Macroeconomic Policy, Volatility and Institutions in ... · Institutions and Macroeconomic Policies in Resource-Rich Arab Economies focuses on the unique features of the Arab World

Macroeconomic Policy, Volatility andInstitutions in Resource-Rich Economies

Kamiar MohaddesUniversity of Cambridge@KamiarMohaddes

Based on joint work with Hadi Salehi Esfahani (University ofIllinois), Jeff Nugent (USC), M. Hashem Pesaran (USC), Mehdi

Raissi (IMF), and Hoda Selim (IMF).Please do not cite or distribute without permission.

I.S.E.O. Summer School 2019June 18, 2019

Institutions and Macroeconomic

Policies in Resource-Rich Arab Economies

edited by

kamiar mohaddes, jeffrey b. nugent,

and hoda selim

2also published by OXFORD UNIVERSITY PRESS

The Macroeconomics of the Arab States of the GulfRaphael Espinoza, Ghada Fayad, and Ananthakrishnan Prasad

The Rent CurseNatural Resources, Policy Choice, and Economic DevelopmentRichard M. Auty and Haydn I. Furlonge

The Tunisian Labor Market in an Era of TransitionEdited by Ragui Assaad and Mongi Boughzala

Crony CapitalismEdited by Ishac Diwan, Adeel Malik, and Izak Atiyas

2

Kamiar Mohaddes, Janeway Fellow in Economics, University of Cambridge, UK; Senior Lecturer and Fellow in Economics, Girton College, University of Cambridge, UK; Research Fellow, Economic Research Forum, Egypt.

Jeffrey B. Nugent, Professor of Economics, University of Southern California, USA; Senior Associate, Economic Research Forum, Egypt.

Hoda Selim, Research Fellow, Economic Research Forum, Egypt.

For over 80 years the Arab region has been deriving massive wealth from its natural resources. Its economic performance

has been at the mercy of the ebbs and flows of oil prices and its resources have been slowly depleting. The critical question

is how Arab countries might escape the oil curse.

Institutions and Macroeconomic Policies in Resource-Rich Arab Economies focuses on the unique features of the Arab World to explain the disappointing outcomes of macroeconomic policy. It explores the interaction between oil and institutions to draw

policy recommendations on how Arab countries can best exploit their oil revenues to avoid the resource curse. Case studies

and contributions from experts provide an understanding of macroeconomic institutions (including their underlying rules, procedures, and institutional arrangements) in oil-rich Arab

economies and of their political economy environment, which has largely been overlooked in previous research.

Institutions and Macroeconomic Policies in Resource-Rich Arab Economies offers novel macroeconomic policy propositions for exchange rate regimes, fiscal policy, and oil wealth distribution that is more consistent with macroeconomic stability and fiscal

sustainability. These policy reforms, if implemented successfully, could go a long way toward helping the resource-rich countries

of the Arab region and elsewhere to avoid the oil curse.

Jacket image: ARV / Alamy Stock Photo

9 780198 822226

ISBN 978-0-19-882222-6

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Institutions and M

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This lecture is based on

I Some of the chapters in our forthcoming book on Institutions andMacroeconomic Policies in Resource-Rich Arab Economies, co-edited withJeffrey B. Nugent (University of Southern California) and Hoda Selim(International Monetary Fund), Oxford University Press (May 2019).

I The U.S. Oil Supply Revolution and the Global Economy, with Mehdi Raissi,forthcoming in Empirical Economics.

I One Hundred Years of Oil Income and the Iranian Economy: A Curse or aBlessing?, with M. Hashem Pesaran (2014), in Parvin Alizadeh and HassanHakimian (eds.), Iran and the Global Economy: Petro Populism, Islam andEconomic Sanctions. Routledge.

I Do Sovereign Wealth Funds Dampen the Negative Effects of CommodityPrice Volatility?, with Mehdi Raissi (2017), Journal of Commodity Markets 8,pp. 18—27.

Motivation

I According to the resource curse paradox, abundance of oil (natural gas, mineralsand other non-renewable resources) is believed to be an important determinantof economic failure.

I We investigate whether the poor performance of resource-rich countries, whencompared to countries which are not endowed with oil and other resources, isdue to the abundance of oil in itself or whether instead the curse is due to pricevolatility in global oil markets and production volatility due to political factors(for instance, wars, and sanctions).

I More importantly, we try to establish whether there is a role for institutions andthe government (fiscal policy) in offsetting some of the negative growth effectsdue to the curse.

Volatility is a Major Problem in the MENA RegionI It is clear from the MENA region that if commodity price volatility is notmanaged properly, it can result in higher GDP growth volatility anddisappointing long-term economic performance.

I GDP growth volatility in the GCC countries has been at least three timeshigher than that of Chile and Norway.

Scatter Plots of GDP Growth and Volatility of Oil RevenueGrowth against Volatility of GDP Growth, 1961-2013

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Source: Mohaddes and Pesaran (2014), One Hundred Years of Oil Income and theIranian Economy: A Curse or a Blessing?, Routledge.Notes: Based on data from World Bank World Development Indicators (WDI), PennWorld Table Version 8.0, and International Monetary Fund International FinancialStatistics databases.

The Volatility CurseI While commodity price booms significantly increase economic growth, volatilityaffects it negatively (for more details see Mohaddes and Raissi, 2017).

I Fiscal and current account balances of commodity-exporting countries areaffected by swings in resources revenues with destabilizing effects on themacroeconomy.

higher volatility in output dampens growth was in fact discussed extensively in the seminal

paper of Ramey and Ramey (1995). Moreover, we note that in our sample of 69 commodity-

dependant countries, there appears to be a positive association between CToT volatility and

GDP growth volatility– which in turn has a negative effect on output growth.

Figure 1: Scatter Plots of GDP Growth and Volatility of CToT against Volatilityof GDP Growth, 1981-2014

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Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)Venezuela (Bolivarian Republic of)

JamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaicaJamaica

Trinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and Tobago

BahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrain

CyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprusCyprus

Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)Iran (Islamic Republic of)

IraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraqIraq

KuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwait

OmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOman

QatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatar

Saudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi Arabia

Syrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab RepublicSyrian Arab Republic

United Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab EmiratesUnited Arab Emirates

EgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgypt

YemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemenYemen

Brunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei DarussalamBrunei Darussalam

MyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmar

IndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesia

AlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeria

AngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngola

BotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswana

CameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroon

CongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongoCongo

EthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopia

GabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambia

GhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhana

GuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuinea

Côte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'Ivoire

KenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenya

MadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascar

MalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawi

MaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMaliMali

MozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambiqueMozambique

NigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNiger

NigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeria

ZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegalSenegal Sierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibia

Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)U.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: Mainland

TogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogo

UgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaUgandaBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina Faso

ZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambia

ArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmeniaArmenia

AzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstan

Republic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian Federation

MongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongolia

.04

.02

0.0

2.0

4.0

6R

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DP

Gro

wth

0 .05 .1 .15 .2 .25Real GDP Growth Volatility

NorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorwayNorway

IcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandIcelandAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentina

Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)ChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChile

ColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombiaColombia

EcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuador

GuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaGuatemalaHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasHondurasNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaraguaNicaragua

PanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaPanamaParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayParaguayPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeru




Trinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrain




KuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitKuwaitOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOman QatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi ArabiaSaudi Arabia






MyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarMyanmarIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesia



BotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroonCameroon



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GambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGambiaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissauGuineaBissau

GuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'IvoireCôte d'Ivoire

KenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenyaKenya MadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMadagascarMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawi

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NamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaSudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)Sudan (Former)U.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandU.R. of Tanzania: MainlandTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogo





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05

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ity


Source: Authors’calculation based on data from Penn World Table Version 9.0 and International MonetaryFund International Financial Statistics databases. These are cross-sectional averages over 1981-2014.

Figure 2 depicts a simple bivariate relationship between CToT volatility and the growth

of real GDP per capita (as well as the growth rate of TFP and physical capital accumulation)

in our sample of 69 countries over the period 1981-2014. They suggest that CToT volatility

exerts a negative impact on economic growth operating mainly through lower productivity

growth and lower physical capital accumulation. The rest of the paper use the CS-ARDL

approach for estimation in order to investigate whether the above indicative results continue

to hold up once we deal with, for instance, possible endogeneity problems, dynamics, cross-

country heterogeneity and cross-sectional dependence. We will also investigate the potential

role of SWFs and institutions in dampening the negative effects of CToT volatility on growth.

10

The Volatility CurseI Do natural resource abundant countries have fewer possibilities fortechnological progress?

