This paper studies how exporting and importing firms match based on their capability by investigating the change in such exporter-importer matching during trade liberalization. During the recent liberalization of the Mexico-U.S. textile and apparel trade, exporters and importers often switch their main partners as well as change trade volumes. We develop a many-to-many matching model of exporters and importers where partner switching is the principal margin of adjustment, featuring Beckerian positive assortative matching by capability. Trade liberalization achieves efficient global buyer-supplier matching and improves consumer welfare by inducing systematic partner switching. The data confirm the predicted partner-switching patterns.

INTERNATIONAL trade mostly takes the form of firm-to-firm transactions in which firms seek and compete for capable buyers and suppliers globally. A case example is Boeing's 787 Dreamliner team that comprises the most capable suppliers from all over the world. Trade research in the last two decades has revealed the huge heterogeneity in the capability of exporters and importers (e.g., their productivity and product quality). Thus, the way in which heterogeneous exporters and importers match along the supply chains may determine the aggregate capability of the industry and the welfare.

This paper examines how exporters and importers match based on their capability by investigating the change in such exporter-importer matching during trade liberalization. From Mexico's customs administrative records, we construct a matched exporter-importer data set for Mexican textile and apparel exports to the United States from 2004 to 2007. The Mexico–United States textile and apparel trade is particularly suitable for our purpose. First, Mexico and the United States are large trading partners with each other, and so trade between them includes numerous heterogeneous exporters and importers.1 Second, the two countries' textile and apparel trade experienced large-scale liberalization. In 2005 the United States removed quotas on textile and apparel imports at the end of the Multifiber Arrangement (MFA). Mexican products already had quota-free access to the U.S. market under the North American Free Trade Agreement (NAFTA), and so the MFA's end effectively removed protection for Mexican products in the U.S. market and forced them to compete with imports from third countries, principally China. The liberalization varied across products substantially and was arguably exogenous because the liberalization schedule was decided at the GATT Uruguay Round (1986–1994) when China's export growth was not expected.

The MFA's end substantially changed the partnerships between Mexican exporters and U.S. importers. Mexican exports to the United States decreased by an extensive margin (stopping exports) and intensive margin (reducing export values). The intensive margin adjustment involved substantial partner switching, often including the exporter's largest main partners. Main partner switching accounted for more than 50% of the intensive margin and caused a more than 230% excess reallocation of exports across U.S. buyers beyond the intensive margin. As we explain in section II, this prevalence of main partner switching in trade liberalization was at odds with anonymous market models (e.g., neoclassical models, oligopoly models), love-of-variety models (the Krugman-Melitz model), and some recent exporter-importer matching models (e.g., Bernard et al., 2018) that combine the love-of-variety model and fixed costs of matching.

Motivated by this new fact, we develop a many-to-many matching model of exporters and importers in an intermediate good market in which partner switching is the principal margin of adjustment. The model combines Sattinger's (1979) frictionless assignment model of a continuum of agents, Melitz's (2003) standard heterogeneous firm trade model, and Bernard et al.'s (2011) multiple-product firm trade model. The model consists of final producers (importers) in the United States and suppliers (exporters) in Mexico and China. Final producers produce multiple products, and suppliers own multiple production lines. A final producer's variety-level capability depends on its firm-level capability and idiosyncratic capability, whereas a supplier's production-line-level capability depends on its firm-level capability and idiosyncratic capability. A final variety matches a production line one-to-one, resulting in the many-to-many matching of final producers and suppliers. The Beckerian PAM of varieties and production lines arises as a stable equilibrium when a variety's capability and production's capability are complements.

The model predicts that the MFA's end induced systematic partner switching that led to efficient buyer-supplier matching and improved consumer welfare. As empirically documented by Khandelwal et al. (2013), at the MFA's end, Chinese suppliers at various capability levels entered the U.S. market. Their entry lowered the capability ranking of each Mexican supplier in the market. Therefore, to achieve PAM, Mexican exporters switched to U.S. importers with lower capability, whereas U.S. importers switched to Mexican exporters with higher capability. We call these types of partner switching “partner downgrading” and “partner upgrading,” respectively. Allowing capable Chinese suppliers to match with capable U.S. final producers, this rematching achieved PAM in the global market, which improved aggregate capability and consumer welfare. By contrast, in an anonymous market in which matching is independent of capability, rematching should not occur in a systematic way or result in an efficiency gain.

We take the model's predictions on partner switching to data. Guided by the theory, we estimate the rankings of firm-level capability of Mexican exporters and U.S. importers by the rankings of their 2004 preliberalization product trade with their main partners. We then compare the partner-switching patterns between liberalized products (the treatment group) and other textile and apparel products (the control group) within Harmonized System (HS) two-digit industries. We find the partner-switching patterns to be consistent with PAM. First, U.S. importers upgrade their Mexican partners more often in the treatment group than in the control group. At the same time, Mexican exporters downgrade their U.S. partners more often in the treatment group than in the control group. Second, among firms that switch their main partners, the capability rankings of new partners are positively correlated with those of old partners. Together, these findings provide strong support for PAM and reject independent random matching. Furthermore, we confirm the model's predictions on firm exit and the number of partners. First, the capability cutoff for Mexican exporters increases. Second, U.S. importers and Mexican exporters decrease their number of partners.

To the best of our knowledge, detecting Beckerian PAM by capability in this way is a novel approach to addressing the endogeneity problem in the conventional approach. When matching matters for a firm's performance, most firm characteristics observable in typical production and customs data (e.g., inputs, outputs, and productivity measures) may reflect partners' unobserved capability as well as the firm's own capability. Therefore, the simple correlation of those characteristics across matches may suffer from endogeneity.2 Instead, our approach utilizes the MFA's end as an exogenous negative shock on the capability ranking of Mexican exporters.

As matched exporter-importer data become available to researchers, the last decade since 2010 saw the burgeoning literature on buyer-supplier relationships in international trade.3 Our paper contributes to a strand of this literature studying exporter-importer matching. Rauch (1996), Casella and Rauch (2002), and Rauch and Trindade (2003) pioneered the theoretical literature by using the assignment model of symmetric firms, whereas our model features firm heterogeneity in capability as in Melitz (2003). Antras et al. (2006) analyzed offshoring as the PAM of managers and workers across countries. The assignment model captures two distinctive features in exporter-importer relationships. First, trading with high-capability firms improves a firm's performance, but the opportunity to trade with them is scarce and something that firms compete for. This view echoes with recent evidence that trading with high-capability foreign firms improves a local firm's performance through various channels.4 Second, buyer-supplier matching is an allocation of scarce trading opportunities. Thus, trade liberalization induces partner switching to achieve a globally efficient matching. We provide the first evidence for this matching mechanism.

Bernard et al. (2018) recently developed another approach combining match-level fixed costs and the love-of-variety (CES) production function.5 A buyer and a supplier are matched when the match surplus exceeds the match-level fixed costs. As the match surplus monotonically increases in the buyer's capability and the supplier's, all the matches are realized except those between low-capability firms.6 Thus, the model can predict the negative degree assortativity reported by Blum et al. (2010), Bernard et al. (2018), and others that a buyer's number of partners is negatively correlated with the average number of firms to which the buyer's partners sell.

Our finding of PAM can be compatible with negative degree assortativity both theoretically and empirically. In appendix D we present a two-tier model of exporter-importer matching that unifies Bernard et al.'s (2018) model and ours to predict negative degree assortativity for the firm-level matching and PAM for the product-level matching. In the model a buyer (e.g., a car maker) has a love-of-variety production function with respect to intermediate goods and decides whether to make or buy each intermediate good (e.g., tires, seats), considering the match surplus and match-level fixed costs, as in Bernard et al. (2018). For each intermediate good (e.g., a set of four tires), a buyer matches a supplier following PAM as in our model. Our data confirm the model's prediction by finding that negative degree assortativity holds when a match is defined at the firm level, but becomes weaker and statistically insignificant when a match is defined at the product level.

Another important strand of the literature studies the dynamics of an exporter's and importer's partner choice in a steady-state environment. Macchiavello (2010) introduced reputation building in an assignment model to explain an exporter's partner upgrading over time. Eaton et al. (2014) and Eaton et al. (2015) developed models incorporating search and learning frictions in partner acquisitions.7 Eaton et al. (2016) modeled random meeting and competition among multiple buyers and suppliers. Monarch (2021) estimated partner-switching costs in a dynamic discrete choice model. Heise (2020) documented the dependence of exchange rate pass-through on the age of trade relationships.

Benguria (2021) and Dragusanu (2014) documented positive correlations between the size and productivity measures of exporters and importers in France-Colombia trade and India–United States trade, respectively. Our model featuring Beckerian PAM also predicts these findings. Benguria (2021) and Dragusanu (2014) developed search effort models of the Stigler (1961) type to explain their findings by a different mechanism: a high-productivity exporter spends greater search efforts finding a high-productivity importer. Their models, however, do not explain Mexican exporters' partner downgrading at the MFA's end. In their models, search costs are sunk and importers are willing to trade with all exporters. Thus, Mexican exporters should continue to trade with preliberalization U.S. partners instead of downgrading partners by paying additional search costs.

Another related literature investigates nonanonymous contracts in given exporter-importer relationships, using matched exporter-importer data. Macchiavello and Morjaria (2015) examined the surplus of long-term relationships relative to anonymous spot trade. Cajal-Grossi et al. (forthcoming) found greater markups in long-term relational trade than spot trade. Bernard and Dhingra (2019) studied firms' relationship investment to avoid inefficiency in spot trade. Ignatenko (2019) reports exporters' price discriminations across importers. Our paper complements this literature by showing exporters match importers in an nonanonymous way too.

The rest of this paper is organized as follows. Section II explains our data and documents new facts on partner switching during liberalization. Section III presents our model and derives predictions. Section IV describes our empirical strategy. Section V presents the main results and robustness checks. Section VI provides concluding remarks. The online appendix provides the calculations, proofs, data construction, extended models, robustness checks, and additional analyses rejecting alternative explanations of our results.

A. The End of the MFA

The MFA and its successor, the Agreement on Textiles and Clothing, are agreements about the quotas on textile and apparel imports among GATT and WTO countries. At the GATT Uruguay Round (1986–1994), the United States (together with Canada, the European Union, and Norway) promised to abolish the quotas in four steps (in 1995, 1998, 2002, and 2005). The MFA's end in 2005 was the largest liberalization, in which liberalized products constituted 49% of imports in 1990.

