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SLIDE 1

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❯♥❞❡rst❛♥❞✐♥❣ ▼✐❣r❛t✐♦♥ ❙❡❧❡❝t✐♦♥ ❢r♦♠ P♦❧❛♥❞

❆♥♥❛ ❘♦ss♦✶

✶❯♥✐✈❡rs✐t② ❈♦❧❧❡❣❡ ▲♦♥❞♦♥ ❛♥❞ ◆■❊❙❘

❈❧❡r♠♦♥t✲❋❡rr❛♥❞✱ ✷✹t❤ ❏❛♥✉❛r② ✷✵✶✹

✶ ❆♥♥❛ ❘♦ss♦

slide-2
SLIDE 2

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

■♥tr♦❞✉❝t✐♦♥

◮ P♦❧❛♥❞ ❡①♣❡r✐❡♥❝❡❞ ❧❛r❣❡ ♠✐❣r❛t✐♦♥ ♦✉t✢♦✇s ❜❡t✇❡❡♥

♠✐❞✲✶✾✾✵s ❛♥❞ ❧❛t❡ ✷✵✵✵s

◮ ❊♠✐❣r❛t✐♦♥ ❤❛s ✐♥❝r❡❛s❡❞ t♦ ❛❧❧ ❝♦✉♥tr✐❡s✱ ✐♥ ♣❛rt✐❝✉❧❛r t♦ t❤❡

❯❑✳

.2 .4 .6 .8 1 % 1998 2000 2002 2004 2006 2008 year GERMANY OTHER UK USA

✷ ❆♥♥❛ ❘♦ss♦

slide-3
SLIDE 3

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❲❤♦ ♠✐❣r❛t❡s❄

◮ ❲❤❛t t②♣❡ ♦❢ s❡❧❡❝t✐♦♥ ♦❢ P♦❧✐s❤ ❡♠✐❣r❛♥ts ✐♥t♦ t✇♦ ♠❛❥♦r

❞❡st✐♥❛t✐♦♥ ❝♦✉♥tr✐❡s ❛♥❞ ✇❤❛t ✐s ❞r✐✈✐♥❣ t❤❡ r❡s✉❧ts❄

◮ P♦s✐t✐✈❡ s❡❧❡❝t✐♦♥ ✭❡❞✉❝❛t✐♦♥✮ ✐♥ ❜♦t❤ ❝♦✉♥tr✐❡s✱ ♠♦r❡ ❤✐❣❤❧② s❦✐❧❧❡❞

✐♥ t❤❡ ❯❑

◮ ◆♦t ❝♦♥s✐st❡♥t ✇✐t❤ t❤❡ ♣r❡❞✐❝t✐♦♥ ♦❢ t❤❡ ❘♦②✴❇♦r❥❛s ♠♦❞❡❧ ✐♥ t❡r♠s

♦❢ s❡❧❡❝t✐♦♥ ♦❢ ❡❞✉❝❛t✐♦♥ ✭t❤❡♦r❡t✐❝❛❧ ❢r❛♠❡✇♦r❦✮

.5 1 1.5 % 1998 2000 2002 2004 2006 2008 year low intermediate high

UK

.5 1 1.5 % 1998 2000 2002 2004 2006 2008 year low intermediate high

GERMANY

✸ ❆♥♥❛ ❘♦ss♦

slide-4
SLIDE 4

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❚❤✐s ♣❛♣❡r

◮ ❉❡s❝r✐♣t✐✈❡ ❛♥❛❧②s✐s ♦❢ t❤❡ s❡❧❡❝t✐♦♥ ❛♥❞ s♦rt✐♥❣ ♦❢ P♦❧✐s❤

❡♠✐❣r❛♥ts ✐♥t♦ t✇♦ ♠❛❥♦r ❞❡st✐♥❛t✐♦♥ ❝♦✉♥tr✐❡s✿ ❯♥✐t❡❞ ❑✐♥❣❞♦♠ ❛♥❞ ●❡r♠❛♥②

◮ P♦❧✐❝② ❞r✐✈❡♥ r❡s✉❧ts❄ ◮ ❖❜s❡r✈❛❜❧❡ ❛♥❞ ✉♥♦❜s❡r✈❛❜❧❡ s❡❧❡❝t✐♦♥ ◮ ❯s✐♥❣ ❞❛t❛ ❢r♦♠ t❤❡ s♦✉r❝❡ ❝♦✉♥tr✐❡s

