A bibliometric approach to international scientific migration

Henk F. Moed, M’hamed Aisati, Andrew Plume and Gali Halevi

Elsevier (Netherlands, UK, USA)

Contents

• Introduction: Migration and co-authorship• The model• Technical aspects • Study countries and approaches• Results: Global patterns• Publication (Scopus) vs. Survey (OECD) data• Accuracy / robustness of migration indicators• Conclusions

Introduction:Migration vs. co-authorship

Which country has these main collaborators?

Main collaborators

Argentina

USA

Portugal

France

Chile

Brazil

Which country has these main collaborators?

Main collaborators

Thailand

India

Singapore

Iran

UK

Malaysia

Which country has these main collaborators?

Main collaborators

France

Hungary

Germany

Italy

Bulgaria

Romania

Which country has these main collaborators?

Main collaborators

UK

China

USA

Nigeria

Australia

Netherlands

South Africa

The model

International migration vs. co-authorship

Relationship Definition Comment

International co-authorship

Authors from institutions located in different countries jointly publish a paper

Country relates to where authors work, NOT to their nationality International

migrationA scientific author moves from one country to another

The model - 1Theoretical concept; Interpretation

Bibliometric construct / feature

Researcher Author id

Research active (in a year) Publishing (in a year)

Currently active Publishing in 2011

Starting a scientific career during years T1-T2

First publication during T1-T2

“Young” in 2011 First publication year > 2000

Moving from country A to B Publishing author’s “work” country changes consecutively from A to B Transients are

deleted

The Model – 2

Master PhD student Post doc Senior

Master degree

PhD degree

Start senior career

T

First publi-cation

2nd p 3rd p 4th p

B B B B B A A

A A B B B A A

A A A A A B A

2

1

3

Technical aspects

Technical aspects (in Scopus)

1. Author-affiliation linking

2. Author profiling

Author-affiliation links

Is Italian science declining? RESPOL, 2011Cinzia Daraio (a) and Henk F. Moed (b)

(a) Department of Management, Univ Bologna, Via U. Terracini, 28, 40131 Bologna, Italy

(b)Centre for Science and Technology Studies (CWTS), Leiden University, The Netherlands

Author-affiliation link records

Author Year Affiliation

...... ...... ......

Daraio, Cinzia 2011 Univ Bologna, ..., Italy

Moed, Henk F. 2011 Univ Leiden,..., Netherlands

---- .... ....

Author profiling in Scopus

• Assigns to each author a unique number• Groups each author’s documents....• ... based on similarity in affiliation,

publication history, subject and co-authors• Aims at high precision ( lower recall)• New algorithm implemented in April 2011• Author feedback system in place

Categorization of authors in year T

TYPE T-4 T-3 T-2 T-1 T T+1 T+2 T+3 T+4

Continuant X X X

Newcomer X X

Mover X X

Transient X

[Active but not publishing]

X X

# Publishing authors per year and type (UK)

CONTINUANTS

MOVERS

TOTAL

TRANSIENTS

NEWCOMERS

Decline in # Newcomers

2009 Cohort can be followed for one year only

Important distinction

Use of author ID data to assess an individual

vs

Statistical analysis of patterns in large datasets

X

Study countries and approaches

Countries with the largest increase in publication output during 2000-2010 (Scopus)

> 10 %: Selected in

current study

Study countries (10 fast growers + 7 “big” countries)

D8 EU BRIC Other

Egypt Romania Brazil Thailand

Iran Portugal China

Malaysia India Australia

Pakistan Germany Japan

Italy USA

Netherlands

UK

Two complementary approaches

Feature Synchronous Asynchroneous

Publication years analyzed

Fixed [2011]

Variable [2001-2011]

Starting years of authors’ careers

Variable [2001-2010]

Fixed [ 2001-2003]

Results: Synchronous approach

Study set: % Young authors currently active in a study country Question: How many of them had stays abroad?

Results: Asynchroneous approach

Study set: authors starting their career in a study countryQuestion: How many moved abroad, how many returned?

Study set: Young authors starting their career in a study countryQuestion: How many move abroad, how many return?

Post Docs returning to their home country?

Post-Docs not returning to their country? Or PhD students returning after attaining their PhD?

Results: Publication (Scopus) data

vs. Survey (OECD) data

Inconsistencies in OECD data on # FTE Res?

CountryOECD

# FTE Res All

(2007)

OECD# FTE Res Gov+HE

(2007)

Scopus# Authors

(2007)

Ratio # authors /# FTE Res

All

Ratio # authors /# FTE Res Gov+HE

UK 254,600 159,100 154,600 0.61 0.97

ITA 93,000 56,200 113,100 1.22 2.01Differences in

ratios between ITA and UK are

almost a factor 2!

Inconsistencies in data on # FTE Res?

