Is he e a Risk-Re u n T ade-o ac oss Occupa ions?
E idence om Spain
Luis Diaz-Se ano♣
Na ional Uni e si y o I eland, Maynoo h
IZA, Bonn
CREB, Uni e si a de Ba celona
Joop Ha og♦
FEE-SCHOLAR, Uni e si ei an Ams e dam
Abs ac : We use da a om Spain o es o an e ec o ea nings isk and skewness
on indi idual wages. We ca y ou sepa a e es ima ion o men, women, public and
p i a e sec o employees. In acco dance wi h p e ious e idence o he US we show
he exis ence o a isk- e u n ade-o ac oss occupa ions in he Spanish labou
ma ke . These esul s a e in con o mi y wi h p e e ences o isk-a e se indi iduals
wi h dec easing absolu e isk a e sion.
JEL code: J3, D8
Keywo ds: Risk-a e sion, skewness a ec ion, occupa ional choices, compensa ing
wage di e en ials.
♣ Depa men o Economics, Na ional Uni e si y o I eland Maynoo h, Co. Kilda e, I eland. E-mail:
[email p o ec ed].
♦ Depa men o Economics. Uni e si ei an Ams e dam. Roe e ss aa , 11. 1018WB Ams e dam.
(The Ne he lands). E-mail: J.Ha [email p o ec ed].
1
1. In oduc ion
In a iskless wo ld, choosing an occupa ion should be, undoub edly, an easy
ask. In such a case, indi iduals can make hei choice jus maximizing he ea nings
ac oss occupa ions. Howe e , in a isky wo ld like ou s, whe e he ac ha wo ke s
a e a e se o luc ua ions in hei incomes is uni e sally ecognised, such a decision
becomes much mo e complica ed. Such a luc ua ions in ea nings is wha we call
ea nings isk.
Economic heo y sugges s ha i hese isks a e o eseeable, hey should be
compensa ed o . In he empi ical li e a u e on compensa ing wage di e en ials he e
exis a wide a ie y o s udies analysing he e ec o di e en sou ces o isk in he
job place on wages. Howe e , his li e a u e is mainly ocused on inju y o a ali y
isks. As Adam Smi h we claim ha in a compe i i e labou ma ke he e mus exis a
way o compensa ion in hose occupa ions ha en ail a highe p obabili y o ailu e
(highe a iance in ea nings) in o de o a ac su icien supply. While unce ain y in
labou income is accoun ed o in mos heo e ical models i has been a ely es ed
apa om some excep ions in he US.
Addi ionally, he e is also a g owing li e a u e ha shows he ele ance o he
skewness o he e u ns in many economic decisions. Fo example, Ga e and Sobel
(1999) and Golec and Tama king (1998) ind e idence ha isk-a e se indi iduals
playing lo e y games and be ing in ho se aces in he US base hei pa icipa ion
decision on he skewness o he p ize dis ibu ions, espec i ely. P ackash e al.
(2003) ound empi ical e idence om La in Ame ican, US and Eu opean capi al
ma ke s ha in es o s do ade expec ed e u n o he po olio o skewness. Diaz-
Se ano (2004) empi ically suppo s ha posi i e skewness a ou homeowne ship in
Ge many and Spain. And Ha og and Vij e be g (2002) in he US and Diaz-Se ano,
2
Ha og and Nielsen (2004) in Denma k shows ha indi iduals app ecia e posi i ely
skewed income dis ibu ions, and hey inco po a e his in o ma ion in o hei
educa ional choices. In his pape , we empi ically es o he Spanish labo ma ke
whe he isk-a e se wo ke s a e compensa ed o ea nings isks in hei occupa ional
choices, and whe he he e is a willingness o pay o posi i e skewness in hei
incomes.
The emainde o he pape is s uc u ed as ollows. In sec ion 2 he exis ing
li e a u e in his issue is ex ensi ely e iewed. Sec ion 3 p esen s he heo e ical
backg ound. In sec ion 4 we desc ibe he empi ical amewo k. In sec ion 5 we
desc ibe he da ase . We ca y ou he empi ical analysis in sec ion 6. And sec ion 7
summa izes and concludes.