◮ ❘♦②✴❇♦r❥❛s ♠♦❞❡❧✿ ♣r♦❜❛❜✐❧✐t② t♦ ❡♠✐❣r❛t❡ ❛s ❛ ❢✉♥❝t✐♦♥ ♦❢ t❤❡

r❡❧❛t✐✈❡ ♣r✐❝❡s ♦❢ s❦✐❧❧s

◮ Pr✐❝❡s ♦❢ ♦❜s❡r✈❛❜❧❡ s❦✐❧❧s ♣r♦①✐❡❞ ❜② t❤❡ r❡t✉r♥ t♦ ❡❞✉❝❛t✐♦♥

✭❋❡r♥á♥❞❡③✲❍✉❡rt❛s ▼♦r❛❣❛ ✭✷✵✶✸✱ ✷✵✶✷✮❀ ●♦✉❧❞ ❛♥❞ ▼♦❛✈ ✭✷✵✶✵✮❀ ■❜❛rr❛r❛♥ ❛♥❞ ▲✉❜♦ts❦② ✭✷✵✵✼✮✮

◮ Pr✐❝❡s ♦❢ ✉♥♦❜s❡r✈❛❜❧❡ s❦✐❧❧s ♣r♦①✐❡❞ ❜② t❤❡ st❛♥❞❛r❞ ❞❡✈✐❛t✐♦♥ ♦❢

r❡s✐❞✉❛❧ ✇❛❣❡s ✭❏✉❤♥✱ ▼✉r♣❤② ❛❞♥ P✐❡r❝❡ ✭✶✾✾✸✮❀ ●♦✉❧❞ ✭✷✵✵✷✮❀

  • ♦✉❧❞ ❛♥❞ ▼♦❛✈ ✭✷✵✶✵✮✮

◮ ❊✛❡❝t ♦❢ ✇❛❣❡ ✐♥❡q✉❛❧✐t② ♦♥ t❤❡ ✐♥❝❡♥t✐✈❡s t♦ ❡♠✐❣r❛t❡ ✭✉s✐♥❣ ❧❛❜♦✉r

♠❛r❦❡t ✐♥❢♦r♠❛t✐♦♥ ❜❡❢♦r❡ ✐♥❞✐✈✐❞✉❛❧s ❡♠✐❣r❛t❡✮

✹ ❆♥♥❛ ❘♦ss♦

slide-5
SLIDE 5

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❚❤✐s ♣❛♣❡r

◮ ❉❡s❝r✐♣t✐✈❡ ❛♥❛❧②s✐s ♦❢ t❤❡ s❡❧❡❝t✐♦♥ ❛♥❞ s♦rt✐♥❣ ♦❢ P♦❧✐s❤

❡♠✐❣r❛♥ts ✐♥t♦ t✇♦ ♠❛❥♦r ❞❡st✐♥❛t✐♦♥ ❝♦✉♥tr✐❡s✿ ❯♥✐t❡❞ ❑✐♥❣❞♦♠ ❛♥❞ ●❡r♠❛♥②

◮ P♦❧✐❝② ❞r✐✈❡♥ r❡s✉❧ts❄ ◮ ❖❜s❡r✈❛❜❧❡ ❛♥❞ ✉♥♦❜s❡r✈❛❜❧❡ s❡❧❡❝t✐♦♥ ◮ ❯s✐♥❣ ❞❛t❛ ❢r♦♠ t❤❡ s♦✉r❝❡ ❝♦✉♥tr✐❡s

◮ ❘♦②✴❇♦r❥❛s ♠♦❞❡❧✿ ♣r♦❜❛❜✐❧✐t② t♦ ❡♠✐❣r❛t❡ ❛s ❛ ❢✉♥❝t✐♦♥ ♦❢ t❤❡

r❡❧❛t✐✈❡ ♣r✐❝❡s ♦❢ s❦✐❧❧s

◮ Pr✐❝❡s ♦❢ ♦❜s❡r✈❛❜❧❡ s❦✐❧❧s ♣r♦①✐❡❞ ❜② t❤❡ r❡t✉r♥ t♦ ❡❞✉❝❛t✐♦♥

✭❋❡r♥á♥❞❡③✲❍✉❡rt❛s ▼♦r❛❣❛ ✭✷✵✶✸✱ ✷✵✶✷✮❀ ●♦✉❧❞ ❛♥❞ ▼♦❛✈ ✭✷✵✶✵✮❀ ■❜❛rr❛r❛♥ ❛♥❞ ▲✉❜♦ts❦② ✭✷✵✵✼✮✮

