『経済研究』（明治学院大学）第 163 号

2022 年

Export Status and Price Markups: Evidence
from Chilean Plants
Yoko Sakamoto

Abstract
In this paper, we analyze how firms’ export status affects price markups using Chilean
plant-level data. We rely on the framework in Gandhi, Navarro and Rivers（2020）to estimate theindustry-level markups, controlling for unobserved heterogeneity. We show that globally operating
firms tend to set higher markups than firms supplying only domestic market, which supports findings in international trade literature. We also consider firms’ characteristics such as foreign technical supports status and material import status and analyze how those factors affect their markups.
We conclude that firms linked with foreign countries either by trade or technology adoption tends
to set higher markups.

1．Introduction
Literature on international trade has found that price markups are significantly higher for exporters than non-exporters（De Loecker and Warzynski（2012）and De Loecker, Gold-berg, Khandelwal and Pavcnik（2014））. In this paper, we also address the relation between markups and export
status and examine if this stylized fact also holds for our analysis.
We start with constructing a structural model following Gandhi et al.（2020）, which provide
estimation method on firms’ markups up to constant with controlling for unobserved heterogeneous
productivities. Following Gandhi et al.（2020）,we rely on firms’ First-Order Condition with respect toflexible inputs（intermediate inputs）for identification. We consider a fixed effect of firms’ export status to estimate how it affects markups. We use panel Data of Chilean firms and estimate markups up
to constant for the same industry in Pavcnik（2002）who estimates the production function for Chilean firms.
In order to analyze how export status affect markups, we consider the fixed effect of export
1

『経済研究』（明治学院大学）第 163 号

and non-export status in our estimation. As a result, we find that exporting firms charge higher
markups than non-exporting firms in all the industry, which supports the stylized facts in the literature. We interpret this result as that firms improve their productivity by conducting exports, which
improves their quality of goods compared with the non-exporters with raising their markups.
In our analysis, we also consider other characteristics of firms such as having foreign technicalsupports and importing intermediate inputs to investigate how those statuses affect markups. As aresult, we find that both of foreign technical supports and material imports status raises markups aswell as export status. Moreover, we find that increase in markups is higher for these two statuses
than for the export status. This suggests adopting foreign advanced technology or using foreign intermediate goods improve firms’ productivities more than export experience.
The rest of the paper proceeds as follows. In section 2, we provide a structural model following Gandhi et al.（2020）to show how we estimate industry-level markups, while controlling for unobserved productivity. In section 3, using Chilean plant-level data, we estimate the effect of export status on markups by allowing fixed effect into the model in section 2. We also estimate how foreigntechnical supports and material imports affect markups. The estimation results are provided in Section 4. Lastly, we conclude in section 5.

2．Structural Model
We construct the model following Gandhi et al.（2020）who propose identification of production function using firm’s optimization problem.
There is a panel consisting of firms j = 1, ..., J over periods t = 1, ..., T. We observe each firm’s
output, labor, capital, and intermediate inputs denoted by（Yjt, Ljt, Kjt, Mjt）with their log values denoted as（yjt, ljt, kjt, mjt）. Assume that each firm has a production function Ft with Hicks neutral productivity shock νjt as follows:

Yjt = F（（νjt）
t Ljt, Kjt, Mjt）exp（1）

where the Hicks neutral productivity shock is decomposed as

νjt = ωjt + εjt.
ωjt stands for a persistent productivity shock following Markovian process, which implies that we
can express:

ωjt = h（ωjt－1）+ ηjt
where ηjt satisfies E [ηjt│ωjt－1] = 0. εjt is the ex-post shock with E [εjt ] = 0.
2

Export Status and Price Markups: Evidence from Chilean Plants

For production inputs, we assume that Kjt and Ljt are determined at or prior to period t－1,
while Mjt is a flexible input determined at period t.
In order to identify firm’s markup, we rely on firm’s optimization problem to identify the production function. Mjt is chosen to minimize the cost satisfying the first order condition as follows:Λjt FM,t（Ljt, Kjt, Mjt）exp（ωjt）ε = ρtwhere Λjt is the “—Lagrange multiplier”, i.e., “—marginal cost” of firm j. Also, ε≡E（exp（εjt））and ρt  
is the partial derivative of F（･,･,･）
with respect tois the price of intermediate inputs. FM,t（･,･,･）
tMjt. Taking a log of both sides of FOC yields:
ln ρt = lnΛjt + ln FM,t（Ljt, Kjt, Mjt）+ ln ε + ωjt

（2）

Taking log of both sides of equation（1）leads toln Yjt = ln F（+ ωjt + εt Ljt, Kjt, Mjt）



（3）

Subtracting equation（3）from（2）and adding（ln Mjt－ln Pt）results inlnρt MjtPt Yjt
sjt

＝ln

Λjt
Pt

－ψjt

＋ln

FM（
Mjt
，
t Ljt，Kjt，Mjt）
F（t Ljt，Kjt，Mjt）＋ln ε－εjt

（4）

ln G（
t Ljt，Kjt，Mjt）

where sjt is the log of nominal share of intermediate inputs, ψjt is the log of markup, and G（Ljt , Kjt ,Mjt）is the elasticity of the production function.
Following Gandhi et al.（2020）, we assume markups are common within industry by restricting the markup term ψjt as ψjt = ψt for all j. So, the equation（4）becomes:

sjt =－ψt + ln G（+ ln ε－εjt
t Ljt , Kjt , Mjt ）（5）

From data, we observe sjt, Ljt, Kjt, and Mjt. We can recover the markup up to constant by estimating
equation（5）: Regress sjt on Ljt, Kjt, and Mjt non-parametrically, and get the constant term which－ψt+ μt + ln ε, the markup up to constant where μ is the constant of ln G（Ljt, Kjt, Mjt）.
As opposed to Gandhi et al.（2020）who assume F（= F（Ljt, Kjt, Mjt）,we allow prot Ljt, Kjt, Mjt）
duction function to change over time. This is possible in our setting since our focus is estimating
cross-sectional difference in markup whereas Gandhi et al.（2020）suggests to estimate the change inmarkups overtime.

