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Antidumping Protection and Market Power of Domestic Firms?
Jozef Konings* and Hylke Vandenbussche**
(*) University of Leuven and CEPR, London
(**) University of Leuven, CEPR
Journal of International Economics, 2005
I. LiteratureTrade Liberalisation and the effects on Markups
(P/MC) J. Levinsohn (1993), JIE : Turkey
Harrison (1994), JIE: Chile N. Pavcnik (2002), RevEcStud: Chile Krishna and Mitra (1998), JDE: India Tybout (2000), JEL, overview
Conclusion: ‘Trade Liberalisation disciplines firms’ Markups’
Antidumping and the effect on Markups (P/MC)
theoretical papers suggest AD-protection facilitates collusion Veugelers and Vandenbussche, EER (1999); Staiger and Wolak (1989); Prusa (1992)
Harrison, (1991) and Prusa (1997) : increasing foreign import values as a result of AD-
Blonigen and Prusa (2001): ‘empirical work on the domestic price effects of AD- is lacking’
II. Methodology Roeger, JPE (1995) Solow (1957): relationship output growth q=log(Q/K) and
capital/labor input ratio n=log(L/K) under Perfect competition
=factor share earned by labor, over sales
= is the Hicks-neutral rate of technical progress = productivity growth rate
Hall (1988) shows that the primal Solow residual (SR) under imperfect competition into a technology component and a markup component µ=P/MC.
In order to estimate the SR, an IV approach is essential due to a correlation between the explanatory variable on RHS and the Hicks neutral technological progress
wL
PQ
( 1)t t it
t t t ttSR q n n
Roeger (1995) constructs a method based on Hall (1988) but which does not require the use of instruments to estimate the SR. This method stems from subtracting the dual or price based Solow residual (DSR) from the primal Solow residual (SR), thereby getting rid of the
(1)
(2)
(1 ) ( 1) ( )
l l t lit it it it it it itSR q l k l k
(1 ) ( 1) ( )l l l it
it it it it it ittDSR w r p w r
w= log of wage rate r= log of the rental price of capital pit = log of the price of firm i in period t
PQ
wL
=labor’s share in saleswL
PQ
Subtracting (2) from (1) we get
(3)
( ) ( ) (1 ) ( )
( 1) ( ) ( )l l
it it it it it it it
t it it it itl
NSR p q w l r k
w l r k
After including material inputs M (e.g. Basu and Fernald, 1995; Oliviera-Martins and Scarpetta, 1999) and after rewriting (1) becomes
(4)
( ) ( )it it it itp q r k
( ) ( ) ( ) ( )l m l m
it it mit it it itt w l p m r k
1 itity x Rewriting (4) we get
the growth rate in sales per value of capital
a composite explanatory variable that represents the growth rates in the various values of the input factors weighted by their respective share in total sales.
ity
itx
Equation (4) shows that in order to obtain an estimate of the markup (), we need information on
sales growth, growth in the wage bill, growth in material costs and growth in the value of capital
Our testable equation (5) then becomes:
1 2
1 2 3it it it
it
it
jt
jt
y
x x AD x GDP
AD GDP
AD : Either dummy equal to 1 for the years during which antidumping protection applies (from 1997 onwards) or a ‘trade weighted-duty’
GDPjt: annual GDP growth per EU country j to control for business cycle effects on markup
1 = the markup before protection
2 = the change in the markup during AD protection
3 = the change in the markup ratio due to business cycle fluctuations
III. Data Company accounts of 4,000 European producers involved in
antidumping investigations including small firms Time series of company accounts: 1992-1999 involving balance
sheet and Income statement items Event year: antidumping initiations in 1996 This allows us to measure markups before 1996 and during
antidumping protection Sunset Clause: AD-measures last 5 years In 1996: 15 different antidumping cases were initiated (15 is about
the average filings per year) 10 cases led to sufficient information in order to apply the Roeger
(1995) method for estimating markups: sales (P.Q), wage bill (W.L), material costs (PM.M), value of capital (RK.K)
We matched the 8-digit trade classification of the good under AD-investigation with the EU import competing firms producing them
NOTES: We have financial information at the firm
level not at the level of the product line UNDERESTIMATION of the effects of AD protection on EU markups.
