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Federal Reserve Bank of Chicago China’s Export Growth and the China Safeguard: Threats to the World Trading System Chad P. Bown and Meredith A. Crowley REVISED June, 2007 WP 2004-28 China’s Export Growth and the China Safeguard: Threats to the World Trading System?†,‡ Chad P. Bown Meredith A. Crowley Brandeis University Federal Reserve Bank of Chicago This Draft: June 2007 Abstract China’s deepening engagement in the global trading system and the threat of its export capacity have affected the negotiation, formation, and rules of international trade agreements. Among other changes, China’s 2001 accession to the World Trade Organization (WTO) introduced new allowances for existing members to deviate from core WTO principles of reciprocity and most-favored-nation (MFN) treatment by giving existing members access to a discriminatory, import-restricting China safeguard based on the threat of “trade deflection.” This paper asks whether there is historical evidence that imposing discriminatory trade restrictions against China during its pre-accession period led to Chinese exports surging to alternative markets. To examine this question, we use a newly constructed data set of product-level, discriminatory trade policy actions imposed on Chinese exports to two of its largest destination markets over the 1992-2001 period. Perhaps surprisingly, we find no systematic evidence that either U.S. or EU imposition of such import restrictions during this period deflected Chinese exports to alternative destinations. To the contrary, we provide evidence that such import restrictions may have a chilling effect on China’s exports of these products to secondary markets - i.e., the conditional mean U.S. antidumping duty on China is associated with a 20 percentage point reduction in the relative growth rate of China’s targeted exports. We explore explanations for these puzzling results as well as potential implications for the sustainability of the rules of the world trading system. JEL No. F10, F12, F13 Keywords: Reciprocity, MFN, GATT, WTO, Trade Deflection, Safeguards, Antidumping †Bown (corresponding author): Department of Economics, MS 021, Brandeis University, Waltham, MA 02454-9110 USA tel: 781-736-4823, fax: 781-736-2269, email: cbown@brandeis.edu, web: http://www.brandeis.edu/˜cbown/ Crowley: Department of Economic Research, Federal Reserve Bank of Chicago, 230 S. LaSalle St, Chicago, IL 60604-1413 USA tel: 312-322-5856, fax: 312-322-2357, email: mcrowley@frbchi.org ‡ For helpful discussions, we thank Tom Prusa, Robert Staiger, Robert Feinberg, Rachel McCulloch, Mike Moore, John Fernald, Xenia Matschke, Paroma Sanyal, Patricia Tovar, William Alford, James Durling, Daniel Klett, Maurizio Zanardi, and seminar participants at Brandeis, the University of Connecticut, Florida International University, the Federal Reserve Bank of Chicago, the IMF Conference on Global Implications of China’s Trade, Investment and Growth, and the 2005 SEA meetings. Jaimie Lien, Jaewoo Nakajima, Saad Quayyum and Matthew Niedzwiecki provided outstanding research assistance. Bown acknowledges financial support from the World Bank. The opinions expressed in this paper are those of the authors and do not necessarily reflect those of the Federal Reserve Bank of Chicago or the Federal Reserve System. All remaining errors are our own. 1 Introduction China’s entry into global markets has had an important effect on the rules of the world trading system. After close to fifteen years of negotiations that began under the General Agreement on Tariffs and Trade (GATT), China was finally granted membership in the World Trade Organization (WTO) in 2001. While China’s accession to the organization was heralded as a significant achievement for trade policy negotiators, its terms of accession introduced new allowances for existing members to deviate from historic and core GATT/WTO principles. In particular, the commitment that members adhere to the fundamental rules of reciprocity and most-favored nation (MFN) treatment, the second of which is also referred to as nondiscriminatory treatment across trading partners, was substantially weakened through the introduction of a newly available “China safeguard” import-restricting policy instrument. A political justification for the new safeguard was that China’s export capacity threatened to disrupt established trade patterns. Furthermore, an unprecedented statutory trigger for use of the import restriction is the phenomenon of “trade deflection,” i.e., where a second country’s imports from China surge because of a first country imposing its own trade restriction that shut Chinese exports out of its market. This paper empirically investigates whether there is historical evidence that the imposition of discriminatory import restrictions on Chinese trade deflected Chinese exports to secondary markets during its pre-accession period. Since the discriminatory China safeguard was not in use during this period, we address the question by matching data on Chinese exports to roughly forty destination markets to a new dataset of discriminatory antidumping measures imposed on China by two of its most important trading partners. To the best of our knowledge, this is the first paper to investigate whether Chinese exports have been deflected to alternative markets when hit with discriminatory trade restrictions. Prior research investigating related questions has found evidence of such trade deflection; nevertheless, the prior evidence has not investigated Chinese exports, as it has been limited to the examination of exports from other countries and/or is focused on specific industries.1 WTO members created a “Transitional Product-Specific Safeguard Mechanism” that can be used against imports from China until 2014 under Section 16 of China’s terms of accession (WTO, 2001). Many characteristics of the new China safeguard are at odds with core WTO principles and estab1 In work motivated by the EU’s 2002 global safeguard policy on steel which invoked a similar concern over trade deflection emanating from the U.S. steel safeguard (EU, 2002), Bown and Crowley (2007) find substantial evidence that the imposition of administered import-restricting trade policies against Japanese exports led to export surges to alternative markets. Durling and Prusa’s (2006) investigation of global exports from the hot rolled steel market provides some evidence for trade deflection, as does Debaere’s (2005) investigation of the shrimp market in response to the EU’s discriminatory revocation of GSP status for Thai exporters. 1 lished instruments of administered import protection traditionally available to its Members.2 First, unlike any other import-restricting policy instrument legally available to the WTO membership, the allowance of a China-specific trade restriction on imports of fairly traded goods is otherwise inconsistent with MFN treatment.3 Second, the use of the new China safeguard also does not require the policy-imposing country to immediately compensate China for withdrawing trade concessions. This, in effect, weakens the commitment to the WTO’s reciprocity principle as well.4 The most radical change introduced by the new China safeguard is the weakened evidentiary criterion that WTO members must satisfy in order to legally impose a new barrier to Chinese trade. Specifically, section 16.8 of China’s accession introduced the following, “If a WTO Member considers that an action [i.e., a China safeguard imposed by another Member]... causes or threatens to cause significant diversions of trade into its market [i.e., ‘trade deflection’], it may request consultations with China and/or the WTO Member concerned... If such consultations fail to lead to an agreement... the requesting WTO Member shall be free, in respect of such product, to withdraw concessions accorded to or otherwise limit imports from China...” (WTO, 2001, p. 10). The implication of section 16.8 is that, if one WTO member imposes a China safeguard, a second WTO member can automatically impose a China safeguard on the same product without having to undertake its own injury investigation. Thus the second country can impose a China safeguard on the same product without having to demonstrate actual evidence of a threat of deflected imports from China, evidence of an actual increase in imports from China, or even evidence of injury (or a threat of 2 Some of the discriminatory elements of the China safeguard are reminiscent Japan’s 1955 entry into the GATT. In particular, a 1987 GATT working party pointed out that, despite the desire at the time for some existing members to introduce a new Japan-specific safeguard, “Japan became a contracting party in September 1955 without any new general safeguard clause being added to the General Agreement. Some [13 out of 34] contracting parties invoked Article XXXV [“Non-Application of the Agreement between Specific Contracting Parties”] on Japan’s accession. In a number of cases, Japan negotiated bilateral trade agreements containing special safeguard clauses which were followed by the countries concerned disinvoking Article XXXV.” (GATT 1987, p. 2) For an additional discussion of the China safeguard, see Messerlin (2004). 3 There are three other primary areas under the WTO in which exceptions to MFN-treatment for import restrictions are broadly permissible: 1) raising discriminatory trade barriers against unfairly traded goods under antidumping or countervailing duty laws; 2) lowering trade barriers in a discriminatory manner under a reciprocal preferential trade agreement; and 3) lowering trade barriers in a discriminatory manner to developing countries unilaterally, such as under the Generalized System of Preferences (GSP). 4 Bagwell and Staiger (1999) provide an economic interpretation of reciprocity under the GATT/WTO, noting that it is primarily a rule for renegotiations that limits a WTO trading partner’s permissible compensatory retaliation when a first country seeks to raise its tariff above a previously agreed-upon level, as would be the case here. 2 injury) to its own domestic industry. This is a substantial difference from all other WTO-authorized import restrictions, which require some evidence and impose a nontrivial resource and political cost on a country seeking to limit the market access previously granted to another WTO member.5 This policy is based on the now codified provision that there exists a substantial threat that one country’s China safeguard will deflect Chinese exports to a secondary market. Thus far, the most public battles over use of the new China safeguard focused on the U.S. and EU imposing import restrictions on fair trade from China in the textile and clothing sectors in 2005.6 Nevertheless, any WTO member can impose a China safeguard on any of China’s exported products, an increasing share of which are outside of the textiles and apparel sectors. Data collected from the WTO and reported in Bown (2007) indicates that at least 10 different WTO members initiated investigations under the new China-safeguard policy between 2002 and 2006, with at least five of those countries imposing trade-restricting measures on products as varied as float glass, polyvinyl chloride and porcelain tiles (Turkey), in addition to textiles and clothing products (U.S., EU, Peru and Colombia). An examination of countries with relatively transparent import policy governance such as Canada (CITT, 2007) and the U.S. (ITC, 2007) indicates that WTO members were quick to include the “trade deflection” provision into their domestic implementing legislation thus making it readily accessible for competing industries and policymakers seeking a new trigger to limit Chinese exports.7 Is there historical evidence that discriminatory trade restrictions imposed on China have disrupted trade flows via trade deflection? To investigate this question we examine the impact of discriminatory trade policies on Chinese product-level exports over its pre-accession 1992-2001 period. We focus on U.S. and EU imposition of product-specific, discriminatory import restrictions.8 As table 1 indicates, one motivation for focusing on the U.S. and EU is that they are two of China’s four largest destination markets for its exports. If China’s exporters are able to deflect trade, these are two of the markets from which we expect trade deflection to derive.9 Moreover, our focus on the effect of U.S. and 5 The standard safeguard investigation requires evidence of injury (or threat thereof) caused by increased imports. Antidumping (countervailing duty) investigations also require evidence of less than fair value pricing (illegal export subsidies) in addition to the evidence of injury caused by imports. For a discussion of the general role of safeguards in the WTO, see Hoekman and Kostecki (2001). 