Silviase/jgov_v1.4_full_ann huggingface dataset:
belong to multiple institutes, such as both a medical school and hospital. However, we have to also note that we can count the same researcher twice if he or she moves to a different city. In our dataset, we have 31,413 identical researcher names in different cities, which amounts to around 9.6% of the total researchers, and possibly includes researchers who moved between cities or who belong to multiple institutes in different cities. Therefore, we need to make further efforts to identify such researchers using other data sources.

The outcome variables are the number of papers each researcher publishes, total number of citations of his or her papers (including self-citations), maximum number of citations of his or her papers, number of papers published in the top journals (as defined above), and number of papers cited in the clinical guidelines. The fractional counting method could be applied to the calculation of the number of published papers of each researcher to account for the existence of coauthors: if two researchers coauthor two papers, it could be considered one for each researcher. Further, we checked the robustness of our naïve counting and found that both methods give similar results. Therefore, we simply count the number of papers for each researcher.

The determinants of the dataset can be classified into two categories: (i) benefits of agglomeration or network effects and (ii) network characteristics. To measure the extent of the agglomeration, we compute the number of active researchers who belong to the same faculties, same universities less the same faculty, same city less the same university, and same country less the same city. By “active researcher,” we mean researchers who have published at least one paper in the WoS database. Alternatively, we can use the number of papers instead of researchers.

These kinds of multi-layered agglomeration effects are transmitted to the individual researcher through the network. To observe these network effects, we use the number of coauthors from the same faculty, university, city, and country.

In order to capture the network characteristics, we specify what kind of and how many coauthors each researcher has. As for these attributes of the coauthors, we prepare the following variables. The total number of coauthors is a simple summation of the coauthors of his or her papers. For computational convenience, we do not omit duplication when a researcher coauthored with the same author multiple times. The total number of papers and citations are also simple summations of those of coauthors. As for the number of prolific coauthors, we define it as the researchers who published (and are contained in the WoS) more than 20 papers. This criterion corresponds to the
99th percentile of our dataset. We also compute the number of highly cited coauthors, the definition of which is that the citations of their papers are above the 99th percentile in Cardiac and Cardiovascular Systems. In the medical field, the last researcher of the author list plays a special role in many cases. The last author is the leader of the laboratory and usually plays an important role in obtaining the research funds. Thus, to obtain the quality factors of coauthors, we also count the number of coauthors whose papers are in the core journals and are cited by the clinical guidelines.

### 4. Descriptive Statistics and Data Analysis

In this section, we describe the basic properties of the data and conduct a data analysis on the relationship between the outcome variables and determinants of research productivity. The overall structure of this section is as follows. We start with outcome variables, such as the number of published papers, citations, and papers published in top journals, and then we present some simple descriptive statistics and their distributions.

Then, we treat with the determinants of research productivity. First, simple descriptive statistics are presented and, then, their relationships with the outcome variables are investigated. These determinants can be grouped into two categories: (i) externalities and (ii) structure and nature of network with coauthors.

The first category, externalities, includes agglomeration and network effect. Agglomeration is the usual economies of scale, while network effect is the beneficial effect of being linked with coauthors. After presenting descriptive statistics and distributions, we examine how outcomes change as agglomeration or the coauthor network change.

The next category of determinants includes the structure of the network and attributes of coauthors. Networks possess various kinds of structural characteristics, such as whether interactions within the network being dense or sparse, whether the coauthor relationship is fixed or flexible, whether the network is centralized or decentralized, and whether the network exhibits the diversity or homogeneity of participants. These structural characteristics are expected to affect research productivity. The attributes of coauthors, which may also be expected to influence research productivity, include whether they are prolific, highly cited, or able to produce papers that are published in core journals or cited in guidelines. Just as in the
case of externalities, first, simple descriptive statistics are presented and, then, their relationships with outcome variables are investigated. It should be noted in advance that the relationships between the outcomes and determinants are not necessarily causal here. To identify the causal effect, we will have to examine changes in the outcomes when the determinants are exogenously altered. Such examination requires very detailed information on the historical and institutional dynamics of academic research and research institutions. We would like to conduct a more precise analysis of these relationships by incorporating historical and institutional information in the future.

## (1) Outcome variables

Let us start with the outcome variables. Conceptually, research outcomes can be classified into two categories: quantity and quality. For quantity variables, we use the number of published papers, citations, and highly cited papers, while quality outcome variables include the number of papers published in top journals and papers cited in clinical guidelines.