2. Li e a u e e iew
In labou economics he e is a g owing li e a u e dealing wi h ea nings isk
and i s e ec s on indi iduals’ beha iou in he labou ma ke . This li e a u e conside s
h ee di e en app oaches, he e ec o isk on he human capi al in es men , on he
occupa ional choices, and on he compensa ing wage di e en ials.
2.1. Ea nings isk and schooling choices
Le ha i and Weiss (1974) buil a wo pe iod model wi h he choice be ween
wo king o s udying du ing he i s pe iod, and s ochas ic ea nings du ing he second
pe iod o hose who a ended educa ion in he i s pe iod. In hei heo e ical model
hey ob ain ha inc easing ea nings isk educes he in es men in educa ion. Kodde
(1986) buil on Le ha i and Weiss model’: in his heo e ical model he inds an
ambiguous e ec , bu his empi ical es ima ion sugges s inc easing demand o
3
educa ion wi h inc easing isk. Snow and Wa en (1990) ound ha human capi al
dec eases wi h inc easing isk in i s u u e e u ns i such in es men is an in e io
ac i i y and indi iduals exhibi dec easing isk a e sion.
Williams (1979) applied dynamic p og amming o he educa ion decisions,
whe e p oduc ion and dep ecia ion o human capi al and u u e wages a e all
s ochas ic. He concludes, unde condi ions, ha highe isk in he p oduc ion o
human capi al educes in es men in schooling. Belzil and Hansen (2002) and Hogan
and Walke (2002) also used he dynamic p og amming amewo k, bo h models ind
ha indi iduals p e e o s ay a school longe wi h inc easing isk in u u e ea nings.
The i s a gue ha i is because while being a school hey ecei e non isky pa en al
income, he second a ibu e his esul o he inc eased alue o wai ing o a good
ea nings d aw.
The wo k o Ha og and Diaz-Se ano (2002) cons i u es one o he ew
empi ical s udies on his issue. They de elop a human capi al model o op imum
schooling leng h wi h s ochas ic ea nings and highligh he pi o al ole o isk
a i udes and he schooling g adien o ea nings isk. Thei empi ical es ima ion uses
Spanish da a on high school g adua es deciding on a ending uni e si y educa ion.
They ind ha he basic esponse o inc easing ea nings isk is nega i e bu ha in
households wi h lowe isk a e sion, he esponse will be dampened subs an ially and
may e en be e e sed o posi i e.
2.2. Occupa ional choices
O azem and Ma ila (1986) de eloped an empi ical model o occupa ional
choice unde unce ain y. They conside ed he i s wo momen s o he ea nings
dis ibu ion (mean and a iance) wi hin a ious al e na i e occupa ions. Thei
4
empi ical esul s con i m ha inc easing mean and dec easing a iance o he ea nings
dis ibu ion inc ease he p obabili y o choosing a gi en occupa ion. Siow (1984)
es ima ed supply cu es assuming unce ain y in he u u e wages and enu e o he
occupa ional choice by a coho o en an s in o an occupa ion. This au ho used a
sample o Ame ican lawye s. De Meza (1984) also analysed occupa ional choices
unde wage unce ain y, his au ho inds ha inc easing isk dec eases indi idual’s
u ili y.
2.3. Risk compensa ion in wages
This li e a u e s a s wi h King (1974) using agg ega ed da a eg essed he
a iance and he skewness by occupa ion cells on a e age ea nings, obse ing a
posi i e e ec o a iance and nega i e o skewness in he US labou ma ke .
