data from both before the implementation of Elev8

program and after Elev8 program. With year variable, we can compare the school

performance and students’ outcomes before and after the implementation of Elev8

program to generate compelling evidence illustrating the efficiency of the program.

However, the year variable in the Excel file is not an independent variable, so transposing

   

39    

year variable is needed. And after the transposing year variable, he needed me to clean

the data in Stata, including getting rid of invalid variables, removing duplicated entries,

cleaning all the leading and tailing space in the data etc. And then he assured me that I

could do it on my own because I have done all these tasks in my days at RFA with the

help and guidance of him and his assistant. So I had my notebook ready with which I

wrote down the steps doing data cleaning from the past experience at RFA, and I took a

good look at the raw data in Excel. I also went back to the syntax file that I have cleaned

data with before, the commands all got freshened up in my mind again. Thus, after

having a discussion of the task with my supervisor and freshening up my memories of the

commands and steps of doing data cleaning, I knew I was ready to do this and challenge

myself.

My task actually went really well which is I did not expect. When I got the raw

data from my supervisor, I first cut out all the information that is not useful according to

the need of my supervisor. As I have figured out myself through cleaning data at RFA, it

is always a good idea to first cut out the variables in Excel instead of write a drop

command in Stata. Then I also cut the table name on the first row in Excel because Stata

automatically recognizes the first row of Excel sheet as variable names. So when I cut the

table name, all the variable names will be on the first row which will be recognized by

Stata. Then I had to do a transpose action in Excel by copying the table that contains all

the variables I need and transpose it when pasting. This way, Excel flip the row and

column so the all the data will vary by year. This is the last step of preparing data in

Excel. Next step, I need to log the data in Excel into Stata. I first opened a new syntax

window in Stata, and put my name and the project name on top within the asterisk box on

  40  

the very top of the syntax. Then I wrote command “cd*****” which tells Stata from

where I want to find and open the raw data. I then did the import command by writing

“import excel "transpose excel.xlsx", firstrow sheet ("Alburquerque") clear” which

signifies the name of Excel file and which sheet within that Excel file I want to use. After

this command, the data was logged into Stata and I could open the data editor window to

visually analyze the data and decide what to do with it next. In data editor window, I

spotted leading and tailing space scattered within the Attendance Rate variable, so I

decided to do a trim command which will help me get rid of the spaces. By writing

“replace Varname=trim(Varname )”, I successfully cleaned the space within ** variable.

Next, in order to make all the variables numerical for the purpose of analysis, I did a

destring command by writing “destring Varname, replace force” which transform all the

string values in the *** variable into numerical; and by adding force to the end, Stata will

forcefully turn all the other format of variables into missing value. And after I did some

basic rename commands that rename the variables based on what I was told, I did a save

command which saves the cleaned data into the same folder as the “cd” command

indicates. I then basically did the same thing with the rest of data from Oakland,

Baltimore, and Chicago and saved them independently. The last step was to combine all

the data from these 4 cities into a one complete dataset that has the same variables in

them. So I first opened the data from Alburquerque as the main dataset, and then did the

append command by writing “append using "dataset name" ” 3 times, and finally the data

from all the other 3 cities were appended to the main dataset. In the end, I just saved the

synthesized data with a new name under the folder I found convenient. In order to check

   

41    

the outcome of the data cleaning and synthesizing, I open the merged data in Stata, and

all the variables came out just as I wanted so the task turned out to be very successful.

This task turned out to be successful in the end which makes me pleased with my

days at RFA. And after I sent the final cleaned dataset to my supervisor, he was very

satisfied with my work and even asked the HR manager to send me an email, inviting me

to their summer internship program. From this challenge task experience, I learned many

things that, I think, will benefit me throughout my career. The most important lesson I

learned is that people should always be confident and not be scared to challenge

themselves with things they are uncertain about. I didn’t even think of taking on such a

task that require me to perform on data cleaning on my own until this challenge exercise

in the syllabus. I get so used to the everyday routine at RFA where I get orders to do

certain things and get help when there are issues and problems that I it never cross my

mind that I should take on a task initiatively and try to do it all by myself. I was

subconsciously intimidated to do a task on my own at RFA because I only had very

limited training on Stata and quantitative method, so I was scared to make mistakes and

disappoint people at RFA who hired me as an intern. However, after I literally took the

first step out and pushed myself to do this on my own, I realized it is not as hard as I

thought it would be despite of some fears and difficulties I experienced when doing it.

Then I came to the conclusion that the best way to acquire a skill is through doing it on

your own, only in that way can you test yourself to see if you have the ability to perform

with that skill independently. And only when you are able to accomplish the task

completely on your own can you say that you successfully master the skill. However, I

also gained some precious experience technically with using Stata. The first thing is that I

  42  

realized, similar to SPSS, one can use the drop-down menu in Stata to do certain

command. People all say how intuitive Stata commands are, I almost forget Stata also has

drop-down menu that works the same way as the command. One short cut to learn a new

command could be through using the drop-down menu first, and then Stata will

automatically write the corresponding commands in the window box. So I can remember

and learn from this command and next time when I have to use that again, I should have

it in my mind that I can just write the command myself. Another small shortcut I learned

from this challenge task is that when cleaning data, sometimes it is faster and more

continent to do some cleaning with raw data in Excel. For example, it is always a good

idea to get rid of the empty rows or columns within the data in Excel so when you log

data in Stata it looks a lot more tidy and organized. And the action of transposing data

can only be accomplished in Excel. Thus the useful experience I learned through this

challenge excise is that Excel could be quite useful when cleaning data in collaboration

with Stata.

 

   

43    

 

 

 

 

   

 

 

 

 

 

 

 

 

SELF  REFLECTION  

  44  

Self-Reflection of Internship at Research for Action

Xiaoyang Sun 04/20/2015

1. Experience at the Site (RFA)

Doing the internship at Research for Action (RFA) is something I never thought I

would do because I always lack confidence in my quantitative knowledge and skill.

