A anesian, Ga en
A icle
Do non-cogni i e skills p oduce he e ogeneous e u ns ac oss di e en
wage le els amongs you h en e ing he wo k o ce? A quan ile mixed
model app oach
Economies
P o ided in Coope a ion wi h:
MDPI – Mul idisciplina y Digi al Publishing Ins i u e, Basel
Sugges ed Ci a ion: A anesian, Ga en (2025) : Do non-cogni i e skills p oduce he e ogeneous e u ns
ac oss di e en wage le els amongs you h en e ing he wo k o ce? A quan ile mixed model
app oach, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 5, pp. 1-20,
h ps://doi.o g/10.3390/economies13050114
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/329394
S anda d-Nu zungsbedingungen:
Die Dokumen e au EconS o dü en zu eigenen wissenscha lichen
Zwecken und zum P i a geb auch gespeiche und kopie we den.
Sie dü en die Dokumen e nich ü ö en liche ode komme zielle
Zwecke e iel äl igen, ö en lich auss ellen, ö en lich zugänglich
machen, e eiben ode ande wei ig nu zen.
So e n die Ve asse die Dokumen e un e Open-Con en -Lizenzen
(insbesonde e CC-Lizenzen) zu Ve ügung ges ell haben soll en,
gel en abweichend on diesen Nu zungsbedingungen die in de do
genann en Lizenz gewäh en Nu zungs ech e.
Te ms o use:
Documen s in EconS o may be sa ed and copied o you pe sonal
and schola ly pu poses.
You a e no o copy documen s o public o comme cial pu poses, o
exhibi he documen s publicly, o make hem publicly a ailable on he
in e ne , o o dis ibu e o o he wise use he documen s in public.
I he documen s ha e been made a ailable unde an Open Con en
Licence (especially C ea i e Commons Licences), you may exe cise
u he usage igh s as speci ied in he indica ed licence.
h ps://c ea i ecommons.o g/licenses/by/4.0/
Academic Edi o : Gheo ghe
H. Popescu
Recei ed: 15 Ma ch 2025
Re ised: 12 Ap il 2025
Accep ed: 14 Ap il 2025
Published: 22 Ap il 2025
Ci a ion: A anesian, G. (2025). Do
Non-Cogni i e Skills P oduce
He e ogeneous Re u ns Ac oss
Di e en Wage Le els Amongs You h
En e ing he Wo k o ce? A Quan ile
Mixed Model App oach. Economies,
13(5), 114. h ps://doi.o g/10.3390/
economies13050114
Copy igh : © 2025 by he au ho .
Licensee MDPI, Basel, Swi ze land.
This a icle is an open access a icle
dis ibu ed unde he e ms and
condi ions o he C ea i e Commons
A ibu ion (CC BY) license
(h ps://c ea i ecommons.o g/
licenses/by/4.0/).
A icle
Do Non-Cogni i e Skills P oduce He e ogeneous Re u ns Ac oss
Di e en Wage Le els Amongs You h En e ing he Wo k o ce?
A Quan ile Mixed Model App oach
Ga en A anesian 1,2
1
Depa men o Human Resou ces Managemen , Sou he n Fede al Uni e si y, 344006 Ros o -on-Don, Russia;
[email p o ec ed]
2Depa men o O ganiza ional Psychology, Sou he n Fede al Uni e si y, 344006 Ros o -on-Don, Russia
Abs ac : This s udy es ima es he labo ma ke e u ns o non-cogni i e skills among
he you h unde 30 yea s old du ing he ea ly ca ee s age. Using da a om he Russian
Longi udinal Moni o ing Su ey (RLMS-HSE) o 2016 and 2019, i examines he e ec s o
he Big Fi e pe sonali y ai s (openness, conscien iousness, ex a e sion, ag eeableness,
and emo ional s abili y) on hou ly wages. To accoun o po en ial he e ogenei y in he
e ec o non-cogni i e skills along he wage dis ibu ion, a quan ile linea mixed model
is employed, es ima ing e u ns a he 10 h, 25 h, 50 h, 75 h, and 90 h pe cen iles while
con olling o epea ed obse a ions wi h andom in e cep s a he indi idual le el. In e se
p obabili y weigh ing is applied o add ess he selec ion o employmen . The esul s
indica e ha openness yields he highes e u ns o young wo ke s, hough i s e ec
diminishes a e con olling o educa ional a ainmen . By con olling o educa ion, he
model iden i ies he e ec o conscien iousness below he median wage le el, and ha
o ex a e sion abo e. Finally, he s udy inds ha he impac o non-cogni i e skills on
wages e ol es o e he li e cou se. Fi s , he e ec s o non-cogni i e skills on wages a y
a lo in he you h g oup and he en i e wo king popula ion (ages 16–65). Fu he mo e,
b eaking he da a down by age coho s e eals how hei signi icance and magni ude shi
a di e en ca ee s ages.
Keywo ds: non-cogni i e skills; wages; you h; quan ile eg ession; mixed models
JEL Classi ica ion: I26; J24; J31
1. In oduc ion
Wha de ines he success o young people in he labo ma ke ? Why do some indi-
iduals ecei e high wages while o he s a e paid poo ly? Con en ional economic heo y
explains wages p ima ily as a unc ion o schooling and expe ience, an app oach known as
he Mince ian wage equa ion (Mince ,1974). While his model has become a co ne s one
in wage de e mina ion, common sense and empi ical e idence sugges ha educa ion and
expe ience alone do no ully accoun o wage a ia ion. I hey did, indi iduals wi h he
same yea s o schooling and wo k expe ience would ea n equally, which is clea ly no he
case. Economis s ha e long acknowledged he exis ence o o he con ibu ing ac o s o
ea nings, bu in he Mince ian amewo k, hese ac o s we e ypically elega ed o he
e o e m, o en desc ibed as “unobse ed abili ies”. The “abili y bias” p oblem, i.e., he
omission o ele an a iables om he wage equa ion, p esen s a signi ican challenge
in es ima ing he e u ns o educa ion (Chambe lain & G iliches,1975;G iliches,1977).
Economies 2025,13, 114 h ps://doi.o g/10.3390/economies13050114
Economies 2025,13, 114 2 o 20
Fu he mo e, hese unobse ed abili ies, while absen om s anda d wage models, a e
highly co ela ed wi h bo h wages and educa ional choices, complica ing he accu a e
es ima ion o he e u ns o educa ion.
Fo many yea s, unobse ed abili ies we e neglec ed by economis s, la gely due o he
di icul y o measu emen . Mos empi ical s udies on wage p emiums measu ed human
capi al using simple indica o s like yea s o schooling, wi hou accoun ing o he quali y
o skills, knowledge, and abili ies. Howe e , by he la e 1980s and ea ly 1990s, as da a
on cogni i e sco es became mo e accessible—ei he h ough linking household su eys
wi h adminis a i e educa ional eco ds o hanks o he sp ead o la ge-scale s anda d-
ized lea ning assessmen s—economis s began o o e come hese empi ical limi a ions
(Hanushek & Woessmann,2008). A signi ican body o esea ch eme ged es ima ing he
e ec o cogni i e skills, o en p oxied by es sco es, on indi idual ea nings (Bishop,1989a,
1989b;G ogge & Eide,1995;Neal & Johnson,1996;O’Neill,1990).
Fo ins ance, Blackbu n and Neuma k (1993) ound ha he ising e u ns o educa-
ion in he Uni ed S a es could be a ibu ed o indi iduals wi h highe cogni i e abili ies.
Simila ly, Hanushek and Kimko (2000) highligh ed ha cogni i e skills, pa icula ly in
ma hema ics and science, play a mo e c ucial ole in labo o ce p oduc i i y han o mal
schooling alone. These indings emphasized ha adi ional measu es o educa ional a ain-
men o school esou ces ail o cap u e he ue cogni i e abili ies o a popula ion, u ging
schola s o inco po a e wo k o ce quali y in o labo p oduc i i y analyses. Fu he esea ch
by Mu nane e al. (2001) demons a ed ha he cogni i e skills measu ed du ing high
school a e s ong p edic o s o wages a decade la e . Howe e , while esea ch documen ed
ha he co ela ion be ween abili y and educa ion has inc eased o e ime (He ns ein &
Mu ay,1994), J. Heckman and Vy lacil (2001) a gued ha i is di icul o disen angle he
e ec s o educa ional a ainmen om abili ies, e en i abili ies a e di ec ly obse ed.