◮ Pr✐❝❡s ♦❢ ✉♥♦❜s❡r✈❛❜❧❡ s❦✐❧❧s ♣r♦①✐❡❞ ❜② t❤❡ st❛♥❞❛r❞ ❞❡✈✐❛t✐♦♥ ♦❢

r❡s✐❞✉❛❧ ✇❛❣❡s ✭❏✉❤♥✱ ▼✉r♣❤② ❛❞♥ P✐❡r❝❡ ✭✶✾✾✸✮❀ ●♦✉❧❞ ✭✷✵✵✷✮❀

  • ♦✉❧❞ ❛♥❞ ▼♦❛✈ ✭✷✵✶✵✮✮

◮ ❊✛❡❝t ♦❢ ✇❛❣❡ ✐♥❡q✉❛❧✐t② ♦♥ t❤❡ ✐♥❝❡♥t✐✈❡s t♦ ❡♠✐❣r❛t❡ ✭✉s✐♥❣ ❧❛❜♦✉r

♠❛r❦❡t ✐♥❢♦r♠❛t✐♦♥ ❜❡❢♦r❡ ✐♥❞✐✈✐❞✉❛❧s ❡♠✐❣r❛t❡✮

✹ ❆♥♥❛ ❘♦ss♦

slide-6
SLIDE 6

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❖✈❡r✈✐❡✇ ♦❢ t❤❡ r❡s✉❧ts

◮ Pr❡❞✐❝t✐♦♥ ♦❢ t❤❡ ♠♦❞❡❧ ❝♦♥✜r♠❡❞ ♦♥❧② ❢♦r ✉♥♦❜s❡r✈❛❜❧❡ s❦✐❧❧s

✐♥ t❤❡ ❯❑

◮ ❍✐❣❤❡r r❡t✉r♥s ✐♥ P♦❧❛♥❞ ❝♦♠♣❛r❡❞ t♦ t❤❡ ❯❑✿ ❛ ❞❡❝r❡❛s❡ ✐♥

t❤❡ r❡❛❧t✐✈❡ r❡t✉r♥s ✐♥ t❤❡ ❞❡st✐♥❛t✐♦♥ ❝♦✉♥tr✐❡s s❤✐❢ts t❤❡ ♣r♦❜❛❜✐❧✐t② t♦ ❡♠✐❣r❛t❡ s♦ t❤❛t ❡♠✐❣r❛♥ts ❛r❡ ♠♦r❡ ♥❡❣❛t✐✈❡❧② s❡❧❡❝t❡❞