However, I pushed myself to do so through the Independent Study in the doctoral

program and realized how rewarding such an experience could be. The most exciting

thing happened when I first started at RFA is when I learned that my supervisor Jian Gao

is originally from China and his assistant MB who also supervises me from time to time

is an alumni of our department. The reason why this fact matters to me is that I used to

get intimidated from asking questions or helps from my teacher and supervisor by the

relationship between supervisors and supervisees, teachers and students; given the fact

that in Chinese culture, teachers and supervisors are greatly respected with high authority,

and students are encouraged to always have a serious relationship in a respectful way

with their teachers, instead of a casual and friend like one. Therefore I never have called

my teachers or supervisors by their first name. There should always be titles such as

Professor or Doctor before their last name. Due to that, I often times get too nervous and

uncomfortable to make myself clear in terms of explaining my questions and issues. I

dread having conversations with my professors. However, at RFA, the fact that my

supervisor is originally from China makes me feel much more relaxed because I know if

there is something complicated, I can explain better in Mandarin, thus there will be less

miscommunication or misunderstanding. And out of habit, I always call my supervisor

Dr. Gao or Professor Gao given the environment and atmosphere at RFA is quite

   

45    

academic just like universities. But my supervisor told me many times I can call him Jian

instead of professor Gao since he doesn’t teach. And with his assistant M who graduated

from Temple not so long ago, I also feel less nervous when asking questions and having

conversations with him. Because our common experience at Temple, it is easier to relate

to each other like peers and colleagues.

Having a more relaxed and colleagueial relationship with people enables me to

ask more important questions and have more useful conversations at RFA, thus I learned

many new knowledge and skills quickly. Before I started at RFA, I also turned to

Lyda.com (which is an online course program provided by Temple) and took the course

on Excel where I learned many skills and shortcuts to improve my efficiently working

with Excel. At one time, my advisor asked me to generate some graphs with Excel in

order to show some trends of the data. I recall the online Excel course arguing trend line

is a proper way to show trend, so I generated trend line with the data he gave me, and he

was very pleased with my work. When reporting what I did with the data by generating

trend lines, I asked him what the nature was of trend lines in Excel. Is it based on the

average of actual data? Or is it a line based on the standard deviation of actual data? I did

some research online first to found out the nature of trend line but could not find any

result. Then my advisor told me that the trend line in Excel, in essence, is a linear

regression, thus the line is a predicated value based on actual data. And because Excel

does not provide p values like SPSS and STATA, people normally don’t use it for

advanced statistically analysis. Instead, people more often use it as a straightforward

mean to do descriptive analysis. He was very pleased with my efficiency and accuracy of

  46  

my work, and complimented me on my curiosity to ask questions and learn new

knowledge.

Through such experience and conversations at RFA, I found myself making huge

progress in many aspects. I, first of all, gained great confidence in my capacity to do

quantitative work. I used to doubt myself on doing quantitative study because statistics

and math are never my strength, however, when I force myself to do so I realized that I

not only are able to do that, I also am able to do it well. Another precious experience at

RFA is that I get more exposure to American culture in a formal social setting and

realized the cultural differences between the U.S. and China, so I learned to be more

flexible in instructing my action at workplace in the U.S. with schemas and frames in the

American cultural tool kit. I gradually built a more relaxing relationship with my

coworkers at RFA and treat them as equal colleagues who I can have conversation and

discussion with, and then I feel less uncomfortable asking questions. Only in that way can

I learn and progress faster as faster at RFA.

2. Professional Inspiration from the Internship

My main purpose of interning at RFA is to get hands on experience with

massive data and become adept working with STATA, and eventually develop the

ability to deal with data as if it is a second nature. Although someone can argue data

cleaning is a tedious and repetitive job, I still seems crucial and important to me. First

of all, I think accurate data is the very premise of any meaningful quantitative study.

Without accurate dataset, the quantitative results could be misleading and even

   

47    

invalid. What is more, we as graduate students only have limited exposure to the

experience of dealing with real data in class. However, a good way to master a skill is

through continuous practice; and become adept by running into all kinds of problems

and incidents when practicing. Thus during interning at RFA, I had a lot opportunities

dealing with real data and became pretty adept at logging data, cleansing and

synthesizing data. At the meanwhile, I also became more efficient with Excel and

explored a lot of very useful functions within Excel that I didn’t know before. And all

these skills add great advantage to me when hunting jobs in the job market given how

everything is computerized nowadays thus most professional jobs require these skills.

The massive contacts with STATA at RFA enables me to apply into practice the

theories I was taught in school and examine the applicability of different statistical

procedures we learned in class to real data. The combination of theory and practice

greatly improved my understanding and capacity in quantitative study which benefits

me a lot when seeking for a professional job in the field of applied social science

research due to the fact that quantitative method is more of a dominant practice.

The intern experience at RFA inspired me in many ways in terms of how I

look at research and the field. It made me more determined to do applied research

because I see how being able to go out collecting data, analyze data and interpret the

data results, in this case, could influence police making and thus educational reform

that address educational inequality and ameliorate that. By doing applied research,

people are literally able to do things that make meaningful changes in the real world

which deepens my appreciation of doing research, and at same time, further sparkles

my interest in this field. I hope to contribute to this field by working as a professional

  48  

in the future and thus this intern experience at RFA definitely adds to my advantage

in that regard.

