Accessible Unlicensed Requires Authentication Published by De Gruyter March 21, 2019

Do Immigrants Compete with Natives in the Greek Labour Market? Evidence from the Skill-Cell Approach before and during the Great Recession

Michael Chletsos and Stelios Roupakias

Abstract

We attempt to identify the impact of immigration on the labour market opportunities of resident workers by analysing data from the Greek Labour Force Survey (1999–2015) as well as census data for 1981, 1991, and 2001. Slicing the national labour market into education and experience segments, we find modest adverse effects on the employment outcomes of natives and usually insignificant effects on earnings. Our results are generally robust to alternative definitions of skill groups and the potential “downgrading” of immigrants. Importantly, we obtain similar results in qualitative terms when we account for potential endogenous selection into skill-cells, by implementing an instrumental variables approach in the spirit of Card (2001). We also show that veteran immigrants compete more heavily with natives than recent immigrants do. In addition, our analysis indicates that the effects of immigration were economically more important during the Greek crisis. Finally, the evidence supports the idea that migrants push natives towards complementary, non-routine tasks.

JEL Classification: F22; J15; J31

Acknowledgements

We thank the Journal Editor, Professor Mariapia Mendola and an anonymous referee for excellent comments that significantly improved the paper. Any remaining errors are, of course, the authors’ responsibility.

Appendix

A Appendix

Table 10:

Bilateral correlations between the key variables used in the paper.

(1)(2)(3)(4)(5)(6)(7)(8)(9)
1. Immigrant share1
2. Unemployment0.5261
3. Employment−0.353−0.5891
4. Average hours worked−0.0448−0.2460.4231
5. Log monthly earnings−0.433−0.7660.5880.08321
6. Abstract task importance−0.455−0.6430.437−0.1730.8051
7. Routine task importance0.3850.2070.1440.554−0.332−0.5721
8. Service task importance−0.258−0.0483−0.190−0.6330.3150.594−0.8681
9. Routine task intensity0.3910.2320.08900.561−0.409−0.6740.982−0.9271
10. Primary education0.2220.182−0.2210.0131−0.328−0.3910.482−0.4390.499
11. Lower secondary education0.3510.164−0.1120.184−0.239−0.2890.280−0.2060.290
12. Upper secondary education−0.226−0.03020.03460.223−0.0169−0.148−0.193−0.0491−0.112
13. University−0.347−0.3160.299−0.4200.5850.828−0.5680.694−0.677
14. Experience [1–5]0.1070.455−0.143−0.120−0.355−0.1590.103−0.02320.0882
15. Experience [6–10]0.1840.217−0.001640.00438−0.189−0.08750.0502−0.03520.0492
16. Experience [11–15]0.1560.03870.0754−0.00835−0.0541−0.03820.0228−0.02650.0267
17. Experience [16–20].0.0527−0.04170.1250.02330.02600.0004370.00244−0.01530.00571
18. Experience [21–25]−0.0518−0.1130.1550.03300.08960.0225−0.02580.00104−0.0176
19. Experience [26–30]−0.130−0.1590.1160.02100.1300.0437−0.03300.00147−0.0265
20. Experience [30–35]−0.155−0.179−0.03100.02260.1790.0844−0.04660.0156−0.0431
21. Experience [36–40]−0.163−0.218−0.2960.02410.1720.133−0.07300.0820−0.0823

Table 11:

Income brackets and the associated share of native wage earners, GRLFS 1999–2014.

1999–20022003–20082009–20112012–2014
Income bracketAttributed wageShareIncome bracketAttributed wageShareIncome bracketAttributed wageShareIncome bracketAttributed wageShare
0–3001500.0240–2501250.0080–5002500.0350–5002500.074
301–6004500.282251–5003750.051501–7006000.064501–7006000.129
601–9007500.453501–7506250.205701–8007500.099701–8007500.144
901–120010500.188751–10008750.300801–9008500.103801–9008500.128
1201–150013500.0371001–125011250.232901–10009500.105901–10009500.132
1501+16500.0171251–150013750.1221001–110010500.1361001–110010500.130
1501–175016250.0371101–130012000.2121101–120011500.103
1751–200018750.0151301–160014500.1531201–130012500.066
2000+21250.0311601–175016750.0351301–150014000.037
1751+18500.0581501+15850.058

  1. Source: Authors’ elaborations based on GRLFS data

Table 12:

Distribution of immigrants in the labour force by level of education and experience.

