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The labor market effects of Venezuelan migration to Colombia: reconciling conflicting results†

Jeremy Lebow
Jeremy Lebow
   | 21 avr. 2022
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Publié en ligne: 21 avr. 2022
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Accepté: 17 févr. 2022
DOI: https://doi.org/10.2478/izajodm-2022-0005
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© 2022 Jeremy Lebow, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Foreign-born population in Colombia.Sources: Colombian National Integrated Household Survey (GEIH) (2013–2019); Population Census (1993, 2005).
Foreign-born population in Colombia.Sources: Colombian National Integrated Household Survey (GEIH) (2013–2019); Population Census (1993, 2005).

Figure 2

Venezuelan migrants in Colombia (2019).Sources: Colombian National Integrated Household Survey (GEIH) (2019).
Venezuelan migrants in Colombia (2019).Sources: Colombian National Integrated Household Survey (GEIH) (2019).

Figure 3

The effect of migration on residual ln(hourly wage) is separately estimated via 2SLS within occupation skill groups.Note: 95% confidence intervals are presented around each point estimate.
The effect of migration on residual ln(hourly wage) is separately estimated via 2SLS within occupation skill groups.Note: 95% confidence intervals are presented around each point estimate.

Figure A1

Education by migrant status.Notes: Data restricted to urban residents of age 15–64 years. GEIH, Colombian National Integrated Household Survey.Source: GEIH (2019).
Education by migrant status.Notes: Data restricted to urban residents of age 15–64 years. GEIH, Colombian National Integrated Household Survey.Source: GEIH (2019).

Figure A2

Concentration of migrants and natives across occupations.Notes: Data restricted to urban residents of age 15–64 years. Occupations ranked according to mean years of completed schooling for natives in the GEIH between 2010 and 2015. GEIH, Colombian National Integrated Household Survey.Source: GEIH (2019).
Concentration of migrants and natives across occupations.Notes: Data restricted to urban residents of age 15–64 years. Occupations ranked according to mean years of completed schooling for natives in the GEIH between 2010 and 2015. GEIH, Colombian National Integrated Household Survey.Source: GEIH (2019).

Figure A3

Relationship between 2019 and 2005 migrant shares.Note: RMSE, root mean square error.
Relationship between 2019 and 2005 migrant shares.Note: RMSE, root mean square error.

Figure A4

Pre-trends in labor market outcomes.Note: Pre-trends estimated according to Eq. (5) in Section 5. They are 95% confidence intervals.
Pre-trends in labor market outcomes.Note: Pre-trends estimated according to Eq. (5) in Section 5. They are 95% confidence intervals.

Figure A5

2SLS coefficient plot – by work sector.Note: 2SLS, two-stage least squares. They are 95% confidence intervals.
2SLS coefficient plot – by work sector.Note: 2SLS, two-stage least squares. They are 95% confidence intervals.

Figure A6

LOWESS plot.Note: LOWESS, locally weighted scatterplot smoothing. The red dotted line displays the least square line of best fit.
LOWESS plot.Note: LOWESS, locally weighted scatterplot smoothing. The red dotted line displays the least square line of best fit.

Preperiod correlates of 2005 Venezuelan share of population

(1) (2) (3) (4) (5)
Population (100,000) −0.001 (0.005) 0.005 (0.003) 0.004 (0.003) 0.004 (0.003) 0.002 (0.003)
100*ln(hourly wage) −0.017** (0.008) −0.018* (0.009) −0.018* (0.009) −0.024* (0.026)
100*ln(hours/week) 0.021 (0.017) 0.021 (0.017) 0.026 (0.019)
100* unemployment rate 0.047 (0.125) 0.049 (0.126)
100* LPP rate 0.041 (0.047)
N 79 79 79 79 79
R2 1.6e−04 0.051 0.073 0.073 0.09
2014 metro characteristic Mc,2005 Mc,2005 Mc,2005 Mc,2005 Mc,2005