3. Overall Self-Evaluation

Near the end of the intern, I realized I have become more confident of myself

since I started at RFA. I also have come to better understand the atmosphere of

professional workplace in the U.S. context by becoming more flexible when dealing with

cultural difference. I have been enlightened by other interns at RFA who are graduate or

undergraduate students from Temple or University of Pennsylvania. I admire their

courage of willing to take the challenging tasks such as interning at RFA which is

something I need to learn from them. And I am extremely impressed by MB

 

 

 

 

 

 

 

 

 

 

   

49    

 

 

 

 

 

 

 

 

 

 

WRITING  SAMPLE    

  50  

Income Inequality among First-Generation Immigrants in the U.S Labor Market:

Examining the Effect of Region of Origin

Xiaoyang Sun

Abstract

Objective. This study explores the effect of region of origin on U.S. immigrants’ economic outcomes. It does so by examining income disparity in the U.S. labor market among first generation immigrants, sorting out the effects of the immigration process from the effects of immigrants’ characteristics and from the responses of the host society (Evans 1984:1086) by controlling for length of residence, English proficiency and educational Attainment. Method. Data for first generation immigrants aged from 25 to 64 who are still active in the labor market was obtained from 2012 ACS (American Community Survey) 5 years sample. OLS Linear regression procedures were used to test the correlation between income disparity and region of origin with educational attainment, English proficiency, and length of residence as control variables. Conclusion. There are annual income disparity existing among first generation immigrants of different region of origin and this disparity is statistically significant. However, as has indicated by the data, the effect of region of origin on annual income becomes weaker after controlling for sex, English proficiency, length of residence, and educational attainment.

Introduction

The United States is often described as a country of immigrants and this idea

remains true even in the contemporary context (Slack and Jensen 2007: 1415). The flock

of immigrants from all over the world not only helps sustain the U.S population at a level

where it can reproduce the population itself, it also supports the U.S economy with

abundant personnel for the labor force. “Beyond population size, the most notable impact

of immigration has been the broadening of the social and cultural diversity of the

American population” (Hirschman 2005: 595). However, despite the great contribution

immigrants make to the U.S society, problems still exist in that “literature on

   

51    

international migration contends that immigrants often experience considerable hardships

when entering the labor market of a new country” (Raigman and Semyonov 1997: 108).

A further issue revealed by some of the research on immigration has shown

differences in annual income relative to region of origin. Thus, an analysis of immigrants

coming to the United States and the difficult adjustments they face should also consider

the effect of region of origin on annual income. Immigrants are an ethnically diverse

group and this diversity is analytically useful because it allows us to “separate the

consequences of immigration per se from consequences of characteristics of immigrants,

notably language, culture, and the possession of modern work skills” (Evans 1984:1065).

For example, on the one hand some immigrants such as Asians or Africans come from

countries where the language and culture are very distinctive from that of the United

States. On the other hand, some immigrants such as Europeans or Australians are from

countries that are rather close to the United States in terms of language and culture, as

well as other social aspects. Therefore, it is critical to examine the work experience, with

annual income as a main indicator in this paper, of immigrants from different regions to

better understand the overall adjustment and challenges they face as part of the

immigration process.

This paper seeks to examine key economic hardships faced by first-generation

immigrants (foreign born) particularly in terms of whether they experience relatively low

annual income within the U.S. labor market. Specifically, I address the following

research questions: 1) Is there income disparity among first-generation immigrants

related to their region of origin? 2) If there is income disparity, what is the extent of such

  52  

disparity? And, 3) After controlling for English proficiency, educational attainment, and

length of residence, are there still annual income differences related to region of origin?

Existing Research

The existing research literature pertaining to income disparity among immigrants

and related to region of origin is quite limited. This is especially true when it comes to

studies of the U.S. context that employ contemporary data. Perhaps the closest study to

what I am proposing was conducted by Evans (1984), titled, “Immigrant Women in

Australia: Resources, Family, and Work.” This study utilized a 1 percent public use

sample from the 1981 Australian Census to study the working experience of immigrant

women in Australia in terms of four indicators: their labor force involvement,

occupational niche, entrepreneurship, and income. Although this research is somewhat

dated, it nonetheless offers a framework for me to follow in examining the contemporary

U.S. context. Thus, my research study follows a similar line of inquiry as Evans but

extends that work in two significant ways. Frist, this paper will use 2012 American

Community Survey (ACS) 5-year sample to study the income disparity in the

contemporary U.S. context. Second, this paper will also look at immigrant men to see if

there are gender differences in annual income among immigrant groups. In what follows,

I organize my review of the literature into three sub-sections: economic hardship

immigrants experience in the U.S. labor market, differences in economic outcomes

related to region of origin, and demographic factors and labor outcomes.

   

53    

Economic Hardship Immigrants Experience in the U.S. Labor Market

Raigman and Semyonov (1997) have indicated that “immigrants often experience

considerable hardships when entering the labor market of a new country” (109). This

widely noticed and highly debated phenomenon has attracted a good number of

researchers to conduct studies pertaining to economic hardships generally experienced by

immigrants. For example, many researchers are interested in the income disparity in labor

markets among foreign born immigrants and native born non-Hispanic whites. Li (2000),

using micro data of 1996 Canadian census, found that “all immigrant groups in Canada

earned less than their native-born counterparts. The magnitude of net earning disparities

between immigrants and native-born Canadians varies, depending on gender, racial origin

and less so on CMA level” (290).

Madamba and De Jong (1997) studied job mismatch as an indicator of poor

economic performance among Asians in the U.S. Their study looked at six subgroups of

Asian workers: Chinese, Indian, Japanese, Vietnamese, Filipinos, and Korean. The results

reveal that “Asian immigrant workers were more likely to experience job mismatch than

the native born. In 1990, recent male immigrants in four of the six Asian groups had

greater job mismatch than did native born worker” (539). Based on the preceding study,

De Jong and Madamba (2001) further examine the economic performance of immigrants

by classifying underemployment into four types: unemployed, part-time employed,

working-poor, and job mismatch. After comparing these four types of underemployment

for immigrants and native born populations, they indicate that overall, “immigrant

underemployment was greater than that of native born” (117). The working-poverty rates

  54  

and unemployment rates were higher among blacks and Hispanics but when it came to

job mismatch, it was highest among Asians.