EducationYears of experience19992003200720112015
Primary1–50.1290.2370.3630.6190.208
6–100.1170.1970.3720.5700.337
11–150.0970.2190.3080.4630.466
16–200.0630.1290.2160.4030.436
21–250.0370.0730.1080.2510.320
26–300.0240.0440.0700.1030.189
31–350.0150.0280.0350.0820.096
36–400.0140.0130.0280.0440.041
Lower secondary1–50.1410.1850.3320.3310.214
6–100.0970.1590.2660.3680.347
11–150.1090.2070.2710.3650.284
16–200.1010.1250.2360.2540.288
21–250.0720.1450.1550.2270.249
26–300.0630.1210.1240.1440.151
31–350.0340.0690.1160.1310.137
36–400.0600.0940.0610.0550.105
High-school graduates1–50.0630.0560.0770.0800.065
6–100.0730.0800.0750.0810.068
11–150.0760.1040.0840.0950.072
16–200.0600.0940.1060.1210.082
21–250.0540.0630.0960.1120.089
26–300.0480.0750.0740.0780.095
31–350.0430.0500.0620.0790.078
36–400.0550.0400.0510.0850.095
University graduates1–50.0490.0250.0290.0270.019
6–100.0670.0640.0460.0240.028
11–150.0680.0470.0720.0300.026
16–200.0590.0520.0490.0490.046
21–250.0630.0620.0350.0450.044
26–300.0490.0440.0390.0550.042
31–350.0630.0590.0370.0330.059
36–400.0330.0730.0390.0660.102

Table 13:

Unemployment rate of natives by skill cell.

EducationYears of experience19992003200720112015
Primary1–50.1640.1620.1890.3900.716
6–100.1210.0970.1720.2770.545
11–150.0780.0810.0700.2720.432
16–200.0790.0440.0560.2190.361
21–250.0590.0430.0710.1910.282
26–300.0510.0400.0390.1730.246
31–350.0520.0420.0380.1580.223
36–400.0430.0350.0410.1670.227
Lower secondary1–50.2500.1700.0810.3390.326
6–100.1530.1270.1000.3570.430
11–150.0840.0670.0590.2090.344
16–200.0600.0540.0290.1790.284
21–250.0530.0360.0450.1490.270
26–300.0410.0200.0350.1250.209
31–350.0360.0150.0330.1400.206
36–400.0560.0490.0070.0770.127
High-school graduates1–50.2210.2050.1500.3610.451
6–100.1210.1070.0960.2210.302
11–150.0760.0680.0560.1530.205
16–200.0340.0490.0260.1150.182
21–250.0400.0310.0390.0880.147
26–300.0340.0220.0170.0990.153
31–350.0420.0370.0210.1070.179
36–400.0350.0330.0260.0810.168
University graduates1–50.2060.1630.1670.3470.327
6–100.0760.0790.0740.1680.270
11–150.0380.0190.0260.0770.137
16–200.0250.0170.0170.0560.118
21–250.0210.0120.0170.0380.088
26–300.0150.0070.0050.0420.086
31–350.0190.0260.0120.0330.083
36–400.0090.0100.0120.0230.066

Table 14:

Employment rate of natives by skill cell.

