Marek Kupiszewski 1, Dorota Kupiszewska 2 Martin Bell 3, Elin Charles Edwards 3, Aude Benard 3, Philipp Ueffing 3 Philip Rees 4, John Stillwell 4, Konstantinos Daras 5 Eighth International Conference on Population Geographies Brisbane, 30 June to 3 July 2015 1. Institute of Geography and Spatial Organization, PAS, Poland 2. Independent Consultant, Warsaw Poland 3. The University of Queensland, Australia 4. The University of Leeds, UK 5. St Andrew s University, UK
One of the most significant functions of internal migration is its alteration of the spatial distribution of populations within countries 1 How do we measure this impact? Does impact vary at different spatial scales? between countries? according to characteristics of regions (rural-urban, settlement type and population density)? over time? Can we propose an underpinning theory of impact? 1 Along with the within country distribution of international migration and the distribution of natural increase
Theory Ravenstein 1885 showed how rural to urban internal migration (1871 and 1881) was essential to the growth of industrial cities in Britain Zelinsky 1971 proposed a developmental sequence of mobilities (including internal migration) which he called the Mobility Transition Geyer and Kontuly 1993 and Geyer1996 synthesized a body of work into a theory of Differential Urbanization which proposed a sequence of flow patterns for a hierarchy of settlements, within and between city regions Empirical investigations Berry, Beale, Champion and many others established that urbanization had become counter-urbanisation in 1970s and 1980s Anglo-America Courgeau 1992 showed elegantly how regression slopes between net migration rate and log 10 population density evolved in France between 1954 and 1990 from an urbanization to a counter-urbanization pattern Rees and Kupiszewski 1999 analysed the patterns of internal migration in 12 European countries, assessing the degree to which urbanization or counterurbanization prevailed. They also showed variation in pattern between ages grouped in life course stages Geyer and Kontuly 2003 assembled 12 case studies in which authors identified for their countries which differential urbanization process characterised which time period The gaps This was a story mainly about the developed Western world. What happened in other countries housing 85% of the world s population?
Urban-rural migration Regional OD matrices or aggregate inflows and outflows Coverage (countries with one or more data sets) Region 1 year 5 year Total UN interval interval countries Africa 3 3 10 14 54 Asia 15 4 13 16 47 Europe 11 27 5 30 43 Latin America 1 0 22 22 32 Northern America 0 2 3 3 3 Oceania 1 1 4 4 14 Total 31 37 57 89 193
1. System-wide indicators of spatial impact CMI, MEI, ANMR 2. Rural-urban redistribution Migration flows and effectiveness 3. Area-specific patterns Net migration by population density
Crude migration intensity Migration effectiveness Index Aggregate net migration rate CMI = GM/PAR MEI = NM/GM ANMR = NM/PAR Thus ANMR = CMI * MEI Country Year Data type No. of regions CMI MEI ANMR Canada 2006 5-year 288 11.8 15.0 1.8 Australia 2011 5-year 333 21.2 8.6 1.8
ANMR depends strongly on the number of spatial units (because CMI does) (so we can t make a ranking of the countries directly based on ANMR) MEI usually stabilises for a large number of spatial units, or changes not very strongly (so may be used for comparing the countries, despite the differing number of regions in migration matrices) If MEI does change, it usually goes up with the number of region, but may go down (Canada)
Kenya Poland Burkina Faso Romania USA Spain Canada Germany 1998 Italy Finland UK 2002 Belgium Australia Germany 2009 UK 2001 Sweden Netherlands Japan UK 2010 Finland Haiti Nepal Mexico Honduras Brazil Bolivia Nicaragua Ghana Chile Ecuador Canada Costa Rica USA Australia Japan MEI, 1-year events or transitions 0 5 10 15 20 25 30 MEI (%) MEI, 5-year transitions 0 10 20 30 40 50 60 70 MEI (%) Large differences in 1- year and 5-year MEI between countries. We may expect high values for Africa, and higher values Eastern Europe than for the Western Europe (because urbanization is still a dominant process in the former regions) Only countries with more than 40 spatial units included Africa Asia Europe Latin America North America Oceania
MEI 70.0 y = -53.031x + 61.916 R² = 0.3131 China 60.0 Viet Nam Haiti 50.0 Egypt Mongolia Kyrgyzstan Cambodia 40.0 Nepal Uganda Vanuatu India El Salvador Dominican Republic South Africa Indonesia Antigua and Barbuda 30.0 Guinea Thailand Mexico Honduras Ecuador Panama Peru Cuba Senegal Ghana Bolivia Iran Morocco Brazil Uruguay 20.0 Iraq Paraguay Chile Mali Colombia Spain France Belize Fiji Portugal Canada Mauritius Malta 10.0 USA Australia Argentina New Zealand Switzerland Japan 0.0 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 HDI