6 See, for example, “Made in Washington: How the Textile Industry Alone Won Quotas on Chinese Imports,” Wall Street Journal, 10 November 2005, p. A1. 7 For the U.S., see ‘Section 422: China Trade Diversion Investigations’ of the U.S. Trade Act of 1974, and for Canada, see ‘Safeguard Inquiry: Trade Diversion Imports from China’ of the Canadian International Trade Tribunal Act. 8 In what follows below, for convenience we may refer to the EU as a “country” since it invokes a singular trade policy stance toward Union non-members such as China. 9 Furthermore, we believe there are good reasons to be less interested in focusing on two other primary export markets for China - Hong Kong and Japan - as the “triggers” for the trade deflection. While Hong Kong was technically 3 EU discriminatory trade policies is motivated by data requirements. Both the U.S. and EU utilize discriminatory, antidumping import restrictions and publish very detailed, product-level information on these policies. Using newly collected data on policy impositions at the product level (Bown, 2005) allows us to directly identify evidence of trade deflection associated with such measures.10 Furthermore, as table 2 indicates, the U.S. and EU are useful countries on which to focus because their antidumping authorities frequently targeted Chinese exports with new, discriminatory import restrictions. Indeed, China faced the most antidumping investigations and the most imposed measures over the 1992-2001 period, nearly twice as many as the next most-targeted exporter. And as the middle columns indicate, under both the U.S. and EU antidumping regimes, China was also a frequent single target of investigation, implying that it often faced the imposition of discriminatory antidumping measures that will be most similar to the WTO’s new China safeguard.11 Moreover, even in investigations that target multiple foreign countries exporting the same product, an importer can discriminate against China by imposing higher antidumping duties or more stringent price undertaking requirements than those that are imposed on non-Chinese exporters of the same product. The second-to-last column provides evidence that China faces higher-than-average antidumping measures as well. Nevertheless, despite China being a frequent target of both countries, as table 3 indicates, there is surprisingly little overlap to the Chinese products that are targeted by both the U.S. and EU regimes. For example, of the 123 unique 6-digit Harmonized System (HS) products exported from China that faced antidumping measures in the U.S. and the EU during the 1990-2001 period, only fourteen of those products were targeted by both countries over that twelve year period. As table 4 indicates, most of these products are in the steel (metals) and chemicals industries, and it is even more rare China’s largest export market in 1997, much of China’s exports sent to Hong Kong are never intended for consumption, but instead for processing and re-export to other markets (Feenstra and Hanson, 2004). Furthermore, while Japan is China’s third largest export market and a potential additional country to investigate, Japan has rarely used antidumping historically. 10 Since China was not a WTO member during the sample period under investigation, even the mere attempt to track other (non-U.S., non-EU) countries’ imposition of new import restrictions against China at the product level is extremely difficult, given that such policies were neither restricted by the WTO nor were countries required to report to the WTO the trade policies imposed against China. 11 An antidumping measure would be less discriminatory than a China safeguard if there were multiple exporters targeted in a multi-country investigation of the same product. Hansen and Prusa (1996) argue that this is likely to occur in the U.S. due to the incentive created by U.S. law for petitioning industries to seek to cumulate imports in injury investigations. Furthermore, note that we do not examine the impact of countervailing duties because the U.S. did not impose any countervailing measures against Chinese products over the 1992-2001 period (Bown, 2005) due to a 1984 Department of Commerce decision (upheld by the 1986 Georgetown Steel case) not to consider anti-subsidy investigations of exports from non-market economies like China and the former Soviet Union. 4 that the impositions occur in the same (or even adjacent) years. With respect to our econometric investigation and results, perhaps surprisingly, we find no systematic evidence that U.S. or EU antidumping restrictions deflected Chinese exports to secondary markets over the 1992-2001 period. We examine the potential impact of contemporaneous as well as lagged effects of such policies, and we employ two distinct econometric approaches. Not only is there no evidence of trade deflection to these markets, there is some weak evidence of a reduction in the relative growth of Chinese exports of these targeted products to secondary markets. One interpretation is that this evidence is consistent with a global “chilling effect” of U.S. and EU antidumping on Chinese exports to alternative markets: i.e., Chinese exporters may be learning that certain products are in politically sensitive sectors and choosing to slow down their export expansion in these products. The size of the estimated effect is substantial as the conditional mean U.S. antidumping duty on China of 125% is associated with a 20 percentage point reduction in the relative growth rate of China’s exports. Our empirical results indicate no historical evidence of import restrictions deflecting Chinese trade and disrupting established trading patterns. Ironically, it may not be China’s export growth and ability to deflect trade that poses a threat to the world trading system. Rather, a more substantial threat to the WTO could be the China safeguard policy that has been designed in part to remedy (the historically non-existent for China) trade deflection, but which allows existing WTO members to easily deviate from the WTO’s core principles of reciprocity and MFN treatment. A substantial theoretical literature examining the GATT/WTO, closely associated with the work of Bagwell and Staiger (2002),12 identifies reciprocity and MFN as some of the weakest rules necessary for countries to rely on to negotiate an efficiency-enhancing trade agreement initially and to sustain the agreement over time in the face of political and economic shocks. From this perspective, our results raise the question of any political-economic benefit to inclusion of the trade deflection provision, when easy access to the new China safeguard generated by this provision imposes costs via risks to the sustainability of the WTO. The rest of this paper proceeds as follows. Section 2 details our empirical approach and the related literature. Section 3 describes the data used in the estimation, and section 4 presents our results and 12 While much of the initial work in this area is contained in Bagwell and Staiger (2002), other recent papers also examine the roles of MFN and reciprocity as they relate to issues surrounding the accession of a substantial trading partner. For example, the principles combine to form a first line of defense against ‘bilateral opportunism,’ or the value of a concession won by one country in an earlier negotiation being eroded due to the outcome of a subsequent set of negotiations to which it is not party (Bagwell and Staiger, 2005). Furthermore, the principles can also be combined to facilitate multilaterally efficient outcomes, even when trade policy negotiations occur bilaterally and sequentially (Bagwell and Staiger, 2004). 5 basic robustness checks using a difference-in-difference estimation approach. In section 5 we provide a last sensitivity analysis using an alternative, instrumental variables estimation approach. Section 6 concludes. 2 Empirical Model and Estimation 2.1 The empirical investigation Our empirical analysis is motivated by a three country theoretical model in Bown and Crowley (2007) which develops a number of predictions relating a change in one country’s trade policy to changes in trade flows among other countries. The most novel predictions are termed “trade deflection” and “trade depression.” When one country (A) imposes a country-specific tariff on imports from another country (B), the consequent rise in exports from the second country (B) to the third country (C) is termed trade deflection. Trade depression refers to the reduction in the third country’s (C’s) exports to the second country (B) when the first country (A) imposes a country-specific tariff on imports from country B. While it will not be the focus of empirical investigation here, the model also predicts “trade destruction,” i.e., that country A’s import tariff against country B will result in a fall in A’s imports from country B. Lastly, the model predicts “trade creation through import source diversion” or, more succinctly “trade diversion,” i.e., that country A’s imports from country C will rise (Viner, 1950). In this paper, we estimate an augmented gravity model of China’s (country B’s) product-level exports to 38 trading partners (countries C) which has been adapted to estimate the effects of U.S. and EU (countries A) imposition of product-level antidumping duties. For clarity of exposition, ignoring China’s other trading partners, what effects on trade flows might we expect when the country imposing the tariff is the U.S. and the foreign countries are Japan (country C) and China (country B)? First, if the U.S. imposes a country-specific tariff against China in the form of an antidumping duty and imposes no tariff against Japan, we might expect deflected trade, an increase in Chinese exports to Japan. Second, if the U.S. imposes a country-specific tariff against Japan in the form of an antidumping duty, but not against China, we might expect that Chinese exports to Japan will fall, i.e., depressed trade. In this case, Japanese exports that are diverted away from the U.S. market by the tariff and sold domestically within Japan depress Japanese imports from China. 