I Is the capital accumulation another important channel through which volatilityaffects GDP per capita growth?

Figure 2: Scatter Plots of CToT Volatility against Real GDP growth, TFPGrowth and Capital Accumulation, 1981-2014




































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Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)Bolivia (Plurinational State of)

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NamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibia

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KazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRepublic of MoldovaRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationRussian FederationMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongoliaMongolia

.05

0.0

5.1

Phys

ical

Cap

ital A

ccum

ulat

ion



3.1 The Long-Run Effects of Volatility

To examine the long-run effects of CToT volatility on output growth, we estimate the fol-

lowing panel CS-ARDL model:

∆yit = c∗yi +

p∑l=1

φil∆yi,t−l +

p∑l=0

β′

ilxi.t−l +

q∑l=0

ail∆yt−l +

q∑l=0

b′

ilxt−l + εit, (11)

where ∆yit is the growth rate of real GDP per capita for country i and year t, xit is a 2× 1

vector of explanatory variables, namely the growth rate of the CToT index, gCToT,it, and

its volatility, σCToT,it. The terms ∆yt and xt denote the simple cross-section averages of

∆yit and xit in year t. Moreover, to determine the channel(s) through which GDP growth

is negatively affected by CToT volatility in our sample, we follow Beck et al. (2000) in

investigating two possible sources which are acknowledged in the literature, namely, TFP

and physical capital investment. We therefore also estimate the following regressions:

∆wit = c∗wi +

p∑l=1

φil∆wi,t−l +

p∑l=0

β′

ilxi.t−l +

q∑l=0

ail∆wt−l +

q∑l=0

b′


where ∆wit = lnWit − lnWit−1 is the growth rate of Wit = {TFP or physical capital percapita for country i and time t}, while ∆wt is the simple cross-sectional average of ∆wit,

with all other variables as defined in equation (11). However, data on TFP and physical

capital for some of the countries in our dataset were not available, and so these regressions

are based on a sample of 49 countries (those which are not denoted by 2 in Table 1).

11

A New Oil Order: A Low Oil Price Environment?I The technological advancements over the last decade have not only reduced thecosts associated with the production of unconventional oil, but they have alsomade extraction of tight oil resemble a manufacturing process in which onecan adjust production in response to price changes with relative ease.U.S. Oil Production & Rig Count

NOTES: Dashed line shows the forecast as of 3/12/19. Rig count

series shows the last weekly count each month.

SOURCES: Baker Hughes; Energy Information Administration.

U.S. Oil Production & Rig Count

Federal Reserve Bank of Dallas

300

500

700

900

1,100

1,300

1,500

1,700

3

5

7

9

11

13

15

17

2013 2014 2015 2016 2017 2018 2019 2020

U.S. crude oil production

Million barrels per day

U.S. oil rig count

Number of active rigs

Jan

11.9

Mar

816

Impact of the U.S. Oil Supply Revolution on Real Output(Mohaddes and Raissi, 2019)

Notes: Figures are median (blue solid) and median target (black long-dashed) impulseresponses to a one standard deviation fall in the price of oil, equivalent to anannualized drop of 51% in year 1 and 45% in year 2, together with the 5th and 95thpercentile error bands. The impact is in percentage points and the horizon is quarterly.

Focusing on the MENA RegionI The results indicate that the U.S. oil revolution and the resulting low oilprice environment will have a detrimental output effect not only for themajor oil (and commodity) exporters in our sample but also for the entireMENA region; primary commodity importers and exporters alike.

I To explore this further, we look at the implications of the U.S. oil supplyrevolution in terms of real GDP for individual countries in the region.

I Focusing on the median target responses we note that, with the exceptionof Algeria and Egypt, all countries experience a permanent fall in realoutput, with the effects being larger than −2% for the major oil exporters(the six GCC countries and Iran), but perhaps surprising also for Syria.For Algeria, OPEC member, the median target responses are negative forthe first 14 quarters after which the responses are very close to zero.

I The effects for oil importers, although being substantial, are neverthelessmuch smaller; being −0.52%, −0.66%, and −0.17% for Jordan, Moroccoand Tunisia respectively. The only exception is Egypt for which weobserve negative responses for the first five quarters after which themedian target response becomes positive 0.15%.

Impact of the U.S. Oil Supply Revolution on Real Outputin the MENA Region

Source: Mohaddes and Raissi (2019), The U.S. Oil Supply Revolution and the GlobalEconomy, Empirical Economics.Notes: Figures are median target impulse responses to a one standard deviation fall inthe price of oil, equivalent to an annualized drop of 51% in year 1 and 45% in year 2.The impact is in percentage points and the horizon is quarterly.

External Income and Price of Oil, in Log Level

4.0

5.0

6.0

7.0

8.0

1979Q2 1987Q1 1994Q4 2002Q3 2009Q42.0

3.0

4.0

5.0

External income Price of oil

Sources: Source: Mohaddes and Raissi (2019), The U.S. Oil Supply Revolution andthe Global Economy, Empirical Economics.Notes: Based on data from International Monetary Fund BoPS and IFS.

Impact of a Negative Oil Price Shock

Source: Mohaddes and Raissi (2019), The U.S. Oil Supply Revolution and theGlobal Economy, Empirical Economics.Notes: Figures are median generalized impulse responses to a one standarddeviation fall in oil prices, together with 95 percent bootstrapped confidencebounds. The impact is in percentage points and the horizon is quarterly.

A New Oil Order: A More Volatile EnvironmentI With oil prices being 50% higher than January 2019, are we in a low oilprice environment?

I This is not just about low oil prices, but more uncertainty and morevolatility.

I Bottom line: policy makers should think about volatility as opposed to(just) sustained low or high prices.

New Source of Volatility: the Trump FactorI President Trump is the new “swing factor” in global oil markets.I What are the effects of the competing policy objectives of the Trumpadministration on the oil market?

I Geopolitical agenda: sanctions on Iran;I Domestic political agenda: lowering American petrol prices; andI Trade wars with China and the EU.

What about OPEC Policy?

I Major source of oil price volatility. Just take the last few years, where anincrease in supply has been quickly reversed.

I In 2015: waging a price war against U.S. shale oil producers;I In 2016: maximizing sales before OPEC agreements;I In 2018: pressure due to sanctions on Iran and President Trump (asdiscussed).

Impact of a Negative Oil Revenue Shock for OPECCountries (Mohaddes and Raissi, 2019)

Notes: Figures are median generalized impulse responses to a one standard deviationfall in oil revenue, together with 95 percent bootstrapped confidence bounds. Theimpact is in percentage points and the horizon is quarterly.

The Role of Institutions and Policy FrameworksI What is the potential role of institutions and policy frameworks, and inparticular fiscal policy, in dampening the negative effect of commodity pricevolatility.

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IranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIranIran

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AlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeria

AngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngola

BotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswana LibyaLibyaLibyaLibyaLibyaLibyaLibyaLibyaLibyaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritaniaMauritania

MoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMoroccoMorocco

NigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNigeria

SudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudan

TunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisia

AzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijan

KazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChina

0.0

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0 .1 .2 .3 .4Fiscal Policy Volatility

Primary Commodity Exporters

USAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUK

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CanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanadaCanada


AustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaAustraliaNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew ZealandNew Zealand

South AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth AfricaSouth Africa ArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazilBrazil

ChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChileChile

EcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuadorEcuador

MexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexicoMexico

PeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeruPeru

VenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuelaVenezuela

Trinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and TobagoTrinidad and Tobago

BahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrainBahrain






OmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudi


SyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyriaSyria

UAEUAEUAEUAEUAEUAEUAEUAEUAEUAEUAE

EgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgyptEgypt IndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaIndonesiaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeria


BotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswana LibyaLibyaLibyaLibyaLibyaLibyaLibyaLibyaLibya




TunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaTunisiaAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijanAzerbaijan

KazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstanKazakhstan

RussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussia

ChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChinaChina

4050

6070

8090

Inst

itutio

nal Q

ualit

y0 .1 .2 .3 .4

Fiscal Policy Volatility


Source: K. Mohaddes, J.B. Nugent, and H. Selim (2019), Institutions andMacroeconomic Policies in Resource-Rich Arab Economies, Oxford University Press.Notes: This volatility is interpreted as the component of discretionary policy which isnot related to smoothing the business cycle, such as changes in political preferences orthe decision by the politicians to generate a short-term boom so as to keep thepopulation happy—as was seen in the region following the Arab Spring.

The Role of SWFs and Institutional QualityI SWFs have been established for a variety of reasons, ranging from fiscalstabilization (that is to help smooth the impact on government spending ofrevenues that are large and volatile), to long-term saving for future needs of theeconomy, or of specific groups such as pensioners, or for future generations.

