This paper proposes a novel approach to estimating returns to human capital of migrants, accounting for the bias stemming from return migration. We apply our method to the case of immigrants from Central and Eastern Europe in the British labor market over 2004-2016 span. We find that the tertiary education of those immigrants was rewarded roughly at par with secondary education of the natives in the UK. We also find that the bias stemming from return migration is relevant for estimating the returns, but yields relatively small bias in the average predicted wages.

1. Migrants returns to human capital
Migrants returns to human capital
Evidence from high skill migration from CEECs to the UK
Joanna Tyrowicz
Pawel Kaczmarczyk, Magdalena Smyk
First ICEF Conference in Applied Economics
September 2018
1 / 15

2. Migrants returns to human capital
Motivation
Return migration as a selection bias
Mechanism: intentions + reality check
roughly 50% of migrants no longer reside in the host economy within 5-10
years of arrival Dustmann & Weiss (2007)
2 / 15

3. Migrants returns to human capital
Motivation
Return migration as a selection bias
Mechanism: intentions + reality check
roughly 50% of migrants no longer reside in the host economy within 5-10
years of arrival Dustmann & Weiss (2007)
Return migration as omitted variable problem: Dustmann & Goerlach (2012)
Return migrants are both positively and negatively selected Reinhold &
Thom (2013); Biavaschi (2016); Bijwaard & Wahba (2014); Bijwaard, Schluter &
Wahba (2014)
Even with (individual) panel data, the bias on human capital variables can be large
Dustmann & Goerlach (2012)
2 / 15

4. Migrants returns to human capital
Motivation
Return migration as a selection bias
Mechanism: intentions + reality check
roughly 50% of migrants no longer reside in the host economy within 5-10
years of arrival Dustmann & Weiss (2007)
Return migration as omitted variable problem: Dustmann & Goerlach (2012)
Return migrants are both positively and negatively selected Reinhold &
Thom (2013); Biavaschi (2016); Bijwaard & Wahba (2014); Bijwaard, Schluter &
Wahba (2014)
Even with (individual) panel data, the bias on human capital variables can be large
Dustmann & Goerlach (2012)
Aim of this paper:
Propose a method that explicitly accounts for the bias
2 / 15

5. Migrants returns to human capital
Motivation
Return migration as a selection bias
Mechanism: intentions + reality check
roughly 50% of migrants no longer reside in the host economy within 5-10
years of arrival Dustmann & Weiss (2007)
Return migration as omitted variable problem: Dustmann & Goerlach (2012)
Return migrants are both positively and negatively selected Reinhold &
Thom (2013); Biavaschi (2016); Bijwaard & Wahba (2014); Bijwaard, Schluter &
Wahba (2014)
Even with (individual) panel data, the bias on human capital variables can be large
Dustmann & Goerlach (2012)
Aim of this paper:
Propose a method that explicitly accounts for the bias
A la Heckman correction
2 / 15

6. Migrants returns to human capital
Motivation
Return migration as a selection bias
Mechanism: intentions + reality check
roughly 50% of migrants no longer reside in the host economy within 5-10
years of arrival Dustmann & Weiss (2007)
Return migration as omitted variable problem: Dustmann & Goerlach (2012)
Return migrants are both positively and negatively selected Reinhold &
Thom (2013); Biavaschi (2016); Bijwaard & Wahba (2014); Bijwaard, Schluter &
Wahba (2014)
Even with (individual) panel data, the bias on human capital variables can be large
Dustmann & Goerlach (2012)
Aim of this paper:
Propose a method that explicitly accounts for the bias
A la Heckman correction
Applied to the case of Poles in the UK (using conventional LFS)
2 / 15

7. Migrants returns to human capital
Motivation
Road map
1 Motivation
2 The case of HE migration from CEECs/PL to UK
3 Identification and data
4 Results
5 Challenge
6 Conclusions
3 / 15

8. Migrants returns to human capital
The case of HE migration from CEECs/PL to UK
Post-accession migration from CEECs to the UK
Several waves of migration before accession
War-period migration (mostly Poles)
1968 migration relatively small in the UK (only Poles and Czechs)
1980s migration also rarely to the UK
4 / 15

9. Migrants returns to human capital
The case of HE migration from CEECs/PL to UK
Post-accession migration from CEECs to the UK
Several waves of migration before accession
War-period migration (mostly Poles)
1968 migration relatively small in the UK (only Poles and Czechs)
1980s migration also rarely to the UK
Post-accession migration
Predominantly young
Positively selected on education (30+ with tertiary degree)
Usually without prior labor market experience → ladder of jobs
4 / 15

