Human Capital Accumulation in Emerging Asia, 1970–2030：ADP 2010
国际经贸高级英语精读1--3课课文翻译
Starting as low-income economies in the 1960s, a few economies in East Asia managed,in a few decades, to bridge all or nearly all of the income gap that separated them from the high-income economies of the Organisation for Economic Co-operation and Development (OECD).Meanwhile many other developing economies stagnated .What made the difference?One way to grow is by developing hitherto unexploited land.Another is to accumulate physical capital:roads, factories, telephone networks.A third is to expand the labor force and increase its education and training.But Hong Kong (China) and Singapore had almost no land.They did invest heavily in physical capital and in educating their populations,but so did many other economies.During the 1960s through the 1980s the Soviet Union accumulated more capital as a share of its gross domestic product (GDP) than did Hong Kong (China), the Republic of Korea, Singapore, or Taiwan (China).And it increased the education of its population in no trivial measure. Yet the Soviets generated far smaller increases in living standards during that period than did these four East Asian economies.Perhaps the difference was that the East Asian economies did not build, work, and grow harder so much as they built, worked, and gr ew smarter.Could knowledge, then, have been behind East Asia’s surge ?If so, the implications are enormous,for that would mean that knowledge is the key to development—that knowledge is development.How important was knowledge for East Asia’s growt h spurt ?This turned out not to be an easy question to answer.The many varieties of knowledge combine with its limited marketability to present a formidable challenge to anyone seeking to evaluate the effect of knowledge on economic growth.How, after all, does one put a price tag on and add up the various types of knowledge?What common denominator lets us sum the knowledge that firms use in their production processes; the knowledge that policymaking institutions use to formulate, monitor, and evaluate policies; the knowledge that people use in their economic transactions and social interactions?What is the contribution of books and journals, of R&D spending, of the stock of information and communications equipment, of the learning and know-how of scientists, engineers, and students? Compound ing the difficulty is the fact that many types of knowledge are accumulated and exchanged almost exclusively within networks, traditional groups, and professional associations.That makes it virtually impossible to put a value on such knowledge.Reflecting these difficulties in quantify ing knowledge,efforts to evaluate the aggregate impact of knowledge on growth have often proceeded indirectly, by postulat ing that knowledge explains the part of growth that cannot be explained by the accumulation of tangible and identifiable factors, such as labor or capital.The growth not accounted for by these factors of production—the residual in the calculation—is attributed to growth in their productivity, that is, using the other factors smarter, through knowledge.This residual is sometimes called the Solow residual, after the economist Robert M. Solow,who spearheaded the approach in the 1950s,and what it purports to measure is conventionally called total factor productivity (TFP) growth.Some also call the Solow residual a measure of our ignorance ,because it represents what we cannot account for. Indeed, we must be careful not to attribute all of TFP growth to knowledge,or there may be other factors lurking in the Solow residual.Many other things do contribute to growth—institutions are an example—but are not reflected in the contributions of the more measurable factors.Their effect is (so far) inextricably woven into TFP growth.In early TFP analyses,physical capital was modeled as the only country-specific factor that could be accumulated to better people’s lives.Technical progress and other intangible factors were said to be universal, equally available to all people in all countries,and thus could not explain growth differencesbetween countries.Their contributions to growth were lumped with the TFP growth numbers.Although this assumption was convenient, it quickly became obvious that physical capital was not the only factor whose accumulation drove economic growth. A study that analyzed variations in growth rates across a large number of countries showed that the accumulation of physical capital explained less than 30 percent of those variations.The rest—70 percent or more—was attributed directly or indirectly to the intangible factors that make up TFP growth (Table 1.1).Later attempts introduced human capital to better explain the causes of economic growth.A higher level of education in the population means that more people can learn to use better technology. Education was surely a key ingredient in the success of four of the fastest-growing East Asian economies: Hong Kong (China), the Republic of Korea, Singapore, and Taiwan (China). Before their transformation from developing into industrializing economies, their school enrollment rates had been much higher than those of other developing countries (Table 1.2).They had also emphasized advanced scientific and technical studies—as measured by their higher ratios of students in technical fields than in even some industrial countries—thus enhancing their capacity to import sophisticated technologies.Moreover, the importance of education for economic growth had long been recognized and established empirically .One study had found that growth in years of schooling explained about 25 percent of the increase in GDP per capita in the United States between 1929 and 1982.Adding education reduced the part of growth that could not be explained,thus shrinking the haystack in which TFP growth (and knowledge) remained hidden.Some analysts even concluded, perhaps too quickly,that physical and human capital, properly accounted for, explained all or virtually all of the East Asian economies’ rapid growth,leaving knowledge as a separate factor out of the picture.One re ason these analysts came up with low values for TFP growth is that they incorporated improvements in labor and equipment into their measurement of factor accumulation.So even their evidence of low TFP growth in East Asia does not refute the importance of closing knowledge gaps.Indeed, it shows that the fast-growing East Asian economies had a successful strategy to close knowledge gaps:by investing in the knowledge embodi ed in physical capital, and by investing in people and institutions to enhance the capability to absorb and use knowledge.Looking beyond East Asia,other growth accounting studies have examined larger samples of countries.Even when human capital is accounted for,the unexplained part of growth remains high.One such study, of 98 countries with an unweighted average growth rate of output per worker of 2.24 percent,found that 34 percent (0.76 percentage point) of that growth came from physical capital accumulation,20 percent (0.45 percentage point) from human capital accumulation,and as much as 46 percent (just over 1 percentage point) from TFP growth.Even more remains to be explained in variations in growth rates across countries. The same study found the combined role of human and physical capital to be as low as 9 percent, leaving the TFP residual at a staggering 91 percent.To take another example:Korea and Ghana had similarly low incomes per capita in the 1950s,but by 1991 Korea’s income per capita was more than seven times Ghana’s.Much of that gap remains unexplained even when human capital is taken into account .All these results are subject to measurement problems.For example, the measured stock of human capital may overstate the actual quantity used in producing goods and services.High rates of school enrollment or attainment (years completed) may not translate into higher rates of economic growthif the quality of education is poor, or if educated people are not employed at their potential because of distortion s in the labor market.Moreover, it is now evident that education without openness to innovation and knowledge will notlead to economic development.The people of the former Soviet Union, like the people of the OECD countries and East Asia, were highly educated, with nearly 100 percent literacy .And for an educated population it is possible,through foreign direct investment and other means,to acquire and use information about the latest production and management innovations in other countries.But the Soviet Union placed severe restrictions on foreign investment, foreign collaboration, and innovation.Its work force did not adapt and change as new information became available elsewhere in the world, and consequently its economy suffered a decline.(excerpted from World Development Report 1998/1999)一些东亚国家在20世纪60年代还是低收入国家，但是在短短的几十年之间，他们成功地弥补了其与经济合作与发展组织（OECD）中高收入国家之间的差距；与此同时，也有许多发展中国家的经济停滞不前。