3

『経済研究』（明治学院大学）第 163 号
Table 1: Summary Statistics
（1）
N

（2）
mean

（3）
sd

（4）
min

（5）
max

sjt
ljt
mjt
kjt

73,531－0.7420.518－7.365

2.896

VARIABLES

73,531

3.712

1.035

－0.7398.812

73,531

10.73

1.663

2.492

18.49

Exporters dummy

73,53173,5319.711
0.0859

2.060
0.280

－0.4770

18.43
1Domestic Suppliers dummy
Foreign Technical Supports dummyMaterial Importers dummy

73,53173,53173,5310.310
0.0507
0.255

0.462
0.219
0.436

0
0
0

1
1
1

3．Estimation
In previous section, we construct a structural model to estimate the markup up to constant following Gandhi et al.（2020）. Using this model, we estimate how firm’s export status and other characteristics affect the markup.
For the estimation, we use the census data of Chilean manufacturing plants provided by Chile’s Instituto Nacional de EstadÍstica, where we observe firm-level outputs（Pt Yjt）, material inputs us, and labor and capital usage（Ljt and Kjt）for several industries. The data
age and prices（Mjt and ρt ）set covers all firms from 1979 to 1996 with 10 or more employees, and includes firm-level observations on amount of exports, expenditure on foreign technology supports, and material imports. The
detailed explanation on the data set and the variable construction can be found in Pavcnik（2002）
and Liu（1993）.Table 1 provides summary statistics for the variables used for estimation. In the estimation, we choose the same industries as Pavcnik（2002）who estimate production function for Chilean firms: food, textile, wood, paper, chemical, glass, basic metals, and machinery.
We basically estimate equation（5）with allowing fixed effect and assuming Gt（Ljt, Kjt, Mjt）asa polynomial of degree two in logs:

sjt＝δt1＋δt0＋ln（γ0，t＋γl，t ljt＋γk，t kjt＋γm，t mjt＋γll，t l 2jt＋γkk，t k 2jt＋γmm，t m 2jt＋γlk，t ljt kjt＋γlm，t ljt mjt＋γkm，t kjt mjt）－εjt（6）where δta is the fixed effect for status a and xjt stands for ln Xjt. By estimating the fixed effect of status a, we recover:δ̂ta＝－Ψ̂ta＋µ̂t＋ln ε̂4Export Status and Price Markups: Evidence from Chilean Plants

We assume that within industry, the production function Ft is common to all of the firms across status. Hence, we can estimate the markup of firms in each status to common constant for each year.
In the estimation, we consider the following categories in Table 2.
Table 2: Statuses to be considered

Category 1
Category 2
Category 3

status 1

status 0

Exporters
With Foreign Technical Supports
Material Importers

Domestic suppliers
Without Foreign Technical Supports
Domestic-Material users

We estimate the effect of status 1 on markup by calculatingΔψ̂t≡－δ̂t1－（－δ̂t0）＝－（－ψ̂t1＋µ̂t＋ln ε̂）－（－（－ψ̂t0＋µ̂t＋ln ε̂））（7）＝ψ̂t1－ψ̂t0

In the estimation, we also consider the case where the markups do not vary overtime. For thiscase, we estimate

sjt＝δ 1＋δ 0＋ln（γ0，t＋γl，t ljt＋γk，t kjt＋γm，t mjt＋γll，t l 2jt＋γkk，t k 2jt＋γmm，t m 2jt

＋γlk，t ljt kjt＋γlm，t ljt mjt＋γkm，t kjt mjt）－εjt（8）

to recoverΔψ≡－δ 1－（δ 0）＝－（－ψ 1＋μt＋ln ε）－（－（－ψ 0＋μt＋ln ε））＝ψ 1－ψ 0（9）

4．Results
4-1．Export status and markup
We first address how export status affects markups by estimating the equation（6）with considering Category 1. We drop data from 1979 to 1989 since we cannot observe export status. Table 3
and Table 4 provide δ̂t1 and δ̂t0 for each industry, respectively.
5

『経済研究』（明治学院大学）第 163 号

̂1 ）Table 3: Exporters’ fixed effects（δt1δ̂19901
δ̂1991
1
δ̂1992
1
δ̂1993
1
δ̂1994
1
δ̂1995
1
δ̂1996

R2
N
＊

food

textile

wood

paper

chemical

glass

metal

machine－4.762＊＊（0.145）－4.769＊＊（0.146）－4.793＊＊（0.145）－4.780＊＊（0.145）－4.765＊＊（0.146）－4.797＊＊（0.146）－4.748＊＊（0.146）0.82－5.426（0.151）＊＊－5.459（0.150）＊＊－5.502（0.150）＊＊－5.530（0.150）＊＊－5.493（0.150）＊＊－5.513（0.150）＊＊－5.466（0.150）＊＊0.88－5.198＊＊（0.228）－5.220＊＊（0.227）－5.270＊＊（0.225）－5.090＊＊（0.225）－5.048＊＊（0.224）－5.030＊＊（0.224）－5.074＊＊（0.225）0.85－5.562（0.157）＊＊－5.573（0.157）＊＊－5.561（0.154）＊＊－5.605（0.153）＊＊－5.550（0.153）＊＊－5.571（0.153）＊＊－5.494（0.150）＊＊0.91－3.929（0.064）＊＊－3.979（0.063）＊＊－3.961（0.062）＊＊－3.966（0.061）＊＊－3.935（0.060）＊＊－3.958（0.060）＊＊－3.910（0.061）＊＊
0.88－5.521（0.148）＊＊－5.551（0.147）＊＊－5.549（0.146）＊＊－5.631（0.146）＊＊－5.692（0.149）＊＊－5.639（0.149）＊＊－5.638（0.148）＊＊0.92－3.656＊＊（0.203）－3.591＊＊（0.199）－3.651＊＊（0.198）－3.493＊＊（0.199）－3.596＊＊（0.193）－3.534＊＊（0.196）－3.550＊＊（0.189）0.89－5.174＊＊（0.290）－5.150＊＊（0.290）－5.201＊＊（0.290）－5.232＊＊（0.289）－5.223＊＊（0.289）－5.244＊＊（0.289）－5.192＊＊（0.288）
0.89

8,916

5,443

2,914

1,691

3,669

1,054

434

4,952

p < 0.05; ＊＊ p < 0.01

According to equation（7）, we recover the effect of export status on markups by using the information in Table 3 and 4. Table 5 summarizes the results.