◮ Pr❡❞✐❝t✐♦♥ ♦❢ t❤❡ ♠♦❞❡❧ ❢♦r ●❡r♠❛♥② ♥♦t ❝♦♥✜r♠❡❞✿ s❡❧❡❝t✐♦♥

❞❡✜♥❡❞ ❜② ♣♦❧✐❝✐❡s

✺ ❆♥♥❛ ❘♦ss♦

slide-7
SLIDE 7

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❘❡❧❛t❡❞ ❧✐t❡r❛t✉r❡

❙❡❧❡❝t✐♦♥ ♦❢ ♠✐❣r❛♥ts✿ ❧✐tt❧❡ ❡✈✐❞❡♥❝❡ ♦❢ t❤❡ ❇♦r❥❛s ✭✶✾✽✼✮ ♠♦❞❡❧

◮ ❉❛t❛ ❢r♦♠ t❤❡ ❞❡st✐♥❛t✐♦♥ ♦r s♦✉r❝❡ ❝♦✉♥tr②❄

◮ ❈❤✐q✉✐❛r ❛♥❞ ❍❛♥s♦♥ ✭✷✵✵✺✮❀ ▼❝❑❡♥③✐❡ ❛♥❞ ❘❛♣♦♣♦rt ✭✷✵✶✵✮❀

  • r♦❣❣❡r ❛♥❞ ❍❛♥s♦♥ ✭✷✵✶✶✮

◮ ❋❡r♥á♥❞❡③✲❍✉❡rt❛s ▼♦r❛❣❛ ✭✷✵✶✸✱ ✷✵✶✷✮✱ ●♦✉❧❞ ❛♥❞ ▼♦❛✈ ✭✷✵✶✵✮❀

❆♠❜r♦s✐♥✐ ❛♥❞ P❡r✐ ✭✷✵✶✷✮ ❛♥❞ ❑❛❡st♥❡r ❛♥❞ ▼❛❧❛♠✉❞ ✭✷✵✶✸✮

◮ ❚❤❡ r♦❧❡ ♦❢ ♣♦❧✐❝✐❡s

◮ ❘❛♠♦s ✭✶✾✾✷✮❀ ❋❡❧✐❝✐❛♥♦ ✭✷✵✵✺✮❀ ❆❜r❛♠✐t③❦②✱ ❇♦✉st❛♥✱ ❛♥❞ ❊r✐❦ss♦♥

✭✷✵✶✷✮

◮ ■♥t❡r♥❛❧ ♠✐❣r❛t✐♦♥✿ ❇♦r❥❛s✱ ❇r♦♥❛rs✱ ❛♥❞ ❚r❡❥♦ ✭✶✾✾✷✮

❙❡❧❡❝t✐♦♥ ♦♥ ✉♥♦❜s❡r✈❛❜❧❡ s❦✐❧❧s

◮ ●♦✉❧❞ ❛♥❞ ▼♦❛✈ ✭✷✵✶✵✮✿ ♦❜s❡r✈❛❜❧❡ ❛♥❞ ✉♥♦❜s❡r✈❛❜❧❡ s❡❧❡❝t✐♦♥

❢r♦♠ ■sr❛❡❧ t♦ t❤❡ ❯❙❆

◮ ❋r♦♠ ▼❡①✐❝♦ t♦ t❤❡ ❯❙❆✿ ❆♠❜r♦s✐♥✐ ❛♥❞ P❡r✐ ✭✷✵✶✷✮ ❛♥❞ ❑❛❡st♥❡r

❛♥❞ ▼❛❧❛♠✉❞ ✭✷✵✶✸✮ ❋❡r♥á♥❞❡③✲❍✉❡rt❛s ▼♦r❛❣❛ ✭✷✵✶✸✱ ✷✵✶✷✮

✻ ❆♥♥❛ ❘♦ss♦

slide-8
SLIDE 8

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

P♦❧✐s❤ ■♠♠✐❣r❛t✐♦♥ t♦ ●❡r♠❛♥② ❛♥❞ t❤❡ ❯❑✲ ❜❡❢♦r❡

◮ ❇❡❢♦r❡ ✷✵✵✹

◮ ●❡r♠❛♥② ◮ ✶✾✾✶ ♥❡✇ r❡❣✉❧❛t✐♦♥ ❛❧❧♦✇❡❞ P♦❧❡s t♦ ❡♥t❡r t❤❡ ❝♦✉♥tr②

✇✐t❤♦✉t ✈✐s❛ ❜✉t ❝♦✉❧❞♥✬t t❛❦❡ ✉♣ ❡♠♣❧♦②♠❡♥t✳ ■♥❝r❡❛s❡ ✐♥ t❡♠♣♦r❛r② ♠✐❣r❛t✐♦♥

◮ ❊♥❞ ♦❢ t❤❡ ✾✵s✿ ♥❡✇ ❣✉❡st ✇♦r❦❡r ♣r♦❣r❛♠♠❡s

✭✐♥st✐t✉t✐♦♥❛❧✐s❛t✐♦♥ ♦❢ t❡♠♣♦r❛r② ♠✐❣r❛t✐♦♥✮

◮ ❯❑ ◮ ■♠♠✐❣r❛t✐♦♥ ❆❝t ♦❢ ✶✾✼✶ ❛♥❞ P♦❧✐s❤ ❧❛❜♦✉r ✐♠♠❣✐r❛♥ts ✐♥ t❤❡

❯❑ ✇❡r❡ s✉❜❥❡❝t t♦ ✐♠♠✐❣r❛t✐♦♥ ❝♦♥tr♦❧s ✭✇♦r❦ ♣❡r♠✐ts ❢♦r ❛ ♣❛rt✐❝✉❧❛r ❡♠♣❧♦②❡r ✐♥ ❛ ♣❛rt✐❝✉❧❛r ❥♦❜ ❢♦r ❛ ❧✐♠✐t❡❞ ♣❡r✐♦❞✮

◮ ✶✾✾✶ t❤❡ ❊❯ ❆ss♦❝✐❛t✐♦♥ ❆❣r❡❡♠❡♥t t♦ ❡st❛❜❧✐s❤ ❜✉s✐♥❡ss ✼ ❆♥♥❛ ❘♦ss♦

slide-9
SLIDE 9

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

P♦❧✐s❤ ■♠♠✐❣r❛t✐♦♥ t♦ ●❡r♠❛♥② ❛♥❞ t❤❡ ❯❑✲ ❛❢t❡r

◮ ❆❢t❡r ✷✵✵✹

◮ ●❡r♠❛♥② ◮ ❋r❡❡ t♦ ♠♦✈❡✱ ❜✉t st✐❧❧ ♥❡❡❞ ♣❡r♠✐ts t♦ ✇♦r❦ ◮ ❙❡❧❢✲❡♠♣❧♦②♠❡♥t ❛♥❞ ❝r♦ss✲❜♦r❞❡r ♣r♦✈✐s✐♦♥ ♦❢ s❡r✈✐❝❡s ✭P♦❧❡s