I Mohaddes and Raissi (2017) show that countries that have a SWF have, onaverage, performed better when it comes to mitigating the negative growtheffects of CToT volatility and managed to sustain a higher level of capitalaccumulation in the face of the extreme volatility in resource revenues.

countries have established SWFs, we use information from the Sovereign Wealth Fund In-

stitute and end up with 29 countries with SWFs in our sample denoted by 1 in Table 1.

Listing the origin of accumulated funds by country in Table 2, we observe that the majority

of these SWFs (19 out of 29) were set-up using revenues from exports of crude oil and gas.

A large portion (10 out of 29) was established in countries that are major oil exporters and

are members of the Organization of the Petroleum Exporting Countries (OPEC), denoted

by ∗ in Table 2. We also provide the inception year of the funds in the same table. However,

note that it usually takes years (if not a decade) to set-up a SWF, as prior to inception,

parliament needs to debate the architecture of the fund; for instance, the amount transferred

to the SWF in each fiscal year and whether there are any provisions for withdrawing from

the fund to finance public expenditure as opposed to just using interest income from the

endowment for the budget. Nevertheless, the fact that a country has established a SWF

provides a valuable signal, as it indicates that the government and the legislature would like

to manage the resource wealth effi ciently and are concerned with (and willing to deal with)

macroeconomic stabilization as well as intergenerational equity.

Table 2: Sovereign Wealth Funds by Origin and Inception

Country Origin Inception Country Origin Inception

Algeria∗ Oil and Gas 2000 Mongolia Minerals 2011Angola∗ Oil 2012 New Zealand Non-Commodity 2003Australia Non-Commodity 2006 Nigeria∗ Oil 2012Azerbaijan Oil 1999 Norway Oil 1990Bahrain Oil 2006 Oman Oil and Gas 1980Bolivia Non-Commodity 2012 Panama Non-Commodity 2012Botswana Minerals 1994 Peru Non-Commodity 1999Brunei Darussalam Oil 1983 Qatar∗ Oil and Gas 2005Chile Copper 2006 Russia Oil 2008Gabon∗ Oil 1998 Saudi Arabia∗ Oil 1952Ghana Oil 2011 Senegal Non-Commodity 2012Indonesia Non-Commodity 2006 Trinidad and Tobago Oil and Gas 2000Iran∗ Oil and Gas 1999 United Arab Emirates∗ Oil 1976Kazakhstan Oil 2000 Venezuela∗ Oil 1998Kuwait∗ Oil 1953

Notes: Some countries have more than one fund, here we have taken the inception year to be that of the first fund, whichtends to be the main one. ∗ indicates that the country is a member of the Organization of the Petroleum Exporting Countries(OPEC). Source: Sovereign Wealth Fund Institute.

Figure 1 plots a simple bivariate relationship between real GDP per capita growth and

its volatility (measured by its standard deviation over the full sample, 1981-2014), and shows

that there exists a negative relationship between the two variables. The observation that

9

Is Oil Abundance an Institutional Curse?In a new volume entitled “Institutions and Macroeconomic Policies in Resource-RichArab Economies” (Oxford University Press, 2019), Jeff Nugent (USC), Hoda Selim(IMF) and I argue that while:

I oil revenues over the last half century have greatly lowered the incentive inresource-rich Arab economies (RRAEs) to develop the institutions (includingpolitical, fiscal and monetary institutions) that have emerged elsewhere,

I one should note that the GCC countries, because of their revenues (in the formof import duties, fees on religious pilgrims, etc.) and small populations, seem tohave not had that incentive even before oil was discovered in the region.

I For instance, prior to oil, given that the current GCC region was largely made upby merchant societies (relatively open to international trade), much of therevenues came from customs duties collected from the merchants, there hadthus been little need in these countries to develop the kinds of fiscal institutionscapable of raising taxes from the local populations. (Saudi Arabia was anexception as external trade was somewhat less important, and fees obtainedfrom visitors on the Hajj were the most important source of Saudi revenues).

Policy ImplicationsI While abundance of oil in itself is growth enhancing there are two mainproblems with this oil income: one is the volatility of oil revenues, and thesecond is that it accrues to the government.

I The fact that oil revenues accrue to the government tends to make thegovernment less immediately accountable for their policies and actions, andincreases incentives for rent-seeking activities.

I Devarajan (2019) shows that in RRAEs accountability is associated withbetter public expenditure outcomes. To overcome the lack ofaccountability of governments for oil revenues and also enhance publicspending effi ciency, he suggests transferring oil revenues directly tocitizens and then taxing them.

I The quality of institutions (political, fiscal and monetary) governingmacroeconomic policy matter more than the abundance of oil and gas revenuesfor macroeconomic outcomes including long-run growth and stability.

I The undesirable consequences of commodity price volatility can beavoided if resource-rich countries are able to improve the management ofvolatility in resource income: strengthen institutions and policymechanisms which act as shock absorbers in the face of high levels of oilrevenue volatility.

Policy Implications

I Better conduct of fiscal policy.

I Revenue re-balancing: raising non-distortionary taxes, such asconsumption tax (VAT) and reducing the dependence on oil revenue;

I Improved tax administration; andI Spending side: better targeting of subsidies (electricity, water, petrol).Urgent reforms are needed in the area of energy subsidy — which results inwaste, economic distortions and air pollution.

I Diversification is key, but this does not mean petrochemical industries!

I It will be important to reform the economic structure, increasingtransparency and openness to private sector initiatives and foreigninvestment and improve the business environment in general.

I Improving the functioning of financial markets is also a crucial step as thisallows firms and households to insure against shocks, decreasing uncertainty andtherefore mitigating the negative effects of volatility on investment andeconomic growth.

Concluding Remarks

I Volatility is a major problem in the MENA region and macroeconomicpolicy has not helped!

I A clear role for institutions and the government (fiscal policy) inoffsetting some of the negative growth effects due to the volatility curse.

I The new oil order is a serious challenge for the MENA region:

I in particular for oil-exporting countries, as lower oil prices weakendomestic demand as well as external and fiscal balances;

I but also for oil importers, as gains from lower oil prices are offset by adecline in external demand/financing by MENA oil exporters given stronglinkages between the two groups through trade, remittances, tourism,foreign direct investment, and grants.

I it implies more uncertainty and more volatility, with adverse effects oneconomic growth.

I The policy choices are not easy and will most likely require a newsocial contract.

Contents lists available at ScienceDirect

Journal of Commodity Markets

journal homepage: www.elsevier.com/locate/jcomm

Do sovereign wealth funds dampen the negative effects ofcommodity price volatility?☆

Kamiar Mohaddesa,⁎, Mehdi Raissib

a Faculty of Economics and Girton College, University of Cambridge, UKb International Monetary Fund, Washington DC, USA

A R T I C L E I N F O

JEL classification:C23E32F43O13O40

Keywords:Economic growthCommodity pricesVolatilitySovereign wealth funds

A B S T R A C T

This paper studies the impact of commodity terms of trade (CToT) volatility on economic growth(and its sources) in a sample of 69 commodity-dependent countries, and assesses the role ofSovereign Wealth Funds (SWFs) and quality of institutions in their long-term growthperformance. Using annual data over the period 1981–2014, we employ the Cross-Sectionallyaugmented Autoregressive Distributive Lag (CS-ARDL) methodology for estimation to accountfor cross-country heterogeneity, cross-sectional dependence, and feedback effects. We find thatwhile CToT volatility exerts a negative impact on economic growth (operating through loweraccumulation of physical capital and lower TFP), the average impact is dampened if a countryhas a SWF and better institutional quality (hence a more stable government expenditure).

1. Introduction

Commodity-dependent countries are a heterogenous mix of high-, middle-, and low-income countries that possess a large shareof the world's natural resources (90% of crude oil reserves for example), and represent close to 20% of world GDP and global exports.Natural resource wealth has enabled some of these countries to accumulate substantial assets (placed in Sovereign Wealth Funds in agrowing number of countries), and provided a buffer against commodity-price shocks in several cases. However, not all resource-richcountries have been able to leverage their assets to raise long-term economic growth due to a number of factors, including pro-cyclical fiscal policies (especially in the Middle East), underdeveloped public financial management frameworks, and fragile politicalsystems. For instance, Frankel et al. (2013) show that quality of institutions can play an important role in making fiscal policy lesspro-cyclical, hence turning commodity wealth into a blessing rather than a curse. Moreover, when governments rely heavily onrevenues derived from commodities, they are subject to commodity price volatility, which if not managed properly, can result inhigher GDP growth volatility and disappointing long-term economic performance.1

This paper studies the impact of commodity price volatility on long-term economic growth in a sample of 69 commodity-

http://dx.doi.org/10.1016/j.jcomm.2017.08.004Received 15 February 2017; Received in revised form 13 July 2017; Accepted 23 August 2017

★We are grateful to Ibrahim A. Elbadawi, Adnan Mazarei, Diaa Noureldin, and Hoda Selim, as well as the participants at the Sovereign Wealth Funds:Stabilization, Investment Strategies and Lessons for the Arab Countries Conference (World Bank) for helpful comments and suggestions. We would also like to thankthe Editor in charge of our paper (Sjur Westgaard) and an anonymous referee for helpful suggestions. Kamiar Mohaddes acknowledges financial support from theEconomic Research Forum (ERF). The views expressed in this paper are those of the authors and do not necessarily represent those of the ERF, InternationalMonetary Fund or IMF policy.⁎ Corresponding author.E-mail address: [email protected] (K. Mohaddes).

