Career Advancement and Social Mobility Effects of Laureate

This document is confidential and may be circulated only with the client’s authorization.

December 2016

Career Advancement and Social Mobility Effects of Laureate Peru Higher Education

An Impact Evaluation

Final Report

This report was prepared by C230 Consultores for:

With the technical support and advice of:

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Table of Contents Acknowledgements ............................................................................................................................................................ 3

0. Executive summary ..................................................................................................................................................... 4

1. Introduction ................................................................................................................................................................ 7

2. Context of the study ................................................................................................................................................... 9

i. Higher education in Peru ........................................................................................................................................ 9

ii. The Laureate education program ......................................................................................................................... 10

iii. Research objectives .............................................................................................................................................. 11

3. Methodology ............................................................................................................................................................ 13

i. Research questions ............................................................................................................................................... 13

ii. Research design .................................................................................................................................................... 13

A. First approach: quasi-experimental design ...................................................................................................... 14

B. Second approach: employers’ perceptions about graduates’ performance and market value....................... 21

C. Third approach: qualitative perception of Laureate graduates ....................................................................... 22

4. Findings ..................................................................................................................................................................... 24

5. Conclusions ............................................................................................................................................................... 38

6. References ................................................................................................................................................................ 40

7. Appendices ............................................................................................................................................................... 42

Appendix I ..................................................................................................................................................................... 42

Appendix II .................................................................................................................................................................... 43

Appendix III ................................................................................................................................................................... 44

Appendix IV ................................................................................................................................................................... 47

Appendix V .................................................................................................................................................................... 48

Appendix VI ................................................................................................................................................................... 50

Appendix VII .................................................................................................................................................................. 51

Appendix VIII ................................................................................................................................................................. 54

Appendix IX ................................................................................................................................................................... 55

Appendix X .................................................................................................................................................................... 56

Appendix XI ................................................................................................................................................................... 61

Appendix XII .................................................................................................................................................................. 63

Appendix XIII ................................................................................................................................................................. 65

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Acknowledgements

The core team that developed this evaluation included: Giuliana Montanez (Laureate Peru – UPC), Ricardo Garcia Anton (Laureate Peru – Cibertec); Emal Dusst, Craig Lundsten, Pete Pinheiro, Todd Wegner, and Bruno Costa from Laureate International Universities; and Raul Abreu, Braulio Torres, Francisco Zavala, and Martin Montenegro from C230 Consultores. The team also thanks Marisol Suarez and Clery Luz Neyra Vera from UPC, and Paolo Puelles from Cibertec for their insightful input. IFC provided technical advice and support to Laureate and the consultant. The core IFC team was led by Gloria Paniagua with support from Mohammed Ali Khan, Charles Lor, and Helle Lilly Andersen. The framework benefited from expert advice provided throughout the evaluation by Miguel Angel Rebolledo Dellepiane. The team would also like to acknowledge review comments by Amer Hasan (World Bank), Sven Harten (DEval), and Professor Henry Levine (Columbia University). Useful input for the evaluation was provided by the following IFC colleagues: Toshiya Masuoka, Luke Haggarty, Claudio Volonte, and Deepa Chakrapani. The team wishes to thank all contributors and reviewers for their input and collaborative spirit.

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0. Executive summary

In 2004, Laureate International Universities, an international network of higher education institutions, acquired two institutions in Peru: the Universidad Peruana de Ciencias Aplicadas (UPC) and Cibertec, a technical/vocational institute. Both UPC and Cibertec offer affordable undergraduate degree programs in a variety of academic disciplines and share common missions to develop competent professionals. Laureate is interested in learning whether the additional access to higher education that UPC and Cibertec provide allows graduates to access good jobs and provides more pathways for social mobility. The objective of this research is to assess the career achievements and social mobility of Laureate graduates in Lima, Peru, after they enter the labor market, compared to their peers from other institutions.

This research is based on a quasi-experimental impact evaluation design, complemented with descriptive statistics and qualitative information. The results of this study were obtained through an ex-post impact evaluation of graduates in Lima. Since no baseline information was collected while these graduates were in school, the study relies on a survey of Laureate alumni and graduates of comparable higher education institutions. These surveys were complemented with a survey of employers and in-depth interviews of Cibertec and UPC alumni.

The impact evaluation relied on comparing outcomes from a treatment group (alumni of UPC and Cibertec) with a control group (alumni of universities and technical/vocational institutes that are direct competitors), defined in consultation with the International Finance Corporation (IFC) and Laureate International Universities. Individuals from the treatment and control groups were paired using two matching methodologies (Coarsened Exact Matching and Genetic Matching) based on a number of observable pre-treatment variables or covariates, including parental education, type of high school (public or private), household socioeconomic status, field of study, and age, among other variables.

The report presents detailed findings under one (main) econometric model, but, to assess robustness of results, three alternative estimations were conducted. Estimations are considered conclusive and robust only if results are significant and in the same direction in all four econometric models used. If results are not robust, we still treat them as valuable findings, though we consider them as weak results.

This study suggests that Laureate’s academic degrees with the highest enrollment rates enhance the performance of graduates in the labor market and provide more pathways for social mobility relative to other affordable education alternatives. In general, Laureate graduates do well; graduates of UPC and Cibertec have higher incomes and greater job benefits than their peers. UPC graduates likewise experience improvements in their socioeconomic status. Still, no effects were observed on some employability variables, such as the time between graduation and first job, meaning that Laureate graduates are not employed faster than their peers from other comparable institutions. Some weak (non-robust) negative results are observed for Cibertec.

Specifically, results show the following:

Income: Laureate (UPC and Cibertec) graduates earn a higher monthly income when compared to their control group counterparts, an impact that was especially high for Cibertec graduates. Cibertec graduates´ higher income (40.5% more income) is in line with the perception of employers, who consistently rate Cibertec graduates as the top performers in their labor market. Regarding UPC graduates, on average, they have a 28.6% higher monthly income than graduates of other universities. These are considered robust findings.

Job quality: UPC graduates have better jobs; that is, they receive more job benefits, namely unemployment insurance and other bonuses. Cibertec graduates showed more job benefits as well. Results are robust for UPC but are not robust for Cibertec.

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Employability: Results show no robust difference in the employability of UPC and Cibertec graduates, compared to their peers from other institutions. Laureate graduates take the same time to be employed in their first job as graduates of other comparable institutions. For Cibertec, however, there is an indication of negative results regarding number of days unemployed throughout their career; this result is not considered robust. For UPC, there is an indication of higher employment rates. In general, Laureate’s employability performance in the labor markets is about the same as its competitors.

Career advancement: UPC graduates outperform their peers in job promotion and the proportion who have held a managerial position. They advance faster in their professional career. This is a robust finding. Results show no robust difference for Cibertec graduates, though there is an indication of weak negative results for Cibertec regarding proportion who have held a managerial position and the number of coworkers the graduate supervises.

Social mobility: More UPC graduates experience a positive change in their socioeconomic level, compared to their peers. This suggests that UPC graduates are better off than their parents, and that UPC graduates are able to improve their socioeconomic levels to a greater degree than their peers. This is a robust result. No robust effects are seen for Cibertec graduates.

Impact estimations also vary by gender. In three of the six positive outcome variables from UPC alumni, female graduates benefited at a higher proportion than male graduates; these are the proportion of graduates promoted at the workplace, holding a managerial position, and income. This could be an indication that UPC helps women advance faster in the labor market than their peers from other universities. For Cibertec, however, this is true only for the number of job benefits. Results also vary by field of study. UPC graduates with Business and Administration and Engineering majors benefit the most. Cibertec’s strongest and well-known Information Technology (IT) program observed the strongest positive impacts.

To supplement the quantitative analysis, evidence was collected through in-depth qualitative interviews. In these interviews, Laureate graduates appreciate both UPC and Cibertec as prestigious brands that open doors to good jobs.

This impact evaluation comes with some methodological caveats that should be considered for future research. There are some concerns about the internal validity of estimations (for example, there are no available academic scores to use for the construction of the control group), as well as other concerns about the external validity (for example, the study uses a limited sample of cohorts, due to quality and availability of administrative data). Prospective and long-term evaluation design can help overcome these caveats in the future. The findings of this study apply only to the Lima (urban) context, and we should therefore be wary of extrapolating the findings to other settings.

Universities and technical/vocational institutes are expected to produce long-term consequences on alumni performance in earnings potential and future career prospects in the labor market. This study shows that completing a university degree program at UPC contributes to positive results in graduates’ professional careers. The study shows a similar, though more limited, positive outlook for Cibertec graduates when compared to their peers from other technical/vocational institutes. UPC in particular has helped its graduates outperform their peers in many professional outcomes and provide more pathways to social mobility. Cibertec, on the other hand, has helped its graduates to outperform their peers particularly in income earned. These results should be viewed through the lens of UPC and Cibertec’s mission of developing competent and skilled professionals, and Laureate’s mission of providing accessible, high-quality higher education and strengthening the labor market. These conclusive results about Laureate graduates with higher income, in combination with some indication about Laureate graduates with better jobs and positive changes in socioeconomic status, do indicate a promising horizon for high quality affordable higher education.

From a policy and business perspective, it is relevant to explore further the mechanisms through which private higher education can improve social mobility and economic opportunities, through system improvements such as better

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curriculum design, stronger career services, and industry linkages. This would allow education institutions and IFC to identify key best practices in order to replicate them across countries, and to inform the policy dialogue about the value of private education.

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1. Introduction

Over the past decade, Peru’s economy has grown at an average annual rate of 5.9% (one of the fastest growth rates in Latin America), which in combination with low inflation of 2.9% per year has helped to reduce poverty in the country by more than 30 percentage points.1 As further evidence of Peru’s economic growth, GDP per capita has increased from $7,530 USD in 2006 to $11,627 USD in 2015.2 Despite this economic growth, Peru still suffers from great inequality, as reflected in its 2015 GINI Index of 44.7. A third of total income is concentrated in the wealthiest top 10 percentiles of Peruvian households, while those in the bottom 10 percentiles have only 1.5% of total income.3 As a result, opportunities for social mobility have become a bigger concern for policymakers in Peru.

Social mobility is a measure of equal opportunity in a society, where the success of an individual is independent of the home and circumstances in which the individual was born. For example, individuals’ social mobility is considered high if they can move upward or downward on the socioeconomic ladder regardless of their parents’ status. On the other hand, their mobility is low if their socioeconomic level is strongly determined by the barriers or advantages associated with their backgrounds.4

Several factors affect social mobility, including household income, parents’ education and occupation, family ethnicity and structure, and geographic location. These factors have a pervasive influence on economic outcomes over time, especially in high-inequality societies.5 There are additional factors on a policy level, like human capital investment or educational programs that can also have an impact on social mobility.

Investing in human capital increases productivity and in turn leads to higher earnings and improved socioeconomic status.6 Evidence shows that higher education contributes to building a more mobile society. In the U.S. labor market, for example, the association between an adult’s socioeconomic origins and current status decreased by one-third between the ’70s and ’80s, which is related to the rising proportion of workers with college degrees.7,8 Obtaining a college degree helps individuals move up in the income distribution relative to their peers without college degrees.9

Previous studies in Peru of the labor market opportunities of urban children compared with their parents show that the expected benefits of countrywide economic growth have been neutralized by the limited social mobility within the Peruvian labor market.10 Laureate International Universities, a network of higher education institutions, offers

1 World Bank IBRD-IDA, Country Overview, Peru, in: http://www.worldbank.org/en/country/peru/overview 2 World Bank, World development indicators, in http://databank.worldbank.org/data/ 3 Ibid. 4 Some researchers also refer to “intra-generational mobility” as the changes in a person’s income level or occupational status during his or her adult life (see for example: Gigliarano, C. & Chelli, F.M. (2016). Measuring inter-temporal intra-generational mobility: an application to the Italian labour market. Qual Quant; and ACS, Gregory & Zimmerman, S. (2008) U.S. Intra-generational Economic Mobility From 1984 to 2004: Trends and Implications. Economic Mobility Project. 5 Beller, E., & Hout, M. (2006). Intergenerational Social Mobility: The United States in Comparative Perspective. The Future of Children. 16(2): 19-36. 6 Lee, Chun-In, & Solon, G. (2006). Trends in Intergenerational Income Mobility. NBER Working Papers Series. 7 Hout, M. (1988) More Universalism, Less Structural Mobility. The American Occupational Structure in the 1980s. American Journal of Sociology 93, 1358–1400. 8 See also Breen (2004; 2009); Vallet (2004); Beller & Hout (2006). 9 Ellwood, D., & Kane, T. (2000). Who Is Getting a College Education? Family Background and the Growing Gaps in Enrollment. In S. Danziger and J. Waldfogel (eds.), Securing the Future: Investing in Children from Birth to College. Russell Sage Foundation. 10 Benavides, M. (2003). Cuando los extremos no se encuentran: un análisis de la movilidad social e igualdad de oportunidades en el Perú contemporáneo. Boletin del Instituto Francés de Estudios Andinos. 32(3), 473-494.

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affordable academic degree programs that aim to provide avenues to social mobility. Laureate is interested in learning whether the access to higher education that it provides is increasing graduates’ social mobility.

This report evaluates the effect of a college undergraduate degree from Laureate institutions in Lima (UPC and Cibertec) on the pathways to social mobility, including specific dimensions like income, changes in socioeconomic status, and career advancement. The objective of this study is to identify how graduates of UPC and Cibertec perform vis-à-vis students who graduate from other higher education institutions, in the labor market and in terms of social mobility. A first impact assessment of Laureate education was performed in Mexico in 2015, making this study in Peru the second such assessment.11 The first study showed that Laureate institutions in Mexico (Universidad del Valle de México and Universidad Tecnológica de México) provide avenues to social mobility through improvements in income and socioeconomic status.

