STEM FOR WHOM? DISPARITIES IN MAJOR CHOICE AND THE EFFECTS OF HIGHER EDUCATION LEGAL REFORM IN COLOMBIA

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1 STEM FOR WHOM? DISPARITIES IN MAJOR CHOICE AND THE EFFECTS OF HIGHER EDUCATION LEGAL REFORM IN COLOMBIA Maria Claudia Soler Master of Arts Paper International Educational Administration and Policy Analysis Graduate School of Education Stanford University July 2014

2 Graduate School of Education Stanford University INTERNATIONAL EDUCATIONAL ADMINISTRATION AND POLICY ANALYSIS STEM for Whom? Disparities in Major Choice and the Effects of Higher Education Legal Reform in Colombia Maria Claudia Soler July 2014 A Master of Arts Paper in partial fulfillment of the requirements for the degree of Master of Arts Approvals: ICE/IEAPA Master s Program Director: Christine Min Wotipka, Ph.D., date Advisor: Anthony Lising Antonio, Ph.D., date

3 Acknowledgements I wish to thank my advisor Anthony Lising Antonio and Christine Min Wotipka for their amazing advise and support with this project and their invaluable guidance and encouragement with my academic and professional decisions. I gratefully acknowledge Sen Zhou and my ICE/IEAPA cohort for their time, constructive feedback and useful discussions on improving this thesis. I am thankful to Davide Malacrino for his patience and technical support regarding the empirical part of the paper, and to Keara Harman for her editorial assistance. I also want to extend my appreciation to Diana Duran and Luis Bernardo Carrillo at the Colombian Ministry of Education for their assistance with the data, as well as to Juan Esteban Saavedra and Luis Omar Herrera for their help to manage the datasets. This paper was also made possible through the generous support provided by the World Bank Joint Japan Graduate Scholarship Program. The views expressed here are exclusively those of the author. All remaining errors are my own.

4 Abstract Increasing access to higher education does not mean equal access for all groups. Decisions involved in the transition to higher education, such as the selection of a major, play an important role in access. This is particularly true in countries such as Colombia where students have to make that decision prior to enrolling. The purpose of this paper is to improve the understanding of the association between student characteristics such as gender, socioeconomic status and academic performance and the selection of a particular major in science, technology, engineering and mathematics (STEM) for different types of degrees (technical, technological and university). It also explores policy effects of a reform as outlined in Law 749, which aimed to increase the institutional supply of engineering and business programs in technical, technological and university levels. Results suggest that women, low-income students and students whose mothers have higher levels of education are more likely to enroll in technical and technological STEM programs than in university STEM programs. Contrarily, students with higher academic performance are more likely to enroll in university STEM programs than in technical and technological programs. In terms of gender, women with higher academic performance are more likely to enroll in STEM majors than higher achieving men. Among other variables, receiving financial aid has a negative effect on the probability of enrollment in STEM majors across all types of degree granting programs, but has a particularly strong effect on those at the universitylevel. Findings also indicate that Law 749 has a positive effect on the creation of STEM programs offered by institutions of higher education. Key words STEM majors, higher education, gender segregation, academic achievement, socioeconomic status, education disparities, and education policy analysis.

