1

The impact of export restrictions on targeted firms: Evidence from

Antidumping against South Korea

Laura Rovegno*

Université catholique de Louvain, IRES

[Version April 2011]

Abstract

The objective of this paper is to analyze how firms adapt their markups when faced with trade restrictions in

their export markets. In particular, I look at the case of Antidumping (AD) duties imposed against South Korea

by China, the US and the EU. Previous studies on AD have generally focused on the impact of these policies on

firms in the domestic market of the country imposing them. This paper contributes to the literature by looking

at the effects on firms in the country targeted by AD. In the analysis presented in this paper, I use a panel South

Korean firms and estimate markups before and after the imposition of duties using balance-sheet firm level

data. To isolate the effect of AD duties, I use difference-in-difference specifications comparing affected firms

with control groups selected using matching techniques. In a pooled-case estimation, I find evidence of an

increase in estimated markups, especially towards the end of the imposition period. However, case-by-case

estimations reveal great heterogeneity in the response across sectors, which may be related to peculiarities of

AD laws in the specific countries targeting them.

Keywords: Antidumping, Import tariffs, Markup, Competition

JEL Classifications: F13; L13; L41

Preliminary version, do not cite without authorisation from the author

The author especially thanks Hylke Vandenbussche and members of the International Economics Group at UCLouvain and KULeuven. The

research in this paper was carried out while the author worked as visiting researcher in LICOS, KULeuven, Belgium. I particularly thank

Florian Mayneris for comments and discussion and Ilke Van Beveren for invaluable help in the implementation of empirical methods. This

paper further benefited from comments by Bart Cockx, Giovanni Facchini, Serena Fatica, Emanuele Forlani, Yundan Gong, Giovanni Peri,

Xiaohui Liu, Joel T. Strange and Christian Viegelahn. I also thank participants of the 2010 CEA Conference in Oxford and the 2010 ESTG

Conference in Lausanne. The author will also like to thank the Korean Statistics Department for data and information. I am also grateful for

financial support from PAI grant n°P6/07 and ARC grant n°09/14-019.

*Address for correspondence: Laura Rovegno, Université catholique de Louvain, Place Montesquieu 3, 1348 Louvain-la-Neuve, Belgium ; laura.rovegno@uclouvain.be

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1. Introduction

Antidumping (AD) is among the most intensively used forms of trade barrier. Much of the

criticism directed at it emphasis its vulnerability to manipulation by domestic firms and its

potential anticompetitive effects (Blonigen & Prusa, 2003, Konings & Vandenbussche, 2005,

Rovegno & Vandenbussche, 2011, Zanardi, 2004). It is therefore particularly relevant to

analyse empirically the presence of such effects. The literature on AD has generally focused

on the impact of these policies on domestic firms protected (Konings & Vandenbussche,

2005, Pierce, 2009, Rovegno, 2010). Here I am interested in the other side of the story – the

effects on targeted foreign firms. In particular, I analyze how South Korean firms adapt their

markups when faced with AD duties in their main export markets.

AD duties are a priori simply import tariffs. If the protected market is large enough, it would

be expected that foreign producers absorb part of the duty by lowering their received price

(border price effect), generally resulting in a decrease also in markups. However, AD duties

are not like other import restriction. They are designed to counteract dumping (price

discrimination cross borders), and hence are precisely aimed at forcing foreign producers to

increase their border price. A particularly important mechanism to achieve this goal is duty

reviews. Since the Uruguay Round, AD duties have to be revised five years after imposition

(sunset reviews), although earlier administrative reviews are also possible. In these

instances, further duties are imposed if evidence of dumping is found. Importantly, dumping

is calculated as the difference in ex-factory prices between goods sold at home or exported

to the market in question (Blonigen & Haynes, 2002, Feenstra, 2004, Macrory et al., 1991),

subtracting not only duties but also transportation costs. Therefore, foreign firms wanting to

avoid further duties should increase the received price of the exports concerned. This usually

will translate into higher markups, especially given that foreign firms not only do not absorb

part of the duty, but have to increase prices further transferring even transport costs to the

final price. As illustrative evidence, Blonigen & Haynes (2002) estimate an average pass-

through of AD duties of 160% for Canadian exports in iron and steel targeted with AD in the

US.

However, firms will not necessarily react in this manner. In a related paper, Blonigen & Park

(2004) look at foreign firms’ pricing behaviour indirectly by analysing what happens to AD

duties during administrative reviews in the US. In their model, foreign firms have a static

incentive to dump, leading to the imposition of AD duties which are adjusted through a

review process. They assume constant marginal cost, and therefore markups follow the

evolution of prices. When AD enforcement is certain (i.e. if a firm dumps, it is targeted with

AD duties equal to the dumping margin), AD duties can cause foreign firms to dump even

more in future periods provided they discount the future sufficiently heavily. Under

uncertainty however, the effect on prices will depend on the foreign firms’ ex-ante belief

about the likelihood of AD measures. Therefore, in the face of AD duties, foreign firms have

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contradicting incentives to increase markups on the one hand, and decrease them on the

other. It is therefore an empirical question as to which will be the case. This is what I intend

to explore in this paper.