6 2.2 The empirical model In light of the WTO rules on the China safeguard, our primary interest is identifying trade deflection, an increase in China’s exports to some country i in response to a trade restriction imposed by another country such as the U.S. or EU. We begin with a basic gravity specification for China’s exports to country i that incorporates trade policy changes introduced by the U.S. and EU on their own imports from China. Ultimately we utilize two different econometric approaches to estimating trade deflection. Each approach relies on a different source of variation in the data to obtain identification and, thus, speaks to the robustness of our results. To begin, assume that China’s exports to country i of a 6-digit HS product h in year t can be written as a standard gravity model, t X xciht = aih + aht + ait + act + 0 US β1j τc,ushj + j=t−2 + t X j=t−2 0 EU β4j τi,euhj + t X t X 0 EU β2j τc,euhj + j=t−2 0 i β5j τc,ihj t X j=t−2 0 US β3j τi,ushj (1) + ciht , j=t−2 where xciht denotes exports from China to country i of 6-digit HS product h in year t, aih is country i’s time-invariant propensity to import good h (e.g., time-invariant trade barriers, transportation costs, distance, culture, etc.), aht is a time-varying cost or productivity shock to good h, ait represents country i’s time-varying aggregate variables (e.g., GDP, the exchange rate, aggregate demand for imports), and act represents China’s time-varying aggregate variables (e.g., GDP, the exchange rate, aggregate supply of exports). The τ ’s in equation (1) are the trade policy changes that might impact China’s exports to country i. Their first subscript indicates the country against which the restrictive trade policy is imposed, the second subscript indicates the country imposing the trade restriction, the third subscript denotes the product h, and the fourth subscript denotes the year j. Specifically, we US include: the U.S. import tariff on good h exported from China (τc,ushj ), the EU import tariff on good EU US h exported from China (τc,euhj ), the U.S. import tariff on good h exported from country i (τi,ushj ), EU the EU import tariff on good h exported from country i (τi,euhj ), and country i’s import tariff on i good h exported from China (τc,iht ). Finally, it may be the case that the impact of a change in a tariff on trade flows to secondary markets occurs only after a time delay. Thus we allow for current trade flows to be affected by both the contemporaneous (j = t) imposition of a new trade restriction, as well as trade policy changes of up to two lags (j = t − 1, t − 2). In equation (1), the coefficients β1j (β2j ) and β3j (β4j ) for j = t−2, t−1, t identify trade deflection and trade depression associated with U.S. (EU) antidumping duties, respectively. If the imposition 7 of a U.S. (EU) antidumping duty against China is associated with an increase in China’s exports to a secondary market, we expect that β1j (β2j ) will be greater than zero. Furthermore, estimates of β3j (β4j ) that are less than zero indicate trade depression; i.e., the imposition of a U.S. (EU) antidumping duty against country i is associated with a decrease in China’s exports to that secondary market. The greatest econometric concerns in estimating trade deflection and trade depression in equation (1) are the potential endogeneity of the tariffs and the relationship between a change in a tariff and any underlying cost or productivity shock affecting a particular 6-digit HS good. With regard to the tariffs, it seems reasonable to assume that the U.S. and EU antidumping duties are set independently vis-à-vis China’s exports to some third country i. Moreover, the correlation between U.S. and EU trade policy changes against China in our sample is a very low 0.0006 suggesting that the U.S. and EU only rarely, if ever, respond to a common cost or technology shock in China. Despite this evidence against the concern that trade policy is responding to a common Chinese technology shock at the 6-digit HS level, we still want to carefully control for product-level shocks so that our estimates of the coefficients β1j through β4j can be interpreted as treatment effects of the policy change. 2.3 Difference-in-difference model to estimate trade deflection Our first approach identifies trade deflection by utilizing variation within a 6-digit HS product across two exporting countries. First, rewrite an analog to equation (1) in which the exporter, China, is replaced with a subscript d to denote a different exporting country with exporting characteristics (described below) similar to China. Then we take the time difference of (1) for China as well as the time difference of the analog equation for exporter d, and we difference those two equations. Under the assumption that importing country i’s trade policy is constant over the time period under consideration,13 we then have: (∆xciht − ∆xdiht ) = ∆act − ∆adt + t X 0 US US β1j (∆τc,ushj − ∆τd,ushj ) j=t−2 + t X 0 EU β2j (∆τc,euhj (2) − EU ∆τd,euhj ) + (∆ciht − ∆diht ). j=t−2 The variable ∆xciht (∆xdiht ) denotes the growth of Chinese (country d) exports of h to country i at time t where ∆xciht ≡ xciht −xciht−1 (xciht +xciht−1 )/2 in our basic specifications. This average measure of the growth rate of exports, used by Davis and Haltiwanger (1992), allows us to include observations of 13 Alternatively, if we assume that country i trade policy varies over time, but is MFN, or nondiscriminatory, we arrive at the same specification. 8 zero trade in our estimation sample. Specifically, this measure caps the growth rate of trade between t − 1 and t at +200% when there is entry into a market and -200% when there is exit from a market. Including observations of China’s entry (and exit) into specific markets is important in our empirical work because we wish to understand if China, as a developing country, is also able to deflect its exports to new markets when it faces trade restrictions that may be shutting it out of the U.S. or EU markets. Nevertheless, so as to check the robustnsess of our results, we also include specifications that use conventional log growth rate measures ∆xciht ≡ lnxciht − lnxciht−1 , omitting all observations on entry and exit by construction. Next, we use year dummies to control for aggregate shocks in China US EU and country d, (∆act and ∆adt ). The variable ∆τc,usht (∆τc,euht ) designates the magnitude of the contemporaneous change in the U.S. (EU) tariff rate against imports from China. Similarly, the US EU variable ∆τd,usht (∆τd,euht ) designates the magnitude of the contemporaneous change in the U.S. (EU) tariff rate against imports from country d. When implementing the model on a sample of data, we choose India as ‘country d’ for a number of reasons. As we detail below, India has considerable similarities with China when it comes to export structure (both by commodity and by destination market), export growth during this time period, and it is also an important target of both U.S. and EU use of antidumping. The coefficients β1j and β2j for j = t − 2, t − 1, t identify trade deflection associated with U.S. and EU antidumping duties. If the imposition of a U.S. antidumping duty against China is associated with an increase in China’s exports relative to India’s (country d’s) exports, we expect that β1j will be greater than zero. Similarly, if an increase in the U.S. antidumping duty against India induces Indian trade deflection, we expect India’s exports to market i to rise relative to China’s exports to i, yielding a positive coefficient on β1j . The same reasoning implies that trade deflection associated with an EU antidumping measure will yield an estimate of β2j that is positive. Note, however, that one implication of this particular difference-in-difference approach is that we cannot identify β3j and β4j - i.e., trade depression - from equation (2). We therefore introduce a framework for estimating trade depression separately in the next section. Finally, while equation (2) forms our baseline specification, as a robustness check we also estimate a variant of the model to examine the possibility of “aggregate deflection” by China and India (country d) to all markets other than the U.S. and EU. Specifically, in this particular sensitivity analysis we sum Chinese exports to China’s top 41 trading partners less the U.S., EU and India (country d) for each product in year t (xrow cht ). Similarly, in accordance with our difference-in-difference strategy, we sum India’s (country d’s) exports to those same (China’s top 41) trading partners less the U.S., and the EU for each product h in each year t (xrow dht ). We then estimate an aggregated analog to equation (2) given by 9 (∆xrow cht − ∆xrow dht ) = ∆act − ∆adt + t X 0 US U.S. β1j (∆τc,ushj − ∆τd,ushj ) j=t−2 + t X (20 ) 0 EU EU row β2j (∆τc,euhj − ∆τd,euhj ) + (∆row cht − ∆dht ). j=t−2 We also expect that aggregate trade deflection associated with U.S. and EU duties will be associated with positive coefficient estimates of β1j and β2j . 2.4 Difference-in-difference model of trade depression We use a similar difference-in-difference approach to estimate trade depression. To fix ideas once again, we are interested in the question of whether China’s exports to a secondary country market fall if that country’s own exports of a 6-digit HS product are subject to a U.S. or EU antidumping trade restriction. In order to obtain identification in this case, we utilize variation in China’s exports to two different countries that faced U.S. and EU antidumping restrictions between 1992-2001. Taking the time difference of (1) for two separate export markets, we write the difference between China’s export growth to countries i and k as: (∆xciht − ∆xckht ) = ∆ait − ∆akt + t X 0 US US β3j (∆τi,ushj − ∆τk,ushj ) j=t−2 + t X 0 EU β4j (∆τi,euhj (3) − EU ∆τk,euhj ) + (∆c,iht − ∆ckht ), j=t−2 where variables are defined as in (2), and we use year dummies to control for aggregate variation in countries i and k. The coefficients β3j and β4j for j = t − 2, t − 1, t identify potential trade depression associated with U.S. and EU trade policies. Trade depression, a decline in China’s exports to countries i or k in the face of an antidumping measure, would imply estimates of β3j and β4j that are less than zero. Note, finally, that there are two subtle differences between equations (3) and (2). First, with respect to Chinese exports to two different countries, even a China-specific 6-digit HS productivity shock falls out of the expression, so the restrictiveness of the assumption about time-varying productivity is less stringent in equation (2). Second, equation (3) implicitly assumes that tariff policies by countries i and k are constant over the time period under consideration. In order to estimate equation (3), we choose countries that infrequently changed their own tariffs over the sample period. For reasons we detail below, we estimate equation (3) on relative Chinese export growth to Japan (i) and Korea (k). 10 3 Variable Construction and Data In this section we discuss the construction of variables used in the estimation. Tables 5 and 6 present summary statistics for the primary data used in the estimation. 3.1 Trade variables The dependent variables in the estimation of equations (2), (20 ), and (3) are constructed from the annual volume of China’s exports to 38 of its top markets for roughly 4700 6-digit Harmonized System (HS) products for the years 1992 to 2001 (table 1). The data derives from the World Integrated Trade System (WITS) COMTRADE data base. The dependent variable of equation (2) also requires data on Indian (country d) exports of the same 4700 products to 38 of China’s top markets. In our robustness checks, we also use data on the value of Chinese and Indian exports to these markets. Our final estimation sample includes observations on the dependent variable from 1993 to 2001. First consider the dependent variable in the estimation of equation (2), the difference between the annual growth of China’s exports to 38 different countries i of commodity h and India’s exports of the same commodities to the same countries. In choosing India as ‘country d’ in equation (2) we were guided by a desire to match as closely as possible China’s mix of export markets, its mix of exported goods, its relatively high aggregate growth rate of exports, and the relatively high number of antidumping measures imposed by the U.S. and EU between 1992-2001. Table 1 presents the shares of exports by country for China and India in 1997, the midpoint of our sample. First, the U.S. and EU are important destination markets for both countries and represent a combined 31.0% (46.1%) of China’s (India’s) total exports. They share a number of other important export markets including Japan, South Korea, Singapore, Taiwan, Russia, Australia, Canada and Malaysia. The biggest difference is that Hong Kong is China’s top export market with a 24.0% export share while Hong Kong receives only 5.6% of India’s exports. One likely explanation for this disparity is Hong Kong’s role in entrepôt trade for exports originating in China (Feenstra and Hanson, 2004).14 Finally, export shares are similar in other years, but they do reflect some changes in the structure of trade over time. Table 7 presents the shares of China’s and India’s exports by broadly defined goods categories for 1997. In terms of the mix of exported goods, the top category for both countries is textiles and apparel, which account for 14.1% (17.5%) of China’s (India’s) exports. Metals including steel, are 14 In the formal estimation, we have run specifications of the model that drop Hong Kong as an export market, and we have also examined whether Hong Kong’s re-exports of Chinese goods might account for trade deflection. None of our results were affected by these considerations, though the estimates are available from the authors upon request. 