The outcomes assessed in this report can be grouped in three categories:

a) Economic Capital and Employment, which refers to various aspects of the alumnus’s professional career and income generation after graduation.

b) Career Advancement, which measures promotions and access to managerial positions. c) Social Mobility, which measures the change in socioeconomic levels during shorter periods of time (i.e., from

pre-enrollment to post-graduation).

This study also tests for differences by gender, degree programs, and socioeconomic background for the principal indicators in each category.

This is an ex-post impact evaluation. It was planned and implemented years after the individuals graduated from their institutions. Therefore, there is no baseline information available. Thus, the study relies on data collected from a survey of graduates (students who completed their studies between 2004 and 2009), a survey of employers, and in-depth interviews of UPC and Cibertec alumni. The analysis is based on a quasi-experimental approach for an impact evaluation, complemented with descriptive statistics and qualitative information.

The report is organized as follows: Section 2 presents the context and research objectives for the present study, providing some background information on the Peruvian higher education market, including UPC and Cibertec. Section 3 presents the research methodology, including its research questions, its research approaches, and basic descriptive statistics of the surveys, as well as the sampling strategy. Section 4 presents the findings of the study, as well as considerations regarding sensitivity analysis of quantitative results and methodological limitations. Section 5 describes conclusions and presents recommendations.

11 C230 Consultores (2015).

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2. Context of the study

i. Higher education in Peru

Over the past decade, more and more Peruvian students have entered higher education, with participation rates exceeding 30%, compared with just 23% in 2000.12 The increase in student participation in higher education has been matched by an increase in the number of private higher education institutions. Today, more than 70% of the 1.5 million higher education students in Peru attend private institutions, compared with one-third in 1985.

Part of the reason that private higher education has grown so much in Peru over the past few years is the limited enrollment capacity of the public university system. Universidad Nacional Mayor de San Marcos, the main public university in Peru, accepts only 8% of applicants every year. Right after high school, many students head to academias, expensive preparatory schools, that help them prepare for the highly competitive admission exams of public universities.13 However, for those students who do not have the resources to prepare themselves for these exams or who are simply not admitted to public universities, the private sector provides an important alternative.

There are two higher education systems in Peru: universities, in which students typically take around five years to obtain a degree, and technical/vocational institutes, which operate with shorter programs (an average of three years) that provide specific technical skills oriented toward job opportunities. There are currently 140 universities (64% private) and 790 technical/vocational institutes (53% private) operating in Peru.14 Three out of four higher education undergraduates study in universities, and one out of four is enrolled in a technical/vocational institute.15 In general, university degrees are more prestigious and more appreciated by employers than degrees from technical/vocational institutes, but universities also involve greater costs for students in terms of both time and tuition. The value of a university degree also depends on the prestige of the institution; it is widely reported that employers avoid hiring graduates from low-ranking universities.16

Graduates face a complex situation in the labor market after completing their studies. The strong economic performance of the past decade (just below 6% GDP growth per year) contributed to employment growth of around 2.4% each year,17 sufficient to absorb Peru’s growing working age population and labor force. Currently, the unemployment rate of the economically active population with technical/vocational education is around 7.8%, and that of those with university education is 10.9%,18 relatively low figures compared with other countries in Latin America.19 The picture gets more complicated, however, once underemployment is considered: over 35% of university graduates between 24 and 45 years old are underemployed.20 Job creation in the past decade was relatively slow, given Peru’s

12 Laureate International Universities. Discussion on Peru presentation, October 9, 2015. 13 Guerrero, G. (2013). ¿Cómo afectan los factores individuales y escolares la decisión de los jóvenes de postular a educación superior? Un estudio longitudinal en Lima, Perú. GRADE- Grupo de Análisis para el Desarrollo. 14 Laureate International Universities. 15 Orozco Silva, L. E. (2009) The Educational Revolution in Colombia: Scopes and Restrictions. In Higher education in Latin America and the Caribbean 2008, eds. Segrera, F., Brock, C. & Dias, J. Caracas: IELAC- UNESCO. 16 Yamada, G. (2012). Las brechas crecientes entre el mercado laboral y la educación superior en el Perú. Universidad del Pacífico y Consejo Nacional de Educación; Castro, J. F. & Yamada, G. (2013). Declining Higher Education Quality Affects Postsecondary Choices: A Peruvian Case. International Higher Education 70(Winter); Gestión. (2013). ¿Qué universidades prefieren las empresas peruanas para contratar egresados?. Gestión. Lima. 17 Ruppert, E. (2015) Peruvian Jobs Diagnostic. World Bank. 18 INEI. (2016). Situación del Mercado Laboral en Lima Metropolitana. Informe Técnico No 8. Mayo-Junio-Julio. 19 International Labor Organization. (2015). Global Employment Trends of Youth. 20 Yamada, G. (2012). Las brechas crecientes entre el mercado laboral y la educación superior en el Perú. Universidad del Pacífico y Consejo Nacional de Educación; Castro, J. F. & Yamada, G. (2013). Declining Higher Education Quality Affects Postsecondary

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steady economic growth, and those jobs created tended to be in low productivity sectors, like wholesale and retail trade, government services, transportation, and construction. Furthermore, the creation of a job does not ensure that it is a high-quality job; in recent years, most of the jobs created pay less than the minimum wage, and three out of four jobs can be classified as underemployment.21

Evaluating the impact of private higher education on the labor outcomes and social mobility of graduates in Peru is especially relevant considering the growth of private institutions in the education system, due to the limited enrollment capacity of the public university system and the complex situation in the labor market that graduates face after completing their studies.

ii. The Laureate education program

Laureate International Universities22 is a leading international network of institutions of higher education. The Laureate network comprises more than 70 universities that provide undergraduate and graduate degree programs to over one million students in 25 countries throughout the Americas, Europe, Africa, Asia, and the Middle East. Laureate’s universities offer hundreds of career-focused undergraduate, master’s, and doctoral programs in fields including architecture, art, business, culinary arts, design, education, engineering, health sciences, hospitality management, information technology, law, and medicine. Though these institutions share curricula, faculty, degree programs, and student exchange opportunities, each institution in the Laureate network operates under its own brand, guided by local leadership, and is actively involved in its local community.

In 2004, Laureate acquired two higher education institutions in Peru: the Universidad Peruana de Ciencias Aplicadas (UPC), in the traditional segment of the educational market, and Cibertec, in the technical/vocational segment. Of the five socioeconomic segments of the population that Peruvian universities typically cater to, UPC caters to the second highest segment, while Cibertec caters to the second lowest segment.23 Both UPC and Cibertec share common missions to form upright, innovative, and competent professionals, through the values of leadership, teamwork, excellence, quality, and innovation.

Universidad Peruana de Ciencias Aplicadas (UPC) UPC has more than 50,000 undergraduate and graduate students enrolled at four campuses in Lima. UPC offers undergraduate and graduate degrees in a variety of fields, including arts, architecture, business, communications, design, education, economics, engineering, medicine and health sciences,

hospitality management, law, and psychology. It also offers enrollment in 20 dual-degree programs with other academic institutions in Brazil, Chile, Spain, the United States, Mexico, and Switzerland.

In contrast to the competitive admissions exams of the Peruvian public higher education system, the UPC admissions process of “Selección Preferente” is more flexible and inclusive, offering personal interviews for students with good performances in high school. This is an important difference with respect to the public university system, where the limited enrollment capacity is reflected in highly competitive admission exams.

Choices: A Peruvian Case. International Higher Education 70(Winter); Gestión. (2013). ¿Qué universidades prefieren las empresas peruanas para contratar egresados?. Gestión. Lima. 21 BBVA, Peru Economic Outlook, First Quarter 2016. 22 Formerly known as Sylvan International Universities, founded in 1998 by Sylvan Learning Systems, a public company focused on educational services for students in primary and secondary education. In 2004 the company shifted focus toward post-secondary education and changed its name to Laureate Education, Inc. Later on, the company was acquired by a new investor and became a private entity in 2007. 23 Laureate International Universities (2015).

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The academic model of UPC is based on five pedagogical principles that underpin its administrative decisions and educational processes: learning through skills, student-centered learning, self-reflective learning, diverse and global learning, and learning toward sustainability. The objective is that UPC graduates acquire the following core competences or skills: critical, innovative and qualitative thinking, information management, citizenship, and oral and written communication skills. The academic programs also include workshops and contests to encourage entrepreneurship and innovation among students.

UPC is the first Peruvian university accredited by the Western Association of Schools and Colleges Senior College and University Commission (WSCUC), one of the six regional U.S. accrediting institutions. This accreditation recognizes the following characteristics:24

• The university’s innovative general education program. • UPC’s comprehensive 360-degree faculty evaluation process. • Its integration of learning outcomes assessment into program review in order to engage in continuous

program improvement. • UPC’s resolution to engage faculty and staff in the unique Integrated System for Academic Quality

(SICA) quality assurance process. • Its effort to engage all stakeholders in the short- and long-term strategic planning processes. • UPC’s commitment to its mission — a mission that is meaningful at the local, regional, national, and

international levels.

Cibertec Cibertec has eight locations around Lima, Trujillo, and Arequipa, with more than 16,000 students pursuing technical/vocational degree programs. Cibertec offers technical/vocational courses in Information Technology, business and administration, communications, and design and engineering.

With a solid ethical base and employment-orientated curricula, Cibertec trains people to succeed in the workplace, and is committed to the development of the country.

The admission process of Cibertec is even more open and inclusive than that of UPC, based on its mission to provide access to quality education to all people who require it. To this end, Cibertec’s educational plan has several pillars: updating courses, maintaining high academic standards, hiring academically certified professors and instructors, as well as a dynamic placement office.

Cibertec is widely recognized, particularly among software and IT firms, and has been accredited by SINEACE, the national system of evaluation, accreditation, and certification of education quality. It has also received awards from a number of major corporations, including Microsoft, Oracle, and Novell.25

iii. Research objectives

This study evaluates returns on education as well as other avenues to social mobility for college graduates from two Laureate institutions in Lima, Peru, vis-à-vis graduates of other higher education institutions in the Lima metropolitan area. To achieve this goal, three specific objectives were established:

24 Accreditation Institutional WSCUC letter, see: http://www.upc.edu.pe/adn-upc/acreditacion-institucional 25 See the complete list: http://www.cibertec.edu.pe/acerca-de-cibertec/premios-y-reconocimientos/

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1. Identify the impact of having a degree from Cibertec or UPC on factors related to income, career advancement, and social mobility, vis-à-vis having a degree from another institution.

The hypothesis underlying this objective is that completing a degree program at UPC or Cibertec results in better professional outcomes and that investing in this private education delivers better chances of upward social mobility compared to other universities. The impact that completing a degree program at UPC or Cibertec has on professional outcomes will be different depending on gender, field of study, and socioeconomic background.

2. Provide an employer’s perspective of the perceived market value of the Laureate alumni.

This objective seeks to understand how employers perceive the performance, skills, and quality of work of Laureate alumni.

3. Collect qualitative evidence from Laureate alumni regarding their career expectations and levels of satisfaction.

This objective examines the motivation of Laureate alumni to enroll in those universities, as well as their professional expectations and perceptions about socioeconomic mobility.

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3. Methodology

i. Research questions

The main hypothesis of the study is that completing a degree program at UPC or Cibertec offers graduates broader avenues for higher income and employability, career advancement, and social mobility. The research questions guide how to measure the impact of a Laureate education on alumni.

Table 1. List of Research Questions, Organized by Categories Categories Aspects Research Questions

1) Income and Employment

a) Employability

Do UPC/Cibertec graduates spend a different amount of time searching for their first job after graduation?

Is the maximum time spent unemployed different for UPC/Cibertec graduates compared with the time spent unemployed for their peers? Are there differences in the unemployment rate of UPC/Cibertec graduates and graduates of other institutions?

b) Income and job quality

Is the income of UPC/Cibertec graduates different than their peers’ from other universities/institutes? Are there differences in the number of job benefits of UPC/Cibertec graduates compared with their peers?

2) Career Advancement

c) Satisfaction with current professional life

Are UPC/Cibertec graduates more or less likely to have a positive perception of the usefulness of their major in the job market? Are UPC/Cibertec graduates more or less likely to be satisfied with their current jobs?

d) Career advancement

Is there a difference in the number of coworkers the UPC/Cibertec graduates supervise and that of graduates of other institutions? Is there a difference in the percentage of UPC/Cibertec graduates who occupy a managerial position compared with their peers? Is there a difference in the proportion of UPC/Cibertec graduates promoted at their workplace, compared with their peers?

3) Social Mobility

e) Changes in socioeconomic status

Have UPC/Cibertec graduates experienced a different change in their socioeconomic status compared with their peers, in relation to their status before graduating?

Source: C230 Consultores

ii. Research design

The study applies a mixed-methods approach to assess the impact of completing a degree at UPC or Cibertec, including a survey of graduates, which is analyzed through quasi-experimental econometric methods; a descriptive survey of employers; and in-depth qualitative interviews with UPC and Cibertec alumni. The guiding element of this evaluation is the first approach: the quasi-experimental design used to estimate impact. The employers´ survey and in-depth interviews are complementary analyses aimed at strengthening the econometric impact analysis.

