1 development based on commodity revenues: theory and russian evidence development based on...
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Development Based on Commodity Revenues:Development Based on Commodity Revenues:Theory and Russian EvidenceTheory and Russian Evidence
Konstantin Sonin, New Economic School
February 18, 2011Leontieff Center, St. Petersburg
2Konstantin Sonin / New Economic School February 18, 2011
Road mapRoad map
Basic tradeoff: – fantastic growth in Aze, Kaz, Rus, Tkm since 1999– volatility and a looming long run “resource curse”
Economic Growth in Resource Rich Countries– theoretical arguments and empirical evidence.
Policy Goals and Policy Tools– development strategy for resource-rich countries
Diversification Policies in Resource Rich Countries– actual policy response of countries to resource riches
Assessment– How successful were they?
3Konstantin Sonin / New Economic School February 18, 2011
Commodity boom underpinned growth...Commodity boom underpinned growth...
Impressive growth records in oil and gas producing countries in the region– Turkmenistan, Azerbaijan, Kazakhstan, Russia
In particular in nominal (US dollar) terms
0
50
100
150
200
250
300
350
0 5,000 10,000 15,000 20,000
Sources: International Monetary Fund, EBRD, and authors' calculations.
Cumulative Real GDP Growth, 1999–2008 (In per cent, vertical axis)
GDP per capita in 1998 at PPP
Gro
wth
Turkmenistan
RussiaKazakhstan
Azerbaijan
0
200
400
600
800
1,000
1,200
0 5,000 10,000 15,000 20,000
Cumulative Nominal US$ GDP Growth, 1999–2008 (In per cent, vertical axis)
GDP per capita in 1998 at PPP
Gro
wth
Kazakhstan
Russia
Azerbaijan
Sources: International Monetary Fund, EBRD, and authors' calculations, based on 2007 data for Turkmenistan.
Turkmenistan
4Konstantin Sonin / New Economic School February 18, 2011
… … but at the expense of great risksbut at the expense of great risks
large growth corrections during crisis: macro volatilityPotential “resource curse” affecting long run growth
-4
-2
0
2
4
6
8
10
12
0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 45 000 50 000
Sources: IMF, Energy Information Administration, and EBRD calculations. Trend lines are fitted based on regressions for a broad sample of 138 countries. Oil-rich countries are defined as countries where oil production valued at international prices exceeded 10 per cent of GDP in 1980. These countries are marked with large circles.
Average Real GDP Growth in Selected Oil-Rich Countries, 1981–2000 (In per cent, annualized, vertical axis)
GDP per capita in 1980 at PPP, US$
Gro
wth
KWSA
Oil sample trend line
QA
LY
AENO Non-oil sample trend line
5Konstantin Sonin / New Economic School February 18, 2011
Commodity rents and developmentCommodity rents and development
Commodity rents may be a blessing– developing countries fail to catch up because of an
underdevelopment trap (fixed costs to investment; externalities across sectors): “big push” needed
– commodity export revenues could finance such big push Commodity rents might be a curse– depress long-run growth by causing macroeconomics
distortions and excess volatility– have a negative effect on political institutions
6Konstantin Sonin / New Economic School February 18, 2011
Commodity rents and investmentCommodity rents and investment
Reliance on commodity exports – leads to high terms-of-trade volatility– discourages investment, especially if financial systems are not sufficiently developed– affects human capital (uncertain returns)
Dutch disease– underinvestment in high learning by doing technologies (manufacturing), or
technologies that are otherwise particularly beneficial for long run growth
7Konstantin Sonin / New Economic School February 18, 2011
Macro lessons learnedMacro lessons learned
in macro, little resembled petrostates of late 70s fiscal conservatism (up until 2008)– budget control– debt repayment– stabilization funds, despite huge political pressure
mild political pressure on Central Bank (up until 2008)
control of ‘white elephants’ (up until 2007)– lesson “unlearned” by 2010
8Konstantin Sonin / New Economic School February 18, 2011
““Resource curse 2.0”: Institutions Resource curse 2.0”: Institutions
Commodity resources discourage investment in good institutions– good institutions limit rent seeking– flexibility to “seek” rents is more valuable to politicians in resource-rich
environment “Institutional Trap”
– if institutions are bad to start with in a resource-rich economy, they are not likely to improve
Interactions with inequality– when the same amount of rents is appropriated by fewer members of the elite,
rent-seeking strategy becomes even more attractive– in resource rich environment, inequality and poor institutions are mutually
reinforcing– high inequality is bad for growth (particularly with imperfect capital markets.
