Governance through Shame and Aspiration:
Index Creation and Corporate Behavior
Akash Chattopadhyay
University of Toronto

Matthew D. Shaffer
Harvard Business School

Charles C.Y. Wang∗
Harvard Business School

November 2017

Abstract
We study whether stock indexes can be a mechanism for transforming long-standing corporate
behavior. After decades of low corporate profitability in Japan, the JPX-Nikkei400 index was
introduced in 2014. Each year the index selected 400 large and liquid firms deemed bestperforming in terms of profitability; membership is considered highly prestigious. We documentthat index-inclusion incentives have led firms to increase return on equity proportionally by
41% on average, via higher margins, efficiency, or shareholder payouts, depending on where
they had slack. These incentives are driven by the prestige associated with the index, rather
than direct-pecuniary or capital-market benefits. Back-of-the-envelope estimates suggest thatthe index-inclusion incentives accounted for 16% of the average increase in aggregate annual
earnings and 20% of the growth in aggregate market capitalization over our sample period.
Stock indexes can affect behavior by functioning as a source of prestige.

Keywords: JPX-Nikkei 400 index; Corporate governance; Prestige incentives; Return on equity; Capital efficiency; Corporate norms
JEL: G18, G34, G38, G41, L51, M14, M52∗
First draft: July 2017. Chattopadhyay (akash.chattopadhyay@rotman.utoronto.ca) is an Assistant Professorof Accounting at the University of Toronto. Shaffer (mashaffer@hbs.edu) is a doctoral candidate at Harvard Business
School. Wang (charles.cy.wang@hbs.edu) is the Glenn and Mary Jane Creamer Associate Professor of Business
Administration at Harvard Business School. We thank Yasunori Nakagami and Chad Cecere for helpful insights on
Japanese corporate governance. We thank Alma Cohen, Ian Gow, Jody Grewal, Paul Healy, Pedro Matos, V.G.
Narayannan, Krishna Palepu, Eugene Soltes, and TJ Wong as well as seminar participants at Harvard Business
School, American University, University of Virginia, Georgetown University, George Washington University, andUniversity of Texas Austin for helpful comments and suggestions. We also thank Raaj Zutshi for excellent research
assistance, and Naoko Jinjo and Nobuo Sato for coordinating interviews in Japan. Comments are welcome.

Governance through Shame and Aspiration

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1

Introduction
There is increasing interest in, and debate about, using stock indexes to shape the standards ofcorporate behavior. A rising number of stock indexes focused on themes relating to particular aspects of corporate behavior, such as corporate governance or environmental impact, have appeared
over the last decade. Most notably, the S&P500 Dow Jones and FTSE Russell, two of the world’s
largest index providers, announced in July 2017 that they were no longer going to include certain
firms with multiple-voting share class structures from their main indexes. As a result, companies
like Snap Inc—whose common shareholders possess no voting rights—could never attain membership to the S&P500 or the S&P 1500 unless the company altered its policies for shareholders’ voting
rights.
The emergence of this apparent alternative mechanism for influencing corporate behavior could
be seen as a response to the (perceived) limits of the standard process of bargaining or contracting
in yielding certain “desirable” outcomes. Such shortcomings may arise, for example, from an
imbalance in the bargaining power between a firm’s CEO and its board or from the stickiness of afirm’s corporate culture.
Despite the significant moves made by the S&P500 Dow Jones and FTSE Russell, little is known
about whether, how effectively, or through what incentive channels stock indexes could influence
corporate behavior. To answer these questions, we study the impact of firms’ incentives to be
included in the JPX-Nikkei 400 (JPX400), a prestigious index designed to select and showcase the
400 top-performing Japanese firms in terms of profitability, capital efficiency, and (by reflection)
corporate governance.
We study the Japanese context because it is a salient example in which the central government(in collaboration with the Japan Exchange Group) instituted a stock index as a policy tool inorder to transform corporate behavior and to solve a fundamental economic problem—low capitalefficiency. Specifically, the JPX400 premiered in 2014 as a response to Japanese policy makers’
concerns that poor capital efficiency has been a key contributor to decades of economic stagnation

Governance through Shame and Aspiration2

in Japan. In this setting, standard contracts have had limited effectiveness due to a long-standing
culture that has prioritized other stakeholders (e.g., customers, suppliers, and employees) in lieu ofshareholders, exhibited strong norms of relatively low pay-to-performance sensitivities, and maintained relatively low levels of executive compensation (Buchanan, Chai, and Deakin, 2013).
This institutional context also allows us to study the potential non-pecuniary incentive effectsof stock indexes. Standard agency theory would not predict the JPX400 to have strong financial
incentive effects for two reasons: first, because Japanese managers’ pay-to-stock-performance sensitivities are generally very low; second, because the index per se offers no formal financial benefits tofirms or managers. Alternatively, managers could be motivated by the prestige or social status accompanying index membership status, or the indirect benefits associated with it. If so, competition
to get into the prestigious club might motivate managers.
If managers were motivated to attain JPX400 membership, we hypothesize that these incentives
would be strongest in firms ranked near the margin of index inclusion, who, all else equal, are most
likely to see their inclusion status change as a function of their effort and performance. Thus, tostudy the ex-ante index-inclusion effects of the JPX400, we exploit the variation in firms’ distancesfrom the threshold of index inclusion.
Our identification strategy is made possible by a unique feature of the JPX400: membershipeach year is determined by a quantitative score based on operating income, ROE, and market
capitalization. The transparency of this ranking algorithm allows managers (as well as researchers)
to assess firms’ relative likelihoods of index inclusion, even though the Japan Exchange Group does
not disclose the official scores. Indeed, based on publicly available information, we are able toproduce synthetic JPX400 rankings that predict actual JPX400 membership with a high degree of
accuracy and explain the variation in the likelihood of index-inclusion across firms.
Using these synthetic ranks we employ a difference-in-differences (DID) design to study theindex-inclusion effects, where “treated” firms are defined as those with synthetic ranks near the
inclusion threshold (ranks 301–500 in our main specification), and “control” firms are those similarGovernance through Shame and Aspiration

3

firms with a much lower probability of gaining inclusion in the index (firms ranked 501–800). Our
DID compares the difference in outcomes between these two groups in the post-period (selection
years 2014–2016) with their difference in the pre-period (2010–2012).1 A unique feature of thisdesign is that, unlike traditional DID designs, a firm’s treatment status varies over time: a firm’s
ranking, and thus its distance from the index-inclusion threshold, varies year by year. Thus ourresearch design in effect combines multiple “experiments” in the post-JPX400 period with multiple
placebo “experiments” in the pre-period to infer the effect of JPX400-inclusion incentives. Finally,
our tests focus on ROE as the primary outcome of interest because, out of the components of the
JPX400 composite score, this measure is the most heavily-weighted determinant, the most directly
controllable by managers, and directly related to policy makers’ goal of increasing capital efficiency.
We document three main empirical findings. First, the firms closest to the JPX400-inclusion
threshold achieve differential and economically significant increases in ROE. We estimate that thesefirms improved ROE by 2.4-percentage-points, a 41% increase relative to the pre-period mean fortreated firms. A battery of tests supports the hypothesis that this effect is driven by index-inclusion
incentives—that is, by the firms’ efforts to improve ROE in order to be included in (or to avoid
exclusion from) the index. In particular, we document that the ROE increases (a) do not appear
to be driven by differential trends, (b) are not due to the realized consequences of index inclusion,and (c) are declining in a firm’s distance from the threshold of inclusion.
Second, we show that it is likely the prestige associated with the index, rather than direct
monetary benefits or other benefits associated with the cost or availability of capital, that primarily
motivates treated firms to improve ROE. We show that managers of the most affected firms do notreceive significantly higher pay, and that executive officers and directors in general have very low pay
sensitivities to stock returns both before and after the introduction of JPX400. Thus, the observed
improvement in performance is unlikely driven by the compensation channel. Moreover, we compare
1

We exclude the year 2013, since firms lacked sufficient time to respond in the first year of the index’s implementation. Nevertheless, including this year does not change our results qualitatively. The index and its implementation
are described in detail in Section 3 and Figure 1.