2SLS estimates with migrant share quadratic

(1) (2) (3) (4)
ln(hourly wage)
All Less than secondary Secondary Postsecondary
Migrant share −1.40 (1.12) −2.37** (1.11) −1.14 (1.02) −0.79 (1.67)
Migrant share2 0.02 (0.05) 0.05 (0.05) 0.02 (0.05) 0.00 (0.08)
Joint F-statistic 62.36 55.46 39.73 25.46
K-P Wald stat. 58.02 35.66 33.97 89.30

2SLS estimates additional robustness

(1) (2) (3) (4) (5) (6)
Original 2SLS Drop Bogotá Drop metro yearly sample <1,000 2014–2019 Difference model Kronmal specification Assign natives to past metro
ln(hourly wage)
All −1.05*** (0.22) −1.03*** (0.22) −1.04*** (0.23) −1.09*** (0.27) −0.66*** (0.15) −1.04*** (0.20)
Less than secondary −1.42*** (0.23) −1.41*** (0.24) −1.41*** (0.25) −1.33*** (0.27) −0.95*** (0.17) −1.38*** (0.22)
Secondary −0.86*** (0.22) −0.87*** (0.22) −0.85*** (0.23) −0.79*** (0.28) −0.54*** (0.15) −0.80*** (0.21)
Postsecondary −0.75* (0.38) −0.69* (0.37) −0.75* (0.39) −0.96** (0.48) −0.46** (0.23) −0.80** (0.36)
ln(hours/week)
All 0.27 (0.27) 0.28 (0.27) 0.27 (0.27) 0.27 (0.32) 0.17 (0.17) 0.29
Less than secondary 0.50 (0.34) 0.52 (0.34) 0.51 (0.34) 0.51 (0.39) 0.34 (0.22) 0.51 (0.34)
Secondary 0.32 (0.21) 0.32 (0.21) 0.30 (0.21) 0.26 (0.26) 0.20 (0.12) 0.32 (0.21)
Postsecondary −0.02 (0.23) −0.02 (0.22) −0.00 (0.22) 0.01 (0.25) −0.01 (0.12) 0.02 (0.22)
Unemployment
All −0.08 (0.07) −0.08 (0.07) −0.09 (0.08) −0.07 (0.07) −0.05 (0.04) −0.07 (0.07)
Less than secondary −0.05 (0.06) −0.05 (0.07) −0.06 (0.07) −0.03 (0.06) −0.04 (0.04) −0.06 (0.06)
Secondary −0.02 (0.09) −0.01 (0.08) −0.03 (0.09) 0.02 (0.09) −0.01 (0.05) −0.01 (0.08)
Postsecondary −0.18* (0.10) −0.18* (0.10) −0.17* (0.10) −0.20* (0.10) −0.11* (0.06) −0.15 (0.10)
LFP
All −0.21* (0.11) −0.21** (0.11) −0.21** (0.10) −0.17* (0.10) −0.13** (0.07) −0.20** (0.10)
Less than secondary −0.23* (0.12) −0.24* (0.12) −0.24** (0.11) −0.16 (0.10) −0.16* (0.08) −0.24** (0.12)
Secondary −0.12** (0.06) −0.12** (0.06) −0.12** (0.06) −0.10 (0.07) −0.07** (0.04) −0.12** (0.05)
Postsecondary −0.24 (0.16) −0.25 (0.16) −0.23 (0.17) −0.27 (0.18) −0.15* (0.08) −0.21 (0.15)
K-P Wald stat. 23.35 23.41 19.28 30.89 24.17 24.47
Number of metro areas 79 78 27 79 79 79

