ESRI Discussion Paper Series No.390

ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central
Headquarters and Overseas Subsidiaries

Yosuke Yasui
Kazuki Kitatsuji
Minoru Masujima
Pravakar Sahoo
Towa Tachibana

March 2024

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “ESRI Discussion Papers” are those of the authors and not those of the
Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

ESG as a Tool for Advancing SDGs from High-Income to Low-Income Countries:
Evidence from Matched Data of Japanese Central Headquarters and Overseas
Subsidiaries 1
Yosuke Yasui ∗Kazuki Kitatsuji † Minoru Masujima ‡Pravakar Sahoo ★ Towa Tachibana ☆
Abstract
This study investigates the impacts of the “Environmental,” “Social,” and “Governance” (ESG)
stances of multinational corporations (MNCs) in high-income countries on the performance of
their overseas subsidiaries situated in low-income countries. This inquiry is particularly relevantin the context of the global push to achieve the Sustainable Development Goals by 2030. To
address this question, we construct unique matched data by combining the ESG scores ofJapanese-listed companies with financial data from their central headquarters (CHQs) in Japanand overseas subsidiaries in low-income economies. The estimation results reveal that
improvements in the ESG scores of CHQs do not positively impact the employment and wagesof their overseas subsidiaries. However, they have a significantly positive effect on laborproductivity. Specifically, improving the “Community” score, which is associated with the levelof social capital, demonstrates an economically significant positive impact.
JEL Classification: D22, F23, O35.
Keywords: ESG, Multinational corporations, Social capital, Labor productivity, Low-income
countries

1 We are grateful to Hiromi Nagane, Akiko Oishi, Hiroyuki Minagawa, and Takashi Ohga, and staff at the Economics

Department in the Japan Research Institute, Limited. We also extend our gratitude to Tatsuyoshi Okimoto, Yutaka
Murayama and the participants of the ESRI Seminar for their helpful comments and suggestions. The views
expressed in this study are those of the authors and do not necessarily reflect the position of the ESRI, the Cabinet
Office, or the Government of Japan. Any remaining errors are our own.
∗ Corresponding author. ESRI, Cabinet Office, Government of Japan and the Japan Research Institute, Limited.
† ESRI, Cabinet Office, Government of Japan and the Japan Research Institute, Limited.
‡ ESRI, Cabinet Office, Government of Japan and the SBI Financial and Economic Research Institute, Limited.
★ Institute of Economic Growth.
☆ Graduate School of Social Sciences, Chiba University.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

I. Introduction
Amidst the global pursuit of achieving the Sustainable Development Goals (SDGs) by 2030, an
increasing number of large firms in high-income economies are incorporating “Environmental,”“Social,” and “Governance” (ESG)-related practices into their corporate identity, contributing to
the SDGs. 2 This strategic consideration has become imperative, as publicly listed firms find it
challenging to attract funds from globally influential investors who are increasingly focusing on
ESG investing.
Despite the growing emphasis on ESG practices, investors face limitations inmonitoring the global supply chains of multinational corporations (MNCs). 3 Consequently,
MNCs may be incentivized to covertly shift the costs 4 of their ESG practices to foreignsubsidiaries, particularly those located in low-income economies. Should this scenario unfold,foreign direct investment by MNCs into low-income countries could roll out as a “curse” rather
than a “blessing,” hindering sustainable growth opportunities for these nations. In the worst-casescenario, heightened ESG practices by MNCs in high-income countries might exacerbate
disparities in environmental and working standards (including human rights) between low- andhigh-income economies, posing a significant challenge to the global achievement of the SDGs.
Given these potential consequences, it is essential to investigate the effectiveness of
ESG practices adopted by MNCs in high-income countries as a tool for advancing SDGs in lowincome countries. To address this critical issue, we conduct an in-depth examination by
constructing a unique and confidential dataset combining the ESG ratings of large Japanese
corporations and the business performance of their foreign subsidiaries.
Various steps have been undertaken to promote ESG in Japan. On February 26, 2014,
the Financial Services Agency in Japan published the “Principles for Responsible Institutional
Investors (Japan’s Stewardship Code)” to promote sustainable growth of companies throughinvestment and dialogue with corporations. Subsequently, on September 16, 2015, theGovernment Pension Investment Fund in Japan underscored its commitment to ESG issues bysigning the United Nations Principles for Responsible Investment.
This pivotal move further urged external domestic and international equity managers to

2 Pérez et al. (2022) mentioned that firms of various sizes across different industries and locations are dedicating
increased resources to enhance their ESG practices. They also described that more than 90% of S&P 500 companies
and approximately 70% of Russell 1000 companies publish ESG reports in various formats.
3 The Economist (September 21, 2017) reported that even MNCs willing to participate in ESG surveys often overlook
the supply chain and provide incomplete responses; according to O’Connor and Labowitz (2017), many existing
approaches do not consider the entire supply chain, especially critical manufacturing endpoints.
4“Environmental” practices involve carbon emission reductions, resource conservation, pollution reduction, andclimate change mitigation. “Social” activities encompass diversity, labor practices, employee relations, human rights,
and community engagement. “Governance” practices include board composition, executive compensation,
shareholder rights, transparency, and ethical business practices. Pursuing these requires substantial effort andcommitment from companies.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

intensify their efforts to steer the sustainable growth of invested firms. Against this backdrop,
MNCs in Japan have proactively embraced ESG practices and initiated the publication ofsustainability reports. These shifts in the ESG stances of Japanese MNCs are evident in the ESG
scores compiled by LSEG Data & Analytics, a financial markets data and infrastructure divisionof the London Stock Exchange Group plc 5 (LSEG [Refinitiv]). As Figure 1 shows, Japanese firms’
ESG scores began to increase rapidly in 2015. By leveraging the variations in changes in ESG
scores within Japanese firms, we identify the association between their ESG practices and theperformance of their foreign subsidiaries in low-income countries, such as growth rates of laborproductivity, wages, and job creation (i.e. employment).

70
GPIF signed the PRI60

ESG Score

50
40
30
20
10
0
2002

2005

2008

2011
2014
Fiscal Year

2017

2020

Figure 1 Development of ESG score of Japanese listed firms
Source: LSEG ESG score.
Note: Average of ESG score of Japanese listed firms.

Our empirical approach is inspired by Bloom et al. (2012b), who explored the impact of
distinct managerial or organizational technologies implemented at central headquarters (CHQs)
5 The London Stock Exchange Group plc. is a United Kingdom-based stock exchange and financial informationcompany headquartered in London, England. LSEG Data & Analytics was previously called Refinitiv, an investment
data and analytics firm, which was purchased by LSEG in January 2021. Refinitiv was established as a spin-off ofThomson Reuters’ financial information division in October 2018.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

on their foreign affiliates. They discovered that these CHQs transferred such technologies tooverseas affiliates, leading to an overall improvement in productivity. To investigate thecorrelation between the ESG practices of Japanese CHQs and the performance of their foreign
offices in low-income economies, we employ an empirical framework similar to that of Bloom etal. (2012b). Specifically, we construct firm-level data to establish connections between CHQs in
Japan and their overseas offices in low-income economies. Our objective is to explore therelationship between changes in ESG practices in CHQs and the performance of their foreign
affiliates. This approach enables us to analyze how the adoption of ESG practices at the centrallevel influences the operational outcomes of affiliated offices in economically challenged regions.
The panel data at the firm level in our study consists of three primary components: (i)ESG scores for a listed Japanese company; (ii) financial data encompassing profits, costs, andemployment data for a CHQ; 6 and (iii) corresponding data for its overseas subsidiaries located in
low-income economies over the fiscal years 2016–2020. The ESG scores are sourced from LSEG
(Refinitiv), while financial data for (ii) and (iii) are derived from uniquely detailed confidential
firm-level statistics provided by the Japanese government. To bolster the robustness of ourestimation, we leverage the panel data structure by incorporating various fixed effects. Thisapproach enables us to control for potential unobserved confounding factors, including global andcountry-level macroeconomic shocks as well as the country-by-industry and specificcharacteristics of each overseas affiliate. Furthermore, to disentangle the impact stemming fromthe managerial or organizational technology and financial profitability of CHQs, we introduce
their Total Factor Productivity (TFP) growth rates and Return on Equity (ROE) into ourestimations. This dual-pronged approach enhances our ability to discern the nuanced effects ofthese factors on the observed outcomes.
Our estimation reveals that an improvement in Japanese CHQ ESG scores is
uncorrelated with the growth rates of wages and jobs in Japanese foreign subsidiaries in lowincome economies. However, a positive correlation is observed with the growth rate of laborproductivity. For example, a one standard deviation increase in standardized ESG scores
correlates with a 9.1% increase in productivity growth. Decomposing the effect of the three pillarsof ESG—“Environmental,” “Social,” and “Governance”—indicates that only the “Social” pillar
has a positive correlation with labor productivity. Furthermore, simultaneous estimation of theeffects of the ten detailed components of ESG scores reveals that an improvement in the“Community” score within the “Social” pillar is linked to a 13.7% increase in labor productivity
growth. The “Community” score measures firms’ positions on fair competition, absence of bribery
and corruption, and contributions to the local community.
Therefore, in low-income countries, where corruption is generally prevalent, Japanese
6 We assume that a CHQ is a major Japanese parent firm for its foreign offices.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