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the ROE response of Nikkei225 firms—whose high public visibility makes for especially strong“shame” (i.e., loss of prestige) due to exclusion, but smaller marginal cost-of-capital benefits—to
the ROE response of non-Nikkei225 firms, who would derive larger cost-of-capital benefits from
inclusion.2 We show that, although non-Nikkei225 firms enjoyed much greater capital market
benefits, in terms of share turnover, from inclusion in the JPX400, Nikkei225 firms in fact exhibit
substantially larger responses, in terms of ROE, to index-inclusion incentives. These findings are
consistent with the hypothesis that it is the prestige associated with index membership, rather thancapital-market benefits, that motivates treated firms to improve their performance.
Third, we show that treated firms improve their ROE by means of improved margins, asset
efficiency, or shareholder payouts. We find evidence that firms improve margins by cutting discretionary expenses like research and development. However, we do not find evidence that treatedfirms improve ROE by accounting-based earnings management, cutting capital investments, or reducing employment or average employee pay. Our findings suggest that treated firms exploit thechannels where they have the most slack to improve their ROE: those with below-median profit
margins differentially improve their margins; those with below-median asset turnover improve efficiency; and companies with above-median cash-to-equity ratios increase shareholder payouts. We
also find that the treated firms’ ROE improvements drive higher market valuations.
Overall, the JPX400 has performed well as a mechanism for improving shareholder equity
efficiency at Japanese firms. In fact, our analyses suggest that, in the aggregate, instituting the
JPX400—setting up a prestigious club that managers wanted to belong to—made a significant
contribution to the overall economy. Our back-of-the-envelope estimates suggest that introductionof the JPX400 increased aggregate net income by JPY1.2 trillion per year, a figure that represents
a 8.9% increase relative to the pre-period average aggregate income of Japanese firms or 16% of thechange in average annual aggregate net income from the pre-period to the post-period. Moreover, we
2

The Nikkei225 is Japan’s leading stock market index and the oldest stock index in Asia. It consists of Japan’s
most highly-respected and well-established firms across 36 industries, as selected by Japan’s top financial publication,
Nihon Keizai Shimbun.

Governance through Shame and Aspiration

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estimate that the incremental total earnings attributable to the JPX400 represent a 6.9% increasein Japanese market capitalization, accounting for 20% of the overall market capitalization growthfrom June 2014 to June 2017.
Our work makes two significant contributions to the literature. First, we contribute to theliterature on the effect of indexes. Most prior work on indexes has examined how index constituents
behave and perform in response to inclusion in an index (e.g., Shleifer, 1986; Harris and Gurel, 1986;
Dhillon and Johnson, 1991; Lynch and Mendenhall, 1997; Chen, Noronha, and Singal, 2004; Doh,
Howton, Howton, and Siegel, 2009). Our study is novel in its focus, methodological approach, andfindings. We focus on the index-inclusion-incentive effects—how firms behave and perform in orderto achieve inclusion or to avoid exclusion—rather than the index-inclusion effects. To estimate theindex-inclusion-incentive effects, we develop a novel empirical strategy that could be adapted toestimate the effects of other indices with transparent inclusion criteria, using publicly observable
performance measures. Overall, our empirical findings provide new large-sample evidence showingthat the desire to acquire (or avoid losing) index membership can be powerful in transforming
certain firms’ behaviors.
Second, we contribute empirical evidence to the literature on incentives. Economics scholars
have long recognized that the pursuit of prestige or status is central to human motivation (e.g.,
Williamson, 1963; Hayek, 1967). Some have argued that the allocation of prestige is vital to aproperly functioning market economy (Zingales, 2015), while others have theorized that its reallocation has been central to large-scale changes in the norms of a society historically Goode(1978). Although an increasing number of theories and predictions about prestige- or status-based
incentive mechanisms have been developed (e.g., Ellingsen and Johannesson, 2007; Frey, 2007;
Moldovanu, Sela, and Shi, 2007; Auriol and Renault, 2008; Besley and Ghatak, 2008; Dur, 2009;
Dubey and Geanakoplos, 2010; Ederer and Patacconi, 2010), there are relatively few empiricalstudies that examine their incentive effects in economic contexts (e.g., Ball, Eckel, Grossman, andZame, 2001; Markham, Dow Scott, and McKee, 2002; Charness, Masclet, and Villeval, 2010; Kosfeld

Governance through Shame and Aspiration

6

and Neckermann, 2011).
Most relevant to us is the small and growing body of literature on the importance of prestige
and status to corporate executives and directors (e.g., Avery, Chevalier, and Schaefer, 1998; Masulis
and Mobbs, 2014; Raff and Siming, 2017; Focke, Maug, and Niessen-Ruenzi, 2017). Adding to thiswork, we show that these incentives could be actively employed, through a stock index, to influence
the behaviors of a large number of firms. Our work thus offers a fresh perspective to the governanceliterature, which has primarily focused on pecuniary incentives. These insights could be valuable
to policy makers seeking to leverage non-pecuniary channels of influence on corporate managers.
Such channels may be particularly useful in the context of the increasing pressures, in the UnitedStates and abroad, to limit executive compensation, and in developing market contexts where there
may be greater constraints to contracting.
The remainder of the paper proceeds as follows. Section 2 discusses the literature on prestige
and human behavior and provides background on the JPX400 index. Section 3 explains our research
design and identification strategy. Section 4 describes our empirical analysis in detail. Section 5
discusses the overall effect of the JPX400 and provides back-of-the-envelope estimates on aggregate
earnings and market-capitalization impacts. Section 6 concludes.

2

Background
This section discusses the institutional, economic, and corporate governance context in Japan.

It also provides a review of the literature on prestige incentives. Finally, it provides the backgroundon the JPX-Nikkei400 index.

2.1

Governance and Reform in JapanIn post-war Japan, there emerged a strong corporate culture that prioritized the interests of employees, suppliers, customers, and strategic stakeholders over those of shareholders (e.g., Yoshimori,
1995; Ito, 2014). Concurrently, strong norms of relatively low levels of management compensation

