migration patterns today and the factors that influence them
TRANSCRIPT
Migration Patterns Today and the Factors that Influence Them
David A. Plane, Professor
School of Geography and Development
University of Arizona, Tucson
The Migration Component of Population Change
The Demographic (Accounting) Equation:Population Change = Births − Deaths + Net Migration
Net Migration = In-migration − Out-migration
At regional and local scale, net migration often the largest component of change—and the most volatile
Migration patterns notoriously hard to forecast
“Those who know enough to forecast migrationknow better than to try.”
—Peter Morrison, RAND demographer
Drivers of Population Change
What are reliable drivers for projecting population? Strong trends likely to continue into the future
At the time-scale of our lifespans and future generations, two incontrovertible trends:
The climate will continue to change
Most people will be getting older, one year at a time
My main contention for today’s talk: Age is a paramount variable for understanding
migration—and for thinking about the linkages between climate-change and US regional/local migration patterns
Migrant streams composed of many differently motivated migrants
At different points in the life course, the reasons to move—and thus the influences on migration— vary widely
Demographic disaggregation is key, yet still myriad reasons why any individual may or may not move
One bifurcation of migration research: Individual/behavioral approaches:
Migration as something people do
Aggregate/geographical approaches: Migration as a reflection of relationships between places and Edward Ullman’s ‘Bases of Spatial Interaction’:
Interregional complementarities
Deterring effect of distance
Intervening opportunities
No such beast as “The Migrant”
Net migration made up of all sorts of people coming into or leaving an area for a plethora of reasons
Complexities of Migration Systems
Many of the influences on migration are highly geographically specific
Plus, migration has a ‘double geography’
Net migration to any particular region or area reflects conditions both there, but also in:
All origins of in-migrants
All potential destinations for out-migrants
At least as challenging to model as climate change!
Migration researchers are like tornado chasers
“Most meteorologists can’t understand it. You have to take a number of factors into account. After a while you develop a feeling for it. Certain patterns appear, points of convergence…
“The atmosphere is a mirror,” he said. “It reflects not only conditions on land, but the temperature and circulation of the ocean currents. When you study the atmosphere, you study everything.”
--William Hauptman, The Storm Season (1992)
So, too, migration is a mirror; when you study migration, you also study everything
Luckily linking climate-change and migration—i.e., everything to everything—is our next speaker’s mission!
My more modest assignment: the current U.S. migration system, and how it, and its influences, play out across regions and local areas
What’s Migration?Percent of U.S. Population by Mover Status: 2010
(Source: American Community Survey Data)
84.6%
3.2%
2.2%
0.6%15.4%
9.4%
Same house (Non-movers)
Movers: Same county
Migrants: Same state
Migrants: Different state
Migrants: From abroad
Stayers Movers
Migrants
6.0%
Migration:
Movement from one territorial or administrative unit to another associated with a long-term or permanent change in residence
Movers: Same County
MigrationPushes and Pulls
Pushes: Negative Aspects Experienced at the Origin
Lack of Economic Opportunity
Wars (Refugee movements)
Famines
Dissatisfaction with (or High Cost of) Housing
Personal or Family Problems
“Itchiness”
Natural Disasters (e.g., Severe Droughts, Floods, Earthquakes)
Pulls: Attractive Perceived Aspects of Destinations
Job Opening or Higher Wages
Lower Living Costs
Family Ties
“Better” Climate
Three Main Categories of Pushes and Pulls:• Economic• Cultural• Environmental
Environmental Push: Hurricane Katrina 2005FEMA (Wikipedia Commons Photo )
1. The Migration or Mobility “Schedule”
Percent
of
people
who
move
Age
0 10 20 6050 7030 40
Peak Mobility:
“Emerging” Adulthood
Parental Shift
Empty-Nesting
Retirement Bulge
Old Age Return
The migration age “schedule”
Origin-destination migration age profiles
Source:
D. A Plane and F. Heins
(2003). Age Articulation
of U.S. Inter-metropolitan
Migration. The Annals of
Regional Science 37:
107–130.
New York to State College, PA
State College, PA to New York
US – all migrants
New York to Sarasota-Bradenton.
