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Board of Governors of the Federal Reserve System
International Finance Discussion Papers
Number 1017, March 2011 --- Screen Reader Version*

Where are Global and U.S. Trade Heading in the Aftermath of the Trade Collapse: Issues and Alternative Scenarios

Joseph W. Gruber⁺, Filippo di Mauro⁺⁺, Bernd Schnatz⁺⁺, and Nico Zorell^++**

NOTE: International Finance Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to International Finance Discussion Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors. Recent IFDPs are available on the Web at http://www.federalreserve.gov/pubs/ifdp/. This paper can be downloaded without charge from the Social Science Research Network electronic library at http://www.ssrn.com/.

Abstract:

Global and U.S. trade declined dramatically in the wake of the global financial crisis in late 2008 and early 2009. The subsequent recovery in trade, while vigorous at first, gradually lost momentum in 2010. Against this backdrop, this paper explores the prospects for global and U.S. trade in the medium term. We develop a unified empirical framework � an error correction model � that exploits the cointegrating relationship between trade and economic activity. The model allows us to juxtapose several scenarios with different assumptions about the strength of GDP growth going forward and the relationship between trade and economic activity. Our analysis suggests that during the crisis both world trade and U.S. exports declined significantly more than would have been expected on the basis of historical relationships with economic activity. Moreover, this gap between actual and equilibrium trade is closing only slowly and could persist for some time to come.

Keywords: International trade, error correction model, great recession

JEL classification: E66, F17

1.  Introduction

The severity and the synchronization of the collapse in world trade during the global financial crisis surprised most forecasters. All standard reference models for trade, which performed reasonably well in explaining trends in international trade prior to the crisis, clearly missed the magnitude of this downturn. After reaching a trough in the first half of 2009, trade also staged a forceful recovery, surprising again forecasters, though this time on the upside. Going forward, the medium-term trajectory for trade crucially depends on plausible assumptions about the long-term implications of the crisis; particularly to what extent the observed trade collapse is having transitory or permanent repercussions for future trends.

This paper provides a structured discussion on where global and U.S. trade may be heading in the aftermath of the most severe financial crisis in post-war history. Crucially, we use a unified empirical framework for both global and U.S. tradethroughout the paper. Setting the scene, we present a stable long-run relationship between trade and GDP, accounting for globalization trends and - in the model for the U.S. - the real exchange rate. In contrast to other studies, we find that world trade responds proportionally to movements in global GDP once globalization trends have been accounted for. Although our models fail to account for the full impact of the global crisis on trade, they can still be used as a framework for assessing trade prospects. Conditioning on IMF projections for global economic activity, we provide a range of scenarios for world trade and U.S. exports. In the most optimistic (baseline) case we assume that the global crisis constitutes only a transitory shock and trade returns to its pre-crisis trend over the medium term. To some extent, this would be consistent with the strong trade recovery recorded in the second half of 2009 and in early 2010.

However, in light of the deceleration in world trade growth in the second half of 2010, our baseline scenario appears to be too optimistic in the short and medium term. We therefore contrast this profile with several plausible scenarios. Starting with world trade, we first explore the consequences of a lower global GDP profile (Section 4.2), assuming that the global crisis might have longer-term repercussions on the world economy, e.g. in the form of prolonged balance-sheet restructuring. In the second set of scenarios (Section 4.3), we assume that the global financial crisis will have persistent effects on the relationship between trade and GDP, e.g. due to an increase in protectionism or other trade-specific frictions, including those related to impaired credit or higher uncertainty. The resulting profile of world trade is fairly close to that projected by most professional forecasters, including the IMF. We also try to disentangle how financial distress alone could impact world trade.

Clearly, some of the factors shaping the future path of world trade will also affect U.S. trade, and vice versa. Therefore, we also examine how U.S. exports would be affected by a crisis-induced change in the relationship between trade and foreign demand (Section 5.1), analogous to our analysis on global trade. Looking at U.S. exports also allows us assess the recent performance of trade across disaggregated regions and commodities. The results for the U.S. imply that U.S. exports remain (as of the third quarter of 2010) depressed relative to their long-run equilibrium, with weakness most apparent in exports to the advanced economies and in the capital goods and automotive product categories. The results also highlight the importance of any assumption regarding the persistence of the residual weakness of U.S. exports to projecting the path of exports in the short to medium run.

The remainder of this paper is organized as follows: The next section presents some stylized facts reviewing developments in global and U.S. trade over the past years and their relationship to economic activity. Thereafter, we provide our benchmark econometric specification for global trade and discuss several plausible scenarios. The last section turns to U.S. trade, presenting an export equation for the U.S. and discussing the implications of the scenarios for U.S. exports.

2.  Where We Stand: Stylized Facts on the Great Trade Collapse and the Subsequent Recovery

Historically, declines in global trade have been rather exceptional events.¹ All the more striking is the severity of the contraction in global trade during the period 2008-09: From peak to trough, world merchandize trade volumes contracted by 19 percent, thereby returning to their 2005 levels (see Chart 1). Moreover, the decline in international trade was truly global in scope and highly synchronized across countries.² Having expanded in the overwhelming majority of countries up to September 2008, exports declined precipitously worldwide as the turmoil on the global financial markets intensified. By January 2009 nine in ten countries recorded a contraction in exports.

As regards the geographical dispersion of the trade collapse in 2008-09, the contraction was of comparable magnitude in the advanced and emerging economies on the import side, whereas, on the export side, the advanced economies were more severely hit than the emerging economies (see Table A.1 in the appendix). The subsequent recovery that started in mid-2009 was significantly faster in the emerging economies on both the import and the export side. In September 2010, the latest available observation, the imports of the emerging economies stood 35 percent above the trough levels of early 2009 and 7 percent above the pre-crisis peak. Particularly striking import growth was recorded in placeAsia and placeLatin America. Similarly, the exports of the emerging economies were 27 percent and 6 percent higher in September 2010 than the trough and peak levels, respectively. Thus, the recovery has so far clearly been driven by the emerging economies. Imports and exports of the advanced economies, by contrast, were still well below the respective pre-crisis peak.

The collapse in world trade significantly exceeded the contraction in global GDP, resulting in a decrease in the trade-to-GDP ratio of about 15 percentage points. While it is well known that trade flows are generally more volatile than overall economic activity (and positively correlated with it), the decline in global trade in the fourth quarter of 2008 and the first quarter of 2009 are clear outliers (see Chart 2). Therefore, it is not surprising that standard quantitative trade models considerably underestimated the downturn in world trade in 2008-09. Interestingly, though, the observations from the second quarter of 2009 onwards were again roughly in line with historical relationships.

Chart 1. World GDP, World Merchandize Trade and U.S. Exports of Goods (indicies, 2008Q3=100, log scale)

Sources: BEA and own calculations.
Note: U.S. exports are defined as total U.S. exports less enduse code 213 (computers).

Data for Chart 1

	World GDP	World trade	U.S. exports
Mar-80			83.64720611
Jun-80	80.94402088		83.82828805
Sep-80	81.05260977		83.45651591
Dec-80	81.26684603		83.3136008
Mar-81	81.47264685	66.70312164	83.44231644
Jun-81	81.56071708	66.99752367	83.37605111
Sep-81	81.70000474	67.15883551	82.85238339
Dec-81	81.72584641	67.31483255	83.06177398
Mar-82	81.75805591	67.42383167	82.11780056
Jun-82	81.86491622	67.39675438	82.19822664
Sep-82	81.89421815	67.51601446	81.36272792
Dec-82	81.98269099	67.4510861	80.37747273
Mar-83	82.14434618	67.63936195	80.42981837
Jun-83	82.36821471	68.02934158	80.36617351
Sep-83	82.58269649	68.40404238	80.44761274
Dec-83	82.82902425	68.81639211	80.89888587
Mar-84	83.07469839	69.41411287	80.71364945
Jun-84	83.25396527	69.6731482	80.90724749
Sep-84	83.44545755	69.98171698	81.19687834
Dec-84	83.59224897	70.15355958	81.57705148
Mar-85	83.77715242	70.28165196	81.30851675
Jun-85	83.97223835	70.50703796	81.22285932
Sep-85	84.1911203	70.72556807	81.0112981
Dec-85	84.3571283	70.97381681	81.38432576
Mar-86	84.4705216	71.11138639	81.34932713
Jun-86	84.63659638	71.56719236	81.42172143
Sep-86	84.78952063	71.90072628	81.53853843
Dec-86	84.9080702	72.04529834	82.06065618
Mar-87	84.9975815	72.2470966	81.77984074
Jun-87	85.2432707	72.62298828	82.27129549
Sep-87	85.44298462	73.08464124	83.02622072
Dec-87	85.70682712	73.49438649	83.65618859
Mar-88	85.87102337	73.54952198	84.52620545
Jun-88	86.03562238	73.86995488	84.89804316
Sep-88	86.23240071	74.33805509	85.01441606
Dec-88	86.45045395	74.75168704	85.61409667
Mar-89	86.68966624	75.10260269	85.94001194
Jun-89	86.83945719	75.49570486	86.66017026
Sep-89	87.0260793	75.59823702	86.57650999
Dec-89	87.21843717	76.04211166	86.65232896
Mar-90	87.41024145	76.47739871	87.17639411
Jun-90	87.54696824	76.67651201	87.27072881
Sep-90	87.70825424	76.93849523	87.26472446
Dec-90	87.7493612	77.31719344	87.31291997
Mar-91	87.7989268	77.50453934	87.58981624
Jun-91	87.9706104	77.84430945	87.84888735
Sep-91	88.08454405	78.22185736	88.20077478
Dec-91	88.22284819	78.37427072	88.37601395
Mar-92	88.42128157	78.76308397	88.65146195
Jun-92	88.49779493	78.91240312	88.49801813
Sep-92	88.59894493	79.02155292	88.8541406
Dec-92	88.6742677	79.23349301	88.95602596
Mar-93	88.7539125	79.20093183	88.81667783
Jun-93	88.85355902	79.40202006	88.87210574
Sep-93	88.95806308	79.68746035	88.6661212
Dec-93	89.10475407	79.98150118	89.28380899
Mar-94	89.27595421	80.39497613	89.14257648
Jun-94	89.42514732	80.93298157	89.58454457
Sep-94	89.59473097	81.36588672	90.26723686
Dec-94	89.76951415	81.95118987	90.45925622
Mar-95	89.91185537	82.31380607	90.63745556
Jun-95	90.03090703	82.77355215	90.72861304
Sep-95	90.20395331	83.05579328	91.100145
Dec-95	90.35368975	83.36674141	91.11420266
Mar-96	90.53951289	83.75614496	91.29319121
Jun-96	90.78058444	83.99744502	91.37479677
Sep-96	90.96092896	84.40432348	91.5958482
Dec-96	91.17790396	84.93403668	92.20745622
Mar-97	91.37038955	85.41945657	92.53274492
Jun-97	91.60172122	86.02613063	93.1201605
Sep-97	91.78797961	86.50302284	93.49657144
Dec-97	91.97195923	86.79888991	93.56437678
Mar-98	92.01769829	87.04059673	93.61280174
Jun-98	92.11174211	87.15088825	93.24461125
Sep-98	92.24500759	87.1606552	93.18539657
Dec-98	92.40750697	87.22076112	93.66713945
Mar-99	92.5899101	87.49143805	93.56589403
Jun-99	92.77876211	88.0006714	93.64688835
Sep-99	92.99506601	88.51570585	93.98793737
Dec-99	93.28621275	89.10495278	94.43295423
Mar-00	93.56681231	89.85519113	94.64428998
Jun-00	93.8061584	90.37278309	94.95283348
Sep-00	93.95143128	90.9308758	95.36325584
Dec-00	94.09217831	91.28603268	95.18620243
Mar-01	94.17217047	91.0760443	95.0635347
Jun-01	94.26898584	90.79338275	94.74831337
Sep-01	94.31756778	90.53572166	94.05653819
Dec-01	94.40539034	90.37745315	93.68263513
Mar-02	94.57969332	90.71177903	93.60735609
Jun-02	94.77598383	91.28434495	94.06691716
Sep-02	94.94425181	91.59908776	94.15237531
Dec-02	95.06256777	91.83061255	93.82370157
Mar-03	95.20383729	92.0495336	93.85054035
Jun-03	95.36310704	92.06783097	93.80733823
Sep-03	95.65538503	92.46380281	94.01443828
Dec-03	95.93967898	93.16893367	94.56114452
Mar-04	96.17401413	93.69539287	94.82575321
Jun-04	96.39906018	94.27430748	95.15641057
Sep-04	96.59427779	94.67953208	95.36940964
Dec-04	96.79148669	95.14011954	95.62435655
Mar-05	97.01511988	95.22501298	95.87814749
Jun-05	97.25514334	95.79995235	96.31687607
Sep-05	97.48902745	96.16451243	96.22091987
Dec-05	97.72361725	96.65052936	96.61361053
Mar-06	98.00024799	97.20744174	97.22591664
Jun-06	98.24470219	97.59546944	97.4921089
Sep-06	98.4642238	97.78291006	97.58218214
Dec-06	98.72557866	98.18088217	98.04032372
Mar-07	98.99902836	98.5854919	98.18875399
Jun-07	99.26442116	98.87405236	98.45736404
Sep-07	99.48229532	99.25849577	98.93551606
Dec-07	99.7391081	99.53386798	99.2465911
Mar-08	99.923273	99.95417332	99.59795208
Jun-08	100.0121865	100.030237	100.0676267
Sep-08	100	100	100
Dec-08	99.72688568	98.7411759	98.93024592
Mar-09	99.46120411	96.85930554	97.4217102
Jun-09	99.61912519	96.84412611	97.28967525
Sep-09	99.82568261	97.55960025	97.90486289
Dec-09	100.0826287	98.09801691	98.8299134
Mar-10	100.3644949	98.70212091	99.26415459
Jun-10	100.6017179	99.23320554	99.57734811
Sep-10	100.8389408	99.76429016	99.83356193
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
Jun-14
Sep-14
Dec-14
Mar-15
Jun-15
Sep-15
Dec-15

Chart 2. World GDP, World Merchandize Trade and U.S. Exports of Goods (quarterly percentage changes)

Source: Own calculations.

