Silviase/jgov_v1.4_full_ann huggingface dataset:
horizontal axis in the case where the number of papers that coauthors published is the highest 10%. Just as in the case of agglomeration, the solid line located to the Southeast implies that a larger number of coauthors' papers will raise the number of citations of a researcher's published papers. As we go from the left to right and, then, to the second and third rows, this trend is preserved. In the left panel in the third row, researchers are divided into two categories: those whose coauthors published guideline-cited papers and those whose coauthors did not. This is because coauthors with guideline-cited papers are rare; thus, even most of the researchers in the highest 10% do not publish guideline-cited papers. The attributes are arranged so that going from left to right and from top to bottom corresponds to the pecking order of outcomes from quantitative to qualitative outcomes. Therefore, quantitative coauthor attributes, as well as qualitative attributes, contribute to larger numbers of papers published by researchers.

Figure 4-2 shows the effects of link attributes on the number of papers published in core journals. A similar trend as before is observed. However, since papers published in core journals are rare, the difference between the case of the highest 10% (solid line) and lowest 10% (dotted line) seems to be smaller than the difference, when the outcome is taken to be the number of citations (Figure 4-1).

In summary, quantitative coauthors are good for quantitative outcomes, while not as beneficial to qualitative outcomes. On the other hand, qualitative coauthors are similarly good both for quantitative and qualitative outcomes.

### 5. Econometric Analysis

This section presents a couple of results of the formal estimation of the determinants of research productivity. This estimation is based on data for individual researchers, and we regress various outcome measures on the determinants of research productivity. The outcome variables are, in the order from quantitative to qualitative, the number of published papers, number of citations and highly cited papers, number of papers published in top journals, and number of papers cited in clinical guidelines.

The first set of determinants, externalities, consists of two groups: agglomeration and network effects. The variables representing agglomeration are the number of papers published by researchers in the institution as well as number of researchers in the institution. Network effects are assumed embodied in the number of coauthors.
with whom a researcher works. As was explained in Section 4, to investigate the range of agglomeration/network effects, the above variables are calculated for various units/areas at different distances.

Among the second set of determinants, the structure and nature of the research network, we are unable to incorporate structural characteristics in the regression models because the variables representing them are calculated on the basis of individual papers. Moreover, concerning the nature of the research network, we specifically use the attributes of coauthors, of which a typical quantitative attribute is whether a researcher's coauthors publish many papers and typical qualitative attributes include whether his or her coauthors publish papers in core journals or they are cited in clinical guidelines.

Since quantitative analysis in this paper is preliminary, we utilize simple linear regression with a technical correction to the dominant number of zeros in the data. Even though the outcome variables are continuous, the Tobit model is employed to account for the fact that the value zero is dominant in the data (i.e., the data is censored). $ ^{4} $ One group of regression models includes agglomeration at various distances as explanatory variables, while another group includes the network effects of different ranges.

First, we estimate models with agglomeration or network effect variables together with the number of papers each researcher published. Of course, when the number of published papers is the dependent variable, the last explanatory variable is omitted. The number of observations is large (322,748) resulting in precise estimates. This enables us to discuss the magnitude of agglomeration/network effects at various distances.

Then, various network characteristics are added to the above models one at a time. The number of observations is the same as the above estimation, again leading to precise estimates.

Table 2 shows the results for models with the number of researchers as agglomeration variables.

We also estimate models with the number of papers as agglomeration variables with similar results; hence, only the results of estimation using the number of researchers
Table 2 : Agglomeration Effect

Explanatory variables	Outcomes
Number of papers	Number of citations
Coefficient	Standard error	t-value	Coefficient	Standard error	t-value
Economies of scale
Faculty	0.00746	0.00017	44.55	0.03501	0.00257	13.64
Institution less faculty	0.00037	0.00002	22.43	0.00819	0.00026	32.12
City less institution	0.00006	0.00001	10.53	0.00001	0.00008	0.07
Nation less city	0.00000	0.00000	-8.11	0.00018	0.00000	53.71
Number of papers	n.a.	n.a.	n.a.	16.26369	0.02660	611.43
Constant	2.13928	0.01042	205.28	-21.29274	0.17143	-124.20
Number of observations	322,748	322,748
Log likelihood	-885,011	-1,593,771

are reported.