as poor with entrepreneurial skills have no access to capital)
9Konstantin Sonin / New Economic School February 18, 2011
““Resource curse 2.0”: EvidenceResource curse 2.0”: Evidence
Oil revenues have adverse impact :– on property rights (Guriev, Kolotilin, and Sonin, JLEO,
2011)– on corporate governance (Durnev and Guriev, 2009)– on media freedom (Egorov, Guriev, and Sonin, 2009)– on democracy (Ross, 2001, 2009)– on regulation and reforms to improve business climate
in non-resource sectors (Amin and Djankov, 2009)– on political stability and likelihood of civil unrest (Ross,
2006)
DiversificationDiversification
10
11Konstantin Sonin / New Economic School February 18, 2011
Why diversification Why diversification
lowers vulnerability to external shocks reduces relative size of resource rents and creates
incentives to improve institutions (commitment device)
12Konstantin Sonin / New Economic School February 18, 2011
Diversification Tools: Public InvestmentDiversification Tools: Public Investment
“Vertical” policies: preferential treatment of specific non-resource industries– difficult to get right, especially in absence of good
institutions– crowd out private investment
“Horizontal” policies: investment in education, infrastructure– more likely to complement private investment– again, less efficient in weak institutional environment
13Konstantin Sonin / New Economic School February 18, 2011
Diversification Tools: Diversification Tools: Macro PoliciesMacro Policies
Sovereign wealth funds– prevent (in short-run) appreciation of currency or hikes in inflation, preserve (in short-run)
competitiveness– smooth government expenditures over time– commitment device to prevent government’s pro-cyclical spending – could be used to finance development policies
Taxation of resource exports – per se cannot play a significant role in redirecting investment in an open economy (capital just flows
to other countries, not to “right sectors”)
14Konstantin Sonin / New Economic School February 18, 2011
Diversification Tools: Diversification Tools: Financial DevelopmentFinancial Development
helps to smooth effects of resource price volatility benefits non-resource sectors, which are more dependent on external finance (cf. Rajan-Zingales) – works as a horizontal industrial policy
helps to match entrepreneurial ideas and funding may help reduce (effects of) inequality instruments: – improved regulation of banks and securities markets– deposit insurance – effective court systems
15Konstantin Sonin / New Economic School February 18, 2011
Diversification Tools: Diversification Tools: Fighting InequalityFighting Inequality
makes it easier to reform institutions in resource rich environments instruments:
– in developing countries typically implemented through government spending rather than taxation
– ideally, through structural policies: labour mobility and education
0
10
20
30
40
50
60
0 10 000 20 000 30 000 40 000 50 000 60 000 70 000 80 000
Sources: UN WIDER, IMF, WTO and EBRD calculations. Higher values of Gini coefficient correspond to higher income inequality. Trend lines are fitted based on regressions for a broad sample of countries, where Gini coefficients are available for 2002–06, taking the latest observation available. Commodity exporters are defined as countries where mining and fuel exports accounted for more than half of total merchandize exports. These countries are marked with large circles.
Gini Coefficients in Selected Commodity Exporters(In per cent, vertical axis)
GDP per capita in 1980 at PPP, US$
GIN
I C
oeff
icie
nt
Commodity exporter sample trend line
Other countries trend line
16Konstantin Sonin / New Economic School February 18, 2011
Examples of Substantial ProgressExamples of Substantial Progress
Diversification away from oil and gas is challenging, but there are examples of substantial progress– Chile: competitive agriculture and fishing (wine, salmon farming)– Malaysia: high-tech manufacturing integrated into South Asian and World
production chains– Indonesia: medium-to-high-tech manufacturing, agriculture– Mexico: high-tech manufacturing based primarily on FDI by US firms
17Konstantin Sonin / New Economic School February 18, 2011
Transition CountriesTransition Countries
Russia and Kazakhstan made diversification cornerstone of development agendaPublic investment increased in all countries over commodity boom period– 3% to 4.5% of GDP in Russia; – 3% to 6% of GDP in Kazakhstan; – 2% to 10% of GDP in Azerbaijan.
