The impact of international competition on executive compensation:
Evidence from Japanese firms during the global trade collapse

September 1, 2019

Hiroyuki Kuwahata#
Eiichi Tomiura*

Abstract
We examine the causal effect of intensified international competition by exploiting aquasi-natural experiment: the export decline triggered by the global financial crisis in the late
2000s. We find that executive compensation rises significantly relative to wages for workers of
the same firm especially among exporters after the global trade collapse in the sample of alllisted firms in Japan. Worker wages tend to decline more in exporting firms after the crisis,
while executives are insulated from the negative shock. We confirm the robustness of our resultsby excluding sporadic exporters and service exporters.
Keywords: executive compensation; inequality; export; natural experiment; global financial
crisis
JEL classifications: F16; F14; F66; M12; J31# Hirosaki University* Hitotsubashi University

11. Introduction
Widening income inequality, especially driven by a drastic rise of executive compensation incontrast to stagnant or even declining wages to workers, has stimulated debates in many
countries around the world. While globalization is often perceived to be linked with unequalincome distribution, we need to identify the causal effect of international competition oncompensation paid to executives and on wages paid to workers by appropriately taking accountof endogeneity. The historic trade collapse triggered by the global financial crisis in the late
2000s is a valuable quasi-natural experiment in this context. This paper compares exporters with
non-exporters in their executive compensation relative to worker wage in Japanese firms before
and after the global trade collapse.
Although the level is still modest compared with the remarkable escalation in the U.S., weobserve rising executive compensation and widening income inequality even in Japan, a country
once notable for its equal income distribution. Income distribution is currently unequal in Japan
compared with many other developed countries in Western Europe and North America, with
Gini coefficient high almost next to the U.S. and U.K. and comparable with Italy and Greece.1
Katz and Autor (1999) categorize Japan as a country with rising wage differentials since the1980s, while France and Germany experienced modest fall or no change during the same period.
Yamada and Kawaguchi (2015) emphasize the rise of wage inequality in Japan amid declining
average real wages since the turn of the century. High rewards to Chief Executive Officers(COEs) in some large globalized firms attract media attention in Japan with controversial
corporate scandal episodes. Hence, the investigation of Japanese inequality deserves attention
on its own right but will contribute to our understanding of inequality in globalized economies
Gini coefficient in Japan is 0.330, which is lower than that in U.S. (0.390) and U.K. (0.360) but
comparable with that in Spain (0.345), Greece (0.340), Portugal (0.336), and Italy (0.333), andhigher than that in Ireland (0.298), France (0.295) and Germany (0.293) at 2015, according to OECD
Income Distribution Database.
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in general.
Investigating how international competition affects compensations is important in a broad
context of the impact of competition on incentives. As offsetting effects of intensifiedcompetition, such as higher bankruptcy risks and reduced gains, make theoretical predictions
ambiguous,2 the investigation of this relation is an empirical question but requires us to handle
the endogeneity problem for identifying the causal effect. For this purpose, this paper focuses on
the case of global trade collapse during the Great Recession in the late 2000s. The world exports
experienced the largest fall since World War II, and the declining speed steeper than that during
the Great Depression in the 1930s. Japanese exporters were among the most seriously hit by thetrade collapse with around one-third of their exports suddenly lost. The investigation of thisquasi-natural experiment is valuable for our research as the U.S.-originated global financial
crisis is obviously exogenous to individual Japanese firms and its impact is likely to vary withthe firm’s globalization before the crisis.
