Open Dissertation Kim.Pdf

The Pennsylvania State University

The Graduate School

THE EXPANSION OF SCIENCE PRODUCTION AT KOREAN UNIVERSITIES,

1970–2017

A Dissertation in

Educational Theory and Policy

by Hyerim Kim

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

August 2019

The dissertation of Hyerim Kim was reviewed and approved* by the following:

David P. Baker Professor of Education and Sociology Dissertation Advisor Chair of Committee

Soo-yong Byun Associate Professor of Education

Maryellen Schaub Associate Professor of Education Education and Public Policy

Liying Luo Assistant Professor of Sociology and Demography

Katerina Bodovski Associate Professor of Education Chair of Graduate Program, Educational Theory and Policy

*Signatures are on file in the Graduate School.

ABSTRACT

Since 1970, when Korean researchers published the first journal article from their country to be cataloged in the Science Citation Index Expanded (SCIE), science production has exponentially expanded among Korean universities. This rapid growth was not driven by a few specific institutions. Rather, it was possible because virtually all universities with STEM programs, regardless of their institutional mission, participated in scholarly publishing. Meanwhile, the

Korean government implemented a special research funding initiative, Brain Korea 21, in 1999 to stimulate science production among universities. Using multilevel linear regression models, this dissertation examines the effect of the BK 21 program; it finds that BK 21 had a positive effect on publications, but that its effect turned insignificant when more comparable groups of universities were examined. This research also demonstrates that the scientific collaboration network in Korea has moved from one strongly dominated by a few specific actors to one with more actors participating more equally and significantly.

By using an exclusive longitudinal data set of publications—a data set that had not previously been examined—the research reflected in this dissertation contributes to an understanding of the expansion of science production in Korea, changes in publication characteristics, the role of a government-funded project in enhancing science productivity, and the formation of a scientific collaboration network among Korean universities and other countries. Given the relatively short history of the Korean higher education system, this study provides a landscape of how science production has been developed in Korea since its beginning.

Keywords: South Korea, research university, science production, SCIE journal articles

iii

TABLE OF CONTENTS

List of Figures ...... vi List of Tables ...... vii Dedication ...... viii

Chapter 1. Introduction ...... 1

Chapter 2. Literature Review ...... 6 1. Worldwide Expansion of Higher Education and Korean Experience ...... 6 2. Policy Intervention to Increase Science Productivity in Universities ...... 8 3. Collaboration in Science Publication ...... 11

Chapter 3. Development of Higher Education in Korea ...... 13 1. Establishment of Higher Education System, 1945–1950s ...... 13 2. Expansion of Higher Education, 1960s–Early 1990s ...... 16 3. Investment in Research for World-class Research University, Since Mid-1990s ...... 21

Chapter 4. Brain Korea 21 Project ...... 25 1. Background ...... 25 2. Goals and Strategies ...... 27 3. Project Design ...... 29 4. Evaluation ...... 33

Chapter 5. Research Design ...... 35 1. Data and Variables ...... 35 2. Analytical Models ...... 41 3. Network Analysis Methods ...... 45

Chapter 6. Results ...... 48 1. Growth of Science Production in Korea since 1970 ...... 48 2. Effects of BK 21 (Phase II) on University Science Production ...... 59

3. Evolution of the Scientific Collaboration Network ...... 75

Chapter 7. Discussion and Suggestions ...... 87 1. Discussion of Findings ...... 87 2. Policy Implications ...... 90 3. Limitations and Direction for Future Research ...... 93

Appendix A: List of Korean Universities with Their Key Characteristics ...... 95 Appendix B: Summary of Science Production in Korean Universities and Collaborating Countries, 1970–2017 ...... 105

References ...... 120

LIST OF FIGURES

Figure 1-1. Annual Number of SCIE Articles Worldwide, 1900–2018 ...... 2

Figure 1-2. Annual Number of SCIE Articles in Korea, 1970–2018 ...... 3

Figure 3-1. Growth of Higher Education in Korea, 1965–2017 ...... 18

Figure 5-1. Illustration of Network Data Construction ...... 47

Figure 6-1. Composition of SCIE Articles by University Missions, 1970–2017 ...... 54

Figure 6-2. Total Number of SCIE Articles by Journal Impact Factor across University Mission,

1997–2017 ...... 57

Figure 6-3. Composition of SCIE Articles by Journal Impact Factor across University Mission,

1997–2017 ...... 58

Figure 6-4. Annual BK 21 Money and External R&D Funds across University Mission and in

Sample Universities (in million U.S. dollars between 2006 and 2012) ...... 64

Figure 6-5. Logged Mean Number of SCIE Articles and Differences across BK 21 Status ...... 65

Figure 6-6. Predicted Mean Number of Articles and Differences across BK 21 Status ...... 66

Figure 6-7. Network Graphs of 12 Selected Years ...... 78

Figure 6-8. Evolution of Normalized Node Centrality, 1973–2017 ...... 83

Figure 6-9. Subgroup Graphs of Four Selected Years ...... 85

LIST OF TABLES

Table 3-1. Number of College Students by Major, 1965–2017 ...... 20

Table 4-1. Overview of the Brain Korea 21 Project ...... 30

Table 6-1. Science Production at Korean Universities, 1970–2017 ...... 50

Table 6-2. Expansion of Universities’ Participation in Science Production ...... 51

Table 6-3. Mean Number of Authors and Organizations per Article by Year ...... 51

Table 6-4. Average Number of SCIE Articles by University Mission ...... 55

Table 6-5. Growth Rate of Mean Publication by University Mission from 1970 to 2017 ...... 56

Table 6-6. Descriptive Statistics of Variables (109 universities, 1999–2012) ...... 60

Table 6-7. Correlations between Predictor and Covariates ...... 61

Table 6-8. Descriptive Statistics of Variables across BK 21 Phase II Status ...... 62

Table 6-9. Analytical Model Results: BK 21 Effects on Science Production ...... 69

Table 6-10. Results of Common Trends Analysis for BK 21 ...... 70

Table 6-11. Descriptive Statistics of Variables across BK 21 Main Program Status ...... 72

Table 6-12. Supplementary Analysis Results: BK 21 Main Program Effects on Science

Production ...... 73

Table 6-13. Results of Common Trends Analysis for BK 21 Main Program ...... 74

Table 6-14. Network Analysis Results ...... 76

vii

DEDICATION

Dedicated to my mom, Hee-Soon Kwun (1950–2017), who walked with God throughout the years of her pilgrimage.

I love you, mom. See you in heaven.

viii

Chapter 1. Introduction

Science production grew exponentially worldwide over the 20th century. The annual number of new science articles was about 9,400 in 1900; this number increased to around 70,000 in 1960 and 280,000 in 1975. This early growth is referred to as “big science” (Zhang, Powell, &

Baker, 2015). With an unpredicted growth in peer-reviewed articles, then, the annual number exceeded one million in 2009 (Web of Science [WOS], 2018). In 2018, more than 1.4 million articles were newly published worldwide. As illustrated in Figure 1-1, the world’s science production, measured by the number of published science articles catalogued in the Science

Citation Index Expanded (SCIE), grew steadily since the 1960s and reached a new level called

“global mega-science” between 1980 and 2010 (Powell et al., 2017). With the absolute growth in the number of publications occurred the globalization of scientific output: The number of countries publishing SCIE articles also increased notably. While about two dozen countries published at least one SCIE article in 1900, by 1950 about three dozen countries participated in science production. By the turn of the 21st century, around 200 countries published SCIE articles

(Mihai & Reisz, 2017).

This massive growth in scholarly output is generally attributed to two phenomena: the expansion of university education and rapidly increased collaboration in science production among universities. As Geiger mentioned (2017), universities are “the home of science” (p. xiii) and no other type of organizations can replace them in knowledge production. Worldwide SCIE publications authored by at least one university-based researcher grew to 85% of the total publications in 2010, and one out of four publications is authored by scientists located in at least two different countries (Zhang et al., 2015). Competition among countries to increase science productivity contributed to the increased role of universities in science production and led to

excellence initiatives—special funding programs for research—worldwide since the late 1990s.

Figure 1-1. Annual Number of SCIE Articles Worldwide, 1900–2018

1,600

1,400

1,200

1,000

800

600

400

Worldwide SCIE Publications (in 1,000's) (in Publications SCIE Worldwide 200

0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2018 Year

Data Source: Web of Science (2018)

South Korea exemplifies these trends, but has been a latecomer to global mega-science, i.e., the exponential growth of science production since 1980, the emergence of universities as a main knowledge producer, and the expanded collaboration among scientists. The country did not develop a modern higher education system until the 1950s, and only one scientific article was published in SCIE journals by Korean researchers1 in 1970. However, research productivity has increased with an exponential annual growth of 20.8% since then, and by 2018 it exceeded

1 “Korean researchers” does not mean their actual citizenship status. Rather it means that the host country of the organization with which authors of the article were affiliated is Korea. This remains the same throughout the dissertation.

57,000 articles, ranking South Korea 11th in the world. This unprecedented growth is generally attributed to rapid expansion of higher education and the development of the research university

(Ministry of Education [MOE], 2018). Like many other countries, Korea also implemented an excellence initiative project, the Brain Korea 21 program, to increase research productivity among universities. At the same time, it is assumed there might have been substantial and growing collaboration among domestic as well as international researchers. However, there exists little or no research on how science production in Korea has developed; nor have any studies attempted to characterize the number of authors, their organizations, or the collaboration patterns among authors during this program.

Figure 1-2. Annual Number of SCIE Articles in Korea, 1970–2018

70,000

60,000

50,000

40,000

30,000

Korean SCIE Publications SCIE Korean 20,000

10,000

0 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 2018 Year

Data Source: Web of Science (2018)

This dissertation is based on bibliometrics of journal articles, more specifically articles on science, technology, engineering, and mathematics, plus health, cataloged in the SCIE, by

Korean researchers. Journal articles go through a peer-review process before publication so that they are considered qualified in a specific field and time period. Thus, identifying the pattern and characteristics of published journal articles by Korean researchers since 1970 will reveal how science production has evolved in South Korea. After constructing a multifaceted, unique data set, the dissertation answers three questions regarding the growth of science production in South

Korea, indicated by annual numbers, journal impact factors, and collaborative qualities of SCIE journal articles from 1970 to 2017:

First, what has been the pattern of growth in science production in Korea since 1970?

Second, within this growth in number of publications, did the Brain Korea 21 program

(Phase II), a government-funded project to transform selected universities into world-class research universities, stimulate university science production?

Third, how has the scientific collaboration network evolved over nearly half a century?

These three questions and their answers will explain how the exponential expansion of science production in Korea was able to occur within a relatively short period of time and will describe the nature of that production in terms of authorship, organizational affiliation, and inter- organizational collaboration during the rapid growth. The research will demonstrate that the

Korean case followed the worldwide trends and that the country has integrated into global science production.

The dissertation consists of seven chapters including the introduction. In Chapter 2, previous literature is reviewed on three primary topics: worldwide expansion of university education, policy interventions to stimulate science productivity in universities, and collaboration

among scientists. Chapter 3 is exclusively devoted to the development of higher education in

South Korea. It provides the historical and organizational contexts of science publication in

Korea. The Brain Korea 21 Project is examined in depth in Chapter 4, with attention paid to the background, goals and strategies, project design, and evaluation. Chapter 5 explains the research methods used to answer the three research questions, and Chapter 6 presents the results. Policy implications and conclusions of the main findings of the dissertation are discussed in Chapter 7.

Chapter 2. Literature Review

1. Worldwide Expansion of Higher Education and Korean Experience

During the last century, and in the last several decades in particular, higher education worldwide has expanded tremendously, in terms of both student enrollments and the research enterprise. In the research analyzing what drives this expansion, Schofer and Meyer (2005) found that the rapid expansion occurred with a similar growth pattern in all types of countries.

The number of students enrolled in tertiary education grew two-hundredfold during last century, and the worldwide gross enrollment ratio in tertiary education grew from 10.1% of the population in 1970 to 36.8% in 2016 (World Bank, 2018). The global expansion of higher education simultaneously led to unparalleled research capacity, which in turn led to an exponential increase in researchers and science production. By securing stable positions for researchers and passing down produced knowledge to the next generation, universities became central in the scientific process (Meyer, Ramirez, Frank, & Schofer, 2007).

When science production was extensively increased in the mid-20th century and referred to as “big science” (de Solla Price, 1963), scientometricians predicted that exponential growth would slow down over the next few decades. Yet, it did not happen. Instead, “big science” transformed into “global mega-science” (Powell et al., 2017). During the first half of the 20th century, the worldwide growth of science production was led by the massive expansion of higher education and increased science capacity in the United States. After mid-century, European countries and East Asia became the center of the growth of science production based on the expansion of higher education (Zhang et al., 2015).

While non-university research organizations continue to produce new science, it has been research universities and an increasing number of less research-intensive universities that led the

expansion of science publications (Powell et al., 2017). This holds true across various countries where organizational forms vary in their contributions to the science production. For example, universities account for half of the science publications in France, four-fifths in Germany, and two-thirds in the United Kingdom. Yet, the research university is crucial in all three countries in leading research output (Powell & Dusdal, 2017). Higher education in China (Zhang, Sun, &

Bao, 2017) and Taiwan (Fu, 2017) also played a similar role, although universities in that country were guided by strong government policies. In Japan, the second largest amount of science production during the 1990s was attributed to its second-tier research universities

(Shima, 2017).

Even in this era of global expansion of higher education, the Korean experience is notable, as the higher education system was established from the ashes after the Korean War in the early 1950s. In 1945, after four decades of Japanese rule, the majority of Koreans were illiterate (Lee, 2005). The Korean War (1950–1953) further devastated social infrastructure, including school facilities, and left Korea as one of the world’s poorest countries. The Korean government embarked on a national development program based on investments in human capital production through formal education, and achieved one of the world’s most rapid rates of economic growth and social development (Kim, 2002). Within three decades, Korea made primary schooling universal, expanded secondary schooling considerably, and dramatically expanded access to higher education (Kim et al., 2015). In fact, the higher education system’s age-cohort enrollment ratio, or net enrollment ratio, grew from 11.4% to 67.6% from 19802 to

2017. Initially focusing on developing basic education, by the new century Korea funded all levels of education on par with many other high-income countries (Organization for Economic

2 No official net enrollment data are available before 1980.

Cooperation and Development [OECD], 2015).

It was only in 1957 that the first doctoral program opened in Korea (Lee, 1999). By contrast, in 2016, Korea ranked fourth in number of researchers per 1,000 employed among 35

OECD member countries (OECD, 2018). In 2013, Thomson Reuters (now Clarivate Analytics) published a special report on the marked, outstanding growth of scientific publication in the five so-called BRICK countries, namely Brazil, Russia, India, China, and Korea (Adams, Pendlebury,

& Stembridge, 2013). Korea is particularly noteworthy as it has a much smaller population than other four countries: The Korean population is about one-third that of Russia, the second smallest country among the five. Research production in Korea has continuously increased, with an extraordinary annual growth rate of 21.8% between 1980 and 2011, higher than all other countries, according to the report. Research output in Korea accounted for about 4.0% of worldwide science production in 2018 (WOS, 2018).

Although the rapid growth of science publication by Korean researchers has been noted both domestically and internationally, it seems that no systematic and historical analysis has been made. This dissertation will fill that gap.

2. Policy Intervention to Increase Science Productivity in Universities

Based on the global expansion of higher education and links between research and economic development, worldwide competition for science production became one of the main characteristics of the global mega-science era. Various countries have implemented research funding policies to improve the science productivity of their universities. Among the global competition, research performance became “the most important factor for assessing the standing of the modern university” (Ramsden, 1999, p. 342).

Starting in the late 1980s, numerous policies using a performance-based funding

approach for research were introduced. At least 14 countries, mainly in Europe, had implemented performance-based research funding policies by 2010 (Hicks, 2012). In one of the evaluative studies of performance funding, Patrick and Stanley (1998) found that in the United Kingdom, it was the so-called “old” universities (which research mainly had been conducted at and received a larger portion of research funding from government) that showed the highest research performance. Similar results were also found among Australian universities (Ramsden, 1999).

Another line of policy is an excellence initiative, which is designed to encourage outstanding research and transform universities into so-called world-class research universities by providing long-term, large-scale funding to selected universities. These initiatives include either institutional funding, project funding, or both. Funds are used for research and research- related measures, such as the improvement of physical laboratory facilities, recruitment of renowned scientists from abroad, and training of graduate students. Since the late 1990s, at least

23 countries implemented these types of policies (Fu, Baker, & Zhang, 2018). According to

OECD (2014), these excellence initiatives have become popular, with over two-thirds of the

OECD member states implementing such policies, mostly since 2000.

To state a few well-known examples, China implemented a special funding program called the 985 Project to build research universities and improve the global reputation of selected

Chinese universities (Ma, 2007). Thirty-nine universities were selected and received special funds from the Chinese central government for nine years. In Korea, the Brain Korea 21 initiative was launched in 1999 as a seven-year program. Research teams and departments from

673 universities were selected to receive special funds. As research publication increased rapidly after the implementation of the BK 21 program, the Korean government extended BK 21 into a

3 This number does not include universities benefitted for non-science fields such as humanities and social sciences.

second phase in 2006 and a third in 2013. In Japan, the Center of Excellence in the 21st Century program was created in 2002 to foster key research units for world-class research. A total of 113 research units at 50 universities were selected based on their research capacity and performance

(Yonezawa, 2003; 2007). Taiwan created a similar program called the World Class University

Program. Ten universities were chosen, based on their level of scientific publications, to receive special additional research funds (Fu et al., 2018). The German Excellence Strategy was initiated in 2006 with similar objectives: to strengthen the top research in Germany and further improve the country’s international competitiveness (German Research Foundation, 2013). The federal and state government provided a total of 1.9 billion and 2.7 billion euros in the first phase (2006 through 2011) and second phase (2012 through 2017), respectively. During the second phase, 45 graduate schools, 43 clusters of excellence, and 11 institutional strategies were funded under the initiative. In 2019, another eight years of funding began with the total annual funding of 533 million euros. France also has been implementing the Investment for Future programme from

2010 to 2020 with a total budget of 47 billion euros (The Investments for the Future Programme, n.d.).

The results of recent studies on the excellence initiatives are mixed. While Shin’s study found greater performance of the more research intensive universities after the first phase of BK

21 in Korea (2009a), other studies on China and Taiwan revealed that the growth rate of publications in lower tier universities was higher than that of the most highly regarded universities (Zhang et al., 2013; Fu et al., 2018) or that the effect of the funding program was greater in the universities that received less money (Chang, Wu, Ching, & Tang, 2009). The

German initiative appears to have succeeded in fostering collaborations between the university and non-university sectors, and has produced excellent research, but has not led to substantial

changes to the overall German research system (Möller, Schmidt, & Hornbostel, 2016).

Additionally, at least one study found that the initiative grants produced a relatively smaller change in the citation rate of the already highest-performing institutions, and a relatively larger change in the lower-performing institutions (Langfeldt et al., 2015).

It is not easy to evaluate the effect of excellence initiatives, since in most cases the already high-performing research units or institutions are most likely to be funded. As a result, evaluation schemes need to isolate policy effects from others derived from characteristics an institution holds regardless of the funding. In case of BK 21, the aforementioned Shin’s evaluation seems the only study on the funding. Given the importance of BK 21 and its sequential developments, it is crucial to look into whether it has affected the science production among Korean universities as it intended.

3. Collaboration in Science Production

The remarkable growth of science production is derived in large part from international collaboration among scientists. While the effect of the collaboration may differ by discipline, geographical distance, and collaboration partners (Abramo, D’Angelo, & Costa, 2008; Smeby &

Try, 2005; Shin & Cummings, 2010), there is no doubt that international collaboration has continuously grown over the course of the 20th century. Science production has shifted from individual work to collaborative effort, and international collaboration increased 25-fold from

1900 to 2015 (Dong, Ma, Shen, & Wang, 2017).

Scientific collaborations are defined as “interactions taking place within a social context among two or more scientists that facilitate the sharing of meaning and completion of tasks with respect to a mutually shared, superordinate goal” (Sonnenwald, 2007, p. 645), and those collaborations often emerge from, and are perpetuated through, social networks. A social

network can be conceptualized as a set of individuals or organizations that are connected to some or all of the others.

Scientific collaboration networks have been widely studied to understand the network structure, actors’ position and roles, and the network’s influence on the research productivity in various fields of research (Luukkonen, Persson, & Sivertsen, 1992; Newman, 2001a; Newman,

2001b; Sonnenwald, 2007). Multi-university collaborations grew faster than any other type of coauthorship structure across all fields of science and social sciences, and produced the most influential research when a top-tier university joined the team (Jones, Wuchty, and Uzzi, 2008), and the between-cities collaboration weakens with the distance among them (Pan, Kaski, &

Fortunato, 2012). Research was also published to investigate evolutional dynamics of scientific collaboration networks (Barabâsi et al., 2002; Abbasi, Hossain, Uddin, & Rasmussen, 2011), however, there seems no research that examines how the research collaboration, either at the individual researcher level or an institutional level, of a specific country evolves. The dissertation will apply several network concepts and methods to look at the development and evolution of Korean scientific collaboration networks from 1970 to 2017. This will be a meaningful contribution to the literature as it will examine the whole developmental trajectory of a science network from the inception in one country.

Chapter 3. Development of Higher Education in Korea4

1. Establishment of Higher Education System, 1945–1950s

Korea gained its independence from 36 years of Japanese colonial rule in 1945.