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A. First approach: quasi-experimental design

Variables of interest The first approach is a statistical comparison of alumni outcomes based on the graduate survey, which collected all necessary information of UPC and Cibertec graduates and of graduates from similar institutions. The outcomes studied are summarized in the following table:

Table 2. Outcome Indicators Related to Social Mobility Categories Variables Outcomes


a) Employability Time between graduation and first job Maximum unemployment time since graduation Proportion of graduates currently employed


Monthly income in Peruvian soles (PEN) Number of benefits received from the workplace

(specifically Social Security, private medical insurance, and year-end bonus)



Perceived usefulness of the major studied for professional career

Satisfaction with current job


Number of coworkers the graduate supervises Percentage of graduates who have held a managerial

position Percentage of graduates promoted at their current

workplace

3) Social Mobility e) Changes in

socioeconomic status

Proportion of graduates who have experienced a positive change in current socioeconomic level compared with socioeconomic level before enrollment26


Treatment Groups For the remainder of this report, whenever the term “treatment” is used, it refers to the fact of having studied and graduated from UPC or Cibertec. For practical reasons, however, we relied on availability of sufficient administrative data to define the sampling frame of the study (specific majors and cohorts). The selection of graduates to be evaluated in this study was focused on academic programs that accounted for most students at UPC and Cibertec, and at the same time had a comparable academic alternative within comparable institutions (see Tables 3 and 4). This selection represents a trade-off between external validity and feasibility of the study. For UPC, the academic degrees considered in the study represent up to 70% of the students currently enrolled; for Cibertec, the figure is around 95%. Additionally, the study established the following selection criteria:

• Narrowest possible period of reference for enrollment years: On the one hand, the intention was to go as far in the past as possible to assess post-graduation experience. On the other hand, the period of reference had to account for Laureate ownership and resemble current academic programs as much as possible.

26 The Asociación Peruana de Empresas de Investigación de Mercados (APEIM) developed a standardized methodology (NSE scale) to measure a household’s socioeconomic level through a question-score representation of the eight different socioeconomic levels that are generally studied in Peruvian society. Socioeconomic levels are determined by means of seven questions that yield a final point score that translates into a specific level. See Appendix XII for more details regarding the NSE scale.

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• Younger graduates (enrollment age): students who enroll at an older age are more likely to have had work experience, which later has an impact on their outcomes in the labor market.

• Exclude external transfers: a graduate who transfers from another higher education institution to a Laureate institution has experienced both educational environments, making it difficult to disentangle the effect of each program.

• Sample size required by the study’s design based on the power calculations (Appendix I).

Table 3. Selection Criteria for UPC and University Graduates Criteria

Years of enrollment 2004 – 2008 Enrollment age 24 or less External transfer Not allowed Internal transfer Allowed Duration of studies 9 years or less (students who took more time to graduate were not considered)

Fields of study Business administration Communication science

Engineering Source: C230 Consultores

Table 4. Selection Criteria for Cibertec and Technical/Vocational Institute Graduates Criteria

Years of enrollment 2005 – 2009 Enrollment age 27 or less External transfer Not allowed Internal transfer Allowed Duration of studies 7 years or less (students who took more time to graduate were not considered)

Fields of study

Business administration Computer science

Marketing Graphic design


Control groups27 The success of the study relied on finding similar individuals who could have attended UPC and Cibertec, but instead attended other similar institutions. To identify those graduates, the study’s authors analyzed the higher education market in Lima to select the universities and technical/vocational institutes that were most similar to UPC and Cibertec, so they would serve as a suitable control group for both UPC and Cibertec. Four criteria were used for selecting the control group institutions:

1. Institutions that were considered by enrolled UPC and Cibertec students as alternative options. 2. Institutions that had similar academic programs as those of UPC and Cibertec. 3. Institutions that were serving the same socioeconomic segment as UPC and Cibertec. 4. Institutions that had similar tuition prices as UPC and Cibertec.

27 We use the term “control group” to refer to a valid comparison group. The term comes with some caveats of this quasi-experimental approach, as noted in the section of methodological limitations.

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Criteria 1 and 4 were assessed using market and student opinion studies performed or commissioned by UPC and Cibertec,28 while criteria 2 and 3 were considered in consultation with UPC and Cibertec staff.

A total of 10 universities and eight technical/vocational institutes were included in the control groups, listed in the table below:

Table 5. List of Control Group Universities and Technical/Vocational Institutes

Universities – UPC control group Technical/Vocational Institutes – Cibertec control group

Universidad del Pacífico Pontifica Universidad Católica del Perú TECSUP

Instituto Peruano de Acción Empresarial – IPAE

Universidad de Lima Universidad Nacional Mayor de San Marcos* ISIL IDAT

Universidad Nacional de Ingeniería* Universidad ESAN IFB Certus

Servicio Nacional de Adiestramiento en Trabajo Industrial -SENATI

Universidad Ricardo Palma

Universidad San Martín de Porres AVANSYS Instituto SISE

Universidad San Ignacio de Loyola

Universidad Tecnológica del Perú

*Public institutions. Source: C230 Consultores

Control group graduates had to meet the same selection criteria as UPC and Cibertec graduates (described in the next section). The quantitative analysis of outcomes can be performed by comparing results found in the surveys between both groups using a matching process that balanced the treatment and control groups.

Data collection Data collection lasted five months, from March through July 2016. C230 Consultores, in consultation with IFC and Laureate, designed the data collection strategy, which was implemented by Instituto Cuánto, a public opinion consultancy that hired and trained local enumerators and later recorded and digitized the data.

UPC and Cibertec graduates in the treatment group were surveyed by phone, based on their contact information saved in the official alumni database. For the control group, enumerators sought to identify and survey alumni in person in Lima, which proved the most challenging part of the information collection process.29

The survey questionnaire was designed to collect all necessary data regarding the outcome indicators, as well as other pre-treatment and observable characteristics that were used during the matching process. The questionnaire included

28 UPC carried out a study based on surveys of recently enrolled students regarding their reasons for deciding to attend UPC, their perception of competing institutions, and their reasons for choosing the academic program that they did, among other subjects (Estudio de incorporados 2015-1 and Estudio de incorporados 2015-2); Cibertec commissioned two similar studies in 2013, executed by the GFK company (Imagen y posicionamiento – Cibertec 2013), of both Cibertec enrolled students and potential students. UPC also developed in 2015 a tuition price table to assess how its prices compared with those of its competitors, while Cibertec commissioned in 2015 another study, executed by Ipsos Marketing, regarding Cibertec’s market elasticity price in the market (Informe final – June 2015). 29 Due to the low number of effective surveys completed each day, the data collection process had to be extended several weeks. This was particularly difficult for the technical/vocational institute graduates group, who seemed especially hard to find in the city.

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a total of 64 questions, plus an additional eight filter questions to make sure that surveyed individuals passed all the selection criteria mentioned above. These questions were distributed in the following sections:

I. Filters II. Socio-demographic aspects

III. Background prior to higher education studies IV. Academic profile V. Financial support

VI. Employment opportunities and benefits VII. Income

VIII. Current employment characteristics IX. Socioeconomic level

A total of 742 surveys were completed for UPC graduates, 560 for Cibertec graduates, 1,057 for university graduates, and 577 for technical/vocational institute graduates. Because Cibertec had a lower number of graduates who enrolled in the years selected for the study and because it was considerably harder to find individuals who passed all necessary filters for its control group, there is a lower number of surveys for these groups. Nonetheless, power calculations conducted for this study showed that a sample size of 800 observations (including both the treatment and control groups) allow a high probability of finding a treatment effect of a considerable size (see Appendix I).

Missing observations in the variables of the database are always a concern. They can potentially introduce a bias to the impact estimate if the missing data is correlated to unobserved factors. For example, a considerable number of surveyed alumni did not complete the full survey, which led to missing values for some indicators (see Appendix V). To avoid dropping these observations completely, and thus possibly biasing the results, a multiple imputation procedure was performed using the Amelia II package developed by James Honaker, Gary King, and Matthew Blackwell (see Appendix V).30

The Matching Strategy Provided that this impact evaluation is constrained by a post-intervention design, the main challenge in comparing the treated and control groups is the difference in observable characteristics prior to enrollment in a higher education degree program (see Appendix II and Appendix III). Matching methodologies allow us to “prune” the observations in the observed data, to reduce the imbalance of the pre-treatment characteristics (or background differences) across groups.

Our evaluation relies primarily on the Coarsened Exact Matching (CEM) methodology, which shows advantages over the alternatives in “its ability to reduce imbalance, model dependence, estimation error, bias, variance, mean square error, and other criteria”. 31 CEM matches individuals using relevant covariates (pre-intervention characteristics), after coarsening these variables into substantively meaningful groups, so as to achieve general balance between the treatment and control groups.

CEM creates a set of strata for observations that share the same value of covariates across treatment and control groups. An observation is kept if it belongs to a stratum where there is at least one individual in both the treatment and control groups. Observations in a stratum where one of the two groups is missing are pruned out. The CEM algorithm assigns a weight of 1 to all treated observations, and identifies weight values for matched observations in

30 This software is free and available to the public at http://gking.harvard.edu/amelia. 31 King, Gary, Stefano M. Iacus, Giuseppe Porro, “Causal Inference without Balance Checking: Coarsened Exact Matching”, Oxford University, 2011, p2.

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the control group in order to find balance in each stratum. Finally, we estimate the effect by running an ordinary least squared regression, where the dependent variables are each one of the outcome indicators in Table 2, and the independent variables are the covariates, and a dummy identifying the treatment group, which captures the Sample Average Treatment Effect.

The treatment and control group observations were matched based on pre-intervention variables that were selected from a group of variables collected through the graduates´ survey (Table 6):

Table 6. Pre-Treatment Variables Available in the Survey Pre-intervention characteristics

o Gender o Household socioeconomic status o Age o Type of high school (public or private) o Year of enrollment o Pre-enrollment employment status o Field of study o Parents’ highest education level o Lima or province origin

If the number of covariates is higher, it becomes more difficult to achieve balance before pruning away a significant number of observations, which is a departure from the original treatment sample. Given this trade-off, different model approaches were implemented, which included different subsets of pre-intervention characteristics. There were two approaches, with equal number of covariates that produced the highest number of matches for treatment observations (see Table 7). Both approaches include a “core” of covariates, namely: year of enrollment, field of study, pre-enrollment household socioeconomic level, gender, and age. All these covariates are exactly matched, except for age, which was coarsened into discrete bins.32 Approach 1 adds a binary variable to identify whether the student attended a public or private university. Approach 2, instead, includes a categorical variable to describe the maximum level of studies attained by parents.

There were important covariates that were not feasible to include in the matching procedure. These include region of origin and rural or urban household, provided that the sample is predominantly urban, around Metropolitan Lima. Standardized test scores could provide valuable information to match students on academic skills, but were not available for this study, and were unfeasible to apply to the individuals surveyed.

Trying different approaches allows identifying how outcome estimates and number of matched observations change with different model specifications in order to assess consistency. Table 7 shows that Approach 1 brings two advantages over Approach 2 (Appendix IV shows how covariates achieve balance in a mean difference test). First, Approach 1 finds more matches for the treatment sample. Second, it reduces the imbalance among covariates further than Approach 2 does.33 Nonetheless, Approach 2 was also implemented in order to assess the consistency of our results.

32 The age categories are younger than 20, 20-25, 25 to 27 to 39 years old, and older, according to the sample distribution. 33 We measure multivariate imbalance using the estimator L1, as proposed by (King, Iacus & Porro, 2011). A value of 1 shows that treatment and control groups have completely separate distributions. A value of 0 suggest that both distributions overlap.

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Table 7. Matching Approaches by University and Institute Groups Universities Technical/Vocational Institutes

Approach 1

• Year of enrollment • Field of study • Household socioeconomic level • Gender • Age • Type of high school (public or private)

n=672, L1=0.35 n=358, L1=0.48

Approach 2

• Year of enrollment • Field of study • Household socioeconomic level • Gender • Age • Parents’ highest education level

n=627, L1=0.45 n=321, L1=0.52 Note: n = sample size; L1= multivariate imbalance. Source: C230 Consultores

As a complement, we also implemented a Genetic Matching (GM). The GM method is a generalized version of the propensity score Mahalanobis distance matching algorithm, by which distance metrics of all covariates are minimized iteratively.34 Based on a similar study conducted in Mexico, CEM is helpful to achieve balance without losing a significant number of observations outside the region of common support. Likewise, GM finds exact matches for observations in the treatment group, allowing one to reduce the imbalance even further, but at the risk of being unable to match a significant number of observations in the control group. For this study, these matching methodologies complement each other, and contrasting both sets of results provides a framework to ensure consistency.35

Since the research applied both matching methodologies for each approach (1 and 2), a total of four estimators were calculated in this study for each of the outcome variables studied. However, since CEM facilitates quicker and easier regressions than GM, and since CEM also allows more straightforward additional analysis and calculations of the matched sample, the results using CEM with Approach 1 (see Table 7) are the ones presented in the next chapter of the report, and they are considered the main results of the analysis.

Based on the graduates survey, 11 outcome indicators were used to assess whether graduates of UPC and Cibertec performed better in the labor market, and had more avenues for social mobility and a higher level of satisfaction with their present status and their education, vis-à-vis graduates of other institutions. Results that are generally consistent across all four estimators calculated are given more importance and are considered more robust. In Appendix VI, the average values of outcome variables by higher education institution are presented, Appendix VII presents the estimators calculated using the main model, and Appendix VIII summarizes the results obtained using all four models. Lastly, unless otherwise specified, results shown will be assumed to be using the first matching approach and using CEM.