McGold ick (1995) buil on King’s wo k and using US mic oda a ob ained he same
esul s. McGold ick and Robs (1996) s udied he e ec o wo ke s mobili y on
compensa ing di e en ials o ea nings isk. They obse e ha wo ke s wi h high
mobili y ecei e smalle compensa ions o ea nings isk, since hey show p e e ence
o unce ain si ua ions. Also in he con ex o he US labou ma ke , Feinbe g (1981a)
es ima ed signi ican wage compensa ion o ea nings isk using a 6-yea s panel. He
es ima ed ea nings- isk as he coe icien o a ia ion o indi idual yea ly ea nings
du ing he sample pe iod. And Feinbe g (1981b) also inds ha wo ke s expe iencing
employmen ins abili y in hei occupa ions a e compensa ed o .
Ha og and Vij e be g (2002) we e he i s o p o ide a o mal modelling on
isk compensa ion in wages. Using US da a hey es ima ed s uc u al equa ions
measu ing isk and skewness by occupa ion-educa ion cells, and hey obse e a
posi i e compensa ion o isk and a penal y o skewness in wages. Diaz-Se ano,

5
Ha og and Nielsen (2003) also es ima ed posi i e compensa ion o isk using Danish
panel da a. They es ima ed isk using jus educa ion cells and p o ide new es ima es
by expe imen ing wi h se e al dynamic measu es o ea nings isk and skewness. They
also ound a signi ican posi i e e ec o he a iance and nega i e o he skewness
on wages.
3. Theo e ical backg ound
As we men ion in he p e ious sec ion, he e is a wide numbe o s udies ha
conside s an e ec o ea nings unce ain y in he educa ional an occupa ional choices.
Howe e , a li le has been said ye abou skewness. Tsiang (1974) ound heo e ical
suppo ha isk-a e se indi iduals display p e e ence o skewness, in addi ion o
a e sion o dispe sion ( isk), o he p obabili y dis ibu ion o he e u ns in economic
decisions ha en ail an unce ain ou come. This esul sugges s ha since inc easing
absolu e isk a e sion is absu d, dec easing absolu e isk a e sion equi es ha
indi iduals app ecia e highe momen s as e.g. skewness. O he well es ablished
heo ies also emphasize a ound his inding. Fo example, p ospec heo y (Kahneman
and T e sky, 1979, 1991) s a es ha he indi idual’s disu ili y caused by a loss is
g ea e han he u ili y caused by a gain o he same size, which goes in he same
di ec ion as Tsiang’s indings. These a gumen s sugges ha i we assume u u e
ea nings a ached o an occupa ional/educa ional choice o be unce ain, bo h he
a iance and skewness o ea nings, in addi ion o he mean, should be conside ed
when analyzing how hese choices a e planned and achie ed.
To unde s and how such a compensa ion mechanism in wages may a ise we
ollow Ha og and Vij enbe g (2002) and Diaz-Se ano e al. (2004). Assume ha a isk-
a e se indi idual has o choose be ween wo occupa ional op ions ha only di e in isk.
6
In he iskless al e na i e, annual ea nings a e gi en as Y , gene a ing u ili y U(Y ), whe e
U( ) is a conca e u ili y unc ion wi h U’ > 0, U” < 0 and U’” > 0 ( he la e condi ion is
necessa y o declining absolu e isk a e sion, see Tsiang, 1974 o Ha og and Vij e be g,
2002). In he isky op ion, income is a single d aw o he es o wo king li e, w i en as
Y +
ε
. Equal expec ed li e ime u ili y equi es
00
() ( )
TT
UY e d E UY e d
ρρ
ε
−−
=+
∫∫ (1)
whe e T is he leng h o wo king li e and
ρ
he ime discoun a e. We can w i e he le -
hand side as
()
0
1
() 1 ()
T T
UY e d e UY
ρρ
ρ
−−
=−
∫ (2)
Fo he s ochas ic e m on he igh -hand side we apply a hi d-o de Taylo expansion
a ound he expec ed alue
Y, one o de up om P a ’s o iginal con ibu ion (P a ,
1964), o
()
23
0
111
( ) 1 ( ) ''( ) '''( )
26
T T
p p
UY e d e UY U Y U Y
ρρ
ε
σκ
ρ
−−
⎡
⎤
+=− + +
⎢
⎥
⎣
⎦
∫
(3)
whe e 2
p
σ is he second momen ( isk) and 2
p
κ
is he hi d momen (skewness) o
ε
a ound he expec ed alue ze o. Equa ing (2) and (3) and ew i ing a li le, a e applying
a i s -o de Taylo expansion a ound
Y o (2), we ge
23 23
23 23
1 '' 1 ''' '' 1 1
2'6'''2 6
p p p p
s
YY UUU
YYYVVV
YUUU
YY YY
σκ σκ
−=− − = −
(4)
whe e V is A ow-P a ’s ela i e isk a e sion and Vs is he simila de ini ion o ela i e
skewness a ec ion (we call i a ec ion, because indi iduals like skewness; see Ha og
and Vij e be g, 2002). Wi h V and Vs posi i e by de ini ion, we no e om (4) ha
7
indi iduals only en e an occupa ion i he pe manen e ec om an unknown
occupa ional ou come is ma ched by a posi i e p emium o he isk ( a iance), while
hey allow an ea nings d op o skewness.