Three facts (taken from previous studies) about the consequences resulting from the MFA's end motivate our analysis.

Fact 1: Surge in Chinese exports to the United States.

According to Brambilla et al. (2010), U.S. imports from China disproportionally increased by 271% in 2005, while imports from most other countries decreased. Using U.S. import quota data from Brambilla et al. (2010), we classify each HS six-digit textile and apparel product into two groups (see appendix B.5 for details): the treatment group of products in which Chinese exports subject to the binding 2004 U.S. import quota, and the control group of other textile and apparel products. We regress the HS six-digit product-year-level exports of China and Mexico on the annual year dummies with product fixed effects separately for the treatment group and control group. Figure 1 shows the coefficients of the annual year dummies with triangles for the treatment group and circles for the control group, separately for Chinese exports and Mexican exports. The difference in the coefficients between the two groups expresses the impacts of the MFA's end on Chinese and Mexican exports after controlling for product-specific effects. In the left panel for Chinese exports, although the coefficients before 2005 are stable and virtually identical between the two groups, after the 2005 quota removal, the coefficient for the treatment group increases much faster than that for the control group.8
Figure 1.
Effects of the MFA's End on Chinese and Mexican Textile and Apparel Exports to the United States

The left panel plots the coefficients of the annual year dummies in the regression of the HS six-digit product-year-level exports of China on the annual year dummies and product fixed effects separately run for the products on which the United States had imposed binding quotas against China in 2004 (the treatment group, triangles) and other textile and apparel products (the control group, circles). The right panel expresses the same information for exports from Mexico to the United States. Data source: UN Comtrade.

Figure 1.
Effects of the MFA's End on Chinese and Mexican Textile and Apparel Exports to the United States

The left panel plots the coefficients of the annual year dummies in the regression of the HS six-digit product-year-level exports of China on the annual year dummies and product fixed effects separately run for the products on which the United States had imposed binding quotas against China in 2004 (the treatment group, triangles) and other textile and apparel products (the control group, circles). The right panel expresses the same information for exports from Mexico to the United States. Data source: UN Comtrade.

Close modal

Fact 2: Mexican exports faced competition from China.

By 2003 Mexico already had tariff- and quota-free access to the U.S. market through NAFTA. With the MFA's end, Mexico lost its advantage over third-country exporters and faced increased competition from Chinese exporters in the U.S. market, as the right panel of figure 1 shows.9 Although the two groups were stable and almost identical before 2005, the exports in the treatment group significantly declined thereafter.

Fact 3: Exports by new Chinese entrants with various capability levels.

From Chinese customs transaction data, Khandelwal et al. (2013) decomposed the increases in Chinese exports to the United States in liberalized products after the removal of the quota into the intensive and extensive margins. Increases in Chinese exports were mostly driven by the entry of new exporters that had not previously exported products. These new exporters have different capability levels than those of incumbent exporters, with many more capable than incumbents.10

B. Partner Switching after the MFA's End

Data.

From Mexico's customs administrative records, we construct a matched exporter-importer data set from June 2004 to December 2011 for Mexican textile and apparel exports (covering HS50 to HS63) to the United States. For each match of a Mexican exporter and a U.S. importer, the data set contains the following information: exporter ID, importer ID, HS six-digit product code, annual shipment value (USD), quantity and unit, an indicator of a duty-free processing reexport program (Maquiladora/IMMEX), and other information.

We assign the exporter ID and importer ID throughout the data set. The exporter ID is the tax number unique to each firm in Mexico. Assigning importer IDs to U.S. firms is challenging. Although the customs records report the name, address, and employment identification number (EIN) of the U.S. importer for each transaction, none of these can uniquely identify a firm because it can use multiple names or change names, own multiple plants or establishments, or change tax numbers. Furthermore, a firm's name and address may be written in multiple ways and suffer from typographical errors. Therefore, simply counting combinations of names, addresses, and EINs would wrongly assign more than one ID to one U.S. importer.

We therefore assign the importer ID by applying a series of record linkage techniques.11 First, we prepare a list of name variations such as fictitious names, previous names, and name abbreviations, a list of addresses of company branches/subsidiaries, and a list of EINs from Orbis by Bureau van Dijk, which covers 20 million company branches, subsidiaries, and headquarters in the United States. Second, the address format is standardized using software certified by the U.S. Postal Service. Third, we match the lists from Orbis to each of the linking variables (name, address, EIN) in the customs data by fuzzy matching. Two types of errors can occur in fuzzy matching: “false matching” (matching records that should not be matched) and “false unmatching” (not matching records that should be matched). The criteria for fuzzy matching are chosen to minimize false unmatching because false matching is easier to identify by manual checks. Fourth, binary-matched records are aggregated into clusters so that each record matches another record in that cluster. Then, we manually check each cluster and remove falsely matched records. A resulting cluster represents a firm and receives an importer ID. Appendix B explains the data construction process in detail.

Data cleansing drops some observations. First, the data set covers only observations from June to December in 2004, and so we drop the observations from January to May in other years to make the information in each year comparable. We obtain similar results when January-May observations are included. Second, although importer information is reported for most normal trade transactions, it is sometimes missing for processing trade transactions under the Maquiladora/IMMEX program in which exporters do not have to report an importer for each shipment.12 We drop exporters that do not report the importer information for most transactions. To address the potential selection issues caused by this action, we distinguish normal trade and processing trade in the analyses below and conduct weighted regressions in appendix B.4.

Table 1 reports the summary statistics for the product-level and firm-level matching. A product-level match occurs if an importer and an exporter trade in a particular product, whereas a firm-level match occurs if an importer and an exporter trade in at least one product. Columns (a) and (b) in table 1 report the mean and median of the product-level matching.13 The first four rows show that eleven to fifteen exporters and fifteen to twenty importers exist in an average product market, but the majority of firms trade with only one partner.14 Rows (5) and (6) show that even for firms that trade with multiple partners, more than 70% of their trade occurs with their single main partner.15

Table 1.

Summary Statistics for the HS Six-Digit Product-Level Matching and Firm-Level Matching in Textile and Apparel Trade from Mexico to the United States

Product-Level MatchFirm-Level Match
2004200720042007
Mean Statistics (Median)(a)(b)(c)(d)
(1) Number of Exporters 15.6 (8) 11.8 (6) 1,340 1,036 
(2) Number of Importers 20.3 (11) 15.2 (8) 2,031 1,541 
(3) Number of Exporters Selling to an Importer 1.1 (1) 1.1 (1) 1.4 (1) 1.3 (1) 
(4) Number of Importers Buying from an Exporter 1.5 (1) 1.4 (1) 2.1 (1) 1.9 (1) 
(5) Value Share of Main Exporter (Number of Exporters > 1) 0.76 0.77 0.75 0.78 
(6) Value Share of Main Importer (Number of Importers > 1) 0.74 0.77 0.73 0.76 
Product-Level MatchFirm-Level Match
2004200720042007
Mean Statistics (Median)(a)(b)(c)(d)
(1) Number of Exporters 15.6 (8) 11.8 (6) 1,340 1,036 
(2) Number of Importers 20.3 (11) 15.2 (8) 2,031 1,541 
(3) Number of Exporters Selling to an Importer 1.1 (1) 1.1 (1) 1.4 (1) 1.3 (1) 
(4) Number of Importers Buying from an Exporter 1.5 (1) 1.4 (1) 2.1 (1) 1.9 (1) 
(5) Value Share of Main Exporter (Number of Exporters > 1) 0.76 0.77 0.75 0.78 
(6) Value Share of Main Importer (Number of Importers > 1) 0.74 0.77 0.73 0.76 

Rows (1) and (2) are the numbers of Mexican exporters and U.S. importers, respectively. Row (3) is the number of Mexican exporters selling to a given U.S. importer. Row (4) is the number of U.S. importers buying from a given Mexican exporter. Row (5) is the share of imports from the main Mexican exporters in terms of the importer's imports. Row (6) is the share of exports to the main U.S. importers in terms of the exporter's exports. Rows (5) and (6) are calculated only for firms with multiple partners. Each row reports the mean with the median in parentheses.

Excess partner switching after the MFA's end.

Our new finding is that exporters and importers actively switch partners during liberalization. Panel A in table 2 reports the changes in Mexican textile and apparel exports to the United States between 2004 and 2007 by incumbent exporters in 2004 separately for liberalized products (quota-bound) and other products (quota-free). The changes in total exports in column 1 are decomposed into the extensive margin in column 2 by exiters that stopped exporting by 2007 and intensive margin in column 3 by continuing exporters in 2007.16 The intensive margin in column 3 is further decomposed into three margins of partner changes: Partner Staying in column 4 expresses the changes in exports to continuing buyers that import from the exporter in both 2004 and 2007, Partner Adding in column 5 expresses those to new buyers in 2007 that did not import from the exporter in 2004, and Partner Dropping in column 6 expresses those to dropped partners that imported from the exporter in 2004 but not in 2007. The parentheses in columns 5 and 6 report the share of export changes by Partner Switchers that simultaneously add and drop partners. These high shares imply that most partner changes are in fact partner switching. Column 7 reports the excess reallocation of partners, that is, |(5)|+|(6)|-|(5)+(6)|.

Table 2.