11 another important category of exports, representing 10.1% (11.8%) of China’s (India’s) exports. In terms of growth rates, average annual real growth of exports between 1993 and 2001 was 15.8% for China and 11.0% for India. In our product-level data set, which excludes exports by each country to the U.S., EU and China or India, average annual growth of the volume of exports (across all markets) was 16.2% for China and 11.9% for India. Given the similarities of trade structure by destination markets and by products, the similar high rates of trade growth, and the similar frequencies of AD investigations that we discuss in the next section, India is the best country to use as a control for China in such a difference-in-difference framework. On the other hand, when we estimate equation (3), we define the dependent variable as the difference between Chinese export growth of product h in year t to Japan and Korea. We choose Japan and Korea as the export destinations i and k for the following reasons: (1) Japan and Korea are at similar stages of development with similar industrial structures, (2) the two countries have similar aggregate rates of import growth from China, and (3) both countries frequently face U.S. and EU antidumping measures during this time period with some overlap of products that China exports, making them potentially good targets for identifying trade depression. While Japan and Korea were not required by WTO rules to report changes in trade policy, including antidumping, against China during the 1992-2001 period and, thus, any reporting may be incomplete, some information is available. Japan reported one antidumping case against China (initiated in 1991) and Korea reported eight investigations between 1992 and 2001. While the information reported may be incomplete, it is supportive of our assumption that Japan and Korea’s trade policies against China did not involve high frequency tariff changes during this period. 3.2 U.S. and EU antidumping policy variables The main explanatory variables of interest are the changes to U.S. and EU import policy facing a commodity h exported from China or from another country. Our estimates use the level of duties imposed by the U.S. and by the EU. For EU cases that result in price undertakings, we use reported dumping margins to proxy for the magnitude of the policy change.15 The information on U.S. and EU measures imposed at the product level derives from a newly compiled data source (Bown, 2005).16 For the case of the U.S. (EU) antidumping, the information in the dataset has been collected from original source government publications such as the Federal Register (Official Journal of the European Communities), where we are able to track the dates of 15 In unreported results, we have also separated antidumping cases that end in duties versus those that end in price undertakings, and this does not affect our results. 16 See the publicly available ‘Global Antidumping Database,’ at http://www.brandeis.edu/˜cbown/global ad/ . 12 investigations, measures imposed, countries affected, and 6-digit HS products that were targeted. Our estimation examines the export growth path for products targeted by an antidumping measure for multiple years around the policy’s actual imposition. For both U.S. and EU antidumping measures examined in the estimation, we identify the focal year t as the initiation year of the antidumping investigation, as opposed to the year the final measure was actually imposed, though frequently they will be the same. One motivation for this choice is that there has been evidence in prior research that even antidumping investigations that do not end in imposed measures can have a destructive effect on imports, due to the uncertainty as to the final disposition of the case (Staiger and Wolak, 1994). Nevertheless, we expect that this decision could lead us to estimate a differential impact of Chinese export growth with respect to the timing of U.S. versus EU measures, and in some specifications we therefore allow for the lagged imposition of policies (t − 1, t − 2) to affect contemporaneous export growth. 4 Empirical Results 4.1 Difference-in difference-estimates of trade deflection Do U.S. and EU antidumping duties deflect Chinese and Indian exports to secondary (non-U.S., non-EU) markets? Our difference-in-difference deflection estimates, presented in table 8, indicate no robust evidence of statistically significant deflection. In fact, rather than an increase in exports to third markets, U.S. antidumping duties may be associated with a “chilling” effect on Chinese exports to such alternative markets. With respect to EU trade policy, the only economically and statistically significant finding is a chilling effect associated with EU duties on steel products. Our baseline specification (1) examines the response of the difference between China’s and India’s yearly growth of the volume of trade to the contemporaneous initiation of an AD investigation that resulted in duties imposed by the U.S. and EU against China and/or India, respectively. At this short time horizon, the difference between the within-year policy changes against China and India has no effect on the difference in the growth of the volume of exports to alternative markets. Given that it could take over a year for a U.S. or EU antidumping investigation to result in the imposition of a definitive import restriction, the finding of no contemporaneous response in not entirely surprising. Our second specification (2) utilizes the same dependent variable, but includes lags of the difference in the change in the U.S. and EU duties, respectively. We include lags in case the full impact of a new antidumping restriction is not felt until the full administrative process (or perhaps even longer) is completed. Furthermore, the timing of the effect of U.S. versus EU policies could vary because of 13 differences in their administrative structures, the likelihood that preliminary measures are imposed earlier on in the investigation, etc. In this specification, we find that at one lag, an increase in the U.S. duty against China (or India) is associated with a reduction in the growth rate of Chinese (or Indian) exports to third countries relative to the growth rate of Indian (or Chinese) exports. We interpret this as evidence of a potential chilling effect of the U.S. policy on Chinese exports to alternative markets. In terms of the magnitude of the estimates reported in specification (2), a 1% increase in the duty against China is associated with the difference in the mean export growth rates between China and India narrowing by 0.302 percentage points. In our sample, mean growth for Chinese exports over this period was 16.2% while mean growth for Indian exports was 11.9%. Thus, raising the duty against China by 1% is associated with a decline in the differential of the average growth rate of exports between the two countries from roughly 4.3% (=16.2% - 11.9%) to 4.0%. If the U.S. were to apply the conditional mean duty against China in the sample (125%), this would imply a 20 percentage point reduction in Chinese export growth relative to Indian export growth of the same product. Proceding across specifications, in column (3) we redefine the dependent variable to be the difference in the growth rates of the value of exports and find that our estimates are robust. A 1% increase in a U.S. AD duty against one country leads that country’s export growth to be 0.3 percentage points lower in the year after initiation of the AD investigation that resulted in a duty. In column (4), we replace the Davis and Haltiwanger definition for the growth rate of exports (used in construction of the dependent variable) with the standard log growth rate measure. This measure, by construction, omits all observations in which China or India enters or exits a particular country’s import market in a given year. We find that the estimate of chilling associated with a U.S. AD duty at a lag of one year is again robust, suggesting our results are not sensitive to allowing for entry and exit. Turning to column (5) of table 8, we redefine our dependent variable to be the difference in the growth rate of China’s and India’s aggregate exports (to 38 markets) for each particular product, and we estimate equation (20 ). Specifically, we aggregate the total value of exports of each 6-digit HS product (less exports to the U.S., EU and India or China) in each year for China and India and then calculate the Davis and Haltiwanger growth rate for each product aggregated across destination markets in each year. Relative to our other specifications in which each observation of product-level export growth to each market i carries equal weight, the aggregated growth specification is less likely to be influenced by outlier observations of very high or low growth coming from modest changes in trade volumes when the level of trade is low. Notably, the mean (and standard deviation) of growth aggregated across products for China and India are 9.3% (0.76) and 11.2% (1.15) respectively, which are considerably lower than the mean (and standard deviation) of export growth for China and India of 17.9% (1.27) and 11.9% (1.54), respectively, from our estimation sample for specification (3). In 14 the aggregated growth specification we find a slightly stonger chilling effect; a 1 percent increase in the U.S. AD duty against China or India is associated with a growth rate for the targeted country that is 0.41 percentage points lower than the non-targeted country in the year following initiation of an investigation that resulted in a duty. Column (6) presents our final specification which is the effect of U.S. and EU antidumping duties on a subsample of steel products (HS chapters 72 and 73). Because the steel industry is an active user of antidumping trade restrictions, we might be concerned that the estimated effects are driven entirely by steel products. Nevertheless, our restricted steel sample indicates no statistically significant effect of U.S. antidumping duties, but there is evidence of a chilling effect associated with EU antidumping measures in the year after the AD investigation is initiated. For this subsample of products, the magnitude of the chilling effect of an EU antidumping duty is slightly larger - a 1 percent increase in the duty against one country is associated with the growth rate for the targeted country being 0.93 percentage points lower than that of the non-targeted country. Thus, while there is no evidence of trade deflection, there is some evidence that U.S. and EU antidumping measures are associated with these targeted Chinese and Indian products slowing down their export growth to secondary markets. One explanation for the “chilling effect” result could be that it is self-imposed - i.e., that Chinese or Indian exporters recognize through the U.S. and EU policy that these products are in politically sensitive product categories. Therefore, in the hope