Furthermore, in a study examining labor market outcomes of immigrants across

European destinations, Adsera and Chiswick (2007) argue that there are great earning

disadvantages for immigrants when compared to native born populations. With the 1994-

2000 waves of ECHP (the European Community Household Panel) survey data, they

reported that, “overall, immigrants in Western Europe earn around 40% less at arrival

than the native born in that destination with the earning differential greater for those born

outside the EU than for immigrants born in other EU countries” (519). Hence, their study

offers further evidence of the labor-market challenges many immigrant groups face.

Gender may also be a factor in immigrant income inequality. With a specific

focus on the labor market outcomes of immigrant women, Schoeni (1998) compares

immigrant women with native born women in the U.S. between 1970 and 1990. The

results show that while only a slight difference in labor market outcomes was found in

the 1970s, this difference grew significantly over time. “Relative to natives, immigrant

women’s participation rate and weekly earnings (among working women) became lower,

and their unemployment rates became higher and by 1990, the wage gap was 14 percent”

(74). Along the same line, Boyd (1984) also argues in his study that “immigrant women

are observed to have occupational statuses which are lower on the average than those of

other sex and nativity groups… (1091)”. Similar findings are provided by Raigman and

Semyonov (1997) in their study of immigrant women in Israel. They used data from the

1983 census of population conducted by Israel’s Central Bureau of Statistics to examine

the “double disadvantage hypothesis”: this hypothesis basically posits that the labor

   

55    

market environment for immigrant women is likely to be extremely harsh compared with

native born men due to the joint effect of gender and immigration. Through the study

they came to the conclusion that “both immigrant men and women, regardless of

ethnicity, experienced declines in labor force participation and suffered occupational

loss” (120). However, the data suggest an even worse labor force participation decline for

immigrant women in the transition of moving to a new country.

There are also studies that look at native born children of immigrants (2nd

generation immigrants) as well. In a study conducted by Takei, Sakamoto, and Kim

(2013) they divide second generation Southeast Asian Americans (SEAA) into six

subgroups comprised of Cambodian, Filipino, Hmong, Laotian, Thai, and Vietnamese.

They find through the study that the earnings of these six groups are less than their white

counterparts even after controlling for age, educational attainment, English proficiency,

marital status, veteran status, disability status, metropolitan residence, and region of

residence. These results indicate that, “The SEAA groups generally appear to be

disadvantaged relative to white men” (211). Chiswick’s (1983) study also compares the

earnings and employment of American-born Chinese, Japanese, and Filipino men with

American-born white men. The findings reveal that when key mediating variables are

held constant, Chinese and white men show similar earnings and employment, with

Japanese men earning 4 percent lower weekly wages. However, “Filipino men…have

substantially lower levels of schooling, employment, and earnings” (211). Although this

study shows some improvement in terms of earnings and employment for native born

Chinese and Japanese men, the negative outcomes for Filipino men reveal that significant

differences may exist among the Asian American population.

  56  

Differences in Economic Outcomes Related to Region of Origin

Sakamoto, Goyette and Kim (2009) note in their study that, “Immigrants are

heterogeneous, reflecting the wide array of their countries of origin as well as varying

degrees of selectivity involved in their immigration circumstances” (259); thus, it is not

hard to see that immigrant as a social status constitutes many subgroups with each

subgroup facing different circumstances and potential hardships. Among all immigrants,

Raigman and Semyonov (1997) presented the “triple disadvantage hypothesis”

contending that female immigrants from less developed countries are more likely to be

unemployed in the labor market in Israel due to the joint effect of gender, country of

origin, and immigrant status. De Jong and Madamba (2001) found similar results

supporting a double disadvantage hypothesis, asserting that immigrants are challenged by

their immigrant status as well as their minority status, when compared with native born

minorities, respectively.

Immigrants in the U.S constitute culturally and ethnically diverse populations of

people coming from all over the world. Along these lines, studies have shown that the

immigration laws and policies could be “explicitly biased against particular nationalities”

(Ewing 2012: 1). This form of “favoritism” in U.S. immigration history dates back to the

1880s when anti-Chinese legislation was passed, although in the 1950s efforts were

undertaken to erase such forms of discrimination. For example, the Immigration and

Nationality Act of 1952 was a one such attempt but it was far from perfect: “Although it

eliminated race as a basis of exclusion from the United States, it retained the racist bias of

the national-origins quota system” (Ewing 2012: 5). Similar evidence of the

   

57    

discriminatory treatment on a national-origins basis is also highlighted by Foner (2013) in

a study that gives specific examples of exclusion policies and laws that “barred the entry

of Asians—in the case of the Chinese, as early as 1882” (Foner 2013: 18). Limitations

against Asian immigration is also noted by Massey et al (1998) when they noted that

during the 1960s Asians were blocked from entry into the U.S., and “racist immigration

laws that explicitly favored northern and western Europeans” were practiced (64). The

studies discussed above by Ewing, Foner, and Massey et al are helpful in terms of

providing evidence of the discriminatory practices historically speaking; however,

empirical studies using more recent data are necessary in order to examine whether such

practices still exist in contemporary U.S. society.

As noted previously, a study conducted by Evans (1984) examined the work

experiences of immigrant women in Australia in terms of four primary indicators: labor

force involvement, occupational niche, entrepreneurship, and income. This study

indicates that overall, immigrants from North America and Western Europe often times

do better than immigrant women from Third World countries in terms of landing better

jobs and gaining occupational prestige. Specifically, after adjusting for educational

attainment and other demographic characteristics, the effect of region of origin on income

difference decreases significantly for Mediterranean women; this suggests that the effect

of region of origin is actually spurious. Based on the empirical results, Evans came to the

conclusion that “the Australian labor market appears to be nearly blind to ethnicity, and

the labor market treats everybody about equally” (p. 1086).