̂ 0）
Table 4: Non-Exporters’ fixed effects（δt0δ̂19900δ̂19910
δ̂19920δ̂19930
δ̂19940δ̂19950
δ̂1996

R2
N
＊

food

textile

wood

paper

chemical

glass

metal

machine－4.632＊＊（0.144）－4.647＊＊（0.144）－4.710＊＊（0.144）－4.757＊＊（0.144）－4.792＊＊（0.144）－4.785＊＊（0.144）－4.745＊＊（0.144）－5.415（0.148）＊＊－5.431（0.148）＊＊－5.482（0.148）＊＊－5.479（0.148）＊＊－5.470（0.148）＊＊－5.484（0.148）＊＊－5.387（0.148）＊＊－5.194（0.224）＊＊－5.175＊＊（0.223）－5.195＊＊（0.224）－5.114＊＊（0.223）－5.105＊＊（0.223）－5.058＊＊（0.223）－5.023＊＊（0.223）－5.438＊＊（0.143）－5.473（0.144）＊＊－5.536（0.144）＊＊－5.609（0.143）＊＊－5.563（0.144）＊＊－5.586（0.144）＊＊－5.571（0.144）＊＊－3.910（0.058）＊＊－3.919（0.058）＊＊－3.948（0.058）＊＊－3.934（0.057）＊＊－3.909（0.057）＊＊－3.874（0.057）＊＊－3.897（0.057）＊＊－5.453（0.133）＊＊－5.430（0.132）＊＊－5.444（0.132）＊＊－5.498（0.132）＊＊－5.518（0.131）＊＊－5.497（0.133）＊＊－5.511（0.132）＊＊－3.386＊＊（0.193）－3.363＊＊（0.195）－3.494＊＊（0.191）－3.582＊＊（0.189）－3.541＊＊（0.192）－3.619＊＊（0.191）－3.622＊＊（0.192）－5.029＊＊（0.289）－5.024＊＊（0.288）－5.067＊＊（0.288）－5.132＊＊（0.288）－5.158＊＊（0.288）－5.159＊＊（0.288）－5.119＊＊（0.288）

0.82

0.88

0.85

0.91

0.88

0.92

0.89

0.89

8,916

5,443

2,914

1,691

3,669

1,054

434

4,952

p < 0.05;＊＊

p < 0.01

From Table 5, we observe that exporting firms set higher markups than non-exporting firms
in each industry. Especially glass and machine show larger difference in markups between exporters
6

Export Status and Price Markups: Evidence from Chilean Plants
Table 5: Effect of export status on markups

Δψ1990Δψ1991
Δψ1992
Δψ1993
Δψ1994Δψ1995
Δψ1996foodtextile

wood

paper

chemical

glass

metal

machine

0.13

0.011

0.004

0.124

0.019

0.068

0.27

0.145

0.122

0.028

0.045

0.1

0.06

0.121

0.228

0.126

0.083

0.02

0.075

0.025

0.013

0.105

0.157

0.134

0.023

0.051

－0.024－0.004

0.032

0.133

－0.0890.1－0.0270.023－0.057－0.013

0.026

0.174

0.055

0.065

0.012

0.029

－0.028－0.015

0.084

0.142

－0.085

0.085

0.003
0.049

0.079
0.034

0.051
0.0094－0.077
0.02

0.013
0.035

0.127
0.124

－0.072
0.066

0.073
0.104

Mean

and non-exporters whereas wood has little difference in markups.
We also try the case where markups do not vary over time by estimating equation（8）. We
recover the effect of export status by calculating equation（9）. The results are summarized in Table 6.
Table 6: Export status and markup with common markup overtime

δ1
δ0
R2
N
Δψ

food

textile

wood

paper

chemical

glass

metal

machine－4.830＊＊（0.155）－4.780＊＊（0.155）0.81－5.449（0.153）＊＊－5.410＊＊（0.152）0.87－5.046＊＊（0.363）－5.030（0.365）＊＊0.85－4.896（0.146）＊＊－4.882（0.141）＊＊
0.91－3.955＊＊（0.056）－3.920＊＊（0.054）0.88－5.575（0.129）＊＊－5.455（0.126）＊＊0.92－3.444（0.197）＊＊－3.383（0.194）＊＊0.89－5.194＊＊（0.301）－5.090＊＊（0.302）
0.89

8,916

5,443

2,914

1,691

3,669

1,054

434

4,952

0.05

0.039

0.016

0.014

0.035

0.12

0.061

0.104

Under this specification, the results that exporters set higher markups than non-exporters sillholds. This fact is consistent with the findings in trade literature.
4-2．Foreign technical supports and markup
In this subsection, we estimate how foreign technical supports affect markups by considering
Category 2. Again, we estimate equation（6）to have δ̂t1 and δ̂t0 for each industry. The results are provided in Table 7 and 8, respectively.
Using the information from Table 7 and 8, we calculate equation（7）to recover the effect offoreign-technical-support status on markups. The results are summarized in Table 9.
Table 7 shows that foreign technical supports increase markups in most of the industries. In
Metal industry, foreign technical supports decreases markups in most of the years and as a results,
the markups are reduced on average.
Higher markups for firms with foreign technical supports suggests that adopting advanced
7

『経済研究』（明治学院大学）第 163 号

̂1 ）Table 7: Foreign-Technical-Support fixed effects（δt1δ̂19791
δ̂19801
δ̂1981
1
δ̂1982
1
δ̂1983
1
δ̂19841
δ̂19851
δ̂1986
1
δ̂19871
δ̂19881
δ̂19891
δ̂19901
δ̂1991
1
δ̂1992
1
δ̂1993
1
δ̂1994
1
δ̂1995
1
δ̂1996

