Asian Economic Policy Review最新文献

Li et al. (2023) use a unique dataset collected from a large-scale survey conducted by the authors to study college students in China. The data provide many interesting and new descriptive statistics about college students, such as their technical certificates received, their internship experiences, and so on. Their study examines how different mechanisms, including human capital, social network, and signaling, help explain the returns to a college education.

The human capital measures include grade point average (GPA), technical certificates, etc. Social networks are measured by Communist Party membership, participation in a student union, parental income and education. Students' skills possessed before enrolling in college such as the type of college enrolled in are assumed to be related to signaling.

Li et al.'s main findings are: (i) human capital is the least useful in raising the starting salary; (ii) social networks are a strong predictor; and (iii) signaling matters. Li et al. suggest the need to reform the higher education system in China.

A novel contribution of Li et al. (2023) is their investigation of the channels explaining the labor market effects of higher education. It complements similar studies that use data from other countries with more sophisticated causality analyses. For example, Dale and Krueger (2002) compare students who attended more selective colleges and those who had seemingly comparable ability but chose to attend less-selective ones and found that they obtained similar earnings in general. Black and Smith (2006) measure college quality with multiple proxies and find that existing studies understated the wage effect of college quality. Zhong (2011) finds that significant return gaps exist between high-ranked and low-ranked college in China.

Li et al. (2023) use students' wages for their first job to measure the effect of human capital. A potential issue with the wage for the first job is the quality of the job match. It is known that job turnover rates for new college graduates are very high. For example, Takeshita et al. (2022) find that the turnover rate for new college graduates is around 20% within a year in Japan. In China, 34% of college graduates leave their first job within 1 year.¹ Therefore, the first job may not reflect a person's true human capital due to the potential wage penalty of the mismatch (Sellami et al., 2017). This study is limited by the data, yet it would be helpful to discuss more of the implications regarding this issue.

As Li et al. also acknowledge, the three mechanisms that determine wages are represented by variables that may overlap for the different mechanisms. For example, the involvement in a student union may be related to their non-cognitive abilities, such as openness, a part o

Over the past four decades, China has built up one of the largest higher education systems in the world which is no small achievement. The Chinese higher education system is very diverse and complex, and varies a great deal in terms of ease of access for students, the quality of teaching and research, and the returns to education. Therefore, summarizing its progress or the lack of it is a daunting task. Li et al. (2023) offer fresh findings on the returns to higher education in China and their determinants. Their main conclusions are that the returns to college education in China have stabilized after a significant rise and the relatively high returns are largely explained away by signaling and social networks channels as opposed to the human capital mechanism. However, Li et al. (2023) can be improved by talking to the broader Chinese literature and incorporating the institutional details of the higher education sector in China into its analysis.

First of all, as Li et al. (2023) rightly point out in the beginning of their paper that the enrollment in Chinese higher education has experienced a 10-fold expansion since 1999 and the resultant system is very diverse and highly stratified. The elite colleges, 211 project colleges, regular 4-year teaching colleges and 3-year vocational colleges operate in utterly different orbits with the elite colleges catching up quickly with the leading universities in the world on many fronts. The expansion concentrated disproportionally on the lower-tier universities, and therefore drove down the returns to an education in these universities as well as the overall returns to higher education to some extent. Another paper coauthored by the lead author of Li et al. (2023), Prof. Hongbin Li, clearly shows that the college premium for young workers declined while the college premium for senior workers increased over the period 1990–2019 (Li et al., 2022). This trend is also evident in work of Chinese scholars, for example, Ding et al. (2012, 2013). Although I understand the sampling is not necessarily representative at the different tiers and further analysis of the four groups of universities may not be possible, the overall conclusion of Li et al. (2023) needs to be qualified.