Public spending on education:– 2.9 to 4% of GDP in Russia; – 3.3 to 4.2% of GDP in Kazakhstan
18Konstantin Sonin / New Economic School February 18, 2011
Policies: Financial DevelopmentPolicies: Financial Development
Despite fast GDP growth (e.g., 8-fold in Russia in US$ nominal terms between 1998 and 2008) credit-to-GDP ratios have been growing rapidly
Credit to the Private Sector (in per cent of GDP)
0
10
20
30
40
50
60
70
Jan-00 Jan-02 Jan-04 Jan-06 Jan-08
Russia
Sources: Central Bank of Russia and EBRD.
Retail
Corporate
0
10
20
30
40
50
60
70
Jan-00 Jan-02 Jan-04 Jan-06 Jan-08
Kazakhstan
Sources: Central Bank of Kazakhstan and EBRD.
Retail
Corporate
0
5
10
15
20
25
Mar-05 Mar-06 Mar-07 Mar-08 Mar-09
Azerbaijan
Sources: Central Bank of Azerbaijan and EBRD.
Retail
Corporate
0
10
20
30
40
50
Dec-99 Dec-01 Dec-03 Dec-05 Dec-07
Other Transition Countries Average
Sources: EBRD Banking Survey, simple average.
Retail
Corporate
19Konstantin Sonin / New Economic School February 18, 2011
Policies: Financial developmentPolicies: Financial development
Rapid growth made possible due to entry of foreign banks – Especially in Kazakhstan
Loan-to-deposit ratios have been very high, well above regional average80
90
100
110
120
130
140
150
160
170
180
190
200
Dec-99 Apr-01 Aug-02 Jan-04 May-05 Oct-06 Feb-08
Loans-to-Deposits Ratio (in per cent)
Sources: Central Banks of Russia, Azerbaijan, Kazakhstan, EBRD Banking Survey and EBRD calculations. Simple average for other transition countries.
Russia
AzerbaijanKazakhstan
Other transition countries (av.)
0
20
40
60
80
100
120
Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08
Growth of Credit to the Private Sector (year on year, in per cent)
Sources: Central Banks of Russia, Ukraine, Kazakhstan and EBRD calculations.
Ukraine
20Konstantin Sonin / New Economic School February 18, 2011
Policies: Financial DevelopmentPolicies: Financial Development
financial sector growth was facilitated by number of structural reforms– deposit insurance, credit bureaus, interest rates disclosure, revisions to legislation on collateral and bankruptcies
non-bank finance has also been growing, albeit at a lower pace only in Russia reforms outpaced the non-oil-rich transition country average
0
1
2
3
4
5
6
7
8
Russia Other transitioncountries (av.)
Kazakhstan Azerbaijan Turkmenistan
Banking NBFI
Source: EBRD, based on transition indicators for banking sector and non-bank financial institutions.
Number of Financial Sector Transition indicator Upgrades(2000–08)
Nil
21Konstantin Sonin / New Economic School February 18, 2011
Policies: Sovereign wealth fundsPolicies: Sovereign wealth funds
Azerbaijan set up State Oil Fund in 1999Kazakhstan established National Fund in 2000– Peaked at 30% of GDP (the largest in relative terms)
Russia: Stabilization Fund in 2004, subdivided into Reserve Fund and National Wealth Fund in 2008
0
50
100
150
200
250
300
Ven
ezue
la
Iran
Aus
tral
ia
Nig
eria
Mal
aysi
a
Rus
sia
Om
an
Aze
rbai
jan
Kaz
akhs
tan
Alg
eria
Qat
ar
Liby
a
Bah
rain
Nor
way
Sau
di A
rabi
a
Kuw
ait
Bru
nei
UA
E
Kiri
bati
Sources: SWF Institute and World Bank. Data for 2008 or latest estimate available.