Governance through Shame and Aspiration

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and of low sensitivity of compensation to shareholder returns also developed. Over the years, shareholders developed strong formal legal rights in Japan—arguably even stronger than the statutory
rights enjoyed by shareholders in the U.S. For example, any 1% shareholder in Japan can place a
proposal on the company’s ballot for the annual general meeting; poison pills are highly restricted;
and directors can be dismissed and elected by simple majority shareholder vote at any time, making staggered boards meaningless (Hill, 2017). However, Japan’s strong corporate norms limited
shareholders’ de facto power. By the early 2000s, shareholders’ interests still had little weight
and legitimacy in managers’ eyes, and activists were rarely successful at convincing even fellow
shareholders to support initiatives to boost payouts or capital efficiency (Buchanan et al., 2013).
These norms persisted despite the government’s efforts to improve the formal corporate-governanceregime (e.g., Allen, Carletti, and Marquez, 2015; Kato, Li, and Skinner, 2017). Many policy makers
in Japan cited the country’s stakeholder-oriented culture as an explanation for its lagging corporatecapital efficiency—its low return on equity (ROE) and return on assets (ROA)—and decades of
economic stagnation.3
Many policy makers came to believe that improving Japanese corporations’ capital efficiency,
by increasing dialogue with and focus on investors, was vital to reviving the economy, an urgent
concern in light of Japan’s looming problems. As the Ito (2014) report notes:
Japan faces a rapidly aging and declining population and a decreasing stock of laborand household financial assets. Japan has no room to waste its limited resources andcapital. Japan must effectively leverage the resources. . . . In other words, increasing
capital efficiency in the broadest sense is crucial from the perspective of Japans survival.
Japanese companies—as a critical source of value creation—must strive to increase capital efficiency through their dialogue with investors, and contribute to the accumulationof a broad range of capital stock that will serve as the foundation for future economic
prosperity.
These goals and concerns were reflected in the Abe’s administration’s “third arrow” of structural3

For example, Chart 3 in the Ito report Ito (2014) cites an average ROE of 5.3% among Japanese firms in the
TOPIX500 in 2012, or roughly one-quarter of the average ROE among U.S. S&P500 firms and one-third of the average
ROE among the firms in the Bloomberg European 500.

Governance through Shame and Aspiration

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reforms, a large fraction of which focused on increasing Japanese corporations’ accountability toand focus on shareholders.4

2.2

The JPX-Nikkei 400 Index

In 2014, in response to these calls for reform, the Japan Exchange Group (JPX) and Nikkei
Inc. launched a prestigious new stock index, the JPX-Nikkei 400 (JPX400). The JPX selected andshowcased the top 400 firms in Japan, as ranked on a measure of profitability, capital efficiency,
and valuation (subject to meeting certain governance and liquidity criteria); see Section 3 for a fulland exact description of the algorithm. Each summer, the JPX re-formed the index membership,
dismissing firms that no longer made the cut and adding firms that had improved their performance.
The index per se offered no direct financial benefits to the constituent firms or their managers, but
membership in the index was considered highly prestigious; it was colloquially referred to as ”theshame index,” in reference to the experience of firms that were excluded. The index was widely seen
as a vital component of the “third arrow” governance reforms of the Abe administration (Economist,2014). The Economist called it the “by far the shiniest toy in the Abenomics box” (Economist,2017). The JPX400’s prestige was further bolstered by the Government Pension Investment Fund’s(GPIF) decision to use it as a benchmark for its passive investments in 2014.5 Anecdotal evidence,news reports, and interviews with top managers in Japan all suggested that many managers—atfirms that were initially included and at those initially excluded—were motivated to improve theirfirms’ performance. Many excluded firms aspired to gain entry; many included firms feared the4
Prime Minister Shinzo Abe’s economic policy, coined “Abenomics” by economists and the media, consists ofthree main components (“three arrows”): the first arrow of monetary easing, the second arrow of fiscal stimulus, andthe third arrow of structural reforms.
5
Two 2014 reforms were designed to encourage manager-shareholder dialogue: the Stewardship Code and the
Corporate Governance Code. The Stewardship Code encouraged institutional investors to pursue long-term returns
and to engage companies in constructive dialogue. The Corporate Governance Code, a “bill of rights” for shareholders,urged companies to respect shareholder rights, improve capital efficiency, engage investors in dialogue on a regularbasis, and appoint at least two external directors to their boards. Neither code is legally binding: institutional
investors and companies subject to the codes are not required to abide by all of their principles; instead they are
required to either comply or explain.

Governance through Shame and Aspiration

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shame of eventual exclusion.6
Two features of the JPX’s selection criteria are noteworthy. First, the algorithm is explicit,
transparent, and, with the exception of a small number of “qualitative adjustments,” based on
publicly available financial data. Thus we can reconstruct firms’ JPX400 ranks, including synthetic(or placebo) ranks for years prior to the existence of the index. As we will discuss in more detailbelow, the transparency and replicability of the selection algorithm is critical to our research design.
Second, the JPX publicly names and tracks the constituents of the index each year, but it does
not publish their underlying ranks. Consequently, the publicity surrounding the index churn eachAugust focuses on companies’ inclusion/exclusion status, but never addresses their relative ranks.
This scenario motivates our assumption that companies near the margin of inclusion/exclusion havestronger incentives than those whose ranks render them relatively “safe.”
The index’s creation, and its churn each August, have generated substantial excitement andattention in the media, but we have seen no attempts to systematically estimate its impact. The
Japan Exchange Group has been tracking and publishing the index’s returns, but for reasons wewill discuss in Section 5 the index returns do not appropriately capture its overall effects on themarket.

2.3

The Role of Prestige

The desire and competition for prestige are important motivators of human behavior. As early
as 1966, Nobel laureate John Harsanyi asserted that “[A]part from economic payoffs, social status(social rank) seems to be the most important incentive and motivating force of social behavior”;
Harsanyi included status among the goods being bargained over in formal game-theoretic modelsof organizations (Harsanyi, 1966). Economic theory models agents as having preferences among
6

After Amada, a well-established 68-year-old tool-maker and a member of the Nikkei225 index, was excluded fromthe inaugural JPX400 index in 2014, company president Mitsuo Okamoto announced that the company intended to
improve its capital efficiency and shareholder returns and to appoint independent directors in order to gain entry
into the prestigious new club. Similarly, some firms that were included in the index, such as Unicharm, announced
measures that aimed to cement their status among the elite by further improving ROE (McLannahan, 2014).

Governance through Shame and Aspiration

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bundles of diverse goods, and it seems reasonable to assume that social prestige is one of the goods
that humans—and perhaps managers in particular—desire. In a review of research on CEOs,
Bertrand (2009) states that individuals are motivated to seek to become CEOs by “the prestige,high social status, and high salaries.”
Several scholars in economic fields have argued for the importance of prestige and status inadvancing our understanding of corporate and financial behavior. Elster (1989), for example,
argues that norms that are “shared by other people and partly sustained by their approval anddisapproval” are stronger determinants of human behavior than the “instrumental rationality”
that has been the traditional focus of economic theory. More recently, Zingales (2015) framessocial prestige as an incentive that emerges via a Hayekian evolutionary mechanism to “fill thegap between (perceived) social and private returns of various activities. For example, fighting the
spread of Ebola is an activity with a very high social return, but a very low private return. People
who engage in such activity are held in high regard by society.” In other words, cultures assign highstatus to—and thus incentivize people to undertake—activities that generate benefits for societythat cannot be internalized, monetized, and/or fully contracted around. Prestige thus becomes a
motivator of behavior that is traded off with those goods.
Nonetheless, economic approaches to incentive contracting and corporate governance havetended to focus on the formal, monetary incentives of managers—perhaps because these incentives are easier to measure and model, and because it is difficult in many settings to distinguish the
influences of managers’ desires for wealth and for social status since the two often move together.
For example, managerial “empire building” is often attributed to the nonpecuniary desire to run alarge and prestigious firm (e.g., increasing its probability of being included in the Fortune 500); it isalso frequently attributed to pecuniary incentives, under the assumption that firm size drives CEO
pay. The empirical literature has found mixed results across different time periods and locales. For
example, Girma, Thompson, and Wright (2006) find that, in the United Kingdom between 1981 and
1996, acquisitions increase CEO pay despite reducing shareholder wealth. In contrast, Avery et al.