文化在社会上的重要影响英语作文
文化在社会上的重要影响英语作文Culture's Profound Impact on SocietyCulture is a multifaceted and integral aspect of human civilization, playing a pivotal role in shaping the fabric of our societies. It encompasses the shared beliefs, values, customs, traditions, and modes of expression that define a particular group or community. The influence of culture on society is far-reaching, permeating various spheres of life, from social structures and political systems to economic development and interpersonal relationships.One of the primary ways in which culture shapes society is through the transmission of shared values and beliefs. These fundamental tenets serve as the foundation for social norms, governing acceptable behavior and guiding individuals in their interactions with others. For instance, the emphasis on collectivism in some Asian cultures may lead to a greater emphasis on group harmony and consensus-building, whereas the individualistic nature of Western societies may foster a more competitive and self-reliant mindset. These cultural differences can significantly influence the way individuals perceive their roles and responsibilities within society, as well as the ways in which they approach problem-solving anddecision-making.Moreover, culture plays a crucial role in the construction of social institutions and power structures. The values and beliefs embedded within a culture often shape the development of political, economic, and educational systems, as well as the distribution of resources and opportunities. In many societies, cultural traditions and hierarchies have historically been used to justify and perpetuate existing power dynamics, often to the detriment of marginalized groups. However, cultural shifts can also challenge these structures, leading to social transformations and the empowerment of previously disenfranchised populations.The influence of culture on societal development is also evident in the realm of economic progress. Different cultural frameworks can foster diverse attitudes towards entrepreneurship, risk-taking, and the accumulation of wealth. For example, the emphasis on innovation and technological advancement in some cultures may contribute to the creation of thriving entrepreneurial ecosystems, while the reverence for tradition and social stability in others may prioritize more conservative economic practices. These cultural differences can have significant implications for a society's ability to adapt to changing economic landscapes and capitalize on emerging opportunities.Furthermore, culture plays a crucial role in shaping the way individuals perceive and interact with their physical and social environments. The aesthetic preferences, artistic expressions, and recreational activities that emerge from a culture can profoundly influence the built environment, urban planning, and the preservation of natural landscapes. The preservation of cultural heritage sites, the promotion of cultural festivals and events, and the integration of traditional art forms into everyday life can contribute to a sense of community identity and pride, fostering social cohesion and a shared understanding of the past.In addition to its impact on societal structures and economic development, culture also plays a vital role in the realm of interpersonal relationships and individual well-being. The values, traditions, and communication styles inherent within a culture can significantly influence how individuals perceive and respond to emotions, express affection, and resolve conflicts. The importance placed on family, the role of elders, and the norms surrounding social interactions can all have profound implications for the mental health and personal fulfillment of individuals within a given society.It is important to note that the influence of culture on society is not static or monolithic. Cultures are dynamic and constantly evolving, responding to changing social, political, and economic conditions. The globalization of communication and the exchange of ideas haveled to the cross-pollination of cultural elements, resulting in the emergence of hybrid and diverse cultural expressions. This process of cultural exchange and adaptation can both enrich and challenge existing societal structures, requiring a nuanced understanding of the complexities involved.In conclusion, the impact of culture on society is multifaceted and profound. From shaping social norms and power structures to influencing economic development and personal well-being, culture is a fundamental aspect of human civilization that permeates every facet of our lives. As we navigate the challenges and opportunities of the modern world, it is crucial to recognize and respect the diversity of cultural perspectives, while also fostering a deeper understanding of the ways in which culture continues to shape the societies in which we live.。
8内生增长理论
1 索洛模型的缺陷
• 索洛模型依赖外生的全要素生产率解释增长，但对 全要素生产率本身没有进行说明。内生增长模型是 将技术水平内生化从而解释经济增长的模型。
• 由于企业的开发、教育、职业培训，增进了人力资 本，通过人力资本的作用提高了全要素生产率，进 而推进了经济增长。人力资本是指个人在某一时点 所积累的技能与教育存量。工人所拥有的人力资本 越多，产出水平就越高。人力资本的积累过程就是 人力资本投资。是否选择人力资本投资决定于人力 资本形成的成本与收益。
U↓ and b↑
Effect of a Decrease in u on the Consumption Path in the Endogenous Growth Model
6 内生增长理论的趋同
内生增长理论良好地解释了穷国与富国之间 以及穷国之间增长差异的原因。
即使各国出来初始人力资本外各方面都相同， 也可能不会趋同。原因在于：
富国与穷国的差异仅仅在于资本存量差异
穷国与富国趋同的原因
对于穷国而言，人均资本存量低于富国，在
生产函数和储蓄函数相同，折旧与人口增长
相同下，穷国的资本存量小，从而人均储蓄
大于资本广化，在均衡条件下，投资增加，
人均资本增加，人均收入增加，并最终与富
国趋同。If ：
k k k k r
r
p
s z f (k) (n d)k
• 工会Labor union • 贸易限制Trade limited
And How
• 鼓励竞争Compete Encourage • 自由贸易Free Trade • 市场化Marketing
二 内生增长模型Endogenous Growth
In this model, the growth rate of per capita income is determined by the efficiency with which human capital is accumulated, and the fraction of available time devoted to human capital accumulation.
人力资本
Human Capital人力资本在经济中，人力资本涉及人类作为生产收入的行为者的生产能力。
这是一个古老的概念，但这个术语只是在过去的25年里才在专业论著中获得广泛的使用。
在这25年里，把资本理论的一些原理扩展到人在生产中的作用方面取得了相当大的进展。
作为现在和未来的产出与收入流的流泉，资本是一个具有价值的存量。
人力资本是体现在人身上的技能和生产知识的存量。
人力资本投资的收益或报酬在于提高了一个人的技能和获利能力，在于提高了市场经济和非市场经济中经济决策的效率。
这个条目概述的是主要思想，参考文献必然是有限的。
更详尽的和其他解释，读者应当查阅布劳（Blaug）、罗森（Rosen）、萨霍塔（Sahota）和威利斯（Willis）撰写的评述，它们还会给读者提供完整的参考文献。
对分析来说，人力资本与非人力资本在形式上的差别，较之它们之间在产权性质上的差别，其重要性不大。
在自由社会中，人力资本的所有权限于体现它的人。
一般说来，一个人甚至不可能自愿地出卖有法律约束的未来获利能力的所有权。
基于这个原因，最好是把人力资本服务的交换作为租金的市场交易来进行分析。
数量分析限于人力资本投资带来的收入和产出流量。
由于一个人不可能出卖其自身的资产所有权，因此工资支付和收益流量被视为等价于人力资本价值的租金。
甚至连在持久的雇用关系中发现的那种长期承诺，最好也视为一系列短期的、可续订的租金契约。
相比之下，法律制度对非人力资本所有权的交易和自愿转让的限制，却要少得多。
事实上，大量的非人力资本资产的市场活动，是企业组织制度的特点。
然而，由于这些区别并不总是严格的，因此在其中必定要保持着某种伸缩性。
奴隶制茺就是可转让人力资本产权的突出例子。
当然，奴隶制的非自愿因素是基本的，但就是自愿的制度也并非无人所知。
类似地，受契约束缚的劳役，就是一个在法律上可以对其他人的人力资本服务强制实施长期契约要求权的例子。
在当今的许多社会里，非人力资本所有权的转让受到严格的法律限制。
实用科技英语翻译 UNIT TWO[10页]
![实用科技英语翻译 UNIT TWO[10页]](https://img.taocdn.com/s3/m/f197221ffd4ffe4733687e21af45b307e871f9ac.png)
6. 【原文】That reality has been seized upon by some groups and scientists disputing the overall consensus and opposing changes in energy policies.
