Abstract
One of the most striking aspects of the recent recession is the collapse in international trade. This paper uses disaggregated data on U.S. imports and exports to shed light on the anatomy of this collapse. The paper finds that the recent reduction in trade relative to overall economic activity is far larger than in previous downturns. Information on quantities and prices of both domestic absorption and imports reveals a 40 percent shortfall in imports, relative to what would be predicted by a simple import demand relationship. In a sample of imports and exports disaggregated at the 6-digit NAICS level, the paper finds that sectors used as intermediate inputs experienced significantly higher percentage reductions in both imports and exports. It also finds support for compositional effects: sectors with larger reductions in domestic output had larger drops in trade. By contrast, the paper finds no support for the hypothesis that trade credit played a role in the recent trade collapse.
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Notes
Chinn (2009) estimates an econometric model of U.S. exports, and shows that the recent level of exports is far below what would be predicted by the model. Freund (2009) analyzes the behavior of trade in previous global downturns, and shows that the elasticity of trade to GDP has increased in recent decades, predicting a reduction in global trade in the current downturn of about 15 percent. Our methodology looks at U.S. imports rather than U.S. or global exports, and takes explicit account of domestic and import prices at the quarterly frequency.
Hummels, Ishii, and Yi (2001) and Yi (2003) document the dramatic growth in vertical trade in recent decades, and di Giovanni and Levchenko (2010) demonstrate that greater sector-level vertical linkages play a role in the transmission of shocks between countries.
Raddatz (2009) shows that there is greater comovement between sectors that have stronger trade credit links, while Iacovone and Zavacka (2009) demonstrate that in countries experiencing banking crises, exports fell systematically more in financially dependent industries. Amiti and Weinstein (2009) show that exports by Japanese firms in the 1990s declined when the bank commonly recognized as providing trade finance to the firm was in distress.
The concurrent change in the exchange rate is relatively subdued. Appendix Figure A1 of the working paper version of our paper (Levchenko, Lewis, and Tesar, 2010) plots the long-run path of the nominal and real effective exchange rates for the United States. Over the period coinciding with the trade collapse, the U.S. dollar appreciated slightly in real terms, but the change has been less than 10 percent.
How much of this decline in international trade is due to the extensive margin, that is, disappearing import categories? Although we do not have up-to-date information on the behavior of individual firms, we can use highly disaggregated data on trade flows to shed light on this question. To that end, we examined monthly import data at the Harmonized Tariff Schedule 8-digit classification, which contains about 10,000 sectors. The number of HTS 8-digit categories with nonzero imports does decline during this crisis, but the change is very small: while the United States recorded positive monthly imports in 9,200–9,300 categories during the year leading up to June 2008, in the first half of 2009 that number fell to about 9,100. These disappearing categories account for less than 0.5 percent of the total reduction in imports over this period. Thus, when measured in terms of highly disaggregated import categories, the role of the extensive margin in the current trade collapse appears to be minimal.
This series starts in 1967, as the breakdown of imports into oil and non-oil is not available for the earlier period.
Our approach is related to another benchmark for analyzing trade volumes: the gravity equation. Starting from Equation (A.2), the total nominal trade volumes can be expressed in terms of prices and the nominal output as: p t fy t f=(1−ω)(P t /p t f)ɛ−1X t , where X t ≡P t (C t +I t ) is nominal GDP. The gravity approach proceeds to express p t f as a function of trade costs and the source country characteristics, usually the source country nominal GDP, X t *. The advantage of the gravity approach is that it uses less information, as it does not rely on knowing domestic and import prices. The main disadvantage is that it imposes additional assumptions on the supply side, by taking a stand on what determines p t f. This leads to an unnecessarily restrictive interpretation of the current experience: any shortfall of actual imports from what is implied by the evolution of nominal GDPs must be attributed to an increase in trade costs (see, for example, Jacks, Meissner, and Novy, 2009). In a sense, by subsuming domestic prices and making strong assumption on import prices, the gravity approach forces actual trade to be on the model-implied demand and supply curves exactly. By contrast, our approach uses explicit information on domestic and import prices to gauge how far we are from the model-implied demand curve.
We also constructed a price index for just consumption and investment based on the consumption and investment prices in the National Income and Product Accounts, and used that instead of the GDP deflator. The results were virtually unchanged.
Throughout this section, we assume that the taste parameter ω is not changing. If ω is thought of as a taste shock in the demand for foreign goods, an alternative interpretation of the wedge would be that it reveals what this taste shock must be in each period to satisfy the first-order condition for import demand perfectly. In the IRBC literature, the parameter ω is sometimes thought of as a trade cost, and its value calibrated to the observed share of imports to GDP. Under this interpretation, it may be that during this crisis trade costs went up, thereby lowering imports. While we do not have comprehensive data on total trade costs at high frequencies, anecdotal evidence suggests that if anything shipping costs decreased dramatically in the course of the recent crisis, due in part to the oil price collapse (Economist, 2009). Thus, taking explicit account of shipping costs would make the wedge even larger.
