Vulnerable immigrants, refugees, and population profiles of us urban areas: with particular attention to agents of

resettlement and mid-size MSAs

Lawrence A. Brown, Tamar E. Mott, Edward J. MaleckiDepartment of Geography

Ohio State University

A Presentation for theInternational Geographical Union Congress, Brisbane 2006

"Give me your tired, your poor,

your huddled massesyearning to breathe free."

The Rhetoric of ImmigrationAnd

An Open Door forRefugees

Coming to America…

Migration

Ellis Island, New York, ca 1900

The Mariel Boatlift (1980)

1980 = Economic Migrants = Sent Homemid-1980s = Refugees = Asylum in U.S.Today = Economic Migrants = Sent Home

Changing Status:Haitian Boat People

Of course, immigration and refugee immigration

-- in many respects the bulk of US immigration --

was critical in building our economic machine

stimulating cultural change

providing an essential labor pool

and changing the human geography of the US

The US is not alone on these dimensions

Refugees may be vulnerable,Refugees may be vulnerable,but they also are valuablebut they also are valuable

Conceptual Context: Conceptual Context:

The Mechanisms of MigrationThe Mechanisms of Migration

Why do People Move??

Long Standing, Widely Accepted Model is that

Migration from an Origin Place O to a Destination Place D is a Function of

1. Job Opportunity Differentials Between O and D2. Wage Differentials Between O and D3. Information Flows That Inform Potential Migrants

of Conditions + Opportunities in D4. Origin Push Due to Conditions Such As Famine,

Persecution, War

Bottom Line --

Things Are Better (Or Thought To Be Better) There than Here

Critical Linkages –

Earlier Migrants Who Communicate to Potential MigrantsIntermediaries / Intermediary Agents

Government and NGO Resettlement AgenciesEmployment or Labor Procurement Agencies

How does the model apply in an Immigration Context??

For (virtually) all immigrants --

◄ Job Opportunity Differentials, Wage Differentials, and/ortheir equivalent (e.g., freedoms + rights of various sorts) willbe (markedly) greater in US

◄ Origin Push Factors will apply to (virtually) all

◄ Information Flows Factor, therefore, will be instrumental indetermining immigrant destinations within US

◄ Information Flows include three particular mechanisms –

● Migration Chains whereby current migrants follow earliermigrants

● Distance Decay effects whereby there is less Friction ofDistance for origins proximate to US

● Intermediary Actors such as --Labor Procurement Agents(ies)Government and NGO Resettlement Agents(ies)

Most aspects of Conventional Migration Model have been widely studied

But, neglected in these efforts are the numerous resettlement programs, which expanded dramatically in half-century since World War II

Significant difference:

◄ Migration-chain-type mechanisms, given enormous amount of attention,tend to REPLICATE the settlement patterns of past immigrants

◄ Resettlement programs have the potential to (often) CHANGE those patterns if (i) their foreign-born groups are not represented by earlier immigrants and (ii) MSAs targeted are often non-traditional destinations for immigrants.

Four steps:

1. Show differential geographies of immigrant impacts on US urban system2. Illustrate how programs alter settlement patterns using Office of Refugee

Resettlement Data on $$$ allocations to US states3, Provide specific examples of government and NGO agency (VOLAGS),

actions, tying each to Foreign-Born MSA Profiles (I-II-III-IV) identifiedearlier

4. Using PUMS data for high-refugee nations, relate Government and NGO agency actions to foreign-born MSA profiles (I-II-III-IV)

Immigration Largely seen as a bi-coastal phenomena

Today, Creeping Inland

Rise of New Immigrant Gateways,Latino Growth in Metropolitan America,

New Ellis Islands

From: Audrey Singer: The Rise of New Immigrant Gateways, Brookings Institution, 2004

Immigration Increasingly Impacts

Middle America, South, Midwest

Areas Not Thought Of as Melting PotsNot the New York-s and Los

Angeles-s

Areas that Historically Have Been

“American” and Caucasian

Nevertheless, knowledge of immigration impacts on population profiles of

MSAs primarily confined to

Los Angeles, New York, Chicago, Miami

Research Tasks:

◄ 49 MSAs > 1 million in 2000 Census,largely comprising our “mid-size”

◄ Group to observe MSA profiles in terms of immigrant population

◄ Consider underlying processes that filter immigrants to different MSAs

◄ Focus especially on intermediaries, public and NGO, such as US Immigrant Refugee Resettlement Program (IRR),

and Hebrew Immigrant Aid Society (HIAS)

MSAs:From Louisville 1.0 million to New York 21.2 million, Distinct break at Philadelphia (6.2 million ), top of our mid-size range. Only San Francisco (7.0 million), Washington (7.6 million), Chicago (9.2 million), Los Angeles (16.4 million), and New York are larger.