Core-Based Statistical Area (CBSA) Population Size Hierarchy
Sources:
• Plane, David A., Christopher J. Henrie, and Marc J. Perry (2005) Migration up and down the urban hierarchy and across the life course, Proceedings of the National Academy of Sciences 102: 15313–18.
• Plane, David A. and Jason R. Jurjevich (2009) Ties that no longer bind? The patterns and repercussions of age-articulated migration, The Professional Geographer 61: 4–20.
• Plane, David A., and Christopher J. Henrie (2012) The Role of Hierarchical Proximity in Migration and Population Growth: Urban Shadow Versus Urban Synergy Effects, Studies in Regional Science, 42: 109-128.
Spatial Demography
Special Feature
Core-Based Statistical Area (CBSA) Population Size Hierarchy
Plane & Henrie (2012)
Seattle and Portland: “Major” Spokane and Eugene: “AA” Bellingham and Medford: “A”
Ellensburg and Grants Pass: “Micropolitan”
Demographic Effectiveness for Ages 20-29
Flows Up the Hierarchy Flows Down the Hierarchy Legend Mega Metropolitan 2% 10%
Major Metropolitan
5% 25%
AAA Metropolitan
AA Metropolitan
A Metropolitan
Micropolitan
Non-CBSA Counties
Demographic Effectiveness of Net Migration Exchanges
Up and Down the CBSA Hierarchy (1995-2000 Decennial Census Data)
Age at Move:
19-29‘Emerging adults’ exhibiting strong preferences for large metros
Plane and Jurjevich (2009)
Demographic Effectiveness for Ages 55-64
Flows Up the Hierarchy Flows Down the Hierarchy Legend Mega Metropolitan 2% 10%
Major Metropolitan
5% 25%
AAA Metropolitan
AA Metropolitan
A Metropolitan
Micropolitan
Non-CBSA Counties
Demographic Effectiveness of Net Migration Exchanges
Up and Down the CBSA Hierarchy (1995-2000 Decennial Census Data)
Age at Move:
55-64
Empty-nesters and early retirees mostly moving down the urban hierarchy
Plane and Jurjevich (2009)
Demographic Effectiveness for Ages 15-24
Flows Up the Hierarchy Flows Down the Hierarchy Legend Mega Metropolitan 2% 10%
Major Metropolitan
5% 25%
AAA Metropolitan
AA Metropolitan
A Metropolitan
Micropolitan
Non-CBSA Counties
Demographic Effectiveness for Ages 20-29
Flows Up the Hierarchy Flows Down the Hierarchy Legend Mega Metropolitan 2% 10%
Major Metropolitan
5% 25%
AAA Metropolitan
AA Metropolitan
A Metropolitan
Micropolitan
Non-CBSA Counties
Demographic Effectiveness for Ages 25-34
Flows Up the Hierarchy Flows Down the Hierarchy Legend Mega Metropolitan 2% 10%
Major Metropolitan
5% 25%
AAA Metropolitan
AA Metropolitan
A Metropolitan
Micropolitan
Non-CBSA Counties
Demographic Effectiveness for Ages 40-49
Flows Up the Hierarchy Flows Down the Hierarchy Legend Mega Metropolitan 2% 10%
Major Metropolitan
5% 25%
AAA Metropolitan
AA Metropolitan
A Metropolitan
Micropolitan
Non-CBSA Counties
Demographic Effectiveness for Ages 80+
Flows Up the Hierarchy Flows Down the Hierarchy Legend Mega Metropolitan 2% 10%
Major Metropolitan
5% 25%
AAA Metropolitan
AA Metropolitan
A Metropolitan
Micropolitan
Non-CBSA Counties
Demographic Effectiveness for Ages 55-64
Flows Up the Hierarchy Flows Down the Hierarchy Legend Mega Metropolitan 2% 10%
Major Metropolitan
5% 25%
AAA Metropolitan
AA Metropolitan
A Metropolitan
Micropolitan
Non-CBSA Counties
Age
15-24
Age
20-29
Age
25-34
Age
40-49
Age
55-64
Age
80+
Demographic Effectiveness
Plane & Jurjevich (2009)
Overall direction of net migration is down the urban hierarchy
Source: Plane, Henrie, and Perry (2005) PNAS
RankMetro Area(Largest Principal City)
Numeric Change2000-2010
Percent Change2000-2010
1 Chicago 48,288 36.22 New York 37,422 9.33 Philadelphia 20,769 9.74 San Francisco 19,712 5.95 Washington, DC 19,502 14.26 Portland, OR 14,857 22.37 Boston 14,776 8.88 Oxnard, CA 14,637 16.39 Seattle 14,006 15.3
10 Los Angeles 12,381 7.4
The 10 metros with the most population growth within 2 miles of their largest principal cities’ city hall
They include Chicago and the ‘Big 4’ Metros of Megalopolis…But 5 of the 10 are in the West