MEI low intensity high efficiency Mean HDI=0.58 70.0 high intensity high efficiency Mean HDI=0.63 60.0 50.0 40.0 30.0 20.0 10.0 0.0 Egypt Mongolia Kyrgyzstan Nepal Cambodia India Uganda Thailand Indonesia Tunisia South Africa Venezuela Mexico Guinea Malaysia Ecuador Panama Iran Honduras Brazil Senegal Iraq Ghana Uruguay Paraguay Morocco Spain Chile Mali Mauritius Barbados Costa Rica Portugal Fiji France Malta Canada United States of America Argentina Australia Switzerland Japan New Zealand low intensity low efficiency Mean HDI=0.66 Haiti China Viet Nam 0 10 20 30 40 50 60 ACMI high intensity low efficiency Mean HDI=0.77 R2=0.23
Four key migration flows, % for 31 countries (sorted by share of urbanurban migration): Urban-urban migration (blue bars) dominates (38%). In New Zealand 80% of migration is within the urban subsystem. Rural-urban migration (red bars) accounts for 21% of moves, and dominates in only 3 countries: Kyrgyzstan (44%), Thailand and Lithuania. Rural-rural migration (purple bars) dominates in South and South-Eastern Asia: Timor Leste (69%), India, Cambodia, Nepal. It also occurs in Africa (Swaziland) and Latin America (Nicaragua)
Effectiveness of migration from rural to urban areas: MER RU = 100 (M RU - M UR )/(M RU +M UR ) Value of rural to urban migration efficiency positive when urbanization prevails, that is net migration from rural to urban is positive
Effective concentration Rural-urban and urban-rural migration Nepal 2001 Vietnam 1999 Kyrgyz Republic 1999 Cambodia 1998 Egypt 2006 Swaziland 1997 Iran 2006 Cameroon 2005 Bulgaria 2009-13 Israel 2004-05 Nicaragua 2005 Czech Republic 2011-12 Poland 2010-12 Estonia 2011 (2000-2011) Malaysia 2010 %R-U %U-R Effective deconcentration MERRU 75.6 62.1 58.0 53.3 46.4 41.0 40.9 30.8 30.7 21.7 14.0 13.8 7.5 4.0 1.1 0.1-0.7-2.8-6.2-6.9-12.0-16.5-17.0-17.1-17.7-19.0-21.1-22.5-58.4 Countries with the highest effectiveness of concentration (urbanization) are located in Asia, mostly South and South-Eastern Deconcentration (sub/counterurbanization) prevails in European post- Soviet countries.
Data on urban-rural flows not available for many countries Urban-rural division is too simple does not reflect diversity of region types and the changing characteristics processes more complex than just urban-rural flows More complex divisions created Examples from Europe categories differ between countries so can t be used for comparison Eurostat/OECD example could be used for comparisons for where the classification exists, but would not cover the whole world
Population density is used as a proxy for the level of concentration Net migration rates can be studied as a function of population density by zone Population-weighted linear regressions NMR = a + blog population density + ε Global coverage of 85 countries 1-year data: 37 countries 5-year data: 57 countries
1954-1962 1962-1968 968-1975 1975-1985 1982-1990 1990-1999 2001-2006 Slope The Idea originates from a 1992 paper by Daniel Courgeau Two further periods have been added, showing a retreat to balanced flows The settlement system moves from strong urbanization to polarization reversal to strong counter-urbanization 1 0.8 0.6 0.4 0.2 0-0.2-0.4-0.6 FRANCE
NMR NMR NMR NMR 15 Mongolia 2000 (zones=21) 8 Haiti 2001 (zones=41) 10 5 0 4 0-5 -10-4 -15-1 0 1 2 3 log Population Density -8 1 2 3 4 log Population Density 5 4 3 2 1 0-1 -2-3 -4 Argentina 2001 (zones=24) -1 0 1 2 3 4 5 log Population Density 20 15 10 5 0-5 -10-15 -20-25 IRAN 2011 (zones=367) -1 0 1 2 3 4 log Population Density
Slope of NMR against log(population density) Number of spatial units
NMR NMR NMR NMR NMR NMR D E V E L O P M E N T log (dens) log(dens) log (dens) log(dens) log (dens) log (dens) 1 2 3 4 TIME a b c 1 2 3 4 Early rapid urbanisation (take off) Mature urbanisation (industrialisation) Counter urbanisation (de-industrialisation) a. Re-urbanisation b. Equilibrium c. Shrinking cities (post-industrialisation)
Slope of NMR by log (Population Density) 10.00 y = -9.9291x + 7.7905 R² = 0.2424 8.00 6.00 Guinea Haiti Uganda Mongolia Kyrgyzstan Panama Fiji 4.00 2.00 0.00-2.00-4.00 Mali Nepal Viet Nam South Africa Honduras Thailand Saint Lucia China Dominican Republic Bolivia Tunisia Cambodia Peru Ecuador Cuba Mexico Senegal Morocco Paraguay Colombia India Japan Brazil Malaysia Switzerland Vanuatu Iraq Costa Rica Nicaragua Indonesia El Salvador Portugal Spain Canada Ghana Belize Egypt Argentina Australia Chile Venezuela New Zealand Mauritius USA Barbados France -6.00 0.30 0.40 0.50 0.60 HDI 0.70 0.80 0.90 1.00
ANMR varies with spatial scale & is not suitable for international comparisons MEI is relatively stable by spatial scale, so offers a good basis for international comparison. MEI varies negatively with the level of a country s development: the higher the HDI, the lower the MEI; the lower the HDI, the higher the MEI. MEIs could be higher than predicted by HDI in some countries where internal migration was (partially) controlled. The Courgeau NMR-log density relationship could be used to refine the rural-urban analysis. The slope was not overly affected by the scale or zonation issues of the MAUP. We showed there was a general relationship across countries between slope and development. We linked these findings back to previous theories of urbanization, demonstrating the potential for more rigorous, cross-national testing of those theories. http://www.gpem.uq.edu.au/image