Ebook Can the Standard International Business Cycle Model Explain the Relation Between Trade and Comovement?
Do countries that trade more with each other have more closely synchronized business cycles? Yes, according to the conventional wisdom. Increased trade simply increases the magnitude of the transmission of shocks between two countries. Although this wisdom has circulated widely for a long time, it was not until recently that empirical research was undertaken to assess its validity. Running cross country or cross region regressions, first Frankel and Rose (FR, 1998), and then, Clark and van Wincoop (2001), Otto, Voss, and Willard (2001), Calderon, Chong, and Stein (2002), Baxter and Kouparitsas (2004), and others have all found that, among industrialized countries, pairs of countries that trade more with each other exhibit a higher degree of business cyclecomovement.
Using updated data, we re-estimate the FR regressions, and find that a doubling of the median (across all country-pairs) bilateral trade intensity is associated with an increase in the country-pair’s GDP correlation of about 0.06. These empirical results are all statistically significant, and they suggest that increased international trade may lead to a significant increase in output comovement.
While the results are in keeping with the conventional wisdom, it is important to interpret them from the lens of a formal theoretical framework. The international real business cycle (RBC) framework is a natural setting for this purpose because it is one of the workhorse frameworks in international macroeconomics, and because it embodies the demand and supply side spillover channels that many economists have in mind when they think about the effect of increased trade on comovement. For example, in the workhorse Backus, Kehoe, Kydland (BKK, 1994) model, final goods are produced by combining domestic and foreign intermediate goods. Consequently, an increase in final demand leads to an increase in demand for foreign intermediates.
The impact of international trade on the degree of business cycle comovement has yet to be studied carefully with this framework, as FR note: “the large international real business cycle literature, which does endogenize [output correlations] ... does not focus on the effects of changing economic integration on ... business cycle correlations.” The goal of this paper is to focus on these effects by assessing whether the international RBC framework is capable of replicating the strong empirical findings discussed above. We develop, calibrate, and simulate an international business cycle model designed to address whether increased trade is associated with increased GDP comovement. Our model extends the BKK model in three ways. First, recent research by Heathcote and Perri (2002) shows that an international RBC model with no international financial asset markets (international financial autarky) generates a closer fit to several key business cycle moments than does the model in a complete markets setting or a one-bond setting. Based on this work, in our model we study settings with international financial autarky, as well as complete markets. Second, in the above empirical work, the authors recognize the endogeneity of trade and instrument for it. In our framework, we introduce transportation costs as a way of introducing variation in trade. Different levels of transportation costs will translate into different levels of trade with consequent effects on GDP comovement.
The typical international business cycle model is cast in a two-country setting. Indeed, in a previous paper (Kose and Yi, 2001), we partially addressed the issue of this paper using a two-country model. We argued that the model was able to explain about one-third to one-half of the FR findings; our conclusion was that the model had failed to replicate these findings. However, it turns out that this setting is inappropriate for capturing the empirical link between trade and business cycle comovement. In particular, in a two-country setting, by definition, the (single) pair of countries constitutes the entire world, and one country is always at least one-half of the world economy. This would appear to grossly exaggerate the impact of a typical country on another. In reality, a typical pair of countries is small compared to the rest of the world. Also, a typical country-pair trades much less with each other than it does with the rest of the world. Moreover, Anderson and van Wincoop (2003) carefully show theoretically and empirically that bilateral trading relationships depend on each country’s trade barrier with the rest of the world. Consequently, a more appropriate framework is one that captures the facts that pairs of countries tend to be small relative to the rest of the world, pairs of countries trade much less with each other than they do with the rest of the world, and bilateral trade patterns depend on trading relationships with the rest of the world. These forces can only be captured in a setting with at least three countries. This is our third, and most important, modification of the BKK model.
Our three-country model is calibrated to be as close to our updated FR regressions as possible. In particular, two of our countries are calibrated to two countries from the FR sample (the “country pair”), and the third country is calibrated to the other 19 countries, taken together (the “rest of the world”). We choose four country pairs, all of which are close to the median bilateral trade intensity and GDP correlation. We solve and simulate our model under a variety of transport costs between the two small countries. Following the empirical research, we compute the change in GDP correlation per unit change in the log of bilateral trade intensity. We find that under either set of market structures, the model can match the empirical findings qualitatively, but it falls far short quantitatively. In our baseline experiment, the model explains at most 1/10th of the responsiveness of GDP comovement to trade intensity found in our updated FR regressions.
A key reason for the model’s weak performance is that the trade intensity for each of our benchmark country pairs is small to begin with. A typical country does not trade much with any other country: the median trade intensity in our sample is 0.0023, or approximately of one percent of GDP. For trade intensities close to the median, a doubling or tripling is not a large increase in level terms. Moreover, we perform simulations indicating that what matters for the model is the change in trade intensity levels, not logs. Consequently, we reestimate the FR regressions using trade intensity levels. We also compute the responsiveness of GDP comovement to trade intensity levels implied by our model and compare it to the new coefficient estimates. Now the model performs better. For the best benchmark country pair, the model implies that a one percentage point increase in trade intensity (roughly one percent of GDP) would increase their GDP correlation by about 0.036, which is more than 1/4th of the empirical findings. Nevertheless, for the other country pairs the model continues to fall short by more than an order of magnitude.
While the model performs even better when we employ a lower Armington elasticity of substitution, there is still a sufficient gap between the model and the empirical findings that it is suggestive of a trade-comovement puzzle. This puzzle would be distinct from the puzzles that Obstfeld and Rogoff (2001) document; in particular, it is different from the consumption correlation puzzle. The consumption correlation puzzle is about the inability of the standard international business cycle models to generate the ranking of cross country output and consumption correlations in the data. Our trade-comovement puzzle is about the inability of these models to generate a strong change in output correlations from changes in bilateral trade intensity. In other words, the consumption correlation puzzle is about the levels and ranking of output and consumption correlations, while the trade-comovement puzzle is about a “slope.”
We conduct two further experiments that might help explain the gap between the model and the empirical findings. In one experiment, we vary all transport costs, not just those between the country-pair, but we attribute all of the correlation changes to changes in the country-pair’s bilateral trade. This experiment yields a slope that is closer to, and in some cases, exceeds, the empirically estimated slopes. We also find that the empirical association between trade and total factor productivity (TFP) comovement is almost as strong as the association between trade and GDP comovement. We conduct an experiment in which as we vary transport costs and trade, the correlation of TFP shocks changes in a way that is consistent with our regressions. Now there are two channels affecting GDP correlation, the pure trade channel, and an indirect channel operating through TFP comovement. Not surprisingly, the model does a much better job in this experiment. Both of our experiments provide guidance for future empirical and modeling work on resolving this puzzle.
In Section II, we update the Frankel-Rose regressions to study the empirical relationship between trade and business cycle comovement. In Section III, we describe our three-country model and its parameterization. Our quantitative assessment of the model is conducted in Section 4. Section 5 concludes.
Contents
I. Introduction
II. Empirical Link between Trade and Comovement
III. The Model
A. Preferences
B. Technology
- The Intermediate Goods Sector
Transportation Costs
The Final Goods Sector
C. Asset Markets
D. Equilibrium
E. Calibration and Solution
- Calibration
Solution
IV. Quantitative Assessment of the Effects of Trade on Comovement
A. Main Results
- Baseline Experiment
Low Elasticity Experiment
B. Two Additional Experiments
- All Transport Costs Change
TFP shock correlation changes as transport costs change
V. Conclusion
Appendix I. Productivity Shock Process and Import Shares
A. Estimating Productivity Shock Process
B. Calculating Import Shares
References
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