Empi ical Economics (2022) 63:725–791
h ps://doi.o g/10.1007/s00181-021-02152-x
G ow h, inequali y and po e y: a obus ela ionship?
Gus a o A. Ma e o1,2 ·Luis Se én3
Recei ed: 12 Oc obe 2020 / Accep ed: 30 Sep embe 2021 / Published online: 23 No embe 2021
© The Au ho (s) 2021
Abs ac
The consequences o po e y and inequali y o g ow h ha e long p eoccupied
academics and policy-make s. This pape e isi s he inequali y-g ow h and po e y-
g ow h links. Using a panel o 158 coun ies be ween 1960 and 2010, we ind ha
he co ela ion o g ow h wi h po e y is consis en ly nega i e: A 10 p.p. dec ease in
he headcoun po e y a e is associa ed wi h a subsequen inc ease in pe capi a GDP
be ween 0.5 and 1.2% pe yea . In con as , he co ela ion o g ow h wi h inequali y is
empi ically agile—i can be posi i e o nega i e, depending on he empi ical speci i-
ca ion and econome ic app oach employed. Howe e , he indi ec e ec o inequali y
on g ow h h ough i s co ela ion wi h po e y is obus ly nega i e. Close inspec ion
shows ha hese esul s a e d i en by he sample obse a ions ea u ing high po e y
a es.
Keywo ds G ow h ·Inequali y ·Po e y ·Indi ec impac s
JEL Classi ica ion O40 ·O11 ·O15 ·E25
1 In oduc ion
Wha is hee ec o po e yonagg ega eincomeg ow h?And hee ec o inequali y?
Academicsandpolicy-make sha elongbeenconce nedwi h heseques ions.Bu hey
ha e ypically been explo ed as sepa a e issues. Ye p ope ly answe ing hem equi es
BGus a o A. Ma e o
[email p o ec ed]
Luis Se én
[email p o ec ed]
1Depa amen o de Economía, Con abilidad y Finanzas, CEDESOG, Uni e sidad de La Laguna,
San C is óbal de La Laguna, Spain
2EQUALITAS, Mad id, Spain
3CEMFI, Mad id, Spain
123
726 G. A. Ma e o, L. Se én
aking hem up join ly, because po e y and inequali y a e in e ela ed ea u es o he
same income dis ibu ion (Bou guignon 2004).
This pape a emp s o ill ha gap by p o iding an empi ical explo a ion o he
g ow h e ec s o bo h po e y and inequali y and, in pa icula , o hei espec-
i e obus ness. The e ec s o po e y ha e been analyzed by nume ous heo e ical
pape s highligh ing a a ie y o mechanisms h ough which po e y may become sel -
pe pe ua ing. Bu empi ical wo k has been mo e limi ed and la gely inconclusi e.
Indeed, a basic implica ion o he heo e ical models o po e y aps—namely, ha
coun ies su e ing om highe le els o po e y should g ow less apidly han com-
pa able coun ies wi h lowe po e y—has been la gely o e looked. This is he key
hypo hesis pu sued in his pape . I can be iewed as a weak e sion o he po e y ap
hypo hesis, in ha o suppo i we do no need o ind e idence o mul iple equilib ia
o income s agna ion, bu jus empi ical p oo ha , o he hings equal, po e y ends
o hold back g ow h.
In con as , he e ec s o inequali y ha e a ac ed massi e empi ical li e a u e,
albei wi h sha ply con lic ing esul s. The p esen pape adds o exis ing wo k by
highligh ing a no el angle, namely he indi ec e ec o inequali y on g ow h acc uing
h ough he impac o inequali y on po e y: gi en he po e y line and he o e all
popula ion’s mean income, an inc ease in inequali y will ypically aise po e y, by
pushing mo e indi iduals below he po e y line.1I po e y a ec s g ow h, so will
inequali y h ough his indi ec channel—in addi ion o any di ec e ec s ha inequal-
i y migh exe on g ow h.
To assess he espec i e g ow h impac s o po e y and inequali y, we es ima e
a educed- o m g ow h equa ion wi h inequali y and po e y added sepa a ely and
join ly o an o he wise s anda d se o g ow h de e minan s (educa ional a ainmen ,
in es men p ices, go e nmen size, deg ee o openness, public in as uc u es, e c.).
Fo he es ima ion, we assemble a la ge panel da a se o non-o e lapping i e-yea
obse a ions comp ising 158 coun ies o e he pe iod 1960–2010. The sample is
hea ily unbalanced, and i s size exceeds by a ha ound in ea lie s udies o he
po e y-g ow h link.
Ou econome ic app oach is based on GMM es ima ion employing in e nal ins u-
men s (A ellano and Bo e 1995; Blundell and Bond 1998; Roodman 2009). In ou
se ing, he choice o his app oach is dic a ed by he sho ime dimension and la ge
c oss-sec ional dimension o ou panel da ase —which makes panel ime-se ies me h-
ods unsui able—and by he po en ial endogenei y o he eg esso s—which demands
an ins umen al a iable app oach. These issues a ec also much o he empi ical li e -
a u e on he links be ween po e y, inequali y and g ow h, which—like ou pape —has
o con end wi h he po en ial p oblem o wo-way causali y be ween he a iables a
he co e o he analysis.
In his con ex , GMM ep esen s a na u al me hodological choice, which we also
sha e wi h much o he ela ed empi ical li e a u e.2Mo eo e , his common empi ical
1The consequences o inequali y o po e y a e highligh ed o example by Bou guignon (2003,2004)o
Ra allion (2005). Ma e o and Se én (2018) p o ide nume ical simula ions illus a ing he magni ude o
he e ec o inequali y on po e y, o gi en a e age income.
2Empi ical analyses o he links be ween agg ega e g ow h, po e y and inequali y commonly use an
ins umen al a iable app oach. A ew pape s ea u e ex e nal ins umen s—e.g., B ueckne e al. (2015),
123
G ow h, inequali y and po e y: a obus ela ionship? 727
me hodologyalsomakesou pape mo eeasilycompa ablewi hexis ingwo k.Finally,
while ou use o GMM o g ow h empi ics is no no el, ou pape is among he
i s o examine igo ously, in a sys em GMM se ing, he po en ial p oblem o weak
ins umen s plaguing much o he empi ical g ow h li e a u e, as i s aised by K aay
(2015) in he con ex o he empi ical ela ionship be ween inequali y and g ow h.
Ou main inding is ha po e y has a obus nega i e and signi ican e ec on
g ow h. As o inequali y, we ind ha he sign and signi icance o i s di ec e ec on
g ow h a e agile. Howe e , i s indi ec e ec ( h ough po e y) is obus ly nega i e.
Fu he inspec ion e eals he p esence o nonlinea i ies, in ha hese esul s a e d i en
by he sample obse a ions ea u ing high po e y: when po e y is low, i s impac on
g ow h is no signi ican , and he indi ec e ec o inequali y on g ow h is he e o e
absen . We each a simila conclusion when we le he g ow h impac o po e y di e
be ween de eloped and de eloping coun ies: I is nega i e and signi ican o he
la e , bu no o he o me .
Ou esul s su i e a ba e y o obus ness checks, including he use o al e na i e
se s o ins umen s and speci ica ions in he GMM es ima ion, di e en po e y lines
and po e y measu es, al e na i e po e y da a, nonlinea and nonpa ame ic spec-
i ica ions, o he use o al e na i e se s o con ol a iables. We also ind ha ou
p e e ed GMM speci ica ion can add ess in a sa is ac o y manne he endogenei y,
unde -iden i ica ion and weak ins umen s p oblems o en encoun e ed in mac oeco-
nomic applica ions o dynamic panel models (Bazzi and Clemens 2013).
Ou pape is embedded in an ex ensi e li e a u e ( ecen ly su eyed by Ce a e al.
2021a) analyzing he mul idi ec ional links among g ow h, inequali y and po e y.
Th ee s ands a e especially ele an in ou con ex . They, espec i ely, ocus on he
impac o po e yong ow h, heimpac o inequali yong ow h,and hecon ibu iono
inequali y and income g ow h o po e y. We p o ide a b ie e iew o hese li e a u e
wo ks in he nex sec ion.
The es o he pape is s uc u ed as ollows. As jus no ed, Sec . 2is de o ed
o a selec i e summa y o he li e a u e on he g ow h-inequali y-po e y nexus. In
Sec . 3, we desc ibe he da a and we lay ou he empi ical s a egy o es o he
e ec s o po e y and inequali y on g ow h. In Sec . 4, we epo he main empi ical
esul s o ou baseline speci ica ion. Sec ion 5 epo s ex ensi e obus ness checks on
ou empi ical esul s. Sec ion 6analyzes how he links o po e y and inequali y wi h
g ow hmigh depend on he p e ailing deg eeso po e y and/o inequali yand gauges
he di ec and indi ec e ec s o inequali y on g ow h. Finally, Sec . 7concludes.
2 The g ow h-inequali y-po e y nexus: a e iew
The seminal wo k o Kuzne s (1955) is he s a ing poin o an ex ensi e li e a u e
analyzing he g ow h-inequali y-po e y nexus (see Bou guignon 2004, and he ecen
su eys by Ce a e al. 2021a,b). Ou pape ela es o se e al s ands o his li e a u e.
Foo no e 2 con inued
assessing he e ec o GDP g ow h on inequali y—bu GMM using in e nal ins umen s (gi en by sui ably
lagged and ans o med eg esso s) is much mo e commonly used: o example, by Pa idge (1997), Fo bes
(2000), Panizza (2002), o Be g e al. (2018), all o which a e conce ned wi h he opposi e di ec ion o
causali y, om inequali y o g ow h.
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728 G. A. Ma e o, L. Se én
Fi s , a long-s anding heo e ical li e a u e has s udied a a ie y o mechanisms
h ough which po e y may de e economic g ow h. I s a gumen s a e mos ly based
on he exis ence o po e y aps, i.e., mechanisms h ough which po e y p e en s a
signi ican sha eo hepopula ion omhelpingigni e heg ow hengine(Aza iadisand
S achu ski2005;Bowlese al.2006;Haide e al. 2018). Unde app op ia e condi ions,
hose mechanisms may lead o mul iple equilib ia and make he nega i e impac o
po e y on g ow h sel - ein o cing. In gene al, he mechanisms highligh ed in he
li e a u e ope a e by educing he incen i es and/o abili ies o he poo o unde ake
isky en ep eneu ial ac i i ies, and/o o accumula e physical and human capi al.
A p ominen mechanism in ol es ‘ h eshold e ec s’ (Aza iadis and D azen 1990),
esul ing, o example, om indi isibili ies o inc easing e u ns o scale.3Fo exam-
ple, i po e y is coupled wi h c edi cons ain s, he esul is ha below a ce ain le el
o income o weal h economic agen s may be oo poo o a o d he in es men s (in
human o physical capi al) o he echnologies necessa y o aise hei income (Galo
and Zei a 1993; Bane jee and Newman 1993). Malnu i ion p o ides ano he example.
In de eloping coun ies, po e y is associa ed wi h high a es o malnu i ion (Das-
gup a and Ray 1986), which impac s cogni i e abili ies and school absen eeism and
is ansmi ed o he child en’s capaci y o lea n. The esul ing educa ional inequali y
is also g ow h-de e ing (Galo and Moa 2004).