These ad ancemen s in esea ch mo i a ed he economis s o u he explo e o he
de e minan s o labo ma ke ou comes beyond cogni i e abili ies, u ning hei a en ion o
pe sonali y ai s. This g owing in e es was summa ized by he concep o non-cogni i e
skills, which e e s o “pa e ns o hough , eelings, and beha io ” (Bo ghans e al.,2008).
These ai s a e de ined as skills because hey mee he “PES” c i e ia: hey a e p oduc i e
( hey c ea e alue in he wo kplace and beyond), expandable ( hey can be de eloped
h ough aining), and social ( hey a e shaped by social con ex s) (G een,2013). The
PES app oach o skills is g ounded in a holis ic amewo k ha in eg a es insigh s om
h ee co e disciplines—psychology, sociology, and economics. While each o hese ields
o e s dis inc concep ualiza ions and de ini ions o skills, o en emphasizing di e en
dimensions (such as indi idual ai s, social con ex s, o ma ke - ele an compe encies),
he PES app oach b idges hese disciplina y di ides. By iden i ying and ocusing on he
sha ed cha ac e is ics, he PES amewo k enables a mo e comp ehensi e unde s anding
o skills as bo h indi idual capaci ies and socially si ua ed asse s. This in eg a i e pe -
spec i e allows o a nuanced analysis o skill de elopmen , deploymen , and e u ns
ac oss he li e cou se, making i pa icula ly aluable o in o ming policy in educa ion and
labo ma ke s.
O en labeled as socio-emo ional skills, so skills, o 21s -cen u y skills, non-cogni i e
skills a e commonly measu ed using he Big Fi e pe sonali y ai s model, which cap-
u es all he a ia ions in pe sonali y based on i e independen dimensions: openness,
conscien iousness, ex a e sion, ag eeableness, and neu o icism (o emo ional s abili y).
Howe e , in a ealis ic sense, he e m “non-cogni i e skills” is used “as a ca ch-all ph ase
o dis inguish ac o s o he han hose measu ed by cogni i e es sco es such as li e acy
and nume acy” (Gu man & Schoon,2016) and can encompass many o he ac o s, like
sel -con ol, g i , mo i a ion, e c. F om an economic pe spec i e, hese skills a e iewed “as
Economies 2025,13, 114 3 o 20
a b oadly de ined second dimension o indi idual he e ogenei y (nex o cogni i e skills)”
in he s udy o li e success de e minan s (Humph ies & Kosse,2017).
The i s no able e e ence o non-cogni i e skills in ela ion o economic ou comes
was made by he Ma xis economis s Bowles and Gin is (1976) in hei seminal book
“Schooling in Capi alis Ame ica”. They a gued ha ai s such as mo i a ion, discipline, and
in e naliza ion o no ms we e c ucial in shaping social class s uc u es and access o jobs.
While o mal educa ion ewa ds a highe IQ, hey emphasized ha he in e -gene a ional
ansmission o social and economic s a us is la gely media ed h ough non-cogni i e
mechanisms. Simila ly, in ano he seminal wo k on educa ion economics o hose imes,
“Who Ge s Ahead? The De e minan s o Economic Success in Ame ica”, Jencks e al. (1979)
p o ided ea ly e idence ha ai s such as indus iousness, pe se e ance, and leade ship
signi ican ly in luence wages, o en o a deg ee compa able o adi ional p edic o s like
educa ion, IQ, and pa en al socio-economic s a us.
Despi e hese ea ly insigh s, he economic signi icance o pe sonali y emained un-
de explo ed, as he s udy o pe sonali y ai s la gely ell wi hin he ealm o psychology
and sociology. In ano he wo k, published 26 yea s a e “Schooling in Capi alis Ame ica”,
Bowles and Gin is e isi ed hei ea lie indings, emphasizing ha he “in e gene a ional
ansmission o economic s a us is accoun ed o by a he e ogeneous collec ion o mecha-
nisms, including he gene ic and cul u al ansmission o cogni i e skills and noncogni i e
pe sonali y ai s in demand by employe s, he inhe i ance o weal h and income-enhancing
g oup membe ships, such as ace, and he supe io educa ion and heal h s a us enjoyed by
he child en o highe s a us amilies” (Bowles & Gin is,2002). They u he highligh ed
ha while he ole o cogni i e skills and educa ion in he in e -gene a ional ansmis-
sion o economic s a us had been ex ensi ely s udied, ac o s such as weal h, ace, and
non-cogni i e ai s had no ecei ed he schola ly a en ion hey dese ed.
I was no un il he mid-2000s ha economis s began o explici ly accoun o pe son-
ali y ai s in hei analyses o wage de e minan s. The pionee ing wo k o J. J. Heckman
e al. (2006) demons a ed ha o many labo ma ke ou comes, he e ec o non-cogni i e
skills is compa able o, o e en g ea e han ha o cogni i e skills. Heckman’s esea ch
also showed ha non-cogni i e skills in luence wages bo h di ec ly, by imp o ing labo
p oduc i i y, and indi ec ly, by shaping schooling and wo k expe ience. In many ways,
hese indings con i med he ones p oposed by Bowles and Gin is (1976), who poin ed
ou ha he quali ies alued by employe s in wo ke s ma ch hose alued by eache s in
s uden s a school.
This body o esea ch sugges s ha cogni i e skills, non-cogni i e skills, schooling,
and occupa ion, oge he wi h socio-economic s a us, o m a complex in e play h ough
which social and economic inequali ies a e pe pe ua ed. Non-cogni i e skills, in pa icula ,
play a c ucial ole in de e mining bo h educa ional and occupa ional choices, he eby
a ec ing wages indi ec ly (Robe s e al.,2007). Fo example, indi iduals wi h highe le els
o ex a e sion may g a i a e owa d leade ship oles, while hose wi h high ag eeableness
may excel in eamwo k-o ien ed en i onmen s. Non-cogni i e ai s also in luence labo
ma ke ou comes h ough ec ui men e ec s, whe e job applican s wi h highe ex a e sion
and conscien iousness, and lowe neu o icism, a e o en pe cei ed mo e a o ably by
employe s, leading o be e job p ospec s.
In ecen yea s, non-cogni i e skills ha e also gained p ominence among Russian
economis s, wi h a la ge body o wo k assessing hei impac on a ious ou comes, such
as academic pe o mance (A anesian e al.,2022), highe educa ion choices (Rozhko a &
Roshchin,2021a,2021b), school- o-wo k ansi ion (A anesian e al.,2024;Zudina,2022),
job sa is ac ion and ype o employmen (Zudina,2023,2024), and e en heal h beha io s
(Roshchina e al.,2022a,2022b;Rozhko a e al.,2023;Rozhko a,2024). No ably, se e al
Economies 2025,13, 114 4 o 20
s udies ha e es ima ed he e ec o non-cogni i e skills on wages in he Russian labo
ma ke . Using da a om he Russian Longi udinal Moni o ing Su ey (RLMS), which
includes a se o ques ions o measu e he Big Fi e pe sonali y ai s, Rozhko a (2019) ound
ha non-cogni i e skills lead o highe wages, hough he e u ns o hese cha ac e is ics
a y by social g oup. In his espec , openness and emo ional s abili y we e iden i ied
as he ai s wi h he mos signi ican posi i e e ec s. The e ec o conscien iousness
became insigni ican a e accoun ing o labo condi ions, while ag eeableness was ound
o nega i ely impac he wages o women. These indings align wi h hose o Maksimo a
(2019), who also no ed ha openness has a s onge posi i e e ec on he labo p oduc i i y
o women, while a lack o emo ional s abili y (i.e., a p opensi y owa d neu o icism)
penalizes men mo e han women. Howe e , non-cogni i e skills we e ound o ha e li le
e ec in explaining he gende pay gap (Rozhko a e al.,2021).