——唐·贾公彦 把一种语言文字的意义用另一种语言文字表达出来（也指方言与民族共同语、方言与 方言、古代语与现代语之间一种用另一种表达）：把代表语言文字的符号或数码用语 言文字表达出来。
——《现代汉语词典》 把已说出或写出的话的意思用另一种语言表达出来的活动。
——《中国大百科全书·语言文字卷》
翻译过程，可以简单地描述为这样的一个循环：
?译文虽然对这些气候问题的争论很激烈但与面对这些问题要做什么不做什么的争论相比这些问题变得相形见绌因为在我们生活的这个世界无论富裕的还是正在兴起的经济体仍然需要矿物燃料作支撑
实用科技英语翻译
UNIT TWO Global Warming
翻译及科技翻译对译者的要求
翻译定义：
Translating is the replacement of textual material in one language (SL) by equivalent textual material in another language (TL).
作为译者，即要娴于双语语言运用，还需要具备广博 的文化知识以及良好的专业知识。就科技翻译来说， 通常科技文章具有“语气正式，陈述客观准确，语言 规范，文体质朴，逻辑性强，专业术语性强”等特点。 这就需要译者具备： 1）高强的英汉语言能力； 2）熟悉英汉文化差异； 3）较好的专业知识背景； 4）熟悉英汉科技文化； 5）严肃认真的态度。
什么是人力资本说英语作文
什么是人力资本说英语作文题目，What is Human Capital?Human capital refers to the knowledge, skills, abilities, and other attributes that individuals possess, which contribute to their ability to perform effectively in economic activities. It encompasses both tangible and intangible qualities that individuals bring to the workforce, including education, training, experience, creativity, and interpersonal skills. Human capital is a crucial component of economic development and growth, as it directly impacts productivity, innovation, and competitiveness in the global marketplace.Education plays a fundamental role in the development of human capital. It equips individuals with the necessary knowledge and skills to succeed in their chosen fields and adapt to the evolving demands of the labor market. Formal education, such as primary, secondary, and tertiary education, provides individuals with foundational knowledgein various subjects, while vocational and technicaltraining programs offer specialized skills relevant to specific industries. Lifelong learning is also essentialfor enhancing human capital, as individuals must continuously update their skills to remain competitive in today's dynamic economy.Experience is another critical factor in the accumulation of human capital. Through practical exposure and hands-on involvement in real-world tasks, individuals develop valuable insights, problem-solving abilities, and decision-making skills that cannot be acquired through formal education alone. Work experience allows individuals to apply theoretical knowledge in practical settings,refine their skills, and learn from both successes and failures. Employers often value experienced workers for their ability to navigate complex challenges and drive organizational success.Creativity and innovation are integral aspects of human capital that drive progress and economic growth.Individuals who possess creative thinking skills cangenerate new ideas, solve problems creatively, and develop innovative solutions to address emerging challenges. Innovation is a key driver of competitiveness in today's knowledge-based economy, as companies strive todifferentiate themselves through product and service innovation. Cultivating a culture of innovation within organizations encourages employees to experiment, take risks, and pursue unconventional ideas, thereby enhancing their human capital and driving organizational success.Interpersonal skills, such as communication, teamwork, and leadership, are also essential components of human capital. In today's interconnected world, the ability to collaborate effectively with others is critical for success in the workplace. Strong communication skills enable individuals to convey ideas clearly, build relationships, and resolve conflicts diplomatically. Effective teamwork fosters cooperation, synergy, and collective achievement, while leadership skills empower individuals to inspire and motivate others towards common goals. By investing in the development of interpersonal skills, individuals can enhance their human capital and become valuable assets totheir organizations.In conclusion, human capital encompasses a broad rangeof knowledge, skills, abilities, and attributes that individuals bring to the workforce. Education, experience, creativity, and interpersonal skills are all essential components of human capital that contribute to economic development and growth. By investing in the development of human capital, individuals can enhance their employability, productivity, and earning potential, while organizationscan improve their competitiveness and drive innovation. Therefore, fostering human capital development should be a priority for governments, businesses, and individuals alike, as it is essential for building a prosperous andsustainable future.。
Economic transition and returns to education in China
1. Introduction Most existing studies find very low returns of income to education in China. Byron and Manaloto (1990), using a sample of 800 adults from 1986, report the rate of return as less than 4%. Knight and Song (1991), using education dummy variables, likewise find that the effect of education on earnings is remarkably slight, based on a sample of 3600 observations from 1986. Two other studies, by Johnson and Chow (1997) and Liu (1998), using the same data from the 1988 Chinese Household Income Project (CHIP-88), also estimate the return in the 3–4% range. Similar results can be found in MaurerFazio (1999). These estimated returns are considerably lower than
∗ Corresponding author. Tel.:+1 404 894 3542; fax:+1 404 894 1890. E-mail address: Haizheng.li@ (H.average and the 9.6% Asian average, as well as the 11.2–11.7% range for low and middle income countries (those with a per capita income of less than $2449) (Psacharopoulos, 1994). Historically, great value has been placed on education in China, and the Confucian emphasis on education is integral to Chinese culture. Because of the Cultural Revolution and the egalitarian regime in the socialist system, however, the value of education has been largely ignored in recent decades. Still, if the earnings premium on education is too low, private demand for education could be jeopardized and economic growth would be hurt as a result of insufficient human capital accumulations. In general, two problems exist in previous studies. First, because of data limitations, they rely on monthly or annual earnings instead of hourly wage rates in estimating returns to education. Clearly, these earnings depend on individual working hours. Because workinghour data are not available, an omitted variable bias will result. In fact, Li and Zax (2000) find that the most edu-
OECD研究概要
ISBN 978-92-64-04774-7The Global Competition for TalentMobility of the Highly Skilled© OECD 2008Executive SummaryInternational mobility of human resources in scienceand technology is of growing importance…The scale and complexity of the migrationof human resources in science and technology(HRST) are increasingAlongside sustained growth in foreign direct investment (FDI), in trade and in the internationalisation of research and development (R&D), mobility of human resources in science and technology (HRST) has become a central aspect of globalisation. Migration of talent now plays an important role in shaping skilled labour forces throughout the OECD area.Mobile talent contribute to the creationand diffusion of knowledgeThe importance of mobility stems from its contribution to the creation and diffusion of knowledge. Not only does it aid in the production and dissemination of codified knowledge, it is also an important means of transmitting tacit knowledge. In the broadest sense, tacit knowledge is any knowledge that cannot be codified and transmitted as information through documentation, academic papers, lectures, conferences or other communication channels. Such knowledge is more effectively transferred among individuals with a common social context and physical proximity.Economic incentives but also access to qualityresearch infrastructure and to leading researchersdrive mobilityVarious factors contribute to the flows of the highly skilled. In addition to economic incentives, such as opportunities for better pay and career advancement and access to better research funding, mobile talent also seek higher quality research infrastructure, the opportunity to work with “star”EXECUTIVE SUMMARYscientists and more freedom to debate. Less amenable to potential government policy, but still important, are family or personal ties that draw talent to certain locations.… and can have important impacts on knowledge creation and diffusion…Mobile people diffuse knowledge directlyand indirectly in their new locationOnce in another country, people diffuse their knowledge. In the workplace, knowledge spreads to colleagues, especially those in close contact. Knowledge also spills over to geographically proximate individuals and organisations and can contribute to the emergence of local concentrations of activity. Mobile HRST also act as a vital complement to the transfer of knowledge through flows of goods and capital across borders.