Data for Chart 2

	World GDP Growth	World trade Growth	World GDP	World trade
Mar-80
Jun-80
Sep-80	0.534233129
Dec-80	1.056733673
Mar-81	1.014914952
Jun-81	0.433067711	1.507374991
Sep-81	0.685781069	0.823142898
Dec-81	0.126877268	0.795914834
Mar-82	0.158166777	0.555461613
Jun-82	0.5257066	-0.137509956
Sep-82	0.143878668	0.607910272
Dec-82	0.435051716	-0.329416841
Mar-83	0.796344799	0.961394432
Jun-83	1.104506881	2.001613402
Sep-83	1.057951088	1.922444262
Dec-83	1.215985839	2.117637848
Mar-84	1.212739715	3.084172693
Jun-84	0.883485553	1.325098962
Sep-84	0.944019818	1.580480758
Dec-84	0.722858989	0.877114287
Mar-85	0.911390583	0.653078282
Jun-85	0.961820752	1.151977913
Sep-85	1.079772081	1.116741654
Dec-85	0.817875042	1.26957124
Mar-86	0.557935567	0.70156685
Jun-86	0.818205385	2.343404736
Sep-86	0.753172912	1.709438519
Dec-86	0.583380146	0.737408908
Mar-87	0.440169413	1.030798701
Jun-87	1.212814516	1.92861273
Sep-87	0.984749875	2.373819485
Dec-87	1.30300744	2.104122708
Mar-88	0.808913023	0.28058696
Jun-88	0.810905227	1.641744665
Sep-88	0.970204996	2.407367115
Dec-88	1.075662092	2.124292236
Mar-89	1.180654069	1.799320714
Jun-89	0.737684194	2.017800826
Sep-89	0.919900745	0.522419904
Dec-89	0.948307175	2.281368642
Mar-90	0.945565155	2.236741265
Jun-90	0.673130331	1.017014246
Sep-90	0.794518858	1.340279561
Dec-90	0.201902382	1.943151374
Mar-91	0.243498607	0.956623166
Jun-91	0.845955041	1.741677183
Sep-91	0.560601773	1.937192208
Dec-91	0.68092227	0.777559637
Mar-92	0.978404968	1.9955675
Jun-92	0.376132365	0.761714065
Sep-92	0.497544598	0.556231613
Dec-92	0.370268708	1.082883361
Mar-93	0.391556333	-0.165336439
Jun-93	0.490131014	1.027153268
Sep-93	0.514085157	1.461156179
Dec-93	0.72236263	1.505511727
Mar-94	0.843562827	2.123477433
Jun-94	0.734729179	2.77181899
Sep-94	0.835564636	2.224366687
Dec-94	0.861293506	3.019141613
Mar-95	0.700867823	1.85986986
Jun-95	0.585858143	2.363899332
Sep-95	0.852698067	1.444662099
Dec-95	0.737414763	1.592764316
Mar-96	0.915944462	1.998627256
Jun-96	1.189885019	1.233816612
Sep-96	0.888820044	2.089248472
Dec-96	1.070314694	2.728519572
Mar-97	0.948939804	2.497542918
Jun-97	1.141537428	3.131086584
Sep-97	0.918099686	2.453133205
Dec-97	0.906816415	1.5149327
Mar-98	0.224679101	1.235909241
Jun-98	0.462509413	0.562066161
Sep-98	0.656033932	0.049647201
Dec-98	0.800519988	0.305920324
Mar-99	0.899011285	1.385062293
Jun-99	0.930943423	2.621658514
Sep-99	1.066986641	2.65191662
Dec-99	1.43881733	3.039790757
Mar-00	1.38633461	3.886263812
Jun-00	1.181318381	2.665259188
Sep-00	0.715354387	2.876784001
Dec-00	0.692991116	1.821264478
Mar-01	0.393267405	-1.061469963
Jun-01	0.476172354	-1.42619491
Sep-01	0.238660504	-1.300877031
Dec-01	0.431847184	-0.801081696
Mar-02	0.858917089	1.713532098
Jun-02	0.967788252	2.952479386
Sep-02	0.829055313	1.612357593
Dec-02	0.582227248	1.183539027
Mar-03	0.695572615	1.118750568
Jun-03	0.784547602	0.093029007
Sep-03	1.444448058	2.032679559
Dec-03	1.404714814	3.648395436
Mar-04	1.156443834	2.711533239
Jun-04	1.110348621	2.98570082
Sep-04	0.962473062	2.080668481
Dec-04	0.972338212	2.368276323
Mar-05	1.103339406	0.432353428
Jun-05	1.184681324	2.964897937
Sep-05	1.154205151	1.869932752
Dec-05	1.157707794	2.500652864
Mar-06	1.366592834	2.870613245
Jun-06	1.206681495	1.991495811
Sep-06	1.082944679	0.957109127
Dec-06	1.290643066	2.043051989
Mar-07	1.350772458	2.07747874
Jun-07	1.310713703	1.477245603
Sep-07	1.07477394	1.972919688
Dec-07	1.26807141	1.409256688
Mar-08	0.907733848	2.158932134
Jun-08	0.437223375	0.387297461
Sep-08	-0.059777462	-0.153543961
Dec-08	-1.331146049	-6.196911926
Mar-09	-1.295154839	-9.120443289
Jun-09	0.777878448	-0.077110866
Sep-09	1.018665125	3.702891217
Dec-09	1.268733779	2.77396674
Mar-10	1.392636166	3.117617641
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
Jun-14
Sep-14
Dec-14
Mar-15
Jun-15
Sep-15
Dec-15

The United States was not an outlier in regard to the worldwide collapse in trade flows that occurred over 2008-09 (see Chart 1). The declines in U.S. exports and imports were neither exceptionally large nor exceptionally small relative to global trends. Real exports of goods and services fell 15 percent from mid-2008 to mid-2009, while real imports contracted 18 percent over the same time period. The declines in both exports and imports were widespread across both categories of goods and trading partners.

As with global volumes more generally, U.S. trade has rebounded sharply since mid-2009. By the third quarter of 2010, real exports and imports had returned to within a hair of their second quarter 2008 levels. The rebound in imports has coincided with relatively weak U.S. GDP growth and the U.S. import-to-GDP ratio has roughly returned to its pre-crisis trend. In contrast to the modest pace of U.S. GDP growth, foreign growth weighted by share of U.S. exports has been fairly robust and the level of foreign GDP has surpassed its pre-crisis peak. Thus, for exports, the comparison to the previous peak understates the degree to which exports remain below their pre-crisis trend.

3.  Forecasting World Trade: An Error Correction Model

Against the backdrop of the recent developments in global and U.S. trade we now move on to develop a model that allows us to assess the medium-term outlook for global trade. Historical patterns over the past 30 years suggest that global trade and global economic activity follow a common upward trend but trade has increased faster than GDP (see Chart 1). While activity rose at an average rate of just above 3 percent over this period, global trade grew at an average rate of about 6 percent per year. This reflects the globalization process in the wake of technological advances and falling transportation costs, the rising importance of vertical specialization (Hummels et al. 2001, Kleinert and Zorell 2010) and, since the early 1990s, the rapid integration of formerly Communist countries - such as China, Russia and the Central and Eastern European countries - into the global economy (see Bussière and Schnatz 2009).

At the same time, global trade was also more cyclical than global GDP as evidenced by the standard deviation of global trade growth, which has been almost four times higher than that of global GDP growth. This partly reflects the fact that business cycles are commonly associated with large swings in private investment, durable goods consumption and inventories, which are relatively trade-intensive.³

Against this background, we estimate a dynamic standard global trade equation which relates world trade (m) to global economic activity (y) and accounts for globalization trends (t). This trend is included to proxy for several factors, which are difficult to measure in a transparent and more direct way. These comprise, for instance, the decline in trade costs recorded in recent decades as global logistics became more efficient and trade barriers were progressively removed. Moreover, the outsourcing of production processes through global supply chains also led to an increase in international trade.

In order to verify that there is a long-term relationship between global economic activity and global trade, we first conduct a standard Johansen (1995) cointegration test, which confirms the presence of one cointegration vector. In the second step, we run an error-correction model of the following form:

$$\Delta m_{t} =ect(m_{t-1} -\beta y_{t-1} -\delta t)+\gamma_{1} \Delta y_{t} +\gamma_{2} \Delta m_{t-1} +\varepsilon_{t}$$

Two coefficients are particularly noteworthy: First, $\beta$determines the long-term relationship between (the log of) trade (m) and (the log of) economic activity (y) at time t. If this coefficient would be (well) above one, as occasionally suggested in the literature,⁴ this would imply that trade would grow without bound as a proportion of GDP, which appears unreasonable, at least as a very long-term representation. Secondly, ect is the error-correction coefficient, or "speed of adjustment", which determines how fast trade reverts to its long-term equilibrium path after a disturbance. As regards the short-term dynamics, we include contemporaneous growth in global economic activity ($\Delta$yt) and lagged world trade growth ($\Delta$mt-1).⁵ $\varepsilon$t represents the white noise residuals. We estimate this relationship over the period 1981Q1-2008Q3, excluding the actual crisis period to avoid that these observations (outliers) may steer the results.⁶

Table 1: Estimation Results From the Global Error Correction Model

Dependent Variable: Quarterly Growth in World Trade	(1) Benchmark	(2) Stock-Watson	(3) Short Sample	(4) Industrial Production
Sample	1981Q1-2008Q3	1981Q1-2008Q3	1995Q1-2008Q3	1985Q1-2008Q3
Methodology	Engle-Granger	Stock-Watson	Engle-Granger	Engle-Granger
Activity Variable	GDP	GDP	GDP	IP
Long-Term Relationship (a): Global Activity	0.978	0.967***	0.981	1.013
Long-Term Relationship (a): Globalization Trend	0.008	0.008***	0.007	0.008
Speed of Adjustment	-0.085***	-0.081***	-0.154***	-0.133***
Short-Term Dynamics: Global activity growth	1.229***	1.506***	1.060***	0.887***
Short-Term Dynamics: Global trade growth (lagged)	0.354***	0.375***	0.442***	0.474***
Test for Autocorrelation: LM(1); p-value	0.58	0.15	0.75	0.40
Test for Autocorrelation: LM(2); p-value	0.23	0.27	0.46	0.26
Test for Heteroscedasticity: ARCH(1); p-value	0.63	0.43	0.94	0.93
Test for Heteroscedasticity: ARCH(4); p-value	0.72	0.70	0.86	0.49
Quandt-Andrews Breakpoint Test	No break	No break	No break	No break
Adj. R2	0.54	0.54	0.61	0.52

(a) Significance of the long-term coefficients can only be reported for the Stock-Watson approach.

Statistical tests confirm that there is a stable long-term relationship between global economic activity and global trade in this equation. Both the ADF and KPSS-tests confirm that the residuals of the long-run relationship in Table 1 are stationary. Moreover, after accounting for globalization trends, our specification (based on the Engle-Granger methodology) suggests that, in the long run, a one percent increase in economic activity raises world trade by about the same amount (see Table 1). The stronger trade growth over this period is captured by the significant linear trend, which suggests that there is an (autonomous) increase in global trade of about 0.8 percent per quarter, which can be attributed to the ongoing globalization process. Moreover, the error-correction coefficient suggests that shocks to global trade are rather persistent and mean reversion is rather slow. Columns (2) - (4) show that our main results are robust to the econometric method, the sample period chosen and the use of the activity variable (GDP or industrial production). In fact, while the short-run coefficient of the GDP-based models is above one, reflecting the higher volatility of trade as compared with GDP, the short-run coefficient in the specification including industrial production is less than one as industrial production is more cyclical than GDP. Lagrange-Multiplier tests reject the presence of autocorrelated residuals and tests for ARCH-effects cannot reject the absence of heteroscedasticity. Finally, we performed Quandt-Andrews breakpoint tests for one or more unknown structural breakpoints in the sample for a specified equation (trimming 15 percent of the data). Again, we cannot find evidence in favor of structural breaks.

The overall fit of the relationship is also good, explaining around 50-60 percent of the variation in global trade. Chart 3 suggests that - based on the results reported in the first column of Table 1 - actual global trade growth follows quite closely the growth rates projected by the model within the sample period. However, in periods of large swings in global trade the model does not fully account for the peaks and troughs in growth. For instance, in the early 2000s, vigorous global trade growth is only partly captured by the model and the decline in trade in the context of the burst of the dotcom bubbles is also only partly projected. The standard error of the regression indicates, however, that the margin of uncertainty is non-trivial.

Chart 3. One-Step-Ahead In-Sample Forecast of World Trade (Quarter-On-Quarter Percentage Change)

Note: The reference period is 1981-2008. The shaded area shows the two-standard error band.