I chose to study the case of South Korea for various reasons. Firstly, due to its location since

it is in Asia where much of the current AD action is happening. During the 1980s Asia was

targeted by traditional users of AD such as US and EU. However, since the late 1990s Asian

countries, especially India and China, have started to use AD much more intensely becoming

the world’s leading users in terms of initiations and measures imposed. South Korea is a

particularly good example to study the effects of AD on targeted firms since, unlike other

Asian countries such as China and India, it is not a relatively heavy user of AD. In fact,

according to WTO Notifications, South Korea is the second most targeted country in the

world after China, both in terms of initiations and measures. It does not however, make the

top 10 of more intensive users. Additionally, South Korea has a consolidated market

economy making it a good example to study what happens when a country is affected by

other countries’ AD.

In the analysis presented in this paper, I use information on AD duties imposed against South

Korea by its main trading partners between 2004 and 2006 coming from the Global

Antidumping Database (World Bank). I use firm level data from 2001 to 2008 which allows

me to estimate markups before and after the protection. To isolate the effect of AD duties

from other phenomena affecting markups in the period, I use a difference-in-difference

specification comparing affected firms with control groups selected using matching

techniques. I find evidence of an increase in estimated markups. However, separate case

estimations reveal strong heterogeneity across sectors.

The paper is organized as follows. In the next section, I describe the empirical methodology

and data sources. In section three I present results, and in section four I conclude.

2. Empirical Methodology

2.1. Data

The information on AD cases used in this paper comes from Global Antidumping Database

(GAD, version 5.1, 2009), funded by Brandeis University and the World Bank1. It contains

information on AD petitions for more than 40 countries in a comprehensive and standard

format that allows the aggregation of information across different countries. Among those

1 Bown (2010). The latest version of the Global Antidumping database is available as part of the World Bank’s

“Temporary Trade Barriers Database” (http://econ.worldbank.org/ttbd/).

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40 countries, it includes the 27 that, according to WTO notifications, have targeted AD

against Korea since 1995. Data from the GAD was contrasted with WTO notification to verify

its completeness.

In this study, I consider AD measures imposed by South Korea main exporting destinations.

These include China, the EU and the US. Other important destination are Hong Kong, which

does not have independent AD activity, and Japan and Taiwan, which did not impose any AD

duties against South Korea in the years considered. In order to have as clean an experiment

as possible, I selected sectors in which there were no overlaps neither with Countervailing

duties or Safeguards nor other AD cases by or against South Korea.

The firm-level data comes from the commercially available database ORIANA (version 2009),

which contains balance sheet information of Asian firms. South Korean firms are classified

under the 5-digit Korean Standard Industry Classification (KSIC) Revision 9. Although the data

starts in 1998, it has good coverage of South Korean firms only for the period 2001 to 2008.

In order to be able to observe firms before and after the imposition of AD duties, I focus on

AD duties imposed between 2004 and 2006.

ORIANA is highly unbalanced. This is not so much due to firms exiting and entering the

database, but mostly to firms not reporting every year all the variables needed for the

analysis presented here (for example, they report turnover but provide no information on

costs). In this paper, I focus on firms that reported all relevant information for at least two

consecutive years before and after the imposition of duties, which in differences implies that

I have at least one observation before and one after the duty. I only use unconsolidated

accounts since I am interested in firms rather than groups, although this makes little

difference in the Korean case since most firms for which complete information is available

report only unconsolidated accounts.

In order to match AD duties (which are identified by HS code) with firms in ORIANA database

(which are classified under the 5-digit KSIC Rev. 9), I used a concordance table provided by

the Korean Statistical Division. To verify that the correct sectors were matched to each case,

I complemented this by comparing case by case the product description of the AD filing with

the KSIC sector description.

Table 1 lists the five cases considered in this study. These include three AD duties imposed

by China, one by the US and one by the EU. The last two columns of table 5 present the

number of firms included for each AD case. The resulting sample of affected sectors is

composed of an unbalanced panel of 321 South Korean firms.

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3.2. Two methods to estimate markups

There are several methods to estimate markups which differ on their robustness, complexity

and data requirements. For this study, I chose two methods which are somewhat at the

extremes of the spectrum but that share the characteristic of providing a separate

estimation for each observation, therefore allowing for variations through time and across

firms.

The first method is observable price-cost margins (PCM). In its original form, PCM are

calculated as follows:

(1)

where are total sales, are total expenditures on materials and are total

expenditures on labour. Assuming unit labour and material costs are linear on output;

equation (1) is a monotonic transformation of the Lerner index. Rearranging, I obtain the

observable markup:

(2)

The preference for equation (2) lies firstly, in that results will be directly comparable to the

markups obtained using the alternative estimation method described below. Secondly, it

avoids the loss of observations when applying logarithms. Usually, PCM takes values

between 0 and 1. However, in exceptional circumstances a particular firm may present a

negative value in a given year. These observations are artificially lost when applying

logarithms.

The main advantage of PCM or observed markups is undoubtedly its simplicity. In particular,

it does not require the use of deflators which are usually not available at firm level. The main

concern when using this method, however, is that it does not allow disentangling effects on

markups from changes in productivity. Additionally, it is a poor measure of markups if labour

is subject to adjustments costs.

For these reasons, I turn to the method developed by De Loecker & Warzynski (2009, 2010;

henceforth DLW), which at the cost of more complexity and higher data requirements,

allows the separation of markups for productivity effects. I present here an overview of the

method, more details are provided in Appendix A.

