IMT Institute for Advanced Studies, Lucca
Lucca, Italy
The Interplay betw een Bureaucracy and Globalization
PhD Program in Political Systems
and Institutional Change
XXV Cycle
By
Irina Mirkina
2013
The dissertation of Irina Mirkina is
approved.
Program m e Coord inator: Giovanni Orsina, LUISS–Gu id o Carli Free
International University for Social Stu d ies
Su p ervisor: And reas Bergh, Lu nd University
Tu tor: Antonio Masala, IMT Institu te for Ad vanced Stu d ies, Lu cca
The d issertation of Irina Mirkina has been review ed by:
Jam es Melton, University College Lond on
Martin Rod e, University of Cantabria
IMT Institute for Advanced Studies, Lucca
2013
Table of content
Table of content
v
List of figu res and tables
vi
Acknow led gem ents
vii
Vita
ix
Pu blications and p resentations
ix
Abstract
xii
1. Introd u ction
1
2. Institu tions in the Era of Globalization: Literatu re Review
4
2.1 Governance and the featu res of bu reau cracy
4
2.2 Measu ring qu ality of bu reau cracy
6
2.3 Globalization: term s, factors, ind icators
11
2.4 Bu reau cracy and globalization
13
3. Globalization and Institu tional Qu ality: A Panel Data Analysis
16
3.1 Scop e of research
16
3.2 Theoretical fram ew ork and p reviou s em p irical evid ence
17
3.2.1 Theoretical exp ectations
17
3.2.2 Previou s stu d ies
20
3.2.3 Su m m ary
22
3.3 Data and m ethod s
24
3.3.1 Data on institu tional qu ality and globalization
24
3.3.2 Method
28
3.4 Resu lts
29
3.4.1 Main resu lts
29
3.4.2 Robu stness tests
33
3.5 Conclu d ing d iscu ssion
35
4. Fiscal Incentives and Foreign Direct Investm ent: A Cau sal Inference
Ap p roach
38
4.1 Scop e of research
38
4.2 Review of literatu re on fiscal incentives and FDI
39
4.3 Effect of tax concession on FDI in Ru ssian regions: a natu ral
exp erim ent d esign
40
4.4 Param etric id entification strategy
46
4.4.1 Difference in d ifferences estim ation
46
4.4.2 Resu lts of the d ifference in d ifferences estim ation
51
4.5 N on-p aram etric id entification strategy
54
4.5.1 Synthetic controls m ethod
54
4.5.2 Resu lts of the synthetic controls m ethod
57
4.5.3 Main find ings
60
4.6 Conclu sion
63
Ap p end ices
65
v
References
Sp ecial p ages
102
120
List of figures and tables
Figu re 1 Scores for governm ent effectiveness and regu latory qu ality
from the WGI in 1996 and 2010
10
Figu re 2 Change in econom ic globalization and change in governm ent
effectiveness for high -incom e and low -incom e cou ntries, 1996-2009 26
Figu re 3 The m arginal effect of econom ic and social globalization on
control of corru p tion at d ifferent levels of GPD p er cap ita
34
Figu re 4 FDI inflow s in Ru ssian regions in 2002 and 2008
41
Figu re 5 Balance of relevant covariates betw een treatm ent and control
grou p s in the p re-treatm ent p eriod
45
Figu re 6 Orange regions that attracted m ore FDI after tax concession 58
Figu re 7 Orange regions that d id not attract m ore FDI after tax
concession
59
Figu re 8 Yellow regions that attracted m ore FDI after tax concession 60
Figu re 9 Balance of confou nd ing factors for orange regions
61
Figu re 10 Balance of confou nd ing factors for yellow regions
62
Table 1 Concep ts, qu estions and d efinitions
7
Table 2 Descrip tive statistics
26
Table 3 Econom ic globalization: baseline m od els; fu ll sam p le
29
Table 4 Social globalization: baseline m od els; fu ll sam p le
30
Table 5 Different typ es of globalization; su b-sam p le of low -incom e
cou ntries
31
Table 6 Different typ es of globalization; su b -sam p le of high-incom e
cou ntries
32
Table 7 Tax rates on investm ent p rofit in Ru ssian regions, p er cent
42
Table 8 Descrip tive statistics
49
Table 9 Difference in d ifferences estim ation based on GMM m od el
51
vi
Acknow ledgements
Prep aring a thesis is a long jou rney in tim e and sp ace, and in the last
three years I have travelled a lot, collecting new exp eriences and
gaining new id eas. Du ring this jou rney, I w as lu cky to m eet and to
w ork w ith m any great p eop le. I w ou ld like to exp ress m y sincere
gratitu d e to all those w ho have insp ired m e, w hom I have had a
p leasu re to w ork w ith, and w ho have m ad e this thesis p ossible.
First and forem ost, m y d eep est gratitu d e goes to m y su p ervisor,
Prof. And reas Bergh, w ho has su p p orted m e w ith his great know led ge
and enthu siastic encou ragem ent w hilst allow ing m e free rein to w ork
in m y ow n w ay. One sim p ly cou ld not w ish for a better su p ervisor.
Profou nd gratitu d e goes to m y tu tor, Prof. Antonio Masala. H e gave
m e freed om , cou rage, and the p riceless p ractical assistance d u ring the
p lanning and d evelop m ent of m y w ork.
This thesis w ou ld not have been p ossible w ithou t the help , su p p ort ,
and p atience of Dr Leonard o Baccini, not to m ention his ad vice and
u nsu rp assed know led ge of the m ost sop histicated statistical m ethod s.
I w ou ld like to esp ecially thank Dr Therese N ilsson. Ou r collaboration
and vivid d iscu ssions brou ght m y research skills to the new level.
Moreover, chap ter 3 of this thesis is largely based on the p roject that w e
have carried ou t together w ith Prof. Bergh and Dr N ilsson.
I am end lessly gratefu l to Prof. Leonard o Morlino for sharing his
im m ense know led ge and rem arkable exp ertise and insp iring w ith his
p ersonal exam p le of an ind efatigable scholar.
My thesis has also benefited from the p roficiency and au thority of
Prof. Qu an Li. Chap ter 4 is based on the stu d y that w e have d one
together w ith Prof. Baccini and Prof. Li.
I am very thankfu l to m y external review ers, Dr Jam es Melton and
Dr Martin Rod e, for their p reciou s com m ents and su ggestions that
allow ed m e to im p rove significantly this thesis.
I w ou ld like to exp ress m y ap p reciation to the researchers and staff at
the Institu t Barcelona d ‘Estu d is Internacionals for giving m e the
op p ortu nity to carry ou t m y research p roject as a visiting fellow . I am
p ersonally thankfu l to Prof. Ju an Díez Med rano, Matthias vom H au ,
Martijn Vlaskam p , Benjam in Kienzle, and Carlos Sánchez Moya.
vii
I d eep ly ap p reciate the research op p ortu nities p rovid ed by the
Dep artm ent of Econom ics at the School of Econ om ics and Managem ent
at Lu nd University and by the Research Institu te of Ind u strial
Econom ics. My sincere and p rofou nd thanks go to Prof. David
Ed gerton and Prof. Magnu s H enrekson.
I have greatly benefited from the institu tional, acad em ic, and p ersonal
su p p ort from the p eop le at IMT; p articu larly, I ow e a d ebt of gratitu d e
to Prof. Giovanni Orsina. I also w ou ld like to exp ress m y thanks to
Serena Argentieri and Daniela Giorgetti for excellent assistance and the
highest effectiveness in every single m atter. I am also gratefu l for
kind ness and cou rtesy of Antonella Barbu ti and Barbara Iakobino.
I am d elighted to have great classm ates, a tireless sou rce of su p p ort,
critiqu e, and joy. I w ish them all the best of lu ck in their fu tu re
end eavou rs.
Albert Schw eitzer once said that, ―At tim es ou r ow n light goes ou t and
is rekind led by a sp ark from another p erson. Each of u s has cau se to
think w ith d eep gratitu d e of those w ho have lighted the flam e w ithin
u s.‖ My u tm ost thanks to Alexand er Mironov are beyond w ord s.
I am also ind ebted to Svetlana Lep eshkina, Oksana Bu takova, and Olga
Slep tsova for being w ith m e in the right p lace at the right tim e.
Sp ecial thanks shou ld be given to Marta Palacín Mejías for her lovely
hosp itality, contagiou s enthu siasm , and for her role as m y p rim ary
contact in Barcelona.
I w ou ld have not m ad e this jou rney w ithou t Enrico De Angelis. H is
w arm su p p ort and affectionate encou ragem ent brighten u p both m y
life and m y research.
Last bu t not least, I w ish to thank m y p arents for everything they have
ever d one for m e. I d eep ly ap p reciate their end less care and
cou ntenance.
viii
Vita
Sep tem ber 24, 1980 Born, Barnau l, Ru ssia
2002
M.Sc. in Econom ics
Dip lom a w ith d istinction
All-Ru ssian State Distance Institu te of Finance and
Econom ics, Moscow , Ru ssia
2002–2008
Lectu rer
Dep artm ent of Finance and Cred it
All-Ru ssian State Distance Institu te of Finance and
Econom ics, Barnau l, Ru ssia
2004–2008
Research associate
Dep artm ent of Econom ics
N ovosibirsk State University of Econom ics and
Managem ent, N ovosibirsk, Ru ssia
2007–2010
Lectu rer
Dep artm ent of Econom ic Science
Altai Institu te of Financial Ad m inistration,
Barnau l, Ru ssia
2010–2013
Ph.D. stu d ent in Political System s and Institu tional
Change
IMT Institu te for Ad vanced Stu d ies, Lu cca, Italy
2010–2011
Research assistant
Dep artm ent of Econom ics and Institu tional
Change
IMT Institu te for Ad vanced Stu d ies, Lu cca, Italy
2011–2012
Visiting scholar
Institu t Barcelona d ‘Estu d is Internacionals, Sp ain
2013
Visiting research fellow
School of Econom ics and Managem ent
Lu nd University, Sw ed en
Publications and presentations
I. Mirkina (2013) 15 Y ears of the Federal Target Programs in Russia: Good
Intentions and Cruel Reality [Kind le Ed ition]. Retrieved from
http :/ / w w w .am azon.com / d p / B00BJ130PQ/ .
ix
L. Baccini, Q. Li, and I. Mirkina (2013) Tax Cut and Foreign Direct
Investment: A Causal Inference A pproach. Pap er p resented at the
International Stu d ies Association Convention, San Francisco, USA .
A. Bergh, I. Mirkina, and T. N ilsson (2013) Globalization and Institutional
Quality: A Panel Data A nalysis. Pap er p resented at the 15th World
Econom y Meeting, Santand er, Sp ain.
I. Mirkina (2011) The Impact of Institutions on the International Relations of
a Country. Pap er p resented at the 8th Convention of the Central and
East Eu rop ean International Stu d ies Association, Istanbu l, Tu rkey.
I. Mirkina (2011) The Interplay between Bureaucracy and Globalization.
Pap er p resented at the 3rd Grad u ate Conference of the Italian Political
Science Association (Società Italiana d i Scienza Polit ica), Tu rin, Italy.
Y. Shvetsov, I. Mirkina (2009) Fed eral'nye tselevye p rogram m y kak
instru m ent u p ravleniya gosu d arstvennym i raskhod am i [Fed eral Target
Program s as an Instru m ent for the State Exp enses]. Finansy, 2009-4.
Y. Shvetsov, I. Mirkina (2007) Gosu d arstvennye investitsii i razvitie
regional‘noi econom iki [State Investm ent and the Regional
Develop m ent]. Federativnye otnoshenia i regional’naya social’noeconomicheskaya politika, 2007-5.
Y. Shvetsov, I. Mirkina (2007) K vop rosu ob effektivnosti fed era l'nyh
tselevyh p rogram m v bu d zhetnoy sfere [On the Effectiveness of the
Fed eral Target Program s in Pu blic Econom y]. Finansy, 2007-6.
I. Mirkina (2007) Vned renie bu d getirovania, orientirovannogo na
resu ltat [Introd u cing Goal-Oriented Bu d geting], in: M anagement of
Organizations and Financial M arkets for Economic Growth: conference
proceedings, Barnau l: International Acad em y
for
Ind u strial
Managem ent.
I. Mirkina (2006) Finansovoe obesp echenie social‘nyh reform [Financial
Basis of the Social Reform s], in: Strategy and Tactic of Russia’s
Development for the Socially Oriented Economics: conference proceedings,
Barnau l: AltGTU.
Y. Shvetsov, I. Mirkina (2005) Problem y realizatsii fed eral‘nyh tselevyh
p rogram m na regional‘nom u rovne [Problem s in Im p lem entation of the
Fed eral Target Program s at the Regional Level]. Finansy, 2005-5.
I. Mirkina (2005) Problem y vned reniya bu d getnogo u cheta [Problem s
of the Bu d geting Reform s]. V Z FEI Discussion Papers Series, 2005-8.
x
I. Mirkina, I. Ru d enko (2004) N alogovaya nagru zka i ee v liyanie na
chistu yu p ribyl firm y [Tax Assessm ent and N et Profit of the Firm ].
V Z FEI Discussion Papers Series, 2004-7.
I. Mirkina (2003) Asp ecty sovm estnoi realizatsii fed eral'nyh tselevyh
p rogram m regional'nym i i fed eral'nym i organam i vlasti [Coop erative
Investm ent throu gh the Fed eral Target Program s]. V Z FEI Discussion
Papers Series, 2003-6.
I. Mirkina, E. Kolobova (2002) Problem y realizacii i finansirovaniya
gosu d arstvnnyh
investicionnyh
p rogram m
[Problem s
of
Im p lem entation and Financing of the State Inves tm ent Program s].
V Z FEI Discussion Papers Series, 2002-5.
xi
Abstract
This research focu ses on a tw ofold issu e: 1) the challenges that
globalization creates to institu tions, having to d eal w ith a new
international ord er; 2) the challenges that state governan ce creates for
global actors, having to ad ap t them selves to the national and su b national p ecu liarities. The globalization –bu reau cracy interp lay is a
p olitically contested p henom enon, in w hich d ifferent m od els of
interactions am ong states, firm s, and citizens ap p ear and transform
both nationally and internationally.
At the national level, the im p act of globalization on qu ality of
governance is exam ined em p irically across cou ntries. The analysis w ith
fixed effects m od els is based on a p anel d ataset, covering over
100 cou ntries in the p eriod 1992–2010. The stu d y exam ines an effect
from both econom ic and social globalization factors on d ifferent
governance featu res, inclu d ing governance effectiveness, regu latory
qu ality, control of corru p tion, accou ntability, p olitical stability, and ru le
of law . The find ings show that variou s governance featu res seem to
d iverge in how easily they resp ond to the new state of affairs that
follow s w ith m ore globalization. Moreover, in line w ith the theoretical
p red ictions, globalization affects institu tions d ifferently d ep end ing on
the cou ntry‘s level of d evelop m ent. The resu lts thu s su ggest that the
p reviou s find ings on p ositive effects of international econom ic flow s on
institu tional qu ality are likely d riven by changes in rich cou ntries.
The im p act of institu tional p olicies on globalization factors is analyzed
at the su b-national level, u sing a p anel d ataset of 82 Ru ssian regions in
the p eriod 1995–2010. The stu d y takes an ad vantage of exam ining
d ifferent typ es of fiscal incentiv es, introd u ced in som e regions of Ru ssia
in 2003, treating them as a natu ral exp erim ent and estim ating the
cau sal effect of tax concessions on foreign d irect investm ent inflow s
w ith tw o cau sal inference techniqu es: d ifference in d ifferences
estim ation and synthetic controls m ethod . The find ings confirm that tax
concessions for investm ent lead to m ore foreign d irect investm ent
inflow s. H ow ever, selective tax concessions for the governm ent
sanctioned im p ortant investm ent p rojects d o not have the exp ected
effect, or the effect is sp orad ic and w eak at best. As governm ents seek
to increase the national and su b-national attractiveness for foreign
investors, these find ings have im p ortant im p lications for the d esign of
institu tional p olicies.
xii
1. Introduction
Globalization transform s m od ern society and econom y. The states,
therefore, face a challenge in ad ap ting them selves to the global factors
and netw orks. They ou ght to im p lem ent new p olicies or to change
existing ones, seeking integration into the international o rd er.
H ow ever, one generally sees the governm ental stru ctu res as highly
inertial and slow to change. Once their form al m ethod s and p roced u res
are established , they tend to u se the sam e m ethod s and ru les, w hether
the situ ation changes or not. And even if bu reau cratic stru ctu res d o
change u nd er an external p ressu re, it is u nclear how far the
d evelop m ent w ou ld go. Thu s, one m ight w ond er abou t the effect that
the rigid and slu ggish state bu reau cracies have on globalization factors.
In som e cases, extensive ad m inistrative bu rd ens and form alities
restrain the d evelop m ent of international trad e, investm ent, and
m igration. On the other hand , in those cou ntries w here m any of the
ad m inistrative p roced u res and bu reau cratic institu tions are w eak or
u nsp ecified , global actors w ou ld find neither field to act, nor ru les to
follow . The qu estion is on a m u tu al effect of bu reau cracy and
globalization in this interp lay. Does globalization red u ce bu reau cratic
barriers throu gh, for exam p le, m u ltilateral agreem ents or internationa l
norm s? Or d oes it create ju st the op p osite, a new kind of
―internationally w id esp read ‖ bu reau cracy? Does bu reau cracy su p p ort
international coop eration by p rovid ing the legal and institu tional
fram ew ork? Or on the contrary, d oes it fight w ith the global featu res,
trying to keep its p u rely national p ow er?
This research focu ses on a tw ofold issu e: 1) the challenges that
globalization creates to the existing national and su b -national
institu tions, having to d eal w ith an exp and ing international ord er; 2)
the challenges that state governance creates for global actors, having to
ad ap t them selves to the d om estic form al and inform al p ecu liarities.
Globalization –bu reau cracy interp lay is a p olitically contested
p henom enon, in w hich d ifferent m od els of interactions a m ong states,
firm s, and citizens ap p ear and transform both nationally and
internationally.
Each state keep s its national interests above all. Bu t globalization
com es, to a large extent, as the w eakening or even rem oval of the
institu tional bu ffers betw een d om estic econom y and global m arkets. (Ó
Riain, 2000) States, therefore, shou ld resp ond to p ressu res from local
societies and global m arkets sim u ltaneou sly that lead s to the changes
1
not only in the state-m arkets interaction bu t also in the state stru ctu res
them selves.
States are interconnected , and none cou ld be totally isolated from the
global ord er. States are integrated into global m arkets throu gh, for
instance, international trad e and p rod u ction d istribu tion (Wallerstein,
2011). Besid es, states com p ete w ith one another to attract m obile cap ital
(Arrighi, 1994). Moreover, bu siness exp ansion requ ires m ore
bu reau cratic w ork on creating and ad ap ting regu lations, w hich lead s to
the exp ansion of the state ap p aratu s (Chase-Du nn and Grim es, 1995).
Different m od els of state's reaction to globalization m ight sp read
throu gh the interaction of states or throu gh the influ ence of
international and su p ranational organizations. Globalization m ay p ose
new p roblem s for states, bu t it also m ay reinforce states to id entify an d
to m anage those p roblem s on behalf of their societies (Meyer et al.,
1997). Contem p orary theories both in p olitical science and in econom ics
em p hasize the w ays in w hich national and global actors interact w ith
each other (Block, 1994; Evans, 1997). The r elations am ong them are
essentially tense, and com p etition fights can often resu lt in a zero -su m
gam e. The d irection and p ace of the cou ntry's d evelop m ent are
d eterm ined by how institu tions regu late these tensions, creating basis
for both national actors, going global, and foreign actors, com ing into
the cou ntry. This d evelop m ent is p ath -d ep end ent and reflects the
m u tu al effect of state and p rivate actors at the su bnational, national,
and international levels. In this resp ect, Band elj (2009) argu es that the
1
global econom y can be concep tu alized as an institu ted p rocess . States
contribu te to the institu tionalization of globalization by ad ap ting
form al p olicies, as w ell as inform al norm s, by p rovid ing both d om estic
and foreign actors w ith organizational resou r ces, and by su p p orting
real econom ic and social interactions (Band elj, 2009).
The globalization–institu tions interactions have received som e
attention am ong the p olitical theorists, bu t so far have not been
exam ined em p irically. Does good governance actu ally p rom ote
international coop eration? Does globalization itself su p p ort better
governance? Do the available m easu res of globalization and
institu tional qu ality p rovid e a reliable tool to answ er these qu estions?
The answ ers to these qu estions have both theoretical and p olicy
im p lications. If globalization or its variou s su b -com p onents are able to
1
She uses Polanyi's (1957 [1944]) id ea about "econom y as instituted process," m eaning
interactions between econom ic and noneconom ic institutions.
2
im p rove qu ality of governance, then international factors shou ld be
taken into accou nt not only in cross-cou ntry com p arisons bu t also in
im p lem entation and analysis of d om estic p olicies.
Before exam ining the strength and d irection of the relationship
betw een d om estic bu reau cracies and globalization factors, one shou ld
d efine how to concep tu alize both bu reau cracy and globalization and
how to assess them . H ence, the th esis p roceed s as follow s. Chap ter 2
d iscu sses institu tional featu res of bu reau cracy and their reflection in
the existing m easu res of governance qu ality. Then it p roceed s to
d escribing globalization as a p rocess and as a historical p henom enon,
and to exp laining how bu reau cracy and globalization shap e and
reshap e each other. Chap ter 3 d iscu sses the im p act of variou s typ es of
globalization on institu tional qu ality at the national level and then
exam ines this im p act em p irically w ith the analysis of a p anel d ata set,
covering over 100 cou ntries in the p eriod 1992–2010. Chap ter 4
d iscu sses the effect from the fiscal elem ents of institu tional p olicies on
globalization factors at the su b-national level. The effect is tested
em p irically, u sing a p anel d ataset of 82 regions of Ru ssia in the p eriod
1995–2010.
3
2. Institutions in the Era of Globalization : Literature Review
2.1 Governance and the features of bureaucracy
One of the m ost im p ortant roles of the state in m arket econom y is in
d eterm ining the ru les of d om estic–international interaction by creating,
im p lem enting, and ad ap ting variou s typ es of regu lations. This role, as
w ell as m any others, heavily d ep end s on the qu ality of governm ent, i.e.
the fu nctioning of execu tive branches and their bu reau cracies.
Im p ortance of bu reau cratic w ork is d efined by the ability of p u blic
officials a) to d eliver services both to m arket actors and to society, and
b) to create and enforce ru les and regu lations (Olsen, 1988; Fu ku yam a,
2013).
Many sou nd theoretical m od els, created in th e XX centu ry, contribu ted
to the analysis and u nd erstand ing of bu reau cratic stru ctu res (Weber,
1978 [1946]; Blau , 1955; Tu llock, 1965; Crozier, 2009[1964]; Lip sky, 1980;
Mises, 1983 [1944]; Dow ns, 1994 [1966]; N iskanen, 1994 [1971]).
Weber's classical analysis p rovid es system atic argu m ents on the
genesis and featu res of bu reau cracy. Weber d escribed in d etails the
fu nd am ental role of bu reau cracy for a cou ntry's d evelop m ent.
Particu larly, he listed several featu res of bu reau cratic stru ctu res,
inclu d ing the follow ing (Weber, 1978: 196-203):
- There are som e fixed ju risd ictional areas, ord ered by law s and
ad m inistrative regu lations,
- There is a hierarchy w ithin a fixed system of su p er - and
su bord ination,
- The officials u nd ergo thorou gh and exp ert training ,
- There are som e general ru les, stable and exhau stive,
- The officials are ap p ointed by a su p erior au thority ,
- There is a career p ath w ithin the office,
- *The officials get a fixed salary.
- *The office constitu tes tenu re for life, and
- *Job requ ires fu ll w orking cap acity of the official
Last three of the listed featu res d o not reflect m od ern reality, thou gh.
As Fuku yam a (2013) p oints ou t, fixed salaries are not com p atible w ith
the m arket-like incentives often offered to bu reau crats u nd er N ew
4
Pu blic Managem ent. Moreover, it is com m on for talented ind ivid u als
from the p rivate sector or the acad em y in m any cou ntries to serve in
governm ent for p eriod s of tim e.
Many stu d ies d ealt w ith the other Weberian featu res later on, having
com e, how ever, to rather d ifferent conclu sions.
Au strian School of econom ics sees bu reau cracy m ainly as an
instru m ent, the tool for execu ting law s and regu lations. State fu nction
cou ld not be realized w ithou t bu reau cratic w ork: ―There is a field ,
nam ely, the hand ling of the ap p aratu s of governm ent, in w hich
bu reau cratic m ethod s are requ ired by necessity‖ (Mises, 1983: 48).
Minim al bu reau cracy is requ ired for the p rotection of p rop erty rights,
p hysical p rop erty, and the p eop le, as w ell as for insu ring social
coop eration am ong the m em bers of society and m arket actors (Mises,
1983: 20).
H ow ever, there is alw ays a d anger of overw helm ing bu reau cracy,
u nable to correct its ow n errors, that w ou ld requ ire som e external
(m arket or social) factors to intervene. ―The bu reau crats see in the
failu re of their p reced ing m easu res a p roof that fu rther inroad s into the
m arket system are necessary‖ (Mises, 1983: 35).
Pu blic choice theorists concentrate on p roblem s of bu reau cratic
accou ntability. This school of research analyzes the p athologies of
overw helm ing institu tions, their im p lications, and the p ossible
m ethod s of control from society. Tu llock (1965) investigated harm fu l
effects of m isinform ation that cou ld be channeled w ithin the hierarchy
of ad m inistrative stru ctu res. Crozier (2009) later conclu d es that the lack
of accou ntability, be it before elected officials, citizens, or p ressu re
grou p s, is attribu table to bu reau cratic inertia (see Meier and Krau se
(2003) for d iscu ssion). N iskanen (1994) extend ed this view , claim ing
that bu reau cracy is p reoccu p ied w ith its ow n bu d get m axim ization and
its interests contrad ict the interests of society, thu s, m aking bu reau crats
u nresp onsive to society d em and s. Dow ns (1994) argu ed that the
ind ivid u al p references of bu reau crats m ight d iffer, thu s p olicy m aking
and coord inating p rocess cou ld becom e d ifficu lt. Finally, Olson (2000)
claim ed that the m ain aim of p olitical d evelop m ent shou ld be the
creation of instru m ents like ru le of law and accou ntability that cou ld
lim it the state‘s d iscretion.