Sovereign Wealth Fund Assets(In per cent of GDP, selected countries)
AssessmentAssessment
22
23Konstantin Sonin / New Economic School February 18, 2011
Assessment: DiversificationAssessment: Diversification
measures of structure of output / exports are distorted by oil price effects– directly (valuation)– indirectly (short-term incentives to produce and export)
Even Norway, Malaysia lost positions in UNIDO “Industrial Competitiveness” indices during the boom compare oil / output structure at similar points in oil price cycle?
0
10
20
30
40
50
60
70
80
Rus
sia
Nor
way
Aus
tral
ia
Mac
edon
ia
Indo
nesi
a
Rom
ania
Geo
rgia
Pol
and
Slo
vak
Rep
.
Cze
ch R
ep.
Mex
ico
Mal
aysi
a
Ger
man
y
2000 2005
Share of Higher-Value-Added Manufacturing in Exports(In per cent, selected countries)
Source: UNIDO.
0
5
10
15
20
Nig
eria
Ven
ezue
la
Kuw
ait
Chi
le
Qat
ar
Sau
di A
rabi
a
Alb
ania
Rus
sia
Nor
way
Aus
tral
ia
Mac
edon
ia
2000 2005
Share of Higher-Value-Added Manufacturing in Exports(In per cent, selected countries)
Source: UNIDO.
24Konstantin Sonin / New Economic School February 18, 2011
Diversification in Russia: ComparisonDiversification in Russia: Comparison
Comparable periods in terms of average oil price:– Dec04-Apr05 and Dec08-Apr09
No evidence of diversification, there may be slight decline in manufacturing
18.5 17.9 14.2
68.3 69.0 75.4
10.3 10.7 7.9
3.0 2.4 2.50
10
20
30
40
50
60
70
80
90
100
2005q1 2008q1 2009q1
Agriculture Manufacturing Other Extraction
Sources: Rosstat and EBRD calculations. Excluding net taxes. Agriculture includes fishing. "Other" include services, and construction.
Russia: Structure of Gross Domestic Product(In per cent, based on quarterly data)
5.9 5.1 6.2
50.545.1
49.8
43.5 49.9 44.0
0
10
20
30
40
50
60
70
80
90
100
Dec04-Apr05 Dec07-Apr08 Dec08-Apr09
Higher-value-added manufacturing Other Crude oil and gas
Sources: Rosstat and EBRD calculations. Higher value added manufacturing goods include machinery, equipment, and vehicles. Other goods include refines oil and petrochemicals.
Russia: Structure of Merchandize Exports(In per cent)
25Konstantin Sonin / New Economic School February 18, 2011
No diversification of Russian GDP in 2002-2008No diversification of Russian GDP in 2002-2008
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2002 2003 2004 2005 2006 2007 2008
Communal utilities, social servicesHealthcareEducationGovernance and defenseReal estateFinanceTransport/TelecomHotels/RestaurantsTradeConstructionElectricity, gas, water, incl.distributionManufacturingMiningFishingAgriculture
26Konstantin Sonin / New Economic School February 18, 2011
Structure of exports: Russia and KazakhstanStructure of exports: Russia and Kazakhstan
exports structure suggests growing oil dependence in Kazakhstan and Azerbaijan– Partly reflects successful exploration, largely led by international firms (PSAs)
in Russia structure of exports was similar at similar points in the oil price cycle
0
10
20
30
40
50
60
70
80
90
100
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
High-tech manufactures Other manufactures Agriculture Mining and fuels
Sources: WTO and authors' calculations.
Russia: Structure of Merchandize Exports(In per cent)
Oil US$ 30 (2008 prices)
Oil US$ 28 (2008 prices)
0
10
20
30
40
50
60
70
80
90
100
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
High-tech manufactures Other manufactures Agriculture Mining and fuels
Sources: WTO and authors' calculations.