s❡♥t t♦ ●❡r♠❛♥②✮ ❜❡❝❛♠❡ ❡❛s✐❡r

◮ ❯❑ ◮ ❋r❡❡ t♦ ♠♦✈❡ ❛♥❞ ✇♦r❦ ✐♥ t❤❡ ❯❑ ♦♥❧② ♥❡❡❞ t♦ r❡❣✐st❡r ✇✐t❤

t❤❡ ❲❘❙

✽ ❆♥♥❛ ❘♦ss♦

slide-10
SLIDE 10

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❉❛t❛

◮ P♦❧✐s❤ ▲❛❜♦✉r ❋♦r❝❡ ❙✉r✈❡②

◮ ❘♦t❛t✐♥❣ ♣❛♥❡❧ ❢r♦♠ ✶✾✾✷ t♦ ✷✵✵✽❀ ❡✈❡r② q✉❛rt❡r ✺✵ t❤♦✉s❛♥❞

✐♥❞✐✈✐❞✉❛❧s ❛r❡ s❛♠♣❧❡❞

◮ ❉❡♠♦❣r❛♣❤✐❝s ❛♥❞ ❧❛❜♦✉r ♠❛r❦❡t ✐♥❢♦r♠❛t✐♦♥ ♦♥ P♦❧✐s❤

r❡s✐❞❡♥ts ✭✐✳❡✳ ♥❡t ♠♦♥t❤❧② ✇❛❣❡s✱ ❡❞✉❝❛t✐♦♥✮

◮ ❋♦❝✉s ♦♥ ✇♦r❦✐♥❣ ❛❣❡ ✭✶✺✲✻✹✮ ♣♦♣✉❧❛t✐♦♥ ◮ ■♥❢♦r♠❛t✐♦♥ ♦♥ ♠❡♠❜❡rs ♦❢ ♣r✐✈❛t❡ ❤♦✉s❡❤♦❧❞s ❧✐✈✐♥❣ ❛❜r♦❛❞ ❢♦r

❛t ❧❡❛st t❤r❡❡ ♠♦♥t❤s

◮ ❆❣❡✱ ❡❞✉❝❛t✐♦♥✱ r♦❧❡ ✐♥ t❤❡ ❤♦✉s❡❤♦❧❞✱ ❞❡st✐♥❛t✐♦♥ ❝♦✉♥tr② ◮ ❙✉❜s❛♠♣❧❡ ♦❢ ❡♠✐❣r❛♥ts ❜❡❢♦r❡ ❡♠✐❣r❛t✐♦♥✿ r❡❝♦✈❡r

✇❛❣❡s ❛♥❞ ♦❝❝✉♣❛t✐♦♥❛❧ s❡❝t♦r ♣r❡✲♠✐❣r❛t✐♦♥✿ ❝♦♠♣❛r❡ ✐t ✇✐t❤ t❤❡ ❢✉❧❧ s❛♠♣❧❡ ♦❢ ❡♠✐❣r❛♥ts ✭s❧✐❞❡ ✷✶✮

◮ ❯❑ ▲❛❜♦✉r ❋♦rs❡ ❙✉r✈❡② ◮ ●❡r♠❛♥ ▼✐❝r♦❝❡♥s✉s

✾ ❆♥♥❛ ❘♦ss♦

slide-11
SLIDE 11

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❉❡s❝r✐♣t✐✈❡ ❙t❛t✐st✐❝s

Non-Emigrants Total UK Germany Other log hourly wages (real) 1.97 1.86 1.81 1.88 1.86 (0.001) (0.013) (0.035) (0.040) (0.015) Education (years) 12.53 12.41 12.77 11.71 12.43 (0.004) (0.062) (0.179) (0.173) (0.071) Primary 8% 7% 5% 8% 7% (0.000) (0.006) (0.016) (0.020) (0.007) Secondary 70% 74% 73% 83% 73% (0.001) (0.012) (0.033) (0.032) (0.014) Tertiary 22% 19% 21% 9% 20% (0.001) (0.011) (0.030) (0.027) (0.012) Age 38 32 29 36 32 (0.017) (0.235) (0.541) (0.780) (0.271) Age 16-25 10% 21% 27% 10% 21% (0.000) (0.010) (0.032) (0.023) (0.012) Age 25-35 29% 47% 56% 38% 47% (0.001) (0.013) (0.036) (0.041) (0.015) Females (%) 46% 25% 29% 20% 25% (0.001) (0.012) (0.033) (0.035) (0.013) Professional 32% 13% 11% 8% 13% (0.001) (0.009) (0.024) (0.026) (0.010) Services 22% 24% 26% 17% 24% (0.001) (0.011) (0.032) (0.031) (0.013) Blue collar 46% 63% 62% 76% 62% (0.001) (0.013) (0.036) (0.037) (0.015)

  • bservations

410,587 1,564 204 162 1,207 Source: Polish LFS, years 1998 to 2008. Emigrants ✶✵ ❆♥♥❛ ❘♦ss♦

slide-12
SLIDE 12

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❊①♣❧❛✐♥✐♥❣ t❤❡ ✇❛❣❡ ❣❛♣

(1) (2) (3) (4) (5) (1) (2) (3) (4) (5) emigrant

  • 0.162*** -0.183*** -0.118***
  • 0.077**
  • 0.105***
  • 0.086**
  • 0.014
  • 0.038
  • 0.012
  • 0.028

(0.034) (0.038) (0.036) (0.035) (0.035) (0.040) (0.042) (0.039) (0.039) (0.039) Controls Education No Yes Yes Yes Yes No Yes Yes Yes Yes Demographics No No Yes Yes Yes No No Yes Yes Yes Region FE No No No No Yes No No No No Yes Occupation FE No No No Yes No No No No Yes No Source: Polish LFS, year 1998 to 2008

  • B. Germany
  • A. UK

Dependent variable: Log hourly wages Note: Individuals aged 16 to 64. Education control is in years of education, demographics control include 5 age-group dummies and a dummy for female. Occupations are grouped in three categories: professionals, service workers and blue collars. Standard errors reported in brackets.* indicates significance at 10%, ** indicates significance at 5%, *** indicates significance at 1% level.