Robustness of 2SLS effects on native internal migration

(1) (2) (3) (4) (5) (6) (7) (8)
Original 2SLS Drop <100 km from border Control trade with Venezuela Year trend × inverse distance to border Year trend × region Drop Bogotá Drop metro yearly sample <1,000 Kronmal specification
Outmigration
All 0.03 (0.03) 0.01 (0.08) 0.03 (0.05) −0.09 (0.12) −0.11** (0.05) 0.05* (0.03) 0.03 (0.03) 0.02 (0.02)
Less than secondary 0.07** (0.04) 0.07 (0.10) 0.07 (0.06) −0.04 (0.14) −0.09 (0.06) 0.10*** (0.03) 0.04 (0.03) 0.05** (0.03)
Secondary 0.10*** (0.03) 0.05 (0.10) 0.13*** (0.04) 0.04 (0.13) 0.04 (0.05) 0.10*** (0.03) 0.07** (0.03) 0.06*** (0.02)
Postsecondary −0.07 (0.05) −0.06 (0.08) −0.13 (0.08) −0.16 (0.15) −0.24*** (0.08) −0.04 (0.04) −0.04 (0.05) −0.04 (0.03)
Inmigration
All −0.08 (0.11) 0.03 (0.28) −0.08 (0.12) 0.26 (0.32) −0.03 (0.09) −0.09 (0.12) −0.07 (0.12) −0.05 (0.07)
Less than secondary −0.04 (0.12) 0.20 (0.29) −0.05 (0.12) 0.44 (0.30) −0.01 (0.10) −0.05 (0.12) −0.02 (0.13) −0.02 (0.08)
Secondary −0.17 (0.11) −0.04 (0.30) −0.19* (0.10) 0.11 (0.36) −0.08 (0.08) −0.18* (0.11) −0.16 (0.11) −0.11* (0.06)
Postsecondary −0.01 (0.14) −0.06 (0.30) 0.03 (0.15) 0.26 (0.41) 0.07 (0.14) −0.02 (0.14) −0.04 (0.13) −0.00 (0.09)
Kleibergen-Paap Wald stat. 17.78 8.88 38.19 6.30 61.57 17.79 14.00 19.49
Number of metro areas 79 73 79 79 79 78 27 79

Robustness of 2SLS wage effects interacted with regional characteristics

Interaction variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Original 2SLS Drop <100 km from border Control trade with Venezuela Year trend × inverse distance to border Year trend × region Year trend × pre-trend Drop Bogotá Drop metro yearly sample <1,000 2014–2019 difference model Kronmal specification Assign natives to past metro
2014 mean ln(hourly wage) −0.36 (0.46) −0.69 (0.60) −0.36 (0.46) −0.31 (0.45) −0.20† (0.42) −0.36 (0.46) −0.47 (0.49) −0.68 (0.62) −0.48† (0.50) −0.63** (0.29) −0.35 (0.40)
2014 unemployment rate 0.07 (0.29) 0.35 (0.82) 0.11 (0.31) 0.58** (0.25) −0.29 (0.33) 0.08 (0.29) 0.08 (0.30) 0.04 (0.33) 0.08 (0.33) 0.11 (0.18) 0.07 (0.26)
2014 informal rate −0.62* (0.34) −0.80** (0.38) −0.63*† (0.34) −0.56* (0.34) −0.76*** (0.27) −0.60* (0.34) −0.62* (0.37) −0.64 (0.40) −0.62† (0.42) −0.34 (0.48) −0.53* (0.31)
2014 own-account rate −0.75** (0.35) −1.15** (0.45) −0.81**† (0.38) −0.73** (0.33) −0.86***† (0.33) −0.73** (0.35) −0.75** (0.37) −0.80* (0.42) −0.71*† (0.43) −0.74* (0.40) −0.61* (0.33)
WB DB (starting a business) 0.29*† (0.16) 0.43 (0.35) 0.34*† (0.18) 0.41* (0.24) 0.28† (0.25) 0.29*† (0.16) 0.28*† (0.16) 0.27*† (0.15) 0.26† (0.16) 0.16† (0.11) 0.26*† (0.14)
WB DB (construction permits) −0.31 (0.50) −0.31 (0.51) −0.31 (0.51) −0.29 (0.52) −0.08† (0.38) −0.28 (0.49) −0.36 (0.51) −0.30 (0.53) −0.35 (0.61) −0.18 (0.23) −0.26 (0.47)
WB DB (registering property) −0.36 (0.43) −0.37 (0.43) −0.38 (0.50) −0.34 (0.39) −0.38† (0.37) −0.37 (0.43) −0.50 (0.45) −0.33 (0.45) −0.34 (0.54) −0.20 (0.16) −0.30 (0.39)
WB DB (paying taxes) −0.06 (0.23) −0.28 (0.53) −0.05 (0.25) 0.30 (0.25) −0.49* (0.27) −0.05 (0.24) −0.06 (0.23) −0.07 (0.24) −0.11 (0.27) 0.01 (0.12) −0.05 (0.22)
2014 per capita GDP −0.13 (0.32) −0.15 (0.33) −0.14† (0.31) −0.20 (0.33) 0.09† (0.37) −0.11 (0.31) −0.18 (0.30) −0.12 (0.33) −0.18† (0.35 −0.14 (0.15) −0.14 (0.30)
N 474 438 474 474 474 474 468 162 79 474 474