1 For instance, over the 1981–2014 period GDP growth volatility in the Gulf Cooperation Council region (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and theUnited Arab Emirates) has been at least three times higher than that of Chile and Norway.

Journal of Commodity Markets 8 (2017) 18–27

Available online 26 August 20172405-8513/ © 2017 Elsevier B.V. All rights reserved.

MARK

Motivation

I Commodity-dependent countries are a heterogenous mix of high-, middle-, andlow-income countries that possess a large share of the world’s natural resources(90 percent of crude oil reserves for example), and represent close to 20 percentof world GDP and global exports.

I Natural resource wealth has enabled some of these countries to accumulatesubstantial assets (placed in Sovereign Wealth Funds in a growing number ofcountries), and provided a buffer against commodity-price shocks in severalcases.

I However, not all resource-rich countries have been able to leverage their assetsto raise long-term economic growth due to a number of factors, including:

I pro-cyclical fiscal policies (especially in the Middle East),I underdeveloped public financial management frameworks,I and fragile political systems.

Motivation

I Frankel et al. (2013) show that quality of institutions can play an importantrole in making fiscal policy less pro-cyclical, hence turning commodity wealthinto a blessing rather than a curse.

I Moreover, when governments rely heavily on revenues derived fromcommodities, they are subject to commodity price volatility, which if notmanaged properly, can result in higher GDP growth volatility and disappointinglong-term economic performance.

I For instance, over the 1981-2014 period GDP growth volatility in the GulfCooperation Council region (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, andthe United Arab Emirates) has been at least three times higher than that ofChile and Norway.

ContributionsI We study the impact of volatility on long-term economic growth in a sample ofcommodity-dependent countries, and we then assess the role of macroeconomicpolicy, quality of institutions, and SWFs in shaping the growth performance ofthese countries in the face of the extreme volatility in resource revenues thatthey have experienced over time.

I IMF’s Fiscal Monitor (2015) argues that strong institutions and appropriatestabilization buffers can increase the chances of a successful public investmentscale-up, while Bahal et al. (2015) show that higher government spending oninfrastructure facilities (like roads, highways, and power) and/or health andeducation may have a complementary impact on private sector investment byraising the marginal productivity of private capital.

I We therefore also study the possible growth channels– i.e. total factorproductivity (TFP) and physical capital accumulation– through which volatility(and policy/institutions/SWFs) affect long-term economic growth.

I We argue that volatility in oil revenues combined with poor governmentalresponses to this volatility drives the resource curse paradox, not theabundance of oil revenues as such.

Literature

I There are different explanations for why resource-rich economies might besubject to a curse, for instance, the Dutch disease (see Corden and Neary 1982,Neary and van Wijnbergen 1986, and Krugman 1987).

I Other explanations focus on the political economy considerations and arguesthat large windfalls from oil and other resources create incentives for therent-seeking activities that involve corruption (Mauro 1995 and Leite andWeidmann 1999), voracity (Lane and Tornell 1996 and Tornell and Lane 1999),and possibly civil conflicts (Collier and Hoeffl er, 2004).

I Some of these considerations have been recently formalized by Caselli andCunningham (2009), with a recent survey provided in van der Ploeg andVenables (2009).

Literature

I A number of recent empirical works have also focused on the role of institutions.

I Mehlum et al. (2006) and Béland and Tiagi (2009), using a cross-sectionalapproach, show that the impact of natural resources on growth anddevelopment depends primarily on institutions.

I While Boschini et al. (2007) illustrate that the type of natural resourcespossessed is also an important factor.

I These authors argue that, when one controls for institutional quality andincludes an interaction term between institutional quality and resourceabundance, a threshold effect arises. This suggests there are levels ofinstitutional quality above which resource abundance becomes growthenhancing.

Literature

I Empirical support for the resource curse was originally provided by Sachs andWarner (1995), but there are a number of reasons why the econometricevidence on the negative effects of resource abundance on output growth mightbe questioned.

I Firstly, the literature relies primarily on a cross-sectional approach to test theresource curse hypothesis, and as such does not fully take account of the timedimension of the data and could suffer from endogeneity and omitted variableproblems, For example, Alexeev and Conrad (2009) show evidence against theresource curse hypothesis by considering a few additional regressors, such asexogenous geographical factors.

I In addition, even when panel data techniques are used, most studies make useof homogeneous panel data models, such as fixed and random effectsestimators, the instrumental variable (IV) technique, and the generalizedmethods of moments model, among others.

Literature

I As discussed in Pesaran and Smith (1995), the problem with these dynamichomogenous panel data techniques, when applied to testing growth effects, isthat they can produce inconsistent and potentially very misleading estimatesof the average values of the parameters, since growth regressions typicallyexhibit a substantial degree of cross-sectional heterogeneity.

I Note also that the early analysis ignores the effects of oil revenue volatility ongrowth, which turns out to be important.

I Accounting for (some of) these shortcomings and using appropriate econometrictechniques, the recent empirical literature seems to provide evidence againstthe conventional resource curse literature and argues for the positive effect ofresource abundance on development and growth, see for instance Arezki andvan der Ploeg (2007), Cavalcanti et al. (2011b, 2014), and Esfahani et al.(2013, 2014).

Literature

I We are certainly not the first ones to emphasize the importance of volatility foreconomic growth. Ramey and Ramey (1995) discuss the consequences of excessvolatility for long-run growth.

I Blattman et al. (2007) investigate the impact of terms of trade volatility on thegrowth performance of 35 commodity-dependent countries between 1870 and1939.

I Aghion et al. (2009), using data on 83 countries over 1960—2000, show thathigher levels of exchange rate volatility can stunt growth, especially in countrieswith relatively under-developed capital markets.

I Bleaney and Greenaway (2001) estimate a model for 14 sub-Saharan Africancountries over 1980—1995 and show that growth is negatively affected by termsof trade volatility, and investment by real exchange rate instability.

Literature

I van der Ploeg and Poelhekke (2009, 2010) find that the volatility ofunanticipated GDP growth has a negative impact on economic growth,conditional on the country’s level of financial development.

I Most closely related to our paper is Cavalcanti et al. (2015), who investigatethe effects of CToT volatility (σCToT ) on long-run economic growth of bothcommodity exporters and importers.

I However, we rely on a higher frequency (and exogenously determined) measureof σCToT , use a different estimation technique, and most importantly, have adifferent focus: namely the role of SWFs and quality of institutions in mitigatingthe negative growth effects of σCToT .

I While we do not explicitly control for other determinants of real GDP growth,the country-specific intercepts, different short-run slope coeffi cients and errorvariances, as well as cross-sectional averages of all the variables (as proxies forunobserved common factors) in the CS-ARDL regressions capture the effects ofsuch unobserved variables/factors.

Data

I To empirically test the relationship between economic growth and commodityterms of trade (CToT) growth, gCToT , and volatility, σCToT , we use annual datafrom 1980 to 2014 on: real GDP per capita, TFP, physical capital accumulation,and a CToT index based on the prices of 45 primary commodities.

I The CS-ARDL method employed in this paper requires a suffi cient number oftime periods for consistent estimation of country-specific coeffi cients. To ensurethis, we include only countries in our sample for which we have at least 25consecutive annual observations on real GDP and CToT.

I Furthermore, we only focus on countries that are commodity dependant– thosefor which the ratio of primary commodities to total exports exceeds 50%.

I Subject to these requirements, we end up with 69 countries in oursample– listed in Table 1.

their export and import baskets. This is in contrast to the "standard" commodity price

indices most commonly used in the literature, such as the "All Primary Commodities Index"

in International Monetary Fund (2012), which attaches the same weight to each country in

the regression analysis. Equation (10) is then used to construct two important variables.