Second, the Chinese labor market is also quite diverse. Dual labor market theory would not adequately capture its complexity. Public sector jobs couple strong stability and high benefits with relatively low nominal salaries. The private sector mostly relies on salaries but leading firms can attract even graduates of elite colleges. Therefore equation (1) in Li et al. (2023) at least needs to control for the sector the graduates landed in. The same goes for the city/region/industries of choice as these greatly shape the kind of work–life balance college gradu

I read Urquiola (2023) with a keen personal interest as well as an academic interest. In my 47-year professional life, half was spent in US academia and the other half in Japanese academia. From time to time, I have compared Japanese universities with their US counterparts. Table 1 is a summary of my experiences.

Indeed, the Japanese research universities now are like US universities in the mid-1800s as described by Urquiola (2023). According to the 2023 Times Higher Education university ranking, the University of Tokyo, a top-ranked Japanese university, places only at #39 in the world.¹

Urquiola (2023) explains how the leading US universities became globally leading research universities in the late 20th century, while they did not have such a reputation in the mid-19th century. There are interesting questions of why and how it became possible for them to make such a transition. Urquiola attributes the success of US universities to a “laissez-faire orientation,” namely, “thousands of institutions largely left to compete with each other, even if most enjoy some form of state financial support.”

Urquiola argues that “selectivity appeared in the USA is not surprising from the point of view of economic theory” and that “economic models fully predict this in a laissez-faire educational market.” There are two mechanisms at work: a “peer effect” and an “information effect,” that is, graduating from prestigious schools carries information of a high caliber. In addition, “network” effects can be expected.

The information effect here seems to be a variation of signaling theory (Spence, 1974). In a signaling model, high-caliber (high productivity) students are willing to do extra (possibly unproductive) tasks that incur costs, if the extra task is used to screen students and the cost of the task is negatively correlated with the caliber of students. If high-caliber (research-oriented) students can perform well in entrance exams (in Japan) or in the preparation of admission documents and course work in college (in the USA) more easily than ordinary students, then the entrance exams, admission process, and course grades can be used as a signal of high-caliber students destined to be researchers (and other high paying jobs).

Citing Epple and Romano (1998) and MacLeod and Urquiola (2015), Urquiola (2023) predicts that a laissez-faire school market will have two kinds of universities: Selective schools with the highest prestige being small and in a strict hierarchy and a larger segment of non-selective schools. Most public (state) universities in the USA belong to the latter. The existence of two types of schools makes it different from Spence's original signaling model.

Once a university has established its status as a top-notch research university, its reputation attracts researchers with excellent publication records wh

Thailand has set itself the target of becoming a developed country by 2037. To achieve this goal, the nation must enhance its technological capabilities to produce higher-value-added products. Over the past two decades, Thailand has worked to improve its competitiveness through innovation by increasing research and development (R&D) investment. The country's gross domestic expenditure on R&D (GERD) rose from 0.25% of the gross domestic product (GDP) in 2000 to 1.33% in 2020. This is driven by a significant increase in private sector R&D expenditure, which rose from 35% of total GERD in 2000 to 68% in 2020. However, firms' R&D output performance, as measured by granted patents, has seen slower progress (Rattanakhamfu & Itthiphatwong, 2019). Some have argued that this is due to weak university-industry linkages (UILs).

In fact, 15 years ago, Brimble and Doner (2007) emphasized the role of university–industry linkages (UILs) in Thailand's technological competencies. Based on their analysis of four sectors—the automotive sector, the textile and garment sector, the agro-industry, and the electronics sector—Brimble and Doner concluded that Thai UILs played a very limited role in the country's economic development. They attributed weak UILs to low competition in the domestic market, inefficient structures and weak incentives in Thai universities, and the fragmentation of the Thai bureaucracy.

Intarakumnerd and Jutarosaga (2023) contribute to the existing literature by examining the role of UILs in three sectors, namely the automotive sector, the electronics sector, and the pharmaceutical sector, based on their analysis of R&D and Innovation (RDI) Surveys between 2014 and 2018 and case studies. They find that despite significant development in the higher education system over the past two decades, Thailand's UILs remain weak. Most UIL activities focus on human resource development, particularly student internships and employee training. Research-related linkages are particularly weak. Among the sectors studied, the pharmaceutical and electronics industries appear to have stronger ties with universities than the automotive sector.