The scholars w ithin a p rincip al–agent fram ew ork look for the m ethod s
of corru p tion control and better accou ntability throu gh m anip u lation of
5
incentives, e.g. com p etition, m anip u lation of w age scales, shortening of
accou ntability rou tes, etc. (see Fu ku yam a (2013) for d iscu ssion). Many
stu d ies, ap p lying the p rincip al–agent view s, fou nd variou s incentives
and m onitoring activities being effective in controlling the behavio u r of
bu reau crats (Miller and Moe, 1983; Moe, 1990; Scholz and Wei, 1986;
Scholz, Tw om bly, and H ead rick, 1991; Weinga st and Moran, 1983).
Other stu d ies of bu reau cracy and bu reau cratic p erform ance exam ined
the im p ortance of the bu reau crats‘ valu es and its im p act on p u blic
p olicy ou tp u ts (Eisner, 1991; Khad em ian, 1992). Several researches
show ed how attem p ts to im p ose var iou s bu d geting system s have been
altered and even sabotaged by bu reau crats in the im p lem entation
p rocess (Wild avsky, 1984 [1964]; Meier and Krau se, 2003). Finally, op en
system s theories of bu reau cracy focu s on how organizations cop e w ith
the p olitical environm ent (Keiser, 1999), as w ell as on a general
fram ew ork for assessing p olitical control (Meier, 2000; Meier and
Krau se, 2003).
2.2 M easuring quality of bureaucracy
To assess em p irically the m u tu al im p act of bu reau cracy and
globalization, one shou ld be able to m easu re the qu ality of governance
w ith a reliable, cross-nationally com p arable tool. Existing m easu res
typ ically rely, in w hole or in p art, on su rveys of either exp ert op inions,
either m arket actors (international and d om estic firm s), or citizens.
Qu estions in su ch su rveys involve national law s and regu lations , the
level of ―red tap e‖ and ad m inistrative bu rd ens, or p ercep tions of
corru p tion (Chong and Cald erón, 2000; Mau ro, 1995).
These m easu res, how ever, have a nu m ber of lim itations. First, as the
u nd erstand ing of governance featu res d iffers am ong the field s and
theories, d ifferent exp erts m ay intend d ifferent things w hen
resp ond ing to the sam e su rvey qu estion (Fu ku yam a, 2013). Another
issu e is the assu m p tion that the interests of firm s (either foreign or
d om estic) and the interests of the nation are sim ilar (Ku rtz and
Schrank, 2007). For instance, som e regu lations, im p osed by the state
(e.g. labor law s, environm ental controls, or antitru st actions) m ay be
assessed as ―bu rd ensom e‖ and ―grow th -inhibiting‖ by the m arket
actors. If, how ever, su ch controls d o not exist, states m ight be ju d ged
less severely by the firm s (Ku rtz and Schrank, 2007). Moreover, as
Rothstein (2011) p oints ou t, the existing ind icators of governance
qu ality p resu m e som e strong norm ative p olicy p references (e.g., less
rather than m ore regu lation), w hich m ight skew their valu es. Finally,
6
since the d irect assessm ent of m any governance asp ects is not p ossible,
m ost of the ind icators ap p roxim ate them (w ith, for exam p le, inp u t –
ou tp u t evalu ations), i.e. ―m easu re w hat is m easu rable‖ (Fu ku yam a,
2013).
Taking into accou nt the listed lim itations, it is w orth d iscu ssing the
ind icators from three w ell-know n sou rces: the World w id e Governance
Ind icators p rod u ced by the World Bank (WGI), the Global
Com p etitiveness Rep ort by the World Econom ic Foru m (GCS), and the
Risk Briefing assessm ent as w ell as the Dem ocracy Ind ex by the
Econom ist Intelligence Unit (EIU) (table 1).
These m easu res have a nu m ber of ad vantages. First, they inclu d e
d ifferent typ es of m easu res: aggregate ind icators (WGI), su rvey (GCS),
2
and exp erts‘ assessm ent (EIU) . Second ly, u nlike som e other ind icators,
these sou rces cover a consid erable nu m ber of cou ntries. The WGI ha s
started w ith 185 cou ntries in 1996 and offered the scores for alread y 210
cou ntries in 2010. The EIU observes 170 cou ntries in the Dem ocracy
Ind ex (issu ed early from 2006) and 179 cou ntries in the Risk Briefing
assessm ents (this nu m ber has increased from 120 cou ntries in 1996).
The biggest d evelop m ent occu rred in the GCS: from 58 cou ntries in
1996 u p to 133 cou ntries in 2010. Third ly, they concentrate, again u nlike
som e others, not only on one asp ect of governance, e.g. corru p tion, bu t
on m any d ifferent featu res of the governm ent fu nctioning, inclu d ing
p olitical rep resentation, stability and balance of p ow ers, red tap e,
corru p tion, liberal p olicies and their im p lem entation, etc.
This broad range of featu res, how ever, creates also som e concep tu al
and m ethod ological p roblem s. First of them is related to the end u ring
d ebate on w hat is governance. Du e to the lack of agreem ent in
d efinitions even am ong the p olitical theorists, the sou rces of nu m erou s
―institu tional‖ ind exes and ind icators try to avoid giving their ow n
d efinitions. Instead , they offer a variety of qu estions and / or m arks to
d escribe certain asp ects of the governm ent‘s w ork. And althou gh it
looks som etim es like tou ching d ifferent p arts of an elep hant in the
d ark, the resu lts seem qu ite encou raging as long as one takes into
accou nt their com p lexity and interd ep end ence (table 1 and ap p end ix
A).
2
The WGI as a set of the aggregate ind icators rely on the GCS and the EIU among the
others.
7
Table 1 Concep ts, qu estions and d efinitions
Governance
feature
Definition of
governance
World w id e
Governance
Ind icators (WGI)
Trad itions and
institutions by
w hich authority in
a country is
exercised
Governm ent
effectiveness
The quality of
public services,
the capacity of the
civil service and
its ind epend ence
from political
pressures; the
quality of policy
form ulation and
im plem entation,
and the cred ibility
of the
governm ent's
com m itm ent to
such policies
Regulatory
quality
The ability of the
governm ent to
provid e sound
policies and
regulations that
enable and
prom ote private
sector
d evelopm ent
Control of
corruption
The extent to
w hich public
pow er is exercised
for private gain,
includ ing both
corruption and
―capture‖ of the
state
Global
Com petitiveness
Report (GCS)
Legal and
ad m inistrative
fram ew ork w ithin
w hich ind ivid uals,
firm s, and
governm ents
interact to generate
w ealth
- Wastefulness of
governm ent
spend ing
- Burd en of
governm ent
regulation
- Efficiency of legal
fram ew ork in
settling d isputes
- Efficiency of legal
fram ew ork in
challenging
regulations
- Transparency of
governm ent
policym aking
-
- Diversion of
public fund s
- Public trust of
politicians
- Irregular
paym ents and
bribes
8
Econom ist Intelligence Unit‘s
Risk Briefing assessm ents
(EIU)
-
- Is the governm ent likely to
open, liberal and probusiness policies for
nationals and foreigners?
- What is the quality of the
bureaucracy in term s of
overall com petency/
training, morale/ d ed ication?
-H ow pervasive is red tape?
- H ow pervasive is
corruption?
- H ow accountable are public
officials?
- Is there a risk that this
country could be accused of
hum an rights abuses?
- Is the tax regim e clear and
pred ictable?
- What is the risk that
corporations w ill face
d iscrim inatory taxes?
- What is the risk from
retroactive taxation?
- What is the risk of
d iscrim inatory tariffs?
- What is the risk of excessive
protection (tariff and non tariff) in the next two years?
-
One can see from com p arisons in table 1 that the lim its of all categories
are highly am bigu ou s and cou ld lead to confu sion. Marks of
governm ent (in)effectiveness in the GCS cou ld be com p ared w ith the
regu latory qu ality in term s of the WGI, w hile the EIU‘s assessm ent of
the governm ent effectiveness inclu d es hu m an rights, bu reau cratic
com p etency, red tap e, corru p tion, and accou ntability all at once. Som e
qu estions for the ind ivid u al ind icators are excessively p recise (for
exam p le, the GCS d istingu ishes seven typ es of bribes), som e others,
how ever, are rather vagu e (e.g. ―H ow accou ntable are p u blic officials?‖
in the EIU).
The second concep tu al p roblem refers to the id ea that Pu tnam et al.
(1993) state as ―d ifferent governm ents m ight be sim p ly good at
d ifferent things.‖ Concentrating only on governm ent effectiveness o r
control of corru p tion w ou ld be by no m eans enou gh for a fu ll analysis;
one shou ld not skip any of the governance featu res. Moreover, it m ight
be necessary to keep in m ind p ossible existence of som e other
governance featu res, not listed exp licitly in the institu tional ind exes.
Com p aring tw o or m ore d im ensions of governance gives som e insights
abou t p ossible internal factors that m ight affect the cou ntry‘s
institu tional qu ality (figu re 1). On the one hand , there are som e ou tliers
scoring relatively w ell for one featu re of governance bu t not for the
others. For instance, Bhu tan w as assessed m u ch higher for governm ent
effectiveness and p olitical stability than for regu latory qu ality and
accou ntability over the w hole p eriod . On the other hand , clear
im p rovem ent or w orsening of the institu tional qu ality in general cou ld
be seen only in few cases. More often, the cou ntries m anage to im p rove
certain asp ects of governance, w hile having a little su ccess in the other
asp ects.
The last m ethod ological p roblem occu rs her e. Since the ind icators
certainly m easu re d ifferent things w ith d ifferent m ethod s, it m ight w ell
be that only the m ost evid ent cases receive reliable scores. That is: in
those cases w here m any ind icators agree on a cou ntry‘s institu tional
qu ality, it hap p ens becau se its im p rovem ent or w orsening is
u nqu estionable. H ow ever, for m any other cou ntries fallen ―in the
m id d le,‖ the scores and ranks are som ew hat relative that cou ld m ake
any d irect cou ntry com p arisons vagu e and d isp u table. For this m atter,
for instance, the au thors of the WGI, besid e the estim ation of
governance ind icators, rep ort also the m argins of error, highlighting
their im p ortance. While taking this into accou nt, m any significant
9
d ifferences am ong cou ntries can in fact be assessed u sing these
ind icators (Kau fm ann et al., 2010). It is, therefore, p referable u sing the
WGI instead of either alternative. In ad d ition to its grow ing p olicy
relevance (World Bank, 2000), it d isp lays reasonable reliability and has
a broad coverage, avoid ing sam p le selection p roblem s at the cou ntrylevel (Ku rtz and Schrank, 2007).
Figu re 1 Scores for governm ent effectiveness and regu latory qu ality
from the WGI in 1996 and 2010
10
2.3 Globalization: terms, factors, indicators
Globalization m ay be d efined as a p rocess, a historical p eriod , or as a
p olitical and econom ic p henom enon.
In the first u nd erstand ing, globalization reveals itself throu gh a variety
of links and interconnections betw een the states. ―It d efines a p rocess
throu gh w hich events, d ecisions and activities in one p art of the w orld
can com e to have a significant consequ ence for ind ivid u als and
com m u nities in qu ite d istant p arts of the globe‖ (McGrew , 1990). This
view is shared by m any scholars, d efining globalization as a set of
―p rocesses d eriving from the chan ging character of the good s and
assets that com p rise the base of the international p olitical econom y‖
(Cerny, 1995: 596).
In the second u nd erstand ing, the term ‗globalization‘ m ight serve for
d escribing a context in w hich events occu r (Reich, 1998). The
globalization p eriod m ight be d escribed as one that began in the m id 3
1970s . Other interp retations d ate globalization from the end of the
1970s and the beginning of the 1980s, becau se of a series of events that
clu stered w ithin a lim ited tim e p eriod : sp read of technologies,
international d ebt crises, the second oil crisis; rise in inflation rates; rise
4
of cap ital m ovem ent, etc.
Finally, globalization is m ost often characterized as a com p lex system
of p olitical and econom ic p henom ena. These inclu d e d iffu sion of
technology, m oving of p rod u ction and cap ital, integration of cap ital
m arkets, and the changes in the international d ivision of labou r (Reich,
1998). All these p henom ena certainly have had historical p reced ents ;
nonetheless, these events have no p reced en ts in term s of sp eed and
intensity. Globalization thu s characterizes the escalation of existing
p rocesses rather than the d evelop m ent of a new one (Reich, 1998).
Su ch intensification creates new challenges and op p ortu nities for
international and d om estic actors, as w ell as for the states (Jones, 1995).
The im p act of globalization varies across cou ntries, d ep end ing on
m any socio-econom ic factors. Great exp ectations abou t global p olitical
convergence based on liberal d em ocratic institu tions across nations
(Fu ku yam a, 1992; Mand elbau m , 2005) d id not fu lly com e into reality.
3
For d iscussion, see H irst, P. and Thom pson, G. (1996). Globalization in Question: The
International Economy and the Possibilities of Governance. Cam brid ge: Polity Press.
4
For d iscussion on this view, see Solomon, R. (1994). The Transformation of the World
Economy, 1980–1993. London: MacMillan.
11
H ow ever, there is som e evid ence in su p p ort of the neoliberal concep t of
globalization, w hich exp ects that the cou ntries w ou ld red efine their
national interests to achieve greater op enness to the w orld econom y
(Ohm ae, 1991; Fried m an, 2005; Rod e and Gw artney, 2012). Critiqu e of
globalization regard s som e u nd esired consequ ences of su ch op enness,
inclu d ing the increase of p overty and socio-econom ic d ivisions
(Mu rp hy, 2001), cu ltu ral conflicts and ethnic nationalism (Mansfield
and Snyd er, 2007), and rise of p rotectionist p olicies (Stalling s, 1995;
Mansfield and Milner, 1997; H elleiner and Pickel, 2005). Su ch
consequ ences w ere esp ecially evid ent in the less d evelop ed cou ntries,
as globalization cou ld u nd erm ine national sovereignty in variou s areas,
from finance to labou r m obility (Strange, 1996).
To analyse the effect and consequ ences of globalization, m ost stu d ies
u se variou s econom ic ind icators, w hich allow reliable cross-cou ntry
com p arisons. Su ch ind icators inclu d e:
- Foreign d irect investm ent inflow s (Blom ström et al., 1994; Garrett,
2001; Borensztein et al., 1998; Carkovic and Levine, 2002),
- International trad e flow s (Frankel and Rom er, 1996; Dollar and Kraay,
2004; Greenaw ay et al., 1999), an d
- Measu res of op enness of trad e (Dollar, 1992) and of cap ital (Chand a,
2005; Rod rik, 1998; Alesina et al., 1994).
While those ind ivid u al ind icators m ight p rovid e a p roxy for som e su b d im ensions of globalization, it is necessary to assess several
globalization factors together, as they are strongly related to each other.
For a long tim e, the only overall–globalization stu d y w as the
Globalization Ind ex by A.T. Kearney/ Foreign Policy Magazine (2002).
There w ere several attem p ts to u p d ate this ind ex after 2005, e.g. CSGR
5
Globalization Ind ex by the University of Warw ick and GlobalInd ex by
6
the TransEu rop e Research N etw ork .
Recent KOF Ind ex of Globalization took som e of the p reviou s
ap p roaches, bu t w ith im p roved m ethod ology and the exp and ed list of
u nd erlying factors.
5
CSGR Globalisation Ind ex is used in Joyce, J. P. (2006). Prom ises Mad e, Prom ises
Broken: A Mod el of IMF Program Im plem entation, Economics and Politics, 18(3): 339-365.
6
The authors d escribe GlobalInd ex in Raab, M., Ruland , M., Schönberger, B., Blossfeld ,
H ., Hofäcker, D., Buchholz, S., and Schm elzer, P. (2008): GlobalInd ex – A sociological
approach to globalization m easurem ent, International Sociology, 23(4): 596-631.
12
In the KOF Ind ex of Globalization, globalization is d efined as ―the
p rocess of creating netw orks of connections am ong actors at m u lticontinental d istances, m ed iated throu gh a variety of flow s inclu d ing
p eop le, inform ation and id eas, cap ital and good s. Globalization is
concep tu alized as a p rocess that erod es national bou nd aries, integrates
national econom ies, cu ltu res, technologies and governance, and
p rod u ces com p lex relations of m u tu al interd ep end ence‖ (Dreher et al.,
2008).
The KOF Ind ex of Globalization takes accou nt of econom ic, social, and
p olitical globalization (see Ap p end ix C for d etails). Econom ic
globalization is m easu red by trad e, foreign investm ent, as w ell as the
tariff restrictions. Social globalization, m eaning m ovem ent of id eas,
inform ation, and p eop le, is estim ated , am ong other factors, by tou rism ,
foreign p op u lation, u se of the Internet, and the nu m ber of both
McDonald ‘s restau rants and Ikea stores. Political globalization is m eant
to ap p roxim ate p olitical coop eration and is m easu red by su ch factors,
as the nu m ber of em bassies and m em bership of international
organizations (Dreher et al., 2008).
2.4 Bureaucracy and globalization
Each state reacts to globalization in its ow n, national-sp ecific w ay.
Und erlying factors for the reaction m ight inclu d e the level of social and
p olitical d evelop m ent, stru ctu re of ind u stries, as w ell as cu ltu ral,
ed u cational, and ethnic stru ctu re of the society. Am ong all the factors,
qu ality of institu tions is of a great im p ortance. Im p lem entation and
enforcem ent of regu lations can becom e a cru cial factor for a foreign or
d om estic investor, tou rist, trad er, or m igrant.
Since the 1980s, the valu e of w orld trad e has increased enorm ou sly and
ind u strial cap ital has becom e u ncond itionally globalized (Ru ggie,
1995). These factors affected governm ents‘ ability to control their
national financial p olicies (Ó Riain, 2000). Migration has becom e a
significant featu re of m any op en econom ies (H eld et al., 1999).
Moreover, globalization has had som e social im p lications (e.g. in its
im p act on gend er equ ality and incom e d istribu tion grou p s).
Som e stu d ies find that these changes have u nd erm ined the role of the
state, rep lacing or com p lem enting national institu tions w ith global
norm s and stru ctu res (Sklair, 1991; Robinson, 1998; M ittelm an, 1997).
Many scholars, how ever, p red ict that globalization m akes the role of
the state m ore im p ortant, since effective governance is cru cial for
13
national interests w ithin the global ord er (Cerny, 1995; Rod e and
Gw artney, 2012). The governm ents m ed iate the connections betw een
the d om estic and global actors, therefore, affecting the w ay, in w hich
the national interests and assets are u sed w ithin the range of
op p ortu nities available in the global econom y (Ó Riain, 2000).
Globalization m ight change lin ks and au thority of institu tions across
the levels of governance (Castells, 1997). This w ou ld m ean
reconfigu ration of the state and form ation of new relations both
betw een states and the local actors and betw een states and the global
actors.
A nu m ber of p olicies and regu lations, u sed to p rom ote d om estic
d evelop m ent, ou ght to be reshap ed u nd er the im p act of globalization.
Mu ltilateral agreem ents change tariff ru les and sim p lify cu stom
p roced u res. Cu rrency u nions change m onetary p olicies, banking
regu lations, and control of cap ital. Migration changes labou r
regu lations and su p p orts, to som e extent, anti-d iscrim inatory p olicies.
Foreign investm ent m ay serve as a stim u lu s for ad op ting m ore liberal
financial p olicies and even for p rom oting d em ocratization. The fo rm er
hap p ens becau se investors seek for m ild er fiscal p olicies (e.g. in the
form of tax concessions), w hich d ecrease costs for investors, instead of
p rotectionist p olicies, w hich increase costs (Bhagw ati et al., 1992;
Brew er, 1993; Ellingsen and Wärneryd , 1999). Moreover, investors d o
exp ect that d em ocratic governm ents can m ore cred ibly com m it to
m arket friend ly p olicies than au thoritarian governm ents can; hence,
foreign investm ent cou ld affect the p ath of p olitical d evelop m ent of a
cou ntry and p rovid e a su p p ort for d em ocratization reform s (Jensen,
2008; Band elj, 2009; bu t see Rod e and Gw artney, 2012). Som e stu d ies
u nd erline the role of foreign cap ital for d em ocratic transition and
global integration of a cou ntry (Bevan and Estrin, 2004; Meyer, 1998;
Schm id t, 1995). Som e even argu ed that those cou ntries w ere m ore
su ccessfu l that bu ilt ―cap italism from ou tsid e‖ (King and Szelényi,
2005; Eyal et al., 1998). Som e other stu d ies, how ever, d em onstrated
controversial im p act of foreign investm ent. While stim u lating a
cou ntry‘s d evelop m ent in a short-ru n, it cou ld create d ep end ence on
foreign cap ital, cu rbing the long-ru n d evelop m ent (Dixon and Bosw ell,
1997; Firebau gh, 1997).
As all institu tional areas are being reshap ed by globalization, there is a
strong need in an em p irical analysis of those changes and their
consequ ences. Globalization brings to test the strength, au tonom y, and
legitim acy of national institu tions. At the sam e tim e, institu tions
14
d eterm ine, to w hat extent variou s globalization factors w ou ld be able
to affect the d om estic ord er. Their interp lay creates a new global ord er,
w here national-sp ecific norm s and institu tions m ight becom e m ore
im p ortant than ever.
15
3. Globalization and Institutional Quality: A Panel D ata Analysis
3.1 Scope of research
In Moscow , Ru ssia, the Sw ed ish fu rnitu re retailer IKEA w as asked to
p ay a bribe only w eeks before the op ening of its flagship store in 2000.
Refu sal to p ay w ou ld lead to electricity being shu t d ow n. IKEA
resp ond ed , not by p aying the bribe, bu t by renting d iesel g enerators
large enou gh to p ow er the entire shop p ing m all. Later, in 2006, it w as
revealed that the Ru ssian execu tive hired to m anage the d iesel
generators for another store, in St. Petersbu rg, took kickbacks from the
rental com p any to inflate the p rice. IKEA‘s exp ansion in Ru ssia w as
halted , and tw o years later senior execu tives w ere d ism issed for
allow ing bribes.
The story abou t IKEA in Ru ssia, told by am ong m any others The N ew
7
York Tim es , illu strates im p ortant asp ects of globalization. Trad e and
foreign d irect investm ents can p otentially im p rove econom ic
d evelop m ent, benefitting both consu m ers and cap ital ow ners. At the
sam e tim e, d ysfu nctional institu tions, su ch as corru p tion, m ay thw art
these p otential benefits. The establishm ent of and actions taken by
firm s op erating in foreign cou ntries m ay also affect norm s and
behavior. On the one hand , IKEA‘s refu sal to p ay bribes m ay facilitate
the fight against corru p tion in Ru ssia. On the other hand , the p rofits
generated by IKEA increase the p otential gains fro m engaging in
corru p t behavior. It is therefore not clear how the d egree of corru p tion
in Ru ssia w ou ld change as a resu lt of IKEA‘s ventu res. More generally,
little is know n abou t the effects of globalization on institu tional qu ality.
This p art of research aim s to shed light on one p articu lar qu estion: Is
increasing globalization on average follow ed by im p roving or
d eteriorating institu tional qu ality?
Changing institu tions w ill generally involve trad e-offs betw een shortand long ru n benefits. Consequ ently, the tim e horizon and exp ectations
of those w ho influ ence institu tions—execu tive au thorities and their
bu reau cracies—w ill be cru cial in d eterm ining the effects of increased
globalization. As the tim e horizon often is d eterm ined by the level of
econom ic d evelop m ent, it is im p ortant to exam ine w hether the
globalization effect on institu tional qu ality varies across levels of
d evelop m ent.
7
Kram er, A. (2009). Ikea Tries to Build Public Case Against Russian Corruption. The N ew
Y ork Times, Septem ber 11, 2009.
16
The analysis em p loys the World Bank‘s World w id e Governance
Ind icators (WGI) to cap tu re several asp ects of institu tional qu a lity and
the KOF Globalization Ind ex to m easu re econom ic and social
globalization. The stu d y also avoid s w hat Blonigen and Wang (2005)
call ―inap p rop riate p ooling of w ealthy and p oor cou ntries ‖ by u sing
both sam p le sp lits and interaction effects. Accord ing to the theoretical
p red ictions, globalization shou ld be typ ically follow ed by im p roved
institu tional qu ality in rich cou ntries, bu t in p oor cou ntries this
relationship w ou ld be the op p osite. Desp ite institu tions changing
slow ly over tim e, the d ifference betw een rich and p oor cou ntries
shou ld allow to d raw significant conclu sions.
This chap ter p roceed s as follow s. The next section d iscu sses
theoretically how globalization and econom ic op enness is exp ected to
affect institu tions and review s recent research. Section 3.3 d iscu sses the
m easu rem ent of institu tions and p resents the d ata, w hile section 3.4
contains the em p irical analysis, inclu d ing several robu stness checks
and tests of the relationship betw een globalization and institu tional
qu ality across levels of d evelop m ent. Section 3.5 conclu d es the analysis
by su m m arizing the resu lts and d iscu sses how to interp ret the find ings.
3.2 Theoretical framework and previous empirical evidence
3.2.1 Theoretical expectations
N orth (1990) d efined institu tions as ―ru les of the gam e‖ that shap e
hu m an interaction and argu ed that ―third w orld cou ntries are p oor
becau se the institu tional constraints d efine a set of p ayoffs to
p olitical/ econom ic activity that d oes not encou rage p rod u ctive
activity‖ (N orth, 1990: 3, 110). A large follow ing literatu re has
em p irically confirm ed qu ality of institu tions as an im p ortant
d eterm inant of econom ic grow th: Knack and Keefer (1995), Rod rik et
al. (2004), Abd iw eli (2003) and Dou cou liagos and Ulu basoglu (2006) to
8
m ention ju st a few . There is no com p lete agreem ent on w hat
institu tions m atter the m ost, thou gh the su rvey by Du rlau f et al. (2005)
p oints to low corru p tion, p olitical stability, p rop erty rights , and ru le of
law all being im p ortant for d evelop m ent. The im p ortance of low
corru p tion for grow th is also confirm ed by H aggard and Tied e (2011).