To preview our principal results, this paper finds that executive compensation relative toworker wage tends to rise after the global trade collapse significantly more in exporters
compared with non-exporters in our sample of all listed firms in Japan. This finding is
consistent with the interpretation that firms provide stronger incentives to executives due tohigher risks after the global negative shock. We also detect a significant effect only on the levelof worker wage, not on the level of executive compensation, suggesting that executives are
insulated from the negative shock but workers face downward pressures on their wages. The
statistically significant change is confirmed by a difference-in-difference format. Firm-specificshocks are controlled, as we focus on within-firm pay differentials. We confirm the robustnessof this finding by excluding switching exporters, service exporters, or outlier firms, or with an
Schmidt (1997) and Raith (2003) are important contributions as theoretical analyses of the impact
of competition on incentive provisions.
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alternative definition of crisis timing, in our sample of all listed firms in all industries in Japan.
Our research is related with previous literature on the rise of executive compensation, andthe impact of globalization on inequality. As these two topics attract wide attention both inacademia and popular press, it is certainly beyond the scope of this paper to review all related
papers. We briefly and selectively refer to some critical papers to motivate our research.
The increasing executive compensation is a hotly debated issue. Among many studies,
Bertrand (2009) documents the long-run trend in the U.S. and discusses a wide range of
explanations, including intensified competition among firms for managerial talents.
International trade, or globalization more generally, is obviously among the strong factors forintensifying competition in our age.
Along the research line most closely related to this paper, several studies examine theimpact of international trade on executive compensation in comparison with wages paid toworkers. Based on Standard & Poor’s U.S. executive-level data, Cuñat and Guadalupe (2009a)
find that import competition, instrumented by exchange rates and tariffs, leads to within-firm
pay differentials.3 Chakraborty and Raveh (2018) examine the India’s import liberalization in
the 1990s and report that a fall of tariff on inputs raises the compensation share of managers.
Although inequality is not their main focus, Cuñat and Guadalupe (2005) analyze the sudden
appreciation of British pound in 1996 and discover that intensified competition increases thepay sensitivity to performance for executives in U.K. firms.4 Our paper on the exports ofJapanese firms is not only the first study of Japanese case to the best knowledge of authors, but
Exchange rate is often used as an instrumental variable in this context. For instance, Bertrand(2004) finds that import competition, instrumented by source-weighted exchange rate, raises thesensitivity of workers’ wage to the state unemployment rate in the U.S., although she did not analyze
executive compensation. While they do not discuss inequality, Bertrand and Mullainathan (2001)
discover that CEOs in the U.S. are rewarded for luck, which is instrumented by industry exchangerate.
4 Cuñat and Guadalupe (2009b) exploit deregulations in the financial sector as a natural experimentand discover a shift of compensation from fixed portion to variable performance-pay, thoughinternational competition is outside of their scope of analysis.
34also unique in using the global trade collapse as a natural experiment in this context, andcomplements previous studies of import-side of international competition. While Keller andOlney (2018) on U.S. executives is the most closely related research with ours on Japanesefirms, their research mainly based on instrumental variables differ from ours in our use of thehistoric trade collapse as a quasi-natural experiment and our main focus on within-firm
differentials with worker wage.
The remainder of this paper is organized as follows. Section 2 describes the firm-level
data used for our research. Section 3 explains the research design for our empirical analysis.
Section 4 reports our main estimation results and discusses their implications. Section 5summarizes several robustness check results. Section 6 concludes.

