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The statistical analysis of longer term historical trends in migration in South Eastern Europe since the 1950s Attila Melegh Demographic Research Institute Hungarian Central Statistical Office Managing Migration and its Effects in the SEE countries SEEMIG Managing Migration and its Effects in SEE – Transnational Actions Towards Evidence Based Strategies www.seemig.eu The project is funded under the 3 rd call of the South-East Europe Programme. Project duration: June 2012 – November 2014 www.seemig.eu Which countries? Managing Migration and its Effects in the SEE countries Universities Statistical Offices Research Institutes Local governments COUNTRIES AUSTRIA BULGARIA HUNGARY ITALY ROMANIA SERBIA SLOVAKIA SLOVENIA •University of Vienna (AT) •University of Trento (IT) •National Statistical Institute of the Republic of Bulgaria •Hungarian Central Statistical Office •Statistical Office of the Republic of Serbia •Institute of Informatics and Statistics (INFOSTAT, Slovakia) •Demographic Research Institute (Hungary) •Romanian Research Institute for Research on National Minorities (Romania) •Institute of Social Sciences (Serbia) •Institute for Economic Research (Slovenia) •Scientific Research Centre of the Slovenian Academy of Sciences and Arts Slovenia •Municipality of Pécs (Hungary) •Harghita County Council (Romania) •Municipality of Sfântu Gheorghe (Romania) •District administration of Montana (Bulgaria) •Maribor Development Agency (Slovenia) •Town councilwww.seemig.eu Turčianske Teplice (Slovakia) •Municipality of Kanjiža (Serbia) Managing Migration and its Effects in the SEE countries SEEMIG strategy background From what activities do we generate ideas? • Data system analysis • Action plans • Master classes • Foresight exercise • Population projections • Migration policy documents • Target is a relevant policy document on a national level www.seemig.eu Managing Migration and its Effects in the SEE countries Transnational strategy Policy area Policy area 1: Harmonization of data collection Policy area 2: Enhancement of data collection methodologies - Policy area 3: Increase of transnational partnerships and cooperation - Policy area 4: Increase data collection on the local level - Policy Recommendations Harmonization of definitions and concepts Continuation of mainstreaming of migration data Harmonization of address registration Improving the methodology of making flow estimates out of global stock data, transnational use of big data Improving the methodology of making emigrant stock estimates out of global migration flow and migrant stock data Improvement and integration of administrative data- systems Establishing transnational dialogues between sending and receiving communities Collection and exchange of data on daily cross-border migration of labour between countries Improvement of transnational databases Enhancement of local data development (top- down from EU level) Launching local surveys and identification of sensitive groups on transnational level Enhancement of institutional capacity of public administrations and public services & new forms of cooperation among different actors on local level www.seemig.eu Problems and questions • What use we can make of new global historicalstatistical sources related to migration and its context? • How to understand longer term developments in migratory patterns and societal links globally and in one region? • What theories we can apply which can guide our research? How we can reflect on existing theories • How to proceed in the future and what to suggest? Major statistical challenge: Migration as a longer term linkage • Generally: It is observed individually and nationally: major issues of comparability, definitions in space and time. Register problem, emigration not recorded • It is not just an individual level phenomenon and it is cross national by definition. Just a proper global and historical structural perspective helps understanding it. • Cumulative and multiple level causation. • It is embedded: family members, networks, agencies, labor market processes and related social institutions, historical migratory links, • Caused by, and plays out, and reinforces global inequalities Global statistics: net migration flows 1. United nations: world population prospects, net migration rates, global scope 2. Net migration residuals. Net migration: the number of immigrants minus the number of emigrants over a period, divided by the personyears lived by the population of the receiving country over that period. It is expressed as the net number of migrants per 1,000 people. For most countries the figure is based on estimates of net international migration derived as the difference between overall population change and natural increase.. 3. Problems of enumeration, not a real category. Net flows. 4. Longer term development in a comparative way. Regional analysis • Census problems: not there but counted in the census (Romania) • If controlled by other historical estimates that Hungary seems to be zero or negative since 2008 Estimated global migration flows Wittgenstein Centre Method • Use of World Bank matrices (Abel) • Europe is not the most important actorSEEMIG not a big global player • increase of migration volume during 2000s (esp. inflows to Italy) • flows from/to SEEMIG region concentrated within Europe • This is not utilized according to merits Source: Abel & Sander 2014 Illustration: Sander & Bauer. No overall pattern Net migration in selected countries 1950-2010, WPP 2012 10 N u m b e r 5 Austria 0 Bulgaria Czech Republic o f Hungary -5 Italy m i g r -10 a n t s Slovakia Germany Romania Albania -15 Georgia p e r 1 0 0 0 -20 -25 -30 No linear development No migration transition Major divergence and path dependency even across political regimes Y ear Migration theory and change • Migration transition (Zelinsky): teleological from net emigration to net immigration due to changes in the economic structure. • Migration cycle: use of various contextual elements: labor market, demographic processes, state actions etc. (Fassmann et al 2013, 2014). • Migration hump: first low level emigration, then high level and then low level plus immigration. Would be migrants could afford migration. Migration theory and change • World-system theory or macro historical school. • Dependency, outmigration