The first is an annual CToT growth series, gCToT,it, which is calculated in two steps: (i)

year-on-year growth rate of the monthly CToT index is taken, and (ii) the average over the

year is calculated. The second is a measure of realized CToT volatility for year t, σCToT,it,

which is constructed as the standard deviation of the year-on-year growth rates of CToTiτduring months τ = 1, ..., 12 in year t. Therefore, in contrast to most studies in the growth

literature which employ time-invariant measures of volatility, we construct a time-varying

measure of commodity price volatility, σCToT,it.

Table 1: List of the 69 Primary Commodity Exporters Included in the Sample

Algeria12 Egypt Madagascar2 Qatar1

Angola12 Ethiopia2 Malawi2 Russia1

Argentina Gabon1 Mali2 Saudi Arabia1

Armenia Gambia2 Moldova Senegal1

Australia1 Ghana12 Mongolia1 Sierra LeoneAzerbaijan12 Guatemala Mozambique SudanBahrain1 Guinea2 Myanmar2 Syria2

Bolivia1 Guinea-Bissau2 Namibia TogoBotswana1 Honduras New Zealand1 Trinidad and Tobago1

Brunei Darussalam12 Iceland Nicaragua TanzaniaBurkina Faso Indonesia1 Niger Uganda2

Cameroon Iran1 Nigeria1 United Arab Emirates12

Chile1 Iraq Norway1 UruguayColombia Jamaica Oman12 Venezuela1

Congo2 Kazakhstan1 Panama1 Yemen2

Côte d’Ivoire Kenya Paraguay Zambia2

Cyprus Kuwait1 Peru1 ZimbabweEcuador

Notes: Countries are classified as commodity exporters if primary commodities constitute more than 50 percent of their exports.1 indicates that the country has a Sovereign Wealth Fund (SWF). The 20 countries which could not be included in the TFPand Physical Capital accumulation regressions due to unavailability of data are denoted by 2.

The CS-ARDL method requires a suffi cient number of time periods for consistent estima-

tion of country-specific coeffi cients. To ensure this, we include only countries in our sample

for which we have at least 25 consecutive annual observations on real GDP and CToT. Fur-

thermore, we only focus on countries that are commodity dependant– those for which the

ratio of primary commodities to total exports exceeds 50%. Subject to these requirements,

we end up with 69 countries in our sample– listed in Table 1. To identify which of these

8

DataI We define a country-specific measure of the CToT index as:

CToTiτ = ∏j

(PjτMUVτ

)Xij/ ∏

j

(PjτMUVτ

)Mij, (1)

where MUVτ is a manufacturing unit value index used as deflator, Xij (Mij ) isthe share of exports (imports) of commodity j in country i’s GDP, and Pjτ isthe individual commodity price in month τ.

I We construct this monthly index based on data (on the prices of 45 primarycommodities) obtained from the IMF IFS databases. Note that by construction,the movements in the CToT index are due to changes in commodity prices asthe export and import shares are taken to be constant over time (i.e. long-termaverages).

I The CToT index allows countries to be influenced by changes in commodityprices differently, depending on the composition of their export and importbaskets.

I This is in contrast to the "standard" commodity price indices most commonlyused in the literature, such as the "All Primary Commodities Index" in IMF IFS,which attaches the same weight to each country in the regression analysis.

Data

I Equation (1) is then used to construct two important variables. The first is anannual CToT growth series, gCToT ,it , which is calculated in two steps: (i)year-on-year growth rate of the monthly CToT index is taken, and (ii) theaverage over the year is calculated.

I The second is a measure of realized CToT volatility for year t , σCToT ,it , which isconstructed as the standard deviation of the year-on-year growth rates ofCToTiτ during months τ = 1, ..., 12 in year t .

I Therefore, in contrast to most studies in the growth literature which employtime-invariant measures of volatility, we construct a time-varying measure ofcommodity price volatility, σCToT ,it .

SWFs

I We end up with 29 countries with SWFs in our sample, with the majority ofthese SWFs (19) set-up using revenues from exports of crude oil and gas.

I A large portion (10 out of 29) are established in countries that are major oilexporters and are members of the OPEC.

I Note that it usually takes years (if not a decade) to set-up a SWF, as prior toinception, parliament needs to debate the architecture of the fund; for instance,the amount transferred to the SWF in each fiscal year and whether there areany provisions for withdrawing from the fund to finance public expenditure asopposed to just using interest income from the endowment for the budget.

I Nevertheless, the fact that a country has established a SWF provides a valuablesignal, as it indicates that the government and the legislature would like tomanage the resource wealth effi ciently and are concerned with (and willing todeal with) macroeconomic stabilization as well as intergenerational equity.

countries have established SWFs, we use information from the Sovereign Wealth Fund In-

stitute and end up with 29 countries with SWFs in our sample denoted by 1 in Table 1.

Listing the origin of accumulated funds by country in Table 2, we observe that the majority

of these SWFs (19 out of 29) were set-up using revenues from exports of crude oil and gas.

A large portion (10 out of 29) was established in countries that are major oil exporters and

are members of the Organization of the Petroleum Exporting Countries (OPEC), denoted

by ∗ in Table 2. We also provide the inception year of the funds in the same table. However,

note that it usually takes years (if not a decade) to set-up a SWF, as prior to inception,

parliament needs to debate the architecture of the fund; for instance, the amount transferred

to the SWF in each fiscal year and whether there are any provisions for withdrawing from

the fund to finance public expenditure as opposed to just using interest income from the

endowment for the budget. Nevertheless, the fact that a country has established a SWF

provides a valuable signal, as it indicates that the government and the legislature would like

to manage the resource wealth effi ciently and are concerned with (and willing to deal with)

macroeconomic stabilization as well as intergenerational equity.

Table 2: Sovereign Wealth Funds by Origin and Inception

Country Origin Inception Country Origin Inception

Algeria∗ Oil and Gas 2000 Mongolia Minerals 2011Angola∗ Oil 2012 New Zealand Non-Commodity 2003Australia Non-Commodity 2006 Nigeria∗ Oil 2012Azerbaijan Oil 1999 Norway Oil 1990Bahrain Oil 2006 Oman Oil and Gas 1980Bolivia Non-Commodity 2012 Panama Non-Commodity 2012Botswana Minerals 1994 Peru Non-Commodity 1999Brunei Darussalam Oil 1983 Qatar∗ Oil and Gas 2005Chile Copper 2006 Russia Oil 2008Gabon∗ Oil 1998 Saudi Arabia∗ Oil 1952Ghana Oil 2011 Senegal Non-Commodity 2012Indonesia Non-Commodity 2006 Trinidad and Tobago Oil and Gas 2000Iran∗ Oil and Gas 1999 United Arab Emirates∗ Oil 1976Kazakhstan Oil 2000 Venezuela∗ Oil 1998Kuwait∗ Oil 1953

Notes: Some countries have more than one fund, here we have taken the inception year to be that of the first fund, whichtends to be the main one. ∗ indicates that the country is a member of the Organization of the Petroleum Exporting Countries(OPEC). Source: Sovereign Wealth Fund Institute.

Figure 1 plots a simple bivariate relationship between real GDP per capita growth and

its volatility (measured by its standard deviation over the full sample, 1981-2014), and shows

that there exists a negative relationship between the two variables. The observation that

9

higher volatility in output dampens growth was in fact discussed extensively in the seminal

paper of Ramey and Ramey (1995). Moreover, we note that in our sample of 69 commodity-

dependant countries, there appears to be a positive association between CToT volatility and

GDP growth volatility– which in turn has a negative effect on output growth.

Figure 1: Scatter Plots of GDP Growth and Volatility of CToT against Volatilityof GDP Growth, 1981-2014




































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Figure 2 depicts a simple bivariate relationship between CToT volatility and the growth

of real GDP per capita (as well as the growth rate of TFP and physical capital accumulation)

in our sample of 69 countries over the period 1981-2014. They suggest that CToT volatility

exerts a negative impact on economic growth operating mainly through lower productivity

growth and lower physical capital accumulation. The rest of the paper use the CS-ARDL

approach for estimation in order to investigate whether the above indicative results continue

to hold up once we deal with, for instance, possible endogeneity problems, dynamics, cross-

country heterogeneity and cross-sectional dependence. We will also investigate the potential

role of SWFs and institutions in dampening the negative effects of CToT volatility on growth.