5 1 Introduction When equal opportunity in education is one path to build a more egalitarian society, understanding access to higher education is important. A developing country like Colombia offers an interesting context for exploring the role background variables and policy reforms play in postsecondary education choices. Located in the most unequal region in the world, Colombia is Latin America s third most unequal country of the region with a GINI index of 55.9 (United Nations, 2013). 1 When it comes to education, the country has improved access as a means to increase educational opportunities, and enrollment rates in higher education programs are above the regional average (42% in 2011) 2 (The World Bank, 2013). However, studies have shown that there are wide disparities in the accessibility and completion of higher education for students of different levels of income (Melguizo, Sanchez, & Marquez, 2013). Although these kinds of disparities in higher education opportunity have been explored, less is known about the disparities in access across different fields of study in higher education in Colombia. One way to better understand such disparities in access is to study the determinants associated with the selection of majors, as well as the institutional supply of degrees in particular fields. Postsecondary majors are not only associated with career choices and life satisfaction (Lugulu & Kipkoech, 2011), but also affect future income (Attanasio & Kaufmann, 2010). In terms of the job market, science, technology, engineering and mathematics (STEM) majors tend to lead to better-paying jobs after graduation (Hill, St. Rose, & Corbett, 2010). Although STEM majors are the most popular among Colombian students, with 31% of the students enrolling each year in the country (SPADIES, 2014), there is still inequality in the selection of majors. Across the 1 The Gini coefficient varies between 0, which indicates complete equality, and 100, which implies complete inequality. GINI index for the most unequal countries in Latin America: Honduras 57; Bolivia 56.3; and Colombia and Guatemala with 55.9 (United Nations, 2013) 2 The Latin American average enrollment in higher education in 2002 was 24.4% (Melguizo, Sanchez, & Marquez, 2013)

6 2 different types of degrees, only 20% of females and 32% of low-income students select STEM majors. Within STEM, approximately 6% of the students select technical degrees, 23% technological degrees and 71% university degrees. 3 Moreover, 49% of the students come from the lowest-income families (SPADIES, 2014). Additionally, Colombia is a particularly intriguing location for this study because students must select majors (field of study) and degrees (technical, technological or university) before they apply to postsecondary institutions. As a result, students choice of major is not influenced by experiences or courses taken at institutions of higher education. Therefore, student characteristics, secondary school characteristics, financial aid or the availability of programs offered by institutions might have a greater effect on the selection of majors and degrees. As there are only a few studies that explore the choice of major in Colombia, this study seeks to contribute in that area by analyzing the impact of student background variables on the selection of STEM majors. By using a probit model, I investigate how socioeconomic status, gender, and academic performance are associated with the probability of choosing a STEM major. In order to achieve this goal, I use the data set SPADIES (Higher Education Institutions Dropout Prevention and Analysis System), 4 which is the largest longitudinal dataset developed by the Ministry of Education in Colombia. SPADIES is used to track all student enrolled in postsecondary institutions nationwide from After controlling for other variables, I expect to find that women, students from a low socioeconomic status, and students with low academic performance are less likely to pursue STEM majors. Additionally, I investigate how this probability of selecting a STEM major varies across different types of degree (technical, technological and university). 3 Differences between the three types of degrees are explained in the background section. Main differences are associated with program length and tuition cost. 4 In Spanish SPADIES means Sistema para la Prevención y en Análisis de la Deserción en Educación Superior.

7 3 Furthermore, because enrollment in STEM majors can also be influenced by the availability of degrees offered by institutions of higher education, I also explore changes in the creation of STEM and non-stem degrees offered by tertiary institutions before and after 2002, when Law (Ministerio de Educacion, 2002) was approved. Using a difference-in-difference estimation, I investigate how this law influenced the institutional supply of STEM programs. For this analysis I use a different data set called SNIES (National System of Higher Education Information), 6 which contains information for about 90% of the higher education institutions in the country. I expect that implementation of the law has corresponded to an increase in the number of STEM programs offered by institutions in technical and technological degrees and that this increase is higher for STEM programs than it is for non-stem programs. This paper is organized as follows: In the first section I describe the higher education system in Colombia. Next, I review previous literature on STEM major choice determinants. In section three, I outline a conceptual framework based on the college choice model that will guide the analysis of the results. In section four, I explain the data and methods used to answer my research questions. In section five, I elaborate on the findings of my analysis, which will be further discussed in section six. Finally, in section seven, I present a final conclusion and areas for future research. Background Higher education in Colombia There are four categories of institutions of higher education in Colombia: universities, university institutions, technological institutions and technical training institutions. Universities carry out the 5 This law established the creation of a transfer credit system and the promotion of shorter degree programs in the fields of engineering and business at the technical and technological levels. A more detailed explanation of this law can be found in the background section. 6 In Spanish SNIES means Sistema Nacional de Información de la Educación Superior.

