2 Introduction There has been a well documented massive decline in trade during the period of the financial crisis. This raised concerns in many circles (academic, policy makers, international organisations, media) regarding a return to protectionism –e.g. in part a key motivation for the establishment of the Global Trade Alerts...and prompted discussion and an emerging literature on the driving forces of the decline in trade CARIS
3 Possible reasons for the decline The main hypotheses put forward for the decline (eg. Baldwin & Evenett, 2009; OECD, 2010) are: 1.The decline in demand arising from the impact of the crisis on GDP / production 2.Difficulties in access to credit, and the price of credit (hence the various policies that were put into place by governments with regard to export credit guarantee schemes) 3.A rise in protectionism 4.The role of vertical specialisation and integrated supply chains which magnifies the effect of any decline in demand. CARIS
4 Aim of paper The aim of this work is to explore (some) of these hypotheses in more detail In particular to examine the role of demand and the role of access to credit / price of credit Also to see if there is any evidence that the impact is different according to either: –If the partner countries are members of a regional trade agreement –By the level of development of the countries concerned Methodology: gravity modelling BUT on the basis of monthly trade flows over three year: the period Jan 07 to Dec 09. CARIS
Ln(Imports) = α + β 1 ln(IPrep) + β 2 ln(IPptn) + β 3 ln(distance) + β 4 (border) + β 5 (landlocked) + β 6 (commoncolony) + β 7 (commonlanguage) + β 8 crisis + β 9 (crisisworsening)+ β 10 (crisisend) + β 11 (RTA) + β 12 (N) + u. Key variables are: –Monthly industrial production –crisis dummies Crisis: begins Aug 07 Crisis worsening: begins Oct 08 Crisis end: begins Aug 09 –LIBOR-OIS spread –Membership of an RTA –level of development (NN, NS, SS): N = high income; S = middle and low income (using World Bank definition) CARIS6 Model specification
Monthly bilateral trade data (IMF DOTS) countries = OECD + Armenia, Bangladesh, Barbados, India, Lithuania, Malaysia, Slovak Republic, Tunisia Limited to those reporting a monthly output variable: 36 month period: Jan 2007- Dec 2009 Standard gravity variables: Distance - great circle (A.Rose), common colony, border (0-1) dummy, landlocked (0-1) dummy, common language (0-1) dummy CARIS7 Description of Data
Pooled regression: eg. is there any evidence that trade is higher or lower on average between RTA members? Panel regressions: what evidence is there for the impact of the crisis on the change in trade and was different for RTA members? Peak-Trough: compares the peak and trough months for trade for each country and again looks at what might explain this. CARIS8 Questions addressed by the empirics?
In aggregate trade between RTA members does NOT seem to be higher, if anything is up to 8% lower However if you break this down, trade between high income RTA partners is up to 13% higher...... While trade between high income and other countries who are members of an RTA is up to 19% lower in comparison to the rest of the sample CARIS9 Is there any evidence that trade is higher or lower on average between RTA members ?
10 What evidence is there for the impact of the crisis on trade Look at change in demand + change in access to credit + role of RTAs Issue of how to measure the issue of credit. Two possibilities –Crisis dummies –Libor-OIS spread Libor = average cost of lending from 16 banks.The Libor-OIS (overnight index swap rate) spread accounts for credit risk hence reflects banks willingness to lend. CARIS
12 What evidence is there for the impact of the crisis on trade The activity variables suggest a 10% decline (in partners) demand leads to a 4%-6% decline in trade. The rise in credit risk (measured by the LIBOR- OIS spread) is associated with up to a 12% fall in trade, –With a slightly bigger impact 13.6% for RTA members on average –And between high income RTA partners by up to 20%. No evidence of a differential impact between high, middle or low income countries. CARIS
13 And comparing the peak and trough months? The change in partner country activity levels impacted on trade by up to just under 4% The decline in trade was up to 20% higher for those countries in an RTA, and up to 80% higher for high income countries in an RTA. Change in credit risk impacted on trade by up to just over 24%. CARIS
14 Summary Fall in demand certainly accounts for a significant decline in trade. Rise in credit risk also appears to have had a big impact on trade RTA membership, and in particular for high- income countries did not appear to shield members from the fall – if anything the reverse. –Role of vertical specialisation? Similarly the impact of increased credit risk on trade appears to fall more on high income RTA countries CARIS
17 Summary Note constrained nature of sample therefore results may be dominated by EU effects so need to be careful in drawing conclusions Policy points to the importance of aggregate demand measures + access to trade finance (+ resisting protection) + the need to understand better why RTA members see a bigger decline and in particular the role of supply chain integration in this. CARIS