10

Figure 2: Scatter Plots of CToT Volatility against Real GDP growth, TFPGrowth and Capital Accumulation, 1981-2014




































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3.1 The Long-Run Effects of Volatility

To examine the long-run effects of CToT volatility on output growth, we estimate the fol-

lowing panel CS-ARDL model:

∆yit = c∗yi +

p∑l=1

φil∆yi,t−l +

p∑l=0

β′

ilxi.t−l +

q∑l=0

ail∆yt−l +

q∑l=0

b′


where ∆yit is the growth rate of real GDP per capita for country i and year t, xit is a 2× 1

vector of explanatory variables, namely the growth rate of the CToT index, gCToT,it, and

its volatility, σCToT,it. The terms ∆yt and xt denote the simple cross-section averages of

∆yit and xit in year t. Moreover, to determine the channel(s) through which GDP growth

is negatively affected by CToT volatility in our sample, we follow Beck et al. (2000) in

investigating two possible sources which are acknowledged in the literature, namely, TFP

and physical capital investment. We therefore also estimate the following regressions:

∆wit = c∗wi +

p∑l=1

φil∆wi,t−l +

p∑l=0

β′

ilxi.t−l +

q∑l=0

ail∆wt−l +

q∑l=0

b′


where ∆wit = lnWit − lnWit−1 is the growth rate of Wit = {TFP or physical capital percapita for country i and time t}, while ∆wt is the simple cross-sectional average of ∆wit,

with all other variables as defined in equation (11). However, data on TFP and physical

capital for some of the countries in our dataset were not available, and so these regressions

are based on a sample of 49 countries (those which are not denoted by 2 in Table 1).

11

The Econometric Model and MethodologyI When panels of data are available, there exist a number of alternativeestimation methods that vary on the extent to which they account forparameter heterogeneity.

I At one extreme is the Mean Group (MG) approach in which separate equationsare estimated for each country and the average of estimated coeffi cients acrosscountries is examined.

I At the other extreme are the traditional estimators in which dynamics aresimply pooled and treated as homogeneous, i.e. FE, RE, and GMM.

I In between the two extremes is the pooled mean group (PMG) estimator ofPesaran and Shin (1999), which is an intermediate case between the averagingand pooling methods of estimation, and involves aspects of both.

I It restricts the long-run coeffi cients to be homogenous over the cross-sections,but allows for heterogeneity in intercepts, short-run coeffi cients (including thespeed of adjustment) and error variances.

The Econometric Model and Methodology

I We employ the Cross-Sectionally augmented Autoregressive Distributive Lag(CS-ARDL) approach for estimation to account for (i) joint endogeneity ofexplanatory variables, (ii) cross-country heterogeneity, and (iii) cross-sectionaldependence.

I Accounting for these factors is particularly important in our panel data analysisas the effect of commodity price volatility on growth varies across cross-sectionunits and depends critically on country-specific factors (such as quality ofinstitutions, level of economic and financial development, strength of publicfinancial management frameworks, and type of stabilization buffers) as well asfeedback effects from determinants of GDP growth.

I Moreover, controlling for observed characteristics specific to countries aloneneed not ensure error cross-section independence. Neglecting such dependenciescan lead to biased estimates and spurious inference, particularly given the rapidincrease in globalization and exposures to global shocks.

The Econometric Model and MethodologyTo examine the long-run effects of CToT volatility on output growth, we estimate thefollowing panel CS-ARDL model:

∆yit = c ∗yi +p

∑l=1

φil∆yi ,t−l +p

∑l=0

β′ilxi .t−l +

q

∑l=0ail∆y t−l +

q

∑l=0b′ilxt−l + εit , (2)

where ∆yit is the growth rate of real GDP per capita for country i and year t , xit is a2× 1 vector of explanatory variables, namely the growth rate of the CToT index,gCToT ,it , and its volatility, σCToT ,it . The terms ∆y t and xt denote the simplecross-section averages of ∆yit and xit in year t .

Moreover, to determine the channel(s) through which GDP growth is negativelyaffected by CToT volatility in our sample, we investigate two possible sources whichare acknowledged in the literature, namely, TFP and physical capital investment. Wetherefore also estimate the following regressions:

∆wit = c ∗wi +p

∑l=1

φil∆wi ,t−l +p

∑l=0

β′ilxi .t−l +

q

∑l=0ail∆w t−l +

q

∑l=0b′ilxt−l + εit , (3)

where ∆wit = lnW it − lnW it−1 is the growth rate of W it = {TFP or physical capitalper capita for country i and time t}, while ∆w t is the simple cross-sectional averageof ∆wit , with all other variables as defined in equation (2).

Gylfason and Zoega (2006) and Esfahani et al. (2014) among others.

A possible explanation for this finding is that economic agents tend to save less in com-

modity abundant countries because they perceive the revenues from primary commodity

exports to be a permanent stream of future income. Another possibility is that the uncer-

tainty arising from commodity price volatility might suppress the accumulation of physical

capital by risk averse investors. Moreover, as noted by Catão et al. (2009), terms of trade

volatility adversely affects capital accumulation and growth by raising the country’s default

risk, hence widening the country spreads, and lowering its borrowing capacity.

Table 3: Estimates of the Long-Run Effects on Real GDP, TFP and PhysicalCapital Growth (1981-2014)

Dependant variableis the growth rate of: Real GDP per capita Total Factor Productivity Physical CapitalLags 1 2 3 1 2 3 1 2 3

(a) Baseline Regressions

CToT Growth 0.0015† 0.0028‡ 0.0041‡ 0.0023† 0.0033‡ 0.0041‡ -0.0009 0.0010 0.0022†(0.0008) (0.0009) (0.0010) (0.0010) (0.0011) (0.0011) (0.0007) (0.0009) (0.0010)

CToT Volatility -0.0021 -0.0020 -0.0047† -0.0054† -0.0085‡ -0.0124‡ -0.0044† -0.0045* -0.0079‡(0.0017) (0.0018) (0.0021) (0.0026) (0.0025) (0.0020) (0.0022) (0.0026) (0.0028)

CD Test Statistic -2.30 -1.57 -0.34 -0.46 0.17 0.37 -2.01 -1.34 -0.20No Countries 69 69 69 49 49 49 49 49 49No Observations 2,218 2,149 2,080 1,577 1,528 1,479 1,577 1,528 1,479

(b) Regressions with the Interactive Sovereign Wealth Fund (SWF) Dummy

CToT Growth 0.0015* 0.0027‡ 0.0038‡ 0.0022† 0.0033‡ 0.0041‡ -0.0009 0.0011 0.0023†(0.0008) (0.0009) (0.0010) (0.0010) (0.0011) (0.0011) (0.0007) (0.0009) (0.0010)

CToT Volatility -0.0039* -0.0043* -0.0067† -0.0061* -0.0078† -0.0113‡ -0.0057† -0.0075† -0.0117‡(0.0023) (0.0024) (0.0028) (0.0033) (0.0032) (0.0025) (0.0026) (0.0031) (0.0036)

Interactive SWF Dummy 0.0041 0.0064* 0.0073* 0.0022 -0.0025 -0.0047 0.0060 0.0112† 0.0110†(0.0033) (0.0034) (0.0038) (0.0052) (0.0051) (0.0045) (0.0048) (0.0051) (0.0051)


Notes: The CS-ARDL specifications are given by equations 11 and 12. Symbols ‡, †, and * denote significance at 1%, 5%, and10% levels, respectively. CD is the cross-section dependence (CD) test of Pesaran (2004, 2015).

To determine the overall effect of changes in CToT growth and its volatility, we calculate

the average percentage effect of the two CToT variables on output per capita and TFP

growth rates as well as on the change in capital accumulation. We use the estimates based

on p = 3 in panel (a) of Table 3 together with the average realized CToT growth (0.3

percent per year) and volatility (2.3 percent per year) across the 69 countries, and obtain an

overall negative effect of between 0.01 and 0.03 percentage points per year (corresponding

13

Empirical Results

I Once we allow for long enough lags, to fully account for short-run dynamics,the results suggest that economic growth is adversely linked to commodity pricevolatility in the long-term.

I Overall, while commodity price booms significantly increase economic growth,volatility affects it negatively.

I This finding can be partly explained by the fact that fiscal and current accountbalances of commodity-exporting countries are affected by swings in resourcesrevenues with destabilizing effects on the macroeconomy.

I Note that the positive growth effect of gCToT provides evidence against thetraditional resource curse hypothesis, which argues that it is the level ofresource abundance that affects economic growth negatively, and is in line withresults obtained recently in the literature; see, for instance, Alexeev and Conrad(2009), Cavalcanti et al. (2011b, 2011a, 2015), El-Anshasy et al. (2015), andEsfahani et al. (2013).

Empirical Results

I The results also indicate that both commodity terms of trade growth andvolatility have significant effects on TFP and physical capital accumulation forcommodity abundant countries, with the coeffi cient of σCToT ,it being negativeand significant across all lag orders.

I The negative association between CToT volatility and TFP growth lends itselfto the argument that natural resource abundant countries have fewerpossibilities for technological progress.