8 4 traditional missions of teaching and research at graduate and undergraduate levels; university institutions (Instituciones Universitarias o Escuelas Tecnológicas), whose main mission is teaching academic disciplines of high specialization; technological institutions (instituciones tecnológicas) which offer technological programs that focus on short-term, academic education in technological fields; and technical training institutions (instituciones técnicas profesionales) that provide short-term vocational education, training and skill upgrading at the tertiary level (The World Bank, 2003). Technological and technical institutions and a lower number of university institutions tend to offer most of existing technical and technological programs. The main difference between technical and technological programs is the duration. While the length of a technical program is 1.5 years on average, technological programs typically last between 2.5 and 3 years. Programs at the university level last approximately 5 years in Colombia. While universities capture of 75% of the enrollment in higher education, technical and technological universities capture approximately 25% (SPADIES, 2014). In 2002, the Colombian government passed Law 749, with two main purposes: to create a system of credit transfer allowing students to gradually progress up the educational ladder; and to encourage institutions of higher education to increase the number of shorter technical and technological degree programs in the fields of engineering and business. Moreover, the transfer credit system 7 attempted to offer students the flexibility to pursue technical degrees and to use their credits to continue their education at the technical and then, at the university level. In 2008, a few years after the implementation of Law 749, the government approved a related law titled Law 1188 in order to establish specific rules for the establishment and 7 This system it is known as ciclos propedéuticos.

9 5 development of technical, technological and university programs. 8 Article No. 6 of Law 1188 states that any institution of higher education may offer programs in all majors within the credit transfer system as long as it meets the requirements stated in Law 1188, as well as the requirements in Decree Although the articles are very detailed, there is no requirement of an equal distribution of degrees across the different fields of study. Hypothetically, this could lead to an overabundance of graduates in non-stem fields, as there are a higher number of non-stem degree-granting programs. While regulation regarding the distribution of new programs may be occurring at the administrative level, such a policy does not appear within the law itself. Who are the students in the higher education system in Colombia? Colombia s higher education system grew considerably in the early 1990s due primarily to an increase in enrollment in technical and technological programs. Total enrollment throughout higher education, regardless of age, 10 increased from 14.24% to 45% between the years 1990 and 2012 (The World Bank, 2013), making enrollment rates in Colombian rates in higher education above the regional average. However, wide disparities in access by level of income, type of institution and completion remain (Melguizo et al., 2013). The enrollment of low-income students in college, regardless of age, increased from 26.64% to over 40% between 2000 and This increase in enrollment of low-income students can be linked to an increase in dropout rates at the technical and technological level, which traditionally have fewer resources to support students and hence exhibit higher dropout rates of about 60 percent (Melguizo et al., 2013). Additionally, 8 Some of the requirements to open a program include: curriculum, methodology, state or art of the field of study that corresponds to the program in national and international settings, and a section explaining the relevance of the program to the needs of the country, among others. 9 Decree 1295 is a specific reglamentation of Law This enrollment is expressed as a percentage of the total population of the five-year age group following on from secondary school leaving (World Bank, 2014)

10 6 private institutions -with are of higher quality and prestige in Colombia- captured almost 70% of total enrollment while public institutions captured only 33.5% 11 (The World Bank, 2003). In terms of academic performance, the composition of students who enter higher education varies. On the high school compulsory exam SABER 11, 12 out of all of the students entering higher education in 2011, 36% of the students had low scores, 42% had average scores and 22% had high scores. Enrollment of students with low scores in higher education has progressively increased in the last few years (for instance from 37% in 2007 to 36% in 2010) which could indicate that tertiary educational institutions collectively broadened their intake, becoming more inclusive of students with prior low academic performance, who usually come from low socioeconomic backgrounds (OECD et al, 2013). Regarding gender, enrollment of both women and men in higher education has increased significantly in Colombia since the system s expansion in The enrollment rate for male students in 1991 was 14.54% compared to 47.75% in Similarly, female students increased significantly in their enrollment rate from 15.51% to 51% in 2012 (See Figure 1). Overall, Colombian women tend to enroll in greater numbers than men and perform better than their male counterparts (slight difference of 0.8 between average scores of females and males in SABER 11 during 2011) (OECD et al., 2013). Likewise, women graduate at higher rates and the gender difference in completion rates is around 10 percentage points for females (The World Bank, 2003). [Figure 1 about here] 11 Unfortunately, my data set does not contain information about the kind of institution where students go and therefore, I could not perform more analysis related to this issue. 12 The exam evaluates student performance in 7 areas: language, mathematics, social sciences, philosophy, biology, chemistry, and physics. The final score is the average score of different scores obtained in all 7 areas. Low scores range from 00,00 to 30,00; average scores range from 30,01 to 70,00; and high scores are equal or greater than 70,01.

