falling trade costs, heterogeneous firms, and industry ... · bureau of the census or by the...

Falling Trade Costs, Heterogeneous Firms, and IndustryDynamics∗

Andrew B. Bernard†

Tuck School of Business at DartmouthNational Bureau of Economic Research

J. Bradford Jensen‡

Institute for International Economics

Peter K. Schott§

Yale School of ManagementNational Bureau of Economic Research

February, 2004

Abstract

This paper examines the response of industries and firms to changes in tradecosts. New firm-level models of international trade with heterogeneous firmspredict that industry productivity will rise as trade costs fall due to the reallocationof activity across plants within an industry. Using disaggregated U.S. importdata, we create a new measure of trade costs over time and industries. As themodels predict, productivity growth is faster in industries with falling trade costs.We also find evidence supporting the major hypotheses of the heterogenous-firmmodels. Plants in industries with falling trade costs are more likely to die orbecome exporters. Existing exporters increase their shipments abroad. Theresults do not apply equally across all sectors but are strongest for industries mostlikely to be producing horizontally-differentiated tradeable goods.Keywords: Plant deaths, survival, exit, exports, employment, tariffs, freight costs, transport costs

JEL classification: F10

∗We thank Marc Melitz, Nina Pavcnik, Jim Tybout and participants at the 2002 TuckTrade Summer Camp and ERWIT 2002 for helpful comments. The research in this paperwas conducted at the Center for Economic Studies. Research results and conclusionsexpressed are those of the authors and do not necessarily indicate concurrence by theBureau of the Census or by the National Bureau of Economic Research. The paper hasnot undergone the review the Census Bureau gives its official publications. It has beenscreened to insure that no confidential data are revealed.v05

†100 Tuck Hall, Hanover, NH 03755, tel : (603) 646-0302, fax : (603) 646-0995, email :[email protected]

‡1750 Massachusetts Avenue, Washington, DC, 20036-1903, email : [email protected]§135 Prospect Street, New Haven, CT 06520, tel : (203) 436-4260, fax : (203) 432-6974,

email : [email protected]

Falling Trade Costs and Industry Dynamics 2

1. Introduction

The potential link between trade liberalization and economic growth isfundamental to both international and development economics. To date,addressing it has proceeded at two levels. The first explores the country-level correlation between openness and per capita GDP and asks whethercountries that reduce trade barriers experience higher income growth thancountries which remain relatively closed. The second investigates a micro-economic link between countries’ trade policies and their firms’ productiv-ity. It asks whether firms achieve higher productivity growth by becomingexporters or by being forced to endure more intense competition with for-eign rivals.

This paper focuses on a new microeconomic channel that stresses pro-ductivity gains via the reallocation of economic activity across firms withinindustries. This approach is guided by new, heterogenous-firm models ofinternational trade by Bernard et al. (2003), Melitz (2003), and Yeaple(2002). These models emphasize productivity differences across firms op-erating in imperfectly competitive industries that encompass horizontallydifferentiated varieties. The existence of trade costs induces only the mostproductive firms to self-select into export markets. As a result, when tradecosts fall, industry productivity rises both because low-productivity, non-exporting firms exit and because high-productivity firms are able to expandthrough exporting. The most productive non-exporters begin to export,and current exporters, also high-productivity firms, expand their foreignsales. In these models, it is the reallocation of activity across firms, notintra-firm productivity growth, that boosts industry productivity.1

We test the industry- and firm-level implications of these models byexamining whether the evolution of U.S. industry productivity is relatedto the costs of engaging in international trade. A key contribution of ouranalysis is the linking of plant-level U.S. manufacturing data to industry-level measures of trade costs. We define trade costs as the sum of advalorem tariff and ad valorem transport costs, and construct them usingproduct-level U.S. international trade data. Our estimates of U.S. trade

1Clerides, Lach, and Tybout (1998), Bernard and Jensen (1999), Bernard and Wagner(1997), and Aw, Chung, and Roberts (2000), for example, find that firm productivitygrowth is not improved after entry into exporting.


costs vary substantially across both industries and time.We report two main results. First, we demonstrate that industry

productivity does indeed rise as trade costs fall. Second, we show thatthe key firm-level implications of the models linking falling trade costs toindustry productivity gains are supported by the data. We find that theprobability of plant death is higher in industries experiencing decliningtrade costs, as is the probability of a plant successfully entering the exportmarket. We also confirm that existing exporters increase their foreignshipments as industry trade costs decline. These results highlight theheterogeneity of firm outcomes within industries and call attention to thefact that there are both winners and losers within industries as a result oftrade liberalization.

The reallocation effects of changing trade costs are not uniform acrossindustries. We find that within-industry reallocation in response to changesin trade costs is strongest for industries where the U.S. has high levels ofbilateral trade — both imports and exports — with other high-income coun-tries. This finding accords with the predictions of the models as theseindustries are the most likely to encompass trade in horizontally differen-tiated varieties, which is the focus of the theory. Looking at all industriespooled together, we find at best a weak correlation between falling tradecosts and improved productivity. This heterogeneity of responses acrossindustries may help explain the fragility of the existing cross-country re-sults linking openness to growth. At the same time, our findings supportthe hypothesis that reductions in trade barriers do lead to productivitygains in a subset of industries.

Our identification of a connection between falling trade costs and in-dustry productivity growth has important implications for the existing lit-erature assessing the impact of trade liberalization on economic growth.Research into this question has been conducted almost exclusively withaggregate cross-country data using various measures of openness to proxyfor changes in trade costs.2 Though several studies, including Ben-David(1993), Sachs and Warner (1995) and Edwards (1998), offer evidence of apositive correlation between levels of trade liberalization and GDP growth,the robustness of this evidence has been challenged, most notably by Ro-

2Proudman and Redding (1998) use UK industry data and show a positive relationshipbetween industry measures of trade and growth.


driguez and Rodrik (2000). By examining more direct measures of tradeliberalization, and linking them to the responses of individual firms withinindustries, we provide more concrete evidence on the extent to which tradeliberalization may affect productivity and therefore GDP growth. Ourresults suggest that changes in openness over time matter for productivitygrowth but that all industries are not affected equally.