But other empirical studies reveal a region of origin effect. For example, Boyd’s

(1984) empirical study focusing on immigrants in Canada highlighted unequal treatment

  58  

among immigrants of different countries of origin. She specifically noted that the

disadvantage of being foreign born varies based on birthplace of immigrant women in

Canada, and that “the analysis indicates that the double negative of being female and

foreign born is less of a factor for the occupational attainment of women born in the

United States and in the United Kingdom, than it is for women born in Europe and

elsewhere” (p. 1091). Also, a study done by Adsera and Chiswick (1999) reported a

similar effect with country of origin impacting labor market outcomes among immigrants

in Europe. They noted in their study that immigrants who are not EU born suffer

economically in the labor market compared to their counterparts who are EU born. There

was an exception to their findings, pointing out that, “the earnings of English Americans

are not significantly different from those born in the EU” (p. 518). Another research

study conducted by Schoeni (1998) also noted the economic outcomes among immigrant

women based on their country of origin, highlighting that “immigrants born in the United

Kingdom and Canada, Europe, Japan, Korea, China, the Philippines, and the Middle East

have had steady or improved wages and unemployment relative to U.S.-born women. At

the same time, immigrants from Mexico and Central America have experienced relatively

high unemployment and low earnings” (57).

Demographic Factors and Labor Outcomes

Demographic factors in analyses of immigration often times consider such

attributes of immigrants such as educational level, age, or the skill level of a particular

immigrant population often compared to members of the same country who decide not to

emigrate. For example, some studies have examined the degree to which a particular

   

59    

group of immigrants possesses more advanced labor skills in some cases “measured by

the share of professionals among entering immigrants” (Lobo and Salvo 1998: 738).

One stream of research relating to demographics and immigration focuses on the

effects of educational attainment and the degree to which immigration decisions are

informed by a belief that a host country offers a greater return on educational investment.

Chen (1995), in a study of Taiwanese immigrants in the U.S., argued that “emigrants are

self-selected from more able persons because they are willing to sacrifice their current

benefits and family ties and are therefore very strongly motivated to pursue economic

improvement in a foreign country” (251). A key finding of Chen’s that Taiwanese

immigrants with higher levels of educational attainment emigrated to the U.S. on the

basis that they believed the U.S. labor market offered greater returns to schooling (260).

Other scholars also have examined immigration decisions in light of perceptions of

greater returns to education. Cobb-Clark (1993) added gender as a variable to the

immigrant self-selection equation analyzing the experiences of women immigrants to the

U.S. She found that, like men, women self-selected to immigrate to the U.S. under

conditions in which the GDP is high but the returns to education are low and tended to do

better in the U.S. labor market.

Although it is not the intent of this paper to examine policy and its impact on

decisions to immigrate or not, it is worth noting that scholars have identified links

between immigration policy and whether certain immigrant populations became more or

less select based on professional qualifications. An example of this type of analysis is

offered by Lobo and Salvo (1998) in their treatment of Asian immigrants to the U.S.

They examined Immigration and Naturalization Service (INS) data for the period 1972 to

  60  

1994 with a particular interest in how changes in immigration policy resulted in more or

less selective populations of Asian immigrants in terms of professional credentials. They

primarily focused on three periods reflective of changes in U.S. immigration policy: 1)

1972-1977, 2) 1978-1991, 3) 1992-1994. One particular period of interest followed the

Immigration Act of 1990, “which allowed for a substantial increase in the entry of those

with professional qualifications” (739). Lobo and Salvo’s results show that the period

from 1978-1991 saw a decrease in professional credentials among Asian immigrants, and

then an increase during 1992 to 1994. As they explained: “With the Immigration Act of

1990, there was once again an upswing in the occupational selectivity of immigrants. The

share of professionals among Asian immigrants increased to 33 percent” (748-49) and

this corresponded with a decrease in Asian immigrants identified as “operators,

fabricators, and laborers” (749). One explanation for this shift was the fact that the 1990

Immigration Act included an expanded allotment of employment preferences.

Conceptual Map

Reflected in the conceptual map below, I expect to see that region of origin and

annual income are correlated and are statistically significant; in other words, I

hypothesize that there are statistically significant differences in the mean annual income

among immigrants based on their different regions of origin. Basically, English

proficiency, length of residence, and educational attainment are the main factors that to

some extent determine the annual income of certain groups of immigrants. As is

   

61    

generally assumed, many English speaking countries (e.g., Australia, Canada, the United

Kingdom, etc.) tend to be among the most advanced countries in terms of economic

development factors. Immigrants to the United States from such countries are more likely

to exhibit higher English proficiency levels and have higher levels of educational

attainment. Consequently, they are more likely to land some of the better jobs that offer

higher annual incomes. Therefore, I hypothesize that after controlling for these two very

important variables (English proficiency and educational attainment), the effect of region

of origin on annual income should decline or even disappear.

Gender English Proficiency Length of Immigration Educational Attainment Region of Origin Annual Income

Much research has noted that length of residence also plays a very important role

in determining annual income among immigrants; in this sense, it is believed that with

the accumulation of social and human capital over the years after becoming a new

immigration, they gradually catch up with native-born Americans in terms of annual

income. Thus, I hypothesize that after controlling for length of residence, the effect of

region of origin on annual income disparity among immigrants may further decline.

  62  

Data and Method

The data for this paper are from the 2012 ACS 5 year sample. Using OLS

regression, with controls for gender, educational attainment, English proficiency, and

length of residence, the emphasis is on measuring the effect of region of origin on income

disparity among first generation immigrants.

The dependent variable INCWAGE_LOG is a continuous variable transformed by

getting the log of INCWAGE in order to make the dependent variable more normally

distributed so it does not violate the normality assumption in OLS regression. The

original INCWAGE (income wage) variable reports each respondent’s total pre-tax wage

and salary, which is their income received as an employee. The amounts are expressed in

contemporary U.S. dollars. I selected INCWAGE as the original dependent variable

instead of total personal income because the wage and income salary received as an

employee is a better indicator to examine the labor market performance of first-

generation immigrants.