3. Empi ical amewo k
Assume ha ea nings o an indi idual can be exp essed as ollows
isj s ij
YY
η
=
, (5)
whe e Yisj a e he obse ed ea nings o indi idual i wi h educa ion S in occupa ion j,
Ys=
µ
sY0 a e he expec ed ea nings o an indi idual wi h educa ion le el S, wi h
Y0=exp(X
β
), and
η
ij=exp(uij) is a e m picking up speci ic e ec s on ea nings o
indi iduals’ occupa ion. This speci ica ion allows indi iduals wi h he same schooling
o ea n di e en wages. Appling loga i hms in bo h sides o (5) we ge
log log
ij s i ij
YXu
µ
β
=
++
, (6)
whe e X a e he obse able de e minan s o Yij, log
µ
s is he ixed e ec o educa ion
on wages, and uij is he e ec on wages o occupa ion j o indi idual i. Exp ession (6)
p o ides he amilia Mince ma k-up model. I wages include a compensa ion o isk
in labou ea nings, uij can be decomposed as ollows
23
ij j j j i
uu
λασ γκ
=+ + +, (7)
whe e
λ
j a e he occupa ion ixed-e ec s, 2
j
σ
is he a iance o ea nings wi hin
occupa ions, 3
j
κ
is he skewness, and ui is a andom e m no mally dis ibu ed wi h 0
mean and cons an a iance ac oss indi iduals. By subs i u ing (7) in (6), he comple e
speci ica ion o indi iduals’ ea nings can be exp essed as
8
23
log log
isjijji
YX u
µλ βασγκ
=+++++
, (8)
The exis ence o a isk compensa ion equi es
α
>0, whe eas he so called
“skewness a ec ion” ha en ails a penal y in wages equi es
γ
<0. We ake as sui able
measu es o 2
j
σ
(he ea e Rj) and 3
j
κ
(he ea e Kj) he a iance and he skewness o
he obse ed ea nings by occupa ions cells, espec i ely.
We decompose ea nings acco ding o he sou ce o a ia ion, i.e. sys ema ic
and unsys ema ic componen . Sys ema ic luc ua ions in ea nings a e caused by
supply a iables (e.g. human capi al), which a e usually known by indi iduals, and
he e o e, ha e no hing o do wi h isk. Howe e , unsys ema ic a ia ions in ea nings
ca ch a ia ions which a e unknown by indi iduals when hey ha e o make hei
choice o educa ion and ensuing occupa ion. They e lec indeed he isk o he
indi idual: hei as ye unknown abili ies, sui abili y o he job, and hence ela i e
posi ion in he occupa ions’ ea nings dis ibu ion. They also e lec demand ac o s
(e.g. business cycle o shocks in ou pu demand) and hey a e expec ed o gene a e
compensa ing wage di e en ials. Hence, sui able measu es o isk and skewness in
wages equi e ha sys ema ic a ia ion in wages shall be pu ged om obse ed
ea nings, since o he wise he ue ela ionship be ween isk and wages migh be
obscu ed.