Variables:Sixteen regions of origin from US Census 2000 – North, West, South, East Europe;East, South Central, Southeast, West Asia; East, Middle, North, South, West Africa; Caribbean; Central and South America Percent MSA Population Foreign Born

Standard Grouping Procedures:Principal Components, Group on PC Scores Using SPSS Quick Cluster and Subjective Judgment

Table 1: Percentage of Major Foreign-Born Groups in the 49 MSAs > 1 Million Population: Descriptive Statistics Standard Coefficient

Mean Deviation Variation Observations Low Place(s) Where Low Occurs High Place(s) Where High Occurs NEurope 4.1% 1.7% 0.408 49 1.2% Miami 8.3% Pittsburgh WEurope 5.1% 2.5% 0.499 49 1.5% Los Angeles 11.1% Cincinnati SEurope 3.8% 5.4% 1.420 49 0.5% Dallas 31.6% Providence EEurope 8.1% 6.3% 0.777 49 1.2% San Antonio 31.0% Cleveland EAsia 8.5% 4.0% 0.473 49 0.9% Miami 19.1% Seattle SCAsia 6.4% 3.0% 0.474 49 1.1% Miami 13.0% Cincinnati SEAsia 11.2% 6.4% 0.573 49 1.0% Miami 29.8% Norfolk WAsia 2.5% 2.4% 0.949 49 0.8% Greenesboro, Miami 17.5% Detroit EAfrica 1.3% 1.5% 1.175 49 0.1% Miami 8.0% Minneapolis (also Columbus (7.8%)) MAfrica 0.1% 0.1% 1.034 49 0.0% Las Vegas 0.6% Raleigh (also Charlotte, Washington) NAfrica 0.8% 0.4% 0.574 49 0.3% Chicago, Miami, San Francisco 2.7% Nashville SAfrica 0.3% 0.2% 0.490 49 0.1% Chicago 0.9% Cincinnati WAfrica 1.5% 1.5% 0.972 49 0.1% Salt Lake City 7.3% Providence Caribbean 7.1% 10.3% 1.445 49 0.6% San Francisco 55.2% Miami CAmerica 29.1% 20.6% 0.708 49 1.7% Buffalo 73.2% San Antonio SAmerica 4.9% 4.2% 0.848 49 1.4% Sacramento 18.8% Orlando %FB 10.6% 7.8% 0.737 49 2.6% Cincinnati, Pittsburgh, Louisville 40.2% Miami Bold indicates Coefficient of Variation > 0.67

Table 1:

On average 10.6% of population each MSA is foreign-born, butranges from 2.6% in Cincinnati, Louisville, Pittsburgh to 40.2% Miami.

Most dominant region of origin is Central America, primarily Mexicans;29.1%; ranges from 1.7% in Buffalo to 73.2% San Antonio.

Next most important origin is Southeast Asia, dominated by Philippines, also Vietnam; averages 11.2%; ranges from 1.0% Miami to 29.8% Norfolk.

East Europe (dominated by Poland, also Russia + Ukraine), East Asia (dominated by China), South Central Asia (dominated by India), Caribbean(dominated by Cuba, Dominican Republic, Jamaica) fall at the 6-8% level;Ranges are for East Europe, from 1.2% in San Antonio to 31.0% in Cleveland;for East Asia, from 0.9% in Miami to 19.1% Seattle; for South Central Asia,from 1.1% in Miami to 13.0% in Cincinnati; for Caribbean, from 0.6% in San Francisco to 55.2% Miami.

How does it happen that some MSAs are exceedingly low for many foreign-born groups, e.g., Miami; while broader range of foreign-born groups represented in other MSAs, e.g., Cincinnati (WEur, SCAsia, SAfr), Providence (SEur, WAfr)?

Coefficient of Variation, Ratio of Standard Deviation to the Mean.

Value < 0.67 (one std dev), indicates wide representation of foreign-borngroup among MSAs; value > 0.67 indicates clumpiness of representation

Only 7 of 16 foreign-born groups are below 0.67; 9 above; some egregious

Latter include South Europe (1.4), West Asia (1.0), East Africa (1.2),Middle Africa (1.0), West Africa (1.0), Caribbean (1.5)

How does it happen that some groups appear only in some places, not others; whereas other groups are more ubiquitous?

Basic statistics, then, indicate complexity of the urban geography of the foreign-born and immigration in the US

Clearly, there are major differences among MSA profiles in terms of the representation and mix of their foreign-born

Resulting Classification Profiles

I. Immigration Destinations Since Beginning of the 20th Century, LargelyLocated in the American Manufacturing Belt -- Target of pre-World War II immigration from Europe, especially Eastern and Southern Europe, and remain so today – Relatively Low Percent Foreign Born Today

II. Caribbean + South American Immigrants – 1960s and Later – Very High Percent Foreign Born – Cubans, Dominicans, Haitians – Often Fleeing Poverty, Political Oppression, etc

III. Central American, Primarily Mexican, Immigrants – 1960s and Later But With Pre-World II Roots in Migrant Labor Programs (agricultural focus), Industrial Recruitment (steel, meat packing, rr), Wetback crossings – All Among the 26 MSAs with the Largest Mexican Representation