34 Diamond, A., & Sekhon, J. (2012). Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies. Review of Economics and Statistics, p4. 35 It is worth mentioning that, for this type of analysis, balance between treatment and control groups, and not representativeness of the sample with respect to the population, is the important matter.

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Methodological limitations Most of the methodological limitations mentioned below could not be controlled for and might influence the results of the study. However, given that only an ex-post analysis was possible, the methodology implemented is the best option available to assess with known reliability the impact of Laureate education in Peru.

• All impact evaluations based on matching methodologies rely on the assumption that observable covariates explain the difference between the treatment and control groups. A “Rosenbaum bounds” test was conducted to assess the sensitivity of the income results to unobserved factors36. We found that even if the chances of joining either UPC or Cibertec are twice as high due to an omitted covariate, the effect on income is still significant.37 However, there is a chance that other unobserved factors are unaccounted for, and these factors might relate to individual self-selection. Given the ex-post design of the impact evaluation, it is impossible to make sure that all factors correlated with the decision to enroll in Laureate institutions are controlled for through the matching process. This could be a source of bias in the results.

• Another possible source of bias is the survey methodology: Laureate graduates were surveyed by phone, while most control group graduates were surveyed in person. This difference in data collection methodologies might bias the results. Multiple efforts were made to obtain telephone databases of control group graduates by contacting directly the universities and institutes, but there was not much success. The questionnaire was designed to be as simple as possible to ensure that responses to telephone interaction were not different from responses to face-to-face interaction.

• Social mobility is a methodological construct with some measurement challenges. Social mobility studies are usually macroeconomic intergenerational analyses. However, this study’s objective was to measure specific changes in specific people. We use the term “pathways or avenues to social mobility” to refer to changes within a shorter period. Such changes are feasible to measure using a survey and targeting specific graduates. We acknowledge that factors such as ethnicity and family structure affect social mobility, but we do consider that sociodemographic variables are valid measurable proxies of such factors. Future research should consider measuring further complexity of social mobility.

• Non-response bias can also impact the internal validity of the study. Missing values on survey questionnaires can potentially introduce a bias if the missing data is correlated with outcome variables through unobserved factors. In other words, if missing values differ systematically between treatment and control groups, and non-response is correlated to the indicators assessed, the impact estimated might be explained by the factors that led some individuals to decide to answer or not answer some of the questions. This study accounts for non-response bias through Multiple Imputation.

• The external validity of the study might be affected by the matching process and by the decision to limit the study to certain fields of study and characteristics of graduates, since the final sample studied might not be a representative sample of either Laureate or control group populations. These constraints of the sample compromise the study’s capacity to extrapolate conclusions to all Laureate education. Likewise, these findings apply to an urban context. Future research could shed light on the impact of higher education in rural contexts.

• A final potential source of bias is the over-representation of certain institutions in the control group, which could explain impact estimated across most or all of the variables analyzed (see Appendix II for details of the control group distribution). For this reason, the study also calculated the results in a sample that specifically excluded these over-represented institutions. Of the six main outcome variables that were tested, the impact

36 Such tests are not published in this report, but are available upon request. 37 Tests were not conducted for other outcome indicators.

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of Laureate education persisted, except for the unemployment rate indicator, which was not significant for UPC graduates, and significant and negative for Cibertec alumni. These results are presented in Appendix XI.

B. Second approach: employers’ perceptions about graduates’ performance and market value

The second approach of this study was an employer survey regarding perceptions of the job skills and market value of UPC and Cibertec graduates, compared with graduates of other higher education institutes.

The employer firms were surveyed by phone and selected using the following two-pronged approach: (i) the graduate survey included a question in the questionnaire where graduates were asked directly where they worked, and (ii) a list of the largest companies in Lima with the highest likelihood of employing graduates from the treatment and control groups.

The questionnaire has a total of 58 questions, which were grouped in the following categories:

Table 8. Questions and Categories of In-Depth Interview Questionnaire

Categories Description

Business profile Type of firm, economic activity, number of workers, proportion of workers with higher education and technical/vocational education

Graduates skills Communications, Analytic, Teamwork, Leadership, Working in an international environment, Organization, Critical thinking, Business ethics

Wages and promotion

Willingness to pay recent UPC or Cibertec graduates vis-à-vis graduates of other institutions Time required for a graduate of UPC or Cibertec to get a promotion vis-à-vis graduates of other institutions

Perception about Laureate graduates’ performance

Employers’ satisfaction with respect to the work performance of UPC or Cibertec graduates

Comparison with graduates of other institutions

Preference for graduates of UPC or Cibertec vis-à-vis graduates of each of the control group institutions

Source: C230 Consultores.

A total of 432 employer surveys were conducted, 360 of which had an opinion of UPC graduates and 222 of which had an opinion of Cibertec graduates. Most of the employers have worked with or had heard of UPC graduates, while about half of them had worked with or had heard of Cibertec graduates.38 Also, most employers surveyed work in companies with over 100 employees. Descriptive statistics of the surveyed firms are shown below:

38 The questionnaire had a section for questions regarding graduates of UPC and its respective control universities, and another section for questions regarding Cibertec and its respective control institutes. If employers answered they had worked with or knew of the work performance of either UPC or Cibertec graduates, they were asked the questions of one or both sections accordingly. If employers did not have an opinion of either UPC or Cibertec graduates, they were not surveyed.

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Figure 1. Distribution of Surveyed Firms That Had an Opinion of UPC Graduates

Source: C230-IFC Employers survey 2016.

Figure 2. Distribution of Surveyed Firms That Had an Opinion of Cibertec Graduates


C. Third approach: qualitative perception of Laureate graduates

The third approach of the research was a series of in-depth qualitative interviews of graduates of UPC and Cibertec. The objectives of the interviews were: (i) to gather more insights about the graduates’ personal achievements, fulfillment of expectations, and perceptions regarding social mobility aspects; and (ii) inquire about issues that arose during the quantitative analysis. Consequently, these interviews focused on topics that remained unclear after studying the graduate and employer surveys, which means that some of them were carried out after all other information was collected.

17%

11%

40%

32%

Number of Employees in Surveyed Companies

0-49 50-99 100-499 > 500

332

19 6 30

100

200

300

400

PrivateCompany

Governmentor Public

Institution

FamilyBusiness

NGONum

ber o

f com

pani

es

Distribution of Companies, by Type of Business

16%

16%

36%

32%

Number of Employees in Surveyed Companies

0-49 50-99 100-499 > 500

206

11 4 10

50

100

150

200

250

PrivateCompany

Governmentor Public

Institution

FamilyBusiness

NGONum

ber o

f com

pani

es

Distribution of Companies, by Type of Business

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A total of 22 graduates were interviewed (11 from UPC and 11 from Cibertec), purposively sampled to represent diversity in gender, employment status, income level, and marital status. Additionally, the selection process strove to include individuals from different academic programs and from different enrollment years.

The qualitative analysis of the interview results was structured around the following six topics:

I. Background prior to higher education studies II. Academic profile and academic life perceptions

III. Experience in the labor market IV. Economic conditions, expectations, and personal goals V. Perceptions regarding social mobility

VI. Social life and current satisfaction level

The analysis of the qualitative interviews was used to complement and better understand the context of graduates and the context of the econometric findings.

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4. Findings

UPC graduates outperformed their control group counterparts in key income, career, and social mobility variables of interest studied (six out of 11). Some of these positive results are seen in income, number of job benefits, percentage who perceived usefulness of their major studied, percentage who have held a managerial position, percentage promoted at their current workplace, and percentage who have experienced a positive change in socioeconomic level. Likewise, UPC graduates displayed weak positive results in two additional outcome variables: a smaller proportion of graduates currently unemployed, and a higher satisfaction with current job; these two variables were significant on the main econometric model but did not hold for all four econometric estimations.

Cibertec graduates outperformed their control technical/vocational institute graduates in one out of the 11 studied outcome variables, namely in monthly income. They had weaker positive results in one additional outcome variable: the number of job benefits, and three weak negative results: the maximum unemployment time, percentage of graduates who have held a managerial position, and number of coworkers the graduate supervises. By “weak results,” we mean that such results do not hold across all estimations, and we consider them less conclusive.

Table 9 summarizes the main findings, which include results that were generally consistent across all four estimations described in the matching strategy, and those outcomes that were significant at least in one estimation.

Table 9. Summary of Study Findings, by Outcome Indicator

Categories Aspects Outcome indicators


a) Employability Time between graduation and first job ▪ ▪ Maximum unemployment time ▪ X Proportion of graduates currently unemployed ▪


Monthly income in soles (PEN) Number of job benefits received from the workplace



Perceived usefulness of the major studied for professional life ▪

Satisfaction with current job ▪


Number of coworkers the graduates supervises ▪ X Percentage of graduates who have held a managerial position in the workplace X

Percentage of graduates promoted at workplace ▪

3) Social Mobility


Proportion of graduates who have experienced a positive change in their current socioeconomic status compared with their socioeconomic status before enrollment

▪

Positive impact in all estimations (robust)

Positive impact in main econometric model, but not significant in at least one out of four estimations

X Negative impact in main econometric model, but no significant in at least one out of four estimations

▪ Not significant impact in main econometric model

To describe the findings in detail, the remainder of this section will focus on the results from the main model of this study (CEM methodology, including covariates in Approach 1). Results were considered negative or positive if they were at least statistically significant at a 95% level. Appendix VI provides a table for all the outcomes, including sample size, means by group, and p-values.

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The analysis included estimations of impacts disaggregated by gender and field of study.39 Additionally, for the monthly income and the socioeconomic level indicators, another analysis was conducted in which the sample was separated into three groups, depending on the individuals’ number of points on the socioeconomic level scale (NSE),40 to determine in which level of the socioeconomic segments the treatment effect was the largest.

i. Employability (access to jobs and unemployment)

“I have many friends [from UPC] who entered big companies through small job posts, and now they are heads of their departments.”

UPC graduate

UPC graduates’ current unemployment rate (4.8%) is lower than that of graduates of other comparable universities (7.9%) (Figure 3). There are no significant differences in the time it takes UPC alumni to find their first job or the maximum time they have been unemployed, compared with their peers from other universities. In contrast, Cibertec graduates do not have a lower unemployment rate, nor do they take a longer or shorter time to find their first job. Also, there was no significant impact consistent with all four estimations on their maximum time unemployed.41

When gender is taken into account, only male UPC alumni were found to have a significantly lower unemployment rate than their counterparts from other universities (Figure 4). In fact, male UPC alumni had an unemployment rate 7.5 percentage points lower than the male control group (4.6% and 12.1%, respectively). Since there was no significant effect on women’s unemployment rate, it is safe to conclude that men’s high treatment effect accounts for the effect seen when both groups are combined. It is worth noting that the unemployment rate of control group female graduates is 5.7 percentage points lower than that of the male control group sample (6.4% and 12.1%, respectively), so the female population in general has a lower average unemployment rate.

Figure 3. Impacts and means on unemployment rate of UPC and control group graduates

Figure 4. Impacts and means on unemployment

rate of UPC and control group graduates by gender

Note: Significance: *p<0.10; **p<0.05; ***p<0.01.

Source: C230-IFC Graduates survey 2016.

39 See Appendix IX for a summary of the findings on outcome variables by fields of study. 40 See Appendix XII for more information regarding the NSE points scale. 41 One out of four estimators calculated shows their maximum time unemployed is 30 days longer than that of graduates from the control group, though this result was not observed in the main model and was only significant at a 90% level.

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In addition to gender, the field of study also appears to be an important factor when considering unemployment. More precisely, UPC graduates from the Engineering academic programs have an unemployment rate 5 percentage points lower (Figure 5). Effects were not significant for the Communications and Business and Administration programs.

As for Cibertec, graduates experience roughly the same unemployment rate and first job search time as their peers. Gender was not a significant factor, but field of study did seem to have an influence on unemployment (Figure 6): IT graduates from Cibertec had a lower unemployment rate (6%) than their peers from the control group (11%). In contrast, Graphic Design graduates from Cibertec had a higher unemployment rate (21%) than their peers.

Cibertec graduates showed a longer time (37 days) in unemployment than their peers over their professional careers. While Cibertec alumni have spent on average 162 days in unemployment, their peers in control institutes only have spent 125.

Figure 5. Impacts and means on unemployment rates of UPC and control

group graduates, by fields of study

Figure 6. Impacts and means on unemployment

rates of Cibertec and control group graduates, by fields of study

Note: Significance: *p<0.10; **p<0.05; ***p<0.01. Source: C230-IFC Graduates survey 2016.

What do employers think of Cibertec graduates?

Cibertec alumni are viewed by employers as capable and skilled workers. In comparison with their peers, Cibertec graduates are ranked first on work performance in general, as well as first in 7 out of the 8 job skills employers were asked about. For university graduates, excluding the three oldest and most established universities, UPC ranked third in work performance.

Figure 7. Work performance of Cibertec and control group graduates, as assessed by employers


6

7

8

(1="very bad", 10="very good")

Scal

eof

one

to te

n

Cibertec SENATI IFB TECSUP IPAEISIL IDAT SISE AVANSYS

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ii. Income and job quality

“Fortunately, I have been working in companies where the pay is good. The company I currently work at also offers significant benefits.”