Governance through Shame and Aspiration

11(1998) finds that the compensation growth of acquiring CEOs does not differ significantly from thatof their matched non-acquiring peers, which the authors interpret as evidence that acquisitivenessis driven by the desire for prestige and status.

2.4

Prestige in Corporate Japan

Both anecdotal evidence and research by Japanese scholars suggest that social status and prestige are important in the Japanese corporate environment. Aoki, Jackson, and Miyajima (2007)
note that CEOs in Japan are overwhelmingly internally promoted, and argue that they are thus
perceived as, and act as, “top employees” of the firm rather than agents of shareholders. The
authors characterize the Japanese corporate environment as “relational,” i.e., focused on long-term
relationships and reputation. Today’s Japanese corporate culture emerged in a post-war periodcharacterized by government-led reconstruction and industrial policy; even early in the 21st century, coordination between Japanese corporations and government ministries remained close, andmanagers saw themselves as guardians of collective “corporate value” rather than shareholder value(Aoki et al., 2007). This corporate culture persists despite shareholders’ strong formal statutory
rights in Japan (Hill, 2017). In 2014 Japanese managers rated shareholders as their fourth mostimportant stakeholders, behind customers, employees, and suppliers.7 Even more strikingly, notjust managers but also shareholders in Japan may be focused on non-pecuniary desiderata. One
salient example of this comes from the experience of activist shareholders in Japan in the early
2000s. As Buchanan et al. (2013) document, activist shareholders were frequently able to get open
shareholder votes on proposals that cash-rich Japanese firms increase their payouts, and faced noformal structural barriers (such as from corporate charters, classified shareholders, or Japanesecourts), but their proposals were simply voted down by investors loyal to management.
We thus assume that social prestige and status are important determinants of managerial behavior in Japan, and we hypothesize that the JPX400 index can influence corporate behavior by
7“Change for the Better: Corporate Governance in Japan,” Schroders TalkingPoint, April 22, 2014, Figure 1,
accessed December 5, 2014.

Governance through Shame and Aspiration

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functioning as a source of prestige. Anecdotal evidence, news reports, and our interviews withJapanese managers and investors all suggest that JPX400 quickly became “the” high-status index,
and inclusion in the index is considered to be prestigious.
Bringing these various strands together, the JPX400 index can be viewed as an effort to influence Japanese corporate behavior by assigning social prestige to inclusion in an index based on
performance measures of interest to policy makers and shareholders. This initiative was consistent
with Japanese policy makers’ belief that improved competitiveness and capital efficiency would
help revive the Japanese economy. In the terms of the Zingales (2015) framework, the JPX400
represents policy makers’ attempt to employ social prestige to direct corporate behavior towardsocially beneficial ends.

3

Empirical Design
We study how the JPX400’s index-inclusion incentives affect firm performance. Firms closer tothe inclusion threshold are most likely to see their inclusion status change as a function of theirperformance. Thus we hypothesize that, all else equal, firms closer to the threshold of inclusion
will differentially improve ROE, the pertinent performance metric that is most readily controllable
by managers.8 We also hypothesize that such an incentive can operate through managers’ concerns
for prestige—either the threat of loss of prestige (shame) or the desire to gain prestige (aspiration).
Testing these hypotheses requires us to measure firms’ index-inclusion incentives ex-ante. Ourmaintained assumption is that, though the JPX does not publish its rankings of firms, managers
are aware (at least approximately) of their relative rankings and of their firms’ proximity to thethreshold of inclusion due to the transparency of the JPX400’s selection algorithm.
Thus our empirical strategy is anchored on a synthetic replication of the JPX400’s rankings ofeligible firms. We first validate these synthetic rankings, then use them as the basis of a difference8

The intuition is similar to the theoretical and empirical finding in Casas-Arce and Martı́nez-Jerez (2009) that, ina multiperiod tournament, agents who are either far ahead of or far behind a reward cutoff reduce their effort relative
to agents who are closer to the margin of winning vs. losing the prize.

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in-differences design to infer how index-inclusion incentives affect firms’ ROE. These steps aredescribed in the following subsections.

3.1

Synthetic JPX400 Ranks and Sample Construction

We obtain Worldscope data on annual fundamentals (including but not limited to all the underlying variables listed in Table A1), and Datastream data on monthly prices, volume, outstanding
shares, and returns, for a comprehensive list of Japanese securities in the period 1990–2017.9 We
omit observations that are missing returns, have an empty “data date” field for fundamentals data,or are duplicated on their Datastream identifier and relevant time indicator. We also merge in
an indicator for Nikkei225 membership, constructed using historical updates on constituent firms
archived on the Nikkei website.10
We then replicate the algorithm used by the Japan Exchange Group to construct the JPX400
index, and employ the resultant synthetic ranks to design our empirical analyses.11 As Figure 1
shows, the JPX selects a new crop of JPX400 members each year on the last day of June, using
available price and volume data and financial-statement data released prior to April.12 We use thesame information used by the JPX to construct our own synthetic rank.
The JPX400 selection algorithm begins by filtering TSE-listed companies on several criteria.
First, it excludes all companies that (1) have been listed on the TSE fewer than three consecutive
years, (2) have had negative book value in any of the past three years, (3) have had negative
operating income in all of the past three years, or (4) are in the process of being de-listed.13 From
9
Selection of firms for inclusion in the JPX400 was initiated in 2013; we collect data as far back as 1990 in orderto perform placebo tests described in detail below.
10
The set of current constituents and historical changes can be obtained at https://indexes.nikkei.co.jp/en/
nkave/index/component. We first construct an annual dataset consisting of all Nikkei225 firms in each year, thenmatch those firms to our baseline sample using their four-digit tickers (Datastream’s “Local Offering Code”). Finally,
we define a Nikkei225 indicator that equals to 1 if a firm belonged to the Nikkei225 on the date when the JPX400
index was announced. We achieve a complete match for Nikkei225 members in all years.
11
For the JPX400 selection algorithm and other important dates, see the JPX-Nikkei Index 400 Guidebook, available
at http://www.jpx.co.jp/english/markets/indices/jpx-nikkei400/.
12
As Figure 1 shows, the inaugural year was an exception to this rule. As we explain below, our empirical design
excludes the first year (2013) of index selection.
13
We designate as TSE-listed all companies listed on the First, Second, and Mothers Sections of the Tokyo StockGovernance through Shame and Aspiration

14

this pool of eligible firms, the JPX then selects the top 1,200 stocks by “trading value” (price times
volume, or the total value of transactions in the stock over the past year). Finally, the JPX thenfilters down these 1,200 stocks to the top 1,000 stocks by market capitalization.
These 1,000 firms, which we refer to collectively as “the ranked set,” are then ranked using thefollowing composite score (T otal Score):

T otal Scorei,t = .4 × ROE Ranki,t + .4 × OpΠ Ranki,t + .2 × M Cap Ranki,t ,(1)

where ROE Ranki,t , OpΠ Ranki,t , and M Cap Ranki,t are firm i’s ranks in the ranked set on 3-year
average ROE, 3-year total operating profit, and market capitalization respectively.
Each year’s index constituents are chosen on the basis of the highest T otal Score, with one
caveat: the JPX reserves the right to make up to 10 “qualitative adjustments” per year basedon corporate governance and disclosure-related factors. These qualitative adjustments are notdetermined by factors that we can observe, but, according to our interview with representativesof Japan Exchange Group (and the empirical evidence presented in the next subsection), they are
insignificant. For our purposes, we treat qualitative adjustments as random noise in the indexinclusion rule. We follow the JPX400 selection algorithm precisely, with the exception of thequalitative adjustments, and create synthetic JPX400 ranks for each year from 1994 through 2016.