The first model is the case where the number of published papers is the outcome variable. The number of researchers in the same faculty of each researcher positively increases the number of papers. When the distance of agglomeration is enlarged to the same university, but different faculty, it still exerts a positive influence on the number of papers, but by a much smaller magnitude. When the range is further expanded, the coefficient on the number of researchers in the same city, but different university, becomes smaller still, and that on the number of researchers in the same country, but different city, even change to negative. The same trend is observed in models with other outcome indicators, including the number of papers published in top journals and those cited in guidelines.

In sum, the agglomeration effect is limited to nearby locations. It is positive at the faculty level, but diminishes very rapidly to university, outer city, and country levels. It might be marginally beneficial to have a large agglomeration of researchers in other faculties in the same university, but agglomeration in the outer city or other parts of the country is scarcely helpful. On the other hand, even if agglomeration is not large at the city or country level, economies of scale could be created within a faculty, like an island in the ocean.
Table 3: Network effect

Explanatory variables	Outcomes	Number of citations
Number of papers	Number of citations
Coefficient	Standard error	t-value	Coefficient	Standard error	t-value
Network effect
Faculty	0.10008	0.00242	41.37	1.05041	0.03697	28.41
Institution less faculty	0.01084	0.00318	3.40	1.44378	0.04857	29.73
City less institution	-0.00127	0.00476	-0.27	1.14055	0.07244	15.74
Nation less city	0.03822	0.00263	14.54	2.33343	0.03970	58.78
Overseas	0.06050	0.00276	21.90	3.04400	0.04155	73.27
Number of papers	n.a.	n.a.	n.a.	16.25688	0.02639	616.06
Constant	1.83387	0.01679	109.25	-25.68246	0.26230	-97.91
Number of observations	322,748	322,748
Log likelihood	-885,701	-1,591,849

Outcomes	Guidelines
Explanatory variables	Coefficient	Standard error	t-value	Coefficient	Standard error	t-value
Network effect
Faculty	-0.02403	0.00355	-6.76	-0.09400	0.01127	-8.34
Institution less faculty	-0.03034	0.00475	-6.39	-0.05423	0.01451	-3.74
City less institution	-0.00399	0.00678	-0.59	0.00636	0.01893	0.34
Nation less city	0.10842	0.00312	34.73	0.09061	0.00804	11.27
Overseas	0.15768	0.00301	52.37	0.11504	0.00740	15.55
Number of papers	0.29206	0.00184	158.82	0.17072	0.00447	38.23
Constant	-4.80490	0.03205	-149.93	-10.21562	0.16679	-61.25

Number of observations	322,748	322,748
Log likelihood	-167,215	-25,606

Next, we estimate models with the number of coauthors at different distances as an agglomeration variable. Table 3 shows these results.

When the number of published papers is the outcome variable, the number of coauthors in the same faculty of each researcher positively increases the outcome. When the distance of agglomeration is enlarged, just as the case of the agglomeration effect, the positive effect diminishes rapidly. The coefficient on city less university is very small and that on country less city is negative.

If we use the number of citations or maximum citations as outcome variables, the declining trend reverses. In the case of the number of citations, for example, the coefficient on faculty is significantly positive, and that on university less faculty increases. After a small dip at city less university, the coefficient strongly increases at country less city. Remarkably, a network with overseas researchers further increases the number of citations.

When the outcome variable is the number of papers published in top journals, the coefficients on faculty, university less faculty, and city less university are surprisingly negative, although the last one is not significant. Again, remarkably, coauthor networks at the country level and overseas exert positive effects on publication in top journals. Moreover, similar results are obtained if the number of papers cited in
guidelines is taken as the outcome variable.