2. Description of data
This section describes our data. The definitions of main variables included in our analysis are
explained in detail to facilitate the understanding of our results reported in the next section. We
also report several descriptive findings from our dataset.
We use firm-level data derived from security reports of listed firms.5 All listed firms inJapan are required to submit annual security reports, which contain standard corporate
variables.6 The government asks each listed firm to report the sum of compensation paid to all
executives combined, implying that the disclosure of compensation for each executive is not

We derive firm-level data from NIKKEI NEEDS provided by Nikkei Inc. and eol provided byPronexus Inc.
6 Our sample covers all the stock markets in Japan, i.e. the first and the second section and
MOTHERS (Market of the high-growth and emerging stocks) of Tokyo stock exchange, the first andsecond section and Hercules of Osaka stock exchange, the first and second section and Centrex of
Nagoya stock exchange, Sapporo securities exchange including Ambitious, and Fukuoka stock
exchange including Q-Board. We exclude firms in the bank industry from our sample because theirsales data are unavailable.
55legally mandatory in Japan.7
To investigate the impact of global financial crisis, we focus on the period from 2006 to
2012. As we drop 20% of the firms due to their unbalanced observations and firms withirregular observations, 9 15,834 observations (2,262 firms for seven years) remain in our
sample.
The main variable for our research is the executive compensation relative to the wage
payment to workers. The numerator is the average compensation, including bonus and stock
option, paid to CEO, board members (torishimari-yaku in Japanese) and corporate/executive
officers (shikko-yakuin in Japanese). The denominator is the average wage, including bonus, toall regular employees (sei-shain in Japanese). We exclude irregular workers, such as short-term
employees and contract workers dispatched from personnel service companies, as they are notcomparable with executives and regular workers in their relations with their employers, and as
the payment to some of these workers are merged with outsourcing expenses in corporate
reports. The average of each firm is calculated by total compensation or wage payment divided
by the number of executives or regular workers. As our data are at the firm level, we are notallowed to identify individual executives or workers.
The other important variable for our analysis is the firm’s export. We define exporters if
the firm directly sells at least some of their products overseas.11 While we can calculate theexport intensity (the share of exports in sales), this paper focuses on the extensive margin ofexport (whether the firm exports).
Table 1 displays summary statistics of the variables used for our regressions in the next
Firms are required to disclose individual compensation only if it exceeds 0.1 billion yen.
We omit firms with executive compensations not higher than worker wage. Section 5.4 discusses
outliers by excluding firms with extremely high executive compensation.
11 This dataset includes exports not only of goods but also services, but exporters are
overwhelmingly in manufacturing industries. As is usual in other standard datasets, we exclude sales
by offshore affiliates but cannot trace indirect exports handled by intermediaries.
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section. Executive compensation, ExComp, is on average around four and half times higher thanworker wage, WorkerW, in our sample. This ratio is low compared with that in many othercountries, possibly reflecting a legacy of relatively equal income distribution in the Japanesesociety and a typically weak incentive scheme in traditional Japanese companies.12 However, as
shown by the standard deviation and the maximal value, firms vary widely in thisexecutive-worker payment ratio.13 By inspecting executive compensation and worker wage
separately, we find that executive compensation tends to differ across firms substantiallycompared with worker wage.
On other variables in Table 1, we note the following characteristics. First, around
one-third of firms are exporters (Export).14 This export participation ratio is higher than that in
the whole population of firms in many countries, including Japan. However, the observation ofhigh exporter ratio is plausible in our sample of listed firms, which tend to be older, larger insize and more capital-intensive. Second, R&D-intensity (R&D/Sales) is on average slightly lessthan two percent, no so high as our sample include non-manufacturing firms. Third, we observe
wide inter-firm variations in Total Factor Productivity (TFP). Finally, a non-negligible share offirms have labor unions and introduce stock options (both defined as binary dummies), again
with wide variations across firms.
Figure 1 displays how executive compensations relative to average worker wage evolve
over years during our sample period. We note that the payment gap between executives andemployees tends to become wider. This trend is consistent with observations of increasing
As the other end of spectrum in international comparison, a similarly defined ratio exceeds 100 in
the U.S. around the same period, according to Figure 1 in Bertrand (2009).
13 Although the wide inter-firm variability improves the precision of our estimation, we also reports
the results from the limited sample excluding outlier firms with extremely high executive
compensations as a robustness check.
14 We define exporters as firms consecutively exporting their products every year in our sample
period and non-exporters as all other firms. We will later consider an alternative definition of
non-exporters.
127inequality in many countries including Japan, as documented by Yamada and Kawaguchi (2015).
However, if we compare exporters with non-exporters, the speed of gap expansion appears to behigher for exporters than for non-exporters in later years. While this visual inspection suggests
the possible impact of global shock on inequality, we will investigate this interpretation in the
difference-in-difference regression format in the next section.

3. Empirical specification
This section is devoted for the explanation of the model and the identification strategy of ouranalysis. As the baseline model, we estimate the following.
𝑙𝑛

𝐸𝑥𝐶𝑜𝑚𝑝𝑊𝑜𝑟𝑘𝑒𝑟𝑊 𝑖𝑗𝑟𝑡

= 𝛼 + 𝛽1 𝐸𝑥𝑝𝑜𝑟𝑡𝑗 + 𝛽2 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛽3 𝐸𝑥𝑝𝑜𝑟𝑡𝑗 ∙ 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝑍𝑗𝑡 𝛾 + 𝜇𝑖 + 𝜌𝑟 + 𝜆𝑡 +𝜀𝑗𝑡

(1)

We index firm, industry, region, and year by j, i, r, and t, respectively. The dependent variable isthe executive compensation relative to the wage payment to workers in logarithm, which wederive from security reports explained in the previous section. By normalizing by the wage levelof workers in the same firm, our regression is regarded as after purging out the firm-specificeffect, such as brand image or market power of the firm. Within-firm investigation is important,as skills of executives and of workers are complementary in positive sorting (high-skill
executives matched with high-skill workers in exporting firms). On the right-hand side of theregression, we include two binary variables Export and Crisis. We define Export to take thevalue one for exporters and zero for non-exporters, while Crisis indicates the year after the
global financial crisis. The definitions of these dummy variables will be explained in detail later.
We also include the interactive term of these two dummies to capture the differential impact of
global shock on exporters compared with non-exporters. Other firm-level controls, which will
be explained below, are summarized by a vector Z. The error term is denoted by . We also8

