Silviase/jgov_v1.4_full_ann huggingface dataset:
### 1. Introduction

Research activity is crucial to technological progress and, hence, to economic growth. Japan, in particular, has to rely on research and technological progress for growth with its declining labor force. Thus, the Japanese government has tried to strengthen science research activities after the Science and Technology Basic Law in 1995.

However, even if the government aggressively assists science research financially, it does not necessarily lead to enhanced research outcomes. Knowledge of the mechanisms of producing research outcomes is needed to effectively enhance science research.

The promotion of academic research poses many questions about the nature of research activities. Does research activity benefit from externalities from agglomeration or the research network? To what distance does research activity exert spillover effects? Do the structure and nature of the research network have prominent effects on research outcomes?

Moreover, in promoting academic research, it should be noted that simply increasing the quantity of research activities is not sufficient to raise the economic welfare of a nation. As is the case with general economic activities, research activity should generate more outputs with the same inputs, or the same outputs with fewer inputs. It is imperative to raise the efficiency of academic research. In particular, to enhance the quality of research, the quality should be explicitly taken into account. The difficulty here is to accurately measure the research quality. Conventional measures of quality include the number of published papers and number of citations. This paper takes advantage of institutional factors in medicine to measure the quality of research and uses some new measures of quality in the analysis, as will be explained below.

NISTEP (2015) quantitatively describes Japan's science and technology (S&T) activities based on paper data, etc. This data source is also based on Web of Science (WoS) by Thomson Reuter. This shows that the volume of scientific papers has increased in the world. About 400,000 papers were published in the early 1980s, while over 1,200,000 papers were published in 2013. On the other hand, the volume of scientific papers produced in Japan has remained at the same level over the last 10 years. However, Japan's share has declined, accounting for 8% in the early 1990s, 8.6% in early 2000, and 5.2% in early 2010, based on the basis of fractional counting. $ ^{1} $
Particularly, the share of papers with high impact, adjusted top 10% papers (or adjusted top 1% papers), was 5.8% (or 4.7%) in the early 1990s, 5.9% (or 4.7%) in early 2000, and 3.6% (or 2.9%) in early 2010. This shows that the share of papers in Japan declined in both quantity and quality.

When we review publishing trends in the world, papers in life science, basic life science, or clinical medicine account for half of all papers. However, the ratio of these papers has slightly decreased during the past 30 years. In Japan, papers on basic life science have slightly decreased, by 2.5 points, from 1981 to 2013, while papers on clinical medicine have increased, by 12 points.

In addition, the style of research activities has recently changed. The number of internationally coauthored papers has increased in the world. In 2013, the share of domestic papers was 76%, while that of internationally coauthored papers was 24%. Moreover, Japan is no exception; the number of internationally coauthored papers in Japan has increased, those focusing on clinical medicine accounting for 19.2%.

This paper investigates the mechanisms of producing high quality research and sheds light on the way to promote academic research by analyzing the determinants of research outcomes.

On the mechanics of producing high quality research outcomes, the analyzed determinants include: (i) externalities, such as the benefits of agglomeration and network effects from researchers in the same institution, in nearby areas, or overseas, and (ii) the effects of the structure of research networks, such as density, flexibility, and variety, as well as the nature of links, such as a link with researchers who publish a lot of papers or quality papers. The beneficial effects of the geographical concentration of research activities and interactions among them include economies of scale, knowledge spillover, and diversity. It is not easy to clearly separate these effects in empirical analyses; in this paper, agglomeration denotes economies of scale at various distances, network effect refers specifically to knowledge spillover through coauthorship, and diversity is contained as one of the elements of the structure and nature of networks, together with the other properties of networks listed below.

Among the first set of determinants, externalities, we start with the analysis of conventional economies of scale (agglomeration), which assert that a researcher profits from other researchers within the same institute (or same city), so that he or
she is more productive when the affiliated institution embraces more researchers, or more papers written by the researchers belonging to the same institute. Then, we move on to the analysis of spillover effects, which investigates whether the beneficial effects of other researchers mentioned above extend to other institutions, nearby areas, and overseas. A noble feature of our analysis is that it is based on individual researchers/papers, as is explained below, and, therefore, the actual linkages among researchers can be used to shed light on the range of network effects.

Then, we examine the second set of determinants, the structure and nature of the network. As for the structure, we examine how densely congregated the coauthors within the network are, and whether a variety of institutions are represented in the network.