We conjecture that the positive long-run average value over this period may reflect a secular reduction in trade costs, which we do not incorporate explicitly into our exercise.
In the baseline analysis we compute the wedges based on log changes over time—in our case, year-over-year changes in quarterly data. An alternative would be to compute them based on deviations from trend in each variable. To do this, we HP-detrended each series, and built a wedge using Equation (1) such that the caret means the log deviation from trend. This procedure yields qualitatively similar results. In 2009:Q2 the overall wedge stands at −20 percent. This is considerably smaller in magnitude than the baseline value we report. However, it is still quite exceptional by historical standards. In the post-1984 period, the standard deviation of the deviation-from-trend wedge is 4.8 percent, and its mean is very close to zero. This implies that the value of 2009:Q2 wedge is 4.3 standard deviations away from the historical average.
This roughly corresponds to the sum of capital goods; automotive vehicles, engines, and parts; consumer durables; and durable industrial supplies and materials.
Our calculation includes in D̂ structures and residential investment in addition to machinery and equipment. This inclusion tends to make the durable wedge smaller, as real estate prices fell more than overall investment goods prices, shrinking the price component of the durable wedge.
All the data used in this subsection come from the OECD.
The change in trade is computed using the total values of exports and imports in each sector, implying that it is a nominal change. As an alternative, we used import price data from the BLS at the most disaggregated available level to deflate the nominal flows. The shortcoming of this approach is that the import price indices are only available at a more coarse level of aggregation (about 4-digit NAICS). This reduces the sample size, especially for exports, and implies that multiple 6-digit trade flows are deflated using the same price index. Nonetheless, the main results were unchanged.
We also re-estimated all of the specifications while dropping oil sectors: NAICS 211111 (Crude Petroleum and Natural Gas Extraction), 211112 (Natural Gas Liquid Extraction), and 324110 (Petroleum Refineries). All of the results below were unchanged.
For more on these product complexity measures, see Cowan and Neut (2007) and Levchenko (2007).
We take medians to reduce the impact of outliers, which tend to be large in firm-level data. Taking the means instead leaves the results unchanged.
Amiti and Weinstein (2009) emphasize that trade credit in the accounting sense and trade finance are distinct. Trade credit refers to payments owed to firms, while trade finance refers to short-term loans and guarantees extended by banks to firms and used to cover international transactions. Indeed, the emphasis in the policy literature has been on the contraction in trade finance (see, for example, Auboin, 2009; IMF, 2009). Unfortunately, we are not aware of any reliable sector-level measures of trade finance used by U.S. firms engaged in international trade.
We created a classification of durables at the 3-digit NAICS level. Durable sectors include 23X (construction) and 325–339 (chemical, plastics, mineral, metal, machinery, computer/electronic, transportation, and miscellaneous manufacturing). All other 1XX, 2XX, and 3XX NAICS categories are considered non-durable for this exercise.
The peak of both total nominal imports and total nominal exports in the recent crisis is August 2008. An alternative dependent variable would be the percentage drop from the peak to the trough. However, that measure is more noisy because of seasonality. Therefore, we consider a year-over-year reduction, sidestepping seasonal adjustment issues.
Another feature of the vertical linkage hypothesis is that imports and exports will be positively correlated within a sector. To check whether this affects the results, we estimated a Seemingly Unrelated Regression model on the imports and exports equations jointly. The coefficients and the standard errors were very similar to the simple OLS estimates reported in the Tables.
The authors are grateful to Davin Chor and Kalina Manova for sharing the interbank lending rate data used in their paper. Their sample of countries does not cover all of the U.S. imports and exports in each sector, but it comes close, with the mean of 95 percent and medians of 97 percent for exports and 98 percent for imports in our sample of 6-digit NAICS sectors.
These results could be sensitive to the timing of the credit contraction. The Table reports the estimates in which ΔIBRATE c is taken over the 12-month period from April of 2008 to April 2009 (the end point of the Chor-Manova data set). The results are unchanged if we instead lag ΔIBRATE c by a further 6 or even 12 months.
It is suggestive from examining the raw data that there is no time trend in this variable. We confirm this by regressing it on a time trend: the coefficient on the time trend turns out to be very close to zero, and not statistically significant.
It may be that while the impact on the median firm is small, there is still a large aggregate effect due to an uneven distribution of trade credit across firms. To check for this possibility, we built the aggregate accounts payable/cost of goods sold series, by computing the ratio of total accounts payable for all the firms to the sum of all cost of goods sold for the same firms. The results from using this series are even more stark: it shows an increase during the crisis, and its 2009:Q1 value actually stands above its long-run average.
Indeed, in the manufacturing-only sample, the trade credit variable is significant but with the “wrong” sign for both imports and exports: it implies that trade in credit-intensive industries fell by less.