The main independent variable in the study BPLGROUPS is a categorical

variable recoded by collapsing the original BPL (birthplace) variable in the ACS data that

indicates the U.S. state, the outlying U.S. area or territory, or the foreign country where

the person was born. I collapsed all of the foreign birthplaces into 10 groups: North

America, South America, Europe, East Asia, Southeast Asia, Middle East, Other Asia,

Africa, Australia and New Zealand, and Others and will compare income disparity among

immigrants based on these 10 regions of origin.

Control variables include the following: EDUC (educational attainment) as a

continuous variable indicates respondents’ educational attainment, as measured by the

   

63    

highest year of school or degree completed; YRSUSA1 (length of residence) as a

continuous variable as well that reports how long a person who was born in a foreign

country or U.S. outlying area had been living in the United States; SPKEAKENG

(English proficiency) a categorical variable that indicates whether the respondent speaks

only English at home, and also reports how well the respondent, who speaks a language

other than English at home, speaks English.

Results

Descriptive Results. Table 1 highlights the basic information of all the variables

included in this paper. Table 2 shows the level of English proficiency, educational

attainment, length of residence, and annual income by region of origin groups. In terms

of the main dependent variable annual income, the data shows that this variable differs

significantly across all region of origin groups. Immigrants from North America on

average make the highest income at about $ 71,992 annually, which is more than twice

that of immigrants from South America, who have the lowest income at about $ 31,379

annually. As for English proficiency, the overall pattern is that immigrants from South

America and East Asia have both the highest percentage of people who do not speak

English and the highest percentage of people who do not speak English very well

therefore. To some extent, we could say that the English proficiency is on average the

lowest among immigrants from South America and East Asia.

In terms of educational attainment, immigrants from the Middle East have on

average 9.20 years of education, which is the highest across all the region of origins. Next

are immigrants from East Asia who have on average 8.63 years of education. At the other

  64  

end of the spectrum are immigrants from South America, who have on average 5.45 years

of education.

In terms of length of residence, immigrants from North America have on average

the longest years of residence in the United States which is 25.9 years. The next longest

length of residence group is immigrants from Europe with on average 25.46 years of

residence in the United States. According to the data, immigrants from Africa have on

average 15.92 years of residence in the United States, which is the shortest among all the

region of origin groups.

Table - 1

Univariate Table of Dependent, Independent, and Control Variables Variable Frequency Percent Mean Stad Dev. Range

Annual Income 913877

45425.02 53691.77 659320 Length of Residence 913877

20.73 12.75 65

Sex Male 499,607 54.67

Female 414,270 45.33 Region of Origin

North America 23,155 2.53 South America 436,610 47.78 Europe 132,449 14.49 East Asia 88,289 9.66 Southeast Asia 103,951 11.37 Middle East 86,221 9.43 Other Asia 1,701 0.19 Africa 35,689 3.91 Australia n New Zealand 5,191 0.57 Others 621 0.07 English Proficiency

Does not speak Eng 57,309 6.27 Speaks only Eng 178,769 19.56 Speaks very well 321,060 35.13 Speaks well 203,390 22.26 Speaks Eng but not very well 153,349 16.78

   

65    

Educational Attainment No Schooling 27,007 2.96

Less than High School 164,423 17.99 High School 243,120 26.6 1-3 years in College 159,264 17.43 BA and BA+ 320,063 35.02 Data Source: ACS 5 Year Sample, 2012

Table - 2        

Bivariate Table of Dependent, Independent and Control Variables Region of Origin

North America

South America Europe East Asia

Southeast Asia

Middle East

Other Asia Africa

Australia n NZ Others

English proficiency (%) Does not

speaks Eng 0.30 11.56 0.59 4.01 1.57 0.69 1.53 0.48 0.15 0.48 Speaks only Eng 79.88 12.12 43.78 16.01 12.91 10.97 15.58 24.46 60.51 32.69 Speaks very well 15.98 27.02 35.19 34.34 44.97 63.34 48.74 52.13 27.09 41.55 Speaks well 2.66 23.86 15.08 29.26 27.18 19.74 23.16 18.19 9.57 18.52 Speaks not well 1.17 25.43 5.36 16.38 13.37 5.27 10.99 4.74 2.68 6.76 Educational Attainment

Mean (in year) 8.60 5.45 8.30 8.63 7.74 9.20 8.12 8.29 8.05 7.33 Stad Dev. 2.19 2.99 2.35 2.70 2.72 2.34 2.90 2.42 2.32 2.33 Length of Residence

Mean (in year) 25.93 20.12 25.46 20.74 21.10 16.88 17.29 15.92 18.30 19.30

Stad Dev. 16.63 11.59 16.01 12.70 10.89 11.33 13.17 11.30 13.13 14.15 Annual Income

Mean (in $) 71991.84

31379.22 61181.7

57999.48

46150.68

68841.59 49637.03

48996.10

63978.54

37179.93

Stad Dev. 81303.75

33302.60

69850.34 61803.1 43656.32 72673.16 59456.09 58852.99 77336.63 33275.85 Data Source: ACS 5 Year Sample, 2012

Thus, differences clearly exist by region of origin in all the aspects presented

above, especially with the relationship between the main dependent variable annual

income and the main independent variable region of origin. We see great disparity in

annual income across all the region of origin groups. Next, this paper will employ OLS

  66  

regression procedure to do multivariate modeling and examine how these significant

disparities hold up after controlling for the control variables such as educational

attainment, English proficiency, and length of residence.

According to Table 3 (below), in the main bivariate analysis, region of origin and

annual income among first generation immigrants are correlated and this correlation is

statistically significant. Based on Model 1, we see region of origin does influence annual

income and that the influence is statistically significant. According to the coefficients in

Model 1, first generation immigrants from all the region of origins on average have a

lower annual income than the North American immigrants, the omitted group. The

biggest annual income disparity exists between those from South America and the

omitted group (North Americans); in fact, South American immigrants make 0.677% less

annually than North American immigrants. On the other hand, the smallest income

disparity appears between those immigrants from the Middle East and the omitted group

with former making 0.023% annually compared to their North American counterparts.