r❡❣✐♦♥❛❧ ❞✐✛❡r❡♥❝❡s ✭s❧✐❞❡ ✷✷✮

✶✶ ❆♥♥❛ ❘♦ss♦

slide-13
SLIDE 13

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❚❤❡♦r❡t✐❝❛❧ ♠♦❞❡❧

❧♥wi = µi +δis +εi

✇❤❡r❡ i = {PL,DE,UK}✱ εi ∼ N

  • ✵,σ ✷

i

  • ✱ ❛♥❞ s = µs +εs

❆ss✉♠♣t✐♦♥s

◮ ❈♦♥st❛♥t ❝♦st ♦❢ ♠✐❣r❛t✐♦♥ ❛❝r♦ss s❦✐❧❧s ❛♥❞ ❞❡st✐♥❛t✐♦♥

❝♦✉♥tr✐❡s

◮ ❖♥❡ ❞✐♠❡♥s✐♦♥ s❦✐❧❧ ✭♦❜s❡r✈❛❜❧❡ ♦r ✉♥♦❜s❡r✈❛❜❧❡✮ ❛♥❞

✐♥❞✐✈✐❞✉❛❧ ❡❛r♥✐♥❣s ♣❡r❢❡❝t❧② ❝♦rr❡❧❛t❡❞ ❛❝r♦ss ❝♦✉♥tr✐❡s

◮ Ranking of individuals is the same across countries

◮ δUK ≤ δDE ≤ δPL✇❤✐❧❡ µUK > µDE > µPL

✭❡①✐st❡♥❝❡ ❝♦♥❞✐t✐♦♥ ✲ ❇♦r❥❛s✱ ❇r♦♥❛rs ❛♥❞ ❚r❡❥♦ ✭✶✾✾✷✮✮

✶✷ ❆♥♥❛ ❘♦ss♦

slide-14
SLIDE 14

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❚❤❡♦r❡t✐❝❛❧ ♠♦❞❡❧ ✭❝♥t✬❞✮

❘❡s✉❧ts ❢r♦♠ t❤❡ ♠♦❞❡❧✿

◮ ▲♦✇❡r r❡t✉r♥ t♦ s❦✐❧❧s ❛ttr❛❝t ❧♦✇❡r s❦✐❧❧s ❧❡✈❡❧

◮ ❈❤♦♦s❡ ❯❑ ✐❢✿ s < µUK −µDE

δDE −δUk

◮ ❈❤♦♦s❡ r❡❣✐♦♥ ●❡r♠❛♥②✿

µUK −µDE δDE −δUk < s < µDE −µPL δPL−δDE

◮ ❈❤♦♦s❡ r❡❣✐♦♥ P♦❧❛♥❞ ✿ s > µDE −µPL

δPL−δDE

✶✸ ❆♥♥❛ ❘♦ss♦

slide-15
SLIDE 15

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

Pr♦❜❛❜✐❧✐t② t♦ ❡♠✐❣r❛t❡

◮ ❋♦❧❧♦✇✐♥❣ t❤❡ ♠♦❞❡❧ ❛ ♣❡rs♦♥ ♠✐❣r❛t❡s ✭Mi pjt = ✶✮ ✐❢✿

I PL,i(s) = µi − µPL +(δi −δPL)s +εi −εPL −π > ✵ ❋♦r ❡❛❝❤ ❞❡st✐♥❛t✐♦♥ i✿

◮ ❙❡❧❡❝t✐♦♥ ♦♥ r❡s✐❞✉❛❧ ✇❛❣❡s

Pr

  • Mi

pjt = ✶

  • =

γ✵ +γ✶xpjt +γ✷(SDpl −SDdest)jt ∗(residual)pjt +γ✸Zjt +τ +αj +εpt

◮ ❙❡❧❡❝t✐♦♥ ♦♥ ❡❞✉❝❛t✐♦♥

Pr

  • Mi

pjt = ✶

  • = γ

✵ +γ

✶xpjt +γ

✷(BETApl −BETAdest)jt ∗

(education)pjt +γ

✸Zjt +τ +αj +εpt

✶✹ ❆♥♥❛ ❘♦ss♦

slide-16
SLIDE 16

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❙❡❧❡❝t✐♦♥ ♦♥ ✉♥♦❜s❡r✈❛❜❧❡ s❦✐❧❧s✲ r❡s✐❞✉❛❧ ✇❛❣❡s

UK Germany UK and Germany (1) (2) (3)

  • 0.00404*
  • 0.00090***

(0.00220) (0.00026) 0.00484** 0.00034 (0.00219) (0.00022)

  • 0.00021

0.00046** 0.00073* (0.00018) (0.00021) (0.00041) Years of education 0.00009***

  • 0.00001

0.00009*** (0.00002) (0.00002)