Kazakhstan: Structure of Merchandize Exports(In per cent)
Oil US$ 30 (2008 prices)
Oil US$ 28 (2008 prices)
27Konstantin Sonin / New Economic School February 18, 2011
Structure of exports: Azerbaidzhan and non-oil countriesStructure of exports: Azerbaidzhan and non-oil countries
in other transition countries share of manufacturing exports has been increasing on average0
10
20
30
40
50
60
70
80
90
100
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
High-tech manufactures Other manufactures Agriculture Mining and fuels
Sources: WTO and authors' calculations.
Azerbaijan: Structure of Merchandize Exports(In per cent)
Oil US$ 30 (2008 prices)
Oil US$ 28 (2008 prices)
0
10
20
30
40
50
60
70
80
90
100
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
High-tech manufactures Other manufactures Agriculture Mining and fuels
Other Transition Countries:Structure of Merchandize Exports(In per cent)
Oil US$ 30(2008 prices)
Oil US$ 28 (2008 prices)
Sources: WTO and authors' calculations, based on weighted average of AM, BG, BY, CZ, EE, GE, HU, KG, LV, LT, MK, MD, MN, PL, RO, SK, SI, TR, UA.
28Konstantin Sonin / New Economic School February 18, 2011
Assessment: Impact of CrisisAssessment: Impact of Crisis
no clear link between commodity dependence and severity of the crisis on average (in terms of macro impact on growth) – indirect measure: deviation of 2009 forecast from the 1999-2008 average growth– if anything, the effect of commodity wealth is positive
all countries in the region drew on their fiscal and monetary reserves to finance sizable fiscal and monetary stimulus packages
-25
-20
-15
-10
-5
0
5
10
0 20 40 60 80 100
Sources: WTO, International Monetary Fund, and authors' calculations, based on World Economic Outlook April 2009 forecasts, 129 countries.
Commodity Dependence and Crisis Impact (Deviation of 2009 growth forecast from the 1999–2008 average growth)
Share of fuel and commodities in merchandize exports
Dev
iatio
n (in
per
cent
age
poin
ts)
Latvia
Qatar
Russia
Yemen
Angola
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
0 20 40 60 80 100
Commodity Dependence and Crisis Impact (Deviation of 2009 growth forecast from the 1999–2008 average growth)
Share of fuel and commodities in merchandize exports
Dev
iatio
n (in
per
cent
age
poin
ts)
Kaz
Russia
Azerbaijan
Latvia
Armenia
Sources: WTO, EBRD, and authors' calculations, based on May 2009 EBRD forecasts.
MongoliaKyrgyzMoldova
Ukraine
29Konstantin Sonin / New Economic School February 18, 2011
Assessment: Financial DevelopmentAssessment: Financial Development
Financial development supported real sector, but also exacerbated commodity cycle– very high leverage – rapid consumer credit growth– credit growth averaging 50%+,
up to 115%, put strain on bank risk management and on supervisors
Overall, some cross-country evidence that while financial development softened the impact of crisis, excessively high loan-to-deposit ratios exacerbated it
Table 2. Determinants of Impact of Global Crisis on Growth
Model A B C D E
Method OLS
Dependent variable Difference between 2009 growth forecast and 1999 –2008 av.
Average growth, 1999–2008 –1.067 –1.031 –1.144 –1.036 –1.108(per cent a year) (0.164)*** (0.174)*** (0.189)*** (0.138)*** (0.149)***
GDP per capita –3.225 –2.799 –3.302 –2.454 –2.338Log, PPP (0.520)*** (0.435)*** (0.521)*** (0.329)*** (0.206)***
Oil rents 0.072 0.073 0.036 0.030(In per cent of GDP) (0.024)*** (0.025)*** (0.021)* (0.018)*
Share of commodities 0.028in merchandize exports (0.013)**
Private sector credit-to-GDP 0.023 0.025 0.017 0.016(0.008)*** (0.008)*** (0.009)* (0.007)**
Loan-to-deposit ratio –0.018 –0.018 –0.019(0.008)** (0.008)** (0.008)**
Quality of institutions, index 0.046 –0.053 0.028(0.129) (0.115) (0.126)
Share of higher-value-added 0.005 0.014 0.007manuf and food in exports (0.017) (0.021) (0.016)
Constant 28.690 23.909 28.199 22.451 20.920(4.873)*** (4.449)*** (4.943)*** (2.711)*** (2.193)***
R2 0.64 0.63 0.61 0.62 0.58
Number of observations 101 101 101 135 135
Notes: Robust standard errors in parentheses. Values significant at the 10% level are marked with *; at the 5% level, with **; at the 1% level, with ***.
