1. Scientific Mobility and Knowledge Networks in High Emigration Countries: Evidence from the Pacific John Gibson, University of Waikato and Motu (with David McKenzie, World Bank) Economic Impacts of Immigration and Population Diversity, University of Waikato, April 2012

2. Competing Narratives Brain Drain – decimates the human capital and fiscal revenues of sending countries Migration → countries without a local research base, weak universities, few doctors etc Brain Gain – potent force for developing local economy through knowledge transfers, remittances, trade, FDI, incentive effects Migration → diaspora help bring world scientific frontier closer and eventually return to develop your own Silicon Valley

3. Issue is also of interest to (potential) destination countries Confusion in potential destination countries about whether it is a good or bad thing to recruit skilled workers (or retain skilled foreign students) from high- emigration developing countries Mayor of New York City (2011 speech to US Chamber of Commerce) : “It’s what I call national suicide… We are investing millions of dollars to educate these students at our leading universities, and then giving the economic dividends back to our competitors – for free. … So instead of staying here to contribute to our economy, they go home and go to work for companies that compete with our own. That makes no sense.” New York Times (March 7, 2012) “America is Stealing the World’s Doctors”

4. What does this paper contribute? empirical micro evidence on knowledge flows from the international mobility of researchers – Focus on the places with highest skilled emigration rates, which are small island countries – Most previous case studies for India, China, Taiwan, which are atypical Find evidence contrary to two claims in the literature – Too few researchers ever return to have much impact – No productivity gain to researchers from migration Migration is important channel for knowledge acquisition and knowledge transfer for researchers from small island countries

5. Prior approaches focus on individuals from a selected profession – highly cited physicists (Hunter et al, EJ) – ISI highly cited (Weinberg, JDE), with e-mail address (Trippl, RegStud) – Israeli economists (Ben-David) – Non-US faculty of US PhD-granting chemistry departments (Gaule) – Italian researchers and scientists (Constant and D’Agosto, AIEL) Problems with this for looking at scale and consequences of scientific mobility: 1.Initial decision to enter discipline may be tied to desire to migrate 2.Training for this occupation/discipline may occur through migration 3.Whether remain in the occupation may depend on migration 4.Frames based on revealed career performance may draw stayers from more extreme end of the tail  Right counterfactual for a high-skilled individual abroad not necessarily someone in the same job at home

6. Our approach Define a target sample of interest whose members can be identified before migration has occurred – ex post define the sample frame in terms of ex ante characteristics Then survey these individuals regardless of their subsequent emigration and occupational choices – No survivorship bias and can test for, and adjust, for differential survey rates

7. Highly Skilled Migrants Target sample of interest: “The best and the brightest” – individuals who were the best students in their countries at the end of high school from 1976-2004 in three Pacific countries: Tonga, PNG and NZ – Approx one-quarter are ever researchers (self-identified) Natural counterfactual for measuring impacts – Outcomes for the high skilled who did not migrate Comparison of returnees and migrants sifts between two theories of migration – local attributes of the country of work make individuals more productive when they are abroad – workers learn how to be more productive when working abroad and can bring that knowledge back with them

8. Country choice Skilled emigration rates are highest for small island states (Docquier-Marfouk, Beine et al) – These are the places where concerns about brain drain most vivid, so seem the right place to look at the consequences – Many more countries like these than like China, India and Taiwan We chose: – New Zealand: 4.3 million, $28,000 GDP per capita, 15.8% brain drain rate (with Ireland, equal highest in OECD) – Papua New Guinea: 6.4 million, $1010 GDP per capita, 19.8% brain drain rate – Tonga: 110,000, $2560 GDP per capita, 65.1% brain drain.