IV. South Central and East Asian, African Immigrants Notable – 1980s and Later Immigration – Focus of Resettlement Programs, often major Asian business enterprise and/or strong universities -- Currently Identified as “New Latino Destinations”

From: Brown, Mott, Malecki “Immigration Reflections on the Population Profiles of US Urban Areas: Patterns and Processes With Particular Attention to Mid-Size MSAs)

I. Immigration Destinations Since the Beginning of the 20th Century, Largely Locatedin the American Manufacturing Belt -- Target of pre-World War II immigration fromEurope, especially Eastern and Southern Europe, and remain so today – Relatively Low Percent Foreign Born Today

II. Caribbean and South American Immigrants – 1960s and Later – Very High Percent Foreign Born – Cubans, Dominicans, Haitians – Often Fleeing Poverty, Political Oppression, etc

III. Central American, Primarily Mexican, Immigrants – 1960s and Later But With Pre-World II Roots in Migrant Labor Programs (agricultural focus), Industrial Recruitment (steel, meat packing, rr), Wetback crossings – All Among the 26 MSAs with the Largest Mexican Representation

IV. South Central and East Asian, African Immigrants Notable – 1980s and Later Immigration – Focus of Resettlement Programs, often major Asian business enterprise and/or strong universities -- Currently Identified as “New Latino Destinations”

Composite of All Four MSA Profiles

Dimensions That Differentiate These Maps

Era of Immigration:Pre World War II, Early 20th Century (I); 1960s and Later (II, III); 1980s and Later (IV)

Destination Areas of Immigrants:American Manufacturing Belt (I), Florida (II), West of the Mississippi (Great Plains + West) (III), East of the Mississippi (Mid-West + Southeast (IV)

Origin Areas of Immigrants:Europe, especially South and East (I), Caribbean and South America (II), Central America, largely Mexico (III), South Central Asia, East Asia, Africa (IV)

Place Characteristics Drawing Immigrants:Industrial Centers (I), Accessibility + Contiguity (II), Accessibility + Contiguity initially w/ Recruitment for Agric + Industry and “Wetbacks” (III), Refugee Resettlement Programs, Foreign Direct Investment, Universities (IV)

Foreign Born Profiles of Largest MSAs (> 5 million, except Miami):Chicago, Dallas, Los Angeles, San Francisco – strong Central American, largely Mexican; New York, Miami – strong Caribbean, South American; Boston, Detroit, Philadelphia – Strong European; Washington – strong South Central Asian, East Asian, African

Take Away Point –

Immigration Patterns areHighly Differentiated Across the Geographic Landscape –

Different Places are Impacted Differently –

It’s Not Just Immigration, but Immigration of What Type, Immigration By Whom, andImmigration Under What Mechanisms

And –

An Increasingly New Dynamic

The Incredibly Shrinking World!!

The Incredible Shrinking World, 1500 to 1960s

Global Shrinkage: Effect ofChanging Transport Technologies

on “Real” Distance

Based on McHale (1969), Fig 1

The Incredible Shrinking US

(1912-1970)

→The Model of Migration,The Operant Mechanisms

Remains the Same, but

→ We Have an IncrediblyShrinking World + Political Circumstances, such that

→ The Substance + NatureOf Immigration Has Changed

Over Time, and therefore,

→ Geographic Landscapes are Impacted Very Differently

RECALL -- Classification Profiles

I. Immigration Destinations Since Beginning of the 20th Century, LargelyLocated in the American Manufacturing Belt -- Target of pre-World War II immigration from Europe, especially Eastern and Southern Europe, and remain so today – Relatively Low Percent Foreign Born Today

II. Caribbean + South American Immigrants – 1960s and Later – Very High Percent Foreign Born – Cubans, Dominicans, Haitians – Often Fleeing Poverty, Political Oppression, etc

III. Central American, Primarily Mexican, Immigrants – 1960s and Later But With Pre-World II Roots in Migrant Labor Programs (agricultural focus), Industrial Recruitment (steel, meat packing, rr), Wetback crossings – All Among the 26 MSAs with the Largest Mexican Representation

IV. South Central and East Asian, African Immigrants Notable – 1980s and Later Immigration – Focus of Resettlement Programs, often major Asian business enterprise and/or strong universities -- Currently Identified as “New Latino Destinations”

1. How programs alter settlement patternsIllustrated by Office of Refugee

Resettlement (ORR) Dataon $$$ allocations to US states

Key Measurement

Refugee Resettlement Index (RRI)

Percent of refugees 1983-2004 for each continental state

divided by

Percentage of foreign-born for same state in 1980

Similar to familiar location quotient

If refugee resettlement mirrored existing settlements of foreign-born, (RRI = 1) latter pattern would be maintained,

If refugee resettlement did not follow foreign-born settlement (RRI > 1) pattern of foreign-born settlement altered

For Figure 1 Map

we also compute

Federal Resettlement Allocations by State, averaged over 1997-2005,per Foreign-Born in 2000

Are Federal Resettlement AllocationsProportional or Disproportional toThe pattern of the foreign-born??