that they might avoid such import restrictions in secondary markets as well, the exporters take it upon themselves to curtail their export growth. Nevertheless, this is only one interpretation, as we cannot rule out the possibility that this chilling effect is the result of the secondary market imposing its own import restrictions. We would only be able to address this distinction by having access to data that would fully control for any product-level changes in trade policy on Chinese imports into these other (i.e., non-U.S., non-EU) markets, a difficult endeavor given the lack of data reporting requirements vis-à-vis China during the pre-WTO accession period of the sample, as we described in the introduction. We do note, however, that alternative markets such as Japan and South Korea that did report use of antidumping to the WTO during this time period targeted China with AD actions in products that were different from those targeted by the U.S. and EU. 4.2 Difference-in-difference estimates of trade depression While there is evidence of a “chilling” effect of U.S. and EU antidumping policies on Chinese exports to third markets, is there evidence that when the U.S. and EU impose such policies on third countries that there is also a trade depressing effect on Chinese exports? Table 9 presents our results on trade 15 depression for Chinese exports to Japan and Korea in the face of those two countries being hit with U.S. and EU antidumping. We find strong evidence that the imposition of U.S. antidumping duties against Japan and Korea is associated with a large, economically and statistically significant decline in Chinese exports to Japan and Korea. Beginning with column (7), our baseline specification uses the difference in the growth of the volume of Chinese exports to Japan and Korea as the dependent variable. We find that a 1% increase in the U.S. AD duty against Japan or Korea is associated with the growth of Chinese exports to the targeted country being roughly 1.5 percentage points lower than growth to the non-targeted country. In contrast we find no evidence of depression associated with EU AD duties. This economically large depression effect of U.S. antidumping is robust across specifications using the different dependent variable. Column (8) presents a similar result when we add lags of the change in the duty. Column (9) reports a somewhat larger effect when we redefine the dependent variable to be the difference in the value of export growth. In column (10) we use a log growth measure in order to eliminate observations on entry and exit. The contemporaneous effect of the depression result still exists, though it is moderated by relative export growth two years later. Lastly, column (11) restricts our sample steel products and finds that the magnitude of the coefficient is roughly equal to the coefficient in the sample of all products, suggesting that the effect in steel products is similar to that in non-steel products. We estimate, but do not report, some additional specifications to help us understand and interpret the magnitude of our depression result. First, we observe that entry and, especially, exit by Chinese exporters from specific markets do not drive our results. To check our results from the log growth measure specification (10), we re-estimate specification (9) but drop all observations of Chinese export growth to Japan or Korea that have a value of +/- 2 (indicating entry and exit). For this specification, our estimate of the effect of the difference in a change in the U.S. duty on product h in year t increases slightly in absolute value relative to specification (9) to -2.02 (standard error=.818) from -1.98. Second, we observe that depression is primarily driven by U.S. AD activity against Japan. A few statistics bring this into view. In our sample of 29474 observations, we have only 16 antidumping duties imposed by the U.S. against Korea, but 42 imposed against Japan.17 Moreover, when we look at the mean growth rates of Chinese exports to Korea and Japan conditional upon a U.S. antidumping duty, we find that Chinese exports to Korea are higher while Chinese exports to Japan are substantially lower. 17 To clarify, although the U.S. imposed antidumping measures on roughly 95 (120) different 6-digit HS export products from Korea (Japan) during this time period, Korea (Japan) only imported 16 (42) of these same products from China. 16 Third, we have performed a number of industry-specific regressions which indicate that depression is driven by a variety of products for which Japan faced antidumping duties over a number of years. Fourth, because two products, ferro-silicon/silico-manganese (HS=720230) and temporary lighters (HS=961310) were subject to antidumping investigations in different years by Japan, Korea, the U.S. and EU, we re-estimated all of our depression specifications in the absence of observations on these products. Our estimates were identical to those reported in table 4 to one decimal place.18 Lastly, to better understand the magnitude of our depression coefficient, we calculate the mean change in the level of the value of Chinese exports to Japan, conditional on a U.S. AD duty being imposed. We find that Chinese exports to Japan fall by about U.S.$1 million when the U.S. imposes an AD duty on its imports from Japan. In our dataset, aggregate Chinese exports to Japan rise from roughly U.S.$15 billion in 1993 to U.S.$44 billion in 2001. Thus, our estimate of depression, while large and economically significant in the markets for some products, is small relative to the total value of Japanese imports from China. 5 Robustness: IV estimates of trade deflection and trade depression 5.1 Panel data regression model Given that our estimates of equations (2) and (3) could be sensitive to the choice of countries d (India), i (Japan), and k (Korea), we present a final check on the robustness of our results by examining an alternative model that relies more on cross-sectional variation across 6-digit products and countries to obtain identification. This has some similarities to the approach taken in Bown and Crowley (2007).19 In this alternative approach, we start with the time difference of (1): 18 Japan reported initiating an AD investigation on imports of ferro-silicon (HS=720230) from China in 1991. The U.S. imposed an antidumping restriction on the same 6-digit product in 1993, the EU in 1996 and Korea in 1997. The EU restricted imports of temporary lighters (HS=961310) from China in 1990 and Korea restricted imports of the same product in 1997. 19 Bown and Crowley (2007) estimate trade deflection and trade depression associated with U.S. antidumping against Japanese exports in a panel data model in which Japanese industry-level covariates proxy for technology and cost shocks. The analysis above, in contrast, uses the difference-in-difference equation (2) that does not require productlevel controls to estimate trade deflection. This is useful because comparably disaggregated data to proxy for technology and costs shocks is not available for China during the sample. Nevertheless, as a robustness check to the panel data model in Bown and Crowley (2007), they also estimated the Japanese sample on a similar model with product-level fixed effects and obtained consistent results, thus motivating our robustness check here. 17 t X ∆xciht = ∆aht + ∆act + 0 US β1j ∆τc,ushj + j=t−2 + t X t X 0 EU β2j ∆τc,euhj j=t−2 0 US β3j ∆τi,ushj j=t−2 + t X 0 EU β4j ∆τi,euhj (4) + ∆ciht , j=t−2 where we assume that country i’s trade policy toward China is constant over the time period under investigation. Then, we use 6-digit product fixed effects and lagged export growth to proxy for time-varying cost or productivity shocks at the product level. Our estimating equation is then: ∆xciht = ah + ∆act + ∆ait + t X j=t−2 + t X j=t−2 0 US β3j ∆τi,ushj + t X 0 US β1j ∆τc,ushj + 0 EU β2j ∆τc,euhj j=t−2 t X 0 EU β4j ∆τi,euhj + β50 ∆xciht−1 (5) + ∆ciht , j=t−2 where in estimating we apply the instumental variables techniques of Anderson and Hsiao (1981, 1982) because the autocorrelation of the dependent variable implies that least squares estimation yields biased estimates.20 In the estimation, we instrument for the lagged growth rate, ∆xciht−1 , with the second lag of the log level of exports, ln(xciht−2 ) if xciht−2 > 1 and a value of zero if the second lag of the level of exports is less than 1.21 By utilizing 6-digit HS product fixed effects in (5) we control for changes in production costs or technology that imply that a particular good h will have a growth rate for exports that is higher or lower than average. Note that commodities with very high average growth rates also tend to be those most likely to be targeted for antidumping measures. As in equations (2) and (3) we use year dummies to control for all aggregate variation in China and country i over time. For estimating equation (5), we calculate annual export growth of China’s exports to 38 different countries i listed in table 1, excluding the U.S., EU and India. 20 An alternative approach such as the Arellano and Bond (1991) GMM estimator which utilizes multiple lags of the level of the dependent variable as an instrument for the lagged growth rate is not computationally feasible in our estimation because of the large number of parameters in (5). 21 Because the bias associated with using a weak instrument may be large, we test the quality of our instrument. First-stage restricted and unrestricted regressions are reported in table A-1 for our baseline specification. For all specifications, the F-statistics of roughly 312,000 are far larger than the 99% critical χ2 (1) of 6.63. We conclude that the second lag of the log level of exports is a strong instrument for the lagged growth rate. 18 5.2 Instrumental variables estimates of trade deflection and trade depression Table 10 presents our estimates of trade deflection and trade depression from a panel of Chinese exports to 38 countries. Our finding of a chilling effect of U.S. AD duties from the difference-indifference equation (2) discussed in section 4.1 appears to be robust across models. Although we find no evidence of chilling in specification (12) which regresses the growth of the volume of Chinese trade on only the contemporaneous initiation of AD cases that resulted in changes in U.S. and EU AD duties, when we include two lags of each change in a duty in specification (13), we find that a 1% increase in the U.S. AD duty against Chinese exports is associated with a 0.127% reduction in the growth of exports in the following year. For the conditional mean U.S. antidumping duty on China’s exports in the sample of 125%, this implies a 15.9 percentage point fall in the growth of Chinese exports to an alternative market. When we redefine the dependent variable to be the value of exports (14), we estimate a chilling effect that is similar in magnitude but which is not statistically significant at standard confidence levels. Part of the explanation for this result is the additional observations added to the sample when we switch to values from volumes, as the COMTRADE data reports many observations for Chinese export values that do not include a volume counterpart. In specification (15), we redefine the dependent variable to be the log growth of the value of exports, and in (16) we redefine it to be the Davis-Haltiwanger growth of the value of exports aggregated across the 38 markets in our sample. Both specifications also yield chilling estimates at one lag, a 1% duty implies roughly a 0.10 and 0.15% reduction in export growth, respectively. The last specification, (17), restricts the sample to steel exports and finds evidence consistent with our difference-in-difference estimates of table 8, i.e., there is no statistically significant evidence of deflection or chilling associated with U.S. imposition of antidumping on Chinese steel. The next set of estimates in table 10 suggest evidence of