9. Tracking and surveying Extensive tracking effort attempted to locate these individuals wherever they are today and survey them – Used visits to schools and home communities, school networks, online search, phone book searches, peer search, etc. – Cost about US$200 for each respondent to track, locate and survey, excluding time cost for PIs Survey then administered online (using SurveyMonkey), in-person, or by phone – of those in the survey frame, 44% responded

10. How good is this survey rate and what non-contact/non-response bias is likely Overall survey rate of 44% compares well to other studies: – Trippl gets 25%, conditional on having an e-mail address for survey of ISI highly cited scientists – Constant et al. get 28% despite using pre-existing database of Italian scientists and researchers Our surveys made repeated passes through the frame, searching with greater intensity for those not found and this gives a test for response bias – supplement all the regressions in our EJ paper with indicator for whether respondent was surveyed latter in the year for each country and find no evidence of differences, suggesting that those not found/not surveyed may not differ from those whose responses we obtained

11. Understanding the determinants (J. Development Economics, 2011) Returnees leave behind much higher incomes and return for mainly non-economic reasons – Family commitments – Scholarship/bonding commitments Returnees do not earn significant income premium over the similarly skilled never- migrants – supports view that local (non-portable) attributes of the country of work make individuals more productive when they are abroad

12. High rates of migration and of return migration in the overall sample  Mistake to think in terms of irreversible “brain drain”

13. Even higher mobility rates for those who are ever researchers (but lower return rates) Note: based on those age 22+, moving for work or study

14. Measuring the consequences (Economic Journal, 2012) largest gains to the migrants themselves, US$20,000-$40,000 per year but often ignored in the migration literature – Labour/immigration literature looks at impact on natives in destination (or compares with natives) – Development literature looks at impact on those left behind Also significant human capital gains Annual remittances exceed fiscal costs for Tonga – Lean public sector, flat income tax and comprehensive consumption tax are good settings for high emigration countries – While there are high remittances from skilled PNG migrants, fiscal costs are higher because of progressive income tax Very little business investment or trade facilitation – Remoteness and low economic density that make emigration attractive, also limit scope for business development

15. Gains and losses (annual) from high skilled migration

16. Researcher sub-sample Self-defined “ever been a researcher – including doctoral and post-doctoral students” – Highest for the NZ sample – 43% – Ca. 10% for PNG and Tonga, which we combine into one group Individuals who could, potentially, publish scientific articles although not all did – Ex ante, policy makers who tinker with innovation and training policy to restrain emigration or attract returnees don’t know which researchers will be successful – Therefore a sample from a relevant population Early career, with average age of 35 – Use Harzing Publish or Perish rather than Web of Science to look for impacts, since many citations are to working papers

17. Researcher activity Current migrants are more active and have larger impacts than the returnees and never-migrants – 50% higher odds of publishing and unconditional number of articles is twice as high as for the returnees (NZ sample) – Total citations almost twice that of the returnees and 10x higher than the never migrants (NZ sample) – Returnees to PNG+Tonga more similar to the current migrants in activity and impacts Reflects some highly productive medical researchers who returned to PNG More constrained return to PNG from bonding provisions

18. Research activity (risk of publishing academic article in last five years) by migration status

19. Research activity (unconditional number of articles in last five years) by migration status

20. Scientific impacts (h-index) by migration status

21. Apparent returnee premium just reflects age differences (NZ sample only) Number of papers, total citations and h-index show no significant difference between returnees and never-migrants once condition on age  Returnees have no significant productivity premium over the similarly skilled never-migrants – supports view that local (non-portable) attributes of the country of work make individuals more productive when they are abroad

22. Researcher networks, dissemination and funding Current migrants have larger co-author networks but little overlap with home country researchers (NZ sample) NZ expatriate researchers have significantly higher grant funding and much higher fraction (30%) are funding from international grants – No significant differences between returnees and never migrants in terms of funding Returnees to PNG+Tonga have largest co-author network and more active conference networks than current migrants

23. Location of co-authors by migration status New ZealandPNG + Tonga

24. Research Dissemination Rare for expatriates to return to present at home country academic conferences or to home country audiences in government/university seminars – Once every five years, on average – Only 10% of conferences that expatriate researchers attended in the past year were in the home country Return migrants may be conduit for dissemination – Attend conferences with similar frequency to expatriates but with 50/50 split between home country and international conferences – Much more active than the never migrants

25. Research funding (US$, all grants, last 3 years) by migration status

26. Conclusions High rates of mobility, with significant return – Contrary to results from a sample frame based on those from developing countries who started their research career in the US Large positive impact of migration on researcher productivity and impact – Contrary to results from a sample frame of highly cited researchers, where the stayers were already from the scientifically advanced countries Return migrants are the ones actively engaged in maintaining international knowledge networks