Starting from a gross-output production function of the form ,

where is an unobserved Hicks-neutral productivity term and , and are

materials, labour and capital respectively; and assuming no adjustment costs in materials,

first order conditions for cost minimization imply:

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(3)

where marginal cost,

is the output elasticity of materials, and

is the expenditure on material costs in total sales. The later is a priori directly

observable, while the output elasticity of materials is obtained by estimating the production

function. Following DWL, I depart from the usual constant output elasticity functions and

estimate a gross-output trans-log production function of the form:

(4)

where , , and other lower case latter indicate logarithms of the

corresponding uppercase variables. is the error term. The estimated output elasticity of

materials is given by:

(5)

Estimating constant output elasticities, as in a Cobb-Douglass production function, would

imply that all the variation observed in markups is due entirely to changes in the expenditure

share. The use of a more flexible functional form allows also for variations in output

elasticities across firms and time. I estimate equation (4) using the one-step method

developed by Wooldridge (2009)2.

I mentioned above that the expenditures shares are directly observable. This is not

completely true since, as shown by DLW, output observed from the data is really

. I use the residuals obtained from the estimation of equation (4) and adjust

expenditures shares as suggested by DLW,

(6)

Finally, estimated markups are calculated as follows,

(7)

2 The programs used to perform production function estimations were obtained from Van Beveren (2010),

Ornaghi & Van Beveren (2011) and Konings & Vanormelingen (2009).

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3.3. Difference-in-difference specification and choice of control groups

In order to isolate the effect of AD duties on markups, I use the following difference-in-

difference specification,

(8)

where is either the observed or estimated markup for firm at time . represents a

set of treatment variables. These take two possible forms. In the basic specification, it is a

dummy taking the value 1 if an AD duty is in place against exports of sector at time . In the

alternative specification, and in order to evaluate whether effects change with time, I use a

set of dummies equal 1 if is the year since the AD duty is in place in sector . I also

include, firm fixed effects ( ), a complete set of year dummies ( ) and a set of control

variables . The latter include the logarithms of capital and labour to sales ratios, both

instrumented using its one period lags. is the error term.

In order to estimate equation (8), adequate control groups are needed3. These were

constructed using matching techniques. There are two issues to consider. First, a firm is

“treated” if an AD duty has been imposed in the sector where it operates, which means that

here “treatment” is a sector-level phenomenon. It also means that the “endogeneity”

associated to the treatment is also a sector-level issue. In consequence, it does not make

much sense to estimate a propensity score at firm level but at sector level. Therefore, a first

group is constructed using a sector-level propensity score matching (PSM) with replacement,

where each affected sector is matched to the two nearest neighbours among sectors not

affected by AD. However, nothing guarantees that the firms operating in matched sectors

are similar to the ones operating in the affected ones, in terms of their firm-level pre-

treatment characteristics. For this reason, I complement the sector-level matching with a

firm-level covariate matching (CVM). This second control group is hence composed by the

firm most similar to the affected ones, operating in the most similar sectors. In order to have

a pool big enough to obtain appropriate firm-level matches, I extended the sector level

matches and considered for each affected sector the five nearest neighbours. Finally, a third

control group was obtained through a firm-level CVM drawing firms from the complete pool

of sector not affected by AD. In all cases, I consider sectors and firm that were not only not

affected by AD by or against South Korea, but neither Countervailing duties nor Safeguards.

A detailed discussion of the matching procedures is presented in Appendix B.

3 Following Konings and Vandenbussche (Konings & Vandenbussche, 2005), the first candidates for such control

group would be firms in sectors where AD petitions were filed but resulted in negative rulings, and hence for which no duties were imposed (termination cases). However, in the case of South Korea there were not enough termination cases to be able to construct a control group.

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3. Results

In this section I present the results of separately estimating (8) using (the observed

markup) and (the estimated markup). Table 2 shows the results for the pooled case,

that is considering all targeted sectors in the same equation and the alternative three

control groups. The first three rows present the results for observed markups and the last

three for estimated markups. The first column presents estimations for the first specification

when I include a single dummy for the whole period when duties are in place. The following

columns present results for the second specification where separate dummies are included

for each year the duty is in place. The dummy for year 5 is excluded from the table, although

it was included in the estimation. The reason is that this year is only observed for one sector,

and therefore is not informative for the pooled estimation. It should also be noted that year

4 is included, although is not observed for two of the five sectors.

No significant effect is found on observed markup. Although there seems to be a change in

the sign of the coefficient from negative to positive over time, suggesting an increase in

markups as the review date approaches, none of the coefficients is significant. When the

regression is run on estimated markups, a rather different picture emerges. In all

specifications there is evidence of an increase in markups. The magnitude of the increase is

sensitive to the control group chosen, but they all coincide in pointing in the direction of a

stronger effect towards the end of the imposition period.

As it is shown in table 1, there are great differences in the number of firms considered for

each affected sectors. In particular, “Electric appliances for the kitchen” represents almost

50% of all affected firms considered, while “Metal wires” represent around 30%. Therefore,

it is likely that these sectors are in fact driving the results. To evaluate this, table 3 presents

the results of running difference-in-differences estimations for each affected sector

separately. For brevity, I include only results of the difference-in-difference estimation using

second control group, constructed combining a sector-level propensity score matching and

firm-level covariate matching (PSM + CVM). However, most conclusions carry over when

using the other two.