I Moreover, while a commodity price boom increases the physical capital stock,higher volatility of commodity prices significantly reduces it. Therefore, capitalaccumulation seems to be another important channel through which volatilityaffects GDP per capita growth; which is in line with what is argued in Gylfasonand Zoega (2006) and Esfahani et al. (2014) among others.

Empirical Results

I A possible explanation for this finding is that economic agents tend to save lessin commodity abundant countries because they perceive the revenues fromprimary commodity exports to be a permanent stream of future income.

I Another possibility is that the uncertainty arising from commodity price volatilitymight suppress the accumulation of physical capital by risk averse investors.

I Moreover, as noted by Catão et al. (2009), terms of trade volatility adverselyaffects capital accumulation and growth by raising the country’s default risk,hence widening the country spreads, and lowering its borrowing capacity.

Empirical Results

I As we expect the long-run growth effects of CToT volatility for primarycommodity exporters to be different from those countries that are notdependant on a handful of primary products, we run the same regressions as in(2) but for a sample of 61 countries that have a more diversified export basket.

I The results for these 61 countries show that CToT volatility is not significantlyrelated to economic growth in the long-run.

I This is mainly because these countries have a more diversified basket of exports,especially manufacturing or service-sector goods, and so they are expected togrow faster and be better insured against price fluctuations in individualcommodities.

The Role of SWFs and Institutional Quality

I While many SWFs have existed for over half a century (such as the KuwaitInvestment Authority which was founded in 1953), a large number of funds havebeen established (by major commodity exporters in particular) over the last twodecades.

I These SWFs accumulated large assets during the most recent oil-price boom(2002—2008), have played a major role in reserve management of commodityrevenues, and contributed to macroeconomic stabilization in several cases.

I SWFs have been established for a variety of reasons, ranging from fiscalstabilization (that is to help smooth the impact on government spending ofrevenues that are large and volatile), to long-term saving for future needs of theeconomy, or of specific groups such as pensioners, or for future generations.

I One of the main short-term objectives of SWFs is to counter the adversemacroeconomic effects of commodity price volatility.

The Role of SWFs and Institutional QualityI We identify 29 countries in our sample as having established SWFs. 19 of theseare funded by revenue from exports of crude oil and gas, of which ten aremembers of the Organization of the Petroleum Exporting Countries (OPEC),and seven are located in the Persian Gulf.

I It is estimated by the Sovereign Wealth Fund Institute that in late 2016 thetotal assets of SWFs were around $7.5 trillion with over 60% of these beingfunded by oil and gas exports.

I The prominent examples are Norway’s Government Pension Fund ($830), AbuDhabi Investment Authority ($773), Saudi Arabia’s Fund (SAMA) ($685),Kuwait Investment Authority ($592), and Qatar Investment Authority ($256),with the number in brackets referring to their market values in billions in June2015.

I Note that given the objective of these funds, on average 65% of the SWF assetsare held in public and private equities (61% Norway; 72% SAMA; 65% Kuwait;68% Qatar; 62% Abu Dhabi—figures based on 2014). See Mohaddes andPesaran (2017) for more details.

Gylfason and Zoega (2006) and Esfahani et al. (2014) among others.

A possible explanation for this finding is that economic agents tend to save less in com-

modity abundant countries because they perceive the revenues from primary commodity

exports to be a permanent stream of future income. Another possibility is that the uncer-

tainty arising from commodity price volatility might suppress the accumulation of physical

capital by risk averse investors. Moreover, as noted by Catão et al. (2009), terms of trade

volatility adversely affects capital accumulation and growth by raising the country’s default

risk, hence widening the country spreads, and lowering its borrowing capacity.

Table 3: Estimates of the Long-Run Effects on Real GDP, TFP and PhysicalCapital Growth (1981-2014)

Dependant variableis the growth rate of: Real GDP per capita Total Factor Productivity Physical CapitalLags 1 2 3 1 2 3 1 2 3

(a) Baseline Regressions

CToT Growth 0.0015† 0.0028‡ 0.0041‡ 0.0023† 0.0033‡ 0.0041‡ -0.0009 0.0010 0.0022†(0.0008) (0.0009) (0.0010) (0.0010) (0.0011) (0.0011) (0.0007) (0.0009) (0.0010)

CToT Volatility -0.0021 -0.0020 -0.0047† -0.0054† -0.0085‡ -0.0124‡ -0.0044† -0.0045* -0.0079‡(0.0017) (0.0018) (0.0021) (0.0026) (0.0025) (0.0020) (0.0022) (0.0026) (0.0028)


(b) Regressions with the Interactive Sovereign Wealth Fund (SWF) Dummy

CToT Growth 0.0015* 0.0027‡ 0.0038‡ 0.0022† 0.0033‡ 0.0041‡ -0.0009 0.0011 0.0023†(0.0008) (0.0009) (0.0010) (0.0010) (0.0011) (0.0011) (0.0007) (0.0009) (0.0010)

CToT Volatility -0.0039* -0.0043* -0.0067† -0.0061* -0.0078† -0.0113‡ -0.0057† -0.0075† -0.0117‡(0.0023) (0.0024) (0.0028) (0.0033) (0.0032) (0.0025) (0.0026) (0.0031) (0.0036)

Interactive SWF Dummy 0.0041 0.0064* 0.0073* 0.0022 -0.0025 -0.0047 0.0060 0.0112† 0.0110†(0.0033) (0.0034) (0.0038) (0.0052) (0.0051) (0.0045) (0.0048) (0.0051) (0.0051)


Notes: The CS-ARDL specifications are given by equations 11 and 12. Symbols ‡, †, and * denote significance at 1%, 5%, and10% levels, respectively. CD is the cross-section dependence (CD) test of Pesaran (2004, 2015).

To determine the overall effect of changes in CToT growth and its volatility, we calculate

the average percentage effect of the two CToT variables on output per capita and TFP

growth rates as well as on the change in capital accumulation. We use the estimates based

on p = 3 in panel (a) of Table 3 together with the average realized CToT growth (0.3

percent per year) and volatility (2.3 percent per year) across the 69 countries, and obtain an

overall negative effect of between 0.01 and 0.03 percentage points per year (corresponding

13


I As before, the long-run effects of σCToT ,it is negative for real GDP per capitagrowth and the channels of impact are lower TFP and physical capitalaccumulation. Note also that the coeffi cient of CToT volatility is negative andstatistically significant for all lag orders.

I More importantly, the estimated coeffi cient of the interactive SWF dummy ispositive and statistically significant in the first and third blocs.

I In other words, countries that have a SWF in our sample have, on average,performed better when it comes to mitigating the negative growth effects ofCToT volatility and managed to sustain a higher level of capital accumulationin the face of the extreme volatility in resource revenues.

I Our results, therefore, suggests that one is better able to dampen the negativelong-run growth effects of CToT volatility with a well-functioning SWF that caneffectively deal with the adverse effects of (excess) commodity pricevolatility– add to the fund when commodity prices are high and transfer less toit or even withdraw from it when prices are low to smooth expenditure.


I For instance, oil exporters in the Persian Gulf, enjoyed a large increase in theirSWFs assets while oil prices were high for most of the past decade, but morerecently many of them have dipped into their SWFs following the collapse in oilprices since 2014.

I Rather than cutting back on public expenditure (social welfare programs, publicsalaries, and infrastructure spending), many governments either withdrew moneyfrom their funds (such as Russia and Saudi Arabia) or alternatively transferredless revenue to these funds.

I To give a concrete example, since 1976 the Kuwaiti government has by lawtransferred a minimum of 10 percent of all state revenues to the FutureGeneration Fund (FGF). However, with oil prices having been high for almost adecade it was announced in March 2013, following an Amir budgetary decree,that the minimum contribution is to be increased to 25 percent. But thefollowing year oil prices fell sharply and remained low, and so the decision wasreversed and the contribution to the FGF was cut back to 10 percent from fiscalyear 2015/16.

The Role of SWFs and Institutional QualityWe next check the robustness of our results to the definition of SWF and re-estimateour growth regressions excluding the seven countries whose SWFs are mainly fundedby non-commodity revenues (Australia, Bolivia, Indonesia, New Zealand, Panama,Peru, and Senegal).

Russia and Saudi Arabia) or alternatively transferred less revenue to these funds. To give

a concrete example, since 1976 the Kuwaiti government has by law transferred a minimum

of 10 percent of all state revenues to the Future Generation Fund (FGF). However, with oil

prices having been high for almost a decade it was announced in March 2013, following an

Amir budgetary decree, that the minimum contribution is to be increased to 25 percent. But

the following year oil prices fell sharply and remained low, and so the decision was reversed

and the contribution to the FGF was cut back to 10 percent from fiscal year 2015/16.