R2
N
＊

food

textile

wood

paper

chemical

glass

metal

machine－2.979＊＊（0.044）－5.967＊＊（0.091）－6.065＊＊（0.091）－6.136＊＊（0.090）－6.081＊＊（0.095）－6.125＊＊（0.091）－6.104＊＊（0.093）－6.032＊＊（0.092）－5.990＊＊（0.095）－5.921＊＊（0.094）－5.867＊＊（0.093）－6.000＊＊（0.096）－5.918＊＊（0.090）－5.959＊＊（0.093）－5.969＊＊（0.088）－5.963＊＊（0.091）－5.993（0.089）＊＊－6.004＊＊（0.088）0.78－2.468（0.124）＊＊－5.072（0.250）＊＊－5.249（0.252）＊＊－5.179（0.253）＊＊－5.073（0.253）＊＊－5.059（0.251）＊＊－5.005（0.251）＊＊－5.004（0.251）＊＊－5.047（0.252）＊＊－4.977（0.256）＊＊－5.004（0.253）＊＊－5.097（0.252）＊＊－5.024（0.251）＊＊－5.126（0.252）＊＊－5.151（0.251）＊＊－5.157（0.253）＊＊－5.147（0.250）＊＊－5.099（0.249）＊＊0.86－2.671（0.121）＊＊－5.624＊＊（0.257）－5.663＊＊（0.290）－5.510＊＊（0.294）－5.241＊＊（0.281）－5.405＊＊（0.265）－5.442＊＊（0.272）－5.446＊＊（0.255）－5.367＊＊（0.282）－5.365＊＊（0.294）－5.790＊＊（0.256）－5.648＊＊（0.252）－5.580＊＊（0.247）－5.506＊＊（0.253）－5.351＊＊（0.249）－5.449＊＊（0.249）－5.401＊＊（0.248）－5.364＊＊（0.241）
0.83－3.449＊＊（0.153）－6.882（0.325）＊＊－6.936（0.340）＊＊－6.799（0.343）＊＊－6.913（0.302）＊＊－6.958（0.301）＊＊－6.725（0.296）＊＊－6.811（0.300）＊＊－6.656（0.297）＊＊－6.863（0.305）＊＊－6.835（0.296）＊＊－6.854（0.301）＊＊－6.618（0.296）＊＊－6.723（0.292）＊＊－6.725（0.291）＊＊－6.712（0.292）＊＊－6.809（0.291）＊＊－6.691（0.291）＊＊0.90－2.170（0.052）＊＊－4.280（0.105）＊＊－4.287（0.107）＊＊－4.416（0.106）＊＊－4.324（0.106）＊＊－4.282（0.105）＊＊－4.180（0.104）＊＊－4.182（0.107）＊＊－4.135（0.106）＊＊－4.177（0.106）＊＊－4.167（0.103）＊＊－4.234（0.102）＊＊－4.182（0.101）＊＊－4.215（0.100）＊＊－4.286（0.100）＊＊－4.232（0.100）＊＊－4.264（0.100）＊＊－4.238（0.098）＊＊0.86－2.214（0.080）＊＊－4.717（0.160）＊＊－4.821（0.168）＊＊－4.792（0.146）＊＊－4.529（0.162）＊＊－4.654（0.145）＊＊－4.736（0.141）＊＊－4.721（0.141）＊＊－4.704（0.151）＊＊－4.596（0.155）＊＊－4.720（0.142）＊＊－4.693（0.139）＊＊－4.697（0.136）＊＊－4.686（0.137）＊＊－4.756（0.134）＊＊－4.807（0.133）＊＊－4.762（0.127）＊＊－4.756（0.133）＊＊0.91－1.833＊＊（0.160）－3.792＊＊（0.315）－3.756＊＊（0.367）－4.132＊＊（0.347）－3.832＊＊（0.338）－4.016＊＊（0.337）－4.085＊＊（0.339）－3.843＊＊（0.337）－3.920＊＊（0.319）－3.920＊＊（0.322）－3.689＊＊（0.335）－4.009＊＊（0.320）－3.815＊＊（0.322）－3.770＊＊（0.321）－3.850＊＊（0.324）－3.870＊＊（0.330）－3.822＊＊（0.332）－3.903＊＊（0.323）0.86－2.071＊＊（0.056）－4.187＊＊（0.113）－4.359＊＊（0.116）－4.177＊＊（0.119）－4.243＊＊（0.124）－4.308＊＊（0.122）－4.162＊＊（0.120）－4.232＊＊（0.123）－4.133＊＊（0.121）－4.110＊＊（0.122）－4.199＊＊（0.119）－4.157＊＊（0.121）－4.159＊＊（0.118）－4.168＊＊（0.120）－4.284＊＊（0.114）－4.342＊＊（0.116）－4.304＊＊（0.118）－4.252＊＊（0.112）0.88

23,817

14,275

7,529

4,214

8,292

2,499

1,140

11,765p < 0.05; ＊＊ p < 0.01

technology can decrease the level of competition in the market. This would be because that firms receiving foreign technical supports monopolize the technology and produce higher quality of goods
which raises markups since people become to have higher willingness to pay to get their products. If
the market is perfectly competitive, foreign technology adoption by some firms decreases markup
8Export Status and Price Markups: Evidence from Chilean Plants

̂ 0）
Table 8: No-foreign-tech-support fixed effect（δt0δ̂19790
δ̂19800δ̂19810
δ̂19820δ̂19830
δ̂19840
δ̂19850
δ̂19860
δ̂19870
δ̂19880
δ̂19890
δ̂19900δ̂19910
δ̂19920δ̂19930
δ̂19940δ̂19950
δ̂1996