UPC graduate

On average, UPC graduates have a 28.6% higher monthly income42 than graduates of other universities (S/.4,624.5 compared with S/.3,595.8 — see Figure 8)43. UPC men and women benefit by about the same number, both in absolute and proportional values, compared with their male and female counterparts in the control group, respectively (see Figure 9).

In addition to gender, field of study plays an important role in the impact of a Laureate education compared with peer universities. The UPC graduates who surpass by the largest amount their control group peers are the Business and Administration alumni, who earn on average S/.1,150 soles more (an income 32.5% higher — see Figure 10). Those with the smallest difference were the Engineering program graduates, who earn on average S/.874 soles more (an income 22.3% higher).

42 Based on data availability, the outcome indicator to measure income initially was based on a discrete variable that identifies those individuals above the 75th percentile of income in Peru (individuals earning more than 3,600 Soles). Of the total sample surveyed, 27.1% answered the question in the continuous form (p49); 45.5% in the discrete form (p50); and 27.4% were missing observations. Missing observations were estimated then with Multiple Imputation, taking advantage of the information collected in question P50 and other sociodemographic variables described in Appendix V. Based on the CEM methodology, the proportion of UPC graduates earning an income above 3,600 soles is 25 percentage points higher than their peers from other universities (61% in UPC vs. 36% in other universities). For Cibertec, the proportion of students above the same threshold is 20 percentage points higher than graduates from other institutes. These results are robust; with a significance level higher than 95% in all econometric estimations. Subsequently, we generated a continuous variable via Multiple Imputation and evaluated it as an outcome indicator. Impacts estimations are fully consistent for both the discrete and continuous variables. The findings in this section are elaborated on the continuous results provided that it is simpler to communicate them in terms of a sum of money, rather than proportions of individuals above the 75th percentile in the income distribution. 43 Average exchange rate during data collection, from 1st March to 16th July, 2016: USD $ 1,00 = PEN S/3.27850, Central Reserve Bank of Peru.

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Figure 8. Impacts and means on monthly income of UPC and control group graduates

Figure 9. Impacts and means on monthly income of UPC and control group graduates, by gender

Figure 10. Impacts and means on monthly income of UPC and control group graduates, by fields of study


“I started working [at my company] in 2014. In September 2015, I was evaluated and received a 500 sol raise. Recently, another company made me an offer, and I mentioned I was thinking of leaving, so they gave me another raise of 700 soles. I liked the effort and the recognition they gave my work, so I stayed.”

Cibertec graduate

The difference between Cibertec alumni and their peers is substantial: Cibertec graduates earn 40.5% more than graduates in the control group (S/.3,350 and S/.2,384, respectively — see Figure 11). This finding was robust. These results when analyzed by gender show that male graduates benefit more from their education compared with male peers than female graduates. Male Cibertec alumni earn on average S/.1,050.8 soles more than their male counterparts from the control group (a 41.4% difference), while female Cibertec alumni earn S/.737.9 more than their female peers (a 37.6% difference — see Figure 12). Hence, Cibertec education has a stronger effect on its male alumni, which may translate to an increase in the income gender gap of its graduates.

Cibertec education has a differentiated effect on graduates from different fields of study. Graduates of the IT academic program benefited the most, earning on average up to S/.1,322 soles more than the control group (see Figure 13). The rest of the programs did not show a significant effect.

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Figure 11. Impacts and means on monthly income of Cibertec and control group graduates

Figure 12. Impacts and means on monthly income of Cibertec and control group graduates, by gender

Figure 13. Impacts and means on monthly income of Cibertec and control group graduates by fields of study


Which socioeconomic segment benefits the most from graduating from UPC or Cibertec in terms of income? Individuals with a high socioeconomic standing profit the most, but Cibertec education also favors individuals of moderate socioeconomic standing. UPC graduates in the highest socioeconomic segment benefited the most when compared with their control group counterparts, and those in the middle benefited more than those in the lowest socioeconomic segment (i.e., the higher the socioeconomic segment, the larger the benefits they receive from Laureate education in terms of income). Cibertec produced similar results, though the difference in benefits between highest and lowest socioeconomic segments was not as large as in UPC, and there was a larger gap between those in the middle and lower segments.

Methodology: Graduates were divided into three groups depending on their level in the socioeconomic NSE scale (see Appendix X). Laureate graduates located in each segment of the scale were compared with control group individuals in the same segment.

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UPC graduates receive 4.46 out of 5 possible job benefits offered under in Peru, while control group graduates receive 4.12 on average (Figure 14). The job benefits considered for this analysis are: pension, private medical insurance, unemployment insurance,44 year-end bonus, and other types of bonuses45 (see figure 18). The total number of job benefits is a proxy to measure job quality (the higher the number, the better the quality of the job).

Male UPC graduates compared favorably with male peers (on average 0.25 more job benefits — see Figure 15), but this is not the case for female UPC graduates, who showed no significant effect.

Figure 14. Impacts and means on number of job benefits of UPC and control group graduates

Figure 15. Impacts and means on number of job benefits of UPC and control group graduates, by

gender


Cibertec graduates receive 3.90 out of 5 possible job benefits offered under in Peru, and graduates from other technical/vocational institutes receive 3.23 (Figure 16). Both Cibertec men and women benefit from treatment effects: women get on average 0.78 more job benefits, and men receive on average 0.63 than their peers from other institutes, respectively (Figure 17). Interestingly, both treatment and control group women receive more job benefits on average than their male counterparts.

44 In Peru it is common to have public unemployment insurance, called CTS (Compensación por Tiempo de Servicios). 45 E.g., profit sharing, bonus payments, among others.

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Figure 16. Impacts and means on number of job benefits of Laureate graduates

Figure 17. Impacts and means on number of job benefits of Cibertec and control group

graduates, by gender


Which job benefit do UPC and Cibertec graduates receive more than their counterparts? Over 90% of UPC graduates and 77% of Cibertec graduates enjoy four of five job benefits: year-end bonus, medical insurance, unemployment insurance, and pension (Figure 18). Across all benefits, Laureate graduates enjoy benefits in larger proportions than their peers who graduated from other higher education institutions, in particular for two benefits: unemployment insurance and other bonuses.

Figure 18. Difference of treatment and control groups by type of job benefit

Source: C230-IFC Graduates survey 2016

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iii. Satisfaction with current professional life

“My biggest satisfaction [at my company] has been the verbal recognition, the wage raise, and recently a formal recognition for outstanding performance here.”

UPC graduate

On a scale of 1 to 10, UPC alumni graded their perception of usefulness of their field of study an average of 7.94, while graduates from the control group graded theirs an average of 7.58 (Figure 19). Job satisfaction was also higher for UPC graduates compared with their peers (Figure 20). On a scale of 1 to 10, UPC alumni graded their satisfaction with their current job an average of 8.02, while the control group graduates graded their satisfaction a 7.70. In the case of Cibertec, there were no significant effects.46

Figure 19. Impacts and means on perception of major usefulness of UPC and control group

graduates

Figure 20. Impacts and means on job

satisfaction of UPC and control group graduates


iv. Career advancement (executive positions and promotions)

UPC alumni outperform their peers from the control group both in terms of promotions and managerial responsibilities. Over half (52%) of UPC graduates have been promoted, compared with 42% of control group graduates (Figure 21). In terms of promotions, UPC women outperform their control group counterparts by 12.7 percentage points (52.8% of UPC female graduates have been promoted, against 40.1% of control group graduates — see Figure 22). UPC graduates from the Business and Administration and Engineering programs have been promoted more, compared with peer alumni in the same fields (an increase of 10 percentage points and 12 percentage points, respectively — see Figure 23). There was no significant difference for Communications graduates.

46 One of the four estimators calculated for the perception of field of study usefulness shows a negative impact at 90% of confidence. Something similar occurred with the outcome variable of job satisfaction.

33

Figure 21. Impacts and means on percentage who have been promoted of UPC and control

group graduates

Figure 22. Impacts and means on percentage who

have been promoted of UPC and control group graduates, by gender

Figure 23. Impacts and means on percentage who have been promoted of UPC and control group graduates, by

fields of study


A third (34%) of UPC alumni have held managerial positions compared with a fifth (21%) of the control group (Figure 24). Both UPC women and men outperform their control group counterparts in the percentage who have held a managerial position. A third (34%) of UPC men have held a managerial position, while only 24% of control group male graduates have done so, whereas among women the percentages are 35% for women from UPC and 18% for the control group (Figure 25). Business and Administration UPC graduates outperform their peers in holding management positions by 15 percentage points, and Engineering graduates do so by 10 percentage points (Figure 26). There was no significant difference for Communications graduates.

34

Figure 24. Impacts and means on percentage who have held a managerial

position of UPC and control group graduates

Figure 25. Impacts and means on percentage who have held a managerial position of UPC and

control group graduates, by gender

Figure 26. Impacts and means on percentage who have held a managerial position of UPC and control group graduates, by fields of study


There were no significant differences in the number of coworkers supervised by UPC alumni compared with those supervised by peers from the control group in any of the four models.

In contrast to UPC, Cibertec alumni did not show a difference from the control group in terms of promotions. This remained true regardless of disaggregation by gender and by field of study. However, negative effects were found regarding the percentage of graduates holding a managerial position (18% vs 25% in the control group) and the number of coworkers supervised (1.53 vs 2.90 in the control group).

35

v. Changes in socioeconomic status47

“My socioeconomic situation has improved by at least 50% compared to my situation before I started my studies; at least 50%, if not more.”

UPC graduate

UPC showed a strong and positive impact on its graduates’ socioeconomic status. More precisely, 19% of UPC alumni had a positive change, as opposed to 12% in the control group (a difference of 7 percentage points — see Figure 28). This suggests that UPC graduates are better off than their parents, and that UPC graduates are able to improve their socioeconomic levels to a larger degree than their peers.

When analyzed by gender, more UPC men (20%) experienced an increase in socioeconomic status compared with control group men (12%), and more UPC women (17%) experienced an increase in socioeconomic status compared with control group women (11%) (see Figure 29). The impact effect is proportionally similar for both UPC male and female graduates.

47 Appendix XI and Appendix XII have information regarding the effects of Laureate education on occupational prestige. These results are not presented in the main findings due to methodological issues.

Graduates’ teamwork, communications, and leadership skills

Employers were asked to rank the top three most important skills of their employees. Overall, the perception was that teamwork skills were the most important, followed by communication skills and leadership skills.

Employers were then asked to grade employees who graduated from Laureate institutions and their peer institutions on these skills. On all three, UPC graduates were graded in second place when compared with four other groups of control graduates. Cibertec graduates ranked second in teamwork skills, when also compared with four groups of different institute graduates, and first in communications and leadership skills.

Figure 27. Average grades of teamwork, communication skills, and leadership skills of Laureate and control group graduates.


5

7

9

Communication Team work Leadership

Scal

e of

one

to te

n

Skills of UPC and other university graduates

UPC ULima, UPacífico and PUCPESAN and USIL UNI and UNMSMURP, USMP and UTP

5

7

9

Communication Team work Leadership

Scal

e of

one

to te

n

Skills of Cibertec and institute graduates

Cibertec SISE, SENATI and AVANSYSIPAE and ISIL TECSUPIFB and IDAT

36

Business and Administration UPC graduates are the only ones who show a significant effect: 8 percentage points between Laureate graduates who underwent a positive change in socioeconomic status compared with their peers (see Figure 30). There was no significant effect in graduates from other programs.

Figure 28. Impacts and means of the percentage who underwent a

positive change of socioeconomic level of UPC and control group

graduates

Figure 29. Impacts and means of the percentage who underwent a positive change of

socioeconomic level of UPC and control group graduates, by gender

Figure 30. Impacts and means of the percentage who underwent a positive change of socioeconomic level of UPC and control group graduates, by fields of study



The same increase in socioeconomic status did not hold for Cibertec graduates, regardless of gender or field of study.48 When analyzed by field of study, there were positive and significant impacts for Cibertec graduates from Information Technology, 10 percentage points more than their peers. However, Graphic Design alumni benefited less than their control group peers (40% experienced a positive change in socioeconomic status, compared with 49% of the control group — see Figure 31). No significant results were found for the rest of the programs.

48 One of the four estimators shows an increase in socioeconomic status of Cibertec alumni (of 6.7 percentage points over the control group) that was statistically significant at the 90% level, though it was reached using the less restrictive set of matching variables.

37

Figure 31. Impacts and means of the percentage who underwent a positive change of socioeconomic level of Cibertec and control group graduates, by fields of study


“I have a stable economic condition. I’m capable of accessing comforts that enable me to have a good social environment.”

UPC graduate

Which socioeconomic segment benefits most from Laureate education in terms of an increase in socioeconomic status?

Individuals with low socioeconomic status benefit most from UPC. UPC graduates in the lowest socioeconomic segment benefited most when compared with their control group counterparts. Those in the middle benefited to a lesser degree. Graduates from the highest segment did not have a positive impact, when compared with their control group counterparts. This implies that the lower the socioeconomic segment, the larger the benefits received from an education at UPC. For their part, none of the segments benefited from Cibertec education in terms of their socioeconomic status (measured in the NSE points scale — see Appendix X).

38

5. Conclusions

Since 2004, Laureate Institutions have played a key role in expanding access to higher education in Peru, through UPC in the university system and Cibertec in the technical/vocational system. The emergence of a vibrant sector of private alternatives to public universities makes it relevant to shift the policy analysis from coverage to quality of education and skills acquired during college years. This study contributes to the understanding that the academic degrees offered by Laureate also enhance graduate performance in the labor market and provide more pathways for social mobility relative to other education alternatives. There are positive and robust results for Laurate graduates, particularly for UPC. No negative results were found for UPC. Those results that are negative for Cibertec are weak (not robust) findings.