Data for Chart 3

	Actual imports	Projected imports	SE Band Top	SE Band Bottom
1980Q1
1980Q2
1980Q3
1980Q4
1981Q1
1981Q2	0.01496127
1981Q3	0.008197736	0.008773626
1981Q4	0.007927641	8.78E-05	100.0141321	99.98604343
1982Q1	0.005539246	0.000484969	100.0145146	99.98645532
1982Q2	-0.001376046	0.004478007	100.0185224	99.99043364
1982Q3	0.0060607	-0.001419294	100.0124827	99.98467871
1982Q4	-0.003299606	0.005059428	100.0189409	99.99117801
1983Q1	0.009568024	0.00747434	100.0215743	99.99337439
1983Q2	0.019818445	0.01629778	100.0302615	100.002334
1983Q3	0.019041988	0.019263704	100.0331349	100.0053925
1983Q4	0.020955275	0.020843343	100.0347525	100.0069342
1984Q1	0.030375679	0.021382123	100.0352864	100.0074778
1984Q2	0.013163963	0.019811715	100.0338297	100.0057937
1984Q3	0.015681212	0.014749912	100.0286451	100.0008547
1984Q4	0.0087329	0.013070151	100.0269155	99.99922477
1985Q1	0.00650955	0.013442229	100.0273443	99.99954018
1985Q2	0.011453932	0.014144255	100.0280795	100.0002091
1985Q3	0.011105521	0.017825173	100.0317377	100.0039127
1985Q4	0.012615798	0.015137422	100.0289872	100.0012876
1986Q1	0.006991173	0.012780275	100.0266161	99.99894444
1986Q2	0.023163686	0.014510778	100.02842	100.0006015
1986Q3	0.01694992	0.01882666	100.0327543	100.004899
1986Q4	0.007347033	0.014417623	100.0282792	100.000556
1987Q1	0.010255222	0.009804655	100.0236368	99.99597251
1987Q2	0.019102507	0.020420205	100.0344352	100.0064052
1987Q3	0.023460825	0.020832927	100.0347392	100.0069266
1987Q4	0.020822917	0.025741274	100.0396751	100.0118074
1988Q1	0.00280194	0.018783961	100.0326777	100.0048902
1988Q2	0.016284137	0.013538661	100.0275293	99.99954804
1988Q3	0.023788469	0.020212268	100.034144	100.0062805
1988Q4	0.021020437	0.023609817	100.0375507	100.0096689
1989Q1	0.017833245	0.023687809	100.037611	100.0097647
1989Q2	0.01997713	0.017305712	100.0311972	100.0034143
1989Q3	0.0052106	0.019877087	100.0337618	100.0059924
1989Q4	0.022557346	0.015991603	100.0299965	100.0019867
1990Q1	0.022120931	0.021640305	100.0355751	100.0077055
1990Q2	0.010118775	0.017746633	100.0316856	100.0038076
1990Q3	0.013313773	0.015355991	100.0292386	100.0014734
1990Q4	0.019245132	0.009442712	100.0233831	99.99550228
1991Q1	0.009520765	0.011263707	100.0252619	99.99726556
1991Q2	0.017266838	0.015257742	100.0291336	100.0013819
1991Q3	0.019186675	0.014425957	100.0283016	100.0005503
1991Q4	0.007745522	0.016088112	100.0299534	100.0022229
1992Q1	0.01975917	0.016247771	100.0301849	100.0023107
1992Q2	0.007588277	0.01295394	100.0269096	99.99899824
1992Q3	0.005546904	0.010477982	100.0243086	99.9966474
1992Q4	0.010770622	0.008814663	100.0226703	99.99495907
1993Q1	-0.001654733	0.010991885	100.024874	99.9971098
1993Q2	0.010219139	0.008941237	100.0229489	99.99493361
1993Q3	0.014505842	0.01364817	100.0276356	99.99966069
1993Q4	0.014942914	0.017579446	100.0315745	100.0035843
1994Q1	0.021012458	0.01921757	100.0332187	100.0052164
1994Q2	0.027340995	0.019629052	100.0336519	100.0056062
1994Q3	0.021999885	0.022063688	100.0360944	100.0080329
1994Q4	0.029744626	0.019989744	100.0339	100.0060795
1995Q1	0.018427858	0.019640251	100.0336888	100.0055917
1995Q2	0.023363919	0.013926748	100.027778	100.0000755
1995Q3	0.014343263	0.018109192	100.031992	100.0042264
1995Q4	0.015802129	0.013676792	100.0274981	99.9998555
1996Q1	0.019789169	0.016314031	100.0301599	100.0024682
1996Q2	0.012262671	0.020809916	100.0347087	100.0069111
1996Q3	0.02067723	0.01510208	100.0289503	100.0012539
1996Q4	0.02691959	0.019943953	100.0338138	100.0060741
1997Q1	0.024668641	0.019952882	100.0338838	100.0060219
1997Q2	0.030830678	0.020867729	100.0348024	100.0069331
1997Q3	0.024235271	0.019334509	100.0334088	100.0052602
1997Q4	0.015035722	0.016244591	100.0302775	100.0022117
1998Q1	0.012283342	0.004805012	100.0187915	99.9908185
1998Q2	0.005604925	0.006565088	100.0205261	99.99260407
1998Q3	0.000496349	0.00715008	100.0211264	99.99317372
1998Q4	0.003054533	0.008279531	100.0223196	99.99423944
1999Q1	0.01375558	0.011460222	100.0254448	99.99747561
1999Q2	0.025878821	0.015884997	100.0297386	100.0020314
1999Q3	0.026173629	0.021079562	100.0349917	100.0071675
1999Q4	0.029945046	0.025034546	100.0390225	100.0110466
2000Q1	0.038126498	0.025055277	100.0390819	100.0110286
2000Q2	0.026303599	0.024049278	100.0383055	100.0097931
2000Q3	0.028361814	0.013616278	100.0277929	99.99943966
2000Q4	0.018048781	0.012935976	100.0272926	99.99857935
2001Q1	-0.010671437	0.005344124	100.01964	99.99104824
2001Q2	-0.014364628	-0.001897027	100.0124714	99.98373455
2001Q3	-0.013094125	-0.003828398	100.0102872	99.98205603
2001Q4	-0.008043076	0.000966719	100.0150822	99.98685122
2002Q1	0.016990167	0.009679155	100.0239331	99.99542525
2002Q2	0.029097331	0.019803283	100.0336981	100.0059084
2002Q3	0.015994972	0.021405229	100.0354112	100.0073992
2002Q4	0.0117659	0.013763684	100.0276049	99.99992251
2003Q1	0.011125388	0.013811322	100.0276389	99.99998378
2003Q2	0.000929858	0.014976869	100.0288277	100.001126
2003Q3	0.020122964	0.02063307	100.0350587	100.0062075
2003Q4	0.035834172	0.027098339	100.0411313	100.0130654
2004Q1	0.026754225	0.028439533	100.0425462	100.0143328
2004Q2	0.029419966	0.024030217	100.0379539	100.0101066
2004Q3	0.020593182	0.022273453	100.0362338	100.0083131
2004Q4	0.023406677	0.018996329	100.032853	100.0051396
2005Q1	0.004314215	0.021079352	100.0349658	100.0071929
2005Q2	0.029217947	0.016532357	100.0306284	100.0024363
2005Q3	0.018526644	0.024146947	100.0380807	100.0102132
2005Q4	0.024698982	0.020464667	100.0343505	100.0065789
2006Q1	0.02830183	0.024719637	100.03865	100.0107893
2006Q2	0.019719249	0.023456727	100.0373988	100.0095147
2006Q3	0.009525579	0.01891641	100.0328107	100.0050221
2006Q4	0.020224617	0.018593189	100.032653	100.0045334
2007Q1	0.020561934	0.023130799	100.0370683	100.0091933
2007Q2	0.014664406	0.022809183	100.0367313	100.008887
2007Q3	0.019537099	0.018369386	100.0322509	100.0044879
2007Q4	0.01399419	0.022346544	100.0362554	100.0084377
2008Q1	0.021359573	0.016538893	100.0303831	100.0026947
2008Q2	0.003865494	0.013015705	100.0269456	99.99908578
2008Q3	-0.00153662	0.001441123	100.0152479	99.98763438
2008Q4	-0.063972409	-0.015446816	99.998931	99.97017537
2009Q1	-0.095635109	-0.032102164	99.98333249	99.95246318
2009Q2	-0.000771406	-0.010071711	100.0118538	99.96800279
2009Q3	0.036359809	0.027793187	100.0446598	100.0109266
2009Q4	0.027361893	0.04240061	100.0590512	100.0257501
2010Q1	0.03070007	0.040123303	100.0563507	100.0238959
2010Q2	0.026989285	0.037833441	100.0539249	100.021742
2010Q3	0.026989285	0.035875008	100.0516924	100.0200576
2010Q4	0.026989285	0.035229006	100.0508589	100.0195991
2011Q1	0.016914662	0.032977777	100.0484579	100.0174977
2011Q2	0.016914662	0.029512933	100.044877	100.0141488
2011Q3	0.016914662	0.029611286	100.0450019	100.0142207
2011Q4	0.016914662	0.029709639	100.045127	100.0142923
2012Q1	0.016914662	0.030752741	100.046197	100.0153084
2012Q2	0.016914662	0.030914696	100.0464035	100.0154259
2012Q3	0.016914662	0.031076651	100.0466104	100.0155429
2012Q4	0.016914662	0.031238606	100.0468179	100.0156593
	0.016914662	0.03153277	100.0471583	100.0159072
	0.016914662	0.031703625	100.0473783	100.016029
	0.016914662	0.031874481	100.0475988	100.0161502
	0.016914662	0.032045336	100.0478199	100.0162708
	0.016914662	0.032327791	100.0481532	100.0165024
	0.016914662	0.032506159	100.0483851	100.0166272
	0.016914662	0.032684528	100.0486176	100.0167514
	0.016914662	0.032862896	100.0488508	100.016875
	0.016914662	0.033041265	100.0490845	100.0169981
	0.016914662	0.033219633	100.0493188	100.0171205
	0.016914662	0.033398002	100.0495536	100.0172424
	0.016914662	0.03357637	100.049789	100.0173637

Chart 4. One-Step-Ahead Forecast of World Trade and Actual Outcomes in the Midst of the Crisis (Quarter-on-Quarter Percentage Change)

Note: Based on the Engle-Granger model estimated up to 2008Q3, including actual GDP realizations in the projections.

Data for Chart 4

DATE	Actual Trade	Model Forecast
1980-01	#N/A	#N/A
1980-04	#N/A	#N/A
1980-07	#N/A	#N/A
1980-10	#N/A	#N/A
1981-01	#N/A	#N/A
1981-04	1.50%	1.50%
1981-07	0.82%	0.82%
1981-10	0.79%	0.79%
1982-01	0.55%	0.55%
1982-04	-0.14%	-0.14%
1982-07	0.61%	0.61%
1982-10	-0.33%	-0.33%
1983-01	0.96%	0.96%
1983-04	1.98%	1.98%
1983-07	1.90%	1.90%
1983-10	2.10%	2.10%
1984-01	3.04%	3.04%
1984-04	1.32%	1.32%
1984-07	1.57%	1.57%
1984-10	0.87%	0.87%
1985-01	0.65%	0.65%
1985-04	1.15%	1.15%
1985-07	1.11%	1.11%
1985-10	1.26%	1.26%
1986-01	0.70%	0.70%
1986-04	2.32%	2.32%
1986-07	1.69%	1.69%
1986-10	0.73%	0.73%
1987-01	1.03%	1.03%
1987-04	1.91%	1.91%
1987-07	2.35%	2.35%
1987-10	2.08%	2.08%
1988-01	0.28%	0.28%
1988-04	1.63%	1.63%
1988-07	2.38%	2.38%
1988-10	2.10%	2.10%
1989-01	1.78%	1.78%
1989-04	2.00%	2.00%
1989-07	0.52%	0.52%
1989-10	2.26%	2.26%
1990-01	2.21%	2.21%
1990-04	1.01%	1.01%
1990-07	1.33%	1.33%
1990-10	1.92%	1.92%
1991-01	0.95%	0.95%
1991-04	1.73%	1.73%
1991-07	1.92%	1.92%
1991-10	0.77%	0.77%
1992-01	1.98%	1.98%
1992-04	0.76%	0.76%
1992-07	0.55%	0.55%
1992-10	1.08%	1.08%
1993-01	-0.17%	-0.17%
1993-04	1.02%	1.02%
1993-07	1.45%	1.45%
1993-10	1.49%	1.49%
1994-01	2.10%	2.10%
1994-04	2.73%	2.73%
1994-07	2.20%	2.20%
1994-10	2.97%	2.97%
1995-01	1.84%	1.84%
1995-04	2.34%	2.34%
1995-07	1.43%	1.43%
1995-10	1.58%	1.58%
1996-01	1.98%	1.98%
1996-04	1.23%	1.23%
1996-07	2.07%	2.07%
1996-10	2.69%	2.69%
1997-01	2.47%	2.47%
1997-04	3.08%	3.08%
1997-07	2.42%	2.42%
1997-10	1.50%	1.50%
1998-01	1.23%	1.23%
1998-04	0.56%	0.56%
1998-07	0.05%	0.05%
1998-10	0.31%	0.31%
1999-01	1.38%	1.38%
1999-04	2.59%	2.59%
1999-07	2.62%	2.62%
1999-10	2.99%	2.99%
2000-01	3.81%	3.81%
2000-04	2.63%	2.63%
2000-07	2.84%	2.84%
2000-10	1.80%	1.80%
2001-01	-1.07%	-1.07%
2001-04	-1.44%	-1.44%
2001-07	-1.31%	-1.31%
2001-10	-0.80%	-0.80%
2002-01	1.70%	1.70%
2002-04	2.91%	2.91%
2002-07	1.60%	1.60%
2002-10	1.18%	1.18%
2003-01	1.11%	1.11%
2003-04	0.09%	0.09%
2003-07	2.01%	2.01%
2003-10	3.58%	3.58%
2004-01	2.68%	2.68%
2004-04	2.94%	2.94%
2004-07	2.06%	2.06%
2004-10	2.34%	2.34%
2005-01	0.43%	0.43%
2005-04	2.92%	2.92%
2005-07	1.85%	1.85%
2005-10	2.47%	2.47%
2006-01	2.83%	2.83%
2006-04	1.97%	1.97%
2006-07	0.95%	0.95%
2006-10	2.02%	2.02%
2007-01	2.06%	2.06%
2007-04	1.47%	1.47%
2007-07	1.95%	1.95%
2007-10	1.40%	1.40%
2008Q1	2.14%	1.65%
2008Q2	0.39%	1.30%
2008Q3	-0.15%	0.14%
2008Q4	-6.40%	-1.54%
2009Q1	-9.56%	-3.21%
2009Q2	-0.08%	-1.01%
2009Q3	3.64%	2.78%
2009Q4	2.74%	4.24%
2010Q1	3.07%	4.01%

Given the strength of the collapse in global trade in 2008-09 compared with the contraction in GDP, it is hardly surprising that the model did not foresee the magnitude of the downturn in trade. Even if we assume that we had perfect foresight of developments in global GDP, the model would have predicted less than half of the actual contraction in global trade in the last quarter of 2008 and in the first quarter of 2009 (see Chart 4), a failure shared with most standard trade models.

4.  World Trade at a Crossroads: A Scenario Analysis

The Great Trade Collapse of 2008-09 was not only hard to anticipate on the basis of standard trade models but also poses major challenges for forecasters in the assessment of world trade going forward. In particular, it is an open question whether the crisis will have transitory or persistent impacts on world trade. Given that only a short period of time has gone by since the end of the downturn in global trade, it is arguably too early to reliably detect possible structural breaks in the data associated with the global financial crisis. Therefore, we take a different approach and use our model of world trade (see Section 3) as a starting point for a comprehensive scenario analysis. More specifically, we analyze in greater detail how the shape of the recovery will look like under alternative assumptions on (i) the future profile of world GDP, (ii) the relationship between global trade and GDP as well as (iii) the impact of renewed financial distress.⁷ Before discussing each of these channels in turn, we briefly describe our baseline scenario.

4.1  A "Mechanistic" Baseline Scenario

In our "mechanistic" baseline scenario, we assume that the coefficients in our benchmark error-correction model in Table 1 remain unchanged and feed the model with a plausible global GDP profile. In more detail, we assume - for the sake of comparability - that world GDP growth will evolve in line with the latest IMF projections (World Economic Outlook, October 2010). Following the 0.6 percent contraction in world GDP in 2009, the IMF expects global economic activity to grow by 4.8 percent in 2010, 4.2 percent in 2011 and 4.5 percent in 2012 in annual terms. Since we are mostly interested in the overall shape of the trade recovery in the medium term, we assume that the quarterly profile of world GDP for any given year is flat: 1.2 percent quarter-on-quarter throughout 2010, 1.0 percent in 2011 and 1.1 percent in 2012. Notably, these quarterly growth rates are considerably above the average rate of 0.8 percent in our reference sample, which implies that the excess capacity in the global economy is gradually (and partially) absorbed over this period (see Table A.2 in the appendix).

Chart 5. Contributions to the One-Step-Ahead Out-of-Sample Projections of the Global Trade Model (Quarter-on-Quarter Growth)

Note: The last observation is 2010Q1.