R2
N
＊

food

textile

wood

paper

chemical

glass

metal

machine－2.934＊＊（0.039）－5.908＊＊（0.078）－5.951＊＊（0.078）－5.902＊＊（0.078）－5.866＊＊（0.078）－5.876＊＊（0.079）－5.846＊＊（0.079）－5.858＊＊（0.079）－5.831＊＊（0.079）－5.817＊＊（0.079）－5.846＊＊（0.079）－5.869＊＊（0.079）－5.879＊＊（0.079）－5.939＊＊（0.079）－5.975＊＊（0.079）－5.999＊＊（0.079）－5.998（0.079）＊＊－5.960＊＊（0.079）0.78－2.482（0.123）＊＊－5.056（0.245）＊＊－5.097（0.245）＊＊－5.020（0.246）＊＊－4.968（0.246）＊＊－4.998（0.246）＊＊－4.957（0.246）＊＊－4.981（0.246）＊＊－4.948（0.246）＊＊－4.961（0.246）＊＊－4.971（0.246）＊＊－5.000（0.246）＊＊－5.021（0.246）＊＊－5.067（0.246）＊＊－5.072（0.246）＊＊－5.060（0.246）＊＊－5.075（0.246）＊＊－4.988（0.246）＊＊0.86－2.679（0.112）＊＊－5.402＊＊（0.224）－5.454＊＊（0.225）－5.375＊＊（0.225）－5.343＊＊（0.225）－5.363＊＊（0.225）－5.340＊＊（0.225）－5.376＊＊（0.225）－5.378＊＊（0.225）－5.353＊＊（0.224）－5.388＊＊（0.225）－5.432＊＊（0.224）－5.422＊＊（0.224）－5.453＊＊（0.224）－5.351＊＊（0.224）－5.331＊＊（0.224）－5.292＊＊（0.224）－5.278＊＊（0.224）0.83－3.386＊＊（0.137）－6.864（0.273）＊＊－6.975（0.272）＊＊－6.809（0.273）＊＊－6.711（0.273）＊＊－6.710（0.273）＊＊－6.638（0.272）＊＊－6.694（0.271）＊＊－6.584（0.271）＊＊－6.572（0.271）＊＊－6.609（0.271）＊＊－6.656（0.272）＊＊－6.699（0.271）＊＊－6.750（0.271）＊＊－6.817（0.271）＊＊－6.767（0.271）＊＊－6.785（0.271）＊＊－6.758（0.271）＊＊0.90－2.031（0.046）＊＊－4.124（0.092）＊＊－4.239（0.092）＊＊－4.150（0.092）＊＊－4.080（0.093）＊＊－4.120（0.093）＊＊－4.084（0.094）＊＊－4.083（0.094）＊＊－4.028（0.093）＊＊－4.030（0.093）＊＊－4.069（0.094）＊＊－4.094（0.093）＊＊－4.126（0.093）＊＊－4.136（0.093）＊＊－4.111（0.093）＊＊－4.092（0.093）＊＊－4.074（0.093）＊＊－4.062（0.093）＊＊0.86－2.327（0.046）＊＊－4.737（0.093）＊＊－4.798（0.095）＊＊－4.624（0.096）＊＊－4.622（0.096）＊＊－4.601（0.096）＊＊－4.536（0.096）＊＊－4.602（0.098）＊＊－4.606（0.097）＊＊－4.542（0.098）＊＊－4.558（0.097）＊＊－4.612（0.097）＊＊－4.602（0.097）＊＊－4.621（0.096）＊＊－4.665（0.096）＊＊－4.689（0.096）＊＊－4.659（0.097）＊＊－4.683（0.097）＊＊0.91－1.890＊＊（0.149）－3.881＊＊（0.296）－3.900＊＊（0.299）－3.850＊＊（0.299）－3.857＊＊（0.301）－3.889＊＊（0.298）－3.848＊＊（0.299）－3.822＊＊（0.298）－3.910＊＊（0.297）－3.827＊＊（0.297）－3.860＊＊（0.301）－3.847＊＊（0.300）－3.851＊＊（0.299）－3.955＊＊（0.301）－3.901＊＊（0.301）－3.919＊＊（0.300）－3.943＊＊（0.300）－3.930＊＊（0.300）0.86－2.012＊＊（0.049）－4.093＊＊（0.098）－4.213＊＊（0.099）－4.104＊＊（0.098）－4.041＊＊（0.099）－4.069＊＊（0.099）－3.999＊＊（0.099）－4.052＊＊（0.099）－3.994＊＊（0.099）－3.991＊＊（0.100）－4.071＊＊（0.100）－4.082＊＊（0.099）－4.079＊＊（0.099）－4.124＊＊（0.099）－4.179＊＊（0.099）－4.200＊＊（0.099）－4.205＊＊（0.099）－4.165＊＊（0.099）0.88

23,817

14,275

7,529

4,214

8,292

2,499

1,140

11,765p < 0.05; ＊＊ p < 0.01

since advanced technology spillovers through market and strengthen the level of competition.
As same as previous section, we consider the case where markups do not change over time.
We estimate equation（8）and recover Δψ in equation（9）.The results are given in Table 10.

9『経済研究』（明治学院大学）第 163 号
Table 9: Effect of foreign-tech-support status on markups

Δψ1979Δψ1980Δψ1981
Δψ1982
Δψ1983
Δψ1984
Δψ1985
Δψ1986
Δψ1987
Δψ1988Δψ1989Δψ1990Δψ1991
Δψ1992
Δψ1993
Δψ1994Δψ1995
Δψ1996Meanfoodtextile

wood

paper

chemical

glass

metal

machine0.045－0.014－0.008

0.063

0.139

－0.113－0.057

0.059

0.059

0.016

0.222

0.018

0.156

－0.02

－0.089

0.094

0.114

0.152

0.209

－0.039

0.048

0.023

－0.144

0.146

0.234

0.159

0.135

－0.01

0.266

0.168

0.282

0.073

0.215

0.105

－0.102

0.202

0.244

－0.093－0.025

0.202

0.249

0.061

0.042

0.248

0.162

0.053

0.127

0.239

0.258

0.048

0.102

0.087

0.096

0.2

0.237

0.163

0.174

0.023

0.07

0.117

0.099

0.119

0.021

0.18

0.159

0.099

－0.011

0.072

0.107

0.098

0.01

0.139

0.104

0.016

0.012

0.291

0.147

0.054

0.093

0.119

0.021

0.033

0.402

0.226

0.098

0.162

－0.171

0.128

0.131

0.097

0.216

0.198

0.14

0.081

0.162

0.075

0.039

0.003

0.158

－0.081

0.056

0.095

－0.036

0.08

0.02

0.059

0.053

－0.027

0.079

0.065

－0.1850.044－0.0060.079

0－0.092

0.175

0.091

－0.051

0.105－0.036

0.097

0.118

－0.055

0.14

0.118

－0.0490.142－0.005

0.072

0.109

0.024

0.19

0.103

－0.121

0.099

0.044

0.111

0.086

－0.067

0.176

0.073

－0.027

0.087

0.101

0.067

0.100

0.065

0.139

0.070

-0.0012

0.120

Table 10 shows that foreign technical supports raises markup for all industries but the Metal
industry. Comparing the results from this subsection with the previous subsection, we find that foreign technical supports raise markups more than export status except for Glass and Metal industries.
One possible explanation of this is that direct technical supports improves productivities more thanexport experience.
Table 10: Foreign-tech-support status and markup with common markup overtime
food