Ins i u ional a angemen s ha place economic oppo uni ies beyond he each o
he poo can likewise esul in educed income g ow h (Mookhe jee and Ray 2002;
Enge man and Sokolo 2006). Ano he po e y-pe pe ua ing mechanism is ela ed
o isk a e sion (Bane jee 2000): Because poo e indi iduals a e ypically mo e isk
a e se, in he absence o well- unc ioning insu ance and c edi ma ke s, hey will skip
p o i able in es men oppo uni ies ha hey deem oo isky.4Po e y can also al e
he decision-making p ocess o indi iduals owa d less g ow h-enhancing ac i i ies.
Fo ins ance, he poo de o e a signi ican ac ion o hei income o sa is ying basic
needs (Shah e al. 2012) and o “ emp a ion” goods (Bane jee and Mullaina han 2010)
and educe he esou ces de o ed o educa ion, heal h and in es men . Poo indi iduals
show also lowe aspi a ions, as hey an icipa e ha hei cu en s a us will impede
hei u u e success (La Fe a a 2019).
In spi e o he di e si y o hese analy ical models, e idence on hei empi ical el-
e ance emains la gely inconclusi e. A ew pape s (see Du lau 2006, o a e iew)
ha e sea ched o a ious empi ical egula i ies consis en wi h hose models, such
as agg ega e non-con exi ies (Aza iadis and S achu ski 2005) and con e gence clubs
(Quah 1993). A b oade empi ical e iew o di e en mechanisms ad anced in he
li e a u e inds li le e idence ha hey may be a wo k, excep pe haps in emo e o
disad an aged a eas (K aay and McKenzie 2014). Mo e ecen ly, la ge-scale andom-
ized e alua ions, such as he one de eloped by Bandie a e al. (2017) in Bangladesh,
3Po e y aps a ising om h eshold e ec s ha e o en been o e ed as a a ionale o a ‘big push’ app oach
o policy. In pa icula , when la ge aid p og ams a e coo dina ed in a mul i- ace ed way, a ‘big push’ can be
e ec i e o enginee g ow h akeo s (Bane jee e al., 2015). Howe e , in a c oss-coun y da ase , Eas e ly
(2006) inds ha akeo s a e a e and, in gene al, hey a e no associa ed wi h ‘big push’ s a egies.
4The a gumen ha isk a e sion leads o unde in es men goes back o S igli z (1969). See also Ageno
and Aizenman (2011), who a gue ha aid ola ili y could induce po e y aps in poo coun ies h ough a
simila mechanism.
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G ow h, inequali y and po e y: a obus ela ionship? 729
yield s ong e idence ha he poo ace impe ec ions in capi al ma ke s ha keep
hem in a low asse -low employmen po e y ap.
Somewha su p isingly, jus a ew pape s ha e aken up he undamen al agg ega e
implica ion o he po e y ap li e a u e— ha , ce e is pa ibus, coun ies wi h highe
po e y should g ow mo e slowly. The lis is limi ed o ou wo king pape e sion,
Ma e o and Se én (2018), plus López and Se én (2015) and Ra allion (2012), all
o which conclude ha po e y is g ow h-de e ing5;Eas e ly(2006) shows a non-
signi ican impac o po e y on g ow h.
The second s and o li e a u e o which ou pape is ela ed is conce ned wi h he
impac o inequali y on g ow h. I includes a la ge numbe o empi ical con ibu ions
eaching con lic ing conclusions; o o e iews, see Voi cho sky (2011), Be g e al.
(2018), and Ce a e al. (2021a). Fo example, Alesina and Rod ik (1994) and Pe o i
(1996) ound a nega i e ela ionship be ween inequali y and g ow h in c oss sec ion
da a, bu subsequen ly, Li and Zou (1998) and Fo bes (2000) ob ained he opposi e
esul using panel da a. Ba o (2000) ound ha inequali y migh a ec g ow h in
di e en di ec ions depending on he coun y’s le el o income, while Panizza (2002)
ound ha esul s migh depend on he model speci ica ion and he quali y and ype
o da a (see also Deininge and Squi e 1998). In u n, Bane jee and Du lo (2003)
concluded ha he esponse o g ow h o inequali y changes has an in e ed U-shape.
Themul iplici yo ac o sa ec ingbo hinequali yandg ow hmigh explains hese
con adic o y esul s. Fo example, ising inequali y could be he esul o g ow h-
enhancing echnological change whose e u ns a e cap u ed by alen ed indi iduals
a he op o he dis ibu ion (Goldin and Ka z 2008). In con as , i en -seeking is
he undamen al o ce behind g owing incomes o he ich, he inc ease in inequali y
could come along wi h declining g ow h (S igli z 2012).
In his line o enqui y, Galo and Moa (2004) a gue ha he eplacemen o
physical capi al accumula ion by human capi al accumula ion as a p ime engine o
economic g ow h has changed he quali a i e impac o inequali y on g ow h. Ma e o
and Rod íguez (2013) emphasize ha he sign o he e ec o inequali y on g ow h
depends on he ype o inequali y conside ed (i.e., inequali y o oppo uni y o o
e o ). Voi cho sky (2005) and, mo e ecen ly, an de Weide and Milano ic (2018)
a gue ha he e ec o inequali y is nega i e o he income g ow h o he poo bu
posi i e o he income g ow h o he ich—i.e., inequali y ends o be sel - ein o cing.
The e ec s o inequali y on g ow h migh also depend on he sec o al s uc u e o he
economy (E man and e Kaa 2019) and on he deg ee o in e gene a ional mobili y
(Aiya and Ebeke 2020).6
In gene al, di e en mechanisms a ec ing g ow h in opposi e di ec ions h ough
di e en channels ac all simul aneously, leading o con lic ing in e ences. In he
empi ical li e a u e, an eme ging consensus iew is ha he long- un e ec o inequal-
i yong ow hissigni ican lynega i e,andonlywhenlookinga ela i elysho pe iods
5Eas e ly (2006) in es iga es (and ejec s) a mo e ex eme hypo hesis, namely ha high po e y coun ies
should show no g ow h.
6E man and e Kaa (2019) show ha highe inequali y inc eases g ow h in physical capi al-in ensi e
indus ies, while i ha ms g ow in indus ies using skilled labo in ensi ely.
123

730 G. A. Ma e o, L. Se én
o ime, he ela ionship may u n posi i e (Hal e e al. 2014; B ueckne e al. 2015;
Be g e al. 2018; B ueckne and Lede man 2018).7
A hi d s and o he li e a u e explo es he links be ween g ow h and inequali y,
on he one hand, and po e y, on he o he . The bulk o his li e a u e, which is qui e
ex ensi e (Ce a e al. 2021a), ocuses on he po e y- educing e ec o g ow h and
he ac o s ha shape i (Dolla and K aay 2002; Bou guignon 2003; Ra allion 2004).
This angle o he po e y-g ow h link is he opposi e o ha pu sued in his pape .
Empi ically, he e is ample consensus ha g ow h educes po e y—i.e., i is “good
o he poo .” Dolla and K aay (2002), and he subsequen upda es using al e na i e
da abases and empi ical app oaches (K aay 2006, Dolla e al. 2016) ind ha he
income o he poo es deciles a ies in he same p opo ion as a e age income, hence
os e ing agg ega e g ow h is p o-poo (see also Fe ei a e al. 2010, o Loayza and
Radda z 2010). Recen wo k con i ms his esul (Fosu 2017; Bluhm e al. 2018;
Be gs om 2020). Fo example, Be gs om (2020) inds ha , in a la ge c oss-coun y
sample, 90% o he a ia ion in po e y is explained by a ia ion in pe capi a GDP.
Howe e , he eason is ha he sample a ia ion in pe capi a income is much la ge
han ha o inequali y;indeed,inmos o he sample coun ies, hees ima edinequali y
elas ici y o po e y exceeds he income elas ici y o po e y—which sugges s ha
declines in inequali y o e a la ge po en ial (as ye un ealized) o educe po e y a es.
Compa a i ely, he li e a u e has paid less a en ion o he impac o inequali y
on po e y (Bou guignon 2003; Ra allion 2005; Fe ei a e al. 2010; Kalwij and Ve -
schoo 2007).Thisis p ecisely hemechanismbehind heindi ec inequali y- o-g ow h
channel analyzed in his pape , and no co e ed in ea lie li e a u e. Mo e ecen ly,
Seh awa and Gi i (2018), he a o emen ioned Be gs om (2020) and Lakne e al.
(2020) ind e idence suppo ing he ole o declining inequali y o po e y educ ion.
3 G ow h, inequali y and po e y: da a and empi ical
implemen a ion
We u n o he desc ip ion o ou empi ical s a egy. Fi s we desc ibe he da a and
hen he econome ic app oach employed in he es ima ion.
3.1 Da a
Sinceou ocusisno oncyclicalg ow h luc ua ions,we ollow heempi icalli e a u e
on inequali y and g ow h and cons uc a panel da a se o non-o e lapping 5-yea
obse a ions on he h ee a iables o in e es : inequali y, g ow h and po e y. We
ocus on he 1960–2010 pe iod, as done by he ecen empi ical li e a u e on inequali y
and g ow h. G ow h is measu ed as he log di e ence o eal pe capi a income o e
he en i e 5-yea in e al, while po e y and inequali y a e measu ed a he beginning
7Amo elimi edli e a u e hasexamined heinequali y-g ow hlink om heopposi e pe spec i e,assessing
how income g ow h a ec s inequali y. I s esul s a e mos ly inconclusi e, howe e . Fo ins ance, while
B ueckne e al. (2015) and Blau (2018) ind ha GDP g ow h educes inequali y, K usell e al. (2000)and
Aghion e al. (2019) each he opposi e conclusion.
123
G ow h, inequali y and po e y: a obus ela ionship? 731
o he in e al. This means we only need o collec po e y and inequali y da a up o
2005.
We use he Gini index o measu e inequali y and ake he UN-WIID2 (2008)
da abase as ou p ima y sou ce o da a on income inequali y. I includes 5313 su eys
o 154 coun ies om 1950 o 2006. We comple e he WIID2 da a wi h in o ma ion
omPo calNe ,whichadds ano he 122coun y-yea (16coun ies)obse a ionso e
he 1960–2010 pe iod. In a numbe o ins ances, he e a e mul iple su eys e e ing
o he same coun y-yea , bu hey o e di e en co e age o use di e en concep s
o income. We es ic ou sample o Gini indexes based on na ionally ep esen a i e
su eys. Mo eo e , da a a e some imes based on income and o he imes on expendi-
u e igu es; income is ne o ans e s and axes in some cases and no in o he s; he
uni o analysis may be he indi idual o he household, e c. To co ec a leas in pa
o his he e ogenei y, we adjus he o iginal Gini da a ollowing Dolla and K aay
(2002).8
Fo economic g ow h, we use na ional accoun s pu chasing-powe -pa i y (PPP)-
adjus ed pe capi a GDP da a om he Penn Wo ld Tables 7.1, he same sou ce used
by Be g e al. (2018) and many o he s udies o inequali y and g ow h, which acili a es
compa abili y wi h hem. Sala-i-Ma in (2006) and Dolla and K aay (2002), among
many o he s, emphasize he ad an ages o using pe capi a GDP ins ead o he mean
le el o income ob ained di ec ly om household su eys. The su ey mean usually
does no ma ch pe capi a income om he na ional accoun s, because o di e ences
in concep s and me hodology, inconsis en da a collec ion me hods, mis epo ing, e c.
