1 Development Based on Commodity Revenues: Theory and Russian Evidence Development Based on Commodity Revenues: Theory and Russian Evidence Konstantin

11

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?


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


… … 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


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


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


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


““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)


““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


Why diversification Why diversification

lowers vulnerability to external shocks reduces relative size of resource rents and creates

incentives to improve institutions (commitment device)


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


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”)


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


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


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


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


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


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


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


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


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.


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)


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


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)


0

10

20

30

40

50

60

70

80

90

100

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007



Kazakhstan: Structure of Merchandize Exports(In per cent)




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



Azerbaijan: Structure of Merchandize Exports(In per cent)



0

10

20

30

40

50

60

70

80

90

100

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007


Other Transition Countries:Structure of Merchandize Exports(In per cent)

Oil US$ 30(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.


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


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


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)


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


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



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



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



World Bank Governance Indicators: Government Effectiveness (Higher values correspond to better institutions)


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


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…


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


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


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


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


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


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


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

Documents

1 Development Based on Commodity Revenues: Theory and Russian Evidence Development Based on Commodity Revenues: Theory and Russian Evidence Konstantin