The first thing that emerges from the table is that there is great heterogeneity in the

response across sectors. For the first sector, “Metal wire”, there is evidence of a strong

increase in estimated markups. This increase is on average 20%; although this magnitude is

sensitive to the control group and drops to 10% when using CVM instead. “Kitchen

Appliances” present the exact opposite result; both observed and estimated markups appear

to be decreasing, although the magnitude of the effect seems much stronger for estimated

markups. This later result is also sensitive to the control group considered and it drop to 3%

if the control group used is CVM. For the remaining sectors, the number of firms observed is

too small to be able to identify an effect. Therefore, these three sectors are presented

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together. Still, no significant average effect is observed, although there seems to be a

decrease in observed markups in the first period, and a decrease in estimated markups

towards the end of the duration of the duty.

Overall, it was noted in the introduction that the sign of the effects of AD duties on markups

of targeted firms can be ambiguous. The empirical results submitted here reinforce this. I

find two opposite reactions in the two sectors for which we have enough data to identify an

effect. One possible explanation for this heterogeneous response is peculiar feature of EU

AD law. “Metal wires”, for which an increase in markups is found, was targeted by the US,

while “Kitchen Appliances”, for which a decrease was found, was targeted by the EU.

According to EU’s “lesser duty” rule, AD duties do not have to match the entire dumping

margin if a lower duty is sufficient to eliminate the material injury to the domestic industry4.

This can have an important effect on firms’ incentives facing AD duty reviews. A foreign firm

targeted with AD in the US may increase its price not only to avoid further duties, but also to

affect their level. The second incentive is not necessarily present for foreign firms facing a

revision of duties in the EU. In fact, these firms can absorb the tariff and still be faced with

relatively low AD duties.

4. Conclusions

In this paper I studied the effect of trade restrictions on South Korean firms’ market power,

by analysing the changes in their markups when they are targeted by AD duties by its main

exporting destinations. I used a panel of firms to estimate markups before and after AD

duties and compared affected firms with three alternative control groups. I also estimated

markups using two alternative methods, observed markups which rely on both costs of

materials and labour, and estimated markups on the basis of costs shares and output

elasticity of materials only. In the pooled case estimation, I find evidence of an increase in

estimated markups and particularly a stronger effect towards the end of the imposition

period. However, case-by-case estimations reveal strong heterogeneity across sectors.

This paper is still work in progress and many robustness checks and extensions remain in the

agenda. In particular, greater exploration in the causes of the reported sector heterogeneity

is required. Also, firm heterogeneity is likely to play a role.

4 The great difference in average AD duty levels between the US and the EU is illustrative of the importance of

this rule: Rovegno & Vandenbussche (2011) report that between 1989 and 2009, the average ad valorem AD duty in the EU was 30%. This was more than double for the US, 70%.

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References

Ackerberg, Daniel A.; Kevin Caves and Garth Frazer. 2006. "Structural Identication of Production Functions." mimeo, UCLA. Baldwin, Robert and Jeffrey Steagall. 1994. "An Analysis of Itc Decisions in Antidumping, Countervailing Duty and Safeguard Cases." Review of World Economics, 130(2), 290-308. Blonigen, Bruce A. and Stephen E. Haynes. 2002. "Antidumping Investigations and the Pass-through of Antidumping Duties and Exchange Rates." The American Economic Review, 92(4), 1044-61. Blonigen, Bruce A. and Jee-Hyeong Park. 2004. "Dynamic Pricing in the Presence of Antidumping Policy: Theory and Evidence." The American Economic Review, 94(1), 134-54. Blonigen, Bruce A. and Thomas J. Prusa. 2003. "Antidumping," Choi, E. K. and J. Harrigan, Handbook of International Trade. Blackwell Publishing Ltd, 251 - 84. Bown, Chad P. 2010. "Global Antidumping Database." available at http://econ.worldbank.org/ttbd/gad/. De Loecker, Jan and Frederic Warzynski. 2009. "Markups and Firm-Level Export Status." National Bureau of Economic Research Working Paper Series, No. 15198. De Loecker, Jan and Frederic Warzynski. 2010. "Markups and Firm-Level Export Status." Unpublished manuscript. Feenstra, Robert C. 2004. Advanced International Trade: Theory and Evidence. Princeton, New Jersey: Princeton University Press. Hansen, Wendy L and Thomas J Prusa. 1996. "Cumulation and Itc Decision-Making: The Sum of the Parts Is Greater Than the Whole." Economic Inquiry, 34, 746-69. Hansen, Wendy L. 1990. "The International Trade Commission and the Politics of Protectionism." The American Political Science Review, 84(1), 21-46. Hansen, Wendy L. and Thomas J. Prusa. 1997. "The Economics and Politics of Trade Policy: An Empirical Analysis of Itc Decision Making." Review of International Economics, 5(2), 230-45. Knetter, Michael M. and Thomas J. Prusa. 2003. "Macroeconomic Factors and Antidumping Filings: Evidence from Four Countries." Journal of International Economics, 61(1), 1-17. Konings, Jozef and Hylke Vandenbussche. 2005. "Antidumping Protection and Markups of Domestic Firms." Journal of International Economics, 65(1), 151-65. Konings, Jozef and Hylke Vandenbussche. 2008. "Heterogeneous Responses of Firms to Trade Protection." Journal of International Economics, 76(2), 371-83. Konings, Jozef and Stijn Vanormelingen. 2009. "The Impact of Training on Productivity and Wages: Firm Level Evidence." LICOS Discussion Paper 244/2009. Levinsohn, James and Amil Petrin. 2003. "Estimating Production Functions Using Inputs to Control for Unobservables." The Review of Economic Studies, 70(2), 317-41. Macrory, Patrick F.J.; Endwin A. Vermulst and Paul P. Waer. 1991. "United States and European Community Antidumping Law: Similarities and Differences." University of Miami Yearbook of International Law, 74, 74-142. Olley, G. Steven and Ariel Pakes. 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry." Econometrica, 64(6), 1263-97. Ornaghi, Carmine and Ilke Van Beveren. 2011. "Using Proxy Variables to Control for Unobservables When Estimating Productivity: A Sensitivity Analysis." mimeo. Pierce, Justin R. 2009. "Plant-Level Responses to Antidumping Duties: Evidence from U.S. Manufacturers." CES research paper 09-38, U.S. Census Bureau. Rovegno, Laura. 2010. "Trade Protection and Market Power: Evidence from Us Antidumping and Countervailing Duties." IRES Discussion Paper 2010-43. Rovegno, Laura and Hylke Vandenbussche. 2011. "Antidumping Practices in the European Union: A Comparative Analysis of Rules and Application in Wto Context," Gaines, S., B. E. Olsen and K. E. Sørensen, Liberalising Trade in the Eu and the Wto: Comparative Perspectives. Cambridge University Press. Forthcoming,