While he in luence o non-cogni i e skills on wages is well documen ed in bo h he
in e na ional and Russian economic li e a u e, he po en ial he e ogenei y o his e ec
ac oss wage dis ibu ion emains unde explo ed. Speci ically, i is unclea whe he he
e u ns o non-cogni i e skills a e uni o m ac oss di e en pay le els o i hese e u ns a y
be ween low-, medium-, and high-paid wo ke s. Al hough exis ing esea ch on Russian
da a has no examined his aspec , se e al in e na ional s udies ha e employed quan ile
eg ession o add ess his ques ion, highligh ing ha e u ns o non-cogni i e skills may
indeed di e ac oss he wage spec um (Collischon,2017;Edin e al.,2022;E en & Ozbeklik,
2013;Lindq is & Ves man,2011).
This s udy builds on his line o inqui y by explo ing he labo ma ke e u ns o non-
cogni i e skills in Russia, wi h a pa icula emphasis on he you h en e ing labo ma ke .
The ocus on he young wo k o ce o e s insigh s in o he ole o non-cogni i e skills in
acili a ing ansi ions om school o en y-le el posi ions and hen o s able employmen .
The ele ance o his ocus is oo ed in he unique challenges he you h ace in he labo
ma ke , making i essen ial o unde s and which skills con ibu e o secu ing be e -quali y
jobs du ing he c i ical ea ly s ages o hei ca ee s. O e all, his s udy aims o con ibu e
o he b oade li e a u e on e u ns o non-cogni i e skills, speci ically wi hin he con ex
o Russia, while add essing he signi ican gap in unde s anding o how hese skills a ec
wage ou comes a di e en poin s in wage dis ibu ion and o di e en popula ion coho s.
Gi en his backg ound, he s udy is guided by he ollowing o e a ching esea ch
ques ion: How do non-cogni i e skills a ec wage among young wo ke s, and do hese
e ec s a y ac oss di e en wage le els and demog aphic subg oups? Speci ically, do low-,
medium-, and high-paid young wo ke s bene i equally om non-cogni i e skills?
To add ess his, he s udy explo es se e al supplemen a y esea ch ques ions, aiming
o unco e he nuanced ole o non-cogni i e skills in wage de e mina ion wi hin he
b oade con ex o li e cou se ansi ions and labo ma ke dynamics:
1.
Do non-cogni i e skills con ibu e o wage ou comes independen ly o educa ional
a ainmen ? While i is well es ablished ha pe sonali y ai s in luence bo h educa-
ional and occupa ional choices, his ques ion seeks o disen angle he di ec e ec s o
non-cogni i e skills om hose media ed by educa ion, assessing hei independen
con ibu ions o wage;
2.
A e he e u ns o non-cogni i e skills highe o indi iduals wi h g ea e educa ional
a ainmen ? This ques ion examines whe he wo ke s wi h a e ia y deg ee expe-
ience ampli ied bene i s om non-cogni i e skills compa ed o hose wi h lowe
educa ional quali ica ions;
3.
Do non-cogni i e skills p oduce di e en e ec s on wages while he gende lens is
adop ed? Gi en he pe sis en gende pay gap in many labo ma ke s, including
Economies 2025,13, 114 5 o 20
in Russia, his ques ion in es iga es whe he ce ain non-cogni i e skills mi iga e o
exace ba e gende -based wage inequali ies, pa icula ly among young wo ke s;
4.
Do he e ec s o non-cogni i e skills on wages a y ac oss di e en age g oups?
Recognizing ha he labo ma ke ewa ds non-cogni i e skills may shi as indi id-
uals p og ess h ough di e en ca ee s ages, and his ques ion adop s a li e cou se
pe spec i e o assess whe he he impac o non-cogni i e skills di e ges o younge ,
mid-ca ee , o olde wo ke s, as well as he wo king popula ion as a whole.
Like p io esea ch on he Russian labo ma ke , his wo k d aws on da a om he
RLMS collec ed in 2016 and 2019. Howe e , i goes beyond p e ious s udies by applying
quan ile mixed models o in es iga e he he e ogenei y in he e u ns o non-cogni i e
skills ac oss di e en wage le els. This app oach allows he s udy o accoun o epea ed
measu es in he longi udinal da a, he eby add essing unobse ed he e ogenei y a he
indi idual le el. Fu he mo e, in o de o add ess he sample bias a ising due o he
sel -selec ion in o employmen , he analysis inco po a es in e se p obabili y weigh s.
2. Da a and Me hodology
The s udy u ilizes he da a o he 26 h and 28 h wa es o he RLMS, which we e
collec ed in 2016 and 2019, espec i ely, and included he module on he Big Fi e pe sonali y
ai s. F om he indi iduals who esponded o he adul ques ionnai e, he s udy zooms
in a he age ange o 16 o 29 yea s old, wi h he lowe bound e e ing o he minimum
wo king age (wi hou any es ic ions) and he uppe one e e ing o he age when he
school- o-wo k ansi ion ends. F om his, he e a e 2673 eco ds epo ing being employed
and mon hly wages, ep esen ing 2170 unique indi iduals. The sample was ec ui ed om
38 egions ha a e no epo ed he e, bu ha a e con olled o in he u he es ima ion.
The sample summa y is p esen ed in Table 1.
Table 1. Sample Summa y.
Va iable 2016 N = 1534 12019 N = 1139 1
Sex
Female 745 (49%) 562 (49%)
Male 789 (51%) 577 (51%)
Age
Mean (SD) 25.64 (2.82) 25.49 (3.06)
A ea
Ru al 282 (18%) 222 (19%)
U ban-Type Se lemen 104 (6.8%) 79 (6.9%)
Ci y 420 (27%) 303 (27%)
Regional Cen e 728 (47%) 535 (47%)
Highes Le el o Educa ion
1. No school 154 (10%) 93 (8.2%)
2. Seconda y School 384 (25%) 255 (22%)
3. Seconda y Voca ional 462 (30%) 416 (37%)
4. Te ia y 534 (35%) 375 (33%)
Expe ience
Mean (SD) 4.65 (2.94) 4.42 (3.01)
Ma i al S a us
1. Single 574 (37%) 528 (46%)
2. Ma ied/Ci il pa ne ship 900 (59%) 575 (50%)
3. Di o ced/Sepa a ed/Widowed 60 (3.9%) 36 (3.2%)
1n (%); Sou ce: Au ho ’s calcula ions based on RLMS-HSE da a.
Economies 2025,13, 114 6 o 20
The log o hou ly wage was de i ed om he epo ed mon hly wage o indi iduals
om hei p ima y employmen and he numbe o wo king hou s pe week. This se es
as he dependen a iable o he s udy. The wages o he 26 h wa e we e adjus ed o he
wages o 2019 (p ima y yea o da a collec ion) based on he Consume P ice Index alues.
The inpu a iables e e o educa ion le el, expe ience, sex, a ea o esidence (u ban o
u al), and non-cogni i e skills in acco dance wi h he Big Fi e axonomy. The ques ions
o measu e he Big Fi e pe sonali y ai s included in he RLMS we e ini ially de eloped
o he Wo ld Bank-suppo ed Skills Towa ds Employabili y and P oduc i i y (STEP)
su ey p og am (Wo ld Bank,2014) and u he adop ed in di e en su ey p og ams
wo ldwide, including he RLMS. Russian e idence assu es ha he scales a e eliable, wi h
he C onbach Alpha coe icien s a ound 0.7. Howe e , he desc ip ion o he su ey ool
and psychome ic p ope ies behind he measu emen a e beyond he scope o he pape
and can be ound elsewhe e (Maksimo a,2019;Gimpelson e al.,2020). The dis ibu ion o
non-cogni i e skills based on he sample o he s udy is p esen ed in Figu e 1.
Figu e 1. Dis ibu ion o non-cogni i e skills, his og ams.