Our analysis also relates to research into the possible link between im-port competition and plant performance surveyed in Tybout (2001). Thegeneral consensus of this literature is that foreign competition both re-duces the domestic market share of import-competing firms and reallocatesdomestic market share from inefficient to efficient firms. Using explicitmeasures of trade costs at the industry level, we also find evidence of re-allocation. However, our results suggest that the reallocation is drivenby plant death and entry into exporting rather than through changingdomestic market shares of surviving plants. Our findings are also con-sistent with studies examining the effects of changes to particular tradingregimes. Head and Ries (1999) and Trefler (2001), for example, find thatthe Canada-U.S. Free Trade Agreement induced substantial rationalizationof production and employment. Our results provide evidence on the firm-level nature of such within-industry rationalization as trade costs fall.

The remainder of the paper is organized as follows: the next sectionassembles the predictions from the theoretical models on the responses tolower trade costs. Section 3 summarizes our dataset and describes howwe construct our measure of trade costs. Section 4 presents the empiricalresults. Section 5 concludes.

2. Theory: Heterogeneous Firms and Trade

Three recent papers by Bernard et al. (2003), Melitz (2003), and Yeaple(2002) — henceforth BEJK, MM, and SY, respectively — develop firm-levelmodels of intra-industry trade that are designed to match a set of stylizedfacts about exporting firms. These facts reveal that relatively few firmsexport and that exporters are more productive, larger, and more likely tosurvive than non-exporting firms. An important contribution of the modelsis their demonstration that such differences can arise even if exportingdoes not itself enhance productivity, a robust empirical finding (Clerides,


Lach and Tybout (1998), Bernard and Jensen (1999), Bernard and Wagner(1997) and Aw, Chung and Roberts (2000)).

In each paper, exporter superiority is shown to be the equilibrium out-come of more productive firms self-selecting into the export market. Thisselection is driven by the existence of trade costs, which only the most pro-ductive firms can absorb while still remaining profitable. All three papersrelate reductions in trade costs to increases in aggregate industry produc-tivity: as trade costs fall, lower productivity, non-exporting firms die, moreproductive non-exporters enter the export market, and the level of exportssold by the most productive firms increases. In this section, we summarizethe foundation and intuition of these implications before taking them to apanel of plant-level data.

MM builds a dynamic industry model with heterogeneous firms produc-ing a horizontally differentiated good with a single factor. The coexistenceof firms with different productivity levels in equilibrium is the result of un-certainty about productivity before an irreversible entry decision is made.Entry into the export market is also costly, but the decision to exportoccurs after firms observe their productivity. Firms produce a unique hor-izontal variety for the domestic market if their productivity is above somethreshold, and export to a foreign market if their productivity is above ahigher threshold. MM restricts the analysis to countries with symmetricattributes to focus solely on the relationship between trade costs and firmperformance.

In equilibrium, a decline in variable trade costs causes a reallocation ofproduction across firms leading to higher industry productivity (Hypoth-esis 1). Falling trade costs mean greater profits for exporters, which arealso the most productive firms, because of their increased access to exter-nal markets and lower per unit costs net of trade. Higher export profitspull higher productivity firms from the competitive fringe into the mar-ket, raising the productivity threshold for market entry and forcing theleast productive non-exporters to shut down (Hypothesis 2). Higher ex-port profits reduce the productivity threshold for exporting, increasing thenumber of firms which export, and increase the value of exports at currentexporters (Hypothesis 3 and 4).

BEJK construct a static Ricardian model of heterogeneous firms, im-perfect (Bertrand) competition with incomplete markups, and international


trade. Firms use identical bundles of inputs to produce differentiated prod-ucts under monopolistic competition. Within a country without trade, onlythe most efficient producer actually supplies the domestic market for a givenproduct.

With international trade and variable trade costs, a firm produces forthe home market if it is the most efficient domestic producer of a particularvariety and if no foreign producer is a lower cost supplier net of trade costs.A domestic firm will export if it produces for the domestic market and if,net of trade costs, it is the low cost producer for a foreign market. Withpositive trade costs, exporters are firms with higher than average produc-tivity. BEJK demonstrate that as trade costs fall, aggregate productivityrises (Hypothesis 1) because high-productivity plants are more likely to ex-pand (Hypotheses 3 and 4) at the expense of low productivity firms whichfail (Hypothesis 2).3

SY is a static, one factor model of trade in differentiated goods. UnlikeMM and BEJK, workers vary in terms of skill. Firm labor productivity isdetermined endogenously as two production techniques are available to pro-duce differentiated goods, either low fixed/high unit cost or high fixed/lowunit cost.4 With trade costs, firms with the highest productivity producethe differentiated good via the high fixed cost technique and export, whilefirms using the low fixed cost technology have lower productivity levels.

A reduction in trade costs increases the incentive for firms to adopt thehigh fixed cost production technique and export. As a result, a largernumber of firms adopt this technology while the absolute number of “do-

3Declining trade costs force low productivity plants to exit the market in both BEJKand MM, but the mechanism by which this occurs differs subtly. In BEJK, low pro-ductivity plants exit because of increased import competition from foreign varieties. InMM, countries’ varieties do not overlap. As a result, an increase in imports raises theprobability of death at all levels of productivity while the death of low productivity plantsis actually driven by the entry into exporting of other domestic firms. In our empiricalwork, we will not be able to distinguish between these two competing sources of plantdeaths.

4MM and BEJK model firms as differing in terms of exogenous total factor produc-tivity (TFP). In SY, TFP is identical across firms, while labor productivity varies withchoice of production technique. TFP and labor productivity are correlated in MM andBEJK due to the presence of fixed costs of production. As a result we avoid the com-plications of computing plant level productivity and focus instead on labor productivityin our empirical tests below.


mestic” firms in the industry falls. Total employment falls and the leastskilled workers leave the industry so that observed labor productivity rises.

Although varying in structure, each of the papers agree on the followingfour testable hypotheses:

Hypothesis 1 A decrease in variable trade costs leads to an aggregate in-dustry productivity gain.

Hypothesis 2 A decrease in variable trade costs raises the probability offirm exit.