  • 0.00003

Occupation FE Yes Yes Yes Controls Yes Yes Yes Observations 410,791 410,749 410,953 Source: Polish LFS Note: Individuals aged 16-64. Other controls include 5 age-category dummies, gender dummy, year Occupation wage residual*Difference between Poland and UK Residual SD in Occupation j Occupation wage residual*Difference between Poland and Germany Residual SD in Occupation j Occupation wage residual Poland OLS on probability to emigrate

✶✺ ❆♥♥❛ ❘♦ss♦

slide-17
SLIDE 17

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❙❡❧❡❝t✐♦♥ ♦♥ ♦❜s❡r✈❛❜❧❡ s❦✐❧❧s ✲ ②❡❛rs ♦❢ ❡❞✉❝❛t✐♦♥

UK Germany UK and Germany (1) (2) (3) 0.00318** 0.01266*** (0.00154) (0.00336) 0.00233

  • 0.01144***

(0.00157) (0.00383)

  • 0.00050***

0.00004

  • 0.00046***

(0.00009) (0.00008) (0.00013) Years of education

  • 0.00003
  • 0.00007
  • 0.00009

(0.00006) (0.00004) (0.00008) Occupation FE Yes Yes Yes Controls Yes Yes Yes Observations 410,791 410,749 410,953 Source: Polish LFS Years of education*Difference between Poland and UK return to education in Occupation j Years of education*Difference between Poland and Germany return to education in Occupation j Note: Individuals aged 16-64. Other controls include 5 age-category dummies, gender dummy, year Occupation wage residual Poland OLS on probability to emigrate

✶✻ ❆♥♥❛ ❘♦ss♦

slide-18
SLIDE 18

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

■♥t❡r♣r❡t❛t✐♦♥

  • .004-.002

.002 .004 Probability to emigrate

  • 3
  • 2
  • 1

1 2 3 4 5 residual wages

  • A. Average difference in the price of skills
  • .004-.002

.002 .004 Probability to emigrate

  • 3
  • 2
  • 1

1 2 3 4 5 residual wages

  • B. Median difference in the price of skills
  • .004-.002

.002 .004 Probability to emigrate

  • 3
  • 2
  • 1

1 2 3 4 5 residual wages

  • C. Difference in the price of skills is zero
  • .004-.002

.002 .004 Probability to emigrate

  • 3
  • 2
  • 1

1 2 3 4 5 residual wages

  • D. Difference in the price of skills at the Ger level

UK

✶✼ ❆♥♥❛ ❘♦ss♦

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SLIDE 19

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❘♦❜✉st♥❡ss ❝❤❡❝❦s

◮ ❆❞❞✐♥❣ r❡❣✐♦♥❛❧ ❞✉♠♠✐❡s ◮ ❨❡❛r s❡❧❡❝t✐♦♥ ◮ ❯♥♦❜s❡r✈❛❜❧❡ ❛♥❞ ♦❜s❡r✈❛❜❧❡ s❡❧❡❝t✐♦♥ t♦❣❡t❤❡r ◮ ❊♥❞♦❣❡♥❡✐t②

✶✽ ❆♥♥❛ ❘♦ss♦

slide-20
SLIDE 20

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❈♦♥❝❧✉s✐♦♥s

◮ ❙✉❜s❛♠♣❧❡ ♦❢ ❡♠✐❣r❛♥ts ❢♦r ✇❤♦♠ ■ ♦❜s❡r✈❡ ♣r❡✲♠✐❣r❛t✐♦♥

✇❛❣❡s ❛♥❞ ♦❝❝✉♣❛t✐♦♥

◮ ❙❡❧❡❝t✐♦♥ ♣❛tt❡r♥ ♦❢ P♦❧✐s❤ ❡♠✐❣r❛t✐♦♥ ✐♥t♦ ❞✐✛❡r❡♥t ❞❡st✐♥❛t✐♦♥

❜❛s❡❞ ♦♥ ✉♥♦❜s❡r✈❛❜❧❡ ❢❛❝t♦rs

◮ ▲♦✇❡r r❡❧❛t✐✈❡ r❡t✉r♥s t♦ s❦✐❧❧ ✐♥ P♦❧❛♥❞ ✇✳r✳t✳ ❞❡st✐♥❛t✐♦♥ ✇✐❧❧

❧❡❛❞ t♦ ✇♦r❦❡rs ✇✐t❤ ❤✐❣❤❡r r❡s✐❞✉❛❧ ✇❛❣❡ t♦ ❧❡❛✈❡✿ ❡①♣❧❛✐♥s ✇❤② ❞✐✛❡r❡♥t ♣❛tt❡r♥ ✐♥ ●❡r♠❛♥② ❛♥❞ t❤❡ ❯❑