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Sabry, Faten. 2000. "An Analysis of the Decision to File, the Dumping Estimates, and the Outcome of Antidumping Petitions." The International Trade Journal, 14(2), 109 - 45. Van Beveren, Ilke. 2010. "Total Factor Productivity Estimation: A Practical Review." Journal of Economic Surveys, no-no. Wooldridge, Jeffrey M. 2009. "On Estimating Firm-Level Production Functions Using Proxy Variables to Control for Unobservables." Economics Letters, 104(3), 112-14. Zanardi, Maurizio. 2004. "Antidumping Law as a Collusive Device." Canadian Journal of Economics/Revue canadienne d'économique, 37(1), 95-122.

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Appendix A: Estimation of trans-log production functions

The trans-log production function presented in equation (4) was estimated using the one-

step estimator developed by Wooldridge (2009). This method has several advantages with

respect to two-step semi-parametric procedures such as Olley & Pakes (1996), Levinsohn &

Petrin (2003) and Ackerberg, Caves & Frazer (Ackerberg et al., 2006). In particular, it does

not require bootstrapping standard errors, allowing accounting for serial correlation and

heteroskedasticity in the errors (Ornaghi & Van Beveren, 2011). Moreover, it is more easily

adaptable to more complex function forms such as the one presented in equation (4).

Separate production functions were estimated for each 3-digit KSIC sector provided they

contained at least 1000 observations. Sectors that did not meet this requirement were

summed up to the closest 3-digit sector. The proxy variable used was materials. The

alternative proxy, investment, was not preferred given that it implies restricting the

estimation to firms reporting positive investment with great loss of data. Gross output was

calculated as turnover deflated by Producer Price Index at 3 to 5 digit, depending on

availability. Material inputs were obtained as cost of materials deflated by Stage

ofProcessing Price Index for Raw and Intermediate Materials. Labour was calculated as cost

of employees deflated by Consumer Price Index. This variable was preferred to number of

employees since the quality of employment data in ORIANA is very poor. Sensitivity checks

were performed using employment data instead and results seemed not affected. Finally,

capital was calculated as total fixed assets deflated by Stage of Processing Price Index for

Capital Equipment. All deflators were obtained from the Bank of Korea.

The set of instruments used was chosen following De Loecker & Warzynski (2010). They

include the lag of labour and its square and the interactions of current capital with the

labour lagged and materials lagged, as well as additional higher polynomials of the lags of

capital and materials in order to indentify all coefficients. Exogeneity of instruments was

tested on the basis of the Hansen test. The data was cleaned from abnormal values such as

negative sales, costs or total fixed assets. Also, I eliminated observations in the 0.5 and 99.5

percentiles of capital intensity and shares of labour and material costs on sales.

For brevity, I do not present estimation results for all sectors. As a sample, table A.1.

presents a summary of production function estimations for the 3-digit sectors containing the

five AD targeted sectors considered in this study. For comparison, it also includes the

estimations using OLS, both for a Cobb-Douglas and trans-log production functions, and

fixed effects.

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Table A.1: Production function estimation

3-digit KSIC Estimator TFP Obs.