Data for Chart 5

		obs	Trade	Contribution of Short-Run Dynamics	Contribution of Error Correction	Contribution of Residuals
1980	1	1980Q1
1980	2	1980Q2
1980	3	1980Q3
1980	4	1980Q4
1981	1	1981Q1
1981	2	1981Q2	0.01496127		-0.004682442
1981	3	1981Q3	0.008197736	0.013689798	-0.004916172	-0.000575891
1981	4	1981Q4	0.007927641	0.004457491	-0.004369738	0.007839889
1982	1	1982Q1	0.005539246	0.00474591	-0.004260941	0.005054277
1982	2	1982Q2	-0.001376046	0.00840222	-0.003924213	-0.005854053
1982	3	1982Q3	0.0060607	0.001280067	-0.002699361	0.007479993
1982	4	1982Q4	-0.003299606	0.007477983	-0.002418555	-0.008359034
1983	1	1983Q1	0.009568024	0.008579926	-0.001105586	0.002093685
1983	2	1983Q2	0.019818445	0.016882081	-0.0005843	0.003520664
1983	3	1983Q3	0.019041988	0.019941484	-0.000677779	-0.000221717
1983	4	1983Q4	0.020955275	0.021587007	-0.000743664	0.000111932
1984	1	1984Q1	0.030375679	0.022224288	-0.000842166	0.008993557
1984	2	1984Q2	0.013163963	0.021552104	-0.001740388	-0.006647752
1984	3	1984Q3	0.015681212	0.016201782	-0.00145187	0.0009313
1984	4	1984Q4	0.0087329	0.014396864	-0.001326714	-0.004337251
1985	1	1985Q1	0.00650955	0.014237331	-0.000795101	-0.00693268
1985	2	1985Q2	0.011453932	0.014064924	7.93E-05	-0.002690323
1985	3	1985Q3	0.011105521	0.017248425	0.000576747	-0.006719652
1985	4	1985Q4	0.012615798	0.013937188	0.001200234	-0.002521625
1986	1	1986Q1	0.006991173	0.011298961	0.001481314	-0.005789102
1986	2	1986Q2	0.023163686	0.01248605	0.002024728	0.008652908
1986	3	1986Q3	0.01694992	0.017413045	0.001413614	-0.001876739
1986	4	1986Q4	0.007347033	0.013142751	0.001274873	-0.00707059
1987	1	1987Q1	0.010255222	0.007995548	0.001809107	0.000450567
1987	2	1987Q2	0.019102507	0.018440797	0.001979408	-0.001317698
1987	3	1987Q3	0.023460825	0.01879785	0.002035077	0.002627898
1987	4	1987Q4	0.020822917	0.024205906	0.001535368	-0.004918357
1988	1	1988Q1	0.00280194	0.017264802	0.001519159	-0.01598202
1988	2	1988Q2	0.016284137	0.010915419	0.002623242	0.002745477
1988	3	1988Q3	0.023788469	0.017624046	0.002588221	0.003576201
1988	4	1988Q4	0.021020437	0.021560944	0.002048874	-0.002589381
1989	1	1989Q1	0.017833245	0.021857721	0.001830089	-0.005854564
1989	2	1989Q2	0.01997713	0.015338852	0.001966861	0.002671417
1989	3	1989Q3	0.0052106	0.018317821	0.001559266	-0.014666487
1989	4	1989Q4	0.022557346	0.013441024	0.00255058	0.006565742
1990	1	1990Q1	0.022120931	0.019542849	0.002097455	0.000480626
1990	2	1990Q2	0.010118775	0.016067624	0.001679009	-0.007627858
1990	3	1990Q3	0.013313773	0.013303484	0.002052508	-0.002042219
1990	4	1990Q4	0.019245132	0.007187348	0.002255364	0.00980242
1991	1	1991Q1	0.009520765	0.009795163	0.001468544	-0.001742942
1991	2	1991Q2	0.017266838	0.013718898	0.001538844	0.002009096
1991	3	1991Q3	0.019186675	0.012976524	0.001449433	0.004760718
1991	4	1991Q4	0.007745522	0.015124941	0.000963171	-0.008342589
1992	1	1992Q1	0.01975917	0.014703896	0.001543875	0.003511399
1992	2	1992Q2	0.007588277	0.011601772	0.001352167	-0.005365663
1992	3	1992Q3	0.005546904	0.008782622	0.00169536	-0.004931078
1992	4	1992Q4	0.010770622	0.006503386	0.002311278	0.001955958
1993	1	1993Q1	-0.001654733	0.008611508	0.002380378	-0.012646618
1993	2	1993Q2	0.010219139	0.005422898	0.003518339	0.001277902
1993	3	1993Q3	0.014505842	0.009915338	0.003732833	0.000857671
1993	4	1993Q4	0.014942914	0.013975103	0.003604343	-0.002636532
1994	1	1994Q1	0.021012458	0.015607456	0.003610114	0.001794888
1994	2	1994Q2	0.027340995	0.01642723	0.003201822	0.007711943
1994	3	1994Q3	0.021999885	0.019894949	0.002168739	-6.38E-05
1994	4	1994Q4	0.029744626	0.018319406	0.001670338	0.009754882
1995	1	1995Q1	0.018427858	0.019102495	0.000537755	-0.001212393
1995	2	1995Q2	0.023363919	0.013695742	0.000231006	0.009437171
1995	3	1995Q3	0.014343263	0.018697113	-0.000587921	-0.003765929
1995	4	1995Q4	0.015802129	0.014101227	-0.000424435	0.002125337
1996	1	1996Q1	0.019789169	0.016793031	-0.000479001	0.003475138
1996	2	1996Q2	0.012262671	0.021534349	-0.000724433	-0.008547245
1996	3	1996Q3	0.02067723	0.015210863	-0.000108784	0.00557515
1996	4	1996Q4	0.02691959	0.020395547	-0.000451594	0.006975637
1997	1	1997Q1	0.024668641	0.021126769	-0.001173887	0.004715759
1997	2	1997Q2	0.030830678	0.022672861	-0.001805131	0.009962949
1997	3	1997Q3	0.024235271	0.022134578	-0.002800069	0.004900762
1997	4	1997Q4	0.015035722	0.019664514	-0.003419923	-0.001208869
1998	1	1998Q1	0.012283342	0.008075658	-0.003270646	0.00747833
1998	2	1998Q2	0.005604925	0.01001471	-0.003449622	-0.000960163
1998	3	1998Q3	0.000496349	0.010017538	-0.002867458	-0.006653731
1998	4	1998Q4	0.003054533	0.00997338	-0.001693849	-0.005224997
1999	1	1999Q1	0.01375558	0.012078248	-0.000618026	0.002295357
1999	2	1999Q2	0.025878821	0.016251831	-0.000366834	0.009993824
1999	3	1999Q3	0.026173629	0.022194784	-0.001115222	0.005094067
1999	4	1999Q4	0.029945046	0.02681167	-0.001777123	0.004910499
2000	1	2000Q1	0.038126498	0.027509608	-0.002454331	0.013071221
2000	2	2000Q2	0.026303599	0.027915867	-0.003866589	0.002254321
2000	3	2000Q3	0.028361814	0.018062235	-0.004445957	0.014745536
2000	4	2000Q4	0.018048781	0.018517301	-0.005581325	0.005112805
2001	1	2001Q1	-0.010671437	0.011206594	-0.00586247	-0.016015561
2001	2	2001Q2	-0.014364628	0.002063163	-0.00396019	-0.012467601
2001	3	2001Q3	-0.013094125	-0.00215126	-0.001677138	-0.009265727
2001	4	2001Q4	-0.008043076	0.000664088	0.000302631	-0.009009795
2002	1	2002Q1	0.016990167	0.007664846	0.002014309	0.007311012
2002	2	2002Q2	0.029097331	0.017844343	0.00195894	0.009294048
2002	3	2002Q3	0.015994972	0.020436803	0.000968426	-0.005410257
2002	4	2002Q4	0.0117659	0.012790919	0.000972765	-0.001997784
2003	1	2003Q1	0.011125388	0.012679154	0.001132168	-0.002685934
2003	2	2003Q2	0.000929858	0.013537934	0.001438936	-0.014047012
2003	3	2003Q3	0.020122964	0.01795165	0.00268142	-0.000510106
2003	4	2003Q4	0.035834172	0.024258482	0.002839857	0.008735833
2004	1	2004Q1	0.026754225	0.026802984	0.00163655	-0.001685308
2004	2	2004Q2	0.029419966	0.023031502	0.000998715	0.005389749
2004	3	2004Q3	0.020593182	0.022175829	9.76E-05	-0.001680271
2004	4	2004Q4	0.023406677	0.019174032	-0.000177703	0.004410348
2005	1	2005Q1	0.004314215	0.021762352	-0.000682999	-0.016765138
2005	2	2005Q2	0.029217947	0.015997967	0.00053439	0.012685591
2005	3	2005Q3	0.018526644	0.024435759	-0.000288812	-0.005620302
2005	4	2005Q4	0.024698982	0.020697004	-0.000232337	0.004234315
2006	1	2006Q1	0.02830183	0.02541488	-0.000695243	0.003582193
2006	2	2006Q2	0.019719249	0.024749066	-0.001292339	-0.003737478
2006	3	2006Q3	0.009525579	0.020210275	-0.001293865	-0.009390831
2006	4	2006Q4	0.020224617	0.019127291	-0.000534102	0.001631428
2007		2007Q1	0.020561934	0.023640585	-0.000509786	-0.002568865
		2007Q2	0.014664406	0.0232741	-0.000464917	-0.008144777
		2007Q3	0.019537099	0.018323144	4.62E-05	0.001167712
		2007Q4	0.01399419	0.022394307	-4.78E-05	-0.008352354
2008		2008Q1	0.021359573	0.016053616	0.000485277	0.00482068
		2008Q2	0.003865494	0.012915465	0.000100241	-0.009150211
		2008Q3	-0.00153662	0.000632369	0.000808754	-0.002977743
		2008Q4	-0.063972409	-0.017010785	0.001563968	-0.048525592
2009	1	2009Q1	-0.095635109	-0.038644947	0.006542783	-0.063532945
		2009Q2	-0.000771406	-0.024302481	0.01423077	0.009300304
		2009Q3	0.036359809	0.012181462	0.015611725	0.008566623
		2009Q4	0.027361893	0.028352208	0.014048402	-0.015038717
2010		2010Q1	0.03070007	0.026672371	0.013450932	-0.009423234
		2010Q2	0.037833441	0.025161271	0.012672171	6.04E-16
		2010Q3	0.038792842	0.027684205	0.011108637	-5.76E-16
		2010Q4	0.037487454	0.028023526	0.009463929	5.07E-16
2011		2011Q1	0.033886277	0.025956608	0.007929669	-1.25E-16
		2011Q2	0.031274984	0.024682942	0.006592042	-1.32E-16
		2011Q3	0.029234737	0.023759379	0.005475359	-2.78E-17
		2011Q4	0.027569085	0.023037783	0.004531302	-1.84E-16
2012		2012Q1	0.0271216	0.023393423	0.003728177	-2.67E-16
		2012Q2	0.026261672	0.023235157	0.003026515	8.47E-16
		2012Q3	0.02532863	0.022931017	0.002397613	-2.22E-16
		2012Q4	0.024448675	0.022601018	0.001847656	-3.85E-16
2013	1	2013Q1	0.023794158	0.022422005	0.001372153	-1.46E-16
2013	2	2013Q2	0.023151445	0.022190515	0.00096093	9.16E-16
2013	3	2013Q3	0.022567287	0.0219632	0.000604087	-2.78E-16
2013	4	2013Q4	0.022053264	0.021756595	0.000296669	-2.78E-16
2014	1	2014Q1	0.021719138	0.021686394	3.27E-05	5.41E-16
2014	2	2014Q2	0.021372823	0.021568221	-0.000195397	-3.92E-16
2014	3	2014Q3	0.021051498	0.021445736	-0.000394237	-4.06E-16
2014	4	2014Q4	0.0207662	0.021332089	-0.000565889	6.59E-16
2015	1	2015Q1	0.020517783	0.021231185	-0.000713402	-4.09E-16
2015	2	2015Q2	0.020303428	0.021143325	-0.000839896	5.86E-16
2015	3	2015Q3	0.020119258	0.021067512	-0.000948254	-4.41E-16
2015	4	2015Q4	0.019961346	0.021002374	-0.001041028	-5.27E-16

Taking our assumptions on the future path of world GDP as given, our benchmark model suggests a strong recovery of world trade over the entire forecast horizon (see Table 2). In terms of annual growth rates, world trade would expand 12.6 percent in 2010, 13.6 percent in 2011 and 10.9 percent in 2012. To a notable extent, these strong growth rates are driven by the equilibrium correction mechanism in the model. The benchmark model interprets the huge deviation of world trade growth from its equilibrium level in late 2008 and early 2009 as an event that will be corrected by above-average growth in world trade - also compared to GDP - going forward. Since the estimated speed of adjustment to disturbances is relatively low, the adjustment process takes several years (see Chart 5).

We should emphasize that the world trade growth rates implied by this mechanistic baseline scenario are well above those in the IMF October 2010 World Economic Outlook, particularly beyond 2010In line with the IMF, we also acknowledge the possibility that the recovery in world trade could be more sluggish going forward, either due to a crisis-induced change in the relationship between trade and GDP or lower-than-anticipated GDP growth. In fact, the main contribution of this paper is a structured discussion and quantification of these considerations within a unified model framework. More specifically, we will present several alternative scenarios with different assumptions about the medium term profile of world GDP and the relationship between trade and GDP and lay out the consequences for the future path of world trade.

Table 2. Baseline Scenario (Annual Percentage Changes)

	Full Sample	2009	2010	2011	2012
World GDP	3.4%	-0.8%	4.8%	4.3%	4.3%
World Trade	6.3%	-11.9%	12.6%	13.6%	10.9%
Memo: IMF	-	-11.0%	11.4%

Note: The last observation is 2010Q1. The IMF forecast refers to the WEO October 2010.

4.2  How Anemic Global GDP Growth Could Derail the Recovery in Trade

As already indicated, the baseline scenario rests on our assumption for the future path of world GDP. Given the unprecedented nature of the recent financial crisis, the shape of the ongoing recovery in the world economy is subject to considerable uncertainty. There is a possibility that future GDP growth at the global level will be significantly lower than currently anticipated. Such a scenario could materialize, for instance, if the adverse repercussions of the ongoing balance sheet restructuring in some of the advanced economies turn out be larger than anticipated, while the emerging economies are not able to take up the slack. Moreover, the precarious fiscal situation and the lingering sovereign debt crises in some developed countries could derail the fragile global economic recovery there. In fact, Reinhart and Rogoff (2009) provide evidence that recoveries from financial crises tend to be more sluggish than "normal" recoveries. Similarly, looking at past recessions associated with financial crises in advanced economies, IMF (2009) finds that, on average, output reached its pre-crisis peak after about 5 1/2 quarters, compared with around three quarters for "normal" recoveries.⁸ This profile is also consistent with the present recovery in advanced economies, which remains rather subdued. Making use of cross-industry variation in external financial dependence, Dell'Ariccia et al. (2008) show that banking crises have indeed independent negative effects on real economic activity, rather than merely occurring during economic downturns.