… in both receiving and sending countries…Countries receiving inflows benefit from a variety ofpositive effects related to knowledge flows and R&DFor receiving countries, the inflow of talent has positive effects relating to knowledge flows, including the possibility of increased R&D and economic activity owing to the availability of additional skilled workers, improved knowledge flows and collaboration with sending countries, increased enrolments in graduate programmes, and potential firm and job creation by immigrant entrepreneurs. Mobility can help to link domestic firms to foreign knowledge and to stimulate spillovers from foreign R&D to local R&D units and the economy at large. At the same time, receiving countries must ensure that inflows of scientists and researchers do not delay reforms to policies that may be limiting the domestic supply of HRST.Much of the literature on highly skilled emigrationfocuses on remittances and brain drainFor sending countries, work on the effects of emigration has often focused on migrant remittances and brain drain, with particular emphasis on the impact on developing countries. Remittances are an important source of income for many low- and middle-income households in developing countries. The main concerns about brain drain centre on the loss of productive labour and itsEXECUTIVE SUMMARYassociated output, the fiscal cost of educating workers who then move abroad,and the potential impact on much-needed institutional development andstructural change. However, these concerns must be balanced against thequestion of whether these researchers and scientists could have foundproductive employment at home.But emigration of skilled workers can also spurhuman capital accumulation in the sending countryEmigration of skilled workers, such as researchers and scientists, can also bebeneficial for creation and diffusion of knowledge in their country of origin. Inparticular, emigration possibilities may encourage the development of skills.In addition, when skilled individuals move to larger and “denser” economiesthey can benefit the sending country by producing “better” knowledge thanthey could at home, accumulating human capital faster and improving theirproductivity, thereby increasing the potential return flows of knowledge. Thiscan increase the global stock of knowledge.… indicating that it is not necessarily a zero-sum gameBrain circulation stimulates knowledge flows andbuilds links between locations“Brain circulation” can stimulate knowledge transfer to sending countries.This may mean the return of skilled migrants to their home country after aperiod abroad, or a pattern of temporary and circular migration betweenhome and abroad. Professionals diffuse the knowledge they acquire to theirhome country and maintain networks, thereby facilitating continuingknowledge exchange. To make the most of brain circulation, the homecountry needs to have sufficient absorptive capacity, and returning talentsneed to be able to re-enter local labour markets at a level that is appropriatefor their skills and knowledge.A country’s diaspora can also act as a conduit…The existence of a diaspora further enhances the transfer of knowledge. Astock of skilled HRST abroad can act as a conduit for flows of knowledge andinformation back to the home country, and social and other links increase theprobability that knowledge will continue to flow back even after individualsmove back or move away. In some emerging economies, diaspora networksplay a vital role in developing science and technology capacity.EXECUTIVE SUMMARY… so that all countries can benefitTaken together, these effects suggest that knowledge flows associated with the emigration of researchers and scientists can provide benefits to sending countries. The mobility of researchers therefore is not necessarily a zero-sum game in which receiving countries gain and sending countries lose.International mobility patterns differ substantiallyacross countriesMost OECD countries are net beneficiaries of highlyskilled migration…Data on international mobility of HRST show that most OECD countries are net beneficiaries, with inflows exceeding outflows. The United States, Canada, Australia and France, in particular, have experienced strongly positive net inflows of tertiary-educated migrants.… but there are significant variationsHowever, a more detailed picture reveals that, in relative terms, New Zealand and Ireland have experienced large outflows. In absolute terms, the United Kingdom and Germany have the highest number of skilled expatriates, while Luxembourg, Norway and the Slovak Republic have the fewest. For some countries, intra-OECD flows add substantially to the stock of highly skilled individuals. For other OE CD countries, non-OE CD migrants play a more important role, and the main sources are Asian, led by China, India and the Philippines.Students are increasingly mobile as wellThe international mobility of students is a further aspect of the internationalisation of HRST. OE CD countries benefit from the inflow of talented students and scholars, and many now actively seek to attract foreign students. Benefits also occur when domestic students study abroad and gain knowledge and experience in another country. Data show that the number of students enrolled outside their country of citizenship has risen sharply since 1995.EXECUTIVE SUMMARYReturn and circular migration is largely drivenby family ties and employment opportunitiesReturn and circular flows of migrants add to the mobility picture. Data show atendency for many “permanent” or long-term migrants to return to theircountry of origin. Return rates appear to be higher for skilled workers and forthose from countries at a greater cultural, economic and geographic distancefrom the host country. This trend is consistent with the notion of a globalisinglabour market in which the mobility of skilled workers is affected by changes inrelative labour market conditions. The decision to return is driven strongly bylifestyle and family considerations and the availability of attractive employmentopportunities at home. For students, the considerations are similar.There is room for improving the collection of dataWhile recent years have seen major efforts to improve data on internationalstocks and flows of the highly skilled, difficulties relating to internationalcomparability, to differing and/or insufficient disaggregation and totimeliness remain. Further work is needed if countries are to betterunderstand patterns and changes in stocks and flows of scientists, engineersand researchers and the broader category of the highly skilled.The evidence on the impact of international mobility islimited…Direct evidence of the impact of mobilityon innovation outcomes is hard to findQuantitative evidence on the impact of mobility patterns is not readilyavailable. Many variables and factors influence science and technologyoutcomes and are hard to disentangle. Nevertheless, data and informationcan be used to build a picture and to see some links between mobility andbroader science and innovation outcomes.Mobility is clearly leading to greaterinternationalisation of the labour marketA clear effect of the mobility of highly skilled workers is the increasinginternationalisation of the labour market for the highly skilled. Both in privateindustry and academia, foreign staff are sought for their specific knowledge orabilities, their language skills and their knowledge of foreign markets.EXECUTIVE SUMMARY… but points to a range of positive impacts on knowledge creation and diffusionSome evidence suggests that immigrant HRSTcontribute strongly to innovationThe links between mobility and innovation are less clear, although some evidence suggests that immigrants contribute strongly to patent applications and creation of technology firms. Studies from several countries highlight a trend towards more international co-authorship of academic articles. Some work suggests that the impact of collaborative work, as measured by citations, is higher than the average impact of national work.Mobility opportunities are growingIn the broader context of R&D and innovation activity, many countries have greatly improved their ability to exploit and perform research and