from previous agrarian and colonial countries. • There is an idea of change: intrusion and “great transformation” • What about state socialism and the move toward capitalist semi-periphery from socialist semi periphery? Böröcz: remittance dependency after the collapse of state-socialism. • What about path dependency? • We need to look for further ideas. Relative economic inequality between SEE and major migratory targets South Eastern Europe and global inequalities: long term perspective • Eastern and South Eastern Europe has not really changed its position for the last 100 and 150 years even across political regimes. Use of proxies when data is not available (Good and Tongshou 1999) • Global comparative and historical statistics: Maddison database. Use of 1990 Geary-Khamis USD and rely on purchasing power parities rather than exchange rates. Projects back and forward these 1990 levels of GDP with indexes checked by specialists • The differentials are almost the same throughout: makes migration all the time „rational” and makes the already existing links „viable”: Socialist and capitalist countries had similar trajectories Type 1: Increase Net Migration over Time, All datapoints in the "Increase" type, fiveyear intervals marked by midpoints, 1950-2010 Source:World Population Prospects, 2010 revision 20,0 15,0 R2 = 0,4781 10,0 Person per 1000 5,0 0,0 1940 1950 1960 1970 1980 1990 2000 -5,0 -10,0 Transition model -15,0 -20,0 Time 2010 Economic inequality and transition model Net Migration rate and GDP/cap difference from w orld average In Greece 19502010, WPP and Maddision databank. 10 120,00% 8 6 80,00% Person per 1000 4 60,00% 2 40,00% 0 1950-1955 1955-1960 1960-1965 1965-1970 1970-1975 1975-1980 1980-1985 1985-1990 1990-1995 1995-2000 2000-2005 2005-2010 20,00% -2 0,00% -4 -6 -20,00% Time Net migration rate GDP per cap difference % difference, Gery-Khamis 1990 dollar 100,00% 0 50,00% -1 1950- 1955- 1960- 1965- 1970- 1975- 1980- 1985- 1990- 1995- 2000- 20051955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 40,00% -2 Person per 1000 30,00% -3 -4 20,00% -5 10,00% -6 0,00% -7 -10,00% -8 -9 -20,00% Tim e Net migration GDP/cap difference % difference from World Average, Geary Khamis, 1990 USD Net m igration and GDP/capita difference in Bulgaria 1950-2010, WPP, 2010, Maddison Linking inequality and migration patterns • Those countries become recently immigrant, which could shift categories (Austria, Germany, Italy). This can be a key in the migration patterns of the region. • Institutional change, EU membership and better transportation enhances this. • Below average countries (poorer semiperiphery) is remaining emigrant or close to an emigrant patterns. World Bank and UN migration matrices 1. Ozden et al, Abel 1960 till 2000, UN matrices since 1990 2. Stocks by country of birth (sometimes foreign citizenship, sometimes ethnicity, sometimes estimated) in pairs, disaggregated by gender 3. Based on censuses but partially estimated. SEE censuses no data or very problematic till the mid 1990s 4. Hungary no census question. Soviet Union ethnicity. 5. Very problematic data for some countries even within SEE. Need to be controlled nationally. 6. Immigrant stocks are not okay till the 1990s 7. Most key target countries (USA, Canada, Germany, Australia have usable censuses) External outward links: SEE countries and major (top 5) destination countries (Country of birth stock, WB matrices) Russia/SU? Receiving centers (at least 3): USA, Germany Canada Turkey Australia France Argentina Hungary Source: WB, 2013 Illustration: Ági Tátrai-Pap. Semi-periphery countries also play a role SEE countries and their major destination countries (Country of birth stock, WB matrices) Russia/SU? Receiving centers (at least 3): Germany USA, Canada Turkey Australia Austria France Israel Source: WB, 2013 Illustration: Ági Tátrai-Pap. SEE countries and their major destination countries (Country of birth stock, WB matrices) Russia/SU? Receiving centers (at least 3): Germany USA, Canada Turkey Australia Austria Israel Switzerland Source: WB, 2013 Illustration: Ági Tátrai-Pap. SEE countries and their major destination countries (Country of birth stock, UN matrices) Receiving centers (at least 3): Germany USA, Canada Australia Austria Italy Source: UN, 2013 Illustration: Ági Tátrai-Pap. Reduction and Europeanization Loss of semiperiphery SEE countries and their major destination countries (Country of birth stock, UN matrices Receiving centers (at least 3): Germany USA, Canada Australia Austria Italy Source: UN, 2013 Illustration: Ági Tátrai-Pap. East/West slope SEE countries and their major destination countries (Country of birth, stock, UN matrices) Receiving centers (at least 3): Germany USA, Canada Austria Italy Switzerland Australia Source: UN, 2013 Illustration: Ági Tátrai-Pap. Major sources Region not united Source: un, 2013 Illustration: Ági Tátrai-Pap. Major sources Source: un, 2013 Illustration: Ági Tátrai-Pap. Major sources Region united and Internal sources Often emigration partners send migrants Source: un, 2013 Illustration: Ági Tátrai-Pap. On these bases what do we learn about migration? • Path dependency and political change is not so crucial. • State socialism and capitalism and migration links survive. Resilience of historical connections. • Global comparative perspective and overall integration of societies • Outmigration to the ”West” while sources within the region mainly • Abel (2014) estimated flows: similar observation. Latin American and Eastern Europe are “emptied”. Issues of dependency Argument • There is much stability in macro economic structures and related migratory links. • There have been sweeping changes in some countries concerning net migration, no overall pattern, theory of change is still missing. • We need to think in terms of not continuous, not homogenous space (some geographic models are to be corrected, new models created) • We need to think in terms of pairs, matrices even in the economy, not just migration • Stable migration links are to be studied carefully Net migration rate and GDP/cap difference from the world average, in Hungary between 1950–2010 Pair differences and ethnic links Net migration flow and GDP per capita ratios between Germany and Hungary, 1954–1999 . Immigration from Romania to Hungary 1995–2005 Romanian workers harvest grapes Thanks [email protected]