a contemporaneous chilling effect of an EU antidumping duty against imports from China on Chinese exports to third countries. This differs slightly from our difference-in-difference estimates presented in table 8 which found no statistically significant relationship between EU antidumping and Chinese exports to third countries. Across the 6 specifications in table 10, estimates of the magnitude of the effect range from a low of a 0.17% fall in the growth of the value of Chinese exports to a high of a 0.52% fall in the value of Chinese exports of steel products when the EU increases its duty by 1%. For the regression on steel products (column 17), although the timing is slightly different, the relative size of the result vis-à-vis the estimate on the full sample of products is in line with the estimates from our difference-in-difference model. In order to understand the differences between the results of our difference-in-difference model 19 and our IV panel model, we can also examine the sources of variation in the data that identify the deflection/chilling effect for EU AD duties. In the difference-in-difference model of trade deflection, identification comes from variation between Chinese and Indian growth rates within a product. However, EU antidumping measures are highly correlated across China and India, especially for steel. The correlation between EU antidumping measures for China and India is 0.31 in our sample compared to only 0.26 for the U.S. Moreover, the correlation for EU measures is higher (0.66) when we limit our sample to steel products compared to a correlation of 0.47 for the U.S. Thus, identification of the effect of EU AD duties is relatively weak in the difference-in-difference model. However, there is some evidence of chilling in the IV panel estimates because identification in that model comes from (a) time variation in the growth rate within a product exported by China and (b) cross sectional variation across products exported by China. Next consider the third panel of table 10 which presents our estimates of trade depression associated with U.S. AD duties against China. In contrast to our results from the difference-in-difference model, there is no robust evidence of trade depression associated with U.S. AD duties from our IV estimates on a panel of 38 of China’s trading partners. While the estimated coefficient on the contemporaneous effect is frequently negative, it is not statistically significant. The lowest panel of estimates in table 10 presents coefficient estimates of potential trade depression arising from EU AD duties. As with the U.S. estimates, there is no robust evidence of trade depression associated with EU antidumping duties. For two specifications, the logged growth measure (column 15) and steel products (column 17), there is one statistically significant coefficient estimate that indicates trade depression. However, as these results are not robust to slight changes in the specification. A simple explanation for the lack of trade depression in the IV panel model can be found by re-estimating the specification in column (13) on a restricted sample of Chinese exports to Japan and Korea only. In this smaller sample we do observe contemporaneous trade depression, consistent with our difference-in-difference estimates reported in table 9. This suggests that Japan and Korea are unusual among China’s export partners and that the phenonomenon of trade depression is likely limited to the few countries that face very high antidumping duties emanating from the U.S. and EU. 5.3 Puzzles and Potential Explanations A number of potentially complementary explanations are consistent with our results that Chinese exporters did not deflect trade during the 1992-2001 period. First, it could be that the Chinese products hit with U.S. and EU antidumping measures are primarily the function of export platform 20 activity that can easily be disassembled and relocated to another country. It could also be that some of the products are highly differentiated with specifications designed (by U.S. or EU retailers) for one particular export market. Or it could be that these other WTO members were applying higher (non-MFN) tariffs against China during its pre-accession period that China was not able to penetrate. Finally, it could relate to the fact that as a “new” entrant into the global economy, Chinese firms did not yet have the networks over the 1992-2001 period to deflect trade to alternate markets, perhaps not yet having paid the market-specific fixed cost of entry. Regardless of the explanation, our result of “missing” trade deflection is puzzling given that there was such concern about the phenomenon among the WTO membership that China’s terms of accession include a safeguard to pre-emptively control it. 6 Conclusion China’s accession to the World Trade Organization (WTO) introduced a new China safeguard that allowed existing members to substantially deviate from the WTO’s core principles of reciprocity and most-favored-nation (MFN) treatment based on the threat of trade deflection. This paper uses a new data set to construct measures of product-level, discriminatory trade policy actions that two of China’s most important trading partners imposed on its exports during the 1992-2001 period. We find no systematic evidence that either U.S. or EU imposition of discriminatory import restrictions during this period deflected Chinese exports to alternative destinations. To the contrary, we provide some evidence that EU and U.S. trade restrictions may have a chilling effect on China’s exports to secondary markets - i.e., the application of the mean U.S. duty is associated with a 20 percentage point reduction in the relative growth of targeted Chinese (vis-à-vis untargeted Indian) exports of the same product. Our results do raise a number of policy concerns. One derives from a comparison of the results in this paper and the empirical evidence of trade deflection from studies of developed countries (e.g., Bown and Crowley, 2007). Developing country exporters may face an additional cost to antidumping if they are unable to deflect trade and recoup some of their losses.22 This could suggest that the failure to reform antidumping in the Doha Round is even more detrimental to developing countries than had previously been considered. The lack of historical evidence of Chinese trade deflection presents a potential additional con22 For example, we found China did not deflect steel exports whereas Japan did deflect steel exports in the face of U.S. antidumping measures. Thus, the lack of trade deflection by developing countries is not simply a product-level phenomenon determined solely by the differences in the countries’ export baskets. 21 cern raised by the terms of China’s WTO accession. 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[27] Prusa, Thomas J. 2001. “On the Spread and Impact of Anti-Dumping.” Canadian Journal of Economics, 34: 591-611. [28] Staiger, Robert W. and Wolak, Frank A. 1994. “Measuring Industry Specific Protection: Antidumping in the United States.” Brookings Papers on Economic Activity: Microeconomics, 51-118. [29] Trefler, Daniel. 1993. “Trade Liberalization and the Theory of Endogenous Protection: An Econometric Study of U.S. Import Policy.” Journal of Political Economy, 101(1): 138-160. [30] Viner, Jacob. 1950. The Customs Union Issue. New York: Carnegie Endowment for International Peace. [31] WTO. 2001. “Accession of the People’s Republic of China.” available on-line at http://www.wto.org/, document number WT/L/432, 23 November. [32] WTO. 2005. “Transitional Product - Specific Safeguard on Imports to Colombia of Certain Textile Products and Stockings and other Hosiery from the People’s Republic of China.” available on-line at http://www.wto.org/, document number G/SG/N/16/COL/1, 3 October. 25 Table 1: China’s and India’s Major Export Markets, 1997 Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Export Market Share of China’s Total Exports, 1997 Share of India’s Total Exports, 1997 0.240 0.179 0.174 0.131 0.050 0.024 0.019 0.011 0.011 0.011 0.010 0.010 0.008 0.007 0.007 0.006 0.006 0.006 0.005 0.005 0.004 0.004 0.004 0.004 0.004 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.002 0.002 0.002 0.002 0.001 0.001 0.001 - 0.056 0.196 0.055 0.265 0.014 0.022 0.011 0.028 0.014 0.013 0.012 0.013 0.001 0.007 0.047 0.004 0.004 0.001 0.020 0.012 0.023 0.003 0.004 0.000 0.010 0.001 0.002 0.004 0.007 0.001 0.005 0.003 0.007 0.003 0.006 0.001 0.002 0.000 0.001 0.000 0.021 Hong Kong United States Japan European Union South Korea Singapore Taiwan Russia Malaysia Australia Canada Indonesia Thailand Philippines United Arab Emirates Vietnam Brazil Panama India Saudi Arabia South Africa Bangladesh Poland Pakistan Macau Switzerland Myanmar Norway Chile Turkey North Korea Iran Argentina Egypt Mexico Nigeria Hungary New Zealand Israel Czech Republic Kazakhastan China Source: compiled by the authors from COMTRADE. 26 Table 2: U.S. and EU Use of Antidumping Measures, 1992-2001 a. U.S. antidumping Investigations resulting in measures (share of target country’s investigations) Only country named in investigation (share of target country’s investigations) Mean margin (%), cond’l on measures imposed Share of U.S. import market 1995-2001, (rank) Exporting country target Antidumping investigations (share of total) 1 2 3 4 5 6 7 8 9 10 China EU Japan South Korea Taiwan Mexico Brazil Canada India South Africa 55 47 38 32 23 21 18 18 16 12 (0.14) (0.12) (0.10) (0.08) (0.06) (0.05) (0.05) (0.05) (0.04) (0.03) 35 20 21 15 13 7 9 5 9 5 (0.64) (0.43) (0.55) (0.47) (0.57) (0.33) (0.50) (0.28) (0.56) (0.42) 26 10 11 3 3 4 1 6 2 1 (0.47) (0.21) (0.29) (0.09) (0.13) (0.19) (0.06) (0.33) (0.13) (0.08) 137.27 29.24 63.11 15.36 19.72 43.60 63.35 22.38 50.37 42.95 0.08 0.19 0.13 0.03 0.03 0.10 0.01 0.19 0.01 0.00 All other 116 (0.29) 50 (0.43) 12 (0.10) 73.50 0.22 Total 396 (1.00) 189 (0.48) 79 (0.20) 66.31 1.00 (5) (2) (3) (7) (6) (4) (12) (1) (19) (26) b. EU antidumping Investigations resulting in measures (share of target country’s investigations) Only country named in investigation (share of target country’s investigations) Mean margin (%), cond’l on measures imposed Share of EU import market 1995-2001, (rank) Exporting country target Antidumping investigations (share of total) 1 2 3 4 5 6 7 8 9 10 China India South Korea Thailand Russia Taiwan Malaysia Ukraine Indonesia Turkey 53 28 26 22 19 16 15 14 13 13 (0.15) (0.08) (0.07) (0.06) (0.05) (0.04) (0.04) (0.04) (0.04) (0.04) 23 15 13 13 10 8 9 7 7 3 (0.43) (0.54) (0.50) (0.59) (0.53) (0.50) (0.60) (0.50) (0.54) (0.23) 27 6 7 1 3 6 1 0 0 3 (0.51) (0.21) (0.27) (0.05) (0.16) (0.38) (0.07) (0.00) (0.00) (0.23) 76.93 80.85 24.58 41.87 99.81 28.11 34.52 132.43 60.77 32.63 0.06 0.01 0.02 0.01 0.03 0.03 0.02 0.00 0.01 0.02 All other 138 (0.39) 67 (0.49) 20 (0.14) 58.85 0.78 Total 357 (1.00) 175 (0.49) 74 (0.21) 60.04 1.00 Note: (4) (20) (9) (21) (6) (7) (18) (50) (23) (13) Antidumping data compiled by the authors from Bown (2005). Import data from COMTRADE. †EU import data is extra-EU imports only. 27 Table 3: U.S. and EU Antidumping Against China’s and India’s Export Products, 1990-2001 Number of unique† 6-digit HS product codes Exports from China facing U.S. antidumping measures 77 Exports from China facing EU antidumping measures 60 Exports from China facing both U.S. and EU antidumping measures 14 Exports from India facing U.S. antidumping measures 36 Exports from India facing EU antidumping measures 32 Exports from India facing both U.S. and EU antidumping measures 8 Notes: data compiled by the authors based on Bown (2005). † ‘Unique’ relates to the fact that some 6-digit HS products may have been investigated or hit with an antidumping measure more than once during the 12 year sample. 