Hypothesis 3 A decrease in variable trade costs increases the number ofexporting firms; new exporters are drawn from the most productive non-exporters (or new entrants).

Hypothesis 4 A decrease in variable trade costs increases export sales atexisting exporters.

3. Data

3.1. U.S. Manufacturing Plants Across Industries and Time

U.S. manufacturing plant data are drawn from the Censuses of Manufac-tures (CM) of the Longitudinal Research Database (LRD) of the U.S. Bu-reau of the Census starting in 1987 and conducted every fifth year through1997. Though CM data are available for earlier periods, we cannot usethem in this study because comprehensive collection of export informationdid not begin until 1987. The sampling unit for the Census is a manufac-turing establishment, or plant, and the sampling frame in each Census yearincludes detailed information on inputs and output on all establishments.Plant output is recorded at the four-digit Standard Industrial Classificationlevel (SIC4).

The samples used in our econometric work below incorporate severalmodifications to the basic data. First, we exclude small plants (so-calledAdministrative records) due to a lack of information on exports. Second,we drop plants in any ‘not elsewhere classified’ industries, i.e. SIC4 in-dustries ending in ‘9’. These modifications leave us with two panels ofapproximately 234,000 plants in 337 manufacturing industries.


3.2. Trade Costs Across Industries and Time

An important contribution of our analysis is the creation of a new setof industry-level trade costs. To most closely match the notion of tradecosts in the theoretical models, we construct ad valorem trade costs thatvary over time and across industries.5

We define variable trade costs for industry i in year t (Costit) as thesum of ad valorem duty (dit) and ad valorem freight and insurance (fit)rates, Costit = dit+ fit. We compute dit and fit from underlying product-level U.S. import data complied by Feenstra (1996). The rate for industryi is the weighted average rate across all products in i, using the importvalues from all source countries as weights.6 The ad valorem duty rate istherefore duties collected (dutiesit) relative to the Free-On-Board customsvalue of imports (fobit),

dit =dutiesitfobit

.

Similarly, the ad valorem freight rate is the markup of the Cost-Insurance-Freight value (cif it) over fobit relative to fobit,

fit =cifitfobit

− 1.

We define the change in trade costs for census year t as the annualizedchange in tariff and freight costs over the preceding five years,

∆Costit−5 =Costit − Costi,t−5

5=[dit + fit]− [di,t−5 + fi,t−5]

5. (1)

In the empirical work below, we relate changes in trade costs between yearst−5 to t (∆Costt−5:ti ) to plant survival, plant export decisions, and changesin the plant’s exports between t to t + 5. The five-year spacing betweentime periods corresponds to the interval between Censuses.

Table 1 reports average tariff, freight and total trade costs across two-digit SIC (SIC2) industries for five-year intervals from 1982-1997 using theimport values of underlying SIC4 industries as weights. Costs are averaged

5Unfortunately it is not possible to construct plant-specific trade cost measures.6We use the concordance provided by Feenstra et al. (2002) to match products to

SIC4 industries.


over the five years preceding the year at the top of the column. Table 1reveals that ad valorem tariff rates vary substantially and are highest inlabor-intensive Apparel and lowest in capital-intensive Paper. Tariff ratesdecline across a broad range of industries over time. Indeed, over theentire period, tariffs decline by more than one quarter in thirteen of twentyindustries. The pace of tariff declines, however, varies substantially acrossindustries.7 Freight costs are highest among industries producing goodswith a low value-to-weight ratio, including Stone, Lumber, Furniture, andFood. Freight costs also generally decline with time, though the patternof declines is decidedly more mixed than it is with tariffs.

Four-digit industries have even greater dispersion in trade cost changes.The average SIC4 industry saw trade costs fall 0.19 percentage points peryear from 1982-92.8 Of the 337 SIC4 industries, we find that 82% expe-rienced declines in tariff rates from 1982 to 1987, while 53% experienceddeclines from 1987 to 1992. For freight costs, 44% of the industries expe-rienced declines from 1982 to 1987, while 66% experienced declines from1987 to 19929 In terms of overall trade costs, 79% of SIC4 industries sawtrade costs decline between 1982 and 87, while 62% had declining tradecosts between 1987 and 1992.

In addition to being a good match to the theory, the trade costs con-structed here have several advantages. First, they are the first to in-corporate information about both trade policy and transportation costs.Second, they vary across industries and time. Finally, they are deriveddirectly from product-level trade data collected at the border.

Even with these advantages, several caveats should be noted. First,changes in the composition of products or importers within industries caninduce variation in dit and fit even if actual statutory tariffs and market

7The median percentage point reduction in product-level ad valorem tariff rates be-tween 1989 and 1997 is 0.6%. Twenty five percent of products experience reductionsgreater than 1.5 percentage points. These differences do not account for changes inproduct codes during this interval or for changes in the non ad valorem component oftariffs, which varies across industries (Irwin 1998). A similar change cannot be computedfor a longer interval because a change in the coding of imports in 1989 precludes directproduct comparison with years after 1989.

8Data on the tariff and freight measures for all 337 (SIC4) industries and years isavailable at http://www.som.yale.edu/faculty/pks4/sub_international.htm.

9Using a different methodology, Hummels (1999) reports a similar decline in aggregatefreight costs during the same period.


transportation costs remain constant. Shifts in United States consump-tion away from imports subject to high tariffs, or towards trading partnerslocated closer to the United States, for example, can decrease the mea-sures of trade costs.10 This concern about the trade cost data is mitigatedto some extent by our focus on the relationship between trade costs andfirm outcomes. From the firm’s perspective, increased imports from closeor low-tariff countries are just as relevant as declines in actual costs for agiven country.

A second caveat is that our trade cost measure is constructed from U.S.import data. Each of the theoretical models described above contemplatessymmetric reductions in trade costs across countries. To the extent thatchanges in U.S. trade policy or transportation rates diverge from those inother countries, measured changes in trade costs may over- or underesti-mate the changes implemented by other countries. This problem is likelyto be more severe for trade policy than for transportation rates. Unfortu-nately, because disaggregate tariff rates and freight costs are unavailable forU.S. export destinations during the period in question, we cannot constructa direct measure of outbound trade costs.11

Finally, our measure of trade costs does not include non-tariff barriers(NTBs) such as quotas or regulatory requirements. NTBs are an importantsource of trade distortions but there is no available industry-level data forour sample period.