subsidiaries with strong anti-bribery and anti-corruption stances can allocate their resources more
effectively to production rather than engaging in rent-seeking activities compared to otherJapanese subsidiaries with weaker stances on “Community.” These findings remain robust evenwhen adopting a dynamic Generalized Method of Moments (Arellano-Bond estimator),
controlling for macroeconomic shocks across different developing countries, considering thedirect influence of productivity and profitability of parent companies in Japan (as a proxy forknow-how), and incorporating the fixed effects of local subsidiaries.
Our study is situated within three key strands of the literature. The first pertains to the
diffusion of social capital through the cross-border arms of MNCs from high- to low-income
countries. Numerous studies have examined the impacts of management know-how on laborproductivity through international trade and foreign direct investment (FDI) (Bloom and Reenen,
2007; Burstein and Monge-Naranjo, 2009; Bloom et al., 2012a; Alviarez et al., 2023). However,the literature specifically focusing on the cross-border diffusion of social capital rather thanmanagement know-how through MNCs remains relatively sparse, with a few exceptions, such as
the work of Bloom et al. (2012b). Their findings indicate that social capital, as proxied by social
trust, promotes decentralized decision-making within firms. This decentralization, in turn, may
enhance productivity by supporting larger firm.
Specifically, our research reveals that firms’ commitment to the principle of“Community” positively influences the labor productivity of their foreign offices in emerging anddeveloping countries. While these principles share similarities with the concept of social trust that
MNCs often engage with in high-income countries, our study is the first to underscore the distinct
significance of this principle within globally promoted ESG practices mandated by governments.
The second strand of the literature concerns the concept of “dirty work export” throughFDI. Existing literature presents two opposing hypotheses. One is the “pollution haven”
hypothesis, 7 which advocates that nations with lax environmental regulations are more attractive
to FDIs. However, Javorcik and Wei (2003) found no support for the “pollution haven” hypothesisin their study on Eastern Europe and the former Soviet Union. Conversely, Cust et al. (2023)
presented a case in which major MNCs with robust corporate governance practices and/or acommitment to high environmental standards drilled oil wells in developing countries, without
necessarily leading to lower forest losses after drilling. 8 The other hypothesis is the “pollution
halo,” which asserts that international firms can introduce globally accepted environmental
norms—along with ecologically sustainable technologies and managerial approaches—to the
7 Refer to Wagner and Timmins (2009), Olney (2013), and Cust et al. (2023) for the definition of “pollution haven”
hypothesis.
8 Even in the U.S., the most developed country in the world, a state with a strong commitment to clean energy can
reduce CO2 emissions. However, another physically distant state that supplies the former state with electricity
increases CO2 emissions by generating more coal combustion (Holland et al., 2016). This “pollution export”
phenomenon could result in overall increased air contamination for the entire country.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

countries in which they operate (Birdsall and Wheeler, 1993). We address the question of whether
MNCs with strong ESG stances in advanced countries transfer the associated costs to their foreignsubsidiaries in low-income economies.
The third strand of the literature relates to ESG scores and firm performance. Many priorstudies have reported that firms with higher ESG scores achieved through carbon emission
reduction, promotion of gender and racial diversity, and active engagement with local
communities have experienced enhanced firm value (Yu et al. 2018; Irawan and Okimoto 2021),
improved credit ratings (Devalle et al. 2017; Okimoto and Takaoka 2024), increased productivity(Albrizio et al. 2017), and stronger financial performance (Friede et al. 2015; Lee et al. 2016). 9
However, the potential influence of adopting ESG practices at CHQs on their affiliated offices in
low-income economies has received comparatively less attention. To the best of our knowledge,
this is the first study that addresses this issue.
The remainder of this paper is organized as follows. Section II presents our empiricalanalysis in which we explain the data and methodology. Section III provides the results androbustness checks. In Section IV, we discuss how the ESG practices of CHQs affect the laborproductivity of their overseas affiliates. Section V concludes the paper.
II. Data and Empirical Design
A. Data
We integrated three datasets to investigate the relationship between the ESG policies of CHQs in
Japan, the performance of their foreign affiliates in low-income countries, and the productivity
and profitability indices of CHQs as proxies for their management expertise.
The first dataset consisted of ESG scores provided by LSEG Data & Analytics, afinancial markets data and infrastructure division of the London Stock Exchange Group plc5
(LSEG). According to LSEG Data & Analytics (2022), ESG scores are grounded in data-driven
methodologies that consider the most significant industry metrics, while minimizing biases
related to company size and transparency. The determination of these scores involves assessing
industry-specific relative performance within the framework of established criteria and acomprehensive data model. 10 To compile ESG scores, specialists manually collect and audit
9 Some studies argue that ESG investing does not necessarily yield a significant return difference between firms withhigh and low ESG scores (Auer and Schuhmacher, 2016; Halbritter and Dorfleitner, 2015).
10 According to LSEG Data & Analytics (2022), the ESG scoring methodology has a number of key calculation
principles: 1) Unique ESG magnitude (materiality) weightings have been included – as the importance of ESG factors
differs across industries, each metric’s materiality is mapped for each industry on a scale of 1 to 10; 2) Transparency
stimulation – company disclosure is at the core of the methodology. With applied weighting, not reporting‘immaterial’ data points does not greatly affect a company’s score, whereas not reporting on ‘highly material’ data
points will negatively affect a company’s score; 3) ESG controversies overlay – Companies’ actions against
commitments are verified, to magnify the impact of significant controversies on the overall ESG scoring. The scoring
methodology aims to address the market cap bias from which large companies suffer by introducing severity weights,

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

information from publicly available sources, including company websites, annual reports, andcorporate social responsibility (CSR) reports. The credibility of these ESG scores in analyzing
firms’ ESG stances is noteworthy. These scores, along with their predecessors such as Thomson
Reuters Asset 4, have gained widespread usage in prior studies. 11
However, it is important to note that the ESG scores for listed Japanese firms primarily
evaluate the CHQs and their group firms in Japan. LSEG ESG specialists capture ESG data frompublicly available documents and/or reports published by listed firms’ CHQs. If the CHQ is an
MNC and therefore holds many overseas affiliates, the documents and/or reports of the CHQ
should contain data on all overseas affiliates. In this case, specialists consider data from alloverseas affiliates. However, most CHQs in Japanese listed firms have difficulty gathering ESG
data from all overseas affiliates. For example, according to the Ministry of Economy, Trade andIndustry (METI) (2023), 85% of the surveyed Japanese firms (a total of 200 firms) answered that
there is “room for improvement” in terms of coordination between the department that oversees
sustainability-related data and consolidated subsidiaries, including overseas affiliates. 12 Against
this backdrop, ESG data on listed Japanese companies only reflect CHQs and their affiliated firms
in Japan. For our analysis, we assumed that the ESG score of a listed Japanese company assesses
the ESG position of the CHQ in Japan.
LSEG ESG scores are organized around three pillars: “Environmental,” “Social,” and“Governance” scores. Each pillar is further subdivided: “Environmental” encompasses“Emission,” “Resource use,” and “Innovation” scores; “Social” includes “Workforce,” “Humanrights,” “Community,” and “Product responsibility” scores; and “Governance” comprises“Management,” “Shareholder,” and “CSR Strategy” scores. Considering that each pillar may
serve distinct functions for SDGs, we conducted a more comprehensive estimation by utilizing
the detailed scores of each pillar, as outlined in Equation (3) in Section B. Note that the numberof Japanese-listed companies with ESG scores from the fiscal years 2015-2019 were 400, 405,
413, 423, and 441, respectively.
The second dataset utilized was the Basic Survey of Overseas Business Activities

which ensure controversy scores are adjusted based on a company’s size; 4) Industry and country benchmarks at thedata point scoring level – to facilitate comparable analysis within peer groups; and 5) Percentile rank scoring
methodology – to eliminate hidden layers of calculations. This methodology enables the production of a score
between 0 and 100.
11 See Ioannou and Serafeim (2012), Beiting et al. (2014), Eccles et al. (2014), Zhou et al. (2020), Irawan andOkimoto (2021), Okimoto and Takaoka (2024), and Wang et al. (2023).
12 METI (2023) mentioned that the offices and consolidated subsidiaries that generate sustainability-related data havelittle interest in “sustainability,” and as such their awareness of the need for sustainability-related data varies widely.
Furthermore, despite the wide variety of sustainability-related data and the vast scope and volume of data, the datacollection system within the corporate group, which serves as the basis for collaboration, has not yet beensystematized. Also, some of the issues specific to overseas subsidiaries were identified, such as the need to redefine
the definition of sustainability-related data as a corporate group because the same definition may not be applied to thecollection of sustainability-related data from overseas subsidiaries due to differences in laws, regulations, andbusiness practices.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"(BSOBA). 13 This is an annual survey of foreign affiliates held by Japanese firms and conductedby the METI. This survey gathers fundamental information on Japanese firms’ overseas businessactivities, including the number of Japanese firms with overseas operations, their sales (exports
to Japan, sales to the country where the subsidiary is located, and exports to other regions such as
North America, Asia, and Europe), foreign countries, profits and losses, employment figures, andthe regions and countries in which they operate. To gauge the performance of foreign affiliates,
we computed the labor productivity of each overseas subsidiary by dividing its foreign currencybased value-added by the number of regular foreign employees. 14
Value-addedisdefinedas[𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 + 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 + 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅]/