A densely populated or concentrated network may have an advantage of transmitting information very rapidly. A network with fixed membership could facilitate a deep exchange of ideas, while a network with changing membership may be favorable for absorbing new ideas. Similar to the latter, a network that is comprised of members from multiple institutions possibly embraces a variety of ideas and, hence, may contribute to breakthroughs in academic research; however, medicine networks among multiple institutions often represent participation in multi-center randomized controlled trials with uniform protocols (i.e., the same objectives and methods).

As for the nature of the research network, it may be that collaborators with specific attributes exert a favorable effect on the quantity and quality of the research outcome. We will examine a wide variety of characteristics. These attributes may be viewed as either quantitative or qualitative. A typical quantitative attribute is whether a researcher's coauthors publish a lot of papers, while typical qualitative attributes include whether coauthors publish papers in core journals or if papers are cited in clinical guidelines. Linkage to the first type, massive producer, may contribute to the quantity of published papers, but may not contribute to the quality of research. The second and third types may have the opposite effects. The full set of attributes examined will be presented later.

Our unit of analysis is individual papers/researchers rather than research institutes, as was the case in previous analyses. This type of analysis permits us to entangle intricate relationships among researchers, and our analyses of the effects of the range and structure of research networks are very much substantiated by this choice of unit.

Furthermore, distinction between individual researchers and institutions enables
policymakers to draw up a promotional plan that is grounded on the real source of quality research. When contribution from individual researchers is independent of institutional contribution, policymakers should target these individual researchers. If a study confounds the contribution from individual researchers with institutional contribution, it could wrongly propose a promotional plan that covers all researchers in the targeted institutions.

Our analyses try to incorporate the quality of research. In doing so, we exploit an institutional feature in medicine for the identification of the research quality. In medicine, evidence-based medicine is systematically encouraged and clinical guidelines are compiled by professional associations. Citations in guidelines could be a good indicator of research quality. In addition, the quality of research is evaluated by professional panels in the process of compilation. Such evaluation may also be used as a quality indicator. In the analyses below, we use the former guideline-related indicator, citation in guidelines, as well as other conventional quality indicators, such as the number of citations and publications in prestigious journals. The identification of prestigious journals is also facilitated by the institutional feature of medicine. The National Institute of Health (NIH) selects the PubMed Core Clinical Journals, which may be regarded as “prestigious” or mainstream journals.

The organization of the paper is as follows. Section 2 reviews the related literature. Section 3 explains the data. Section 4 analyzes the determinants of research efficiency in a descriptive way, while Section 5 formally estimates quantitative models. Finally, Section 6 concludes.

### 2. Literature Review

First, we review which determinants of research outcomes were applied by previous works. Particularly, we focus on networks on research.

With regard to the network effect, Banerjee et al. (2013) examine how participation in a microfinance program diffuses through social networks. They estimate structural models of diffusion that allow us to (i) determine the relative roles of basic information transmission versus other forms of peer influence, and (ii) distinguish information passing by participants and non-participants.

As for the network characteristics, Azoulay, Zivin, and Wang (2010) analyze the spillover effects between researchers, using coauthorship along with those eminent life
scientists who suddenly died, or superstars. They estimate the magnitude of spillovers generated by 112 academic superstars who died prematurely and unexpectedly, thus providing an exogenous source of variation in the structure of their collaborators' coauthorship networks. Following the death of a superstar, they find that collaborators experience, on average, a lasting 5 to 8% decline in their quality-adjusted publication rates.

Newman (2001a) constructs a bibliographical database in which two scientists are considered connected if they have coauthored one or more papers together. They study a variety of statistical properties of the networks, including the number of papers written by authors, authors per paper, collaborators of scientists, existence and size of a giant component of connected scientists, and degree of clustering in the networks. Further, they highlight some apparent differences in collaboration patterns between the subjects studied. In the following paper, we study a number of measures of centrality and connectedness in the same networks.