textile

wood

paper

chemical

glass

metal

machine－5.969＊＊（0.075）－5.867＊＊（0.074）0.77－5.325（0.258）＊＊－5.265（0.260）＊＊0.85－5.485（0.231）＊＊－5.388＊＊（0.227）
0.83－6.103＊＊（0.216）－6.074（0.208）＊＊0.89－4.429（0.097）＊＊－4.281（0.097）＊＊
0.86－4.637（0.094）＊＊－4.568（0.090）＊＊0.90－3.812＊＊（0.284）－3.824＊＊（0.283）0.86－4.166＊＊（0.095）－4.052＊＊（0.096）0.87

23,817

14,275

7,529

4,214

8,292

2,499

1,140

11,765

Δψ

0.102

0.06

0.097

0.029

0.148

0.069

－0.0120.114＊

p < 0.05; ＊＊ p < 0.01

δ1
δ0

R2
N

10

Export Status and Price Markups: Evidence from Chilean Plants

4-3．Material import status and markup
Lastly, in this subsection, we conduct the same exercise as in previous two subsections to estimate how material imports affect markups. We estimate equation（6）with considering Category 3 toobtain δ̂t1 and δ̂t0 for each industry. The results are shown in Table 11 and 12, respectively.

̂1 ）Table 11: Material Import Status fixed effect（δt1δ̂19791
δ̂19801
δ̂1981
1
δ̂1982
1
δ̂1983
1
δ̂19841
δ̂19851
δ̂1986
1
δ̂19871
δ̂19881
δ̂19891
δ̂19901
δ̂1991
1
δ̂1992
1
δ̂1993
1
δ̂1994
1
δ̂1995
1
δ̂1996

R2
N
＊

food

textile

wood

paper

chemical

glass

metal

machine－3.018＊＊（0.044）－6.069＊＊（0.088）－6.145＊＊（0.088）－6.145＊＊（0.089）－6.085＊＊（0.089）－6.140＊＊（0.089）－6.109＊＊（0.088）－6.072（0.088）＊＊－6.082＊＊（0.089）－6.077＊＊（0.088）－6.057＊＊（0.088）－6.020＊＊（0.089）－6.037＊＊（0.089）－6.049＊＊（0.088）－6.109＊＊（0.088）－6.104＊＊（0.087）－6.090＊＊（0.088）－6.080＊＊（0.087）0.78－2.690（0.098）＊＊－5.480（0.196）＊＊－5.526（0.196）＊＊－5.493（0.196）＊＊－5.431（0.196）＊＊－5.474（0.196）＊＊－5.425（0.196）＊＊－5.403（0.196）＊＊－5.382（0.196）＊＊－5.398（0.196）＊＊－5.389（0.196）＊＊－5.448（0.196）＊＊－5.450（0.196）＊＊－5.497（0.196）＊＊－5.502（0.196）＊＊－5.501（0.195）＊＊－5.519（0.196）＊＊－5.451（0.196）＊＊0.86－2.789（0.128）＊＊－5.685＊＊（0.256）－5.729＊＊（0.256）－5.717＊＊（0.257）－5.648＊＊（0.258）－5.659＊＊（0.257）－5.605＊＊（0.256）－5.620＊＊（0.258）－5.544＊＊（0.257）－5.496＊＊（0.260）－5.717＊＊（0.257）－5.635＊＊（0.261）－5.619＊＊（0.260）－5.578＊＊（0.259）－5.469＊＊（0.259）－5.537＊＊（0.258）－5.472＊＊（0.260）－5.515＊＊（0.258）0.84－3.422＊＊（0.115）－6.947（0.231）＊＊－7.010（0.232）＊＊－6.903（0.233）＊＊－6.831（0.232）＊＊－6.881（0.231）＊＊－6.740（0.232）＊＊－6.765（0.231）＊＊－6.708（0.230）＊＊－6.730（0.230）＊＊－6.742（0.232）＊＊－6.821（0.232）＊＊－6.777（0.232）＊＊－6.804（0.232）＊＊－6.853（0.231）＊＊－6.831（0.232）＊＊－6.840（0.232）＊＊－6.762（0.232）＊＊0.90－2.244（0.059）＊＊－4.512（0.119）＊＊－4.608（0.119）＊＊－4.604（0.119）＊＊－4.503（0.120）＊＊－4.544（0.120）＊＊－4.492（0.120）＊＊－4.457（0.120）＊＊－4.401（0.120）＊＊－4.418（0.120）＊＊－4.441（0.120）＊＊－4.484（0.120）＊＊－4.508（0.120）＊＊－4.502（0.120）＊＊－4.486（0.120）＊＊－4.459（0.119）＊＊－4.453（0.119）＊＊－4.423（0.119）＊＊0.86－2.375（0.056）＊＊－4.759（0.115）＊＊－4.885（0.115）＊＊－4.819（0.119）＊＊－4.763（0.117）＊＊－4.790（0.118）＊＊－4.772（0.117）＊＊－4.825（0.117）＊＊－4.880（0.117）＊＊－4.646（0.119）＊＊－4.634（0.119）＊＊－4.693（0.118）＊＊－4.690（0.117）＊＊－4.655（0.114）＊＊－4.741（0.112）＊＊－4.827（0.113）＊＊－4.777（0.115）＊＊－4.737（0.113）＊＊0.91－1.936＊＊（0.147）－3.889＊＊（0.293）－3.902＊＊（0.295）－3.997＊＊（0.297）－3.964＊＊（0.300）－4.002＊＊（0.298）－3.987＊＊（0.297）－3.840＊＊（0.297）－4.034＊＊（0.297）－3.981＊＊（0.297）－3.961＊＊（0.300）－4.000＊＊（0.299）－3.993＊＊（0.301）－4.011＊＊（0.303）－3.896＊＊（0.302）－3.942＊＊（0.301）－3.912＊＊（0.308）－3.865＊＊（0.305）0.86－2.336＊＊（0.059）－4.726＊＊（0.118）－4.815＊＊（0.118）－4.832＊＊（0.118）－4.740＊＊（0.119）－4.793＊＊（0.119）－4.704＊＊（0.119）－4.704＊＊（0.119）－4.630＊＊（0.119）－4.614＊＊（0.119）－4.713＊＊（0.119）－4.695＊＊（0.119）－4.686＊＊（0.119）－4.707＊＊（0.118）－4.783＊＊（0.118）－4.791＊＊（0.118）－4.785＊＊（0.118）－4.774＊＊（0.117）0.88