control for year-, region- and industry-fixed effects.16
In the baseline case, we define the exporter dummy Export to take the value one for firms
exporting their products consecutively every year in our sample period and zero otherwise. This
strict definition concentrating on always exporters is to avoid possible contaminations byswitching exporters (exporting sporadically or intermittently). To check the robustness of ourresults, however, the next section will consider alternative definitions of exporter dummy. Our
analysis of export complements previous studies of the impact of import on compensation (e.g.
Cuñat and Guadalupe 2009a, and Chakraborty and Raveh 2018).
We identify the effect of globalization on executive compensation by estimating (1). Our
identification strategy is built on the premise that the global financial crisis is an exogenous
event for individual firms in Japan. No single Japanese firm influenced or triggered the global
financial crisis, which originated in the finance-related sector in the U.S. We define Crisis=1 foryears from 2010 to avoid turmoil amid the global crisis, as total exports by our sampled firms
stopped declining at that year. We will later discuss an alternative threshold year demarcating
pre- and post-crisis period for checking the robustness of our main results.
The negative impact of the U.S.-originated crisis on Japanese firms is mainly throughdeclined exports. Japan’s total exports dropped to two-thirds in 2009 as a part of global trade
collapse, and returned to the pre-crisis level only after a decade in 2018.17 While the exports to
U.S. was particularly hit by the crisis (decreased to nearly half from pre-crisis peak 2007 to2009), Japan’s exports to other destinations also fell as well possibly linked by global value
chains. In contrast, the changes in domestic demand during the period were by far modest(Japan’s real domestic demand decreased by 4% in 2009). Consequently, we focus on thisWe classify industries into 29, comparable with the two-digit level. We divide Japan into sevenregional blocs (Hokkaido-Tohoku, Kanto, Chubu, Kansai, Chugoku, Shikoku, andKyushu-Okinawa). See Table 1 for summary statistics.
17 These numbers are in yen values according to trade statistics by the Japan’s Ministry of Finance.
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export channel and compare the impact of the crisis on exporters relative to that on
non-exporters.
As firm-level controls Z, we include the following variables: Total Factor Productivity(TFP), R&D intensity (R&D-sales ratio), firm age (years since the establishment of the firm),
the dummy for firms introducing stock option, and the dummy for firms with labor union. TFP
is estimated by the method of Levinsohn and Petrin (2003).18 We take logarithm for all
variables, except dummies and R&D-sales ratio.19

4. Estimation results
4.1. Baseline results
Our baseline estimation results are reported in Table 2. Robust standard errors clustered at thefirm level are shown in parentheses. We investigate the result in each column of this table, as
they contain important information for our research.
The first column includes only the exporter dummy and the crisis year dummy on theright-hand side of the regression, except for the constant term. Both dummies are significantly
positive, indicating that executive compensation relative to worker wage of a firm tends to behigher in exporting firms than in non-exporters or after the global trade collapse than before thecrisis. Our finding of significantly positive effect of exporting is consistent with the result from
U.S. data by Keller and Olney (2018).20 On the impact of an exogenous shock, the significant
relation with a crisis reported in this table is in line with the finding of Chakraborty and Raveh(2018) from the reform liberalizing imports in India. This basic result appears to be in line withWe estimate the production function linking revenue with labor (the number of employees) andcapital (tangible fixed assets) and use materials as the proxy variable. TFP is defined as the residualfrom this regression.
19 We do not take logarithm for R&D/sales, as this variable itself is the ratio. However, all of ourmain results on compensations are intact even with R&D intensity in logarithm.
20 Kuwahata (2018) also detects a positive relation with exporting based on a propensity-scorematching from the same Japanese dataset as ours, though not considering the crisis.
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the interpretation that executives are highly rewarded for managing complex and/or riskyinternational business and also in line with our observation of wider inequality after the global
crisis as confirmed by Yamada and Kawaguchi (2015).
Next, we inspect the following two cases. The second column of Table 2 adds theinteractive term Export*Crisis, while the third column additionally includes industry, region,and year dummies. While the exporter dummy and the crisis year dummy remain significantly
positive as in previous cases, the interactive term is significantly positive. As we have detailed
firm-level data, we control for relevant firm attributes in the last column of this table.
The column (4) of Table 2 reports the results with firm-level controls in addition todummies for industry-, region-, and year-fixed effects. We confirm the finding from the columns(2) and (3) even after controlling for firm-level characteristics, i.e. executive compensation
normalized by wages of workers tends to be significantly higher in exporters especially after thecrisis. In other words, the gap between exporters and non-exporters has become noticeably
wider after the global trade collapse. The significant interactive term is also consistent withtheoretical predictions, such as those by Schmidt (1997) and Raith (2003), in that intensifiedcompetition induces firms (owners) to provide relatively stronger incentives to executives inorder to make them work harder to avoid liquidation. Prendergast (2002) also formalizes theeffect of risk on incentives as a delegation and surveys previous empirical results as strong
evidence for the positive effect of risks on incentives.
On other variables in the column (4), several informative points should be noted. Amongthem, we should first note that TFP is significantly positive. This result on TFP is consistent
with previous findings, as Keller and Olney (2018) report that technology increases the pay gap
between executives and workers in U.S. firms.21 As TFP is highly correlated with firm size, this21

Keller and Olney (2018) use capital expenditure as a proxy for skill-biased technology.
11

observation is also in line with Gabaix and Landier (2008), as they claim that the increase in
CEO pay in the U.S. during 1980-2003 can be attributable to the expansion of firm size. If we
control for TFP, the significance of firm’s exporter status (Export without interaction) is
absorbed as predicted by the established stylized fact on productivity premium of exporters ininternational trade literature. R&D intensity is also estimated to be significantly positive.
We also find that the payment differential between executives and workers tends to be
more moderate in older firms in Japan, possibly reflecting the traditional Japanese style. In
contrast, even among Japanese firms, executives are more likely to be highly rewarded if thefirms introduce stock option, while they are less rewarded in comparison with workers of thesame firms if labor union are organized in the firms. All these results on firm-level variables are
consistent with our prior predictions.