Changes in Mexican Textile and Apparel Incumbent Exports to the United States from 2004 to 2007 (Million USD)

Partner Margin Decomposition
Traditional MarginsPartner MarginsExcess
TotalExtensiveIntensiveStayAddDropReallocation
(1)(2)(3)(4)(5)(6)(7)
A. Aggregate decomposition 
Quota-bound -950.9 -887.4 -63.4 -25.1 83.5 -121.9 167.1 
% of (3)   100% 39.5% -131.7% 192.2% 263.4% 
Switcher share     (0.95) (0.82)  
Quota-free -223.0 -179.6 -43.4 -24.0 37.5 -56.9 75.1 
% of (3)   100% 55.4% -86.6% 131.2% 173.2% 
Switcher share     (0.79) (0.87)  
B. HS six-digit product-level regression coefficients 
Binding -4.441∗∗ -4.052∗∗ -0.389 -0.132 0.388∗∗ -0.645∗∗ 0.706∗∗ 
(standard error) (2.046) (1.883) (0.306) (0.230) (0.165) (0.274) (0.296) 
HS2 FE Yes Yes Yes Yes Yes Yes Yes 
Main partner margin decomposition 
 Intensive Non-Main Main Partner Margins Main Partner 
 Margin Partner Stay Add Drop Excess Reallocation  
 (1) (2) (3) (4) (5) (6)  
C. Aggregate decomposition  
Quota-bound -63.4 -15.2 -13.7 72.9 -107.4 145.8  
% of (1) 100% 24.0% 21.6% -114.9% 169.3% 229.8%  
Quota-free -43.4 -14.2 -10.9 38.7 -56.9 77.4  
% of (1) 100% 32.8% 25.1% -89.2% 131.3% 178.4%  
D. HS six-digit product-level regression coefficients  
Binding -0.389 -0.080 -0.095 0.332∗∗ -0.545∗∗ 0.602∗∗  
(standard error) (0.306) (0.082) (0.205) (0.141) (0.238) (0.240)  
HS2 FE Yes Yes Yes Yes Yes Yes  
Partner Margin Decomposition
Traditional MarginsPartner MarginsExcess
TotalExtensiveIntensiveStayAddDropReallocation
(1)(2)(3)(4)(5)(6)(7)
A. Aggregate decomposition 
Quota-bound -950.9 -887.4 -63.4 -25.1 83.5 -121.9 167.1 
% of (3)   100% 39.5% -131.7% 192.2% 263.4% 
Switcher share     (0.95) (0.82)  
Quota-free -223.0 -179.6 -43.4 -24.0 37.5 -56.9 75.1 
% of (3)   100% 55.4% -86.6% 131.2% 173.2% 
Switcher share     (0.79) (0.87)  
B. HS six-digit product-level regression coefficients 
Binding -4.441∗∗ -4.052∗∗ -0.389 -0.132 0.388∗∗ -0.645∗∗ 0.706∗∗ 
(standard error) (2.046) (1.883) (0.306) (0.230) (0.165) (0.274) (0.296) 
HS2 FE Yes Yes Yes Yes Yes Yes Yes 
Main partner margin decomposition 
 Intensive Non-Main Main Partner Margins Main Partner 
 Margin Partner Stay Add Drop Excess Reallocation  
 (1) (2) (3) (4) (5) (6)  
C. Aggregate decomposition  
Quota-bound -63.4 -15.2 -13.7 72.9 -107.4 145.8  
% of (1) 100% 24.0% 21.6% -114.9% 169.3% 229.8%  
Quota-free -43.4 -14.2 -10.9 38.7 -56.9 77.4  
% of (1) 100% 32.8% 25.1% -89.2% 131.3% 178.4%  
D. HS six-digit product-level regression coefficients  
Binding -0.389 -0.080 -0.095 0.332∗∗ -0.545∗∗ 0.602∗∗  
(standard error) (0.306) (0.082) (0.205) (0.141) (0.238) (0.240)  
HS2 FE Yes Yes Yes Yes Yes Yes  

In panels A and C, each column reports the changes in Mexican textile and apparel exports to the United States between 2004 and 2007 by incumbent exporters in 2004 for quota-bound products and other quota-free products. In panel A, the changes in total exports in (1) are decomposed into the extensive margin by exiters in (2) and the intensive margin by survivors in (3). The intensive margin in (3) is decomposed into (4) exports to continuing partners, (5) exports to new partners, and (6) exports to dropped buyers. Column 7 is |(5)|+|(6)|-|(5)+(6)|. In panel C, the intensive margin changes by survivors in (1) are decomposed into (2) exports to nonmain partners, (3) exports to continuing main partners, (4) exports to new main partners, and (5) exports to dropped main partners. Column 6 is |(4)|+|(5)|-|(4)+(5)|. In panels B and D, each column reports the product-level regressions of each margin on the quota-bound product dummy (Binding) with the HS two-digit fixed effects. Standard errors are clustered at the HS six-digit product level. Significance: 10%, ∗∗5%, ∗∗∗1%.

As table 1 suggests, the switching of main partners plays a major role in the adjustment. In panel C in table 2, the intensive margin in column 1, which is column 3 in panel A, is decomposed according to main partner's involvement: export changes not involving main partners in column 2, exports to continuing main partners in 2004 and 2007 in column 3, those to new main buyers in 2007 that were not main buyers in 2004 in column 4, and those to dropped main buyers that were main buyers in 2004 but not in 2007 in column 5. Column 6 reports the excess reallocation associated with main partners, that is, |(4)|+|(5)|-|(4)+(5)|.

Table 2 shows that in liberalized industries, main partner switching [columns 4+5] accounted for 54% of the intensive margin [column 1] and caused a more than 230% excess reallocation of exports across U.S. buyers beyond the intensive margin. This prevalence of main partner switching is at odds with anonymous market models (perfectly competitive and oligopoly models) and love-of-variety models (the Krugman-Melitz model) including some production networks models (e.g., Bernard et al., 2018). First, as we show in section III, in anonymous markets where firms are indifferent about partners, partner changes should be minimized to save partner-switching costs. Exporters may either add or drop buyers but should not switch among surviving buyers; that is, the excess reallocations in panels A and C should be zero. Second, as appendix D shows, in models combining the love-of-variety model and match-specific fixed costs, firms add and drop marginally important partners rather than main partners. Thus, the large main partner excess export reallocation in panel C is puzzling to these models.

The decompositions in panels A and C show the overall importance of partner switching. To examine the impact of liberalization at the disaggregated level, we regress each margin of the HS six-digit product-level exports on the dummy variable of quota liberalization (the Binding dummy) with the HS two-digit fixed effects. Panels B and D in table 2 report the estimated coefficients. The large and statistically significant coefficients in columns 5–7 in panel B and columns 4–6 in panel D confirm the significant roles of partner switching.

This section develops an exporter-importer matching model in which partner switching is the principal margin of adjustment. Section IIIA sets up the model for the case of one-to-one matching, and section IIIB derives the main insights about partner switching in trade liberalization. Section IIIC introduces many-to-many matching and derives predictions that we take to the data.

A. Matching Model of Exporters and Importers

The model includes three types of a continuum of firms, namely, U.S. final producers, Mexican suppliers, and Chinese suppliers.17 U.S. final producers may be retailers or wholesalers. The model has two stages. In Stage 1 a U.S. final producer matches with a supplier from either Mexico or China to form a team that produces one variety of differentiated final goods. Suppliers tailor intermediate goods and transact them only within the team. Firms match under perfect information, and each firm joins only one team. This one-to-one frictionless matching model is the simplest model predicting PAM. Introducing search frictions does not change the qualitative predictions that we take to the data.18 In Stage 2 teams compete in the U.S. final good market under monopolistic competition.

The U.S. representative consumer maximizes the CES utility function:
U=δρlnωΩθ(ω)αq(ω)ρdω+q0s.t.ωΩp(ω)q(ω)dω+q0=I,
where Ω is the set of available differentiated final goods, ω is the variety of differentiated final goods, pω is the price of ω, q(ω) is the consumption of ω, θ(ω) is the capability of the team producing ω, q0 is the consumption of the numeraire good, and I is the exogenously given income. α0 and δ>0 are the given parameters. Consumer demand for a variety with price p and capability θ is derived as q(p,θ)=δθασPσ-1p-σ, where σ1/1-ρ>1 is the elasticity of substitution and PωΩp(ω)1-σθωασdω1/(1-σ) is the ideal price index.

The team's capability θ=θ(x,y) is increasing in the final producer's capability x and supplier's capability y in that team, that is, θ1θ(x,y)/x>0 and θ2θ(x,y)/y>0. One finds a fixed mass MU of final producers in the United States, MM of suppliers in Mexico, and MC of suppliers in China. The cumulative distribution function (CDF) for U.S. final producers' capability is F(x) with support [xmin,xmax]. For simplicity, a Chinese supplier is a perfect substitute for a Mexican supplier of the same capability. The capability of Mexican and Chinese suppliers follows an identical distribution with the CDF G(y) and support [ymin,ymax].19

Production technology is of the Leontief type. When a team with capability θ produces q units of final goods, the team supplier produces q units of intermediate goods at costs cyθβq+fy; then the final producer assembles these intermediate goods into final goods at costs cxθβq+fx, where ci and fi are positive constants (i=x,y). The team's total costs are c(θ,q)=cθβq+f, where ccx+cy and ffx+fy. The externalities within teams make firms' marginal costs dependent on both their partner's capability and their own capability.20 For simplicity, we assume that the firm's marginal costs depend on the team's capability. The team's capability θ shifts both demand and marginal costs depending on α and β. Therefore, θ may represent productivity (e.g., Melitz, 2003) and/or quality (e.g., Verhoogen, 2008; Baldwin & Harrigan, 2011).

Stage 2.

We obtain an equilibrium by backward induction. The team's optimal price is p(θ)=cθβ/ρ. Hence, team revenue R(θ), total costs C(θ), and joint profits Πθ become
R(θ)=σAθγ,C(θ)=σ-1Aθγ+f,andΠθ=Aθγ-f,
(1)
where each team takes AδσρPcσ-1 as given and γασ-βσ-1>0 is assumed so that the team's profit increases in θ. All the calculations are in appendix A.1. We normalize γ=1 by choosing the unit of θ as the comparative statics on any of α, β and σ is not our main interest. The price index P=c/ρΘ1/(σ-1) decreases in the team's aggregate capability ΘMθdH(θ), where M and H(θ) are active teams' mass and capability distribution, respectively.

Stage 1.

Firms choose their partners and decide how to split team profits, taking A as given. Profit schedules, πxx and πyy, and matching functions, mxx and my(y), characterize equilibrium matching.21 A final producer with capability x matches with a supplier having capability mxx and receives the residual profit πxx after paying profits πy(mxx) to the partner. my(y) is the inverse function of mx(x), where mx(my(y))=y.