Four RRI categories in Figure 1 --1.0 or less indicates state is, at best, matching its FB proportion;

1.75 indicates 75 percent more resettlement than expected by FB level3.0 indicates 300 percent more;4.0 indicates 400 percent more

Highest impacts on states not traditionally thought of as immigrant destinations – South Dakota (RRI=3.8; $$=53), Georgia (3.8; 15), Kentucky (3.7; 18), Minnesota (3.5; 38), North Dakota (3.0; 136), Missouri (2.8; 33), Iowa (2.8; 31), Washington (2.6, 29)

Lowest impact states include ones that are traditional destinations and highly studied – California (RRI=0.9;$$= <10), Texas (0.8; <10), New York (0.7; <10), Illinois (0.6, <10), New Jersey (0.4; <10)

Where divergence between RRI and ORR dollars, appears to be result of secondary migration by refugees that are more difficult to settle due to language differences and the like

Finding: resettlement programs + agencies have significant impact on geography of the foreign-born and redistribution of the Foreign-Born

2. Examples of government and NGOagency (VOLAGS), actions

tying each toForeign-Born MSA Profiles

The Inter-Leafing of Migration Patterns --

Late 19th century immigration laid a spatial foundation by which early 20th century immigration was channeled and, in turn, these together channeled migration following World War II -- Foundation was, of course, largely European

Mechanisms -- migration chainsmigration chains, established communities, persons/ families within who sponsor immigrants, provide a path for assimilation

Facilitators / Intermediaries are a multitude of government, but especially NGO ecumenical agencies -- who connect(ed) refugees with destinations

For many nationalities, then, present-day MSA profiles of foreign-born reflect earlier settlement patterns, as new immigrants and refugees are drawn in by existing communities

VOLAGS:

Hebrew Immigrant Aid Society (HIAS), founded 1881American Committee for Christian German Refugees, founded 1934 Catholic Committee for Refugee Victims of Nazi Persecution, 1936 American Fund for Czechoslovak Relief, post-WWIITolstoy Foundation, post WWIIPolish Immigration and Refugee Committe, post WWIIAmerican Council for Nationalities Service, 1975World Relief Organization, 1979Young Mens Christian Association (YMCA), 1979

Currently, most refugee resettlement in US handled by ten VOLAGS -- Church World Service, Ethiopian Community Development Council, Episcopal Migration Ministries, Hebrew Immigration Aid Society (HIAS), International Rescue Committee, Immigration and Refugee Services of America, Lutheran Immigration on Refugees Services, United States Conference of Catholic Bishops, World Relief Corporation, State of Iowa Bureau of Refugee Services

Example: Migration Chains + VOLAGS

1880-1924, 70 pct Slav, Lithuanian, Latvian, Jewish immigrants settled in urban-industrial centers, primarily cities in Profile I -- Boston, Cleveland, Detroit, Philadelphia, Pittsburgh -- New York (Profile II), Chicago (Profile III) -- Largely spontaneous migration, but assisted by Volags

Post-WWII, US Displaced Persons Act (1948) and VOLAGS played a major role --

Immigrants from Europe re-settled in existing centers for various nationalities; e.g., Cleveland (Profile I) influx from Poland, Slovenia, Croatia, Hungary. In response to 1956 Hungarian Uprising, refugees were settled in Cleveland + Pittsburgh (Profile I) where Hungarians had moved after suppression of Hungary’s 1848 war of independence.

Example: Eastern Europe and VOLAGS --

Pre-World War II Eastern Europeans resettled in Atlantic Coast cities where they disembarked -- New York (Profile II) , Philadelphia (Profile I), Baltimore (Washington MSA, Profile IV)

Jewish populations tended to remain in these locales

Poles, Hungarians, Czechs often moved to newer cities of industrializing America -- Buffalo (Profile I), Detroit (Profile I), Milwaukee (Profile III), Chicago (Profile III), Cleveland (Profile I); Slovaks, Croatians, Slovenes, Ukrainians favored Pennsylvania

Post-WWII, following these established chains, Pittsburgh (Profile I) received Jewish displaced persons; Polish refugees were re-settled with sponsor families in states with large Polish-American communities such as Connecticut, Illinois, Michigan, New Jersey, New York

HIAS, in 1999 resettled refugees from former Soviet Union to NYC (Profile II), San Francisco (Profile III), Chicago (Profile III), Los Angeles (Profile III), Philadelphia (Profile I), Boston (Profile I)

NB: Eastern Europeans don’t necessarily go to Profile I MSAs (as might be expected) because other Profiles are characterized by more recent immigrants such as Caribbean (II) and Mexicans (III) but retain their strong Eastern Europe base

Example: Secondary MigrationSecondary Migration via Migration Chains

1960s, Cubans already had history in Miami (Profile II). Initially, Cuban refugees were re-settled in Miami/Dade County. Area could not support such heavy increases in population. Federal government directed refugees away from Miami

61 pct of 495,000 Cubans 1961-1981 relocated in, e.g., Chicago, New Orleans, Los Angeles, NYC; Elizabeth, Union City, West New York, other NJ cities.