131

Observed revenue shares 0.823 0.043 554.97

1501

OLS Cobb-Douglas 0.887 0.069 0.010 1.093 1.838

OLS Trans-log 0.885 0.090 0.001 1.084 2.016

FE Trans-log 0.880 0.080 0.009 1.080 2.110

Wooldridge Trans-log 0.881 0.091 0.026 1.082 5.080

172

Observed revenue shares 0.808 0.044 440.41

3940

OLS Cobb-Douglas 0.892 0.073 0.016 1.115 1.606

OLS Trans-log 0.876 0.097 0.013 1.090 2.665

FE Trans-log 0.886 0.093 0.010 1.102 1.742

Wooldridge Trans-log 0.883 0.098 0.020 1.098 3.566

259

Observed revenue shares 0.771 0.053 446.41

14658

OLS Cobb-Douglas 0.846 0.100 0.021 1.121 2.089

OLS Trans-log 0.843 0.120 0.017 1.106 3.762

FE Trans-log 0.849 0.112 0.014 1.113 3.702

Wooldridge Trans-log 0.840 0.121 0.017 1.107 2.858

283

Observed revenue shares 0.823 0.047 238.26

1349

OLS Cobb-Douglas 0.876 0.098 0.008 1.085 1.806

OLS Trans-log 0.866 0.118 0.006 1.061 2.922

FE Trans-log 0.905 0.096 0.007 1.109 2.276

Wooldridge Trans-log 0.896 0.121 0.010 1.096 2.698

285

Observed revenue shares 0.749 0.067 324.468

1639

OLS Cobb-Douglas 0.800 0.149 0.023 1.123 2.235

OLS Trans-log 0.801 0.188 0.005 1.096 3.582

FE Trans-log 0.834 0.154 0.016 1.143 3.209

Wooldridge Trans-log 0.823 0.191 0.025 1.112 8.912

Appendix B: Construction of control groups

Sector-level propensity score

In other to implement the sector-level propensity score matching (PSM), I combined industry

data from the Mining and Manufacturing Survey (MMS) provided by the Korean Statistical

Department, and trade data from UN Comtrade. In the MMS sectors are classified under the

KSIC Rev. 8. I considered sectors at 5-digit level and transformed the data into Rev. 9 using a

concordance table also provided by the Korean Statistical Department. Around 80% of

sectors have a one-to-one match from one revision to the other. For the remaining sectors,

more than half of them had a one-to-two or one-to-three concordance. For these industries,

data was aggregated into larger “super” sectors. A similar solution was used for sectors with

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two-to-two concordances or two-to-three. Sectors where this was not possible were

dropped out of the sample (14 for Rev. 8 and 6 for Rev. 9). Series on imports and exports by

industry were constructed matching HS-classified UN Comtrade data with KSIC Rev. 9 using

an HS-KSIC concordance table provided by the Korean Statistical Division. 55 sectors are

excluded from the concordance table, and hence had to be dropped out of the sample. The

resulting industry database includes 371 5-digit sectors and spans from 2000 to 2006.

Following Blonigen & Park (2004) and Konings & Vandenbussche (2008), I estimate

propensity scores using a multinomial logit where the dependent variable takes three

possible values: 1 if no AD activity is present in the sector in that year, 2 if an AD petition was

filed against South Korea in that sector but did not resulted in duties (termination cases) and

3 if an AD duty was imposed in that sector that year. The model is estimated using all

manufacturing sectors, and not only the 5 cases selected for this study. Table B.1 present the

results. The basis outcome is “1”, and hence coefficients should be interpreted in relation to

this.

The choice of variables to include in the model was based on the large body of literature

studying the determents of AD duties at the sector level (Baldwin & Steagall, 1994, Blonigen

& Park, 2004, Hansen & Prusa, 1996, Hansen, 1990, Hansen & Prusa, 1997, Knetter & Prusa,

2003, Sabry, 2000). All these studies focus on the protected side; that is they explain the

presence of AD on the basis of characteristics of the sectors receiving the protection.

However, they still provide guidance regarding what variables to include in a selection

equation. Import penetration has been reported across studies to be an important

determinant. From the perspective of sectors in the targeted country, this suggests including

export intensity as well as the share of Korean exports on imports of the three destinations

considered (China, the EU and the US). As table B.1 shows, the coefficient for export

intensity is positive as expected and significant both as a determinant of petitions and

measures. As for export shares, only Korean export shares on Chinese and US imports are

significant as determinants of measures. Additionally, I included the growth rate of Korean

exports to these three markets but these resulted in non-significant coefficients. Contrary,

the growth rate of worldwide imports in the sector is significant and presents a negative

sign. This is quite intuitive and implies that AD filings and measures are more likely in sectors

that are experiencing a downturn worldwide.

AD activity has been reported in the literature to concentrate in specific sectors. To account

for this, I include the number of AD petitions against sector in the previous 3 years. As

expected, past activity has a positive and significant effect both in petitions and measures. I

experimented with other alternatives such as number of measures instead of petitions, as

well as numbers for the previous 5 instead of 3 years. Results are unchanged.

15

Table B.1: Determinants of AD petitions and measures. Multinomial logit estimation

Dependent variable: "1" if no petitions; "2" if petitions but no tariffs; "3" if tariffs. Base outcome: "1"

Results for outcome "2" (1) (2) (3)

Export intensity lagged 0.0687** 0.0713** 0.0708**

(2.032) (2.197) (2.106)

Export intensity lagged and squared -0.000345 -0.000349 -0.000347

(-1.511) (-1.594) (-1.522)

Share Korean exports in Chinese imports in sector lagged 0.0150 0.0168 0.0153

(0.791) (0.863) (0.812)

Share of Korean exports in EU imports in sector lagged 0.00320 -0.00949

(0.0875) (-0.277)

Share of Korean exports in US imports in sector lagged 0.134** 0.143** 0.140***

(2.429) (2.498) (2.698)

Growth rate of Korean exports to China in sector lagged 0.000274

(1.261)

Growth rate of Korean exports to EU in sector lagged -0.00724

(-1.253)

Growth rate of Korean exports to US in sector lagged -0.00135

(-0.265)