Addi ionally, o manyo hecoun y-yea obse a ions o whichweha ein o ma ion
on inequali y, we do no ha e ma ching in o ma ion on mean income om he same
sou ce, which hampe s he cons uc ion o a la ge panel da ase . In con as , na ional
accoun s da a a e epo ed yea ly o all coun ies, using a homogenous me hodology,
which, in addi ion, allows us o compa e ou empi ical esul s wi h hose o he ample
mac oeconomic li e a u e on income inequali y and g ow h.
Rega ding po e y da a, we ollow he s a egy p oposed by Dolla and K aay
(2002), López and Se én (2015), Sala-i-Ma in (2006) and Pinko skiy and Sala-i-
Ma in (2013). These au ho s poin ou ha combining po e y and income g ow h
da a om household su eys and na ional accoun s may lead o misleading conclu-
sions, because o he inconsis encies be ween he wo sou ces jus no ed. To a oid
his p oblem, hey use PWT da a o cons uc bo h income g ow h and po e y mea-
su es, wi h he la e compu ed assuming ha household income ollows a logno mal
dis ibu ion. Thus, we cons uc a se o po e y measu es ( he headcoun a io P0,
he po e y gap P1 and he squa ed po e y gap P2) using a logno mal app oxima ion
on he basis o he obse ed pe capi a GDP le els and Gini coe icien s.9We also
8Speci ically, we pool he sample and eg ess he Gini coe icien on a cons an , egional dummies and
dummy a iables indica ing whe he he su ey is s a ed in e ms o g oss income o consump ion ( he
omi ed ca ego y is income ne o axes and ans e s). We hen sub ac he es ima ed mean di e ence
be ween hese wo al e na i es and he omi ed ca ego y o a i e a a se o Gini indices ha no ionally
co espond o hedis ibu iono incomene o axesand ans e s.The esul so heseadjus men eg essions
a e a ailable upon eques , bu hey show simila conclusions as in Dolla and K aay (2002).
9The UN-WIID2 Gini index is no always a ailable o he i s yea o each 5-yea in e al. In such cases,
we alloca e he a ailable obse a ion(s) o he closes s a ing yea o a 5-yea in e al, wi h a limi o 2 yea s
123
732 G. A. Ma e o, L. Se én
expe imen wi h al e na i e, widely used po e y lines: US$ 1.25, US$ 2 and US$ 4
pe pe son pe day, in 2005 PPP US$ (see Appendix 1 o de ails).
This app oach allows a conside able inc ease in sample size. Despi e he p og ess
made in ecen yea s, mainly h ough he Po calNe p ojec , su ey-based po e y da a
a e s ill ela i ely sca ce, a leas in compa ison wi h he size o he s anda d c oss-
coun y ime-se ies g ow h da ase . Using he logno mal app oxima ion, we assemble
746obse a ionsonpo e yo e non-o e lapping5-yea in e als,co e ing156coun-
ies be ween 1960 and 2005 (an a e age o almos i e obse a ions pe coun y).10 In
con as , using he Janua y 2020 e sion o Po calNe o e he same 1960–2005 ime
span, we can cons uc a da ase o 383 po e y obse a ions o e non-o e lapping
5-yea in e als o 144 coun ies, oughly hal he size o ou sample—i.e., an a e age
o less han 3 obse a ions pe coun y, wi h da a o he as majo i y o coun ies
s a ing in 1990 o la e .11
As a as we a e awa e, ou s is he la ges sample used o da e o s udy he impac o
po e y on g ow h. I exceeds by a he samples used by he wo ea lie pape s analyz-
ing he po e y-g ow h nexus in a panel eg ession se ing: López and Se én (2015)
assemble a sample comp ising 325 obse a ions om 85 coun ies o e 1960–2000,
while Ra allion (2012) uses unbalanced panel da a om Po calNe co e ing up o 97
de eloping coun ies o e a sho e ime span, 1981–2005.
Table 1p esen s summa y s a is ics on annual g ow h, mean income, inequali y and
po e y o he common sample o hese a iables in he unbalanced 1960–2010 panel.
The able shows he wide ange o pe capi a income le els (exp essed in 2005 US
dolla s in PPP e ms) in he sample— om jus o e $200 ( he Democ a ic Republic
o Congo in he mid-2000s) o abou $73,000 (Luxembou g in 2005). The median
obse a ion co esponds o B azil in he mid-1970s, wi h pe capi a income abou
$5500. The o e all sample mean is abou $9800, much la ge han he median, which
e lec s a wo ld income dis ibu ion skewed o he igh .
Rega dinginequali y,bo h hemedianand hemeano heGinicoe icien equal0.4,
which ma ches he alues ound o he U.S. (in 2000), Bu kina Faso (in 1995), Tu key
(in 2010) o Singapo e (in 1970). The maximum alue (abo e 0.74) co esponds o
Foo no e 9 con inued
o di e ence. When mo e han one obse a ion is a ailable wi hin he 2-yea limi , we ake he a e age.
Because o he s ong ine ia o inequali y and po e y ime se ies, using a 1-yea limi ins ead o 2 yea s,
o no using means, yields e y simila esul s (Dolla and K aay 2002).
10 Ou da a comp ise 121 da a poin s co esponding o 32 low-income coun ies, 180 o 41 lowe -middle
income coun ies, 240 o 44 uppe -middle income, 57 o 11 high-income non-OECD, and 206 o 30 high-
income OCDE coun ies. The sample includes 18 obse a ions (2 coun ies) om No h Ame ica, 248 (48
coun ies) om Eu ope and Cen al Asia, 159 (28 coun ies) om La in Ame ican and he Ca ibbean, 53
(12 coun ies) om Middle Eas and No h A ica, 144 (40 coun ies) om Sub-Saha an A ica, 56 (9
coun ies) om Sou h Asia and 126 (19 coun ies) om Eas Asia and he Paci ic.
11 This sample size would be oo small o many o ou exe cises, and hus o he obus ness es s using
Po calNe da a epo ed in Sec ion V below, we eso o he in e pola ed Po calNe se ies, which allows
inc easing he sample size o 556 obse a ions. These in e pola ed in o ma ion s a in 1981 and a e epo ed
e e y h ee yea s. Thus, o cons uc a non-o e lapping 5-yea panel da a simila o he one used in ou
baseline speci ica ion and ma ch he iming o po e y da a wi h ha o he o he a iables (g ow h and
o he con ols), we use a “closes ” c i e ion o ake he a e age i wo po e y obse a ions a e one yea
abo e and one below he assigned yea . We should also no e ha he cu en Po calNe se ies uses a po e y
line o 1.90 2011 US$, which eplaces i s p e ious line o 1.25 2005 US$ (see Fe ei a e al. 2016, o mo e
de ails).
123
G ow h, inequali y and po e y: a obus ela ionship? 733
Table 1 G ow h, inequali y and po e y da a: summa y s a is ics
Median Mean S d P10 P90 Min Max
GDP pe
capi a
g ow h
0.025 0.025 0.030 −0.012 0.061 −0.086 0.201
Real pe
capi a
income
5651.1 9792.5 10,462.5 816.4 26,053.7 207.5 73,243.0
Gini coe -
icien
0.394 0.402 0.100 0.280 0.543 0.157 0.742
P0 (US$
1.25)
0.005 0.096 0.177 0.000 0.364 0.000 0.906
P0 (US$
2)
0.023 0.162 0.247 0.000 0.610 0.000 0.969
P0 (US$
4)
0.130 0.287 0.336 0.000 0.881 0.000 0.999
P1 (US$
1.25)
0.001 0.040 0.086 0.000 0.146 0.000 0.602
P1 (US$
2)
0.006 0.073 0.132 0.000 0.270 0.000 0.722
P1 (US$
4)
0.038 0.151 0.212 0.000 0.523 0.000 0.855
P2 (US$
1.25)
0.000 0.023 0.056 0.000 0.077 0.000 0.497
P2 (US$
2)
0.002 0.044 0.089 0.000 0.158 0.000 0.594
P2 (US$
4)
0.016 0.100 0.156 0.000 0.356 0.000 0.750
Headcoun po e y a e (P0); po e y gap (P1); squa ed po e y gap (P2); al e na i e po e y lines: US$ 1.25,
US$ 2 and US$ 4, pe pe son pe day (2005 PPP). Po e y is ob ained om a logno mal app oxima ion on he
basis o he obse ed pe capi a GDP (PWT 7.1) le els and Gini coe icien s (UNU-WIDER 2008). See Appendix
1 o de ails
Zimbabwe in 1995, and he minimum (below 0.16) co esponds o Bulga ia in 1975.
A ound 80% o he obse a ions all in he ange be ween 0.28, a alue ound among
Wes e n Eu opean coun ies, and 0.54, a alue ound among La in Ame ican and
Sub-Saha an A ican coun ies.
Po e y ises by cons uc ion wi h he po e y line and declines as he po e y mea-
su e changes om P0 o P2 (i.e., as one conside s mo e bo om-sensi i e measu es).
Fo ou logno mal po e y es ima es, he able shows ha median headcoun po e y
P0 is 0.6% using US$ 1.25 pe day as po e y line, bu i aises o 2.3% wi h a US$ 2
po e y line, and o 13% wi h US$ 4. Likewise, he median P1 anges om less han
0.1% o US$ 1.25 o abou 4% o US$ 4, while he median P2 anges om less han
0.1% o US$ 1.25 o almos 2% o US$ 4. Al hough he mean and he median o
hese po e y measu es a e ela i ely small, he he e ogenei y in he sample is qui e
high, since he anges o he a ious po e y measu es un om a minimum o ze o
( e lec ing he p esence o high-income coun ies in he sample) o a maximum whose
123
740 G. A. Ma e o, L. Se én
Table 2 G ow h, po e y and inequali y: panel OLS es ima es
M1. Skele on model M2. Ex ended wi h educa ion and in .
p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P0, lag −
0.0450***
(−5.70)
−
0.0440***
(−5.71)
−
0.0328***
(−3.88)
−
0.0334***
(−4.02)
−
0.0382***
(−4.58)
−
0.0380***
(−4.70)
−
0.0424***
(−3.84)
−0.0430***
(−4.03)
Gini, lag −
0.0415***
(−
3.63)
−
0.0393***
(−3.50)
−0.0252**
(−2.12)
−0.0266**
(−2.31)
−
0.0399***
(−3.35)
−
0.0396***
(−3.36)
−0.0354**
(−2.44)
−0.0366***
(−2.60)
log y,lag −
0.00781***
(−5.43)
−0.00143
(−
1.61)
−
0.00873***
(−5.88)
−
0.00892***
(−4.80)
−
0.00381***
(−3.08)
−
0.00952***
(−5.05)
−
0.00803***
(−5.43)
−
0.00303***
(−3.20)
−
0.00920***
(−5.97)
−
0.0213***
(−6.13)
−
0.0143***
(−4.18)
−0.0215***
(−6.31)
In . de la o ,
lag
−
0.00482**
(−2.23)
−
0.00629**
(−2.20)
−0.00453*
(−1.91)
Female educ.,
lag
−0.00299
(−1.16)
−0.00300
(−1.09)
−0.00176
(−0.66)
0.00560***
(3.79)
0.00370**
(2.44)
0.00509***
(3.48)
Male educ.,
lag
0.00747***
(2.89)
0.00671**
(2.40)
0.00567**
(2.13)
In la ion −0.00728
(−1.41)
−0.00409
(−0.84)
−0.00728
(−1.41)
−
0.0232***
(−3.66)
−0.0165**
(−2.58)
−0.0217***
(−3.31)
T ade
openness
(log)
0.0113***
(4.25)
0.0136***
(5.05)
0.0115***
(4.34)
Go . size
(log)
−0.00110
(−0.40)
−0.00191
(−0.67)
−0.00135
(−0.48)
In as uc u e,
lag
0.00847***
(2.94)
0.00830***
(2.78)
0.00754***
(2.70)
123

G ow h, inequali y and po e y: a obus ela ionship? 741
Table 2 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion and in .