2SLS wage effects interacted with regional characteristics

ln(hourly wage)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Migrant share −0.97*** (0.26) −1.14** (0.55) −1.02*** (0.23) −0.55 (0.39) −1.28*** (0.23) −0.94*** (0.30) −1.23*** (0.20) −1.01*** (0.37) −1.01*** (0.30)
Migrant share interacted with: 2014 mean ln(hourly wage) −0.36 (0.46)
2014 unemployment rate 0.07 (0.29)
2014 informal rate −0.62* (0.34)
2014 own-account rate −0.75** (0.35)
2017 WB DB (starting a business) 0.29* (0.16)
2017 WB DB (construction permits) −0.31 (0.50)
2017 WB DB (registering property) −0.36 (0.43)
2017 WB DB (paying taxes) −0.06 (0.23)
2014 per capita GDP −0.13 (0.32)
K-P Wald stat. 12.38 9.51 14.11 13.27 36.46 11.55 8.73 9.03 13.31
N 474 474 474 474 474 474 474 474 474

2SLS effects on employment by type of work

(1) (2) (3) (4) (5)
Formal salaried Informal salaried Own account Employer K-P Wald stat.
All −0.09*** (0.02) −0.02 (0.07) −0.04 (0.09) 0.01 (0.02) 25.69
Female −0.09*** (0.03) −0.00 (0.06) −0.03 (0.10) 0.01 (0.01) 25.35
Male −0.08** (0.03) −0.03 (0.10) −0.05 (0.09) 0.02 (0.03) 26.10
Age 15–24 years −0.10*** (0.03) −0.02 (0.08) −0.31*** (0.11) −0.01 (0.00) 24.77
Age 25–34 years −0.02 (0.05) −0.01 (0.08) 0.07 (0.11) −0.00 (0.02) 23.15
Age 35–44 years −0.09** (0.04) −0.04 (0.08) 0.10 (0.09) 0.04 (0.03) 25.02
Age 45–54 years −0.10** (0.04) −0.00 (0.06) 0.02 (0.08) 0.03 (0.03) 29.18
Age 55–64 years −0.10** (0.05) −0.03 (0.05) −0.01 (0.11) 0.05** (0.02) 29.02
Less than secondary −0.10*** (0.02) −0.05 (0.08) −0.06 (0.10) 0.02 (0.03) 31.28
Secondary −0.05 (0.04) −0.07 (0.07) 0.06 (0.11) −0.00 (0.02) 26.91
Postsecondary −0.08* (0.05) 0.01 (0.06) −0.04 (0.10) 0.03 (0.02) 19.22

Labor market effects of immigration

(1) (2) (3)
OLS 2SLS Test (1) = (2) (p-value)
ln(hourly wage) −0.73* (0.42) −1.05*** (0.22) 0.209
ln(hours/week) 0.08 (0.26) 0.27 (0.27) 0.399
Unemployment 0.01 (0.13) −0.08 (0.07) 0.255
Labor force participation −0.10 (0.12) −0.21* (0.11) 0.355
K-P Wald stat 23.35
N 474 474
Year FE, City FE X X