When considering the effects of networks on innovation, we have to also scrutinize the diversity, as the networks can be regarded as media of the diversity between different groups. As for the concept of diversity, though many disciplines use their own characterization, Stirling (1998) organizes the idea into three subordinates: variety, balance, and disparity. Variety refers to the number of categories; the higher the number of categories, the greater the variety. This is the most common aspect of diversity, and some papers use the term "diversity" in reference to this subordinate. For example, Lay and Dreher (1992) examine the determinant of variety using the different best practices in the modes of usage of numerically controlled machine tools. Moreover, balance refers to the pattern in the apportionment of that quality across the relevant categories. The more equal the fractions, the more even the balance and greater the diversity. This aspect is commonly used in such contexts as income inequality and market structure. Further, disparity refers to the nature and degree to which the categories themselves are different from one another. As achieved by Nguyen et al. (2005) with regard to oil refining technology, this is usually captured by some form of distance measure. Stirling (2007) proposes some quantitative heuristics that try to capture all of these subordinate factors of diversity.

This characterization of diversity has a significant influence on subsequent studies. Applying the three aspects, Yegros-Yegros, Rafols, and D'Este (2015) analyzes the effect of the degree of interdisciplinarity on the citation impact of individual
publications. They operationalize interdisciplinarity as disciplinary diversity in the references of a publication. Interestingly, they find that variety has a positive effect on impact, whereas balance and disparity have a negative effect. Another example is van Rijnsoever et al. (2015), which investigates the influence of network position and composition of innovation projects on the creation diversity of an emerging technology, using variety and balance among the three components.

However, previous works do not necessarily consider all of these various aspects of the network. Some focus on the structure of networks such as distances and centrality (e.g., Newman (2001b)). Others mainly deal with the nature of the network such as the quantity and quality of collaborators' outcomes (e.g., Azoulay, Ding, and Stuart (2007)). Our paper analyzes both the structure and nature of the network using a variety of indicators.

Second, we review which measurements have been applied by previous works as outcomes of innovative activities. This is important when we analyze the efficiency of innovation.

Carlino and Kerr (2015) marshal the measurement of technological innovation in three ways: (i) by the inputs used in the innovation process, such as R&D expenditures or venture capital (VC) investment; (ii) by intermediate outputs of the innovation effort, such as the number of patents; or (iii) by some final measure of innovative work, such as the number of new product announcements.

The earlier studies often use R&D expenditures as a measurement of output because of the scarcity of available data. Needless to say, such measurements are not appropriate to examine the productivity and efficiency of the innovation because the ratio of outcomes to input does matter in measuring the productivity.

As for the intermediate output of the innovation, Azoulay, Ding, and Stuart (2007) analyzes the impact of agglomeration on patenting. They examine the individual, contextual, and institutional determinants of faculty patenting behavior in a panel dataset spanning the careers of 3,884 academic life scientists. They conclude that the patent stock of the university affects the individual researcher's patenting behavior. Azoulay, Ding, and Stuart (2009) examine the influence of faculty patenting activity on the rate, quality, and content of public research outputs in a panel dataset. In the process of the estimation, they also use the patent stock of the affiliate. Moreover, Lichtenberg (2013) examines the relationship across diseases between the long-run growth in the number of publications about a disease, and change in the age-adjusted
mortality rate from the disease. Further, Duranton and Kerr (2015) present a review on agglomeration in the context of geoeconomics.

An example of research that adopts the third measurement is Feldman and Audretsch (1999). It uses the United States Small Business Administration's Innovation Data Base (SBIDB), which consists of new product introductions compiled from the new product announcement sections of over 100 technology, engineering, and trade journals, spanning every manufacturing industry. They show considerable support for the thesis that diversity, rather than specialization, better promotes technological innovation. Although most previous works in the areas of biomedicine measure the outcome using such indicators as the quantity and quality of patents, some studies adopt more direct indicators of the research outcome. For example, Lichtenberg (2013) uses the mortality rate of the disease concerned. On the other hand, we analyze using the information from the clinical guidelines as the direct measure of the research outcome in the medical field, as well as the bibliometrical data. We regard clinical research as an index to show how much academic research is actually utilized for medical practice.

Regarding the literature in Japan, there are some papers that deal with the mechanism of scientific research. Saka, Igami, and Tomizawa (2015) relate the WoS with the Database of Grants-in-Aid for Scientific Research of Japan, and examine the relationship between the grants-in-aid and papers within the WoS. Their research covers not only the medical field, but also entire disciplines. On the one hand, their scope is limited to Japan. Yonetani, Ikeuchi, and Kuwahara (2013) use the WoS and Survey of Research and Development by the Japanese Statistics Bureau to investigate the relationship between the number of published papers and deterministic variables such as the number of researchers and research funds. The unit of this research is university, and the researcher basis analysis is not carried out. On the other hand, we focus on researcher basis analysis. Using researcher as the unit of analysis enables us not only to examine the relationship between research outcomes, agglomeration, and networking in more detail, but also to shed light on the problem that the policymakers should target, individual researchers or institutions.