4.2. Different responses of executive compensation and worker wage
While our analysis in the previous section discovers that the global negative shock widens theinequality between executives and workers, it will be informative if we can identify whether
executive compensation or worker pay dictates this rising inequality. To answer this inquiry, thissection reports the regression results separately for executive compensation and worker wage.
First, we estimate the following specification:
𝑙𝑛𝐸𝑥𝐶𝑜𝑚𝑝𝑖𝑗𝑟𝑡= 𝛼 𝐸 + 𝛽1𝐸 𝐸𝑥𝑝𝑜𝑟𝑡𝑗 + 𝛽2𝐸 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛽3𝐸 𝐸𝑥𝑝𝑜𝑟𝑡𝑗 ∙ 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝑍𝑗𝑡 𝛾 𝐸 + 𝜇𝑖𝐸 + 𝜌𝑟𝐸 + 𝜆𝐸𝑡 + 𝜀𝑗𝑡𝐸(2)

The dependent variable in (2) is the absolute level of executive compensation, not divided byaverage worker wage, in logarithm. Variables on the right-hand side of the regression are kept
exactly the same as in the baseline case (1) to facilitate comparisons. Parameters, fixed effects
and the error term in (2) are expressed with superscript E for executives.
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The estimation results of executive compensation are shown in Table 3. The exporter
dummy without interaction is significant positive, implying that executive compensation tends
to be higher in exporters. As the crisis dummy without interaction is also significantly positive,
executive compensation becomes higher on average after the crisis. However, the interactive
term Export*Crisis turns out to be statistically insignificant, suggesting that the gap between
exporters versus non-exporters in executive compensations tend to be determined basically inthe same way before and after the great trade collapse.23 In other words, exporters are likely tomaintain executive compensation significantly higher than non-exporters even after thesubstantial decline of their exports. This finding of no significant impact of negative shock on
executive compensation is consistent with the result from U.S. firms by Keller and Olney(2018). Our finding also suggests that executives are insulated from the global negative shock,in line with the claim by Bertrand and Mullainathan (2001) that “while CEOs are always
rewarded for good luck, they may not always be punished for bad luck” (p.908) based on a casestudy of oil companies in the U.S. Garvey and Milbourn (2006) also confirm this asymmetry inStandard and Poor’s compensation data.
On other variables in the last column of Table 3, we confirm basically the same results asin Table 2, except for the firm’s age turned into being statistically insignificant. Our finding ofnegative relation between executive compensation and labor union is consistent with Huang etal. (2017) based on U.S. data.24
Second, we estimate the same model for the average wage of workers. The estimation
results for workers are reported in Table 4. The dependent variable in this case is replaced by theabsolute level of average wage payment to workers in logarithm. In contrast to the previous
As our measure of executive compensation is the average for each firm, the widening payment
gap between CEO and other executives within each firm could be consistent with this observation.
24 Huang et al. (2017) further find that firms with strong unions tend to pay their CEOs less
especially when they have union elections and contract negotiations.
23

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table, we find a significantly negative coefficient on the interaction term. This indicates thatexporters tend to decrease the average wage of their workers noticeably more thannon-exporters after the great trade collapse. This finding is consistent with the stylized fact(wider wage premium of exporters during expanding exports), such as Baumgarten (2013)
based on German manufacturing employer-employee matched data.25 We also emphasize that
our worker wage is the average paid to regular employees, thus not affected by expanding use ofoutsourcing and/or low-wage contract workers.
In addition to the statistical significance of the interactive term, we note its magnitude.
While it ranges between 13 to 15% in the first three columns without firm-level controls, thecoefficient on Export without interaction shrinks to less than four percent after controlling forfirm attributes. The estimated coefficient on the interactive term is almost in the same
magnitude. This indicates that the exporters’ premium in worker wage nearly vanishes after thegreat trade collapse if we control for TFP and other firm-level characteristics. Consequently, thechange due to the negative shock should be substantial.
On other variables in the column (4) of Table 4, we confirm the significantly positive
relationship with TFP, R&D intensity and the firm’s age. As expected, we find no significant
effect of stock option but positive effect of labor union on wages of workers.
By combining these two tables, we conclude that our finding of wider inequality reported
in Table 2 is driven by the shrinkage of exporter premium in average worker wage, not by thehigher speed of executive compensation rise of exporters relative to non-exporters, after thecrisis. The response of executive compensation to the global negative shock was not discernibly
different between exporters and non-exporters, while firms adjust the exporter premium inworker wage. Swift downward adjustment of wages of workers in contrast with insensitive
He also finds that most of the increase in wage inequality occurred between establishments andwithin workers with the same educational attainment.
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response of executive compensation appears consistent with the report by Yamada and
Kawaguchi (2015) of rising wage inequality mainly due to relatively smaller wage decline
among high-wage groups during around the same period in Japan. While one cannot pin down
the exact underlying mechanism behind such different responses, our regressions are
informative for discussing our wider inequality in globalized economies.