𝐸𝐸𝐸𝐸𝐸𝐸ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟. 15 Since the original figures of the BSOBA are denominated in Japanese yen,

we converted them into local currency-denominated figures using the exchange rate. While laborproductivity is typically measured as value-added per man-hour, we calculated productivity on a
per-man basis owing to data limitations. Subsequently, we took the first difference in the natural
logarithm of labor productivity to approximate its growth rate. Note that this growth rate is on anominal rather than real basis; therefore, in the estimation, price changes in the product can beabsorbed by time-by-country dummies and industry-by-country dummies to some extent.
Additionally, for each foreign subsidiary, we calculated wages by dividing the total payroll by the
number of regular foreign employees. 16 The wage growth rate was computed in the same manneras the labor productivity growth rate. In terms of employment growth rate, we calculated the firstdifference in the logarithm of the number of regular foreign employees.
The third dataset was the Basic Survey of Japanese Business Structure and Activities(BSJBSA), an annual survey conducted by the METI. This survey is designed to collect annual
statistics from all Japanese firms meeting specific criteria, namely those with 50 or more regular
employees and a capital of 30 million yen or higher (equivalent to approximately 0.3 million USD
based on the average exchange rate in calendar year 2020, according to International Financial
Statistics, which was 106.77 yen per US dollar). The BSJBSA encompasses all industries except
the financial, insurance, and real estate sectors. 17 Approximately 30,000 firms respond to this
13 In accordance with the Statistics Law in Japan, the statistics employed in this study are independently produced andprocessed. This is achieved by obtaining questionnaire information from the National Statistics Center, specificallyfrom the Basic Survey of Overseas Business Activities and the Basic Survey of Corporate Activities. It is important tonote that these statistics are different from those produced and published by the METI.
14 The count of regular foreign employees is determined by subtracting the number of dispatchers from Japan fromthe total number of regular employees.
15 Operating profits are defined as Sales minus Cost of goods sold minus Selling and general administrative expenses.
Our definition of Value-added is that of the BSOBA, which mostly aligns with that of Morikawa (2010), the sum of
the operating profits (total sales minus operating cost), rent, wage, depreciation and paid tax. However, due to data
limitations in BSOBA, we could not include Depreciation in the calculation of Value-added.
16 We assumed that the wage payments for the Japanese expatriates were accounted for in the CHQ’s profit and loss
statement.
17 In the econometric analysis, industries highly correlated with oil prices, those closely linked to real estatedevelopment, and even media-related sectors that are challenging to measure in terms of productivity were excluded,
despite their limited numbers.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

survey annually. Utilizing the data from the BSJBSA, we calculated the growth rate of TFP for a
Japanese CHQ using the nonparametric cost-share-based index number method, as detailed by
Morikawa (2010, 2023). 18
We merged the three aforementioned datasets (LSEG ESG scores, BSOBA, and
BSJBSA) by establishing connections between a listed company with LSEG ESG scores and the
BSJBSA CHQ. Additionally, we established links between the CHQ of the BSJBSA and its
overseas subsidiaries of BSOBA, as illustrated in Figure 2. Low-income countries for our sample
are defined as World Bank-designated developing countries in which Japanese foreign affiliates
are located (see the countries listed in Table A1). It is worth noting that the majority of the foreignsubsidiaries are wholly-owned, with the parent company holding 100% of its shares.

LSEG ESG Score

BSOBA

BSJBSA

CHQ A

Subsidiary A1
Subsidiary A2

CHQ A

・
・
・

Overseas subsidiary data

・・・

ListedFirm A

Parent company data・・・

Subsidiary B1
Subsidiary B2

・・・

・
・
・

CHQ B

・
・
・

CHQ B

・
・
・

Subsidiary ANA

ListedFirm B・・・

Subsidiary BN B

ESG Score

Labor productivity, Wage, andEmployment of the subsidiary

Total factor productivity
and Return on Equity of the
CHQ

Figure 2 Matched dataset

Table 1 presents the summary statistics of the ESG scores, outcome variables for foreignsubsidiaries in low-income countries, real TFP growth rate, and change in the ROE of their parent
companies. Furthermore, Table 2 demonstrates the correlation matrix between each pillar score,
the real TFP growth rate, and the change in the ROE, indicating strong correlations between
overall ESG score with “Environmental,” “Social,” and “Governance,” as well as between eachof the three pillars with their respective detailed components. Notably, the detailed components
exhibited weak correlations with each other. This enabled us to estimate the effects of these18 Refer to A3 in the appendix for the details on the calculation of real TFP growth rate.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

detailed components simultaneously, addressing the challenge of multicollinearity, as
demonstrated in Equations (2) and (3) in the following section. Figure 3 illustrates the sectoral
distribution of Japanese foreign subsidiaries in low-income countries. Although the
Manufacturing and Wholesale sectors have notable shares, Japanese foreign subsidiaries arewidely distributed across various industries.
Table 1 Summary statistics and description of ESG scores
Panel A: Japanese overseas subsidiaries located in low-income countries
FY2017–FY2020
Growth rate of labor productivity
Growth rate of wage
Growth rate of employment

Mean
3.91
6.29
−0.47

SD
73.77
56.82
39.28

N
5,706
6,382
9,923

Panel B: Japanese central headquarters located in Japan
FY2016–FY2019
Description

Mean

SD

N

−0.01

0.23

12,872

�
ΔESG
�
ΔE

Environmental

0.02

0.24

12,872Emission strategy

0.02

0.36

12,872

�_Resource use
ΔE

Resource use strategy

0.02

0.27

12,872Innovation stance to tackle
environmental issues

0.02

0.31

12,872ΔS�

Social
Welfare policy toward its
employees

−0.01

0.32

12,872

−0.02

0.44

12,872

ΔS� _Human right

Policy to respect human right
Local community and fair-trade
policyStance to take a responsibility to its
products

−0.02

0.58

12,8720.00

0.46

12,8720.04

0.37

12,872Governance

−0.04

0.46

12,872

−0.04

0.51

12,872

−0.01

0.54

12,872

−0.02

0.31

12,872ΔTFPManagement strategy
Policy regarding whether there arepolicies that put more weight onshareholders’ opinions
Corporate social responsibility
strategy
Growth rate of real TFP(demeaned)

−1.33

24.45

10,583

ΔROE

Change in ROE (demeaned)

−0.17

22.94

10,921

�_Emmision
ΔE

�_Innovation
ΔE
ΔS� _Workforce

ΔS� _Community

ΔS� _Product responsibility

�ΔG

�_Management
ΔG�_Shareholder
ΔG

�_CSR strategy
ΔG

Notes. All ESG scores are standardized (Z-score normalized).

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

Table 2 Correlation matrix

�
ΔESG
�
ΔE

�
ΔESG

1

�
ΔE

ΔS�

�
ΔG

ΔTFP

ΔROE

0.50

1

0.63

0.03

1

0.62

0.09

−0.02

1

ΔTFP

0.05

0.12

−0.05

0.05

1

ΔROE

0.03

−0.00

−0.03

0.10

−0.09

ΔS�
�
ΔG

�
ΔE
�_Emmision
ΔE

�
ΔE

1

�_Emmisio ΔE
�_Resource ΔE
�_Innovatio ΔTFP
ΔE
n
use
n

ΔROE

0.61

1

0.49

0.13

1

0.76

0.20

0.07

1

ΔTFP

0.12

0.21

0.00

0.04

1

ΔROE

−0.00

0.02

0.01

−0.02

−0.09

�_Resource use
ΔE
�_Innovation
ΔE

ΔS�
ΔS�
ΔS� _Workforce

1

1

Δ S� _Workfo Δ S� _Human ΔS� _Commu ΔS� _Product ΔTFP
nity
responsibilit
rce
right
y

ΔROE

1
0.51

1

0.77

0.14

1

0.43

−0.04

0.09

1

0.20

0.05

−0.04

0.04

1

ΔTFP

−0.05

0.02

−0.01

−0.05

−0.05

1

ΔROE

−0.03

−0.05

−0.02

0.01

−0.01

−0.09

ΔS� _Human rightΔS� _Community
ΔS� _Productresponsibility

�ΔG
�_Management
ΔG

�
ΔG

1

�_Sharehold ΔG
�_Managem ΔG
�_CSR
ΔG
er
ent
strategy

ΔTFP

ΔROE

0.95

1

0.34

0.06

1

0.15

0.01

0.02

1

ΔTFP

0.05

0.03

0.05

0.03

1

ΔROE

0.10

0.12

−0.03

−0.00

−0.09

�_Shareholder
ΔG
�_CSR strategy
ΔG

11

1

1

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

�_Emmision
ΔE
�_Resource use
ΔE
�_Innovation
ΔE

�_Emmisio ΔE
�_Resource ΔE
�_Innovatio ΔS� _Workforc ΔS� _Humanrig ΔS� _Communi
ΔE
n
use
n
e
ht
ty
1