To es ima e he he e ogenei y in he e u ns o non-cogni i e skills, he s udy adop s a
quan ile linea mixed model, a eg ession echnique ha ex ends he adi ional quan ile
eg essions (Koenke ,2005;Koenke & Basse ,1978;Koenke & Hallock,2001) o mixed-
e ec s models (Ba es e al.,2015;Pinhei o & Ba es,2000) ha allow o accoun ing o
he epea ed measu es in he longi udinal da a h ough he indi idual andom e ms. In
his analysis, he chosen le els e e o he wages a he 10 h, 25 h, 50 h, 75 h, and 90 h
pe cen iles, aiming o cap u e he e ec o non-cogni i e skills on he p oduc i i y o low-,
medium-, and high-paid wo ke s ac oss he whole spec um o emune a ion.
Economies 2025,13, 114 7 o 20
Mul iple eg essions a e ca ied ou in he cu en s udy, wi h some a ia ions in
he model speci ica ion. The model whe e non-cogni i e skills en e he Mince ian wage
equa ion as addi ional inpu s is he ex ended model o he s udy. I complemen s he
baseline equa ion, which is he same excep o excluding educa ional a ainmen . Wi h
espec o ha , he es ima ed quan ile eg ession equa ion o he ex ended model can be
w i en as ollows:
log(Wage)τ=β0+ui+β1·Educa ion Le el +β2·Expe ience +β3·Expe ience2+β4·A ea +β5·Sex+
β6·Ma ial S a us +β7·Region −1+β7·Openness +β8·Conscien iousness +β9·Ex a e sion +β10·
Ag eeableness +β11 ·Emo ional S abili y +ϵτ
whe e
log(Wage)τ
is he p edic ed log o he hou ly wage o quan ile
τ
(
τ
= 0.10, 0.25,
0.50, 0.75, 0.90);
β0
is he in e cep ;
ui
is he andom in e cep speci ic o each indi idual
i
, cap u ing he indi idual-le el a ia ion;
β1
o
β10
e e o he es ima ed coe icien s
associa ed wi h he espec i e p edic o s; and ϵτis he e o e m o quan ile τ.
The models we e es ima ed o accoun o he sample bias a ising due o he non-
andom selec ion o esponden s o employmen . Wi h ega ds o his, in e se p obabili y
weigh s using p opensi y sco es we e calcula ed and inse ed in o he model. The p obabil-
i y o employmen was modeled as a unc ion o exogenous ac o s such as age, sex, le el
o educa ion, egion, and a ea o esidence. The models ca ied ou in his analysis we e
es ima ed using he lqmm (Ge aci,2014) package in R (R Co e Team,2024). The package was
de eloped o add ess he g owing need o apply quan ile es ima ion o he longi udinal
da a, le e aging he powe o mixed-e ec s models. In o he wo ds, lqmm ep esen s “a
lexible s a is ical ool o analyze da a om sampling designs such as mul ile el, spa ial,
panel o longi udinal, which induce some o m o clus e ing” (Ge aci,2014). In e se
p obabili y weigh s we e calcula ed using he Weigh I (G ei e ,2024) package, which was
de eloped o p o ide a one-s op collec ion o unc ions o gene a e he balancing weigh s
o longi udinal and obse a ional s udies.
3. Resul s
3.1. Which Skills Demons a e he Highes P oduc i i y?
The analysis in es iga es he he e ogeneous e u ns o non-cogni i e skills ac oss he
wage dis ibu ion, ocusing on he 10 h, 25 h, 50 h, 75 h, and 90 h pe cen iles. The models
a e es ima ed wi h in e se p obabili y weigh s o accoun o selec ion o employmen .
The esul s om hese models a e summa ized in Table 2. While he e ec o o he con ol
a iables is beyond he scope o his s udy, he eg ession esul s sugges ha in he coho
o he labo ma ke en an s, only openness p oduces posi i e and s a is ically signi ican
e ec on wages. Mo eo e , his e ec holds ac oss all le els o dis ibu ion, wi h he o e all
end showing ha he highe he pe cen ile o hou ly wage, he highe he e u n o
openness. As such, while i s e ec accoun s o 5.7% (p< 0.05) a he 10 h pe cen ile o
hou ly wage, i eaches 7.6% (p< 0.01) o he you h a he 90 h pe cen ile o hou ly wage
dis ibu ion. The model did no iden i y he e ec s o o he non-cogni i e skills. Howe e ,
in he nex s age, i is impo an o unde s and whe he he e ec s o non-cogni i e skills
change once he model is ex ended and includes educa ion le el.
Economies 2025,13, 114 8 o 20
Table 2. Resul s o quan ile mixed eg ession, e u ns o non-cogni i e skills, baseline model (wi hou
educa ion) wi h in e se p obabili y weigh s.
Va iable Q10 Q25 Q50 Q75 Q90
In e cep 3.914 (0.16) *** 4.371 (0.06) *** 4.647 (0.08) *** 4.744 (0.09) *** 5.058 (0.1) ***
Expe ience 0.089 (0.02) *** 0.087 (0.01) *** 0.065 (0.01) *** 0.081 (0.01) *** 0.024 (0.01) .
Expe ience Sqd. −0.005 (0) ** −0.006 (0) *** −0.004 (0) *** −0.004 (0) *** 0 (0)
A ea: Se lemen 0.088 (0.1) 0.005 (0.07) 0.041 (0.07) 0.122 (0.08) 0.144 (0.08) .
A ea: Ci y 0.257 (0.1) * 0.05 (0.07) 0.135 (0.06) * 0.236 (0.06) *** 0.289 (0.07) ***
A ea: Reg Cen e 0.258 (0.2) 0.068 (0.19) 0.19 (0.18) 0.26 (0.18) 0.303 (0.18)
Sex: Male 0.461 (0.07) *** 0.288 (0.03) *** 0.242 (0.03) *** 0.291 (0.03) *** 0.257 (0.04) ***
Family: Ma ied −0.062 (0.06) −0.051 (0.02) * −0.004 (0.02) −0.001 (0.03) 0.011 (0.03)
Family: Di o ced −0.055 (0.09) −0.022 (0.07) 0.023 (0.06) 0.025 (0.08) 0.077 (0.1)
Openness 0.057 (0.02) * 0.059 (0.02) ** 0.055 (0.02) ** 0.073 (0.02) *** 0.076 (0.02) **
Conscien iousness 0.018 (0.03) 0.016 (0.02) 0.01 (0.01) −0.012 (0.01) −0.021 (0.02)
Ex a e sion 0.006 (0.03) 0.001 (0.01) 0.01 (0.01) 0.016 (0.01) 0.007 (0.01)
Ag eeableness 0.036 (0.02) 0.009 (0.02) 0.012 (0.01) 0.002 (0.02) 0.005 (0.02)
Emo ional S abili y 0.028 (0.02) 0.02 (0.01) 0.009 (0.01) −0.005 (0.01) −0.021 (0.02)
Region Con olled Con olled Con olled Con olled Con olled
No. G oups 2170 2170 2170 2170 2170
No. Obs 2673 2673 2673 2673 2673
No e: p< 0.001 (***); p< 0.01 (**); p< 0.05 (*); and p< 0.1 (.). The e e ence ca ego ies o ca ego ical a iables a e
as ollows: ‘Female’ o Sex and ‘Village’ o A ea. Sou ce: Calcula ions o he au ho based on he RLMS da a o
2016 and 2019.
3.2. Non-Cogni i e Skills and Re u ns o Educa ion
The ex ended model inco po a es educa ional a ainmen in o he analysis, esul ing
in a Mince ian wage equa ion augmen ed wi h non-cogni i e skill measu emen s. The
inclusion o educa ion subs an ially al e s he es ima ed e ec s o non-cogni i e skills,
pa icula ly o openness and conscien iousness, while in oducing signi ican e ec s o
educa ion i sel . The esul s a e p esen ed in Table 3.
Table 3. Re u ns o non-cogni i e skills, esul s o ex ended quan ile mixed model (wi h educa ion)
and in e se p obabili y weigh s.