5. Robustness checks
5.1. Exit from exporting
This paper defines exporters to be the firms exporting consecutively in all years during thesample period. This strict definition implies that firms are categorized as non-exporters even if
they export in some years but do not export in other years. Therefore, our baseline case merges
these sporadic or intermittent exporters with firms that never export. As a robustness check, thissub-section concentrates on “never exporters” in defining non-exporters by excluding firms
switching their exporter status from our analysis. As a result, the sample size shrinks from15,834 to 13,104 observations.
The first column of Table 5 reports the estimation results from this limited sample. Allfirm-level attributes, and industry, region, and year dummies are included as in the baseline
regressions.26 The variable of our interest Export*Crisis is positive and statistically significant
at 1% level. Thus, our principal finding on the interactive term remains robust even if weconcentrate on the comparison between always exporters versus never exporters. We confirm
that firms switching their exporter status do not affect our result.

5.2. Alternative definition of crisis years
Table 5 shows the estimation results with all these controls, but we confirm that our main findings
are robust even without them as in the first three columns in Table 2.
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We have compared the years before and after 2010 to focus on the post-adjustment period, but
this sub-section reports results with an alternative definition of the crisis years for a robustness
check purpose. Among various alternative candidates, we choose to define the crisis dummy totake the value one for and after the year 2009, one year earlier than the baseline definition, inorder to concentrate on the steep decline of export just after the global financial crisis.
The second column of Table 5 displays the regression results with this alternative
definition of the crisis dummy. As in the baseline case, the interaction term Export*Crisis issignificantly positive. Estimated coefficients on other variables also remain basically the sameeven after changing the definition of the crisis dummy. Consequently, we confirm that our main
results are not dictated by the definition of the crisis year dummy.

5.3. Exporting of goods versus services
While exporters are overwhelmingly observed in manufacturing industries, non-negligible
portion of exporters are in non-manufacturing industries. As the global trade collapse in the
2000s is often characterized by a sudden drop of automobile and other machinery exports, it
might be useful to distinguish exports of goods from service in our analysis. Our baseline casecovers firms in all industries, but this sub-section concentrates on firms exporting goods as arobustness check purpose.
The column (3) of Table 5 reports the regression results from the sample of firms in goods
exporting industries. We include not only manufacturing, but also wholesale and retail trade into
this category. Although some firms categorized as non-manufacturers may export goods andthose categorized as manufacturers may export services, no data on exports disaggregated byproduct categories are available in standard financial statements. The estimates in the tableconfirm the robustness of our main finding. Even if we exclude firms in service industries, the16interaction term Export*Crisis remains significantly positive. The estimated coefficient of thisvariable barely differs from that in the baseline case, indicating that the magnitude of the impactof crisis appears to be around the same in goods-exporting firms. As no standard theory predicts
different responses of compensation to a crisis between firms exporting goods and services, ourconfirmation of no difference is also consistent with our prior belief.

5.4. Outliers
As in any standard datasets, we observe substantial variations across firms in the ratio ofexecutive compensation over wage of workers. However, the inter-firm variability isparticularly wide in executive compensation. This observation is in line with our daily exposure
to the news and episodes of extremely high compensations to some notable CEOs. Therefore,
this sub-section omits outlier firms to check the robustness of our main results from the wholesample.
The last column of Table 5 reports the estimation results from a sample excluding outlier
firms. We drop firms if their ratio of executive compensation over average worker pay areamong the highest 50 in any year. As a result, 32 firms are omitted. In this limited sample, therelative executive compensation divided by worker pay is contained within the ratio 30. The
regression results shown in this table confirm the robustness of our main finding from the wholesample. The interaction term Export*Crisis remains significantly positive even in this limited
sample. Our finding of wider inequality after the crisis is hence not dictated by exceptionallyhigh CEO compensation in outlier firms.
This additional confirmation by excluding outliers is also in line with our other findings.
As we have reported in Section 4.2, our finding of wider inequality is almost exclusively by the
decline of worker wage, not by the rise of executive compensation, especially among exporters
17

after the crisis. Since most of the inter-firm variations are in executive compensation, not inworker pay, the observation of extremely high CEO compensation in outlier firms is unlikely toaffect our result.