0.13

1

0.20

0.07

1

−0.02

0.01

0.08

1

−0.15

0.03

−0.01

0.14

1

−0.08

0.06

−0.06

−0.04

0.09

1

0.22

0.23

0.05

0.05

−0.04

0.04

0.15

0.16

−0.01

0.00

−0.03

−0.07

0.00

0.10

−0.03

−0.04

0.02

0.01

0.01

0.03

0.00

0.05

0.14

0.00

ΔTFP

0.21

0.00

0.04

0.02

−0.01

−0.05

ΔROE

0.02

0.01

−0.02

−0.05

−0.02

0.01

ΔS� _Workforce

ΔS� _Human rightΔS� _Community

ΔS� _Productresponsibility
�_Management
ΔG�_Shareholder
ΔG

�_CSR strategy
ΔG

�_Emmision
ΔE
�_Resource use
ΔE
�_Innovation
ΔE
ΔS� _Workforce
ΔS� _Human rightΔS� _Community
ΔS� _Productresponsibility
�_Management
ΔG�_Shareholder
ΔG

�_Managem ΔG
�_Sharehold ΔG
�_CSRstrate ΔTFP
ΔS� _Product ΔG
responsibilit ent
er
gy
y

ΔROE

1
0.01

1

�_CSR strategy
ΔG

0.14

0.06

1

−0.09

0.01

0.02

1

ΔTFP

−0.05

0.03

0.05

0.03

1

ΔROE

−0.01

0.12

−0.03

−0.00

−0.09

1

Notes. All ESG scores are standardized (Z-score normalized). ΔTFP and ΔROE are demeaned. The periods span from fiscal year 2016 to fiscal
year 2019. Bold numbers indicate that the correlation is greater than 0.3.

12

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

0

20

40

60

0

20

40

60

Manufacturing

WholesaleService

Transportation

OthersInformation and communication

RetailConstruction

Low-income countries

All countries

Figure 3 Sectoral distribution of Japanese overseas subsidiaries
Notes. “Low-income countries” indicates the sectoral distribution of Japanese overseas subsidiaries located in low-income countries.
“All countries” indicates the sectoral distribution of all Japanese overseas subsidiaries.

B. Empirical Model
Considering the above, we ran the following regression models for overseas affiliates, 𝑓𝑓 ,belonging to a Japanese CHQ, ℎ, operating in industry 𝑗𝑗 in country 𝑐𝑐.

� ℎ,𝑡𝑡−1 +𝜙𝜙𝑡𝑡 + 𝛿𝛿𝑐𝑐 × 𝜙𝜙𝑡𝑡 +𝛾𝛾𝑗𝑗 × 𝛿𝛿𝑐𝑐 + 𝜃𝜃𝑓𝑓 + Λ′ 𝑿𝑿ℎ,𝑡𝑡−1 + 𝜀𝜀𝑓𝑓ℎ,𝑡𝑡∆𝑦𝑦𝑓𝑓ℎ,𝑡𝑡 = 𝛽𝛽𝐸𝐸𝐸𝐸𝐸𝐸 ∆𝐸𝐸𝐸𝐸𝐸𝐸(1)

where ∆𝑦𝑦𝑓𝑓ℎ,𝑡𝑡 represents outcome variables such as the growth rate of labor productivity, wages,
� ℎ,𝑡𝑡−1 is the ESG score of the CHQ, ℎ,and local employment in the overseas subsidiary, 𝑓𝑓. 𝐸𝐸𝐸𝐸𝐸𝐸

which is standardized against the average of 𝐸𝐸𝐸𝐸𝐸𝐸ℎ,𝑡𝑡−1 for all Japanese CHQs for each year.19
� ℎ,𝑡𝑡−1 is the first difference in 𝐸𝐸𝐸𝐸𝐸𝐸
� ℎ,𝑡𝑡−1 . 𝜙𝜙𝑡𝑡 is a dummy variable for each year. 𝛿𝛿𝑐𝑐 is a
∆𝐸𝐸𝐸𝐸𝐸𝐸

dummy variable for each low-income country. Thus, 𝛿𝛿𝑐𝑐 × 𝜙𝜙𝑡𝑡 controls for macroeconomic

19 We standardized the ESG scores because they exhibited an upward trend even after taking the first difference.

However, it is important to note that the estimation results showed minimal changes in terms of the signs andsignificance of most coefficients when using the first difference in the original ESG scores instead of the standardized
ones.

13

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

shocks 20 specific to each developing country on the growth rate of the outcome variables. 𝛾𝛾𝑗𝑗 is adummy variable for each industry, and 𝛾𝛾𝑗𝑗 × 𝛿𝛿𝑐𝑐 controls for industry-by-country fixed effects. 𝜃𝜃𝑓𝑓

represents the fixed effect of an overseas subsidiary. 𝑿𝑿ℎ,𝑡𝑡−1 is a vector of controls related to a
Japanese CHQ, such as its productivity (real TFP) and profitability (ROE); 𝜀𝜀𝑓𝑓ℎ,𝑡𝑡 is a random
disturbance term.

� ℎ,𝑡𝑡−1 into three pillars to assess each effect, along with
Equation (2) breaks down ∆𝐸𝐸𝐸𝐸𝐸𝐸

the same control variables as in Equation (1).

∆𝑦𝑦𝑓𝑓ℎ,𝑡𝑡 = 𝛽𝛽𝐸𝐸 ∆𝐸𝐸�ℎ,𝑡𝑡−1 + 𝛽𝛽𝑆𝑆 ∆𝑆𝑆̃ℎ,𝑡𝑡−1 + 𝛽𝛽𝐺𝐺 ∆𝐺𝐺�ℎ,𝑡𝑡−1 +𝜙𝜙𝑡𝑡 + 𝛿𝛿𝑐𝑐 × 𝜙𝜙𝑡𝑡 +𝛾𝛾𝑗𝑗 × 𝛿𝛿𝑐𝑐 + 𝜃𝜃𝑓𝑓 + Λ′ 𝑿𝑿ℎ,𝑡𝑡−1 + 𝜀𝜀𝑓𝑓ℎ,𝑡𝑡 (2)Equation (3) decomposes the elements of each pillar as follows: “Environmental” issegmented into three components: “Emission reduction,” “Resource use,” and “Innovation”
scores. “Social” is further divided into four components: “Workforce,” “Human rights,”“Community,” and “Product responsibility” scores. “Governance” is subdivided into threecomponents: “Management,” “Shareholders,” and “CSR strategy” scores. Detailed descriptionsof each score can be found in Panel B of Table 1.
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑢𝑢𝑢𝑢𝑢𝑢
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼∆𝑦𝑦𝑓𝑓ℎ,𝑡𝑡 = 𝛽𝛽𝐸𝐸,1 ∆𝐸𝐸�ℎ,𝑡𝑡−1+ 𝛽𝛽𝐸𝐸,2 ∆𝐸𝐸�ℎ,𝑡𝑡−1+ 𝛽𝛽𝐸𝐸,3 ∆𝐸𝐸�ℎ,𝑡𝑡−1
𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊
𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑡𝑡𝑡𝑡
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟+𝛽𝛽𝑆𝑆,1 ∆𝑆𝑆̃+ 𝛽𝛽𝑆𝑆,2 ∆𝑆𝑆̃+ 𝛽𝛽𝑆𝑆,3 ∆𝑆𝑆̃+ 𝛽𝛽𝑆𝑆,4 ∆𝑆𝑆̃ℎ,𝑡𝑡−1ℎ,𝑡𝑡−1ℎ,𝑡𝑡−1
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀
𝐶𝐶𝐶𝐶𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜+ 𝛽𝛽𝐺𝐺,2 ∆𝐺𝐺�ℎ,𝑡𝑡−1+ 𝛽𝛽𝐺𝐺,3 ∆𝐺𝐺�ℎ,𝑡𝑡−1+𝛽𝛽𝐺𝐺,1 ∆𝐺𝐺�ℎ,𝑡𝑡−1+𝜙𝜙𝑡𝑡 + 𝛿𝛿𝑐𝑐 × 𝜙𝜙𝑡𝑡 +𝛾𝛾𝑗𝑗 × 𝛿𝛿𝑐𝑐 + 𝜃𝜃𝑓𝑓 + Λ′ 𝑿𝑿ℎ,𝑡𝑡−1 + 𝜀𝜀𝑓𝑓ℎ,𝑡𝑡ℎ,𝑡𝑡−1(3)

We estimated these three equations using firm-level data. To address potential
endogeneity and reverse causality issues, we lagged the ESG scores and control variables
related to a Japanese CHQ by one period (one-year lag), assuming that a change in the ESG
practices of CHQs influences their foreign affiliates’ performance in one year. Furthermore, we
controlled for other external factors by incorporating overseas office time-invariant
characteristics using the fixed effect, 𝜃𝜃𝑓𝑓 .
III. Estimation Results
A. Effects on Productivity
The results presented in Table 3 include the outcomes of Equation (1) and the four specifications
derived from Equation (2) delineated in Columns (1) and (2)–(5). Each column shows thecoefficients estimated by the subsidiary-level fixed effects model with robust standard errors.