10One way to avoid this problem is to aggregate product-level changes rather thanlevels up to SIC4 industries. In principle, one could compute the change in tariff andfreight rates across country-product pairs and then average across these changes forindustry observations. In practice, however, such a procedure encounters a number ofproblems. Most importantly, the U.S. changed import product categorization systemsbetween 1988 and 1989, i.e. in the middle of our sample. In addition, the set of countriesimporting a given product varies substantially from year to year, yielding numerous zerosfor product-level tariff changes.11To check the appropriateness of using import data for both inward and outward U.S.

trade costs, we compare U.S. and European Union tariffs changes across industries from1992-1997 (after the end of our sample) using the TRAINS database compiled by theUnited Nations Conference on Trade and Development. TRAINS data is unavailable forour sample period. This database tracks product-level tariffs for a limited, but growing,set of countries starting in 1990. Using these data, we find that the correlation ofUnited States and European Union ad valorem tariff rate changes across SIC4 industriesis positive and significant at the 1% level. This correlation indicates that the inwardand outward tariffs are moving in the same direction across industries.


3.3. Identifying Industries With Relatively High Varieties Trade

Each of the three models discussed in Section 2 is based upon interna-tional trade in horizontally differentiated varieties. In addition to examin-ing trends across all manufacturing industries, we also examine support forHypotheses 1 through 4 across a subset of industries more likely to capturetrade in horizontally differentiated varieties.12 Starting from the assump-tion that trade between the United States and other developed economiesis the most likely to be characterized by a taste for variety, we select SIC4industries using U.S. import and export penetration ratios vis a vis theOECD.13

For industry i, we define the OECD import and export penetrationratios for the United States, nmi and nxi respectively, as

nmi =fobOECDi

fobi + qi − xi(2)

nxi =xOECDi

qi(3)

where fobOECDi and xOECDi are the value of U.S. imports from and exportsto the OECD in industry i, fobi and xi are total U.S. imports and exportsin industry i, and qi is the total value of U.S. production in i. Thoughnmi and nxi vary substantially across industries, they are relatively stableacross time. We use values for 1987, the midpoint of our two panels, toconstruct our industry subsample. We refer to industries with import andexport penetration greater than the 67th percentile as high bilateral OECDtrade industries (or high-OECD).14 The sixty-seven industries above thesecutoffs are reported in Table 2. Two thirds of the industries in this samplecome from SIC 35 to 38, precisely the sectors (Industrial Machinery, Elec-

12Rauch (1999) divides four-digit SITC industries into differentiated and homogenouscategories. The difficulties associated with concording these categories to four-digit SICindustries preclude their use here.13We use the 23 countries in the OECD as of 1974: Australia, Austria, Belgium,

Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan,Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland,Turkey, and the United Kingdom.14Varying the cutoff percentile around 67 does not materially affect the results reported

below.


tronics, Transportation and Instruments) that are most likely to containdifferentiated products.

4. Empirical results

In this section, we examine the relationships between trade costs andindustry- and plant-level outcomes described in Section 2.

4.1. Industry Productivity Growth

The most important implication of all three models presented above isthat lower trade costs increase aggregate productivity (Hypothesis 1). Asthe models are all single factor models, they do not differentiate betweenlabor productivity and total factor productivity. Here we report results forboth, but in subsequent sections we focus on labor productivity.15 We esti-mate a simple regression of the 5-year change in SIC4 industry productivityon the decline in industry trade costs in the previous five years,

∆Productivityit = ct + β1∆Costit−5 + δi + δt + εit, (4)

where ∆Productivityit is the average annual percent change in industryproductivity, either real value added per worker or TFP, from year t toyear t+ 5, ∆Costit−5 is the annualized percent change in total trade costsbetween years t− 5 and t, and δi and δt are sets of industry and year fixedeffects. Data on real value added per worker and five-factor total factorproductivity are drawn from Bartelsman et al. (2000). Our use of priorchanges in trade costs to predict subsequent behavior is helpful for tworeasons. First, it biases the empirical work against Hypotheses 1 to 4by excluding contemporaneous reallocation. Second, it helps to mitigateproblems of endogeneity and omitted variables.

One feature of all three models is that they are designed to focuson within-industry reallocation, rather than differences across industries.However, the theories are silent on the appropriate empirical scope of anindustry. We estimate all our specifications with both two-digit (SIC2) and

15Constructing plant level total factor productivity levels and changes over time isespecially difficult given the small number of periods in our data and the five-year intervalbetween observations.


four-digit (SIC4) fixed effects. The inclusion of two-digit industry fixed ef-fects allows for potential substitution across four-digit industries within asector. Fixed effects at the four-digit level restrict the analysis to within-industry changes in trade costs over time.

The results with SIC2 industry fixed effects are reported in columns1 (all industries) and 2 (high-OECD industries) of Table 3 with robuststandard errors adjusted for clustering on four-digit industries. Consistentwith the three models, both TFP and labor productivity are negativelyassociated with changes in trade costs, i.e. falling trade costs are followedby more rapid industry productivity growth. For the all-industry sample,the coefficients are relatively small and only significant at the 10% level forTFP.

For the sample of high-OECD industries, the coefficients are large andsignificant at the 1% level, suggesting a one percentage point annual declinein trade costs is associated with annual productivity growth rates 1.0 to1.6 percentage points higher.

Results using SIC4 industry fixed effects are reported in columns 3 and4 of Table 3 and show the same pattern of results. The coefficients onthe change in trade costs for the all-industry sample are not significant foreither TFP or labor productivity. However, in the high-OECD sample,both coefficients are again large, negative and significant.

The industry regressions suggest that falling trade costs are associatedwith faster industry productivity growth but this relationship is strongestfor a sample of industries with high bilateral trade levels with OECD coun-tries. We now turn to the specific within-industry predictions of the models(Hypotheses 2 through 4).