Unfortunately, however, the country was divided by the Allied victors, the United States and the

Soviet Union, on a temporary basis. The United States occupied territory south of the 38th parallel of the peninsula, and its military administration ruled for three years. In August 1948, the

Republic of Korea, often informally called South Korea, was established in the south. The

Democratic People’s Republic of Korea, or North Korea, was established one month later. In

June 1950, the North began a full-scale invasion of the South. The Korean War lasted for three years. Thus, society was in a chaotic and turbulent situation. Nonetheless, a nascent education system was set up and partially implemented during this period of time. Expectation and hope for the newly established independent country filled the people’s minds, and the government strived to provide education to its citizens to the extent possible. While most resources were invested in primary education, the foundations for the modern higher education system were also set up during this period.

Establishing the Higher Education System

When Korea was liberated in 1945, higher education barely existed, because of the colonial Japanese policy of restricting educational opportunity. As such, the 1945 liberation can be considered as the inception of higher education in Korea (Kim, 2008). The higher education system was designed mainly under the U.S. military administration, replacing the former restrictive policies with liberal, laissez-faire policies (Kim, 2008; Lee, 1999). The Seoul National

University, the first comprehensive university in the country, was founded by an ordinance of the

4 This chapter is based on my chapter about the growth of Korean higher education and its science production (Kim & Choi, 2017).

U.S. military administration in 1946; in that same year, three private colleges were officially recognized as four-year universities5.

After the establishment of the Korean government in 1948, the Education Act was enacted at the end of 1949. This law played a key role in defining educational ideals, establishing the education system, and setting operating rules. The school system of six-year elementary school, three-year middle school, three-year high school, and four-year university was set up.

Higher education consisted of universities (offering degree programs lasting from four through six years), teachers’ colleges (four-year programs), and junior colleges (offering two- or three- year programs). The enactment of the Education Act also marked the start of the government control over higher education (Lee, 1999). Even though the Korean education system was based largely on the American model, the Act stated clearly the overall authority of the government to oversee operations of public as well as private institutions, including decisions on the substantive academic issues, such as conferring degrees.

The most notable higher education policy initiative during the Korean War (1950–1953) was the establishment of the Wartime Associated University, created by the Education Ministry through the Special Decree on University Education. The Wartime Associated University system made use of institutions that had been closed or evacuated during the war. Through this system, more than 6,000 college students could be educated in dispersed temporary campuses outside

Seoul (“6.25 와 전시연합대학 [Korean War and the Wartime Associated University],”2004,

January 15). After the armistice agreement was signed in July 1953, those temporary campuses were used for the provincial national universities, which played a major role in the early growth

5 Three universities are Ewha Womans University (former Ewha College), Yonsei University (Yonhi College), and Korea University (Bosung College).

of higher education and later science production.

The first doctoral program in the country was opened at Seoul National University (SNU) in 1957. SNU established a Graduate School of Public Administration and a Graduate School of

Public Health two years later. They were the first professional graduate schools that later became one of the streams of advanced education for training professionals and application of theories to practice. Two other streams are general graduate schools for academic research, and special graduate schools for continuous education for workers and adults.

The Rapid Growth of Private Institutions

During this period, policy efforts focused on providing basic educational opportunities to all eligible children, and most resources were allocated to primary education (Lee, 2008).

Publicly funded institutions alone could not accommodate the surging demand for tertiary education. This led to the rapid establishment of numerous private institutions. These also played an important role in producing science researchers, even though expanded private institutions were legitimated through the promise of enhanced access to higher education rather than the production of new knowledge.

From 1945 to 1950, the U.S. military administration and the Korean government carried out a land reform whereby Koreans with large landholdings were required to sell most of their land at a lower price. In order to avoid this legal obligation, landowners chose to establish private schools, enabling educational foundations to become the new owners of the land. The government also encouraged the creation of new private institutions. As a result, private institutions began to account for an increasingly large share of Korean higher education (Oh,

2004). Indeed, by the academic year 2017, almost 80% of college students were enrolled in private institutions.

Based on the establishment of these private institutions, the total number of higher education institutions, including junior colleges, rapidly grew from 19 to 74 within the decade ending in 1955. During this short period, the overall number of students in the country also massively expanded, to more than 10 times the number in 1945. However, many of the new institutions were considered substandard; in response, the Education Ministry announced the

Decree on University Establishment Standards in 1955. The decree provided minimum standards for a new institution in terms of facilities and number of instructors. It also mandated that existing institutions satisfy the same criteria within a certain time period. As a result, the number of newly established institutions fell dramatically. However, the decree set the standard mainly related to teaching; the other functions of higher education, such as research and social services, were not considered in the regulation (Lee, 1999).

2. Expansion of Higher Education, 1960s–Early 1990s

After the April Revolution in 1960,6 the new government strove to create a democratic educational system, part of its efforts to build a democratic society. Zeal for liberalization and democratization pervaded the education sector; however, only a year after the revolution, the government was overthrown by a military coup and the country was led by the military government for the next three decades. The military government implemented a series of five- year economic development plans starting in 1962, with the primary goal of creating an educated workforce for the economy (Kim, 2002). During this period, primary and secondary education became universal. Higher education also expanded. At the end of this period, the net enrollment rate in higher education was about 35% (Kim, 2008).

6 The April Revolution, or April 19th Revolution, was a democratic uprising of students and labor groups in April 1960. It overthrew the autocratic Syngman Rhee presidency. The uprising was triggered by the discovery of the body of a student in Masan Harbor; the student had been killed by a tear-gas shell in the protest against the rigged general election in March 1960.

Higher education policy shifted from liberal and laissez-faire to strict control. The government tried to adjust the number of college students to fit workforce demand and to control university quality, especially at private institutions. Yet, demand for higher education grew ever faster, with the number of students increasing accordingly. At the same time, the government established research-focused institutions and helped staff those institutions by recruiting Korean scholars who had left the country to return, using salaries, housing, and other perquisites to attract them.

In the early 1960s, the government imposed multiple policies in order to control the number of institutions and to guarantee education quality. Higher education was expected to provide a skilled workforce, needed for fulfilling the country’s ambitious economic development plans. The policymakers wanted to deemphasize programs in the humanities and make industrial training a higher priority. Guided by these principles, the government attempted to systematically foster talent by setting the total number of university enrollments and massively overhauling national, public, and private universities. In 1963, the Private School Act was enacted, placing private institutions under public control (Kim, 2008). Subsequently, restrictive policies were implemented, such as the 1966 Decree on University Student Quota, designed to curb the quantitative growth of the college student population. A national university entrance preparatory exam system was introduced in 1969 to maintain a certain quality of newly admitted students and ameliorate social ills associated with the severe competition for college entrance. Several measures to control the quality of higher education were also implemented during this period.

Institutional evaluation of graduate schools was conducted for the first time in 1977, and program evaluation was also done in that year. These evaluations were initiated by the government, but were handed over to the university association in the 1980s.

Figure 3-1. Growth of Higher Education in Korea, 1965–2017

180 100.0%

90.0% 160 152 154 154 147 80.0% 140 135 69.3% 67.5% 67.6% 66.1% 70.0% 120 105 60.0% 100 52.5%

83 50.0% 78 80 65 36.0% 40.0% 57 60 56 56 30.0% 22.9% 23.6% 40 20.0% 11.4% 35 35 20 27 24 26 26 26 10.0% 20 22 14 15 15 0 0.0% 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017

Net Enrollment Rate in Higher Education (%) Number of Public Universities Number of Private Universities

Data Source: KEDI (2018) * In this figure, “universities” include art, culture, and sports specialized universities, missionary, and science and technology institutions, which are excluded from the main analysis. As titled the “Growth of Higher Education,” junior colleges are not included here.

Even though the government tried to control the expansion of higher education, the number of higher education institutions continuously increased during this period. There were 70 institutions in 1965; the number had risen to 107 by 1990, as presented in Figure 3-1. The student population also grew almost tenfold, partially due to the failure of the above-mentioned policies. As the excessive competition for college entrance became a major social issue, the government introduced a system in 1980 to manage the population of graduates, rather than the number of admitted students. Through this policy, an additional 30% of applicants were admitted annually under the assumption that colleges and universities would drop this amount of students through rigorous academic criteria before graduation. Policymakers faced serious criticism from educators regarding this selection goal, as it was not deemed pedagogically desirable. In practice, colleges and universities did not select out nearly as many students as policymakers had planned.

The policy failed, as considerably more students were allowed to graduate, and it was virtually abolished by 1985.

Illustrating the expansion of higher education with the number of college students by their major, Table 3-1 shows that more than one-third have majored in science and engineering fields since 1965. From 1965 to 2017, the growth in undergraduate enrollments in science, engineering, and medical science and pharmacy was more than 20 times. Significant development was also made in the number of researchers: Whereas only 105 doctoral degrees were awarded by Korean higher education institutions in 1965, the annual number had increased to 1,400 doctoral degrees awarded by 1990, reflecting a 10.9% annual rate increase. Indeed, this represents faster growth than the undergraduate student population. The expanded research workforce served as key capacity for the continuous growth of science production.

Beyond the extraordinary expansion, another notable phenomenon in Korean higher

Table 3-1. Number of College Students by Major, 1965–2017

Medical Social Natural Arts & Year Humanities Engineering Sciences & Education Total Sciences Sciences Sports Pharmacy

1965 19,227 29,037 19,452 17,647 9,382 6,512 4,386 105,643

1970 17,786 35,734 25,919 33,345 12,845 7,782 13,003 146,414

1975 20,829 37,343 45,771 44,421 16,813 12,621 31,188 208,986

1980 44,209 85,197 70,828 105,352 22,111 21,871 53,411 402,979

1985 150,141 257,738 336,624 39,408 53,177 94,796 931,884

1990 156,164 286,814 419,891 40,430 69,029 67,838 1,040,166

1995 166,480 306,487 523,002 44,707 84,660 62,399 1,187,735

2000 241,043 424,176 242,565 491,201 59,850 139,282 67,281 1,665,398

2005 251,466 522,941 235,045 519,300 64,043 187,464 79,380 1,859,639

2010 267,549 613,973 243,441 526,193 78,180 213,507 85,998 2,028,841

2015 264,619 603,377 255,151 562,506 118,137 223,262 86,241 2,113,293

2017 246,666 569,982 242,274 564,952 125,983 217,072 83,690 2,050,619 (12.0%) (27.8%) (11.8%) (27.6%) (6.1%) (10.6%) (4.1%) (100.0%)

Data Source: KEDI (2018)

education is that no distinctive classification of all higher education institutions exists. In the

United States, the Carnegie Classification of Institutions of Higher Education functions as a framework to categorize individual organizations by their mission and other characteristics

(Fernandez & Baker, 2017). Such classification can serve as an effective tool to develop institutional-level policies or to control for organizational characteristics in academic research.

While policymakers as well as researchers in Korea have much interest in classifying HEIs, this is a very controversial issue (Shin, 2009b) because the majority of four-year institutions in Korea aspire to be research universities. Despite criticism of the lack of such classification, the widespread model and goal-setting to become research universities led to broadened access to graduate education and contributed to the rapid expansion of science production through the expanded education and training of researchers.

3. Investment in Research for World-class Universities, Since Mid-1990s

The education policy paradigm in Korea went through a sharp change in the 1990s.

While priority had been given to expanding educational opportunities to accommodate ever- growing demands during the 1980s, educational policy starting in the mid-1990s became more concentrated on the excellence and specialization of each institution (Lee, 1999). Global competitiveness of Korean colleges and universities was also emphasized in an era of globalization and large-scale research funding projects, such as the Brain Korea 21, rooted on the competition strategy initiated by the Education Ministry.

University Reform Policy, Mid-1990s

The number of institutions and their enrollments continued to grow in the 1990s. The higher education enrollment rate of an age-cohort was 23.6% in 1990; by 2000, it had doubled, reaching 52.5%. Despite the quantitative expansion, the quality of college education and research

remained a significant concern. As mentioned earlier, most four-year institutions pursued standing as research universities, all with a similarly broad composition of academic departments. This phenomenon was often described as the “department store” model of university, and was perceived as individual institutions lacking unique characteristics or specializations and having substandard quality in general. Accordingly, policymakers initiated special efforts to improve the quality of institutions and tried to build world-class research universities with a new policy approach.

Educational policy during this period shifted to emphasize “customer choice” based on the belief that autonomy and competition would, eventually, raise overall quality.7 This led to a new approach in higher education as well: funding based on competition and evaluation.

Connecting financial support to evaluation results was a very new approach in educational policymaking in Korea (Ahn & Ha, 2015). The government anticipated that competition and evaluation would cause universities to specialize in an effort to enhance their quality, and that this specialization would bring about a desired diversity among higher education programs across the country. Indeed, a number of specific projects were initiated by the Ministry of

Education during this time period.

In addition, the government also promoted basic research through establishing “centers of excellence,” namely the Engineering Research Centers (ERC) and Science Research Centers

(SRC) (Choung & Hwang, 2013). With the goal of effective utilization of limited resources, such centers were established within universities in order to promote industry-academy collaborations

7 The then president Kim Young Sam formed the Presidential Commission on Education Reform (PCER). PCER announced an education reform plan, known as the 5.31 Education Reform Proposals (ERP). The ERP has significantly influenced Korean education policies since its release in 1995 (Ahn & Ha, 2015). ERP implemented an overhaul of the entire education system from its users’ viewpoints, and was based on market principles (Kim, 2002).

and interdisciplinary research. Trends in research funds during the 1990s indicate the emphasis put on such collaborations, as team-based research funds more than doubled from 22% to 51%, while funds for individual research diminished from 78% to 49% (Choung & Hwang, 2013).

Brain Korea 21 (1999–)

Derived from the belief that quality of research is crucial for enhancing the global competitiveness of Korean higher education institutions, the Ministry of Education (MOE) announced a new research funding program called Brain Korea 21 (or BK 21) in 1998. The first phase of the program (1999–2005) invested more than 1,300 billion Korean Won (approximately

1.2 billion US dollars) (MOE, 2006). Unlike regular research funding programs that focused on research publications, BK 21 was designed to support the development of research personnel, such as master’s and doctoral students and postdoctoral researchers, as well as project teams within graduate schools, to increase overall science capacity in Korea. BK 21 included the vision of creating “world-class” graduate programs, building graduate school capacity, and advancing the regional universities.

As a university reform policy, BK 21 had dual policy objectives when it was initiated: to cultivate science capacity among research universities and to ameliorate social ills related to excessive competition for college entrance. In order to achieve these purposes, MOE set up several preconditions for BK 21 funding: Institutions were required, for example, to reduce undergraduate programs, introduce a multi-departmental admission system, and admit a certain number of graduate students from other universities (MOE, 2006). MOE also encouraged institutions to manage research funds with transparency as well as to evaluate professors (Ahn &

Ha, 2015).

BK 21 is considered to be the first policy to apply the “choice and concentration

principle” to higher education, and to create an environment supportive of research at universities, which has increased research output while developing outstanding research personnel (Lee, 2008). Although BK 21 has a subprogram for humanities and social sciences, the program’s main focus has been on natural sciences and technology, which have been perceived as crucial for nation development and succeeding in global competition. BK 21 aimed to build a self-sustaining system of knowledge production in science and technology. Nine times more funds went to projects in science and technology than those in the humanities and social sciences. Partially as a result of such programs, the absolute number of peer-reviewed articles in

SCIE journals (WOS, 2018) published by Korean researchers rose from 10,447 (16th in the world) in 1998 to 27,976 (10th in the world) in 2006. In the same period, the percentage of

Korean SCIE research in global proportion rose from 1.51% to 3.14%. Additionally, the number of SCIE articles published by professors who took part in BK 21 doubled during the program

(Ministry of Education and Korean Research Foundation [MOE & KRF], 2006). The ratio of students to academic staff also improved as a result of the introduction of further research positions, such as post-doctorates and contract professors. A detailed explanation of BK 21 is provided in the next chapter.

Chapter 4. Brain Korea 21 Project

1. Background

In 1998, the Ministry of Education and Human Resources Development (MOE) announced the Brain Korea 21 project. BK 21 is a special funding project to establish globally competitive research universities and graduate programs and to develop high-quality researchers in Korea. It was originally designed as a seven-year project from 1999 to 2006, and based on the increased number of publications and researchers after the project (MOE, 2006), BK 21 developed into sequential projects: Phase II from 2006 to 2013, and Phase III from 2013 to

20208.

The idea of establishing a world-class research university was originally conceived in the education reform package called “May 31 Education Reform” in 1997. The Presidential

Commission on Education Reform suggested creating a fund to build up research universities in order to eliminate regional disparities in college quality, particularly between Seoul and non-

Seoul areas. The commission expected that this initiative would ensure a sufficient supply of qualified researchers for worldwide competitive R&D in the 21st century. This idea, however, did not develop into policy, as it was the last year of the Kim Young-Sam administration.

Instead, the idea of establishing a world-class research university was brought back in the following Kim Dae-Jung administration with a different policy objective: reducing private tutoring (MOE, 2000; MOE & KRF, 2006).

Private tutoring among schoolchildren, whose main goal was to secure admission to the

8 In November 2018, MOE announced that Phase IV would be implemented from 2020 to 2027 (MOE, 2018).

few best colleges in the nation, was increasing9. The uniform college admission system, upheld by the rigid college rankings, was blamed for this excess of private education. Policymakers believed that to solve the issue of private tutoring, overall structural change in higher education should be made first in order to reform the college admission system. In May of 1998, the education minister briefed the president on the University Reform Plan consisting of three projects: establishing a world-class research university, advancing regional universities, and promoting professional schools. The University Reform Plan aimed to replace competition for college admission with competition among graduate schools, to improve research and education quality, and, eventually, to reduce private tutoring. Based on this plan, MOE began designing the

BK 21 project.

One of the initial objectives of the BK 21 was to transform Seoul National University

(SNU) into an institution centered on graduate study, by reducing the number of undergraduates.

SNU had been considered the best college in Korea; consequently, it was the center of competition for admission. Involving SNU was thus inevitable in the process of overall higher education reform. In the initial stage of the BK 21 design, the idea of providing exclusive funding for SNU emerged among policymakers, with the provision of intensive financial support for graduate programs conditional upon the reduction of undergraduate programs. Later, however, the idea was abandoned because SNU’s reform plan did not meet the policymakers’ expectations10 (MOE, 2000; MOE & KRF, 2006). Instead, BK 21 was designed as an open- competition project for which any university with doctoral programs could apply.

9 For example, the total expenditure for private tutoring in 1998 was equivalent to two-thirds of the government education budget (KEDI, 1998; MOE & KEDI, 1998).

10 Ahn and Ha (2015) state that there was resistance from SNU faculty to a government-driven reform, as well as objection from other universities to exclusive financial support for SNU.

2. Goals and Strategies

As a university reform policy, BK 21 had two goals: normalizing primary and secondary education by reducing excessive private tutoring, and fostering a culture of competition and collaboration among universities by abolishing college rankings. It also aimed to establish world- class graduate schools within a relatively short period of time, so that Korea would lead the upcoming knowledge-based economy. To achieve these goals, four strategies were set up: selection and concentration, investment in human resources, financial support tied to institutional reform, and balanced development across disciplines and regions.

BK 21 was the first higher education policy in Korea to claim a “selection and concentration” strategy for seeking excellence, while prior research funds were allocated to provide balanced support across disciplines and universities (MOE & KRF, 2006). MOE wanted to develop globally competitive graduate programs using limited resources with the strategy of concentration. Although the strategy caused substantial controversy11, it also created great interest among universities (MOE, 2000). Due to the concentration strategy, BK 21 funding was not only much greater but also more stable for longer periods than other project-based funding programs. As a result, the selection process became highly competitive (Seong, Popper,

Goldman, & Evans, 2008).

The second strategy was investment in human resources. From the beginning, MOE defined BK 21 as a human resources development project for establishing a world-class research university. The importance of human capital in a global and knowledge-based economy was

11 A few members of the Korea Federation of National University Professor Association and the Korea Association of University Professors argued that funding based on the concentration strategy would reinforce existing college rankings and weaken overall competitiveness of Korean universities (MOE, 2000). After they went on a protest march, BK 21 was amended to drop mandatory evaluation of professors and to separate the subprogram on humanities and social sciences from the main project.

emphasized, and universities were considered to be an integral part of training such professionals. MOE confirmed that BK 21, which would invest about 300 million dollars annually in universities, would provide a stable assistantship for graduate students and stipend for young researchers. This strategy distinguishes BK 21 from excellence initiatives in other countries, such as the 985 Project in China or the Center of Excellence in the 21st Century (COE

21) program in Japan. For instance, 985 Project funds were used mainly to develop new research centers, improve facilities, and build international networks with foreign institutions (World

Education News & Reviews, 2006). Providing assistantships to graduate students was not allowed (Chinese Ministry of Finance, 2010).

The third strategy was to tie financial support to institutional reform within participating universities. One of the goals of BK 21 was to make institutional and cultural changes among

Korean universities. Participating universities had to fulfill several requirements,12 such as reducing the number of undergraduate students, admitting a certain percentage of graduate students from other colleges (not their own undergraduate alumni), adopting a new academic system under which undergraduate students were permitted to choose majors in their sophomore year13, and introducing a centralized and transparent management system of research funds within the university14.

The last strategy was a balanced development of research environments across

12 Requirements for universities varied according to the subprograms under which they were selected and funded, with one exception: All participating universities were obliged to introduce a centralized management system for their research funds.

13 This was to give more options for students and transform college education into a demand-driven system as well as strengthen the industry-university tie.

14 MOE’s initial plan was to include a performance-based promotion and payment system for university professors, but later it was excluded as a result of massive opposition from the faculty (MOE, 2000).

universities and regions. It was a supplementary strategy to the selection and concentration strategy. One of the subprograms aimed to advance undergraduate programs and to promote the university-industry collaboration in regional universities, specifically those located outside of

Seoul and its vicinity, so as to encourage high-achieving students to go on to colleges in their own region. Once a department was funded by BK 21, it was not allowed to receive funds from other funding programs.