20 These shocks include omitted unobserved country-level shocks such as monetary and fiscal policies as well as
regulatory changes.

14

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

Table 3 Effects of Japanese CHQs’ ESG score on labor productivity of their foreign subsidiaries
Dependent variable: The growth rate of labor productivity in Japanese overseas subsidiaries(1)
9.051*(5.021)

� -1
ΔESG

(2)

(3)

(4)

(5)

3.511
(4.479)8.305**(4.098)0.134(2.799)0.067(0.060)0.003(0.056)
4,896
0.032
✓
✓
✓
✓

3.749
(4.415)

�-1
ΔE

8.361**(4.084)ΔS� -1

�-1
ΔG

ΔTFP-1 of Japanese CHQs
ΔROE-1 of Japanese CHQs
Observation
Adjusted R2
Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year

0.057
(0.060)−0.006(0.055)
4,896
0.031

0.055
(0.060)−0.003(0.056)
4,896
0.031

0.071
(0.060)0.003(0.057)
4,896
0.032

0.511
(2.763)0.059(0.060)−0.004(0.056)
4,896
0.031

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

Notes. Estimation results using the fixed effects model. Standard errors robust to clustering by foreign subsidiaries are reported inparentheses. The estimation period spans from fiscal years 2017 to 2020. Foreign subsidiaries located in low-income countries are
the focus of the analysis. The intercept term is omitted. * p<0.10, ** p<0.05, *** p<0.01.

Column (1) indicates that ∆𝐸𝐸𝐸𝐸𝐸𝐸−1 , the one-year lag of the first difference in thestandardized ESG score, has a significant positive impact on the productivity growth rate within
Japanese overseas affiliates in low-income countries, albeit with slightly less significance. Thiscoefficient of interest suggests that a one-point increase (equivalent to a one standard deviation
increase) in the ESG score of a Japanese CHQ corresponds to a 9.1 percentage increase in thegrowth rate of its foreign subsidiary’s labor productivity, holding all other factors constant. This
substantial impact becomes more apparent when juxtaposed with the average growth rate of laborproductivity, which stands at 3.91 during the sample period spanning the fiscal years 2017–2020.
� −1 into
Columns (2)–(5) present the outcomes of Equation (2), which breaks down ∆𝐸𝐸𝐸𝐸𝐸𝐸
three pillars: ∆𝐸𝐸�−1 ,∆𝑆𝑆̃−1, and ∆𝐺𝐺�−1 . 21 These pillars denote the one-year lag of the first differencein the standardized “Environmental,” “Social,” and “Governance” scores, respectively. Uponcomparing Columns (2)–(5), it becomes evident that only ∆𝑆𝑆̃−1 exhibits a statistically significant

21 It is worth noting that, given the correlations across each pillar are less than 0.3, as indicated in Table 2, themulticollinearity issues in Column (5) of Table 3 and Column (1)–(4) of Table 4 seem inconsequential.

15

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"� −1 primarily
positive effect. This suggests that most of the significant positive coefficient of ∆𝐸𝐸𝐸𝐸𝐸𝐸
stems from the impact of ∆𝑆𝑆̃−1. Furthermore, the impact of ∆𝑆𝑆̃−1 appears to be comparable to that� −1.
of ∆𝐸𝐸𝐸𝐸𝐸𝐸

In Columns (1)–(4) of Table 4, we deconstructed the three pillars into their ten
constituent elements.21 Consistent with the findings in Column (4) of Table 3, ∆𝑆𝑆̃_𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶−1,

one of three elements of ∆𝑆𝑆̃−1 , displays a significant positive coefficient. Conversely, none of theelements of ∆𝐸𝐸�−1 and ∆𝐺𝐺�−1 exhibit significant positive coefficients. Furthermore, the coefficientof interest indicates that a one-point improvement (equivalent to one standard deviation) of aJapanese CHQ’s “Community” score corresponds to a noteworthy 13.7 percentage point increasein the growth rate of its foreign subsidiaries, all else being equal. This impact is substantial
compared with the mean growth rate observed during the sample period. The “Community” scoreassesses a listed company’s commitment to being civically responsible, protecting public health,and respecting business ethics, especially in the sense of fair competition, anti-bribery, anticorruption, and community involvement. Therefore, Japanese overseas subsidiaries of the CHQ
that engage in community-related activities can enhance their productivity in low-income
countries, particularly those conducted in Japan.
In Column (4) of Table 4, the coefficient of ∆𝐺𝐺� _𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜−1 reveals a modestly

significant negative influence on the growth rate of labor productivity. In addition, the absolutevalue of the coefficient, 4.7, is not large compared with that of the coefficient of∆𝑆𝑆̃_𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶−1 . The “Shareholder” score evaluates a listed company’s effectiveness towards

equal treatment of shareholders and the use of anti-takeover devices. Thus, Japanese overseas
offices that prioritize shareholder interests may inadvertently hinder productivity in low-income
countries.

Furthermore, all coefficients of “Environmental”-related practices are insignificant.
These results indicate that promoting “Environmental” practices by Japanese CHQs does not
result in a decrease in labor productivity among their overseas affiliates. This finding contradicts
both the “pollution haven” and “pollution halo” hypotheses specifically in the context of ESG
promotion by MNCs. 22
It is worth noting that we obtained a statistically significant positive coefficient of∆𝑆𝑆̃_𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶−1 when estimating the same data by the Difference Generalized method of

moments (Arellano and Bond, 1991), as illustrated in Table A1. The positive impact of“Community” on labor productivity is trustworthy. However, the Arellano-Bond estimator

22 The coefficient of ΔE
�_Emission-1 becomes significantly negative when we estimated Equation (3) within thesample of manufacturing subsidiaries (the estimation results are available upon request). This finding indicates that if
a CHQ strengthens an emission reduction policy, the productivity growth rate of its foreign offices in the
manufacturing sector would decrease, which may support the “pollution halo” hypothesis. In contrast, the coefficientof ΔS� _Community-1 remains significantly positive even in the case of the manufacturing subsidiaries.

16

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

revealed that the coefficient of ∆𝐺𝐺� _𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜−1 becomes insignificant. Given the limited

availability of panel data, employing the Difference GMM estimation would substantiallydiminish the sample size and would not be able to perform a two-step estimation. Hence, we opted
for the fixed effects model as our preferred estimation method.
Table 4 Effects of Japanese CHQs’ ESG score on labor productivity of their foreignsubsidiaries: Detailed components of ESG score
Dependent variable: The growth rate of labor productivity in Japanese overseas subsidiaries
�_Emission-1
ΔE

�_Resource use-1
ΔE
�_Innovation-1
ΔE

(1)
4.184
(3.766)−1.033(4.700)1.978(3.594)

(2)

(3)

6.248
(3.937)−2.468(2.034)
12.545***(3.051)
2.597(3.069)ΔS� _Workforce-1

ΔS� _Human right-1
ΔS� _Community-1

ΔS� _Product responsibility-1

0.047(0.062)−0.002(0.056)
4,896
0.031

0.070
(0.060)0.006(0.056)
4,896
0.037

1.789
(2.286)−3.630(2.724)−1.670(4.372)0.057(0.060)−0.005(0.055)
4,896
0.031

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

�_Management-1
ΔG
�_Shareholder-1
ΔG

�_CSR Strategy-1
ΔG

ΔTFP-1 of Japanese parent firm
ΔROE-1 of Japanese parent firm
Observation
Adjusted R2
Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year

(4)
2.549
(4.095)−6.222(5.116)
4.280(3.697)
6.067(4.134)−2.012(2.117)
13.697***(3.097)4.249(3.321)
2.997(2.320)−4.736*(2.820)0.437(4.439)0.060(0.062)0.008(0.054)
4,896
0.038
✓
✓
✓
✓

Notes. Estimation results using the fixed effects model. Standard errors robust to clustering by foreign subsidiaries arereported in parentheses. The estimation period spans from fiscal years 2017 to 2020. Foreign subsidiaries located in lowincome countries are the focus of the analysis. The intercept term is omitted. * p<0.10, ** p<0.05, *** p<0.01.

17

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

B. Effects on Wages
Table 5 presents the estimation results when wage growth rate serves as the outcome variable in� −1, ∆𝐸𝐸�−1 , and ∆𝑆𝑆̃−1 exhibit statisticallyEquation (2). Columns (1)–(3) reveal that none of ∆𝐸𝐸𝐸𝐸𝐸𝐸

significant estimates. In contrast, ∆𝐺𝐺�−1 demonstrates a significantly negative coefficient. This

negative association maintains its significance even after controlling for all other detailed
components of ESG scores. This finding implies that a one-point increase (equivalent to a one
standard deviation) in the “Governance” score is associated with a decrease in the wage growthrate of local employees by approximately 5.4 percentage point, holding all other factors constant.