11 7 What do students study? In Colombia, students can select among 51 majors that are categorized into eight fields of study. 13 A 2013 report released by the World Bank and the OECD considered the enrollment imbalance by discipline in Colombia. Taking into account the total number of students who enrolled in higher education between 1998 and 2013, approximately 30% of the students concentrated in majors such as engineering, architecture and urban studies. This is followed by 28% of the students who selected economics, management and related disciplines, while mathematics and the natural sciences accounted for only 2% of the total number of students enrolled (See Figure 2). [Figure 2 about here] Although women enroll at high rates in higher education, there are great disparities in their enrollment across different fields of study. For example, while 43.2% men select STEM majors, only 20% women choose this path. At the same time, within the various STEM fields, engineering enrollment rates are higher than that of mathematics for both women and men. 14 In terms of the type of degree, regardless of major or subject, 7.4% of the students select technical degrees, 17.25% select technological degrees and 75.2% choose university degrees (SPADIES, 2014). The type of degree awarded has an effect on later income: the average income per month for people with technical degrees is approximately $538, $700 for technical degrees and $915 for university degrees. 15 (Ministry of Education, 2014). 13 The eight fields of study are 1. Agronomy, veterinary and related disciplines; 2. Arts; 3. Education; 4. Health; 5. Social sciences and humanities; 6. Economics, business and related disciplines; 7. Engineering, architecture, urban studies, and related disciplines; and 8. Mathematics and natural sciences % of women are in engineering and approximately 2% in mathematics and the natural sciences. For the case of men, 41% select engineering and 2% mathematics and the natural sciences. 15 The numbers were obtained using the average income for each type of degree, regardless of majors and years of experience.

12 8 Considering that women enroll less frequently in STEM majors and low-income students enroll more frequently in technical and technological degrees that tend to have lower returns, we can see how the expansion of the higher education system in Colombia exaggerates disparities in higher education enrollment. Although there is evidence of socioeconomic disparities in higher education, little is known about other disparities therein. By exploring the effects of the interaction between different background variables such as gender, socioeconomic status and academic performance that impact students selection of STEM majors, the findings from this study will contribute to the growing body of evidence on issues of access and equity in Colombia. Literature Review A growing body of literature has explored factors that influence the decision to pursue STEM fields. In this section I explore four of these factors: gender, socioeconomic status, academic performance, and the availability of STEM programs offered by institutions. Gender and major choice While differences in the participation of female and male students in higher education in countries like the United States and Latin America have decreased, a higher percentage of men earned bachelor s degrees in STEM fields compared to women in the U.S. (28% vs. 22%) (National Center for Education Statistics, 2012). By comparison, the percentage of women enrolled in Latin American colleges and universities is rather high. In Brazil and Uruguay total women s enrollment in higher education is 53%; in Argentina 47%; and in Colombia, 51% during 2013 (SPADIES, 2014). Ramirez & Wotipka (2001) present a relevant study through the use of a comparative, cross-national research design with cross-sectional and panel regression methods. Instead of simply looking at the increase of women in universities, they ask whether overall expansion masks important differences in the likelihood of expansion across different fields of study. They find that