### 3. Data and Variables

One of the focuses of our analysis is to utilize the rich information of individual
paper/researcher basis data, rather than institute basis data. We construct a paper-basis dataset and researcher-basis dataset, mainly using the Thomson Reuters' WoS Core Collection, $ ^{2} $ with some additional data sources, such as the NIH grant database and references of clinical guidelines. WoS is a citation database that covers over 12,000 journals in more than 250 disciplines.

In the paper-basis dataset, we collect all of journal articles published in the journals categorized as Cardiac and Cardiovascular Systems in the WoS between 2008 and 2014. The data covers not only journals in specific countries, but also highly ranked journals all over the world, which is measured by the impact factor. The total number of the articles is 160,355. This dataset contains each paper's authors, author affiliations, each affiliation's address, and number of citations. Using the affiliation's address, we aggregate the number of papers to the faculty, university, city, and country levels to observe the extent of agglomeration. In addition, we examine whether each paper is cited by the clinical guidelines in the field, that is, the 2014 AHA/ACC Guideline for the Management of Patients With Non-ST-Elevation Acute Coronary Syndromes (Amsterdam et al. (2014)), 2013 ACCF/AHA Guideline for the Management of Heart Failure (Yancy et al. (2013)), and 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction (O'Gara et al. (2013)).

Concerning the measurement of the quality of research, we utilize new indicators that arise from institutional features of medicine along with traditional indicators, such as the number of citations. These new indicators include the number of papers published in "Core Clinical Journals" and those cited in clinical guidelines. The list of Core Clinical Journals is selected and provided in PubMed by the National Library of Medicine, and the guidelines are compiled by medical associations. As such, we exploit such official and semi-official judgment about the quality of published papers.

In the paper-basis dataset, the major research output variable is the number of citations of each article, including self-citations. In the WoS, the highly cited papers are defined as papers whose number of citations is within the top 1%.

The reputation of the journal where the article is published is also an important measure of the quality of the article. In order to specify such top journals, we choose journals on the PubMED Core Clinical Journals list. $ ^{3} $ In the WoS's Cardiac and
Cardiovascular Systems area, the following journals are on the list: American Heart Journal, American Journal of Cardiology, Circulation, Annals of Thoracic Surgery, Heart, Heart & Lung, Journal of the American College of Cardiology, Journal of Thoracic and Cardiovascular Surgery, and Progress in Cardiovascular Diseases. Among these journals, we exclude two, Annals of Thoracic Surgery and Journal of Thoracic and Cardiovascular Surgery, because they focus on surgery. We defined the "core journals" as the remaining seven journals.

As the Literature Review indicates, the effects of the structure and nature of networks can be classified into three categories: variety, balance, and disparity. Variety can be captured by the number of authors in a network, balance connotes no dominant coauthor that can be captured by equal frequency of coauthorship within a network, and disparity within a network means that the network contains coauthors coming from different fields, nationalities, and cultures. Balance could be captured by the fixedness of the network, but we are unable to calculate such an index due to our limited capability. Disparity is captured by colleague density and the number of participating universities. As an indicator that reflects the structure of the coauthor network of each paper, we compute colleague density. This is the share of colleagues in coauthors at each scope, that is, within the same faculty, within the same university but different faculty, within the same city but different university, and within the same country but different city. It is calculated by dividing the number of colleague relationships with total number of possible pairs of authors. The higher this density, the more colleague relations exist in the coauthorship of the paper.

In calculating the number of universities to which the authors belong, when an author belongs to multiple universities, we count them all for this index.

The researcher-basis dataset covers all authors of the articles contained in the above-mentioned paper-basis dataset. However, we exclude 27,536 researchers whose faculty we cannot identify. As such, the total number of the researchers is 322,748, and this dataset contains each researcher's list of published articles.

In this researcher-basis dataset, we have to identify researchers across papers. As the WoS does not have unique IDs for researchers, we must make a judgement as to whether a researcher in a paper is identical to another researcher with the same name in a different paper; therefore, we regard researchers with the same name in the same city as an identical. Note that we also regard researchers with the same name, but in a different institute, in the same city as identical. This is because researchers often