In summary, Intarakumnerd and Jutarosaga offer valuable insights into the evolution of UILs in Thailand. To further enhance their contribution to the field, Intarakumnerd and Jutarosaga should consider the following revisions:

First, by providing descriptive statistics on the firms in the sample so that readers can understand the firms' characteristics from the RDI survey, such as firm size and nationality. Although Intarakumnerd and Jutarosaga argue that there is a bias towards large firms, as all have asset sizes of more than THB 10 million, this is not the case when firm size is measured by sales. In fact, the proportion of small, medium, and large firms are relatively equal (each around 30%–39%) in all survey years.

Second, by off

Investment in education is right as regarded one of the most important strategies for social and economic development in the long term. Historically, the private and social returns to schooling are higher at the primary and secondary levels compared to the tertiary level. However, as countries become more developed and wealthier, the returns to tertiary education could exceed that of primary education (Psacharopoulos and Patrinos, 2018). As Malaysia heads towards graduating from middle-income status, human capital development is a key policy the country's Twelfth Malaysia Plan 2021–2025 (12MP, see Malaysia, 2021). For tertiary education, the goal is to improve the quality of universities. In this regard, Tham and Chong (2023) provide a timely assessment of the quality and policy challenges to improve the quality of higher education in Malaysia.

A key policy challenge highlighted by Tham and Chong is how to measure the quality of higher education. As they rightly point out, the official performance indicators used such as use of selective international university rankings are clearly problematic given that the paradox of high rankings juxtaposed with under-employment of university graduates. Furthermore, they also opine that the implementation of quality assurance and accreditation systems have not ensured the quality of teaching in universities.

In reflecting the challenges facing higher education in Malaysia, it is worth examining some of the key institutional and political factors as well as constraints that have shaped the trajectory of the sector in Malaysia. These include the impacts of affirmative action policies (the New Economic Policy [NEP], and its successor variants) in both student intake and faculty hiring in public universities. Ethnic (Bumiputra vs. non-Bumiputra and regional [Peninsular vs. Sabah and Sarawak]) dimensions continue to be emphasized in the 12MP. In addition, the effects of the use of Bahasa Malaysia (Malay language) as the medium of instruction at all levels of education continue to be debated. In the past, this language requirement in teaching had also limited opportunities for hiring foreign academics.

The liberalization of higher education in the 1980s was partly undertaken in response to such policies. Furthermore, the subsequent internationalization of higher education created more space for the role of market forces which mitigate some of these policies. Although the emergence and development of private higher education have invigorated higher education in Malaysia, it has created a dualistic system that raises issues related in inequality. The issues of quality and inequality are intertwined. More studies are needed on the quality gap between public and private higher education in Malaysia and the extent to which it has exacerbated inequality. This has also implications of social cohesion.

Needless to say, the quality of higher education in Mala

China's higher education system has undergone a rapid expansion over the last two decades. By drawing on hand-collected data, we explore students' experiences in college and in the labor market post-graduation in the wake of this expansion. According to our data, the largest employer of college graduates in the labor market was the state sector, followed by the domestic private sector. To explain the returns to college education in China, we explore three mechanisms: human capital, social networks, and signaling. We find that human capital measures, apart from a student's college English test scores, cannot explain the college wage premium, whereas both social networks (for example, membership of the Communist Party) and signaling matter significantly. This seems to indicate that in China, connections are crucial for student success in the labor market, whereas the higher education system itself is more a system for selecting talented individuals than it is for educating them. Finally, students allocate their time accordingly, for example, by spending more time studying English in college than any other subject.

Many developing countries including those in East Asian region have been interested in the way to stimulate university industry linkages (UILs) in order to resume their economic growth and to improve their innovation capabilities, which are seen as a key to escaping from the middle income trap (Yusuf & Nabeshima, 2007).