13 9 in the case of engineering, the absolute level of enrollees in the science and engineering sectors lags behind that of enrollees in other fields of study for both women and men (Ramirez & Wotipka, 2001). Moreover, Ramirez & Wotipka affirm that differences increase when comparing the percentage of women in science and engineering, as the number in the latter is significantly higher. Women are especially underrepresented in most STEM majors. For example, in 2007 in the United States, women earned 17% of bachelor s degrees in engineering, compared to 79% of bachelor s degrees in education (Michael, Hussar, & Snyder, 2009). After graduation, gender segregation in college major choice is reflected in gender segregation in the workforce, with significant economic consequences for women. According to Jacobs (1996) sex typing of fields 16 can explain the selection of majors. He affirms that sex typing of fields is a worldwide phenomenon that varies between countries because although women tend to select traditional majors such as education, psychology and health at higher rates, there are some exceptions depending on the country. For instance, in Kuwait, 51.6% of engineering students are women, compared with 3.3% in Switzerland and Japan. In Poland, 62.7% of mathematics and computer science degrees were awarded to women, compared with 35.9% in the United States and 21.0% in Egypt. Jacobs (1996) suggests that gender segregation of fields of study can be accounted for partly by the college experience, and that sex typing of fields is related to the college environment. This last explanation is arguably not the case in Colombia, because students select their majors before being exposed to college. Yet, sex typing of fields could still influence the decisions made during secondary education. 16 Sex typing is the stereotypical categorization of people, or their appearance or behavior, according to conventional perceptions of what is typical of each sex (Oxford Dictionaries, 2014). A non-traditional major for females is defined as one where fewer than 25% of the composition of students is female. Non-traditional majors for females include science and technology. For men, education, psychology and health, fall into this category.

14 10 In fact, some studies show that gender segregation may be influenced by experiences prior to enrollment in college. Schneeweis & Zweimüller (2012) studied the gender composition in Austrian schools during grades 5-8 in order to understand the reasons behind the selection of secondary school for female students. 17 Their findings suggest that girls are less likely to choose a traditionally female dominated school and are more likely to choose a technical school at the age of 14 if they were exposed to a higher percentage of girls in previous grades. Socioeconomic status and major choice There are different variables associated with socioeconomic status that can be explored in term of their influence on educational decisions. Parental education, family income and the family head s occupational status were found to be positively associated with higher educational achievement in different studies considered by Leppel, Williams, & Waldauer (2001). Likewise, other studies have looked at the association between socioeconomic background indicators and the selection of particular majors. Leppel et al. (2001) analyzed American data from the National Center for Education Statistics (NCES) 1990 Survey of Beginning Postsecondary Students (BPS) and concluded that male students whose mothers are in professional or in executive occupations are more likely to enter education in the humanities and social sciences and less likely to select the sciences and engineering than other male students. In addition, research on business majors conducted by Green (1992) showed a higher proportion of males from wealthier families in business majors compared to females with the same background. The author suggests that while men are motivated by money and status, women from wealthier families might feel more secure about their financial future and are more willing to explore majors not directly linked to high- 17 The Austrian higher education system is organized in such way that after grade 8, students have to choose a school type (intermediate vocational school, or higher vocational school), offering a range of vocational orientations (technical, business, domestic science, tourism and kindergarten teacher training).

15 11 paying jobs. What the author does not examine, however, is if there are other variables that could explain these decisions among women. Evidence provided by Davies & Guppy (1997) shows that students from households with lower socioeconomic status were more likely to choose more lucrative fields of study. Other studies explain that students expectations about future income significantly affect their final decision to enroll in higher education (Attanasio & Kaufmann, 2009; Montmarquette, Cannings, & Mahseredjian, 2002). These studies from a more economic view illustrate trends in major choice, but they do not discuss whether low-income students are able to afford the cost of a more lucrative field of study (which tend to be more expensive than other fields) and may therefore choose shorter programs such as professional technical and technological in the fields of study perceived as lucrative. Consequently, it is possible that socioeconomic status does not affect the selection of STEM majors as much as it affects the type of degree: i.e. whether it is at the technical, technological or university level. Students from low-income households might be more likely to enroll in STEM majors, but less likely to do so at the more-expensive university level, even though it has higher returns in the job market Academic performance and major choice The degree to which academic performance affects choice of major has been widely studied. Trusty et al. (2000) considered the effects of eighth-grade test scores (in mathematics, reading, science and history/geography) on postsecondary educational choices. By using a subsample of participants from the 1988 to 1994 panel sample of the National Educational Longitudinal Study of 1988, 18 they reported that measures of academic performance were linked to the selection of major. For men, above-average mathematics performance was the strongest predictor of choosing 18 The subsample is of 7,645 participants and represents U.S. students two years beyond high school, who indicated a specific major field of study at a postsecondary institution.