The most consistent result across Laureate institutions was a positive and significant difference in income when compared to peers from comparable institutions. On average, UPC graduates in Lima show a 28.6% higher monthly income than graduates of other universities, and Cibertec graduates in Lima earn 40.5% more than graduates of other technical/vocational institutes. These results are consistent with a previous impact evaluation for Laureate institutions in Mexico (Universidad del Valle de México [UVM] and Universidad Tecnológica de México [Unitec]). Laureate International Universities in both Mexico and Peru are providing affordable high quality education. Graduates of UVM, Unitec, UPC, and Cibertec outperform their peers and experience improvement in the avenues toward social mobility. The labor markets of both Mexico and Peru recognize the quality of Laureate’s higher education.

The study found other specific impacts for both institutions. For UPC, the most robust effects were found on income, on the percentage of graduates who underwent a positive change in socioeconomic status, the percentage who have experienced a job promotion, the percentage who have held a managerial position, number of job benefits, and perceived usefulness of their major. However, no robust effects were found on employment indicators. Cibertec graduates’ strongest and most robust result was seen in income and a weaker positive effect on number of job benefits. Nonetheless, there is some indication of negative results for Cibertec´s graduates on number of days in unemployment, Cibertec´s graduates holding fewer managerial positions, and number of coworkers the Cibertec graduates supervise.

Gender analyses show encouraging results. On the one hand, male graduates, both from control and treatment groups across institutions, earn higher incomes than females. But on the other hand, UPC women seem to be closing the gap, with a higher effect than their male peers, although this does not hold true for Cibertec. Results also show some indications that Laureate may be contributing to reducing the gender gap, specifically in career advancement outcomes for UPC.

Laureate alumni perceive that having a UPC or Cibertec degree made it easier to enter the job market and start a career. More precisely, Laureate graduates appreciate both UPC and Cibertec as prestigious brands that open doors to good jobs. In broader terms, they are grateful to their almae matres and are proud of having studied there.

While the results are generally positive, they nonetheless highlight ongoing areas of opportunity for improvement. In particular, employers did not value UPC graduates as highly as Cibertec graduates. Also, Laureate could try to boost employment rates for their graduates. Therefore, there is a case for Laureate institutions to engage with employers to listen to their main concerns and discuss how the academic programs are structured. In particular, there is an area of opportunity to improve the quality of career services for recent graduates, which could include expanding job banks, strengthening internship programs, and engaging employers for strategic alliances to implement job-training programs.

With respect to methodological limitations, further research would benefit from prospective impact evaluation that allows an opportunity to gather more administrative information and to measure social mobility during longer periods of time. Additional administrative information would allow mitigating some potential biases (for example, including

39

academic performance in the econometric models) and would allow including more cohorts and majors for better external validity of conclusions.

In line with the recommendations from the Mexico report, from a business and policy perspective, it is relevant to further explore the mechanisms through which private higher education can improve social mobility and economic opportunities, such as curriculum design, industry linkages, and career services. This would allow education institutions to identify key best practices in order to replicate them across countries, and to inform the policy dialogue about the value of private education.

From Laureate’s perspective, this study shows strong positive results. Laureate stakeholders could benefit from considering the implications of this study. Students make educational decisions without sufficient information about the higher education market. Laureate provides higher returns on educational investment than their competitors, and Peruvian prospective students should know this so they can make better decisions. Laureate should publicize the findings of this study to convey that Laureate is a sound investment vis-à-vis its competitors. Affordable private education providers, like those in the Laureate International Universities network, are delivering accessible and high-quality higher education. Private higher education is worth exploring further as a solution to education access, and as a key element in building stronger labor markets and a stronger Peru.

“I believe [my university] does have an impact in the development [of Peru], mostly through the education it offers its students … in the way it forms professionals, and those professionals — in the end — can make a difference.”

UPC graduate

40

6. References

Beller, E., & Hout, M. (2006). Intergenerational Social Mobility: The United States in Comparative Perspective. The Future of Children, 16(2), 19-36.

Benavides, M. (2003). Cuando los extremos no se encuentran: un análisis de la movilidad social e igualdad de oportunidades en el Perú contemporáneo. Boletin del Instituto Francés de Estudios Andinos, 32(3), 473-494.

Blackwell, M., Iacus, S., King, G., & Porro, G. (2010). CEM: Coarsened Exact Matching. See in http://gking.harvard.edu/files/gking/files/cem-stata.pdf.

C230 Consultores (2015). Affordable Higher Education in Mexico: Implication for Career Advancement and Social Mobility”. Prepared for International Finance Corporation (IFC) and Laureate International Universities.

Castro, J. F., & Yamada, G. (2013). Declining Higher Education Quality Affects Postsecondary Choices: A Peruvian Case. International Higher Education.

Diamond, A., & Sekhon, J. (2012). Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies. Review of Economics and Statistics. See in http://sekhon.berkeley.edu/papers/GenMatch.pdf.

Ellwood, D., & T. Kane. (2000). Who Is Getting a College Education? Family Background and the Growing Gaps in Enrollment. In Danziger, S., & Waldfogel, J. (eds.), Securing the Future: Investing in Children from Birth to College. Russell Sage Foundation.

Gestión (2013). ¿Qué universidades prefieren las empresas peruanas para contratar egresados?. In Gestión. Lima.

Guerrero, G. (2013). ¿Cómo afectan los factores individuales y escolares la decisión de los jóvenes de postular a educación superior? Un estudio longitudinal en Lima, Perú. Grupo de Análisis para el Desarrollo (GRADE). Lima, Perú.

Honaker, J., & King, G. (2010). What to Do about Missing Values in Time-Series Cross-Section Data. American Journal of Political Science, Vol. 54(2), 561-581.

Honaker, J., King, G., & Blackwell, M. (2015). AMELIA II: a Program for Missing Data. See in: https://cran.r-project.org/web/packages/Amelia/vignettes/amelia.pdf.

Hout, M. (1988). More Universalism, Less Structural Mobility. The American Occupational Structure in the 1980s. American Journal of Sociology, 93, 1358–1400.

Iacus, S., King, G. & Porro, G. (2011). Causal Inference without Balance Checking: Coarsened Exact Matching. Oxford University Press.

INEI (2014). Encuesta Nacional de Egresados Universitarios 2014. Database available at http://iinei.inei.gob.pe/microdatos/.

INEI (2016). Situación del Mercado Laboral en Lima Metropolitana, Informe Técnico No 8. Mayo-Junio-Julio, 2016.

King, G., & Lam, P. (2015). Comments on C230's Final Report. Beecher Analysis Group.

King, G., & Lam, P. (2014). Comments on C230's Inception Report. Beecher Analysis Group.

Laureate International Universities (2015). Discussion on Peru presentation, October 9, 2015.

41

Lee, C., & Solon, G. (2006). Trends in Intergenerational Income Mobility. NBER Working Papers Series.

Mendoza, A. (2015). Inequality in Peru: Reality and Risks Policy and Campaign Team. Oxfam in Peru, Lima.

Orozco, L. (2009). The Educational Revolution in Colombia: Scopes and Restrictions. In Higher education in Latin America and the Caribbean 2008, eds. Segrera, F., Brock, C. & Dias, J. IELAC- UNESCO, Caracas.

Sekhon, J. S., & Grieve, R. (2009). A Nonparametric Matching Method for Covariate Adjustment with Application to Economic Evaluation. See in http://sekhon.berkeley.edu/papers/GeneticMatching_SekhonGrieve.pdf.

Universidad de Ciencias Aplicadas (UPC). (2015). Estudio de incorporados 2015-1: UPC total. Conocimiento del consumidor y mercado — Dirección de inteligencia y nuevas soluciones.

Universidad de Ciencias Aplicadas (UPC). (2015). Estudio de incorporados 2015-2: UPC total. Conocimiento del consumidor y mercado — Dirección de inteligencia y nuevas soluciones.

Yamada, G. (2012). Las brechas crecientes entre el mercado laboral y la educación superior en el Perú, Universidad del Pacífico y Consejo Nacional de Educación.

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7. Appendices

Appendix I

Power calculations

Power calculations were performed to make sure the study used a sufficiently large sample, in such a way that the analysis of the database yielded a good probability of identifying a statistically significant difference on two chief indicators: monthly income and employment rate. Results shown below assumed a sample size of 400 observations in the treatment group and 400 in the control group.

Calculations conducted show that the study had an 80% chance of detecting a 13.52% difference in monthly income between the treatment and the control groups, and that this effect would be statistically significant at a 10% level. This translates to a difference of 265 Peruvian soles (S/. 265), according to the national monthly income average of university graduates. Also, taking into account the average monthly income from the control group (S/. 2,113)49 and that of UPC graduates (S/. 2,461)50, the power calculations show that given the size of our sample, the analysis would be able to detect a difference such as seen between these averages.

In the case of the employment rate, the calculations show that the analysis would have had an 80% chance of detecting an effect of 5.47 percentage points that was statistically significant at a 10% level. Unfortunately, for this evaluation it was not possible to dispose of data regarding the average employment rate of either Cibertec or control group graduates. Nonetheless, the overall calculations yielded adequate proof that they would be able to detect a treatment effect that would be statistical significant.

Figure 32. Detectable effect sizes of income and unemployment rates as assessed by the power calculations.

Detectable size effect Notes

Income 13.52% 800 sample size. Statistical power of 80% Significance level of 10%.

Employment rate 5.47 percentage points

49 This average did not include graduates from U. ESAN or UTP, since they were not considered in the survey. 50 Averages were calculated using the 2014 National University Graduate survey (Encuesta Nacional de Egresados Universitarios 2014), collected by the Instituto Nacional de Estadística e Informática (INEI).

43

Appendix II

Pre-matching control and treatment group differences

There was a noticeable difference between treatment and control group distributions, regarding indicators of socioeconomic status. Specifically, the treatment group came from wealthier families than their control group counterparts.

Figure 33. Treatment and control groups’ sample distribution before matching


2%2% 2%8%4%

21%35%

45%45%

52%

56%36%50%

25%7% 9%

0%

20%

40%

60%

80%

100%

UPC ControlUniversities

Cibertec ControlInstitutes

Perc

enta

ge

Socioeconomic level prior to enrollment

E D C B A

90.6%65.3% 59.6%

26.7%

9.4%34.7% 40.4%

73.3%

0%

20%

40%

60%

80%

100%



Perc

enta

ge

Type of high school

Private Public

2%1% 3% 3% 7%10%26% 32%

44%13%

14%26%

22%67%46%

38% 23%9% 11%

2% 2%

0%

50%

100%



Perc

enta

ge

Parents' highest education level

None ElementaryHigh school Technological higher educationBachelor degree Post graduate studies

44

Appendix III

Matched sample distribution

• UPC and university group

Figure 34. Distribution of UPC and control groups’ university graduates, after matching.

3% 3%

47% 47%

50% 50%

0%

20%

40%

60%

80%

100%

Laureate Control

Perc

enta

ge

Socioeconomic prior to enrollment

E D C B A

9% 10%11% 10%

72% 72%

8% 8%

0%

20%

40%

60%

80%

100%

Laureate Control

Perc

enta

ge


None ElementaryHigh school Technological degreeBachelor degree Post graduate studies

53% 53%

47% 47%

0%

20%

40%

60%

80%

100%

Laureate Control

Perc

enta

ge

Gender

Men Women

63% 63%

10% 10%

27% 27%

0%

20%

40%

60%

80%

100%

Laureate Control

Perc

enta

geFields of study

B&A Communications Engineering

45


Table 10. Number of Observations in the University Group Sample, Before and After Matching.


• Cibertec and technical/vocational institute group

Figure 35. Distribution of Cibertec and control group’s technical/vocational institute graduates.

14%3%

10%

4%9%39%

5%13%

3%

Control group universities

ESAN PUCP UPacífico ULima UNIUNMSM URP USIL USMP UTP

38% 38%

57% 57%

3% 3%

0%

20%

40%

60%

80%

100%

Laureate Control


E D C B A

39% 38%

23% 24%

37% 37%

0%

20%

40%

60%

80%

100%

Laureate Control

Perc

enta

ge


None ElementaryHigh school Technological degreeBachelor degree Post graduate studies

46


Table 11. Number of Observations in the Institute Group Sample, Before and After Matching.


74% 74%

26% 26%

0%

20%

40%

60%

80%

100%

Laureate Control

Perc

enta

geGender

Men Women

37% 37%

59% 59%

1% 1%3% 3%

0%

20%

40%

60%

80%

100%

Laureate Control

Perc

enta

ge

Fields of study

B&A Information TechnologiesMarketing Graphical Design

40%

1%

9%12%

10%

9%

15%

2%Control group institutes

IDAT AVANSYS IFB IPAEISIL SENATI SISE TECSUP

47

Appendix IV

Pre- and post-matching mean difference tests of matching covariates Table 12. Mean Difference Tests of Matching Variables, Before and After Matching.

Source: C230-IFC Graduates Survey 2016.