EducationYears of experience19992003200720112015
Primary1–50.7330.7350.6390.4420.214
6–100.8100.8320.7440.5770.364
11–150.8610.8290.8510.5900.423
16–200.8640.8820.8540.6950.528
21–250.8780.8830.8560.7370.636
26–300.8810.8920.8730.7350.684
31–350.8550.8720.8630.7410.679
36–400.7870.8000.8140.6600.629
Lower secondary1–50.6930.7600.8180.5760.560
6–100.8280.8500.8600.6000.512
11–150.8980.9040.9060.7340.606
16–200.9110.9150.9400.7920.690
21–250.9250.9400.9290.8020.687
26–300.9320.9420.9180.8230.746
31–350.8800.9020.8920.8090.716
36–400.7210.7120.8480.7690.717
High-school graduates1–50.7400.7620.7980.5880.501
6–100.8650.8770.8850.7650.677
11–150.9090.9100.9320.8300.777
16–200.9410.9310.9590.8650.796
21–250.9400.9430.9410.8750.836
26–300.9180.9400.9440.8480.791
31–350.8020.8500.8470.7870.719
36–400.5990.6340.6850.6240.519
University graduates1–50.7440.7680.7900.6290.612
6–100.9090.9100.9120.8180.715
11–150.9510.9770.9680.9100.853
16–200.9660.9770.9710.9260.857
21–250.9410.9640.9590.9300.854
26–300.9020.9110.9090.8800.834
31–350.7530.7800.8070.7700.720
36–400.4700.5030.5310.5140.423

Table 15:

Average hours worked of natives by skill cell.

EducationYears of experience19992003200720112015
Primary1–542.29944.38439.68141.08246.195
6–1043.01943.42843.08842.68035.343
11–1542.24542.92144.08943.64833.987
16–2043.34243.92143.06041.33843.391
21–2544.58944.67643.87042.06142.815
26–3044.57043.44344.67342.78642.269
31–3543.19943.73944.16541.42142.631
36–4043.03743.78943.79140.57341.604
Lower secondary1–541.60443.20641.00137.78342.570
6–1043.68943.83843.77242.03940.279
11–1544.09743.41344.32743.55640.802
16–2044.96444.54044.30942.59044.203
21–2545.04244.52344.66243.76740.663
26–3045.03845.57244.75142.79842.768
31–3543.63045.73943.96742.24041.852
36–4044.55643.94044.64843.45046.182
High-school graduates1–542.72141.98341.76340.61738.887
6–1043.31543.44543.27641.81141.711
11–1544.05844.25643.52742.56942.468
16–2044.05844.19344.35742.83442.593
21–2543.82943.58843.31443.55043.351
26–3043.69043.98343.92343.87642.607
31–3544.14943.29743.21442.98143.743
36–4044.24042.82643.85042.68342.001
University graduates1–539.94040.78340.13339.66839.804
6–1040.84540.23340.37540.70639.475
11–1540.61040.19739.92841.58241.123
16–2040.36040.50139.91540.80040.585
21–2539.16840.16638.89939.11540.050
26–3038.90539.22538.96939.52639.311
31–3538.68339.04337.60539.39838.216
36–4037.34838.25338.59039.62341.304

Table 16:

Monthly earnings of natives by skill cell.

EducationYears of experience19992003200720112015
Primary1–5631815678572589
6–10759942834826602
11–158431052899858754
16–208911070971879721
21–2594911231005928744
26–3094711621099975805
31–3597612291087975845
36–4095912111081994867
Lower secondary1–5681979789722671
6–107591041853700708
11–158171151918865687
16–209161084991919861
21–25101211221079974792
26–301027121110351032809
31–351040127911381017833
36–401048126311431176879
High-school graduates1–5724962804805640
6–108131006895845695
11–159101123998911775
16–2099511641084995825
21–251062121011041030868
26–301084123411751103872
31–351200125912331157954
36–401175129911701147996
University graduates1–59101217981895704
6–101057121411911091870
11–1512541382131012371020
16–2013791651139613011168
21–2515841516150613781190
26–3015031558157114151357
31–3516721856167815101317
36–4015041613160014751334

B Appendix

Calculating effective skills for migrants

Let Am denotes age of entry in Greece and AT age of entry into the labour market. In the fashion of Borjas (2003),[43] we distinguish between immigrants who migrated as adults (i.e. Am<AT), and as children (i.e. AmAT). Adult migrants’ labour market experience is assumed to be given by the sum of pre- AAm and a post-migration AmAT experience. On the other hand, child migrants only have experience acquired abroad AAT. We weigh experience by the unknown factors α, β and γ in order to account for potential downgrading. Formally, effective experience for each of the two immigrant groups is then given by the following relationship:

X=αAmAT+βAAm,ifAm>ATγAATifAmAT

To estimate the weights, we run the following generic assimilation regression:[44]

logw=si+ϕcIC+ϕDID+λNNExper+λCICExper+λD0IDExper_abroad+λD1IDExper_Greece+δY+ρπ+ϕ

where si is a vector of education fixed effects, IC and ID indicate whether the immigrant entered the country as a child and as adult, respectively, N indicates whether the worker is native-born, Y are cohort of immigration dummies and π are period fixed effects. The weights are then calculated as follows:

α=λD0λN,β=λD1λN,γ=λCλN.

.

The results reported in Table 17 indicate that experience acquired in Greece is almost equally valued by Greek employers. In contrast, the value of pre-immigration experience is much lower. Applying the information from this table in the above formulas, we find that the values α, β and γ are 0.2, 1.2, and 0.9, respectively. Using the estimated weights, we then compute the effective experience and adjust the immigrant share accordingly. Given that we almost fully eliminate source country experience, immigration counts fall dramatically in high experience cells.

Table 17:

The impact of experience on the long monthly earnings of natives and immigrants.

Group
Coefficient of:NativesChild immigrantsAdult immigrants
Source country experience̶̶̶̶0.003***
(0.001)
Source country experience squared ÷10̶̶−0.000**
(0.000)
Greek experience0.029***0.027***0.013***
(0.000)(0.001)(0.001)
Greek experience squared ÷10−0.005***−0.005***−0.002***
(0.000)(0.000)(0.001)
Mean value of:
Source country experience̶̶11.1
Greek experience18.99.410.4
Marginal value of an additional year of experience for immigrants:
Source country experience̶0.002
(0.000)
Greek experience̶0.0170.010
(0.000)(0.000)
Marginal value of an additional year of experience for natives, evaluated at mean value of relevant sample of immigrants̶0.0200.008
(0.000)(0.000)

  1. Notes. Robust standard errors are reported in parentheses. The regression pools data for the 1999–2015 period and has 290,880 observations. The regression also includes dummy variables indicating whether the worker is an adult immigrant or a child immigrant; the interaction between a vector of variables indicating the worker’s educational attainment and dummy variables indicating whether the worker is an adult or a child immigrant; dummy variables indicating the calendar year in which the immigrant arrived (2015–2011, 2010–2006, 2005–2001, 2000–1996, 1995–1991, 1990–1986, 1985–1981, and before 1981), and the interaction of this vector with a dummy variable indicating whether the immigrant arrived as an adult.

  2. * p < 0.1; ** p < 0.05; *** p < 0.01.

To control for a potential downgrading by education, we follow Dustmann and Frattini (2014) and impute to each intermediate or high-skilled immigrant who arrived as an adult the year-specific average level of education of natives in the same occupation (we use the two-digit ISCO88 occupation classification), assuming that the unemployed form an extra occupational group. As can be seen in Table 18, the imputed shares of high-school and university graduates are far lower than the observed ones. As a result, immigrants are over-represented in the group of workers with lower secondary education. Finally, Figure 5 reports the observed against the adjusted for effecting experience and schooling immigrant share.

Table 18:

Observed (1) versus imputed (2) education distribution of immigrants who arrived as adults.

(1)(2)
Primary28.7728.91
Lower secondary26.1337.71
High school34.2429.32
University10.854.05

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Note

The main data used in this paper can be accessed through the Hellenic Statistical Authority (ELSTAT). We would like to thank Maarten Goos, Alan Manning, and Anna Salomons for generously giving us access to their data on task measures. We have benefited from helpful comments by seminar participants at the EALE 2017 conference. We also thank George Borjas and Giovanni Peri for useful suggestions.

Published Online: 2019-03-21

© 2019 Walter de Gruyter GmbH, Berlin/Boston