Table 5 Effects of Japanese CHQs’ ESG score on wages of their foreign subsidiaries
Dependent variable: The growth rate of wage in Japanese overseas subsidiaries
� -1
ΔESG

(1)
−2.296(3.466)(2)(3)

ΔROE-1 of Japanese parent firm
Observation
Adjusted R2
Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year

1.561
(3.776)
3.752(2.546)−5.407**(2.451)
0.033(0.043)−0.038(0.069)
5,408
0.009
✓
✓
✓
✓

3.497
(2.524)ΔS� -1ΔTFP-1 of Japanese parent firm

(5)

0.466
(3.611)

�-1
ΔE

�-1
ΔG

(4)

0.025
(0.042)−0.053(0.068)
5,408
0.007

0.024
(0.042)−0.054(0.068)
5,408
0.007

0.029
(0.042)−0.052(0.068)
5,408
0.007

−5.243**(2.379)0.030(0.043)−0.041(0.069)
5,408
0.009

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

Notes. Estimation results using the fixed effects model. Standard errors robust to clustering by foreign subsidiaries are reported inparentheses. The estimation period spans from fiscal years 2017 to 2020. Foreign subsidiaries located in low-income countries are thefocus of the analysis. The intercept term is omitted. * p<0.10, ** p<0.05, *** p<0.01.

Table 6 presents a comprehensive breakdown of the results presented in Table 5,
allowing for an exploration of the factors encompassed by ∆𝐺𝐺�−1 that contribute to a negative
impact on wage growth rate. In Column (3) of Table 6, it becomes apparent that the primary driverof this adverse effect is ∆𝐺𝐺� _𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜−1. In Column (4), the significance of the coefficient for∆𝐺𝐺� _𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜−1 persists when incorporating all detailed components of ESG scores in the18

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

estimation. This finding aligns with those in the preceding section, suggesting that an increase in∆𝐺𝐺� _𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜−1 tends to exacerbate the labor productivity growth rate. Specifically, a local

subsidiary held by a Japanese CHQ with a higher “Shareholder” score than other CHQs maintains
a lower wage growth rate. Consequently, subsidiaries face challenges in hiring or retaining highly
productive local employees, leading to a reduction in the labor productivity growth rate.
In both Columns (1) and (4), within the “Environment” scores, the coefficient of∆𝐸𝐸� _𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅_𝑢𝑢𝑢𝑢𝑢𝑢−1 exhibits a negative impact on wage growth, albeit with slightly lesssignificance. The “Resource use” score reflects a company’s performance and capacity to reduce
the use of materials, energy, or water, and to find more eco-efficient solutions by improving supply
chain management. Japanese CHQs may be compelled to make capital investments in an attempt
to curtail resource use, involving initiatives such as recycling products and resources anddeveloping high-value-added products, leading to escalating costs. Simultaneously, their overseas
subsidiaries may have a heightened incentive to reduce labor costs.
Notably, both coefficients of ∆𝐸𝐸� _𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑢𝑢𝑢𝑢𝑢𝑢−1 and ∆𝐺𝐺� _𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜−1 maintainnegative values that become insignificant when estimating the Arellano-Bond estimator, as shownin Table A2. However, since employing the difference GMM estimation would substantiallydiminish the sample size and does not allow us to perform a two-step estimation, we prefer the

fixed effects model results to the Arellano and Bond estimators.
Table 6 Effects of Japanese CHQs’ ESG score on wages of their foreign subsidiaries:
Detailed components of ESG score
Dependent variable: The growth rate of wage in Japanese overseas subsidiaries
�_Emission-1
ΔE

�_Resource use-1
ΔE
�_Innovation-1
ΔE

(1)
2.398
(2.986)−7.648*(4.214)1.649(2.500)

(2)

(3)

0.542
(2.757)0.221(1.622)2.511(2.144)1.234(2.684)

ΔS� _Workforce-1

ΔS� _Human right-1
ΔS� _Community-1

ΔS� _Product responsibility-1−3.128(1.937)

�_Management-1
ΔG

19

(4)
3.395
(3.176)−7.680*(4.560)2.245(2.504)0.988(2.876)0.902(1.555)3.370(2.157)
2.119(2.777)−2.500(2.050)ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

�_Shareholder-1
ΔG

�_CSR Strategy-1
ΔG

ΔTFP-1 of Japanese parent firm
ΔROE-1 of Japanese parent firm
Observation
Adjusted R2
Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year

0.017
(0.043)−0.049(0.068)
5,408
0.008

0.029
(0.042)−0.052(0.068)
5408
0.007

✓
✓
✓
✓

✓
✓
✓
✓

−5.283*** −5.606***(2.034)(2.099)−0.3870.391(2.863)(2.800)0.029
0.025(0.043)(0.043)−0.041−0.034(0.068)(0.068)
5,408
5,408
0.010
0.011
✓
✓
✓
✓

✓
✓
✓
✓

Notes. Estimation results using the fixed effects model. Standard errors robust to clustering by foreign subsidiaries arereported in parentheses. The estimation period spans from fiscal years 2017 to 2020. Foreign subsidiaries located in lowincome countries are the focus of the analysis. The intercept term is omitted. * p<0.10, ** p<0.05, *** p<0.01.

C. Effects on Employment
Table 7 shows the estimation results of Equations (1) and (2) when including the growth rate of
employment of foreign subsidiaries in developing countries as the outcome variable. Columns(1)–(5) show that neither ESG nor any of its pillars have a significant impact on the growth rate.
Table 7 Effects of Japanese CHQs’ ESG score on employment of their foreign subsidiaries
Dependent variable: The growth rate of employment in Japanese overseas subsidiaries
� -1
ΔESG

(1)
−0.609(1.782)(2)

ΔROE-1 of Japanese parent firm
Observation
Adjusted R2

(5)

1.296
(1.252)−0.026(0.020)0.001(0.029)
8,374
0.023

−2.490(2.100)−1.583(1.350)1.444(1.288)−0.026(0.021)0.001(0.029)
8,374
0.023

−1.553(1.340)ΔS� -1ΔTFP-1 of Japanese parent firm

(4)

−2.345(2.039)

�-1
ΔE

�-1
ΔG

(3)

−0.024(0.020)0.005(0.029)
8,374
0.022

20

−0.021(0.020)0.005(0.029)
8,374
0.023

−0.027(0.020)0.004(0.029)
8,374
0.022

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

Notes. Estimation results using the fixed effects model. Standard errors robust to clustering by foreign subsidiaries are reported inparentheses. The estimation period spans from fiscal years 2017 to 2020. Foreign subsidiaries located in low-income countries are thefocus of the analysis. The intercept term is omitted. * p<0.10, ** p<0.05, *** p<0.01.

Even after breaking down the ESG pillars into detailed components, as Columns (1)–(4) in Table 8 illustrate, none of the components have significant effects on the employment
growth rate. Though the “Product responsibility” score has a significant negative effect in Column(2), the significance vanishes in Column (4), which includes each detailed ESG component.
Table 8 Effects of Japanese CHQs’ ESG score on wages of their foreign subsidiaries: Detailed
components of ESG score
Dependent variable: The growth rate of employment in Japanese overseas subsidiaries
�_Emission-1
ΔE

�_Resource use-1
ΔE
�_Innovation-1
ΔE(1)−1.385(1.781)−0.219(2.338)−0.527(1.570)(2)−1.221(1.255)0.438(0.793)−1.918(1.169)−1.937*(1.056)

ΔS� _Workforce-1

ΔS� _Human right-1
ΔS� _Community-1

ΔS� _Product responsibility-1
�_Management-1
ΔG
�_Shareholder-1
ΔG

�_CSR Strategy-1
ΔG

ΔTFP-1 of Japanese parent firm
ΔROE-1 of Japanese parent firm

(3)

−0.019(0.021)0.550(0.029)21−0.029(0.020)0.002(0.029)1.458(1.012)−0.543(1.080)−0.021(1.595)−0.025(0.020)0.001(0.028)(4)−1.182(1.825)0.232(2.551)−0.462(1.563)−1.399(1.319)0.335(0.786)−1.796(1.209)−1.614(1.169)1.550(1.111)−0.431(1.093)−0.208(1.598)−0.026(0.021)−0.002(0.028)

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

Observation
Adjusted R2
Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year

8,374
0.022

8,374
0.023

8,374
0.023

8,374
0.023

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

Notes. Estimation results using the fixed effects model. Standard errors robust to clustering by foreign subsidiaries arereported in parentheses. The estimation period spans from fiscal years 2017 to 2020. Foreign subsidiaries located in lowincome countries are the focus of the analysis. The intercept term is omitted. * p<0.10, ** p<0.05, *** p<0.01.