p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
Num. obs 745 745 745 676 676 676 656 656 656 477 477 477
R2-adjus ed 0.096 0.072 0.112 0.124 0.108 0.130 0.120 0.107 0.135 0.149 0.125 0.161
Unbalanced panel wi h da a a 5-yea in e als o e 1960–2010. The dependen a iable is he annual g ow h a e o pe capi a GDP. The explana o y a iables a e eal pe capi a GDP (in logs), he headcoun po e y a e
(P0) using US$ 2 as po e y line, he Gini coe icien , and al e na i e se s o addi ional con ols ha a y ac oss models M1 (skele on model), M2 (educa ion and in es men p ices), M3 (policy a iables) and M4 (policy
a iables and in as uc u es). Explana o y a iables a e all lagged one pe iod (5 yea s), wi h he excep ion o he policy a iables in models M3 and M4, which a e aken as con empo aneous 5-yea a e ages. A cons an
e m and ime dummies a e included in all models. Robus s a is ics in pa en heses: ***deno es signi icance a 1%, **a 5%, *a 10%
123
742 G. A. Ma e o, L. Se én
Table 3 G ow h, po e y and inequali y: wi hin-g oup es ima es
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P0, lag −
0.0665***
(−3.79)
−
0.0764***
(−4.45)
−
0.0664***
(−3.89)
−
0.0792***
(−4.65)
−
0.0716***
(−3.86)
−
0.0861***
(−4.53)
−
0.0514**
(−2.46)
−
0.0633***
(−2.82)
Gini, lag 0.0378
(1.27)
0.0643**
(2.27)
0.0454
(1.46)
0.0742**
(2.46)
0.0576**
(2.00)
0.0865***
(2.98)
0.0457
(1.48)
0.0641**
(2.05)
log y,lag −
0.0409***
(−6.28)
−
0.0303***
(−4.91)
−
0.0429***
(−6.18)
−
0.0394***
(−5.51)
−
0.0256***
(−4.02)
−
0.0426***
(−5.86)
−
0.0667***
(−8.31)
−
0.0557***
(−6.62)
−
0.0709***
(−8.38)
−
0.0643***
(−8.11)
−
0.0580***
(−8.04)
−
0.0674***
(−8.18)
In . de la o ,
lag
−
0.00916**
(−2.33)
−
0.0116**
(−2.33)
−
0.00899**
(−2.31)
Female educ.,
lag
−0.00122
(−0.15)
−0.0124
(−1.63)
−0.00196
(−0.26)
0.00471*
(1.79)
0.00261
(1.04)
0.00603**
(2.39)
Male educ.,
lag
0.00637
(0.75)
0.0148*
(1.85)
0.00935
(1.15)
In la ion −
0.0224***
(−3.87)
−
0.0221***
(−3.87)
−
0.0219***
(−3.72)
−
0.0368***
(−4.93)
−
0.0369***
(−5.45)
−
0.0366***
(−5.10)
T ade
openness
(log)
0.0258***
(3.50)
0.0312***
(3.57)
0.0238***
(3.49)
Go . size
(log)
−
0.0237***
(−2.94)
−
0.0196**
(−2.57)
−
0.0251***
(−3.26)
In as uc u e,
lag
0.0177***
(3.75)
0.0236***
(5.90)
0.0170***
(3.44)
Num. obs 745 745 745 676 676 676 656 656 656 477 477 477
123
G ow h, inequali y and po e y: a obus ela ionship? 743
Table 3 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
R2-adjus ed 0.202 0.171 0.216 0.218 0.192 0.236 0.325 0.302 0.348 0.336 0.329 0.350
Num.
coun ies
156 156 156 131 131 131 147 147 147 88 88 88
See no e in Table 2
123
744 G. A. Ma e o, L. Se én
(inM4)ca yposi i eandsigni ican coe icien s(Calde óne al.2015).Incon as , he
e ec so maleand emaleseconda yeduca iondependonmodelspeci ica ion.Female
educa ion ca ies a posi i e and signi ican coe icien in M4, bu u ns insigni ican
in M2, while he coe icien o male educa ion is gene ally posi i e. Simila ly, among
he policy a iables, he coe icien o go e nmen size is gene ally nega i e, bu i is
signi ican only o he WG es ima es.
Table 4shows es ima ion esul s o i s -di e ence GMM, while Table 5shows
he esul s o he baseline sys em GMM speci ica ion (limi ing he ins umen ma ix
o wo lags). In Appendix 3(Tables 15 and 16), we epo esul s unde al e na i e
app oaches o educing he dimension o he sys em GMM ins umen se : collapsing
he ma ix o ins umen s while using all lags as ins umen s (Table 15), and limi ing
hem o wo lags and collapsing he ins umen s a he same ime (Table 16). Fo
i s -di e ence GMM (Table 4), we use h ee lags in he ma ix o ins umen s so
as o ha e he same numbe o o hogonali y condi ions as in he baseline sys em
GMM es ima ion, hus making he esul s mo e easily compa able.18 The p alues
o he Hansen es s sugges ha in i ually e e y case, he null o join alidi y o
all ins umen s canno be ejec ed. Mo eo e , he Di e ence-in-Hansen es esul s,
whose p alues always exceed 0.10, poin owa d he supe io i y o sys em GMM o e
i s -di e ence GMM.
The pa ame e es ima es o he a iables o in e es ollow he same pa e n ound
ea lie . The coe icien on he po e y headcoun is consis en ly nega i e and highly
signi ican , ega dless o he choice o model and speci ica ion. In con as , he coe i-
cien o he inequali y a iable a ies in sign and signi icance depending on he GMM
app oach and he con ols used in he es ima ion. I is always posi i e and in one case
signi ican o i s -di e ence GMM, consis en wi h ou esul s o he WG es ima es
in Table 3and pa o he ea lie li e a u e (e.g., Fo bes 2000). Howe e , i is nega-
i e and, in some cases, signi ican o sys em GMM, consis en wi h ou esul s o
pooled-OLS and ano he s and o he li e a u e (e.g., Be g e al. 2018, and e e ences
he ein). The nega i e e ec o po e y on g ow h is obus o changes in model spec-
i ica ion and es ima ion me hod, while he e ec o inequali y on g ow h, which has
been he ocus o a massi e li e a u e, is no .
The heo e ical model ou lined in López and Se én (2015) and explo ed in Ma e o
and Se én (2018) helps a ionalize ou empi ical esul s. In ha model, poo indi idu-
als—i.e., hose whose ini ial endowmen is below a minimum consump ion le el—do
no sa e and do no con ibu e o he economy’s agg ega e g ow h. In he absence o
inancial ma ke s, he model shows ha po e y is unambiguously g ow h-de e ing,
while inequali y can a ec g ow h di ec ly, h ough he sa ings o he non-poo , and
indi ec ly, h ough i s e ec on po e y. While he indi ec e ec is nega i e, he di ec
e ec is ambiguous (as ound by he empi ical li e a u e), and so is he o e all impac
o inequali y on g ow h.
As a u he diagnos ic check on he GMM es ima es o Tables 4,5,15,16,we
inspec ed he esiduals o c oss-sec ionaldependence,usingPesa an’s(2021)CD es ,
18 Da a o he in as uc u e index included in M4 a e a ailable o only 88 coun ies unde sys em GMM
and 79 unde he i s -di e ence GMM speci ica ion. Using wo lags as ins umen s o es ima e his model
would esul in he numbe o ins umen s exceeding he c oss sec ion dimension o he da a. Thus, we limi
he numbe o ins umen s o jus one lag.
123
G ow h, inequali y and po e y: a obus ela ionship? 745
Table 4 G ow h, po e y and inequali y: i s -di e ence GMM es ima es
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy
a iables
M4. Ex ended wi h policy and
in as uc u es
P0, lag −
0.0941***
(−2.59)
−
0.0981***
(−2.63)
−
0.150***
(−
3.35)
−
0.150***
(−5.16)
−
0.0997*
(−
1.79)
−
0.0947**
(−
2.19)
−
0.117**
(−
2.25)
−
0.103***
(−2.75)
Gini, lag 0.0253
(0.37)
0.113
(1.10)
0.0330
(0.47)
0.131**
(2.01)
0.0690
(0.73)
0.112
(1.56)
0.0434
(0.43)
0.115
(1.32)
log y,lag −
0.106***
(−4.15)
−
0.0876***
(−3.89)
−
0.119***
(−5.45)
−
0.100***
(−
3.48)
−
0.0756***
(−4.60)
−
0.0948***
(−4.47)
−
0.154***
(−
3.71)
−
0.105***
(−
4.50)
−
0.149***
(−
4.47)
−
0.103***
(−
3.24)
−
0.0883***
(−4.01)
−
0.106***
(−5.25)
In . de la o ,
lag
−0.0117
(−
1.19)
−0.0177
(−1.47)
−0.00942
(−0.90)
Female educ.,
lag
0.0616**
(2.39)
0.0148
(0.82)
0.0465***
(2.70)
0.00688
(0.71)
−0.00271
(−0.20)
0.00611
(0.57)
Male educ.,
lag
−
0.0525*
(−
1.92)
−0.0140
(−0.59)
−0.0319
(−1.51)
In la ion 0.0005**
(2.51)
0.0005**
(2.26)
0.0004*
(1.91)
0.0001
(0.06)
0.0017
(0.98)
0.0009
(0.83)
T ade
openness
(log)
0.0396
(1.56)
0.0532**
(2.24)
0.0457*
(1.95)
123

746 G. A. Ma e o, L. Se én
Table 4 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy
a iables
M4. Ex ended wi h policy and
in as uc u es
Go . size
(log)
−0.0265
(−
1.50)
−0.0350
(−
1.51)
−
0.0407**
(−
2.05)
In as uc u e,
lag
0.00750
(0.37)
0.0245*
(1.78)
0.00778
(0.42)
m2- es (p
alue)
0.854 0.924 0.568 0.565 0.503 0.336 0.505 0.833 0.750 0.658 0.641 0.622
AR(3) (p
alue)
0.0261 0.00635 0.0238 0.117 0.102 0.416 0.186 0.155 0.264 0.142 0.146 0.187
Hansen (p
alue)
0.167 0.199 0.0571 0.412 0.269 0.330 0.581 0.431 0.759 0.397 0.575 0.424
Num. obs 502 503 502 467 468 467 248 249 248 345 346 345
Num.
coun ies
130 130 130 113 113 113 84 84 84 79 79 79
Num.