2SLS estimates by demographic groups

(1) (2) (3) (4) (5)
ln(hourly wage) ln(hours/week) Unemployment LFP K-P Wald stat.
All −1.05*** (0.22) 0.27 (0.27) −0.08 (0.07) −0.21* (0.11) 23.35
Male −1.16*** (0.22) 0.37** (0.17) −0.05 (0.08) −0.18** (0.08) 24.34
Female −0.91*** (0.25) 0.19 (0.39) −0.12 (0.08) −0.22* (0.13) 22.22
Age 15–24 years −0.94*** (0.20) 0.14 (0.46) −0.01 (0.12) −0.54*** (0.18) 21.34
Age 25–34 years −0.97*** (0.22) 0.31 (0.21) −0.13 (0.10) −0.08 (0.07) 22.11
Age 35–44 years −1.10*** (0.23) 0.29 (0.20) −0.06 (0.07) −0.07 (0.06) 23.39
Age 45–54 years −1.01*** (0.26) 0.34 (0.25) −0.10** (0.05) −0.11 (0.10) 26.11
Age 55–64 years −1.21*** (0.30) 0.24 (0.38) −0.02 (0.05) −0.10 (0.11) 23.71
Less than secondary −1.42*** (0.23) 0.50 (0.34) −0.05 (0.06) −0.23* (0.12) 28.72
Secondary −0.86*** (0.22) 0.32 (0.21) −0.02 (0.09) −0.12** (0.06) 25.64
Postsecondary −0.75* (0.38) −0.02 (0.23) −0.18* (0.10) −0.24 (0.16) 17.22

Characteristics of migrants and nonmigrants

Nonmigrants Migrants
Male (%) 48.5 (50) 49.6 (50)
Age (years) 36.5 (13.9) 31.5 (11.3)
Labor force participation (%) 71.7 (45) 79.4 (40.5)
Unemployment (%) 11.4 (31.7) 14.8 (35.5)
Median hourly wage (2010 USD) 2.3 (6.1) 1.6 (4.8)
Hours/week 45.2 (15.9) 49.6 (17.4)
Own account (%) 25.3 (43.5) 32.1 (46.7)
Informal (%) 56 (49.6) 88.3 (32.1)
N 447,264 21,730

Wage estimate sensitivity: 1-year migration measure

Panel A: including return migrants
(1) (2) (3) (4)
OLS IV: 2005 census IV: 1993 census IV: inverse distance
Geographic unit: CZ metro areas
1-year migrant share (data until 2019) −1.45 (1.29) −3.53*** (0.80) −3.96*** (0.90) −3.82*** (0.67)
1-year migrant share (data until 2017) −2.18 (2.35) −4.80*** (1.36) −5.15*** (1.25) −5.74*** (0.91)
Number of units 79 79 79 79
Geographic unit: administrative metro areas
1-year migrant share (data until 2019) −1.38 (1.67) −3.05*** (0.89) −3.60*** (0.82) −3.58*** (0.59)
1-year migrant share (data until 2017) −2.76 (3.03) −4.40** (1.74) −5.03*** (1.35) −5.65*** (0.96)
Number of units 23 23 23 23
Geographic unit: department
1-year migrant share (data until 2019) −1.80 (1.65) −4.01*** (1.01) −4.31*** (1.19) −4.17*** (0.63)
1-year migrant share (data until 2017) −3.58 (3.15) −5.38*** (1.59) −5.29*** (1.63) −6.33*** (0.92)
Number of units 24 24 24 24