Growth rate of worldwide imports in sector lagged -0.0731*** -0.0828*** -0.0812***

(-2.737) (-2.780) (-2.955)

AD petitions against sector in the previous 3 years 0.919*** 0.937*** 0.929***

(3.383) (3.492) (3.499)

Price cost margins lagged -0.0177 -0.0184 -0.0186

(-0.379) (-0.393) (-0.412)

Capital intensity lagged 1.684 1.667 1.690

(1.427) (1.409) (1.443)

Results for outcome "3" (1) (2) (3)

Export intensity lagged 0.138** 0.141*** 0.139***

(2.321) (2.587) (2.609)

Export intensity lagged and squared -0.00119** -0.00121** -0.00123**

(-2.027) (-2.226) (-2.261)

Share Korean exports in Chinese imports in sector lagged 0.0581** 0.0593** 0.0530**

(2.204) (2.276) (1.972)

Share of Korean exports in EU imports in sector lagged -0.0684 -0.0732

(-1.233) (-1.389)

Share of Korean exports in USA imports in sector lagged 0.0844* 0.0911** 0.0499

(1.867) (2.182) (0.834)

Growth rate of Korean exports to China in sector lagged -0.00255

(-0.944)

Growth rate of Korean exports to EU in sector lagged 0.0000

(0.109)

Growth rate of Korean exports to US in sector lagged 0.0000

(0.449)

16

Growth rate of worldwide imports in sector lagged -0.0717*** -0.0760*** -0.0759***

(-4.424) (-4.782) (-4.708)

AD petitions against sector in the previous 3 years 0.613*** 0.631*** 0.616***

(3.114) (3.378) (3.140)

Price cost margins lagged -0.0887*** -0.0877*** -0.0865***

(-3.357) (-3.634) (-3.603)

Capital intensity lagged 4.891*** 4.786*** 4.689***

(3.191) (3.296) (3.166)

Year dummies Yes Yes Yes

Chi-squared statistic 227.9*** 199.4*** 272.6***

Pseudo-R2 0.366 0.367 0.364

Observations 1219 1284 1284

Note: Robust standard errors in parentheses. ***/**/* denotes statistically different from zero at 1/5/10 % levels respectively.

Regarding sectors specific characteristics, I firstly included price-cost margins to account for

the degree of competition. They do not seem to have an effect on petitions but they do for

measures. Its coefficient is negative suggesting that sectors where competition is tougher

present a higher probability of being targeted with AD measures. Also, more capital

intensive industries appear more likely to be targeted with AD measures. I experimented

with other sector characteristics such as value added per worker and labour intensity, which

resulted in non-significant coefficients. The rates of change in these variables were not

significant neither. Also the fact that the sectors have been targeted in the past presents a

positive and significant coefficient. This reflects the fact that AD activity tends to

concentrates in the same sectors. I also experimented with measures of past AD protection

by South Korea to allow for the possibility of retaliation; however those variables resulted in

non-significant coefficients.

Propensity scores were obtained on the basis of the model in column (3) in which only

significant variables are kept. They were calculated as the prediction of outcome “3” and

matches found among sectors where no AD activity was present (sectors that reported

outcome “1” every year).

Firm-level covariate matching

The firm-level covariate matching (CVM) was performed using firm characteristics that are

relevant for the analysis and methods used in this paper. There is a trade off between the

number of covariates to include in the matching (and therefore the amount of information

on the firm to use) and the quality of the matching obtained. For this reason, I experimented

with different set of variables prioritizing the variables of interest, observed and estimated

markups, while running balancing test for all relevant variables. Two sets of covariates

appeared to perform best. One includes observed markups, capital intensity (total fixed

assets/sales), labour intensity (labour costs/sales), and growth rate of sales (log difference).

The second includes the same variables, except that observed markups are substituted by

17

estimated markups and estimated total factor productivity. Alternative control groups were

obtained using these two sets of covariates. I could not use any indicators that require the

number of workers, such as value added or capital per worker, since information on

employment is not available for many firms in various years, and therefore this would have

greatly reduced the sample. Given the unbalanced nature of the panel, I considered the

average value of each variable in the period before the imposition of the duty. I also defined

the matching algorithm as to find when possible an untreated firm that has the same panel

structure to the affected one. For example, if a given firm is observed two times before the

duty and once after the duty, it was matched with a firm that was also observed twice

before and once after.

Balancing tests

Table B.2 presents mean equality balancing tests for the alternative control groups. Although

matching was performed on averages before treatment, balancing tests are carried out on

levels since otherwise we will be inappropriately averaging over firm pre-treatment

averages.

Table B.2: Mean equality balancing tests

PSM + CVM CVM

Treated PSM Set 1 Set 2 Set 1 Set 2

Observable markup Mean 1,228 1,239 1,214 1,226 1,219 1,299 p-value 0,115 0,114 0,802 0,331 0,000

Estimated markup Mean 1,046 1,123 1,062 1,039 1,101 1,047 p-value 0,000 0,032 0,293 0,000 0,877

Capital intensity (total fixed assets/sales) Mean 358,6 370,3 285,7 318,2 329,7 391,3 p-value 0,567 0,000 0,053 0,194 0,163

Labour intensity (labour costs/sales) Mean 0,057 0,062 0,053 0,053 0,055 0,059 p-value 0,006 0,113 0,056 0,440 0,239

Growth rate of sales Mean 0,196 0,151 0,168 0,150 0,195 0,214 p-value 0,027 0,243 0,055 0,963 0,501

Value added per worker Mean 33,589 30,243 32,435 44,326 45,845 56,264 p-value 0,022 0,531 0,000 0,000 0,000

Estimated total factor productivity Mean 6,562 2,158 3,501 5,328 5,377 6,447 p-value 0,000 0,000 0,000 0,000 0,375

For the first control group constructed using the sector-level PSM, mean equality is rejected

for all variables except observable markups and capital intensity. This is illustrates the

importance of combining it with a firm level matching.