ins umen s
39 39 54 84 84 99 59 59 70 47 47 55
SeeNo einTable2. Es ima ions a e done using 2-s ep i s -di e ence GMM educing he numbe o ins umen lags o h ee. The ins umen se s a s a −3, and he a iance co a iance
ma ix is compu ed using he small sample co ec ion o Windmeije (2005). Robus s a is ics in pa en heses. ***deno es signi icance a 1%, **a 5%, *a 10%
123
G ow h, inequali y and po e y: a obus ela ionship? 747
Table 5 G ow h, po e y and inequali y: sys em GMM es ima es
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P0, lag −
0.117***
(−3.81)
−
0.121***
(−5.34)
−
0.0883***
(−3.71)
−
0.0846***
(−4.12)
−
0.0666***
(−2.96)
−
0.0697***
(−3.85)
−0.0506*
(−1.88)
−0.0503**
(−2.32)
Gini, lag −
0.107***
(−
2.92)
−
0.0955**
(−2.12)
−0.0553
(−
1.44)
−0.0587
(−1.57)
−
0.0908***
(−3.30)
−
0.106***
(−4.10)
−
0.00339
(−
0.08)
−0.0346
(−1.25)
log y,lag −
0.0200***
(−4.60)
−
0.00156
(−
0.61)
−
0.0228***
(−6.19)
−
0.0166***
(−4.61)
−
0.00210
(−
0.92)
−
0.0173***
(−5.30)
−
0.0160***
(−4.72)
−
0.00653**
(−2.50)
−
0.0184***
(−5.95)
−
0.0362***
(−4.72)
−0.0168
(−
1.53)
−
0.0299***
(−3.29)
In . de la o ,
lag
−0.00137
(−1.38)
−
0.00339*
(−
1.93)
−
0.000830
(−0.70)
Female educ.,
lag
0.00122
(0.20)
0.00267
(0.44)
0.00385
(0.57)
0.00714**
(2.47)
0.000557
(0.15)
0.00557*
(1.83)
Male educ.,
lag
0.00241
(0.36)
−
0.00158
(−
0.23)
−0.00144
(−0.19)
In la ion 0.001*
(1.93)
0.0012***
(2.91)
0.001***
(2.60)
0.0004
(0.68)
0.001**
(2.02)
0.001*
(1.83)
T ade
openness
(log)
0.0255***
(3.26)
0.0264***
(2.83)
0.0217***
(2.58)
123
748 G. A. Ma e o, L. Se én
Table 5 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
Go . size
(log)
−0.00306
(−0.42)
−0.00861
(−0.91)
−0.00349
(−0.44)
In as uc u e,
lag
0.0222***
(3.07)
0.0189**
(2.03)
0.0166**
(2.01)
m2- es (p
alue)
0.108 0.223 0.215 0.0574 0.117 0.117 0.279 0.434 0.493 0.227 0.203 0.313
AR(3) (p
alue)
0.942 0.659 0.671 0.735 0.729 0.785 0.622 0.609 0.474 0.845 0.773 0.746
Hansen (p
alue)
0.138 0.160 0.279 0.225 0.242 0.572 0.224 0.295 0.616 0.163 0.269 0.477
Di -Hansen
o le els (p
alue)
0.220 0.640 0.248 0.382 0.490 0.848 0.375 0.662 0.783 0.372 0.617 0.850
Num. obs 745 745 745 676 676 676 656 656 656 477 477 477
Num.
coun ies
156 156 156 131 131 131 147 147 147 88 88 88
Num.
ins umen s
54 54 76 120 120 142 116 116 138 82 82 97
See no e Table 4Es ima ions a e done using 2-s ep sys em GMM educing he numbe o ins umen lags o wo. The ins umen se s a s a −3, and he a iance co a iance ma ix is compu ed
using he small sample co ec ion o Windmeije (2005). The di e ence Hansen es assesses he alidi y o he ins umen s o he le el equa ion in sys em GMM. Robus s a is ics in pa en heses.
*** deno es signi icance a 1%, ** a 5%, * a 10%
123
G ow h, inequali y and po e y: a obus ela ionship? 749
and ocusing on he model e sions including bo h po e y and inequali y. Resul s a e
shown in Table 17 (Appendix 4). In he majo i y o cases, he es esul s a e suppo i e
o he empi ical speci ica ion. This is pa icula ly he case o he models including
policy a iables (models M3 and M4 in he a o emen ioned ables), o which he es
ails in all cases o ejec he null o c oss-sec ional independence. Fo he s ipped-
down model M1, which omi s all con ols, esul s a e mo e mixed, as he es ails o
ejec he null a he con en ional 5% le el in some exe cises ( hose in Tables 4and
5) bu ejec s i in o he s ( hose in Tables 15,16). The excep ion is model M2, o
which he es consis en ly inds signi ican e idence o c oss-sec ional dependence.19
O e all, we ake hese esul s as suppo ing he iew ha models M3 and M4 a e
co ec ly speci ied. Howe e , he p esence o esidual c oss-sec ional co ela ion in
model M2— i s explo ed by Pe o i (1996) and Fo bes (2000), sugges s ha he
model’s es ima ed s anda d e o s may be inco ec .20
4.1 Weak ins umen s analysis
Bazzi and Clemens (2013) ha e aised he po en ial p oblem o weak ins umen s
when using sys em GMM es ima ion in g ow h eg essions. Weak iden i ica ion a ises
when he ins umen s a e only weakly co ela ed wi h he endogenous eg esso s,
and i s consequence is ha es ima o s pe o m poo ly (Nelson and S a z 1990). To
assess he s eng h o he ins umen s employed in ou sys em GMM es ima ions—in
pa icula , he iden i ica ion o he po e y and inequali y pa ame e s—we use ools
designed o se ings ea u ing mul iple endogenous eg esso s. We ollow Sande son
and Windmeije (2016) (SW he ea e ), who p opose a condi ional Fs a is ic based
on Ang is and Pischke (2009) o es whe he , in a mul i a ia e se ing, a pa icula
endogenous eg esso is weakly ins umen ed. Fo each such eg esso , a condi ional
es is cons uc ed by “pa ialing-ou ” linea p ojec ions o he emaining endogenous
eg esso s. SW show ha he condi ional Fs a is ic can be assessed agains he S ock
and Yogo c i ical alues, and he weakness can hen be exp essed in e ms o he size
o he bias o he IV (o 2SLS) es ima o ela i e o ha o he OLS es ima o . The null
hypo hesis is ha he ins umen s a e weak. I is ejec ed i he condi ional Fs a is ic
exceeds he co esponding c i ical alue, and we use a c i ical alue allowing o a
30 pe cen maximal ela i e bias. We also pe o m a Chi-squa e unde -iden i ica ion
es sepa a ely o each eg esso . He e, he null hypo hesis is ha he ma ix o coe i-
cien s om he i s -s age condi ional eg essions is no ull ank, signaling a comple e
19 The obus ness exe cises in sec ion V ollow he same pa e n ega ding c oss-sec ional dependence
es s: The esiduals o models M3 and M4 show no e idence o dependence, while in mos cases, hose o
model M3 yield he opposi e conclusion. Model M1 again yields mixed esul s.
20 The absence o c oss-sec ional dependence in models M3 and M4 (and, o a lesse ex en , M1) may seem
su p ising gi en ha sho - e m g ow h luc ua ions ypically display signi ican in e na ional como emen .
Howe e , ou use o 5-yea a e ages g ea ly mi iga es he como emen usually ound a annual (o highe )
equency. In addi ion, he inclusion o ime dummies in ou empi ical speci ica ions also helps soak up
common ac o s a ec ing g ow h in mul iple coun ies. Las ly, he p esence o s a is ically signi ican
policy a iables in models M3 and M4 likely helps soak up any emaining c oss-sec ional co ela ion in
hese speci ica ions, unlike in models M1 and M2.
123
756 G. A. Ma e o, L. Se én
Table 7 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion
and in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P0, lag −
0.142***
(−
4.86)
−
0.152***
(−
6.15)
−
0.101***
(−4.93)
−
0.0944***
(−4.78)
−
0.0652***
(−3.12)
−
0.0765***
(−4.41)
−0.0557
(−
1.54)
−
0.0629***
(−2.62)
Gini,
lag
−
0.107***
(−
2.92)
−0.0689
(−
1.55)
−
0.0553
(−
1.44)
−0.0412
(−1.24)
−
0.0908***
(−3.30)
−
0.107***
(−3.74)
−
0.00339
(−
0.08)
−0.0206
(−0.58)
Hansen
(p
alue)
0.124 0.160 0.207 0.292 0.242 0.688 0.195 0.295 0.618 0.181 0.269 0.543
Po e y Gap, P1, Po e y line US$ 1.25
P1, lag −
0.263***
(−
3.74)
−
0.227***
(−
4.57)
−
0.145***
(−3.35)
−
0.138***
(−2.90)
−
0.200***
(−4.89)
−
0.158***
(−4.49)
−
0.144**
(−
2.28)
−0.121**
(−2.45)
Gini,
lag
−
0.107***
(−
2.92)
−
0.0658*
(−
1.66)
−
0.0553
(−
1.44)
−0.0486
(−1.21)
−
0.0908***
(−3.30)
−
0.0789***
(−2.63)
−
0.00339
(−
0.08)
−0.0182
(−0.57)
Hansen
(p
alue)
0.319 0.160 0.346 0.184 0.242 0.620 0.194 0.295 0.520 0.129 0.269 0.630
Po e y gap, P1, po e y line US$ 2.0
123

G ow h, inequali y and po e y: a obus ela ionship? 757
Table 7 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion
and in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P1, lag −
0.191***
(−
3.65)
−
0.176***
(−
5.08)
−
0.118***
(−3.46)
−
0.111***
(−3.66)
−
0.141***
(−4.17)
−
0.119***
(−4.20)
−
0.0895**
(−
1.98)
−0.0854**
(−2.57)
Gini,
lag
−
0.107***
(−
2.92)
−
0.0756*
(−
1.93)
−
0.0553
(−
1.44)
−0.0524*
(−1.72)
−
0.0908***
(−3.30)
−
0.0925***
(−3.45)
−
0.00339
(−
0.08)
−0.0356
(−1.10)
Hansen
(p
alue)
0.210 0.160 0.290 0.253 0.242 0.491 0.217 0.295 0.493 0.134 0.269 0.470
Po e y gap, P1, po e y line US$ 4.0
P1, lag −
0.169***
(−
3.57)
−
0.183***
(−
5.61)
−
0.125***
(−3.41)
−
0.120***
(−4.23)
−
0.0993***
(−3.76)
−
0.104***
(−4.71)
−
0.0706*
(−
1.86)
−0.0709**
(−2.33)
Gini,
lag
−
0.107***
(−
2.92)
−
0.0871**
(−
2.03)
−
0.0553
(−
1.44)
−0.0451
(−1.15)
−
0.0908***
(−3.30)
−
0.102***
(−3.78)
−
0.00339
(−
0.08)
−0.0326
(−1.08)
Hansen
(p
alue)
0.0985 0.160 0.267 0.208 0.242 0.581 0.208 0.295 0.683 0.191 0.269 0.540
Squa ed po e y gap, P2, po e y line US$ 1.25
123
758 G. A. Ma e o, L. Se én
Table 7 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion
and in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P2, lag −
0.413***
(−
4.15)
−
0.347***
(−
4.18)
−
0.197***
(−2.85)
−0.177**
(−2.36)
−
0.300***
(−4.54)
−
0.237***
(−4.22)
−
0.230**
(−
2.35)
−0.197**
(−2.56)
Gini,
lag
−
0.107***
(−
2.92)
−0.0518
(−
1.07)
−
0.0553
(−
1.44)
−0.0370
(−1.15)
−
0.0908***
(−3.30)
−
0.0709**
(−2.52)
−
0.00339
(−
0.08)
−0.00956
(−0.30)
Hansen
(p
alue)
0.431 0.160 0.420 0.172 0.242 0.567 0.212 0.295 0.528 0.153 0.269 0.662
Squa ed po e y gap, P2, po e y line US$ 2.0
P2, lag −
0.266***
(−
3.70)
−
0.239***
(−
4.81)
−
0.150***
(−3.63)
−
0.149***
(−3.77)
−
0.203***
(−4.90)
−
0.164***
(−4.45)
−
0.139**
(−
2.19)
−0.116**
(−2.41)
Gini,
lag
−
0.107***
(−
2.92)
−
0.0652*
(−
1.68)
−
0.0553
(−
1.44)
−0.0442
(−1.31)
−
0.0908***
(−3.30)
−
0.0820***
(−2.79)
−
0.00339
(−
0.08)
−0.0241
(−0.81)
Hansen
(p
alue)
0.270 0.160 0.318 0.210 0.242 0.587 0.211 0.295 0.521 0.127 0.269 0.622
Squa ed po e y gap, P2, po e y line US$ 4.0
123
G ow h, inequali y and po e y: a obus ela ionship? 759
Table 7 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion
and in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P2, lag −
0.201***
(−
3.58)
−
0.196***
(−
5.44)
−
0.131***
(−3.06)
−
0.127***
(−3.78)
−
0.132***
(−3.98)
−
0.123***
(−4.59)
−
0.0850*
(−
1.85)
−0.0833**
(−2.26)
Gini,
lag
−
0.107***
(−
2.92)
−
0.0801**
(−
2.03)
−
0.0553
(−
1.44)
−0.0493
(−1.53)
−
0.0908***
(−3.30)
−
0.0980***
(−3.89)
−
0.00339
(−
0.08)
−0.0327
(−1.55)
Hansen
(p
alue)
0.136 0.160 0.285 0.248 0.242 0.499 0.188 0.295 0.487 0.160 0.269 0.603
See no e in Table 4
123
760 G. A. Ma e o, L. Se én
14). Fu he inspec ion e eals ha he co ela ion is highe o he mo e ecen da a,
eaching 0.93 in 2005 and 0.96 in 2010.