innovation over the past decade. This is changing the geographical spread and intensity of research and scientific activity. The growing sums spent on R&D in non-OE CD countries and their human capital resources, coupled with the increasingly internationalised activities of technology firms, all suggest that the opportunities for talent mobility will continue to grow.A wide range of policies aim at attracting and retainingHRST…Most countries offer a range of policiesOECD policies reveal a wide range of “intensity” in countries’ approach to the mobility of HRST. Most countries see it as important in a context of retaining and attracting talent and have policies to encourage and assist mobility.These range from economic incentives to encourage inflows, immigration-oriented assistance, procedures for recognising foreign qualifications, social and cultural support, and support for research abroad. Some countries focus on just a few policy mechanisms, while others offer “something for everyone”.However, few have a specific mobility strategyOnly a few countries’ policy approaches are part of an explicit mobility strategy.For those in which policies are not part of such a strategy, there is a greater risk of incoherence among policies on inflows, outflows and the diaspora. Ideally,EXECUTIVE SUMMARYmobility policies should be part of a wider mobility strategy that contributes tothe country’s economic and social objectives and sets out the rationale forintervention. There is generally more support for inflows of researchers andother HRST than for outflows, perhaps because countries judge outwardmobility to be adequate or because they are reluctant to encourage outwardmobility, despite arguments about the benefits of brain circulation.National policies generally target the same HRST…National policies appear generally to target the same population, with littleorientation towards national scientific and technological interests. Sincemany countries offer support for mobility, as opposed to permanentmigration, researchers may use these policies to work in a number ofcountries. It is difficult to know if the similarity of mobility policies representsa move towards best practice, as few policies have been evaluated.… and most do not impose geographical restrictionsIn most cases, national policies do not place restrictions on the country oforigin (inward mobility) or of destination (outward mobility). In theory, then,mobility policies often have a global focus.Policy for the futureWhat should future mobility policy look like?OECD countries already have a wide selection of policy tools at their disposal,which they use more or less intensively to promote HRST mobility. Thequestion then is, what is the role for international mobility policy in thefuture, given what is known about mobility and knowledge flows and aboutcurrent mobility, R&D and innovation patterns?Identifying a clear rationale for interventionis the first stepIn designing future mobility policies, a key first step is to identify a rationalefor intervention and clear objectives. For mobility, the main rationale may bethe potential positive externalities from knowledge spillovers andinformation asymmetry issues. The obstacles to mobility commonly citedinclude legal and administrative barriers, lack of funding, personal issues andlanguage.EXECUTIVE SUMMARYAs many mobility policies have not been evaluated,best practice has yet to emergeFew policies have been evaluated, so it is difficult to point to best practices.However, some lessons can be drawn from evaluation material provided by countries in response to the OECD questionnaire, including the importance of setting appropriate funding levels and programme durations for the target population. More work on evaluation would be valuable.Removing barriers to circular mobilityand fostering the diaspora may prove fruitfulGiven differences among countries, it is not possible to identify a “recipe” for what governments should do more of, what they should do less of, and what should stay the same. One promising avenue, however, is removal of barriers to short-term and circular mobility. Shorter (and potentially repeated) periods abroad may avoid some of the obstacles that currently hinder mobility, and would support knowledge flows associated with brain circulation and the diaspora.Countries must ensure that the broad environmentfor science and innovation is soundMoreover, policy coherence is important not only for mobility policies but also to ensure that the broader environment for innovation and scientific endeavour is sound. In particular, to improve innovation outcomes, it is not sufficient simply to increase the number of HRST; these people must operate in a system that enables them to use, create and disseminate knowledge.Countries should also remove obstaclesto the domestic supply of HRSTFinally, an important message from this study is that the global competition for talent is growing. Many OE CD countries and a growing range of non-member economies aim to attract the same pool of highly skilled researchers and scientists. Relying extensively on international flows and mobility policies to fill existing or future gaps in supply may therefore entail risks.Policy will also need to focus on addressing shortcomings in national policies that may limit the supply of HRST.。
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ADB EconomicsWorking Paper SeriesHuman Capital Accumulationin Emerging Asia, 1970–2030Jong-Wha Lee and Ruth FranciscoNo. 216 | September 2010ADB Economics Working Paper Series No. 216Human Capital Accumulationin Emerging Asia, 1970–2030Jong-Wha Lee and Ruth FranciscoSeptember 2010Jong-Wha Lee is Chief Economist of the Asian Development Bank and Ruth Francisco is a Ph.D. candidate at the University of the Philippines. The authors thank Jinyoung Kim, Francis Lui, and participants in the workshops at the Bank of Korea and the Chinese University of Hong Kong for helpful comments. The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the Asian Development Bank, its Board of Governors, or the governments they represent.Asian Development Bank6 ADB Avenue, Mandaluyong City1550 Metro Manila, Philippines/economics©2010 by Asian Development BankSeptember 2010ISSN 1655-5252Publication Stock No. WPS102409The views expressed in this paperare those of the author(s) and do notnecessarily reflect the views or policiesof the Asian Development Bank.The ADB Economics Working Paper Series is a forum for stimulating discussion and eliciting feedback on ongoing and recently completed research and policy studies undertaken by the Asian Development Bank (ADB) staff, consultants, or resource persons. The series deals with key economic and development problems, particularly those facing the Asia and Pacific region; as well as conceptual, analytical, or methodological issues relating to project/program economic analysis, and statistical data and measurement. The series aims to enhance the knowledge on Asia’s development and policy challenges; strengthen analytical rigor and quality of ADB’s country partnership strategies, and its subregional and country operations; and improve the quality and availability of statistical data and development indicators for monitoring development effectiveness.The ADB Economics Working Paper Series is a quick-disseminating, informal publication whose titles could subsequently be revised for publication as articles in professional journals or chapters in books. The series is maintained by the Economics and Research Department.ContentsAbstract v I. Introduction 1 II. Educational Progress in Emerging Asia, 1970–2010 2A. Educational Attainment Trend 2B. The Role of Population Structure and Enrollment Ratesfor Educational Progress 7 III. Determinants of Educational Investment 11A. Determinants of Educational Capital Growth:What does the Literature Say? 11B. Empirical Estimation of Educational Investment 14 IV. Projections of Human Capital Growth in Emerging Asia 18A. Projection Method 19B. Estimates of Average Years of Schooling for EmergingAsian Economies, 2015–2030 20 V. Concluding Remarks 21 Appendix: Estimation of Enrollment Rates Based on Logistic Trend 23 References 24AbstractEmerging Asian economies have made strong progress in improving educational capital in the past 40 years. High educational attainment, especially at the secondary level, has significantly improved emerging Asia’s educational achievement. Regressions show that better parental education and income, lower