Schiller - Measuring researchers mobility

OECD Blue Sky Forum 2016 Daniel Schiller, Alexander Cordes 0

Measuring Researcher Mobility

A Comparison of Different Datasets and Methods

with an Empirical Application of Micro-Data for the United States and Germany

Prof. Dr. Daniel Schiller Chair of Economic and Social Geographiy, University of Greifswald

[email protected]

Dr. Alexander Cordes Ministry of Economic Affairs, Labour, and Transport of Lower Saxony

[email protected]

The research has received funding from the German Expert Commission for Research and Innovation (EFI).


Agenda

• Properties of a reporting system

• Assessment of German and international data sources

• Case study: Natural Language Processing (NLP)

• Micro data analysis for Germany and the United States


Properties of a Reporting System

• Definitions

Researchers, e.g. occupational codes (ISCO)

Mobility, e.g. six months as a minimum, short-term, mid-term, long-term

• More complex analysis requires more information on mobile researchers

Calculation of migration balances

Mobility patterns

Career paths

Interdendence of mobility and performance, etc.

• From surveys or information from mobility programmes towards comprehensive and representative datasets


Assessment of German Data Sources

• Regularly collected statistics containing individual information: micro census, socio-economic panel (SOEP)

• Process data from employment administration (integrated employment biographies (IEB) of the federal labour office)

• Data of the immigration offices (central register of foreigners)

• Panel studies of career development of university alumni and doctoral candidates (e.g alumni register of HIS, ProFile-doctoral candidate panel of iFQ)

• Efforts of the science council (Wissenschaftsrat) implementing a data set “research” with a collection of available data in a standardized format

Absence of any national registers of researchers


Most Promising International Data Sources

• Census Data

Main limitation: size of census population too small for differentiated analysis in most countries

• Publication Data

Main limitation: lack of information on country of birth/education

Interesting data for combination with information from other sources

• Reconstruction of CVs via Natural Language Processing (NLP)


Case Study: Natural Language Processing (NLP) • Natural Language Processing (NLP): the attempt to gain, save and process

relevant information of (un-)structured texts.

• Vast amounts of personal information on researchers is accessible online

university websites, personal homepages, open source CVs, public profiles on social networks, publications, other online material

• Implementation within five steps

Step 1: Establishment of a reference database

Step 2: Collection of electronic documents of persons in the reference database

Step 3: Identifying mobility traces in the documents

Step 4: Evaluation of mobility traces

Step 5: Creation of career paths of persons in the reference data base


Micro Data Analysis for Germany and the United States

• Why a comparison of Germany and the US?

Similar in terms of R&D intensity and distribution (public/private)

US: largest target country, central position in research networks

Germany: largest EU country, non-English speaking, less open research system

• Population survey data (annual 1%-samples of the population)

US: American Community Survey (ACS), approx. n = 3,000,000

Germany: Microcensus, approx. n = 800,000

• Identification of mobile researchers in the survey data

Researcher: education (higher education), occupation, (sector)

Mobility: year of immigration, only migrants who were 28 years or older when they immigrated (exclusion of student mobility)


5.4

10.89.6

6.8

0

2

4

6

8

10

12

scientific occupations non-scientific occupations

GER USAShare of Migrants in Scientific and Non-scientific Occupations

Source: own calculations based on ACS and Microcensus


Share of Researchers among Non-Migrants and Migrants in Germany and the United States

7.2

3.6

5.4

7.6

0

1

2

3

4

5

6

7

8

non-migrants migrants

GER USA

Source: own calculations based on ACS and Microcensus


Employment Structure in the US by Country of Birth

16.5

6.85.1 4.5 4.6 4.6 5.1

7.2

4.8

2.13.2

2.2 2.4 2.01.1 0.4

3.1 3.62.3 2.3

3.7

4.4

4.64.3 4.4 3.8

4.4

3.2

3.4

2.4

2.72.8

1.4 1.9

1.1

0.4

1.81.9

1.5 1.5

2.3

5.6

4.84.7 4.8

3.62.8

2.4

2.7

3.21.5

1.4

1.2 1.2

0.9

0.1

1.31.0

0.7 0.8

2.1

3.9

5.86.0 5.4

3.3 3.1 2.0

3.5

4.8

3.0

1.7

2.2 1.4

1.2

0.2

1.5 1.3

0.9 1.0

24.6

20.6 20.319.5

19.1

15.3 15.314.9

14.4

12.5

10.5

8.2

7.26.4

4.3

1.1

7.6 7.8

5.4 5.6

0.0

5.0

10.0

15.0

20.0

25.0

postsecondary teachers

life, physical, and social science occupations

architecture and engineering occupations

computer and mathematical occupations

Source: own calculations based on ACS


Conclusions

• German and international data sources

Data situation in Germany worse than in some other countries

Use of single data sources is always connected with limitations

• Natural Language Processing – Big Data

Taking advantage of publicly available information about researchers

Formation of a consortium with experts in big data analysis, computer linguistics, and STI

• Microcensus data

Population survey data is feasible to inform science and policy about researchers mobility, at least for USA and GER

In-depth analysis of the data via discrete choice modelling is possible


Measuring Researcher Mobility

A Comparison of Different Datasets and Methods

with an Empirical Application of Micro-Data for the United States and Germany

Prof. Dr. Daniel Schiller Chair of Economic and Social Geographiy, University of Greifswald

[email protected]

Dr. Alexander Cordes Ministry of Economic Affairs, Labour, and Transport of Lower Saxony

[email protected]

The research has received funding from the German Expert Commission for Research and Innovation (EFI).

Schiller - Measuring researchers mobility

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