As the evid ence of the im p ortance of institu tional qu ality accu m u lates,
it is natu ral to exam ine if and how institu tions change. Institu tions
8
H ow ever, Richter and Tim m ons (2012) argue that the siz e of the effect institutions have
on econom ic grow th is relatively sm all.
17
shap e hu m an interaction, bu t at the sam e tim e institu tions are enforced
and u p held by hu m an interaction. Accord ing to N orth (1998),
institu tional change is increm ental and occu rs w hen influ ential agents
p erceive they cou ld d o better by altering the existing institu tional
fram ew ork. Typ ically, institu tions are assu m ed to change slow ly over
tim e (Acem oglu et al., 2005; Kingston and Caballero, 2009).
N evertheless, as d iscu ssed in the p reviou s chap ter, there are several
reasons w hy increasing globalization m ay foster institu tional change,
m any of w hich su ggest that at least som e asp ects of globalization
shou ld be beneficial for institu tional qu ality.
A fu nd am ental reason to exp ect trad e and econom ic op enness to
im p rove institu tional qu ality is Montesqu ieu ‘s id ea that m arket
9
interactions act as a civilizing force. The su rvey by H irschm an (1982)
em p hasizes that the p ractice of com m ercial transactions generates
feelings of tru st and em p athy for others. Later, exp erim ental research in
East Africa rep orted by Ensm inger (2004) ind icates a strong
relationship betw een m arket exchange and fairness. Ensm inger notes
several p ossible exp lanations, su ch as the id ea that p eop le are learning
in the m arket that fair-m ind ed ness is rew ard ed . Another p ossibility is
that m arket exp eriences help p eop le learn how to coord inate
su ccessfu lly w ith other anonym ou s ind ivid u als u sing conventions
based on fairness (cf. You ng, 1993).
Differences in institu tional qu ality across cou ntries can also be seen as a
sou rce of com p arative ad vantage. As d iscu ssed in Bergh and H öijer
(2008), globalization can increase the com p etition betw een cou ntries
w ith d ifferent institu tions, fostering institu tional reform s in cou ntries
w ith low institu tional qu ality. An exam p le is p rovid ed by Al-Marhu bi
(2005), w ho notes that the cost associated w ith bad p olicies su ch as
u nexp ected m onetary exp ansions m ay be higher (and the benefits
sm aller) in the cou ntries that are op en to w orld m arkets. As a resu lt,
op enness generates incentives to create governance stru ctu res su ch as
ind ep end ent central banks and au tonom ou s tax agencies to free
m onetary p olicy and tax collection from p olitical influ ence.
Finally, as also d iscu ssed by Al-Marhu bi (2005), globalization m ay also
affect institu tions throu gh closer integration and op enness, com ing
w ith an increasing global flow of inform ation that p rovid es alternative
sou rces for know led ge and id eas. Su ch inform ation sp illovers m ay
9
―[w ]herever manners are gentle there is com m erce, and w herever there is com m erce,
m anners are gentle‖ (Montesquieu, 1749, as cited in H irschm an, 1982).
18
m ake citizens m ore d em and ing and help nu rtu re civil institu tions. As
d iscu ssed by Ostrom (2005), the fact that inefficient institu tions p ersist,
can be p artly exp lained by p eop le being m isinform ed or having biased
view abou t the ou tcom es of variou s institu tional arrangem ents. Many
asp ects of globalization can p otentially m itigate su ch m isinform ation
and bias, thereby fostering institu tional reform .
While the OECD focu s sp ecifically on citizens‘ role in p olicym aking
(OECD, 2009), inform ation sp illovers are also likely to affect
institu tional qu ality in a low -incom e setting. A recent illu strative
exam p le is the initiatives to tackle corru p tion in d evelop ing cou ntries
by encou raging anonym ou s rep orts of bribe-p aying u sing p u blic
10
w ebsites. Su ch activities m ay im p rove governance system s and
p roced u res and red u ce the scop e for corru p tion.
Other m echanism s d o not necessarily su ggest that institu tions im p rove
as a resu lt of globalization. While generally sym p athetic to the id ea of
m arket integration as a civilizing force, H irschm an (1982) also noted as
a cou nteracting force the tend ency of com m erce to ind u ce an elem ent
of calcu lation and instru m ental reason in m any sp heres of life.
Another likely consequ ence of globalization is that the d istribu tion of
p ow er w ithin cou ntries w ill change throu gh, for exam p le, the
transm ission of technology (Rom er, 1990). Along these lines, Acem oglu
and Robinson (2006) show that the trad e ind u ced transfers of skillbiased technology increase the incom e share of the m id d le class, in tu rn
im p roving their p olitical p ow er and u ltim ately generating better
p rotected p rop erty rights. Sim ilarly, Acem oglu et al. (2005a) argu e that
the Atlantic trad e strengthened com m ercial interests in Western
Eu rop e.
H ard y and Magu ire (2008) d efine institu tional entrep reneu rs as actors
w ho have an interest in p articu lar institu tional arrangem ents and w ho
leverage resou rces to create new institu tions or to transform existin g
ones, and p rovid e several exam p les of how institu tions su ch as labou r
regu lation, entry barriers and p rop erty rights have been changed as a
resu lt of su ch ind ivid u al initiatives. Im p ortantly, it is not necessarily
the case that the changes follow ing glob alization strengthen the
incentives for institu tional entrep reneu rs to im p rove institu tions.
10
See e.g. w w w.ipaid abribe.com —a w ebsite covering inform ation reported by
anonym ous ind ivid uals on w hen having to pay a bribe in more than 450 Ind ian cities.
19
Changing institu tions w ill in m any cases involve trad e-offs betw een
short ru n and long ru n benefits. For exam p le, both violations of ru le of
law and of p rop erty rights are likely to bring short ru n gains to those
involved , w hile harm ing the long ru n econom ic d evelop m ent. The
sam e goes for corru p tion and several p olicy ru les as w ell, su ch as
inflationary m onetary p olicies, w hich m ay bring su bstantial benefits to
sm all elite grou p s in the short ru n w hile being d evastating for long ru n
econom ic d evelop m ent.
For this reason, the tim e horizon and exp ectations of those w ho
influ ence institu tions w ill be cru cial in d eterm ining the effects of
increased globalization. Referring again to the case of IKEA in Ru ssia, it
m ay w ell be the case that those w ho engage in corru p t activity realize
that corru p tion is likely to d im inish the long ru n benefits from having
IKEA exp and ing in Ru ssia, bu t w hen fu tu re benefits are heavily
d iscou nted , the short ru n benefits of increased corru p tion are higher. In
the latter case, the p resence of IKEA creates an incentive for
bu reau cracies to alter institu tions in ord er to easier be able to reap short
ru n benefits from corru p tion, p rop erty rights violations, and sim ilar
activities. If, how ever, m ore w eight is p u t on long ru n econom ic
d evelop m ent, the effect of IKEA w ou ld be to increase the retu rn to
institu tional im p rovem ents.
In p oor cou ntries, u ncertainty and instability is typ ically higher, and
life exp ectancy is shorter. Consequ ently, the incentives to im p rove
institu tions to foster long ru n econom ic d evelop m ent are low er, and
the incentives to w orsen institu tions (throu gh, for exam p le, accep ting
corru p tion) are likely to be higher (see Acem oglu an d Verd ier, 2000;
Fjeld stad and Tu ngod d en, 2003; and Altind ag and Xu , 2009 for fu rther
d iscu ssions). Thu s, the correlation betw een globalization and
su bsequ ent im p rovem ents in institu tional qu ality w hen exam ined
u sing p anel d ata w ith cou ntry fixed effects is exp ected to be sm aller
(and p ossibly negative) in p oor cou ntries than in rich cou ntries.
3.2.2 Previous studies
In the w ake of increasing globalization, several stu d ies have exam ined
w hether factors su ch as trad e and econom ic op enness affect
institu tions, esp ecially ru le of law and the level of corru p tion. Most of
these stu d ies find a p ositive effect of op enness on institu tional qu ality,
bu t none has so far exam ined how the effect varies w ith the level of
d evelop m ent.
20
Recently, Bhattacharyya (2012) find s a p ositive effect of op enness on
p rop erty rights. Op enness is qu antified as the nu m ber of years a
cou ntry has been op en, as d efined by the Sachs and Warner op enness
ind ex, u p d ated by Wacziarg and Welch (2003). The institu tional
ind icator u sed is exp rop riation risk and rep u d iation of contracts as
m easu red by the International Cou ntry Risk Gu id e. The stu d y u ses
p anel d ata, covering betw een 65 and 103 cou ntries from 1980 to 2000.
Resu lts ind icate that a one sam p le stand ard d eviation increase in the
fraction of years a cou ntry has rem ained op en since 1950 lead s to a 2.2
p oint increase in the p rop erty rights institu tions ind ex. To accou nt for
p otential end ogeneity, Bhattacharyya (2012) verifies the resu lts by
instru m enting trad e op enness u sing exp ort p artners‘ gr ow th rate. In a
lim ited sam p le of 31 cou ntries, he find s a sim ilar relationship betw een
tariffs and execu tive constraints over the p eriod 1865 to 1940.
Levchenko (2011) find s that the cou ntries w hose exogenou s
geograp hical characteristics p red isp ose them t o exp orting in so-called
institu tionally intensive sectors have significantly higher institu tional
qu ality. The conclu sion is reached by p red icting the institu tional
intensity of exp orts u sing geograp hic characteristics and exam ining the
relationship w ith the ru le of law ind ex from Kau fm ann et al. (2005)
averaged across 1996–2000 for a cross section of 141 cou ntries.
The Kau fm ann ind icators (Kau fm ann et al., 1999, w hich are the sam e as
in Knack and Keefer, 1995) are also u sed by Al-Marhu bi (2005), w here
the average of the six d im ensions are exp lained u sing several ind icators
of econom ic op enness: trad e flow s, the Sachs and Warner ind ex, the Dind ex (Dollar, 1992) and the fou rth d im ension of the econom ic freed om
ind ex, freed om to trad e internationally (Gw artn ey and Law son, 2002).
Regressions inclu d ing 81 to 125 cou ntries ind icate that op enness (all
m easu res excep t the D-ind ex) in 1985 is associated w ith im p roved
governance in 2000 and 2001.
A p ositive relationship is also fou nd by Wei (2000) u sing corru p tion
(taken from Bu siness International and Transp arency International)
and trad e flow s as a share of GDP in a cross section of 169 cou ntries in
1978–1980 and 184 cou ntries in 1994–1996. Sim ilar resu lts are reached
by Bonaglia et al. (2001), w ho have fou nd corru p tion (from
Transp arency International and International Cou ntry Risk Gu id e)
being related to the GDP share of im p orts in 53 to 119 cou ntries, u sing
p ooled OLS regressions for variou s p eriod s betw een 1980 and 1998.
The negative effect of im p ort op enness on corru p tion is confirm ed by
21
IV-regressions w here p op u lation, an English -sp eaking d u m m y, area,
and rem oteness are u sed as instru m ents for op enness.
While the stu d ies review ed above all tend to find a p ositive
relationship betw een op enness and institu tional qu ality, these resu lts
are not confirm ed by N icolini and Paccagnini (2011). Their stu d y u ses
p olitical rights and civil liberties (from Freed om hou se) as w ell as trad e
flow s/ GDP in a p anel d ata setting w ith 197 cou ntries from 1976 to 2004
p eriod . Using Granger cau sality tests, they fail to find a cau sal
relationship betw een trad e and institu tions in any d irection. Their u se
of yearly d ata is in contrast to other stu d ies and m ay bias the resu lts
tow ard s 0 if there are m easu rem ent errors in the d ata and institu tions
change only little from year to year.
The m echanism s d iscu ssed in p reviou s stu d ies are sim ilar to those
d escribed in section 3.2.1. For exam p le, Bhattacharyya (2012) d ep arts
from N orth (1990) by noting that N orth d oes not m ention the im p act of
international trad e on technological p rogress via technology transfer.
The m ost theoretically elaborated p ap er is Levchenko (2011), w here
institu tions p lay d u al roles: they generate rents for som e p arties w ithin
the econom y and they are a sou rce of com p arative ad vantage in trad e.
The d u al role of institu tions m eans that d ifferent consequ ences of
increasing trad e op enness are p ossible. In technologically sim ilar
cou ntries, trad e w ill lead to a ―race to the top ‖ in institu tional qu ality
(sim ilar to the p ersp ective in Bergh and H öijer, 2008). H ow ever, w hen
technological d ifferences are bigger, trad e flow s are d riven by other
than institu tional sou rces of com p arative ad vantage. In this case, trad e
d oes not create an incentive to im p rove institu tions bu t rather increases
the incentives for the p arties earning rents to m ake institu tions w orse.
The em p irical resu lts ind icate that institu tions d o ind eed im p rove as a
resu lt of trad e op enness in cou ntries that can exp ect to cap tu re the
institu tionally intensive sectors after trad e op ening. The theoretical
p ossibility that som e cou ntries w ill actu ally m ake institu tions w orse as
a resu lt of trad e op enness is not su p p orted by the em p irical analysis.
Levchenko (2011) su ggests that the p resence of the OECD cou ntries
w ith very high institu tional qu ality m eans that no other cou ntry w ill
find it op tim al to red u ce its qu ality of institu tions after trad e op ening.
3.2.3 Summary
Research so far lend s som e su p p ort to the view that econom ic op enness
on average p rom otes institu tional im p rovem ent. The resu lts of the m ost
22
recent find ings fit w ell w ith old er stu d ies su ch as Wei (2000) and
Bonaglia et al. (2001) w here m ore op en econom ies are show n to exhibit
less corru p tion. Institu tional im p rovem ents can therefore be seen as a
p otential m ech anism in the em p irical relationship betw een
globalization and econom ic grow th (Dreher, 2006; Rod e and Coll,
2012).
Several qu estions are how ever left op en. Previou s stu d ies, relying on
cross-sectional variation am ong cou ntries, d o not teach u s anything
abou t variation over tim e. Im p ortantly, this also hold s w hen
instru m ental variables are u sed in cross sections. When Levchenko
(2011) show s that cou ntries, w hich geograp hical characteristics
p red isp ose them to exp ort in institu tionally intensive sectors , have
higher institu tional qu ality, this find ing d oes not im p ly that an increase
in trad e flow s w ou ld be follow ed by institu tional im p rovem ent.
The tw o existing p anel d ata stu d ies arrive at d ifferent resu lts.
Bhattacharyya (2012) find s a p ositive relationship b etw een op enness
and better p rotected are p rivate p rop erty, w hereas N icolini and
Paccagnini (2011) d o not find any relationship betw een trad e and
institu tions u sing yearly bilateral trad e flow d ata and the Freed om
H ou se ind ex for p olitical rights and civil liberties. Given that
institu tions change slow ly over tim e, the resu lt m ay be exp lained by
their u se of yearly d ata.
It is also clear from p reviou s research that m ost of the econom ic gains
that can be exp ected from institu tional im p rovem ent d o take som e tim e
to m aterialize, w hile the gains from breaches of contracts, corru p tion ,
and other form s of institu tional d eterioration are m ore or less
im m ed iate for those involved in these activities. Thu s institu tions m ay
w ell change for the w orse in less d evelop ed cou ntries w here the
bu reau crats w ho shap e institu tions have shorter tim e horizons. So far,
how ever, no stu d y has noted that the effect of globalization on
institu tions is likely to d ep end on the level of d evelop m ent as
su ggested by the tim e horizon argu m ent, and thu s p otentially su ffers
from inap p rop riate p ooling of low -incom e and high-incom e cou ntries,
as d iscu ssed by Blonigen and Wang (2005).
Finally, the literatu re on globalization and institu tional qu ality has so
far em p loyed strict econom ic m easu res of globalization, su ch as trad e
flow s or the Sachs Warner ind ex that classifies a cou ntry as either op en
or not. The p rocess of globalization, how ever, is a broad and
m u ltid im ensional p henom enon w ith econom ic, social, and p olitical
23
com p onents (Arribas et al., 2009; Dreher et al., 2008) that m ay affect
institu tions d ifferently, su ggesting that a strict focu s on econom ic
m easu res m ight lim it ou r u nd erstand ing of the relationship betw een
globalization and institu tional change.
3.3 Data and methods
3.3.1 Data on institutional quality and globalization
Choosing a m easu re of institu tional qu ality involves several trad e -offs.
For som e p articu lar institu tions, su ch as d em ocracy, d ata are available
for all m ajor states since 1800 in the Polity IV Project. Other
institu tional m easu res are m ore com p rehensive, bu t cover m u ch few er
11
cou ntries and years. Follow ing the d iscu ssion in the p reviou s chap ter,
the m ost reliable choice so far is the World w id e Governance Ind icators
(WGI) by the World Bank (Kau fm ann et al., 2010). This d ataset cap tu res
several asp ects of institu tional qu ality, begins in 1996 and covers
193 cou ntries by 2010, and has not been u sed before in this line of
research. Inference u sing year-to-year changes is not ad vised , bu t
averaging over short tim e p eriod s yield s a reasonably com p rehensive
m easu re of variou s asp ects of institu tional qu ality for both high -incom e
and low -incom e cou ntries. For a d iscu ssion on the u se of WGI in
research, see Ap aza (2009).
For interp retation, it is im p ortant to know that the WGI com p ile and
su m m arize inform ation from several sou rces, inclu d ing both exp ert
assessm ents (su ch as the World Bank‘s Cou ntry Policy and Institu tional
Assessm ent, u sed by Chau vet and Collier, 2008) and p u blic su rveys
(su ch as the Afrobarom eter su rveys). Each sou rce is assigned to one of
six aggregate ind icators, w hich are then averaged and m ad e
com p arable across cou ntries u sing an u nobserved com p onents m od el
(Kau fm ann et al., 2010). The cru cial assu m p tion in this p roced u re is
that the observed d ata from each sou rce are a linear fu nction of the
12
u nd erlying, u nobserved level of governance. The six aggregate
ind icators are government effectiveness, control of corruption, regulatory
quality, political stability, rule of law, and voice and accountability. Previou s
stu d ies su ch as Knack and Keefer (1995) and Al-Marhu bi (2005) u se the
average of these ind icators. The find ings in H aggard and Tied e (2011),
how ever, su ggest that d evelop ing cou ntries vary in the w ay d ifferent
11
A good collection of available institutional data is accessible through the Quality of
Governance (QoG) dataset by the University of Gothenburg (Teorell et al., 2011).
12
More d etails are available on http:/ / info.world bank.org/ governance/ w gi/
24
typ es of institu tional qu ality are com bined . Sp ecifically, they note that a
large clu ster of d evelop ing cou ntries com bines high corru p tion levels
w ith relatively w ell fu nctioning p rop erty rights, w hereas a second
sm aller clu ster is w orse in both d im ensions and also very violent. For
the scop e of this analysis, it is necessary, therefore, to u se these six
ind icators sep arately instead of averaging them . Details of six m easu res
and their sou rces are p resented in Ap p end ix B.
As m entioned above, institu tions are typ ically assu m ed to change
slow ly over tim e. This seem s to be bad new s for p oor cou ntries that
m ight be stu ck w ith d ysfu nctional institu tions. Bu t as recently noted by
Berggren et al. (2012), institu tional ind icators m ight change a lot over
tim e and the p attern varies su bstantially betw een cou ntries . The sam e
ap p lies to the sam p le stu d ied here. Data ind icate notable changes in
institu tional qu ality, also am ong the p oorest cou ntries.
Follow ing the d iscu ssion in the p reviou s chap ter, the log of the KOF
Index of Globalization (Dreher et al., 2008) is u sed a m easu re of
globalization. Details of the KOF Ind ex and its su b-com p onents are
p resented in Ap p end ix C.
As an illu stration, figu re 2 p lots the change in econom ic globalization
as m easu red by the KOF Ind ex against the change in governm ent
effectiveness from the WGI over the p eriod 1996–2009. As exp ected , the
scatter p lot illu strates that m ost cou ntries exp erienced increasing
econom ic globalization over the p eriod . Interestingly, focu sing on the
u p p er p art of the figu re show s that a nu m ber of cou ntries su bstantially
im p roved their institu tional qu ality d u ring the sam e tim e p eriod .
Figu re 2 su ggests a w eak p ositive correlation betw een globalization
and institu tional qu ality. Variation is how ever large am ong
observations, and d ifferentiating betw een rich and p oor cou ntries
allow s seeing a som ew hat stronger correlation seem ing to exist in high incom e contexts. Fu rtherm ore, figu re 2 reveals that there are no
obviou s ou tliers in the sam p le.
The baseline sp ecification inclu d es several control variables, all
su ggested by p reviou s em p irical research on the d eterm inants of
institu tional qu ality. Table 2 show s d escrip tive statistics on the
variables of interest.
25
1
Figu re 2 Change in econom ic globalization and change in governm ent
effectiveness for high -incom e and low -incom e cou ntries, 1996-2009
GEO
RWA
SRB
.5
ETH
EST KOR
BDI
KAZ
MKD
SLV
HRV
ALB
BIH
LVA
LTU
ARM
CZE
MUS
ROM
CHN
SYR
SVNSVK
RUSSLE
JPNGNB
DNK MYS
CYP
BFA
IDN
DZA
BGR
LKACOL
MLIAZE MLT
SGP
FIN
ZAR
ISR ARE VNM
VUT
AUS
BRA ZMBPRY
URY
AGO
GHA
JOR
KHM
KWT
UKR
IND
BRBTZA
JAM
BWA NZL CRI
OMN
GUY
TTO
HND
MDG
SWE
FRA COG
SWZ
CMR
THA
CHE
CHLUGA
POL
MEX CAF PRT
BHR
MWI
AUT
PAN
IRN
CAN
NGA
DOM
GRC KEN
SDN
MOZ
GIN
ECU
HTI
NAM
MAR
GTM
HUN
BEN
MDA
TUN
NPL
NOR
PAK
LSO
EGY
NIC
BHS
BGD
ISL
ZAF
MNG
BEL
USA
PNG
NLD
GAB
YEM
DEU
CPV SEN
GBR
LUX
IRL
ITABOL MMR
PER
TGO
KGZ
FJI
BLR
BLZ
ESP
MRT
TCD
0
TUR
NER
-.5
VEN
-1
ARG
CIV
MDV
ZWE
-20
-10
0
10
Economic Globalization
high-income countries
20
30
low-income countries
GDP per capita (PPP ad ju sted , in constant USD), total population (both in
logs), and a share of total rent from natural resources in GDP are taken
from the World Develop m ent Ind icators d atabase (World Bank, 2010).
Pop u lation is u sed as a p roxy for cou ntry size. While richer cou ntries
typ ically have better institu tions, the find ings of both Treism an (2000)
and Fism an and Gatti (2002) su ggest that cou ntry size has the op p osite
effect, p resu m ably d u e to the d ifficu lties in shar ing of p ow er and
resp onsibilities betw een central and local au thorities. Rent from natu ral
resou rces is inclu d ed to m easu re natu ral resou rces abu nd ance, fou nd
associated w ith higher levels of corru p tion by both Ad es and Di Tella
(1999) and Treism an (2000), and also w ith low er qu ality of governm ent
in general by Anthonsen et al. (2012).
Ad d itional controls are u sed as robu stness checks, inclu d ing the log of
total net development assistance and aid from the WDI, the p ercentage of
ad u lt p op u lation (age > 15) w ith com p leted secondary education (Barro
and Lee, 2010), and the Polity IV index ranking a cou ntry‘s p olitical
institu tions by giving each cou ntry a score from -10 to 10, ranging from
26
p u re au tocracy to consolid ated d em ocracy (Marshall and Jaggers,
2012).
Table 2 Descrip tive statistics
Variable
Mean
Governm ent
effectiveness
-0.06
Std .
d ev.
1
n
N
Min
Max
191
752
-2.32
2.30
Control of
corruption
-0.06
1
191
752
-1.92
2.51
Political stability
-0.05
1
193
758
-3.23
1.64
Regulatory quality
-0.07
1
191
752
-2.53
2.16
Rule of law
-0.07
1
193
763
-2.53
1.96
Voice and
accountability
-0.04
1
193
769
-2.22
1.67
Log of Econom ic
globalization
3.99
0.35
148
739
2.31
4.58
Log of Econom ic
globalization:
Flow s
Log of Econom ic
globalization:
Restrictions
Log of Social
globalization
4.00
0.41
173
864
2.21
4.61
3.95
0.46
137
684
1.60
4.57
3.67
0.55
183
912
1.82
4.53
Log of Social
globalization:
Personal contacts
Log of Social
globalization:
Information flow s
Log of Social
globalization:
Cultural proxim ity
Log of GDP per
capita, PPP
3.80
0.54
179
892
1.85
4.59
3.96
0.47
175
872
1.48
4.60
2.78
1.28
190
947
0.69
4.59
8.49
1.29
168
868
5.25
11.18
27
Source
World w id e
Governance
Ind icators (2011)
World w id e
Governance
Ind icators (2011)
World w id e
Governance
Ind icators (2011)
World w id e
Governance
Ind icators (2011)
World w id e
Governance
Ind icators (2011)
World w id e
Governance
Ind icators (2011)
Dreher et al.
(2008), updated in
2012
Dreher et al.
(2008), updated in
2012
Dreher et al.
(2008), updated in
2012
Dreher et al.
(2008), updated in
2012
Dreher et al.
(2008), updated in
2012
Dreher et al.
(2008), updated in
2012
Dreher et al.
(2008), updated in
2012
World
Developm ent
Ind icators (2012)
Table 2 (cont.)
Variable
Mean
Log of Population
size
15.46
Std .
d ev.