income inequality, declining fertility, and higher public educational expenditures account for higher educational enrollment. But Asia’s average years of schooling are forecast to increase to 7.6 years by 2030, from 7.0 in 2010, significantly slower than the increase of 4.1 years from 1970 to 2010. That would put emerging Asia’s educational capital in 2030 at only the 1970 level of the advanced countries, or still 3.5 years behind the level of advanced countries in 2010. For sustained human development, Asian economies must invest in improving educational quality and raising enrollment rates at the secondary and tertiary levels.I. IntroductionWell-known for its remarkable economic achievements, emerging Asia1 has grown by over 6.7% a year since the 1970s, making it the world’s fastest-growing region. Numerous studies have explored this remarkable growth record. They point to several primary explanations, including high saving and investment ratios, a well-educated labor force, macroeconomic stability, and export-oriented development strategies.Among these factors, there has been considerable focus on high savings and the region’s emphasis on exports. By contrast, even though the role of emerging Asia’s notable educational achievements in economic growth has often been emphasized, there has been little investigation into how this was achieved in the last 5 decades. Of the limited literature, Friedrickson and Tan (2008) attribute the success to several factors including (i) high rates of economic growth, (ii) an emphasis on the policies needed to promote high economic growth, (iii) the rapid transition from high to low fertility rates, and (iv) strong public institutions. Lee (2001) discusses the measures Asian countries adopted to expand the quantity and quality of education and emphasizes the role of government in setting educational priorities to meet changing demands, and improve the efficiency of resource utilization. Meanwhile, a number of papers have recognized the positive impact of cultural and religious features in East Asia on educational outcomes. Indeed, studies on Asian- American students’ educational expectations and school performance also suggest that family orientation or training, which generally embody Asian culture and values, playan active role in their high expectations and outstanding performance in schools in the United States (US) (Leung 2001, Goyette and Xie 1999, Chen and Stevenson 1995, Peng and Wright 1994). That is, their parents tend to have high educational expectations of their children and provide strong support (Lee and Barro 2001).This paper investigates how emerging Asia achieved its rapid human development from 1970 to 2010, and projects educational progress in the next 20 years.2 Section II presents a brief overview of the region’s educational progress in the past 40 years. Section III reviews the empirical literature on the determinants of educational investment, and examines the empirical relationship between school enrollment rates and income and nonincome factors. Section IV discusses the estimation methodology for generating educational projections and 1Emerging Asia includes the People’s Republic of China; Hong Kong, China; India; Indonesia; the Republic of Korea;Malaysia; Pakistan; the Philippines; Singapore; Taipei,China; Thailand; and Viet Nam. Accounting for about 95% of developing Asia’s gross domestic product (GDP) and 86% of its population, they represent regional trends well.2We focus on educational capital as a main component of human capital. However, human development can be more broadly defined as comprising other factors such as health and skills.presents our projections of educational attainment of estimates for 2015–2030. The final section provides concluding remarks.II. Educational Progress in Emerging Asia, 1970–2010 This section provides a brief overview of the record of human capital accumulation in emerging Asia from 1970 to 2010 compared to other regions. We use Barro and Lee’s (2010) estimates of educational attainment of the adult population as a measure ofhuman capital. The data provide estimates of population distribution by educational level and average years of schooling, disaggregated by sex and by 5-year age groups among the population aged 15 years and over for 146 countries at 5-year intervals from 1950 to 2010.Let us denote h j t a , as the proportion in age group a , for whom j is the highest level of schooling attained, i.e., j = 0 for no school, 1 for primary, 2 for secondary, and 3 for higher at time t . There are 13 5-year age groups ranging from a = 1 (15–19 years) to a = 13 (75 years and over). For those aged 15 years and above, this proportion of population (h j 15+) is simply the average of h j a across all age groups, weighted by the corresponding population share by age group, l a :h l h tt a taa 15113+==∑(1)From this, the numbers of years of schooling by age group and for the population aged 15 and above are computed, respectively, as follows:s h Dur t a j ta j t a j =∑,, (2)s l s h Dur tt a t a a j tj t j 151131515+=++==∑∑,,. (3)where Dur j indicates the duration by educational level.A. Educational Attainment TrendAs can be seen in Figure 1, compared to other regions, emerging Asia has shown strong growth in educational attainment in the past 4 decades. In 2010, its population aged 15 and over had an average 7 years of schooling, up by 4.1 years from just 2.9 years in 1970. By contrast, in the same period, the high-income countries raised their average years of schooling by 3.3 years (from 7.7 to 11.0), while developing countries (including emerging Asia) generally added 3.7 years (from 3.4 to 7.1).Emerging Asia’s strong progress is due mainly to a big leap in average years of primary and secondary schooling, which accounts for almost 90% of its overall increase. Average 2 | ADB Economics Working Paper Series No. 216years of primary and secondary schooling increased 1.9 and 2.0 years, respectively. In particular, average secondary schooling increased from less than 0.5 year in 1970 to almost 2.5 years in 2010. Tertiary education has grown rapidly, increasing from almost zero in 1970 to 0.3 in 2010. Nonetheless, this still falls a bit short of that in all developing countries. It is interesting to note that the educational progress in emerging countries in the past 40 years has brought them to almost the same educational level as the advanced countries 50 years ago. Educational level and distribution for 2010 in emerging Asia are comparable to those of the advanced countries in the late 1960s, and 4 years behind the current level of educational capital in advanced countries (11 years of education).Figure 1: Educational Attainment of Total Population 15 Years and Above: Emerging Asia, Developing Countries, and Advanced Countries201019901970201019901970201019901970PrimarySecondaryTertiaryAverage Years of Schooling Note: Graphs by region.Source: Barro and Lee (2010).Yet there are substantial educational gaps among emerging Asian countries, as seen in Table 1. The levels of educational attainment in Taipei,China (11.4) and the Republic of Korea (11.7) in 2010 are higher than the average in the advanced economies (11). By contrast, in India and Pakistan, although educational progress has been rapid in the past 40 years, the average remains below 6 years—or the average educational attainment of the advanced countries more than 6 decades ago. Average attainment in both Viet Nam (6.5) and Indonesia (6.3) also remain low in 2010.Table 1: Educational Attainment Trends of the Total Population, 15 Years and over*Economy Year PopulationAged 15+(million)NoSchoolingPercentage of Population Aged 15 and overPrimary Secondary Tertiary Average Years ofSchooling Total Full Total Full Total Full Total Pri Sec TerEmerging Asia 19701,035.949.534.717.214.6 3.4 1.20.7 2.93 2.410.480.0419901,732.133.230.118.133.216.2 3.5 2.0 4.93 3.49 1.330.1120102,473.417.425.918.348.028.08.6 4.57.05 4.30 2.490.2620202,818.714.623.917.651.029.510.5 5.27.50 4.48 2.700.3220303,102.115.022.215.851.829.911.1 6.17.56 4.48 2.740.34 China, People’s Rep. of1970500.641.936.919.220.3 4.40.80.5 3.50 2.950.530.031990835.422.234.520.441.326.5 1.9 1.1 5.74 4.24 1.430.0620101,090.7 6.524.114.460.451.09.0 4.38.36 4.95 3.150.2720201,162.9 4.120.612.563.755.911.5 5.08.89 5.16 3.400.3320301,215.9 3.119.710.765.858.811.5 5.19.00 5.26 3.410.33 Hong Kong, China1970 2.524.141.124.232.318.3 2.6 1.5 6.30 4.05 2.160.081990 4.512.624.916.651.232.011.3 