Table 8shows es ima ion esul s o models M1, M2, M3 and M4 using he
Po calNe in e pola ed po e y se ies and ou p e e ed sys em GMM speci ica ion.
Compa ison wi h Table 4 e eals ha he esul s a e obus o he use o his al e na i e
sou ce o po e y da a: Po e y consis en ly ca ies a nega i e coe icien , signi ican
in all cases bu one. In u n, he coe icien on inequali y is also nega i e in mos
ins ances, bu insigni ican in h ee ou o eigh cases.
5.4 Addi ional con ols
Nex , we assess he obus ness o ou esul s o he use o al e na i e con ols. We
ocus on wo ex ensions. Fi s , we conside al e na i e measu es o educa ion o p oxy
o human capi al. Second, we conside a se o ins i u ional quali y a iables. Resul s
a e shown in Table 19 in he Appendix 6.
In model M2, we added male and emale educa ion sepa a ely, ollowing Pe o i
(1996) and Owen e al. (2002). He e, we es ima e se e al a ian s o model M2, using
a e age yea s o schooling, on he one hand, and he pe cen age o he popula ion wi h
a leas p ima y o seconda y educa ion, on he o he hand ( i s and second columns
in Table 19).
In u n, we conside wo o he mos widely used measu es o he quali y o ins i-
u ions (see also Table 13 in Appendix 2): an index o democ a ic accoun abili y
(“democ acy”), and an index o go e nmen s abili y (“s abili y”), in o ma ion aken
om he poli ical isk module o he In e na ional Coun y Risk Da abase.22 Columns
3, 4 and 5 o Table 19 ex end models M2, M3 and M4 wi h hese ins i u ional a iables;
column 6 epo s he es ima ion esul s when join ly including all he a iables om
M2, M3 and M4.
Finally, and jus o illus a i e pu poses, we epo (in he las column o he able)
es ima es o a model including all he con ols. They should be aken wi h cau ion,
howe e , gi en he sha p educ ion in sample size (by almos hal ela i e o columns
1–2) and he high deg ee o collinea i y among he eg esso s.
Es ima ed coe icien s o he pe cen age o popula ion wi h p ima y and seconda y
educa ion a e posi i e and signi ican . In he ex ended speci ica ions wi h ins i u ional
a iables, he coe icien s o bo h he quali y o democ acy and go e nmen s abili y
a e posi i e and, in mos cases, signi ican , con i ming ha he quali y o ins i u ions
is posi i ely co ela ed wi h g ow h. Mo e impo an ly, he baseline es ima ion esul s
o po e y (consis en ly nega i e) and inequali y (i s sign and signi icance depends on
he pa icula speci ica ion) a e obus o he inclusion o all hese addi ional con ols.
22 The e a e o he ins i u ional dimensions, such as he con ol o co up ion, he mili a y in powe , he
deg ee o in e na ional con lic s, o he Poli y2 a iable ( om he Poli y IV p ojec ). Including all hese
dimensions/ a iables simul aneously would in oduce se ious p oblems o collinea i y in he es ima ed
model.
123
G ow h, inequali y and po e y: a obus ela ionship? 761
Table 8 Sys em GMM es ima es: obus ness o he use o Po calNe da a
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P0, lag −
0.0959***
(0.0244)
−
0.103***
(0.0325)
−
0.0675**
(0.0272)
−0.0537*
(0.0323)
−
0.0637**
(0.0285)
−
0.0635**
(0.0323)
−0.0327*
(0.0183)
−0.0206
(0.0448)
Gini, lag −
0.0794*
(0.0407)
−0.0271
(0.0571)
−
0.0783**
(0.0366)
−
0.0777**
(0.0383)
−
0.105***
(0.0363)
−
0.0865**
(0.0412)
0.0218
(0.0491)
0.000979
(0.0694)
log y,lag −
0.0134***
(0.00496)
−
0.000522
(0.00322)
−
0.0166***
(0.00498)
−
0.00978**
(0.00465)
0.00155
(0.00333)
−
0.00822*
(0.00498)
−
0.0141**
(0.00594)
−
0.00678**
(0.00290)
−
0.0160***
(0.00576)
−
0.0246**
(0.0103)
−
0.0289**
(0.0123)
−0.0351***
(0.0121)
In . de la o ,
lag
−
0.0256***
(0.00749)
−
0.0355***
(0.0119)
−
0.0270***
(0.00792)
Female educ.,
lag
0.000286
(0.00584)
0.00111
(0.00629)
0.000747
(0.00695)
0.000651
(0.00376)
0.00286
(0.00440)
0.00464
(0.00476)
Male educ.,
lag
0.00145
(0.00634)
−0.00156
(0.00668)
−
0.0000402
(0.00700)
In la ion −
0.000204
(0.000193)
0.000531*
(0.000297)
0.000430
(0.000353)
−
0.000114
(0.000248)
0.000823
(0.000520)
0.000887
(0.000602)
T ade
openness
(log)
0.0284**
(0.0117)
0.0320**
(0.0144)
0.0280*
(0.0144)
Go . size (log) −0.00351
(0.0125)
0.00171
(0.0102)
0.00139
(0.0118)
In as uc u e,
lag
0.0208***
(0.00773)
0.0284***
(0.0105)
0.0286*
(0.0151)
123

762 G. A. Ma e o, L. Se én
Table 8 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
m2- es (p
alue)
0.125 0.345 0.140 0.00886 0.0692 0.0129 0.115 0.990 0.383 0.0668 0.219 0.109
AR(3) (p
alue)
0.0812 0.231 0.383 0.152 0.255 0.475 0.247 0.478 0.470 0.136 0.0734 0.466
Hansen (p
alue)
0.0304 0.102 0.0159 0.0934 0.153 0.173 0.114 0.318 0.206 0.246 0.182 0.196
Di -Hansen
o le els (p
alue)
0.422 0.459 0.087 0.710 0.586 0.556 0.432 0.932 0.816 0.618 0.317 0.205
Num. obs 522 522 522 474 474 474 491 491 491 360 360 360
Num.
coun ies
136 136 136 116 116 116 130 130 130 81 81 81
Num.
ins umen s
32 43 48 102 100 96 102 97 96 80 67 68
See no e in Table 5. F om Po calNe , he po e y line is 1.90 US$ 2011, which upda es he p e ious line o 1.25 US$ 2005 (Fe ei a e al. 2016). We use he in e pola ed po e y se ies p o ided in
Po calNe , which s a in 1981 and a e epo ed e e y 3 yea s. To cons uc a non-o e lapping 5-yea s panel da a simila o he one used in ou baseline speci ica ion, and ma ch po e y da a wi h all o he
a iables (g ow h and o he con ols), we use a “closes ” c i e ia o ake he a e age i wo po e y obse a ions a e 1 yea abo e and one below he assigned yea
123
G ow h, inequali y and po e y: a obus ela ionship? 763
5.5 Al e na i e econome ic speci ica ions
We also pe o med a numbe o o he obus ness checks conce ning he empi ical
speci ica ion and es ima ion app oach. To sa e space, we jus p o ide a b ie sum-
ma y he e ( esul s a e a ailable upon eques ). Fi s , we modi ied he sys em GMM
es ima ion employing di e en lag s uc u es—e.g., using yi −s,pi −s,gi −sand xi −s,
o s≥4 o he i s -di e ence equa ion and yi −4,pi −4,gi −4and xi −4 o
he le el equa ion—o using 1-s ep ins ead o 2-s ep es ima es. We also expe imen ed
wi h a modi ied e sion o he basic empi ical equa ion including a quad a ic e m in
he Gini coe icien . The main conclusion is ha he signi ican ly nega i e e ec o
po e y on g ow h is qui e obus o all hese a ia ions in speci ica ion and es ima ion
app oach, while he inequali y-g ow h ela ionship is highly agile.
Finally, we also e-es ima ed he models in a pu e c oss sec ion o coun ies, wi h
he a iables exp essed as a e ages o e he en i e sample pe iod, cap u ing wha
could be iewed as he long- un ela ionship be ween hem. The es ima ed po e y
coe icien emains uni o mly nega i e and signi ican , al hough i s p ecision declines
somewha ela i e o he panel es ima es. In u n, inequali y ends o show a nega i e
and signi ican coe icien , mo e equen ly han in he panel es ima es, consis en
wi h ecen e idence (e.g., Hal e e al 2014;Be ge al.2018) ha inequali y exe s a
nega i e long- un impac on g ow h.
6 Po e y egimes
6.1 The e ec o po e y and inequali y on g ow h
Thenonpa ame icanalysisin hep ecedingsec ionhin eda possiblenonlinea e ec s
o po e y and inequali y on g ow h. To ake a deepe look, we es ima e al e na i e e -
sions o Eqs. (1)–(3) allowing o di e en coe icien s on lagged po e y and lagged
inequali y depending on whe he he lagged alue o P0 lies abo e o below he sample
median (2.7% o ou baseline P0, see Table 1). We ollow he same s a egy condi-
ioning ins ead on he lagged le el o inequali y, and es ima e Eqs. (1)–(3) allowing
o di e en coe icien s on po e y and inequali y depending on whe he he lagged
Gini coe icien lies abo e o below i s sample median (39.8%, see Table 1). Table 9
epo s es ima es dis inguishing whe he po e y is abo e o below he median—wha
we shall label he ‘high po e y egime’ and ‘low po e y egime,’ espec i ely. In u n,
Table 10 epo s he es ima es dis inguishing whe he inequali y is abo e o below he
median— he ‘high inequali y egime’ and ‘low inequali y egime,’ espec i ely. In
bo h cases, we use he baseline sys em GMM speci ica ion (Table 5).