Matching variables

Pre-matching Post-matching

UPC Control universities Cibertec Control

institutes UPC Control universities Cibertec Control

institutes Age

Observations 742 1057 560 577 672 761 358 318 Mean 26.447 27.969 26.905 29.050 26.424 26.654 27.142 27.492

ttest t(1797)=13.23 p=0.000 t(1135)=10.78 p=0.000 t(1431)=-1.46 p=0.143 t(674)=-1.09 p=0.278 Socioeconomic level prior to enrollment (NSE)


ttest t(1797)=-13.82 p=0.000 t(1135)=-6.10 p=0.000 t(1431)=0.00 p=1.000 t(674)=0.00 p=1.000 Year of enrollment


ttest t(1797)=-4.49 p=0.000 t(1135)=-4.39 p=0.000 t(1431)=0.00 p=1.000 t(674)=0.00 p=1.000 Gender (1 = male)


ttest t(1797)=4.58 p=0.000 t(1135)=-6.43 p=0.000 t(1431)=0.00 p=1.000 t(674)=0.00 p=1.000 Type of high school (1 = private)


ttest t(1797)=-12.84 p=0.000 t(1135)=-11.93 p=0.000 t(1431)=0.06 p=0.949 t(674)=0.07 p=0.945

48

Appendix V

Number of missing values for each outcome variable, and by group (university or institute).

Table 13. Number of Missing Values for Each Outcome Variable.

Outcome Variables

Proportion of non-missing values

Difference (control-treated)

z-stat p-value

Control Treatment

UPC and university

control group

Time between graduation and first job 0.916 0.939 -0.024 -1.873 0.0611

Maximum unemployment time 0.981 0.978 0.003 0.394 0.6937

Proportion of graduates currently unemployed 0.994 0.985 0.009 1.974 0.0483

Monthly income in soles (PEN) 0.860 0.765 0.094 5.146 0.0000

Number of job benefits received from the workplace 0.862 0.937 -0.075 -5.046 0.0000 Perceived usefulness of the major studied for

professional life 0.991 0.997 -0.006 -1.559 0.1191

Satisfaction with current job 0.888 0.941 -0.052 -3.818 0.0001

Number of coworkers the graduate supervises 0.869 0.937 -0.067 -4.621 0.0000 Percentage of graduates who have held a managerial

position in the workplace 0.979 0.984 -0.005 -0.712 0.4767

Percentage of graduates promoted at the workplace 0.860 0.935 -0.075 -5.048 0.0000

Proportion of graduates who have experienced a positive change in their current socioeconomic status 0.959 0.962 -0.003 -0.316 0.7521

Cibertec and

institute control group

Time between graduation and first job 0.920 0.980 -0.060 -4.641 0.0000

Maximum unemployment time 0.979 0.988 -0.008 -1.091 0.2752

Proportion of graduates currently unemployed 0.988 0.991 -0.003 -0.528 0.5972

Monthly income in soles (PEN) 0.908 0.809 0.099 4.810 0.0000

Number of job benefits received from the workplace 0.778 0.882 -0.104 -4.660 0.0000 Perceived usefulness of their major in their professional

life 0.967 0.998 -0.031 -3.994 0.0001

Satisfaction with current job 0.854 0.877 -0.022 -1.105 0.2692

Number of coworkers the graduates supervises 0.813 0.864 -0.051 -2.356 0.0185 Percentage of graduates who have held a managerial

position in the workplace 0.950 0.984 -0.034 -3.207 0.0013

Percentage of graduates promoted at the workplace 0.813 0.870 -0.057 -2.618 0.0088

Proportion of graduates who have experienced a positive change in their current socioeconomic status 0.946 0.827 0.119 6.373 0.0000


49

VARIABLES INCLUDED IN THE IMPUTATION PROCEDURE

Gender Age Parent’s education # coworkers charge Usefulness of major studied

Institution College Time unemployed Promotion Income (cont) Field of study Lima Time to first job Job benefits Income (discrete) Year of enrollment Working status Manager Satisfaction current

job Father occupation

Mother occupation Year-end of studies specialization master Doctorate Variables of graduate’s questionnaire (see questionnaire)

N1_A N10_A N9_B P34 P47_4 N3_A N1_B N10_B P39 P47_5 N5_A N3_B P14 P42_1 P47_6 N6_A N5_B P19 P42_2 P47_7 N7_A N6_B P26 P47_1 P47_8 N8_A N7_B P27t P47_2 P51_1 N9_A N8_B P31 P47_3 C_B

50

Appendix VI

Average values of outcome variables by higher education institution.

Table 14. Average Values of Outcome Variables of Graduates by Higher Education Institution.

Income Unemployment rate Current socioeconomic level Satisfaction with

current job Maximum time

unemployed # obs A B C D E

U. Peruana de Ciencias Aplicadas (UPC) PEN 4,508 5.0% 59% 40% 0% 0% 0% 8.02 144.9 627

U. del Pacífico PEN 5,297 8.0% 66% 34% 0% 0% 0% 7.52 81.7 13 Pontificia U. Católica del Perú PEN 2,707 7.0% 52% 47% 1% 0% 0% 7.21 124.2 76 U. de Lima PEN 4,084 13.5% 82% 15% 3% 0% 0% 7.85 236.9 38 U. Nacional Mayor de San Marcos PEN 3,640 5.3% 21% 70% 9% 0% 0% 8.02 136.8 85 U. Nacional de Ingeniería PEN 3,233 23.5% 63% 28% 9% 0% 0% 7.81 61.1 32 U. ESAN PEN 5,338 0.0% 100% 0% 0% 0% 0% 9.18 - * 3 U. Ricardo Palma PEN 3,564 12.3% 54% 44% 2% 0% 0% 7.81 154.4 236 U. San Martín de Porres PEN 2,791 3.6% 36% 58% 7% 0% 0% 8.32 172.3 112 U. San Ignacio de Loyola PEN 3,766 0.0% 39% 61% 0% 0% 0% 7.91 82.4 38 U. Tecnológica del Perú PEN 3,462 2.7% 34% 50% 16% 0% 0% 8.00 129.8 26 Cibertec PEN 3,373 10.0% 9% 68% 23% 0% 0% 7.86 143.4 321 Tecsup PEN 3,386 25.7% 2% 98% 0% 0% 0% 7.47 570.7 2 IPAE PEN 2,270 4.3% 10% 81% 9% 0% 0% 8.12 125.3 33 ISIL PEN 2,201 4.6% 13% 76% 11% 0% 0% 7.77 188.9 28 IDAT PEN 2,268 9.8% 12% 61% 26% 1% 0% 8.02 142.8 92 IFB Certus PEN 2,180 15.1% 9% 50% 39% 2% 0% 8.26 229.9 34 SENATI PEN 1,587 4.3% 5% 57% 35% 3% 0% 7.48 87.4 24 AVANSYS PEN 1,128 0.0% 0% 26% 74% 0% 0% 10.00 141.7 2 SISE PEN 1,831 1.0% 9% 55% 36% 0% 0% 7.29 128.4 39


* Graduates who said they had never been unemployed were omitted when calculating the average maximum unemployment time. In this case, all graduates answered they had never been unemployed, so there were no observations to calculate an average from.

51

Appendix VII Main model estimations of first matching approach and using CEM.

Table 15. Main Model Estimators. UPC vs. Control universities Cibertec vs. Control institutes N - treated 729 N - treated 358 N - control 984 N - control 318 N - matched obs. 1713 N - matched obs. 676

Parameters Cl (95%) Parameters Cl (95%) [L , U] [L , U]

Income

Estimator 1028.76 764.73 1292.80 965.93 674.57 1257.28 S.E. 134.71 148.65 P-value 0.0000 0.0000 Y-treated 4624.52 3350.81 Y-control 3595.76 2384.88 Difference_Y (%) 28.6% 40.5% N - treated 636 313 N - control 678 275 N. matched obs. 1314 588

Managerial position

Estimator 0.1310 0.0827 0.1792 -0.0739 -0.1389 -0.0089 S.E. 0.0246 0.0332 P-value 0.0000 0.0261 Y-treated 0.3445 0.1812 Y-control 0.2135 0.2551 Difference_Y (%) 61.35% -28.96% N - treated 672 358 N - control 761 318 N. matched obs. 1433 676

Number of coworkers in charge of at workplace

Estimator 0.03 -1.29 1.36 -1.38 -2.66 -0.09 S.E. 0.68 0.66 P-value 0.9595 0.0360 Y-treated 3.36 1.52 Y-control 3.33 2.90 Difference_Y (%) 0.9% -47.6% N - treated 635 312 N - control 675 275 N. matched obs. 1310 587

Percentage promoted at workplace

Estimator 0.0947 0.0401 0.1493 0.0312 -0.0472 0.1095 S.E. 0.0279 0.0400 P-value 0.0007 0.4354

52

Y-treated 0.5192 0.3621 Y-control 0.4245 0.3309 Difference_Y (%) 22.3% 9.42% N - treated 635 312 N - control 675 275 N. matched obs. 1310 587

Number of job benefits


Percentage unemployed

Estimator -0.0303 -0.0561 -0.0046 -0.0103 -0.0551 0.0345 S.E. 0.0131 0.0229 P-value 0.0212 0.6517 Y-treated 0.048 0.091 Y-control 0.079 0.102 Difference_Y (%) -38.35% -10.09% N - treated 672 358 N - control 761 318 N. matched obs. 1433 676

Time between graduation and first job

Estimator 7.68 -13.27 28.63 9.97 -16.05 35.99 S.E. 10.69 13.28 P-value 0.4726 0.4530 Y-treated 104.87 116.27 Y-control 97.19 106.30 Difference_Y (%) 7.9% 9.37% N - treated 220 139 N - control 279 135 N. matched obs. 499 274

Maximum time unemployed

Estimator -11.28 -31.98 9.42 37.24 6.33 68.15 S.E. 10.56 15.77 P-value 0.2855 0.0184 Y-treated 142.56 162.80 Y-control 153.84 125.56 Difference_Y (%) -7.33% 29.65%

53

N - treated 220 139 N - control 279 135 N. matched obs. 499 274

Perceived usefulness of major studied

Estimator 0.36 0.20 0.52 -0.16 -0.40 0.07 S.E. 0.08 0.12 P-value 0.0000 0.1742 Y-treated 7.94 7.70 Y-control 7.58 7.86 Difference_Y (%) 4.74% -2.03% N - treated 672 358 N - control 761 318 N. matched obs. 1433 676

Satisfaction with current job

Estimator 0.33 0.18 0.48 -0.02 -0.30 0.26 S.E. 0.08 0.14 P-value 0.0000 0.9101 Y-treated 8.02 7.85 Y-control 7.70 7.87 Difference_Y (%) 4.28% -0.254% N - treated 672 358 N - control 761 318 N. matched obs. 1433 676

Positive change in socioeconomic level (NSE)

Estimator 0.0712 0.0339 0.1085 0.0391 -0.0253 0.1034 S.E. 0.0190 0.0328 P-value 0.0002 0.2344 Y-treated 0.1904 0.2457 Y-control 0.1192 0.2066 Difference_Y (%) 59.73% 18.92% N - treated 672 358 N - control 761 318 N. matched obs. 1433 676

NOTE: Difference (%)=(Y_treated - Y_control)/Y_control

Source: C230-IFC Graduates Survey.

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Appendix VIII

Summary of statistically significant effects of the four econometric models

Table 16. Summary of Significant and Non-significant Effects by Outcome Variable and by Matching Model.

UPC and

control group Cibertec and control group

Sets of matching variables 2 1 2 1

Income CEM S+ S+ S+ S+ GenM S+ S+ S+ S+

Managerial position CEM S+ S+ N- S- GenM S+ S+ N- N+

Number of coworkers in charge of at workplace

CEM N+ N+ S- S- GenM N- N+ N- N-

Percentage promoted at workplace CEM S+ S+ N+ N+ GenM S+ S+ N+ N+

Number of job benefits CEM S+ S+ S+ S+ GenM S+ S+ S+ N+

Percentage unemployed CEM S- S- N- N+ GenM N- N- N- N+

Time between graduation and first job

CEM N+ N+ N- N- GenM N- N+ N+ N-

Maximum time unemployed CEM N- N- N+ S- GenM N- N- S+ N-

Perceived usefulness of major studied CEM S+ S+ N- N+ GenM S+ S+ N- S-

Satisfaction with current job CEM S+ S+ N- N- GenM S+ N+ N- S-

Percentage with a positive change in socioeconomic level (NSE)

CEM S+ S+ N+ N+ GenM S+ S+ S+ N+

"S" means a significant effect (at a 95% statistical level or higher) "N" means that no significant effect was found "+" means the estimator is a positive number "-" means the estimator is a negative number

Source: C230-IFC Graduates Survey.

Note: Results presented in the Findings section are from the main model of this study (CEM methodology, including covariates in Approach 1). Results were considered negative or positive if they were at least statistically significant at a 95% level. Estimations from the three alternative estimations to assess robustness of results are considered negative or positive if they were at least statistically significant at a 90% level.

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Appendix IX

Table of study findings by field of study

Table 17. Summary of Findings for Outcome Variables, by Fields of Study

UPC

Categories Outcome Indicators Business Administration Communications Engineering


• Proportion of graduates currently unemployed ▪ • Monthly income in soles (PEN)


• Percentage of graduates who have held a managerial position in the workplace ▪

• Percentage of graduates promoted at the workplace ▪

3) Social Mobility • Proportion of graduates who have experienced a positive change in their current socioeconomic status compared with their socioeconomic status before enrollment

▪

Cibertec

Categories Outcome Indicators Business Administration

Information Technology Marketing Graphic

Design 1) Income and Employment

• Proportion of graduates currently unemployed ▪ ▪ X • Monthly income in soles (PEN) ▪ ▪ ▪


• Percentage of graduates who have held a managerial position in the workplace ▪ ▪ ▪ ▪

• Percentage of graduates promoted at the workplace ▪ ▪ ▪ ▪

3) Social Mobility • Proportion of graduates who have experienced a positive change in their current socioeconomic status compared with their socioeconomic status before enrollment

▪ ▪ ▪ X

Source: IFC-C230 Graduate Survey 2016.