The dataset includes 160,355 papers and 322,748 researchers in total. Panel (a) of Table 1 shows the descriptive statistics based on individual papers.

The average number of authors per paper is seven. On average, each paper is cited 12 times by other papers, and around 8.2% of papers are published in the core journals.

Panel (b) exhibits the descriptive statistics aggregated to researchers. Note that this dataset contains only researchers who published at least one paper. A vast majority of researchers published no papers at all, we suppose, and, thus, are excluded from the dataset. An average researcher in this dataset publishes 2.4 papers. The total number of citations of each researcher's papers is, on average, 31. Only 0.22 of each researcher's papers are published in the core journals, and 0.02 are cited in the guidelines.

In addition, the distributions of the outcome variables are very skewed. An average researcher publishes very few papers (perhaps none) and, even if he or she publishes any papers, they are not very often cited by other papers. Even fewer researchers publish papers that are published in the core journals or cited in the guidelines. To represent such skewed distributions, we draw Gini diagrams in Figure 1.

In the upper left panel, the horizontal axis indicates the cumulative proportions of researchers and the vertical axis indicates the cumulative proportions of the number of papers. If a 45-degree line is observed in a Gini graph, the proportion of papers is
Table 1 : Descriptive Statistics

Variable	Obs.	Mean	Std. Dev.	Min.	Max.
Number of Authors	160,355	7.053	4.117	1	70
Number of Citations	160,355	12.038	27.788	0	2,575
Core Journal Dummy	160,355	0.082	0.275	0	1
Number of Citations by the Guidelines	160,355	0.003	0.060	0	3
Number of Affiliates of Authors	158,831	3.338	2.859	1	71
Share of Colleagues of Faculty in Coauthors	150,076	0.527	0.358	0	1
Share of Colleagues of Univ. less Faculty	150,076	0.080	0.156	0	1
Share of Colleagues of City less Univ.	150,076	0.041	0.113	0	1
Share of Colleagues of Country less Faculty	150,076	0.112	0.207	0	1
Share of Foreign Coauthors	150,076	0.240	0.283	0	1
(b) Researcher-basis Dataset
Attributes of Researcher
Number of Papers	322,748	2.399	3.777	1	121
Total Number of Citations	322,748	31.083	80.478	0	6,672
Number of Papers in the Core Journals	322,748	0.221	0.914	0	38
Number of Papers Cited by the Guidelines	322,748	0.019	0.241	0	23
Agglomeration Measurements
Number of Researchers of the Same Faculty	322,748	19.803	41.470	0.2	1,133
Number of Researchers of the Same Univ. less the Same Faculty	322,748	303.443	437.040	0	3,686
Number of Researchers of the Same City less the Same Univ.	322,748	913.423	1,282.317	0	6,328
Number of Researchers of the Same Country less the Same City	322,748	29,823.540	31,708.870	0	84,552
Coauthorship Network
Number of Coauthors from the Same Faculty	322,748	4.523	3.046	0	51
Number of Coauthors from the Same Univ. less the Same Faculty	322,748	1.493	2.245	0	29
Number of Coauthors from the Same City less the Same Univ.	322,748	0.558	1.408	0	42
Number of Coauthors from the Same Country less the Same City	322,748	1.204	2.593	0	61
Number of Coauthors from Abroad	322,748	0.843	2.434	0	63
Attributes of Coauthors
Total Number of Coauthors	322,748	19.272	36.371	1	1,350
Total Number of Papers	322,748	134.487	685.188	0	55,110
Total Number of Citations	322,748	2,128.344	12,658.690	0	1,314,506
Number of Prolific Coauthors	322,748	1.782	9.690	0	520
Number of Highly Cited Coauthors	322,748	1.644	14.014	0	1,356
Number of the Last Author	322,748	16.549	77.494	0	4,280
Number of the Core Journal Papers	322,748	15.023	96.144	0	8,324
Number of Papers Cited by the Guidelines	322,748	0.977	8.660	0	734
Institutional Capabilities
Univ. Fund	1,119	1,594,117	3,601,130	3,183	31,500,000
NIH Grants for the Univ.	2,865	181,000,000	518,000,000	1,834.74	4,350,000,000
Figure 1: Inequalities of Outcomes

commensurate with the proportion of the outcome, which implies that the distribution of the outcome is “equal.” On the other hand, if the curve is strongly convex to below, the distribution is “unequal,” or skewed. The graph in the upper left panel is very much skewed. Up to around 20% of the cumulative proportion of the citation is zero, which means that around 20% of papers are not cited at all. A little more than 70% of papers represent only 20% of citations. The very steep rise of the curve at the end of the horizontal line shows that only a very small number of papers account for the vast majority of citations. The rest of the graph is drawn in terms of individual researchers. Again, the distribution of the number of papers among researchers is skewed, and is more so in the case of the number and maximum number of citations. Further, the distributions become extremely skewed for the number of core journal papers and guideline-cited papers.