10. Migrants returns to human capital
Identification and data
Identification
Estimate the returns to (Polish) human capital in the UK
ln(w/h)i = α + βindividuali + γ(occupation, industry)i +
δnationalityi ∗ HEi + θnationalityi ∗ HEi ∗ CEECi + i
The estimates of δ and θ biased due to return migration
5 / 15

11. Migrants returns to human capital
Identification and data
Identification
Estimate the returns to (Polish) human capital in the UK
ln(w/h)i = α + βindividuali + γ(occupation, industry)i +
δnationalityi ∗ HEi + θnationalityi ∗ HEi ∗ CEECi + i
The estimates of δ and θ biased due to return migration
This bias has Heckman nature: latent (unobserved) variable
ln(w/h) =
0 if migrant returned (PRM > PRM)
α + ξcontrolsi + i if migrant still in the UK (PRM ≤ PRM)
5 / 15

12. Migrants returns to human capital
Identification and data
Identification
Estimate the returns to (Polish) human capital in the UK
ln(w/h)i = α + βindividuali + γ(occupation, industry)i +
δnationalityi ∗ HEi + θnationalityi ∗ HEi ∗ CEECi + i
The estimates of δ and θ biased due to return migration
This bias has Heckman nature: latent (unobserved) variable
ln(w/h) =
0 if migrant returned (PRM > PRM)
α + ξcontrolsi + i if migrant still in the UK (PRM ≤ PRM)
How to get PRM and PRM?
5 / 15

13. Migrants returns to human capital
Identification and data
Drivers of return migration
Individual wealth literature
Booysen, Van Der Berg, Burger, Von Maltitz & Du Rand (2008); Moser & Felton (2009)
6 / 15

14. Migrants returns to human capital
Identification and data
Drivers of return migration
Individual wealth literature
Booysen, Van Der Berg, Burger, Von Maltitz & Du Rand (2008); Moser & Felton (2009)
Empirical work on the UK by Dustmann & Okatenko (2014)
accommodation
[own home, mortgage or rented]
nationality of the partner (wife/husband/civil partner)
[UK, same nationality, different nationality]
children – living in UK [yes/no];
years since migrating to the UK
6 / 15

15. Migrants returns to human capital
Identification and data
Drivers of return migration
Individual wealth literature
Booysen, Van Der Berg, Burger, Von Maltitz & Du Rand (2008); Moser & Felton (2009)
Empirical work on the UK by Dustmann & Okatenko (2014)
accommodation
[own home, mortgage or rented]
nationality of the partner (wife/husband/civil partner)
[UK, same nationality, different nationality]
children – living in UK [yes/no];
years since migrating to the UK
Heterogenous across ethnicities / ancestries
6 / 15

16. Migrants returns to human capital
Identification and data
Drivers of return migration
Individual wealth literature
Booysen, Van Der Berg, Burger, Von Maltitz & Du Rand (2008); Moser & Felton (2009)
Empirical work on the UK by Dustmann & Okatenko (2014)
accommodation
[own home, mortgage or rented]
nationality of the partner (wife/husband/civil partner)
[UK, same nationality, different nationality]
children – living in UK [yes/no];
years since migrating to the UK
Heterogenous across ethnicities / ancestries
Language skills
6 / 15

17. Migrants returns to human capital
Identification and data
Drivers of return migration
Individual wealth literature
Booysen, Van Der Berg, Burger, Von Maltitz & Du Rand (2008); Moser & Felton (2009)
Empirical work on the UK by Dustmann & Okatenko (2014)
accommodation
[own home, mortgage or rented]
nationality of the partner (wife/husband/civil partner)
[UK, same nationality, different nationality]
children – living in UK [yes/no];
years since migrating to the UK
Heterogenous across ethnicities / ancestries
Language skills
→ Obtain individual level proxy of PRM (using PCA)
6 / 15

18. Migrants returns to human capital
Identification and data
British LFS (i.e. only the hosting economy)
Large, representative survey, quasi-rotating
Wages only in q2 of each year
Roughly 7% are immigrants to the UK
As of 2004, many CEECs migrants which sets 2004-2015 data sets
40 940 from CEECs, out of which 24 367 from Poland
In total 2.5 million observations for working individuals above 16
7 / 15

19. Migrants returns to human capital
Identification and data
Obtainig PRM
1 all four characteristics from the literature and the country of origin
(related to residence, partner, children and duration of stay)
2 the above + languages skills
8 / 15

20. Migrants returns to human capital
Identification and data
Obtainig PRM
1 all four characteristics from the literature and the country of origin
(related to residence, partner, children and duration of stay)
2 the above + languages skills
3 all four characteristics from the literature within the country of origin
(related to residence, partner, children and duration of stay)
4 the above + languages skills
8 / 15