IV. Discussion
Our estimation results indicate that if a Japanese CHQ improves its “Community” score, its
overseas offices in low-income countries are likely to attain higher labor productivity growth. A
one-point (equivalent to one standard deviation) improvement in the standardized “Community”
score corresponds to about a 13.7 percentage point increase in the annual labor productivity
growth rate. This effect is considered economically significant when compared with the mean andstandard deviation of labor productivity growth rate among foreign subsidiaries, which are 3.91
and 73.77 percentage points, respectively.
Why does the “Community” score have such a strong positive association withproductivity? LSEG, the provider of ESG scores, demonstrates that the “Community” score is anindicator illustrating a listed company’s commitment to being civically responsible, protecting
public health, and respecting business ethics, especially in the sense of fair competition, antibribery, anti-corruption, and community involvement. The “Community” score is calculated as asynthetic index incorporating True/False judgments of a firm’s policy regarding “Fair
Competition,” “Bribery and Corruption,” “Business Ethics,” “Community Involvement,”“Improvement Tools Business Ethics,” “Whistleblower Protection and Corporate ResponsibilityAwards,” and monetary donation amounts. Considering this concept and methodology of the“Community” score, we propose two channels through which “Community” positively influences
the productivity of foreign subsidiaries in low-income countries. The first is the influence of afirm’s social capital on productivity. Based on the “Community” score methodology, a Japanese
CHQ with a higher “Community score” is likely to possess a higher level of social capital. Bloom
et al. (2012b) investigated the relationship between the social capital of MNCs and the
productivity of their foreign subsidiaries, and found that MNCs with high social capital increase
decentralized decision-making in their foreign affiliates. This decentralization results in the
expansion of firm size, and the resulting scale effects contribute to improved productivity inforeign affiliates. Applying this line of reasoning to our study, if Japanese CHQs with higher social

22

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

capital effectively instill this business attitude toward their overseas affiliates, they are likely toengage in decentralized decision-making. This allows affiliates to better adapt to their local
markets, leading to larger firms and improved productivity. To examine this reasoning, we reestimated Equation (3) by incorporating relevant cross-terms, specifically, each ESG component
multiplied by the share of sales (excluding those to Japan) compared with total sales. In Column(1) of Table 9 we retrieved the coefficient of only ∆𝑆𝑆̃_𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶−1 for reference. However,Column (4) of Table 4 and Column (2) of Table 9 show coefficients of ∆𝑆𝑆̃_𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶−1 and

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆−1 +𝑇𝑇ℎ𝑖𝑖𝑖𝑖𝑖𝑖-𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡−1. A significant positive coefficient of the∆𝑆𝑆̃_𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶−1 ×
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆−1

cross-term suggests that foreign subsidiaries, which prioritize the global market over the Japanesemarket, can enhance their productivity when decentralized from Japanese CHQs with higher
social capital. This finding provides supporting evidence for a channel linking a firm’s social
capital to productivity.
Table 9 Productivity enhancing effect of Japanese CHQ's “Community” score depending on
scale opportunities of their foreign subsidiaries
Dependent variable: The growth rate of labor productivity in Japanese overseas subsidiaries(1)
13.697***(3.097)

ΔS� _Community-1

Local Sales−1 + Third−country Exports−1
ΔS� _Community-1 ×

Observation
Adjusted R2
Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year

Total Sales−1

4,896
0.038

(2)
0.997
(6.025)0.191**(0.079)
3,646
0.055

✓
✓
✓
✓

✓
✓
✓
✓

Notes. Estimation results using the fixed effects model. The coefficients of all components of ESG scores excluding
Community score, CHQs’ TFP and ROE, and the intercept term are omitted. Standard errors robust to clustering by foreignsubsidiaries are reported in parentheses. The estimation period spans from fiscal years 2017 to 2020. Foreign subsidiaries
located in low-income countries are the focus of the analysis. * p<0.10, ** p<0.05, *** p<0.01.

The second channel relates to firms preventing rent-seeking activities for productivity.
The fewer overseas affiliates engaged in rent-seeking activities, the more assets they allocate toproduction, leading to higher productivity. On one hand, foreign subsidiaries situated in countries
with less corrupt environments do not consider bribing authorities or trading partners; instead,they can focus on their production under a fair competition business culture. Conversely, overseas

23

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

affiliates in countries perceived to be highly corrupt might be tempted to engage in rent-seeking
for their businesses. As illustrated by the corruption perception index (CPI) in Figure 4, most lowincome countries have low CPIs, indicating a prevalent perception of corruption compared to
middle-to-high-income countries. These circumstances may incentivize firms operating in lowincome countries to engage in rent-seeking behavior for their business. However, such a business
attitude can be detrimental to their productivity (Mauro, 1995; Mironov and Zhuravskaya, 2016;
Gründler and Potrafke, 2019). Hence, a higher “Community” score of Japanese CHQs deters theirforeign affiliates in low-income countries from rent-seeking, resulting in an improvement in theirproductivity.

Figure 4 Corruption perception index
Source. Transparency International (https://www.transparency.org/en/cpi/2022).

In contrast, firms located in middle-to-high-income countries generally have lessreliance on rent-seeking activities. As a result, the effect of the “Community” score on the
productivity of the foreign affiliates in these countries tends to be weak. To examine thisconjecture, we re-estimated Equation (3) by adding cross-terms. Specifically, each ESG
component was multiplied by the CPI of the country where Japanese foreign affiliates are located,using the sample of all countries. Column (3) of Table 10 shows the estimated coefficients of∆𝑆𝑆̃_𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶−1 and ∆𝑆𝑆̃_𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶−1 × 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼−1 , and thecoefficient of the cross-term is significantly negative. Based on this estimation result, Figure 5
depicts the effects of Japanese CHQ’s “Community” score on their overseas subsidiaries,
conditional on CPI. The line with square markers in the graph indicates that in countries with a24

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

higher CPI, the productivity-enhancing effect of the “Community” score tends to decline. In thesame figure, the CPI of low-income countries (our primary focus) ranges from 10 to 60.
Consequently, the effect on the productivity of foreign affiliates in these low-income countries
becomes more pronounced than that of affiliates in middle-to-high-income countries. This findingaligns with the channels of firms’ rent-seeking activities in terms of productivity.

+30

Effect of Japanese CHQs' "Community" scoreon their overseas subsidiaries conditional on
Corruption perception index (Right axis)

90

+20

80

Frequency

70

+10

60
+0

50
40

-10

Low-income
countries (Left axis)

30

Middle-to-High Income
countries (Left axis)

20

-20

10
0

Effect of one-point increase in "Community" score on
productivity, %

100

-30
0
10 20 30←High corruption

40

50

60

70

80 90 100
Low corruption→

Corruption perception index
Figure 5 Corruptive environment and “Community” score’s productivity enhancing effectNotes. The square-marked line indicates the effect of Japanese CHQs “Community” score on their overseas subsidiaries, conditionalon the corruption perception index, using the estimation result of Column (3) of Table 10. The blue bar depicts the distribution of lowincome countries where Japanese foreign affiliates are located, while the white bar depicts the distribution of middle-to-high-income
countries where Japanese foreign affiliates are located.

Table 10 Productivity enhancing effect of Japanese CHQ's “Community” score depending on
corruption perception of countries their foreign subsidiaries locates
Dependent variable: The growth rate of labor productivity in Japanese overseas subsidiaries(1)
13.697***(3.097)

ΔS� _Community-1

Local Sales−1 + Third−country Exports−1
ΔS� _Community-1 ×
Total Sales
−1

25

(2)
−2.841(5.019)0.177***(0.062)(3)
23.399***(7.076)(4)
10.690(8.415)0.208***(0.064)

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

ΔS� _Community-1 × Corruption Perception Index-1
SampleObservation
Adjusted R2
Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year−0.250**(0.118)−0.293**(0.142)

LowincomecountriesAll
countriesAll
countriesAll
countries

4,896
0.038

7140
0.044

9,795
0.029

7,098
0.047

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

Notes. Column (1) is the estimation results for Japanese foreign subsidiaries in the low-income countries, while Column (2) to (4) are those for Japanese
foreign subsidiaries all over the world. The coefficients of all components of ESG scores other than Community score and TFP and ROE of Japanese
CHQs are not shown. Standard errors robust to clustering by foreign subsidiaries are reported in parentheses. The estimation period spans from fiscal
years 2017 to 2020. * p<0.10, ** p<0.05, *** p<0.01.

In terms of policy implications, our estimates underscore the substantial benefits ofcommunity-related stances at Japanese CHQs. Importantly, these initiatives do not merely transfer
costs to foreign affiliates but instead contribute positively to the productivity of these firms. Thisfinding provides supporting evidence that CHQs have a certain degree of direct control overpolicies related to fair competition, anti-corruption, and anti-bribery activities in their foreignsubsidiaries. Therefore, MNCs are encouraged to place greater emphasis on the “Community”
score within the ESG framework. This strategic focus not only aligns with the SDGs but also
promises increased returns for MNCs.
This study has some limitations that warrant consideration. First, it is based exclusively
on Japanese firms and their overseas affiliates. A comparative perspective reveals that MNCs fromother high-income countries, particularly U.S. MNCs, often adopt distinct approaches tomanaging human resources or employment relationships in their overseas subsidiaries (Ferner etal., 2013). Consequently, the effects of ESG practices on labor productivity in foreign offices may
differ for MNCs in other advanced countries apart from Japan. Future research that encompasses
a broader spectrum of advanced-country MNCs could provide insights into the comparative
impacts of “Community” practices on labor productivity.
Second, our focus was confined to firm-level analyses. To comprehensively gauge themacroeconomic impacts of MNCs’ ESG practices on low-income economies, it is imperative to
explore the potential spillover effects of MNCs’ overseas subsidiaries on local firms. Investigatingthese spillover effects would shed light on the broader economic implications of ESG practices,
especially in the context of fostering sustainable development in low-income economies. Thisavenue offers a promising direction for future research on the economic impacts of ESG initiatives.