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Appendix X

Characterization of UPC and Cibertec graduates of the highest and lowest quartiles of socioeconomic standing.

A research question that emerged during the analysis was to understand the differences between the graduates who achieved a larger change in their socioeconomic status and those graduates who changed the least on these indicators, or that even had a negative change in them.

First, Laureate graduates were ordered by the size of the change in NSE points between their current values and their status before enrollment, as well as the size of the change between their current status and their parents’ status. This way, Laureate graduates were divided into quartiles to study those who achieved the highest socioeconomic change, and those who achieved the least change—the highest and lowest quartiles, respectively.

In general terms, graduates from the lowest quartiles (those who achieved the least change) came from wealthier families, in terms of socioeconomic levels, and studied in private high schools. Graduates from the highest quartiles (those who achieved the highest change) tend to be older and to have better jobs (higher wages, lower unemployment rate, and a higher percentage who have held a managerial position).

Findings from this analysis were consistent with those of the rest of the study, since graduates from wealthier families had a slimmer chance of improving their status, due to the fact that their socioeconomic level was already very high, and it was more difficult to increase it further. In fact, since their status was so high, it was easier to reduce their socioeconomic level (i.e., the points they scored on the NSE scale) than it was to increase it.

Socioeconomic status 1. UPC graduates:

UPC graduates who achieved the least change (lowest quartile) are described as follows:

• Most of them come from households from the highest socioeconomic level (A). • 7.3% attended a public high school. • A lower percentage of graduates studied Engineering. • A higher percentage are women. • The average monthly income is S/.4,294. • The unemployment rate is 7.4% • 39.7% of graduates have a managerial position

UPC graduates who achieved the highest change (highest quartile) are described as follows:

• Only a fraction are from level A (8.5%). • 16% attended a public high school. • A higher percentage of graduates studied Engineering. • A higher percentage are men. • The average monthly income is S/.4,720. • The unemployment rate is 3.9% • 48.3% of graduates have a managerial position • Graduates from the highest quartile are also 0.8 years older, on average.

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Lowest quartile:

Figure 36. Distribution of UPC graduates from the lowest quartile, in regard to change in socioeconomic status compared with their status before enrollment.


Highest quartile:

Figure 37. Distribution of UPC graduates from the highest quartile, in regard to change in socioeconomic status compared with their status before enrollment.

30.7%

69.4%


B A

43.5%

56.5%

Gender

Women Men

11%

29%

18% 17%12%

9%3% 1%

24 25 26 27 28 29 30 31+Years of age

Age

62.9%

13.7%23.4%

Field of study


12.8%

78.7%

8.5%Socioeconomic level prior to enrollment

C B A

38.3%

61.7%

Gender

Women Men

58


2. Cibertec graduates:

Cibertec graduates who achieved the least change (lowest quartile), are described as follows:

• A great number come from households of the highest socioeconomic levels (A and B). • A lower percentage of them attended a public high school. • A higher percentage studied Graphical Design. • A considerably higher proportion of graduates are men. • The average monthly income is S/.3,538. • The unemployment rate is 9.34%.

Cibertec graduates who achieved the highest change (highest quartile) are described as follows:

• A lower number come from households of the highest socioeconomic levels (A and B). • A higher percentage studied Business & Administartion. • The average monthly income is S/.4,097. • The unemployment rate is 5.26%.

6%13%

21%

14%

24%

17%

3% 1%

24 25 26 27 28 29 30 31+Years of age

Age

57.5%

7.5%

35.1%

Fields of study


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Lowest quartile:

Figure 38. Distribution of Cibertec graduates from the lowest quartile, in regard to change in socioeconomic status compared with their status before enrollment.


Highest quartile:

Figure 39. Distribution of Cibertec graduates from the highest quartile, in regard to change in socioeconomic status compared with their status before enrollment.

11.3%

75.5%

13.2%


C B A

7.5%

92.5%

Gender

Women Men

4%

9%6% 6%

11%15% 13%

17%

8% 8%4%

23 24 25 26 27 28 29 30 31 32 33Years of age

Age

17.0%

73.6%

1.9% 7.55%Field of study

B&A IT Marketing GD

1.75%

68.4%

29.8%


D C B

31.6%

68.4%

Gender

Women Men

60

Source: C230-IFC Graduates Survey 2016

2% 0%

9% 7%

14%

9%12% 12%

14%

4%

9%

4%2% 2% 2%

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36Years of age

Age

26.3%3.5%

70.2%

Fields of study

B&A Marketing IT

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Appendix XI

Findings on occupational prestige

Following the same methodology applied to the 2015 impact assessment of Laureate education in Mexico,51 this Peru study calculated the effect of Laureate education on the occupational prestige of its graduates, as is presented below. However, as the analysis is based on the CAMSIS occupational prestige scale (see Appendix VII) that is calculated for each country individually, and since this scale has not been calculated for Peru, the study’s authors used the most recent scale calculated for a neighbor country in Latin America as a proxy, which turned out to be the Mexico 2010 scale. Since there are consistency issues in using the scale calibrated for a different country, these results were not presented in the main findings. These results should be considered with caution when making conclusions. We present them for the sake of transparency and consistency across studies.

Table 18 . Findings on Occupational Prestige for Cibertec and UPC Alumni

Category Aspect Outcome indicator

3) Social Mobility


Proportion of graduates who have experienced a positive change in occupational prestige compared with their parents

▪ X

Positive impact in all estimations Positive impact in at least one out of four estimations

X Negative impact in at least one out of four estimations ▪ No significant impact

Positive results were not found for changes in occupational prestige, which was measured by the percentage of graduates who underwent a positive change of at least 10 points when compared with their parents’ occupational prestige. Male graduates were compared against their fathers, and female graduates were compared against their mothers.

No significant difference was seen for either UPC or Cibertec on this indicator, when studying them in an aggregated analysis.52

Using the second matching approach (with the least restrictive matching variables), the results indicate that only Cibertec men had a significant (and negative) impact for occupational prestige, at a 90% significance level. Specifically, 56% of Cibertec men had a positive change in occupational prestige, compared with 64% of control group male graduates.

When considering field of study, only Cibertec IT alumni showed a significant and negative impact: Cibertec IT graduates were 7.5 percentage points behind their control group peers (57% of Cibertec graduates showed a positive change in occupational prestige, compared with 65% of control group graduates).

In the analysis of these results, it is important to note the differences in the distribution of occupational prestige of the parents of Cibertec alumni and the parents of control group alumni. Cibertec alumni parents average 61.6 points on

51 C230 Consultores. Affordable Higher Education in Mexico: Implication for Career Advancement and Social Mobility. Prepared for International Finance Corporation (IFC) and Laureate International Universities in 2015. 52 Two of the four estimators calculated for Cibertec were negative and statistically significant at a 95% level, although this effect was not found on the main model and only in the estimations that used the less restrictive set of matching variables. These significant estimators showed that around 57% of Cibertec graduates underwent a positive change in occupational prestige, compared with 65% of graduates from other institutes.

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the occupational prestige scale, while control group parents average 58.1 points. Furthermore, a higher percentage (20%) of Cibertec parents are professionals with higher education degrees, like lawyers, doctors, economists, engineers, etc., compared with control group parents (12%), while a higher percentage of control group parents (23%) hold low-paying jobs, like bakers, farmers, masons, etc., than Cibertec parents (12%). This indicates that it would be harder for a Cibertec graduate to surpass his or her parent’s occupational prestige by at least 10 points than it would be for a control group graduate. This can at least partially explain the negative result on some of the estimators.

Figure 40. Impacts and means of the percentage who underwent a positive change in occupational prestige of Cibertec and control group graduates, by by fields of study.


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Appendix XII

Occupational prestige (CAMSIS)

The occupational prestige scale used in this study is based on the methodology developed by the Cambridge Social Interaction and Stratification Scale (CAMSIS) project, an international comparative assessment of the structures of social interaction and stratification across a number of countries. At its core lies the construction of occupational scales for each constituent country. Each country has a different prestige scale based on the value that each society puts on different occupations. The scale values represent an occupational unit's relative position within the national classification of social interaction and stratification. The theory behind CAMSIS implies that occupational groups are robust indicators of social stratification because employment still provides the major mechanism by which social and economic rewards are distributed directly or indirectly in modern societies and is the single most significant indicator of someone’s location in the overall structure of advantage and disadvantage (Blackburn & Prandy, 1997). It is important to understand that “occupational group” here is defined to include status differences in employment (self-employed or supervisory, for example), in addition to the conventional understanding of the meaning of “occupation.” Furthermore, CAMSIS is gender-sensitive; i.e., different scales are calculated for men and women, since holding the same occupation might have different implications for the person’s social position, depending on his or her gender.

The theory behind CAMSIS scales (developed from the original “Cambridge Scale”) covers not just a particular measure, but a whole distinctive theoretical approach to social stratification. Using large-scale survey datasets, sociologists are able to carry out empirical analyses of individual level data on features of, or attitudes about, occupations.

The CAMSIS approach to studying the relative stratification position of the incumbents of occupations exploits data on social connections between the incumbents of occupations. CAMSIS scales are scores given to occupations, which indicate their position in the social stratification structure and can be used to compare occupational prestige across generations.

CAMSIS measures can be constructed by using data on 130 pairs of occupations linked by a social interaction, such as marriage, friendship, or parent-child relationships. First, a two-way cross-tabulation of the occupations for the first and second members of the pair is prepared. A score is assigned to each occupation to indicate its position within the empirical dimension(s) of social interaction. Prestige scores go from 0 (for instance, unoccupied or homemakers), which means that society regards this occupation as being low on the prestige scale; to (approximately) 100 points (for example, doctors), which means that this occupation is highly regarded by society.

For this project, the relationship studied was the parent-child level. The main idea behind the analysis was to measure the upward mobility of the graduates, comparing their CAMSIS scores with their parents’ scores. This analysis was done for gender-specific pairs: mother-daughter and father-son.

The CAMSIS score is calculated for each individual country or society, since social stratification and interaction depend on a particular country’s cultural and historical context. As there was no CAMSIS score available for Peru, this study used Mexico’s 2010 versions, since it proved to be the closest country with a CAMSIS score that had an updated version no older than 2010. Venezuela’s 2001 and Colombia’s 1973 were not considered as the best option, due to their temporal distance.

For more information, visit http://camsis.stir.ac.uk/.

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Socioeconomic status (NSE)

The socioeconomic levels used in this study are measured using the NSE methodology developed by APEIM (Asociación Peruana de Empresas de Investigación de Mercados) organization of Peru. The methodology uses the answers to seven questions regarding different aspects of an individual’s household conditions, which are the following:

N1. What is the last education level the person acting as head of your household has achieved?

N2. Which of the following goods do you possess at your home in a working condition? (Computer, laptop or tablet / Microwave oven / Washing machine / Refrigerator)

N3. Which of the following goods or services do you possess at your home? (Car for private use / Domestic cleaning service)

N4. What is the predominant flooring material at your home?

N5. To what medical services system is the head of your household affiliated?

N6. What is the predominant construction material of the outer walls of your home?

N7. To what sewage system is the bathroom of your home connected?

Each question has a specific number of points assigned to it, depending on the answer given. There are eight different socioeconomic levels, going from A2 to E, where A2 is the highest and E is the lowest, and a certain person’s level is determined by summing up the points of all seven questions and comparing it to a table that indicates what point values are assigned to each level.

For this particular study, only five levels were used, since the highest six levels were grouped into three, in the following way: levels A1 and A2 were grouped into level A, B1 and B2 were grouped into level B, and C1 and C2 were grouped into level C. This way, the study was conducted using levels A, B, C, D and E.

In this way, the variable of interest measures the percentage of graduates who have a positive change in their NSE between their current value and that before entering the university/institution.

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Appendix XIII

Table of estimations omitting over-represented control group institutions (URP and IDAT).

Table 19. Estimations Omitting Observations from Over-represented Control Group Institutions. UPC vs. Control universities Cibertec vs. Control institutes

N - treated 563 N - treated 242 N - control 423 N - control 166 N - matched obs. 986 N - matched obs. 408

Parameters Cl (95%) Parameters Cl (95%) [L , U] [L , U]

Income


Unemployment rate

Estimator -0.0251 -0.0553 0.0052 0.0490 -0.0054 0.1034 S.E. 0.0154 0.0277 P-value 0.1047 0.0781 Y-treated 0.0504 0.0971 Y-control 0.0754 0.0481 Difference_Y (%) -49.73% 50.47% N - treated 563 242 N - control 423 166 N. matched obs. 986 408

Percentage with a positive change in socioeconomic level


Number of job benefits

Estimator 0.5930 0.3414 0.8447 0.5028 0.0735 0.9321 S.E. 0.1284 0.2190

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P-value 0.0000 0.0222 Y-treated 4.4905 3.9685 Y-control 3.8975 3.4657 Difference_Y (%) 13.21% 12.67% N - treated 530 213 N - control 383 144 N. matched obs. 913 357

Managerial position


Percentage promoted at workplace


NOTE: Difference (%)=(Y_treated - Y_control)/Y_control


Documents

Career Advancement and Social Mobility Effects of Laureate