## (2) Externalities: agglomeration and network effect

Let us move on to the first category of the determinants of research productivity, namely externalities. Among these, the variables representing agglomeration are the number of papers written by researchers in the institution as well as the number of researchers in the institution. To investigate the range of economies of scale, the above
variables are aggregated for the faculty to which a researcher belongs; for the institution, but outside of the faculty, to which a researcher belongs; for the city to which the institution belongs, excluding the institution to which a researcher belongs; and for the nation to which the institution belongs, excluding the institution to which a researcher belongs.

Referring back to Table 1, faculties embrace around 20 researchers, on average. The average number of researchers in the same university, but outside of the faculty to which a researcher belongs, is 303. Moreover, for the same city, but outside of the university, this number is 913, and for the same country, but outside the city, it is 29,823.

The other externality is network effect, which is assumed to be embodied in the number of coauthors with whom a researcher works. Again, to examine the range of network effects, we count the number of coauthors in the faculty to which a researcher belongs; in the institution, but outside of the faculty, to which a researcher belongs; in the city to which the institution belongs, excluding the institution to which a researcher belongs; and in the nation to which the institution belongs, excluding the institution to which a researcher belongs.

An average researcher collaborates with 4.5 researchers within the same faculty. The number of coauthors is 1.5 within the same university, but different faculties; 0.6 within the same city, but outside the university; 1.2 within the same country, but outside the city; and 0.8 for coauthors abroad.

So far, we have presented basic descriptive statistics for both agglomeration and network effect. Now, we will examine their relationship with the outcomes. Figure 2-1 shows the cumulative distribution functions used to investigate what happens to the distribution of outcomes when supposed determinants change.

The case of the number of papers is very similar to the case of the number of citations; hence, we refrain from reporting the former. The solid line in the upper left panel, for example, depicts the cumulative distribution of faculties as the number of citations changes, in the case where agglomeration measured by the number of researchers is the highest 10%. The solid line shows the share of faculties with the number of citations, on the horizontal axis, at this level of agglomeration. Very roughly, the share is around 80% for faculties with five or less papers, which researchers in the faculties publish, and nearly 100% for faculties with 10 or less papers. The dashed line does the same exercise in the case where agglomeration is the middle 45 to 55%, and
Figure 2-1 : Shapes of Cumulative Distribution Functions of the Number of Citatioons by the Degree of Agglomeration

Note: To make the difference of distributions easy at which to look, they are drawn without highest 5%.

the dotted line in the case the lowest 10%. By comparing the three lines, one can see the change in the distribution when the agglomeration changes. In this panel, the solid line for the highest 10% runs is located Southeast, the dotted line for the lowest 10% Northwest, and the dashed line in the middle. This means that as the agglomeration increases from the lowest (dotted line) to the highest (solid line), the share of the faculties with a larger number of papers increases. This, in turn, implies that larger agglomeration corresponds to a higher outcome. Along the first row, one can proceed to the right to find out that the agglomeration of a university outside of faculty, as well as that of a city outside of a university, generally contributes to an increase in the number of papers published by the researcher. In these cases, however, the shift from the dotted to dashed line is much larger than that from the dashed to solid line, implying that marginal increases in the number of papers become less prominent as we move farther away from the faculty to which a researcher belongs. In other words, diminishing marginal returns to scale are observed. The lower right panel depicts the case of agglomeration of a country, excluding the city to which a researcher belongs. Agglomeration at this level does not contribute to an increased number of papers.

These trends change slightly when we use the number of publications in core journals as the outcome variable in Figure 2–2.
Figure 2–2 : Shapes of Cumulative Distribution Functions of the Number of Core Journal Papers by the Degree of Agglomeration

Note: To make the difference of distributions easy at which to look, they are drawn without highest 1%.