2.10
n
N
Min
Max
190
945
9.72
21.00
Total natural
resources rent (%
of GDP)
Percentage of
population (age
15+) w ith
com pleted
secondary
ed ucation
Log of Net
d evelopm ent
assistance and aid
Revised com bined
Polity IV score
9.06
17.16
185
922
0
20.56
13.53
142
710
0.60
19.03
1.55
139
774
12.61
23.19
3.02
6.57
159
791
-10
10
Source
World
Developm ent
Ind icators (2012)
164.95 World
Developm ent
Ind icators (2012)
67.79 Barro and Lee
(2010)
World
Developm ent
Ind icators (2012)
Marshall and
Jaggers (2012)
3.3.2 M ethod
The relationship betw een globalization and institu tional qu ality is
exam ined w ith the follow ing equ ation
IQ it = αi + βiGit-1 + γ iX it-1 + u it
(1)
w here IQ stand s for institu tional qu ality, G is globalization, X refers to
a set of controls, and u it is the error term . To accou nt for u nobservable
heterogeneity p otentially correlated w ith the exp lanatory variables,
cou ntry fixed effects are inclu d ed , and a rand om effects m od el is u sed
13
for a robu stness check.
The m od el is estim ated u sing fou r year averages over five p eriod s:
1992–1995, 1996–1999, 2000–2003, 2004–2007, and 2008–2010. Averages
are u sed to m inim ize the effects of noise and single year flu ctu ations in
the d ata. To m itigate p otential end ogeneity, ind ep end ent variables are
lagged , so that average globalization from 1992–1995 is u sed to exp lain
institu tional qu ality over 1996–1999. The only variable that is u sed w ith
its actu al valu es is a share of p op u lation w ith second ary ed u cation, as
this d ata only covers the years 1990, 1995, 2000, 2005, and 2010. To
enhance com p arability across d ifferent sp ecifications, the sam p le is
13
The main specification does not includ e tim e fixed effects as shocks sim ultaneously
affecting institutional quality in several countries are unlikely. When includ ing tim e fixed
effects, tim e d umm ies are not jointly significant at trad itional levels.
28
restricted across the m od els of the sam e sp ecification, thu s, the effective
sam p le is lim ited by d ata availability.
3.4 Results
3.4.1 M ain results
Tables 3 and 4 p resent fixed effects estim ation resu lts for the
relationship betw een econom ic and social globalization and the six
d im ensions of institu tional qu ality, u sing the fu ll sam p le.
Econom ic globalization seem s to be follow ed by im p roving institu tions,
w ith fou r ou t of six d im ensions reaching statistical significance at least
at the ten p er cent level. In contrast, the estim ates for social
globalization are sm all and never significant. The control variables
generally have the exp ected sign, w ith p op u lation size and rents from
natu ral resou rces being negatively related to institu tional qu ality.
Table 3 Econom ic globalization: baseline m od els; fu ll sam p le
Variables
Econom ic
Globalization
Population
GDP per
Capita, PPP
Total natural
resources rents
Constant
Observations
N um ber of
countries
R-squared
Governm ent Effectiveness (GE)
0.114
(1.49)
-0.468
(4.08)***
0.266
(3.91)***
-0.002
(0.7)
4.715
(2.66)***
Control of
Corruption
(CC)
0.113
(1.19)
-0.253
(1.78)*
-0.023
(0.27)
-0.005
(1.79)*
3.543
(1.62)
Regulatory
Quality
(RQ)
0.262
(2.84)***
-0.622
(4.50)***
0.057
(0.69)
-0.004
(1.62)
8.473
(3.98)***
Voice and
Accountability
(VA)
0.304
(3.22)***
-0.082
(0.58)
-0.168
(2.01)**
-0.006
(2.04)**
1.323
(0.61)
Rule of
Law
(RL)
Political
Stability
(PS)
0.208
(2.54)**
-0.354
(2.88)***
0.073
(1)
-0.005
(2.17)**
4.012
(2.12)**
0.248
(1.73)*
-0.232
(1.08)
0.025
(0.2)
-0.002
(0.43)
2.251
(0.68)
392
101
392
101
392
101
392
101
392
101
392
101
0.96
0.94
0.95
0.95
0.96
0.91
Absolute value of t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
29
Table 4 Social globalization: baseline m od els; fu ll sam p le
Variables
Social
Globalization
Population
GDP per Capita,
PPP
Total natural
resources rents
Constant
Observations
N um ber of
countries
R-squared
GE
CC
RQ
VA
RL
PS
0.04
(0.54)
-0.295
(2.56)**
0.284
(4.29)***
-0.003
(1.72)*
2.041
(1.16)
417
109
-0.088
(0.94)
0.06
(0.41)
0.053
(0.64)
-0.008
(3.12)***
-1.376
(0.62)
417
109
0.066
(0.73)
-0.314
(2.24)**
0.135
(1.68)*
-0.004
(1.81)*
3.573
(1.66)*
417
109
-0.005
(0.05)
0.207
(1.43)
-0.046
(0.55)
-0.007
(2.65)***
-3.188
(1.44)
417
109
-0.097
(1.21)
-0.017
(0.14)
0.209
(2.93)***
-0.007
(3.18)***
-1.393
(0.73)
417
109
-0.179
(1.29)
0.219
(1.02)
0.215
(1.75)*
-0.002
(0.66)
-4.986
(1.52)
417
109
0.96
0.94
0.95
0.95
0.96
0.91
Absolute value of t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
To allow for d ifferent effects in d evelop ed and d evelop ing cou ntries,
the fu ll sam p le in d ivid ed into su b-sam p les of high-incom e and low incom e cou ntries (w ith the threshold at GDP p er cap ita of 4000 US
d ollars). Moreover, to gain d eep er know led ge on w hat globalization
factors affect institu tional qu ality, the econom ic and social globalization
m easu res shou ld be d isaggregated . Tables 5 and 6 p resent the effects of
five typ es of globalization on six d im ensions of institu tional qu ality , for
low -incom e and high-incom e cou ntries sep arately.
The resu lts reveal som e p atterns that are not visible in the p ooled
sam p le. First, econom ic flow s correlate w ith w orsened institu tions in
low -incom e cou ntries, w ith significant effects for governm ent
effectiveness and control of corru p tion. For rich cou ntries, the sign is
the op p osite for all d im ensions, and significantly so for governm ent
effectiveness, control of corru p tion , and p olitical stability.
Second ly, the p ersonal contacts as a p art of social globalization
correlate negatively w ith institu tional qu ality in low -incom e cou ntries,
bu t not in high-incom e cou ntries.
30
Table 5 Different typ es of globalization; su b-sam p le of low -incom e
cou ntries
(controls are inclu d ed bu t not show n)
Variables
Econom ic Flow s
N um ber of Countries
R-squared
Econom ic Restrictions
N um ber of Countries
R-squared
Personal Contacts
N um ber of Countries
R-squared
Information Flow s
N um ber of Countries
R-squared
Cultural Proxim ity
N um ber of Countries
R-squared
GE
-0.154
(2.10)**
60
0.88
0.275
(3.24)***
52
0.89
-0.191
(1.35)
59
0.88
0.035
(0.56)
59
0.89
0.032
(0.85)
61
0.89
CC
-0.24
(2.75)***
60
0.81
0.227
(2.25)**
52
0.81
-0.47
(2.81)***
59
0.8
-0.12
(1.62)
59
0.81
-0.009
(0.19)
61
0.80
RQ
-0.114
(1.49)
60
0.89
0.28
(3.13)***
52
0.88
-0.124
(0.81)
59
0.89
0.04
(0.6)
59
0.89
-0.021
(0.51)
61
0.89
VA
-0.033
(0.35)
60
0.90
0.334
(3.26)***
52
0.91
-0.488
(2.82)***
59
0.91
0.008
(0.1)
59
0.90
-0.066
(1.4)
61
0.90
RL
-0.073
(0.92)
60
0.90
0.353
(4.15)***
52
0.91
-0.278
(1.87)*
59
0.90
-0.081
(1.2)
59
0.91
-0.037
(0.91)
61
0.91
PS
-0.075
(0.51)
60
0.84
0.581
(3.51)***
52
0.85
-0.533
(1.95)*
59
0.86
-0.14
(1.12)
59
0.85
-0.061
(0.81)
61
0.85
Absolute value of t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Third ly, both cu ltu ral p roxim ity and inform ation flow s are follow ed by
institu tional im p rovem ents, often w ith significant effects, in high incom e cou ntries bu t not in low -incom e cou ntries. Finally, m ore liberal
trad e p olicies (as m easu red by the econom ic restrictions d im ension of
the KOF Ind ex) correlate w ith better institu tions in low -incom e
cou ntries, bu t not so in high-incom e cou ntries.
Ru nning sep arate regressions for low -incom e and high-incom e
cou ntries d rastically changes the resu lts, in line w ith the theoretical
exp ectations, d iscu ssed above. In p articu lar the p ositive relationship
betw een econom ic globalization and institu tional qu ality in the
baseline analysis seem s to be fu lly d riven by the relationship in richer
cou ntries. Sim ilarly, social globalization im p roves institu tional qu ality
in high-incom e cou ntries d u ring the tim e p eriod stu d ied . On the other
hand , econom ic globalization is not correlated w ith institu tional qu ality
31
in the less d evelop ed context, and social globalization is negative and
significant for fou r ou t of six institu tional m easu res.
Table 6 Different typ es of globalization; su b -sam p le of high-incom e
cou ntries
(controls are inclu d ed bu t not show n)
Variables
Econom ic Flow s
N um ber of Countries
R-squared
Econom ic Restrictions
N um ber of Countries
R-squared
Personal Contacts
N um ber of Countries
R-squared
Information Flow s
N um ber of Countries
R-squared
Cultural Proxim ity
N um ber of Countries
R-squared
GE
0.218
(2.96)***
46
0.98
-0.07
(0.64)
44
0.97
0.077
(0.35)
48
0.97
0.212
(2.20)**
47
0.97
0.066
(2.46)**
48
0.97
CC
0.2
(1.93)*
46
0.96
-0.084
(0.57)
44
0.96
0.285
(0.93)
48
0.96
0.466
(3.61)***
47
0.96
0.066
(1.78)*
48
0.96
RQ
0.147
(1.25)
46
0.95
0.302
(1.93)*
44
0.95
-0.033
(0.1)
48
0.95
0.098
(0.7)
47
0.95
0.032
(0.8)
48
0.95
VA
0.077
(0.8)
46
0.97
0.001
(0.01)
44
0.96
0.42
(1.58)
48
0.97
0.201
(1.72)*
47
0.97
0.101
(3.15)***
48
0.97
RL
0.096
(1.14)
46
0.97
-0.165
(1.4)
44
0.97
-0.013
(0.06)
48
0.97
0.122
(1.18)
47
0.97
0.028
(0.97)
48
0.97
PS
0.214
(1.72)*
46
0.94
-0.099
(0.58)
44
0.95
0.395
(1.15)
48
0.95
0.185
(1.24)
47
0.95
0.096
(2.29)**
48
0.95
Absolute value of t statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
In short, only liberalization of econom ic restrictions seem s to d o m ore
good to institu tions in low -incom e cou ntries than in high -incom e
cou ntries. The other fou r asp ects of globalization all cap tu re som e kind
of hu m an interaction su ch as trad e, foreign investm ent, tou rism ,
cu ltu ral integration, and su rfing the Internet. To the extent that
increases in these activities affect institu tions, it lead s to im p rovem ents
in high-incom e cou ntries and to w orse institu tions in low -incom e
cou ntries. This is very m u ch in line w ith the p rior theoretical
argu m ents and su ggests that the resu lts in p reviou s stu d ies u sing
sam p les inclu d ing rich and p oor cou ntries together shou ld be
interp reted w ith care.
32
3.4.2 Robustness tests
The robu stness of the resu lts is verified w ith a nu m ber of ad d itional
control variables u sed in the em p irical literatu re on institu tional
change: Political regim e, rep resented by the Polity IV score, the share o f
ad u lt p op u lation w ith com p leted second ary ed u cation and total net
d evelop m ent assistance and aid .
The m ain d ifference betw een rich and p oor cou ntries rem ains w hen
inclu d ing these variables (resu lts are not rep orted bu t available from
the au thor). As exp ected , Polity IV score is not significant given that it
changes little over tim e. Ed u cation has a significant negative effect on
p olitical stability bu t is otherw ise not significant. Most interestingly, aid
is typ ically p ositive and significant, su ggesting either that aid im p roves
institu tional qu ality or that m ore aid is given to those cou ntries w here
institu tions are im p roving. Previou s find ings have associated aid w ith
a d ecline in institu tional qu ality (Brau tigam and Knack, 2004) or fou nd
it having only a sm all im p act on institu tional change (Knack, 2004).
This im p act m ight also d ep end on the m od e of aid (Selaya and Thiele,
2012). H ow ever, accord ing to Wright (2009), there are factors that m ight
intervene in the aid -institu tions relationship , cau sing aid to im p rove a
cou ntry‘s accou ntability.
In the m ain analysis, the strategy to id entify d ifferences betw een rich
and p oor cou ntries is to d ivid e the fu ll sam p le. A d ifferent strategy is to
inclu d e an interaction term betw een globalization and GDP. Doing so
im p roves the p ow er of the estim ations and allow s u s to calcu late the
m arginal effect of globalization on institu tional qu ality at d ifferent
levels of GDP p er cap ita. Figu re 3 illu strates the m arginal effect of
globalization on control of corru p tion cond itional on the valu e of
logged GDP p er cap ita, and confid ence intervals at 95 p er cent. Once
again the p attern fou nd in the baseline analysis is confirm ed : Econom ic
globalization is follow ed by im p roved (here: less corru p ted )
institu tions in rich bu t not in p oor cou ntries. Social globalization
how ever seem s to w orsen institu tions in p oor bu t not in rich cou ntries.
33
Figu re 3 The m arginal effect of econom ic and social globalization on
control of corru p tion at d ifferent levels of GPD p er cap ita
34
3.5 Concluding discussion
In 1749 Montesqu ieu su ggested that m arket interactions m ight act as a
civilizing force. Follow ing his line of reasoning, the establishm ent of
the Sw ed ish fu rnitu re retailer IKEA in Ru ssia in 2000 cou ld affect
norm s and behaviou r in su ch m anner that ensu ing firm s establishing in
the cou ntry w ou ld not have to share sim ilar exp eriences w ith bribes
and corru p tion. If this held tru e globally, it w ou ld be w elcom e new s for
d evelop ing cou ntries. Sad ly how ever, this seem s not to be the case.
Researchers have recently started to em p irically exam ine the
relationship betw een econom ic globalization and institu tional qu ality.
The existing literatu re d oes how ever not consid er that the relationship
betw een globalization and institu tional change m ay d iffer across
contexts of d evelop m ent. This research com p lem ents this new and
grow ing literatu re on op enness and institu tional qu ality, by argu ing
that state execu tives and their bu reau cracies are likely to have d ifferent
tim e horizons in low -incom e and high-incom e cou ntries. As a resu lt,
increasing econom ic and social interactions follow ing w ith
globalization are likely to affect institu tions d ifferently d ep end ing on
the level of d evelop m ent.
Using a p anel d ataset based on the World w id e Governance Ind icators,
this stu d y u ncovered a heterogeneou s effect of globalization on
institu tions, esp ecially for the control of corru p tion m easu re. Most
notably, the control of corru p tion seem s to w orsen w hen less
d evelop ed cou ntries trad e m ore, w hile this is not the case in high incom e cou ntries. Sim ilarly, m ore social interaction across national
bord ers seem s to w orsen institu tional qu ality in low -incom e contexts,
bu t not so w hen the level of econom ic d evelop m ent is higher.
In contrast, m ore liberal trad e p olicies seem to bring abou t p ositive
institu tional change in low -incom e cou ntries, su ggesting that the new
set of constraints and new ru les are m ore im p ortant than m ore
econom ic transactions in this context.
Three m ajor conclu sions can be m ad e from this analysis. First, the
globalization p rocess is ind eed m u ltid im ensional and d ifferent p arts of
the p rocess (and their sp ecific character) seem to affect institu tions very
d ifferently. Second , d ifferent typ es of institu tions seem to d iffer in how
easily they resp ond to the new state of affairs that follow s w ith m ore
globalization. For exam p le, althou gh both the ru le of law ind icator and
the control of corru p tion ind icator cap tu re how w ell citizens and the
35
state resp ect institu tions that govern econom ic and social interactions
am ong them , it is m ainly control of corru p tion that is affected by the
globalization p rocess in this setting. Third , in line w ith the theoretical
p red ictions, globalization affects institu tions d ifferently d ep end ing on
the cou ntry‘s level of d evelop m ent. The em p irical resu lts thu s su ggest
that the p reviou s find ings on p ositive effects of econom ic flow s on
institu tional qu ality are likely d riven by changes in rich cou ntries.
Theoretical exp ectation that d ifferences in tim e horizon cau ses
institu tional entrep reneu rs to react d ifferently to increasing
globalization is thu s su p p orted . Bu t w hat are the exact m echanism s
exp laining these heterogeneou s effects of globalization? A su ggestion
for fu tu re research w ou ld be to investigate the role of ed u cation,
p olitical p articip ation, and d em ocratic institu tions.
In less d em ocratic low -incom e cou ntries, p olitical p ow er is often
concentrated in the hand s of an econom ic elite, for exam p le, the m ajor
p rod u cers and investors in the econom y. In this case, the short tim e
horizon argu m ent ap p lies, and social and econom ic globalization d oes
not p rovid e strong incentives to im p rove p rop erty rights for the w hole
econom y (i.e. for those m arket actors w ho d o not belong to the elite) or
to red u ce corru p tion by altering the existing fram ew ork. As d iscu ssed
by Acem oglu (2008), the econom ic elite p rotect their p rop erty rights
and ensu re that they d o not fear exp rop riation, bu t this typ e of
organization also typ ically enable the elite to get a m onop oly p osition
for them selves and exclu d e others to take ad vantage of p rofit
op p ortu nities, in essence violating the p rop erty rights of fu tu re
p otential p rod u cers.
In m ore d em ocratic cou ntries, things m ight be d ifferent bu t not
necessarily so. The effect of globalization on institu tions in d em ocracies
w ill d ep end on the d egree to w hich p eop le vote and d em and
accou ntability of local and national p oliticians. Again, tim e horizon is
likely to m atter, both d irectly and ind irectly via the ed u cation channel.
As show n by e.g. Cam p ante and Chor (2011), there is a robu st link
betw een ind ivid u al schooling and p olitical p articip ation. On average,
schooling is low er in d evelop ing cou ntries both as a resu lt of p overty
bu t also d u e to shorter tim e horizons: as show n by Jayachand ran and
Lleras-Mu ney (2009), low life exp ectancy low ers the valu e of hu m an
cap ital investm ents.
Exam ining the consequ ences of globalization on the w orld ‘s p oor is
u nd erstand ably a vivid research area. For exam p le, Bergh and N ilsson
36
(2010) have d em onstrated , also u sing the KOF Ind ex of Globalization,
that both econom ic and social globalization are p ositively related to life
exp ectancy, and that this relationship hold s also w hen rich cou ntries
are exclu d ed from the sam p le. In contrast, the resu lts of this stu d y
rather su ggest that globalization su ch as trad e and tou rism bring
benefits to institu tional qu ality in rich cou ntries bu t not in p oor ones.
Thu s, there are m any reasons to focu s fu tu re research on the links from
variou s form s of globalization to social norm s and institu tions in
d evelop ing cou ntries.
37
4. Fiscal Incentives and Foreign D irect Investment: A Causal
Inference Approach
4.1 Scope of research
In the p ast several d ecad es, m any governm ents at national and su b national levels in Eu rop e, Am erica, and Asia have sou ght to attract
foreign d irect investm ent (FDI) by setting u p investm ent p rom otion
agencies and offering variou s fiscal incentives (UN CTAD, 2000;
OECD, 2003). While p olicym akers believe tax incentives help to attract
FDI, m u ltinational enterp rise m anagers d o not typ ically rank taxes as
very im p ortant for investm ent d ecisions in their su rvey resp onses
(Tavares-Lehm ann et al., 2012). Su ch incongru ence of beliefs and
p ercep tions is p u zzling. Fu rther ad d ing confu sion to the p u zzle is the
fact that, after som e m ixed find ings on taxation and FDI in the early
em p irical stu d ies from 1950s to 1990s, a large bod y of econom etric
stu d ies in the p ast d ecad e ap p ear to reach the consensu s that low er tax
rates encou rage FDI (Tavares-Lehm ann et al., 2012; H ines, 1999;
Devereu x, 2006; De Mooij and Ed erveen, 2008).
This stu d y offers another em p irical analysis of the qu estion w hether tax
concession cau ses m ore FDI. In p articu lar, this research ad d resses three
w eaknesses from the p reviou s stu d ies. First, m ost em p irical stu d ies
focu s on the national level tax rates, bu t in m any cou ntries, tax rates on
corp orate p rofit are often affected by su b -national governm ents as w ell.
Ignoring su b-national d ifferences in corp orate tax rates lead s to
m easu rem ent error in the tax variable and biased estim ates. Second ly,
m any em p irical stu d ies p ool together very d ifferent cou ntries, w hose
u nobservable heterogeneity tend s to bias the estim ated effect of tax rate
on FDI. Third ly, existing econom etric stu d ies focu s on estim ating
correlation betw een tax rate and FDI, rather than id entify ing the cau sal
effect of tax incentives on FDI. The solu tion to these three p oints is to
ap p ly the cau sal inference ap p roach to a natu ral exp erim ent scenario
w ithin a single cou ntry w here regional governm ents at one p oint have
been granted au tonom y to cu t corp orate p rofit tax. The research seeks
to p rod u ce m ore cred ible estim ates of the cau sal effect of tax concession
on FDI.
Ru ssian regions after the year 2002 p rovid e the id eal setting for su ch an
analysis. It w as the first tim e the fed eral governm ent gave regional
governm ents the au tonom y to red u ce their p art of corp orate p rofit tax.
The 82 Ru ssian regional governm ents ad op ted three d ifferent corp orate
p rofit tax regim es: a statu s qu o flat rate, tax concessions for investm ent
38
p rofit, and tax concessions for the p rofit from im p ortant investm ent
p rojects. This exogenou sly im p osed au tonom y allow s test ing the cau sal
effects of d ifferent tax concessions on FDI across regions w ithin a single
cou ntry over tim e.
Tw o cau sal inference techniqu es are ap p lied : a p aram etric
id entification strategy, based on d ifferences in d ifferences (DID)
estim ation, and a non-p aram etric id entification strategy, based on
synthetic controls m ethod .
This chap ter p roceed s as follow s. Section 4.2 p rovid es a brief overview
of the em p irical literatu re on tax and FDI. Section 4.3 d iscu sses w hy
Ru ssian regions p rovid e an id eal n atu ral exp erim ent case. Section 4.4
p resents the p aram etric id entification strategy and the resu lts for the
DID analysis. Section 4.5 d iscu sses the non-p aram etric id entification
strategy and the resu lts from synthetic control m ethod . Section 4.6
conclu d es.
4.2 Review of literature on fiscal incentives and FDI
Many scholars p rovid e extensive review s of stu d ies on the top ic (see,
e.g., Tavares-Lehm ann et al., 2012; H ines, 1996; Devereu x, 2006; De
Mooij and Ed erveen, 2008). Em p irical stu d ies typ ically regress a
m easu re of foreign investm ent on som e tax variable(s) w hile
controlling for other factors affecting investm ent. As noted in som e
su rveys of the literatu re, these stu d ies d iffer in variou s resp ects. The
d ep end ent variable is often FDI flow s, FDI stock, the nu m ber of foreign
locations, or investm ent in p rop erty, p lant, and equ ip m ent. The tax
variables also d iffer across stu d ies, inclu d ing statu tory tax rate, tax
base, average tax rate, effective tax rate, effective m arginal tax rate,
effective average tax rate, and bilateral corp orate effective tax rates. The
d esign cou ld be tim e series, cross-sectional, and p anels, and cou ld be at
firm , ind u stry, su b-national, cou ntry, and bilateral levels. Stu d ies from
earlier d ecad es tend to reach m ixed find ings regard ing the im p act of
corp orate taxes on FDI, bu t m ost recent research (e.g.,
Becker et al., 2012; Bellak and Leibrecht, 2009; Bellak et al., 2009;
Blonigen and Davies, 2004; Egger et al., 2009; Gru bert and Mu tti, 2004)
tend s to find that corp orate taxes have significant effects on FDI.
As noted earlier, m ost of the extant stu d ies tend to focu s on national
corp orate tax rates and p ool heterogeneou s cou ntries together in their
sam p les. Only a very sm all nu m ber of stu d ies exam ine the im p act of
corp orate taxes by su b-national governm ents on FDI. For exam p le,
39
Bartik (1985), Slem rod (1990), Pap ke (1991), and H ines (1996) exam ine
how state-level corp orate incom e taxes affect FDI allocation
(investm ent and / or p lant location) am ong 50 US states. Sw enson (1994)
stu d ies how average tax rates affect aggregate FDI inflow s in 18
d ifferent ind u stries into 50 US states in the p eriod 1979–1991.
Becker et al. (2012) stu d y the effect of bu siness tax rates by Germ an
m u nicip alities on location d ecisions of m u ltinational com p anies. Since
in m any cou ntries, other than the USA and Germ any, su b-national
governm ents have som e au tonom y levying taxes on corp orations, m ore
research is in ord er to stu d y the im p act of tax p olicies of regional
governm ents on FDI. Focu sing on w ithin -cou ntry variations hold s
constant the u nobserved heterogeneity betw een cou ntries that tend s to
bias the estim ated effect of tax on FDI, thu s isolating the effect of
interest.
More im p ortantly, extant em p irical stu d ies largely estim ate the
correlation betw een tax and FDI and have little confid ence in claim ing
and find ing the cau sal effect of tax on FDI. It has been d em onstrated
that p olicy evalu ation based on conventional regression m od els
w ithou t ad d ressing cau sal inference issu es is m ost likely to generate
biased estim ates (Abad ie et al., 2010; Abad ie and Gard eazabal, 2003;
Abad ie, 2004; Ru bin, 1977; H olland , 1986; Angrist and Pischke, 2008).