23,817

14,275

7,529

4,214

8,292

2,499

1,140

11,765p < 0.05; ＊＊ p < 0.01

11

『経済研究』（明治学院大学）第 163 号

̂ 0）
Table 12: Material Non-import Status fixed effect（δt0δ̂19790
δ̂19800δ̂19810
δ̂19820δ̂19830
δ̂19840
δ̂19850
δ̂19860
δ̂19870
δ̂19880
δ̂19890
δ̂19900δ̂19910
δ̂19920δ̂19930
δ̂19940δ̂19950
δ̂1996

R2
N
＊

food

textile

wood

paper

chemical

glass

metal

machine－2.965＊＊（0.042）－5.972＊＊（0.085）－6.012＊＊（0.085）－5.964＊＊（0.085）－5.925＊＊（0.085）－5.932＊＊（0.085）－5.901＊＊（0.085）－5.920＊＊（0.085）－5.888＊＊（0.085）－5.868＊＊（0.085）－5.904＊＊（0.085）－5.941＊＊（0.085）－5.949＊＊（0.085）－6.014＊＊（0.085）－6.044＊＊（0.085）－6.072＊＊（0.085）－6.077（0.085）＊＊－6.035＊＊（0.086）0.78－2.631（0.098）＊＊－5.355（0.195）＊＊－5.399（0.195）＊＊－5.307（0.196）＊＊－5.253（0.196）＊＊－5.276（0.196）＊＊－5.235（0.196）＊＊－5.283（0.196）＊＊－5.254（0.196）＊＊－5.266（0.196）＊＊－5.291（0.196）＊＊－5.312（0.196）＊＊－5.334（0.196）＊＊－5.380（0.196）＊＊－5.383（0.196）＊＊－5.362（0.196）＊＊－5.377（0.196）＊＊－5.280（0.196）＊＊0.86－2.716（0.125）＊＊－5.477＊＊（0.250）－5.525＊＊（0.250）－5.438＊＊（0.250）－5.404＊＊（0.250）－5.425＊＊（0.250）－5.408＊＊（0.250）－5.451＊＊（0.250）－5.462＊＊（0.250）－5.444＊＊（0.250）－5.465＊＊（0.250）－5.528＊＊（0.250）－5.517＊＊（0.250）－5.551＊＊（0.250）－5.447＊＊（0.250）－5.420＊＊（0.250）－5.388＊＊（0.250）－5.364＊＊（0.250）0.84－3.389＊＊（0.115）－6.866（0.229）＊＊－6.990（0.228）＊＊－6.805（0.229）＊＊－6.706（0.230）＊＊－6.693（0.230）＊＊－6.636（0.229）＊＊－6.707（0.227）＊＊－6.572（0.227）＊＊－6.567（0.227）＊＊－6.618（0.227）＊＊－6.656（0.228）＊＊－6.705（0.227）＊＊－6.766（0.227）＊＊－6.835（0.227）＊＊－6.779（0.227）＊＊－6.806（0.227）＊＊－6.791（0.227）＊＊0.90－2.164（0.059）＊＊－4.413（0.118）＊＊－4.513（0.119）＊＊－4.383（0.118）＊＊－4.348（0.119）＊＊－4.359（0.119）＊＊－4.323（0.120）＊＊－4.378（0.120）＊＊－4.327（0.120）＊＊－4.320（0.120）＊＊－4.393（0.120）＊＊－4.409（0.119）＊＊－4.432（0.119）＊＊－4.461（0.119）＊＊－4.445（0.118）＊＊－4.417（0.118）＊＊－4.405（0.118）＊＊－4.415（0.118）＊＊0.86－2.322（0.050）＊＊－4.773（0.099）＊＊－4.817（0.101）＊＊－4.624（0.103）＊＊－4.598（0.103）＊＊－4.582（0.103）＊＊－4.520（0.103）＊＊－4.568（0.105）＊＊－4.546（0.104）＊＊－4.559（0.105）＊＊－4.604（0.103）＊＊－4.645（0.104）＊＊－4.636（0.103）＊＊－4.671（0.103）＊＊－4.701（0.104）＊＊－4.711（0.103）＊＊－4.691（0.104）＊＊－4.731（0.104）＊＊0.91－1.916＊＊（0.149）－4.006＊＊（0.299）－4.043＊＊（0.303）－3.886＊＊（0.302）－3.904＊＊（0.302）－3.949＊＊（0.300）－3.877＊＊（0.305）－3.976＊＊（0.301）－3.944＊＊（0.297）－3.867＊＊（0.296）－3.899＊＊（0.300）－3.902＊＊（0.300）－3.880＊＊（0.298）－3.996＊＊（0.299）－4.029＊＊（0.299）－4.037＊＊（0.300）－4.048＊＊（0.298）－4.088＊＊（0.298）0.86－2.238＊＊（0.058）－4.558＊＊（0.116）－4.697＊＊（0.116）－4.521＊＊（0.116）－4.477＊＊（0.116）－4.502＊＊（0.116）－4.433＊＊（0.116）－4.517＊＊（0.117）－4.464＊＊（0.117）－4.473＊＊（0.117）－4.561＊＊（0.117）－4.575＊＊（0.117）－4.574＊＊（0.117）－4.623＊＊（0.117）－4.675＊＊（0.117）－4.699＊＊（0.117）－4.714＊＊（0.117）－4.659＊＊（0.117）0.88