We use a wo-s ep me hod o es o isk compensa ion in he ea nings
equa ion (8). Fi s ly, we es ima e equa ion (6), whe e we assume ha log
s
i
X
µ
β
+
collec s he sys ema ic a ia ion in ea nings, and hence he es ima ed esiduals a e
used o calcula e R and K by occupa ions as ollows
15
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18
Table 1: Sample desc ip i e s a is ics
Full sample Women Men Public sec o P i a e sec o
Mean STD Mean STD Mean STD Mean STD Mean STD
Age 37,14 12,09 34,03 11,40 38,54 12,14 39,10 11,48 36,46 12,23
Women 0,31 0,46 0,38 0,49 0,29 0,45
Ma ied 0,36 0,48 0,31 0,46 0,38 0,48 0,41 0,49 0,34 0,47
Household size 4,14 1,54 4,02 1,57 4,19 1,52 3,93 1,51 4,22 1,54
# o child en 0,57 0,50 0,52 0,50 0,59 0,49 0,55 0,50 0,58 0,49
Yea s o schooling 8,39 4,29 9,21 4,40 8,03 4,19 10,78 4,65 7,56 3,83
Log(yea ly wages) 13,87 0,83 13,55 0,89 14,01 0,75 14,19 0,74 13,76 0,83
Dummies indus y
Ag icul u e 0,08 0,26 0,05 0,21 0,09 0,28 0,01 0,12 0,10 0,30
Ene gy 0,02 0,14 0,00 0,07 0,03 0,16 0,02 0,14 0,02 0,14
Chemical 0,03 0,17 0,01 0,11 0,04 0,20 0,02 0,13 0,04 0,19
Mechanics 0,08 0,26 0,02 0,14 0,10 0,30 0,02 0,13 0,10 0,29
O he manu ac u es 0,13 0,33 0,13 0,34 0,12 0,33 0,01 0,11 0,16 0,37
Cons uc ion 0,10 0,30 0,01 0,11 0,14 0,35 0,02 0,14 0,13 0,34
Comme ce 0,16 0,37 0,19 0,39 0,15 0,35 0,02 0,12 0,21 0,41
T anspo 0,06 0,23 0,03 0,16 0,07 0,26 0,08 0,28 0,05 0,22
Banking 0,05 0,22 0,05 0,21 0,05 0,22 0,01 0,10 0,06 0,24
O he se ices 0,30 0,46 0,51 0,50 0,21 0,41 0,79 0,40 0,13 0,34
Dummies occupa ion
Manage ial 0,01 0,10 0,00 0,05 0,01 0,12 0,01 0,10 0,01 0,10
P o essionals 0,09 0,29 0,13 0,33 0,08 0,27 0,23 0,42 0,04 0,20
Scien is s 0,07 0,26 0,11 0,31 0,05 0,23 0,12 0,33 0,05 0,22
Technicians 0,13 0,33 0,20 0,40 0,09 0,29 0,19 0,39 0,10 0,31
Cle ical 0,21 0,41 0,37 0,48 0,14 0,34 0,16 0,37 0,23 0,42
Quali ied wo ke s 0,06 0,24 0,00 0,07 0,08 0,28 0,04 0,18 0,07 0,25
Cle ical 0,08 0,28 0,01 0,11 0,12 0,32 0,05 0,22 0,10 0,30
Manual wo ke s 0,33 0,47 0,18 0,38 0,39 0,49 0,14 0,35 0,39 0,49
O he 0,02 0,14 0,00 0,07 0,03 0,16 0,06 0,24 0,01 0,08
Public wo ke s 0,26 0,44 0,32 0,46 0,23 0,42
Dummies loca ion
U ban a ea 0,58 0,49 0,62 0,49 0,56 0,50 0,70 0,46 0,53 0,50
Region 1 0,40 0,49 0,39 0,49 0,40 0,49 0,40 0,49 0,40 0,49
Region 2 0,37 0,48 0,37 0,48 0,37 0,48 0,40 0,49 0,36 0,48
Region 3 0,23 0,42 0,24 0,43 0,23 0,42 0,20 0,40 0,24 0,43
Sample size 18132 5620 12512 4675 13457
19
Table 2: P obi es ima es o emale pa icipa ion
Coe icien Z-s a
Cons an
P ima y educa ion
Seconda y educa ion
Highe educa ion
Age
Age squa e
Child en
Ma ied
Numbe o wage ea ne s
U ban a ea
-3.6399 (-18.6)
0.1565 (2.5)
0.3684 (8.4)
1.0198 (4.3)
0.1098 (8.7)
-0.0016 (-6.8)
-0.2909 (-4.7)
0.3725 (7.1)
0.5089 (3.8)
0.1374 (4.2)
0 – Non-pa icipan
1 – Pa icipan
15569
5620
Sample size 21189
No es: Endogenous a iable is emale pa icipa ion.