But once Cuban-Americans adjusted to US, learned English, became independent of federal assistance, many relocated back to Miami and other Profile II Florida cities

Likewise, resettlement of Hmongs originally directed to scattered communities (e.g., Columbus, Philadelphia, Portland, Seattle, Wausau Wisconsin), but secondary migration led to strong clusters in California (40% of US Hmongs, esp Central Valley + Fresno), Minnesota (26%, esp Minneapolis-St. Paul (Profile IV), Wisconsin (19%, esp Milwaukee (Profile III ).

Example: Indochinese

US government proposed a “wide dispersal” policy, but VOLAGS that implemented it (beginning about 1975) used their own criteria

Operated as autonomous entities and used networks throughout US to find sponsors for refugees. States west of Mississippi received the highest refugee densities, as did the four states where transit camps were located -- California, Pennsylvania, Arkansas, Florida

With no Indochinese population base, sponsors tended to be Caucasian and, later, some Chinese

Hence, primary settlement narrated by VOLAGSprimary settlement narrated by VOLAGS was succeeded by secondary migrationsecondary migration, leading to a high level of spatial clustering -- like Cubans and Hmongs

Layering or Interleafing Process

● Refugee group preceded by earlier migs of same/similar group,migration-chains operate (info flows + sponsorship in US); Example,displaced persons post WWII, mimicked settlement patterns of earlierEur migs; likewise recent immigs Eastern Europe + former Soviet Union

● Refugee groups have few/no precursors, geographic patterns oftenaltered as agencies target non-traditional immigrant destinations;Example, post-1960s immigrants from Asia, Africa, Caribbean

● Secondary migration might occur, esp among refugees not represented by earlier migs, not in culturally-comfortable communities, not near other family or community members; Example, Hmong, Vietnamese, Somalis, and Cubans; Usual mechanisms such as wage/job opp differentials, culture, community, family, and migration chains.

Urban Profiles example -- European refugees tended towards Profile I MSAs as expected, but also to Profile II + III MSAs, characterized today by Caribbeans and Latin Americans; Reflects that earlier generations of Europeans well represented in New York (II) and Chicago (III), but more recent immigrants altered characterization of these MSAs.

3. Using PUMS datafor high-refugee nations,

relate Government and NGO agency actionsto foreign-born MSA profiles (I-II-III-IV)

Impact of refugee resettlement programs specifically in terms of 49 MSAs considered, used 2000 PUMS 5% sample to construct Table 3

12 origin countries for which refugee status, and resettlement agencies, have been predominate impetus for migration -- Ukraine, USSR/Russia, Yugoslavia, Cuba, Cambodia, Laos, Vietnam, Eritrea, Ethiopia, Liberia, Somalia, Sudan

Using 49 MSAs as base, % of each immigrant group in each MSA is computed; then re-expressed as ratio to the % foreign-born found in that MSA -- produces our Refugee Resettlement Index (RRI) -- similar to familiar location quotient

All things being equal, assumption that each MSA’s share of an immigrant group would equal its share of the foreign-born, yielding a value of 1

Ratios above 1 indicate MSA is receiving more of a given group than expected; ratios below 1 indicate receiving less than expected

Table 3 highlights cells where RRI is greater than 1.75 and 3.0, indicating that the number of persons from a particular origin is 75% or 300% greater than expected given the number of foreign-born in the MSA

NB: RRIs are not solely the result of resettlement efforts; also reflect secondary migration following resettlement which has been common for some groups

Examples include Hmongs and Cubans, discussed above in terms of their secondary migration

Also Somalis -- Refugee resettlement programs led Somalis to Columbus Ohio (Profile IV) directly, but also considerable secondary migration, triggered by Columbus’ economy and social networks

Similarly, Somali refugees have moved in significant numbers from Atlanta (Profile IV) to Portland and Lewiston Maine

Metropolitan Statistical Areas Ukraine

USSRRussia Yugo Cuba Cambd Laos Viet Eritrea Ethiop Liberia Somal Sudan

TotalRefugees Total pop

Total Foreign-born

RRI ofRefgeesto FB

PROFILE I

Boston, MA 1.14 2.05 0.40 0.13 1.91 0.22 1.21 0.93 0.93 1.03 1.87 1.19 51472 5819100 721060 1.27

Buffalo-Niagara Falls, NY 2.67 2.35 3.38 0.20 0.41 0.53 0.98 0.75 0.00 0.00 11.62 6.51 4986 1170111 51381 1.73

Cleveland, OH 6.20 2.67 11.86 0.12 1.19 0.99 0.69 0.62 0.32 1.87 0.00 1.51 18588 2945831 135397 2.45

Detroit, MI 1.32 1.13 6.87 0.08 0.32 1.38 0.41 0.00 0.29 1.77 0.13 0.32 22817 5456428 383970 1.06

Hartford-Bristol-Middleton-New Britain, CT 1.58 0.94 0.53 0.26 0.48 1.94 1.14 0.00 0.13 0.30 0.13 0.00 7172 1183110 120355 1.06