Alternatively, we used the average level of inventories to imports (resp., exports) over the longer period, 2001–07, and the results were unchanged. We also used the percentage change in inventories that happened contemporaneously with the reduction in trade, and the coefficient was insignificant: it appears that there is no relationship between changes in inventories and changes in trade flows over this period.
Alessandria, Kaboski, and Midrigan (2010) argue for the importance of inventory adjustment as an explanation for why trade fell by more than output. The quantitative exercise in that paper focuses on the auto sector. As evident from Table 2, while the auto sector experienced large reductions in cross-border trade, it is far from the only sector that did so. In addition, at the outset of the crisis the automotive vehicles, engines, and parts sector accounted for 9 percent of U.S. exports and 11 percent of U.S. imports. At a purely mechanical level, the auto sector accounted for at most one-sixth of the total reduction in either imports and exports, and thus it is important to consider other sectors in quantitative assessments of the inventory hypothesis.
This formulation may appear to sidestep the special feature of durable goods, namely that it is the stock of durables that enters utility. In our formulation, equation (A.3) defines the flow of new durable goods, rather than the stock. Our assumption is then that the flow of new durable goods is a CES aggregate of the flows of foreign and domestic durable purchases, d t d and d t f. We can then define the stock of durables by its evolution D t =(1−δ)Dt−1+D t , with the stock D t entering the utility function. An alternative assumption would be that foreign and domestic durables have separate stocks, and consumer utility depends on a CES aggregate of domestic and foreign durable stocks (this is the assumption adopted by Engel and Wang, 2009). A priori, we find no economic reason to favor one set of assumptions over the other, while our formulation is much more amenable to analyzing prices and quantities jointly. This is because statistical agencies record quantities and prices of purchases, which are flows.
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Additional information
*Andrei Levchenko is an Assistant Professor, Department of Economics, University of Michigan and a Faculty Research Fellow, National Bureau of Economic Research. Logan Lewis is a PhD Candidate, Department of Economics, University of Michigan. Linda Tesar is a Professor and Chair, Department of Economics, University of Michigan, and a Research Associate, National Bureau of Economic Research. The authors are grateful to Lionel Fontagné, Gordon Hanson, Kalina Manova, David Weinstein, the editors (Pierre-Olivier Gourinchas and Ayhan Kose), two anonymous referees, and workshop participants at the University of Michigan, Federal Reserve Board of Governors, Dartmouth College, Federal Reserve Bank of Dallas, the IMF/Banque de France/PSE Conference on “Economic Linkages, Spillovers and the Financial Crisis,” Princeton IES Summer Workshop, SED-Montreal, and the NBER Summer Institute for helpful suggestions. Çağatay Bircan provided excellent research assistance. Financial support from Cepremap is gratefully acknowledged.
Appendix I
Appendix I
Wedges Derivation
We begin with the simplest two-good IRBC model of Backus, Kehoe, and Kydland (1995). There are two countries, Home and Foreign, and two intermediate goods, one produced in Home, the other in Foreign. There is one final good, used for both consumption and investment. The resource constraint of the Home country in each period is given by:
where C t is Home consumption, I t is Home investment, y t h is the output of the Home intermediate good that is used in Home production, and y t f is the amount of the Foreign intermediate used in Home production. In this standard formulation, consumption and investment are perfect substitutes, and Home and Foreign goods are aggregated in a CES production function. The parameter ω allows for a home bias in preferences.
The household (or, equivalently, a perfectly competitive final goods producer) chooses the mix of Home and Foreign intermediates optimally:
where p t h is the price of the domestically produced good and p t f is the price of the imported good, both expressed in the home country's currency. This yields the standard demand equations:
where
is the standard CES price level.
Log-linearizing these, we obtain the import demand relationship in log changes given in Equation (1).
The derivation is essentially the same for subcomponents of final demand. In particular, suppose that durable goods consumption in the Home country, D t , is an aggregate of Home and Foreign durable varieties:
where d t h is the domestic durable variety consumed in Home, and d t f is the Foreign durable variety consumed in Home. In other words, a “final durable goods” producer aggregates domestically produced durable intermediates with foreign-produced durable intermediates to create a durable good that can be used either as purchases of new durable consumption goods or capital investment.Footnote 30 Cost minimization then produces the expression for the durable wedge in Equation (2).
Similarly, suppose that investment and consumption goods are different, but both are produced from domestic and foreign varieties
In this formulation, domestic consumption goods c t d are different from domestic investment goods i t d, and the same holds for the foreign consumption and investment goods. These production functions then lead to the consumption and investment wedges in Equations (3) and (4).
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Levchenko, A., Lewis, L. & Tesar, L. The Collapse of International Trade during the 2008–09 Crisis: In Search of the Smoking Gun. IMF Econ Rev 58, 214–253 (2010). https://doi.org/10.1057/imfer.2010.11
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DOI: https://doi.org/10.1057/imfer.2010.11