Geographies of Intergenerational Immigrant Labour Markets International Population Geographies Conference University of Liverpool June 2006 Jamie Goodwin-White Division of Social Statistics School of Social Sciences University of Southampton

Theoretical Background   Concern with how immigrants and their children are doing over time and across generations   Spatial dimensions of immigrant economic progress (Waldinger, Clark, Ellis, Zhou 2001)   Local contexts of labor market inequality (McCall 2001, Bound and Freeman 1992)

Questions   Does the geography of immigrants matter for estimating wages and wage gaps?

Questions   Does the geography of immigrants matter for estimating wages and wage gaps?   Is there a selectivity to internal migration that relates to wage outcomes? Is it different for immigrants and natives? How?

Models 1: Geographically Weighted Regression   Does the geography of immigrants matter for estimating wages and wage gaps?   How to weight population to account for different spatial distributions

Geo-weighted Regressions: Data and Methodology   year-old men from 5% PUMS   (currently employed, and with positive wages and hours data)   Split into immigrant Mexicans and native-born whites

Covariates and weights   Ln(hourly wage)   Age, education (+ fb interactions), married, years of experience (age-5-educ), arrival cohort (for fb)   Model iterations with 1) pweight and 2) mexweight   mexweight: proportion of fb mex (by metro) used to weight regression

Findings Geoweighted models show: 1) 1) higher wages for both groups 2) 2) wage penalty to immigrants increases slightly General models predicted logged hourly wage: pweightmexweight nbw2.31 ($10.15)2.40 ($10.98) fbm-.18 ($8.51)-.21 ($8.94)

Additional findings: covariates   Significant and dramatic wage penalty to recent arrivals in geoweighted models

Additional findings: covariates   Significant and dramatic wage penalty to recent arrivals in geoweighted models   Significantly reduced wage penalty to those native whites with less than college education

Additional findings: covariates   Significant and dramatic wage penalty to recent arrivals in geoweighted models   Significantly reduced wage penalty to those native whites with less than college education   Large returns to BA increase sizably in geoweighted models - especially for immigrants

Summary: Distinctive Contexts of Immigrant Geographies   Does the geography of immigrants matter for estimating wages and wage gaps?   Yes, areas where immigrant (Mexicans) live are higher waged but more unequal   But much of this penalty is for recent arrivals   Payoffs to higher education, especially for immigrants - and fewer penalties for least-educated when recent arrivals taken into account

Models 2: Heckman Selectivity Models   Is there a selectivity to internal migration that relates to wage outcomes? Is it different for immigrants, their second generation adult children and natives? How?   In other words, are those who move likely to be those who expect to benefit from the move?

Models 2: Heckman Selectivity Models   Two-step models: Probit on instrumental variable migration (moved at metro level last 5 years) Selectivity parameter (lambda or inverse mills ratio) comes from taking unmeasured variance from this model and using as covariate in OLS wage model

Heckman Migration-Selectivity Models: Data and Methodology   year-old men from 2000 PUMS   (currently employed, and with near full-time full-year data)   Split into immigrants, natives, and 1.5 generation

Two-stage covariate selection   Wage models include: race(white,black, asian,hispanic, other), age, education (BA), lambda   Migration models also include: family size, new employment growth, and percent immigrant

Findings   Initial OLS: age, education, race generally work as expected. Immigrant Hispanics and native Blacks fare particularly poorly. 1.5 Generation Hispanics, whites, Blacks fare better then native-born counterparts. Wages have very slight negative relationship to move   Heckman selectivity-corrected models show strong positive selectivity effect of migration on wages  =0.42 (native-born), ~ =.20 (immigrants and 1.5 generation)

Changes due to migration selectivity controls   BA degree effect stronger still in Heckman models - underestimated without migration selectivity controls (especially for immigrants)   Much of penalty to being Hispanic ( for immigrants, and 1.5 generation only) abated by controlling for migration selectivity  =0.42 (native-born), ~ =.20 (immigrants and 1.5 generation)

Changes due to migration selectivity controls   BA degree effect stronger still in Heckman models - underestimated without migration selectivity controls (especially for immigrants)   Much of penalty to being Hispanic ( for immigrants, and 1.5 generation only) abated by controlling for migration selectivity  =0.42 (native-born), ~ =.20 (immigrants and 1.5 generation)   Immigrant concentration has slight negative effect on wages, but not for native-born

Summary   Is there a selectivity effect on how migration effects wages?   Yes - and a strongly significant one. It is stronger (more efficient for natives than the foreign-stock). Uncorrected wage regression models show negative bias to migration’s effect on wages.   Migration is a way in which immigrant and 1.5 generation Hispanics attenuate severe wage penalties relative to the native- born. Or not.   Strong payoffs to BA, especially for immigrants, indicate that internal migration could also be an intergenerational labor market strategy.

Conclusions   It is critical to understand immigrant and second generation geographies when assessing immigrant economic incorporation.   Internal migration and different group’s selectivity thereof is part and parcel of this.   Proximity to co-ethnics or other immigrants is only part of the equation on immigrants’ residential patterns and labor market outcomes - especially over time and for the second generation.