23,817

14,275

7,529

4,214

8,292

2,499

1,140

11,765p < 0.05; ＊＊ p < 0.01

Using the fixed effects from Table 11 and 12, we calculate equation（7）to recover the effect ofmaterial import status on markups. The results are summarized in Table 13.
Table 13 shows that firms importing their material of production set higher markups thanthose who use only domestically produced intermediate goods in most of the industries except for12

Export Status and Price Markups: Evidence from Chilean Plants

the metal industry. We can interpret this fact as foreign material improves the quality of product,
which increases the markups. In this sense, material imports have the same effect of productivity improvement by foreign technical supports.
Table 13: Effect of material imports status on markups

Δψ1979Δψ1980Δψ1981
Δψ1982
Δψ1983
Δψ1984
Δψ1985
Δψ1986
Δψ1987
Δψ1988Δψ1989Δψ1990Δψ1991
Δψ1992
Δψ1993
Δψ1994Δψ1995
Δψ1996Meanfoodtextile

wood

paper

chemical

glass

metal

machine

0.053

0.059

0.073

0.033

0.08

0.053

0.02

0.098

0.097

0.125

0.208

0.081

0.099

－0.014－0.117

0.168

0.133

0.127

0.204

0.02

0.095

0.068

－0.141

0.118

0.181

0.186

0.279

0.098

0.221

0.195

0.111

0.311

0.16

0.178

0.244

0.125

0.155

0.165

0.06

0.263

0.208

0.198

0.234

0.188

0.185

0.208

0.053

0.291

0.208

0.19

0.197

0.104

0.169

0.252

0.11

0.271

0.152

0.12

0.169

0.058

0.079

0.257

－0.136

0.187

0.194

0.128

0.082

0.136

0.074

0.334

0.09

0.166

0.209

0.132

0.052

0.163

0.098

0.087

0.114

0.141

0.153

0.098

0.252

0.124

0.048

0.03

0.062

0.152

0.079

0.136

0.107

0.165

0.075

0.048

0.098

0.12

0.088

0.116

0.102

0.072

0.076

0.054

0.113

0.112

0.035

0.117

0.027

0.038

0.041

－0.016

0.015

0.084

0.065

0.119

0.022

0.018

0.041

0.04

－0.133

0.108

0.032

0.139

0.117

0.052

0.042

0.116

－0.095

0.092

0.013

0.142

0.084

0.034

0.048

0.086

－0.136

0.071

0.045

0.171

0.151

－0.029

0.008

0.006

－0.223

0.115

0.116

0.137

0.144

0.082

0.090

0.109

－0.0075

0.159

Lastly, again, we consider the case of common markups overtime. We estimate equation（8）and recover Δψ in equation（9）.The results are given in Table 14.
Table 14 shows that the result of higher markups for foreign material users still holds for thisTable 14: Material import status and markup with common markup overtime
food

textile

wood

paper

chemical

glass

metal

machineR2
N－5.929＊＊（0.078）－5.813＊＊（0.078）0.77
23,817－5.434（0.205）＊＊－5.297（0.206）＊＊0.86
14,275－6.009（0.283）＊＊－5.856＊＊（0.281）
0.83
7,529－6.734＊＊（0.199）－6.652（0.198）＊＊
0.89
4,214

－4.523（0.077）＊＊－4.433（0.075）＊＊
0.86
8,292

－4.697（0.096）＊＊－4.595（0.093）＊＊
0.91
2,499

－3.839＊＊（0.288）－3.855＊＊（0.290）0.86
1,140－4.676＊＊（0.104）－4.528＊＊（0.104）0.88
11,765

Δψ

0.116

0.137

0.153

0.082

0.09

0.102

－0.0160.148＊

p < 0.05; ＊＊ p < 0.01

δ1
δ0

13

『経済研究』（明治学院大学）第 163 号

setting. Comparing the effect export status on markups, we conclude that foreign material usage
raises markups more than export status form most of the industries expect for Glass and Metal industries. This means that material imports are more effective in improving in productivity than final
goods exports. Also, compared with the effect of foreign-technical-support status on markups, material imports raise markup more than adopting foreign technology except for the chemical and metalindustries. This result suggests that foreign material usage is more effective in improving productivity than having technical supports from foreign countries.

5．Conclusion
In this paper, we analyze how firms’ export status and other characteristics affect markups.
We show that exporting firms set higher markups than non-exporting firms, which is the consistent
result with the finding from previous international trade studies such as De Loecker and Warzynski（2012）and De Loecker et al.（2014）. We also find that firms having foreign technical supports orimporting foreign materials have higher markups than those who do not have those statuses. Hence
we can conclude that firms who are linked with abroad by any channel charges higher markups.
Our analysis has an implication of the effect of opening trade and productivity improvementon competition. Although we have empirical stylized fact that export status raises markups, some of
the theoretical works show that opening trade has pro-competitive effect. Our paper answers if material imports and foreign technology adoption have either procompetitive effect or counter-competitiveeffect（i.e., rising markups）, which is not addressed by previous studies. According to the results inthis paper, foreign technology usage and foreign material usage has even more stronger countercompetitive effect than export status. One possible explanation for this would be followings. Foreignmaterial usage and foreign technology adoption improves firms’ productivity more than experiencing
export, but this improvement only happens among those firms and does not spillover to other firms
since the market is imperfectly competitive. As a result, only those who rely on foreign material or
technology produce higher quality of goods which raises markups for those products and the degreeof increase in markups is higher for those two statuses than export status since it improves firms’
productivity more.

References
Gandhi, Amit, Salvador Navarro, and David A Rivers, “On the identification of gross output production functions,” Journal of Political Economy, 2020, 128（8）,2973-3016.
Liu, Lili, “Entry-exit, learning, and productivity change Evidence from Chile,” Journal of Development Economics,
1993, 42（2）,217-242.

14

Export Status and Price Markups: Evidence from Chilean Plants
Loecker, Jan De and Frederic Warzynski, “Markups and Firm-Level Export Status,” The American Economic Review, 2012, 102（6）,pp. 2437-2471.
—, Pinelopi K Goldberg, Amit K Khandelwal, and Nina Pavcnik, “Prices, markups and trade reform,” mimeo,
2014.
Pavcnik, Nina, “Trade liberalization, exit, and productivity improvements: Evidence from Chilean plants,” The Review of Economic Studies, 2002, 69（1）,245-276.

15