Intarakumnerd and Jutarosaga (2023) focus on UILs in Thailand and examine how changes in the higher education sectors in Thailand have contributed to broadening the relationships with firms in key sectors in Thailand. They review the reforms implemented in the higher education system in Thailand and describe in detail various efforts that have been introduced to stimulate UILs in Thailand. The discussion of the higher education system is supplemented by a discussion of the research and development (R&D) activities of firms based on the Thai R&D and Innovation Survey and a detailed look at three strategic sectors for Thailand: the automotive, hard disk drive, and pharmaceutical industries. Intarakumnerd and Jutarosaga find that much of the interactions between universities and firms center around skill development and relative few activities focus on research. They conclude that UILs in Thailand remain weak, even though UILs have become more sophisticated and interactions between universities and firms have increased compared to the past.

While Intarakumnerd and Jutarosaga presents the current state-of-affairs in terms of UILs in Thailand, it would be helpful to deepen the discussion relating to the following points.

First, the paper could include more assessment of the strengths and weaknesses of the instruments that have been introduced recently in Thailand to encourage UILs. A study by Brimble and Doner (2007) identifies the lack of R&D by firms, the lack of incentives and support for universities, and the slow bureaucracy as the likely causes for the lack of UILs in Thailand. From that time, according to Intarakumnerd and Jutarosaga (2023), the government has introduced a number of efforts to stimulate UILs. While some improvements are seen among universities in terms of more emphasis on research, the interactions between universities and firms are not as forthcoming as expected. More discussion on why firms do not utilize these initiatives, despite the fact that compared to the past more firms are engaging in innovation activities in general, would be quite helpful.

Second, the paper could discuss the presence of multinational firms in Thai industries in terms of UILs in more depth. Thailand has been successful in rapid industrialization, mainly through foreign direct investment. In many cases, the innovation activities of foreign subsidiaries are conducted in the home country (or other “hot spot” locations), it is not necessary to conduct them where the actual production is located. According to Intarakumnerd and Jutarosaga, there d

By emphasizing that the Japanese government started to make higher education a core part of national economic and fiscal policies around the turn of the 21st century, Yonezawa (2023) investigates how these policies have been developed, what the main points of these policies are, and how they have impacted Japan's higher education. Yonezawa (2023) is clearly structured and rich in content. The explanations of the failure of Japanese higher education and the concluding remarks on the value of the policies created by the Abe Cabinet are impressive and interesting.

According to existing research (Kaneko, 1995; Nakamura, 1978; Pempell, 1978), since the late 1950s when Japan set the basic goal of economic policies to achieve economic development, higher education was placed in a strategic position for growth. Higher education was expected to train the human resources needed to achieve this goal. In the late 1950s, higher education was treated only peripherally in the economic plan as the “promotion of science and technology,” but in the 1960 Doubling Income Plan, it was placed under the title of “Chapter 3: Human Capacity Building and Promotion of Science and Technology.” The “Report of the Education and Training Subcommittee,” which was annexed to this plan, was further included under the title of “Report of the University Committee on Education and Training” and the “Report of the University Committee on Science and Technology.” The “Report of the Subcommittee on Education and Training” even proposed an increase in the number of university science and engineering faculties and technical high schools.

From the late 1950s to the 1960s, the idea of higher education as an investment rather than a mere consumption was introduced to Japan, mediated by such keywords as “human resources,” “human investment,” “educational investment,” and “manpower.” Since then, though differing in degrees over time, Japan's higher education has been expected to play a significant and decisive role in facilitating economic development and fostering manpower and professionals, particularly by the government and industry.

The impact of economic and fiscal policies on changes in Japan's higher education may have become more significant and considerable since the 21st century, but there is little doubt that the development of higher education was already conceived as one important part of Japan's policies of economic growth, science, and technology since the late 1950s. Also, when compared to other East Asian countries like China and South Korea, Japanese higher education is characterized by its closer partnership between higher education, government, and industry, and especially a stronger influence from industry on shaping national higher education policies and undergraduate education.

It is difficult to precisely evaluate the effectiveness of a specific policy unless it can be qualitati