2SLS estimates’ robustness

(1) (2) (3) (4) (5) (6)
Original 2SLS Drop <100 km from border Control trade with Venezuela Year trend × inverse distance to border Year trend × region Year trend × pre-trend
ln(hourly wage)
All −1.05*** (0.22) −1.02 (0.68) −1.07*** (0.21) −0.53 (0.67) −0.59** (0.28) −1.06*** (0.22)
Less than secondary −1.42*** (0.23) −1.43** (0.64) −1.45*** (0.22) −0.45 (0.57) −0.57 (0.40) −1.42*** (0.23)
Secondary −0.86*** (0.22) −0.93 (0.68) −0.86*** (0.22) −0.54 (0.58) −0.32 (0.23) −0.87*** (0.22)
Postsecondary −0.75* (0.38) −0.65 (0.99) −0.76** (0.35) −0.33 (1.18) −0.71** (0.30) −0.82** (0.36)
ln(hours/week)
All 0.27 (0.27) −0.50* (0.28) 0.27 (0.28) −0.68*** (0.24) 0.47* (0.24) 0.28 (0.26)
Less than secondary 0.50 (0.34) −0.58 (0.37) 0.51 (0.35) −0.74** (0.31) 0.67** (0.30) 0.52 (0.34)
Secondary 0.32 (0.21) −0.29 (0.28) 0.32 (0.21) −0.37 (0.25) 0.50** (0.23) 0.32* (0.17)
Postsecondary −0.02 (0.23) −0.52* (0.28) −0.04 (0.25) −0.80*** (0.22) 0.16 (0.22) −0.02 (0.22)
Unemployment
All −0.08 (0.07) −0.15 (0.20) −0.09 (0.07) −0.38** (0.16) −0.25* (0.13) −0.12 (0.08)
Less than secondary −0.05 (0.06) −0.13 (0.17) −0.06 (0.06) −0.34** (0.13) −0.17* (0.10) −0.07 (0.07)
Secondary −0.02 (0.09) −0.21 (0.19) −0.02 (0.08) −0.41*** (0.16) −0.21 (0.14) −0.04 (0.08)
Postsecondary −0.18* (0.10) −0.21 (0.24) −0.18** (0.09) −0.47** (0.24) −0.37** (0.18) −0.19* (0.10)
LFP
All −0.21* (0.11) 0.07 (0.18) −0.20* (0.11) 0.05 (0.23) −0.20** (0.09) −0.13 (0.09)
Less than secondary −0.23* (0.12) 0.09 (0.23) −0.23* (0.12) 0.04 (0.31) −0.20* (0.11) −0.17 (0.11)
Secondary −0.12** (0.06) −0.02 (0.15) −0.09 (0.07) 0.00 (0.17) −0.14*** (0.05) −0.11 (0.07)
Postsecondary −0.24 (0.16) 0.13 (0.16) −0.25 (0.16) 0.17 (0.19) −0.26** (0.13) −0.21 (0.15)
K-P Wald stat. 23.35 13.07 28.14 13.95 88.40 23.61
Number of metro areas 79 73 79 79 79 79

2SLS effects on employment by occupation skill group

(1) (2) (3) (4) (5) (6) (7)
Occupation Underemployed K−P Wald stat.
Group 1 Group 2 Group 3 Group 4 Group 5
All −0.06*** (0.02) −0.03 (0.08) 0.07 (0.04) −0.01 (0.02) −0.09*** (0.02) 0.29** (0.13) 25.69
Female 0.02 (0.03) −0.05 (0.05) 0.02 (0.06) −0.03 (0.02) −0.07*** (0.02) 0.24* (0.12) 25.35
Male −0.15*** (0.03) −0.00 (0.10) 0.12*** (0.02) 0.02 (0.03) −0.11*** (0.02) 0.35** (0.14) 26.10
Age 15–24 years −0.16*** (0.03) −0.19** (0.07) −0.01 (0.07) −0.04 (0.05) −0.01 (0.02) 0.15 (0.11) 24.77
Age 25–34 years −0.11*** (0.03) 0.01 (0.13) 0.11** (0.05) 0.15*** (0.04) −0.11** (0.05) 0.49*** (0.16) 23.15
Age 35–44 years 0.00 (0.03) 0.08 (0.09) 0.08** (0.04) −0.03 (0.04) −0.18*** (0.04) 0.36** (0.14) 25.02
Age 45–54 years −0.04 (0.05) 0.07 (0.05) 0.12*** (0.04) −0.08* (0.04) −0.04 (0.03) 0.35** (0.15) 29.18
Age 55–64 years 0.09** (0.04) −0.12 (0.09) 0.02 (0.08) −0.05 (0.03) −0.06*** (0.02) 0.09 (0.08) 29.02
Less than secondary −0.09*** (0.03) −0.04 (0.07) −0.01 (0.06) −0.02* (0.01) −0.01** (0.00) 0.18 (0.11) 31.28
Secondary −0.14*** (0.03) −0.04 (0.06) 0.15*** (0.05) −0.00 (0.05) 0.00 (0.01) 0.28* (0.15) 26.91
Postsecondary −0.02 (0.02) −0.02 (0.09) 0.12** (0.05) 0.04 (0.04) −0.20*** (0.07) 0.49*** (0.17) 19.22