Jacksonville, FL 0.55 1.14 1.39 1.54 2.01 0.54 1.54 1.67 1.01 3.33 0.00 1.37 4175 1100491 59586 1.25

Louisville, KY/IN 1.74 1.93 0.00 2.66 0.18 0.00 2.41 0.00 2.16 0.00 4.41 4.05 2775 1025598 27933 1.77

Norfolk-VA Beach-Newport News, VA 0.77 0.90 0.14 0.54 0.65 1.12 1.73 0.46 1.64 1.22 1.21 0.00 4759 1569541 70370 1.21

Philadelphia, PA/NJ 4.24 2.17 1.18 0.23 4.46 0.84 2.05 1.22 1.06 8.08 0.11 3.34 59872 6188463 433919 2.46

Pittsburgh-Beaver Valley, PA 2.50 2.80 3.08 0.16 0.31 0.12 1.19 0.00 0.83 0.74 0.00 0.12 5439 2358695 62286 1.56

Providence-Fall River-Pawtucket, MA/RI 0.69 0.60 0.15 0.07 9.00 4.78 0.22 0.00 0.05 8.75 0.00 0.00 11058 1188613 142784 1.38

Rochester, NY 5.43 2.34 2.41 0.66 0.96 4.53 1.58 2.87 3.21 0.28 5.95 1.73 9362 1098201 62794 2.66

St. Louis, MO-IL 1.46 1.52 2.72 0.11 0.98 0.67 2.24 1.61 1.49 0.08 3.39 0.44 11309 1333914 114508 1.76

Seattle-Everett, WA 14.43 9.91 2.23 0.41 22.56 13.93 16.57 46.16 21.04 0.28 31.08 10.09 67822 2603607 80945 14.95

PROFILE II

Miami-Hialeah, FL 0.09 0.21 0.03 11.92 0.02 0.01 0.06 0.03 0.01 0.03 0.00 0.12 6791 3876380 1558152 0.08

New York-Northeastern NJ 1.65 1.75 1.31 0.20 0.12 0.05 0.13 0.09 0.16 0.78 0.01 0.37 204901 21199865 5182255 0.71

Orlando, FL 0.23 0.35 0.26 2.27 0.80 0.81 1.36 2.29 0.55 0.26 0.00 1.28 8946 1644561 197119 0.81

Tampa-St. Petersburg-Clearwater, FL 0.49 0.69 2.26 3.74 0.55 1.82 1.27 0.12 0.28 0.08 0.00 0.65 12965 2395997 233907 0.99

West Palm Beach-Boca Raton-Delray Beach, FL 0.29 1.16 1.18 3.44 0.46 0.26 0.43 0.05 0.00 1.08 0.00 2.35 6189 1131184 196852 0.56

PROFILE III

Austin, TX 0.15 0.26 0.47 0.33 0.89 0.32 2.03 0.64 0.73 0.17 0.00 1.42 8720 1249763 152834 1.02

Chicago-Gary-Lake, IL 1.56 1.16 3.11 0.24 0.52 0.42 0.47 0.35 0.31 0.40 0.22 0.40 71061 9157540 1466940 0.86

Dallas-Fort Worth, TX 0.17 0.31 0.15 0.17 1.03 0.67 1.33 0.80 2.16 0.29 0.15 0.83 36557 5221801 784642 0.83

Denver-Boulder-Longmont, CO 0.72 1.87 0.65 0.07 0.87 2.36 1.60 2.64 2.01 0.59 0.26 2.80 22751 2581506 277127 1.46

Grand Rapids, MI 0.49 0.69 1.30 0.54 7.00 4.53 3.64 3.49 0.38 0.61 1.72 0.82 8005 1088514 56066 2.55

Greensboro-Winston Salem-High Point, NC 0.12 0.63 1.07 0.31 1.79 1.34 1.88 0.00 1.33 3.44 5.65 9.22 5785 1251509 71565 1.44

Houston-Brazoria, TX 0.04 0.28 0.29 0.23 0.82 0.47 2.57 1.38 0.36 0.65 0.11 0.67 60945 4669571 895944 1.21

Kansas City, MO-KS 1.11 1.10 1.36 0.70 1.08 3.97 2.58 2.24 1.76 3.48 3.00 7.89 9427 1776062 80539 2.09

Las Vegas, NV 0.12 0.49 1.28 1.00 0.85 1.72 0.77 1.15 3.57 0.10 0.15 0.37 11694 1563282 258494 0.81

Los Angeles-Long Beach, CA 0.38 0.37 0.33 0.19 1.42 0.20 0.89 0.22 0.38 0.07 0.03 0.14 172505 16373645 5067615 0.61

Memphis, TN/AR/MS 1.00 2.02 0.00 0.39 3.92 2.67 4.06 0.00 2.57 0.00 6.35 2.81 5910 1135614 37670 2.80