• Alan Ehrenhalt has trumpeted this downtown population revival as “The Great Inversion”
• Book is an interesting set of case studies of selected metros’ development trends—including Portland’s
A good object lesson in regard to differing perspectives afforded by examining ABSOLUTE versus RELATIVE change…
IN TERMS OF ABSOLUTE CHANGE:
More metros gained than lost people within 2 miles of their principal city’s City Hall
188 metros gained, in aggregate, 708,925 in their ‘downtowns’
178 metros lost, in toto, 443,670 in their ‘downtowns’
Net nationwide metro gain <2 miles of City Hall: 265,255 Majority of gains within 1 mile: 141,001
Between 1 to 2 miles: 124,254 (despite 3x greater potential land area)
http://trimet.org/howtoride/max.htm
How Great is this ‘Great Inversion?’
BUT FROM A PERSPECTIVE OF RELATIVE CHANGE:
National gain of population <2 miles of City Hall: 265,255…
Whereas OVERALL U.S. metro population gain: 25,247,936
Percentage increase:10.8% overall metro growth1.7% growth within 2 miles of City Hall2.9% within 1 mile
Share of population living with 2 miles actually decreased:6.8% in 20006.2% in 2010
But very constrained land available in Downtowns
Big issues for future: Housing affordability Densification of the inner city beyond innermost rings Urbanization of suburbs
How Great is this ‘Great Inversion?’
Location Quotients for 25 to 34 year olds
How much of central area growth is due to ‘emerging adults’?
Percent change of 25-34 yr olds
To what extent will the ‘new urbanism’ become the new ‘suburbanism’?
The 2010 Census Special Report’s on-line content includes a fun, interactive, metro comparison tool
Wikipedia Commons Photos
Land availability strongly mediates the relationships between density, accessibility and affordability
Miami and Dallas-Ft. Worth have approximately same total population
But Miami is denser in almost every distance band from City Hall
Source: Henrie, C. C., and D. A. Plane (2006) Decentralization of the Nation’s Main Street: New Coastal-Proximity-Based Portrayals of Population Distribution in the United States, 1950–2000, Professional Geographer 58: 448–459.
Percentage Growth Rates, 2000-2010
United States 9.71
In Coastline County 6.10
Atlantic 7.11Gulf of Mexico 13.73
Pacific 6.67
Great Lakes -1.76
Not in Coastline County 11.74
Coastal population growth
• Long-term “stacking up”
• …but recently, interior areas are growing faster
Population-weighted density gives a sobering perspective on high coastal population concentrations – and implications for emergency preparedness and potential migration induced by sea-level rise and extreme weather events…
ICLUS,Version 2
Integrated Climate and Land Use Scenarios
EPA-based project to explores future changes in human population, housing density, and impervious surface for the United States
Projections broadly consistent with peer-reviewed storylines of population growth and economic development now widely used by the climate change impacts community
Two different climate models to illustrate the effect of changing climate variables on migration patterns :
First Institute of Oceanography-Earth System Model (FIO-ESM)
Hadley Global Environment Model 2 Atmosphere-Ocean (HadGEM2-AO)
Land use change projected down to 90 x 90 meter grid
The spatial allocation model incorporates National Land Use Dataset [US-NLUD] based on the 2011 National Land Cover Database
2000 to 2010 base period used to project out to 2100 Big inter-decadal variation in both climate and migration patterns
2000-2010 decade included ‘The Great Recession’
Aggregate migration data used, IRS county-to-county flows, so no linkage to age and age composition
Updates to the Demographic and Spatial AllocationModels to Produce Integrated Climate and Land UseScenarios (ICLUS) Version 2
DRAFT document, for comments only, available on the EPA website:
https://www.epa.gov/iclus