We focus on stable matching that satisfies the following two conditions: (i) individual rationality, wherein all firms earn nonnegative profits, πxx0 and πyy0 for all x and y, and (ii) pairwise stability, wherein each firm is the optimal partner for the other team member:
πxx=Aθ(x,mx(x))-f-πy(mx(x))=maxyAθx,y-πy(y)-f,πyy=Aθ(my(y),y)-f-πx(my(y))=maxxAθx,y-πx(x)-f.
(2)
From the envelop theorem, we obtain22
πx'(x)=Aθ1(x,mx(x))>0andπy'(y)=Aθ2(my(x),y)>0.
(3)
Thus, profits increase in capability. The capability cutoffs xL and yL exist such that only final producers with xxL and suppliers with yyL engage in trade, which satisfy
πx(xL)=πy(yL)=0andMU[1-F(xL)]=MM+MC1-G(yL);
(4)
that is, the number of active final producers equals that of active suppliers.
Differentiating equation (3) by x, we obtain the derivative of the matching function:
mx'(x)=Aθ12πx''-Aθ11,whereθ122θxyandθ112θx2.
(5)

The denominator in equation (5) is positive from the second-order condition, and so the sign of θ12 is the same as the sign of mx'(x), namely, the sign of sorting in stable matching (e.g., Becker, 1973). For simplicity, we consider three cases in which the sign of θ12 is constant for all x and y: (1) Case C (Complement) θ12>0, (2) Case I (Independent) θ12=0, and (3) Case S (Substitute) θ12<0.23 In Case C we have PAM (mx'(x)>0): high-capability firms match with high-capability firms, whereas low-capability firms match with low-capability firms. In Case S we have negative assortative matching (mx'(x)<0): high-capability firms match with low-capability firms. In Case I we cannot determine a matching pattern (i.e., mx[x] cannot be defined as a function) because each firm is indifferent about partner capability. Therefore, we assume that matching is random and independent of capability in Case I.

Case I is a useful benchmark because it nests two important classes of standard models. The first is anonymous market models in which each firm is indifferent about partner capability. The second is heterogeneous firm trade models with one-sided heterogeneity in which firm heterogeneity exists either among exporters (θ1=θ12=0) or among importers (θ2=θ12=0). In the following we focus on Case C and Case I in the main text, and we examine Case S in appendix A.3.

In Case C the following “matching market-clearing” condition determines mx(x):
MU[1-F(x)]=(MM+MC)[1-G(mx(x))]forallxxL.
(6)
Figure 2A describes condition equation (6). The left rectangle has width MU, and the right one has MM+MC. The left vertical axis expresses the value of F(x) and the right one the value of G(y). The left gray area equals the mass of final producers with higher capability than x, MU1-F(x), and the right gray area equals the mass of suppliers that match with them, MM+MC1-G(mx(x)). The matching function mx(x) equalizes the size of the two gray areas.
Figure 2.

Matching Model's Predictions

Figure 2.

Matching Model's Predictions

Close modal
Finally, we obtain the cutoff xL as follows. In both Cases C and I, the team with the capability cutoff θL comprises a final producer with xL and a supplier with yL. In Case C, mx(x) determines aggregate capability ΘxL=MUxLθ(x,mx(x))dF(x) and the capability cutoff θLxL=θ(xL,mx(xL)) as functions of xL. In Case I, let θ(x,y)θx(x)+θy(y). Condition equation (4) determines yL(xL) as a function of xL. Then ΘxL=MUxLθxxdF(x)+(MM+MC)yL(xL)θy(y)dG(y) and θL(xL)=θxxL+θy(yL(xL)) become functions of xL. From equations (1), (4), and A=δ/σΘ, the team with the capability cutoff earns zero profits:
ΠθL=δθ(xL)σΘxL-f=0.
(7)
Equation (7) uniquely determines xL because ΘxL is decreasing and θL(xL) is increasing in xL.

B. Consequences of Chinese Firm Entry at the End of the MFA

This section analyzes the effect of the MFA's end on matching. Motivated by fact 3 shown in section II that new Chinese entrants had different levels of capability, we model the event as an increase in the mass of Chinese suppliers (dMC>0). We assume that a firm changes its partner only if it strictly prefers the new match over the current match. We denote the variables and functions before the MFA's end by “B” (before) and variables after the MFA's end by “A” (after).

Case C.

Figure 2B shows how matching changes from mxB(x) to mxA(x) for the given capability x. Area A expresses U.S. importers with capability higher than x. They initially match with suppliers in areas B+C that have higher capability than mxB(x). After the MFA's end, the original matches become unstable because some U.S. importers are willing to switch to the new entrants. In the new matching, final producers in area A match with suppliers in areas B+D that have higher capability than mxA(x). A U.S. final producer with capability x switches its main partner from one with capability mxB(x) to one with higher capability, namely,  mxA(x). We call this change “partner upgrading” by U.S. final producers. This in turn implies “partner downgrading” by Mexican suppliers. Mexican suppliers with capability mxA(x) match with final producers with strictly higher capability than x before the MFA's end. Not all Mexican suppliers can match with new partners, however, and those with low capability exit the market, as proven in appendix A.2.

Case I.

Figure 2C shows that the MFA's end increases the supplier's cutoff from yLB to yLA, as proven in appendix A.2. Whether xL increases or decreases is generally ambiguous, and so the figure depicts the case in which xL unchanged. As low-capability suppliers in Area C exit, U.S. importers that matched with them switch to new Chinese suppliers in Area D. Other firms do not change their partners, although they change the price and quantity of goods traded. Firms are indifferent about their partners as long as those partners have a capability level above the cutoffs.

Rematching gains from trade.

The MFA's end causes two adjustments. First, new Chinese suppliers with high capability enter the market, and Mexican suppliers with low capability exit. This replacement effect occurs in both Cases C and I, and it corresponds to the extensive margin adjustment in table 2. Second, incumbent firms rematch. This rematching effect occurs only in Case C and corresponds to the partner excess reallocation in table 2.

We show that the rematching effect in Case C is a new mechanism of gains from trade that did not exist in standard trade models nested in Case I (perfectly competitive models and Krugman-Melitz models with one-sided heterogeneity). We consider a hypothetical “no-rematching” equilibrium at which firms switch partners only if their current partners exit the market, and we denote variables in this equilibrium by “NR.” The following proposition compares the price indices across the three cases (the proof is in appendix A.2).

Proposition 1.

In Case C, PA<PNR<PB, while in Case I, PA=PNR<PB.

The effect of liberalization on the price index PB-PA can be decomposed into the replacement effect PB-PNR and rematching effect PNR-PA. The gain from the replacement effect is well known in the heterogeneous firm trade literature. In Case C the rematching effect creates an additional consumer gain. The proof applies a classic theorem in matching theory that stable matching maximizes the aggregate payoff, AΘ-Mf, for the given A (Koopmans & Beckmann, 1957; Shapley & Shubik, 1971; Gretsky et al., 1992) and proves that aggregate capability increases as ΘA>ΘNR>ΘB.24 In other words, trade liberalization improves consumer welfare by improving global buyer-supplier matching and aggregate capability.

Proposition 1 also implies that a preferential trade agreement can create inefficient “matching diversion.” High-capability U.S. final producers are diverted to match with low-capability Mexican suppliers instead of high-capability Chinese suppliers.25

C. Many-to-Many Matching

This section introduces many-to-many matching in an intermediate good market. A final producer produces multiple product varieties, and a supplier owns multiple production lines. Matching occurs between varieties and production lines, resulting in many-to-many matching.

One finds N final products and one intermediate good. The consumer's utility is given by
U=s=1NδρlnωΩsθ(ω)αq(ω)ρdω-s=1NωΩsp(ω)q(ω)dω+I,
where Ωs is the set of varieties of product s. A final producer produces at most one variety of each product, following Bernard et al. (2011). Let χis=xi+ηis be the product capability of firm i for product s, where xi is firm capability and ηis is i.i.d. idiosyncratic capability with Eηis=0 and support [ηmin,ηmax]. xi and ηis are independent and have densities fx(x) and fη(η), respectively.

A supplier owns multiple production lines. Each line specializes in a particular variety. A supplier with firm capability y owns n(y) production lines and can match with at most n(y) buyers. One reason for such buyer capacities is a manager's span of control. A supplier requires a manager's resource to collaborate with each buyer. We assume that n(y) is weakly increasing in y.

The production line k of supplier j with firm capability yj has line capability υjk=yj+ɛjk, where ɛjk is i.i.d. idiosyncratic capability with Eɛjk=0 and support [ɛmin,ɛmax]. yj and ɛjk are independent. Their marginal densities are gy(y) and gɛ(ɛ), respectively, which are common for both Mexican and Chinese suppliers. We assume that fη(η) and gɛ(ɛ) are log concave.26 In other respects, the model has the same structure as the one in section IIIA.

Matching occurs between a final product variety and a supplier production line. The conditions for stable variety-to-line matching are similar to those in section IIIA. Stable matching consists of the matching function υ=mχ(χ) and χ=mυ(υ) between product capability χ and line capability υ, the variety's profit schedule πχ(χ), and the line's profit schedule πυ(υ). Following equation (1), we can obtain a match's joint profit as Πχ,υ=Aθχ,υ-f, where f is the fixed cost per product. The stability conditions continue to be equation (2), and the sign of θ12 determines the sign of sorting. The matching market-clearing condition in Case C is similar to that in equation (6):
M˜U[1-F˜(χ)]=M˜M+M˜C1-G˜(mχ(χ)),
(8)
where M˜UMUN is the total mass of varieties, M˜MMMn and M˜CMCn are the total mass of production lines in Mexico and China, respectively, and nyminymaxn(y)gy(y)dy is the mean mass of production lines. The CDFs of product capability χ and line capability υ are F˜(χ)-χfχ(t)dt and G˜(υ)-υn(t)ngυ(t)dt, respectively, where fχ(χ) and gυ(υ) are the densities of χ and υ, respectively.27 The conditions for Cases I and S can be derived analogously. The cutoff capabilities of varieties χL and lines υL satisfy similar conditions to those in equations (4) and (7).
Although variety-to-line matching is one-to-one, firm-to-firm matching is many-to-many. We approximate the number of a final producer's partners by the number of production lines matching with the final producer, and the number of a supplier's partners by the number of varieties matching with the supplier.28 Note that the number of a final producer's active products follows a binomial distribution with success probability [1-Fη(χL-x)] and the number of trials N, while the number of a supplier's active production lines follows a binomial distribution with [1-Gɛ(υL-y)] and n(y), where Fη and Gɛ are the CDFs of η and ɛ, respectively. Therefore, the mean number of Mexican partners for a final producer with capability x, NM(x) and the mean number of partners for a Mexican supplier with firm capability y, nS(y) are given by
NM(x)=MMN[1-Fη(χL-x)]MM+MCandnS(y)=n(y)[1-Gɛ(υL-y)].
(9)
Thus, the mean number of partners is increasing in firm capability and decreasing in the cutoffs.