Although BK 21 was launched as a policy to reduce private tutoring at the beginning, funds for graduate schools attracted more attention among universities. In the second phase of

BK 21, establishing world-class graduate schools became the main goal, and changes were made in the subprograms accordingly. While all four strategies remained the same, support for graduate schools (but not undergraduate programs) in the regional universities was added to the strategies.

3. Project Design

The first phase of BK 21 consisted of three subprograms: establishing world-class graduate schools, advancing regional universities, and building graduate school capacity. More than half of the total BK 21 funding was granted under the subprogram for establishing world- class graduate schools, and nine-tenths of that amount went to the science and technology disciplines. About one-fourth of the total BK 21 funding was allocated to advance regional universities15. The third program, aimed at building graduate school capacity, was relatively smaller than the first two. Universities that were not selected for the first two programs

15 Only regional universities were eligible for this subprogram, while universities located in Seoul and its vicinity were excluded. In addition, science and technology institutions, such as the Korea Advanced Institute of Science and Technology (KAIST) and the Pohang University of Science and Technology (POSTECH), were also ineligible for this subprogram even though they were located outside Seoul.

Table 4-1. Overview of the Brain Korea 21 Project

Phase I Phase II Period 1999. 8–2006. 2 2006. 3–2013. 2 Funds 1,340,911 million Korean Won 1,754,789 million Korean Won (approximately 1.2 billion US dollars) (approximately 1.6 billion US dollars) Subprograms and 1. Establishing world-class graduate programs 1. Establishing world-class graduate programs and regional funding amount universities (in million KRW) i) Science & technology 547,248 i) Basic science and technology 281,895 (40.8%) (16.1%) ii) Humanity & social Science 56,739 i) Applied science and technology 804,165 (4.2%) (45.8%) iii) Lab facility16 190,000 ii) Humanity & Social Science 194,763 (14.2%) (11.1%) iv) Advancing regional universities 320,721 (23.9%) 2. Graduate school capacity building 2. Graduate school capacity building (Core programs) i) Specialized programs 59,108 i) Basic science & technology 63,980 (4.4%) (3.6%) ii) Core programs I 115,424 ii) Applied science & technology 237,668 (8.6%) (13.5%) iii) Core programs II 51,671 iii) Humanity & Social Science 54,649 (3.9%) (3.1%) 3. Raising high-quality research manpower i) MBA programs 28,849 (1.6%) ii) Medical programs 88,820 (5.1%) Data Sources: MOE & KRF (2006); National Research Foundation of Korea (2013)

16 This funding was exclusively granted to SNU.

were mainly funded by the third program17. A total of 1,340,911 million Korean won (equivalent to approximately 1.2 billion US dollars18) was awarded to 67 universities from 1999 through

2005.

Subprograms remained almost the same during the second phase, and the main focus was still on science and technology. One difference was that establishing world-class graduate schools replaced advancing undergraduate programs in regional universities. This is because the goal of nationwide university structural change in the first phase was weakened in the second phase. Instead, developing globally competitive graduate schools, regardless of location, was emphasized. Sixty-nine universities received a total of 1,754,789 million Korean won

(approximately 1.6 billion dollars) across the seven years from 2006 through 2012. Science and engineering received about 85% of the total BK 21 funds during both phases.

In order to be funded in either of the first two phases, department or groups of departments were required to form a project team and to apply for the funds. In the first phase, there were several preconditions for qualifying for BK 21 funding: The project team needed to have a doctoral program with enrolled students; the department needed to have a minimum number of faculty19; all faculty members and graduate students were required to agree to participate in the project; only faculty members who published at least four articles during the previous three-year period were eligible to be a team leader; and only a team in which 40 or more percent of the faculty published at least two articles in last three years could apply for BK

17 The first two programs were funded from the newly allocated budget from the finance ministry, while the third one was redesigned from the existing research fund projects.

18 Throughout the dissertation, a fixed exchange rate of 1,100 Korean Won to US dollars is used regardless of an annual variation.

19 The minimum number varies across academic fields. For example, seven for liberal arts and social science, ten for basic science, and 10 to 25 for applied science programs.

21 funds20. All project teams were required to secure matching funds from industry, local governments, or their own university, equivalent to three to 10 percent of the BK 21 funds received. In the second phase, some of these requirements were relaxed in order to expand the opportunity to participate: The need for a minimum number of faculty was loosened by as much as 50%, and, the requirement regarding minimum numbers of publications was waived.

Applications were evaluated through multiple processes. Evaluation committees were formed with researchers who were not members of the applicant department. They evaluated whether applications satisfied preconditions first, then appraised their plan for institutional reform. Finally, they were briefed on the research plan by the applying project team and made a final decision. In addition, an Overseas Advisory Board (OAB)21 was formed to review the selected applications and give comments. In the case that multiple teams were ranked equally,

OAB made the selection.

BK 21 funds were used mainly to provide assistantships for graduate students, including master’s students, and to hire postdoctoral researchers and contract-based researchers. MOE set a standard level of assistantships or payment in order to guarantee a minimum level of support.

However, BK 21 funds could not be used to cover the cost of faculty labor or lab facilities22. All funded universities were appraised annually vis-à-vis the plans they initially submitted. If performance was deemed to be poor after two years, the team was dropped from the project.

20 This requirement was an example for the first subprogram: science and technology field in the establishing world-class graduate school program. Each subprogram and academic discipline had a different requirement.

21 OAB was composed of renowned overseas Korean scholars before any specific project was planned and was involved in the design of BK 21 from the initial stage.

22 A subprogram of lab facility for SNU in the first phase was an exception to this.

4. Evaluation

BK 21 attracted a great deal of attention from universities despite the relatively small size of the total funding. The total amount of BK 21 Phase I grants represented just 5% of government R&D expenditures, 10% of university R&D funds from various sources, and only

1% of gross R&D expenditures nationwide (Seong, Popper, Goldman, & Evans, 2008). Yet, once a university was selected, the amount of BK 21 money as a proportion of the total R&D funding the university received was much larger than any other research source, because of the strategy of selection and concentration. For example, the BK 21 grant accounted for more than

20% of university R&D funds from all sources in the case of the Changwon National University and the Hannam University in 2006. Also, BK 21 grants proved to be more stable for longer periods than other research funds, as long as the recipient passed the annual and mid-term evaluations. As a result, the selection process was highly competitive. For example, in both phases, the number of universities that applied for the grant in the science and engineering field was more than twice the number selected (MOE & KRF, 2006; National Research Foundation of

Korea [NRFK], 2013).

After BK 21 was implemented, the absolute number of publications in SCIE journals by

Korean researchers increased, as stated earlier. According to NRFK (2013), almost 2,000 postdoctoral and contracted researchers, 16,000 master’s students, and 8,000 doctoral students were affected annually by the BK 21 second phase, which expanded research opportunities for them23. Given the high profile of the project, BK 21 has attracted great interest from all stakeholders from the beginning. Based on the expansion of science production and the research

23 Corresponding figures for the first phase look slightly larger than the second, but they are only available as a grand sum throughout seven years, making accurate comparisons difficult.

workforce after the initiation of BK 21, the government developed it into a sequential project.

Unfortunately, though, no rigorous evaluation has been undertaken to determine whether the growth in publications by Korean universities was stimulated by BK 21, or whether it was already increasing with the expansion of higher education, regardless of BK 21.

Chapter 5. Research Design

Various research methods were employed to answer three research questions in the dissertation: What has been the pattern of growth in science production in Korea since 1970?;

Within this growth in number of publications, did the Brain Korea 21 program stimulate university science production?; How has the scientific collaboration network evolved during the last nearly five decades? Publication data from Clarivate Analytics’ (former Thomson Reuters’)

Web of Science were mainly used to answer the first question. To evaluate the effect of BK 21

Phase II (hereafter, BK 21 means its Phase II) on university science production, two different multi-level models, fixed- and random-effects models, were employed. Considering the preconditions for BK 21 participation, a subset of data was created for the evaluation consisting of 109 universities across 14 years, from 1999 through 2012. Regarding the development and evolution of Korean scientific collaboration networks from 1970 to 2017, several network analysis concepts and methods were applied.

1. Data and Variables

DATA. Panel data were assembled from a variety of publicly open sources, including

Clarivate Analytics’ (former Thomson Reuters’) Web of Science, Korean Educational

Development Institute (KEDI), Ministry of Education (MOE), and National Research

Foundation of Korea (NRFK). The data span 1970 to 2017. The panel consists of 146 universities in South Korea that have departments or programs in science, technology, engineering, math, and health (hereafter, STEM)24. In Korea, about 210 four-year universities existed as of 2018. Among those 210 institutions, universities of education (specialized

24 As long as a university had STEM faculty, that university was included in the population. It should be noted that number of faculty data by institution were available only since 1997.

institutions for teacher training)25, seminaries, and art- and sport-specialized universities were excluded from the population. Sample sizes were 7,008 at level-1 (year) and 146 at level-2

(university).

To evaluate the effect of BK 21, a subset of data was pooled from the full data set.

Firstly, as mentioned earlier, departments and programs without registered doctoral students were ineligible for BK 21 funding. Therefore, 27 such universities were excluded from the evaluation. Secondly, to evaluate the effect of BK 21, analytical models examined the period from 1999 (seven years before BK 21 implementation) to 2012 (when BK 21 ended). Nine universities established after 1998 were also excluded from the analysis. Finally, panel data consisting of 109 universities across 14 years (from 1999 to 2012) were created.

DEPENDENT VARIABLE. The primary dependent variable in the research was the annual number of articles published by each university. Specifically, publicationit represents the number of publications by university i in year t. Publication refers to a peer-reviewed article published in the scientific journals cataloged in the Science Citation Index Expanded (SCIE), acquired from Clarivate Analytics’ Web of Science [WOS]. According to Adams (2011), WOS indexes 12,000 journals, approximately equal to a quarter of the regularly published academic journals worldwide. Those journals are highly selective and account for more than 95% of the citations among academic articles. About 9,000 journals out of the 12,000 are catalogued in the

SCIE. A full set of information26 for each and every article published by Korean researchers was

25 Ten universities of education existed as of April in 2018. All of them are public universities. The Korea National University of Education was also excluded from the population in the dissertation as it serves a role similar to universities of education, despite a different official classification.

26 This includes 38 items including author’s name, title of the article, language, keywords, abstract, author’s address, corresponding author, funding agency, cited references, total times cited count, journal name, publication date, and article identification number.

downloaded from the WOS website. As mentioned in Chapter 1, “Korean” does not refer to the author’s actual citizenship status; rather, it refers to the host country of the institution with which the author was affiliated was Korea. As the first year when Korean researchers published an

SCIE journal article was 197027, publication data for the years 1970 to 2017 were downloaded; the download took place in June 2018.

As the main research interest of the study was science production of Korean universities and their collaboration networks, information on the organization with which an author is affiliated is crucial. It was noticed that the organization information extracted from the author address in WOS data was inconsistent and included numerous abbreviations as well as typos. In order to identify a university, organizations that are probably higher education institutions were sorted out using keywords like “university,” “college,” or “institute” and then manually cleaned.

After the data were cleaned using STATA©, the number of articles by each university was compared to the summary analysis provided in the WOS website to see if a substantial number of publications was missed in the process of cleaning. As shown in the Appendix B, the differences were not significant28.

Because of the importance of an author’s organizational affiliation, mergers between universities or establishment of a new university required careful attention. If universities A and

B were merged into university C, A and B were treated as C for the period prior to the merger.

27 According to the WOS website, Korean researchers have been publishing articles in SCIE journals every year since 1970. Before that, two articles were published—in 1908 and 1916, respectively—by non- Korean researchers who were affiliated with a Korean institution. The 1908 article was published by Esther L. Shields, a nurse working at the Severance Hospital, which was established by a U.S. missionary in Seoul. The 1916 article was written by Frank W. Schofield, a bacteriologist in the Union Medical College, a school affiliated with the Severance.

28 The summary analysis in the WOS website, however, is not without any miscount, because a variety of English names were used for one university. There also exist many misspellings, typos, or official changes to the university names in some cases.

The number of publications by A and B before the merger was summed up and coded under C. If a university and a junior college were merged, however, the number of publications by the junior college was not considered, since junior colleges were not included in this research. In the case of a newly established university in the middle of the examined period, the publication data in the previous years before the establishment were coded as missing. If that university did not publish any articles after its establishment, the data were coded as zero.

In summing publications by institution, articles coauthored by faculty at the same institution were counted once, following the methods used by previous literature, e.g., Porter and

Toutkoushian (2006), Shin (2009a), and Powell et al. (2017). Some of previous literature found lagged effects of the funding on publication (Shin, 2009a; Fu et al., 2018), while others did not

(Zhang et al., 2013). In the dissertation, there was no substantial difference in the results regarding lagged effects. Therefore, this dissertation reports results without lag.

INDEPENDENT VARIABLE. The main independent variable of interest was whether a university was funded or not by BK 21. The variable BK21it is equal to 1 if the university i was funded by BK 21 in year t, and 0 otherwise. When necessary, it was noted with P2 in parenthesis to differentiate from Phase I.

As a supplementary analysis, the effect of the main subprogram of BK 21 was tested. The main subprogram aimed to establish world-class graduate programs, and the funding allocated for this accounted for more than 60% of the total BK 21 budget. Only a small number of universities were funded by this subprogram. The variable BK21mainit is equal to 1 if the university i was funded by the main program in year t, and 0 if the university i applied for but did not receive funding from the main program in year t.

BK 21 university indicates whether the university was funded by BK 21, and BK 21 money shows the total amount of BK 21 funding that a university received over the seven-year period.

COVARIATES. There are a variety of university characteristics that influence its science production. The analytical model controlled for these characteristics.

Private is whether the university is private (=1) or public (=0). In Korea, there are three types of public universities: national schools established and managed by the central government, national university corporations established by the central or local government but entitled with enhanced autonomy, and public schools established and managed by local government. All three types were coded as public universities in the dissertation.

An institutional mission orientation was coded as a dummy variable. While a university classification is commonly used in some countries, e.g. the Carnegie Classifications in the U.S., there is no such classification in Korea. Given that mission orientation is associated with research production (for example, Patrick & Stanley, 1998; Ramsden, 1999; Hicks, 2012), the dissertation applied Shin’s classification of Korean higher education institution (2009b) for dummies of four types of universities: General, Doctoral, Research active, and Research universities. Although Shin’s classifying work was conducted in 2009, the mission orientation variable was treated as a time-constant.

Seoul+ represents whether the university is located in Seoul or its vicinity, to take into account the overall disparity in human resources and social infrastructure between Seoul and its vicinity, and other parts of the country. If the university is located in Seoul or its vicinity, it was coded as 1; otherwise it was coded as 0.

In addition to these time-invariant characteristic variables, several time-varying variables

were also created. Undergraduateit, masterit, phdit, and facultyit represent the number of undergraduates, master’s students, doctoral students, and faculty members, respectively, in the

STEM department of university i in year t. Graduate students, Ph.D. students in particular, are the main source of research assistance, and their participation is crucial in conducting science and engineering research. However, these three variables, relating to the numbers of each type of student in a given institution, usually are highly correlated. After looking at the variance explained by each of them, the model includes doctoral students only.

NRF basic research fundit shows the total amount of research funds university i received from the National Research Foundation of Korea for basic research in science and engineering in year t. Literature shows that the amount of R&D expenditure one university uses is a predictor of research publication (e.g., Zhang et al., 2013). Unfortunately, data on R&D funds for individual universities in Korea were available only beginning in 2006, thus covering only half of the analysis period, and only the years after the initiation of BK 21 Phase II. Instead, NRF basic research funds data were available from the year 1990. Given the high correlation between two

(.971), NRF basic research funds data were substituted for the total R&D money.

External R&D fundsit shows the total amount of research funds university i received from various sources in year t. The amount of funds that a university received from the BK 21 is not included in this external R&D fundsit. This variable is used for the descriptive statistics only.

Data on time-varying variables were not available for the entire period. Undergraduateit and facultyit were available from 1998; masterit, and phdit from 1999; NRF basic research fundit from 1990; and external R&D fundsit from 2006.

2. Analytical Models

As the dissertation analyzes panel data, a classical regression model was not relevant to apply. One of the critical assumptions of a classical regression model is that the residuals are not correlated to predictors. When units are nested within different groups in the data, as is the case with these panel data (repeated observations on university), there could be dependency among units within a same group, which would not be the case among units in different groups. In the current data, observations of one university across multiple years would be correlated due to the unique characteristics that university holds. In this regard, multilevel regression should be employed in the research. To identify the effect of BK 21, two different approaches were employed: a fixed-effects model and a random-effects model.

Fixed-effects Model: Differences-in-Differences Model

A fixed-effects model is designed to control for the unobserved heterogeneity of the individual university. The model specification with fixed effects is written as

ln publicationit =  +  BK21it + t + ai + Xit + it where i indicates individual university, and t indicates year;  is the causal effect of BK 21; t is the year effect; ai is a vector of unobserved but fixed confounders from university time-stable characteristics; Xit is a vector of observed time-varying covariates such as number of faculty, number of doctoral students, and NRF basic research funds. All of these time-varying variables were log transformed. In this model, ai allows for unobserved heterogeneity in the model capturing all time-constant factors that affect publication. Therefore, as long as the model includes ai, time-invariant variables such as private, seoul+, or any of the institutional mission dummies cannot be added in the model. Unobserved individual university effects are represented

by coefficients on dummies for each university, while the year effects are measured by the coefficients on time dummies.

The dependent variable was log transformed to make a normal distribution as previous literature has done, e.g., Zhang et al. (2013). In the case of zero publication, which frequently happened during the 1970s and 1980s, “1” was added before the transformation to have a transformed value of zero. As the dependent variable is log transformed, the coefficients of predictors should be interpreted as the percentage change in science publication associated with a one-unit change in the predictors.

This fixed-effects model employs the differences-in-differences (DD) estimation strategy, which is not only often used for policy evaluation (Hagood, 2019) but also is becoming more widely used in higher education program evaluation (Cellini, 2008). The DD strategy considers the intervention of a policy as a plausible source of exogenous variation (Tandberg & Hillman,

2014)29. In the dissertation, the model identifies changes in science production before and after

BK 21, and then compares the differences against universities that were not funded by BK 21.

According to Wooldridge (2007), DD estimation “removes biases in second period comparisons between the treatment and control group that could be the result from permanent differences between those groups, as well as biases from comparisons over time in the treatment group that could be the result of trends” (p. 2-3). Given the disparities in science-production capacity among universities, DD estimation can be an efficient tool to measure the effect of the policy intervention, namely, BK 21.

29 A basic DD model specifies treatment, time, and interaction of them. While the time component is identified as either before or after the treatment in the basic model, year fixed effect (t) was included in this study, which was more rigorous than the binary identification of time.

The key identifying assumption in DD, known as a common trend, is that trends in a dependent variable would be the same in both treatment and control groups in the absence of treatment. This requires an examination of whether both groups have similar trends during pre- treatment period, and in the dissertation, two methods were utilized to test the common trends.

First, a placebo treatment period was replaced with the treatment variable for universities that were funded by BK 21 as if BK 21 were implemented on those years rather than the actual BK

21 years. If results show a significant positive effect from the placebo treatment, it means there was a different trend between treatment and control groups before BK 21 (Torun & Tumen,

2016). The second method is a lead-lag analysis in which the treatment variable was replaced with a series of time dummies (up to five years before and after BK 21). This analysis tested preexisting trends in publication and showed the short-term and long-term effects of BK 21

(Hagood, 2019).

Random-effects Model

An alternative to the fixed-effects specification of groups in panel data is “random effects,” which is based on the assumption that ai (all time-constant covariates that affect the dependent variable) is not correlated with any of the regressors in the model. Instead, it is

2 assumed that ai has a zero mean and variance  . The most important consequence of random effects is that the residuals for a given university are correlated across years, and standard errors need to be clustered in the university level (Angrist & Pischke, 2008). The model specification with random effects is written as

ln publicationit = i +  BK21it + t + Ti + Xit + it where i represents varying intercepts according to individual university i; and Ti is a vector of time-stable covariates such as governance, institutional mission, and location. Two different

random-effects models were designed with and without Ti to examine the effects of time-stable characteristics on scientific publications.

If the groups are regarded as unique categories or a researcher wishes to control as much as possible for between-group differences without modeling them, a fixed-effects approach is preferable. However, if the groups are regarded as a sample from a population, or a researcher wishes to estimate and test group-level variables, or the number of level-1 units is small, the random-intercepts approach is more relevant. In the dissertation, data include various universities and the range of each variable is relatively large. Therefore, it is important to control for between-university differences as much as possible. On the other hand, the number of level-1

(year) units is not that large, and this may result in the small difference in the residual variance between the fixed-effects and random-effects models.

Limitations of Analytical Models and Supplementary Analysis

The most notable weakness of the analytical models described above is that it is almost impossible to utilize well-defined control groups because of the project design of BK 21 as well as the selection bias of research funding programs. As described in Chapter 4, BK 21 consists of multiple subprograms, which resulted in an increased number of universities funded by BK 21.

Although the amount of funds varies, more than half of the entire universities, 67 out of 109, were benefitted from BK 21. This not only reduces the number of control groups but also makes comparison between treatment and control groups questionable. This is because universities that did not receive BK 21 money at all will be substantially different from their counterparts in terms of science capacity even if they have a similar trend in the pre-treatment period.

Considering this limitation, supplementary analysis was conducted with a focus on the main subprogram of BK 21. As described in Table 4-1, the subprogram of “Establishing world-

class graduate programs and regional universities” (or BK 21 main program) accounted for more than 70% of the total BK money, while only half of recipient universities received funds from this subprogram. The universities that applied for this main program but failed to be selected served as control groups and the effect of the main program was examined.