4.2. Plant Deaths

To examine the effect of changing trade costs on plant survival, we es-timate a probit with levels and interactions of plant productivity, exportstatus and the change in trade costs. The presence of both a productivitymeasure and the exporter dummy reflects the lack of a one-to-one relation-ship between exporting and productivity in the Bernard et al (2003) model.The probability of death for a plant in industry i between year t and year


t+ 5 is given by

Pr (Dpt+5 = 1|Xpt, Zit) = Φ¡ct + β1RPpt + β2∆Costit−5 + β3Ept (5)

+β4 [Ept ×∆Costit−5] + β5 [RPpt ×∆Costit−5]+β6 [Ept ×RPpt ×∆Costit−5] + δi + δt

¢where RPpt (relative productivity) is percentage difference in plant laborproductivity from that of the mean plant in the SIC4 industry in yeart, ∆Costit−5 is the annual average change in industry trade costs in thepreceding 5 years, and Ept is a dummy variable indicating whether theplant is an exporter in year t. As above, we report results with SIC2 andSIC4 industry fixed effects while allowing for robust standard errors andclustering in four-digit industries.

All three models predict that low productivity, non-exporters shouldbe more likely to fail, and that a decline in trade costs should raise theprobability of death (Hypothesis 2). Since all the plant-level empiricalspecifications include either SIC2 and SIC4 industry fixed effects, the im-plicit null hypothesis is that deviations from the average industry changein trade costs are correlated with plant outcomes.

Columns 1 and 2 in Table 4 report the results for plants in the all-industry and high-OECD samples respectively, controlling for SIC2 fixedeffects.16 Plant labor productivity and plant export status have large,negative, and significant coefficients. As expected, low productivity plantsare more likely to die and exporters are substantially less likely to fail. Aspredicted by the models, declines in industry trade costs also increase theprobability of death. While the coefficient on the change in industry tradecosts is negative in both specifications, it is only significant (at the 1%level) for the industries with high OECD bilateral trade, i.e. the industriesthat are the focus of the theoretical models.

The signs of the coefficients on the interaction terms suggest that ex-porters are less likely to die as trade costs fall and that higher productiv-ity among exporters reduces the probability of failure as trade costs fall.For non-exporters the probability of death rises more for high-productivityplants as trade costs fall, significantly so for plants in the preferred sample16All coefficients reported in the table are changes in the marginal probability evaluated

at the mean of the regressors.


of industries with high-OECD trade. This latter coefficient is surprising,as the theory (and common sense) suggests that high-productivity non-exporting plants should not face a higher probability of shutdown in theface of declining trade costs.

Columns 3 and 4 in Table 4 contain analogous specifications controllingfor SIC4 fixed effects. For the all-industry sample, the coefficient on thetrade cost variable has an unexpected positive, although insignificant, coef-ficient. Falling trade costs are not associated with an increased probabilityof death in the wide sample of industries. However, for the sample of high-OECD industries, declines in industry trade costs significantly increase theprobability of death.

The estimates from the OECD sample with SIC4 fixed effects suggestthat the economic magnitude of the change in trade costs on the probabilityof death is substantial. For a plant with average productivity, a reductionin trade costs of 0.9 percentage points per year (equivalent to a one standarddeviation change) leads to a 3.7 percentage point increase in the probabilityof death for a non-exporter and a 0.2 percentage point increase in theprobability of death for an exporter. The average probability of death inthe sample is 26.8%.

As with the industry productivity regressions, here we find that the pre-dictions of the theoretical models are strongest for the high-OECD sample:falling trade costs lead to higher probabilities of death.

4.3. New Exporters

In addition to increasing the probability of plant deaths, all three modelsalso predict that high-productivity non-exporters start exporting as tradecosts fall (Hypothesis 3). We estimate the probability that a non-exporterbecomes an exporter as a function of plant labor productivity, the change inindustry trade costs, and their interaction for the sample of non-exporters.17

17A more complete specification on the decision to export would include an estimateof the sunk costs of exporting and include the entire panel of plants (for example, seeRoberts and Tybout 1997 or Bernard and Jensen 2004). Here, we are interested in thechange in the probability of exporting for non-exporters as trade costs fall.


The probit is given by

Pr (Ept+5 = 1|Ept = 0,Xpt, Zit) = Φ¡ct + β1RPpt + β2∆Costit−5 (6)

+β3 [RPpt ×∆Costit−5] + δi + δt¢.

The results reported in Table 5 again yield support for the predictions ofthe model, especially for the high-OECD industries.18 Declines in industrytrade costs significantly increase the probability of becoming an exporterfor the high-OECD industries. For the all-industry sample, the effect is notsignificant and has the wrong sign with SIC4 fixed effects. As expected,plant labor productivity is strongly positively associated with entering theexport market. The interaction between plant productivity and the changein trade costs also has the expected sign, although significant only for broadsample. High-productivity non-exporters are even more likely to becomeexporters when trade costs decline.

The magnitude of the effect of falling trade costs is substantial. Fora non-exporter with average productivity, a one standard deviation reduc-tion in trade costs increases the probability of exporting by 5.8 percentagepoints. The average probability of becoming an exporter in the high-OECDsample is 27%.

These results, coupled with the increased probability of death as tradecosts fall, offer support for the two major predictions of the models. Inparticular, they highlight the heterogeneity of outcomes across plants thatvary in terms of their export status and labor productivity. In response tofalling trade costs, some plants, typically low productivity non-exporters,are more likely to die, while higher productivity non-exporters take advan-tage of the lower trade costs and begin exporting. It is important to notethat the results only hold for the set of industries that have high bilateraltrade flows with the OECD.18All coefficients reported in the table are changes in the marginal probability evaluated

at the mean of the regressors.


4.4. Export Growth

BEJK and MM offer specific predictions that exports will increase atcurrent exporters as trade costs decline (Hypothesis 4).19 We estimate thepercentage change in exports for current exporters as a function of thechange in industry trade costs,

%∆EXPpt = ct + β1∆Costit−5 + δi + δt + εpit (7)

where %∆EXPpt =EXPpt+5−EXPpt2(EXPpt+5+EXPpt)

.20

The results, shown in Table 6, provide some support for the predictionthat exports increase more rapidly for plants in industries with falling tradecosts. The trade cost measure is negative in all specifications, althoughfor high-OECD trade industries it is not significant when SIC4 fixed effectsare included. As current exporters are higher productivity plants, thisexpansion of export sales serves to increase the reallocation effect of fallingtrade costs and boost aggregate industry productivity.