Va iable Q10 Q25 Q50 Q75 Q90
In e cep 3.88 (0.16) *** 4.327 (0.1) *** 4.534 (0.08) *** 4.607 (0.09) *** 4.718 (0.1) ***
Edu: Seconda y 0.15 (0.06) * 0.02 (0.04) 0.102 (0.04) ** 0.173 (0.04) *** 0.225 (0.04) ***
Edu: Voca ional 0.23 (0.06) *** 0.105 (0.04) * 0.191 (0.04) *** 0.246 (0.04) *** 0.322 (0.06) ***
Edu: Te ia y 0.314 (0.06) *** 0.265 (0.04) *** 0.353 (0.04) *** 0.41 (0.04) *** 0.468 (0.06) ***
Expe ience 0.044 (0.01) ** 0.04 (0.02) * 0.053 (0.01) *** 0.064 (0.01) *** 0.026 (0.01) .
Expe ience Sqd. −0.002 (0) −0.002 (0) −0.003 (0) ** −0.003 (0) ** 0 (0)
Sex: Male 0.427 (0.05) *** 0.332 (0.03) *** 0.292 (0.02) *** 0.308 (0.03) *** 0.351 (0.04) ***
A ea: Se lemen 0.113 (0.08) 0.013 (0.06) 0.023 (0.05) 0.074 (0.06) 0.114 (0.08)
A ea: Ci y 0.18 (0.11) . 0.018 (0.07) 0.125 (0.06) * 0.155 (0.06) * 0.241 (0.06) ***
A ea: Reg Cen e 0.175 (0.24) 0.089 (0.22) 0.171 (0.22) 0.164 (0.21) 0.249 (0.22)
Openness 0.036 (0.02) . 0.034 (0.02) . 0.029 (0.01) . 0.043 (0.02) * 0.042 (0.02) .
Conscien iousness 0.042 (0.02) . 0.013 (0.01) 0.008 (0.01) −0.008 (0.01) −0.005 (0.02)
Ex a e sion 0.024 (0.03) 0.007 (0.02) 0.017 (0.01) 0.025 (0.01). 0.009 (0.02)
Ag eeableness 0.035 (0.03) 0.011 (0.01) 0.005 (0.01) 0.008 (0.01) −0.02 (0.01)
Emo ional S abili y 0.015 (0.02) 0.019 (0.01) 0.002 (0.01) −0.007 (0.01) −0.023 (0.02)
Region Con olled Con olled Con olled Con olled Con olled
No. G oups 2170 2170 2170 2170 2170
No. Obs 2673 2673 2673 2673 2673
No e: p< 0.001 (***); p< 0.01 (**); p< 0.05 (*); and p< 0.1 (.). The e e ence ca ego ies o ca ego ical a iables
a e as ollows: ‘Female’ o Sex, ‘Below Seconda y’ o Educa ional A ainmen , and ‘Village’ o A ea. Sou ce:
Calcula ions o he au ho based on he RLMS da a o 2016 and 2019.
Economies 2025,13, 114 15 o 20
No ably, openness eme ges as he mos p oduc i e non-cogni i e skill. Wi h a big gap,
i is ollowed by emo ional s abili y and conscien iousness. While he magni ude o each
ai ’s e ec a ies, hei in luence emains consis en ac oss wage le els, unde sco ing he
b oad applicabili y o hese ai s in enhancing p oduc i i y ac oss he wage spec um.
Though he s onges e ec o openness is in line wi h wo p e ious s udies on e u ns
o non-cogni i e skills in he Russian labo ma ke (Maksimo a,2019;Rozhko a,2019),
mixed e idence exis s in he in e na ional body o wo k, wi h s udies poin ing o bo h
nega i e (Muelle & Plug,2006) and posi i e (Seibe & K aime ,2001) ela ionships.
In con as , ex a e sion does no signi ican ly bene i lowe -paid wo ke s bu has a
meaning ul e ec o hose in middle- and high-wage b acke s, indica ing ha in e pe sonal
skills may yield a highe e u n in oles associa ed wi h g ea e esponsibili y o complexi y.
In ha espec , mul iple models we e used o es ima e e u ns o non-cogni i e skills
wi h and wi hou educa ional a ainmen . C ucially, e en when educa ional a ainmen
is inco po a ed in o he model, non-cogni i e skills con inue o show s a is ically signi ican
e u ns. This inding aligns wi h much o he economic li e a u e ha emphasizes he obus ness
o non-cogni i e skills in explaining wage di e en ials. The p esen esul s, he e o e, a i m
ha non-cogni i e skills con ibu e uniquely o ea nings, beyond he gains a ibu ed o o mal
educa ion. This esul aligns wi h e idence ha adop ed a di e en app oach: assessing e u ns
o educa ion i s , and hen including non-cogni i e skills in he model, which esul ed in a
subs an ial d op in he educa ional coe icien s (Bowles e al.,2001).
The ac ha non-cogni i e skills almos do no p oduce signi ican e u ns amongs
he you h wi hou p o essional quali ica ions (i.e., seconda y school comple ed o e en
below) highligh s he ala ming posi ion o he you h wi h basic educa ion in he la-
bo ma ke ; hei in eg a ion in o he wo ld o wo k is no media ed by non-cogni i e
skills. The same applies o he you h wi h a e ia y deg ee, unde sco ing he e y lim-
i ed ole o pe sonal cha ac e is ics in he success o labo ma ke en an s wi h a highe
le el o educa ion.
An in iguing disco e y o his s udy e e s o he heigh ened p oduc i i y o non-
cogni i e skills among indi iduals wi hou ad anced educa ional quali ica ions (i.e., sec-
onda y oca ional educa ion). While ini ially unexpec ed, his esul is heo e ically consis-
en . Fi s , highe educa ion is o en associa ed wi h he de elopmen o cogni i e skills
ha can independen ly boos p oduc i i y, po en ially educing eliance on non-cogni i e
ai s. Con e sely, o hose wi h lowe academic quali ica ions, non-cogni i e skills may
compensa e o ewe echnical o cogni i e compe ences, hus playing a mo e cen al
ole in p oduc i i y. Addi ionally, non-cogni i e ai s may hold pa icula alue in posi-
ions o asks ha equi e pe sonali y and beha io al compe encies, a he han ad anced
academic quali ica ions. Fo example, in oles whe e so skills such as adap abili y and
in e pe sonal acumen d i e job pe o mance, less-educa ed wo ke s may ely on hese
ai s o succeed and s and ou . Finally, labo ma ke demands may also play a ole. In
ec ui ing o posi ions ha do no equi e ad anced deg ees, employe s migh p io i ize
candida es’ non-cogni i e skills, assessing pe sonali y ai s such as esilience, wo k e hic,
and adap abili y as p oxies o po en ial and eliabili y.
To u he unde s and gende -based wage di e en ials, models wi h in e ac ion be-
ween non-cogni i e skills and sex we e es ima ed o male and emale wo ke s. The
analysis e eals ha ag eeableness is he only ai penalizing e u ns o young men in
compa ison o young women, albei wi h a modes ma gin. This inding sugges s ha ,
while non-cogni i e skill de elopmen may suppo wage gains o women, i s po en ial
o closing he gende wage gap emains limi ed, a conclusion in line wi h he exis ing
na a i e in Russia and beyond ha di e ences in non-cogni i e skills a e no he main
ac o in gende wage di e en ials (No dman e al.,2015;Rozhko a e al.,2021;Togna a
Economies 2025,13, 114 16 o 20
e al.,2018). Finally, he di e en na u e o ewa ds o non-cogni i e skills o women and
men iden i ied in ag eeableness highligh s he ole o gende no ms in he labo ma ke
(Glewwe e al.,2022).
Finally, his s udy explo es he ole o non-cogni i e skills in p omo ing sus ainable
employmen o young people. The analysis inds ha he e u ns o non-cogni i e skills
among indi iduals aged 18–29 di e ma kedly om hose obse ed in he b oade wo king
popula ion. This di e gence likely e lec s he unique challenges ha young people ace as
hey na iga e en y in o he labo ma ke and s i e o s able employmen . Mo e b oadly,
his inding sugges s ha he p oduc i i y o non-cogni i e skills e ol es o e wo ke s’
li espans, adap ing as indi iduals ma u e and na iga e di e en li e ansi ions. This li e
cou se pe spec i e on non-cogni i e skills is new wi h espec o labo ma ke ou comes.