Institutions MatterInstitutions Matter
30
31Konstantin Sonin / New Economic School February 18, 2011
Assessment: InstitutionsAssessment: InstitutionsTable 3. Determinants of Export Structure
Model A B C D E
Method OLS
Dependent variable Share of hva manufacturing and food in exports, 2001 –03
Exports structure in 1991–92 0.784 0.806 0.803 0.815 0.756(0.061)*** (0.059)*** (0.058)*** (0.073)*** (0.067)***
GDP per capita 1.779 –2.874 –2.472 –3.664 –5.608Log, PPP (0.935)* (1.769) (1.818) (2.057) (2.094)**
Oil rents –0.230 0.013 –0.051 0.027 0.159(In per cent of GDP) (0.114)** (0.127) (0.150) (0.151) (0.144)
Oil rents * SWF dummy –0.045(0.103)
Quality of institutions, index 1.222 1.074 3.779 1.130(0.549)** (0.620)* (0.943)*** (0.484)**
Private sector credit-to-GDP 0.009 0.012(period average) (0.041) (0.093)
Constant –5.487 30.282 26.709 39.716 51.026(7.724) (14.615)** (14.824)* (16.101)** (18.151)**
R2 0.72 0.75 0.76 0.79 0.79
Number of observations 96 89 86 43 25
Notes: Robust standard errors in parentheses. Values significant at the 10% level are marked with *; at the 5% level, with **; at the 1% level, with ***. In Column D only countries withthe value of index of institutions below the median are included. In Column E only countries where commodities accounted for more than 40 per cent of merchandize exports at the startof the period are included.
To look at diversification, compare export structures in 1991–92 and 2001–03 (oil at US$ 30-31 in 2008 prices)
Use share of food and higher-value-added manufacturing in exports (WTO data)– Technologically distanced from oil
and gas– Bulk of developed countries’
exports (from 70% in Germany to 30% in Australia, but less than 10% in Rus, Kaz, Aze)
32Konstantin Sonin / New Economic School February 18, 2011
Assessment: InstitutionsAssessment: Institutions
export structures are generally “sticky” (correlation is 0.85) oil rents suppress diversification, but this effect becomes
insignificant when quality of institutions is included quality of institutions is statistically and economically significant one standard deviation improvement in the quality of institutions is
associated with a 4 to 6 p.p. increase in share of higher-value-added manufacturing and food in merchandize exports– relationship is even stronger in the subsample of countries with weaker
institutions (index below median)– result holds in a small subsample of countries where commodities accounted
for 40%+ of merchandize exports at the start of the period financial development per se or existence of sovereign wealth fund
do not appear to have significant impact
33Konstantin Sonin / New Economic School February 18, 2011
Assessment: InstitutionsAssessment: Institutions
improving institutions remains a challenge
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
1996 1998 2000 2002 2003 2004 2005 2006 2007 2008
AZE KAZ RUS TMN Other trans. countries (av)
Source: World Bank and Kaufmann et al. (2009).
World Bank Governance Indicators: Overall (Higher values correspond to better institutions)
-2.0
-1.5
-1.0
-0.5
0.0
1996 1998 2000 2002 2003 2004 2005 2006 2007 2008
AZE KAZ RUS TMN Other trans. countries (av)
Source: World Bank and Kaufmann et al. (2009).
World Bank Governance Indicators: Rule of Law (Higher values correspond to better institutions)
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1996 1998 2000 2002 2003 2004 2005 2006 2007 2008
AZE KAZ RUS TMN Other trans. countries (av)
Source: World Bank and Kaufmann et al. (2009).
World Bank Governance Indicators: Voice and Accountability (Higher values correspond to better institutions)
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1996 1998 2000 2002 2003 2004 2005 2006 2007 2008
AZE KAZ RUS TMN Other trans. countries (av)
Source: World Bank and Kaufmann et al. (2009).