Wage estimate sensitivity

Panel A: including return migrants
(1) (2) (3) (4)
OLS IV: 2005 census IV: 1993 census IV: inverse distance
Geographic unit: CZ metro areas
Migrant share −0.73* (0.42) −1.05*** (0.22) −1.13*** (0.21) −1.16*** (0.17)
K-P Wald stat. 23.35 21.78 28.54
Number of units 79 79 79 79
Geographic unit: administrative metro areas
Migrant share −0.67 (0.49) −0.94*** (0.26) −1.03*** (0.20) −1.08*** (0.16)
K-P Wald stat. 29.58 19.88 46.11
Number of units 23 23 23 23
Geographic unit: department
Migrant share −0.83 (0.51) −1.19*** (0.28) −1.21*** (0.30) −1.27*** (0.18)
K-P Wald stat. 19.07 20.90 35.20
Number of units 24 24 24 24

Wage estimate sensitivity: 2014–2018 difference model

Panel A: including return migrants
(1) (2) (3) (4)
OLS IV: 2005 census IV: 1993 census IV: inverse distance
Geographic unit: CZ metro areas
Foreigner share (2018 GEIH) −0.26 (0.60) −1.07*** (0.39) −1.05*** (0.35) −1.24*** (0.24)
Foreigner share (2018 census) −0.54 (0.49) −1.26*** (0.46) −1.21*** (0.42) −1.45*** (0.28)
Number of units 79 79 79 79
Geographic unit: administrative metro areas
Foreigner share (2018 GEIH) −0.09 (0.63) −0.87** (0.43) −0.89*** (0.33) −1.07*** (0.23)
Foreigner share (2018 census) −0.39 (0.54) −1.13** (0.57) −1.09** (0.43) −1.35*** (0.29)
Number of units 23 23 23 23
Geographic unit: department
Foreigner share (2018 GEIH) −0.25 (0.59) −1.02*** (0.39) −0.97** (0.41) −1.19*** (0.23)
Foreigner share (2018 census) −0.46 (0.79) −1.36*** (0.50) −1.32** (0.53) −1.53*** (0.29)
Number of units 24 24 24 24

Robustness check of 2SLS effects on occupation skill group and type of work

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Original 2SLS Drop <100 km from border Control trade with Venezuela Year trend × inverse distance to border Year trend × region Year trend × pre-trend Drop Bogotá Drop metro yearly sample <1,000 2014–2019 difference model Kronmal specification Assign natives to past metro
Occup skill Group 1 −0.06*** (0.02) −0.05 (0.08) −0.05** (0.03) 0.04 (0.07) −0.00 (0.04) −0.06** (0.02) −0.07*** (0.02) −0.07*** (0.02) −0.06*** (0.02) −0.04*** (0.02) −0.06** (0.02)
Occup skill Group 2 −0.03 (0.08) 0.15 (0.10) −0.05 (0.07) 0.21** (0.10) 0.03 (0.08) −0.01 (0.08) −0.03 (0.08) −0.03 (0.08) −0.04 (0.07) −0.02 (0.04) −0.03 (0.08)
Occup skill Group 3 0.07 (0.04) 0.16** (0.08) 0.08 (0.05) 0.21*** (0.07) 0.05 (0.05) 0.09** (0.04) 0.06 (0.04) 0.06 (0.04) 0.08* (0.05) 0.04 (0.03) 0.06 (0.04)
Occup skill Group 4 −0.01 (0.02) 0.03 (0.05) 0.00 (0.03) 0.04 (0.06) 0.02 (0.02) −0.01 (0.02) −0.01 (0.02) −0.00 (0.02) −0.00 (0.02) −0.01 (0.02) −0.01 (0.02)
Occup skill Group 5 −0.09*** (0.02) −0.11* (0.06) −0.09*** (0.02) −0.14** (0.07) −0.11*** (0.04) −0.09*** (0.02) −0.08*** (0.02) −0.09*** (0.02) −0.09*** (0.03) −0.06*** (0.02) −0.09*** (0.02)
Underemployed 0.29** (0.13) 0.52 (0.32) 0.26** (0.12) 0.57* (0.32) 0.31** (0.12) 0.29** (0.13) 0.28** (0.13) 0.30** (0.13) 0.33** (0.13) 0.18** (0.08) 0.28** (0.12)
Formal salaried −0.09*** (0.02) −0.10** (0.05) −0.08*** (0.03) −0.07 (0.04) −0.09* (0.05) −0.09*** (0.02) −0.08*** (0.02) −0.09*** (0.02) −0.07*** (0.03) −0.06*** (0.01) −0.06*** (0.02)
Informal salaried −0.02 (0.07) 0.07 (0.16) 0.01 (0.07) 0.18 (0.15) 0.09 (0.06) −0.04 (0.06) −0.03 (0.07) −0.01 (0.07) −0.07 (0.08) −0.01 (0.05) 0.00 (0.06)
Own account −0.04 (0.09) 0.09 (0.21) −0.06 (0.08) 0.13 (0.22) −0.08 (0.13) −0.04 (0.09) −0.04 (0.09) −0.05 (0.09) 0.01 (0.07 −0.03 (0.05) −0.07 (0.09)
Employer 0.01 (0.02) 0.04 (0.05) 0.02 (0.02) 0.07 (0.04) 0.06*** (0.02) 0.02 (0.02) 0.01 (0.02) 0.02 (0.02) 0.02 (0.02 0.01 (0.01) 0.01 (0.02)
Kleibergen-Paap Wald stat. 25.69 12.80 30.99 14.50 102.73 24.59 25.77 20.69 33.17 26.38 26.37
Number of metro areas 79 73 79 79 79 79 78 27 79 79 79