3.2

Research Design

We utilize these synthetic rankings to test how index-inclusion incentives affect firm behavior.
Our main dependent variable of interest is ROE. Of the three components of the index-selection
score (equation 1), ROE is the only scaled variable; thus its ranking is the most controllable bymanagers. The other two components, market capitalization and operating income, are unscaled;thus their variation is largely driven by firm size. Managers may be able to increase firm size via
seasoned equity offerings or acquisitions, but these actions would be likely to generate a competing
Exchange and those listed on the JASDAQ.

Governance through Shame and Aspiration15

effect on a firm’s rankings for the JPX400 by increasing the equity base and, ceteris paribus,
decreasing ROE. Thus we expect to observe the incentive effects of the JPX400 most cleanly in
ROE.
Our main tests compare the ROE of the firms closest to the threshold of JPX400 inclusion—
those with synthetic ranks of 301–500—to that of firms outside the threshold of inclusion—thosewith synthetic ranks of 501–800. We make this comparison in 2014–2016 only, excluding 2013
because the JPX400’s inaugural constituents were announced at the end of that year, affording
firms only three months to respond to index-inclusion incentives. Next, to assess the index-inclusion
effect on ROE, we benchmark this first difference against the baseline difference between firms withsynthetic ranks of 301–500 and those with synthetic ranks of 501–800 prior to introduction of the
JPX400 (in the years 2010–2012). This second difference accounts for the possibility that thetreatment assignment, based on the largely deterministic JPX400 selection mechanism, could be
associated with baseline differences in ROE or other determinants of rank. Unlike in traditional
difference-in-differences (DID) designs, in our setting a firm’s treatment status varies over time: its
ranking, and thus its distance from the index-inclusion threshold, varies year by year. Therefore ourresearch design in effect combines multiple “experiments” in the post-JPX400 period with multiple
placebo “experiments” in the pre-period to infer the effect of inclusion incentives.
Together, our basic research design is summarized in the following DID specification:

ROEi,t+1 = α + β1 T reati,t × P ostt + β2 T reati,t + γXi,t + ft + i,t ,(2)where T reati,t is an indicator equaling 1 for firms ranked 301–500 and 0 for firms ranked 501–800
in a given selection year t; ROEi,t+1 is a firm’s return on equity in the following fiscal year; P ostt
is an indicator for the period after the introduction of the inaugural JPX400 constituents, equaling
1 for years 2014–2016 and 0 for years 2010–2012; Xi,t is a vector of contemporaneous firm controls;
and ft represents time fixed effects.
The main coefficient of interest, β1 , captures the mean ROE differences between the treatment

Governance through Shame and Aspiration16

and control firms in the post-JPX400 period, relative to the differences between placebo treatment
and control firms in the pre-JPX400 period. Our identifying assumption is that any baseline
differences in future ROE—that is, in the absence of JPX400-inclusion incentives—between firms
near the inclusion threshold (treated) and those further from the threshold (controls), if they exist,
are stable over time and thus accounted for by pre-period differences between placebo treatment
and control firms.
We believe that this assumption is most defensible when conditioned on contemporaneous ROE,
an important predictor of future ROE, because the distribution of contemporaneous ROE canchange after the introduction of the JPX400. Thus, our most robust specification—the specification
on which we rely most heavily throughout our empirical analysis—includes contemporaneous ROE(or the lagged dependent variable more generally) as a control. Another way to interpret thisspecification is that its DID estimate (β1 ) identifies the treatment effect by comparing the meandifferences in firm-level changes in ROE between the treatment and control firms in the postJPX400 period to the mean differences in firm-level changes between placebo treatment and control
firms in the pre-JPX400 period.
Finally, we note that the DID coefficients produced by this research design represent a conservative estimate of the JPX400-inclusion incentive effect for the treated group of firms. This is
the case because our research design uses as controls those firms that are less influenced by indexinclusion incentives (those ranked 501–800)—effectively assuming that they are unaffected by thoseincentives. To the extent that these firms do respond to some degree to the incentives of JPX400,
our DID estimates would be downward-biased.

3.3

Validation of Synthetic Ranks

Our research design relies on a synthetic replication of the JPX400 ranks, which is necessary
because the JPX publishes the index constituents but not the underlying rankings. Thus we
begin by empirically validating our synthetic rankings and testing whether their variations areGovernance through Shame and Aspiration

17

meaningfully associated with the probability of JPX400 inclusion (and thus with the true rankings).
We estimate the following OLS regression:

Actual Inclusioni,t = α + βP redicted Inclusioni,t + γXi,t + i,t ,(3)where Actual Inclusioni,t is an indicator for actual JPX400 membership, P redicted Inclusioni,tis an indicator that takes a value of 1 for all firm-year observations for which our synthetic JPX400
rank is less than or equal to 400 and zero otherwise, and Xi,t is a vector of contemporaneous firmlevel controls. To ensure that our analysis of prediction accuracy includes all false negatives, theregression sample includes all firms with synthetic ranks of 1–2000 (based on the top 2,000 firms
in terms of trading value) for the selection years 2013–2015.
Columns 1 and 2 in Table 1 report the results of this analysis. Firm-level controls are incrementally introduced in column 2. Column 1 shows that, unconditionally, a synthetic rank lower
than or equal to 400 is associated with a 92% probability of index inclusion. Column 2 adds firmlevel controls to assess the extent to which prediction errors, such as those due to “qualitative
adjustments,” are systematically correlated with firms’ fundamentals. We find that Log Market
Cap alone exhibits a significant relationship with the likelihood of JPX400 inclusion; no other firm
attribute—Book to Market, Sales Growth, LT Debt to Equity, or Cash to Equity—does so. Because
the JPX400 is weighted toward large firms, the positive association between firm size and inclusion
likelihood is likely due to the small number of false negatives being on average much larger in size
than the full sample of 2,000 firms.
Columns 3 and 4 in Table 1 report results of similar analyses, but instead of
P redicted Inclusion they use indicators of synthetic rank ranges—1–200, 201–400, 401–600, andover 800—as the main predictors of interest. These tests represent a more granular assessment of
whether the variation in the synthetic rank is meaningfully associated with the likelihood of JPX400
inclusion. Because the quintile indicators are exhaustive, these specifications are estimated without
a separate intercept term.

Governance through Shame and Aspiration

18

The results in columns 3 and 4 show that the probability of JPX400 inclusion decreases with
our synthetic ranks. By interpreting the coefficients in column 3, we find that, unconditionally,
firms we ranked 1–200 have a 99.1% likelihood of inclusion, and firms we ranked 201–400 havean 87.9% likelihood of inclusion. Lower-ranked firms have low likelihoods of inclusion: firms weranked 601–800 have a 0% likelihood of inclusion. Again, because our synthetic ranks produce asmall number of false negatives, we find a statistically significant but economically small likelihood(0.5%) of inclusion for firms we ranked above 800. The results reported in column 4, which include
firm fundamentals as additional controls, are virtually identical to those in column 3. As in column
2, the only firm characteristic significantly associated with the likelihood of inclusion is firm size,
because false-negative firms are on average larger.
Collectively, these results show that our reconstructed synthetic JPX400 rankings possess ahigh degree of validity. That these ranks cannot be improved on by additional controls other thansize suggests that our prediction errors are unlikely to cause systematic biases in our main results.