World Bank Governance Indicators: Government Effectiveness (Higher values correspond to better institutions)
34Konstantin Sonin / New Economic School February 18, 2011
CorruptionCorruption
-2
-1
-1
-1
-1
-1
0
0
0
1996 1998 2000 2002 2003 2004 2005 2006 2007 2008
Azerbaijan Kazakhstan Russia Turkmenistan Other transition countries (average)
World Bank Governance Indicators - Control of corruption
35Konstantin Sonin / New Economic School February 18, 2011
MWIBDIZAR
TZANERSLEGNB
MDG
YEMZMB
MLI
ETH
ERI
NGABEN
RWA
BFA
CAFKEN
MOZ
COG
TJKTCD
UGATGONPL
CIVHTI
SEN
GMB
KGZCOM
SLB
ZWELAOBGDUZB
SDN
MDAMNGDJI
MRT
CMR
GINAGOPAK
GHA
PNGKHM
BOLVNM
LSO
GEOHND
IND
NICSYR
IDN
JAMEGY
ECUGUY
MAR
GTM
LKA
PRY
SWZARM
AZE
PHLSLV
ALB
JOR
LBNPERFJI
VEN
CHNUKRGABDZAMKD
COL
NAM
PAN
KAZBLR
IRNDOM
TUN
BRA
TUR
THABGRROM
URY
CRI
MEX
RUS
MYS
ZAF
CHLBWA
MUSHRV
LVAPOL
ARG
LTU
TTO
EST
SAUSVK
HUN
PRT
CZEBHRKOR
SVN
GRC
NZL
ARE
ISRKWT
ESP
ITA
DEU
SGP
FRAJPN
AUS
BEL
FIN
SWENLDGBRCANAUTDNKCHE
IRL
NOR
USA
-2-1
01
23
e(
cont
rol_
of_
corr
upt
ion
| X
)
-2 -1 0 1 2e( loggdppcppp | X )
coef = .69167874, se = .04034047, t = 17.15
A Russian problem…A Russian problem…
Log GDP per capita, PPP, 2005
Russia is 1.04 st.dev. below the line;
Same if control for education, size, inequality etc…
36Konstantin Sonin / New Economic School February 18, 2011
Media freedom and Government EffectivenessMedia freedom and Government Effectiveness
PRK
CUB
MMRTKM
LBYERI
ZWE
GNQSDNBLRUZB
RWA
LAOSYR
SOM
CHN
VNM
ZAR
IRN
TUN
SAU
SWZ
YEMKAZ
BDI
TJK
TGO
TCD
LBR
GIN
GMB
ARE
AZEVEN
OMNBHR
KGZ
IRQ
NPL
CIV
MYS
ETHBGD
RUS
CMR
EGY
AFG
DJI
BTN
SGP
HTI
GAB
AGO
MDAZMB
MRT
DZA
ARM
CAF
COLMAR
QAT
KHM
JOR
KENPAKLBN
SLE
UKRGTM
IDN
KWT
LKA
PRYGEO
GNB
MWINERNGA
MKD
HND
TZA
COG
ALBMDG
TURROM
BIHMOZUGA
COM
PAN
THA
MEX
LSO
NIC
ARG
ECU
SLV
BRA
PER
YUG
BFADOM
IND
HRV
SENBGR
MNG
ITA
PHL
BOL
TMP
SLB
BEN
BWA
FJI
NAM
PNG
URY
KORISR
GRCMUS
GHA
ZAFTTO
CHL
GUYMLI
CZE
CYP
ESP
AUT
HUNSVK
FRA
JPN
POLCRI
SVN
AUSGBR
LTU
USA
EST
CAN
LVA
DEU
JAM
IRL
PRT
NZLNLDCHE
BEL
NORDNKFINSWE
-2-1
01
2e
( g
ove
rnm
ent
_effe
ctiv
ene
ss |
X )
-.4 -.2 0 .2 .4e( mf100 | X )
coef = 3.0090516, (robust) se = .23436654, t = 12.84
Egorov, Guriev, and Sonin (2009)
Replay
37Konstantin Sonin / New Economic School February 18, 2011
Media freedom and Control of CorruptionMedia freedom and Control of Corruption
PRK
CUB
MMRTKM
LBY
ERI
ZWE
GNQ
SDN
BLRUZB
RWASYR
SOM
LAOVNMCHN
ZAR
TUN
IRN
SAU
SWZYEM
KAZ
BDI
TJKLBRTGO
TCDGIN
OMN
ARE
VEN
GMB
AZEKGZ
BHR
IRQ
CIV
MYS
NPL
EGY
ETHCMRRUS
BGDAFG
DJI
HTI
GAB
AGO
BTN
SGP
ZMBMDA
MRT
ARMDZACOLMAR
CAF
QAT
KHM
JOR
KENPAK
LBN
UKRSLEIDN
KWT
GTMGEO
LKA
PRY
GNBMWINER
NGA
TZAHNDALB
COG
MKD
MDGTURROM
BIH
MOZCOM
PAN
UGA
THANICMEXLSOSLV
ECU
ARG
BRABFA
YUGPERIND
DOMSEN
HRVBGR
BOL
PHL
ITA
MNG
BWA
BENTMPSLB
FJINAM
URY
PNG
KOR
ISRGRCMUS
ZAF
GHA
CHL
TTO
GUYMLI
CYP
CZE
ESP
HUN
AUT
SVK
JPNFRA
POL
CRI
SVN
AUS
LTU
GBR
LVA