26

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

V. Conclusion
This study investigated the impact of MNCs based in high-income countries on the performanceof their overseas subsidiaries in low-income countries, with a specific focus on ESG aspects. Thisinquiry is significant in the context of the global initiative to achieve SDGs by 2030. To address
this issue, we constructed unique matched data by combining the ESG scores of Japanese-listedcompanies with financial data from their CHQs in Japan and overseas subsidiaries in low-income
economies. The estimation results revealed that improvements in the ESG scores of CHQs do not
positively affect the growth rate of employment or the wages of overseas subsidiaries. However,there was a significant positive effect on labor productivity growth. Notably, enhancements in the“Community” score of the “Social” pillar demonstrated an economically meaningful positive
impact.
The “Community” score encompasses fair competition, anti-bribery and anti-corruption
measures, and engagement with the local community, which are closely associated with the levelof social capital of firms. Accordingly, we argued that CHQs with social capital tend to
decentralize decision-making in their foreign subsidiaries, allowing them to expand their firm size
(scale) when facing global markets. Consequently, subsidiaries could enhance their productivity.
Moreover, among low-income countries, where corruption and bribery are more prevalent than in
middle-to-high-income countries, foreign subsidiaries of CHQs that adhere to fair competition
and anti-bribery policies can utilize their assets for production, as opposed to engaging in rentseeking activities. This discovery suggests that actively promoting “Community”-related
initiatives by MNCs in high-income countries, as well as the preferential acceptance bygovernments in low-income countries of foreign direct investment from MNCs with such a stance,
contributes to the economic growth of low-income countries.
Funding: This study received no external funding.
Conflicts of Interest: The authors declare no conflicts of interest.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

APPENDIX
A. Variable Construction Procedures
A1. Constructing outcome variables using Basic Survey of Overseas Business Activities
Labor productivity.
Labor productivity of overseas local subsidiaries = Nominal value added / (Number of permanent
employees - Number of seconded employees from Japan)
Nominal value added = [(Sales − Cost of goods sold − Selling and general administrative expenses) +Total salaries + Rent] / Exchange rate
Exchange rate = Japanese Yen / Local currency
Growth rate of labor productivity = ∆ln(labor productivity) * 100
Employment.
Number of employees in overseas local subsidiaries = Number of permanent employees − Number of
seconded employees from Japan
Growth rate of the number of employees = ∆ln(Wages of overseas local subsidiaries) * 100
Wage.
Wages of overseas local subsidiaries = Total salaries / Exchange rate / (Number of permanent
employees − Number of seconded employees from Japan)
Growth rate of wage = ∆ln(number of employees) * 100
A2. Constructing independent variables using LSEG (Refinitiv) ESG Score
Standardized ESG Score.
� h,t−1 =ESG

ESG Scoreh,t−1 − 1/n ∑h ESG Scoreh,t−1�Var(ESG Scoreh,t−1 )A3. Constructing control variables using the Basic Survey of Japanese Business Structure andActivities
TFP. 23
Nominal value-added = Operating profits + depreciation + Wages + Welfare costs + Rent + Paid
taxes 24
Real value-added = Nominal value-added / Industry-Specific GDP deflator 25
Capital input = Book value of tangible assets
23 This method of calculating TFP is commonly employed in productivity studies and is referenced in Syverson’s (2011)
survey.
24 The definition of value-added closely aligns with that of Morikawa (2010, 2023). Note that paid tax refers to non-incomebased business taxes, such as fixed asset tax, automobile tax, and stamp duty that are incurred as part of business operations.
It excludes corporate tax, resident tax, and income-based business tax.
25 The industry-specific GDP deflator is sourced from the National GDP, compiled by the Cabinet Office in Japan.

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Labor input = [Number of full-time employees + Number of part-time employees (working hours
conversion for full-time employees 26)]*Industry-level working hours 27
Cost share of labor (α） = Total payrolls / Nominal value-addedΔln(Real TFP) = Δln(Real Value-added) − α*Δln(Labor input) − (1 − α)*Δln(Capital input)
ROE = Operation profits / Net assets

26 This conversion of the number of part-time employees into the equivalent number of full-time employees is available in
BSJBSA, considering the ratio of part-time to full-time employees’ working hours.
27 Since BSJBSA lacks information on working hour data for individual firms, industry-specific working hours are sourced
from Labor force statistics, compiled by the Ministry of Internal Affairs and Communications in Japan.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

Table A1 List of countries included in our analysis
Low-income countries
Algeria
AngolaBangladesh
Bolivia
Botswana
BrazilBulgaria
Burkina Faso
Cambodia
China
Colombia
Cote d'Ivoire
EcuadorEgypt
El Salvador
Ethiopia
Fiji
Ghana
GuatemalaIndia
Indonesia
Iran
Iraq
JamaicaKazakhstan
Kenya
Laos
Lebanon
Liberia
Libya
MalawiMalaysia
Mauritius
MexicoMongolia
MoroccoMozambique
MyanmarNamibia
Nigeria
Pakistan
Paraguay
Peru
Philippines
Romania
RussiaMiddle-to-high-income countries
Argentina
Australia
Austria
BahamasBahrainBelgium
BermudaBrunei
CanadaCanary islands
Cayman islands
Chile
Croatia
CyprusCzechia
Denmark
Estonia
FinlandFranceGermany
GreeceGuamHong Kong
Hungary
IrelandIsrael
Italy
Korea, South
KuwaitLatviaLithuania
Luxembourg
Macau
Micronesia
Netherlands
New Caledonia
New Zealand
NorwayOman
PanamaPolandPortugal
Puerto Rico
Qatar
Saudi Arabia
Singapore34

ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

Samoa
SerbiaSouth Africa
Sri Lanka
Tanzania
ThailandTunisia
TurkeyUgandaUkraineUzbekistan
Venezuela
Vietnam
ZambiaZimbabwe

Slovakia
Slovenia
Solomon Islands
Spain
SwedenSwitzerland
TaiwanTrinidad and Tobago
United Arab Emirates
United Kingdom
United States of America
Uruguay
Virgin islands

Notes. “Low-income countries” are the World Bank-designated developing countries where Japanese
overseas subsidiaries are located. “Middle-to-high-income countries” are countries other than the lowincome countries where Japanese overseas subsidiaries are located.

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ESRI Discussion Paper Series No,390"ESG as a Tool for Advancing SDGs from High-Income to Low-Income
Countries: Evidence from Matched Data of Japanese Central Headquarters and Overseas Subsidiaries"

Table A2 Arellano-Bond estimator for the effects of Japanese CHQs’ ESG score on outcomesof their foreign subsidiaries

Dependent variable:
One−year lag of the dependent
variable
�_Emission−1
ΔE

�_Resource use−1
ΔE
�_Innovation−1
ΔE
ΔS� _Workforce−1

ΔS� _Human right−1
ΔS� _Community−1

ΔS� _Product responsibility−1
�_Management−1
ΔG
�_Shareholder−1
ΔG

�_CSR Strategy−1
ΔG

ΔTFP of Japanese parent firm
ΔTFP−1 of Japanese parent firm
ΔROE of Japanese parent firm
ΔROE−1 of Japanese parent firm
Observation
Fixed effects
Overseas subsidiary
Country×Year
Country×Industry
Year

(1)

(2)

(3)

The growth rate oflabor productivity

The growth rate ofwage

The growth rate of
employment−0.260***(0.046)−0.662(5.407)−1.706(6.052)4.990(4.431)9.106*(4.979)−3.807(2.448)13.534***(3.928)−3.876(4.522)
5.121(3.194)1.059(4.438)−3.812(5.334)0.165(0.128)0.416***(0.158)0.051**(0.023)−1.712**(0.737)
1,973−0.208***
0.037
6.597
(4.117)−5.588(5.766)
3.176(2.898)1.292(3.361)1.379(1.864)2.985(2.495)0.357(3.701)−2.699(2.289)−4.167(3.419)3.455(3.373)0.102*(0.059)0.249*(0.132)0.042**(0.018)−1.363**(0.554)2,236−0.124*0.071−2.519(2.462)−0.292(3.086)−4.216*(2.505)−1.261(1.735)−1.337(1.094)−0.691(1.678)0.346(2.149)2.534(1.582)−1.876(1.083)−1.329(2.135)0.056(0.038)0.040(0.062)−0.008(0.007)0.215(0.187)
3,471

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

Notes. Estimation results using the difference Generalized Method of Moments (Arellano and Bond, 1991). All components of the ESG score
are treated as predetermined variables, while the real TFP growth rate and the ROE change of the CHQs are treated as endogenous variables.
Standard errors robust to clustering by foreign subsidiaries are reported in parentheses. Note that a two-step estimator is not available because
the matrix of the two-step estimator is not full rank. The estimation period spans from fiscal years 2017 to 2020, with a focus on foreignsubsidiaries located in low-income countries. The intercept term is omitted. * p<0.10, ** p<0.05, *** p<0.01.

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