5.19.31 4.99 3.990.332010 6.412.514.811.856.841.815.9 6.410.37 5.16 4.760.452020 6.88.812.310.058.445.620.48.011.22 5.40 5.250.5720307.2 6.210.57.060.648.822.79.311.75 5.53 5.580.64 India1970330.666.227.112.7 5.60.1 1.10.6 1.60 1.330.240.031990538.751.619.114.225.40.5 3.9 2.2 3.43 2.30 1.010.122010829.132.720.919.140.70.8 5.8 3.2 5.13 3.32 1.630.1820201,002.826.921.018.944.9 1.07.1 3.7 5.66 3.60 1.850.2220301,146.327.618.415.646.0 1.08.1 4.7 5.76 3.55 1.950.26 Indonesia197069.445.446.521.67.8 2.30.40.2 2.87 2.530.320.011990116.443.430.415.224.417.1 1.8 1.1 4.35 2.94 1.350.062010172.617.352.637.727.624.0 2.5 1.4 6.29 4.51 1.700.082020196.017.449.437.029.526.2 3.8 2.4 6.61 4.59 1.900.122030217.217.948.137.328.825.5 5.2 4.2 6.73 4.60 1.940.19 Korea, Rep. of197018.524.339.137.630.814.3 5.8 3.3 6.38 4.50 1.700.18199031.812.222.121.246.832.918.89.19.32 5.24 3.520.56201040.9 3.69.49.046.837.140.112.011.74 5.77 4.93 1.04202042.9 2.5 6.0 5.445.336.546.212.112.23 5.83 5.23 1.17203043.0 1.7 3.4 3.146.637.348.312.312.51 5.89 5.41 1.21 Malaysia1970 6.036.542.521.319.27.0 1.70.6 4.19 3.170.970.05199011.317.529.517.744.222.68.8 2.77.67 4.60 2.850.23201019.48.515.29.761.439.914.9 4.510.19 5.32 4.480.39202023.97.111.8 6.763.641.917.5 5.110.68 5.42 4.810.45203027.6 6.310.5 4.164.242.019.0 5.910.86 5.43 4.930.50continued.Economy Year PopulationAged 15+(million)NoSchoolingPercentage of Population Aged 15 and overPrimary Secondary Tertiary Average Years ofSchooling Total Full Total Full Total Full Total Pri Sec TerPakistan197034.280.08.7 6.59.9 4.8 1.4 1.1 1.580.940.580.05199063.066.212.09.919.411.1 2.4 2.1 2.92 1.64 1.190.092010112.538.021.819.834.622.3 5.5 4.5 5.65 3.05 2.400.202020150.329.325.923.938.028.4 6.8 5.7 6.54 3.48 2.800.252030187.930.222.420.739.033.28.47.2 6.87 3.45 3.110.31 Philippines197020.014.955.720.018.89.110.7 6.0 5.36 4.040.980.33199036.1 5.241.321.034.415.618.812.87.44 5.06 1.750.63201060.8 4.224.814.542.120.229.022.68.95 5.44 2.48 1.03202076.9 3.319.111.447.420.130.225.49.32 5.57 2.63 1.11203091.4 3.117.110.147.717.332.228.69.49 5.61 2.66 1.22 Singapore1970 1.334.329.612.534.211.5 1.9 1.0 5.20 3.43 1.710.061990 2.410.249.725.336.79.1 3.3 1.9 6.60 4.65 1.850.102010 3.98.227.214.546.326.618.311.09.19 5.12 3.480.592020 4.67.322.711.747.129.623.012.89.80 5.23 3.850.722030 4.8 5.520.010.347.732.226.814.510.36 5.38 4.160.83 Taipei,China19708.923.643.836.927.114.1 5.6 2.0 6.10 4.38 1.570.15199014.99.528.722.446.727.915.2 4.78.80 5.25 3.150.40201019.0 2.413.411.746.132.438.27.811.37 5.81 4.650.92202020.3 1.59.68.045.333.243.78.511.88 5.87 4.97 1.04203020.20.9 6.0 4.648.337.244.88.712.23 5.90 5.25 1.07 Thailand197020.024.967.211.6 6.9 2.1 1.00.7 3.65 3.310.300.03199037.211.668.516.315.17.8 4.8 4.4 5.42 4.360.870.18201051.911.748.019.527.915.612.310.57.25 4.81 1.990.46202054.711.541.121.530.421.217.014.98.30 5.19 2.480.64203056.111.238.526.131.724.918.617.38.89 5.48 2.700.72 Viet Nam197024.031.140.916.227.29.30.80.4 4.00 2.83 1.140.02199040.413.273.740.211.2 5.3 1.9 1.2 4.17 3.500.610.06201066.1 4.258.034.631.615.3 6.1 2.9 6.45 4.20 2.060.18202076.6 3.653.733.535.317.97.3 3.3 6.87 4.31 2.350.21203084.6 3.355.437.334.117.17.2 4.1 6.85 4.38 2.240.23 *1970, 1990, and 2010 figures are estimates; 2020 and 2030 figures are projections.Pri = primary; Sec = secondary; Ter = tertiary.Sources: Barro and Lee (2010) and authors’ estimates.Table 1: continued.In terms of the increase in educational attainment since 1970, Malaysia is on top (6 years); followed by the Republic of Korea (5.4); and Taipei,China (5.3) (see Figure 2). By contrast, Viet Nam increased only 2.4 years; Indonesia, India, the Philippines, and Thailand added around 3.5 years; and Hong Kong, China and Pakistan added 4 years.Figure 2: Educational Attainment of Total Population 15 Years and Above: Emerging Asia, Developing Countries, and Advanced CountriesRepublic of Korea Taipei,China Hong Kong, China Malaysia Philippines Singapore China, People’s Rep. of Thailand Indonesia Viet Nam Pakistan India Emerging Asia 203020101970203020101970203020101970203020101970203020101970203020101970203020101970203020101970203020101970203020101970203020101970203020101970203020101970PrimarySecondaryTertiaryAverage Years of Schooling Note: 1970 and 2010 figures are estimates; 2030 figures are projections.Sources: Barro and Lee (2010) and authors’ estimates.B. The Role of Population Structure and Enrollment Rates for Educational ProgressThe continuous high educational attainment of the younger cohorts has been the major factor behind emerging Asia’s significant educational progress.If we distinguish the population between two major age groups (15–24 and 25 and above), we can express equation (1) as follows:h l h l h tj tt j t tj ,,,()15152415241524251+−−−+=+−(4)Differencing equation (4) between time t and t-5, while assuming that population structure is stable over time, we get:∆∆∆h l h l h tj tt j t tj ,,,()15152415241524251+−−−+=+− (5)Therefore, the increase in average years of schooling for the population aged 15 and above is mainly determined by the increase in average years of schooling for the young population aged 15–24, because the educational attainment for the population aged 25 and above is relatively stable over time. The change in educational attainment for the young population is determined by the change in school enrollment rates.3Equation (5) highlights two important factors in educational growth over time: (i) the role of population structure and (ii) the role of high enrollment rates. 1.Young Population Structure and Educational Capital GrowthEquation (5) implies that, holding all else constant, educational capital in countries with a greater proportion of young people will grow faster than in countries with an older population structure. To illustrate, consider two countries (say, A and B) with the same distribution of attainment among those aged 15 years and over, but one with relativelya greater proportion of young people than the other (i.e., l A1524−> l B 1524−). Holding all else constant, for the same increase in enrollment, the change in the aggregate educational capital in country A will be greater than in country B.4In emerging Asian countries, a relatively young population structure was indeed an important factor in its rapid educational capital growth in the past 40 years. Those aged 15–24 years represented 31.2% of the total population in 1970, the same as the3 Note that estimating h tj a, by forward extrapolation implies that h h enrol tj tj j ,,152451524−−−=+∆. See Barro and Lee (2010).4It is important to note that a young population structure could be considered as both an opportunity and a constraint for a nation in building its educational capital. On one hand, it is a good opportunity because given the same improvements in enrollment and completion rates, a nation with a greater proportion of young people could increase its educational capital at a faster rate than a nation with lesser young people. On the other hand, to improve the quantity and quality of education, a younger population structure requires more resources.average of developing countries generally, but well above the 23.7% of the advanced countries. This implies that a 1 percentage point increase in the proportion of 15–24-year-olds achieving a particular level translates to a 0.31 percentage point increase in the proportion of the population 15 and above reaching that level in both emerging Asia and developing countries. Meanwhile, in advanced countries, the same increase among the 15–24-year-olds translates to a 0.24 percentage increase among the population aged 15 years and over. However, this population dividend for educational growth has declined significantly in emerging Asia, as the share of its population aged 15–24 decreased to 23.7 % in 2010. It has similarly declined, albeit at a more limited pace, to 25.8% in 2010 in developing countries. Meanwhile, in advanced countries, it has further declined to 17.1% in 2010.In India, Malaysia, and Pakistan, population structure remains an important factor in the relatively rapid educational progress beyond 2010. In 2010, the population aged 15–24 years was a third of the total aged 15 years and above in Pakistan; 27% in India; and 26% in Malaysia (see Table 2). This means that across all levels, every percentage point increase in the proportion of 15–24-year-olds achieving that level translates to a 0.33 percentage point increase in the proportion of 15 years and above achieving that level (but 0.26 in India