3.4

Summary Statistics

Table 2 reports pre-period summary statistics for the sample of firms used in our main analyses(firms with synthetic ranks of 301–800). The first five columns report the distributional statistics(quartiles, mean, and standard deviation) of covariates for the full sample; the rightmost threecolumns report the means for the treatment group (ranked 301–500) and the control group (ranked
501–800) and the t-statistics of their differences. Definitions of covariates appear in Table A1.
It is noteworthy that our treated and non-treated firms differ in their means on several variables
related to profitability and size, such as ROA, Asset Turnover, Log NOA, and Log Market Cap.
This pattern is to be expected, since the treatment firms are ranked higher than the control firms,
and the synthetic ranks are driven by size and profitability. These differences motivate our inclusionof linear controls for firm characteristics.
Note that, though we could have included firms ranked 1–300 and/or 801–1,000 in our control

Governance through Shame and Aspiration

19

group, untabulated tests revealed that doing so accentuates the degree of covariate imbalance. Thus
our design combines a pre-estimation matching of similar firms with linear controls to account for
local differences.

4

Empirical Analysis
This section presents empirical tests of our hypotheses about the effects of the JPX400 on

corporate behavior.

4.1

JPX400 Inclusion Threshold and Future ROE

To test the hypothesis that firms closer to the threshold of JPX400 inclusion are more likely
to improve their ROE, we estimate equation (2). Table 3 reports estimation results for various
specifications of our main DID regression, which compares the forward ROE of firms around thethreshold of inclusion (the treated group of firms ranked 301–500) to that of firms under thethreshold of inclusion (the control group of firms ranked 501–800).
Column 1 in Table 3 estimates a basic DID specification, without time fixed effects and without
any other controls. The DID estimate of the treatment effect—the coefficient on T reat × P ost—of
2.8 percentage points is statistically significant at the 1% level, and represents a 48% increase in
ROE relative to the pre-period treatment-group mean ROE of 5.85%.
Also noteworthy is the positive and significant (at the 1% level) coefficient on Post, an estimateof the secular trend in ROE. The point estimate of 0.018 implies that firms assigned to the control
group had a 1.8-percentage-point higher ROE in the post period, or a 35% increase relative tothe pre-period control-group mean of 5.12%. In the context of this economically significant Post
coefficient, the DID estimate can be interpreted in two ways. At one extreme, we can attribute allof the secular trend to the effects of the other contemporaneous governance reforms, such as the
Corporate Governance Code and the Stewardship Code. If we do so, our DID estimates suggestthat the incremental effect on ROE of being close to the index-inclusion threshold is at least as large

Governance through Shame and Aspiration20

as the effects of these reforms. On the other hand, to the extent that part of the Post coefficientreflects the effects of the JPX400-inclusion incentives on firms ranked 501–800, our DID coefficient
would be downward-biased.
Columns 2-4 present estimates from increasingly robust DID specifications relative to column 1.
Column 2 replaces the Post indicator with time-fixed effects; column 3 adds industry-fixed effects
and linear controls for contemporaneous firm attributes that can can also explain future ROE,
specifically Log Market Cap, Book to Market, Sales Growth, LT Debt to Equity, and Cash to Equity;and column 4 includes contemporaneous ROE as an additional firm-level control. Most notably,
the coefficient on Treat×Post remains similar in size and statistical significance; by contrast, theadjusted R2 of the regression increases from 2.19% in column 2 to 30.31% in column 4, mitigating
concerns about omitted variable biases (Oster, 2017). Interpreting the coefficient in column 4, themost robust specification, we report a DID coefficient of 2.4 basis points, or a 41% increase relative
to the pre-period treatment-group mean.
As Section 2 explains, our identifying assumption is that any baseline differences in future
ROE between the treated and control firms are stable over time. We conduct two tests of thisassumption. The first test assesses whether there is evidence that the differences in ROE between
the two groups are changing in the pre-period. Column 5 reports a specification that augments the
specification in column 4 with the following additional interaction variables: T reati,t × Y ear2011
and T reati,t × Y ear2012 , where T reati,t is defined as in equation 2, and Y ear2011 and Y ear2012
are indicators for the selection years 2011 and 2012. These interaction coefficients are insignificant
both economically and statistically (at the 10% level), meaning that relative to the baseline of 2010
the differences between treated and control firms do not differ in 2011 or 2012.
We also conduct a second, and more expansive, set of tests based on placebo test regressions
going back to 1994. Figure 3 graphs the results of five placebo DID estimates, in which we implement
our main regression specification (see column 4 in Table 3) for five sets of 7-year sample periods
prior to the introduction of the JPX400. In each year, we rank firms based on the JPX400 selection

Governance through Shame and Aspiration

21

algorithm and the composite score of equation (1), and create placebo treatment and control
indicators as in our main tests. Following the precise setup of our main empirical tests, we take7-year samples (using year 4 as the post-period indicator), drop year 4 from the analysis, and define
the last three years of the sample as the post period; then we estimate the DID specification ofcolumn 4 in Table 3. Under our identifying assumption, we expect to find placebo DIDs that are
statistically no different from 0. Indeed, Figure 3 shows that none of our placebo DID estimates are
statistically different from 0, and that most of the point estimates are close to 0, providing further
support for our identifying assumption.

4.2

Establishing Index-Inclusion Incentives

The results of Table 3 suggest that firms near the threshold of inclusion in the JPX400 significantly increased ROE. These findings could be consistent with managers improving ROE in
hopes of gaining or maintaining membership in the index, but they could also reflect the ex-postconsequences or benefits of membership in the index or of some omitted incentives that happen tocorrelate with the JPX400 ranks.
We run a series of tests to show that our findings are explained by ex-ante JPX400-inclusion
incentives. Table 4 reports the results of various tests, all based on the baseline specificationof equation 2, that employ alternative definitions of treatment and control-group firms, different
samples, or alternative measurements of treatment intensity.
We first test for the possibility that the ROE effects are due to consequences or ex-post benefitsof inclusion in the index. Column 1 in Table 4 reports the estimates from a specification that splits
our treatment indicator into two separate indicators: one for firms which achieve actual inclusionin the JPX400 (Treat x JPX-included ), and the other for firms which are excluded (Treat x JPXexcluded ). Note that this partition is exactly defined only for the post-period. For the pre-period,
where there’s no “actual inclusion” in the JPX400, we proxy for inclusion with the condition thata firm’s rank is between 1–400. In other words, in the pre-period, the treatment indicator isGovernance through Shame and Aspiration

22

partitioned into firms with ranks 301–400 and firms ranked 401–500. If ex-post benefits of inclusion
explains our results, we would expect to see a larger coefficient on those treated firms that wereactually selected for inclusion in the JPX400. We find, however, that the estimated DID coefficientsfor the two types of treatment firms are nearly identical to each other—0.023 for treated firms thatwere included in the JPX400 and 0.025 for treated firms that were excluded. These estimates arealso similar to our baseline DID estimates in Table 3. Both coefficients are statistically significant
at the 5% level; furthermore, they are not statistically different from each other, which is consistent
with ROE improvement being driven by firms’ ex-ante incentives to pursue inclusion in or avoid
exclusion from the index.
Column 2 in Table 4 provides the results of an alternative test in which we also include firms
ranked 1–300 as placebo treatments. If our ROE effects are driven by ex post index-membershipbenefits, we would expect to see a coefficient on Rank 1–300 ×Post that is similar in sign andmagnitude to the coefficient on T reat × P ost. However, we find that the coefficient on Rank 1–
300 ×Post is (1) statistically no different from zero at the 10% level and (2) statistically different,at the 1% level, from the coefficient on T reat × P ost, which remains at 0.023. Thus, firms ranked
1–300 exhibit no differential response in Forward ROE as compared to firms ranked 501–800; thispattern is consistent with firms facing stronger incentives to improve ROE when they are nearer tothe threshold of index inclusion.
The results in Table 4, column 2, also help to rule out the possibility that our results are driven
by unobserved factors that could be a function of firms’ JPX400 ranks. If that were the case, wemight expect the coefficient on Rank 1–300 ×Post to be large and significant. We thus conduct thefollowing cross-sectional placebo test. Because the JPX400 selection algorithm filters down to the
top 1,000 firms in Japan by market capitalization and liquidity, we use the next 1,000 firms, thoseranked 1,001 to 2,000 by market capitalization, rank them according to equation 1, and re-run
our main test. If some omitted factor associated with the algorithm’s rank-ordering were driving
our results, we would expect the effect to appear in this placebo sample of smaller firms. But