To sum up, with respect to the quantitative outcome, a network effect is very limited to nearby locations, just as the agglomeration effect. Once the indicators of high quality research are used as the outcome variables, neighborhood networks are not effective or even harmful. However, distant networks, especially those overseas, are very effective in conducting quality research.

In Tables 2 and 3, we can also see the effect of the number of papers published by each researcher. An interesting question might be whether producing a lot of papers implies that the researcher is conducting high quality research. Moreover, the results in these tables are virtually identical; as the number of published papers increases, research outcomes increase. This is true for all outcome measures, including the number of published papers, number of citations, number of papers published in top journals, and number of papers cited in clinical guidelines. Therefore, producing many papers would enhance both the quantity and quality of research.

The magnitude of impact, however, is diminishing; it is highest for the number of citations, decreases with the maximum citations and core journal papers, and is lowest with guideline citations. Of course, this is natural considering the scarcity of core journal or guideline-cited papers. However, it has an important implication that, even if the number of citations strongly increases with the number of published papers, the numbers of core journal papers and guideline citations increase only modestly. A quantitative increase in the number of papers may not be an efficient way to raise the quality of research.

Table 4 shows the results of the estimation of the models with network characteristics.

We added each attribute of coauthors into the model one at a time, and also included agglomeration or network effect variables as well as the number of papers of the researcher, which is naturally excluded when the outcome variable is the number of papers. Moreover, when the number of papers is the outcome variable, all network characteristics, both quantitative and qualitative, are significantly positive. The number of papers that coauthors publish and number of citations of the coauthors' papers increase the number of papers of each researcher, although the effect seems marginal. Further, this effect is much stronger when coauthors are prolific authors or authors of highly cited papers. High quality coauthors, such as those publishing in core journals or cited in guidelines, also exert strong network effects, while the effect of
Table 4 : Network Attributes (with agglomeration variables)

Outcome variables
Number of papers	Number of citations
Attributes of coauthors	Coefficient	Standard error	t-value	Coefficient	Standard error	t-value
Number of papers coauthors publish	0.00335	0.00001	431.03	0.00633	0.00018	34.43
Number of citations of coauthors' papers	0.00016	0.00000	365.66	0.00148	0.00001	163.73
Number of prolific coauthors	0.24715	0.00053	463.56	0.43364	0.01336	32.45
Number of highly cited coauthors	0.10126	0.00044	229.79	0.91517	0.00753	121.48
Number of last authors	0.03091	0.00007	462.81	0.05338	0.00167	31.93
Number of core journal papers	0.01997	0.00006	333.28	0.06661	0.00121	55.18
Number of guideline-cited papers	0.14503	0.00072	200.81	0.11697	0.01225	9.55
Outcome variables
Core journal papers	Guideline-cited papers
Attributes of coauthors	Coefficient	Standard error	t-value	Coefficient	Standard error	t-value
Number of papers coauthors publish	-0.00011	0.00001	-9.91	-0.00010	0.00003	-3.67
Number of citations of coauthors' papers	0.00000	0.00000	-4.54	0.00000	0.00000	-2.96
Number of prolific coauthors	-0.01077	0.00085	-12.70	-0.01007	0.00204	-4.94
Number of highly cited coauthors	0.00059	0.00048	1.25	-0.00216	0.00109	-1.98
Number of last authors	-0.00109	0.00011	-10.39	-0.00062	0.00023	-2.70
Number of core journal papers	0.00101	0.00007	13.81	-0.00001	0.00015	-0.09
Number of guideline-cited papers	0.00277	0.00076	3.63	0.02632	0.00125	21.07

coauthors who have experienced last authorship is modest.

When the outcome variable is the number of citations, the overall trend is similar to the case of the number of papers, although the effect of each characteristic is generally larger, reflecting the fact that citations are 15 times larger than the number of publications.