All of the coefficients I discussed above are statistically significant at .000 level.

In Model 2, I added sex as a control variable. Accordingly, we see some changes

in the coefficients of region of origin although there is no clear pattern to the changes.

The coefficients for some of the groups go up, while for some other groups they go down.

The changes in coefficients are statistically significant after adding sex to the model;

however, adding sex does not change the direction of the coefficients.

Table - 3

Multivariate Results.

   

67    

OLS Regression of Annual Income on Region of Origin, Sex, English Proficiency, Length of Residence, and Educational Attainment Among First Generation Immigrants in the U.S.

Region of Origin Model 1 Model 2 Model 3 Model 4 Model 5 South America -.677** -.705** -.383** -.393** -.208** Europe -.122** -.125** -.057** -.076** -.057** East Asia -.178** -.167** .052** .047** -.049** Southeast Asia -.302** -.280** -.110** -.116** -.094** Middle East -.023** -.066** .01 .041** -.052** Other Asia -.380** -.424** -.289** -.262** -.264** Africa -.353** -.377** -.308** -.262** -.226** Australia and New Zealand -.146** -.166** -.137** -.090** -.011 Others -.523** -.540** -.453** -.430** -.287** Sex

Women

-.405** -.426** -.428** -.442** English Proficiency

Speaks only English

.837** .724** .425** Speaks very well

.862** .796** .467**

Speaks well

.488** .446** .281** Not very well

.187** .165** .125**

Length of Residence

.008** .010** Educational Attainment

Less Than High School

.008** High School

.110**

1-3 Years in College

.262** BA and BA+

.763**

Intercept 10.665 10.864 10.052 9.954 9.705 p-value .000 .000 .000 .000 .000

Adjusted R2 0.065 0.102 0.171 0.179 0.24

Data Source: ACS 5 Year Sample, 2012

In Model 3, I added English proficiency to the model and this control variable

renders a pattern in the coefficients for region of origin: it greatly decreases the

coefficients in all groups, and even changes the direction of the correlation between some

groups and the omitted group. Further, all the changes in coefficients are statistically

significant except for immigrants from the Middle East. As Model 3 indicates, the biggest

  68  

jump in coefficients happens in the comparison between South America and the omitted

group. Controlling for English proficiency changes the coefficient for South America

from -.677 to -.383, suggesting that South American immigrants start to catch up with

North American immigrants in annual income, when holding English proficiency

constant. Another noticeable change in the coefficients exists in East Asia and the Middle

East. The direction of coefficients of both these two groups is reversed after controlling

for English proficiency, although the coefficient for the Middle East is not statistically

significant. The changes of the coefficients after adding English proficiency clearly

indicate that English language skill matters in the U.S. labor market for immigrants. In

sum, adding English proficiency makes the association between region of origin and

annual income weaker, and overall people who speak better English tend to have higher

annual incomes. Adding English proficiency to the model also greatly increases the

adjusted R2 value, further indicating that English proficiency is a significant variable to

include when considering income disparity among immigrants.

I added length of residence to Model 4 and this variable changes the effect of

region of origin on annual income differently by groups (all the coefficients are

statistically significant). The changes in coefficients vary by region of origin groups

which demonstrates that length of residence works for some immigrant groups, but not

for all of them. For example, among all the 9 region of origin groups, length of residence

decreases the annual income of immigrants coming from South America, Europe,

Southeast Asia, and the Middle East as compared to immigrants from North America.

However, for the other region of origin groups, such as East Asia, Other Asia, Africa,

Australia and New Zealand, and Others, controlling for length of residence actually

   

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increases their annual income, which means that the more years immigrants coming from

these regions spend in the United States, the less income disparity there will be between

them and immigrants from North America.

In Model 5, I added educational attainment as the last control variable. Obviously

this variable has a lot explanatory power in that it greatly increases the adjusted R2 values

of the model from .179 to .240 (statistically significant). There are some interesting

changes for the coefficients of all the region of origin groups after adding educational

attainment to the model. Overall, educational attainment changes the effect of region of

origin on annual income by groups. Firstly, it changes the region of origin coefficients of

East Asia and the Middle East from positive to negative and it also increases the effect of

region of origin on annual income for the groups of Other Asians. These changes indicate

that education is not helping immigrants from these three regions make more money

annually. However, education does help immigrants from South America, Europe,

Southeast Asia, Africa, Australia and New Zealand, and Other regions achieve a higher

annual income as we can see from the changes in the coefficients of these groups: All the

coefficients go down after holding educational attainment constant although the

coefficient is not statistically significant for Australia and New Zealand.

Overall, after adding the four control variables, the correlations between region of

origin and annual income among first generation immigrants are still statistically

significant for all the region of origin groups except for Australia and New Zealand.

Compared with the zero order model which only has the main dependent variable and

main dependent variable, adding the four control variables makes the effect of region of

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origin on annual income much weaker for all the region of origin groups except for the

Middle East. And the adjusted R2 value greatly increased from .065 to .240 with

statistical significance after adding all the control variables. This supports my contention

that the variables included in the model are quite useful in capturing influences on

immigrants’ annual income.

Like length of residence, educational attainment in this paper works differently

across region of origin groups. It narrows the annual income gap for most of the region of

origin groups such as South America, Europe, Australia and New Zealand, etc. However,

when it comes to regions like East Asia, Other Asia, and the Middle East, it expands the

income gap.