28 Table 4: China’s Export Products Targeted by Both U.S. and EU Antidumping, 1990-2001 Product† (HS 1992 code) Foundry Coke (270400) Persulfates (283340) Sulfanilic Acid (292142) Coumarin (293221) Ferrosilicon (720221) Ferrosilicon (720229) Silicomanganese (720230) Steel Plate (720842) Steel Plate (720843) Iron Waterworks Fittings (730719) Carbon Steel Pipe Fittings (730793) Lug Nuts (731816) Pure Magnesium (810411) Pure Magnesium (810419) Year of EU AD Measure Against China Year of U.S. AD Measure Against China 1999 1994 2001 1994 1992 1992 1996 1999 1999 1999 1994 1996 1997 1997 2000 1996 1991 1994 1992 1992 1993 1996 1996 1992 1991 1990 2000 2000 Notes: data compiled by the authors based on Bown (2005). † Production description based on that listed in the U.S. antidumping investigation. 29 Table 5: Data Summary Statistics for Difference-in-Difference Approach to Trade Deflection Sample Size Difference-in-Difference Model of Deflection Mean Standard Deviation Dependent Variables Difference in volume of export growth of product h 227555 0.0431 1.9788 Yearly growth of the volume of China’s exports of product h 227555 0.1621 1.2355 Yearly growth of the volume of India's exports of product h 227555 0.1190 1.5700 Difference in value of export growth of product h 259595 0.0602 1.9812 Yearly growth of the value of China’s exports of product h 259595 0.1797 1.2690 Yearly growth of the value of India’s exports of product h 259595 0.1195 1.5471 Difference in value of export growth of product h to ROW 37378 -0.0192 1.3695 Yearly growth of the value of China’s exports of product h to ROW 37378 0.0932 0.7600 Yearly growth of the value of India’s exports of product h to ROW 37378 0.1124 1.1565 227555 0.0012 0.0361 429 125.12 80.51 Explanatory Variables U.S. AD duty against China less U.S. AD duty against India U.S. AD duty against China conditional on a duty (%) U.S. AD duty against India conditional on a duty (%) EU AD duty against China less EU AD duty against India 156 41.44 35.00 227555 0.0002 0.0272 EU AD duty against China conditional on a duty (%) 392 67.06 38.11 EU AD duty against India conditional on a duty (%) 319 65.64 66.48 U.S. AD duty against China less U.S. AD duty against India 259595 0.0011 0.0346 U.S. AD duty against China conditional on a duty (%) 459 123.28 80.22 U.S. AD duty against India conditional on a duty (%) 156 41.43 34.62 0.0265 EU AD duty against China less EU AD duty against India 259595 0.0002 EU AD duty against China conditional on a duty (%) 411 67.46 38.51 EU AD duty against India conditional on a duty (%) 319 65.58 67.18 U.S. AD duty against China less U.S. AD duty against India 37378 0.0010 0.0351 U.S. AD duty against China conditional on a duty (%) 57 141.44 88.41 U.S. AD duty against India conditional on a duty (%) 25 44.75 33.49 37378 0.0002 0.0178 EU AD duty against China conditional on a duty (%) 37 57.04 33.05 EU AD duty against India conditional on a duty (%) 19 63.55 65.25 EU AD duty against China less EU AD duty against India 30 Table 6: Data Summary Statistics for Difference-in-Difference Approach to Trade Depression Difference-in-Difference Model of Depression Sample Size Mean Standard Deviation Dependent Variables Difference in volume of export growth of product h 25975 -0.0763 1.4853 25975 0.1439 1.0256 25975 0.2202 1.2432 29474 -0.0686 1.5173 Yearly growth of the value of China’s exports to Japan 29474 0.1744 1.0121 Yearly growth of the value of India’s exports of product h 29474 0.2430 1.2628 25975 0.0004 0.0121 U.S. AD duty against Japan conditional on a duty (%) 39 35.82 24.99 U.S. AD duty against Korea conditional on a duty (%) 15 16.36 14.31 Yearly growth of the volume of China’s exports to Japan Yearly growth of the volume of China's exports to Korea Difference in value of export growth of product h Explanatory Variables U.S. AD duty against Japan less U.S. AD duty against Korea EU AD duty against Japan less EU AD duty against Korea 25975 0.0001 0.0124 EU AD duty against Japan conditional on a duty (%) 9 81.44 29.37 EU AD duty against Korea conditional on a duty (%) 11 36.09 26.46 U.S. AD duty against Japan less U.S. AD duty against Korea 29474 0.0004 0.0127 U.S. AD duty against Japan conditional on a duty (%) 42 38.29 26.22 U.S. AD duty against Korea conditional on a duty (%) 16 16.77 13.92 EU AD duty against Japan less EU AD duty against Korea 29474 0.0001 0.0116 EU AD duty against Japan conditional on a duty (%) 9 81.44 29.37 EU AD duty against Korea conditional on a duty (%) 12 34.20 26.06 31 Table 7: China’s and India’s Major Export Products, 1997 Harmonized System Chapters 01-05 06-15 16-24 25-27 28-38 39-40 41-43 44-49 50-63 64-67 68-71 72-83 84-85 86-89 90-97 Description Animal and Animal Products Vegetable Products Foodstuffs Mineral Products Chemicals & Allied Industries Plastics / Rubber Leather Wood & Wood Products Textiles & Apparel Footwear / Headgear Stone / Glass Metals Machinery / Electrical Transportation Miscellaneous Source: compiled by the authors from COMTRADE. 32 Share of China's Total Exports Share of India's Total Exports 0.000 0.000 0.137 0.027 0.157 0.035 0.013 0.069 0.141 0.004 0.047 0.101 0.170 0.027 0.065 0.000 0.000 0.076 0.018 0.157 0.039 0.006 0.042 0.175 0.011 0.040 0.118 0.202 0.022 0.089 Table 8: Difference-In-Difference Approach to Trade Deflection: The Impact of U.S. and EU Antidumping on China’s Export Growth Relative to India’s Export Growth, 1992-2001 Dependent Variable: Yearly growth† of China’s exports of product h to country i less yearly growth of India’s exports of product h to country i Export quantities (1) Add lagged policy changes (2) Export values (3) Log growth measure (4) Aggregated exports to ROW (5) Steel products only (6) -0.033 (-0.115) -0.023 (-0.115) -0.051 (-0.112) 0.054 (-0.158) 0.084 (-0.201) -0.396 (-0.277) Duty imposed on product h in year t-1 -0.302*** (0.109) -0.302*** (0.106) -0.270* (0.156) -0.414** (0.196) -0.101 (-0.223) Duty imposed on product h in year t-2 0.080 (-0.116) -0.020 (-0.114) -0.005 (-0.166) -0.203 (-0.229) 0.042 (-0.208) 0.138 (-0.152) 0.104 (-0.147) 0.132 (-0.198) 0.234 (-0.397) -0.363 (-0.454) Duty imposed on product h in year t-1 -0.056 (-0.147) 0.059 (-0.144) 0.131 (-0.191) 0.298 (-0.403) -0.926** (0.437) Duty imposed on product h in year t-2 0.117 (-0.146) 0.105 (-0.144) 0.255 (-0.182) -0.165 (-0.411) -0.031 (-0.412) Yes Yes Yes Yes Yes Yes 227555 227462 259595 110691 37378 12916 0.0033 0.0034 0.0021 0.0022 0.0032 0.0033 Explanatory Variables U.S. AD duty against China less U.S. AD duty against India Duty imposed on product h in year t EU AD duty against China less EU AD duty against India Duty imposed on product h in year t 0137 (-0.152) Other Controls Year dummies Observations 2 R Notes: † Subscript h is a 6-digit HS product, and t is a year, the growth rate is defined using the Davis and Haltiwanger (1992) measure described in the text and is thus bounded between -2 (exit) and 2 (entry). In parentheses are standard errors, with ***, **, and * denote variables statistically significant at the 1, 5, and 10 percent levels, respectively. 33 Table 9: Difference-In-Difference Approach to Trade Depression: The Impact of U.S. and EU Antidumping on China’s Export Growth to Japan Relative to Korea, 1992-2001 Dependent Variable: Yearly growth† of China’s exports of product h to Japan less yearly growth of China’s exports of product h to Korea Export quantities (7) Add lagged policy changes (8) Export values (9) Log growth measure (10) Steel products only (11) -1.480* (0.756) -1.627** (0.778) -1.979*** (0.693) -3.499*** (1.255) -1.999** (0.926) Duty imposed on product h in year t-1 0.990 (-0.685) 0.823 (-0.630) 0.403 (-0.891) 1.243 (-0.854) Duty imposed on product h in year t-2 0.563 (-0.626) 0.531 (-0.599) 2.187** (0.875) -0.663 (-0.825) 0.035 (-0.741) 0.261 (-0.755) -0.247 (-0.883) -0.027 (-1.546) Duty imposed on product h in year t-1 -0.261 (-0.837) -0.027 (-0.772) -0.946 (-0.984) 3.332 (-2.778) Duty imposed on product h in year t-2 0.213 (-0.771) -0.062 (-0.719) 1.495* (0.879) -2.525 (-2.781) Yes Yes Yes Yes Yes 25975 25966 29474 21123 1483 0.0130 0.0131 0.0134 0.0119 0.0499 Explanatory Variables U.S. AD duty against Japan less U.S. AD duty against Korea Duty imposed on product h in year t EU AD duty against Japan less EU AD duty against Korea Duty imposed on product h in year t 0.033 (-0.741) Other Controls Year dummies Observations 2 R Notes: † Subscript h is a 6-digit HS product, and t is a year, the growth rate is defined using the Davis and Haltiwanger (1992) measure described in the text and is thus bounded between -2 (exit) and 2 (entry). In parentheses are standard errors, with ***, **, and * denote variables statistically significant at the 1, 5, and 10 percent levels, respectively. 34 Table 10: IV Approach and Panel Estimates: The Impact of U.S. and EU Antidumping Measures on China’s Exports to Secondary Markets, 1992-2001 Dependent Variable: Yearly growth† of China’s exports of product h to country i Export quantities (12) Add lagged policy changes (13) Export values (14) Log growth measure (15) Aggregated exports to ROW (16) Steel products only (17) 0.027 (0.055) 0.005 (0.056) -0.012 (0.060) -0.029 (0.068) -0.030 (0.107) 0.216 (0.136) Duty imposed on product h in year t-1 -0.127*** (0.045) -0.073 (0.051) -0.102* (0.053) -0.154* (0.091) -0.133 (0.124) Duty imposed on product h in year t-2 -0.017 (0.046) -0.029 (0.047) 0.117** (0.055) -0.102 (0.100) -0.112 (0.095) -0.257*** (0.095) -0.169* (0.097) -0.176* (0.094) -0.311** (0.145) -0.515*** (0.177) Duty imposed on product h in year t-1 -0.075 (0.086) 0.007 (0.085) 0.067 (0.089) 0.114 (0.133) -0.093 (0.116) Duty imposed on product h in year t-2 -0.060 (0.095) -0.045 (0.100) 0.002 (0.108) -0.115 (0.179) -0.153 (0.153) -0.334 (0.502) 0.052 (0.478) -0.976 (0.800) -0.020 (0.095) 0.049 (0.641) Duty imposed on product h in year t-1 0.914** (0.386) 0.609 (0.376) 0.262 (0.572) 0.014 (0.092) 0.353 (0.508) Duty imposed on product h in year t-2 0.546 (0.360) 0.491 (0.309) -0.139 (0.423) 0.100 (0.082) 0.249 (0.419) -0.038 (0.271) 0.129 (0.341) 0.094 (0.235) -0.046 (0.085) 0.047 (0.261) Duty imposed on product h in year t-1 -0.498 (0.332) -0.226 (0.326) -0.646** (0.329) 0.018 (0.079) -0.490 (0.49) Duty imposed on product h in year t-2 -0.334 (0.301) 0.102 (0.378) 0.511 (0.442) -0.049 (0.078) -1.369** (0.599) Explanatory Variables U.S. AD duty against China [Trade Deflection] Duty imposed on product h in year t EU AD duty against China [Trade Deflection] Duty imposed on product h in year t -0.229** (0.093) U.S. AD duty against country i [Trade Depression] Duty imposed on product h in year t -0.309 (0.505) EU AD duty against country i [Trade Depression] Duty imposed on product h in year t -0.036 (0.272) Other Controls Instruments for growth of China’s exports of h to country i in t-1 Yes Yes Yes Yes Yes Yes Product h fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes 478931 478851 563430 355555 38282 28762 0.09 0.09 0.09 0.04 0.12 0.10 Observations R2 Notes: † Subscript h is a 6-digit HS product, and t is a year, the growth rate is defined using the Davis and Haltiwanger (1992) measure described in the text and is thus bounded between -2 (exit) and 2 (entry). In parentheses are White’s heteroskedasticity-consistent standard errors corrected for clusters defined on the variable defined as the 6-digit HS product and year combination. ***, **, and * denote variables statistically significant at the 1, 5, and 10 percent levels, respectively. 