While a subs an ial body o wo k exis s wi h espec o he e ec s o non-cogni i e skills
h ough he li e cou se lens on heal h beha io s (Ca e e al.,2019;Chi eji,2010), educa ion
(R. Elkins & Schu e ,2020;Hsin & Xie,2017), and e en in e -gene a ional mobili y (K öge
e al.,2024), his pe spec i e can also enhance labo ma ke s udies.
5. Resea ch Limi a ions
This s udy p esen s se e al limi a ions. Fi s , a c ucial sho coming e e s o he
absence o a measu e o cogni i e skills, which would enable a mo e nuanced anal-
ysis o unobse ed abili ies. Ideally, li e acy and nume acy es sco es would be in-
cluded as c i ical p edic o s alongside non-cogni i e skills o cap u e cogni i e ac o s
di ec ly. In o he wo ds, p oxies o in elligence could se e as a aluable supplemen o
enhance he model’s obus ness and he unde s anding o he ole ha skills p oduce o
wage di e en ials.
A second limi a ion lies in he exclusion o occupa ional and o ganiza ional cha ac-
e is ics. While including hese ac o s could p o ide u he insigh s in o how he wo k
en i onmen in luences he e ec s o non-cogni i e skills, doing so poses some con o e sy.
The li e a u e sugges s ha non-cogni i e skills, which s em om pe sonali y ai s, in lu-
ence how indi iduals a e so ed in o occupa ions. This so ing occu s h ough pe sonal
p e e ences o ce ain p o essions (o en guiding educa ion choices oo) and pe cei ed
p oduc i i y ac o s unique o speci ic occupa ions (File ,1986). The decomposi ion o pay
gaps based on pe sonali y ai s e eals ha pay dispa i ies a o ing ce ain pe sonali y
ypes can be a ibu ed o pe sonali y-d i en di e ences in occupa ional pa hs (Nandi &
Nicole i,2014). In his espec , he po en ial inclusion o occupa ion in he model migh
subs an ially bias he es ima ion o he e u ns o non-cogni i e skills, as occupa ion would
also be a majo channel o wage di e en ials.
The hi d limi a ion is o do wi h he da a. The RLMS only collec ed he non-cogni i e
skills da a in he 2016/2017 and 2019/2020 wa es, wi h he da a om he su ey module
being publicly a ailable 4 yea s a e i was collec ed. The e o e, he s udy o non-cogni i e
skills and labo ma ke ou comes in he Russian con ex does no conside o he socio-
economic and demog aphic ansi ions ha happened in he coun y a e 2020. Fu he -
mo e, hese ex e nal ins i u ional ac o s a e beyond he scope o he analysis, wi h he
ocus being on he indi idual cha ac e is ics obse ed om he labo supply side.
Fu he mo e, he change in non-cogni i e skills o e ime is ano he conside a ion.
While p e ious esea ch in economics con i ms ha hese cha ac e is ics become s able in
adul hood (Cobb-Cla k & Schu e ,2012,2013;R. K. Elkins e al.,2017), analogous s udies
on he example o he Russian con ex ha e no been published ye , bu a e being ca ied
ou . Howe e , p e ious e idence in economic esea ch assu e ha in a 4-yea in e al, he
changes in non-cogni i e cha ac e is ics a e p ima ily no signi ican , he e o e he andom
in e cep e m o indi idual (ID) adop ed in he cu en s udy should be a su icien
Economies 2025,13, 114 17 o 20
solu ion o accoun o he non-independence o obse a ions, due o he longi udinal
na u e o he da a.
Finally, hough he s udy le e ages he mixed-model design and inco po a es indi-
idual andom in e cep s, i does no ully add ess he endogenei y due o he omi ed
a iable bias ha could po en ially impac bo h non-cogni i e skills and wages. In his
espec , he indings o he s udy should no be in e p e ed in a causal manne .
6. Conclusions
The indings o his s udy unde sco e he he e ogeneous impac o non-cogni i e
skills on wages, e ealing a nuanced and non-linea ela ionship be ween hese ai s
and labo ma ke ewa ds. In his espec , he esul s p o ide a esponse o he majo
esea ch ques ion guiding his s udy, namely, whe he he e u ns o non-cogni i e skills
a e he e ogeneous ac oss he di e en pay le els. This he e ogenei y sugges s ha he
e u ns o non-cogni i e skills a y signi ican ly ac oss he wage dis ibu ion, an insigh ha
has impo an implica ions o u u e esea ch. In o he wo ds, low-, medium-, and high-
paid young wo ke s bene i di e en ly om hei non-cogni i e skills, wi h openness being
he mos in luen ial skill h oughou . Subsequen s udies could explo e non-linea i y in i s
e ec o non-cogni i e skills on o he socio-economic ou comes and beha io s, o e ing a
b oade pe spec i e on how hese skills shape indi idual economic and social ajec o ies.
Ano he c i ical inding is ha non-cogni i e skills yield signi ican wage e u ns, e en
a e con olling o educa ional a ainmen , hus add essing ano he esea ch ques ion
o he s udy. This esul highligh s he unique and obus con ibu ion o non-cogni i e
ai s o wages beyond wha o mal educa ion alone can explain. This inding ein o ces
he a gumen ha hough non-cogni i e skills a ec ea nings indi ec ly, h ough educa-
ional choices, hey a e essen ial in shaping indi idual p oduc i i y and economic success,
i espec i e o one’s o mal schooling.
Gi en ha he hi d ques ion guiding his inqui y is ela ed o he e ec o non-
cogni i e skills on wages wi h espec o di e en le els o educa ion, he s udy e eals
ha non-cogni i e skills a e pa icula ly ad an ageous o indi iduals wi h lowe le els
o educa ional a ainmen , sugges ing ha hese ai s play a compensa o y ole o hose
wi hou ad anced academic quali ica ions. In oles ha do no necessi a e a high le el o
o mal educa ion, non-cogni i e skills appea o be ins umen al in achie ing labo ma ke
success. This insigh ca ies p ac ical implica ions o wo k o ce de elopmen policies,
especially hose aimed a suppo ing indi iduals wi h limi ed o mal educa ion in accessing
s able, ewa ding employmen . On he o he hand, u u e esea ch in his domain could
po en ially add ess he e ec o highe educa ion on he de elopmen and accele a ion o
non-cogni i e skills.
Finally, his s udy shows ha he e u ns o non-cogni i e skills a e dis inc ly di e en
o younge wo ke s compa ed o he gene al wo king popula ion, unde sco ing how
he p oduc i i y o hese ai s e ol es ac oss one’s li espan. As indi iduals ma u e and
na iga e a ious li e ansi ions, he ole o non-cogni i e skills in con ibu ing o economic
ou comes changes, e lec ing he shi ing demands and p io i ies a di e en ca ee s ages.
Fo young adul s, non-cogni i e skills hold pa icula impo ance in acili a ing en y in o
he wo k o ce and secu ing pa hways o sus ainable employmen .
Funding: This esea ch ecei ed no ex e nal unding.
Da a A ailabili y S a emen : The da a used in he s udy a e publicly a ailable on he websi e o he
Russian Longi udinal Moni o ing Su ey: h ps://www.hse. u/en/ lms/ (accessed on 1 Decembe 2024).
Con lic s o In e es : The au ho decla es no con lic o in e es .