3. Network Analysis Methods

In the terminology of social network analysis, people or groups are called “nodes,”

“vertices,” or “actors,” and connections among them are referred to as “edges,” “ties,” or “links.”

Both nodes and edges can be defined in different ways depending on research interest. In a scientific collaboration network, edges are coauthorship relations among authors (nodes). An edge exists between two nodes if they have a coauthored article. Since scientific collaboration may span disciplinary, institutional, and national boundaries, nodes can be replaced with author’s affiliation.

As the main interest of the third research question in the dissertation is in the collaboration networks of Korean universities, articles that do not have any author affiliated with a Korean university30 were dropped from this network analysis. Construction of the scientific collaboration networks data consisted of three steps. In the first step, a network ID was created.

Each Korean university was given a specific network ID, and organizations located outside of

Korea were given an identical network ID according to their host country. This approach allowed investigation of the role of the foreign country in the scientific collaboration networks of Korean universities. Second, a two-mode data matrix was constructed by year, with article identification

30 The full data include all SCIE journal articles that have at least one author affiliated with a Korean institution, including non-university institutions such as junior colleges, research institutes, corporations, and hospitals. If an article was published by all of these non-university affiliated authors, that article has nothing to do with the university collaboration network.

and network ID. This two-mode matrix constructs a network that ties author’s affiliation

(represented with the network ID of a university or a country) to articles. In this matrix, there are no direct ties between authors, nor ties between articles. Rather, the ties are connecting authors to articles. Finally, two-mode matrices were transformed into one-mode valued adjacency matrices.

In these adjacency matrices, the nodes in rows and columns represent the author’s affiliation

(i.e., a Korean university or a foreign country) and the edges signify coauthorship between two nodes. The value of the edges denotes the number of coauthorships between two nodes in the given year. As researchers can coauthor with other researchers in their own university or country, self-loops were allowed in the data. The steps in network data construction are illustrated in

Figure 5-1. The final data set contains 621,392 articles involving 146 Korean universities and

193 foreign countries over 48 years (from 1970 to 2017).

Figure 5-1. Illustration of Network Data Construction

Step 1. Giving a network ID to each organization Daejin University (0023) Ewha University (0034) Hongik University (0057) Kyunghee University (0095) Pohang University of Science and Technology (0110) Seoul National University (0122)

Research Institutes (categorized): 600

US: 1183

Step 2. Creating two-mode data Two-mode data between Article ID and Organization ID

Step 3. Converting to one-mode data One-mode data between Organization IDs

Chapter 6. Results

1. Growth of Science Production in Korea since 1970

The first SCIE journal article authored by Korean scientists was published in 1970. Since then, the number of articles published by Korean researchers has grown at an exponential rate.

With the rapid expansion of science publication has come various changes in the characteristics of publications, authors, and collaborators.

Table 6-1 presents the expansion of science production by Korean scientists. Only one article was published by a Korean university through collaboration with one foreign country31 in

1970. After almost five decades, however, the number of publications now exceeds 55,000 with more than 160 collaborating countries. The role of universities in science production is noteworthy in this expansion, as it was relatively low in the 1970s. Articles by non-university authors made up as much as 32.4% of all published articles in Korea in 1976; that number began to decrease in the 1990s, and since 2000, the proportion has been less than one in 10.

Simultaneously, the number of universities that published SCIE journal articles increased to 146 universities by 2017. This means that virtually all universities with STEM departments or programs participated in science production.

The number of collaborating partners was also increased. While only two dozen foreign countries had coauthored articles with Korean scientists before 1990, the number grew to 70 countries in 2000, and 158 by 2015. This reflects the international collaboration among scientists

(Dong et al., 2017; Zhang et al., 2015), with Korea also participating in the global mega-science trend. Researchers from the United States have always been the most frequent collaborators: Out of 695,000 articles published by Korean scientists since 1970, 95,000 articles were coauthored

31 It was Japan, which was an important collaborating partner for Korean scientists in the earlier period.

with researchers based in the U.S. Japan was the second most significant partner until 2010; about 30,000 articles were published through collaborations with Japanese scholars. Since 2011, though, China has replaced Japan as the second-largest collaborator with Korean scientists.

Germany, India, the United Kingdom, Canada, and France are also important collaborators for

Korean science production32.

The number of author(s) per article also has changed. Articles by solo authors accounted for as much as one-third of all articles published in the earlier period. However, since the mid-

1990s, when universities expanded their publication rate, the ratio of solo-authored articles has continuously declined. In 2017, only 2.5% of articles were published by a solo author. This is due to increased collaboration among researchers within the country as well as across borders.

During the earlier period, collaboration with researchers in another domestic institution than the author’s own, let alone collaboration with foreign institutions, was not well-developed. Fewer than 10% of articles were coauthored with colleagues at domestic institutions until 1990. In fact, the number was lower than the percentage of articles coauthored with scientists at foreign institutions. This means that Korean researchers collaborated more with foreign scientists than their own countrymen. This trend has reversed since 1998. As of recently, almost half of all published articles are coauthored with domestic institutions, and about one-third are coauthored with foreign institutions. Finally, about 70% of all articles are currently published with scholars from multiple institutions.

32 The total number of coauthored articles with each country is provided in Appendix B.

Table 6-1. Science Production at Korean Universities, 1970–2017

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017

Number of SCIE articles published 1 38 113 523 1,420 5,158 13,385 25,581 39,110 54,082 55,241 (with at least one author affiliated with Korean organization) Articles by at least one author 1 28 92 478 1,101 4,292 11,850 22,693 35,422 49,139 50,053 affiliated with Korean university (100.0%) (73.7%) (81.4%) (91.4%) (77.5%) (83.2%) (88.5%) (88.7%) (90.6%) (90.9%) (90.6%) Articles by non-university Korean 0 10 21 45 319 866 1,535 2,888 3,688 4,943 5,188 author(s) only (0.0%) (26.3%) (18.6%) (8.6%) (22.5%) (16.8%) (11.5%) (11.3%) (9.4%) (9.1%) (9.4%)

Number of Korean universities 1 11 16 43 62 97 130 138 143 144 146 (which published at least one article)

Number of foreign countries 1 4 9 14 24 58 70 99 116 158 163 (which coauthored at least one article with Korean organization)

Articles by solo author 0 13 12 51 96 388 719 1,177 1,470 1,582 1,363 (0.0%) (34.2%) (10.6%) (9.8%) (6.8%) (7.5%) (5.4%) (4.6%) (3.8%) (2.9%) (2.5%)

Articles by multiple authors 1 25 101 472 1,324 4,770 12,666 24,404 37,640 52,500 53,878 (100.0%) (65.8%) (89.4%) (90.2%) (93.2%) (92.5%) (94.6%) (95.4%) (96.2%) (97.1%) (97.5%)

Articles with collaboration in the 1 17 42 139 364 1,860 6,943 15,130 23,823 34,804 36,984 institution level (100.0%) (44.7%) (37.2%) (26.6%) (25.6%) (36.1%) (51.9%) (59.1%) (60.9%) (64.4%) (67.0%)

Articles coauthored with domestic 1 1 2 23 93 779 4,656 10,444 16,470 24,530 25,834 institution(s) (100.0%) (2.6%) (1.8%) (4.4%) (6.5%) (15.1%) (34.8%) (40.8%) (42.1%) (45.4%) (46.8%) Articles coauthored with foreign 1 17 41 122 292 1,181 2,924 6,370 10,239 15,294 16,847 institution(s) (100.0%) (44.7%) (36.3%) (23.3%) (20.6%) (22.9%) (21.8%) (24.9%) (26.2%) (28.3%) (30.5%)

Table 6-2. Expansion of Universities’ Participation in Science Production

1970 1980 1990 2000 2010 2017 Number of universities which published

at least 1 article 1 16 62 130 143 146

more than 100 articles 2 31 65 77

more than 1,000 articles 3 17 24

Table 6-2 illustrates the growth in science publications on the part of universities since

1970. Only three universities published more than 10 articles in 1980, and only two universities published more than 100 articles in 1990. In 2017, however, 77 universities published more than

100 articles, and 24 universities published more than 1,000 articles.

Table 6-3. Mean Number of Authors and Organizations per Article by Year

Mean number of Median number of year authors organizations authors organizations

1970 2 3 2 3

1975 2.2368 1.6579 2 1

1980 3.7257 1.708 3 1

1985 2.9522 1.3977 3 1

1990 3.869 1.5148 3 1

1995 9.5219 2.2867 3 1

2000 7.0925 2.5288 4 2

2005 7.0356 2.7755 4 2

2010 8.9126 3.1492 5 2

2015 10.504 3.8366 5 3

2017 12.671 4.3586 5 3

Simultaneously, the mean number of authors per article continuously increased during the last nearly five decades. As presented in Table 6-3, fewer than four researchers, on average, published an article together before 1990, but nowadays the number is more than a dozen per paper. However, this is partially reflective of a number of articles published by hundreds or thousands of scientists. For example, about 1,400 papers were published since 2000 with at least

500 authors each; of those articles, half have more than 1,000 authors. This represents global collaboration among scientists but it may exaggerate the average number of authors per article.

To avoid overestimating of the trend, Table 6-3 also presents the median number of authors; this number increased at a more moderate rate, from two in the 1970s to five in the 2010s.

The rapid growth of science production in Korea has been driven neither by a few institutions nor by any specific type of university. Rather, it has been possible because virtually all universities with STEM programs, regardless of their institutional mission, published articles.

Figure 6-1 illustrates the composition of SCIE articles by university missions. Science production by research universities, the most research-intensive universities, continuously increased during the 1970s. Almost nine (0.872) out of 10 SCIE articles published in Korea in

1979 had an author affiliated with a research university. Since then, however, the research universities’ shares have steadily decreased to about one-third (0.359), while the proportion of articles by other types of universities continuously increased: general universities by tenfold, doctoral universities by fivefold, and research active universities by threefold, between the early

1980s and around 2010. Although the absolute number of articles published and the publication growth rate were both larger among more research-intensive universities, as shown in Table 6-4 and Table 6-5, it is obvious that universities that are less research focused also expanded their science production.

The quality of articles was also not decided by the university mission. Article quality needs to be evaluated with different measures depending on discipline, but the journal impact factor can be a moderate tool to measure overall quality. Figure 6-2 presents the increases in number of publications since 1997 by quartiles of journals distributed along a standard impact factor, with the fourth tier being journals with the impact factor ranked in the top 25% of a given academic field33. Regardless of institutional mission, all universities published articles in all tiers of journals. It is not the case that research-intensive universities published only in journals with higher impact factor. Figure 6-3 presents the composition of articles by quartiles of journals across different university missions. It is notable that the percent of the higher tiers have been increasing across all types of universities since 1997, even though there were variations in the middle of period and with a different rate between different university missions.

33 Journal impact factor data were available starting from the year 1997. Data were downloaded from the WOS website in November 2018 and merged with the publication data using journal titles.

Figure 6-1. Composition of SCIE Articles by University Missions, 1970–2017

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

General universities Doctoral universities Research active universities Research universities

* In order to compare the contribution of each university mission to science production, this figure counted articles which had at least one Korean university-affiliated author; articles by non-university author(s) only were excluded.

Table 6-4. Average Number of SCIE Articles by University Mission

Research General Doctoral Research Total Year Active university (99) university (26) university (7) (146) university (14)

1970 0.0 1.0 0.0 0.0 1.0 1971 0.0 1.0 0.0 0.0 1.0 1972 0.0 0.0 1.0 0.0 4.0 1973 0.0 2.0 2.7 3.8 3.5 1974 0.0 1.0 1.8 4.3 3.7 1975 0.0 1.5 2.0 3.6 3.5 1976 1.0 1.0 1.5 2.7 2.4 1977 1.0 1.0 2.2 7.4 4.5 1978 0.0 1.0 1.8 9.4 6.2 1979 0.0 0.0 1.3 13.6 7.5 1980 1.0 1.0 1.3 20.0 7.1 1981 1.0 1.8 2.1 33.2 10.3 1982 2.0 2.8 2.8 38.0 9.9 1983 1.8 3.3 4.5 42.5 12.0 1984 1.4 2.9 4.3 44.3 11.1 1985 1.7 2.8 6.2 59.0 12.2 1986 1.9 2.7 7.9 63.7 11.9 1987 2.0 3.5 9.6 82.5 14.5 1988 1.8 3.6 11.3 78.6 16.5 1989 1.7 4.1 14.6 90.1 18.4 1990 3.1 5.8 16.4 107.7 22.9 1991 2.5 7.9 21.8 120.1 24.0 1992 3.0 9.0 27.2 140.9 25.7 1993 4.8 13.7 41.1 190.6 32.2 1994 5.2 19.7 52.1 240.4 37.1 1995 7.7 30.5 84.4 361.9 53.2

1996 10.3 42.8 113.9 470.9 65.6 1997 12.3 52.6 143.4 600.9 78.7 1998 15.8 75.9 193.9 758.6 83.7 1999 16.9 83.8 242.7 881.9 96.0 2000 17.4 100.9 294.7 1,009.0 103.0 2001 21.3 123.3 347.9 1,188.9 119.1 2002 25.2 139.4 397.8 1,295.3 129.3 2003 29.4 172.1 487.6 1,450.0 146.3 2004 36.5 211.3 593.3 1,641.7 171.9 2005 38.2 223.7 652.0 1,830.9 185.4 2006 39.7 249.3 714.9 1,963.1 200.2 2007 37.3 262.7 749.8 2,013.7 204.9 2008 46.2 316.5 887.6 2,303.7 237.1 2009 49.1 353.8 1,001.9 2,468.6 251.7 2010 56.2 390.5 1,145.3 2,669.4 273.5 2011 65.2 460.2 1,264.4 2,942.4 301.3 2012 76.4 487.6 1,381.7 3,174.0 331.9 2013 82.4 517.4 1,444.4 3,294.9 342.0 2014 88.2 550.7 1,527.6 3,445.1 361.4 2015 92.3 577.7 1,618.1 3,657.9 375.6 2016 90.6 586.0 1,644.9 3,801.9 379.0 2017 89.0 606.1 1,664.3 3,823.9 378.4

Table 6-5. Growth Rate of Mean Publication by University Mission from 1970 to 2017

Research General Doctoral Research Total Period Active university (99) university (26) university (7) (146) university (14)

1970–2017 0.112 0.154 0.179 0.177 0.116

Figure 6-2. Total Number of SCIE Articles by Journal Impact Factor across University Mission, 1997–2017

General University Doctoral University

30,000 30,000

25,000 25,000

20,000 20,000

15,000 15,000

10,000 10,000

5,000 5,000

0 0

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Tier 1 Tier 2 Tier 3 Tier 4 Tier 1 Tier 2 Tier 3 Tier 4

Research Active University Research University

30,000 30,000

25,000 25,000

20,000 20,000

15,000 15,000

10,000 10,000

5,000 5,000

0 0

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Tier 1 Tier 2 Tier 3 Tier 4 Tier 1 Tier 2 Tier 3 Tier 4

Figure 6-3. Composition of SCIE Articles by Journal Impact Factor across University Mission, 1997–2017

General University Doctoral University

100% 100%

80% 80%

60% 60%

40% 40%

20% 20%

0% 0%

Tier 1 Tier 2 Tier 3 Tier 4 Tier 1 Tier 2 Tier 3 Tier 4

Research Active University Research University

100% 100%

80% 80%

60% 60%

40% 40%

20% 20%

0% 0%

Tier 1 Tier 2 Tier 3 Tier 4 Tier 1 Tier 2 Tier 3 Tier 4

2. Effects of BK 21 (Phase II) on University Science Production

Descriptive Statistics

Summary statistics for the variables in the analytical models are presented in Table 6-6.

A total of 109 universities were included in the subset of data for the evaluation of BK 21 (Phase

II). Among them, 67 universities received BK 21 funding of as much as 267 million dollars each over a seven-year period. A similar number of universities (63) were funded in the first phase.

Each university received about 5 million dollars annually for basic research from NRF from

1999 to 2012, and 31.8 million dollars for R&D from 2006 to 2012. A total of 265 faculty members worked in the universities, along with 577 master’s students and 203 doctoral students on average. Of the universities, 82 are private, and 44 are located in Seoul or its vicinity. Seven universities are research universities, 14 research active universities, and 26 doctoral universities.

The rest are general universities. It is noteworthy that there are large ranges of almost all variables. This is because the data include all universities as long as they have STEM departments or programs.

Table 6-7 presents correlations between variables. As mentioned earlier, NRF basic research funds and external R&D funds are highly correlated (.971). This validates a replacement of R&D funds with NRF funds in the analytical model. Even though the amount of BK 21 money is not included in the analytical model, Table 6-7 demonstrates that the amount of BK 21 funding is highly correlated with the number of doctoral students (.844), and with both NRF

(.922) and R&D funds (.937). The correlation was greater in Phase II compared to Phase I.

Additionally, the number of doctoral students is highly correlated with NRF funds as well.

Table 6-8 describes the magnitude of differences between BK 21 universities and non-

BK 21 universities. As stated earlier, substantial differences between treatment and control

Table 6-6. Descriptive Statistics of Variables (109 universities, 1999–2012)

Definition Mean SD Min Max Obs. Article Number of articles published in SCIE journals in a given year 308.1 589.5 034 6,412 1,526 BK 21 university (P2) Whether the university was selected and funded by BK 21 phase 0.615 0.487 0 1 1,526 II in a given year BK 21 university (P1) Whether the university was selected and funded by BK 21 phase I 0.578 0.494 0 1 1,526 BK 21 money (P2) Total amount of BK 21 money a university received from 2006 to 13,300 33,200 0 267,000 1,526 2012 under BK 21 Phase II (in million Korean Won) BK 21 money (P1) Total amount of BK 21 money a university received from 1999 to 10,900 43,100 0 437,000 1,526 2005 under BK 21 Phase I (in million Korean Won) NRF basic research Total amount of basic research funds which a university received 4,990 12,900 0 169,000 1,526 funds in a given year (in million Korean Won) External R&D Total amount of external R&D funds which a university received 31,800 55,600 0 504,000 760 funds in a given year (in million Korean Won, 2006–2012) Faculty Number of faculty in STEM in a given year 264.2 244.6 2 1,480 1,519 PhD student Doctoral students in STEM in a given year 203.2 355.9 0 2,871 1,519 Master student Master students in STEM in a given year 577.0 771.6 0 4,692 1,519 Undergraduate Undergraduate students in STEM in a given year 5,247.3 3,380.5 154 18,961 1,519 Private Private university 0.752 0.432 0 1 1,526 Seoul+ university University located in Seoul and its vicinity 0.404 0.491 0 1 1,526 Institutional mission using Shin’s classification (2009) Research Seven research universities 0.064 0.245 0 1 1,526 Research active Fourteen research active universities 0.128 0.335 0 1 1,526 Doctoral Twenty-six doctoral universities 0.239 0.426 0 1 1,526 General 103 universities which are not classified in any of above category 0.569 0.495 0 1 1,526

34 Four universities have no publications in the earlier years.

Table 6-7. Correlations between Predictor and Covariates

BK 21 BK 21 BK 21 BK 21 Faculty PhD NRF R&D Private Seoul+ University Mission univ univ money money students funds funds Research (P2) (P1) (P2) (P1) Research active Doctoral General

BK 21 univ (P2) 1.000

BK 21 univ (P1) 0.657 1.000

BK 21 money (P2) 0.317 0.319 1.000

BK 21 money (P1) 0.191 0.216 0.871 1.000

Faculty 0.532 0.507 0.720 0.514 1.000

Doctoral students 0.372 0.370 0.844 0.645 0.726 1.000

NRF funds 0.330 0.333 0.922 0.824 0.716 0.876 1.000

R&D funds 0.368 0.371 0.937 0.806 0.760 0.878 0.971 1.000

Private -0.278 -0.317 -0.227 -0.250 -0.207 -0.237 -0.187 -0.193 1.000

Seoul+ -0.003 -0.055 0.159 0.078 0.080 0.108 0.172 0.216 0.257 1.000

University mission

Research 0.207 0.223 0.777 0.564 0.512 0.811 0.739 0.779 -0.023 0.166 1.000

Research active 0.303 0.272 0.217 0.031 0.500 0.268 0.224 0.233 -0.160 0.075 -0.101 1.000

Doctoral 0.442 0.434 -0.108 -0.048 0.105 -0.086 -0.062 -0.052 0.023 0.022 -0.147 -0.216 1.000

General -0.688 -0.668 -0.439 -0.260 -0.682 -0.510 -0.465 -0.499 0.099 -0.152 -0.301 -0.440 -0.642 1.000

Table 6-8. Descriptive Statistics of Variables across BK 21 Phase II Status

BK 21 universities Non-BK 21 universities Article 476.2 (700.7) 40 (42.1) BK 21 university (P1) 0.836 (0.371) 0.167 (0.373) BK 21 money (P2) 21,700 (40,200) - BK 21 money (P1) 17,400 (53,900) 488 (1,740) NRF basic research funds 7,900 (15,800) 357 (556) External R&D funds 48,000 (65,700) 5,870 (6,510) Faculty 366.4 (261.3) 102.5 (61.7) PhD student 310.6 (419.8) 33.1 (32.7) Master student 865.8 (864.5) 120.0 (118.6) Undergraduate 6,755.0 (3,348.4) 2,860.2 (1,573.7) Private 0.657 (0.475) 0.905 (0.294) Seoul+ university 0.403 (0.491) 0.405 (0.491) Institutional mission Research 0.104 (0.306) 0 (0) Research active 0.209 (0.407) 0 (0) Doctoral 0.388 (0.488) 0 (0) General 0.299 (0.458) 1 (0) Observations 938 588 Groups (University) 67 42

groups probably exist, which will limit the validity of an analytical model. BK 21 universities published about 500 SCIE articles annually, whereas non-BK 21 universities published only 8% of those. Eight out of 10 BK 21 Phase II universities had been funded in the first phase as well.