35 Table A-1: Testing Instrument Quality: First Stage Regressions Dependent Variable: Yearly growth† of China’s exports of product h to country i in t-1 Unrestricted first stage regression (13) Restricted first stage regression (13) Duty imposed on product h in year t 0.049 (0.039) 0.088 (0.065) Duty imposed on product h in year t-1 0.022 (0.050) 0.006 (0.071) Duty imposed on product h in year t-2 -0.113*** (0.038) -0.174*** (0.058) Duty imposed on product h in year t 0.009 (0.085) 0.005 (0.114) Duty imposed on product h in year t-1 -0.131* (0.077) -0.181** (0.090) Duty imposed on product h in year t-2 -0.005 (0.068) -0.005 (0.088) Duty imposed on product h in year t 0.243 (0.361) -0.548 (0.509) Duty imposed on product h in year t-1 0.379 (0.315) 0.184 (0.442) Duty imposed on product h in year t-2 0.685** (0.291) 0.672* (0.355) Duty imposed on product h in year t 0.376 (0.297) 0.265 (0.313) Duty imposed on product h in year t-1 0.433 (0.287) 0.152 (0.309) Duty imposed on product h in year t-2 -0.305 (0.275) -0.672* (0.398) -0.131*** (0.000) -- Product h fixed effects Yes Yes Year fixed effects Yes Yes 534768 534768 0.39 0.03 Explanatory Variables U.S. AD duty against China EU AD duty against China U.S. AD duty against country i EU AD duty against country i Other Controls Second lag of the log level of China’s exports of h to country i Observations R 2 Notes: † Subscript h is a 6-digit HS product, and t is a year, the growth rate is defined using the Davis and Haltiwanger (1992) measure described in the text and is thus bounded between -2 (exit) and 2 (entry). In parentheses are White’s heteroskedasticity-consistent standard errors corrected for clusters defined on the variable defined as the 6-digit HS product and year combination. ***, **, and * denote variables statistically significant at the 1, 5, and 10 percent levels, respectively. 36 Working Paper Series A series of research studies on regional economic issues relating to the Seventh Federal Reserve District, and on financial and economic topics. Does Bank Concentration Lead to Concentration in Industrial Sectors? Nicola Cetorelli WP-01-01 On the Fiscal Implications of Twin Crises Craig Burnside, Martin Eichenbaum and Sergio Rebelo WP-01-02 Sub-Debt Yield Spreads as Bank Risk Measures Douglas D. Evanoff and Larry D. Wall WP-01-03 Productivity Growth in the 1990s: Technology, Utilization, or Adjustment? Susanto Basu, John G. Fernald and Matthew D. Shapiro WP-01-04 Do Regulators Search for the Quiet Life? The Relationship Between Regulators and The Regulated in Banking Richard J. 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Levine and Bhashkar Mazumder WP-02-08 The Immediacy Implications of Exchange Organization James T. Moser WP-02-09 Maternal Employment and Overweight Children Patricia M. Anderson, Kristin F. Butcher and Phillip B. Levine WP-02-10 The Costs and Benefits of Moral Suasion: Evidence from the Rescue of Long-Term Capital Management Craig Furfine WP-02-11 On the Cyclical Behavior of Employment, Unemployment and Labor Force Participation Marcelo Veracierto WP-02-12 Do Safeguard Tariffs and Antidumping Duties Open or Close Technology Gaps? Meredith A. Crowley WP-02-13 Technology Shocks Matter Jonas D. M. Fisher WP-02-14 Money as a Mechanism in a Bewley Economy Edward J. Green and Ruilin Zhou WP-02-15 Optimal Fiscal and Monetary Policy: Equivalence Results Isabel Correia, Juan Pablo Nicolini and Pedro Teles WP-02-16 Real Exchange Rate Fluctuations and the Dynamics of Retail Trade Industries on the U.S.-Canada Border Jeffrey R. Campbell and Beverly Lapham WP-02-17 Bank Procyclicality, Credit Crunches, and Asymmetric Monetary Policy Effects: A Unifying Model Robert R. Bliss and George G. Kaufman WP-02-18 Location of Headquarter Growth During the 90s Thomas H. Klier WP-02-19 The Value of Banking Relationships During a Financial Crisis: Evidence from Failures of Japanese Banks Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman WP-02-20 On the Distribution and Dynamics of Health Costs Eric French and John Bailey Jones WP-02-21 The Effects of Progressive Taxation on Labor Supply when Hours and Wages are Jointly Determined Daniel Aaronson and Eric French WP-02-22 3 Working Paper Series (continued) Inter-industry Contagion and the Competitive Effects of Financial Distress Announcements: Evidence from Commercial Banks and Life Insurance Companies Elijah Brewer III and William E. Jackson III WP-02-23 State-Contingent Bank Regulation With Unobserved Action and Unobserved Characteristics David A. Marshall and Edward Simpson Prescott WP-02-24 Local Market Consolidation and Bank Productive Efficiency Douglas D. Evanoff and Evren Örs WP-02-25 Life-Cycle Dynamics in Industrial Sectors. The Role of Banking Market Structure Nicola Cetorelli WP-02-26 Private School Location and Neighborhood Characteristics Lisa Barrow WP-02-27 Teachers and Student Achievement in the Chicago Public High Schools Daniel Aaronson, Lisa Barrow and William Sander WP-02-28 The Crime of 1873: Back to the Scene François R. Velde WP-02-29 Trade Structure, Industrial Structure, and International Business Cycles Marianne Baxter and Michael A. Kouparitsas WP-02-30 Estimating the Returns to Community College Schooling for Displaced Workers Louis Jacobson, Robert LaLonde and Daniel G. Sullivan WP-02-31 A Proposal for Efficiently Resolving Out-of-the-Money Swap Positions at Large Insolvent Banks George G. Kaufman WP-03-01 Depositor Liquidity and Loss-Sharing in Bank Failure Resolutions George G. Kaufman WP-03-02 Subordinated Debt and Prompt Corrective Regulatory Action Douglas D. Evanoff and Larry D. Wall WP-03-03 When is Inter-Transaction Time Informative? Craig Furfine WP-03-04 Tenure Choice with Location Selection: The Case of Hispanic Neighborhoods in Chicago Maude Toussaint-Comeau and Sherrie L.W. Rhine WP-03-05 Distinguishing Limited Commitment from Moral Hazard in Models of Growth with Inequality* Anna L. Paulson and Robert Townsend WP-03-06 Resolving Large Complex Financial Organizations Robert R. Bliss WP-03-07 4 Working Paper Series (continued) The Case of the Missing Productivity Growth: Or, Does information technology explain why productivity accelerated in the United States but not the United Kingdom? Susanto Basu, John G. Fernald, Nicholas Oulton and Sylaja Srinivasan WP-03-08 Inside-Outside Money Competition Ramon Marimon, Juan Pablo Nicolini and Pedro Teles WP-03-09 The Importance of Check-Cashing Businesses to the Unbanked: Racial/Ethnic Differences William H. Greene, Sherrie L.W. Rhine and Maude Toussaint-Comeau WP-03-10 A Structural Empirical Model of Firm Growth, Learning, and Survival Jaap H. Abbring and Jeffrey R. Campbell WP-03-11 Market Size Matters Jeffrey R. Campbell and Hugo A. Hopenhayn WP-03-12 The Cost of Business Cycles under Endogenous Growth Gadi Barlevy WP-03-13 The Past, Present, and Probable Future for Community Banks Robert DeYoung, William C. Hunter and Gregory F. Udell WP-03-14 Measuring Productivity Growth in Asia: Do Market Imperfections Matter? John Fernald and Brent Neiman WP-03-15 Revised Estimates of Intergenerational Income Mobility in the United States Bhashkar Mazumder WP-03-16 Product Market Evidence on the Employment Effects of the Minimum Wage Daniel Aaronson and Eric French WP-03-17 Estimating Models of On-the-Job Search using Record Statistics Gadi Barlevy WP-03-18 Banking Market Conditions and Deposit Interest Rates Richard J. Rosen WP-03-19 Creating a National State Rainy Day Fund: A Modest Proposal to Improve Future State Fiscal Performance Richard Mattoon WP-03-20 Managerial Incentive and Financial Contagion Sujit Chakravorti, Anna Llyina and Subir Lall WP-03-21 Women and the Phillips Curve: Do Women’s and Men’s Labor Market Outcomes Differentially Affect Real Wage Growth and Inflation? Katharine Anderson, Lisa Barrow and Kristin F. Butcher WP-03-22 Evaluating the Calvo Model of Sticky Prices Martin Eichenbaum and Jonas D.M. Fisher WP-03-23 5 Working Paper Series (continued) The Growing Importance of Family and Community: An Analysis of Changes in the Sibling Correlation in Earnings Bhashkar Mazumder and David I. Levine WP-03-24 Should We Teach Old Dogs New Tricks? The Impact of Community College Retraining on Older Displaced Workers Louis Jacobson, Robert J. LaLonde and Daniel Sullivan WP-03-25 Trade Deflection and Trade Depression Chad P. Brown and Meredith A. Crowley WP-03-26 China and Emerging Asia: Comrades or Competitors? Alan G. Ahearne, John G. Fernald, Prakash Loungani and John W. Schindler WP-03-27 International Business Cycles Under Fixed and Flexible Exchange Rate Regimes Michael A. Kouparitsas WP-03-28 Firing Costs and Business Cycle Fluctuations Marcelo Veracierto WP-03-29 Spatial Organization of Firms Yukako Ono WP-03-30 Government Equity and Money: John Law’s System in 1720 France François R. Velde WP-03-31 Deregulation and the Relationship Between Bank CEO Compensation and Risk-Taking Elijah Brewer III, William Curt Hunter and William E. Jackson III WP-03-32 Compatibility and Pricing with Indirect Network Effects: Evidence from ATMs Christopher R. Knittel and Victor Stango WP-03-33 Self-Employment as an Alternative to Unemployment Ellen R. Rissman WP-03-34 Where the Headquarters are – Evidence from Large Public Companies 1990-2000 Tyler Diacon and Thomas H. Klier WP-03-35 Standing Facilities and Interbank Borrowing: Evidence from the Federal Reserve’s New Discount Window Craig Furfine WP-04-01 Netting, Financial Contracts, and Banks: The Economic Implications William J. Bergman, Robert R. Bliss, Christian A. Johnson and George G. Kaufman WP-04-02 Real Effects of Bank Competition Nicola Cetorelli WP-04-03 Finance as a Barrier To Entry: Bank Competition and Industry Structure in Local U.S. Markets? Nicola Cetorelli and Philip E. Strahan WP-04-04 6 Working Paper Series (continued) The Dynamics of Work and Debt Jeffrey R. Campbell and Zvi Hercowitz WP-04-05 Fiscal Policy in the Aftermath of 9/11 Jonas Fisher and Martin Eichenbaum WP-04-06 Merger Momentum and Investor Sentiment: The Stock Market Reaction To Merger Announcements Richard J. Rosen WP-04-07 Earnings Inequality and the Business Cycle Gadi Barlevy and Daniel Tsiddon WP-04-08 Platform Competition in Two-Sided Markets: The Case of Payment Networks Sujit Chakravorti and Roberto Roson WP-04-09 Nominal Debt as a Burden on Monetary Policy Javier Díaz-Giménez, Giorgia Giovannetti, Ramon Marimon, and Pedro Teles WP-04-10 On the Timing of Innovation in Stochastic Schumpeterian Growth Models Gadi Barlevy WP-04-11 Policy Externalities: How US Antidumping Affects Japanese Exports to the EU Chad P. Bown and Meredith A. Crowley WP-04-12 Sibling Similarities, Differences and Economic Inequality Bhashkar Mazumder WP-04-13 Determinants of Business Cycle Comovement: A Robust Analysis Marianne Baxter and Michael A. Kouparitsas WP-04-14 The Occupational Assimilation of Hispanics in the U.S.: Evidence from Panel Data Maude Toussaint-Comeau WP-04-15 Reading, Writing, and Raisinets1: Are School Finances Contributing to Children’s Obesity? Patricia M. Anderson and Kristin F. Butcher WP-04-16 Learning by Observing: Information Spillovers in the Execution and Valuation of Commercial Bank M&As Gayle DeLong and Robert DeYoung WP-04-17 Prospects for Immigrant-Native Wealth Assimilation: Evidence from Financial Market Participation Una Okonkwo Osili and Anna Paulson WP-04-18 Institutional Quality and Financial Market Development: Evidence from International Migrants in the U.S. Una Okonkwo Osili and Anna Paulson WP-04-19 Are Technology Improvements Contractionary? Susanto Basu, John Fernald and Miles Kimball WP-04-20 7 Working Paper Series (continued) The Minimum Wage and Restaurant Prices Daniel Aaronson, Eric French and James MacDonald WP-04-21 Betcha can’t acquire just one: merger programs and compensation Richard J. Rosen WP-04-22 Not Working: Demographic Changes, Policy Changes, and the Distribution of Weeks (Not) Worked Lisa Barrow and Kristin F. Butcher WP-04-23 The Role of Households’ Collateralized Debts in Macroeconomic Stabilization Jeffrey R. Campbell and Zvi Hercowitz WP-04-24 Advertising and Pricing at Multiple-Output Firms: Evidence From U.S. Thrift Institutions Robert DeYoung and Evren Örs WP-04-25 Monetary Policy with State Contingent Interest Rates Bernardino Adão, Isabel Correia and Pedro Teles WP-04-26 Comparing location decisions of domestic and foreign auto supplier plants Thomas Klier, Paul Ma and Daniel P. McMillen WP-04-27 China’s Export Growth and the China Safeguard: Threats to the World Trading System WP-04-28 Chad P. Bown and Meredith A. Crowley 8
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