We have presented results testing a series of predictions from the newround of trade models. The results confirm the general predictions andprovide evidence that declining trade costs have asymmetric effects on dif-ferent firms in the same industry. An important caveat is that the modelsare not designed to explain the responses of firms in every industry tochanges in trade costs but rather focus on trade with similar countries indifferentiated products. Our results are systematically strongest for thesample of industries that have high bilateral trade with the OECD, i.e.the industries most likely to be characterized by ‘new’ trade models withheterogeneous firms.

4.5. Robustness

In this section, we check the robustness of the results for the high-OECDsample in two ways. First we add the relative capital intensity of the plantto the specification for plant deaths and entry into exporting. Second wesplit the trade cost measure into its separate tariff and freight components.

19While SY does not offer a specific prediction on the response of exports at existingexporters, it does have plant exports rising with a decline in trade costs.20This growth rate is bounded between 2 and -2. Plants that stop exporting have a

growth rate of -2.


Table 7 reports the specification for the high-OECD sample includingrelative capital intensity and its interactions.21 None of the effects of tradecosts changes with the addition of capital intensity. The coefficient on thetrade cost measure actually increases in magnitude in both specifications.In Table 8, we split the trade cost measure into its constituent parts forboth the plant death and export entry. Tariffs and freight costs have theexpected negative signs in both specifications.

5. Conclusions

In this paper, we examine the response of industries and plants tochanges in tariff and transport costs. Using three recent models of in-ternational trade with heterogeneous firms, we develop testable predictionson the reallocation of activity across plants within industries as a result ofchanges in trade costs. To test these hypotheses, we create a new datasetof trade costs by industry over a twenty-year period and link the measuresto plant-level data on the entire U.S. manufacturing sector.

Our results highlight an important new channel by which trade policymay affect industry performance. This channel stresses the reallocationof economic activity towards more productive firms within industries inresponse to trade liberalization and transport cost declines. We find thatindustries with falling trade costs have relatively high subsequent produc-tivity growth. This growth is driven by three forms of reallocation. First,lower trade costs increase the probability of plant death, especially for lowerproductivity, non-exporting plants. Second, surviving high-productivity,non-exporters are more likely to enter the export market, thus expandingtheir sales. Third, existing exporters, already the largest and most pro-ductive establishments, see their exports grow more quickly as trade costsfall.

The results in this paper provide a new round of evidence in favorof trade models designed to understand high bilateral trade flows betweensimilar countries. In particular, the models perform best when we consider

21The relative capital intensity measure is defined as the percentage difference of thecapital intensity of the plant relative to that in the mean plant in the industry in thatyear. Capital is measured as the sum of the book value of machinery, equipment andstructures.


precisely those industries that are characterized by bilateral trade betweencountries at similar income levels.

The results suggest that symmetric reductions in trade costs with simi-lar partners are indeed associated with faster industry productivity. Theseeffects are limited to industries most likely to be characterized by trade invarieties. Alternative theories, perhaps emphasizing comparative advan-tage, may be needed to explain the responses of firms in other industriesto changes in trade costs.


References

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Two-Digit SIC Industry 1982 1987 1992 1982 1987 1992 1982 1987 1992

20 Food 5.7 5.1 4.4 10.2 9.7 8.9 15.9 14.8 13.421 Tobacco 10.4 14.1 16.7 5.9 5.2 2.9 16.3 19.3 19.522 Textile 17.0 13.2 11.2 6.0 6.4 5.4 23.1 19.6 16.623 Apparel 23.3 20.7 16.9 8.6 7.6 6.3 31.8 28.3 23.224 Lumber 3.2 2.3 1.7 11.1 6.5 7.5 14.2 8.8 9.225 Furniture 5.9 4.1 4.1 9.4 8.6 8.5 15.3 12.8 12.626 Paper 0.9 0.8 0.6 3.9 3.1 4.4 4.7 4.0 4.927 Printing 1.7 1.2 1.1 5.9 5.5 5.1 7.5 6.6 6.228 Chemicals 3.8 4.3 4.4 6.4 4.8 4.5 10.1 9.1 9.029 Petroleum 0.4 0.5 0.9 5.2 5.1 8.3 5.6 5.5 9.330 Rubber 7.4 7.9 11.3 7.5 6.8 6.9 14.9 14.7 18.231 Leather 9.0 10.7 11.2 8.3 7.2 5.5 17.3 17.8 16.732 Stone 8.9 6.4 6.5 12.0 11.1 9.6 20.9 17.5 16.133 Primary Metal 4.6 3.8 3.4 6.9 6.3 6.0 11.5 10.1 9.434 Fabricated Metal 6.6 5.1 4.3 6.8 5.9 5.0 13.4 11.0 9.335 Industrial Machinery 4.2 3.9 2.4 4.0 4.0 2.9 8.2 7.9 5.336 Electronic 5.0 4.6 3.3 3.4 3.1 2.4 8.3 7.6 5.637 Transportation 1.9 1.6 2.3 4.5 2.5 3.1 6.4 4.1 5.438 Instruments 6.8 5.2 4.3 2.7 2.8 2.5 9.5 8.0 6.839 Miscellaneous 9.6 5.7 5.2 5.0 4.9 3.6 14.6 10.6 8.8Average 4.8 4.4 4.2 5.6 4.4 4.1 10.4 8.8 8.3

Total Rate (d it +f it )(Percent)

Notes: Table summarizes ad valorem tariff, freight and total trade costs across two-digit SIC industries.Costs for each two-digit industry are weighted averages of the underlying four-digit industries employed inour empirical analysis, using U.S. import values as weights. Figures for each year are the average for the fiveyears preceding the year noted (e.g. the costs for 1982 are the average of costs from 1977 to 1981). The finalrow is the weighted average of all manufacturing industries included in our analysis.