To explore how world trade would respond to a lower GDP growth profile, we consider three alternative scenarios in which quarterly world GDP growth is, respectively, 0.3, 0.5 and 0.8p.p. lower than in the baseline scenario over the entire forecast horizon. As Table 3 shows, the corresponding annual growth rates in world trade are indeed well below the baseline scenario. However, even under the extreme assumption of anemic global GDP growth of less than 1/2 percent over the next two years (i.e. the third scenario), our simple model predicts that annual world trade growth is significantly above its long-term average of 6.3 percent throughout 2010 and 2011 and close to this average rate in 2012. Again, this reflects the strong impact of the error-correction mechanism implemented in the model. In the following, we explore this issue in greater detail.

Table 3. World Trade Growth Under Alternative Assumptions on World GDP Growth (Annual Percentage Growth)

	2010	2011	2012
baseline	12.6%	13.6%	10.9%
Δy 0.3p.p. lower	12.0%	11.7%	9.2%
Δy 0.5p.p. lower	11.6%	10.4%	8.1%
Δy 0.8p.p. lower	10.9%	8.4%	6.4%
Memo: IMF	11.4%	7.0%	-

Note: The last observation is 2010Q1. The IMF forecast refers to the WEO October 2010.

4.3  Impacts of changes in the relationship between world trade and world GDP

So far, we have implicitly assumed that, in the absence of renewed disturbances, world trade will gradually return to levels consistent with its long-run equilibrium relationship with global GDP. In other words, we assumed that the crisis had only transitory effects that are unwinding over the horizon. Moreover, we have taken for granted that the post-crisis equilibrium relationship will be the same as that observed before the crisis. In the following, we relax these assumptions and analyze the consequences for the future path of world trade.

The recovery in global trade observed in the second half of 2009 and the first half of 2010 indeed suggests that a significant part of the trade downturn in the crisis was transitory and has been unwinding.⁹ In particular, the inventory dynamics magnified both the downturn and the subsequent upturn in global trade (see Alessandria et al. 2010). This is also in line with the argument that the global financial crisis led to a drop in demand for intermediate inputs, which explains the sharp fall in global trade (see Bems et al. 2010). In mid-2010, however, inventories have been approaching levels in line with historical averages, which implies that the restocking is likely to have a less pronounced effect in the period ahead. The same argument holds for the possible disruption of global supply chains.¹⁰ Moreover, the strength of the trade recovery in this period was also supported by factors that were themselves transitory. Most notably, fiscal stimulus measures such as the car scrapping schemes provided strong support to global trade.¹¹ Given the need to retrain public finances, however, support from these measures is also receding.

Chart 6. Impacts of Changes in the Relationship Between Global Trade and GDP

Note: The last observation is 2010Q1. Projectionsup to 2012Q4.

Data for Chart 6

DATE	World Trade	Baseline	Downward Shift	New Normal
1980-01	#N/A	#N/A	#N/A	#N/A
1980-04	#N/A	#N/A	#N/A	#N/A
1980-07	#N/A	#N/A	#N/A	#N/A
1980-10	#N/A	#N/A	#N/A	#N/A
1981-01	66.70312	3.38980	3.38980	3.38980
1981-04	66.99752	3.40476	3.40476	3.40476
1981-07	67.15884	3.41296	3.41296	3.41296
1981-10	67.31483	3.42088	3.42088	3.42088
1982-01	67.42383	3.42642	3.42642	3.42642
1982-04	67.39675	3.42505	3.42505	3.42505
1982-07	67.51601	3.43111	3.43111	3.43111
1982-10	67.45109	3.42781	3.42781	3.42781
1983-01	67.63936	3.43738	3.43738	3.43738
1983-04	68.02934	3.45720	3.45720	3.45720
1983-07	68.40404	3.47624	3.47624	3.47624
1983-10	68.81639	3.49719	3.49719	3.49719
1984-01	69.41411	3.52757	3.52757	3.52757
1984-04	69.67315	3.54073	3.54073	3.54073
1984-07	69.98172	3.55641	3.55641	3.55641
1984-10	70.15356	3.56515	3.56515	3.56515
1985-01	70.28165	3.57166	3.57166	3.57166
1985-04	70.50704	3.58311	3.58311	3.58311
1985-07	70.72557	3.59422	3.59422	3.59422
1985-10	70.97382	3.60683	3.60683	3.60683
1986-01	71.11139	3.61382	3.61382	3.61382
1986-04	71.56719	3.63699	3.63699	3.63699
1986-07	71.90073	3.65394	3.65394	3.65394
1986-10	72.04530	3.66128	3.66128	3.66128
1987-01	72.24710	3.67154	3.67154	3.67154
1987-04	72.62299	3.69064	3.69064	3.69064
1987-07	73.08464	3.71410	3.71410	3.71410
1987-10	73.49439	3.73492	3.73492	3.73492
1988-01	73.54952	3.73773	3.73773	3.73773
1988-04	73.86995	3.75401	3.75401	3.75401
1988-07	74.33806	3.77780	3.77780	3.77780
1988-10	74.75169	3.79882	3.79882	3.79882
1989-01	75.10260	3.81665	3.81665	3.81665
1989-04	75.49570	3.83663	3.83663	3.83663
1989-07	75.59824	3.84184	3.84184	3.84184
1989-10	76.04211	3.86440	3.86440	3.86440
1990-01	76.47740	3.88652	3.88652	3.88652
1990-04	76.67651	3.89664	3.89664	3.89664
1990-07	76.93850	3.90995	3.90995	3.90995
1990-10	77.31719	3.92920	3.92920	3.92920
1991-01	77.50454	3.93872	3.93872	3.93872
1991-04	77.84431	3.95598	3.95598	3.95598
1991-07	78.22186	3.97517	3.97517	3.97517
1991-10	78.37427	3.98292	3.98292	3.98292
1992-01	78.76308	4.00268	4.00268	4.00268
1992-04	78.91240	4.01026	4.01026	4.01026
1992-07	79.02155	4.01581	4.01581	4.01581
1992-10	79.23349	4.02658	4.02658	4.02658
1993-01	79.20093	4.02493	4.02493	4.02493
1993-04	79.40202	4.03515	4.03515	4.03515
1993-07	79.68746	4.04965	4.04965	4.04965
1993-10	79.98150	4.06459	4.06459	4.06459
1994-01	80.39498	4.08561	4.08561	4.08561
1994-04	80.93298	4.11295	4.11295	4.11295
1994-07	81.36589	4.13495	4.13495	4.13495
1994-10	81.95119	4.16469	4.16469	4.16469
1995-01	82.31381	4.18312	4.18312	4.18312
1995-04	82.77355	4.20648	4.20648	4.20648
1995-07	83.05579	4.22083	4.22083	4.22083
1995-10	83.36674	4.23663	4.23663	4.23663
1996-01	83.75614	4.25642	4.25642	4.25642
1996-04	83.99745	4.26868	4.26868	4.26868
1996-07	84.40432	4.28936	4.28936	4.28936
1996-10	84.93404	4.31628	4.31628	4.31628
1997-01	85.41946	4.34095	4.34095	4.34095
1997-04	86.02613	4.37178	4.37178	4.37178
1997-07	86.50302	4.39601	4.39601	4.39601
1997-10	86.79889	4.41105	4.41105	4.41105
1998-01	87.04060	4.42333	4.42333	4.42333
1998-04	87.15089	4.42894	4.42894	4.42894
1998-07	87.16066	4.42943	4.42943	4.42943
1998-10	87.22076	4.43249	4.43249	4.43249
1999-01	87.49144	4.44624	4.44624	4.44624
1999-04	88.00067	4.47212	4.47212	4.47212
1999-07	88.51571	4.49830	4.49830	4.49830
1999-10	89.10495	4.52824	4.52824	4.52824
2000-01	89.85519	4.56637	4.56637	4.56637
2000-04	90.37278	4.59267	4.59267	4.59267
2000-07	90.93088	4.62103	4.62103	4.62103
2000-10	91.28603	4.63908	4.63908	4.63908
2001-01	91.07604	4.62841	4.62841	4.62841
2001-04	90.79338	4.61405	4.61405	4.61405
2001-07	90.53572	4.60095	4.60095	4.60095
2001-10	90.37745	4.59291	4.59291	4.59291
2002-01	90.71178	4.60990	4.60990	4.60990
2002-04	91.28434	4.63900	4.63900	4.63900
2002-07	91.59909	4.65499	4.65499	4.65499
2002-10	91.83061	4.66676	4.66676	4.66676
2003-01	92.04953	4.67788	4.67788	4.67788
2003-04	92.06783	4.67881	4.67881	4.67881
2003-07	92.46380	4.69893	4.69893	4.69893
2003-10	93.16893	4.73477	4.73477	4.73477
2004-01	93.69539	4.76152	4.76152	4.76152
2004-04	94.27431	4.79094	4.79094	4.79094
2004-07	94.67953	4.81154	4.81154	4.81154
2004-10	95.14012	4.83494	4.83494	4.83494
2005-01	95.22501	4.83926	4.83926	4.83926
2005-04	95.79995	4.86848	4.86848	4.86848
2005-07	96.16451	4.88700	4.88700	4.88700
2005-10	96.65053	4.91170	4.91170	4.91170
2006-01	97.20744	4.94000	4.94000	4.94000
2006-04	97.59547	4.95972	4.95972	4.95972
2006-07	97.78291	4.96925	4.96925	4.96925
2006-10	98.18088	4.98947	4.98947	4.98947
2007-01	98.58549	5.01003	5.01003	5.01003
2007-04	98.87405	5.02470	5.02470	5.02470
2007-07	99.25850	5.04424	5.04424	5.04424
2007-10	99.53387	5.05823	5.05823	5.05823
2008-01	99.95417	5.07959	5.07959	5.07959
2008-04	100.03024	5.08345	5.08345	5.08345
2008-07	100.00000	5.08192	5.08192	5.08192
2008-10	98.74118	5.01795	5.01795	5.01795
2009-01	96.85931	4.92231	4.92231	4.92231
2009-04	96.84413	4.92154	4.92154	4.92154
2009-07	97.55960	4.95790	4.95790	4.95790
2009-10	98.09802	4.98526	4.98526	4.98526
2010-01	98.70212	5.01596	5.01596	5.01596
2010-04	#N/A	5.05379	5.04113	5.03827
2010-07	#N/A	5.09259	5.06385	5.05643
2010-10	#N/A	5.13007	5.08541	5.07303
2011-01	#N/A	5.16396	5.10478	5.08784
2011-04	#N/A	5.19524	5.12326	5.10212
2011-07	#N/A	5.22447	5.14139	5.11636
2011-10	#N/A	5.25204	5.15940	5.13075
2012-01	#N/A	5.27916	5.17831	5.14608
2012-04	#N/A	5.30542	5.19753	5.16186
2012-07	#N/A	5.33075	5.21683	5.17787
2012-10	#N/A	5.35520	5.23612	5.19401
2013-01	#N/A	5.37899	5.25550	5.21036
2013-04	#N/A	5.40215	5.27487	5.22681
2013-07	#N/A	5.42471	5.29420	5.24332
2013-10	#N/A	5.44677	5.31349	5.25987
2014-01	#N/A	5.46849	5.33284	5.27654
2014-04	#N/A	5.48986	5.35218	5.29326
2014-07	#N/A	5.51091	5.37150	5.31002
2014-10	#N/A	5.53168	5.39078	5.32678
2015-01	#N/A	5.55219	5.41003	5.34356
2015-04	#N/A	5.57250	5.42924	5.36034
2015-07	#N/A	5.59262	5.44843	5.37713
2015-10	#N/A	5.61258	5.46760	5.39392

Other factors argue in favor of a more permanent adverse impact of the worst financial crisis in post-war history on global trade, consistent with empirical evidence showing that imports recover only sluggishly in countries that underwent a financial crisis, even when conditioning on output (e.g. IMF 2010). Firstly, Kannan (2010) presents evidence showing that stressed credit conditions partly explain the fact that recoveries from financial crises tend to be sluggish. In light of the ongoing sovereign debt crises and the feeble state of the banking sector in many advanced economies, global financial conditions are unlikely to fully return to the levels recorded prior to the crisis in the near term.¹² International trade would be particularly exposed to such deterioration in financial conditions, as it is generally more sensitive to financial distress than domestic transactions (Amiti and Weinstein 2009). To some extent, this can be explained by the higher working capital requirements and risk associated with cross-border trade.¹³ Therefore, the financial crisis made the downturn particularly trade-intensive. This might have been amplified by a drying-up of trade finance in the midst of the crisis (Auboin 2009, Chauffour and Farole 2009, Dorsey 2009).

Second, policymakers might increasingly succumb to protectionism and thereby lower the trade intensity of world GDP. While protectionism has so far been contained, protectionist pressures have surfaced amid a sluggish recovery in many advanced economies, persisting global imbalances and the growing risk of "currency wars" (Evenett 2010, Kee et al. 2010). Evidence on the Great Depression suggests that governments are particularly prone to protectionism if they cannot resort to alternative expansionary policies, such as fiscal or monetary policy (Eichengreen and Irwin 2009).

Third, a shift in demand from tradable to non-tradable goods could also be reinforced by the crisis-induced increase in government expenditure relative to GDP across the globe, as the latter is typically characterized by more pronounced home bias than private absorption.

In terms of the error correction model described in Section 3, there are several ways to capture persistent changes in the relationship between trade and GDP. In our "downward shift" scenario, we assume that there will be no further error correction in world trade going forward. Loosely speaking, we force the model not to interpret the remaining deviation from the long-run relationship of world trade and world GDP over the projection horizon as a transitory event that is to be further corrected in the subsequent quarters. As a consequence, there is a permanent one-off downward shift in the level of trade. Technically speaking, we set the residuals from the first-step Engle-Granger equation to zero from 2010Q2 onwards. Under this scenario, the projected profile for world trade growth for 2011 would be much lower, but still above the path projected by the IMF (see Chart 6 and Table 4).