We next check the robustness of our results to the definition of SWF and re-estimate

our growth regressions (11) excluding the seven countries whose SWFs are mainly funded by

non-commodity revenues (Australia, Bolivia, Indonesia, New Zealand, Panama, Peru, and

Senegal). The results are reported in Table 4 and echo those in Table 3: while the coeffi cient

on volatility is negative and statistically significant across all lag orders, the coeffi cient of

the interactive dummy is always positive and significant.

Table 4: Estimates of the Long-Run Effects on Real GDP Growth when Consid-ering Institutions and Different SWF Groupings, (1981-2014)

Excluding Non-Commodity SWFs Role of InstitutionsFull sample (69 Countries) 29 Countries with SWF

Lags 1 2 3 1 2 3

CToT Growth 0.0014* 0.0025‡ 0.0035‡ 0.0025† 0.0020 0.0026*(0.0008) (0.0009) (0.0010) (0.0012) (0.0013) (0.0014)

CToT Volatility -0.0056† -0.0069‡ -0.0124‡ -0.0283* -0.0364† -0.0527‡(0.0023) (0.0024) (0.0027) (0.0161) (0.0169) (0.0167)

Interactive SWF Dummy 0.0080† 0.0127‡ 0.0167‡ - - -(0.0033) (0.0035) (0.0038)

Interactive Institutional Term - - - 0.0004* 0.0005† 0.0007‡(0.0002) (0.0002) (0.0002)

CD Test Statistic -2.49 -1.82 -0.53 1.06 0.54 -0.06No Countries 69 69 69 29 29 29No Observations 2,218 2,149 2,080 927 898 869

Notes: The dependant variable is the growth of real GDP per capita. See also notes to Table 3.

Our results are in line with a number of recent papers that investigate the role of

oil/stabilization funds in (i) reducing fiscal pro-cyclicality and (ii) smoothing government

consumption.11 Coutinho et al. (2013) argue that resource funds tend to dampen fiscal

pro-cyclicality. Sugawara (2014) shows that government expenditure volatility is lower in

11Note that there is a large literature investigating whether fiscal policy is pro-cyclical in emerging anddeveloping countries. See, for instance, Villafuerte and Lopez-Murphy (2010), Erbil (2011), and Céspedesand Velasco (2014), who focus on a sample of oil producing and commodity abundant countries and arguethat overall fiscal policy has been pro-cyclical in these countries.

16


I Our results are in line with a number of recent papers that investigate the roleof oil/stabilization funds in (i) reducing fiscal pro-cyclicality and (ii) smoothinggovernment consumption:

I Coutinho et al. (2013) argue that resource funds tend to dampen fiscalpro-cyclicality.

I Sugawara (2014) shows that government expenditure volatility is lower incountries with stabilization funds.

I Koh (2016) illustrates that fiscal policy becomes more counter-cyclical after theestablishment of oil funds, and that these funds are typically associated withsmoother government consumption.

I Moreover, Shabsigh and Ilahi (2007) argue that oil funds help reducemacroeconomic volatility in oil exporting countries, more specifically, thevolatility of broad money, prices, and (to some extent) the real exchange rate.


Given the large heterogeneity within the 29 SWF countries in our sample, a follow-upquestion is the potential role of institutions and policy frameworks, and in particularfiscal policy, in dampening the negative effect of CToT volatility.

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AlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeriaAlgeria


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Source: K. Mohaddes, J.B. Nugent, and H. Selim (2019), Institutions andMacroeconomic Policies in Resource-Rich Arab Economies, Oxford University Press.

Scatter Plots of Institutional Quality against Fiscal PolicyVolatility, 1961-2013

USAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSAUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUKUK


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OmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanOmanQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarQatarSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudiSaudi


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RussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussiaRussia

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4050

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Oil Producers

Source: K. Mohaddes, J.B. Nugent, and H. Selim (2019), Institutions andMacroeconomic Policies in Resource-Rich Arab Economies, Oxford University Press.

Notes: This volatility is interpreted as the component of discretionary policy which isnot related to smoothing the business cycle, such as changes in political preferences orthe decision by the politicians to generate a short-term boom so as to keep thepopulation happy—as was seen in the GCC following the Arab Spring.

Russia and Saudi Arabia) or alternatively transferred less revenue to these funds. To give

a concrete example, since 1976 the Kuwaiti government has by law transferred a minimum

of 10 percent of all state revenues to the Future Generation Fund (FGF). However, with oil

prices having been high for almost a decade it was announced in March 2013, following an

Amir budgetary decree, that the minimum contribution is to be increased to 25 percent. But

the following year oil prices fell sharply and remained low, and so the decision was reversed

and the contribution to the FGF was cut back to 10 percent from fiscal year 2015/16.

We next check the robustness of our results to the definition of SWF and re-estimate

our growth regressions (11) excluding the seven countries whose SWFs are mainly funded by

non-commodity revenues (Australia, Bolivia, Indonesia, New Zealand, Panama, Peru, and

Senegal). The results are reported in Table 4 and echo those in Table 3: while the coeffi cient

on volatility is negative and statistically significant across all lag orders, the coeffi cient of

the interactive dummy is always positive and significant.

Table 4: Estimates of the Long-Run Effects on Real GDP Growth when Consid-ering Institutions and Different SWF Groupings, (1981-2014)

Excluding Non-Commodity SWFs Role of InstitutionsFull sample (69 Countries) 29 Countries with SWF

Lags 1 2 3 1 2 3

CToT Growth 0.0014* 0.0025‡ 0.0035‡ 0.0025† 0.0020 0.0026*(0.0008) (0.0009) (0.0010) (0.0012) (0.0013) (0.0014)

CToT Volatility -0.0056† -0.0069‡ -0.0124‡ -0.0283* -0.0364† -0.0527‡(0.0023) (0.0024) (0.0027) (0.0161) (0.0169) (0.0167)

Interactive SWF Dummy 0.0080† 0.0127‡ 0.0167‡ - - -(0.0033) (0.0035) (0.0038)

Interactive Institutional Term - - - 0.0004* 0.0005† 0.0007‡(0.0002) (0.0002) (0.0002)

CD Test Statistic -2.49 -1.82 -0.53 1.06 0.54 -0.06No Countries 69 69 69 29 29 29No Observations 2,218 2,149 2,080 927 898 869

Notes: The dependant variable is the growth of real GDP per capita. See also notes to Table 3.

Our results are in line with a number of recent papers that investigate the role of

oil/stabilization funds in (i) reducing fiscal pro-cyclicality and (ii) smoothing government

consumption.11 Coutinho et al. (2013) argue that resource funds tend to dampen fiscal

pro-cyclicality. Sugawara (2014) shows that government expenditure volatility is lower in

11Note that there is a large literature investigating whether fiscal policy is pro-cyclical in emerging anddeveloping countries. See, for instance, Villafuerte and Lopez-Murphy (2010), Erbil (2011), and Céspedesand Velasco (2014), who focus on a sample of oil producing and commodity abundant countries and arguethat overall fiscal policy has been pro-cyclical in these countries.

16


I The results, not surprisingly, illustrate that within the SWF sample, countrieswith stronger institutions, have been better able to mitigate the negative growtheffects of CToT volatility.

I These results are in line with Frankel et al. (2013), who argue that the betterinstitutions in developing countries are, the more likely they are to pursue lessprocyclical or more countercyclical fiscal policy, as well as Sugawara (2014) whoshows that the two significant factors in reducing government expenditurevolatility are stronger institutions and fiscal rules.

I Overall, our results suggest that while volatility represents a fundamentalbarrier to economic prosperity, the establishment of SWFs, as well asappropriate institutions, can help mitigate the negative effects.

I Therefore, creating a mechanism of short-term management of commodity pricevolatility through stabilization funds should be a priority for commoditydependant countries, complemented by well-functioning public financialmanagement systems.

Policy ImplicationsI The undesirable consequences of commodity price volatility can be avoided ifresource-rich countries are able to improve the management of volatility inresource income by setting up forward-looking institutions such as SovereignWealth Funds, or adopting short-term mechanisms such as stabilization fundswith the aim of saving when commodity prices are high and spendingaccumulated revenues when prices are low.

I The government can also intervene in the economy by increasing public capitalexpenditure when private investment is low, using proceeds from thestabilization fund.

I Alternatively the government can use these funds to increase thecomplementarities of physical and human capital, such as improving the judicialsystem, property rights, and human capital. This would increase the returns oninvestment with positive effects on capital accumulation, TFP, and growth.

I Improving the functioning of financial markets is also a crucial step as thisallows firms and households to insure against shocks, decreasing uncertainty andtherefore mitigating the negative effects of volatility on investment andeconomic growth.

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