Table 9shows ha , unde he low po e y egime, he impac o po e y on g ow h
is nega i e bu s a is ically insigni ican . Howe e , i is nega i e and highly signi ican
unde he high po e y egime. In u n, he es ima ed coe icien on he Gini index is in
mos cases nega i e, bu i u ns signi ican only o high po e y a es and o he M1
and M3 model speci ica ions. Thus, like wi h he uncondi ional es ima es, while he
esul o po e y is obus , he esul o inequali y is no . In con as , Table 10 shows
ha , when we condi ion on he lagged le el o inequali y, he es ima ed coe icien s on
123
764 G. A. Ma e o, L. Se én
Table 9 Es ima ion esul s by po e y egimes: baseline sys em GMM
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and in as uc u es
P0, lag (P0 ≤
Median)
0.427
(0.49)
−0.101
(−
0.16)
−0.729
(−
0.92)
−0.944
(−
1.29)
−0.293
(−
0.44)
−0.430
(−
0.61)
1.230
(1.44)
−0.320
(−
0.41)
−1.019
(−0.84)
P0, lag (P0 >
Median)
−
0.125***
(−
4.30)
−
0.124***
(−
5.04)
−
0.0881***
(−
4.08)
−
0.0928***
(−
5.25)
−
0.0733***
(−
3.25)
−
0.0784***
(−
3.65)
−
0.0868***
(−
2.84)
−
0.0586***
(−
2.66)
−0.0927**
(−2.31)
Gini, lag (P0 ≤
Median)
−0.0553
(−
0.81)
−0.0589
(−
1.24)
−
0.00103
(−
0.02)
−0.00504
(−
0.10)
−0.0689
(−
1.52)
−0.0759
(−
1.50)
−0.0285
(−
0.46)
−0.0224
(−
0.55)
−0.0114
(−0.14)
Gini, lag (P0 >
Median)
−
0.138***
(−
2.83)
−
0.0980***
(−
2.63)
−
0.0544
(−
1.23)
−0.0547
(−
1.46)
−
0.0969***
(−
2.85)
−
0.107***
(−
3.17)
−0.0546
(−
1.00)
−0.0409
(−
1.40)
−0.0759
(−0.95)
log y,lag −
0.0208***
(−
4.56)
−
0.0153***
(−
2.73)
−
0.0275***
(−
5.31)
−
0.0160***
(−
4.45)
−
0.0115*
(−
1.89)
−
0.0236***
(−
6.43)
−
0.0179***
(−
4.86)
−
0.0134***
(−
2.73)
−
0.0243***
(−
5.32)
−
0.0420***
(−
2.98)
−
0.0374***
(−
3.29)
−
0.0344***
(−
4.65)
−
0.0504***
(−6.72)
m2 (p alue) 0.097 0.294 0.205 0.106 0.138 0.168 0.379 0.527 0.539 0.195 0.456 0.389 0.652
Hansen (p
alue)
0.0990 0.0476 0.256 0.211 0.111 0.354 0.408 0.238 0.476 0.163 0.425 0.990 0.122
Num. obs 745 745 745 676 676 676 655 655 655 477 477 477 477
Num. coun ies 156 156 156 131 131 131 147 147 147 88 88 88 88
Num.
Ins umen s
55 55 85 100 100 130 97 97 127 77 77 127 41
Baseline sys em GMM es ima es: 1 lag in he ins umen ma ix, s a ing a −3. See also he no e o Table 4. In he las column o he able, o u he educe he numbe o ins umen s in model M4, we conside he
case wi h 2 lags, s a ing a −3, and using he collapse op ion. The sample is di ided acco ding wi h he sample median o P0, which is 2.7% o ou baseline P0 wi h po e y line o 2US$
123
G ow h, inequali y and po e y: a obus ela ionship? 765
Table 10 Es ima ion esul s by inequali y egimes: baseline sys em GMM
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and in as uc u es
P0, lag (Gini ≤
Median)
−
0.109***
(−
3.33)
−
0.0957***
(−
4.04)
−
0.0789***
(−
4.54)
−
0.0924***
(−
4.64)
−
0.0561**
(−
2.51)
−
0.0681***
(−
3.09)
−
0.0529**
(−
2.06)
−0.0527*
(−
1.68)
−0.0700**
(−1.97)
P0, lag (Gini >
Median)
−
0.146***
(−
4.87)
−
0.115***
(−
3.93)
−
0.0976***
(−
4.15)
−
0.0696***
(−
2.93)
−
0.105***
(−
4.03)
−
0.0821***
(−
3.64)
−
0.102***
(−
3.50)
−
0.0530**
(−
2.13)
−0.0935**
(−2.57)
Gini, lag (Gini ≤
Median)
0.0343
(0.32)
−0.0374
(−
0.48)
0.119*
(1.86)
0.0885
(1.60)
−0.0408
(−0.52)
−0.0461
(−
0.73)
0.0405
(0.36)
0.00487
(0.08)
0.0931
(0.71)
Gini, lag (Gini >
Median)
−0.0397
(−0.53)
−0.0484
(−
0.91)
0.0345
(0.71)
0.0223
(0.55)
−0.0683
(−1.22)
−0.0647
(−
1.51)
−0.00858
(−
0.10)
−0.00988
(−
0.22)
0.0139
(0.17)
log y,lag −
0.0230***
(−
5.98)
−
0.00713***
(−2.91)
−
0.0203***
(−
6.25)
−
0.0152***
(−
5.05)
−
0.00585*
(−
1.82)
−
0.0163***
(−
5.55)
−
0.0186***
(−
4.63)
−
0.00851***
(−3.26)
−
0.0190***
(−
6.22)
−
0.0262***
(−
2.73)
−
0.0301***
(−
3.68)
−
0.0353***
(−
4.53)
−
0.0368***
(−3.86)
m2 (p alue) 0.1000 0.130 0.142 0.0681 0.0628 0.0770 0.352 0.475 0.441 0.203 0.359 0.277 0.285
Hansen (p alue) 0.116 0.0375 0.168 0.130 0.0793 0.430 0.242 0.137 0.377 0.0907 0.232 0.988 0.254
Num. obs 745 745 745 676 676 676 655 655 655 477 477 477 477
Num. coun ies 156 156 156 131 131 131 147 147 147 88 88 88 88
Num.
Ins umen s
55 55 85 100 100 130 97 97 127 77 77 127 41
Baseline sys em GMM es ima es: 1 lag in he ins umen ma ix, s a ing a −3. See also he no e o Table 4. In he las column o he able, o u he educe he numbe o ins umen s in model M4, we conside he case wi h
2 lags, s a ing a −3, and using he collapse op ion. The sample is di ided acco ding wi h he sample median o he Gini index, which is 39.8%
123
772 G. A. Ma e o, L. Se én
is no pe mi ed by s a u o y egula ion o exceeds he pe mi ed use, you will need o ob ain pe mission
di ec ly om he copy igh holde . To iew a copy o his licence, isi h p://c ea i ecommons.o g/licenses/
by/4.0/.
Appendix 1: Logno mal app oxima ion o al e na i e po e y
measu es
Following Dolla and K aay (2002), López and Se én (2015) o Pinko skiy and
Sala-i-Ma in (2013), we cons uc a se o po e y igu es ( he headcoun a io, P0,
he po e y gap, P1 and he squa ed po e y gap, P2) using a logno mal app oxima ion
on he basis o he obse ed pe capi a income le els and Gini coe icien s, which a e
a ailable much mo e widely han su ey-based po e y da a.
The use o he logno mal app oxima ion o he dis ibu ion o income da es back o
Gib a (1931). The li e a u e employs also o he unc ional o ms, such as he Pa e o,
he gamma o he Weibull dis ibu ion, bu he logno mal is he mo e widely used.
Indeed, López and Se én (2006) compa e he quin ile income sha es gene a ed by
a logno mal dis ibu ion wi h hei obse ed coun e pa s using da a om o e 1000
household su eys and ind he logno mal app oxima ion i s he da a ex emely well,
so ha hey a e unable o ejec he null hypo hesis ha pe capi a income ollows a
logno mal dis ibu ion.
Unde logno mali y, gi en he Gini coe icien (g), he s anda d de ia ion (σ)o
he log o income is gi en by σ−11+g
2, whe e (·) is he s anda d no mal
cumula i edis ibu ion unc ion.Using hisexp essionand helogo pe capi aincome
(y), we can compu e he FGT amily o po e y measu es o a gi en po e y line zas:
P0log(z)−y
σ+σ
2
P1log(z)−y
σ+σ
2−ey
zlog(z)−y
σ−σ
2
P2log(z)−y
σ+σ
2−2ey
zlog(z)−y
σ−σ
2+ey
z2eσ2log(z)−ν
σ−3σ
2.
Appendix 2: Da a desc ip ion and c oss-co ela ions
See Tables 13,14.
123

G ow h, inequali y and po e y: a obus ela ionship? 773
Table 13 Desc ip ion o he a iables
Name Desc ip ion Sou ce Num.Obs.
( es ic ed
o P0 and
Gini
sample)
Sample
a e age
S anda d
de ia ion
Pe capi a
eal GDP
Le el o ac i i y
and deg ee o
de elopmen :
PPP Con e ed
GDP Pe Capi a
(Chain Se ies), a
2005 cons an
p ices
Penn Wo ld
Tables 7.1
749 9793 US$
(PPP-
2005)
10,462
(PPP-2005)
Po e y The headcoun
a io P0 (le el o
po e y), he
po e y gap P1
(in ensi y), and
he squa ed
po e y gap P2
(se e i y). Fo he
log-logis ic
measu e, he
baseline po e y
line is US$ 2; o
Po calNe , we
use US$ 1.90 as
po e y line
Own
calcula ion
based on
logno mal
app oxima-
ion;
Po calNe
749 (logno -
mal)
556
(Po cal.)
16.18%
(P0)
7.34 (P1)
4.40%
(P2)
18.56
(Po cal.)
24.75% (P0)
13.22%
(P1)
8.92% (P2)
21.14%
(Po cal.)
Gini
coe icien
Measu e o income
inequali y
(be ween 0 and
1). Based only on
na ionally
ep esen a i e
su eys (a ea,
popula ion and
age), and based
on income (ne o
ans e s and
axes) and
expendi u e
igu es
UN-WIID2
(2008);
Po calNe
749 40.20% 9.98%
Yea s o
seconda y
educa ion
( o al,
male and
emale)
A e age yea s o
seconda y
educa ion o he
male popula ion
and he a e age
yea s o
seconda y
educa ion o he
emale
popula ion
Ba o and Lee
(2013)
Educa ional
A ainmen
Da a
684 1.95 ( o al)
1.77
( emale)
2.15
(male)
1.42 ( o al)
1.44
( emale)
1.44 (male)
123
774 G. A. Ma e o, L. Se én
Table 13 (con inued)
Name Desc ip ion Sou ce Num.Obs.