6. Concluding remarks
This paper has analyzed whether and how much executive compensation changes relative to theaverage wage of workers at the firm level by comparing exporting firms with firms selling all
their output in the home country before and after the exogenous and serious trade collapse
triggered by the global financial crisis. We detect that the ratio of executive compensation overworker wages tends to be higher among exporters than that among non-exporters and after thecrisis than before the crisis, but further find that the change after the crisis is likely to be more
substantial among exporters. The robustness of this main finding has been confirmed byomitting firms intermittently exporting their products, firms exporting services, or firms withextremely high executive compensation, and with an alternative definition of the crisis timing.
Although we cannot exactly determine the underlying mechanism behind this observation,
our estimation results could be consistent with the interpretation that firms provide relatively
strong incentives to executives in order to manage possibly complex and risky export businessfacing the global negative shock while cutting wages of workers relative to firms concentrating
on stable domestic market. Our finding of changes in intra-firm pay differential suggests a pieceof evidence on non-negligible impact of globalization on income inequality.
Our finding of different response of executive compensation relative to worker wage inexporters has an informative implication for a broad range of research and policy discussions,but there are several issues left for future independent studies. For example, the control of
personal characteristics of executives and/or workers will improve the precision of estimation if
18

we are able to assemble compensation data at the individual level with employer-employee
matched data. On the globalization of firms, it will be useful to expand our scope fromexporting toward other equally important channels, such as foreign direct investment.

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5
4.5
4
3.5

ExComp / WorkerW

5.5

6

Figure 1 Exporters vs. non-exporters in executive compensation relative to worker wage

2006

2007

2008

2009
Year

Exporters

2010

2011

2012

Non-exporters

Note: The solid line represents the ratio of executive compensation over worker wage ofexporters, while the dashed line is for non-exporters.

21

Table 1 Summary statistics
Variable

Mean

Std. Dev.

Min

Max

ExComp / WorkerW

4.541

3.753

1.002

109.699

ExComp26.52725.2894660.750

WorkerW

5.735

1.309

0.299

16.051

Export

0.331

0.471

0

1

TFP

889.836

1706.114

35.95554890.740

R&D / Sales

0.017

0.034

0

0.917

Age

61.31422.156

12

137

Labor union

0.400

0.490

0

1

Stock option

0.282

0.450

0

1

Agriculture, forestry, and fisheries

0.003

0.056

0

1

Mining

0.001

0.036

0

1

Construction

0.060

0.238

0

1

Food manufacturing

0.043

0.204

0

1

Apparel and textile products

0.017

0.130

0

1

Paper and pulp products

0.010

0.100

0

1

Chemical products

0.075

0.263

0

1

Pharmaceuticals

0.016

0.125

0

1

Petroleum and coal products

0.004

0.059

0

1

Rubber products

0.008

0.086

0

1

Glass and ceramic

0.024

0.154

0

1

Iron and steel

0.018

0.133

0

1

Nonferrous metals

0.012

0.109

0

1

Metal products

0.033

0.178

0

1

General machinery

0.084

0.277

0

1

Electric machinery

0.091

0.287

0

1

Transport equipment

0.038

0.191

0

1

Precision instruments

0.016

0.127

0

1

Miscellaneous manufacturing

0.036

0.186

0

1

Telecommunications

0.069

0.254

0

1

Land transportation

0.013

0.114

0

1

Industry dummies

22

Air transportation

0.001

0.030

0

1

Wholesale trade

0.111

0.314

0

1

Retail trade

0.092

0.289

0

1

Securities dealers

0.002

0.042

0

1

Other finance business

0.004

0.063

0

1

Real estate

0.025

0.157

0

1

Warehousing

0.013

0.113

0

1

Services

0.082

0.274

0

1

Hokkaido and Tohoku

0.023

0.151

0

1

Kanto

0.572

0.495

0

1

Chubu

0.137

0.344

0

1

Kansai

0.214

0.410

0

1

Chugoku

0.018

0.133

0

1

Shikoku

0.007

0.084

0

1

Kyushu and Okinawa

0.028

0.166

0

1

Region dummies

Note: We include 15,834 observations. ExComp, WorkerW, and Sales are in million yen.