Because the equilibrium conditions remain the same as in section IIIA the effects of the MFA's end on the matching functions, capability cutoffs, and price indices are qualitatively the same as those in section IIIB. Let Pt (t{A,B,NR}) be the product-level price indices. Then the following lemma holds with essentially the same proofs as in section IIIB.

Lemma 1.

(i) In Case C after the MFA's end: mχA(χ)>mχB(χ) for the given χ; mυA(υ)<mυB(υ) for the given υ; υLA>υLB; and PA<PNR<PB. (ii) In Case I after the MFA's end, υLA>υLB and PA=PNR<PB.

Predictions of main partner choices, exit, and number of partners.

We derive the model's predictions of firm-to-firm matching that we take to the data. Our data on Mexico–United States trade record only partner switching by firms engaging in Mexico–United States trade both before and after the MFA's end. We call these firms U.S. continuing importers and Mexican continuing exporters.

We examine a firm's main partner choice because of its importance in our dataset. First, consider Case C. Let χi*xi+maxsηis be the highest product capability of final producer i. The mean firm capability of final producer i's main partner is y¯t(χi*)E[y|y+ɛ=mχt(χi*)] for t{A,B}. Similarly, the mean firm capability of supplier j's main partner is x¯t(υj*)E[x|x+η=mυt(υj*)] for t{A,B}, where υj*yj+maxkɛjk. A final producer i upgrades its main partner if y¯A(χi*)>y¯B(χi*), and downgrades if y¯A(χi*)<y¯B(χi*). Similarly, a supplier j upgrades its main partner if x¯A(υj*)>x¯B(υj*), and downgrades if x¯A(υj*)<x¯B(υj*).

As shown in appendix A.2.2, the log concavity of fη(η) and gɛ(ɛ) implies that E[x|x+η=mυ(υj*)] increases in mυ(υj*) and that E[y|y+ɛ=mχ(χi*)] increases in mχ(χi*). Therefore, from Lemma 1, U.S. continuing importers upgrade Mexican main partners, whereas Mexican continuing exporters downgrade U.S. main partners. Another testable implication is that the relative ranking of main partner's firm capability preserves. For each pair of final producers i and j, if y¯B(χi*)>y¯B(χj*), then y¯A(χi*)>y¯A(χj*) holds; similarly, for each pair of suppliers k and h, if x¯B(υk*)>x¯B(υh*), then x¯A(υk*)>x¯A(υh*) holds. That is, the ranking of new partners' firm capability is positively correlated with the ranking of that of old partners.

In Case I, no systematic partner change occurs. No U.S. continuing importers or Mexican continuing exporters change main partners. The firm capability ranking of new partners is independent of the ranking of old partners. In summary, we establish the following proposition.

Proposition 2.

In Case C after the MFA's end, (C1) U.S. continuing importers upgrade Mexican main partners, while Mexican continuing exporters downgrade U.S. main partners, and (C2) the firm capability ranking of new main partners is positively correlated with that of old main partners. In Case I after the MFA's end, (I1) No U.S. continuing importers or Mexican continuing exporters change main partners and (I2) the firm capability ranking of new main partners is independent of the ranking of old main partners.

We derive the model's predictions of firm exit and the number of partners that holds in both Cases C and I. First, the firm capability cutoff for Mexican suppliers yL=υL-ɛmax increases. Second, from equation (9), the number of partners NM(x) and nS(y) decrease.

Proposition 3.

In Cases C and I after the MFA's end, (E1) the firm capability cutoff for Mexican exporters rises and (E2) both U.S. importers and Mexican exporters reduce their partners.

A. Proxy for Firm Capability Rankings

To test the predictions in Propositions 2 and 3, we estimate the ranking of firm capability as follows. Let I(x) be the mean imports of the intermediate good by U.S. importers with firm capability x from the main partners and let X(y) be the mean exports by Mexican exporters with firm capability y to the main partners. The following lemma holds from the monotonic relationship between firm capability and within-match trade (the proof is in appendix A.2.3).

Lemma 2.

In Case C and Case I, I(x) and X(y) are monotonically increasing functions.

For each HS six-digit product, we rank all the U.S. importer and all the Mexican exporters, using their imports and exports of the product from their main partner in 2004, respectively. We use these rankings using 2004 data throughout our sample period (2004–2007) during which the ranking is stable.29 Section VD presents the results using alternative rankings.

We first create three variables using these rankings for each product g in country c: (1) firm i's own ranking, OwnRankigc, (2) the ranking of the firm's main partner of product g in 2004, OldPartnerRankigc, and (3) the ranking of the firm's main partner of product g in 2007, NewPartnerRankigc. We choose 2004–2007 as the sample period to avoid potential confounding from the impact of the 2008 financial crisis on Mexican exports. These rankings are standardized using the number of firms to fall into the range of [0, 1]. Smaller rankings indicate higher capability (e.g., first ranking means the best). OldPartnerRankigc differs from NewPartnerRankigc if and only if the firm switches its main partner during 2004–2007. Finally, the partner upgrading dummy Upigsc equals one if NewPartnerRankigsc<OldPartnerRankigsc, and the partner downgrading dummy Downigsc equals one if NewPartnerRankigsc>OldPartnerRankigsc.

B. Specifications

Partner changes (C1 and I1).

The following regressions test predictions C1 and I1:
Upigsc=βUcBindinggs+λs+ɛUigsc,Downigsc=βDcBindinggs+λs+ɛDigsc,
(10)
where c,i, g, and s represent the country (United States and Mexico), firm, HS six-digit product, and sector (HS two-digit level), respectively. The dummy variable Bindinggs equals one if Chinese exports of product g to the United States faced a binding quota in 2004, which is constructed from Brambilla et al. (2010). λs represents the HS two-digit-level fixed effects.30ɛUigsc and ɛDigsc are the error terms. Appendix B.5 explains the construction of the binding dummy and other variables. The regression sample includes both continuing U.S. importers and Mexican exporters.

The coefficients of interest βUc and βDc in equation (10) are identified by comparing the treatment and control groups within HS two-digit sectors. The treatment is the removal of binding quotas on Chinese exports to the United States. The coefficients estimate the removal's impact on the probability of partner upgrading and downgrading, respectively. The HS two-digit fixed effects control for basic product characteristics such as textile and apparel and knit and woven.

Prediction C1 for PAM states that at the MFA's end, all the continuing U.S. importers upgrade their main partners, whereas all the continuing Mexican exporters downgrade. Although the frictionless matching model predicts that all the firms will change their partners, in reality, other factors such as transaction costs are likely to prevent some from making such a change, at least in the short run. Accordingly, we reformulate prediction C1 as follows: U.S. importers' partner upgrading and Mexican exporters' partner downgrading will occur more frequently in the treatment group than in the control group, which corresponds to βUUS>0, βDUS=βUMex=0, and βDMex>0 in equation (10).

Prediction I1 for independent matching states that at the MFA's end, no continuing U.S. importer and Mexican exporter would change their partners. In reality, some idiosyncratic shocks appearing as error terms in equation (10) could induce partner changes. Thus, we reformulate prediction I1 as follows: no difference should exist in the probability of partner changes in any direction between the treatment and control groups, which corresponds to βUUS=βDUS=βUMex=βDMex=0 in equation (10).

Our regression equation (10) does not suffer from the endogeneity problem that existed in the conventional correlation approach to detecting PAM that regresses an exporter's characteristics on those of an importer. For instance, the cross-sectional regression of an exporter's rank on an importer's rank could produce a mechanical positive correlation regardless of the sign of sorting.31 We use firm characteristics (trade volume) only to construct the outcome variables on the left-hand side. Any discrepancy between the true capability ranking and trade ranking should appear in the error terms ɛUigsc and ɛDigsc, which might reflect the capability of the firm and its partners, and other unobservable firm and product characteristics. However, as long as the binding dummy is uncorrelated with these unobservables, βUc and βDc are consistently estimated.32

Another advantage of equation (10) is controlling for the various unobservable determinants of a firm's partner rankings. First, idiosyncratic shocks to demand and cost may change firm capability and generate partner switching. As long as these shocks appearing as error terms in equation (10) are uncorrelated to the MFA liberalization, they should not bias our estimates. Second, the dependent variables are constructed from time differences in partner rankings. Time differencing controls for all the time-invariant firm-specific determinants of the level of partner rankings.

Old and new partner rankings (C2 and I2).

To test predictions C2 and I2, we estimate the following regression for firms that switched partners during 2004–2007:
NewPartnerRankigc=αc+γcOldPartnerRankigc+ɛigcforfirmiwithNewPartnerRankigcOldPartnerRankigc.
(11)

Prediction C2 predicts γc>0, and prediction I2 predicts γc=0.

Two additional points need to be mentioned. First, if we run equation (11) only for firms that do not change partners, then γc equals one by construction. To avoid this mechanical correlation, we estimate equation (11) only for firms that change partners. Second, the regression equation (11) combines both the treatment and the control groups since prediction C2 should hold for both groups in Case C.33

Capability cutoff changes (E1).

We test prediction E1 using two models. First, we estimate a product-level difference-in-difference model of the export cutoffs for the preliberalization (2001–2004) and postliberalization (2004–2007) periods:34
lnExportCutoffgsr=δ1Bindingg+δ2Bindingg×Afterr+δ3Afterr+λs+ugsr.
(12)

For surviving exporters in the final year of period r, the minimum of their exports of product g in the initial year of period r proxies for the capability cutoff, ExportCutoffgsr. Since importer information is unavailable before 2004, we use Mexican exporters' product exports as the capability proxy, which is highly correlated with exports to the main partners in the 2004–2007 data. Afterr is an indicator of whether period r is 2004–2007, λs represents the HS two-digit-level fixed effects, and uigsc are the error terms.

We use the difference-in-difference specification to test the predictions about the cutoff changes. In equation (12), the cutoff increase in prediction E1 implies δ2>0 as the coefficient of interest. On the contrary, δ1 estimates the difference in the levels of the cutoffs between the liberalized and nonliberalized products. We perform a placebo check of no difference in the prior trends in the cutoffs by estimating equation equation (12) for the two preliberalization periods (1998–2001 and 2001–2004).