Governance through Shame and Aspiration23

the estimated coefficient on Placebo Group Treat×Post in column 3 indicates no such evidence,
consistent with the findings in column 2. We thus conclude that it is unlikely that some omitted
factor correlated with JPX400 ranks is confounding our results.
We perform a final test to show that the ROE effects we document are attributable to firms’
desires to preserve or attain JPX400 membership. Because firms nearer to the threshold of inclusion
are the most likely to see their inclusion status change as a function of their performance, wehypothesize that index-inclusion incentive effects on ROE are stronger for those firms closer to thethreshold of inclusion. To capture the variation in treatment intensity, we sort the 1,000 “ranked
set” firms into five quintiles based on the negative of the absolute value of their distance from therank-400 cutoff. The resultant variable, Quintile(Closeness), ranges from 0 to 4 and is increasing
in proximity to the JPX400 cutoff: higher values represent more intense index-inclusion incentives.
Column 4 reports the results of our estimates using the entire ranked set and this alternative treatment measure. We find a positive and significant coefficient (at the 5% level) on Quintile(Closeness)×Post. Together with the results in columns 1-4, these findings suggest that theobserved effects on ROE are likely driven by firms’ desire to be included in the index.

4.3

Incentive Channels: Prestige vs. Capital-Market Benefits

We now turn to why firms wanted to be part of the JPX400 index. We first test to see whether
managers attempted to gain membership in the JPX400 because they stand to directly gain monetarily. Table 5, column 1 tests this hypothesis by estimating our primary DID specification but
using Forward Log Avg Exec Pay (log of the mean executive officer and directors’ total compensation) as the dependent variable of interest. In this specification, we also include as contemporaneouscontrols: Log Avg Exec Pay and Log Avg EE Pay (log of the mean employee total compensation),
whose coefficients are reported, as well as Log Market Cap, Log Book to Market, Sales Growth, LT
Debt to Equity, and Cash to Equity, whose coefficients are suppressed for simplicity of presentation.
Table 5 shows that the executive officers and directors of the treated firms did not earn signifi-

Governance through Shame and Aspiration

24

cantly higher compensation on average. The point estimate is a 3.2% increase, which is economicallysmall particularly relative to the large on-average performance improvement among treated firms.
Moreover, this effect is statistically insignificant at the 10% level.
This finding is consistent with a low level of pay-performance sensitivity in Japan in that highpowered incentives are not the norm, which we verify. In columns 2 and 3 of Table 5 we estimatea standard pay-to-performance regression using Log Avg Exec Pay as the dependent variable andAnnual Return as the main explanatory variable. Our specification also tests for differences in thissensitivity between time periods and between treated and control group of firms. In column 2 wefind a very low average pay-to-stock-return-performance sensitivity in Japan. Among the control
firms in the pre-period, a 100% increase in the stock price is associated with merely a 4.4% increasein pay. In untabulated results, we estimate a similar specification using U.S. executive compensation
data, and find a pay-to-stock-return-performance sensitivity of 20%, which is at about 4.5 times aslarge as Japan. Moreover, we don’t find that this sensitivity changes after the introduction of the
JPX400, for either the treatment or control group of firms. Finally, in column 3 we find that theseresults remain unchanged after including industry-fixed effects.
Overall, our findings here suggest that managers’ desire to improve firm performance to gain
membership into the JPX400 is unlikely to be driven by direct monetary benefits. Our hypothesisis that firms coveted membership in the index for reasons of prestige—the aspiration to acquire
prestige or the desire to avoid shame at loss of prestige. In light of the perceived prestige ofmembership in the JPX400 and its colloquial nickname—the “shame” index—this is a plausible
premise. It is also possible, however, that firms coveted membership in the index for reasons
relating to capital-market benefits, such as greater liquidity or lower cost of capital (both realistic
expectations given that the GPIF promised to track the index and that other institutional investors
could be expected to follow suit).
To determine which of these two possible explanations drives the overall treatment effect on
ROE, we exploit cross-sectional variation in the treatment response between Nikkei225 and non-

Governance through Shame and Aspiration

25

Nikkei225 firms. The Nikkei225 is Japan’s leading stock index, closely tracked by institutional
investors; it consists of Japan’s largest, best-established, and most liquid firms across 36 industries. These firms are likely to derive less incremental capital-market benefits from inclusion in an
additional stock index than would non-Nikkei225 firms. On the other hand, Nikkei225 firms arelikely to be highly sensitive to prestige incentives, such as the shame of exclusion. Thus, under ourprestige-incentives hypothesis, we would expect to observe a larger ROE response from Nikkei225
firms than from non-Nikkei225 firms.
We first validate the premise of this exercise by testing how liquidity improvements differ between the non-Nikkei225 firms who make it into the JPX400 and the Nikkei225 firms who make itinto the JPX400. In column 1 of Table 6, we regress ∆Liquidity (measured as the change in shareturnover) on ∆JPX400 Membership x Nikkei225 (change in JPX400 membership interacted withan indicator for being a Nikkei225 member), ∆JPX400 Membership x non-Nikkei225 (change in
JPX400 membership interacted with an indicator for being a non-Nikkei225 member), a Nikkei225
indicator, and a set of firm controls as in column 3 of Table 2. This regression is estimated on asample of firms that were not part of the JPX400 in the current year, so that the two interaction
variables capture the average liquidity effects from JPX400 inclusion among those firms already in
the Nikkei225 and those firms not in the Nikkei225.
Column 1 of Table 6 shows that while non-Nikkei225 firms enjoy a 10.6% average increase inliquidity as a result of gaining JPX400 membership, an economically significant effect that is also
statistically significant at the 5% level, we do not find a statistically significant effect among those
Nikkei225 firms that gain entry into JPX400. F-test comparing the two coefficients, reported in
the last two rows of the table, suggests that these two effects are statistically different at the 5%
level.
Having established that the capital market benefits of gaining JPX400 membership, measured
by liquidity or share turnover, is significantly larger among those firms not already part of the
Nikkei225, we proceed to test the prestige-incentive hypothesis empirically in two ways. First, weGovernance through Shame and Aspiration

26

employ our main DID specifications but split our treatment group indicator into Nikkei225 andnon-Nikkei225 subgroups. Such a specification allows for treatment differences but assumes that
there are no Nikkei225 and non-Nikkei225 specific effects; that is, it does not allow the controls todiffer based on Nikkei225 status. This translates into estimating the following equation:

ROEi,t+1 = α + β1 T reati,t × N ikkei225i,t × P ostt
+β2 T reati,t × non-N ikkei225i,t × P ostt + β3 T reati,t × N ikkei225i,t+β4 T reati,t × non-N ikkei225i,t + γXi,t + ft + i,t .