Discussion and Conclusion

As globalization becomes an irreversible trend, greater and greater numbers of

people are becoming geographically mobile. Indeed, people are constantly migrating,

“especially but not exclusively from less developed societies to more developed ones”

(Evans, 1984: 1086). And the United States, known throughout the world as the “land of

opportunity,” continues to be a site of attraction for people from around the world, thus

making research studies of U.S. immigration critical to understanding the dynamic and

changing context of American society. What especially makes immigration to the United

States interesting is that there is enormous cultural and demographic diversity among

immigrants, in terms of the differentiation in their race and ethnicity, educational

background, language and work skills, and socioeconomic status. All of these factors are

   

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likely to influence their process of incorporation into the U.S. economy and society,

including the labor market. Therefore, acquiring knowledge about how the personal

attributes of immigrants might influence their incorporation process is necessary and

crucial to understanding a variety of social issues, all of which are likely to implicate

policy making related to immigration. Accordingly, in this paper I examined the

economic and labor force performance among first generation immigrants related to

region of origin by using annual income as the main indicator. Economic and labor force

performance is to some extent one of the most crucial facets of immigration

incorporation. Thus, looking at annual income disparity among immigrants of different

region of origin offers a glimpse, to some degree, about how personal attributes such as

educational attainment and English proficiency might come into play as one aspect of

immigration incorporation.

Based on the data in this paper, I found that overall, immigrants from North

America make the highest annual income among all the other region of origin groups.

This served to confirm my hypothesis that region of origin does have an effect on annual

income among first generation immigrants, with people from more developed and

industrialized societies such as countries in North America, Europe, etc. being more

likely to have higher income. And whereas, immigrants from less developed and

industrialized societies such as South America, Africa, etc. are more likely to have much

lower incomes. As the studies of Evans (1984) and Boyd (1984) revealed, immigrant

women tend to make even less annual income than immigrant men due to the “double

disadvantage” of being an immigrant and a woman; the results of this paper are basically

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consistent with the previous research that immigrant women on average make 0.405%

less annual income than immigrant men.

English proficiency also plays a very important role in deciding annual income

among immigrants. Again, my study confirms what previous studies have also shown.

Therefore, we see that controlling for English skill increases annual income much more

for immigrants from regions where English is not the first or official language, such as

East Asia, South America, etc.; it only slightly increases annual income in regions where

English is mainly the mother tongue or official language, such as Australia and New

Zealand, North America, etc.

The effect of length of residence, however, in this paper is not completely

consistent with previous research. Specifically, there is a general view that the longer

immigrants spend time in the United States, the higher their income becomes; some

studies go as far as to argue that the annual incomes of immigrant catch up with native

born whites over time. But what I found in this study is that length of residence only

increases the annual income of some region of origin groups such as East Asia, Other

Asia, and Africa. At the same time, length of residence may decrease the annual income

of immigrants coming from South America, Europe, and the Middle East, at least when

their income is compared to that of the control group.

Educational attainment is a key factor in much of the literature that examines

economic outcomes of immigrants. In this paper, educational attainment proves to be an

important variable explaining much variation in immigrant income disparity, as revealed

in the changes of the adjusted R2 values. Like length of residence, educational attainment

narrows the annual income gap for most of the region of origin groups such as South

   

73    

America, Europe, Australia and New Zealand, etc. However, when it comes to regions

like East Asia, Other Asia, and the Middle East, controlling for educational attainment

actually expands the income gap between these origin groups and the omitted group as

demonstrated by the increased coefficients in Model 5. Such results, however, are not

entirely surprising, as some of the previous literature helps to explain this phenomenon.

For example, Madamba and De Jong (1997), in their study of immigrants’ economic

performance with job mismatch as the main indicator, revealed that, “Asian immigrant

workers were more likely to experience job mismatch than the native born” (539). Hence,

job mismatch helps to partially explain why the increased coefficients appear mostly

among immigrants from Asian regions.

Future Study and Limitations

The study focuses on the economic performance among first generation

immigrants with a particular interest in examining if region of origin (nativity) influences

immigrant annual income. Although with a very specific focus, additional control

variables other than the four introduced in this paper could be added to the main model to

better sort out the relationship and association between region of origin and annual

income. Socioeconomic status could be an appropriate factor to add to the model as

several studies have pointed out that the socioeconomic status immigrants carry with

them to the United States can greatly influence their experience, including their work

experience in the labor market.

Another factor that may also have predictive power is the variable race, given a

history of race-based discrimination in the United States. As has been mentioned in

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previous literature, some discriminatory immigration laws have been proposed based on

race. An added complexity here though is that race and region of origin are not always

consistent in terms of the presumed race of immigrants from a particular region, in part a

result of the impact of globalization and the formation of multicultural and multiracial

societies. In other words, it is not always accurate to assume that an immigrant from

Africa is to be classified in terms of race as “black,” and similarly, that an immigrant

from Europe is to be classified racially as “white.” Therefore, future studies ought to take

into account race, but do so without presuming one’s racial identity on the basis of region

or nationality. I would presume that race continues to matter in U.S. society and will

show up as a factor in predicting annual income among immigrants.

Another limitation of this paper is that the divisions of the region of origin used in

this study are not informed by previous studies due to the limited research adopting such

a line of inquiry. Basically, I collapsed all the foreign birthplaces into 10 regions

according to the divisional system used in the ACS. This seemed like a logical step but it

is not without problems. For example, some of the divisions overlap in the ACS data set;

a case in point is that Central Europe and Eastern Europe are all included in Europe and

therefore the division I employed might be too diverse and thus generalizing about

immigrants from this region may fail to capture the region’s actual complexity. In other

words, the regions I employed in this paper might be too broad since Europe is quite a

heterogeneous region, culturally and demographically speaking. The same may be said of

the breakdowns of the Asian region, where vast differences exist among countries

included in the East Asian region for example. Thus, future research may benefit from a

   

75    

more refined delineation of foreign birthplaces and hence enable more detailed

comparisons among immigrants and relative to labor market outcomes.

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Xiaoyang Sun

Temple University Department of Sociology

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