School attendance 2SLS linear probability models

In school In school In school
In labor force Out of labor force
All ages (including border locations)
Migrant share 0.07** (0.03) −0.03 (0.08) 0.11* (0.06)
Sample mean 18.76 7.14 11.62
Age ≤24 years (including border locations)
Migrant share 0.20* (0.10) −0.19 (0.24) 0.39* (0.23)
Sample mean 54.60 14.43 40.17
All ages (excluding border locations)
Migrant share 0.13 (0.11) 0.20* (0.10) −0.07 (0.09)
Sample mean 18.82 7.17 11.65
Age ≤24 years (excluding border locations)
Migrant share 0.28 (0.34) 0.49* (0.27) −0.21 (0.33)
Sample mean 54.69 14.45 40.24

Effects on native internal migration

(1) (2) (3) (4) (5) (6) (7)
Outmigration Inmigration
OLS 2SLS Sample mean OLS 2SLS Sample mean K-P Wald stat.
All 0.06 (0.06) 0.03 (0.03) 6.58 −0.04 (0.25) −0.08 (0.11) 10.17 17.78
Male 0.08 (0.06) 0.06 (0.04) 6.76 0.01 (0.26) −0.04 (0.12) 10.16 18.13
Female 0.05 (0.06) 0.01 (0.03) 6.42 −0.07 (0.23) −0.11 (0.11) 10.17 17.48
Age 15–24 years 0.07 (0.08) 0.05 (0.06) 7.67 −0.02 (0.32) −0.06 (0.16) 14.88 16.69
Age 25–34 years 0.05 (0.08) −0.03 (0.05) 9.49 −0.09 (0.32) −0.13 (0.15) 13.02 16.74
Age 35–44 years 0.05 (0.09) 0.03 (0.05) 6.64 −0.06 (0.25) −0.10 (0.11) 9.03 17.02
Age 45–54 years 0.07 (0.05) 0.07** (0.03) 3.97 −0.04 (0.14) −0.09 (0.06) 5.62 20.18
Age 55–64 years 0.04 (0.03) 0.05* (0.03) 3.09 −0.04 (0.13) −0.09 (0.07) 4.24 20.09
Less than secondary 0.10* (0.06) 0.07** (0.04) 5.54 −0.01 (0.25) −0.04 (0.12) 8.87 21.90
Secondary 0.08 (0.08) 0.10*** (0.03) 6.62 −0.08 (0.23) −0.17 (0.11) 10.04 17.97
Postsecondary 0.00 (0.08) −0.07 (0.05) 7.70 −0.02 (0.28) −0.01 (0.14) 11.71 13.30
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