Es ima es include dummies o egion.
20
Table 3: P obi es ima es o public-p i a e sec o choice
Coe icien Z-s a
In e cep
P ima y educa ion
Seconda y educa ion
Highe educa ion
Ac ually s udying
Age
Age squa e
Female
Child en
Ma ied
U ban
Region 1
Region 3
Scien is s
P o essionals
Ope a o s
Adminis a i e
Whi e-colla s
Blue-colla s
-4.1242 (-16.1)
0.3698 (4.6)
0.9197 (10.1)
1.6174 (16.0)
0.4602 (6.5)
0.0998 (7.9)
-0.0008 (-7.3)
0.2351 (7.4)
-0.1505 (-3.4)
0.1630 (3.7)
0.2127 (7.1)
0.0728 (1.6)
-0.4938 (-9.7)
0.5552 (6.1)
0.1760 (2.1)
0.1258 (1.6)
-0.3535 (-4.8)
-0.6774 (-7.4)
-0.9382 (-13.0)
Public sec o
P i a e sec o
4675
13417
Sample size 18092
No e: Endogenous a iable is wo king in he p i a e sec o .

21
Table 4: OLS and wo-s ep es ima es o equa ion (2) used o pu ge sys ema ic ea nings.
Full sample Males Females Females1Public2
P i a e2
Cons an
Schooling
Exp.
Exp. squa e
Exp. cube
Female
U ban
Region 1
Region 3
Co ec ion e m
12.2383
(397.8)
0.0969
(71.1)
0.0757
(18.2)
-0.0014
(-7.2)
6.4·10-6
(2.5)
-0.4507
(-41.2)
0.1319
(12.6)
-0.0937
(-8.2)
0.1044
(7.9)
12.1515
(334.6)
0.0849
(57.8)
0.0937
(18.9)
-0.0018
(-8.1)
8.9·10-6
(3.1)
0.1374
(11.9)
-0.0963
(-7.6)
0.1105
(7.5)
11.6686
(212.2)
0.1182
(44.3)
0.0843
(11.3)
-0.0025
(-6.7)
2.6·10-5
(4.9)
0.1274
(6.1)
-0.0872
(-3.8)
0.1052
(4.1)
11.6906
(103.7)
0.1198
(41.1)
0.0835
(11.1)
-0.0025
(-6.7)
2.5·10-5
(4.9)
0.1293
(6.2)
-0.0860
(-3.7)
0.1079
(4.2)
-0.0403
(-1.3)
13.0405
(110.1)
0.0658
(17.5)
0.0736
(10.4)
-0.0017
(-7.4)
1.4·10-5
(3.4)
-0.3178
(-17.9)
0.1263
(6.2)
-0.0829
(-4.2)
0.1147
(4.6)
-0.2856
(-7.1)
12.4335
(328.0)
0.0675
(24.8)
0.0613
(12.3)
-0.0011
(-4.8)
2.6·10-6
(0.8)
-0.5673
(-42.4)
0.1268
(10.4)
-0.1006
(-7.4)
0.2034
(12.2)
0.3992
(9.9)
Sample size 17919 12512 5251 5251 4675 13457
No es: (1) Heckman’s selec i i y co ec ion
(2) Lee’s selec i i y co ec ion
22
Table 5: Summa y s a is ics o es ima ed R ( isk) and K (skewness).
