Job‐education mismatches among immigrants in the US
Brigitte Waldorf
Purdue University, USA
Collaborators:
Julia Beckhusen, US Census Bureau
Raymond Florax, Purdue University and VU University Amsterdam
Jacques Poot, University of Waikato
International Workshop
ECONOMIC IMPACTS OF IMMIGRATION AND POPULATION DIVERSITY
University of Waikato, April 2012
Part A.
Overview:
concept, causes, consequences, and operationalization
Matching People and Jobs
Perfect match
Education
High
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
Matching People and Jobs
Perfect match
Education
High
Undereducation
Education
High
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
Overeducation
Matching People and Jobs
Education
High
Perfect match
Education
High
Undereducation
Education
High
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
CAUSES
Imperfect information
Screening
Technological change
HC transferability*
HC acquisition*
CONSEQUENCES
Job-education
Mismatch
Job satisfaction
Productivity
Wages
Operationalizing job-education mismatches:
• for every occupation j at time t define the required education as mjt ± sjt
required
YoS (yrs of schooling)
under
over
Mean ± st.dev
Examples for 1980:
Cook:
11.7 ± 2.5 yrs
Manager:
14.3 ± 2.7 yrs
Physicians:
19.6 ± 2.6 yrs
Note: mean and standard deviations are
calculated for male US–born employees
in occupation j at time t
• based on data from the US census 1980, 1990, 2000, ACS 2009
Part B.
The poorly educated:
prevalence and determinants of undereducation
Education
Undereducation among the poorly educated:
Immigrants versus US-born employees
Hypothesis
Uimmigrants > Unatives
High
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
Education
Undereducation among the poorly educated:
Immigrants versus US–born employees
Hypothesis
Uimmigrants > Unatives
Yes!
Immigrants positively selected
High
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
Education
Undereducation among the poorly educated:
Immigrants versus US–born employees
Hypothesis
Uimmigrants > Unatives
Yes !
immigrants positively selected
ethnic enclave economy
High
Occupation
Req: High
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Ex: Accountant
Req: Low
No !
barriers, segmentation
Modelling the determinants of undereducation
Indicator variable,Y, measured for person i with occupation j at time t :
YoS
under
yit = 1
if YoSit < mjt – sjt
required
yit = 0
if mjt – sjt < YoSit < mjt+sjt
Probit model: Prob (Y = 1|X) = F(Xb)
Modelling the determinants of undereducation
Indicator variable,Y, measured for person i with occupation j at time t :
YoS
under
yit = 1
if YoSit < mjt – sjt
required
yit = 0
if mjt – sjt < YoSit < mjt+sjt
Probit model: Prob (Y = 1|X) = F(Xb)
X includes:
Estimation:
•
•
•
•
• pooled immigrants & native men
• pooled 1980, 1990, 2000, & 2009
• separate for two education levels:
demographic attributes
immigration-specific variables
locational context variables
fixed effects (period, birthplace, state)
less than high school n = 180,474
high school n = 128,312
Results:
Unexpected?
Inconsistent?
Less than high
school
I
Demographic
attributes
Age
+
Married
+
White
+
Hispanic
+
Asian
o
Immigration–
specific variables
English
–
Citizen
–
Sojourn
+
Locational context
Metro
–
UE
o
Wage
o
Fixed effects
Period
↑
State
Birthplace
Mex, China↑
Results:
Less than high
school
I
N
Demographic
attributes
Age
+
+
Married
+
+
White
+
+
Hispanic
+
+
Asian
o
o
Immigration–
specific variables
English
–
Citizen
–
Sojourn
+
Locational context
Metro
–
–
UE
o
o
Wage
o
+
Fixed effects
Period
↑
↑
State
Birthplace
Mex, China↑
Results:
Less than high
school
I
N
Demographic
attributes
Age
+
+
Married
+
+
White
+
+
Hispanic
+
+
Asian
o
o
Immigration–
specific variables
English
–
Citizen
–
Sojourn
+
Locational context
Metro
–
–
UE
o
o
Wage
o
+
Fixed effects
Period
↑
↑
State
Birthplace
Mex, China↑
High school
I
+
+
+
–
o
+
+
–
+
o
+
↑
JAP, AUS, NZ↑
Results:
Less than high
school
I
N
Demographic
attributes
Age
+
+
Married
+
+
White
+
+
Hispanic
+
+
Asian
o
o
Immigration–
specific variables
English
–
Citizen
–
Sojourn
+
Locational context
Metro
–
–
UE
o
o
Wage
o
+
Fixed effects
Period
↑
↑
State
Birthplace
Mex, China↑
High school
I
N
+
+
+
–
o
+
+
+
–
o
+
+
–
+
o
+
+
o
+
↑
↑
JAP, AUS, NZ↑
Part C.