Economies 2025,13, 114 18 o 20
Re e ences
A anesian, G., Bo o skaya, M., Masych, M., Dikaya, L., Ryzho a, V., & Ego o a, V. (2024). How a a e NEET you h alling behind in
hei non-cogni i e skills? An econome ic analysis o dispa i ies. Economies,12(1), 25. [C ossRe ]
A anesian, G., Bo o skaya, M., Ryzho a, V., Ki ik, V., Ego o a, V., & Be mous, A. (2022). Can we imp o e lea ning ou comes o
schoolchild en om he poo es amilies by in es ing in o hei non-cogni i e skills? Causal analysis using p opensi y sco e
ma ching. Vop osy Ob azo aniya/Educa ional S udies Moscow,1, 13–53. [C ossRe ]
Ba es, D., Maechle , M., Bolke , B., & Walke , S. (2015). Fi ing linea mixed-e ec s models using lme4. Jou nal o S a is ical So wa e,67,
1–48. [C ossRe ]
Bishop, J. H. (1989a). Achie emen , es sco es and ela i e wages (89-22). Cen e o Ad anced Human Resou ce S udies, New Yo k S a e
School o Indus ial Labo Rela ions, Co nell Uni e si y.
Bishop, J. H. (1989b). Is he es sco e decline esponsible o he p oduc i i y g ow h decline? Ame ican Economic Re iew,79(1), 178–197.
Blackbu n, M. L., & Neuma k, D. (1993). Omi ed-abili y bias and he inc ease in he e u n o schooling. Jou nal o Labo Economics,
11(3), 521–544. [C ossRe ]
Bo ghans, L., Duckwo h, A. L., Heckman, J. J., & e Weel, B. (2008). The economics and psychology o pe sonali y ai s. Jou nal o
Human Resou ces,43(4), 972–1059. [C ossRe ]
Bowles, S., & Gin is, H. (1976). Schooling in capi alis Ame ica: Educa ional e o m and he con adic ions o economic li e. Basic Books.
Bowles, S., & Gin is, H. (2002). The inhe i ance o inequali y. Jou nal o Economic Pe spec i es,16(3), 3–30. [C ossRe ]
Bowles, S., Gin is, H., & Osbo ne, M. (2001). The de e minan s o ea nings: A beha io al app oach. Jou nal o Economic Li e a u e,39(4),
1137–1176. [C ossRe ]
Ca e , J. L., Richa ds, M., Ho op , M., & Ha ch, S. L. (2019). The oles o non-cogni i e and cogni i e skills in he li e cou se de elopmen
o adul heal h inequali ies. Social Science & Medicine,232, 190–198. [C ossRe ]
Chambe lain, G., & G iliches, Z. (1975). Unobse ables wi h a a iance-componen s s uc u e: Abili y, schooling, and he economic
success o b o he s. In e na ional Economic Re iew,16(2), 422. [C ossRe ]
Chi eji, N. (2010). Time p e e ence, noncogni i e skills and well being ac oss he li e cou se: Do noncogni i e skills encou age heal hy
beha io ? Ame ican Economic Re iew,100(2), 200–204. [C ossRe ] [PubMed]
Cobb-Cla k, D. A., & Schu e , S. (2012). The s abili y o Big-Fi e pe sonali y ai s. Economics Le e s,115(1), 11–15. [C ossRe ]
Cobb-Cla k, D. A., & Schu e , S. (2013). Two economis s’ musings on he s abili y o locus o con ol. The Economic Jou nal,123(570),
F358–F400. [C ossRe ]
Collischon, M. (2017). The e u ns o pe sonali y ai s ac oss he wage dis ibu ion. SOEP Pape No. 921. SSRN Elec onic Jou nal.
A ailable online: h ps://ss n.com/abs ac =3043648 (accessed on 1 Decembe 2024). A ailable a accessed on.
Collischon, M. (2019). The e u ns o pe sonali y ai s ac oss he wage dis ibu ion. Labou ,34(1), 48–79. [C ossRe ]
Edin, P.-A., F ed iksson, P., Nybom, M., & Öcke , B. (2022). The ising e u n o noncogni i e skill. Ame ican Economic Jou nal: Applied
Economics,14(2), 78–100. [C ossRe ]
Elkins, R., & Schu e , S. (2020). Explo ing he ole o pa en al engagemen in non-cogni i e skill de elopmen o e he li ecou se.
Jou nal o Popula ion Economics,33(3), 957–1004. [C ossRe ]
Elkins, R. K., Kassenboehme , S. C., & Schu e , S. (2017). The s abili y o pe sonali y ai s in adolescence and young adul hood. Jou nal
o Economic Psychology,60, 37–52. [C ossRe ]
E en, O., & Ozbeklik, S. (2013). The e ec o noncogni i e abili y on he ea nings o young men: A dis ibu ional analysis wi h
measu emen e o co ec ion. Labou Economics,24, 293–304. [C ossRe ]
File , R. K. (1986). The ole o pe sonali y and as es in de e mining occupa ional s uc u e. ILR Re iew,39(3), 412–424. [C ossRe ]
Ge aci, M. (2014). Linea quan ile mixed models: The lqmm package o laplace quan ile eg ession. Jou nal o S a is ical So wa e,57,
1–29. [C ossRe ]
Gimpelson, V. E., Zudina, A. A., & Kapeliushniko , R. I. (2020). Non-cogni i e componen so human capi al: E idence om ussian
da a. Vop osy Ekonomiki,11, 5–31. [C ossRe ]
Glewwe, P., Song, Y., & Zou, X. (2022). Labo ma ke ou comes, cogni i e skills, and noncogni i e skills in u al China. Jou nal o
Economic Beha io & O ganiza ion,193, 294–311. [C ossRe ]
G een, F. (2013). Wha is skill? (pp. 9–26) Ox o d Uni e si y P ess. [C ossRe ]
G ei e , N. (2024). Weigh I : Weigh ing o co a ia e balance in obse a ional s udies. A ailable online: h ps://CRAN.R-p ojec .o g/
package=Weigh I (accessed on 20 Decembe 2024).
G iliches, Z. (1977). Es ima ing he e u ns o schooling: Some econome ic p oblems. Econome ica,45(1), 1. [C ossRe ]
G ogge , J., & Eide, E. (1995). Changes in college skills and he ise in he college wage p emium. The Jou nal o Human Resou ces,30(2),
280. [C ossRe ]
Gu man, L. M., & Schoon, I. (2016). A syn hesis o causal e idence linking non-cogni i e skills o la e ou comes o child en and adolescen s
(pp. 171–198). Sense Publishe s. [C ossRe ]
Economies 2025,13, 114 19 o 20
Hanushek, E. A., & Kimko, D. D. (2000). Schooling, labo - o ce quali y, and he g ow h o na ions. Ame ican Economic Re iew,90(5),
1184–1208. [C ossRe ]
Hanushek, E. A., & Woessmann, L. (2008). The ole o cogni i e skills in economic de elopmen . Jou nal o Economic Li e a u e,46(3),
607–668. [C ossRe ]
Has ie, T. J., & Tibshi ani, R. J. (2017). Gene alized addi i e models (pp. 136–173). Rou ledge. [C ossRe ]
Heckman, J., & Vy lacil, E. (2001). Iden i ying he ole o cogni i e abili y in explaining he le el o and change in he e u n o schooling.
Re iew o Economics and S a is ics,83(1), 1–12. [C ossRe ]
Heckman, J. J., S ix ud, J., & U zua, S. (2006). The e ec s o cogni i e and noncogni i e abili ies on labo ma ke ou comes and social
beha io . Jou nal o Labo Economics,24(3), 411–482. [C ossRe ]
He ns ein, R. J., & Mu ay, C. A. (1994). The bell cu e: In elligence and class s uc u e in ame ican li e. F ee P ess.
Hsin, A., & Xie, Y. (2017). Li e-cou se changes in he media ion o cogni i e and non-cogni i e skills o pa en al e ec s on child en’s
academic achie emen . Social Science Resea ch,63, 150–165. [C ossRe ]
Humph ies, J. E., & Kosse, F. (2017). On he in e p e a ion o non-cogni i e skills. Wha is being measu ed and why i ma e s. Jou nal
o Economic Beha io & O ganiza ion,136, 174–185. [C ossRe ]
Jencks, C., Ba le , S., Co co an, M., C ouse, J., Eagles ield, D., Jackson, G., McClelland, K., Muese , P., Olneck, M., Schwa z, J., Wa d,
S., & Williams, J. (1979). Who ge s ahead? The de e minan s o economic success in Ame ica (p. 397). Basic Books.