Tariff Rate (d it ) Freight Rate (f it )(Percent) (Percent)

Table 1: Ad Valroem Trade Costs by Two-Digit SIC Industry and Year


SIC4 Industry SIC4 Industry2091 Canned and cured fish and seafoods 3567 Industrial furnaces and ovens2371 Fur goods 3571 Electronic computers2421 Sawmills and planing mills, general 3572 Computer storage devices2435 Hardwood veneer and plywood 3578 Calculating and accounting equipment2611 Pulp mills 3593 Fluid power cylinders + actuators2812 Alkalies and chlorine 3596 Scales and balances, exc. laboratory2816 Inorganic pigments 3621 Motors and generators2822 Synthetic rubber 3624 Carbon and graphite products2833 Medicinals and botanicals 3652 Prerecorded records and tapes2865 Cyclic crudes and intermediates 3672 Printed circuit boards3111 Leather tanning and finishing 3674 Semiconductors and related devices3211 Flat glass 3675 Electronic capacitors3292 Asbestos products 3676 Electronic resistors3297 Nonclay refractories 3678 Electronic connectors3313 Electrometallurgical products 3692 Primary batteries, dry and wet3334 Primary aluminum 3694 Engine electrical equipment3341 Secondary nonferrous metals 3713 Truck and bus bodies3425 Saw blades and handsaws 3714 Motor vehicle parts and accessories3492 Fluid power valves + hose fittings 3724 Aircraft engines and engine parts3511 Turbines and turbine generator sets 3743 Railroad equipment3523 Farm machinery and equipment 3751 Motorcycles, bicycles, and parts3532 Mining machinery 3761 Guided missiles and space vehicles3533 Oil and gas field machinery 3822 Environmental controls3541 Machine tools, metal cutting types 3823 Process control instruments3542 Machine tools, metal forming types 3825 Instruments to measure electricity3546 Power-driven handtools 3827 Optical instruments and lenses3547 Rolling mill machinery 3844 X-ray apparatus and tubes3552 Textile machinery 3845 Electromedical equipment3553 Woodworking machinery 3851 Ophthalmic goods3554 Paper industries machinery 3861 Photographic equipment and supplies3555 Printing trades machinery 3914 Silverware and plated ware3556 Food products machinery 3915 Jewelers' materials + lapidary work3562 Ball and roller bearings 3931 Musical instruments3563 Air and gas compressors 3951 Pens and mechanical pencils3566 Speed changers, drives, and gears

Notes: This figure lists SIC4 industries where the U.S. has high OECD import and export penetration (i.e.greater than the 67th percentile) in 1987. Industries are sorted by SIC code. OECD import penetration isU.S. import value from OECD countries divided by U.S. consumption. OECD export penetration is U.S.export value to OECD countries divided by U.S. production.

Table 2: Industries with High Bilateral OECD Trade


Regressor

∆Cost -0.134 -1.594 *** -0.164 -1.456 **(0.122) (0.527) (0.174) (0.645)

R2

Regressor

∆Cost -0.098 * -1.047 *** -0.127 -0.880 **(0.058) (0.354) (0.095) (0.422)

R2

Industry Fixed Effects

Year Fixed Effects

Industry Sample

Observations

Notes: Industry-level OLS regression results. Robust standard errors adjusted for clustering atthe SIC4 industry level are in parentheses. ∆Labor Productivity is average annualized change invalue added per worker from years t+1 to t+5. Value added and total employment for eachSIC4 industry are from Bartelsman, Becker and Gray (2000). ∆TFP is the average annualizedchange in Bartelsman, Becker and Gray (2000) five-factor total factor productivity from yearst+1 to t+5. ∆Cost is the change in total trade costs between years t-5 and t. High-OECDindustries are SIC4 industries with relatively high OECD import and export penetration (seetext). Regressions cover the years 1982 to 1997. ***Significant at the 1% level; **Significantat the 5% level; *Significant at the 10% level. Coefficients for the regression constant anddummy variables are suppressed.

1,004 203 1,004 203

All High-OECD All High-OECD

∆TFP ∆TFP

Yes Yes Yes Yes

∆TFP

SIC2

0.07 0.09

∆Labor Productivity




SIC4 SIC4

0.21 0.18 0.52 0.58

0.42 0.49

∆TFP

SIC2

Table 3: Industry Productivity Growth, 1982-97


Regressor

∆Cost -0.793 -5.233 *** 0.595 -3.358 **(0.678) (1.681) (0.541) (1.622)

Relative Productivity -0.043 *** -0.023 *** -0.043 *** -0.024 ***(1.492) (0.008) (0.004) (0.007)

x ∆Cost -0.204 -5.662 *** -0.117 -5.606 ***(0.416) (1.409) (0.479) (1.326)

Exporter -0.119 *** -0.139 *** -0.121 *** -0.134 ***(0.006) (0.009) (0.004) (0.009)

x ∆Cost 0.648 2.786 0.159 3.203(0.795) (2.271) (0.621) (2.194)

x ∆Cost x Relative Productivity 0.533 5.024 *** 0.726 4.969 **(0.733) (2.208) (0.786) (2.079)


Year Fixed Effects

Industry Sample

Observations

Log likelihoodNotes: Plant-level probit regression results where the reported coefficients represent the change themarginal probability of plant death at the mean of the regressors. Robust standard errors adjusted forclustering at the SIC4 industry level are in parentheses. Dependent variable indicates plant death betweenyears t and t+5. ∆Cost is the change in total trade costs between years t-5 and t. Relative Productivity isplant's labor productivity relative to its' industry's mean. Exporter is an indicator variable equalling unity ifplant is an exporter in year t. High-OECD industries are SIC4 industries with relatively high OECD importand export penetration (see text). Regressions cover two panels: 1982 to 1987 and 1987 to 1992.***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. Coefficientsfor the regression constant and dummy variables are suppressed.