BK 21 universities received 22 times more research funds from NRF than non-BK 21 universities. The total amount of R&D funds that BK 21 universities received was eight times greater than non-BK 21 universities. This not only shows the difference between two groups, but also it indicates that BK 21 funds were concentrated on a smaller, select group of universities.

Variables regarding science capacity, namely the number of faculty members and student size, confirm that relatively larger universities were benefitted from BK 21. All of the non-BK 21 universities were general universities. Given that Shin’s classification (2009) was based purely on research performance, it is clear that all universities with better research performance were selected for BK 21, even though BK 21 Phase II did not include criteria regarding publications.

Figure 6-4 illustrates the distribution of BK 21 money across different types of universities. The more a university is research intensive, the more it received external R&D funds as well as BK 21 money (bar graphs in Figure 6-4). The line graph presents that the ratio of BK 21 money to total R&D funds gets higher as a university becomes more research intensive. Again, this demonstrates the implemented selection and concentration strategy in BK

21.

Despite substantial differences, the expansion of science production shows similar trends in both groups as illustrated in Figure 6-5. While non-BK 21 universities virtually did not publish at all until the early 1980s, the number of publications that they produced has been boosted since

1990. Differences between two groups increased until 1997 and then became stable in the rest of the period. Figure 6-5, though, does not consider the existing gap between two groups in terms of

Figure 6-4. Annual BK 21 Money and External R&D Funds across University Mission and in Sample Universities (in million U.S. dollars between 2006 and 2012)

500.0 0.100

450.0 0.090

400.0 0.080

350.0 0.070

300.0 0.060

250.0 0.050

200.0 0.040

150.0 0.030 Annual Annual external R&D funds

100.0 0.020 21 BK moneyratio to funds R&D

50.0 0.010

0.0 0.000 [Kangwon Research [Seoul General [Kyungnam Doctoral [Kyunghee Research National active National university University] university University] university University] university University] Annual BK21 money 0.3 0.3 1.0 2.2 4.6 5.2 16.0 38.1 Annual external R&D funds 11.3 8.9 26.7 47.5 65.5 78.0 196.7 409.3 BK21 ratio to R&D funds 0.031 0.036 0.037 0.047 0.067 0.067 0.079 0.093

Annual external R&D funds Annual BK21 money BK21 ratio to R&D funds

Figure 6-5. Logged Mean Number of SCIE Articles and Differences across BK 21 Status

0 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

BK21 universities Non-BK21 universities Differences

Figure 6-6. Predicted Logged Mean Number of Articles and Differences across BK 21 Status

6 2

5.5 1.8

5 1.6

4.5 1.4

BK21 BK21 Universities - 4 1.2

3.5 1

3 0.8

Predicted Predicted Mean Logged of Articles SCIE 2.5 0.6 Differences Differences between BK21 and Non 2 0.4

1.5 0.2

1 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

BK21 universities Non-BK21 universities Differences

science capacity. Once the faculty size, number of doctoral students, and NRF research funds are controlled, we can see similar trends prior to BK 21, as shown in Figure 6-6. Science production was increasing rapidly in both groups, and differences were actually decreasing from 1999 to

2003.

BK 21 Effects on Science Production

Table 6-9 presents estimated regression coefficients with robust standard errors. All four models report statistically significant positive effects of BK 21. For example, according to the results of a fixed-effects model, it is expected to see about a 42.5% increase in the number of articles produced by a given university if that university received BK 21 funding (this is calculated as exp (0.354323) = 1.4252155). This finding remains the same across all three random-effects models. Results also show that the number of doctoral students and the amount of NRF basic research funds have significant and positive effects on the number of research publications as well. In the case of doctoral students, it is expected to see about a 1.4% increase in the number of publications from a given university for any 10% increase in the number of doctoral students. However, the magnitude of the impact of NRF basic research funds on science production is much less, as we expect only a 0.12% increase in publications for any 10% increase in the amount of NRF funds.

Random-effects models reveal several additional aspects among Korean universities that a fixed-effects model does not. First, the variable of whether a university was funded by BK 21

Phase I was added in the random-effects model. Results show that BK 21 Phase I had a statistically significant positive effect—even much larger than Phase II—on science production.

However, the effect becomes insignificant once the university characteristic of institutional mission is added in the model. Secondly, while private universities showed no advantage over

their public counterparts, universities located in Seoul and its vicinity published more articles than universities outside. Again, however, this becomes insignificant after controlling for the university mission. Lastly, university mission has the biggest impact on science publications. For example, a doctoral university published at least two times more articles than a general university. This is a rather expected result since the category of the institutional mission was generated based on the number of publications (Shin, 2009b). Since there is no other reliable classification of Korean universities, it is inevitable to use Shin’s classification. Still, this means that the third random-effects model violates the exogeneity assumption between the predictor

(here, the institutional mission is one of covariates) and the predicted.

Although universities can differ, as this study includes all universities with STEM programs in particular, this difference is assumed to be captured by the university fixed effect in the first fixed-effects model. Then, the critical assumption is whether publication trends would be the same in two groups—BK 21 universities and non-BK 21 universities—in the absence of the treatment (Angrist, & Pischke, 2008). To test if this assumption is met, placebo treatment and lead-lag analysis were run. Placebo treatment period was set and performed during the period prior to BK 21 as if BK 21 had been implemented in these years rather than the original years

(Torun, & Tumen, 2016). As shown in Table 6-10, placebo cutoff years do not yield any meaningful result. Lead-lag analysis also provides similar results. The time dummy variables estimate the change in number of publications in the given period before or after BK 21, relative to the year of implementation of BK 21. The lead-lag results provide evidence in support of the common trends assumption, as there are no treatment effects in the years leading up to BK 21 implementation. Additionally, the results show that the BK 21 effect emerged one year after the implementation.

Table 6-9. Analytical Model Results: BK 21 Effects on Science Production Model 1 Model 2 Model 3 Model 4

(Fixed effects) (Random intercepts) (Random intercepts) (Random intercepts) BK 21 Phase II 0.354323** 0.3849787** 0.3827439** 0.3658758** (0.1207972) (0.1229206) (0.1232705) (0.1197158) Faculty (logged) 0.3035801 0.60294** 0.6334552*** 0.4478251** (0.1925447) (0.197427) (0.1982672) (0.1564801) Doctoral student (logged) 0.144034** 0.1746009*** 0.1711799+ 0.1471593*** (0.0450591) (0.0440564) (0.0442395) (0.0403906) NRF basic research funds 0.0128914* 0.0126129+ 0.0124012+ 0.0143489** (logged) (0.0050796) (0.0049771) (0.0049898) (0.0049993) BK 21 Phase I 0.7389708*** 0.7435386*** 0.258073 (0.2121015) (0.1955827) (0.1379391) Private university 0.0333433 - 0.0290348

(0.1575788) (0.1155045) Location in Seoul+ 0.3759272*** 0.1098001

(0.0962825) (0.0733328) Mission orientation

(reference = General) Doctoral 0.6827587***

(0.1424259) Research active 1.263735***

(0.2228706) Research 1.947807***

(0.251592) Year fixed effects Yes Yes Yes Yes University effects Fixed Random Random Random Intercept 1.477779+ - 0.5343021 - 0.8508049 0.1164538 (0.8738265) (0.8370956) (0.8642448) (0.6913652) Observations 1,519 1,519 1,519 1,519 Groups (University) 109 109 109 109 R2 within 0.6941 0.6877 0.6868 0.6927 R2 between 0.8950 0.8595 0.8785 0.9242 R2 overall 0.7539 0.8290 0.8462 0.8875 Significance code: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1

Table 6-10. Results of Common Trends Analysis for BK 21

Placebo Test Lead-Lag Analysis Placebo Treatment (1999-2005) 0.0732856 (0.1084811) 5 years before BK 21 Phase II 0.1205366 (0.0763637) 4 years before BK 21 Phase II 0.0637002 (0.0659062) 3 years before BK 21 Phase II - 0.0053295 (0.0682434) 2 years before BK 21 Phase II - 0.0278073 (0.0679016) 1 year before BK 21 Phase II 0.0991075 (0.0702941) 1 year after BK 21 Phase II 0.4600777*** (0.1331088) 2 year after BK 21 Phase II - 0.0175221 (0.0605656) 3 year after BK 21 Phase II 0.0369606 (0.0473414) 4 year after BK 21 Phase II - 0.0101368 (0.0460693) Faculty (logged) 0.3008756 (0.1908689) Doctoral student (logged) 0.1460756** (0.0461172) NRF basic research funds (logged) 1.0144083** (0.0046733) 0.0154291+ (0.0059691) Intercept 1.361806*** (0.0671699) 1.450807+ (0.8607602) Year fixed effects Yes Yes University fixed effects Yes Yes Observations 1,489 1,519 Groups (University) 109 109 R2 within 0.8529 0.6962 R2 between 0.1218 0.8929 R2 overall 0.3631 0.7711

Significance code: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1

Results of Supplementary Analysis

The evaluation of the BK 21 main program was conducted with better defined control groups. In this supplementary analysis, 46 universities that applied for the BK 21 main program were examined. Although differences still exist, they become less between treatment (BK 21 main program universities) and control (non-BK 21 main program universities) groups, as Table

6-11 demonstrates.

Interestingly, the analytical models provide none of significant effects of the BK 21 main program on science production. Table 6-12 demonstrates that only faculty size has a significant positive effect on number of publications across different models. Results of the common trends analysis are consistent with these findings as well (Table 6-13). None of the time dummy variables has a significant effect on publication. This means there was no effect of BK 21 on science production before and after the period of BK 21 implementation. The insignificant effect of research funding program among universities with similar capacity is in line with previous literature, e.g., Zhang et al. (2013) and Chang et al. (2009).

Table 6-11. Descriptive Statistics of Variables across BK 21 Main Program Status

BK 21 main program universities Non-BK 21 main program universities Article 774.9 (855.0) 143.7 (133.8) BK 21 university (P1) 0.371 (0.484) - BK 21 money (P2) 39,300 (49,300) 2,300 (2,480) BK 21 money (P1) 30,800 (72,000) 3,080 (3,130) BK 21 science money (P2) 30,400 (45,900) - BK 21 science money (P1) 13,400 (38,700) - NRF basic research funds 13,500 (20,100) 2,170 (2,590) External R&D funds 77,200 (79,700) 20,400 (12,000) Faculty 493.9 (264.9) 191.9 (108.9) PhD student 510.7 (499.7) 87.9 (55.9) Master student 1,354.1 (953.7) 358.1 (181.7) Undergraduate 8,380.8 (3,635.7) 4,888.9 (1,706.4) Private 0.6 (0.490) 0.636 (0.483) Seoul+ university 0.429 (0.495) 0.636 (0.483) Institutional mission Research 0.2 (0.400) - Research active 0.371 (0.484) - Doctoral 0.4 (0.490) 0.364 (0.483) General 0.286 (0.167) 0.636 (0.483) Observations 938 588 Groups (University) 35 11

Table 6-12. Supplementary Analysis Results: BK 21 Main Program Effects on Science Production Model 1 Model 2 Model 3 Model 4

(Fixed effects) (Random intercepts) (Random intercepts) (Random intercepts) BK 21 Science Phase II 0.3399139 0.34932 0.3588391 0.3163071 (0.3047848) (0.3050335) (0.3080024) (0.3078497) Faculty (logged) 1.319255** 0.9382538** 0.9916029*** 0.7933756*** (0.420995) (0.1406867) (0.1491104) (0.1482516) Doctoral student (logged) 0.0071734 0.1261159* 0.1290802* 0.0352262 (0.0838983) (0.0596248) (0.0597283) (0.0692417) NRF basic research funds 0.0471907 0.0612474 0.0603268 0.0539269 (logged) (0.0455068) (0.0555721) (0.0550928) (0.0514232) BK 21 Science Phase I 0.4815562** 0.3414819 - 0.0783269 (0.1660062) (0.2425198) (0.1301276) Private university - 0.0317296 - 0.0832646

(0.1775044) (0.1131702) Location in Seoul+ 0.34986 0.23781

(0.2369094) (0.1772824) Mission orientation

(reference = General) Doctoral 0.7123709***

(0.1815344) Research active 1.048003***

(0.2032975) Research 1.73814***

(0.3154033) Year fixed effects Yes Yes Yes Yes University effects Fixed Random Random Random Intercept -3.534485 - 2.416535+ - 2.823027 - 1.728036 (2.550769) (1.22878) (1.370367) (1.489806) Observations 637 637 637 637 Groups (University) 46 46 46 46 R2 within 0.6850 0.6822 0.6825 0.6816 R2 between 0.6852 0.7691 0.7850 0.8636 R2 overall 0.6839 0.7465 0.7606 0.8220 Significance code: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1

Table 6-13. Results of Common Trends Analysis for BK 21 Main Program

Placebo Test Lead-Lag Analysis Placebo Treatment (1999-2005) 0.0041052 (0. 1252747) 5 years before BK 21 Phase II - 0.0424851 (0.1349799) 4 years before BK 21 Phase II - 0.0163156 (0.1179765) 3 years before BK 21 Phase II - 0.108465 (0.0793288) 2 years before BK 21 Phase II 0.0702524 (0.0961912) 1 year before BK 21 Phase II 0.1834572 (0.1324706) 1 year after BK 21 Phase II 0.3083704 (0.2831137) 2 year after BK 21 Phase II - 0.0349389 (0.0601942) 3 year after BK 21 Phase II 0.0773551 (0.1137699) 4 year after BK 21 Phase II 0.0118762 (0.0599514) Faculty (logged) 1.276934** (0.4209627) Doctoral student (logged) 0.0127313 (0.0794837) NRF basic research funds (logged) 0.0165218+ (0.0073993) 0.0465017 (0.0458008) Intercept 2.336596*** (0.1295451) - 3.312579 (2.525492) Year fixed effects Yes Yes University fixed effects Yes Yes Observations 640 637 Groups (University) 46 46 R2 within 0.9195 0.6879 R2 between 0.2111 0.6850 R2 overall 0.4723 0.6846

Significance code: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1

3. Evolution of the Scientific Collaboration Network

Overall Metrics

Table 6-14 presents a summary of some of the basic results for the scientific collaboration network in Korea in 11 selected years across almost five decades. The number of articles that have at least one author affiliated with a Korean university has continuously increased. At the same time, collaboration among researchers also expanded, and the average number of authors per article doubled. In contrast, the proportion of articles from solo authors decreased by half.

Nodes and Edges. The number of nodes and edges continuously increased throughout the entire period from 1970 through 2017. In 1970 there existed only one Korean university and one foreign country in the collaboration networks. In 1975, the number of universities in the network increased to 11, meaning that 11 Korean universities published at least one STEM+ article in the SCIE journals either as a solo author or with coauthors (from another Korean university or a foreign country). Nearly half a century later, this number had grown by a factor of

13, and 146 Korean universities participated in the network in 2017. The number of foreign countries in the network grew by a factor of 40, from four in 1975 to 158 in 201735. An edge

(with a value of 1) is one occurrence of a coauthorship between two nodes in this network. As coauthorships count the relationship between two nodes in the network, an article with multiple authors can produce more coauthorships than usually expected. In 1975 the number of coauthorships was 25; by 2017, the number had grown to 16,858.

35 The numbers of foreign countries in Table 6-14 are slightly different from those in Table 6-1 (p. 49). This is because countries that coauthored only with non-university organizations in Korea (in other words, who did not coauthor with a Korean university) were excluded from the network analysis.

Table 6-14. Network Analysis Results

Graph Metric 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 Nodes (Authors' affiliation) 3 15 23 58 89 155 201 237 255 300 309 Korean Universities 1 11 16 43 62 97 130 138 143 144 146 Foreign Countries 1 4 7 13 22 53 66 94 107 151 158 Non-university Korean Orgs by Category 1 0 0 2 5 5 5 5 5 5 5 Articles 1 28 92 478 1,101 4,292 11,850 22,693 35,422 49,139 50,053 Articles by Solo Organization 0 17 57 355 802 2,664 5,459 8,784 13,389 17,132 16,218 % of Solo Org Authored Articles 0.0% 60.7% 62.0% 74.3% 72.8% 62.1% 46.1% 38.7% 37.8% 34.9% 32.4% Mean Nodes (i.e. org) per Article 3.00 1.43 1.45 1.31 1.35 1.65 1.85 2.03 2.14 2.34 2.50 Total Edges (Coauthorship) 6 25 45 156 327 1,446 3,063 5,878 7,999 14,547 16,858 Network Density 1.000 0.095 0.087 0.059 0.061 0.108 0.142 0.202 0.239 0.318 0.348 Maximum Geodesic Distance (Diameter) 1 5 5 6 6 4 3 3 4 3 4 Average Geodesic Distance (GD) 1.000 2.432 2.274 2.464 2.435 2.079 1.959 1.852 1.812 1.708 1.672 SD of GDs amongst Reachable Pairs 0.000 1.22 0.927 0.883 0.782 0.551 0.494 0.483 0.512 0.507 0.515 Overall Network Betweenness 0.0% 17.0% 27.0% 30.3% 43.0% 24.3% 18.6% 7.5% 6.2% 4.4% 4.4% Centralization Connected Components 1 6 8 11 7 4 2 1 3 1 2 Single-Vertex Connected Components 0 4 6 10 6 3 1 0 2 0 1 Connectedness 1.000 0.352 0.419 0.682 0.869 0.962 0.99 1 0.984 1 0.994 Size of the Giant Component 3 9 15 48 83 152 200 237 253 300 308 As a percentage (out of total vertices) 100.0% 60.0% 65.2% 82.8% 93.3% 98.1% 99.5% 100.0% 99.2% 100.0% 99.7% Maximum Edges in a Connected 6 18 36 146 321 1,443 3,062 5,878 7,997 14,547 16,857 Component As a percentage (out of total edges) 100.0% 72.0% 80.0% 93.6% 98.2% 99.8% 100.0% 100.0% 100.0% 100.0% 100.0% Subgroups 1 8 11 16 13 8 5 2 4 2 3 Subgroups excluding single vertex 1 4 5 6 7 5 4 2 2 2 2 subgroup Modularity 0.2 0.5 0.4 0.4 0.4 0.3 0.2 0.3 0.2 0.2 0.2 Average Node Degree 2.0 1.3 1.9 3.4 5.3 16.7 28.5 47.6 60.7 95.0 107.1 Average Node Clustering Coefficient 1 0.111 0.224 0.403 0.446 0.604 0.713 0.739 0.739 0.78 0.803

Network Density. The increase of the edges between nodes affects the network density.

The density of a graph refers to a proportion of network ties, or edges, that exist out of all possible ties (Borgatti, Everett, & Johnson, 2013), and it describes the general level of linkage among the nodes in a network (Scott, 1991). The scientific collaboration network in Korea continued to become denser from 0.095 in 1975 to 0.348 in 2017. Figure 6-7, drawn by the

NetDraw program of UCINET© (a comprehensive package for the analysis of social network data) depicts this development and the evolution of the network by showing network graphs of the 12 different years. In addition to the increasing density in Figure 6-7, the number of isolates

(a node without any ties attached) decreased to one in 2000 and none in most of the later years.

This means that, in 2000, only one Korean university out of 130 had no coauthorship with another university or foreign country. In contrast, four out of 11 universities had no coauthorships in 1975.

Another interesting phenomenon in Figure 6-7 occurred in 2015 and 2017. Since 2000

(except for these two years), network graphs show Korean universities and collaborating countries clustered together while only a couple of nodes are located in the territory of the other group. In 2015 and 2017, however, the two groups are well mixed in the graphs. There are no notable differences in the overall metrics trend in those two years so that it is not easy to identify why the network graphs for those years look quite different. It is noticeable that the location of the most influential actors, which are tagged with network ID in Figure 6-7, is different in 2015 and 2017: They are scattered in the wider areas in those years, while they are concentrated in the center in other years. According to Cronin (2015), NetDraw generates a visualization of the data using an algorithm to push the most connected nodes to the center of the figure and the least

Figure 6-7. Network Graphs of 12 Selected Years

1970 1975

1980 1985

* In the network graphs above, blue nodes represent Korean universities, yellow nodes represent foreign countries, and gray nodes represent non- university Korean organization categories.

Figure 6-7. Network Graphs of 12 Selected Years (cont’d)

1990 1995

2000 2005

Figure 6-7. Network Graphs of 12 Selected Years (cont’d)

2010 2015

2016 2017

connected ones to the periphery. Considering this, it is possible to speculate that the influence of the few powerful nodes was relatively weakened in 2015 and 201736.

Geodesic and Diameter. A fundamental concept in network analysis is the “geodesic,” or the shortest path between two nodes. The average geodesic distance remained between 1.91 and 2.676 from 1973 to 1996, and continuously decreased in subsequent years. In 2017, the average geodesic distance was 1.672. This means that a randomly chosen node in the network can reach another connected node in fewer than two steps. A diameter of a graph is the length of the largest geodesic between any pair of nodes. It quantifies how far apart the farthest two nodes in the network are. The diameter in the Korean scientific collaboration network was the largest in

1985 and 1990, at 6, and has since declined to and remained at approximately 3.5. This result excludes pairs of nodes that are not connected at all.

Results of Centrality Analysis

The centrality analysis investigates which node (a Korean university or a foreign country) was most influential in the Korean scientific collaboration network and how it changed over the

48-year period. This is about determining the centrality of a node. While “centrality” measures are helpful in evaluating the importance of a node in the network, “centralization” of a network describes the extent to which network cohesion is organized around particular focal nodes (Scott,

1991).

Among various centrality measures, substantial interest in many social network studies is given to the “betweenness” of a node, which is a measure of how often a given node falls along the shortest path between pairs of nodes. This quantity is an indicator of who the most influential actor in the network is, i.e., the ones who control the flow of information among most others.