Koenke , R. (2005). Quan ile eg ession. Camb idge Uni e si y P ess. [C ossRe ]
Koenke , R., & Basse , G. (1978). Reg ession quan iles. Econome ica,46(1), 33. [C ossRe ]
Koenke , R., & Hallock, K. F. (2001). Quan ile eg ession. Jou nal o Economic Pe spec i es,15(4), 143–156. [C ossRe ]
K öge , L. K., Palacios-Abad, A., & Radl, J. (2024). Non-cogni i e skills and in e gene a ional inequali y: Re iewing he powe o pe sonali y
(pp. 400–414). Edwa d Elga Publishing. [C ossRe ]
Lindq is , E., & Ves man, R. (2011). The labo ma ke e u ns o cogni i e and noncogni i e abili y: E idence om he swedish
enlis men . Ame ican Economic Jou nal: Applied Economics,3(1), 101–128. [C ossRe ]
Maksimo a, M. (2019). The e u n o non-cogni i e skills on he ussian labo ma ke . Applied Econome ics,53, 55–72.
Mince , J. A. (1974). Schooling, expe ience, and ea nings. Na ional Bu eau o Economic Resea ch—NBER. A ailable online: h ps://
www.nbe .o g/books-and-chap e s/schooling-expe ience-and-ea nings (accessed on 1 Decembe 2024).
Muelle , G., & Plug, E. (2006). Es ima ing he e ec o pe sonali y on male and emale ea nings. ILR Re iew,60(1), 3–22. [C ossRe ]
Mu nane, R. J., Wille , J. B., B aa z, M. J., & Duhaldebo de, Y. (2001). Do di e en dimensions o male high school s uden s’ skills
p edic labo ma ke success a decade la e ? E idence om he NLSY. Economics o Educa ion Re iew,20(4), 311–320. [C ossRe ]
Nandi, A., & Nicole i, C. (2014). Explaining pe sonali y pay gaps in he UK. Applied Economics,46(26), 3131–3150. [C ossRe ]
Neal, D. A., & Johnson, W. R. (1996). The ole o p ema ke ac o s in black-whi e wage di e ences. Jou nal o Poli ical Economy,104(5),
869–895. [C ossRe ]
No dman, C. J., Sa , L. R., & Sha ma, S. (2015). Cogni i e, non-cogni i e skills and gende wage gaps: E idence om linked
employe -employee da a in bangladesh. IZA Discussion Paspe No. 9132. SSRN Elec onic Jou nal. A ailable online: h ps://
ss n.com/abs ac =2655081 (accessed on 1 Decembe 2024).
O’Neill, J. (1990). The ole o human capi al in ea nings di e ences be ween black and whi e men. Jou nal o Economic Pe spec i es,4(4),
25–45. [C ossRe ]
Pinhei o, J. C., & Ba es, D. M. (2000). Mixed-e ec s models in sand s-PLUS. Sp inge . [C ossRe ]
R Co e Team. (2024). R: A language and en i onmen o s a is ical compu ing. R Founda ion o S a is ical Compu ing. A ailable online:
h ps://www.R-p ojec .o g/ (accessed on 1 Decembe 2024).
Robe s, B. W., Kuncel, N. R., Shine , R., Caspi, A., & Goldbe g, L. R. (2007). The powe o pe sonali y: The compa a i e alidi y o
pe sonali y ai s, socioeconomic s a us, and cogni i e abili y o p edic ing impo an li e ou comes. Pe spec i es on Psychological
Science,2(4), 313–345. [C ossRe ]
Roshchina, Y., Roshchin, S., & Rozhko a, K. (2022a). De e minan s o COVID-19 accine hesi ancy and esis ance in Russia. Vaccine,
40(39), 5739–5747. [C ossRe ] [PubMed]
Roshchina, Y., Roshchin, S., & Rozhko a, K. (2022b). Be ween nudges and manda es: The d i e s o COVID-19 accina ion in en ions
and subsequen up ake in Russia. Vaccine,41(34), 5053–5062. [C ossRe ] [PubMed]
Rozhko a, K. (2019). The e u n o noncogni i e cha ac e is ics in he Russian labo ma ke . Vop osy Ekonomiki,11, 81–107. [C ossRe ]
Rozhko a, K. (2024). Does pe sonali y p edic heal h? Non-cogni i e skills, heal h beha iou s, and longe i y in Russia. Popula ion and
Economics,8(1), 132–155. [C ossRe ]
Rozhko a, K., & Roshchin, S. (2021a). Non-cogni i e cha ac e is ics and highe educa ion choices. Vop osy Ob azo aniya/Educa ional
S udies Moscow,4, 35–73. [C ossRe ]
Rozhko a, K., & Roshchin, S. (2021b). The impac o non-cogni i e cha ac e is ics on he highe educa ion choice-making: An economis
pe spec i e. Vop osy Ob azo aniya/Educa ional S udies Moscow,3, 138–167. [C ossRe ]
Economies 2025,13, 114 20 o 20
Rozhko a, K., Roshchin, S., & Roshchina, Y. (2023). Do non-cogni i e skills ma e o alcohol consump ion? E idence om Russia.
Jou nal o Compa a i e Economics,51(2), 564–576. [C ossRe ]
Rozhko a, K., Yemelina, N., & Roshchin, S. (2021). Can non-cogni i e skills explain he gende wage gap in Russia? An uncondi ional
quan ile eg ession app oach. SSRN Elec onic Jou nal. [C ossRe ]
Seibe , S. E., & K aime , M. L. (2001). The Fi e-Fac o Model o Pe sonali y and Ca ee Success. Jou nal o Voca ional Beha io ,58(1),
1–21. [C ossRe ]
Togna a, N. R., Vale io, A., & Sanchez Pue a, M. L. (2018). Do cogni i e and noncogni i e skills explain he gende wage gap in middle-
income coun ies: An analysis using STEP da a (Policy Resea ch Wo king Pape WPS 7878). Wo ld Bank G oup. A ailable
online: h p://documen s.wo ldbank.o g/cu a ed/en/479671478193527141/Do-cogni i e-and-noncogni i e-skills-explain- he
-gende -wage-gap-in-middle-income-coun ies-an-analysis-using-STEP-da a (accessed on 1 Decembe 2024).
Wo ld Bank. (2014). STEP skills measu emen su ey: Inno a i e ools o assessing skills. Social p o ec ion and labo discussion pape N
1421. Wo ld Bank G oup. A ailable online: h p://documen s.wo ldbank.o g/cu a ed/en/516741468178736065/STEP-skills
-measu emen -su eys-inno a i e- ools- o -assessing-skills (accessed on 1 Decembe 2024).
Wood, S. N. (2006). Gene alized addi i e models. Chapman; Hall/CRC. [C ossRe ]
Wood, S. N. (2011). Fas s able es ic ed maximum likelihood and ma ginal likelihood es ima ion o semipa ame ic gene alized linea
models. Jou nal o he Royal S a is ical Socie y Se ies B: S a is ical Me hodology,73, 3–36. [C ossRe ]
Zudina, A. (2022). Non-cogni i e skills o NEET you h in Russia. Vop osy Ob azo aniya/Educa ional S udies Moscow,4, 154–183.
[C ossRe ]
Zudina, A. (2023). The human capi al o Russian in o mal wo ke s: A non-cogni i e skills pe spec i e. Uni e se o Russia,32(1), 37–60.
[C ossRe ]
Zudina, A. (2024). Subjec i e e u ns o non-cogni i e skills in he Russian labo ma ke : The case o job sa is ac ion. The Moni o ing o
Public Opinion Economic & Social Changes,1(179), 3–25. [C ossRe ]
Disclaime /Publishe ’s No e: The s a emen s, opinions and da a con ained in all publica ions a e solely hose o he indi idual
au ho (s) and con ibu o (s) and no o MDPI and/o he edi o (s). MDPI and/o he edi o (s) disclaim esponsibili y o any inju y o
people o p ope y esul ing om any ideas, me hods, ins uc ions o p oduc s e e ed o in he con en .