and 0.26 in Malaysia). For Singapore, which has the lowest proportion of young adults (13%), every 1 percentage point increase in young adults’ educational attainment translates to only a 0.13 percentage point increase in the total population’s average educational attainment.Table 2: Trends of Educational Attainment of Population Aged 15–24 Years OldEconomy and Year Population(million)Percent ofPopulation,15+Percentage of Population AverageYears ofSchoolingNoSchoolingPrimary Secondary TertiaryTotal Full Total Full Total FullEmerging Asia1970323.531.229.14327.426.8 5.6 1.10.4 4.5 2010585.523.7 4.116.916.064.839.114.2 4.79.2 China, People’s Rep. of1970158.231.616.344.830.538.47.40.50.1 5.5 2010218.620.00.1 3.5 2.375.972.820.5 6.110.9 Hong Kong, China19700.831.3 3.940.329.954.329.3 1.60.58.9 20100.913.20.6 2.7 2.782.670.014.1 4.312.6 India1970100.430.452.33724.39.60.2 1.10.4 2.5 2010224.727.17.125.625.661.80.9 5.5 1.87.1 Indonesia197021.230.62164.832.513.9 3.70.30.1 4.3 201041.424.0 6.855.455.435.427.4 2.40.87.7continued.Economy and Year Population(million)Percent ofPopulation,15+Percentage of Population AverageYears ofSchoolingNoSchoolingPrimary Secondary TertiaryTotal Full Total Full Total FullMalaysia1970 2.134.814.547.931.735.112.5 2.20.3 6.42010 5.026.0 3.50.60.378.354.717.7 3.812.0 Pakistan197010.831.473.710.47.814.17.0 1.8 1.6 2.2 201037.533.319.430.930.845.828.8 3.8 3.17.2 Philippines19707.236.0 5.954.523.42710.412.6 4.2 6.3 201018.229.9 2.611.58.358.321.427.523.49.7 Korea, Rep. of1970 5.730.7241.440.150.724.3 5.80.88.62010 6.616.2 1.2 3.9 3.939.135.255.7 6.312.7 Singapore19700.535.59.729.914.858.519.4 1.70.67.8 20100.716.97.410.38.166.466.415.9 5.110.8 Taipei,China1970 3.033.3 3.844.438.844.623.97.30.78.32010 3.116.50.100.045.539.254.4 6.513.0 Thailand1970 6.834.1 6.980.617.111.6 3.70.90.4 4.8 201010.620.48.39.89.85734.724.916.910.6Viet Nam1970 6.928.910.831.515.657.117.20.80.3 6.4 201018.227.60.13224.657.524.310.4 3.28.8 Source: Barro and Lee (2010).2. Higher Enrollment Rates and Educational Capital GrowthEquation (5) also implies that the continuous inflow of a better-educated young population could drive the rapid growth of the educational stock among the population aged 15 years and over. That is, between two countries (A and C) with initially the same population structure (l A a=l C a) and educational attainment, the change in educational capital in A will be greater if the inflow of the better educated young population in A is greater thanin B. Aside from having a young population structure, improvements in emerging Asia’s enrollment rates were also significant in the past 40 years. In emerging Asia as a whole, the proportion of the young population with no schooling declined by 25 percentage points from 1970 to 2010 as enrollment rates increased at all levels (see Table 2). The proportion of the more educated young population increased from less than 28% in 1970 to 79% in 2010. With the lower illiteracy rate and greater enrollment rates at the higher levels of education (see Table 3), education among the population 15–25 years old has increased by 4.7 years in the past 40 years.Table 2: continued.Table 3: Trends in Enrollment Rates (percent)Economy Primary (adjusted)Secondary (adjusted)Tertiary (gross)197020051970200519702005 China, People’s Rep. of68.2100.023.576.00.922.0Hong Kong, China87.097.033.081.67.332.0 India57.0100.020.051.38.29.1 Indonesia67.0100.013.662.0 2.815.5 Malaysia87.0100.033.069.0 1.729.0 Pakistan33.083.410.428.4 2.5 4.0 Philippines95.7100.032.383.319.928.0 Korea, Rep. of94.0100.038.093.08.071.3 Singapore95.079.244.068.1 6.863.9 Taipei,China96.098.071.693.016.982.0 Thailand75.6100.010.971.0 3.346.0 Viet Nam81.1100.07.682.70.113.8 Note: Primary and secondary enrollment rates were “adjusted” for the proportion of population in each age group that should be enrolled in a particular level of education.Source: UNESCO reports (various years) as compiled by Barro and Lee (2010).The proportion of those who have no schooling has declined significantly in both Pakistan (from 73.7% in 1970 to 19.4% in 2010) and India (from 52.3% to 7.1%), where illiteracy rates were highest among emerging Asian countries in 1970. The proportionof 15–24-year-olds reaching at least the primary and secondary levels in Pakistan has increased more than threefold (to 30.9% and 45.8%), and the tertiary level from 1.8% to 3.8%. In India in the same period, these figures have increased more than sixfold and fivefold, respectively. But while India’s primary enrollment rate is now at par with most emerging countries, its secondary enrollment rate is still very low compared with other emerging countries in Asia.In the People’s Republic of China (PRC) and Hong Kong, China, progress amongthe young population has largely been due to improvement in secondary education. Hong Kong, China posted the highest proportion of the population of 15–24-years-old to reach the secondary level (82.6% in 2010 from 54.3% in 1970), followed by Malaysia with 78.3%, and the PRC with 75.9%.In the Republic of Korea and Taipei,China, gains at the tertiary levels have been huge among 15–24-year-olds: in the Republic of Korea, 55.7% of the population now reaches the tertiary level from 5.8% in 1970; and in Taipei,China, 54.4% from 7.3%.Table 2 also implies that completion rates among emerging Asia’s young population have improved significantly, especially at the primary and secondary levels, contributing to increasing average years of schooling. Nonetheless, these improvements vary across countries: secondary completion rates in the PRC; Hong Kong, China; and the Republic of Korea were 85% and above in 2010, compared to India’s secondary completion rate in India below 2% in 2010.III. Determinants of Educational InvestmentAs explained in the previous section, the rapid growth of educational stock among the adult population in the emerging Asian economies is driven by higher school enrollment levels in the young population. We now review the empirical literature on what determines educational capital growth across countries.A. Determinants of Educational Capital Growth:What does the Literature Say?1. Parents’ Income and EducationHousehold characteristics such as income, parental education, and household size affect the probability that a child will enroll in, attend, and complete school. Household income determines whether a household can afford to send children to school. Poor households are often unable to provide even a daily allowance to the studying child, let alone pay for tuition fees and school supplies. Often, they cannot forego the income from their children’s employment, spare the child’s help with the household chores, or the assistance the child provides in caring for younger siblings, especially when both parents are at work.The educational level of the parents also affects the desire to see their children in school and influences the child’s achievement once there. More educated parents may havea stronger desire to provide education for their children, and therefore provide more materials and school-related activities. A more educated adult population may alsobe more capable of training and producing more educated students. That is, there willbe more competent teachers available for instruction, especially at the higher levels of education, who are capable of using new technologies that enhance the qualityof learning.Past empirical studies provide evidence that family background does indeed affect investment in children’s education. Focusing on basic schooling outcomes, such as dropout and enrollment rates and educational attainment, earlier studies find that parental income and education have significant impact (for example, see Masters 1969 and Flug et al. 1998). Masters finds that children of less educated parents in the US are 20 times more likely to drop out than children of parents who are both high school graduates. Flug et al. emphasize that parental education and income account for more than 70% of the total variance in enrollment across countries. According to Haveman and Wolfe (1995), every 10% increase in family income is associated with a 0.2%–2% increase in school attainment among children.Cross-country education production function analysis by Lee and Barro (2001) also indicates that family characteristics, such as income and the education of parents, are strongly related to different school outcomes (that is, internationally comparable test scores, repetition rates, and dropout rates).。