36 However, this speculation doesn’t answer why the network graphs of only two years, 2015 and 2017, are visualized differently: The graph of 2016 remains the same as other years.

“One particularly notable feature of the results is that the betweenness measure gives very clear winners among the scientists in the network” (Newman, 2001a, p. 2). The following section presents results of betweenness measures on node centrality and looks at trends in network centralization.

Node centrality. Figure 6-8 shows changes in normalized centrality measures of the 20 most influential nodes in the network. Among Korean universities, Seoul National University

(SNU) is continuously ranked at the top. The U.S. remained the most important actor in the

Korean scientific collaboration network throughout the whole period. Japan was the second most influential in the earlier period, but its importance fell behind China in 2010, and since then, the two countries have held comparable values of centrality. Apart from the fluctuating centralities of individual nodes, the notable trend of the 20 nodes’ normalized centralities in Figure 6-8 is that the differences among nodes has continuously declined since the early 1990s. For example, in 1990 the centrality of the U.S. was 0.439, and it was more than three times larger than the second most central country, Japan. However, the centrality results of all 20 nodes in 2017 remained between 0.004 and 0.046. This indicates that the influence of individual nodes weakens in the network over time.

Network Centralization. As discussed earlier, network centralization refers to the extent to which a network is dominated by a single node (Borgatti et al., 2013). Therefore, the trend of leveling off, as found in the node centrality results, leads to the decreased network centralization after 1990, as reported in Table 6-14. This implies that the scientific collaboration network in

Korea has moved from a network strongly dominated by a few specific actors to one with more actors participating more equally.

Figure 6-8. Evolution of Normalized Node Centrality, 1973–2017

Node Centrality of 10 Most Influential Korean universities 0.5 0.45 0.4 SNU 0.35 0.3 0.25 0.2 0.15 0.1 0.05

1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 1973

Node Centrality of 10 Most Influential Collaborating Countries 0.5 US 0.45 0.4 0.35 0.3 0.25 0.2 Japan 0.15 0.1 0.05

Results of Subgroup Analysis

Often, groups of actors interact with each other to such an extent that they could be considered a separate entity (Wasserman & Faust, 1994). There is also often a continuous phase transition, with increasing density of edges in a graph at which is formed a “giant component,” a connected subset of actors whose size scales extensively (Newman, 2001a). Is this valid in the

Korean scientific collaboration network as well? The research described in this dissertation looked at the subgroups formulated in the network and their changes. The role of the most central actors in the subgroup formation was also investigated.

Composition of Subgroups. The results of clustering analysis are reported in Table 6-14.

The subgroup analysis was conducted using the Girvan-Newman algorithm with NodeXL Pro© program, as it is the most frequently applied for subgroup analysis (Cronin, 2015). The total number of subgroups, excluding the single node subgroup, has decreased since 1990, when the largest number of subgroups (seven) existed. Figure 6-9 depicts how subgroups are clustered in selected four years. Across these four years, it is observed that the largest subgroup is clustered around the U.S. and SNU, and over time there are fewer subgroups, meaning more and more nodes are integrated into the bigger subgroups. This finding is consistent with several network statistics below.

Clustering Coefficients. Newman (2001b) stated that real networks “possess local communities in which a higher than average number of people know one another” (p. 408), and in this sense, “real networks are clustered” (p. 407). One way of investigating the existence of such clustering in network data is to measure the fraction of transitive triples, also known as the clustering coefficient, in a network. In a scientific collaboration network, the clustering coefficient is an important property of networks since it indicates how much a node’s

Figure 6-9. Subgroup Graphs of Four Selected Years

1975 1990

2005 2017

collaborators are willing to work with each other, and it represents the probability that two of its collaborators wrote an article together. (Barabâsi et al., 2002). In Korean networks, the overall network clustering coefficient, or the average node clustering coefficient, continuously

increased, as shown in Table 6-14. This indicates that the probability of two collaborators of one node working together has been increasing within the network.

Size of the Giant Component. A component is a maximal set of nodes in which every node can reach every other by some path in a disconnected graph. In small networks, i.e., those with few nodes and edges, all nodes belong to a small subgroup of collaborations or communications. However, with the increase in the total number of edges, there often comes the point at which a “giant component” forms. In the network containing a giant component, the giant component fills a large portion of the graph, and all other components are trivial. Table 6-

14 presents the giant component’s size for each year, both as the total number of vertices and as a fraction of system size. In the early years, the size of the giant component was about 60% of the total nodes; it continually increased in subsequent years, plateauing and stabilizing at approximately 99% in 2000. This pattern is consistent with the findings of previous research, e.g., Newman (2001a; 2001b).

Modularity. Networks with high modularity have dense connections between the nodes within modules and sparse connections between nodes in different modules. In the current data, with increases of graph density and size of the giant component, and a decrease of the number of subgroups, the network modularity has decreased. This means that nodes collaborate more with nodes in different subgroups in the later years.

Chapter 7. Discussion and Suggestions

1. Discussion of Findings

This dissertation proposed and answered three research questions about science production at Korean universities since 1970. Using SCIE journal articles, an exclusive data set was created to identify how science production was started and developed in Korea, to evaluate the role of a government funding project on science productivity, and to deconstruct how the scientific collaboration network was formulated and has evolved since the first science article was published in Korea.

The first finding in this dissertation was that science production in Korea grew exponentially during the last nearly five decades. The number of SCIE articles published by

Korean scientists increased at an annual rate of 18.3% between 1980 and 2017. During this time period, only China (at 17.9%) expanded science production at an annual growth rate comparable to Korea. This dissertation found that this unprecedented growth was largely the result of the expansion of higher education. Virtually all Korean universities with STEM programs have been participating in science production since 2000, accounting for about 90% of the total SCIE publications in the country. This phenomenon is in line with global trends. It is undeniable that the university is the most important and critical contributor in the generation of new knowledge

(Geiger, 2017).

It was obvious that the role of less research-intensive universities also increased continuously since 1980. During the first 10 years of science production (1970–79), the most research-intensive universities in Korea led the expansion. However, since 1980 their shares have steadily decreased. Additionally, it was also shown that all universities published articles in all tiers of journals. In addition, the percentage of articles published in the higher-tier journals

have been increasing across all types of universities. This means that the growth in the number of

SCIE publications would not have been possible without the development of less research- intensive universities. While the top-level institutions usually attract public attention, it is easy to ignore the importance of the relatively lower level of universities in science production. This dissertation provides the empirical evidence for why we need to invest resources in those universities as well.

The second main finding was that the Brain Korea 21 project (Phase II, 2006–2012) had a positive effect on science production. At the end of 20th century, various countries began to invest relatively larger amounts of money in scientific productivity. BK 21, initiated in the midst of such a policy movement, was a special funding project to increase science production and transform a few selected universities into world-class research universities. This dissertation found that, if a university was selected and funded by BK 21, the result was an approximately

42.5% increase in the number of SCIE articles by that university. In that sense, the goal of BK 21 was achieved.

The meaning of this result, however, should be interpreted with caution. As Möller and colleagues (2016) argued, the best institutions benefit from self-enforced processes confirming and strengthening their status, often referred to as “the Matthew effect.” It is not difficult to assume that best universities in Korea benefited during the selection process of BK 21, regardless of policymakers’ intention. In fact, research performance was excluded from the selection criteria of BK 21 Phase II. Yet, the selection results of BK 21 are highly correlated with the publication performance of each university. The total amount of BK 21 money was correlated even more highly with publication performance. Therefore, it is tricky to evaluate the net effect of the money on scientific productivity. The project design of BK 21, an open

competition-based funding to research teams (not the institution as a whole), made it even harder to formulate a well-designed control group.

The supplementary analysis was run in order to correct this weakness. With a smaller group of universities, the supplementary analysis provided a better control group to evaluate the effect of the BK 21 main program. It did not have a significant effect on scientific productivity, a finding that is consistent with previous research such as Fu et al. (2018) and Zhang et al. (2013).

This insignificant effect may be due to a diffusion effect of institutional norms, or greater inter- institutional competition. Whatever caused the parallel growth of science production among non- funded universities, it is obvious that isomorphism around science productivity was generated among modern Korean universities.

The third finding is that a scientific collaboration network was formed and evolved in

Korea during the last nearly half-century. As science production expanded rapidly, there was an evolution of characteristics in articles. This dissertation found that the average number of authors per article continued increasing, from two to 12. The number of institutions with which authors are affiliated also doubled to four in 2017. The scientific collaboration network in Korea continued to get denser: The network density grew from 0.095 in 1975 to 0.348 in 2017. This means that out of all possible collaboration ties among Korean universities and foreign countries, more than one-third were realized. This is a great development of collaboration among scientists; however, this means two-thirds of possible ties have not yet occurred. It may not be possible to determine the ideal fixed extent of network density in science production, yet it seems clear that it will be easier for scientists in a more closely connected network to identify collaborators with the same research interests, as well as to update cutting-edge research quickly, than for those not in such a network.

This dissertation also determined how the collaboration with foreign countries has evolved since 1970. Korea joined the science enterprise relatively later than most other countries.

Korea was a latecomer in the global mega-science boom and it was inevitable that it would receive help from outside. This research determined that, until 1998, Korean scientists coauthored with partners in foreign countries more than among themselves: The number of articles coauthored with foreign institutions was larger than the number of articles coauthored with domestic partners until 1997. It is undeniable that Korean science production is indebted to many advanced countries, the United States in particular. Throughout nearly five decades, the

U.S. was the most influential country in Korean science production, and it remains the closest collaborator today.

However, as the network expands and gets dense, the influence of an individual institution or foreign country, including the U.S., in the network becomes weaker. This implies that the scientific collaboration network in Korea has moved from a network strongly dominated by a few specific actors to the one with more actors participating more equally.

2. Policy Implications

What do the findings of this dissertation imply for future policy in Korea? Science production in Korea rapidly expanded over nearly half a century, largely as a result of the growth of higher education. As more and more universities joined science production, the absolute number of SCIE publications increased accordingly. In sum, a quantitative growth or expansion of science capacity led the exponential growth of science publication for 48 years.

An important consideration is that Korea has one of the lowest fertility rates in the world.

The average number of babies born per woman of reproductive age continuously decreased and by 1984 became lower than two (World Bank, 2018). It dropped further, to 1.052, by 2017, and

was expected to fall to an all-time low, 0.96, in 2018 (Haas, 2018). Accordingly, the population of school-age cohorts has also shrunk. The Korean government has been implementing a university restructuring policy since early 2000s, as it was projected that the population of college-age cohorts would be smaller than the number of seats in the universities in the near future. Mergers between universities and colleges were encouraged and often incentivized by policymakers. As a result, the number of enrolled students in universities nationwide began decreasing, starting in 2014 (KEDI, 2018).

This demographic change challenges the prospects for Korean science production as well.

As demonstrated in this dissertation, the exponential growth in science publication was led by the quantitative growth of science capacity. The size of human resources, such as the number of doctoral students and faculty, have significant positive effects on publication. However, it seems difficult to increase the number of researchers further, as Korea already has one of the largest number of researchers relative to its population size (OECD, 2018). Alternative approaches should be proposed in order to tackle this challenge.

A strong argument can be made that there is still room to improve scientific productivity among Korean scientists. Although the number of SCIE publications has grown rapidly, the country’s productivity still trails some other countries. For example, in 2018 Canada ranked 10th in the world with more than 60,000 SCIE articles, and Korea was next with 57,000 papers. Given the differences in total population size (35 million in Canada vs. 51 million in Korea) as well as the number of researchers per 1,000 employed (8.4 in Canada vs. 13.8 in Korea in 2016), it is clear that Korea has room to improve science productivity. It may be argued that the quality of articles is more important than the number of newly published papers; however, Korea cannot expect global competitiveness if its overall science production shrinks.

Another implication of this dissertation is that policymakers should consider the effects of a given policy beyond the direct target groups. As the results of the supplementary analysis showed, the BK 21 main program had a positive effect, but it was not significant. This means that while universities that received funds from the project increased scientific productivity, the increase was not substantial given the increased science production among those that did not receive funds. In some cases, the activities of benefited institutions can spill over and create positive externalities, even if unintended (OECD, 2014). Therefore, policymakers should consider what kinds of effects could occur over the over the whole system beyond the direct target groups.

Lastly, policymakers need to pay more attention to how the foreign collaborating partners are changing. As found by network analyses, the U.S. has been the most influential country for

Korean researchers, yet its relative importance continues to change. During last two decades, the number of papers coauthored with researchers in several other countries increased at a much higher magnitude than that of the U.S.: For example, collaboration with India increased by a factor of 30.5; with China, by a factor of 26.5; and with Australia, by a factor of 22.5. During the same period, papers coauthored with U.S. scientists increased about sevenfold, which is considerable—and still dominant in absolute figures—but is relatively lower than those three countries. In fact, the U.S. suffers from a relative decline in scientific productivity in global science production, as other nations, Asian and European countries in particular, invest heavily in their science capacity (Powell et al., 2017). Policymakers should be aware of these changes and be able to provide policy supports to researchers as needed.

3. Limitations and Directions for Future Research

This dissertation has several methodological weaknesses. The development of the science production was examined mostly through descriptive statistics, and therefore does not provide any causal inference between the expansion of higher education and increases in the number of publications. The project design of BK 21 made it hard to evaluate its net effect on science publications. While excellence initiatives in other countries such as China and Taiwan would provide better control groups, an evaluation of BK 21 has intrinsic limitations even with the supplementary analysis. Lastly, in the network analysis, to look at the role of foreign countries, all of the authors’ organizations were collapsed into one node if they were located outside Korea and lie in the same country. This means that there could be substantial differences between two types of nodes in the network: a university and a country. This may require different approaches to the interpretation of the results, such as node centrality. However, it was not considered because the purpose of the dissertation is to look at the changes in the overall international networks rather than to investigate changes in each foreign collaborating institution.

Directions for the future research are suggested in two ways. Firstly, empirical research on the expansion of science production should be examined. The literature shows that the pattern of expansion of higher education was similar in all types of countries, regardless of various economic and political systems and levels of development (e.g., Schofer & Meyer, 2005). Would the pattern be the same in science production as well? So far, academic research seems highly stratified and scientific performance highly skewed (e.g., Cole & Cole, 1973; Möller et al.,

2016). However, as this dissertation has found, science production in Korea has diffused to larger groups of institutions throughout its development. While there still exist different levels of

science production in quantity as well as quality, it may be the case that the system becomes a more evenly developed system than becoming more skewed.

Secondly, the association between collaboration and scientific productivity may need to be investigated. The third part of this dissertation focused on the development and the evolution of collaboration networks among universities in Korea. Left unexamined is the way collaboration networks affect the science publication, and vice versa. The literature suggests that collaboration with top-tier universities produces more influential publications (Jones et al., 2008). If any significant association exists between collaboration and publication, supportive policies can be proposed to improve the science competitiveness of Korean researchers.

In spite of the above-mentioned weaknesses, this dissertation contributes to the literature in several ways. By using a comprehensive and exclusive longitudinal data set of publications at the institutional level that had never before been created and examined, it contributes to the understanding of the expansion of science production in Korea, changes in publication characteristics, the role of a government funding project in enhancing science productivity, and a formation of scientific collaboration among Korean universities and other countries. Given the relatively short history of the Korean higher education system, this dissertation provides a landscape of how science production has been developed in Korea since its beginning.

Appendix A. List of Korean Universities with Their Key Characteristics id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

1 Ajou University 아주대학교 1973 X X RA 16,032,000,000 22,798,866,432

2 Andong National University 안동대학교 1991 G 1,924,000,000 1,347,792,640

3 Anyang University 안양대학교 1991 X X G - -

4 Baekseok University 백석대학교 1994 X G - -

5 Busan University of Foreign 부산외국어대학교 1981 X G - - Studies 6 CHA University 차의과학대학교 1996 X X G - -

7 Catholic Kwandong University 가톨릭관동대학교 1955 X G - -

8 Catholic University of Korea 가톨릭대학교 1947 X X RA 1,772,999,936 6,634,120,192

9 Catholic University of Pusan 부산가톨릭대학교 1990 X G - -

10 Changwon National University 창원대학교 1979 D 13,697,999,872 10,414,248,960

11 Cheongju University 청주대학교 1947 X G - 1,844,013,184

12 Chodang University 초당대학교 1995 X G - -

13 Chonbuk National University 전북대학교 1951 RA 23,701,000,192 25,039,169,536

14 Chonnam National University 전남대학교 1951 RA 23,715,000,320 44,083,118,080

15 Chosun University 조선대학교 1948 X D 11,086,999,552 12,166,433,792

16 Chung-Ang University 중앙대학교 1953 X X RA 1,932,000,000 9,247,849,472

37 University missions are classified into four groups (Shin, 2009b): research (R), research active (RA), doctoral (D), and general (G) universities.

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

17 Chungbuk National University 충북대학교 1951 RA 16,556,000,256 31,760,982,016

18 Chungnam National University 충남대학교 1952 RA 17,868,001,280 41,629,233,152

19 Daegu Catholic University 대구가톨릭대학교 1995 X D 2,467,000,064 1,476,532,736

20 Daegu Haany University 대구한의대학교 1980 X G - 610,224,576

21 Daegu University 대구대학교 1956 X G 5,164,000,256 2,228,269,568

22 Daejeon University 대전대학교 1980 X G - -

23 Daejin University 대진대학교 1991 X X G - -

24 Dankook University 단국대학교 1947 X X D 1,248,999,936 4,245,856,256

25 Dong-Eui University 동의대학교 1979 X D 2,916,999,936 718,177,536

26 Donga University 동아대학교 1946 X D 11,079,000,064 7,345,414,144

27 Dongduk Women's University 동덕여자대학교 1950 X X G 425,000,000 -

28 Dongguk University 동국대학교 1946 X X D 1,836,000,000 569,683,712

29 Dongseo University 동서대학교 1992 X G - 2,539,830,272

30 Dongshin University 동신대학교 1986 X G - -

31 Dongyang University 동양대학교 1994 X G - -

32 Duksung Women's University 덕성여자대학교 1950 X X G - -

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

33 Eulji University 을지대학교 1967 X G - -

34 Ewha Womans University 이화여자대학교 1946 X X RA 13,253,999,616 27,103,881,216

35 Far East University 극동대학교 1997 X G - -

36 Gachon University 가천대학교 1981 X X G - 1,560,136,064

37 Gangneung-Wonju National 강릉원주대학교 1979 G 3,291,000,064 1,879,403,520 University 38 Gimcheon University 김천대학교 2010 X G - -

39 Gwangju University 광주대학교 1983 X G - -

40 Gyeongju University 경주대학교 1987 X G - -

41 Gyeongnam National University 경남과학기술대학교 1993 G - - of Science and Technology 42 Gyeongsang National University 경상대학교 1948 D 23,743,000,576 24,958,930,944

43 Halla University 한라대학교 1997 X G - -

44 Hallym University 한림대학교 1982 X D 5,900,000,256 2,601,600,000

45 Hanbat National University 한밭대학교 1984 G 10,053,000,192 1,512,648,960

46 Handong Global University 한동대학교 1994 X G - -

47 Hankuk University of Foreign 한국외국어대학교 1954 X X G - - Studies 48 Hankyong National University 한경대학교 1993 X G - 709,752,000

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

49 Hanlyo University 한려대학교 1994 X G - -

50 Hannam University 한남대학교 1956 X G 3,928,000,000 3,771,140,096

51 Hansei University 한세대학교 1993 X X G - -

52 Hanseo University 한서대학교 1991 X G - -

53 Hanshin University 한신대학교 1980 X X G - -

54 Hansung University 한성대학교 1972 X X G - -

55 Hanyang University 한양대학교 1948 X X R 25,137,000,448 86,380,666,880

56 Honam University 호남대학교 1981 X G - -

57 Hongik University 홍익대학교 1949 X X D 2,879,000,064 2,461,243,136

58 Hoseo University 호서대학교 1980 X G - 3,073,043,968

59 Hyupsung University 협성대학교 1983 X X G - -

60 Incheon National University 인천대학교 1979 X G - 1,350,289,408

61 Inha University 인하대학교 1954 X X RA 9,187,999,744 30,257,457,152

62 Inje University 인제대학교 1979 X D 5,025,999,872 5,872,825,856

63 International University of Korea 한국국제대학교 2003 X G - -

64 Jeju International University 제주국제대학교 2012 X G - -

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

65 Jeju National University 제주대학교 1952 D 9,676,999,680 4,335,864,320

66 Jeonju University 전주대학교 1953 X G - -

67 Jesus University 예수대학교 2003 X G - -

68 Joongbu University 중부대학교 1993 X G - -

69 Jungwon University 중원대학교 2009 X G - -

70 KC University 케이씨대학교 1966 X X G - -

71 Kangnam University 강남대학교 1948 X X G - -

72 Kangwon National University 강원대학교 1970 D 14,682,000,384 15,476,918,272

73 Kaya University 가야대학교 1993 X G - -

74 Keimyung University 계명대학교 1954 X D 3,601,999,872 246,556,000

75 Kkottongnae University 꽃동네대학교 1998 X G - -

76 Kongju National University 공주대학교 1948 G 2,944,000,000 3,991,618,816

77 Konkuk University 건국대학교 1946 X X RA - 22,790,352,896

78 Konyang University 건양대학교 1990 X G - -

79 Kookmin University 국민대학교 1946 X X D 7,834,999,808 2,952,599,296

80 Korea Advanced Institute of 한국과학기술원 1971 R 88,722,997,248 69,307,375,616 Science and Technology

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

81 Korea Aerospace University 한국항공대학교 1952 X G - 793,376,768

82 Korea Maritime and Ocean 한국해양대학교 1946 G 4,433,999,872 2,438,411,264 University 83 Korea National University of 한국교통대학교 1962 G 5,576,999,936 - Transportation 84 Korea Nazarene University 나사렛대학교 1996 X G - -