( es ic ed
o P0 and
Gini
sample)
Sample
a e age
S anda d
de ia ion
A ained
educa ion
(p ima y
and
seconda y)
Pe cen age o
popula ion ( o al)
wi h a leas
p ima y o
seconda y
educa ion
Ba o and Lee
(2013)
Educa ional
A ainmen
Da a
684 19.5
(p ima y)
16.2 (sec-
onda y)
12.8
(p ima y)
13.3
(seconda y)
In es men
p ices
Domes ic p ice o
in es men goods
ela i e o ha o
he U.S. as a
measu e o
ma ke
dis o ions
Penn Wo ld
Tables 7.1
745 0.65
( ela i e
o US)
0.31 ( ela i e
o US)
In la ion GDP de la o , as an
indica o o
mac oeconomic
s abili y
Wo ld
De elopmen
Indica o s,
Wo ld Bank
667 16.35% 32.05%
Deg ee o
openness
Volume o ade
wi h espec o i s
GDP
Penn Wo ld
Table 7.1
749 75.8% 49.7%
Go e nmen
size
The a io o public
consump ion o
GDP: as an
indica o o he
bu den imposed
by he
go e nmen on
he economy
Penn Wo ld
Table 7.1
749 9.65% 5.41%
123
G ow h, inequali y and po e y: a obus ela ionship? 775
Table 13 (con inued)
Name Desc ip ion Sou ce Num.Obs.
( es ic ed
o P0 and
Gini
sample)
Sample
a e age
S anda d
de ia ion
In as uc .
Index
Composi e index o
public
in as uc u e
including:
elecommunica-
ion sec o
(numbe o main
elephone lines
pe 1000
wo ke s), he
powe sec o ( he
elec ici y
gene a ing
capaci y in MW
pe 1000
wo ke s), he
anspo a ion
sec o ( he leng h
o he oad
ne wo k—in km.
pe sq. km. o
land a ea)
Wo ld
De elopmen
Indica o s,
Wo ld Bank.
Based on
Calde ón
e al. (2015)
528 0.39 1.33
Democ acy Deg ee o
Democ acy:
whe he he e a e
ee and ai
elec ions and he
deg ee o
go e nmen ’s
accoun abili y.
Range o alues
be ween
0—minimum
democ acy—and
6—maximum
democ acy)
In e na ional
Coun y Risk
Da abase
474 4.15 1.46
123
776 G. A. Ma e o, L. Se én
Table 13 (con inued)
Name Desc ip ion Sou ce Num.Obs.
( es ic ed
o P0 and
Gini
sample)
Sample
a e age
S anda d
de ia ion
Go e nmen
s abili y
Deg ee o
Go e nmen
s abili y:
measu es he
go e nmen ’s
abili y o ca y
ou i s decla ed
p og am(s) and
i s abili y o s ay
in o ice. Range
o alues be ween
1—minimum
s abili y—and
12—maximum
s abili y
In e na ional
Coun y Risk
Da abase
474 7.71 2.06
123
G ow h, inequali y and po e y: a obus ela ionship? 777
Table 14 Co ela ion ma ix
G ow h pcGDP (log) P0 (US$ 2)
logno mal
P0 (Po calne ) Gini Second.y
o al
Second.y
emale
Second.y
male
P ima y
a ain. (%)
G ow h 1.000
pcGDP(log) 0.152 1.000
P0 (US$ 2) −0.176 −0.828 1.000
P0 (Po cal.) −0.162 −0.843 0.892 1.000
Gini −0.184 −0.484 0.357 0.363 1.000
Sec.y o al 0.198 0.766 −0.592 −0.651 −0.447 1.000
Sec.y emale 0.195 0.777 −0.595 −0.650 −0.409 0.989 1.000
Sec.y male 0.196 0.738 −0.575 −0.638 −0.477 0.988 0.956 1.000
P im.a 0.007 0.202 −0.154 −0.193 −0.088 −0.151 −0.146 −0.150 1.000
Sec.a 0.214 0.641 −0.529 −0.579 −0.404 0.877 0.865 0.871 −0.184
In .P ice −0.008 0.280 −0.033 −0.139 −0.220 0.238 0.235 0.235 0.012
In la ion −0.079 −0.058 −0.022 0.000 0.071 −0.031 −0.033 −0.029 −0.072
Open 0.298 0.143 −0.168 −0.192 −0.087 0.266 0.285 0.240 −0.120
Go .Size −0.115 −0.332 0.381 0.453 0.080 −0.267 −0.248 −0.282 −0.071
In as 0.197 0.922 −0.781 −0.812 −0.499 0.741 0.743 0.722 0.218
Democ 0.126 0.682 −0.443 −0.516 −0.377 0.503 0.539 0.456 0.219
Go .S ab 0.354 0.164 −0.094 −0.150 −0.099 0.225 0.221 0.223 0.020
123

778 G. A. Ma e o, L. Se én
Table 14 (con inued)
Second. a ain.
(%)
In . p ice
( ela i . US)
In la Open, adjus .
(log)
Go . size (log) In as (index) Democ (0–6
index)
Go . s ab.
(0–12 index)
G ow h
pcGDP(log)
P0 (US$ 2)
P0 (Po cal.)
Gini
Sec.y o al
Sec.y emale
Sec.y male
P im.a
Sec.a 1.000
In .P ice 0.173 1.000
In la ion −0.017 −0.051 1.000
Open 0.308 −0.014 −0.138 1.000
Go .Size −0.182 −0.104 −0.041 −0.129 1.000
In as 0.657 0.251 −0.120 0.228 −0.307 1.000
Democ 0.432 0.306 −0.133 0.173 −0.214 0.704 1.000
Go .S ab 0.186 −0.040 −0.098 0.267 −0.110 0.259 0.158 1.000
Va iables a e ans o med in he same way as o he eg ession analysis. Fo example, pe capi a GDP in logs; po e y and he Gini coe icien in le els; openness in logs and
adjus ed by popula ion, kilome e s, o be oil expo e s and landlock; go e nmen size in logs, e c. We conside wo al e na i e measu es o he headcoun po e y a e: i s ,
using a logno mal app oxima ion (Dolla and K aay 2002; Sala-i-Ma in 2006; López and Se én 2015), and, o a educed sample, using he Po calNe da abase
123
G ow h, inequali y and po e y: a obus ela ionship? 779
Appendix 3: Al e na i e sys em GMM es ima ion esul s
See Tables 15,16.
Table 15 G ow h, po e y and inequali y: sys em GMM es ima es (collapse, all lags)
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P0, lag −
0.157***
(−4.14)
−
0.138***
(−4.06)
−
0.104***
(−3.85)
−
0.0860***
(−3.77)
−
0.0963***
(−4.17)
−
0.0918***
(−4.61)
−
0.0626**
(−2.06)
−0.0581**
(−2.08)
Gini, lag −
0.165**
(−
2.53)
−
0.153***
(−3.01)
−
0.0798*
(−
1.77)
−0.0444 −0.0289
(−0.87)
−
0.0766**
(−2.30)
0.0105
(0.32)
−0.0215
(−0.60)
(−1.05)
log y,lag −
0.0264***
(−4.83)
−
0.00454
(−
1.35)
−
0.0271***
(−5.36)
−
0.0216***
(−4.22)
−
0.00310
(−
1.18)
−
0.0181***
(−4.37)
−
0.0172***
(−5.09)
−
0.0057**
(−2.20)
−
0.0194***
(−5.80)
−
0.0324***
(−3.87)
−
0.0174**
(−2.13)
−
0.0303***
(−3.58)
In . de la o ,
lag
−
0.00251*
(−1.66)
−
0.0032
(−
1.42)
−0.0021
(−0.91)
Female educ.,
lag
0.00284
(0.60)
0.00890
(1.54)
0.00805
(1.40)
0.0068***
(2.61)
0.0022
(0.59)
0.0059**
(2.20)
Male educ.,
lag
0.00123
(0.27)
−
0.00899
(−
1.35)
−0.00664
(−1.03)
In la ion 0.0008**
(2.13)
0.0011***
(2.81)
0.0006
(1.49)
0.0009***
(3.57)
0.0009***
(2.79)
0.0007**
(2.14)
123
780 G. A. Ma e o, L. Se én
Table 15 (con inued)
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
T ade
openness
(log)
0.0202*
(1.79)
0.0372***
(4.17)
0.0159*
(1.65)
Go . size
(log)
0.0199
(1.52)
0.00859
(0.88)
0.00910
(0.97)
In as uc u e,
lag
0.0165**
(2.32)
0.0175***
(2.61)
0.0143*
(1.90)
m2- es (p
alue)
0.147 0.313 0.332 0.0710 0.179 0.115 0.236 0.427 0.397 0.226 0.179 0.262
AR(3) (p
alue)
0.832 0.527 0.575 0.817 0.837 0.955 0.631 0.544 0.532 0.802 0.887 0.804
Hansen (p
alue)
0.00333 0.000907 0.0517 0.0972 0.0428 0.114 0.118 0.0331 0.293 0.266 0.286 0.499
Di -Hansen,
le els (p
alue)
0.135 0.108 0.636 0.463 0.413 0.470 0.746 0.462 0.845 0.569 0.442 0.756
Num. obs 745 745 745 676 676 676 656 656 656 477 477 477
Num.
coun ies
156 156 156 131 131 131 147 147 147 88 88 88
Num.
ins umen s
40 40 55 85 85 100 82 82 97 82 82 97
See No e Table 4. Es ima ions a e done using 2-s ep sys em GMM (all lags s a ing a −3), bu collapsing he ma ix o ins umen s. The ins umen se s a s a −3. The di e ence Hansen
es assesses he alidi y o he ins umen s o he le el equa ion in sys em GMM. Robus s a is ics in pa en heses. ***deno es signi icance a 1%, **a 5%, *a 10%
123
G ow h, inequali y and po e y: a obus ela ionship? 781
Table 16 G ow h, po e y and inequali y: sys em GMM es ima es (collapse, educe)
M1. Skele on model M2. Ex ended wi h educa ion and
in . p ices
M3. Ex ended wi h policy a iables M4. Ex ended wi h policy and
in as uc u es
P0, lag −
0.183***
(−5.21)
−
0.167***
(−4.98)
−
0.143***
(−5.55)
−
0.141***
(−5.96)
−
0.131***
(−2.82)
−
0.126***
(−3.59)
−
0.0888***
(−3.31)
−
0.0849***
(−3.40)
Gini, lag −
0.161*
(−
1.65)
−0.0798
(−0.96)
−0.136
(−
1.54)
−0.0411
(−0.70)
−0.0575
(−1.50)
−
0.0972**
(−2.19)
0.0447
(0.86)
−0.0322
(−0.91)
log y,lag −
0.0306***
(−5.34)
0.00134
(0.29)
−
0.0299***
(−5.03)
−
0.0296***
(−5.08)
0.00413
(0.78)
−
0.0280***
(−5.10)
−
0.0255***
(−3.80)
−
0.00586*
(−1.82)
−
0.0260***
(−4.04)
−
0.0449***
−0.0218
(−1.63)
−
0.0431***
(−5.02)
(−4.50)
In . de la o ,
lag
0.000917
(0.26)
0.00168
(0.36)
0.00159
(0.39)
Female educ.,
lag
0.00563
(0.60)
0.00250
(0.29)
0.00329
(0.37)
0.00757**
(2.06)
−0.00199
(−0.39)
0.00654*
(1.85)
Male educ.,
lag
−0.00274
(−0.30)
−
0.00947
(−
1.00)
−0.00197
(−0.22)
In la ion 0.0009**
(1.96)
0.0007
(0.89)
0.0003
(0.34)
0.0013**
(2.33)
0.0017*
(1.90)
0.0014**
(2.44)
T ade
openness
(log)
0.0199
(1.29)
0.0385***
(3.86)
0.0179
(1.43)
123
788 G. A. Ma e o, L. Se én
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