The highly educated:
prevalence and determinants of overeducation
Overeducation among the highly educated
Education
Immigrants versus US-born employees
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
Hypothesis
Oimmigrants > Onatives
High
Occupation
Req: High
Ex: Accountant
Req: Low
Overeducation among the highly educated
Education
Immigrants versus US-born employees
Ex: Ph.D.
Ex: Nuclear physicist
Medium
Ex: Assoc.
Req: Medium
Low
Ex: < HS
Ex: Taxi Driver
High
Hypothesis
Oimmigrants > Onatives
Immigrants
90
Req: Low
90
80
% overeducated
80
% overeducated
Ex: Accountant
100
100
70
60
US–born
50
40
20
Req: High
Doctoral / Professional Degree
Master's Degree
30
Occupation
Gap: 17 to 20 % pts
70
50
40
US–born
30
20
10
10
Immigrants
60
Gap: 10 to 17 % pts
0
0
1980
1990
2000
2009
1980
Yes!
Barriers, HC transfer
1990
2000
2009
Declining overeducation with increasing sojourn length
Follow synthetic arrival-cohorts over time:
• immigrants arriving in the 1970s
• immigrants arriving in the 1980s
100
90
'80s Master's
'70s Master's
80
70
'70s Doctoral
60
'80s Doctoral
50
40
30
1980
1990
2000
2010
Modelling the determinants of overeducation
Indicator variable, Z, measured for person i with occupation j at time t :
YoS
required
zit = 0
if mjt – sjt < YoSit < mjt+sjt
over
zit = 1
if YoSit > mjt + sjt
Probit model: Prob (Z = 1|X) = F(Xb)
X includes:
Estimation:
•
•
•
•
• pooled immigrants & native men
• pooled 1980, 1990, 2000, & 2009
• separate for two education levels
demographic attributes
immigration-specific variables
locational context variables
fixed effects (period, birthplace, state)
• master’s /prof. degree n = 144,716
• doctoral degree n = 106,558
Results:
expected, except: →
legal restrictions for
non-citizens
smooth HC transfer →
Doctoral degree
I
N
Demographic
attributes
Age
+
+
Married
+
–
Black
+
+
Hispanic
+
+
Asian
o
o
Immigration–
specific variables
English
–
Citizen
+
Sojourn
–
Locational context
Metro
+
o
UE
–
o
Wage
+
–
Fixed effects
Period
↓
↓
State
Birthplace
NZ, JAP, CAN↓
Results:
? married to whom? →
power couples
tied stayers / movers
expected, except: →
legal restrictions for
non-citizens
smooth HC transfer →
Doctoral degree
I
N
Demographic
attributes
Age
+
+
Married
+
–
Black
+
+
Hispanic
+
+
Asian
o
o
Immigration–
specific variables
English
–
Citizen
+
Sojourn
–
Locational context
Metro
+
o
UE
–
o
Wage
+
–
Fixed effects
Period
↓
↓
State
Birthplace
NZ, JAP, CAN↓
For racial / ethnic minorities:
• barriers?
• segmentation?
• discrimination?
Results:
? married to whom? →
power couples
tied stayers / movers
expected, except: →
legal restrictions for
non-citizens
Opportunity-rich regions→
Immigrants more likely to
be overeducated
smooth HC transfer →
Doctoral degree
I
N
Demographic
attributes
Age
+
+
Married
+
–
Black
+
+
Hispanic
+
+
Asian
o
o
Immigration–
specific variables
English
–
Citizen
+
Sojourn
–
Locational context
Metro
+
o
UE
–
o
Wage
+
–
Fixed effects
Period
↓
↓
State
Birthplace
NZ, JAP, CAN↓
For racial / ethnic minorities:
• barriers?
• segmentation?
• discrimination?
← Depressed regions:
natives’ more likely to be overeducated
Part D.
Summary and future research
Summary
•
Immigrants more poorly matched than natives.
•
Undereducation among the very poorly educated immigrants:
–
–
•
very common, increasing over time, almost 90% in 2009
inversely related to assimilation: ethnic enclave effect?
Overeducation among highly educated immigrants:
–
–
decreases with sojourn length
varies by birthplace → HC transfer
Future research
► Effect D over time?
► More attention to locational context
► Data requirements: job attributes