85 Korea Polytechnic University 한국산업기술대학교 1998 X X G - -

86 Korea University 고려대학교 1946 X X R 42,685,001,728 96,462,553,088

87 Korea University of Technology 한국기술교육대학교 1991 X G 3,348,000,000 839,268,672 & Education 88 Korean Bible University 한국성서대학교 1997 X X G - -

89 Kosin University 고신대학교 1946 X G - -

90 Kumoh National Institute of 금오공과대학교 1980 G 8,315,000,320 - Technology 91 KunsanNational University 군산대학교 1979 G 4,775,000,064 3,188,539,904

92 Kwangju Women's University 광주여자대학교 1997 X G - -

93 Kwangwoon University 광운대학교 1963 X X D 2,209,999,872 4,585,040,896

94 Kyonggi University 경기대학교 1947 X X G 234,000,000 -

95 Kyung Hee University 경희대학교 1951 X X RA 11,489,999,872 36,413,313,024

96 Kyungdong University 경동대학교 1997 X G - -

100

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

97 Kyungil University 경일대학교 1996 X G - -

98 Kyungnam University 경남대학교 1946 X G 4,366,000,128 2,228,141,824

99 Kyungpook National University 경북대학교 1951 RA 40,579,997,696 60,371,431,424

100 Kyungsung University 경성대학교 1988 X G - -

101 Kyungwoon University 경운대학교 1997 X G - -

102 Mokpo Catholic University 목포가톨릭대학교 2000 X G - -

103 Mokpo National Maritime 목포해양대학교 1993 G - - University 104 Mokpo National University 목포대학교 1946 G 7,468,999,680 -

105 Mokwon University 목원대학교 1965 X G - -

106 Myongji University 명지대학교 1948 X X D 1,890,000,000 8,628,989,952

107 Nambu University 남부대학교 1999 X G - -

108 Namseoul University 남서울대학교 1994 X G - -

109 Pai Chai University 배재대학교 1981 X G 3,464,000,000 -

110 Pohang University of Science 포항공과대학교 1986 X R 57,192,001,536 78,068,793,344 and Technology 111 Pukyong National University 부경대학교 1996 D 21,465,999,360 9,763,200,000

112 Pusan National University 부산대학교 1946 RA 20,625,999,872 81,306,378,240

101

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

113 Pyeongtaek University 평택대학교 1990 X X G - -

114 Sahmyook University 삼육대학교 1962 X X G - -

115 Sangji University 상지대학교 1974 X G - -

116 Sangmyung University 상명대학교 1965 X X G - -

117 Sehan University 세한대학교 1994 X G - -

118 Sejong University 세종대학교 1978 X X D 406,000,000 8,896,274,432

119 Semyung University 세명대학교 1990 X G - -

120 Seokyeong University 서경대학교 1992 X X G - -

121 Seoul Hanyoung University 서울한영대학교 1996 X X G - -

122 Seoul National University 서울대학교 1946 X R 436,730,986,496 266,542,071,808

123 Seoul National University of 서울과학기술대학교 1982 X G - - Science and Technology 124 Seoul Women's University 서울여자대학교 1961 X X G 315,000,000 -

125 Seowon University 서원대학교 1992 X G - -

126 Shingyeong University 신경대학교 2004 X X G - -

127 Silla University 신라대학교 1969 X G - -

128 Sogang University 서강대학교 1960 X X D 4,582,000,128 18,586,931,200

102

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

129 Songwon University 송원대학교 2012 X G - -

130 Sookmyung Women's University 숙명여자대학교 1948 X X D - 5,183,032,832

131 Soon Chun Hyang University 순천향대학교 1978 X D 5,113,999,872 1,404,356,352

132 Soongsil University 숭실대학교 1954 X X D 1,903,000,064 4,403,160,576

133 Sun Moon University 선문대학교 1990 X G - 1,193,019,264

134 Sunchon National University 순천대학교 1982 G 6,841,999,872 1,548,864,128

135 Sungkonghoe University 성공회대학교 1992 X X G - -

136 Sungkyul University 성결대학교 1991 X X G - -

137 Sungkyunkwan University 성균관대학교 1946 X X R 24,364,001,280 66,532,950,016

138 Sungshin Women's University 성신여자대학교 1965 X X G - -

139 Tongmyong University 동명대학교 1996 X G - -

140 U1 University 유원대학교 1993 X G - -

141 Uiduk University 위덕대학교 1996 X G - -

142 Ulsan University 울산대학교 1969 X RA 5,299,999,744 10,609,920,000

143 University of Seoul 서울시립대학교 1973 X D 527,000,000 12,808,693,760

144 University of Suwon 수원대학교 1981 X X G - -

103

id university in Korean founded Seoul private mission BK 21 fund BK 21 fund in + 37 (in KRW, phase I) (in KRW, phase II)

145 Wonkwang University 원광대학교 1951 X D 11,125,000,192 3,564,500,224

146 Woosong University 우송대학교 1995 X G - -

147 Woosuk University 우석대학교 1979 X G 291,000,000 -

148 Yeungnam University 영남대학교 1967 X D 22,261,000,192 8,263,121,408

149 Yonsei University 연세대학교 1957 X X R 50,841,001,984 119,573,684,224

150 Youngsan University 영산대학교 1997 X G - -

104

Appendix B. Summary of Science Production in Korean Universities and Collaborating Countries, 1970–2017

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Ajou University - - - 3 8 60 255 480 739 1,048 1,076 12,574 12,710 98.9% Andong National University - - - - 3 13 45 117 137 164 122 2,381 2,415 98.6% Anyang University ------8 21 23 35 29 372 Baekseok University ------1 39 2 33 10 232 Busan University of Foreign - - - - - 1 3 13 6 2 2 98 Studies CHA University ------36 100 261 369 433 3,780 Catholic Kwandong University ------11 40 135 217 207 2,022 1,969 102.7% Catholic University of Korea - 1 2 3 6 36 225 459 1,012 1,662 1,615 17,051 17,188 99.2% Catholic University of Pusan - - - - 5 8 1 8 23 45 56 401 Changwon National University - - - - 3 11 53 127 218 228 158 3,211 3,263 98.4% Cheongju University - - - - 4 9 30 50 51 91 118 1,308 1,234 106.0% Chodang University ------5 9 11 12 144 Chonbuk National University - - 1 14 11 82 218 610 1,073 1,607 1,597 18,040 18,275 98.7% Chonnam National University - - 1 6 22 119 399 815 1,315 1,928 1,933 22,628 22,830 99.1% Chosun University - - - 1 4 29 101 330 513 593 590 8,030 8,246 97.4% Chung-Ang University - - - 1 11 44 180 429 913 1,400 1,647 14,336 14,425 99.4% Chungbuk National University - - - 7 16 74 234 542 661 905 926 12,011 12,150 98.9% Chungnam National University - - - 9 17 107 320 730 1,246 1,466 1,510 18,966 19,167 99.0% Daegu Catholic University - - - - 1 22 50 116 242 337 322 3,750 3,730 100.5% Daegu Haany University - - - - - 1 3 36 60 98 125 1,035 Daegu University - - - - 2 15 30 87 172 254 206 2,733 2,392 114.3% Daejeon University - - - - - 8 12 46 44 112 117 1,217 1,032 117.9% Daejin University ------21 32 49 35 38 628

105

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Dankook University - - - 4 1 36 132 243 436 929 844 8,552 8,619 99.2% Dong-Eui University - - - - 4 7 46 112 207 168 131 2,784 2,775 100.3% Donga University - - - 2 9 34 81 230 370 489 440 6,257 6,313 99.1% Dongduk Women's University - - - - - 2 8 25 27 28 36 526 Dongguk University 1 - - - 7 42 102 249 507 939 1,054 9,194 9,281 99.1% Dongseo University - - - - - 8 8 38 31 47 43 705 Dongshin University - - - - 3 3 28 36 31 47 38 676 Dongyang University ------7 5 9 13 5 174 Duksung Women's University - - - 1 2 13 16 35 43 72 60 852 Eulji University ------20 50 200 312 271 2,848 2,959 96.2% Ewha Womans University - - - 4 7 68 233 509 840 1,194 1,214 14,232 14,436 98.6% Far East University ------4 8 25 14 180 Gachon University - - - - - 8 17 42 363 948 972 6,796 7,321 92.8% Gangneung-Wonju National - - - 1 2 29 46 135 168 249 225 2,893 2,711 106.7% University Gimcheon University - - - - - 1 3 1 1 15 9 88 Gwangju University ------3 8 15 20 18 218 Gyeongju University ------4 5 2 6 1 75 Gyeongnam National University ------4 5 2 85 100 597 of Science and Technology Gyeongsang National University - - 1 - 14 76 168 430 803 956 975 12,572 12,975 96.9% Halla University ------2 12 10 13 10 200 Hallym University - - - 3 4 38 163 263 481 901 1,005 9,238 9,307 99.3% Hanbat National University ------104 138 160 190 2,221 2,204 100.8% Handong Global University - - - - - 1 7 9 13 33 42 355 Hankuk University of Foreign - - - - 2 10 40 47 90 160 152 1,671 1,677 99.6% Studies

106

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Hankyong National University ------11 38 62 116 117 1,188 1,095 108.5% Hanlyo University ------6 2 1 13 8 73 Hannam University - - - 1 3 20 56 102 139 113 107 2,100 1,975 106.3% Hansei University ------2 3 4 3 54 Hanseo University - - - - - 3 24 52 64 102 53 1,144 1,131 101.1% Hanshin University ------2 19 10 7 12 158 Hansung University - - - - - 5 6 17 19 13 18 325 Hanyang University - 1 - 13 26 137 588 1,307 1,761 2,699 2,825 33,579 33,931 99.0% Honam University - - - - - 1 6 39 17 34 28 373 Hongik University - - - 1 - 35 99 139 244 319 391 4,073 4,128 98.7% Hoseo University - - - - - 5 31 51 158 135 130 2,074 2,080 99.7% Hyupsung University ------5 2 5 32 Incheon National University - - - - - 7 31 86 94 357 429 2,836 2,763 102.6% Inha University - 1 1 11 28 92 424 830 1,087 1,254 1,277 18,300 18,710 97.8% Inje University - - - - 3 26 86 285 570 949 958 9,807 9,954 98.5% International University of ------15 16 16 22 218 Korea Jeju International University ------1 1 9 Jeju National University - - - 1 4 14 63 202 413 546 502 6,115 6,095 100.3% Jeonju University - - - - 5 9 26 38 36 57 65 908 911 99.7% Jesus University ------1 4 Joongbu University ------1 12 26 21 32 300 Jungwon University ------13 44 34 289 KC University ------1 - 1 3 Kangnam University ------2 12 7 10 15 163 Kangwon National University - - - 1 17 53 192 364 763 1,087 1,122 12,250 12,397 98.8%

107

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Kaya University ------2 2 1 8 7 68 Keimyung University - - - 1 2 33 79 140 250 458 490 4,857 4,906 99.0% Kkottongnae University ------1 2 1 9 Kongju National University - - - - - 10 41 102 254 337 357 3,842 3,846 99.9% Konkuk University - - - 1 5 52 150 494 1,112 1,582 1,513 16,860 16,982 99.3% Konyang University - - - - - 3 37 38 106 199 188 1,824 1,796 101.6% Kookmin University - - - - 4 11 64 154 227 311 337 3,957 4,002 98.9% Korea Advanced Institute of - 3 33 198 353 803 1,312 1,563 2,020 2,488 2,540 43,237 44,319 97.6% Science and Technology Korea Aerospace University - - - - - 4 12 86 62 152 133 1,471 1,030 142.8% Korea Maritime and Ocean - - 1 2 5 16 21 87 123 162 144 1,951 1,847 105.6% University Korea National University of - - - - 1 - 9 42 139 190 223 1,924 1,016 189.4% Transportation Korea Nazarene University ------4 1 23 4 129 Korea Polytechnic University ------5 24 39 71 103 733 757 96.8% Korea University - 1 7 21 47 215 630 1,695 2,584 3,601 3,902 44,321 44,812 98.9% Korea University of Technology ------8 51 45 98 96 1,031 1,074 96.0% & Education Korean Bible University ------2 4 9 3 76 Kosin University - - - 2 6 8 17 28 64 141 175 1,378 1,215 113.4% Kumoh National Institute of - - - - - 10 32 4 1 5 3 317 Technology Kunsan National University - - - 2 - 11 71 123 124 185 195 2,381 2,342 101.7% Kwangju Women's University ------1 - 1 11 4 75 Kwangwoon University - - - - 2 35 95 158 240 377 427 4,819 4,896 98.4% Kyonggi University - - - - - 7 33 71 117 193 234 2,165 2,113 102.5% Kyung Hee University - 1 1 4 14 86 257 649 1,724 2,358 2,441 25,002 25,242 99.0% Kyungdong University ------1 - - 21 20 91

108

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Kyungil University - - - - - 8 22 31 32 72 68 861 808 106.6% Kyungnam University - - - - 4 6 36 82 95 81 71 1,557 1,503 103.6% Kyungpook National University - 5 1 12 39 127 489 1,079 1,603 2,125 2,313 26,887 27,073 99.3% Kyungsung University - - - - 1 27 50 58 53 75 63 1,348 1,331 101.3% Kyungwoon University ------6 9 7 16 11 210 Mokpo Catholic University ------1 - - 12 Mokpo National Maritime ------3 13 14 18 17 275 University Mokpo National University - - - 3 - 5 24 74 93 177 187 1,889 1,889 100.0% Mokwon University - - - - - 3 12 34 32 49 32 647 643 100.6% Myongji University - - - - 4 18 80 139 178 301 304 3,682 3,701 99.5% Nambu University ------3 15 14 33 23 286 Namseoul University ------2 23 62 22 347 Pai Chai University - - - - - 15 45 50 41 49 28 942 942 100.0% Pohang University of Science - - - - 2 238 635 904 1,233 1,496 1,475 23,172 24,465 94.7% and Technology Pukyong National University - - - - - 2 132 344 555 678 774 8,864 9,030 98.2% Pusan National University - - 2 11 33 179 447 934 1,550 2,194 2,192 26,549 26,087 101.8% Pyeongtaek University ------2 2 - 6 6 34 Sahmyook University - - - - - 2 4 15 40 134 78 830 834 99.5% Sangji University - - - - - 4 9 28 40 86 70 890 836 106.5% Sangmyung University - - - 1 1 8 18 49 95 116 117 1,469 1,421 103.4% Sehan University ------5 17 11 9 4 199 Sejong University - - - - - 8 84 388 402 628 871 7,833 7,916 99.0% Semyung University - - - - - 3 8 41 46 73 59 817 812 100.6% Seokyeong University - - - - - 5 8 34 27 14 11 307 Seoul Hanyoung University ------2

109

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Seoul National University - 6 27 88 235 838 2,266 3,783 5,427 6,969 7,511 97,620 98,840 98.8% Seoul National University of - - - - - 4 24 84 234 339 388 3,383 2,268 149.2% Science and Technology Seoul Women's University - - - - 1 5 17 50 68 96 86 1,111 Seowon University - - - - - 2 6 9 8 44 34 325 Shingyeong University ------3 1 3 3 26 Silla University ------14 50 60 87 70 1,020 Sogang University - 2 - 10 22 78 169 243 419 555 514 7,726 7,845 98.5% Songwon University ------2 Sookmyung Women's University - 1 - 2 3 17 54 96 148 188 234 2,685 2,701 99.4% Soon Chun Hyang University - - - 1 5 29 72 163 371 653 730 6,448 6,495 99.3% Soongsil University - - 1 2 2 21 56 181 226 349 344 4,000 4,067 98.4% Sun Moon University - - - - - 8 78 86 95 115 105 1,926 1,932 99.7% Sunchon National University - - - - 5 8 45 103 169 279 254 3,244 3,284 98.8% Sungkonghoe University ------1 1 1 - 18 Sungkyul University ------1 14 7 32 31 225 Sungkyunkwan University - - - 7 20 69 621 1,579 2,399 3,942 4,042 42,209 43,075 98.0% Sungshin Women's University - - - 2 - 2 11 14 52 64 76 736 714 103.1% Tongmyong University ------2 24 17 37 44 385 U1 University ------4 6 Uiduk University ------13 13 12 30 16 273 Ulsan University - - - 1 12 56 295 568 1,159 1,931 2,046 20,061 21,077 95.2% University of Seoul - - 1 1 2 12 78 172 290 415 484 4,907 4,953 99.1% University of Suwon - - - - - 17 42 83 56 83 94 1,380 1,250 110.4% Wonkwang University - - - - 5 46 133 230 342 405 451 5,744 5,692 100.9% Woosong University ------4 18 23 35 18 316 299 105.7%

110

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Woosuk University - - - - - 5 27 50 52 57 67 998 974 102.5% Yeungnam University - - 1 9 12 60 192 318 737 1,261 1,307 12,464 12,463 100.0% Yonsei University - 7 13 27 71 233 1,011 1,985 3,262 4,410 4,472 54,385 55,057 98.8% Youngsan University ------1 14 2 26 21 169 Junior colleges - - 1 4 8 50 265 625 828 1,871 2,038 17,758 Research institutions 1 8 7 28 277 701 2,364 4,613 6,886 10,874 11,937 130,479 Hospitals - - 8 3 10 42 203 470 1,151 2,126 2,378 20,601 Corporations - 2 2 14 61 419 1,165 2,544 3,740 4,667 4,974 63,821 others - - 3 2 5 50 304 1,152 1,957 2,561 2,838 30,418 Afghanistan ------4 3 14 Angola ------4 Anguilla ------8 12 Albania ------1 - 3 5 13 Netherlands Antilles ------1 - 2 United Arab Emirates ------1 2 9 48 95 350 Argentina - - - - - 1 20 33 54 51 52 795 Armenia ------2 9 51 120 169 1,107 Australia - - - - 1 20 66 186 335 728 855 6,969 Austria - - - - 2 22 5 68 109 221 316 2,428 Azerbaijan ------3 5 4 16 57 Burundi ------4 4 Belgium - 1 1 - - 4 14 53 120 296 410 2,693 Benin ------5 13 35 Burkina Faso ------1 - 4 7 29 Bangladesh - - - - - 1 1 9 57 112 125 867

111

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Bulgaria - - - - - 14 27 20 46 113 142 1,145 Bahrain - - - - - 1 - - - 7 10 40 Bahamas ------1 Bosnia and Herzegovina ------1 2 7 22 Belarus - - - - - 2 6 11 45 90 136 888 Belize ------3 1 7 Bermuda ------1 - - - - 2 Bolivia - - - - - 1 - - - 2 4 16 Brazil - - - - - 16 28 77 157 296 401 3,019 Barbados ------2 3 12 Brunei Darussalam ------14 8 46 Bhutan ------9 11 Botswana ------1 - 5 3 14 Canada - 1 2 3 8 46 175 405 584 755 862 9,647 Switzerland - - - 2 1 36 72 163 275 471 579 5,277 Chile - - - 1 - 1 3 16 52 108 152 904 China - - - 4 11 64 226 656 1,454 2,550 3,266 25,624 Cote d'Ivoire ------6 4 32 Cameroon ------4 7 20 90 Congo, The Democratic ------1 4 11 27 Republic of Congo ------2 Colombia - - - - - 13 20 30 69 143 174 1,434 Comoros ------1 1 Costa Rica - - - - 1 - - 3 3 4 13 71 Cuba ------1 9 27 34 198

112

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Cayman Islands ------1 1 3 Cyprus - - - - - 13 27 10 34 90 136 962 Czech Republic - - - - - 3 13 52 136 264 337 2,494 Germany - - 2 12 10 95 207 406 728 1,255 1,491 13,874 Dominica ------1 - 2 Denmark - - - 1 2 21 13 30 76 232 258 1,785 Dominican Republic ------1 - - 2 1 5 Algeria ------8 12 31 113 Ecuador - - - - - 1 17 27 28 22 127 591 Egypt - - - - - 1 5 19 65 277 371 2,060 Eritrea ------1 Spain - - - - - 54 90 117 247 571 681 5,395 Estonia ------1 35 105 149 794 Ethiopia ------21 57 148 Finland - - - 1 - 15 37 57 121 264 314 2,526 Fiji ------1 2 5 7 6 46 France - - 1 3 15 67 140 244 492 753 950 8,851 Micronesia, Federated States of ------10 16 Gabon ------1 1 7 United Kingdom - - 1 5 3 81 188 411 650 1,123 1,421 12,616 Georgia ------2 33 92 133 736 Ghana ------4 14 28 96 Guinea ------1 2 Guadeloupe ------2 - - 4 Gambia ------3 5 3 25

113

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Guinea-Bissau ------3 2 14 Greece - - - - - 16 2 17 124 206 240 2,005 Grenada ------1 1 3 Greenland ------2 1 5 Guatemala ------2 3 1 15 French Guiana ------2 Guam ------1 - 1 Hong Kong - - - - 2 5 - - - - - 48 Honduras ------1 - - 8 Croatia ------3 11 69 153 177 1,277 Haiti ------3 Hungary - - - - - 15 42 56 75 190 295 2,112 Indonesia - - - 1 1 3 2 22 38 84 141 808 India - - - - - 31 69 299 752 1,431 1,584 13,127 Ireland ------3 51 82 149 200 1,373 Iran, Islamic Republic of - - - - - 2 1 14 112 317 406 2,332 Iraq ------17 27 92 Iceland ------5 12 23 29 155 Israel - - - - 1 23 28 59 97 156 188 1,708 Italy - - - - 1 74 99 174 313 699 860 6,909 Jamaica ------5 8 26 Jordan - - - - - 1 - 2 6 14 30 117 Japan 1 4 11 37 71 265 654 1,275 1,593 1,829 2,057 29,496 Kazakstan ------12 18 9 32 234 Kenya ------5 21 32 125

