Abstract
We study China’s growth and integration (trade and financial) in a two-country DSGE model with a dynamic exporting decision, pricing-to-market, incomplete financial markets, and aggregate shocks to trade barriers, productivity, and preferences. We estimate the changes in technology, trade costs, and preferences accounting for the dynamics of China’s gross and net trade flows, export participation, real exchange rate, and growth from 1990 to 2014. We find a large unanticipated decline in bilateral trade barriers with persistent, but not permanent, innovations that include an important gradual, phased-in component. Since the Great Recession, average bilateral barriers have stabilized at low levels even as barriers on Chinese imports have risen substantially relative to exports. Trade stagnation since 2011 largely reflects the completed transition to past trade reforms rather than an increase in trade barriers or reversal in the expected pace of future integration. Trade is forecast to decline almost 1 percent per year starting in 2017. Changes in trade barriers are an important determinant of China’s trade balance and its accumulation of foreign assets, accounting for as much as 70 percent of the foreign assets accumulated by 2014. Shocks to trade barriers and in China are found to have increased ROW consumption by 11.9 percent and employment by 0.6 percent but lowered ROW output by less than 1 percent relative to 1990.
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Notes
Two striking features of China’s economic growth over the last 20 years have been a sustained trade integration, with imports and exports expanding at roughly double the pace of economic activity, and large trade surpluses. For instance, China’s share of US trade (exports and imports) has grown from 2.5 percent in 1992 to 10.6 percent in 2013. This growth has been unbalanced with Chinese exports to the USA on average three times Chinese imports from the USA. Indeed, China has grown from being a slight debtor to the rest of the world of about 5 percent of its GDP in 1992 to being creditor of about 21 percent of its GDP in 2011 (Lane and Milesi-Ferretti, 2007).
The first two channels are emphasized in Alessandria and Choi (2015), while the third is absent as they consider two symmetric countries.
Chari and others (2007) emphasize that gaps in first-order conditions, or wedges, in simple GE models can be used to capture the effects of policies or shocks in richer models.
There is a long tradition of considering the dynamic response of aggregate trade flows and relative prices (Magee, 1973; Junz and Rhomberg, 1973; Meade, 1988; Backus and others, 1994). Examples of papers that estimate the dynamic response to relative prices include Hooper and others, 2000, and Gallaway and others, 2003.
Only WTO members were included in the phase-out of under the ATC and so China’s quotas remained quite constrained until 2001 when it joined the WTO, at which point it jumped to the new levels of quotas.
See Chang and others (2015) for a discussion of the different saving motives of the government and household sector in this period.
Trade here is driven by product differentiation, trade costs, and producer heterogeneity. We abstract from other comparative advantage-based sources of heterogeneity.
The taste for the imported good, a, is a normalization. It is straightforward to increase the trade cost and lower the taste parameter without changing the properties of the model.
We also estimated a version with different fixed costs but found these differences to not be significant when there are also shocks to fixed costs.
The idiosyncratic productivity is assumed to be the same since we would need more data on the characteristics of ROW exporters and there as some important changes in the China survey sample that would need to be considered. Additionally, many of these differences show up as affecting the amount of cross-country substitution from a country-specific shock and get absorbed into the estimated Armington elasticity.
We chose to use the real trade share rather than the nominal trade share for three reasons. First, it is consistent with matching real GDP. Second, we would like to evaluate the impact of trade and thus would like to capture how real expenditures have shifted to traded goods. Finally, some of the fall in nominal trade since the Great Recession reflects a decline in commodity prices and our model is not well suited to capture these types of price changes. Results matching the nominal trade share are available.
These movements seem somewhat consistent with the alternative measures of the extensive margin documented.
Without the trend trade cost shock, we estimate nearly unit root common trade costs. We do not follow this approach as introspection and the estimates of the persistence and variance of trend shock suggest trends matter.
Solving the model for larger shocks is computationally challenging given the persistence of the productivity process.
Shocks to the discount factor account for about 43 percent of the unconditional variance of the nominal export–import ratio.
We focus on this measure rather than the Lane and Milesi-Ferretti (2007) measure since this measure makes fewer adjustments for valuation effects.
For comparison, Rabanal and others (2011) estimate a quarterly diffusion rate of 0.7 percent for advanced economies. The much smaller diffusion rate here may arise because we do not distinguish between a size difference from employment and productivity.
We say explicitly, since domestic absorption in the current model includes investment and consumption.
If we use the nominal trade balance as a share of GDP the correlation is actually positive at 0.12.
Using the Chang and others (2015) data from 1980 to 2013, we find a positive correlation between the trade balance and investment rate of 0.36. In their data, the persistently high trade surpluses since 2000 are associated with a higher investment rate.
As before China’s trade share decreases with common trade costs and China productivity and is hardly affected by the other shocks.
Aside from the parameters in the benchmark model, the capital share is \(\alpha =0.36\), the annual depreciation rate is \(\delta =0.1,\) and the adjustment cost is set to yield investment that is two times as volatile as output.
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*George Alessandria is with the University of Rochester and NBER, Rochester, NY, USA; and email address is: George.Alessandria@gmail.com. Horag Choi is with the Monash University, Melbourne, Australia; and email address is: Horag.Choi@gmail.com. Dan Lu is with the University of Rochester, Rochester, NY, USA; and email address is: DanLv27@gmail.com. We thank Raphael Auer, Oleg Itskhoki, Joe Steinberg, Przemyslaw Wozniak, and participants at the Atlanta Fed/Emory International Economics Workshop, Bank of Canada-Toronto Workshop on Chinese Economy, IMF, and the NBER ITI, IFM, and MATS meetings for helpful comments. Carter Mix provided excellent research assistance.
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