An alternative way to model a more inward-oriented growth profile is to assume that the elasticity of trade to GDP would be lower than before the crisis, at least for some time. We add these alternative elasticities to the downward shift in the level of trade implemented before and call this scenario "new normal". In more detail, we assume that both the short-run elasticity $\gamma_{1}$ and the long-run elasticity ($\beta$) shrink by a factor of 0.8. In this case, the positive impact of an increase in world GDP on world trade is lower than in the baseline scenario. Taking the GDP profile as given, the trajectory of world trade is now significantly flatter than in the "downward shift" or the baseline scenario, and very close to the IMF projection.¹⁴

Table 4. World Trade Growth Under Alternative Scenarios (Annual Percentage Growth)

	2010	2011	2012
baseline	12.6%	13.6%	10.9%
"downward shift"	11.7%	9.3%	8.2%
"new normal"	11.2%	7.7%	6.6%
Memo: IMF	11.4%	7.0%	-

Note: The IMF forecast refers to the WEO October 2010. See main text for details on the various scenarios.

At first sight, it appears that the "new normal" scenario is at odds with the data. After all, world trade expanded rapidly in the second half of 2009 and the first half of 2010, casting doubt on the notion of lower trade elasticities. However, structural changes in the global economy might so far have been concealed by the countervailing impact of temporary factors, such as the inventory cycle and the extensive fiscal stimuli across the globe. These factors may have temporarily stimulated world trade (also by propping up world GDP), but their impacts are already waning. Indeed, world trade growth slowed down considerably in the second half of 2010. It seems fair to conclude that the jury is still out on this, as more sophisticated econometric analysis has to wait until a sufficient number of post-crisis observations become available.

Clearly, imposing a lower elasticity of trade to GDP (Section 4.3) is observationally equivalent to assuming a lower GDP profile compared to the baseline (Section 4.2) as far as the future path of world trade is concerned. Nevertheless, these scenarios differ with respect to the underlying rationale. More specifically, the "new normal" scenario assumes that trade will be hit more than overall economic activity, due to trade-specific frictions such as an increase in protectionism. By contrast, the "lower GDP growth" scenarios are concerned with the possibility of prolonged economic weakness in overall economic activity, which translates "one-to-one" into a lower trade profile.

In summary, there is a possibility that the fallout of the financial crisis will affect the relationship between international trade and economic activity, at least in the near term. Our scenario analysis illustrates that this would dramatically alter the future path of world trade. Crucially, such a scenario has to be imposed by the modeler, as the equilibrating forces are inherent to our error correction model. In the following section, we develop an alternative to this approach by augmenting the model with a financial distress variable. As a result, the model will be able to generate a rather sluggish recovery in world trade without turning off the error correction mechanism.

4.4  How Financial Distress Affects World Trade

As already indicated the near-term growth in world trade might be hindered by the fact that financial conditions have not yet returned to pre-crisis levels. In this section we try to account for stressed credit conditions more explicitly. We explore the role of financial conditions in determining the future path of world trade by augmenting the short-term dynamics of the error correction model with a widely-used measure of financial distress: the Chicago Board Options Exchange Market Volatility Index (VIX). The VIX measures the implied volatility of S&P 500 index options and is available from 1990 onwards. Spikes in the VIX tend to coincide with periods of financial distress (see Chart 7).

Chart 7. Financial Distress Indicators (VIX, Global Crisis Index, Ted Spread)

Sources: Bloomberg, Laeven and Valencia (2010) and IMF.
Note: The global "crisis index" is a GDP-weighted index of financial crises in individual countries. The crisis episodes are taken from Laeven and Valencia (2010) and the GDP weights from teh IMF-WEO database.

Data for Chart 7

	Crisis index	Ted spread (rhs)	VIX
1980Q1	4.256
1980Q2	4.256
1980Q3	4.256
1980Q4	4.256
1981Q1	7.135
1981Q2	7.135
1981Q3	7.135
1981Q4	7.135
1982Q1	6.807
1982Q2	6.807
1982Q3	6.807
1982Q4	6.807
1983Q1	6.551
1983Q2	6.551
1983Q3	6.551
1983Q4	6.551
1984Q1	5.43
1984Q2	5.43
1984Q3	5.43
1984Q4	5.43
1985Q1	4.094	0.674
1985Q2	4.094	0.872
1985Q3	4.094	0.911
1985Q4	4.094	0.757
1986Q1	0.683	0.865
1986Q2	0.683	0.721
1986Q3	0.683	0.806
1986Q4	0.683	0.568
1987Q1	0.511	0.7645
1987Q2	0.511	1.3185
1987Q3	0.511	1.464
1987Q4	0.511	1.5885
1988Q1	25.545	1.0835
1988Q2	25.545	1.1805
1988Q3	25.545	1.103
1988Q4	25.545	1.262
1989Q1	1.285	1.1285
1989Q2	1.285	1.0785
1989Q3	1.285	1.0165
1989Q4	1.285	0.596
1990Q1	5.629	0.456	22.17
1990Q2	5.629	0.383	18.72
1990Q3	5.629	0.9625	25.2
1990Q4	5.629	0.995	26.11
1991Q1	7.812	0.442	22.52
1991Q2	7.812	0.4945	17.15
1991Q3	7.812	0.371	16.63
1991Q4	7.812	0.3545	17.46
1992Q1	6.894	0.1685	17.49
1992Q2	6.894	0.2895	15.63
1992Q3	6.894	0.384	13.88
1992Q4	6.894	0.2965	14.74
1993Q1	9.568	0.293	13.26
1993Q2	9.568	0.2325	12.97
1993Q3	9.568	0.398	12.11
1993Q4	9.568	0.316	12.42
1994Q1	12.627	0.3845	13.43
1994Q2	12.627	0.66	14.52
1994Q3	12.627	0.727	12.56
1994Q4	12.627	0.813	15.25
1995Q1	8.972	0.38	12.05
1995Q2	8.972	0.4906	12.22
1995Q3	8.972	0.5421	12.49
1995Q4	8.972	0.548	12.9
1996Q1	6.974	0.3327	15.48
1996Q2	6.974	0.425	16.37
1996Q3	6.974	0.593	16.78
1996Q4	6.974	0.3915	17.22
1997Q1	18.133	0.4514	19.94
1997Q2	18.133	0.6143	19.92
1997Q3	18.133	0.6695	22.45
1997Q4	18.133	0.4655	27.11
1998Q1	27.23	0.5909	21.32
1998Q2	27.23	0.6095	21.53
1998Q3	27.23	0.9515	29.76
1998Q4	27.23	0.6136	29.53
1999Q1	12.332	0.525	27.25
1999Q2	12.332	0.5885	24.4
1999Q3	12.332	1.2328	23.31
1999Q4	12.332	0.6733	22.63
2000Q1	12.409	0.419	23.13
2000Q2	12.409	0.9144	24.89
2000Q3	12.409	0.6013	19.17
2000Q4	12.409	0.5038	26
2001Q1	11.3	0.5928	25.75
2001Q2	11.3	0.1803	23.99
2001Q3	11.3	0.219	25.37
2001Q4	11.3	0.1563	27.93
2002Q1	1.003	0.253	21.35
2002Q2	1.003	0.1804	21.64
2002Q3	1.003	0.2433	35.07
2002Q4	1.003	0.1902	30.7
2003Q1	0.843	0.1707	30.02
2003Q2	0.843	0.2707	21.53
2003Q3	0.843	0.2224	19.32
2003Q4	0.843	0.2348	17.43
2004Q1	0.151	0.1725	16.66
2004Q2	0.151	0.3488	16.23
2004Q3	0.151	0.3197	15.44
2004Q4	0.151	0.3526	13.65
2005Q1	0.057	0.3536	12.79
2005Q2	0.057	0.4001	13.41
2005Q3	0.057	0.5274	12.25
2005Q4	0.057	0.4622	12.78
2006Q1	0	0.3977	12.04
2006Q2	0	0.505	14.53
2006Q3	0	0.4981	13.61
2006Q4	0	0.3539	11.03
2007Q1	24.54	0.3224	12.56
2007Q2	24.54	0.558	13.73
2007Q3	24.54	1.4331	21.59
2007Q4	24.54	1.4657	22.07
2008Q1	42.068	1.3726	26.12
2008Q2	42.068	1.0525	20.67
2008Q3	42.068	3.1481	25.07
2008Q4	42.068	1.3489	58.6
2009Q1	40.875	0.991	45
2009Q2	40.875	0.4175	33.03
2009Q3	40.875	0.1794	25.53
2009Q4	40.875	0.203	23.11
2010Q1		0.1414	20.15
2010Q2		0.3635	26.31
2010Q3
2010Q4
2011Q1
2011Q2
2011Q3
2011Q4
2012Q1
2012Q2
2012Q3
2012Q4
2013Q1
2013Q2
2013Q3
2013Q4
2014Q1
2014Q2
2014Q3
2014Q4
2015Q1
2015Q2
2015Q3
2015Q4

Interestingly, the VIX turns out to be insignificant when included in levels or first differences (results not reported here). Hence, in general, the impact of financial conditions on world trade appears to be well-captured by the other variables of the model, particularly world GDP. However, the results change notably when we differentiate between "normal times" and periods of severe financial distress. Technically speaking, we construct a dummy variable that is equal to one if the VIX deviates significantly (i.e. one-and-a-half standard deviations) from its longer-term mean, and zero otherwise. This financial distress dummy turns out to be statistically significant and has a negative sign, as expected (see Table 5, third column). The estimated coefficient is relatively small, with quarterly world trade growth being around 1p.p. lower in periods of financial distress as compared to "normal" times, ceteris paribus. This is equivalent to about one standard deviation of world trade growth. However, one should bear in mind that the dummy captures the effects of financial distress on world trade over and above those already captured by the other variables, particularly world GDP and the history of world trade itself. In other words, our results indicate that severe financial distress has a significant effect on world trade growth, over and above its indirect impact through GDP. Moreover, episodes of severe financial distress may last more than one quarter, in which case the estimated impact has to be cumulated.

Table 5: The Impact of Financial Distress - Estimation Results From the Global Error Correction Model

Dependent Variable: Quarterly Growth in World Trade	(1) Benchmark: 1981Q1-2008Q3	(2) Short Sample: 1990Q1-2008Q3	(3) VIX: 1990Q1-2008Q3	(4) Ted Spread: 1985Q1-2008Q3	(5) Crisis Index: 1980Q1-2008Q3
Long-Term Relationship: Global Activity	0.978	0.978	0.981	0.981	0.981
Long-Term Relationship: Globalization Trend	0.008	0.008	0.007	0.007	0.007
Speed of Adjustment	-0.085***	-0.097***	-0.100***	-0.101***	-0.083***
Short-Term Dynamics: Global Activity Growth	1.229***	1.179***	1.281***	1.257***	1.279***
Short-Term Dynamics: Global Trade Growth (Lagged)	0.354***	0.416***	0.384***	0.377***	0.351***
Financial Distress	-	-	-0.006*	-0.003*	-0.003*
adj. R2	0.54	0.56	0.59	0.53	0.55

To illustrate the impacts of renewed financial distress on the future path of world trade, we consider a scenario in which financial conditions remain strained over the entire forecast horizon. More specifically, we feed the estimated model (see Table 5, column 3) with a financial distress dummy that is equal to unity from 2010Q2 onwards. Unfortunately, our simple model does not allow us to capture interactions between financial distress and GDP growth, since both variables are treated as exogenous. Therefore, we make the additional (and admittedly ad-hoc) assumption that financial distress will be associated with a 0.8 p.p. reduction in GDP growth over the horizon compared to the baseline (as in Section 4.2). The corresponding path of world trade is well below the baseline (see Chart 8). At the end of the forecast horizon, trade is 12 percent lower than it would have been in the baseline scenario with benign financial conditions. Even if we assume that GDP growth remains unchanged with respect to the baseline, annual world trade growth would be around one percentage point lower than in the baseline in both 2011 and 2012.

Obviously, it is not straightforward to capture the "global financial conditions" with a single variable. To check for the robustness of our results, we have therefore considered alternative measures of financial conditions, including the Ted spread and a GDP-weighted index of financial crises. The key finding remains that these financial variables are insignificant when included in non-binary form, but turn out to be significant as a dummy variable (see Table 5, columns 4 and 5).

Chart 8. The Impacts of Financial Distress (Log Scale)

Note: The last observation is 2010Q1. Projections up to 2012Q4. For details see main text.