Risk Skewness
Mean STD Mean STD
To al 0.631 0.867 3.502 3.140
Indus y
Ag icul u e 0.804 0.897 5.608 1.421
Ene gy 0.561 0.333 2.681 1.838
Chemical 0.588 0.887 2.255 1.999
Mechanics 0.500 0.935 2.653 1.952
O he manu ac u es 0.660 0.774 3.493 3.331
Cons uc ion 0.801 0.424 8.096 4.493
Comme ce 0.809 1.506 2.976 1.810
T anspo 0.791 0.549 4.100 2.959
Banking 0.492 0.694 2.529 1.811
O he se ices 0.457 0.505 2.143 2.070
Educa ion
Incomple e p ima y 0.666 0.572 4.973 3.738
P ima y 0.678 0.835 3.955 3.421
Seconda y 0.602 1.057 2.971 2.247
Uni e si y 0.484 0.816 1.690 1.361
Job le el
Manage ial 0.782 0.463 1.380 0.974
P o essionals 0.421 0.094 1.284 0.453
Scien is s 0.600 0.346 1.907 1.750
Technicians 0.419 0.024 3.081 1.230
Cle ical 0.597 0.663 2.601 2.051
Quali ied wo ke s 1.813 2.807 5.840 2.683
Cle ical 0.455 0.117 3.280 1.516
Manual wo ke s 0.649 0.482 5.046 4.215
O he 0.240 0.151 1.153 0.487
Age
18 o 25 0.600 0.541 3.424 3.009
25 o 35 0.624 0.846 3.386 3.045
35 o 45 0.645 1.003 3.461 3.157
45 o 55 0.643 0.898 3.751 3.376
55 o 65 0.665 1.062 3.699 3.213
Sec o
P i a e 0.685 0.952 3.853 3.281
Public 0.476 0.525 2.492 2.427
Gende
Men 0.676 0.974 3.993 3.418
Women 0.532 0.550 2.409 2.019
23
Table 6: Es ima ion o e u ns o schooling, e u ns o isk and skewness penal y o se e al wo k o ce g oups1. ( -s a is ics in pa en hesis)
Full sample Males Females Public2
P i a e2
In e cep
Schooling
Risk
Skewness
Co ec ion e m
12.4281
(227.6)
0.0686
(37.3)
12.4140
(227.5)
0.0682
(37.1)
0.0377
(6.3)
12.4250
(226.0)
0.0679
(36.8)
0.0390
(6.5)
0.0199
-0.0027
(-2.7)
-0.0096
12.4033
(212.8)
0.0610
(30.3)
0.0401
(6.8)
12.4221
(213.1)
0.0601
(29.7)
0.0424
(7.4)
0.0246
-0.0045
(-2.8)
-0.0155
11.5004
(154.3)
0.0784
(19.4)
0.0140
(1.5)
11.8744
(110.9)
0.0783
(19.4)
0.0528
(4.2)
0.0359
-0.0527
(-4.8)
-0.1012
13.5375
(67.4)
0.0531
(8.8)
0.0127
(0.4)
-0.3270
(-3.8)
13.5005
(61.5)
0.0513
(8.5)
0.2330
(3.2)
0.0862
-0.0890
(-3.4)
-0.0898
-0.3403
(-4.0)
12.2944
(116.5)
0.0525
(16.9)
0.0240
(7.3)
0.3288
(4.5)
12.3326
(116.1)
0.0521
(16.8)
0.0278
(7.9)
0.0181
-0.0063
(-3.0)
-0.0119
0.3273
(4.5)
Sample size 17919 12512 5251 4675 13457
No es: (1) Full model speci ica ion includes a cubic polynomial on yea s o expe ience, and dummy con ols o gende , amily s a us, geog aphical, indus y
and occupa ions
(2) Lee’s selec i i y co ec ion