-133,461 -17,235 -131,096 -17,009

235,790 30,447 235,790 30,447


Yes Yes Yes Yes

SIC2 SIC2 SIC4 SIC4

Plant Death Plant Death Plant Death Plant DeathProbit Probit Probit Probit

Table 4: Probability of Death, 1987-97


Regressor

∆Cost -0.670 -7.928 *** 0.311 -5.192 ***(0.467) (2.438) (0.309) (1.727)

Relative Productivity 0.028 *** 0.070 *** 0.031 *** 0.073 ***(0.004) (0.009) (0.003) (0.009)

x ∆Cost -0.472 * -0.434 -0.527 * -0.324(0.277) (1.548) (0.305) (1.628)


Year Fixed Effects

Industry Sample

Observations

Log likelihoodNotes: Plant-level probit regression results where the reported coefficients represent the change themarginal probability of exporting at the mean of the regressors. Robust standard errors adjusted forclustering at the four-digit SIC industry level are in parentheses. Dependent variable indicates plantbecomes an exporter between years t and t+5. ∆Cost is the change in total trade costs between years t-5 andt. Relative Productivity is plant's labor productivity relative to its' industry's mean. High-OECD industriesare SIC4 industries with relatively high OECD import and export penetration (see text). Regressions covertwo panels: 1982 to 1987 and 1987 to 1992. ***Significant at the 1% level; **Significant at the 5% level;*Significant at the 10% level. Coefficients for the regression constant and dummy variables are suppressed.

-42,663 -5,758 -39,294 -5,622

109,699 10,429 109,699 10,429


Yes Yes Yes Yes

SIC2 SIC2 SIC4 SIC4

Export Next Export Next Export Next Export NextProbit Probit Probit Probit

Table 5: Probability of Entering the Export Market, 1987-97


Regressor

∆Cost -1.079 ** -2.419 * -0.717 * -0.173(0.484) (1.490) (0.425) (1.692)


Year Fixed Effects

Industry Sample

Observations

R2

Notes: Plant-level OLS regression results. Robust standard errors adjusted for clustering at the four-digitSIC industry level are in parentheses. Dependent variable is plant's normalized real export growth betweenyears t and t+5. (See text for description of normalization.) ∆Cost is the change in total trade costs betweenyears t-5 and t. Relative Productivity is plant's labor productivity relative to its' industry's mean. High-OECD industries are SIC4 industries with relatively high OECD import and export penetration (see text).Regressions cover two panels: 1982 to 1987 and 1987 to 1992. ***Significant at the 1% level;**Significant at the 5% level; *Significant at the 10% level. Coefficients for the regression constant anddummy variables are suppressed.

0.01 0.02 0.05 0.03

35,099 8,068 35,099 8,068


Yes Yes Yes Yes

SIC2 SIC2 SIC4 SIC4

∆Exports ∆Exports ∆Exports ∆ExportsOLS OLS OLS OLS

Table 6: Growth of Exports, 1987-97


Regressor∆Cost -5.706 *** -8.621 ***

(1.844) (2.966)

Relative Productivity -0.018 ** 0.062 ***(0.007) (0.009)

x ∆Cost -5.793 *** -1.723(1.353) (1.785)

Relative K/L -0.008 ** 0.015 **(0.004) (0.007)

x ∆Cost 0.248 2.518(1.407) (1.554)

Exporter -0.138 ***(0.009)

x ∆Cost 3.152(2.302)

x ∆Cost x Relative Productivity 5.221 **(2.292)

x ∆Cost x Relative K/L -0.197(2.338)

Industry Fixed EffectsYear Fixed EffectsIndustry SampleObservationsLog likelihoodNotes: Plant-level probit regression results where the reported coefficientsrepresent the change the marginal probability of plant death at the mean of theregressors. Robust standard errors adjusted for clustering at the four-digit SICindustry level are in parentheses. Dependent variables are plant death andbecomming an exporter between years t and t+5. ∆Cost is the change in total tradecosts between years t-5 and t. Relative productivity and relative K/L are plant'slabor productivity and capital-labor ratio relative to its' industry's mean. Exporteris an indicator variable equalling unity if plant is an exporter in year t. High-OECD industries are SIC4 industries with relatively high OECD import and exportpenetration (see text). Regressions cover two panels: 1982 to 1987 and 1987 to1992. ***Significant at the 1% level; **Significant at the 5% level; *Significant atthe 10% level. Coefficients for the regression constant and dummy variables aresuppressed.

-17,245 -5,76830,477 10,429

High-OECD High-OECDYes Yes

SIC4 SIC4

Plant Death Export NextProbit Probit

Table 7: Robustness: Capital Intensity, 1987-97


Regressor∆Tariff -1.432 -10.500 ***

(3.065) (3.533)

∆Freight -3.406 ** -4.635 **(1.447) (2.007)

Relative Productivity -0.022 *** 0.070 ***(0.007) (0.011)

x ∆Tariff -4.663 * -2.420(2.487) (3.148)

x ∆Freight -6.202 *** 0.861(1.176) (2.566)

Exporter -0.133 ***(0.011)

x ∆Tariff 4.009(4.243)

x ∆Freight 2.358(2.345)

x ∆Tariff x Relative Productivity 6.013 *(3.517)

x ∆Freight x Relative Productivity 3.450(2.976)

Industry Fixed EffectsYear Fixed EffectsIndustry SampleObservationsLog likelihoodNotes: Plant-level probit regression results where the reported coefficientsrepresent the change the marginal probability of plant death at the mean of theregressors. Robust standard errors adjusted for clustering at the four-digit SICindustry level are in parentheses. Dependent variables are plant death andbecomming an exporter between years t and t+5. ∆Tariff and ∆Freight are thechange in tariff and freight costs between years t-5 and t. Relative Productivity isplant's labor productivity relative to its' industry's mean. Exporter is an indicatorvariable equalling unity if plant is an exporter in year t. High-OECD industries areSIC4 industries with relatively high OECD import and export penetration (seetext). Regressions cover two panels: 1982 to 1987 and 1987 to 1992.***Significant at the 1% level; **Significant at the 5% level; *Significant at the10% level. Coefficients for the regression constant and dummy variables aresuppressed.

-17,007 -5,62030,447 10,429

High-OECD High-OECDYes Yes

Probit Probit

SIC4 SIC4

Plant Death Export Next

Table 8: Robustness: Separate Tariff and Freight Costs, 1987-97

falling trade costs, heterogeneous firms, and industry ... · bureau of the census or by the...

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