This stu d y takes ad vantage of recent p rogress in the cau sal inference
m ethod s to p rovid e better estim ates of the cau sal effect of tax
concession on FDI.
4.3 Effect of tax concession on FDI in Russian regions: a natural experiment
design
This research aim s to exp lain how tax concessions for investm ent p rofit
m ight influ ence FDI inflow s in Ru ssian regions. The d ep end ent
variable is the am ou nt of foreign d irect investm ent inflow s into a
Ru ssian region in a given year, m easu red in 2000 constant d ollars and
log transform ed (figu re 4). Data are from Fed eral Statistic Service of
14
Ru ssia (Rosstat) . Foreign investm ent refers to the investm ent of
foreign cap ital into the objects of entrep reneu rial activity, as w ell as
other kind s of p rop erty and intellectu al valu es, inclu d ing services and
inform ation. The FDI d ata reflect a d irect-investor ow nership of at least
10 p er cent of the ord inary shares in the equ ity cap ital of an enterp rise,
14
FDI Data are reported by both Rosstat and the Bank of Russia in accordance w ith the
m ethod ology set out by the International Monetary Fund , but only Rosstat offers the d ata
d isaggregated by regions and by sectors.
40
resid ent in Ru ssia, by a d irect investor, resid ent of a foreign cou ntry.
Direct investm ent can be in form of equ ity cap ital, r einvested earnings,
intra-com p any loans, and financial leasing.
Figu re 4 FDI inflow s in Ru ssian regions in 2002 and 2008
FDI com p rises not only the initial transaction establishing the
relationship betw een an investor and an enterp rise, bu t also all
su bsequ ent transactions betw een them ; how ever, it d oes not inclu d e
investm ent m ad e in m onetary institu tions and banks (for statistical
41
15
p u rp oses, the latter is inclu d ed into other foreign investm ent) . Figu re
4 m ap s the intensity of FDI inflow s in 82 Ru ssian r egions. It is
interesting to note the striking increase of FDI inflow s in som e regions
betw een the years 2002 and 2008.
The tax variable u nd er stu d y is the corp orate p rofit tax rate. Changes in
corp orate p rofit tax in Ru ssian regions w ith the new Tax Cod e in 2002
p rovid e a natu ral exp erim ent to evalu ate the cau sal effect of tax
concession on FDI inflow s. As in m any other cou ntries, Ru ssian p rofit
tax is the tax on the incom e of legal entities, im p osed on net annu al
p rofits. The revenu es from p rofit tax are one of the m ain sou rces of the
regional bu d gets revenu es, m aking for 20 u p to 70 p er cent of their non transferable incom e. All other taxes existing in Ru ssia are either low
(hence, insignificant for the regional tax revenu es), either im p osed at
the fed eral level (so that the regional au thorities d o not have any p ow er
over the tax rates). Even thou gh corp orate p rofit tax rate in Ru ssia had
both a fed eral and a regional com p onent, setting the rates for both
com p onents w as trad itionally the p rerogative of the fed eral
governm ent. Bu t the Ru ssian Tax Cod e, w hich entered into force in
2002, introd u ced a new regim e for corp orate p rofit tax: The regions
w ere given the au tonom y to red u ce the regional p art of the p rofit tax
rate. With this new ly granted p ow er, the Ru ssian regions exp erim ented
w ith three typ es of p rofit tax regim es: a flat rate for corp orate p rofit tax
in general, tax concessions for d irect investm ent p rofit, and tax
concessions for p rofit from im p ortant investm ent p rojects. Table 7
p resents m ore d etailed inform ation abou t these p rofit tax rates in 80
Ru ssian regions from 1992 to 2010.
Table 7 Tax rates on investm ent p rofit in Ru ssian regions, p er cent
N um ber of Group
regions
37
Green
9
Orange1
19921993
32
32
19942001
35
35
2002
2003
2004
24
24
24
24
24
20 to
22.5
24
20 to
23.5
24
1
25
Orange2
Yellow 1
32
32
35
35
24
24
24
24
8
Yellow 2
32
35
24
24
15
20052008
24
20 to 22
23.5
19.5 to
23
20
20092010
20
15.5 to
17.5
19
15 to
18.5
15.5
Meyer and Pind (1999) and Vinhas d e Souza (2008) add ress the issue of low reliability
of Russian statistics on FDI in d etails. Som e m ethodology com parisons are presented also
in Foreign direct investment statistics: how countries measure FDI 2001. (2003). Washington,
D.C.: International Monetary Fund .
42
Based on the total p rofit tax rates, table 7 categorizes 80 Ru ssian
regions, exclu d ing tw o ou tliers (Kaliningrad Oblast and Jew ish
Au tonom ou s Oblast), into three grou p s, labelled w ith d ifferent colou rs
henceforth for sim p lifying references w ithin this research .
Green regions have only one flat tax rate, follow ing the fed eral
d ecisions abou t its d ecrease. Orange regions keep the stand ard tax rates
at the sam e level as green regions bu t introd u ce tax concessions for
investm ent p rojects. Y ellow regions also keep the stand ard tax rates at
the sam e level as green and orange r egions bu t introd u ce tax
concessions for so-called ―im p ortant investm ent p rojects.‖
The regional tax concessions, w hich occu rred in som e regions bu t not
in others d u ring the p eriod 2002–2008, p rovid e an id eal case for testing
their cau sal effect on FDI. The sam p le d esign is based on the follow ing
reasoning.
Over tim e, the fed eral p art of the tax rate rose and shrank, w hereas the
regional p art increased stead ily since 2003, bu t the total rate w as the
sam e d u ring 2002-2008, an im p ortant fact for the research d esign.
The total tax rate for p rofit, thou gh changed before 2002 and in 2009,
w as stable at 24 p er cent d u ring 2002–2008. Thu s, this stu d y focu ses on
the p eriod 2002–2008, w hich allow s hold ing constant the total tax rate
for p rofit and evalu ating p recisely the effect of the fiscal p olicy shock
im p lem ented at the regional level regard ing investm ent p rofit in 2003.
The control grou p in the analysis is green regions. Their tax rate on
p rofit from d irect investm ent stayed at 24 p er cent from 2002 to 2008,
even w hen regions w ere allow ed to low er their tax rates. It is w orth
noting three regions are exclu d ed from the green grou p , nam ely
Moscow City, Moscow Oblast, and Chechen Rep u blic, becau se they are
ou tliers in m any of their attribu tes, rend ering incom p ara ble
com p arisons. H ence, the p ool of green regions inclu d es 34 regions.
The first treatm ent grou p is orange regions. An investor in su ch a
region is eligible for the red u ced tax rate for the net incom e received
from d irect investm ent. N ote here that nine regions (referred to as
―orange1‖ in table 7) d ecrease their tax rates for investm ent p rofit in
2003 (entered into force in 2004), so that their total tax rates w ere p laced
som ew here betw een 20 and 22.5 p er cent. Orange2 inclu d es one region
(Bu ryatia Rep u blic), w hich has im p lem ented its tax concession not in
2003 bu t in 2004 (entered into force in 2005). Since the tax concession in
Bu ryatia is likely to be influ enced by other regions and thu s
43
end ogenou s, the treatm ent grou p inclu d es only nine orange1 regions
that im p lem ented tax concession sim u ltaneou sly as soon as they w ere
able to d o so.
The second treatm ent grou p is yellow regions. These thirty -three
regions have the sam e total rate for corp orate p rofit as the green
regions, bu t they d ecrease the tax rate for so-called im p ortant
investm ent p rojects. If an investor receives an ap p roval by the regional
governm ent to be inclu d ed in the list of investm ent p rojects consid ered
im p ortant for regional d evelop m ent, he/ she becom es eligible to ap p ly
the red u ced tax rate on investm ent p rofit. Since there are no com m on
criteria for ―im p ortance‖ of investm ent p rojects, each regional
ad m inistration selects them ind ep end ently. This p rovid es regional
bu reau cracy w ith a great d eal of d iscretionary d ecision -m aking p ow er.
Based on the tim ing of tax p olicy change, this grou p is also d ivid ed into
tw o typ es (referred to as ―yellow 1‖ and ―yellow 2‖ in table 7). Yellow 1
consists of 25 regions that low ered their rates for im p ortant investm ent
p rojects in 2003 (entered into force in 2004), so that the total tax rates
w ere p laced som ew here betw een 20 and 23.5 p er cent. Yellow 2 consists
of eight regions that low ered their rates for im p ortant investm ent
p rojects in variou s years later than 2004, p robably influ enced by
changes in other region s. In the fu rther analysis, the second treatm ent
grou p inclu d es only 25 yellow 1 regions that im p lem ented tax
concession sim u ltaneou sly as soon as they w ere able to d o so.
N ow one m ay w ond er w hether the treatm ent regions are
geograp hically clu stered . Ap p end ix D m ap s the geograp hical locations
of the three key grou p s: green1 (control), orange1 (treatm ent grou p 1),
and yellow 1 (treatm ent grou p 2). It show s little evid ence that either
orange or yellow regions are geograp hically clu stered , thou gh they
tend to be located in the Western p art of Ru ssia.
To fu rther ensu re that the control and treatm ent regions w ere sim ilar in
other variou s d im ensions before regional tax concessions in 2003, the
d ifference of m eans is tested betw een green and yellow / orange regions
w ith resp ect to FDI inflow s, GRP grow th rate, regional bu d get d eficit,
16
and nu m ber of p u blic officials p er cap ita . N one of the tests show s a
significant d ifference betw een control and treatm ent regions. Figu re 5
16
A d etailed d escription of the covariates is provid ed in the follow ing section. Regional
d eficit is obtained as follow s: Regional bud get revenue – (Bud getary transfers from
fed eral bud get + Regional bud get expenses). Data are in m illion US d ollars (logged ) and
com e from the Rosstat‘s dataset.
44
show s the box p lots for each variable for green, orange, and yellow
regions.
Figu re 5 Balance of relevant covariates betw een treatm ent and control
grou p s in the p re-treatm ent p eriod
N ote: t-tests show that d ifferences in m eans are not statistically d ifferent from zero.
The d istribu tions of FDI inflow s, econom ic grow th, regional bu d get
d eficit, and the p er cap ita nu m ber of p u blic officials are very sim ilar in
the p re-treatm ent p eriod betw een treatm ent (orange and yellow ) and
control regions. There is little evid ence that orange and yellow regions
im p lem ented tax concessions to catch u p w ith green regions, or that
green regions faced m ore bu d getary constraints su ch that they cou ld
45
not cu t tax, or that institu tional cap acity w as very d ifferent w hen tax
concession w as ad op ted in som e regions bu t not in ot hers. H ence, the
treatm ent and control regions are qu ite balanced in the p re -treatm ent
p eriod .
4.4 Parametric identification strategy
4.4.1 Difference in differences estimation
In com p arative stu d ies, researchers com p are the u nits exp osed to the
treatm ent w ith one or m ore u nexp osed u nits (Abad ie et al., 2010).
While estim ating the effect of tax concession on FDI, one faces the
fu nd am ental p roblem of cau sal inference: the im p ossibility of
observing the cou nterfactu al, i.e., the ou tcom e for the sam e u nit in t he
absence of the treatm ent. Id eally, to overcom e this p roblem , one w ou ld
cond u ct an exp erim ent in w hich tax concessions are rand om ly assigned
to the Ru ssian regions. The d ifference betw een the average level of FDI
for the treated regions and the average level of FDI for the control
grou p w ou ld constitu te the cau sal effect of the tax concession. This is
becau se both grou p s w ou ld be com p arable w ith resp ect to
(u n)observed confou nd ers. Of cou rse, in reality, tax concessions are
never com p letely rand om ly assigned to the regions. If confronted w ith
non-rand om assignm ent, cau sal inference m ethod s serve to overcom e
the obstacles to estim ating cau sal effects (Ru bin, 1974, 1977; H olland ,
1986; Angrist and Pischke, 2008).
The d ifference in d ifferences estim ation (hen ceforth DID) allow s
ap p roxim ating rand om ization by d esign and generating cau sal
inference. Within the DID fram ew ork as d iscu ssed in Angrist and
Pischke (2008), the estim ation can take the follow ing form in the
context of FDI and tax concession betw een one treatm ent and one
control grou p .
(2)
w here
d enotes observed FDI inflow in region i and p eriod t,
d enotes rand om error,
ind icates a d u m m y for the treatm ent
region,
d enotes the tim e-invariant region fixed effect in the absence
of a tax concession,
d enotes a tim e d u m m y that equ als one after
tax cu t is introd u ced ,
d enotes the tax cu t year fixed effect com m on
am ong regions, and
d enotes the interaction term
betw een treatm ent regions that im p lem ent tax concessions at som e
p oint and the tax cu t year d u m m y. In this setu p ,
d enotes the
46
d ifference in d ifferences effect of interest, i.e. the effect of tax
concessions on FDI.
The setu p in equ ation 2, w hen ap p lied to Ru ssian regions in this
analysis, requ ires several m od ifications. First, there are m any regions
w ithin each grou p (both treatm ent and control) tha t have u nobserved
heterogeneity to control for. H ence, a fixed effect d u m m y for each
region
is inclu d ed instead of
.17 Second , instead of one tax
cu t year, there are m u ltip le years, as each year m ight have had a
sp ecific effect com m on to all regions. H ence, a year fixed effect d u m m y
for each year
is inclu d ed instead of
.18 Third , there are tw o
d ifferent typ es of tax concessions im p lem ented by Ru ssian regions in
2003. H ence, the effects of tw o concession typ es shou ld be estim ated
sep arately. Sp ecifically, OrangeCut is a d u m m y that scores one for those
regions that cu t tax on investm ent p rofit in 2003 and zero otherw ise.
Y ellowCut is a d u m m y that scores one for th ose regions that cu t tax on
p rofit from im p ortant investm ent p rojects in 2003 and zero otherw ise.
Their coefficients rep resent the tax concession effect on FDI in this
setting. Finally, one shou ld control for observed covariates, w hich have
been com m only fou nd to affect FDI. Su ch covariates not only control
for com p ositional effects bu t also im p rove the p recision of the
estim ates. Thu s, the DID regression m od el is d efined as follow s:
(3)
To accou nt for variou s confou nd ing factors, several control variables
are inclu d ed , nam ely GRP p er cap ita, p op u lation, trad e, real econom ic
grow th, natu ral resou rces p otential, investm ent risk, nu m ber of p u blic
officials p er cap ita, p olitical stability, sp ecial econom ic zone, labo u r
cost, hu m an cap ital, sp atial correlation, and lagged d ep end ent variable.
Tim e-varying covariates are lagged by one year to avoid the p ost treatm ent bias.
Gross regional product (GRP) per capita, m easu red in 2000 constant USD
and log transform ed , ind icates the level of econom ic d evelop m ent in a
region for a given year. The variable logged population accou nts for
17
H ausm an test show s that region fixed effects are m ore preferable than region random
effects.
18
Wald test confirm s that year fixed effects are necessary in som e models.
47
regional m arket size, often an im p ortant d river in attracting FDI, and
also serves as a p roxy for regional labou r force (Ahrend , 2000;
Led yaeva, 2007). The variable trade, m easu red as the logged su m of
im p ort to and exp ort from a region, controls for the effect of trad e on
FDI, w hich cou ld be p ositive (e.g., intra-firm trad e) or negative (e.g.,
tariff ju m p ing investm ent). The variable real econom ic grow th (GRP
Growth) is another trad itional m easu re of regional econom ic
d evelop m ent. Data for these variables com e from the Rosstat d ataset.
FDI often d ep end s on the p resence of natu ral resou rces that has been
fou nd significant in som e p reviou s stu d ies (Asied u and Lien, 2011;
Kayam , H isarciklilar, and Yabru kov, 2007). To control for it, there is
regional rating of natural resources potential, com p iled by the Ru ssian
rating agency "Exp ert." Another control ind icator is ranking of
investment risks relative to the Ru ssian average, com p iled by the sam e
agency. Both variables range from 1 to 82, w ith 1 being the best rank,
19
thu s both variables are exp ected to have a negative sign .
The number of public officials per capita is inclu d ed as a sim p lified p roxy
for bu reau cratic qu ality of regions. Inflated p u blic ad m inistration often
associates w ith higher ad m inistrative bu rd ens and is exp ected to
d iscou rage foreign investors. Data on nu m ber of p u blic officials com e
from the Rosstat d ataset. The analysis also em p loys a p roxy of p olitical
stability in a region, m easu red as the governor' s tenure in office. Longer
governor tenu re is associated w ith m ore stable and p red ictable
institu tional p olicy, attracting m ore investm ent inflow s. This variable
w as calcu lated by the au thor.
Another control variable is a d u m m y for so-called Special Economic
Z ones (SEZ). Sp ecial Econom ic Zones w ere created in som e regions over
years in form of ind u strial areas, tou rism zones, and innovation p arks.
The resid ents of a SEZ receive tax holid ays for the first 10–15 years of
their activity and p ay zero im p ort tariffs. SEZ are exp ected to attract
20
foreign com p anies, hence this d u m m y shou ld have a p ositive sign .
This variable w as calcu lated by the au thor .
The m od el also inclu d es an average nominal wage per capita and a share
of employed with secondary education in total em p loym ent. As Broad m an
19
It is w orth noting that both rating w ere w id ely used in som e previous stud ies, but often
found insignificant (see Broad m an and Recanatini (2001) for d iscussion).
20
It is necessary to keep in m ind , though, that most of the SEZ w ere established in 20062007 and they becam e fully functional in 2009-2010, thus, their actual im pact m ight be
beyond the period und er stud y.
48
and Recanatini (2001) argu e, both the cost and the qu ality of labou r
m ay be key factors in attracting investm ent. Und er assu m p tion that the
investors seek for low er labou r costs and for high-skilled w orkers,
nom inal w age is exp ected to be negatively linked to investm ent, w hile
a share of em p loyed w ith higher ed u cation shou ld have a p ositive
im p act on FDI. Data for these tw o variables com e from the Rosstat
d ataset.
As d iscu ssed earlier, no p attern of sp atial correlation of FDI flow s
am ong regions is observed visu ally from figu re 4. N onetheless,
conservative estim ate shou ld inclu d e a spatial lag of FDI. Sp ecifically,
the lagged d ep end ent variable is m u ltip lied by a connectivity m atrix
that cap tu res contiguity am ong all the Ru ssian regions in the sam p le.
Concretely, the connectivity m atrix has ones for those regions that
share the bord er and zeros for those regions that d o not. This variable
controls for the fact that FDI m igh t be geograp hically clu stered . The
lagged d ep end ent variable in the sp atial lag (rather than m od elling a
sim u ltaneou s effect of FDI) allow s avoid ing end ogeneity in estim ating
su ch a m od el (Beck et al., 2006). Descrip tive statistics on the variables of
21
interest as w ell as their sou rces is p resented in table 8 .
Becau se FDI tend s to have inertia, the m od el inclu d es the lagged
d ep end ent variable on the right hand sid e to control for tem p oral
d ep end ence. N ote that u sing both the lagged d ep end ent variable and
region fixed effects on the right hand -sid e w ou ld m ake OLS estim ates
biased (N ickell, 1981). H ence, the Arellano-Bover/ Blu nd ell-Bond
system GMM estim ator ap p lies. It em p loys the m om ent cond itions of
lagged levels as instru m ents for the d ifferenced equ ation together w ith
the m om ent cond itions of lagged d ifferences as instru m ents for the
level equ ation. The Arellano-Bover/ Blu nd ell-Bond estim ator allow s
also to id entify the effects of tim e invariant variable, p rovid es a larger
set of m om ent cond itions both to overcom e som e w eak instru m ent
biases of first d ifferenced estim ators and to red u ce the finite sam p le
bias in p anels w ith short T and p ersistent regressors, and ad d resses the
end ogeneity of variou s variables w ith ap p rop riate instru m ents. In
p articu lar, the lagged d ep end ent variable, p er cap ita GRP, trad e
op enness, real econom ic grow th, sp atial lag, share of p u blic officials,
and investm ent risk are all treated as end ogenou s and all other
21
Kamchatka Krai in 2005 is the only outlier in the sam ple acco rd ing to the Cook's D
value. Rem oving it does not change the results.
49
covariates as exogenou s22. Robu st stand ard errors are estim ated to
correct for heterosked asticity (Rood m an, 2009)23.
Table 8 Descrip tive statistics
Variable
FDI flow s
Obs.
1159
Mean
9.18
Std .d ev.
2.50
Min
0.10
Max
16.38
Source
Rosstat (2012)
Orange regions
1722
0.04
0.19
0
1
Yellow regions
1722
0.10
0.30
0
1
GRP per capita
GRP growth
1292
1209
7.69
2.85
0.81
1.31
5.35
-4.46
10.66
5.26
Author‘s
calculations
Author‘s
calculations
Rosstat (2012)
Rosstat (2012)
Population
Trad e
Rating of natural
resources potential
Rating of investm ent
risk
Length of governor's
stay in pow er
Public officials per
capita
SEZ
1712
1116
1230
7.16
6.49
41.50
0.85
1.79
23.68
3.89
0.10
1.00
9.35
12.37
82.00
1230
41.50
23.68
1.00
82.00
1312
6.26
4.39
1.00
20.00
1299
0.01
0.01
0.004
0.06
1312
0.07
0.26
0.00
1.00
Spatial lag
1312
43.26
22.82
0.00
111.00
N om inal wage per
capita
Em ployed w ith
secondary ed ucation
1710
5.16
0.99
2.42
7.91
Author‘s
calculations
Author‘s
calculations
Rosstat (2012)
1185
21.05
5.68
7.30
51.20
Rosstat (2012)
Rosstat (2012)
Rosstat (2012)
Rating agency
Expert (2012)
Rating agency
Expert (2012)
Authors'
calculations
Rosstat (2012)
DID estim ation id entifies a cau sal effect if and only if the p arallel
trend s assu m p tion hold s. That is, the average ou tcom es for treatm ent
and control grou p s shou ld follow a p arallel trend ove r tim e. Only then
one can u se the observable d ifference in ou tcom es for the control grou p
as the cou nterfactu al for the treatm ent grou p . In the absence of a
p rop er test for the p arallel trend assu m p tion, region -sp ecific tim e
trend s are inclu d ed on the right hand -sid e for som e m od els to check if
the coefficients change (Angrist and Pischke, 2008: 238).
22
23
Variance inflation factor (VIF) statistics show no concern for m ulticollinearity.
Breusch-Pagan test show s that it is necessary to correct for heterosked astacity.
50
4.4.2 Results of the difference in differences estimation
Table 9 show s the DID estim ation resu lts. For the system GMM
estim ator to be valid , tw o assu m p tions and d iagnostic tests are
im p ortant. First, for the instru m ents to be valid , the error term shou ld
be free from serial correlation. With first d ifferencing in the system
GMM, in ord er for the m om ent cond itions to be valid , the d ifferenced
errors shou ld be serially correlated at ord er one, bu t not at any higher
ord er. H ence, the AR(1) and AR(2) tests in first d ifferenced resid u als
are im p ortant. Second , an assu m p tion u nd erlying the valid ity of the
system GMM estim ates is that the instru m ents are exogenou s. The
Sargen/ H ansen overid entification restriction tests allow test ing the
joint nu ll hyp othesis that the instru m ents are valid and u ncorrelated
w ith the error term , and that the exclu d ed instru m ents are correctly
exclu d ed . Resu lts for both tests in table 9 su ggest that serial correlation
and instru m ent exogeneity are reassu ring.
The first tw o colu m ns in table 9 rep ort the estim ates of the sam p le w ith
all Ru ssian regions for w hich d ata are available, w ith varying nu m ber
of control variables. The coefficients of both OrangeCu t and Yellow Cu t
are p ositive, thou gh only the form er ones are statistically significant at
the conventional level. Thu s, p relim inary evid ence su ggests that tax
concessions for investm ent p rofit increase FDI inflow s, bu t that tax
concessions for p rofits from only im p ortant investm ent p rojects d o not.
Table 9 Difference in d ifferences estim ation based on GMM m od el
Variables
OrangeCut
YellowCut
Lagged FDI
GRP per capita
Population
Trad e
GRP Growth
(1) FDI
0.68*
(0.39)
0.40
(0.33)
0.09
(0.06)
-0.22
(0.88)
6.09
(9.57)
-0.64**
(0.25)
0.02
(0.11)
(2) FDI
0.76*
(0.39)
0.45
(0.32)
0.09
(0.06)
-0.26
(0.86)
5.42
(9.56)
-0.63**
(0.25)
0.01
(0.11)
(3) FDI
0.66
(0.42)
0.21
(0.35)
0.08
(0.06)
0.21
(1.11)
4.93
(11.24)
-0.65**
(0.28)
0.02
(0.12)
51
(4) FDI
0.72*
(0.42)
0.28
(0.35)
0.08
(0.06)
0.32
(1.11)
3.07
(10.83)
-0.66**
(0.28)
0.02
(0.12)
(5) FDI
1.18*
(0.71)
0.84
(0.67)
-0.06
(0.07)
1.92
(1.90)
89.49
(62.58)
-0.51
(0.35)
0.12
(0.15)
(6) FDI
1.20*
(0.71)
0.86
(0.68)
-0.06
(0.07)
2.11
(1.99)
89.83
(61.95)
-0.52
(0.35)
0.12
(0.15)
Table 9 (cont.)