Milwaukee, WI 1.68 1.67 7.87 0.21 0.41 12.73 0.71 0.00 0.91 0.00 1.58 1.43 11242 1689572 89284 2.25

New Orleans, LA 0.08 0.46 1.31 2.04 1.31 0.14 7.10 0.00 0.46 0.00 0.00 0.00 11005 1337726 64169 3.06

Oklahoma City, OK 0.16 0.34 0.40 0.13 0.43 3.46 5.32 0.93 0.00 0.45 0.00 0.00 8662 1083346 61810 2.50

Phoenix, AZ 0.08 0.38 1.68 0.15 0.49 0.70 0.77 1.01 0.36 0.49 0.25 3.54 15608 3251876 457483 0.61

Portland-Vancouver, OR 4.07 3.42 0.60 0.19 2.71 3.49 2.97 1.51 1.79 0.16 1.08 0.83 40969 2265223 248068 2.95

Sacramento, CA 5.53 2.64 0.47 0.07 0.69 13.31 3.02 0.70 0.64 0.25 0.00 0.39 52023 1796857 260111 3.57

Salt Lake City-Ogden, UT 0.62 0.76 0.90 0.10 1.30 2.64 1.26 0.00 0.34 0.00 2.16 1.86 10084 1592383 161924 1.11

San Antonio, TX 0.03 0.06 0.01 0.09 0.06 0.16 0.22 0.00 0.08 0.16 0.00 0.00 4270 2813833 606254 0.13

San Diego, CA 0.09 0.16 0.29 0.03 0.51 0.80 0.70 0.28 0.27 0.04 0.62 1.21 48907 7039362 1902304 0.46

San Francisco-Oakland-Vallejo, CA 2.27 1.97 0.67 0.14 1.06 0.38 2.34 0.53 0.66 0.23 0.19 0.44 41227 3554760 414355 1.77

PROFILE IV

Atlanta, GA 0.66 0.73 0.31 0.42 1.82 1.77 2.35 4.88 5.47 4.37 7.02 1.73 45789 4112198 423105 1.93

Charlotte-Gastonia-Rock Hill, SC 0.62 0.59 0.33 0.53 2.32 2.81 2.45 6.21 1.94 5.04 5.44 0.00 10539 1499293 99760 1.88

Cincinnati OH/KY/IN 1.18 1.60 2.89 0.15 1.23 0.66 1.55 11.00 1.04 3.79 0.00 5.26 4569 1979202 51236 1.59

Columbus, OH 1.39 1.90 2.01 0.13 4.08 2.95 1.60 4.46 5.90 3.43 39.46 4.96 11915 1540157 71417 2.98

Indianapolis, IN 0.99 1.31 0.66 0.29 0.38 0.93 1.53 14.39 0.96 2.62 0.00 1.20 4082 1607486 54343 1.34

Minneapolis-St. Paul, MN 1.58 2.23 0.79 0.14 3.70 26.34 2.98 10.64 10.27 18.94 31.15 5.68 66424 2968806 210344 5.63

Nashville, TN 0.86 1.17 1.06 0.37 1.03 9.60 1.34 1.26 3.79 0.16 7.36 17.51 6914 1231311 57614 2.14

Raleigh-Durham, NC 0.27 1.60 0.66 0.29 0.45 0.09 0.94 1.34 0.08 3.48 0.87 1.11 5298 1187941 108803 0.87

Washington, DC/MD/VA 0.38 0.90 0.36 0.18 1.15 0.54 1.76 5.26 7.20 3.71 2.43 4.60 84554 7608070 980621 1.54

Total Populations 1418839 161517899 25308014

Notes:

1.75 RRI or Greater bold

3.0 RRI or Greater bold

Table 3: Refugee Resettlement Indices (RRI) for Selected Origin Countries and US MSAs

Carib Asia AfricaEurope

Refugee Resettlement Indices are computed as follows:

(Foreign Born Population from Country i settled in MSA j / Total Foreign Born Population from Country i for the 49 MSAs) /

(Foreign Born Population in MSA j / Total Foreign Born Population for the 49 MSAs)

Source: US Census 2000, PUMS

1. consider three European origins -- Ukraine, USSR/Russia, Yugoslavia

Strongest representation in Profile I MSAs of Buffalo, Cleveland, Detroit, Milwaukee, Philadelphia, Pittsburgh, Rochester, Seattle, as anticipated

Recall, Profile I group was target of pre-World War II immigration from Europe, especially Southern and Eastern Europe, and remain so today

Strong representation also in Profile III MSAs of Chicago, Portland, Sacramento -- all of which have strong Eastern European base

Illustrates LayeringLayering or InterleafingInterleafing Process; Migration Migration ChainChain mechanisms

2. African origins -- Eritrea, Ethiopia, Liberia, Somalia, Sudan

Strongest representation Profile IV MSAs Atlanta, Charlotte, Cincinnati, Columbus, Indianapolis, Minneapolis, Nashville, Raleigh-Durham, Washington -- often 300% > expected