We control for potential ‘selection bias’ of the AD- measures by using a two-stage Heckman procedure
IV. Results 1 21 2 3it it it itit jt jty x x AD x GDP AD GDP
Protection cases
(1)
Termination Cases
(2)
Counterfactual (3)
(1) Corrected for Selection
Bias
1
1.163*** (0.010)
1.25*** (0.012)
1.21*** (0.007)
1.178*** (0.030)
2 0.079*** (0.013)
0.011 (0.015)
-0.006 (0.01)
0.087** (0.036)
3 -0.086 (0.059)
-1.08** (0.577)
-0.036 (0.043)
-0.12 (0.154)
Inverse Mills
-
-
-
0.002 (0.003)
R2 0.83
0.8 0.85 0.83
Number of observations
8708 7214 15591 8708
Estimation Results for Pooled Cases (Fixed Effects), using AD Duty levels
Protection
Cases
(1)
Protection
Cases
(3)
Protection Cases
(using lagged imports t-2)
(4)
1= P/c 1= P/c 1= P/c
1
1.163*** (0.008)
1.59*** (0.066)
1.74*** (0.09)
2= P/c 2= P/c 2= P/c 2
0.302*** (0.037)
-
-
X interacted with log of dumping imports
-
-0.028***
(0.004)
-0.036***
(0.006) X interacted with log of non-
dumping imports -
-0.013 (0.010)
-0.018 (0.013)
3
-0.101* (0.058)
-0.198***
(0.058)
-0.30*** (0.077)
R2 0.83
0.83
0.81
Number of observations
8708
8708
5934
Estimations case-by-case using Fixed Effects
1 21 2 3it it it itit i jt jty x x AD x GDP AD GDP Markup
1 before
protection (2)
Before AD
(3)
Markup 2
change after
protection (4)
During AD
(5)
R2
Protection cases
Cotton fabrics 1.27*** (0.028)
P > MC 0.065*** (0.038)
P > MC 0.86
Leather Handbags
1.24*** (0.011)
P> MC 0.055*** (0.014)
P> MC 0.89
Farmed Atlantic Salmon
1.039 (0.030)
P > MC 0.129** (0.07)
P > MC 0.75
Seamless Pipes and Tubes
0.993 (0.033)
P = MC -0.049 (0.06)
P = MC 0.75
Polyester Fibre and yarns
1.088* (0.050)
P > MC 0.191*** (0.075)
P > MC 0.72
Bed Linen 1.61*** (0.064)
P>MC 0.36*** (0.101)
P>MC 0.90
Stainless steel fastner
1.17*** (0.019)
P > MC 0.091** (0.023)
P > MC 0.91
Termination Case (1)
Markups 1
before protection
(2)
Before AD
(3)
Markups 2
change after
protection (4)
During AD
(5)
R2
Synthetic Fibre ropes
1.19*** (0.039)
P>MC 0.033 (0.044)
P>MC 0.88
Luggage & Travel Goods
1.27*** (0.013)
P>MC 0.007 (0.017)
P>MC 0.83
Video Tapes 1.38*** (0.175)
P>MC 0.080 (0.24)
P>MC 0.63
Figure 3: Evolution of Imports in tons in
‘Seamless Steel Tubes Case’
Evolution Imports Seamless Steel Pipes and Tubes
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
years
Imp
ort
s in
met
ric
ton
s
EXTRA-EUR Dumping Non-Dumping
Figure 4: Evolution of Imports in tons in ‘Leather Handbags’
Evolution Imports Leather Handbags
0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10
years
Imp
ort
s in
met
ric
ton
s
EXTRA-EUR Dumping Non-Dumping
IV. Robustness Checks
CounterFactual Markups 1
before protection
Markups 2
change after protection
R2
Processing of Meat 1.131*** (0.008)
0.000 (0.012)
0.95
Processing of fruit and vegetables
1.175*** (0.026)
-0.005 (0.035)
0.80
Grain Mill Products
1.132*** (0.020)
-0.016 (0.028)
0.89
Wine 1.153*** (0.026)
-0.021 (0.032)
0.84
Outwear 1.338*** (0.019)
0.020 (0.026)
0.79
Inorganic basic chemicals
1.223*** (0.042)
0.067 (0.056)
0.77
Plastics in primary form
1.203*** (0.022)
0.033 (0.031)
0.86
Cement 1.344*** (0.044)
-0.074 (0.053)
0.80
Copper 1.217*** (0.039)
-0.105** (0.050)
0.83
Metal Structures 1.224*** (0.023)
-0.031 (0.030)
0.80
Table 6: Results For Counterfactual (Fixed Effects)
Table 7: Initiator and Single Product Effects Initiator
Effect Single
Product Firms
Multiple product
firms 1= P/c 1= P/c 1= P/c
X
1.21*** (0.01)
0.929
(0.045)
1.169*** (0.011)
2= P/c 2= P/c 2= P/c X X AD-dummy
0.061*** (0.013)
0.14** (0.062)
0.075*** (0.014)
X X AD-dummyX initiator dummy
0.196** (0.09)
-
-
X X country GDP growth -0.082 (0.06)
0.20
(0.27)
-0.057 (0.063)
R2 0.87
0.74
0.83
Number of observations
6997
475
8236
V. Conclusion Market power of European firms increases on average
by 8% points during Antidumping protection. When import diversion is strong, effects of AD protection
on markups is largely offset This result is robust to estimation techniques (OLS, fixed
effects, correcting for selection bias) Our results are likely to be underestimates, the rise in
markup we find is stronger for single product firms The rise in markup is strongest for ‘Initiators’ The increase in Markup (P/MC) can be an increase in
price OR an increase in productivity lowering MC The productivity paper shows that at least part of the
increase in markups is driven by efficiency gains