Variables
Rating of
natural
resources
Public officials
per capita
SEZ
Rating of
investm ent risk
Spatial lag
Length of
governor's stay
in pow er
N om inal wage
per capita
(1) FDI
0.03
(0.05)
(2) FDI
0.02
(0.05)
(3) FDI
0.03
(0.05)
(4) FDI
0.03
(0.05)
(5) FDI
0.06
(0.05)
(6) FDI
0.06
(0.05)
307.10**
(134.40)
-0.02
(0.41)
-0.01**
(0.01)
-0.00
(0.02)
0.04
(0.03)
307.43**
(134.69)
0.03
(0.42)
-0.01***
(0.01)
-0.00
(0.02)
0.04
(0.03)
349.16**
(170.37)
0.03
(0.45)
-0.01**
(0.01)
0.01
(0.02)
0.03
(0.03)
347.09**
(169.36)
0.07
(0.46)
-0.01**
(0.01)
0.00
(0.02)
0.04
(0.03)
337.22
(347.89)
0.34
(0.54)
-0.02**
(0.01)
-0.00
(0.02)
-0.00
(0.04)
321.63
(334.63)
0.34
(0.54)
-0.02**
(0.01)
-0.00
(0.02)
-0.00
(0.04)
0.01
(0.03)
0.00
(0.02)
0.02
(0.03)
-0.01
(0.02)
Em ployed w ith
secondary
ed ucation
Arellano-Bond
0.00**
0.00**
0.00**
0.00**
0.00**
test for AR(1)
Arellano-Bond
0.17
0.16
0.19
0.21
0.5
test for AR(2)
Sargan-Hansen
53.97
47.31
31.75
29.85
11.60
test -- χ2
Dropping
no
no
yes
yes
yes
regions
Region FE
yes
yes
yes
yes
yes
Year FE
yes
yes
yes
yes
yes
Region-specific
no
no
no
no
yes
Tim e Trend s
Observations
458
458
378
378
378
N um ber of id
69
69
56
56
56
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
The χ2 test is not statistically significant at the conventional level, show ing
m od els are not over-id entified
0.02
(0.03)
-0.01
(0.02)
0.00**
0.61
5.67
yes
yes
yes
yes
378
56
that the
Mod els (3) and (4) show the resu lts w ith both ou tliers and the regions,
w hich im p lem ented tax concessions after 2004, d rop p ed . Exclu d ing
these regions allow s assessing that the resu lts are neither d riven by
ou tliers nor by the regions, w hich fiscal p olicy cou ld have been
influ enced by som e other regions that introd u ced tax concessions
earlier. The effect of OrangeCu t and Yellow Cu t is p ositive in both
52
m od els, bu t only the effect of OrangeCu t in Mod el (4) is statistically
significant.
Mod els (5) and (6) show the resu lts w hen controlled for region-sp ecific
tim e trend s. As noted , inclu d ing su ch trend s is a w ay to check w hether
the p arallel-trend assu m p tion hold s. Coefficients for both OrangeCu t
and Yellow Cu t are p ositive, bu t only the form er ones are statistically
significant.
The resu lts allow conclu d ing that tax concession for investm ent p rofit
cau ses significantly m ore FDI inflow s, bu t that tax concession for
selective im p ortant investm ent p rojects d oes not. The im p ortant new
find ing is that not all tax concessions increase FD I inflow s. These
resu lts are robu st regard less of w hether the sam p le is restricted or not
and regard less of control for region -sp ecific tim e trend s. H ow large is
the su bstantive effect of tax concession on investm ent p rofit? With the
coefficient estim ate of m od el 6 (1.2) being the m ost reliable estim ate, tax
concession for investm ent p rofit increases FDI inflow s by 232 p er cent.
This estim ate is very close to the sem i-elasticity estim ates in the
p reviou s em p irical stu d ies.
Only a few control variables tu rn ou t to be statistically significant in the
m od els. This is in line w ith p reviou s stu d ies on FDI in Ru ssia (Strasky
and Pashinova, 2012). In ad d ition, it is also consistent w ith the earlier
d iscu ssion that the regions betw een treatm ent and control grou p s
ap p ear to be rou ghly balanced on m any d im ensions.
To fu rther ensu re the valid ity of the find ings in table 9, several p lacebo
tests are rep orted in Ap p end ix E. First, in the baseline m od el, FDI is
rep laced w ith three variables that shou ld be orthogonal to ta x
concessions: nu m ber of su icid e in the fem ale u rban p op u lation, nu m ber
of d ivorce, and nu m ber of m arriage. Tax concession shou ld have no
effect on su ch d ep end ent variables. If it d oes, it w ou ld im p ly that
orange and yellow regions d iffer su bstantially fro m the other regions
that d id not im p lem ent tax concessions. Su ch social d ifferences
betw een treatm ent and control grou p s cou ld affect both the level of FDI
and tax concession, threatening the w hole id entification strategy.
Therefore, it is reassu ring that both the orange regions treatm ent and
the yellow regions treatm ent are never statistically significant at the
conventional level. Second , both treatm ents are rep laced w ith tw o
d u m m ies that score one resp ectively for orange regions and yellow
regions in p re-treatm ent p eriod s and zeros for all the other regions and
in the years 2004–2008. Su ch d u m m ies are com m only referred as lead s
53
in the econom etrics literatu re. Again these tw o variables d o not have a
statistically significant effect.
N ote that the lagged d ep end ent variable is never statistically
significant. The m ain m od els are thu s recreated w ithou t lagged FDI,
u sing Driscoll-Kraay stand ard errors to control for sp atial correlation
and first ord er au tocorrelation (Ap p end ix F). Resu lts of this
sp ecification are sim ilar to those rep orted in table 9.
4.5 N on-parametric identification strategy
4.5.1 Synthetic controls method
DID estim ation is ―based on the p resu m p tion of tim e-invariant (or
grou p -invariant) om itted variables‖ (Angrist and Pischke, 2008: 243).
For m any cau sal qu estions, inclu d ing the scop e of this research , the
id ea that om itted variables are tim e-invariant is not alw ays p lau sible.
Althou gh the sp ecification above inclu d es a lagged d ep end ent variable,
as w ell as both region and year fixed effects, cond itions for consistent
estim ates of su ch m od els are qu ite d em and ing (Angrist and Pischke,
2008: 245). This section com p lem ents the p aram etric estim ation w ith
the synthetic control m ethod —a non-p aram etric estim ation techniqu e,
w hich allow s bu ild ing a m ore cred ible cou nter-factu al (Abad ie and
Gard eazabal, 2003; Abad ie, 2004). The synthetic controls m ethod w as
p ioneered by Abad ie and Gard eazabal (2003) in a stu d y on terrorism in
the Basqu e Cou ntry and fu rther d evelop ed by Abad ie et al. (2010). The
id ea behind synthetic controls m ethod is a sim p le one: a com bination of
control u nits often p rovid es a better com p arison for the treated u nit
than any single u nit alone. In other w ord s, a synthetic controls
ap p roach allow s bu ild ing a m ore cred ible cou nterfactu al to test the
effect of tax concession on FDI inflow s.
The synthetic controls m ethod w orks by testing w hether tax concession
im p lem ented by a region i in 2003 lead s to a larger inflow of FDI in the
years 2004-2008 com p ared to the sim ilar Ru ssian regions that d id not
im p lem ent any tax concession. Becau se com p arison u nits are m eant to
ap p roxim ate the cou nterfactu al w ithou t the treatm ent, it is im p ortant to
restrict the d onor p ool to regions w ith ou tcom es that are thou ght to be
d riven by the sam e stru ctu ral p rocess as the treatm ent regions bu t that
w ere not su bject to stru ctu ral shocks to the ou tcom e variable d u ring the
p eriod of stu d y (Abad ie et al., 2012). The com p arison regions, w hich
constitu te the synthetic control grou p , are selected u sing an algorithm
based on their sim ilarity to the treated region i before the treatm ent,
54
both w ith resp ect to confou nd ing factors and p ast level of FDI. In other
w ord s, ―the synthetic control algorithm estim ates the m issing
cou nterfactu al as a w eighted average of the ou tcom es o f p otential
controls‖ (Billm eier and N annicini, 2012: 12).
Key elem ents of the estim ation are the w eights of the synthetic control
u nits. Sp ecifically, the synthetic controls algorithm estim ates the
w eights in a non-p aram etric w ay so that the d istance (or p seu d od istance) betw een the vector of p re-treatm ent covariates of the treated
region and the vector of p re-treatm ent covariates of the p otential
24
synthetic control is m inim ized . For instance, being Am u r Oblast the
treated region i, the synthetic controls algorithm d etects the closest
regions to Am u r Oblast accord ing to a large nu m ber of characteristics
cap tu red by the control variables am ong all regions, w hich d o not
im p lem ent any tax concessions, i.e., green regions. One can then
com p are w hether the increase in FDI for Am u r Oblast is large
com p ared to the increase in FDI for regions chosen as synthetic control
u nits. This p roced u re then rep eats for all the treated regions in the
sam p le, i.e., for both orange and yellow grou p s.
To bu ild the synthetic controls grou p , all control variables, d escribed in
the p aram etric estim ation, are ap p roxim ated in the p re-treatm ent
25
p eriod . A fu rther covariate privatization is inclu d ed , w hich cap tu res
the nu m ber of com p anies p rivatized in each region i in year t. Althou gh
p rivatization reform s are d ecid ed by the fed eral governm ent, one
shou ld m ake su re that FDI inflow s d oes not increase d u e to other
institu tional reform s. Im p ortantly, the lagged d ep end ent variable is
also inclu d ed in the p re-treatm ent p eriod . Doing so allow s isolating
any anticip atory effects, i.e., those control variables that change in
anticip ation of fu tu re tax concessions before su ch tax concessions are
actu ally im p lem ented . All p red ictor variables are averaged over the
entire p re-treatm ent p eriod , from 1995 to 2002.
Moreover, follow ing Billm eier and N annicini (2012), tw o typ es of
exp erim ent are im p lem ented . Typ e-A exp erim ent restricts the choice of
24
Follow ing Abad ie, Diamond, and H ainm ueller (2010) and Billm eier and N annicini
(2012), a constrained quad ratic program m ing routine is used , w hich find s the best fitting
W-w eights cond itional on the regression based V-m atrix. The mod el relies on a fully
nested optim ization p roced ure that searches am ong all (d iagonal) positive sem i-d efinite
Vm atrices and sets of W-w eights for the best fitting convex combination of the control
units.
25
It is im possible to includ e this covariate in the DID estim ator, since the num ber of
observations would d rop substantively.
55
synthetic control u nits to those regions that are in the sam e econom ic
26
zone of the treated u nit . The intu ition here is to ad ju st the control u nit
by intentionally p ooling the regions w ith sim ilar characteristics in su ch
a w ay that the p re-treatm ent variation in the ou tcom e w ou ld be
m inim al betw een the treated and control u nits. The id ea behind the
typ e-A exp erim ent is that ―researchers trying to m inim ize biases
cau sed by interp olating across regions w ith very d ifferent
characteristics m ay restrict the d onor p ool to regions w ith sim ilar
characteristics to the region exp osed to the event or intervention of
interest‖ (Abad ie et al., 2010).
Typ e-B exp erim ent u ses all the regions in the green grou p . There is a
clear trad e-off betw een these tw o typ es of exp erim ents. The form er
exp erim ent m inim izes the p ossibility of com p aring the treated u nits
w ith heterogeneou s regions, since m any confou nd ing factors are likely
to clu ster geograp hically. The latter exp erim ent increases the sam p le
size and the p ow er of the test. Ap p end ix G lists the regions inclu d ed in
the control grou p for each treated u nit for both typ e-A and typ e-B
exp erim ents.
The synthetic controls ap p roach has three m ain ad vantages over DID
estim ation. The first ad vantage is its transp arency since the regions that
end u p in the cou nterfactu al, as w ell as their w eights, can be easily
id entified . The second ad vantage is flexibility since the control grou p
can be ap p rop riately restricted to those regions that are m ost sim ilar to
the treatm ent u nit, m aking the com p arison m ore m eaningfu l than in
p aram etric estim ation. Third , ―w hile p anel m od els only control for
confou nd ing factors that are tim e invariant (fixed effect) or share a
com m on trend (d ifference in d ifferences), the m od el sp ecified above
allow s the effect of u nobservable confou nd ing factors to vary w ith
tim e‖ (Billm eier and N annicini, 2012: 13).
The synthetic controls m ethod d oes not com e w ithou t shortcom ing. As
Billm eier and N annicini (2012: 13) note, the synthetic controls m ethod
w ou ld still su ffer from reverse cau sation if the tim ing of tax
concessions w ere d ecid ed by exp ectations on fu tu re increase in FDI.
Althou gh su ch a p ossibility exists, the qu alitative evid ence su ggests
that the tim ing of the tax reform in 2002–2003 can be consid ered
com p letely exogenou s to the level of FDI inflow s in Ru ssian regions.
26
The Russian regions are clustered geographically into 12 econom ic zones. The regions
w ithin each econom ic zone w ere explored at the sam e tim e, hence they shared com m on
history; they also often have sim ilar clim ate, transpor t infrastructure, and ind ustrial m ix.
56
What cannot be consid ered exogenou s is the d ecision o f regions to u se
su ch a reform to grant tax concession.
4.5.2 Results of the synthetic controls method
The resu lts are p resented grap hically for both typ e-A and typ e-B
exp erim ents, p rim arily for orange regions. Figu res 6–8 rep resent
grap hically the tim e series of the d ep end ent variable, log of FDI, for the
treated u nit (solid line) and for the synthetic control u nit (d ashed line),
both in the entire p re-treatm ent p eriod , i.e. 1995–2002, and in the p osttreatm ent p eriod , i.e. 2003–2008.
Ap p end ix H com p ares the resu lts from typ e-A and typ e-B exp erim ents
for each treated region w ith the constru cted synthetic control. Figu re 6
show s that five orange regions face a su bstantial increase of FDI
inflow s after tax concession: Am u r Oblast, Bryansk Oblast, Rostov
Oblast, Ud m u rt Rep u blic, and Saint Petersbu rg. N ote that for tw o
regions, Kabard ino-Balkar Rep u blic and Rep u blic of Kalm ykia, FDI
d ata are not available. In su m , for five ou t of seven regions tax
concessions lead to a notew orthy increase of FDI inflow s betw een 2003
and 2008.
Figu re 6 show s several inform ative featu res. Saint Petersbu rg and
Ud m u rt Rep u blic had a low er level of FDI com p ared to their control
grou p s in the p re-treatm ent p eriod , w hereas both orange regions
cau ght u p first and then ou tp erform ed their control grou p s in term of
FDI inflow s d u ring the tax concession p eriod . Conversely, Am u r
Oblast, Bryansk Oblast and Rostov Oblast had higher level of FDI
com p ared to their control grou p in 2003, thou gh the gap w as m inim al,
and they fu rther increased the gap as a resu lt of tax concessions.
H ow ever, Rostov Oblast faced a stead y d ecline of FDI p ost 2007.
Moreover, for Am u r Oblast and Bryansk Oblast there is evid ence of an
im p ortant anticip atory effect alread y in 2002. Finally, w hile the
evid ence of an increase of FDI is w eak for Chu vash Rep u blic and Perm
Krai, their FDI increased in 2007 and 2008 and rem ained higher than in
their control grou p s.
57
Figu re 6 Orange regions that attracted m ore FDI after tax concession
There are a cou p le of fu rther consid erations w orth m aking. First, all the
covariates are qu ite balanced betw een the treated u nit and the control
grou p . This is qu ite evid ent also from the figu res, in w hich the level of
FDI is very sim ilar betw een treated and control u nits in the p re treatm ent p eriod . Second , the root m ean squ are p red iction error
(RMSPE) is qu ite low for all seven regions, confirm ing that the overall
fit of the m od els is good . All in all, the synthetic controls analysis seem s
to confirm the resu lts of the DID estim ation: tax concession lead s to
m ore FDI in the orange regions over tim e.
58
Figu re 7 Orange regions that d id not attract m ore FDI after tax
concession
Figu re 8 show s ten yellow regions that exp erience a rise in FDI inflow s
after tax concession: Kalu ga Oblast, Khabarovsk Krai, Kom i Rep u blic,
Ku rgan Oblast, Leningrad Oblast, N ovosibirsk Oblast, Lip etsk Oblast,
Pskov Oblast, Rep u blic of Tatarstan, and Yaroslavl Oblast. H ow ever,
su ch a p ositive effect of tax concessions on FDI is not alw ays confirm ed
by both typ e-A and typ e-B exp erim ents. For other 15 regions, there is
no evid ence that tax concessions for ―im p ortant investm ent p rojects‖
have had any effect on FDI. N ote that yellow regions tend to have
low er level of FDI com p ared to their control grou p in the p re -treatm ent
p eriod , thou gh the gap is qu ite sm all. Thu s, these regions m ight have
u sed tax concession to catch u p w ith the other Ru ssian regions.
All covariates are qu ite balanced betw een the treated u nit s and the
control grou p s and the RMSPE is qu ite low . All in all, the sy nthetic
controls analysis seem s to confirm again the resu lt of the DID
estim ation: tax concession for ―im p ortant investm ent p rojects‖ has only
w eak im p act on FDI in the yellow regions.
59
Figu re 8 Yellow regions that attracted m ore FDI after tax concession
4.5.3 M ain findings
Given the large nu m ber of case stu d ies inclu d ed in the synthetic control
analysis, it is w orth to fu rther d iscu ss the m ain find ings. In p articu lar, it
is necessary to highlight som e com m on characteristics of th ose regions
that su ccessfu lly increased FDI inflow s after having im p lem ented tax
60
concessions. Ap p end ix I su m m arizes the resu lts for the tw o grou p s of
regions and rep ort relevant variables, w hich m ight have an im p act on
FDI in com bination w ith tax concession. For a given variable x the
sym bol ―‡‖ im p lies that a region i lays above the m ean for that sp ecific
variable.
Som e variables stand ou t as clear intervening factors in attracting FDI.
Before d iscu ssing su ch factors, figu res 9 and 10 show the box p lots of
each variable for treated u nits and for the control grou p . The
d istribu tions of treated and control u nits are generally balanced . That is
cru cial for the w hole id entification strategy, since it m itigates the
concerns abou t confou nd ing factors exp laining both w hy FDI inflow s
increase and w hy regions d ecid e to im p lem ent tax concession. In other
w ord s, regions, w hich cu t tax rates, are ―as good as rand om ly
assigned ‖ once cond itioned on control variables.
Figu re 9 Balance of confou nd ing factors for orange regions
N ote: t-tests show that d ifferences in m ean are not statistically d ifferent from zero
First, having good transp ort infrastru ctu re and a high p ercentage of
u rban p op u lation seem s to be an im p ortant intervening factor in
attracting FDI inflow s for both orange and yellow regions. This is
61
hard ly su rp rising. Both high level of u rbanization and an extensive
transp ort netw ork red u ce transp ort costs and , generally, sim p lify
m arket exp ansion (Led yaeva, 2007; Kayam et al., 2007; Iw asaki and
Su ganu m a, 2005; Broad m an and Recanatini, 2001). For instance, the
regions, w hich territory lies w holly or p artially behind the Arctic Circle
(e.g. Rep u blic of Karelia, Kom i Rep u blic, Yaku tia Rep u blic, and
Yam alo-N enets Au tonom ou s Okru g), face the lack of p rop er transp ort
infrastru ctu re, as bu ild ing of either p aved road s or railw ays is
extrem ely d ifficu lt in the cond itions of p erm afrost and grou nd ice.
Moreover, any air connections in these regions interru p t d u ring the
w inter m onths. Even thou gh these regions are m ain p rod u cers in
m ining ind u stry, esp ecially in oil and gas extraction, the natu ral
resou rces end ow m ent, ap p arently, cou ld not be the only factor to
attract foreign investm ent in there in the absence of ap p rop riate
transp ort infrastru ctu re.
Figu re 10 Balance of confou nd ing factors for yellow regions
N ote: t-tests show that d ifferences in m eans are not statistically d ifferent from zero
Second ly, virtu ally every region that increased FDI after tax concession
has a relatively high share of Ru ssian p op u lation. As Broad m an and
Recanatini (2001) p oint ou t, the ethnic com p osition of p op u lation m ight
62
be a p roxy for social (in)stability. The higher the ethnic d iversity in a
region is, the higher the p ossibility of social conflicts and violence,
w hich m ight d iscou rage foreign investors.
Third ly, natu ral resou rces d o not ap p ear to m atter in attracting FDI for
those regions that im p lem ented tax concessions. Ind eed , virtu ally all of
the orange and yellow regions, w hich increase FDI inflow s after tax
concession, have a share of m ining ind u stry below m ean. This find ing
is in line w ith the p reviou s stu d ies: Ru ssian FDI inflow s are attracted
m ainly by the m anu factu ring sector and not by natu ral resou rces
end ow m ent (Vinhas d e Sou za, 2008; Brad shaw , 1997; Asied u and Lien,
2011; Strasky and Pashinova, 2012).
Finally, there is w eak evid ence that the regions, w hich increase FDI
after tax concession, are geograp hically clu stered . This is su rely not the
case for su ccessfu l orange regions, w hich are not in the sam e econom ic
zone. Su ccessfu l yellow regions are also sp r ead -ou t across the w hole
cou ntry, thou gh N orth ern and Western regions seem to p erform
betw een than Sou th ern and Eastern ones. All in all, there is little
evid ence that FDI are geograp hically clu stered in Ru ssia, a resu lt
consistent w ith the find ing in the p aram etric estim ation as w ell.
4.6 Conclusion
This stu d y ap p lies the cau sal inference ap p roach to answ er the
qu estion of w hether tax concession increases FDI. Even thou gh there is
a hu ge bod y of em p irical literatu re on tax p olicy and FDI, their find ings
have several caveats. This research ad d resses those w eaknesses by
taking ad vantage of the exogenou sly (fed erally) im p osed fiscal p olicy
shock in Ru ssian regions after 2002, treating it as a natu ral exp erim ent
and estim ating the cau sal effect of tax concession on FDI inflow s w ith
tw o cau sal inference techniqu es: d ifference in d ifferences estim ation
and synthetic controls m ethod . It fu rther allow s investigat ing the
effects of tw o d ifferent typ es of tax concessions: one for investm ent
p rofit and the other for p rofit from im p ortant investm ent p rojects.
The find ings of this stu d y are interesting and illu m inating. First, u sing
the new cau sal-inference oriented techniqu es, the find ing of p reviou s
stu d ies is confirm ed : tax concession for investm ent p rofit lead s to m ore
FDI inflow s. The estim ated size of effect is also consistent w ith the m ost
com m on estim ate in the em p irical literatu re. Second find ing is that not
all tax concessions increase FDI inflow s. Selective tax concession on
63
governm ent sanctioned im p ortant inv estm ent p rojects d oes not have
the exp ected effect, or the effect is sp orad ic and w eak at best.
These find ings have im p ortant im p lications. Governm ents that u se
fiscal incentives to attract foreign cap ital shou ld be aw are that p olicy
d esign m atters. Consistent tax concession p olicies are also transp arent
and stable and thu s likely to be effective, m aking the tax concession
w orth the investor's w hile. On the other hand , w hen governm ent p icks
and chooses w inners, it likely introd u ces m ore am bigu ity, u ncertainty,
and rent-seeking behaviou r. When governm ent cu ts tax selectively,
investors m ay not resp ond to su ch often id iosyncratic benefits w ith
system atic enthu siasm and investm ents.
One p op u lar view in the fiscal d ecentralization literatu re is that
d ecentralization em p ow ers su b-national u nits, increases efficiency in
allocation of resou rces, and lead s to m ore investm ent and faster
econom ic p erform ance. The find ings of this stu d y sp ell a cau tionary
tale, offering a cond itional view instead . Whether fiscal au ton om y lead s
to m ore attractiveness for international m arket actors shou ld d ep end
on the typ e of p olicy the su b-national governm ent ad op ts.
64
Appendices
Ap p end ix A.
Table A1. Concep ts, qu estions, and d efinitions in ind icators of
governance qu ality
Governance
feature
Definition of
governance
World w id e
Governance
Ind icators (WGI)
Trad itions and
institutions by
w hich authority in
a country is
exercised
Governm ent
effectiveness
The quality of
public services, the
capacity of the civil
service and its
ind epend ence from
political pressures;
the quality of policy
form ulation and
im plem entation,
and the cred ibility
of the governm ent's
com m itm ent to
such policies
Control of
corruption
The extent to w hich
public pow er is
exercised for
private gain,
includ ing both
petty and grand
form s of
corruption, as w ell
as ―capture‖ of the
state by elites and
private interests
Global
Com petitiveness
Report (GCS)
Legal and
ad m inistrative
fram ew ork w ithin
w hich ind ivid uals,
firm s, and
governm ents
interact to generate
w ealth
- Wastefulness of
governm ent
spend ing
- Burd en of
governm ent
regulation
- Efficiency of legal
fram ew ork in
settling d isputes
- Efficiency of legal
fram ew ork in
challenging
regulations
- Transparency of
governm ent
policym aking
- Diversion of public
fund s
- Public trust of
politicians
- Irregular
paym ents and
bribes
65
Econom ist Intelligence
Unit‘s Risk Briefing
assessm ents (EIU)
-
- Is the governm ent likely
to espouse and
im plem ent open, liberal
and pro-business policies
for nationals and
foreigners?
- What is the quality of
the bureaucracy in term s
of overall
com petency/ training;
m orale/ d ed ication; and
com pensation/ status?
-H ow pervasive is red
tape?
- H ow pervasive is
corruption am ong public
officials?
- H ow accountable are
public officials?
- Is there a risk that this
country could be accused
of serious hum an rights
abuses?
-
Table A1. Concep ts, qu estions, and d efinitions in ind icators of
governance qu ality(cont.)
Governance
feature
Regulatory
quality
World w id e
Governance
Ind icators (WGI)
The ability of the
governm ent to
provid e sound
policies and
regulations that
enable and prom ote
private sector
d evelopm ent
Global
Com petitiveness
Report (GCS)
-
Econom ist Intelligence
Unit‘s Risk Briefing
assessm ents (EIU)
- Is the tax regim e clear
and pred ictable?
- What is the risk that
corporations w ill face
d iscrim inatory taxes?
- Is the corporate tax rate
low ?
- What is the risk from
retroactive taxation?
- What is the risk that the
country w ill be subject to
a trad e embargo
sponsored either by a
m ajor international
organization, a significant
trad ing partner, or one or
m ore of the G8 countries?
- What is the risk of
d iscrim inatory tariffs?
- What is the risk of
excessive protection
(tariff and non-tariff) in
the next two years?