23

Table 2 Baseline regression results
Dependent variable: ln (ExComp / WorkerW)

ExportCrisis

(1)

(2)

(3)

(4)

0.15510.14290.17320.0216(0.0210)***(0.0215)***(0.0256)***(0.0238)

0.13130.12190.25070.2524(0.0066)***(0.0080)***(0.0118)***(0.0120)***0.02850.02850.0393(0.0139)**(0.0139)**(0.0139)***

Export*Crisisln (TFP)

0.2338(0.0121)***

R&D / sales

0.9290(0.3577)***–0.0952ln (Age)(0.0299)***Stock option

0.0984(0.0180)***–0.0534

Labor union(0.0188)***Constant

1.23651.24061.208

0.3003(0.0112)***(0.0113)***(0.0995)***(0.1574)*

Industry dummy

No

No

Yes

Yes

Region dummy

No

No

Yes

Yes

Year dummy

No

No

Yes

Yes

Adjusted R-squared
0.03260.03270.07160.2089Notes: We cover 15,834 observations in all cases. Robust standard errors clustered at the firm levelare in parentheses. Statistical significance is shown by asterisks: *** 1%, ** 5%, and * 10%.

24

Table 3 Response of executive compensation
Dependent variable: ln (ExComp)

ExportCrisis

(1)

(2)

(3)

(4)

0.28680.29070.29820.0585(0.0243)***(0.0244)***(0.0288)***(0.0241)**

0.09390.09690.24650.2431(0.0067)***(0.0081)***(0.0119)***(0.0120)***–0.0091–0.0091

0.0072(0.0144)(0.0144)(0.0142)

Export*Crisisln (TFP)

0.3486(0.0124)***R&D / sales

1.8282(0.3774)***–0.0457ln (Age)(0.0297)
Stock option

0.0968(0.0179)***–0.0381

Labor union(0.0194)**
Constant

2.93032.92902.77531.0056(0.0123)***(0.0124)***(0.0986)***(0.1578)***Industry dummy

No

No

Yes

Yes

Region dummy

No

No

Yes

Yes

Year dummy

No

No

Yes

Yes

Adjusted R-squared
0.0562
0.05620.10720.3503
Notes: We cover 15,834 observations in all cases. Other notes to Table 2 also apply to all the tables
in what follow.

25

Table 4 Response of worker wage
Dependent variable: ln (WorkerW)

ExportCrisis

(1)

(2)

(3)

(4)

0.13170.14780.12500.0369(0.0085)***(0.0085)***(0.0097)***(0.0082)***–0.0374–0.0250–0.0042–0.0093(0.0018)***(0.0021)***(0.0026)(0.0025)***–0.0376–0.0376–0.0321(0.0036)***(0.0036)***(0.0034)***

Export*Crisisln (TFP)

0.1148(0.0043)***

R&D / sales

0.8992(0.1304)***ln (Age)

0.0496(0.0123)***–0.0016

Stock option(0.0067)
Labor union

0.0153(0.0072)**

Constant

1.69381.6884

1.56730.7052(0.0058)***(0.0058)***(0.0402)***(0.0660)***Industry dummy

No

No

Yes

Yes

Region dummy

No

No

Yes

Yes

Year dummy

No

No

Yes

Yes

0.28980.5054

Adjusted R-squared
0.08180.0832
Notes: We cover 15,834 observations in all cases.

26

Table 5 Robustness check results
Dependent variable: ln (ExComp / WorkerW)
(1)

(2)

(3)

switchingalternative

service

exporters

definition ofexporters

excluded

the crisis year

excluded–0.0087

0.02130.00440.0295(0.0301)(0.0242)(0.0257)(0.0226)

0.19840.25540.26220.2495(0.0131)***(0.0120)***(0.0147)***(0.0117)***0.04280.03010.04520.0393(0.0146)***(0.0141)**(0.0153)***(0.0136)***

0.23970.23380.2436

0.2163(0.0138)***(0.0121)***(0.0143)***(0.0113)***1.12590.9272

1.2581

0.6913(0.5124)**(0.3578)***(0.5015)**(0.3388)**–0.0815–0.0952–0.1406–0.0837(0.0331)**(0.0299)***(0.0420)***(0.0285)***0.09490.09850.12240.0941(0.0199)***(0.0180)***(0.0207)***(0.0172)***–0.0496–0.0534–0.0438–0.0525(0.0211)**(0.0188)***(0.0217)**(0.0181)***0.22170.30050.48380.3703(0.1764)(0.1575)*(0.1945)**(0.1534)**

Industry dummy

Yes

Yes

Yes

Yes

Region dummy

Yes

Yes

Yes

Yes

Year dummy

Yes

Yes

Yes

Yes

Adjusted R-squared

0.21650.20880.22400.1990

ExportCrisisExport*Crisisln (TFP)R&D / salesln (Age)
Stock option
Labor union
Constant(4)
outlier firms
excluded# observations
13,104
15,834
11,501
15,610Notes: In the second column, the crisis dummy takes the value one for and after the year 2009 andzero before 2009.

27