Data for Chart 8

DATE	World Trade	Baseline	Financial Distress
1980-01	#N/A	#N/A	#N/A
1980-04	#N/A	#N/A	#N/A
1980-07	#N/A	#N/A	#N/A
1980-10	#N/A	#N/A	#N/A
1981-01	66.70312	3.38980	3.38980
1981-04	66.99752	3.40476	3.40476
1981-07	67.15884	3.41296	3.41296
1981-10	67.31483	3.42088	3.42088
1982-01	67.42383	3.42642	3.42642
1982-04	67.39675	3.42505	3.42505
1982-07	67.51601	3.43111	3.43111
1982-10	67.45109	3.42781	3.42781
1983-01	67.63936	3.43738	3.43738
1983-04	68.02934	3.45720	3.45720
1983-07	68.40404	3.47624	3.47624
1983-10	68.81639	3.49719	3.49719
1984-01	69.41411	3.52757	3.52757
1984-04	69.67315	3.54073	3.54073
1984-07	69.98172	3.55641	3.55641
1984-10	70.15356	3.56515	3.56515
1985-01	70.28165	3.57166	3.57166
1985-04	70.50704	3.58311	3.58311
1985-07	70.72557	3.59422	3.59422
1985-10	70.97382	3.60683	3.60683
1986-01	71.11139	3.61382	3.61382
1986-04	71.56719	3.63699	3.63699
1986-07	71.90073	3.65394	3.65394
1986-10	72.04530	3.66128	3.66128
1987-01	72.24710	3.67154	3.67154
1987-04	72.62299	3.69064	3.69064
1987-07	73.08464	3.71410	3.71410
1987-10	73.49439	3.73492	3.73492
1988-01	73.54952	3.73773	3.73773
1988-04	73.86995	3.75401	3.75401
1988-07	74.33806	3.77780	3.77780
1988-10	74.75169	3.79882	3.79882
1989-01	75.10260	3.81665	3.81665
1989-04	75.49570	3.83663	3.83663
1989-07	75.59824	3.84184	3.84184
1989-10	76.04211	3.86440	3.86440
1990-01	76.47740	3.88652	3.88652
1990-04	76.67651	3.89664	3.89664
1990-07	76.93850	3.90995	3.90995
1990-10	77.31719	3.92920	3.92920
1991-01	77.50454	3.93872	3.93872
1991-04	77.84431	3.95598	3.95598
1991-07	78.22186	3.97517	3.97517
1991-10	78.37427	3.98292	3.98292
1992-01	78.76308	4.00268	4.00268
1992-04	78.91240	4.01026	4.01026
1992-07	79.02155	4.01581	4.01581
1992-10	79.23349	4.02658	4.02658
1993-01	79.20093	4.02493	4.02493
1993-04	79.40202	4.03515	4.03515
1993-07	79.68746	4.04965	4.04965
1993-10	79.98150	4.06459	4.06459
1994-01	80.39498	4.08561	4.08561
1994-04	80.93298	4.11295	4.11295
1994-07	81.36589	4.13495	4.13495
1994-10	81.95119	4.16469	4.16469
1995-01	82.31381	4.18312	4.18312
1995-04	82.77355	4.20648	4.20648
1995-07	83.05579	4.22083	4.22083
1995-10	83.36674	4.23663	4.23663
1996-01	83.75614	4.25642	4.25642
1996-04	83.99745	4.26868	4.26868
1996-07	84.40432	4.28936	4.28936
1996-10	84.93404	4.31628	4.31628
1997-01	85.41946	4.34095	4.34095
1997-04	86.02613	4.37178	4.37178
1997-07	86.50302	4.39601	4.39601
1997-10	86.79889	4.41105	4.41105
1998-01	87.04060	4.42333	4.42333
1998-04	87.15089	4.42894	4.42894
1998-07	87.16066	4.42943	4.42943
1998-10	87.22076	4.43249	4.43249
1999-01	87.49144	4.44624	4.44624
1999-04	88.00067	4.47212	4.47212
1999-07	88.51571	4.49830	4.49830
1999-10	89.10495	4.52824	4.52824
2000-01	89.85519	4.56637	4.56637
2000-04	90.37278	4.59267	4.59267
2000-07	90.93088	4.62103	4.62103
2000-10	91.28603	4.63908	4.63908
2001-01	91.07604	4.62841	4.62841
2001-04	90.79338	4.61405	4.61405
2001-07	90.53572	4.60095	4.60095
2001-10	90.37745	4.59291	4.59291
2002-01	90.71178	4.60990	4.60990
2002-04	91.28434	4.63900	4.63900
2002-07	91.59909	4.65499	4.65499
2002-10	91.83061	4.66676	4.66676
2003-01	92.04953	4.67788	4.67788
2003-04	92.06783	4.67881	4.67881
2003-07	92.46380	4.69893	4.69893
2003-10	93.16893	4.73477	4.73477
2004-01	93.69539	4.76152	4.76152
2004-04	94.27431	4.79094	4.79094
2004-07	94.67953	4.81154	4.81154
2004-10	95.14012	4.83494	4.83494
2005-01	95.22501	4.83926	4.83926
2005-04	95.79995	4.86848	4.86848
2005-07	96.16451	4.88700	4.88700
2005-10	96.65053	4.91170	4.91170
2006-01	97.20744	4.94000	4.94000
2006-04	97.59547	4.95972	4.95972
2006-07	97.78291	4.96925	4.96925
2006-10	98.18088	4.98947	4.98947
2007-01	98.58549	5.01003	5.01003
2007-04	98.87405	5.02470	5.02470
2007-07	99.25850	5.04424	5.04424
2007-10	99.53387	5.05823	5.05823
2008-01	99.95417	5.07959	5.07959
2008-04	100.03024	5.08345	5.08345
2008-07	100.00000	5.08192	5.08192
2008-10	98.74118	5.01795	5.01795
2009-01	96.85931	4.92231	4.92231
2009-04	96.84413	4.92154	4.92154
2009-07	97.55960	4.95790	4.95790
2009-10	98.09802	4.98526	4.98526
2010-01	98.70212	5.01596	5.01596
2010-04	#N/A	5.05379	5.04171
2010-07	#N/A	5.09259	5.06492
2010-10	#N/A	5.13007	5.08677
2011-01	#N/A	5.16396	5.10616
2011-04	#N/A	5.19524	5.12448
2011-07	#N/A	5.22447	5.14236
2011-10	#N/A	5.25204	5.16009
2012-01	#N/A	5.27916	5.17880
2012-04	#N/A	5.30542	5.19789
2012-07	#N/A	5.33075	5.21710
2012-10	#N/A	5.35520	5.23632
2013-01	#N/A	5.37899	5.25564
2013-04	#N/A	5.40215	5.27496
2013-07	#N/A	5.42471	5.29425
2013-10	#N/A	5.44677	5.31348
2014-01	#N/A	5.46849	5.33279
2014-04	#N/A	5.48986	5.35209
2014-07	#N/A	5.51091	5.37136
2014-10	#N/A	5.53168	5.39060
2015-01	#N/A	5.55219	5.40980
2015-04	#N/A	5.57250	5.42897
2015-07	#N/A	5.59262	5.44811
2015-10	#N/A	5.61258	5.46723

5.  An Examination of U.S. Exports

Having discussed the outlook for global trade, we now examine the performance of and prospects for U.S. exports. Our interest in the U.S. is twofold. First, given the United States weight in global trade, developments in U.S. exports are clearly an important driver of aggregate trade volumes. Second, focusing on a single country allows for an examination of bilateral trade relationships that is not possible when considering aggregates.

5.1  An Error Correction Model for U.S. Exports

What do the scenarios for world trade outlined above imply for the trajectory of U.S. exports? In order to assess the impact of the various global trade scenarios on U.S. exports we first construct a baseline model that mimics the structure of the global error correction model outlined earlier.

The global model was modified along two key dimensions. First, a trade-weighted measure of the real exchange rate was included as an independent variable in the U.S. model. In the global model, relative price changes could be ignored, as they would be expected to change the cross-country pattern of trade but leave the aggregate volume of trade unaffected. However, when considering U.S. exports separately, relative prices play a potentially important role in explaining both the long-run equilibrium level of exports as well as the short-term dynamics. Second, attempts to include a trend were met with counterintuitive results and hence the trend term was dropped from the estimation. (The estimated trend was negative with the effect of blowing up the income elasticity.)

The estimated parameters of the U.S. model are presented in Table 6. The estimated long-run income elasticity is in the vicinity of 1 1/2, greater than that in the global model, but in line with other models of U.S. exports (including the Federal Reserve's U.S. International Transactions model). The real exchange rate has a significant negative coefficient and is estimated to have a long lag structure, with changes in the real exchange rate continuing to impact the level of exports for three years. Shocks to U.S. exports are persistent, but the speed of adjustment back to equilibrium is considerably faster than that estimated in the global model. A lagged dependent variable was included in the regression; however, the coefficient was insignificant and is not reported. Overall the regression explains 48 percent of the variation in U.S. exports, and hence performs on par with the global model.

Table 6. Estimation Results From the U.S. Exports Error Correction Model

Dependent Variable: Quarterly Growth in U.S. Real Exports of Goods*	Benchmark
Sample	1981Q1-2008Q3
Methodology	Engle-Granger
Activity variable	Export -Weighted Foreign GDP
Long-Term Relationship (a): Global Activity	1.474
Long-Term Relationship (a): Real Exchange Rate: Sum (0 to -12)	-0.725
Speed of adjustment	-0.193***
Short-Term Dynamics: Lagged Global Activity Growth	1.755***
Short-Term Dynamics: Real Exchange Rate: Sum (0 to -12)	-0.772***
Test for Autocorrelation: LM(1); p-value	0.73
Test for Autocorrelation: LM(4); p-value	0.45
Test for Heteroscedasticity: ARCH(1); p-value	0.28
Test for Heteroscedasticity: ARCH(4); p-value	0.12
Adj. R2	0.48

Goods excluding end-use code 213 (Computers). Due to idiosyncratic price developments, real computer exports exhibit movements that are not well explained by the model determinates and obscure the overall analysis.

Projected U.S. exports from the baseline model are plotted in Chart 9, along with the model solution for the long-run level of exports. For the purposes of the projections the real exchange rate is assumed to be unchanged from 2010:Q3 onward and foreign GDP expands in line with the IMF WEO forecast. As can be seen by comparing the long-run equilibrium with the data, a large model error opened up over the 2008-09 period, such that even given the decline in foreign GDP, U.S. exports appeared exceptionally weak. By the third quarter of 2010, the error had closed a bit from the mid-2009 trough, but the gap remains substantial. The "baseline" forecast, indicated by the dashed-circle line, calls for the rapid expansion of exports over the next two years, boosted importantly by the model's error correction mechanism.

The chart also plots the "downward shift" and "new normal" scenarios discussed previously. In the "downward shift" scenario it is assumed that there is no further error correction in exports, such that the gap between level of exports in the third quarter of 2010 and the estimated long-run equilibrium level remains constant. Under this scenario, export growth in the projection is determined only by the path of foreign GDP growth and the lagged effect of previous exchange rate changes. The "new normal" scenario maintains the no further bounce back assumption of the "downward shift" scenario, but also assumes a 20 percent decline in the foreign income elasticity of exports going forward.

Chart 9: Baseline Projection and Alternative Scenarios: 2010Q4 - 2012Q4

Data for Chart 9

	Long-Run Model	Baseline	Downward Shift	New Normal	Data
2005Q1	804.3680785				789.4561635
2005Q2	819.0313923				813.9278254
2005Q3	838.0442365				808.5115141
2005Q4	859.2795717				830.9078691
2006Q1	881.394423				867.0737987
2006Q2	903.378696				883.2834409
2006Q3	917.9043594				888.8367421
2006Q4	927.8629372				917.6276972
2007Q1	947.295152				927.1540652
2007Q2	969.5116165				944.6457943
2007Q3	989.6590069				976.6033351
2007Q4	1004.916719				997.9724102
2008Q1	1025.762512				1022.671757
2008Q2	1046.31277				1056.645459
2008Q3	1050.011709				1051.685027
2008Q4	1051.73516				976.245276
2009Q1	1022.712097				878.9673039
2009Q2	979.1736093				870.9290463
2009Q3	1000.073126				909.0191704
2009Q4	1023.162715				969.4535634
2010Q1	1049.597979				999.1927719
2010Q2	1073.316174				1021.206649
2010Q3	1100.646923	1039.575708	1039.575708	1039.575708	1039.575708
2010Q4	1106.818113	1057.82054	1045.404479	1045.404479
2011Q1	1130.324542	1088.443809	1067.606615	1063.14507
2011Q2	1147.663365	1112.364637	1083.983365	1077.032467
2011Q3	1167.237807	1137.41828	1102.471687	1093.198149
2011Q4	1188.709484	1163.501461	1122.751972	1111.473795
2012Q1	1211.611155	1190.290764	1144.382906	1131.338638
2012Q2	1236.077557	1218.034578	1167.491749	1152.476872
2012Q3	1258.56559	1243.33011	1188.731996	1172.057531
2012Q4	1280.293889	1267.44357	1209.254664	1191.11884

A comparison of projected export growth rates is presented in Table 7. As can be seen by comparing the "baseline" forecast with the "downward shift" scenario, the assumption of further export bounce back in the baseline is adding almost 2 1/2 percentage points to export growth in 2011 and over 1 percentage point in 2012. Thus, the prospect of further bounce back and whether or not it materializes is central to any near-term and medium-term forecast for U.S. export growth. Giving the importance of bounce back, the lower income elasticity assumed in the "new normal" scenario is second-order in the near-term, although would be of increasing importance going forward.

Table 7. U.S. Export Growth Under Alternative Scenarios

	2010	2011	2012
Baseline	8.7	9.5	8.6
"Downward Shif"	7.5	7.1	7.4
"New Normal"	7.5	6.1	6.9

5.2  U.S. Exports by Destination and the Impact of a Multi-Speed Recovery

One of the advantages of examining U.S. trade as opposed to world trade is the ability to focus on bilateral trading relationships. In this section we consider U.S. exports by destination disaggregated across advanced foreign economies (AFEs) and emerging market economies (EMEs). This disaggregation will allow two further avenues for examining the prospects for U.S. exports. First, we can decompose the model error into components due to trade with the AFEs and EMEs. Since, as outlined earlier, the prospect for further bounce back is key to the outlook for U.S. export growth; insight into the distribution of the error can provide important information as to whether bounce back will actually materialize. Second, given that the pace of EME growth is expected to far exceed AFE growth over the next few years, a forecast embodied in the IMF WEO projection, we can examine the implications of the projected multi-speed recovery for U.S. exports.

We estimate separate error-correction models for U.S. exports to AFE and EME country groupings. The models are similar in structure to that estimated for aggregate exports. For each country group, the dependent variable is the growth rate of real goods exports from the U.S. to that country group and the independent variables are export-weighted GDP and real exchange rates calculated within each grouping. The results of the regressions are reported in Table 8. A few notable differences are apparent across the two regions. First, the advanced economies have a higher income elasticity but lower price elasticity for U.S. exports compared with the emerging market economies. Second, exports to the emerging market economies tend to bounce back more quickly to their long-run equilibrium than exports to the advanced economies.

Table 8. Estimation Results From the U.S. Exports Error Correction Model - Disaggregated Markets

Dependent Variable: Quarterly Growth in U.S. Real Exports	(1) AFEs	(2) EMEs
Sample	1981Q1-2008Q3	1981Q1-2008Q3
Long-Term Relationship: GDP	1.656	1.335
Long-Term Relationship: Real Exchange Rate (0 to -12)	-0.484	-0.904
Speed of Adjustment	-0.148***	-0.244***
Short-Term Dynamics: Lagged GDP Growth	2.169***	1.538***
Short-Term Dynamics: Real Exchange Rate (0 to -12)	-0.437***	-1.507***
adj. R2	0.36	0.33

Having estimated separate trade models for the AFE and EME countries, we can forecast exports to the two country groupings independently. Before looking at the implications of the disaggregated forecasts for the U.S. export outlook, it is instructive to use the model forecast to examine the 2008-09 trade collapse and the current state of the bounce back in exports. The disaggregated models allow us to decompose the deviation between actual exports and the model-predicted long-run equilibrium (apparent in Chart 9) into components due to underperformance of exports to the AFEs and EMEs separately. As shown in Chart 10, the collapse in exports in 2008-09 resulted in roughly equal model errors for the AFEs and EMEs, with exports underperforming relative to GDP by about the same amount in both regions. The model errors in both regions decreased through the second half of 2009, as exports to both regions bounced back; however, exports to the EME countries appear to have retraced slightly more of their unexplained weakness. So far through 2010, neither the AFE nor the EME model errors has closed much further, and exports have remained weak relative to GDP in both markets. This does not necessarily imply a loss in U.S. export market share, as it may partly reflect a change in the elasticity of trade to GDP across all exporters worldwide, as discussed in Section 4. The model-implied weakness of exports to the EMEs may come as a surprise given the strong growth of apparent exports to the region over 2010; however, EME GDP growth has also been robust over the period and the model would have had exports increase by an even greater amount than was realized.