Source:
Kaufmann, D., Kraay, A., and Mastruzzi, M. (2010). The Worldwide Governance Indicators:
M ethodology and A nalytical Issues. World Bank Policy Research Working Paper 5430.
66
Table A2: Concep ts, qu estions, and d efinitions in ind icators of qu ality
of d em ocracy
Governance
feature
Voice and
accountability
Rule of law
World w id e
Governance
Ind icators (WGI)
The extent to w hich
a country‘s citizens
are able to
participate in
selecting their
governm ent, as w ell
as freed om of
expression, freed om
of association, and a
free m ed ia
The extent to w hich
agents have
confid ence in and
abid e by the rules
of society, includ ing
the quality of
contract
enforcem ent and
property rights, the
police, and the
courts, as w ell as
the likelihood of
crim e and violence
Global
Com petitiveness
Report (GCS)
-
Econom ist Intelligence
Unit‘s Risk Briefing
assessm ents (EIU)
-
- Property rights
- Intellectual
property protection
- Jud icial
ind epend ence
- Favoritism in
d ecisions of
governm ent officials
- H ow vulnerable is the
legal process to
interference or
d istortion to serve
particular interests?
- What is the risk that
contract rights w ill not
be enforced ?
- To w hat extent is the
jud icial process speed y
and efficient?
- To w hat extent do the
authorities favour
d om estic interests over
foreign com panies in
legal m atters?
- What is governm ent
policy on actively
prom oting com petition
and curbing unfair
business practices?
- To w hat d egree are
private property rights
guaranteed and
protected ?
- What is the risk that
business financial
statem ents are
inconsistent or
m islead ing?
- Are price controls in
place, and w hat is the
risk that these would be
extended in tim es of
econom ic stress?
67
Table A2: Concep ts, qu estions, and d efinitions in ind icators of qu ality
of d em ocracy (cont.)
Political
stability and
absence of
violence
The likelihood that
the governm ent w ill
be d estabilized by
unconstitutional or
violent m eans,
includ ing terrorism
- Business costs of
terrorism
- Business costs of
crim e and violence
- Organized crim e
- Reliability of police
services
- What is the risk of
significant social unrest
d uring the next tw o
years?
- H ow clear,
established , and
accepted are
constitutional
m echanism s for the
ord erly transfer of
pow er from one
governm ent to another?
- Is there a risk that
international
d isputes/ tensions w ill
negatively affect the
econom y and/ or
polity?
Source:
Kaufmann, D., Kraay, A., and Mastruzzi, M. (2010). The Worldwide Governance Indicators:
M ethodology and A nalytical Issues. World Bank Policy Research Working Paper 5430.
68
Ap p end ix C. Su b-d im ensions of the KOF Ind ex of Globalization
Ind ices
A. Econom ic Globalization
i) Actual Flow s
Trad e (percent of GDP)*
Foreign Direct Investm ent, stocks (percent of GDP)*
Portfolio Investm ent (percent of GDP)*
Incom e Paym ents to Foreign Nationals (percent of GDP)*
ii) Restrictions
H id d en Im port Barriers
Mean Tariff Rate
Taxes on International Trad e (percent of current revenue)
Capital Account Restrictions
B. Social Globalization
i) Data on Personal Contact
Telephone Traffic*
Transfers (percent of GDP)*
International Tourism *
Foreign Population (percent of total population)
International letters (per capita)
ii) Data on Inform ation Flow s
Internet Users (per 1000 people)*
Television (per 1000 people)
Trad e in N ew spapers (percent of GDP)
iii) Data on Cultural Proxim ity
N um ber of McDonald 's Restaurants (per capita)
N um ber of Ikea (per capita)
Trad e in books (percent of GDP)
C. Political Globalization
Em bassies in Country*
Mem bership in International Organizations*
Participation in U.N . Security Council Missions*
International Treaties
Weights in total
36%
50%
21%
28%
24%
27%
50%
24%
27%
26%
23%
37%
34%
25%
3%
26%
21%
24%
35%
33%
36%
31%
31%
45%
45%
10%
26%
25%
28%
22%
26%
Source:
Dreher, A. (2006). Does Globalization Affect Grow th? Em pirical Evid ence from a new
Ind ex, Applied Economics 38(10): 1091-1110.
Dreher, A., Gaston, N ., and Martens, P. (2008). M easuring Globalization - Gauging its
Consequence, N ew York: Springer.
N otes:
The percentage ind icates the w eight used to d erive the ind exes of political, econom ic, and
social globalization.
*: These variables have been used in the AT.Kearney/ Foreign Policy Ind ex as w ell.
69
Ap p end ix E. Placebo Tests
Variables
Orange regions
Yellow regions
(7)
Suicid e
1.90
(1.31)
0.84
(1.06)
(8)
Divorce
0.18
(0.17)
0.02
(0.13)
(9)
Marriage
0.05
(0.16)
-0.03
(0.11)
Orange regions (lead s)
-0.68
(0.42)
-0.06
(0.32)
Yellow regions (lead s)
Lagged Suicid e
0.07
(0.07)
Lagged Divorce
0.50***
(0.06)
Lagged Marriage
0.00
(0.06)
Lagged ln(FDI)
ln(GRP per capita)
ln(Population)
ln(Trad e)
1.99
(2.71)
6.95
(20.32)
0.25
(0.45)
-0.48*
(0.26)
7.16**
(2.95)
-0.21***
(0.05)
0.55*
(0.31)
1.85
(1.88)
-0.01
(0.04)
0.00***
0.00***
0.00***
0.19***
(0.06)
0.03
(0.28)
6.79
(6.57)
-0.59***
(0.18)
0.10
(0.08)
0.01
(0.04)
112.00
(132.34)
-0.35
(0.54)
-0.01***
(0.00)
0.00
(0.02)
0.02
(0.03)
0.01
(0.03)
0.00
(0.01)
0.00***
0.56
0.13
0.04**
0.04**
GRP Growth
N atural Resources
Public officials per capita
SEZ
Rating of investm ent risk
Spatial lag
Length of governor's stay in
pow er
Second ary ed ucation
N om inal wage per capita
Arellano-Bond test for
AR(1)
Arellano-Bond test for
AR(2)
(10)
ln(FDI)
70
Ap p end ix E. Placebo Tests (cont.)
Variables
(7)
Suicid e
54.68
(8)
Divorce
53.92
(9)
Marriage
57.66
(10)
ln(FDI)
26.93
Observations
439
455
455
540
N um ber of id
64
65
65
56
Sargan-Hansen test
N otes: All m od els includ e region and year fixed effects. Robust stand ard e rrors in
parentheses *** p<0.01, ** p<0.05, * p<0.1. The χ2 test is not statistically significant at the
conventional level, show ing that our m od els are not over -id entified .
71
Ap p end ix F. Regression w ith Driscoll-Kraay stand ard errors and first
ord er au tocorrelation
VARIABLES
(11) FDI
(14) FDI
(15) FDI
(16) FDI
Orange regions
0.49*
0.51*
0.46
0.52*
(0.22)
(0.21)
(0.25)
(0.24)
0.26
0.26
0.15
0.19
(0.24)
(0.24)
(0.26)
(0.28)
-0.53
-0.46
0.65
0.75
(0.38)
(0.40)
(0.45)
(0.46)
1.53
0.04
1.22
-1.31
(3.67)
(4.15)
(6.53)
(8.40)
-0.10
-0.11
-0.05
-0.07
(0.25)
(0.25)
(0.24)
(0.24)
0.13
0.11
0.09
0.07
(0.14)
(0.13)
(0.12)
(0.11)
-0.01
-0.01
-0.02
-0.02
(0.02)
(0.02)
(0.02)
(0.02)
327.69*** 339.32*** 456.83*** 471.82***
(75.83)
(80.12)
(97.32)
(100.87)
0.28
0.26
0.11
0.13
(0.22)
(0.22)
(0.24)
(0.23)
-0.01**
-0.01**
-0.01**
-0.01*
(0.00)
(0.00)
(0.01)
(0.01)
0.05
0.04
0.06
0.06
(0.03)
(0.03)
(0.04)
(0.04)
0.00
0.00
0.01
0.01
(0.01)
(0.01)
(0.01)
(0.01)
1.19*
(0.56)
0.82***
(0.19)
2.97
(1.71)
93.72
(50.36)
0.23
(0.32)
0.07
(0.16)
0.06
(0.05)
402.56**
(109.80)
-0.03
(0.45)
-0.02
(0.01)
0.03
(0.06)
-0.02
(0.02)
1.18*
(0.58)
0.82***
(0.21)
3.11*
(1.59)
93.36
(51.04)
0.22
(0.32)
0.04
(0.15)
0.06
(0.05)
433.63**
(125.73)
-0.05
(0.44)
-0.02
(0.01)
0.03
(0.05)
-0.02
(0.02)
0.00
(0.00)
yes
0.00
(0.07)
0.01
(0.01)
0.00
(0.00)
yes
Yellow region
GRP per capita
Population
Trad e
GRP Growth
N atural Resources
Public officials per
capita
SEZ
Rating of
investm ent risk
Spatial lag
Length of
governor's stay in
pow er
N om inal wage per
capita
Second ary
ed ucation
Constant
(12) FDI
(13) FDI
0.00
(0.00)
no
0.03
(0.05)
0.01
(0.01)
0.00
(0.00)
no
-8.89
(47.76)
yes
0.05
(0.06)
0.02
(0.01)
5.65
(59.73)
yes
Region FE
yes
yes
yes
yes
yes
yes
Year FE
yes
yes
yes
yes
yes
yes
Region-specific
Tim e Trend s
Observations
no
no
no
no
yes
yes
485
485
395
395
395
395
N um ber of groups
74
74
60
60
60
60
Dropping regions
72
Ap p end ix G. List of the regions u sed as control u nits for each treated
region w ithin a relevant econom ic zone (Exp erim ent A) and w ithin
Ru ssia as a w hole (Exp erim ent B)
The regions that have tax concessions for investment (orange group)
A mur Oblast
Experiment A . Synthetic control is based on: Chu kotka Au tonom ou s
Okru g (0.425), Sakhalin Oblast (0.575). Other p otential control u nits
inclu d e: Kam chatka Krai, Magad an Oblast, Prim orsky Krai.
Experiment B. Synthetic control is based on: Chu kotka Au tonom ou s
Okru g (0.083), Kirov Oblast (0.034), Krasnoyarsk Krai (0.05), Rep u blic
of Ingu shetia (0.138), Sakhalin Oblast (0.407), Zabaykalsky Krai (0.289).
Other p otential control u nits inclu d e: Altai Krai, Arkhangelsk Oblast,
Belgorod Oblast, Chelyabinsk Oblast, Irku tsk Oblast, Ivanovo Oblast,
Kam chatka Krai, Karachay-Cherkess Rep u blic, Krasnod ar Krai, Ku rsk
Oblast, Magad an Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny
N ovgorod Oblast, Om sk Oblast, Oryol Oblast, Prim orsky Krai,
Rep u blic of Ad ygea, Rep u blic of Bashkortostan, Rep u blic of Khakassia,
Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Sverd lovsk Oblast,
Tom sk Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh
Oblast.
Bryansk Oblast
Experiment A . Synthetic control is based on: Ivanovo Oblast (0.047),
Ryazan Oblast (0.953). Other p otential control u nits inclu d e: Oryol
Oblast, Tu la Oblast, Tver Oblast.
Experiment B. Synthetic control is based on: Altai Krai (0.039),
Chelyabinsk Oblast (0.001), Ivanovo Oblast (0.225), Kam chatka Krai
(0.095), Kirov Oblast (0.046), N izhny N ovgorod Oblast (0.352), Oryol
Oblast (0.059), Rep u blic of Ad ygea (0.094), Rep u blic of Ingu shetia
(0.085), Tu la Oblast (0.004). Other p otential control u nits inclu d e:
Arkhangelsk Oblast, Belgorod Oblast, Chu kotka Au tonom ou s Okru g,
Irku tsk Oblast, Karachay-Cherkess Rep u blic, Krasnod ar Krai,
73
Krasnoyarsk Krai, Ku rsk Oblast, Magad an Oblast, Mari El Rep u blic,
Mu rm ansk Oblast, Om sk Oblast, Prim orsky Krai, Rep u blic of
Bashkortostan, Rep u blic of Khakassia, Rep u blic of N orth Ossetia Alania, Ryazan Oblast, Sakhalin Oblast, Sverd lovsk Oblast, Tom sk
Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky
Krai.
Chuvash Republic
Experiment A . Synthetic control is based on: Kirov Oblast (0.327), Mari
El Rep u blic (0.368), N izhny N ovgorod Oblast (0.305).
Experiment B. Synthetic control is based on: Belgorod Oblast (0.23),
Ivanovo Oblast (0.214), Om sk Oblast (0.056), Oryol Oblast (0.188),
Rep u blic of Ad ygea (0.132), Rep u blic of Bashkortostan (0.154), Rep u blic
of Ingu shetia (0.025). Other p otential control u nits inclu d e: Altai Krai,
Arkhangelsk Oblast, Chelyabinsk Oblast, Chu kotka Au tonom o u s
Okru g, Irku tsk Oblast, Kam chatka Krai, Karachay -Cherkess Rep u blic,
Kirov Oblast, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk Oblast,
Magad an Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny
N ovgorod Oblast, Prim orsky Krai, Rep u blic of Khakassia, Rep u blic o f
N orth Ossetia-Alania, Ryazan Oblast, Sakhalin Oblast, Sverd lovsk
Oblast, Tom sk Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast,
Voronezh Oblast, Zabaykalsky Krai.
Perm Krai
Experiment A . Synthetic control is based on: Chelyabinsk Oblast (0.706),
Rep u blic of Bashkortostan (0.294). Other p otential control u nits inclu d e:
Sverd lovsk Oblast.
Experiment B. Synthetic control is based on: Chelyabinsk Oblast (0.261),
Kam chatka Krai (0.128), Krasnod ar Krai (0.008), Mu rm ansk Oblast
(0.311), N izhny N ovgorod Oblast (0.059), Sakhalin Oblast (0.021),
Sverd lovsk Oblast (0.212). Other p otential control u nits inclu d e: Altai
Krai, Arkhangelsk Oblast, Belgorod Oblast, Chu kotka Au tonom ou s
Okru g, Irku tsk Oblast, Ivanovo Oblast, Karachay -Cherkess Rep u blic,
74
Kirov Oblast, Krasnoyarsk Krai, Ku rsk Oblast, Magad an Oblast, Mari
El Rep u blic, Om sk Oblast, Oryol Oblast, Prim orsky Krai, Rep u blic of
Ad ygea, Rep u blic of Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of
Khakassia, Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Tom sk
Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast,
Zabaykalsky Krai.
Rostov Oblast
Experiment A . Synthetic control is based on: Krasnod ar Krai (0.641),
Rep u blic of Ad ygea (0.304), Rep u blic of Ingu shetia (0.055). Other
p otential control u nits inclu d e: Karachay-Cherkess Rep u blic, Rep u blic
of N orth Ossetia-Alania.
Experiment B. Synthetic control is based on: Belgorod Oblast (0.056),
Krasnod ar Krai (0.078), N izhny N ovgorod Oblast (0.211), Oryol Oblast
(0.001), Prim orsky Krai (0.1), Rep u blic of Ad ygea (0.204), Rep u blic of
Ingu shetia (0.003), Rep u blic of N orth Ossetia-Alania (0.001), Sverd lovsk
Oblast (0.314), Tom sk Oblast (0.033). Other p otential control u nits
inclu d e: Altai Krai, Arkhangelsk Oblast, Chelyabinsk Oblast, Chu kotka
Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai,
Karachay-Cherkess Rep u blic, Kirov Oblast, Krasnoyarsk Krai, Ku rsk
Oblast, Magad an Oblast, Mari El Rep u blic, Mu rm ansk Oblast, Om sk
Oblast, Rep u blic of Bashkortostan, Rep u blic of Khakassia, Ryazan
Oblast, Sakhalin Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast,
Voronezh Oblast, Zabaykalsky Krai.
St. Petersburg
Experiment B. Synthetic control is based on: Krasnod ar Krai (0.247),
Prim orsky Krai (0.458), Sakhalin Oblast (0.296). Other p otential control
u nits inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast,
Chelyabinsk Oblast, Chu kotka Au tonom ou s Okru g, Irku tsk Oblast,
Ivanovo Oblast, Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov
Oblast, Krasnoyarsk Krai, Ku rsk Oblast, Magad an Oblast, Mari El
Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod Oblast, Om sk Oblast,
Oryol Oblast, Rep u blic of Ad ygea, Rep u blic of Bashkortostan, Rep u blic
75
of Ingu shetia, Rep u blic of Khakassia, Rep u blic of N orth Ossetia -Alania,
Ryazan Oblast, Sverd lovsk Oblast, Tom sk Oblast, Tu la Oblast, Tu va
Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky Krai.
Udmurt Republic
Experiment A . Synthetic control is based on: Rep u blic of Bashkortostan
(1). Other p otential control u nits inclu d e: Chelyabinsk Oblast,
Sverd lovsk Oblast.
Experiment B. Synthetic control is based on: Kirov Oblast (0.051), Mari
El Rep u blic (0.185), Mu rm ansk Oblast (0.104), Om sk Oblast (0.451),
Prim orsky Krai (0.21). Other p otential control u nits inclu d e: Altai Krai,
Arkhangelsk Oblast, Belgorod Oblast, Chelyabinsk Oblast, Chu kotka
Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai,
Karachay-Cherkess Rep u blic, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk
Oblast, Magad an Oblast, N izhny N ovgorod Oblast, Oryol Oblast,
Rep u blic of Ad ygea, Rep u blic of Bashkortostan, Rep u blic of Ingu she tia,
Rep u blic of Khakassia, Rep u blic of N orth Ossetia -Alania, Ryazan
Oblast, Sakhalin Oblast, Sverd lovsk Oblast, Tom sk Oblast, Tu la Oblast,
Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky Krai.
The regions that have tax concessions for important investment
(yellow group)
A ltai Republic
Experiment A . Synthetic control is based on: Om sk Oblast (0.518),
Tom sk Oblast (0.482). Other p otential control u nits inclu d e: Altai Krai.
Experiment B. Synthetic control is based on: Chu kotka Au tonom ou s
Okru g (0.049), Magad an Oblast (0.078), Rep u blic of Ingu shetia (0.113),
Rep u blic of N orth Ossetia-Alania (0.76). Other p otential control u nits
inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast, Chelyabinsk
Oblast, Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai, KarachayCherkess Rep u blic, Kirov Oblast, Krasnod ar Krai, Krasnoyarsk Krai,
76
Ku rsk Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod
Oblast, Om sk Oblast, Oryol Oblast, Prim orsky Krai, Rep u blic of
Ad ygea, Rep u blic of Bashkortostan, Rep u blic of Khak assia, Ryazan
Oblast, Sakhalin Oblast, Sverd lovsk Oblast, Tom sk Oblast, Tu la Oblast,
Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky Krai.
A strakhan Oblast
Experiment B. Synthetic control is based on: Arkhangelsk Oblast (0.25),
Krasnod ar Krai (0.001), Ku rsk Oblast (0.001), Om sk Oblast (0.353),
Rep u blic of Ingu shetia (0.021), Sakhalin Oblast (0.2), Tu va Rep u blic
(0.173). Other p otential control u nits inclu d e: Altai Krai, Belgorod
Oblast, Chelyabinsk Oblast, Chu kotka Au tonom ou s Okru g, Irku tsk
Oblast, Ivanovo Oblast, Kam chatka Krai, Karachay-Cherkess Rep u blic,
Kirov Oblast, Krasnoyarsk Krai, Magad an Oblast, Mari El Rep u blic,
Mu rm ansk Oblast, N izhny N ovgorod Oblast, Oryol Oblast, Prim orsky
Krai, Rep u blic of Ad ygea, Rep u blic of Bashkortostan, Rep u blic of
Khakassia, Rep u blic of N orth Ossetia-Alania, Ryazan Oblast,
Sverd lovsk Oblast, Tom sk Oblast, Tu la Oblast, Tver Oblast, Voronezh
Oblast, Zabaykalsky Krai.
Kaluga Oblast
Experiment A . Synthetic control is based on: Oryol Oblast (0.51), Tver
Oblast (0.49). Other p otential control u nits inclu d e: Ivanovo Oblast,
Ryazan Oblast, Tu la Oblast.
Experiment B. Synthetic control is based on: Chelyabinsk Oblast (0.213),
Krasnod ar Krai (0.298), Rep u blic of Ad ygea (0.321), Rep u blic of
Ingu shetia (0.011), Sakhalin Oblast (0.157). Other p otential control u nits
inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast, Chu kotka
Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai,
Karachay-Cherkess Rep u blic, Kirov Oblast, Krasnoyarsk Krai, Ku rsk
Oblast, Magad an Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny
N ovgorod Oblast, Om sk Oblast, Oryol Oblast, Prim orsky Krai,
Rep u blic of Bashkortostan, Rep u blic of Khakassia, Rep u blic of N orth
Ossetia-Alania, Ryazan Oblast, Sverd lovsk Oblast, Tom sk Oblast, Tu la
77
Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky
Krai.
Kemerovo Oblast
Experiment A . Synthetic control is based on: Altai Krai (0.112), Om sk
Oblast (0.506), Tom sk Oblast (0.382).
Experiment B. Synthetic control is based on: Chelyabinsk Oblast (0.4),
Krasnoyarsk Krai (0.089), Rep u blic of Bashkortostan (0.235), Rep u blic
of Khakassia (0.274), Sverd lovsk Oblast (0.002). Other p otential control
u nits inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast,
Chu kotka Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast,
Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov Oblast,
Krasnod ar Krai, Ku rsk Oblast, Magad an Oblast, Mari El Rep u blic,
Mu rm ansk Oblast, N izhny N ovgorod Oblast, Om sk Oblast, Oryol
Oblast, Prim orsky Krai, Rep u blic of Ad ygea, Rep u blic of Ingu shetia,
Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Sakhalin Oblast,
Tom sk Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh
Oblast, Zabaykalsky Krai.
Khabarovsk Krai
Experiment A . Synthetic control is based on: Chu kotka Au tonom ou s
Okru g (0.044), Magad an Oblast (0.499), Prim orsky Krai (0.355),
Sakhalin Oblast (0.101). Other p otential control u nits inclu d e:
Kam chatka Krai.
Experiment B. Synthetic control is based on: Chelyabinsk Oblast (0.202),
Kam chatka Krai (0.167), Oryol Oblast (0.002), Prim orsky Krai (0.569),
Tom sk Oblast (0.059). Other p otential control u nits inclu d e: Altai Krai,
Arkhangelsk Oblast, Belgorod Oblast, Chu kotka Au tonom ou s Okru g,
Irku tsk Oblast, Ivanovo Oblast, Karachay -Cherkess Rep u blic, Kirov
Oblast, Krasnod ar Krai, Krasnoyarsk Kra i, Ku rsk Oblast, Magad an
Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod Oblast,
Om sk Oblast, Rep u blic of Ad ygea, Rep u blic of Bashkortostan, Rep u blic
of Ingu shetia, Rep u blic of Khakassia, Rep u blic of N orth Ossetia -Alania,
78
Ryazan Oblast, Sakhalin Oblast, Sverd lovsk Oblast, Tu la Oblast, Tu va
Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky Krai.
Khanty-M ansi A utonomous Okrug-Y ugra
Experiment A . Synthetic control is based on: Altai Krai (0.521), Om sk
Oblast (0.479). Other p otential control u nits inclu d e: Tom sk Oblast.
Experiment B. Synthetic control is based on: Krasnoyarsk Krai (0.229),
Rep u blic of Bashkortostan (0.354), Sakhalin Oblast (0.418). Other
p otential control u nits inclu d e: Altai Krai, Arkhangelsk Oblast,
Belgorod Oblast, Chelyabinsk Ob last, Chu kotka Au tonom ou s Okru g,
Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai, Karachay -Cherkess
Rep u blic, Kirov Oblast, Krasnod ar Krai, Ku rsk Oblast, Magad an
Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod Oblast,
Om sk Oblast, Oryol Oblast, Prim orsky Krai, Rep u blic of Ad ygea,
Rep u blic of Ingu shetia, Rep u blic of Khakassia, Rep u blic of N orth
Ossetia-Alania, Ryazan Oblast, Sverd lovsk Oblast, Tom sk Oblast, Tu la
Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky
Krai.
Komi Republic
Experiment A . Synthetic control is based on: Mu rm ansk Oblast (1).
Other p otential control u nits inclu d e: Arkhangelsk Oblast.
Experiment B. Synthetic control is based on: Arkhangelsk Oblast (0.001),
Chelyabinsk Oblast (0.283), Chu kotka Au tonom ou s Okru g (0.076),
Irku tsk Oblast (0.001), Kam chatka Krai (0.171), Krasnod ar Krai (0.001),
Krasnoyarsk Krai (0.004), Rep u blic of Bashkortostan (0.025), Sakhalin
Oblast (0.435), Sverd lovsk Oblast (0.001), Zabaykalsky Krai (0.001).
Other p otential control u nits inclu d e: Altai Krai, Belgorod Oblast,
Ivanovo Oblast, Karachay-Cherkess Rep u blic, Kirov Oblast, Ku rsk
Oblast, Magad an Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny
N ovgorod Oblast, Om sk Oblast, Oryol Oblast, Prim orsky Krai,
Rep u blic of Ad ygea, Rep u blic of Ingu shetia, Rep u blic of Khakassia,
79
Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Tom sk Oblast, Tu la
Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast.
Kurgan Oblast
Experiment A . Synthetic control is based on: Rep u blic of Bashkortostan
(1). Other p otential control u nits inclu d e: Chelyabinsk Oblast,
Sverd lovsk Oblast.