16 12 majors such as forestry, medical technology and other technologies. For women, reading performance was the strongest predictor for majors in education, nursing and ethnic studies, which were selected by 72% of women. Turner & Bowen (1999) examined how verbal and quantitative scores in the Scholastic Aptitude Test (SAT) affect the probability of majoring in certain fields. They find that for both men and women, the highest probability of choosing to study math or physical sciences is associated with top tier math and verbal scores, while the probability of choosing engineering bears less relation to the verbal scores. Additionally, females with modest SAT math scores have a close to zero probability of majoring in engineering, whereas males with similarly poor scores are much more likely to do so. In Colombia, the study of the relationship between academic performance and the selection of majors has only recently emerged. Most of the studies focus on particular disciplines such as education. Baron, Bonilla, Cardona, & Ospina (2013) studied the decision to pursue education majors in Colombia. Results show a negative relationship between poor performance on standardized tests taken prior to entering higher education and the probability of studying and graduating with degrees in education, relative to other areas of study. This probability is five times higher for people with the lowest scores than it is for those with high scores. Moreover, this difference broadens when females are considered in the analysis. Other studies have tried to explain this composition effect using data on the salaries of graduates in Colombia. Baron (2010) shows that average earnings of graduate students in education are 28% less compared to graduate students in economics, business and accounting. At the same time, Baron et al. (2013) suggest that there is an inverse relationship between academic performance on the high school exam and the probability of graduating with an education major. The Roy-Borjas model suggests that this might happen because students with higher academic abilities tend to choose other majors and

17 consequently students who graduate in education have lower academic performance in test scores (Borjas, 1987; Roy, 1951). 13 Institutional supply of programs and the selection of a type of program With the increasing existence of two-year colleges, and with students choosing to attend noncollegiate postsecondary institutions, the postsecondary enrollment decision has become a polychotomous choice among very different options (Ordovensky, 1995). Instead of studying the decision of going or not going to college, Corman & Davidson (1984) analyze the alternative of postsecondary occupational schools as well as four-year and twoyear colleges. In their model, the authors assume that students are evaluating the costs and benefits of each type of educational alternative and selecting the alternative that yields the greatest net benefit. 19 Results suggest that tuition level, unemployment rates and unemployment in managerial jobs compared with the overall unemployment rate appear to be the key economic variables explaining relative enrollment rates (p. 138). For both men and women, elasticity is higher for junior colleges than for four-year colleges, that is, price is more important in the case of junior colleges. Additionally, using data from the High School and Beyond Survey of 1980 high school seniors, Ordovensky (1995) examined postsecondary enrollment decisions with particular focus on the effects of institutional cost and proximity. Findings suggest that four-year colleges exhibit the least own-cost elasticity, and in two-year colleges vocational enrollments are more responsive to changes in cost than academic enrollments. Unfortunately, data on program costs in Colombia are not available at this point and it was not possible to include this variable in the model. However, 19 The major costs of postsecondary education are tuition, transportation, lodging, and foregone earnings while in school. Among the benefits are higher post-school wages, greater non-pecuniary returns in the labor market and increased efficiency and satisfaction outside the labor market (Corman & Davidson, 1984)