The product-level regression equation (12) raises two potential concerns. First, it fails to control for firm heterogeneity within products. Second, a rise in the export cutoff may not imply more firm exits from the market. Therefore, we also estimate the following threshold model of a firm's exit. In each period r, Mexican supplier i receives a random i.i.d. shock ɛir to its profit, which captures the idiosyncratic factors inducing firm exit in the absence of liberalization (e.g., Eaton et al., 2014). The firm chooses to exit if ɛir is below the threshold ɛ¯iry. Prediction E1 implies two predictions: (i) the MFA's end increases the threshold ɛ¯ir(y) for the given capability y and (ii) the threshold ɛ¯ir(y) is a decreasing function of the firm's capability y. Then we estimate the following firm-level regression for Mexican firm i that exports product g to the United States in the initial year of period r{2001-04,2004-07}:
Exitigsr=δ1Bindingg+δ2Bindingg×Afterr+δ3Afterr+δ4lnExportsigr+δ5Afterr×lnExportsigr+λs+uigsr.
(13)
The dummy variable Exitigsr equals one if the firm stops exporting during period r. lnExportsigr is the log of the firm's total exports of product g in the initial year of period r, which proxies for firm capability. Regression equation (13) uses the level of exports instead of their ranking because the level of capability determines the firm's exit, and the ranking of capability determines the matching. Predictions (i) and (ii) are expressed as follows: (i) δ2>0, that is, the end of the MFA increased the exit probability for a given capability level, and (ii) δ4<0 and δ4+δ5<0, that is, small low-capability firms are more likely to exit.35

Number of partners (E2).

To test prediction E2, we regress the changes in the number of partners on the binding dummy for U.S. importers and Mexican exporters:
Δ#Partnersigsc=ζ1cBindinggs+λs+ɛigsc,c{Mex,US},
(14)

where Δ#Partnersigsc is the changes in the number of firm i's partners in product g during 2004–2007, λs represents the HS two-digit-level fixed effects, and ɛigsc are the error terms. Prediction E2 implies ζ1Mex<0 and ζ1US<0.

A. Partner Changes

Panel A in table 3 examines partner changes during 2004–2007 using linear probability models.36 The columns with odd numbers report the estimates of βdc (c=US,Mex and d=U,D) from the baseline regressions equation (10). We find that βUUS in column 1 and βDMex in column 7 are positive and statistically significant, while βDUS in column 3 and βUMex in column 5 are close to and not statistically different from zero. These signs of βdc support Case C and reject Case I. The removal of binding quotas from Chinese exports increased the probability of U.S. importers' partner upgrading by 5.2 percentage points and the probability of Mexican exporters' partner downgrading by 12.7 percentage points.37 These effects are quantitatively large compared with the sample averages of UpigsUS and DownigsMex, which are three and fifteen percentage points, respectively.38

Table 3.

Partner Change during 2004–2007

A. Benchmark regression
Liner probability models
UpUSDownUSUpMexDownMex
(1)(2)(3)(4)(5)(6)(7)(8)
Binding 0.052∗∗ 0.041* -0.017 0.004 -0.003 -0.000 0.127∗∗∗ 0.130∗∗∗ 
 (0.021) (0.023) (0.027) (0.042) (0.020) (0.018) (0.035) (0.049) 
OwnRank  -0.001  -0.074*  0.004  -0.087 
  (0.024)  (0.042)  (0.014)  (0.054) 
Binding × OwnRank  0.034  -0.070  -0.007  -0.018 
  (0.049)  (0.074)  (0.026)  (0.087) 
HS2 FE Yes Yes Yes Yes Yes Yes Yes Yes 
Obs. 718 718 718 718 601 601 601 601 
B. Placebo check: Partner change in different periods 
 Linear probability models  
 UpUS DownMex  
 2007–2011 2008–2011 2009–2011 2007–2011 2008–2011 2009–2011  
 (1) (2) (3) (4) (5) (6)  
Binding -0.001 0.027∗∗ -0.000 -0.007 0.047 0.005  
 (0.018) (0.011) (0.006) (0.036) (0.031) (0.020)  
HS2 FE Yes Yes Yes Yes Yes Yes  
Obs. 449 575 747 393 499 655  
A. Benchmark regression
Liner probability models
UpUSDownUSUpMexDownMex
(1)(2)(3)(4)(5)(6)(7)(8)
Binding 0.052∗∗ 0.041* -0.017 0.004 -0.003 -0.000 0.127∗∗∗ 0.130∗∗∗ 
 (0.021) (0.023) (0.027) (0.042) (0.020) (0.018) (0.035) (0.049) 
OwnRank  -0.001  -0.074*  0.004  -0.087 
  (0.024)  (0.042)  (0.014)  (0.054) 
Binding × OwnRank  0.034  -0.070  -0.007  -0.018 
  (0.049)  (0.074)  (0.026)  (0.087) 
HS2 FE Yes Yes Yes Yes Yes Yes Yes Yes 
Obs. 718 718 718 718 601 601 601 601 
B. Placebo check: Partner change in different periods 
 Linear probability models  
 UpUS DownMex  
 2007–2011 2008–2011 2009–2011 2007–2011 2008–2011 2009–2011  
 (1) (2) (3) (4) (5) (6)  
Binding -0.001 0.027∗∗ -0.000 -0.007 0.047 0.005  
 (0.018) (0.011) (0.006) (0.036) (0.031) (0.020)  
HS2 FE Yes Yes Yes Yes Yes Yes  
Obs. 449 575 747 393 499 655  

In panel A, the dependent variables Upigsc and Downigsc are dummy variables indicating whether during 2004–2007 firm i in country c switched its main partner of HS six-digit product g in country c' to one with a higher or lower capability ranking, respectively. Bindinggs is a dummy variable indicating whether product g from China faced a binding U.S. import quota in 2004. OwnRankigs is the normalized ranking of firm i in 2004. All the regressions include the HS two-digit (sector) fixed effects. Standard errors are in parentheses and clustered at the HS six-digit product level. Significance: * 10%, ∗∗ 5%, ∗∗∗ 1%.

The columns with even numbers in panel A in table 3 add the firm's own ranking and its interaction with the binding dummy. Both large and small firms switch their partners as the model predicts. Figure 3 illustrates these results by drawing the kernel-weighted local mean regressions of the partner change dummies on the firm's own ranking for apparel products.39 The dashed lines and areas represent the regression lines with 90% confidence bands for the treatment group, and the solid lines and areas represent those for the control group. A higher probability of U.S. importers' upgrading and Mexican exporters' downgrading in the treatment group is found uniformly for all the capability rankings. By contrast, little difference between the two groups in the probability of U.S. importers' downgrading and Mexican exporters' upgrading is found.
Figure 3.
Partner Change during 2004–2007 and Initial Capability Rankings: Apparel Products

The dark gray lines and areas represent the kernel-weighted local mean regression lines with 90% confidence bands for the treatment group, and the light gray lines and areas represent those for the control group. The confidence interval for U.S. upgrading for the control group is degenerated because no upgrading occurred there.

Figure 3.
Partner Change during 2004–2007 and Initial Capability Rankings: Apparel Products

The dark gray lines and areas represent the kernel-weighted local mean regression lines with 90% confidence bands for the treatment group, and the light gray lines and areas represent those for the control group. The confidence interval for U.S. upgrading for the control group is degenerated because no upgrading occurred there.

Close modal

Panel B in table 3 examines partner changes in the later periods of 2007–2011 and 2009–2011 to check our assumption that both the treatment and the control groups exhibit similar partner change patterns if the treatment is absent.40 For each period, we reconstruct the capability rankings based on trade in the new initial years and re-create the upgrading and downgrading dummies. If the transition from the old to the new equilibrium was largely completed by 2007, we should observe no difference in partner changes between the two groups. Small and insignificant estimates for βUUS and βDMex in 2007–2011 and 2009–2011 support our assumption.41

We conduct numerous robustness checks in appendix E.3. First, we include as additional controls several product-level and firm-product-level characteristics that statistically differ between the treatment and control groups.42 Second, we conduct three exercises to address potential within-firm interactions in firms trading multiple products and firms with multiple partners. We add the number of products that a firm trades and its interaction with the binding dummy, address the case that the main partner switching occurs within initial partners, and distinguishing firms that had a single partner and those that had multiple partners. Finally, we adopt alternative variable definitions. We define partner switching using rank bins, define quota binding under alternative criteria, and use alternative year windows. Our results are robust to all of these alternatives.43

B. New and Old Partners Ranks

Figure 4 reports regression equation (11), which tests predictions C2 and I2, with the corresponding scatterplots. For those U.S. importers that changed their main partners between 2004 and 2007, the left panel displays the rankings of their old partners on the horizontal axis and those of their new partners on the vertical axis. The right panel draws a similar plot for Mexican exporters. The lines represent ordinary least squares (OLS) regression equation (11). Figure 4 and the regressions show significant positive relationships. Firms that matched with relatively high-capability partners in 2004 switched to relatively high-capability partners in 2007. This result again supports Case C and rejects Case I. Appendix E.4 replicates figure 4 for a sample only including the treatment group. The results are very similar to those in figure 4.
Figure 4.
Old and New Partner Ranks

The left panel plots the ranking of new main partners in 2007 against the ranking of old main partners in 2004 for U.S. importers that changed their main partners between 2004 and 2007. The right panel draws similar partner rankings for Mexican exporters. The lines represent OLS fits.

Figure 4.
Old and New Partner Ranks

The left panel plots the ranking of new main partners in 2007 against the ranking of old main partners in 2004 for U.S. importers that changed their main partners between 2004 and 2007. The right panel draws similar partner rankings for Mexican exporters. The lines represent OLS fits.

Close modal

C. Capability Cutoff Changes and Number of Partners

Table 4 reports the tests of prediction E1. Column 1 reports the baseline specification of product-level regression equation (12), and column 2 includes as additional control variables the product characteristics for the initial year in each period and their interactions with the after dummy. These controls, when available, are the same as in footnote 42.44 The estimates of the positive and significant δ2 confirm the prediction that the MFA's end increased the capability cutoff for Mexican exporters. Column 5 reports the baseline specification of firm-level regression (13), and column 6 includes the product characteristic variables and their interactions with the after dummy. The estimates of the positive and significant δ2 confirm that the MFA's end increased their exit probability for a given capability level. In addition, the negative estimates of δ4 and δ4+δ5 confirm that small exporters are more likely to exit the market.