✶✾ ❆♥♥❛ ❘♦ss♦

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SLIDE 21

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❚❤❛♥❦ ②♦✉✦

❝♦♥t❛❝t✿ ❛✳r♦ss♦❅✉❝❧✳❛❝✳✉❦ ❛✳r♦ss♦❅♥✐❡sr✳❛❝✳✉❦

✷✵ ❆♥♥❛ ❘♦ss♦

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SLIDE 22

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❆♣♣❡♥❞✐① ✲ ❋✉❧❧ ❛♥❞ s✉❜s❛♠♣❧❡ ✭❜❛❝❦ t♦ s❧✐❞❡ ✾✮

full restricted difference full restricted difference education (years) 13.267 12.721 0.546*** 11.922 11.681 0.241 (0.042) (0.181) (0.186) (0.032) (0.171) (0.174) primary 0.036 0.064

  • 0.028

0.080 0.094

  • 0.014

(0.003) (0.017) (0.018) (0.004) (0.022) (0.022) secondary 0.676 0.725

  • 0.049

0.808 0.820

  • 0.012

(0.008) (0.033) (0.034) (0.006) (0.032) (0.032) tertiary 0.287 0.211 0.077** 0.112 0.086 0.027 (0.008) (0.030) (0.031) (0.005) (0.025) (0.026) female (%) 0.391 0.287 0.104*** 0.329 0.189 0.140*** (0.008) (0.033) (0.034) (0.007) (0.033) (0.034) age (years) 28.826 29.194

  • 0.368

34.126 36.045

  • 1.919**

(0.131) (0.544) (0.559) (0.157) (0.788) (0.803) age 16-25 0.307 0.271 0.035 0.193 0.097 0.096*** (0.008) (0.032) (0.033) (0.006) (0.022) (0.023) age 25-35 0.520 0.550

  • 0.031

0.381 0.384

  • 0.003

(0.009) (0.036) (0.037) (0.008) (0.040) (0.041) age 35-45 0.103 0.106

  • 0.003

0.228 0.296

  • 0.068*

(0.005) (0.023) (0.024) (0.006) (0.037) (0.037) age 45-55 0.059 0.072

  • 0.013

0.163 0.196

  • 0.033

(0.004) (0.019) (0.020) (0.006) (0.031) (0.032) age 55-64 0.011 0.000 0.011*** 0.034 0.027 0.007 (0.002) (0.000) (0.002) (0.003) (0.013) (0.013) Source: Polish LFS, years 1998 to 2008. Note: Individuals aged 16 to 64.Full sample is the whole sample of emigrants, restricted sample is the sample of emigrants for whom I observe positive wages. Primary educated are those who left education at age 16 (or less), secondary educated are those who left education between 17 and 20 years old and UK Germany ✷✶ ❆♥♥❛ ❘♦ss♦

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SLIDE 23

■♥tr♦❞✉❝t✐♦♥ ▲✐t❡r❛t✉r❡ ❘❡✈✐❡✇ ❛♥❞ P♦❧✐❝✐❡s ❉❛t❛ ▼♦❞❡❧ ❛♥❞ r❡s✉❧ts ❈♦♥❝❧✉s✐♦♥

❆♣♣❡♥❞✐① ✲ ❘❡❣✐♦♥❛❧ ❞✐✛❡r❡♥❝❡s

regions (1) (2) (1) (2) (3) (1) (2) (3) 1.906 1.717 0.189*** 1.936

  • 0.029

(0.002) (0.045) (0.045) (0.067) (0.067) 1.934 1.679 0.255*** 1.685 0.250*** (0.001) (0.042) (0.042) (0.054) (0.054) 1.962 1.837 0.125 1.858 0.104 (0.002) (0.099) (0.099) (0.072) (0.073) 2.032 2.054

  • 0.022

2.053

  • 0.021

(0.001) (0.078) (0.078) (0.107) (0.107)

  • bservations

Source: Polish LFS, years 1998 to 2008 Note: Individuals aged 16 to 64, reporting positive wages. In each Panel (A,B,C), column (1) reports the regional distribution

  • f individuals in the reference groups, column (2) reports the log hourly wages (in 2008 prices) between 1998 and 2008.

Column (3) in panels B and C, reports the difference in log hourly wages between non-emigrants and emigrants. Regions are ranked according to the distribution of regional average log hourly wages (in 2008 prices), between 1998 and 2008. The group below 25th includes: Kuyavian-Pomeranian, Swietokrzyskie, Warmian-Masurian and Lódz; the group 25th to 50th includes: Greater Poland, Subcarpathian, Lubusz and Lublin; the group 50th to 75th includes: West Pomeranian, Opole, Pomeranian and Lower Silesian; the group above 75th includes Podlaskie, Lesser Poland, Silesian and Masovian. Standard errors reported in brackets.* indicates significance at 10%, ** indicates significance at 5%, *** indicates significance at 1% level. 410,587 162 204

  • A. Non-Emigrants
  • B. Emigrants to Germany
  • C. Emigrants to the UK

20% 24% 24% 27% below 25th 25th and 50th 50th and 75th above 75th 26% 20% 33% 21% 20% 36% 20% 29%

❜❛❝❦ t♦ s❧✐❞❡ ✶✶

✷✷ ❆♥♥❛ ❘♦ss♦