What does the error decomposition in Chart 10 tell us regarding the prospects for continued error correction in U.S. exports? The relative importance of the AFE countries in the continued underperformance of U.S. exports is not surprising given that a number of trading partners in this grouping suffered severe financial crises. As noted earlier, empirical evidence points to only a sluggish recovery of imports in countries impacted by financial crisis (IMF 2010). It therefore seems likely that this residual may persist for some time. The persistence of the EME model error is harder to explain, in that countries in this grouping largely avoided the financial crisis and have had robust recoveries in GDP. It could be that GDP growth in these countries has been boosted largely by sectors that are less import intensive, i.e. government spending and infrastructure, and as such the model, ignorant of the source of GDP growth, is disappointed by the relative paucity of imports drawn in by such growth. As the impact of fiscal stimulus fades and increased government spending is replaced by other components of GDP, it is likely that imports will recover, pushing U.S. exports to the region closer in line with the level of GDP. However, the persistent gap between actual exports and their estimated equilibrium level might also partly reflect a change in the relationship of trade to GDP, as discussed in Section 4.

A disaggregated consideration of AFE and EME exports also allows us to examine the likely impact of the multi-speed recovery embodied in the IMF's outlook for world growth. In the October 2010 WEO the IMF projects that advanced economies will grow at a sluggish 0.5 percent quarter-on-quarter pace in 2011, picking up a bit to 0.7 percent in 2012. In contrast, emerging market economies are projected to expand at a robust 1.6 percent rate through 2011 and 2012. Using the IMF's growth projections and keeping the real exchange rate flat for both AFEs and EMEs, we are able to project U.S. exports to each region separately, and then aggregate up to a forecast for total U.S. export growth. As shown in Table 9, it turns out that the multi-speed recovery has only a small impact on the forecast in comparison with the baseline aggregate model considered in the previous section.

Chart 10. Decomposition of U.S. Export Model Error Across Destination Markets

Data for Chart 10

	Advanced Foreign Economies	Emerging Market Economies
2008Q1	-9.421582993	-1.249106382
2008Q2	0.891056232	1.521879445
2008Q3	-7.020549593	0.854011752
2008Q4	-42.12989677	-39.81057967
2009Q1	-73.54469013	-73.618414
2009Q2	-64.172204	-43.71566898
2009Q3	-52.20919641	-37.00143234
2009Q4	-29.90918792	-22.60148125
2010Q1	-39.95843823	-10.48353307
2010Q2	-37.60724436	-14.97761244
2010Q3	-32.24357901	-29.53783986

The relatively muted impact of the multi-speed recovery on projected export growth is a result of two factors. First, the AFEs and EMEs each accounted for roughly half of U.S. exports and half of global output in the third quarter of 2010. Thus, from a weighting perspective it makes little difference if we consider global GDP growth or disaggregated growth. Second, the higher growth rate of the EME economies is almost balanced by their lower estimated income elasticity and vice versa for the AFE economies, as such that there is little net impact on the projection for export growth.

Another scenario the disaggregated models allow us to consider is a multi-speed adjustment back to long-run equilibrium. Given that the financial crisis has been centered in the AFEs, it is possible that U.S. exports to the region will remain below their long-run equilibrium for an extended period of time. The "prolonged AFE weakness" scenario then is one where there is no further error correction on the part of exports to the AFEs while exports to the EMEs are allowed to bounce back in line with the model. As shown in Table 9, the lack of bounce back in the AFEs lowers projected export growth by 1 percentage point in 2011 and 0.6 percentage point in 2012.

Table 9. U.S. Export Growth With a Multi-Speed Recovery

	2010	2011	2012
Aggregate Baseline	8.7	9.5	8.6
"Multi-Speed Recovery"	8.6	9.8	9.0
"Prolonged AFE Weakness"	8.3	8.8	8.4

5.3  U.S. Exports by Commodity

Having considered disaggregated markets, we now turn to an examination of U.S. exports of disaggregated commodities. By separately modeling U.S. exports by category of good we can take another cut at examining the weakness of exports through 2008-09 and the prospects for growth going forward.

We estimated error correction models of the same style as in the previous two sections for a comprehensive breakdown of exports into six major commodity headings. For each regression the independent variables, GDP and the real exchange rate, are identical to the series used in the aggregate regression in Table 6. As can be seen in Table 10, there is considerable variation in the income and price elasticity of exports across commodities. Exports of capital goods show the greatest price sensitivity, while food and "other" goods are relatively price inelastic. Consumer goods are the most income elastic, while exports of foods and "other" goods (importantly military goods) are less sensitive to changes in income.

On the surface, the higher income elasticity of consumer goods may appear surprising given that consumption, particularly of non-durables, is generally less cyclical than investment. In part, this surprising result is likely the outcome of the specific composition of U.S. exports of consumer goods. For example in the second quarter of 2008, fully 20 percent of U.S. real consumer goods exports consisted of artwork, jewellery, and gemstones, probably exceeding the weight on such goods in all but a few consumption bundles. More surprising given the high estimated income elasticity, is that pharmaceuticals comprised roughly a quarter of U.S. consumer goods exports, and it seems likely that such exports would be particularly income inelastic.¹⁵

Table 10. Estimation Results From the U.S. Exports Error Correction Model - Disaggregated Commodities

Dependent Variable: Quarterly Growth in U.S. Real Exports	(1) Food	(2) Industrial Materials	(3) Capital Goods	(4) Autos	(5) Consumer Goods	(6) Other
Sample	1995Q2-2008Q3	1995Q2-2008Q3	1995Q2-2008Q3	1995Q2-2008Q3	1995Q2-2008Q3	1995Q2-2008Q3
Long-Term Relationship: Global GDP	0.772	1.316	1.480	1.615	2.105	0.885
Long-Term Relationship: Real Exchange Rate: Sum (0 to -12)	-0.318	-0.504	-0.889	-0.715	-0.766	0.0254
Speed of Adjustment	-0.431***	-0.315***	-0.183***	-0.754***	-0.319***	-0.446***
Short-Term Dynamics: Lagged Global Growth	0.324	1.219***	1.770***	1.964***	1.813***	1.332***
Short-Term Dynamics: Real Exchange Rate: Sum (0 to -12)	0.114	-0.295	-.187	-0.345	-0.783***	2.934***
adj. R2	0.18	0.26	0.33	0.42	0.32	0.20

As was done with destination markets, the separate equations across commodity groups allow us to decompose the deviation from long-run equilibrium into the components due to each commodity. As shown in Chart 11, the unexplained decline in exports that occurred over the 2008-09 period was concentrated in two categories, autos and capital goods. The importance of autos in explaining the model error is particularly pronounced given that this sector accounted for only 10 percent of goods exports in the first half of 2008 and yet accounts for over half of the model error during the trade collapse. The closing of the model error that occurred in the second half of 2009 primarily reflected a narrowing of the large error in autos, while the error in the capital goods model has proven surprisingly resilient. In addition, exports of industrial materials have actually been stronger than the model would have predicted, decreasing the aggregate residual.

One factor contributing to the sluggishness of the recovery in capital goods exports could be the historical persistence of deviations from long-term equilibrium in this sector. As shown in Table 10, the estimated "speed of adjustment" is slowest in the capital goods sector.

Another factor could be that capital goods are impacted more by financial distress than other commodity groups. To investigate this possibility we estimated the capital goods error-correction model including the financial distress variable discussed in section 4.4. However, the coefficients on the financial distress variables did not enter significantly into the regression, perhaps reflecting the relatively short sample period of the regression.

Chart 11. Decomposition of U.S. Export Model Error Across Commodities

Data for Chart 11

	Food	Industrial Materials	Capital Goods	Autos	Consumption Goods	Other
2008Q1	8.315294	8.975791864	-8.835310211	-3.722137904	-1.034581082	1.936481546
2008Q2	5.742516	20.06167573	-5.166901731	-2.136770268	-0.267963737	3.920243788
2008Q3	3.874646	18.83039358	-2.889042297	-0.253633181	1.566452086	-4.552864947
2008Q4	1.784555	-0.665392492	-26.26753546	-22.79905606	-9.514327558	0.747385956
2009Q1	-1.12709	-18.578373	-41.60765369	-51.77876779	-12.13955017	0.662152552
2009Q2	1.049736	0.033903452	-39.92313238	-45.5370034	-3.31816471	1.309838819
2009Q3	0.0908	17.67369083	-41.80790851	-34.14148636	-2.249931431	-3.507847794
2009Q4	9.062531	18.43252128	-36.37561505	-27.90608984	0.307345806	3.051791187
2010Q1	8.206051	23.86771099	-41.62845886	-24.33320402	-1.35152703	-0.944633939
2010Q2	-0.12207	27.36038833	-41.45737764	-23.03861511	-9.202017156	3.377279599
2010Q3	3.386163	19.59701713	-41.72626819	-27.63627033	-11.18977035	2.043179097

6.  Conclusions

Our analysis suggests that during the crisis both world trade and U.S. exports declined significantly more than would have been expected on the basis of historical relationships with economic activity. Moreover, this gap between actual and equilibrium trade is closing only slowly and, under reasonable assumptions, could persist for some time to come. Our analysis highlights that in assessing the near- and medium-term outlook for trade, both globally and for the U.S. specifically, assumptions regarding the speed and degree to which the gap between actual and equilibrium trade closes are absolutely central to any projection for trade going forward. It is too soon to say with any certainty whether the crisis will have only transitory or persistent effects on the relationship between trade and economic activity. However, we have constructed plausible scenarios through which the impact on trade is both long-lasting and meaningful.

Our analysis has shown that many of the factors shaping the future path of world trade will affect U.S. exports in a similar fashion. However, it is also clear that world trade and U.S. trade are highly interdependent (di Mauro et al. 2010). While these linkages are not endogenous to our models, some of the scenarios discussed in this paper can nevertheless be interpreted along these lines. For instance, the scenario with lower world GDP growth (Section 4.2) could partly reflect lower activity growth in the U.S. Of course, the interdependency also works in the other direction. For instance, a possible break in the elasticity of world imports to GDP might further complicate the task of forecasting U.S. exports on the basis of foreign GDP.

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Appendix

Table A.1. International Trade in Goods During the Recent Episode (Percentage Changes)

	80s	90s	2000s (Up to 2008Q3)	2000s (Up to 2010Q1)	Full Sample
Δy: Mean	0.8	0.7	0.9	0.8	0.8
Δy: Median	0.8	0.8	1.1	1.0	0.9
Δy: Std. Dev.	0.3	0.3	0.4	0.6	0.3
Δy: Observations	38	40	35	41	113
Δm: Mean	1.4	1.7	1.6	1.2	1.5
Δm: Median	1.3	1.7	2.0	2.0	1.7
Δm: Std. Dev.	0.8	0.8	1.3	2.5	1.0
Δm: Observations	35	40	35	41	110
Δm/Δy(*)	1.7	2.2	1.7	1.4	1.9
Correlation	0.65	0.59	0.75	0.91	0.65

(1) "China" includes Hong Kong
Note: The country-specific peaks and troughs are based on the period 2008-09. The underlying data are seasonally adjusted and transformed into three-month-moving averages. Aggregates include intra-regional trade.
Sources: CPB and ECB staff calculations.

Table A.2. Descriptive Statistics (Based on Quarterly Percentage Growth Rates)

	80s	90s	2000s (up to 2008Q3)	2000s (up to 2010Q1)	full sample
Δy: Mean	0.8	0.7	0.9	0.8	0.8
Δy: Mean	0.8	0.8	1.1	1.0	0.9
Δy: Mean	0.3	0.3	0.4	0.6	0.3
Δy: Mean	38	40	35	41	113
Δm: Mean	1.4	1.7	1.6	1.2	1.5
Δm: Mean	1.3	1.7	2.0	2.0	1.7
Δm: Mean	0.8	0.8	1.3	2.5	1.0
Δm: Mean	35	40	35	41	110
Δm/Δy(*)	1.7	2.2	1.7	1.4	1.9
Correlation	0.65	0.59	0.75	0.91	0.65

Note: "Full sample" refers to 1980Q1-2008Q3.

Footnotes

⁺  International Finance Division, Board of Governors of the Federal Reserve System, Washington DC 20551 U.S.A. [email protected]. Return to text

⁺⁺  European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany. [email protected], [email protected], [email protected]. Return to text

^**  The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System nor those of the European Central Bank. We have benefited from comments from Tamarah Shakir and the participants at the Workshop on "The Impact of the Crisis on the Assessment of the International Environment: Lessons for Modelers and Practitioners", December 16-17 2010. Return to text

1.  In the aftermath of the burst of the dotcom bubble in 2001, global trade recorded a peak-to-trough decline of about 4 1/2 percent. Before that there were globally only rare quarterly incidents of small negative growth rates in world trade. Return to text

2.  For up-to-date analysis of business cycle synchronisation and the role of trade linkages, see di Mauro et al. (2010). Return to text

3.  For the role of durable goods, see Engel and Wang (2008) and Wang (2010). Return to text

4.  Freund (2009) suggests an elasticity larger than 3, while Cheung and Guichard (2009) find a long-run elasticity of about 2-2.5. This finding may be due to the fact that Cheung and Guichard argue that including a time trend leads in their data to inconclusive results. Accordingly, the autonomous globalisation trends, which are not (or only indirectly) specified in their model, may bias their parameter estimates for the relationship between economic activity and trade upwards. Return to text

5.  We also considered additional lags of $\Delta m$ and $\Delta y$ . However, the refined lag structure was not robust across different sub-samples. Return to text

6.  It is well known that such outliers can produce misleading inferences in the application of unit root tests; see, e. g. Maddala and Kim (2003). Return to text

7.  Another possibility - not discussed in this paper - would be a structural break in the globalisation (time) trend. Return to text

8.  See IMF (2009), pp. 103-138. Return to text

9.  See ECB (2010) for an overview of the different factors shaping the downturn and the subsequent recovery in world trade. Return to text

10.  See Di Giovanni and Levchenko (2009) for evidence on how the shock was transmitted via international production networks. See also di Giovanni and Levchenko (2010) and Levchenko et al. (2010). Return to text

11.  For an overview of the recent measures to support the car industry, see D. Haugh et al. (2010). Return to text

12.  Banking crises like the one observed in the recent past are typically followed by other episodes of financial distress, particularly as far as sovereign debt is concerned. See Reinhart and Rogoff (2009). Return to text

13.  For more details on the time lags involved in international trade, see Djankov et al. (2006) and Hummels (2001). Return to text

14.  If only the long-run elasticity changed, the discrepancy between the "downward shift" and the "new normal" scenarios would be relatively small in the short and medium term - due to the low speed of adjustment - but increase considerably over time. Return to text

15.  In fact, looking just at the most recent downturn and rebound in exports, the greatest volatility in consumer goods exports was recorded in the "gem diamonds" category which plunged 50 percent from the second quarter of 2008 through the second quarter of 2009 only to then rebound 50 percent through the third quarter of 2010. In contrast, exports of pharmaceuticals actually increased 25 percent during the downturn in trade and have fallen back a bit during the rebound. Return to text

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