When both are combined (PSM + CVN), we get appropriate balancing tests depending on the

set of covariates used. For observable markups, I do not reject the null of mean equality for

18

both sets. For estimated markups however, mean equality is none rejected only for the

control groups constructed on the basis of firm level matching using the set 2 of covariates,

which include this variable. Matching on set 1 on the other hand, performs better in terms of

other variables such as labour intensity, growth rate of sales and value added per worker.

Therefore, PSM + CVN using set 1 of covariates is the preferred control group for estimations

on observable markups, while PSM + CVN using set 2 is preferred for regressions on

estimated markups.

As for the free CVN, both sets perform well in terms of capital intensity, labour intensity and

growth rate of sales, but they only provide good balancing tests for the measure of markup

included in the corresponding set of covariates. Importantly, mean equality for total factor

productivity is not rejected only for the latest control group. For this reason, even if results

using this control group are not reported in all tables, they are commented in the main text

when relevant.

Appendix C: Tables

Table 1: AD cases considered.

Product 5-digit KSIC sector Imposing country

Type of protection

Year of Imposition

No. Firms

Steel wire strand Metal wires USA AVD 2004 96

Optical fiber Optical fiber cables China AVD 2005 18

Kraft liner/linerboard Paper and paperboard China AVD 2005 12

Refrigerators Electric appliances for kitchen EU AVD 2006 158

Spendex Spinning of man-made fibers China AVD 2006 37

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Table 2: Effect of AD duties on markups.

Difference-in-difference estimation on pooled cases.

Dependent variable Control group AD AD1 AD

2 AD

3 AD

4 Obs No firms

(observed markups)

PSM -0.00395 -0.00668 -0.00571 0.000526 0.0161 5,262 1,018 (0.00586) (0.00609) (0.00691) (0.00894) (0.0104)

PSM + CVM -0.0101 -0.0101 -0.0145* -0.00405 0.0032 3,338 642 (0.00617) (0.00630) (0.00742) (0.00915) (0.0111)

CVM -0.00275 -0.00689 -0.00120 0.00389 0.0178 3,345 643 (0.00654) (0.00662) (0.00733) (0.0102) (0.0114)

(estimated markups)

PSM 0.0876*** 0.0767*** 0.0855*** 0.0842*** 0.161*** 5,262 1,018 (0.00826) (0.00733) (0.00846) (0.0108) (0.0120)

PSM + CVM 0.0171** 0.0201*** 0.0188** -0.00731 0.0533*** 3,338 642 (0.00851) (0.00767) (0.00939) (0.0119) (0.0160)

CVM 0.0113 0.0164** 0.0110 -0.0153 0.0376** 3,338 642 (0.00998) (0.0083) (0.0112) (0.0149) (0.0188)

Note: ***/**/* denotes statistically different from zero at 1/5/10 % levels respectively. Standard errors for regressions on observed markups were clustered by firm while standard errors for regressions on estimated markups were cluster bootstrapped (400 repetitions). PSM indicates the control group constructed on the basis of a sector-level propensity score matching; PSM + CVM refers to a control group constructed by combining a sector-level propensity score matching with a firm-level covariate matching; and CVM denotes a control group constructed using only a firm-level covariate matching.

Table 3: Effect of AD duties on markups.

Difference-in-difference estimation by sector.

Sector Dependent variable AD AD

1 AD

2 AD

3 AD

4 AD

5 Obs. No. Firms

Metal wire

0.0121 0.00826 0.00878 0.0254** 0.00853 0.0113 984 192 (0.00815) (0.0104) (0.0101) (0.0128) (0.0115) (0.0114)

0.206*** 0.240*** 0.196*** 0.159*** 0.204*** 0.189*** 984 192

(0.00638) (0.00943) (0.00931) (0.00963) (0.0157) (0.0148)

Electric appliances for the kitchen

-0.0215* -0.0135 -0.0416*** -0.0111

1,620 316

(0.0116) (0.0120) (0.0151) (0.0187)

-0.113*** -0.0710*** -0.114*** -0.195***

1,620 316

(0.00803) (0.00855) (0.0101) (0.0131)

Other three sectors

-0.0200 -0.0294** -0.00743 -0.0202 0.0108 734 134

(0.0134) (0.0122) (0.0166) (0.0249) (0.0417)

0.0086 0.00746 0.00638 0.0269 -0.0412**

734 134

(0.0119) (0.00928) (0.0139) (0.0184) (0.0193) Note: ***/**/* denotes statistically different from zero at 1/5/10 % levels respectively. Standard errors for regressions on observed markups were clustered by

firm while standard errors for regressions on estimated markups were cluster bootstrapped (400 repetitions). All reported estimations obtained using a control group constructed by combining a sector-level propensity score matching with a firm-level covariate matching (PSM + CVM).