If we take publication in core journals as an outcome measure, the picture changes drastically. Quantitative indicators generally lose effectiveness, while qualitative indicators remain positive. The coefficients on the number of papers or citations, prolific or last authors become negative, and those on core journal publications or guideline citations retain effectiveness. The difference between quantitative and qualitative indicators looms large in the case of guideline citation. Almost all quantitative attributes are negative. High quality coauthors who published core journal papers or guideline cited papers strongly increase the number of core journal papers and guideline-cited papers by each researcher. However, the effect of the number of papers published in core journals by coauthors on the number of guideline-cited papers is an exception, which is puzzling.
In summary, the quantitative attributes of coauthors are beneficial to quantitative outcomes, but not to qualitative outcomes. On the other hand, the qualitative attributes of coauthors are beneficial for both quantitative and qualitative outcomes. Yet, fostering collaboration to improve the quality of research requires more than encouraging prolific or highly cited coauthors; high quality researchers are essential to high quality collaboration.

Before ending this section, some remarks are in order concerning the endogeneity of the explanatory variables. Establishing collaboration and building networks with other researchers are not exogenously imposed actions. One chooses to collaborate and enter into a network because to do so is beneficial for research. If this is the case, we cannot simply interpret the correlation between research outcomes and links with excellent coauthors as indication of the causal effect of links with excellent coauthors on research outcomes. Instead of links generating outcomes, it may be that expected outcomes entail links with excellent collaborators. In other words, the effect on outcomes of links with distant researchers are, at least partially, not a cause, but a consequence, of expected outcomes.

In these situations, it would be better to control endogeneity by, for example, resorting to the instrumental variable (IV) method. However, the difficulty of finding adequate instruments prevents us to do so. An example of a candidate IV is the distance of a researcher from other researchers. The researchers with whom to collaborate are often those who are near to the researcher (in the sense of geography, languages, research styles, preferences, psychological tendencies, etc.). Thus, aggregate distance (in a suitable sense) from other researchers could be used as an IV, and the controlling of endogeneity is left to future research.

### 6 \. Conclusion

This paper investigates the mechanisms of the production of high quality research and sheds light on the way in which to promote academic research, by analyzing the determinants of research outcomes. In addition, this paper contributes to the measurement of the quality of academic research by introducing several new indicators of quality.

The analyzed determinants include the benefits of agglomeration and network effects, and effects of the structure of research networks as well as the nature of links.
The agglomeration effect is limited to nearby locations. To view this in an opposite direction, even if agglomeration is not large at the city or country level, economies of scale could be created within a faculty, like an island in the ocean.

With respect to the quantitative outcome, the network effect is very limited to nearby locations, just as the agglomeration effect. However, distant networks, especially those overseas, are very effective in conducting quality research.

Concerning the network characteristics, the linkage with prolific coauthors and/or organizers greatly contributes to a quantitative increase in research outcomes, but very little to qualitative outcomes. Instead, the linkage with quality coauthors enhances quality research. In other words, fostering collaboration to improve the quality of research requires more than encouraging prolific or highly cited coauthors.

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Plos One, 10 (8). doi: 10.1371/journal.pone.0135095
資料 3

# 平成 27 年度業務実績等報告書 (自己評価書)

独立行政法人 国際交流基金
## 目次

Ⅰ 評価の概要及び総合評定

No. 1 地域・国別事業方針による事業の実施 …… 1-1～9
No. 2 文化芸術交流事業の推進及び支援 …… 2-1～12
No. 3 海外日本語教育、学習の推進及び支援 …… 3-1～17
No. 4 海外日本研究・知的交流の促進 …… 4-1～14
No. 5 「アジア文化交流強化事業」の実施 …… 5-1～16
No. 6 東日本大震災からの復興に資する事業の実施 …… 6-1～5
No. 7 国際文化交流への理解及び参画の促進と支援 …… 7-1～6
No. 8 海外事務所の運営、京都支部の運営／国際文化交流のための施設の整備に対する援助等の事業 …… 8-1～8
No. 9 業務運営の効率化 …… 9-1～14
No. 10 予算、収支計画及び資金計画 …… 10-1～10
No. 11 人事に関する計画／施設・設備の整備・運営 …… 11-1～5