114

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Kyrgyzstan ------1 1 12 20 Cambodia ------7 7 15 91 Kuwait ------2 2 2 9 16 73 Lao, People's Democratic ------2 7 6 44 Republic Lebanon - - - - - 1 - 1 6 20 28 117 Liberia ------2 Libyan Arab Jamahiriya ------1 3 2 10 Saint Lucia ------1 - 4 Liechtenstein ------1 - - 5 Sri Lanka ------8 108 158 562 Lesotho ------1 Lithuania - - - - - 1 - 2 38 93 137 762 Luxembourg ------1 1 37 31 153 Latvia - - - - - 1 1 2 1 15 118 213 Macao ------1 Saint-Martin (French part) ------1 1 Morocco ------2 5 16 17 106 Monaco ------2 1 1 13 34 Moldova, Republic of ------2 3 6 42 Madagascar ------1 2 2 4 20 Maldives ------1 Mexico - - - - 1 15 25 66 122 232 291 2,325 Macedonia, The Former ------1 - - 3 2 25 Yugoslav Republic of Mali ------1 2 1 1 10 Malta ------1 - 3 1 12

115

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Myanmar ------1 4 14 23 91 Montenegro ------2 1 2 10 Mongolia ------4 16 33 59 295 Mozambique ------2 3 1 10 34 Mauritius ------1 - 1 7 14 Malawi ------4 9 27 Malaysia - - - - - 4 6 22 77 266 343 1,854 Namibia ------2 3 8 New Caledonia ------2 - 3 6 27 Niger ------1 - 7 Nigeria ------2 2 6 23 41 156 Nicaragua ------1 - - 2 Netherlands - - 1 - 1 43 61 106 188 327 468 3,889 Norway - - - - - 3 1 16 57 122 181 1,075 Nepal ------2 5 12 36 60 346 New Zealand - - - - - 4 9 43 82 189 258 1,737 Oman ------2 57 50 203 Pakistan ------2 14 112 420 653 3,031 Panama ------3 4 13 52 Peru - - - - - 1 1 1 7 45 56 293 Philippines - - - - 10 2 3 17 38 91 117 807 Palau ------1 Papua New Guinea ------2 1 4 5 21 Poland - - - - - 25 70 116 182 303 441 3,778 Korea, Democratic People's ------1 2 6 Republic of

116

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Portugal ------6 15 74 168 247 1,379 Paraguay - - - - 1 - - - 1 7 6 30 Palestine ------10 13 French Polynesia ------1 2 1 1 17 Qatar ------7 136 164 677 Reunion ------1 - - 2 Romania - - - - - 14 34 30 39 83 100 980 Russia Federation - - - - 1 89 170 381 328 504 683 7,611 Rwanda ------5 11 27 Saudi Arabia - - - - 1 - - 2 59 485 495 2,535 Serbia and Montenegro ------1 7 - - - 26 Sudan ------6 11 37 Senegal ------3 1 3 17 Singapore - - - - - 5 20 52 186 367 462 3,047 Solomon Islands ------2 - - 3 9 Sierra Leone ------1 1 4 El Salvador ------2 1 4 San Marino ------1 - 2 Serbia ------40 127 169 971 Suriname ------2 3 Slovakia - - - - - 1 1 7 64 70 65 761 Slovenia ------4 49 43 57 62 734 Sweden - - - 1 - 12 20 97 147 312 403 3,156 Swaziland ------1 1 5 Seychelles ------2 3 9

117

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Syrian Arab Republic ------1 2 6 2 19 Chad ------1 Togo ------5 Thailand - - - - 3 4 12 30 92 285 373 2,222 Tajikistan ------3 3 7 Tonga ------1 4 Trinidad and Tobago ------4 3 17 Tunisia ------1 1 5 17 23 115 Turkey - - - - - 2 6 15 86 269 359 2,069 Taiwan, Republic of China - - 1 1 3 18 88 199 337 506 610 5,797 Tanzania, United Republic of ------5 24 26 150 Uganda ------1 3 9 16 56 Ukraine - - - - - 4 13 29 78 165 208 1,840 Uruguay ------1 - - 11 14 57 United States - 11 28 72 193 744 1,775 3,492 5,359 7,444 7,762 95,688 Uzbekistan ------3 12 9 13 12 212 Holy See (Vatican City State) ------1 4 8 Saint Vincent and the ------1 - 1 Grenadines Venezuela ------3 5 9 12 93 Vietnam - - - - - 2 6 48 148 373 469 2,820 Vanuatu ------1 3 Samoa ------1 - - - 1 Kosovo ------1 - 2 Yemen ------1 7 11 South Africa - - - - - 1 5 16 29 108 136 877

118

Korean University / 1970 - 2017 Analysis in 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 A/B Collaborating Country (A) WOS (B) Zambia ------1 - 4 10 30 Zimbabwe ------2 15

119

References

Abbasi, A., Hossain, L., Uddin, S., & Rasmussen, K. J. (2011). Evolutionary dynamics of

scientific collaboration networks: multi-levels and cross-time analysis.

Scientometrics, 89(2), 687-710.

Abramo, G., Andrea D'Angelo, C., & Di Costa, F. (2008). Assessment of sectoral aggregation

distortion in research productivity measurements. Research Evaluation, 17(2), 111-121.

Adams, J. (2011). Thomson Reuters Global Research Report: United Kingdom. UK, Leeds:

Evidence.

Adams, J., Pendlebury, D., & Stembridge, B. (2013). Building BRICKS: Exploring the global

research and innovation impact of Brazil, Russia, India, China and South Korea.

Philadelphia, PA: Thomson Reuters.

Ahn, B. Y., & Ha, Y. S. (2015). 5.31 교육개혁 그리고 20 년: 한국교육의 패러다임 전환

[5.31 Education reform and 20 years: paradigm shift in Korean education]. Seoul, South

Korea: Hakjisa.

Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's

companion. Princeton University Press.

Barabâsi, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of

the social network of scientific collaborations. Physica A: Statistical mechanics and its

applications, 311(3), 590-614.

Borgatti, S. P., Everett, M. E., & Johnson, J. C. (2013). Analyzing social networks. London, UK:

Sage Publications.

Cellini, S. R. (2008). Causal inference and omitted variable bias in financial aid research:

Assessing solutions. The Review of Higher Education, 31(3), 329-354.

120

Chang, D. F., Wu, C. T., Ching, G. S., & Tang, C. W. (2009). An evaluation of the dynamics of

the plan to develop first-class universities and top-level research centers in Taiwan. Asia

Pacific Education Review, 10(1), 47-57.

Chinese Ministry of Finance. (2010). 财政部敎育部关于印发《“985 工程”专项资金

管理办法》的通知 [Finance Ministry and Education Ministry Joint Announcement on

the Management of the “985 Project funds”]. Retrieved from

http://www.mof.gov.cn/zhengwuxinxi/caizhengwengao/2010nianwengao/wengao11/201

101/t20110125_427937.html

Choung, J. Y., & Hwang, H. R. (2013). The evolutionary patterns of knowledge production in

Korea. Scientometrics, 94(2), 629–650.

Cole, J. R. and Cole, S. (1973). Social stratification in science. Chicago, IL: University of

Chicago Press.

Cronin, B. (2015). Getting started in social network analysis with NETDRAW [PDF file].

Retrieved from

https://gala.gre.ac.uk/id/eprint/16200/7/16200%20CRONIN_Getting_Started_in%20SNA

_with_NETDRAW_2015.pdf de Solla Price, D. J. (1963). Big science, little science. New York, NY: Columbia University.

Dong, Y., Ma, H., Shen, Z., & Wang, K. (2017, August). A century of science: Globalization of

scientific collaborations, citations, and innovations. In Proceedings of the 23rd ACM

SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1437-

1446). ACM.

Fernandez, F., & Baker, D. P. (2017). Science production in the United States: An unexpected

synergy between mass higher education and the super research university. In Powell, J. J.,

121

Baker, D. P., & Fernandez, F. (Eds.), The century of science: The global triumph of the

research university (pp. 85-111). Bingley, UK: Emerald Publishing Limited.

Fu, Y. C. (2017). Science production in Taiwanese universities, 1980–2011. In Powell, J. J.,

Baker, D. P., & Fernandez, F. (Eds.), The century of science: The global triumph of the

research university (pp. 173-203). Bingley, UK: Emerald Publishing Limited.

Fu, Y. C., Baker, D. P., & Zhang, L. (2018). Engineering a world class university? The impact of

Taiwan’s World Class University Project on scientific productivity. Higher Education

Policy, 1-16.

Geiger, R. (2017). Foreword. In Powell, J. J., Baker, D. P., & Fernandez, F. (Eds.), The century

of science: The global triumph of the research university (pp. xiii-xvi). Bingley, UK:

Emerald Publishing Limited.

German Research Foundation. (2013). Excellence initiative at a glance. Retrieved from

https://www.dfg.de/download/pdf/dfg_im_profil/geschaeftsstelle/publikationen/exin_bros

chuere_en.pdf

Haas, B. (2018, September 3). South Korea’s fertility rate set to hit record low of 0.96. The

Guardian. Retrieved from https://www.theguardian.com/world/2018/sep/03/south-

koreas-fertility-rate-set-to-hit-record-low

Hagood, L. P. (2019). The financial benefits and burdens of performance funding in higher

education. Educational evaluation and policy analysis.

https://doi.org/10.3102/0162373719837318

Hicks, D. (2012). Performance-based university research funding systems. Research policy,

41(2), 251-261.

Jones, B. F., Wuchty, S., & Uzzi, B. (2008). Multi-university research teams: Shifting impact,

122

geography, and stratification in science. Science, 322(5905), 1259-1262.

Kim, G. J. (2002). Education policies and reform in South Korea, In Secondary education in

Africa: Strategies for renewal, (pp. 29-39). Washington, DC: World Bank.

Kim, Y. C. (2008). Universalization of tertiary education. Seoul, South Korea: Korean

Educational Development Institute.

Kim, J., Cho, W., Moon, M., Park, H., Cho, J. M., Park, J., & Song, J. (2015). Education for all

2015 national review: Republic of Korea. Paris, France: United Nations Educational,

Scientific, and Cultural Organization.

Kim, H., & Choi, J. (2017). The growth of higher education and science production in South

Korea since 1945. In Powell, J. J., Baker, D. P., & Fernandez, F. (Eds.), The century of

science: The global triumph of the research university (pp. 205-226). Bingley, UK:

Emerald Publishing Limited.

Korean Educational Development Institute [KEDI]. (1998). 한국의 교육비 조사연구 [Survey

on the educational expenditures in Korea]. Seoul, South Korea: Author.

Korean Educational Development Institute [KEDI]. (2018). University Statistics [Data file].

Retrieved from http://kess.kedi.re.kr

Langfeldt, L., Benner, M., Sivertsen, G., Kristiansen, E. H., Aksnes, D. W., Borlaug, S. B., ... &

Pelkonen, A. (2015). Excellence and growth dynamics: A comparative study of the

Matthew effect. Science and Public Policy, 42(5), 661-675.

Lee, C. J. (2005). Korean model of secondary education development: Approaches, outcomes

and emerging tasks. In WBI, “Seminar on Growth Strategies for Secondary Education in

Asia”, Kuala Lumpur, Malaysia.

Lee, H. H. (1999). 해방 후 고등교육정책의 흐름 [Development of the higher education policy

123

after liberation]. Higher Education, 100, 12-19.

Lee, S. J. (2008). National development strategy and education policy. Seoul, South Korea:

Korean Educational Development Institute.

Luukkonen, T., Persson, O., & Sivertsen, G. (1992). Understanding patterns of international

scientific collaboration. Science, technology, & human values, 17(1), 101-126.

Ma, W. (2007). The flagship university and China’s economic reform. In P. G. Altbach & J.

Balan (Eds.), World class worldwide: Transforming research universities in Asia and

Latin America, (pp. 31-53). Baltimore, MD: Johns Hopkins University Press.

Meyer, J. W., Ramirez, F. O., Frank, D. J., & Schofer, E. (2007). Higher education as an

institution. In Gumport, P. J. (Ed.), Sociology of higher education: Contributions and

their contexts (187-221). Baltimore, MD: JHU Press.

Mihai, I. A., & Reisz, R. D. (2017). STEM+ productivity, development, and wealth, 1900–2012.

In Powell, J. J., Baker, D. P., & Fernandez, F. (Eds.), The century of science: The global

triumph of the research university (pp. 249-276). Bingley, UK: Emerald Publishing

Limited.

Ministry of Education [MOE]. (2000). 두뇌한국 21(BK 21) 사업 [Brain Korea 21 (BK 21)

Project]. Seoul, South Korea: Ministry of Education.

Ministry of Education [MOE]. (2006). 2 단계 BK 21 사업 기본계획 [The Master Plan on the

BK 21 Project, Phase II]. Seoul, South Korea: Ministry of Education.

Ministry of Education [MOE]. (2018). 세계 수준의 연구중심대학 육성과 대학원

교육·연구역량 강화를 위한 공론화의 장 열려 [Public hearing held on the plan of BK

21]. Retrieved from

124

https://moe.go.kr/boardCnts/view.do?boardID=294&boardSeq=75957&lev=0&searchTy

pe=SC&statusYN=C&page=1&s=moe&m=020402&opType=N

Ministry of Education & Korean Educational Development Institute [MOE & KEDI]. (1998).

Statistical yearbook of education. Seoul, South Korea: Authors.

Ministry of Education and Korean Research Foundation [MOE & KRF]. (2006). 대학원

연구중심대학 육성 사업(BK 21) 백서 [White paper on the establishing world-class

graduate schools and research university project (BK 21) project]. Seoul, South Korea:

Authors.

Möller, T., Schmidt, M., & Hornbostel, S. (2016). Assessing the effects of the German

Excellence Initiative with bibliometric methods. Scientometrics, 109(3), 2217-2239.

National Research Foundation of Korea [NRFK]. (2013). 2 단계 BK 21 사업 백서 [White

Paper on Second Stage of Brain Korea 21]. Seoul, South Korea: Author

Newman, M. E. (2001a). Scientific collaboration networks. II. Shortest paths, weighted

networks, and centrality. Physical review E, 64(016132), 1-8.

Newman, M. E. (2001b). The structure of scientific collaboration networks. Proceedings of the

National Academy of Sciences, 98(2), 404-409.

Oh, S. B. (2004). 사립대학 팽창 과정 탐색： 해방 후 농지개혁기를 중심으로 [Exploration

of private university expansion process: Based on land reform after the liberation]. The

Journal of Korean Education. 31(3). 53–73.

Organization for Economic Cooperation and Development [OECD]. (2014). Promoting research

excellence: New approaches to funding. Paris, France: OECD Publishing.

Organization for Economic Cooperation and Development [OECD]. (2015). Education at a

glance 2015: OECD indicators. Paris: OECD Publishing. doi:10.1787/19991487

125

Organization for Economic Co-operation and Development [OECD]. (2018). Researchers Total,

Per 1,000 employed, 2000–2016 [Data file]. Retrieved from

https://data.oecd.org/rd/researchers.htm#indicator-chart

Pan, R. K., Kaski, K., & Fortunato, S. (2012). World citation and collaboration networks:

uncovering the role of geography in science. Scientific reports, 2: Article number 902, 1-

Patrick, W. J., & Stanley, E. C. (1998). Teaching and research quality indicators and the shaping

of higher education. Research in higher education, 39(1), 19-41.

Porter, S. R., & Toutkoushian, R. K. (2006). Institutional research productivity and the

connection to average student quality and overall reputation. Economics of education

review, 25(6), 605-617.

Powell, J. J., & Dusdal, J. (2017). The European center of science productivity: Research

universities and institutes in France, Germany, and the United Kingdom. In Powell, J. J.,

Baker, D. P., & Fernandez, F. (Eds.), The century of science: The global triumph of the

research university (pp. 55-83). Bingley, UK: Emerald Publishing Limited.

Powell, J. J., Fernandez, F., Crist, J. T., Dusdal, J., Zhang, L., & Baker, D. P. (2017).

Introduction: The worldwide triumph of the research university and globalizing science.

In Powell, J. J., Baker, D. P., & Fernandez, F. (Eds.), The century of science: The global

triumph of the research university (pp. 1-36). Bingley, UK: Emerald Publishing Limited.

Ramsden, P. (1999). Predicting institutional research performance from published indicators: A

test of a classification of Australian university types. Higher Education, 37(4), 341-358.

Schofer, E., & Meyer, J. W. (2005). The worldwide expansion of higher education in the

twentieth century. American sociological review, 70(6), 898-920.

126

Scott, J. (1991). Networks of corporate power: A comparative assessment. Annual review of

sociology, 17(1), 181-203.

Seong, S., Popper, S. W., Goldman, C. A., & Evans, D. K. (2008). Brain Korea 21 phase II: A

new evaluation model. Santa Monica, CA: Rand Corporation.

Shima, K. (2017). Changing science production in Japan: The expansion of competitive funds,

reduction of block grants, and unsung heroes. In Powell, J. J., Baker, D. P., & Fernandez,

F. (Eds.), The century of science: The global triumph of the research university (pp. 113-

140). Bingley, UK: Emerald Publishing Limited.

Shin, J. C. (2009a). Building world-class research university: The Brain Korea 21

project. Higher Education, 58(5), 669.

Shin, J. C. (2009b). Classifying higher education institutions in Korea: A performance-based

approach. Higher education, 57(2), 247-266.

Shin, J., & Cummings, W. (2010). Multilevel analysis of academic publishing across disciplines:

Research preference, collaboration, and time on research. Scientometrics, 85(2), 581-594.

Smeby, J. C., & Try, S. (2005). Departmental contexts and faculty research activity in

Norway. Research in higher education, 46(6), 593-619.

Sonnenwald, D. H. (2007). Scientific collaboration. Annual review of information science and

technology, 41(1), 643-681.

Tandberg, D. A., & Hillman, N. W. (2014). State higher education performance funding: Data,

outcomes, and policy implications. Journal of education rinance, 39(3), 222-243.

The Investments for the Future Programme. (n.d.). Retrieved from

https://www.utc.fr/en/research/projects-registered-with-the-investments-for-the-future-

incentive-programme/the-investments-for-the-future-programme.html

127

Torun, H., & Tumen, S. (2016). The effects of compulsory military service exemption on

education and labor market outcomes: Evidence from a natural experiment. Economics of

Education Review, 54, 16-35.

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. London,

UK: Cambridge University Press.

Web of Science [WOS]. (2018). The database. Retrieved from www.webofknowledge.com

Wooldridge, J. (2007). What’s new in econometrics? Lecture 10 difference-in-differences

estimation. NBER Summer Institute. Retrieved from https://www.nber.org/WNE/Slides7-

31-07/slides_10_diffindiffs.pdf

World Bank. (2018). Data. [Data file]. Retrieved from https://data.worldbank.org/indicator/

World Education News & Reviews. (2006). International rankings and Chinese higher

education reform. Retrieved from https://wenr.wes.org/2006/10/wenr-october-2006-

international-rankings-and-chinese-higher-education-reform

Yonezawa, A. (2003). Making “world-class universities.” Higher education management and

policy, 15(2), 9-23.

Yonezawa, A. (2007). Japanese flagship universities at a crossroads. Higher education, 54(4),

483-499.

Zhang, H., Patton, D., & Kenney, M. (2013). Building global-class universities: Assessing the

impact of the 985 Project. Research policy, 42(3), 765-775.

Zhang, L., Powell, J. J., & Baker, D. P. (2015). Exponential growth and the shifting global center

of gravity of science production, 1900–2011. Change: The magazine of higher

learning, 47(4), 46-49.

128

Zhang, L., Sun, L., & Bao, W. (2017). The rise of higher education and science in China.

In Powell, J. J., Baker, D. P., & Fernandez, F. (Eds.), The century of science: The global

triumph of the research university (pp. 141-172). Bingley, UK: Emerald Publishing

Limited.

6.25 와 전시연합대학 [Korean War and the Wartime Associated University]. (2004, January

15). Daily UNN. Retrieved from

http://www.unn.net/ColumnIssue/Univ50Detail.asp?idx=14&n4_page=1&n1_category=1

129

VITA

HYERIM KIM [email protected]

Education: B.A., Seoul National University, Seoul, South Korea, 2004 Chinese Language & Literature

Ph.D., The Pennsylvania State University, University Park, PA, USA, 2019 Education Theory and Policy

Professional Experience in Education & Human Development:

2013–2015 Senior Deputy Director, Ministry of Education, South Korea - Coordinated higher education policies across divisions in the ministry - Worked on the University Restructure Plan, and the Master Plan for Higher Education

2010–2013 Deputy Director, Ministry of Education, South Korea - Worked on the Comprehensive Plan to Deregulate Higher Education Institutions - Designed the absolute grading system in secondary schools - Managed a supporting program for underprivileged students in high schools

2007–2010 Associate Expert in Higher Education Asia-Pacific Programme of Educational Innovation for Development UNESCO Regional Bureau for Education in Asia-Pacific, Bangkok, Thailand - Coordinated and assisted the revision of the Asia-Pacific Regional Convention on the Recognition of Higher Education Qualifications - Organized the Asia-Pacific Regional Preparatory Conference for the 2009 World Conference on Higher Education

2004–2007 Deputy Director, Ministry of Education, South Korea - Worked on the authorization junior colleges to run degree granting programs - Organized international meetings including the Inaugural Korea-China-Japan Educational Director-General Meeting

Awards and Scholarships:

2017–2019 Teaching Assistantship, Educational Policy Studies, Penn State 2015–2017 Fulbright Graduate Study Award 2012 The Presidential Citation for Outstanding Performance, Government of Korea 2000–2003 Scholarship, Ilju Academy and Culture Foundation