In the upper left panel, the solid line is located Southeast, and the dotted line Northwest, and this tendency is preserved in the upper right graph. In the left graph on the lower row, the solid line tends to be located above the dashed line and, in the right panel on the second row, three lines overlap. Therefore, the benefits of agglomeration are rather limited to the faculty and university.

With respect to network effect, overall trends are the same as the case of agglomeration; hence, we again refrain from reporting the results.

In sum, positive externalities, both agglomeration and network effect, are observed with diminishing effects, as the range of externalities goes farther away from a researcher, the exception being the collaboration with foreign researchers.

## (3) Structure and nature of network

Next, we examine 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.

First, we examine the effect of coauthor density on the number of citations. This density is calculated only for individual papers, so that outcomes, such as the number of papers or core journal papers, are meaningless. In the left panel of Figure 3, as is usual,
Figure 3: Shapes of Cumulative Distribution Functions of the Number of Citations

Note: To make the difference of distributions easy at which to look, they are drawn without highest 1%.

the horizontal axis indicates the number of citations, while the cumulative share is shown on the vertical axis.

The upper left graph exhibits that the solid line runs from North to West of the dashed and dotted lines, implying that the denser the coauthor network at the faculty level, the smaller the number of citations. This result is contrary to our hypothesis that intensive interaction among collaborators entails better outcomes. The hypothesis is also rejected for other levels of coauthorship, except for foreign coauthors (the graphs are omitted here). Thus, collaboration with foreign researchers is beneficial to quality research. The rejection of the density hypothesis is disappointing, but there may be another interpretation. This measure could be a measure of diversity rather than of the density of the network. The hypothesis is that the versatility of opinions and methods in research is beneficial, and that diversity is measured by the share of colleagues of the same institution within the coauthors of a researcher. Under this interpretation, the negative relationship between outcomes and the above measure of “density” (actually a measure of diversity) represents a beneficial effect of collaborating with researchers from other institutions. The left panel of Figure 3, as explained above, shows that collaboration with researchers from other faculties within the same university does not exhibit any beneficial effect, which contradicts the diversity hypothesis.
Another measure of diversity is the number of universities from which coauthors participate. If coauthors come from a larger number of universities, it may imply that a variety of opinions and research methods are incorporated into the research, enhancing its quality. This measure of diversity is calculated for individual papers so that it is only meaningful for the number of citations among outcomes. In the right panel of Figure 3, the solid line lies to the Southeast of the dashed and dotted lines, which implies that the number of citations of a paper increases as the number of universities increases; hence, the diversity of the coauthors increases. We also note a complication coming from endogeneity: in medicine, multiple institutions participate in a clinical trial to facilitate an accumulation of observations. A large number of observations naturally imply the higher quality of the clinical trial. Therefore, multiple participation may represent a larger number of observations, rather than diversity of opinions or methods.

As for the attributes of coauthors, we 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 many papers, while typical qualitative attributes include whether his or her coauthors publish papers in core journals or that are cited in clinical guidelines.

The full range of outcome variables, listed from the most quantitative to most qualitative, is as follows: number of papers that coauthors published and citations of the papers published by coauthors, number of coauthors who publish a very large number of papers or highly cited papers, number of coauthors who are the last authors in their papers, and number of papers published in core journals or cited in guidelines.

Referring back to Table 1, each researcher collaborates with 19 coauthors, on average, and the number of papers published by coauthors, averaged over researchers, is 134, which are cited 2,128 times in total. The most cited coauthors' paper is cited 818 times. On average, a little less than two coauthors are prolific or highly cited researchers (defined in the data section). Further, more than 16 coauthors have experienced last authorship. The coauthors of each researcher published 15 papers in core journals and had roughly one paper cited in guidelines.

Figures 4–1 and 4–2 show the cumulative distribution functions of researchers whose coauthors possess indicated attributes.

The solid line in the upper left panel of Figure 4-1, for example, depicts the cumulative distribution of researchers, with the number of citations indicated by the
Figure 4-1: Shapes of Cumulative Distribution Functions of the Number of Citations by the Degree of Link Attributes

Note: To make the difference of distributions easy at which to look, they are drawn without highest 1%.

Figure 4-2 : Shapes of Cumulative Distribution Functions of the Number of Core Journal Papers by the Degree of Link Attributes

Note: To make the difference of distributions easy at which to look, they are drawn without highest 1%.