Table 4.

Capability Cutoff Changes and Number of Partners

A. Capability cutoff changes
Product-level difference-in-differenceFirm-level difference-in-difference
lnExportCutoffgsrExitigsr
Period 12001–20041998–20012001–20041998–2001
Period 22004–20072001–20042004–20072001–2004
(1)(2)(3)(4)(5)(6)(7)(8)
Binding (δ1-1.255∗∗∗ -0.668∗∗∗ -1.074∗∗∗ -0.786∗∗∗ -0.040∗∗∗ -0.028∗∗ -0.021 0.009 
 (0.281) (0.246) (0.248) (0.249) (0.014) (0.013) (0.016) (0.014) 
Binding × After (δ21.031∗∗ 1.188∗∗ 0.106 0.324 0.076∗∗∗ 0.089∗∗∗ -0.003 -0.034∗∗ 
 (0.479) (0.490) (0.178) (0.244) (0.017) (0.021) (0.013) (0.015) 
After (δ3-3.402∗∗∗ -0.863 -0.230 0.809 -0.361∗∗∗ -0.345∗∗∗ -0.119∗∗∗ -0.212∗∗∗ 
 (0.364) (1.620) (0.151) (0.785) (0.042) (0.077) (0.034) (0.056) 
lnExport (δ4    -0.058∗∗∗ -0.056∗∗∗ -0.069∗∗∗ -0.066∗∗∗ 
     (0.002) (0.003) (0.003) (0.003) 
lnExport× After (δ5    0.020∗∗∗ 0.020∗∗∗ 0.011∗∗∗ 0.008∗∗ 
     (0.003) (0.003) (0.003) (0.003) 
Controls No Yes No Yes No Yes No Yes 
HS2 FE Yes Yes Yes Yes Yes Yes Yes Yes 
Obs. 696 696 652 652 22,625 22,624 24,043 22,142 
B. Number of partners 
   Change in number of partners 
   Mexico  United States  
   (9)  (10)  
  Binding -0.65∗∗  -0.12*  
   (0.33)  (0.06)  
  HS2 FE Yes  Yes  
  Obs. 601  718  
A. Capability cutoff changes
Product-level difference-in-differenceFirm-level difference-in-difference
lnExportCutoffgsrExitigsr
Period 12001–20041998–20012001–20041998–2001
Period 22004–20072001–20042004–20072001–2004
(1)(2)(3)(4)(5)(6)(7)(8)
Binding (δ1-1.255∗∗∗ -0.668∗∗∗ -1.074∗∗∗ -0.786∗∗∗ -0.040∗∗∗ -0.028∗∗ -0.021 0.009 
 (0.281) (0.246) (0.248) (0.249) (0.014) (0.013) (0.016) (0.014) 
Binding × After (δ21.031∗∗ 1.188∗∗ 0.106 0.324 0.076∗∗∗ 0.089∗∗∗ -0.003 -0.034∗∗ 
 (0.479) (0.490) (0.178) (0.244) (0.017) (0.021) (0.013) (0.015) 
After (δ3-3.402∗∗∗ -0.863 -0.230 0.809 -0.361∗∗∗ -0.345∗∗∗ -0.119∗∗∗ -0.212∗∗∗ 
 (0.364) (1.620) (0.151) (0.785) (0.042) (0.077) (0.034) (0.056) 
lnExport (δ4    -0.058∗∗∗ -0.056∗∗∗ -0.069∗∗∗ -0.066∗∗∗ 
     (0.002) (0.003) (0.003) (0.003) 
lnExport× After (δ5    0.020∗∗∗ 0.020∗∗∗ 0.011∗∗∗ 0.008∗∗ 
     (0.003) (0.003) (0.003) (0.003) 
Controls No Yes No Yes No Yes No Yes 
HS2 FE Yes Yes Yes Yes Yes Yes Yes Yes 
Obs. 696 696 652 652 22,625 22,624 24,043 22,142 
B. Number of partners 
   Change in number of partners 
   Mexico  United States  
   (9)  (10)  
  Binding -0.65∗∗  -0.12*  
   (0.33)  (0.06)  
  HS2 FE Yes  Yes  
  Obs. 601  718  

All the regressions include the HS two-digit (sector) fixed effects. In panel B, the dependent variable is the corresponding variable 2007–2011, 2008–2011, and 2009–2011. Standard errors are shown in parentheses and clustered at the HS six-digit product level. Panel A: lnExportCutoffgsr is the log of the minimum of firm-product-level exports in the initial year of period r. Exitigsr is a dummy variable indicating whether Mexican firm i stops exporting product g to the United States in period r. Bindinggs is a dummy variable indicating whether product g from China faced a binding U.S. import quota in 2004. Afterr is a dummy variable indicating whether period r is after 2004. lnExportigr is the log of firm i's exports of product g in the initial year of period r. Columns 2, 4, 6, and 8 include the product-level controls. Panel B: the dependent variables are the change in the number of partners during 2004–2007. Significance: * 10%, ∗∗ 5%, ∗∗∗ 1%.

Columns 3 and 7 show placebo checks that estimate regressions equations (12) and (13) using two periods before the MFA liberalization, 1998–2001 and 2001–2004, respectively.45 Columns 4 and 8 include the control variables. In all the placebo checks, the estimated δ2 is close to zero and statistically insignificant or shows a negative sign. These results reject the concern that the estimate of δ2 captures a prior difference in the trend between the two groups.

Panel B in table 4 reports regression equation (14). The negative and significant coefficients of the binding dummy in columns 1 and 2 confirm Prediction E2 that both U.S. importers and Mexican exporters reduce the number of partners in liberalized industries.

D. Does Capability Reflect Quality or Productivity?

We have studied exporter-importer matching by capability without specifying whether capability determining matching is quality or productivity. To shed a light on this question, we create rankings based on two alternative variables: a firm's unit price with the main partners in 2004 and a firm's quality estimated using the method of Khandelwal et al. (2013). If the exporter's capability mainly reflects quality rather than productivity, the two rankings may agree with the capability ranking. On the other hand, if the exporter's capability mainly reflects productivity, the unit price ranking may become the reverse of the capability ranking.

Appendix E.5 shows that the main results are robust to the price and quality rankings.46 Therefore, an exporter's quality determines whether it can match with high-capability importers. This result is consistent with the literature's finding that quality is an important determinant of a firm's export participation (e.g., Kugler & Verhoogen, 2012).

E. Alternative Explanations

In appendix C we examine four alternative hypotheses for our findings. The first hypothesis is negative assortative matching under which trade rankings may not agree with true capability rankings. The second hypothesis is repeated random independent matching. Suppose random partner change occurs in every period and exhibits mean reversion. The exit of low-capability Mexican exporters may create a positive correlation between the binding dummy and downgrading by Mexican exporters. The third hypothesis is that Mexican exporters switch a product segment from large-scale production with small markups to small-scale production with large markups. The final hypothesis is that a U.S. importer's partner switches from small to large suppliers to seek large production capacity. For these hypotheses, we conduct additional analyses and show that none of them fully explain our results.

This study presented theory and evidence for a simple mechanism of exporter-importer matching: Beckerian PAM by capability. Beckerian PAM offers several new insights into buyer-supplier relationships in international trade. As our model showed, rematching in trade liberalization brings about two new gain-accruing channels. First, at the sector or aggregate levels, trade liberalization improves efficiency by rematching buyers and suppliers. Quantifying these matching-induced gains from trade is an important topic for future research. Second, at the individual level, firms see improved performance when they upgrade their partners. Regarding the second channel, Beckerian PAM has two implications that can be brought to data in future studies. First, the benefits to local firms increase in the capability of foreign partners. Second, only firms with high capability can maintain stable relationships with high-capability foreign firms. The latter suggests the importance of capability development policies to complement trade promotion policies.

1

In 2004 the United States was the largest textile and apparel market for Mexico, and Mexico was the second largest source for the United States. Indeed, 91.9% of Mexican exports are shipped to the United States, and 9.5% of United States imports are from Mexico.

2

For instance, Oberfield (2018) showed that a buyer's employment is positively correlated with a seller's employment in a model in which buyers match sellers randomly and independently of capability.

3

Domestic buyer-supplier matched data has recently become available for research on domestic production networks (e.g., Bernard et al., 2019; Dhyne et al., 2021).

4

See, for example, De Loecker (2007) and Atkin et al. (2017) for learning technologies; Macchiavello (2010) and Macchiavello and Morjaria (2015) for reputation building; Tanaka (2020) for improving management; and Verhoogen (2008) for quality upgrading. Trading with foreign multinational firms is also found to improve firm's performance (e.g., Javorcik, 2004).

5

Bernard et al. (2021) and Lim (2018) introduced idiosyncratic match-level fixed costs in the model of Bernard et al. (2018) and analyzed the formulation of domestic production networks. Carballo et al. (2018) applied the ideal variety approach instead of using the love-of-variety model, which incorporates the interaction between the buyer's taste for ideal varieties and the seller's productivity.

6

In the assignment model, by contrast, the match surplus is a nonmonotonic function. For a given firm, the match surplus is maximized at the capability of its equilibrium partner as we show in section IIIA equation (2).

7

Lu et al. (2017) analyzed importers' switching intermediates in a search-learning model.

8

After this substantial surge in import growth, the United States and China had agreed to impose new quotas until 2008, but imports from China never returned to their pre-2005 levels because (1) the new quota system covered fewer product categories than the old system (Dayaratna-Banda and Whalley, 2007) and (2) the new quotas were substantially greater than the MFA levels (see table 2 in Brambilla et al., 2010).

9

In theory, Mexican firms can export products to the United States that are produced from materials imported from China; however, the number of such cases is negligible because of NAFTA's restrictive rules of origin, which require “yarn forward” (U.S. Customs and Border Protection, 2014). The yarn must be made in Mexico to be qualified as NAFTA products; therefore, only fibers can be imported from China. However, Mexico's fiber imports from China was 7 million USD in 2004 and accounted for only 0.08% of Mexico's textile and apparel exports to the United States.

10

Khandelwal et al. (2013) reported that incumbent exporters are mainly state-owned firms, whereas new exporters include private and foreign firms, which are typically more productive. In addition, the distribution of unit prices set by new entrants has a lower mean but greater support than that by incumbent exporters.