Experiment B. Synthetic control is based on: Kam chatka Krai (0.238),
Om sk Oblast (0.131), Rep u blic of Bashkortostan (0.054), Rep u blic of
Khakassia (0.287), Tom sk Oblast (0.001), Tu va Rep u blic (0.289). Other
p otential control u nits inclu d e: Altai Krai, Arkhangelsk Oblast,
Belgorod Oblast, Chelyabinsk Oblast, Chu kotka Au tonom ou s Okru g,
Irku tsk Oblast, Ivanovo Oblast, Karachay -Cherkess Rep u blic, Kirov
Oblast, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk Ob last, Magad an
Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod Oblast,
Oryol Oblast, Prim orsky Krai, Rep u blic of Ad ygea, Rep u blic of
Ingu shetia, Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Sakhalin
Oblast, Sverd lovsk Oblast, Tu la Oblast, Tver Oblast, Voronezh Oblast,
Zabaykalsky Krai.
Leningrad Oblast
Experiment B. Synthetic control is based on: Krasnod ar Krai (0.202),
N izhny N ovgorod Oblast (0.02), Prim orsky Krai (0.284), Sakhalin
Oblast (0.393), Sverd lovsk Oblast (0.1). Other p otential cont rol u nits
inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast, Chelyabinsk
Oblast, Chu kotka Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast,
Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov Oblast,
Krasnoyarsk Krai, Ku rsk Oblast, Magad an Oblast, Mar i El Rep u blic,
Mu rm ansk Oblast, Om sk Oblast, Oryol Oblast, Rep u blic of Ad ygea,
Rep u blic of Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of
Khakassia, Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Tom sk
Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast,
Zabaykalsky Krai.
80
Lipetsk Oblast
Experiment A . Synthetic control is based on: Ku rsk Oblast (0.678),
Voronezh Oblast (0.322). Other p otential control u nits inclu d e:
Belgorod Oblast.
Experiment B. Synthetic control is based on: Chu kotka Au t onom ou s
Okru g (0.022), Irku tsk Oblast (0.45), Magad an Oblast (0.016), Om sk
Oblast (0.287), Tom sk Oblast (0.225). Other p otential control u nits
inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast, Chelyabinsk
Oblast, Ivanovo Oblast, Kam chatka Krai, Kar achay-Cherkess Rep u blic,
Kirov Oblast, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk Oblast, Mari El
Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod Oblast, Oryol Oblast,
Prim orsky Krai, Rep u blic of Ad ygea, Rep u blic of Bashkortostan,
Rep u blic of Ingu shetia, Rep u blic of Khakassia, Rep u blic of N orth
Ossetia-Alania, Ryazan Oblast, Sakhalin Oblast, Sverd lovsk Oblast,
Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast,
Zabaykalsky Krai.
N ovosibirsk Oblast
Experiment A . Synthetic control is based on: Altai Krai (0.914), Om sk
Oblast (0.086), Other p otential control u nits inclu d e: Tom sk Oblast.
Experiment B. Synthetic control is based on: Chelyabinsk Oblast (0.379),
Krasnod ar Krai (0.327), Magad an Oblast (0.294). Other p otential control
u nits inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast,
Chu kotka Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast,
Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov Oblast,
Krasnoyarsk Krai, Ku rsk Oblast, Mari El Rep u blic, Mu rm ansk Oblast,
N izhny N ovgorod Oblast, Om sk Oblast, Oryol Oblast, Prim orsky Krai,
Rep u blic of Ad ygea, Rep u blic of Bashkortostan, Rep u blic of Ingu shetia,
Rep u blic of Khakassia, Rep u blic of N orth Ossetia -Alania, Ryazan
Oblast, Sakhalin Oblast, Sverd lovsk Oblast, Tom sk Oblast, Tu la Oblast,
Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky Krai.
Orenburg Oblast
81
Experiment A . Synthetic control is based on: Chelyabinsk Oblast (0.334),
Rep u blic of Bashkortostan (0.554), Sverd lovsk Oblast (0.112).
Experiment B. Synthetic control is based on: Belgorod Oblast (0.413),
Chu kotka Au tonom ou s Okru g (0.019), Krasnod ar Krai (0.39), Ku rsk
Oblast (0.115), Om sk Oblast (0.014), Rep u blic of Khakassia (0.049).
Other p otential control u nits inclu d e: Altai Krai, Arkhangelsk Oblast,
Chelyabinsk Oblast, Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai,
Karachay-Cherkess Rep u blic, Kirov Oblast, Krasnoyarsk Krai,
Magad an Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny
N ovgorod Oblast, Oryol Oblast, Prim orsky Krai, Rep u blic of Ad ygea,
Rep u blic of Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of N orth
Ossetia-Alania, Ryazan Oblast, Sakhalin Oblast, Sverd lovsk Oblast,
Tom sk Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh
Oblast, Zabaykalsky Krai.
Penza Oblast
Experiment B. Synthetic control is based on: Altai Krai (0.045), Kiro v
Oblast (0.222), Mari El Rep u blic (0.121), Om sk Oblast (0.267), Oryol
Oblast (0.212), Tu va Rep u blic (0.134). Other p otential control u nits
inclu d e: Arkhangelsk Oblast, Belgorod Oblast, Chelyabinsk Oblast,
Chu kotka Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast,
Kam chatka Krai, Karachay-Cherkess Rep u blic, Krasnod ar Krai,
Krasnoyarsk Krai, Ku rsk Oblast, Magad an Oblast, Mu rm ansk Oblast,
N izhny N ovgorod Oblast, Prim orsky Krai, Rep u blic of Ad ygea,
Rep u blic of Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of
Khakassia, Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Sakhalin
Oblast, Sverd lovsk Oblast, Tom sk Oblast, Tu la Oblast, Tver Oblast,
Voronezh Oblast, Zabaykalsky Krai.
Pskov Oblast
Experiment B. Synthetic control is based on: Oryol Oblast (0.093),
Rep u blic of Ingu shetia (0.262), Ryazan Oblast (0.001), Sakhalin Oblast
(0.153), Tu la Oblast (0.491). Other p otential control u nits inclu d e: Altai
Krai, Arkhangelsk Oblast, Belgorod Oblast, Chelyabinsk Oblast,
82
Chu kotka Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast,
Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov Oblast,
Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk Oblast, Magad an Oblast,
Mari El Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod Oblast, Om sk
Oblast, Prim orsky Krai, Rep u blic of Ad ygea, Rep u blic of
Bashkortostan, Rep u blic of Khakassia, Rep u blic of N orth Ossetia Alania, Sverd lovsk Oblast, Tom sk Oblast, Tu va Rep u blic, Tver Oblast,
Voronezh Oblast, Zabaykalsky Krai.
Republic of Dagestan
Experiment A . Synthetic control is based on: Karachay-Cherkess
Rep u blic (0.633), Krasnod ar Krai (0.055), Rep u blic of Ingu shetia (0.312).
Other p otential control u nits inclu d e: Rep u blic of Ad ygea, Rep u blic of
N orth Ossetia-Alania.
Experiment B. Synthetic control is based on: Altai Krai (0.208), Mari El
Rep u blic (0.338), Rep u blic of Bashkortostan (0.049), Rep u blic of
Ingu shetia (0.343), Rep u blic of N orth Ossetia -Alania (0.061). Other
p otential control u nits inclu d e: Arkhangelsk Oblast, Belgorod Oblast,
Chelyabinsk Oblast, Chu kotka Au tonom ou s Okru g, Irku tsk Oblast,
Ivanovo Oblast, Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov
Oblast, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk Oblast, Magad an
Oblast, Mu rm ansk Oblast, N izhny N ovgorod Oblast, Om sk Oblast,
Oryol Oblast, Prim orsky Krai, Rep u blic of Ad ygea, Rep u blic of
Khakassia, Ryazan Oblast, Sakhalin Oblast, Sverd lovsk Oblast, Tom sk
Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast,
Zabaykalsky Krai.
Republic of Karelia
Experiment A . Synthetic control is based on: Arkhangelsk Oblast (0.291),
Mu rm ansk Oblast (0.709).
Experiment B. Synthetic control is based on: Chu kotka Au tonom ou s
Okru g (0.088), Irku tsk Oblast (0.021), Mu rm ansk Oblast (0.062), Om sk
Oblast (0.216), Oryol Oblast (0.301), Rep u blic of Bashkortostan (0.125),
83
Sakhalin Oblast (0.188). Other p otential control u nit s inclu d e: Altai
Krai, Arkhangelsk Oblast, Belgorod Oblast, Chelyabinsk Oblast,
Ivanovo Oblast, Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov
Oblast, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk Oblast, Magad an
Oblast, Mari El Rep u blic, N izhny N ovgorod Obla st, Prim orsky Krai,
Rep u blic of Ad ygea, Rep u blic of Ingu shetia, Rep u blic of Khakassia,
Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Sverd lovsk Oblast,
Tom sk Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh
Oblast, Zabaykalsky Krai.
Republic of M ordovia
Experiment A . Synthetic control is based on: Kirov Oblast (0.494), Mari
El Rep u blic (0.038), N izhny N ovgorod Oblast (0.468).
Experiment B. Synthetic control is based on: Mu rm ansk Oblast (0.044),
Oryol Oblast (0.539), Rep u blic of Ad ygea (0.145), Rep u blic of
Ingu shetia (0.008), Tom sk Oblast (0.142), Tu va Rep u blic (0.122). Other
p otential control u nits inclu d e: Altai Krai, Arkhangelsk Oblast,
Belgorod Oblast, Chelyabinsk Oblast, Chu kotka Au tonom ou s Okru g,
Irku tsk Oblast, Ivanovo Oblast, Kam chatka Kra i, Karachay-Cherkess
Rep u blic, Kirov Oblast, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk
Oblast, Magad an Oblast, Mari El Rep u blic, N izhny N ovgorod Oblast,
Om sk Oblast, Prim orsky Krai, Rep u blic of Bashkortostan, Rep u blic of
Khakassia, Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Sakhalin
Oblast, Sverd lovsk Oblast, Tu la Oblast, Tver Oblast, Voronezh Oblast,
Zabaykalsky Krai.
Republic of Tatarstan
Experiment B. Synthetic control is based on: Chelyabinsk Oblast (0.185),
Irku tsk Oblast (0.271), Krasnod ar Krai (0.189), Krasnoyarsk Krai (0.03),
Magad an Oblast (0.085), Rep u blic of Bashkortostan (0.207), Tom sk
Oblast (0.032). Other p otential control u nits inclu d e: Altai Krai,
Arkhangelsk Oblast, Belgorod Oblast, Chu kotka Au tonom ou s Okru g,
Ivanovo Oblast, Kam chatka Kr ai, Karachay-Cherkess Rep u blic, Kirov
Oblast, Ku rsk Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny
84
N ovgorod Oblast, Om sk Oblast, Oryol Oblast, Prim orsky Krai,
Rep u blic of Ad ygea, Rep u blic of Ingu shetia, Rep u blic of Khakassia,
Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Sakhalin Oblast,
Sverd lovsk Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh
Oblast, Zabaykalsky Krai.
Sakha (Y akutia) Republic
Experiment A . Synthetic control is based on: Chu kotka Au tonom ou s
Okru g (0.246), Prim orsky Krai (0.754). Other p otential control u nits
inclu d e: Kam chatka Krai, Magad an Oblast, Sakhalin Oblast.
Experiment B. Synthetic control is based on: Arkhangelsk Oblast (0.339),
Chu kotka Au tonom ou s Okru g (0.07), Magad an Oblast (0.002),
Prim orsky Krai (0.365), Zabaykalsky Krai (0.223). Other p otential
control u nits inclu d e: Altai Krai, Belgorod Oblast, Chelyabinsk Oblast,
Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai, Karachay -Cherkess
Rep u blic, Kirov Oblast, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk
Oblast, Mari El Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod Oblast,
Om sk Oblast, Oryol Oblast, Rep u blic of Ad ygea, Rep u blic of
Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of Khakassia, Rep u blic
of N orth Ossetia-Alania, Ryazan Oblast, Sakhalin Oblast, Sverd lovsk
Oblast, Tom sk Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast,
Voronezh Oblast.
Stavropol Krai
Experiment A . Synthetic control is based on: Krasnod ar Krai (0.845),
Rep u blic of Ad ygea (0.026), Rep u blic of Ingu shetia (0.13). Other
p otential control u nits inclu d e: Kar achay-Cherkess Rep u blic, Rep u blic
of N orth Ossetia-Alania.
Experiment B. Synthetic control is based on: Kirov Oblast (0.102),
N izhny N ovgorod Oblast (0.335), Om sk Oblast (0.016), Oryol Oblast
(0.222), Prim orsky Krai (0.317), Ryazan Oblast (0.008). Other p o tential
control u nits inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast,
Chelyabinsk Oblast, Chu kotka Au tonom ou s Okru g, Irku tsk Oblast,
85
Ivanovo Oblast, Kam chatka Krai, Karachay-Cherkess Rep u blic,
Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk Oblast, Magad an Oblast,
Mari El Rep u blic, Mu rm ansk Oblast, Rep u blic of Ad ygea, Rep u blic of
Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of Khakassia, Rep u blic
of N orth Ossetia-Alania, Sakhalin Oblast, Sverd lovsk Oblast, Tom sk
Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast,
Zabaykalsky Krai.
Tyumen Oblast
Experiment A . Synthetic control is based on: Altai Krai (0.568), Om sk
Oblast (0.432). Other p otential control u nits inclu d e: Tom sk Oblast.
Experiment B. Synthetic control is based on: Chelyabinsk Ob last (0.137),
Krasnoyarsk Krai (0.549), Sakhalin Oblast (0.314). Other p otential
control u nits inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast,
Chu kotka Au tonom ou s Okru g, Irku tsk Oblast, Ivanovo Oblast,
Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov Oblast,
Krasnod ar Krai, Ku rsk Oblast, Magad an Oblast, Mari El Rep u blic,
Mu rm ansk Oblast, N izhny N ovgorod Oblast, Om sk Oblast, Oryol
Oblast, Prim orsky Krai, Rep u blic of Ad ygea, Rep u blic of
Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of Khakassia, Rep u blic
of N orth Ossetia-Alania, Ryazan Oblast, Sverd lovsk Oblast, Tom sk
Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast,
Zabaykalsky Krai.
V olgograd Oblast
Experiment B. Synthetic control is based on: Chelyabinsk Oblast (0.33),
Krasnod ar Krai (0.064), Ku rsk Oblast (0.076), Om sk Oblast (0.096),
Prim orsky Krai (0.369), Rep u blic of Ingu shetia (0.001), Sverd lovsk
Oblast (0.063). Other p otential control u nits inclu d e: Altai Krai,
Arkhangelsk Oblast, Belgorod Oblast, Chu kotka Au tonom ou s Okru g,
Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai, Karachay -Cherkess
Rep u blic, Kirov Oblast, Krasnoyarsk Krai, Magad an Oblast, Mari El
Rep u blic, Mu rm ansk Oblast, N izhny N ovgorod Oblast, Oryol Oblast,
Rep u blic of Ad ygea, Rep u blic of Bashkortostan, Rep u blic of Khakassia,
86
Rep u blic of N orth Ossetia-Alania, Ryazan Oblast, Sakhalin Oblast,
Tom sk Oblast, Tu la Oblast, Tu va Rep u blic, Tver Oblast, Voronezh
Oblast, Zabaykalsky Krai.
V ologda Oblast
Experiment A . Synthetic control is based on: Arkhangelsk Oblast (0.298),
Mu rm ansk Oblast (0.702).
Experiment B. Synthetic control is based on: Chu kotka Au tonom ou s
Okru g (0.115), Krasnoyarsk Krai (0.308), N izhny N ovgorod Oblast
(0.574), Sakhalin Oblast (0.001), Tu la Oblast (0.002). Other p otential
control u nits inclu d e: Altai Krai, Arkhangelsk Oblast, Belgorod Oblast,
Chelyabinsk Oblast, Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai,
Karachay-Cherkess Rep u blic, Kirov Oblast, Krasnod ar Krai, Ku rsk
Oblast, Magad an Oblast, Mari El Rep u blic, Mu rm ansk Oblast, Om sk
Oblast, Oryol Oblast, Prim orsky Krai, Rep u blic of Ad ygea, Rep u blic of
Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of Khakassia, Rep u blic
of N orth Ossetia-Alania, Ryazan Oblast, Sverd lovsk Oblast, Tom sk
Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky
Krai.
Y amalo-N enets A utonomous Okrug
Experiment A . Synthetic control is based on: Altai Krai (0.316), Om sk
Oblast (0.449), Tom sk Oblast (0.235).
Experiment B. Synthetic control is based on: Belgorod Oblast (0.281),
Mu rm ansk Oblast (0.278), Sakhalin Oblast (0.321), Tom sk Oblast (0.12).
Other p otential control u nits inclu d e: Altai Krai, Arkhangelsk Oblast,
Chelyabinsk Oblast, Chu kotka Au tonom ou s Okru g, Irku tsk Oblast,
Ivanovo Oblast, Kam chatka Krai, Karachay-Cherkess Rep u blic, Kirov
Oblast, Krasnod ar Krai, Krasnoyarsk Krai, Ku rsk Oblast, Magad an
Oblast, Mari El Rep u blic, N izhny N ovgorod Oblast, Om sk Oblast,
Oryol Oblast, Prim orsky Krai, Rep u blic of Ad ygea, Rep u blic of
Bashkortostan, Rep u blic of Ingu shetia, Rep u blic of Khakassia, Rep u blic
of N orth Ossetia-Alania, Ryazan Oblast, Sverd lovsk Oblast, Tu la
87
Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky
Krai.
Y aroslavl Oblast
Experiment A . Synthetic control is based on: Ryazan Oblast (0.543), Tu la
Oblast (0.457). Other p otential control u nits inclu d e: Iva novo Oblast,
Oryol Oblast, Tver Oblast.
Experiment B. Synthetic control is based on: Magad an Oblast (0.045),
N izhny N ovgorod Oblast (0.2), Rep u blic of Ad ygea (0.146), Rep u blic of
N orth Ossetia-Alania (0.031), Ryazan Oblast (0.355), Sverd lovsk Oblast
(0.223). Other p otential control u nits inclu d e: Altai Krai, Arkhangelsk
Oblast, Belgorod Oblast, Chelyabinsk Oblast, Chu kotka Au tonom ou s
Okru g, Irku tsk Oblast, Ivanovo Oblast, Kam chatka Krai, Karachay Cherkess Rep u blic, Kirov Oblast, Krasnod ar Krai, Krasnoyarsk Krai,
Ku rsk Oblast, Mari El Rep u blic, Mu rm ansk Oblast, Om sk Oblast, Oryol
Oblast, Prim orsky Krai, Rep u blic of Bashkortostan, Rep u blic of
Ingu shetia, Rep u blic of Khakassia, Sakhalin Oblast, Tom sk Oblast, Tu la
Oblast, Tu va Rep u blic, Tver Oblast, Voronezh Oblast, Zabaykalsky
Krai.
88
Ap p end ix H . Resu lts of the synthetic controls analysis: treated u nits
and their synthetic controls
The regions that have tax concessions for investment (orange group)
89
90
The regions that have tax concessions for important investment
(yellow group)
91
92
93
94
95
96
97
98
99
Ap p end ix I. Regional factors that m ight have an im p act on
attractiveness for foreign investm ent
Region
Am ur
Oblast
Bryansk
Oblast
Rostov
Oblast
Saint
Petersburg
H igh share H igh share
of urban
of Russian
population population
H igh share
Developed Ind epend enc Low
of em ployed
transport
e from
share of
w ith
infrastructure
transfers
m ining
secondary
from the
ind ustry
ed ucation
fed eral
bud get
Orange regions that attracted more FDI after tax cut
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
Other orange regions
Chuvash
Republic
Kabard inoBalkar
Republic
Republic of
Kalm ykia
Perm Krai
Ud m urt
Republic
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
Y ellow regions that attracted more FDI after tax cut
Kaluga
Oblast
Khabarovsk
Krai
Kom i
Republic
Kurgan
Oblast
Leningrad
Oblast
Lipetsk
Oblast
N ovosibirsk
Oblast
Pskov
Oblast
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
100
‡
Ap p end ix I. Regional factors that m ight have an im p act on
attractiveness for foreign investm ent (cont.)
Region
Republic of
Tatarstan
Yaroslavl
Oblast
H igh share H igh share
of urban
of Russian
population population
‡
‡
‡
H igh share
Developed Ind epend enc Low
of em ployed
transport
e from
share of
w ith
infrastructure
transfers
m ining
secondary
from the
ind ustry
ed ucation
fed eral
bud get
‡
‡
‡
‡
‡
‡
Other yellow regions
Altai
Republic
Astrakhan
Oblast
Kem erovo
Oblast
KhantyMansi
Okrug
Orenburg
Oblast
Penza
Oblast
Republic of
Dagestan
Republic of
Karelia
Republic of
Mord ovia
Yakutia
Republic
Stavropol
Krai
Tyum en
Oblast
Volgograd
Oblast
Vologda
Oblast
YamaloN enets
Okrug
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
‡
101
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Special pages
Ap p end ix B. The m ost rep resentative sou rces for th e World w id e Governance Ind icators (WGI)
WGI source
Governm ent
effectiveness
Control of
corruption
Regulatory quality
Meaning
Perceptions of the
quality of the civil
service and of its
ind epend ence from
political pressures,
the quality of policy
form ulation and
im plem entation,
and the cred ibility
of the governm ent's
com m itm ent to
such policies
Perceptions of the
extent to which
public pow er is
exercised for
private gain,
includ ing both
petty and grand
form s of
corruption, as w ell
as "capture" of the
state by elites and
private interests
Perceptions of the
ability of the
governm ent to
form ulate and
im plem ent sound
policies and
regulations that
perm it and
prom ote private
sector d evelopm ent
Perceptions of the
extent to which a
country's citizens
are able to
participate in
selecting their
governm ent, as
w ell as freed om of
expression,
freed om of
association, and a
free m ed ia
Econom ist
Intelligence
Unit
Quality of
bureaucracy /
institutional
effectiveness
Excessive
bureaucracy / red
tape
Corruption among
public officials
Unfair com petitive
practices
Price controls
Discrim inatory
tariffs
Excessive
protections
Discrim inatory
taxes
Dem ocracy Ind ex
Vested interests
Accountability of
public officials
H um an rights
Freed om of
association
Reporters
Without
Bord ers
Voice and
accountability
Press Freed om
Ind ex
120
Rule of law
Perceptions of the
extent to which
agents have
confid ence in and
abid e by the rules
of society, and in
particular the
quality of contract
enforcem ent,
property rights, the
police, and the
courts, as w ell as
the likelihood of
crim e and violence
Organized crim e
Fairness of jud icial
process
Speed iness of
jud icial process
Expropriation
Intellectual
property rights
protection
Political stability
and absence of
violence
Perceptions of the
likelihood that the
governm ent w ill be
d estabilized or
overthrow n by
unconstitutional or
violent m eans,
includ ing
politicallym otivated violence
and terrorism
Ord erly transfers
Arm ed conflict
Violent
d em onstrations
Social unrest
International
tensions / terrorist
threat
Ap p end ix B. The m ost rep resentative sou rces for the World w id e Governance Ind icators (cont.)
Freed om
H ouse
World
Econom ic
Forum
Global
Com petitive
ness Report
Corruption
Political rights
Civil liberties
Press
Freed om
Ind ex
Civil society
Electoral process
Infrastructure
Public
trust
in Tax
system Transparency
of
Quality of prim ary politicians
d istortionary
governm ent
ed ucation
Diversion of public Trad e barriers
policym aking
fund s
Local com petition
Freed om of the
Bribery: Trad e
Ease of starting a press
Bribery: Utilities
new business
Favouritism
in
Bribery: Taxes
Anti-m onopoly
d ecisions
of
Bribery: Jud iciary
policy
governm ent
State capture
officials
Effectiveness
of
law -m aking bod y
International Bureaucratic
Country
quality
Risk Guid e
US
State
Departm ent
Global
Insight
Business
Cond itions
Ind icators
Corruption
Bureaucracy
Corruption
Policy consistency
and
forward
planning
Investm ent profile
Military in politics
Dem ocratic
accountability
Tax effectiveness
Legislation
Institutional
perm anence
121
Jud icial fram ew ork
and ind epend ence
Cost of crim e/ Cost of terrorism
violence
Reliability of police
services
Jud icial
ind epend ence
Efficiency of legal
fram ew ork
for
challenging
regulations
IPR protection
Property rights
Informal sector
Law and Ord er
Governm ent
stability
Internal conflict
External conflict
Ethnic tensions
Trafficking
in
people
Jud icial
Civil unrest
ind epend ence
Terrorism
Crim e
Ap p end ix B. The m ost rep resentative sou rces for the World w id e Governance Ind icators (cont.)
Institutional
Profiles
Database
Quality
of
the Level
of
petty,
supply of public large-scale
and
good s:
ed ucation political corruption
and basic health
Capacity of political
authorities
to
im plem ent reform s
Afrobarom eter
Governm ent
hand ling of public
services
(health,
ed ucation)
Ease of Starting a
business
Ad m inistered
prices and m arket
prices
Com petition:
prod uctive sector:
ease
of m arket
entry for new firm s
Com petition
betw een
businesses:
com petition
regulation
arrangem ents
H ow
many
governm ent
officials d o you
think are involved
in corruption?
H ow
many tax
officials d o you
think are involved
in corruption?
122
Political rights and
functioning
of
political institutions
Freed om of the
press
Freed om
of
assem bly
and
d em onstration
Respect
for
m inorities
Transparency
of
econom ic policy
Aw ard of public
procurem ent
contracts
and
d elegation of public
service
Free m ovem ent of
persons,
inform ation, etc.
H ow m uch d o you
trust
the
parliam ent?
H ow satisfied are
you w ith the w ay
d em ocracy works
in your country?
Free
and
fair
elections
Security of persons
and good s
Organized crim inal
activity
Effectiveness
of
fiscal system
Security
of
property rights
Security
of
contracts betw een
private agents
Settlem ent
of
econom ic d isputes
Intellectual
property protection
Agricultural sector:
security of rights
and
property
transactions
Over the past year,
how often have you
feared crim e in
your own hom e?
H ow m uch d o you
trust the courts of
law ?
Trust in police
Conflicts of ethnic,
religious, regional
nature
Violent actions by
und erground
political
organizations
Violent
social
conflicts
External
public
security
Ap p end ix D. Classification of control and treatm ent grou p s, based on tax concession for investm ent
123