Country# FTE Res

All(2007)

# FTE Res Gov+HE

(2007)

# Authors Scopus

(2007)

Ratio # authors /# FTE Res

All

Ratio # authors /# FTE Res Gov+HE

DEU 290,800 116,600 150,400 0.52 1.29

UK 254,600 159,100 154,600 0.61 0.97

ITA 93,000 56,200 113,100 1.22 2.01

NLD 49,700 23,800 46,300 0.93 1.95

Differences in ratios between {ITA, NLD} and

{DEU, UK} almost a factor 2

Results: Migration vs. Co-authorship

Migration and co-authorship patterns are statistically different

N=694Mean=0.75STD=0.49Skewness=1.59

Analyzed later

Map of countries with Ratio migration/collaboration > 1.2

Study country = TO COUNTRY

FROM Country

No. Co-authored papers

No. Migrating authors

Ratio % Migration / % Co-authorship

PAK IND 276 118 3.6PRT BRA 1,971 423 3.4IND PAK 276 96 3.3NLD IRN 492 80 3.1USA IND 12,013 3,307 2.8NLD PAK 145 21 2.8CHN TWN 3,979 1,048 2.6USA IRN 3,039 780 2.6BRA PRT 1,971 352 2.4MYS NGA 122 31 2.1

Language similarity drives migration stronger than it drives co-authorship

Political tensions affect migration less than they affect co-authorship

Tentative error rates in migration indicators

Indicator Set N Mean variation

STD Skew-ness

Rough error rate

# or % authors per country

All 17 11.1 2.9 -0.25 10 %

# or % migrating authors per country pair

Top 100All

100

650

2.1

11.1

1.2

28.8

1.4

7.0

3 %>10%

Ratio migration/Collaboration per country pair

Top 100All

100

650

2.8

10.6

2.4

27.6

2.4

7.4

5 % >10%

Variation = 100*(max-min)/

(2*mean)

Conclusions

• Migration analysis generates new insights into the global scientific network

• Relative indicators based on large numbers are insensitive to errors in author profiles.

Thank you for your attention

PART II Robustness / accuracy of migration indicators

Case study: Data sample analysed

• 100 author-ids in chemistry randomly selected from Scopus

• Search for other author-ids that relate to the same authors as those represented in the sample

• Precision of the 100 author-ids themselves was not examined

• Emphasis on recall rather than precision

Author Countries

Asian countries (China, Japan and Korea) are over-represented (n=34)

CHNUSA

JPNDEUKOR

Findings – 1

• 27% of the researchers did have additional author ID's

• For the 27 % researchers that did have additional ID's, in total 51 additional author IDs were found

• Half of the additional author IDs had 1 paper only; and 75 % at most 2

100 sample authors are linked with 151 author-ids

# Author-ids with 1 paper = 2 x # authors with 1 paper

# Author-ids with 2 papers = 1.6 x # authors with 2 paper

# Author-ids with >=20 papers is 1.1 x # authors with >=20 papers

Implications for the migration study

Homonyms Synonyms

Definition/ Examples

One author id relates to different persons (common names)

A researcher has more than one author id (split identities)

Tendencies The more common a name, the larger the number of articles

Second identity for author ids with few articles only (typically 1-3)

In analysis Newcomers as from 2001

Transients are not taken into account

Sensitivity Analysis: publication thresholds

1. ALL

2. P > 2

3. P > 3

Sensitivity analysis: publ per year (Ppyr) thresholds

1. All

2. Ppyr < 7.0

3. Ppyr < 5.0

No. Authors moving to a study country

12 3

% Authors moving to a study country

12 3

Ratio % migration/% co-authorship per country pair for author sets 1 and 2

Rank order hardly changes

1

2

Variation in # or % migrating authors per country-pair

Rank No. AuthorsMean

variation STD

1-100 1002 2.08 1.71

101-200 329 3.50 2.91

201-300 130 5.40 6.50

301-400 81 7.94 11.53

401-500 54 8.61 17.25

501-600 22 18.28 34.07

1-650 270 11.14 28.77

Variation = 100*(max-min)/

(2*mean)

+

Tentative error rates in migration indicators

Indicator Set N Mean variation

STD Skew-ness

Rough error rate

# or % authors per country

All 17 11.1 2.9 -0.25 10 %

# or % migrating authors per country pair

Top 100All

100

650

2.1

11.1

1.2

28.8

1.4

7.0

3 %>10%

Ratio migration/Collaboration per country pair

Top 100All

100

650

2.8

10.6

2.4

27.6

2.4

7.4

5 % >10%

Variation = 100*(max-min)/

(2*mean)

Conclusions

• Migration analysis generates new insights into the global scientific network

• Relative indicators based on large numbers are insensitive to errors in author profiles.