11

An excellent reference for record linkage is Herzog et al. (2007). In addition, we benefited from the lecture slides on “Record Linkage” by John Abowd and Lars Vilhuber.

12

The Maquiladoras program started in 1986, and the IMMEX program replaced it in 2006. Under these programs, firms in Mexico can import the materials and equipment to be used for exports duty free. Exporters must register the importer's information in advance but need not report it for each shipment.

13

Table 1 removes products with only one exporter or one importer, which accounts for 3% of trade. Including them decreases the numbers in Columns 1 and 2 but barely changes those in the other columns.

14

Appendix E.1 presents versions of table 1 for 2005 and 2006 and for the regression samples that exclude new exporters and new importers after 2005 that might have started with only one partner. The statistics on the numbers of partners in columns 3–6 remain close to those in table 1.

15

The large shares of trade with main partners in table 1 are not driven by small firms that affect total trade to only a small extent. In an earlier version of this paper, we reported that main-to-main matches, where the exporter is the importer's main partner for the product and the importer is the exporter's main partner, account for about 80% of total trade.

16

In appendix E.2, the extensive margin is decomposed into dropping products and leaving the United States.

17

Our model is a partial equilibrium version of that of Sugita (2015), who presented a two-country general equilibrium model with endogenous firm entry.

18

The general conclusion of the theoretical literature on search frictions (e.g., see Smith, 2011 for an excellent survey) is that as long as the complementarity within matches is large enough, PAM holds on average, as in the frictionless matching model that we consider.

19

The identical distribution of Chinese and Mexican suppliers is assumed only for graphical exposition. Appendix A.1 derives the main predictions without this assumption.

20

An example of a within-team externality is quality control. Producing high-quality goods might require extra costs of quality control in each production stage because one defective component could destroy the whole product (Kremer, 1993). Another example is knowledge spillovers. Through the teaching and learning (e.g., joint R&D), each member's marginal cost may depend on the entire team's capability.

21

Roth and Sotomayor (1990) and Browning et al. (2014) provide excellent backgrounds on matching models

22

The use of differentiation is a convenient shortcut for deriving the sorting pattern, following Sattinger (1979). Lemma 5 in appendix D presents a general proof of sorting that can be applied to finite agents.

23

In Cases C and S, θ is also called strict supermodular and strict submodular, respectively. An example for Case C is the quality complementarity of tasks in a production process (e.g., Kremer, 1993). For instance, a high-quality part may be more useful when combined with other high-quality parts. An example of Case S is spillovers through learning and teaching. Gains from learning from highly capable partners might be greater for low-capability firms. Grossman and Maggi (2000) provided further examples.

24

The intuition of the theorem follows from the definition of the supermodularity of θ such that for any x>x' and y>y', θ(x,y)+θ(x',y')>θ(x',y)+θ(x,y'). Applying the theorem to proposition 1 is not trivial since A is endogenous in our setting.

25

Ornelas, Turner, and Bickwit (2021) theoretically analyzed matching diversion by a preferential trade agreement in a model with one-sided heterogeneity.

26

The class of distributions with log-concave densities includes a wide range of unimodal parametric distributions such as normal, uniform, logistic, Frechet, and many others.

27

These densities are obtained by convolution as fχ(χ)=max{xmin,t-ηmax}min{ηmin,t-xmax}fx(s)fη(t-s)dsdt and gυ(υ)=max{ymin,t-ɛmax}min{ɛmin,t-ymax}gy(s)gɛ(t-s)dsdt.

28

Strictly speaking, the number of a final producer's partners could be fewer than the number of lines matching with the final producer. However, since production lines are heterogenous, the probability that one supplier provides multiple production lines to the same final producer is negligible when firms are of a continuum and small when they are finite.

29

The correlations of the rankings in 2004 and 2007 are higher than 0.85 for all the products and similar between the treatment and control groups.

30

We include the HS two-digit-level fixed effects instead of the HS four-digit-level fixed effects because of their collinearity with the binding dummy. When the binding dummy is regressed on only the HS four-digit-level fixed effects, R2 is 0.86 in both the U.S. and the Mexican samples, which means that only 14% of the variation in the binding dummy can be used to estimate βUC and βDC in equation (10). On the other hand, when the binding dummy is regressed on only the HS two-digit-level fixed effects, R2 is 0.48 for the U.S. sample and 0.50 for the Mexican sample, which leaves sufficient variation. We also drop those HS two-digit sectors (HS 50, 51, 53, 56, 57, and 59) in which no variation in the binding dummy at the HS two-digit level occurs.

31

Suppose importers are homogeneous in capability (i.e., θ1=0), such as homogenous warehouses. This is a special case of Case I, and no sorting occurs. Then the ranking of importer's trade equals that of unobserved exporter's capability, which yields a positive mechanical correlation of exporters' and importers' rankings. Oberfield (2018, proposition 6) showed this point in a more general model.

32

In our data, some firms export or import multiple products. If a pair of U.S. and Mexican firms traded in multiple products with each other in 2004 and if they switched to new main partners for all their products (maybe to save transaction costs), then this might bias our estimates. However, this is unlikely because such pairs account for only 8% of Mexican exporters that switched partners.

33

For instance, if an industry-wide shock induces a Mexican exporter's partner to downgrade in both the treatment and the control groups, the model with PAM should predict γc>0 for both groups. In appendix E.4, we present the regression equation (11) only for the treatment group.

34

We thank a referee for suggesting the product-level regression of the export cutoff.

35

One might think of introducing the triple interaction Bindingg×Afterr×lnExportsigr to examine whether the treatment effect on the exit probability decreases in the firm's initial exports. However, this alternative specification is unsuitable for testing prediction E1. As observed in other customs data (e.g., Eaton et al., 2014), the exit probability of small exporters is high even without liberalization. For instance, the exit rate of the smallest 20% exporters before 2004 is greater than 0.85, whereas that for the top 20% is around 0.55. Thus, the treatment effect on the exit probability is naturally estimated to be small for these small exporters, but this does not necessarily contradict prediction E1.

36

The probit regressions in appendix E.3.1 provide similar results for all the regressions.

37

βDMex is estimated to be larger than βUUS because of the following partner changes within initial partners, which is consistent with the theoretical model. Suppose that a Mexican exporter had been exporting to two U.S. importers in 2004 and that these two U.S. importers buy only from that exporter. Then in 2007 the exporter stopped exporting to its 2004 main partner and exported only to the second importer. This is counted as partner downgrading for the exporter but not as partner upgrading for the two importers. This causes βDMex to be estimated as larger than βUUS. Appendix E.3.5 shows the results are robust when distinguishing a firm's main partner change within and beyond initial partners.

38

These numbers do not mean that 97% of U.S. importers and 85% of Mexican exporters traded with the same main partner in both 2004 and 2007. In the data set, only 12% of U.S. importers and 12% of Mexican exporters traded with the same main partner in both 2004 and 2007. The sample averages of UpigsUS and DownigsMex are likely to underestimate the probabilities of partner changes in the population. Our data observe partner upgrading and downgrading only if the firm, new partner, and old partner are all continuing firms. Partner switching to firms in other countries and firms not existing in 2004 are excluded.

39

We used the Epanechnikov kernel and chose the bandwidth to minimize the integrated mean squared error. Appendix E.3.2 shows the plot for textile products.

40

Checking the assumption by examining partner changes before 2004 is not feasible because our data contain partner information only from June 2004 onward. At the aggregate level, figure 1 demonstrates the absence of differential time trends in aggregate exports before the removal of the MFA quota in 2005.

41

The 2008–2011 result differs from those in the other periods. One reason may be that the global financial crisis of 2008 might have introduced noise into the rankings because Mexican exports declined markedly in the second half of 2008.

42

These product-level characteristics are the number of exporters, number of importers, log product trade, and product-type dummies on whether products are for men, women, or not specific to gender and those on whether products are made of cotton, wool, or synthetic textiles. These firm-product-level characteristics are the log of a firm's product trade with the main partner, share of Maquiladora/IMMEX trade in a firm's product trade, number of partners, and dummy of whether a U.S. importer is an intermediary firm.

43

One exception is the regression of the U.S. importer when all the product-level and firm-product-level characteristics are included as controls together. The coefficient becomes insignificant but remains qualitatively the same (βUUS is 73% of the benchmark estimate with p-value 0.12).

44

They are the number of exporters, log product trade, and product-type dummies.

45

For this analysis we use the customs transaction data set for 1998–2004, which does not have U.S. importer information. See appendix B.1 or the data construction.

46

Appendix E.5 shows our results are robust with the ranking based on a firm's total product trade.

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Author notes

We are grateful to comments from three anonymous referees and the editor, Amit Khandelwal, especially their encouragement for many-to-many matching extensions. We thank Andrew Bernard, Bernardo Blum, Kerem Coşar, Don Davis, Swati Dhingra, Lukasz Drozd, Michael Gechter, Julia Cajal-Grossi, Meixin Guo, Daniel Halvarsson, Keith Head, Wen-Tai Hsu, Mathias Iwanowsky, Hiroyuki Kasahara, Ben Li, Alberto Ortiz, Nina Pavcnik, James Rauch, Bob Rijkers, Esteban Rossi-Hansberg, Peter Schott, Yuta Suzuki, Heiwai Tang, Yong Tang, Catherine Thomas, Kosuke Uetake, Yasutora Watanabe, Yuta Watabe, David Weinstein, Shintaro Yamaguchi, and Makoto Yano and participants at seminars and conferences for their comments. We thank the Secretaria de Economia of Mexico and the Banco de Mexico for help with the data. Financial support from the Private Enterprise Development in Low-Income Countries (PEDL), the Wallander Foundation, the Asociacion Mexicana de Cultura, and JSPS KAKENHI (Grants Number 22243023, 26220503, 15H05392, 17H00986, 18K19955, and 19H01477) is gratefully acknowledged. This research benefits from the IDE-JETRO project and the RIETI project. Francisco Carrera, Diego de la Fuente, Zheng Han, Carlos Segura, Yuri Sugiyama, Yuta Suzuki, Jumpei Takubo, Makoto Tanaka, and Stephanie Zonszein provided excellent research assistance.

A supplemental appendix is available online at https://doi.org/10.1162/rest_a_01114.

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Supplementary data