21. Migrants returns to human capital
Identification and data
Distribution of PRM
9 / 15

22. Migrants returns to human capital
Results
Results
We estimate
ln(w/h)i = α + βindividuali + γ(occupation, industry)i +
δnationalityi ∗ HEi + θnationalityi ∗ HEi ∗ CEECi + λPRMi + i
The conventional a la Heckman interpretation of λ
But we do not have the “first stage” regression, so cannot obtain
correlation estimate between i and error term from the first stage
(conventionally ρ)
→ assume some arbitrary values for ρ: 0.3, 0.5, 0.7
10 / 15

23. Migrants returns to human capital
Results
Results
Variables No PRM PRM1 PRM 3 PRM2 PRM1 PRM4 PRM3
(1) (2) (3) (4) (5) (6) (7)
HE 0.22*** 0.23*** 0.23*** 0.22*** 0.22*** 0.22*** 0.22***
CEECs 0.04*** 0.06*** 0.06*** -0.01*** -0.02*** -0.02*** -0.02***
PL -0.0002 0.002*** 0.0001 -0.04*** -0.04*** -0.04*** -0.04***
HE # CEECs -0.13*** -0.14*** -0.14*** -0.18*** -0.18*** -0.18*** -0.18***
HE # PL -0.05*** -0.05*** -0.05*** 0.004 0.004 0.004 0.004
N (weighted) 345,528,095 33,672,185
R2
0.45 0.45 0.45 0.44 0.44 0.44 0.44
PRM (λ) -0.14*** -0.07*** -0.10*** -0.10*** -0.09*** -0.07***
Implied average truncation effects - for arbitrary values of ρ
ρ = 0.7 -3.92 -3.92 -4.06 -3.87 -4.06 -3.87
ρ = 0.5 -2.82 -2.81 -2.92 -2.78 -2.92 -2.78
ρ = 0.2 -1.14 -1.14 -1.18 -1.12 -1.18 -1.12
11 / 15

24. Migrants returns to human capital
Results
Results
Estimates of δ are significant and negative across the specifications
Estimates of θ stops being significant with controls for language (but
much smaller sample!)
Estimates of λ significant and negative: wages of migrants overstated
Latent truncation appears more of a level effect, rather than slope
12 / 15

25. Migrants returns to human capital
Results
Results
Estimates of δ are significant and negative across the specifications
Estimates of θ stops being significant with controls for language (but
much smaller sample!)
Estimates of λ significant and negative: wages of migrants overstated
Latent truncation appears more of a level effect, rather than slope
Results similar across quantiles
Robust to education measurement
12 / 15

26. Migrants returns to human capital
Challenge
In principle, we DO know the probablity of return migration
People report year of arrival and survey is administered at equal intervals
Denote N0,6 = no of people who report arriving in UK in 2000
and participate in BLFS in 2006
Denote N0,7 = no of people who report arriving in UK in 2000
and participate in BLFS in 2007
π (return in t=6 | not returning earlier) = 1 − N0,7/N0,6
13 / 15

27. Migrants returns to human capital
Challenge
In principle, we DO know the probablity of return migration
People report year of arrival and survey is administered at equal intervals
Denote N0,6 = no of people who report arriving in UK in 2000
and participate in BLFS in 2006
Denote N0,7 = no of people who report arriving in UK in 2000
and participate in BLFS in 2007
π (return in t=6 | not returning earlier) = 1 − N0,7/N0,6
Data granularity permitting, exists within cells (nationality, age, education,
gender, etc.)
13 / 15

28. Migrants returns to human capital
Challenge
In principle, we DO know the probablity of return migration
People report year of arrival and survey is administered at equal intervals
Denote N0,6 = no of people who report arriving in UK in 2000
and participate in BLFS in 2006
Denote N0,7 = no of people who report arriving in UK in 2000
and participate in BLFS in 2007
π (return in t=6 | not returning earlier) = 1 − N0,7/N0,6
Data granularity permitting, exists within cells (nationality, age, education,
gender, etc.)
One problem: this is survey data ...
13 / 15

29. Migrants returns to human capital
Conclusions
Preliminary conclusions and way onwards
What we have so far
PCA for correlates of return migration ...
... with a la Heckman transformation
14 / 15

30. Migrants returns to human capital
Conclusions
Preliminary conclusions and way onwards
What we have so far
PCA for correlates of return migration ...
... with a la Heckman transformation
With these estimates: penalty for CEECs human capital
(and Poles on top of that)
What we need / want to do:
Overidentified SMM for “probabilities” of return
Think of applying it to a larger sample
(but data on household/wealth needed)
14 / 15

31. Migrants returns to human capital
Conclusions
Thank you and
I am happy to take questions!
w: grape.org.pl
t: grape org
f: grape.org
e: j.tyrowicz@grape.org.pl
15 / 15