18 14 the tuition cost of technical programs is lower than that of technological programs and both are lower than the cost of university programs in Colombia (SNIES, 2014). In general, studies that explore major choice in Colombia are still in a phase of slow growth and little is known about the effect of the institutional supply of technical and technological degrees in postsecondary decision-making. Literature has instead focused on overall enrollment in higher education. I contribute to this growing literature by adding an analysis of variables that impact STEM enrollment for different types of degrees. Additionally, drawing from the idea of the college choice model, I explore changes at the institutional level related to the availability of STEM major programs in the country. Conceptual framework The study of postsecondary decisions has been explored from different angles. Some studies have focused on factors affecting a student s level of educational aspiration and their decision on whether or not to attend college. Others have paid attention to a student s choice of which college to attend (Chapman, 1981). The two aforementioned studies have assumed that the characteristics of the postsecondary institutions factor into this decision, in addition to both the background and current characteristics of the student and her or his family. These components have been integrated in more conceptual theories that view college choice as a developmental process (Chapman, 1981; Perna, 2006). Models of college choice tend to have an American influence; i.e. they assume that prior to enrollment, the selection of college is seen as far more relevant than the selection of major. In this paper I use the model of college choice to integrate explanations about determinants of STEM major choice with the existence of institutional factors that influence enrollment in specific types of degrees. In particular, I argue that the selection of a STEM major across different types of degrees is shaped by student background in conjunction with other institutional variables such as the availability of diverse tertiary programs.

19 15 The conceptual framework that guides this study is the college choice model developed by Hossler & Gallagher (1987), which understands college choice as a developmental process. It draws upon other college choice models and on previous studies that demonstrate that the characteristics of educational organizations also influence the student choice process. In this way, the model reveals the potential impact of state and federal policymakers, as well as the possible impact of individual institutions (Hossler & Gallagher, 1987). The college choice model developed by the authors proposes three stages: predisposition, search and choice. During the first phase students determine whether or not they would like to continue their education beyond high school. The second stage (which applies only to those students who do wish to continue their education) consists of gathering information about institutions of higher education and formulating a set of possible choices. That is, a group of institutions to which students will actually apply. In this stage, college characteristics such as cost, financial aid available, location and availability of desired programs play an important role. Finally, during the choice stage, students evaluate the choice set and decide which institution they will attend (Hossler & Gallagher, 1987). Here students perceptions of quality as well as price are relevant. Since students in Colombia select majors when they apply to college, the components of the college choice model is useful explaining some of the hypotheses that I test in this paper. School acceptance selectivity issues in technical schools, technological schools and universities can also determine institutional destination for students. Unfortunately, due to the lack of data on this subject I am assuming that in the process of deciding a major and an institution, students pursue only a set of possible choices where they can realistically expect to be admitted. The first hypothesis is that women are less likely to select a STEM major in a university program than a STEM major in a technical or technological program. As in other countries,

20 16 proportionally fewer Colombian female students pursue STEM majors than do men. The difficulties of finding a job in male-dominated fields such as STEM can represent a high risk that women prefer not to assume. In fact, there is evidence in the finance literature that women in general tend to invest more in low risk investments and hence are found to be more risk-averse than men (Watson & McNaughton, 2007). However, this first hypothesis tests if gender differences in STEM enrollment are reduced when comparing different types of degrees. In this sense, women who select STEM majors, trade less time in higher education selecting shorter and perhaps less selective degrees with more time post-graduation looking for a job in maledominated fields such as STEM. Of course, this self-selection into a different degree is also guided by institutional policy such as selectivity and minimum requirements that students have to meet, most prominently academic performance. The second hypothesis is that among students who select STEM majors, those with a history of higher academic performance are more likely to select a university degree than a technical or technological degree. Pursuing a STEM university degree as opposed to a shorter degree in the same field could be considered a more academically demanding experience and that students with lower-academic performance might perceive this as a difficult challenge and therefore they might opt for a technical or technological degree. 20 Exploring the effect of whether a higher academic performance is associated with a higher probability of enrolling in STEM for women than for men is another interesting issue associated with this hypothesis. The selection of a type of degree has been linked to institutional characteristics such as tuition cost (Corman & Davidson, 1984). According to Chapman (1981), socioeconomic status helps to explain how students distribute themselves differently across various types of colleges 20 This self-selection into different types of degree it is also guided by institutional policy such as selectivity and minimum requirements that students have to meet and that usually includes academic performance as requirement.

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