CANUSA
EST
DEU
JAM
IRL
PRT
NZL
CHE
BEL
NLDNOR
DNKSWE
FIN-2
-10
12
3e
( co
ntro
l_o
f_co
rru
ptio
n | X
)
-.4 -.2 0 .2 .4e( mf100 | X )
coef = 2.8623033, (robust) se = .26412487, t = 10.84
Egorov, Guriev, and Sonin (2009)
Replay
38Konstantin Sonin / New Economic School February 18, 2011
Next South Korea?Next South Korea?
Income per capita, purchasing power parity. Source of data and forecast: World Economic Outlook October 2009, IMF.
$-
$5,000
$10,000
$15,000
$20,000
$25,000
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
GD
P p
er c
apit
a, P
PP
Russia Korea 11 years earlier
39Konstantin Sonin / New Economic School February 18, 2011
Institutions much worse in Russia Institutions much worse in Russia
0 25 50 75 100
Voice and Accountability
Political stability and absence of violence/terrorism
Government Effectiveness
Regulatory quality
Rule of law
Control of corruption
Korea, % rank in 1997 Russia, % rank in 2008
40Konstantin Sonin / New Economic School February 18, 2011
Conclusion Conclusion
commodity revenues provide significant opportunities for financing investment but may also negatively affect growth– terms of trade volatility has negative impact on investment– structural shifts in accumulation/allocation of physical/human capital– incentives to engage in rent-seeking rather than improve institutions
diversification may be pursued via variety of strategies– direct investment in non-resource sectors – investment in education and infrastructure, fiscal redistribution– financial sector development– sovereign wealth funds
41Konstantin Sonin / New Economic School February 18, 2011
Conclusion, Conclusion, ctdctd
diversification policies can be successful, but success crucially depends on institutions– democracy, media freedom, property rights, corporate governance, low tolerance for corruption– improving these institutions is a particularly challenging task in oil-rich societies
post-communist oil-rich countries have done well in terms of prudent macro policies financial sector development – played an important role in supporting the real sector– extraordinary financial services boom fuelled by external borrowing in part amplified the effects
of the commodity cycle
42Konstantin Sonin / New Economic School February 18, 2011
SourcesSources
Chapter 4 of the 2009 EBRD Transition Report – background paper “Development Based on Commodity Revenues”, with Sergei
Guriev and Alexander Plekhanov Own work on resource-dependence
– Why Resource-Poor Dictators Allow Freer Media (with Georgy Egorov and Sergei Guriev), American Political Science Review, November 2009
– Determinants of Nationalizations in the Oil Sector (with Sergei Guriev and Anton Kolotilin), Journal of Law, Economics, and Organization, 2011
Sergei Guriev and Ekaterina Zhuravskaya work– Why Russia is Not South Korea, Journal of International Affairs, 2010