Recall, Profile IV cities immigrant destinations since 1980s, often target of refugee resettlement programs + other agency efforts, often major Asian businesses and/or strong universities

Strong representation in Profile I MSAs Buffalo, Jacksonville, Louisville, Philadelphia, Providence, Rochester, St Louis, Seattle; Profile III MSAs Greensboro, Kansas City, Las Vegas, Memphis, Phoenix

Examples specific flows: Greensboro (Profile III) from strife torn areas in Nigeria, Sudan, Niger; now 12, 000 residents of African origin. Similarly, since civil war in Liberia in 1989, 10,000+ refugees to Rhode Island, many living in Providence (Profile I)

Reflects purposefulpurposeful dispersaldispersal by resettlement agencies/progs

3. three Asian origins -- Cambodia, Laos, Vietnam

Strongest representation in Profile III MSAs Grand Rapids, Kansas City, Memphis, Milwaukee, New Orleans, Oklahoma City, Portland, Sacramento -- areas associated with high Central American, primarily Mexican, foreign-born

Strong representation also found in Profile IV MSAs Columbus, Minneapolis, Nashville; consistent with designation as often target of refugee resettlement programs and major Asian business establishments

Profile I Asian recipients include Philadelphia, Providence, Rochester, Seattle.

All MSAs mentioned had overall RRI greater than 1.75, indicating general receptivitygeneral receptivity to refugee populations, or attraction in terms of secondary migrationssecondary migrations. PurposefulPurposefulDispersalDispersal policies also played a significant role.

4. Cuban immigrants are largely found in Profile II MSAs -- Miami, Orlando, Tampa, West Palm Beach

Virtually no other significant flows to these MSAs.

Purposeful DispersalPurposeful Dispersal policies led to significant secondary secondary migrationmigration to these MSAs

Appears, that Profile II MSAs -- urban areas which experienced a marked inflow of Caribbean and South American immigrants beginning in the 1960s and have a very high percent of foreign-born today -- are relatively isolated in terms of current day refugee resettlement dynamics

Fifth, nine largest MSAs (>5 million) show low participation in refugee resettlement process -- Boston with overall RRI of 1.27, Chicago with 0.86, Dallas with 0.83, Detroit with 1.06, Los Angeles with 0.61, New York with 0.71; Philadelphia with 2.47, San Francisco with 1.77, Washington with 1.54

Only Philadelphia with overall RRI of 2.46 and San Francisco with 1.77 are above our 1.75 threshold

Highest overall RRIs are in Seattle with 14.95, Minneapolis with 5.63, Sacramento with 3.57, New Orleans with 3.06; 14 other MSAs below 5 million are above 1.75 overall RRI

Clearly, then, mid-sized MSAs have been the major participants in the refugee resettlement process, leading to significant shifts in their demographic profiles

Sixth, consider how foreign-born data in Table 5 fits with the foreign-born Profiles delineated earlier in this paper

Quite good for Profiles I, II, and IV in the sense that --

(i) strong representation in first two MSA sets of Europeans and Cubans, as expected

(ii) strong representation in Profile IV MSAs of Africans and Asians, focus of recent resettlement efforts

Deviations from profile patterns also interesting -- e.g., Africans + Asians w/ strong representation in several MSAs falling under Profile I + Profile III -- some due to secondary migration, but resettlement programs (w/ purposeful dispersal practices) also signif -- Europeans in Profile II + III reflects Layering or Interleafing process

The result of secondary mig, resettlement programs, and related spontaneous migration, is ongoing change to foreign-born profiles of US MSAs -- undoubtedly they will look quite different in next decennial census

Overall, empirical exercise enforces the relevance of distinguishing between refugees whose nationalities are well represented in population profiles, and those who are not

Refugees of former group tend to mimic existing settlement patterns due to mechanisms such as migration chains + established communities which sponsor refugees + provide a mechanism for assimilation

By contrast, refugee groups not (well) represented in the host population establish new settlement patterns!

Essential distinction, separates WW II displaced persons, largely from Europe, from majority of subsequent refugees from Cuba; Southeast Asian nationalities related to Vietnam War (e.g., Cambodia, Laos, Vietnam), Afghanistan, Ethiopia, Somalia, Liberia, Dominican Republic, El Salvador, Haiti; + refugees from remote republics of former USSR (e.g., Azerbaijan, Belarus, Moldova, Tajikistan, Uzbekistan)

Finally, refugees who change the US map of the foreign-born have a multiplier effect through migration chains + their own community’s ability to sponsor more such immigrants

Accordingly, while Columbus Ohio will never be Queens neighborhood described in Crossing the BLVD, in the twenty-first century we find ourselves with burgeoning refugee communities of Laotians, Cambodians, Hmong, Ethiopians, Somalis, Rwandans -- not to mention many other ethnic groups that are spontaneous migrants such as Indians, Koreans, Mexicans, Nigerians

Conclude, then, that while refugees constitute only a portion of total immigration, 8 percent in 2000 for the US, their effects on changing the foreign-born profiles of MSAs + other communities, and on changing fabric of society, are disproportionately large