(4)

In this estimation, we are interested in the sign and significance of the two triple interaction
coefficients as well as testing for their equality. Under the prestige-incentive hypothesis, beta1
should be larger in magnitude than and statistically different from beta2 .
To complement these tests, we compare the DID in ROE of the two subsets by employing a
difference-in-difference-in-differences (DDD) design. This test is more general because it allows for
Nikkei225 and non-Nikkei225 specific effects. More specifically, we estimate the following equation:

ROEi,t+1 = α + β1 T reati,t × N ikkei225i,t × P ostt + β2 N ikkei225i,t × P ostt
+β3 T reati,t × P ostt + β4 N ikkei225i,t + β5 T reati,t + γXi,t + ft + i,t .

(5)

In this estimation, we are interested in the sign and significance of the triple interaction coefficient.
Under the prestige-incentive hypothesis, β1 should be positive and statistically significant.
In addition to the above tests, we also estimate complementary versions that use Quintile(Closeness),the continuous treatment measure introduced in Table 4, in place of the binary treatment measure
Treat. All other variables used in these empirical specifications are as described in the discussionsof Tables 3 and 4.
In estimating these equations, we use the expanded data set spanning the the entire ranked setof 1,000 firms. For one, we do so because the Quintile(Closeness) treatment measure is definedGovernance through Shame and Aspiration

27

on and designed to exploit the variation in treatment intensity across the full set of ranked firms(1–1,000). Moreover, the expanded sample helps to alleviate concerns about the precision withwhich we can estimate the parameters of (and the statistical power with which we can test theestimated parameters of) these triple-interaction models, in particular given the relatively sparsepresence of Nikkei225 firms in the earlier control group, i.e., firms ranked 500–800.
Columns 1 and 2 of Table 6 report the results of estimating equation 4 using our binary treatment measure and the Quintile(Closeness) treatment measure, respectively. In column (1), theestimated coefficient of 0.066 on Treat×Nikkei225 ×Post is six times the estimated coefficient of
0.011 on Treat×non-Nikkei225 ×Post (a difference that is statistically significant at the 5% level),consistent with prestige incentives being the primary driver of the overall ROE effects. Similarly, incolumn 2, the estimated treatment response of Nikkei225 firms is larger in magnitude—over threetimes as large—and statistically different from that of non-Nikkei225 firms at the 5% level.
Columns 3 and 4 report coefficient estimates from equation 5. In column 3, using our binary
treatment measure, we report a coefficient of 0.050 on the triple interaction, Treat×Nikkei225 ×Post;
the DDD estimate is both economically and statistically significant. The point estimate indicatesthat the treatment response for the Nikkei225 firms is more than 5 percentage points higher
than that of the non-Nikkei225 firms; moreover, these results suggest that the DID estimate
for Nikkei225 firms is statistically different from the DID estimate for non-Nikkei225 firms at
the 5% level. Column 4 repeats this exercise using the Quintile(Closeness) treatment measure.
We find a positive and significant (at the 10% level) coefficient on the triple interaction, Quintile(Closeness)×Nikkei225 ×Post, further evidence of a stronger treatment response for Nikkei225
firms. Collectively, these results suggest that the dominant incentive channel driving the overall
ROE effect is prestige rather than capital-market benefits associated with index inclusion.

Governance through Shame and Aspiration

4.4

28

Drivers of the ROE Response

Next we study which levers managers pull to generate the improvement in ROE that we document. Table 7 reports the results of regressions examining the behavior of the major drivers of
ROE: return on assets, which is driven by profit margins and asset utilization, and financial leverage. In columns 1-4 of Panel A, we replicate our main baseline specification (column 4 of Table3) but use Forward ROA, Forward Profit Margin, Forward Asset Turnover, and Forward Leverage
respectively as dependent variables. In Panel B, in order to examine the cross-sectional variationin the types of channels that different firms may utilize to increase ROE, we run each specificationof Panel A for the subset of companies with above-median and below-median contemporaneous
values in the ROE driver of interest.
The results in Table 7 suggest that the improvement in ROE is largely driven by improving
operations (increasing ROA) rather than by changing capital structure. Panel A, column 1, reports
a statistically significant (at the 5% level) treatment effect on Forward ROA of 0.53 percentage
points, a 17% increase over the treatment group’s pre-period mean of 3.1%. Panel A, column 2,
suggests that this increase in ROA is primarily driven by improved Forward Profit Margin: thestatistically significant (at the 5% level) treatment effect estimate of 0.0074 constitutes a 15.3%
increase in margins relative to their pre-period average of 4.9%. Panel B, columns 1 and 2, documentthat the increase in net margins is driven by the subsample of treatment firms with below-medianmargins: the coefficient on T reat × P ost is significant only for the below-median profit-margin
sample, and the point estimate of the treatment effect is 0.0158, more than twice the treatment
effect for the full sample.
In contrast, our analysis of Forward Asset Turnover suggests that improvement in asset efficiency is not the dominant channel for improvement in ROA. The treatment effect for the fullsample, reported in Panel A, column 3, is not statistically significant at the 10% level. However,the subsample analysis in Panel B, columns 3 and 4, reveals a statistically significant increase inutilization among those firms with below-median asset turnover. Nevertheless, this effect, at aroundGovernance through Shame and Aspiration29

1.96% of the treatment group’s average pre-period turnover, is economically much smaller than thetreatment effect in profit margins.
Finally, we find no evidence that financial leverage was significantly impacted by firms’ JPX400inclusion incentives, either for the overall sample (Panel A, column 4) or for the subsamples ofabove-median and below-median-leverage firms (Panel B, columns 5 and 6). These findings are
not entirely surprising, since JPX400’s ranking algorithm does not necessarily incentivize firms toincrease leverage per se. Under Modigliani and Miller (1958) assumptions, a profitable firm making
a simple ceteris paribus increase in financial leverage—such as by issuing debt and repurchasing
shares—would increase the company’s ROE but might decrease its total market capitalization, acomponent of the selection algorithm.
On the other hand, it is possible that our measure of financial leverage (net debt over equity)
is too noisy a measure to capture JPX400’s potential effects on firm’s capital-structure decisions.
We thus complement the analysis of financial leverage by examining the effect of the JPX400inclusion incentives on payout policy, using shareholder payout ratio (i.e., dividends plus repurchases
divided by shareholder’s equity) as the dependent variable of interest. Treated firms could increase
shareholder payouts to boost their ROE, by reducing retained earnings and the book value ofshareholders equity, particularly if doing so leads shareholders to increase valuation multiples (thusdampening the potential tradeoff in market capitalization). In this way we can interpret shareholder
payouts as a financing choice that firms can employ to boost ROE, but where we are more likely
to find a statistically measurable effect than in net debt leverage. This variable is also of interest
given that, as Ito (2014) asserts, Japanese policy makers were interested in changing Japanese
corporations’ cash-hoarding cultures.
In column 1 of Table 8, we find a statistically significant effect (at the 5% level) on shareholder
payouts overall. Moreover, in columns 2 and 3 we show that that this effect is driven by the
subsample of firms with more excess cash—that is, those with above-median cash-to-equity ratios.
This overall effect size—0.59 percentage points—is economically large, representing approximately aGovernance through Shame and Aspiration

30

21% increase in the payout ratio relative to the pre-period treatment group mean. Relative to total
cash and investment on treatment firms’ balance sheets, however, the magnitude of the payouts
is quite small: our estimates suggest that shareholder payouts represent 1.5% of treatment-grouppre-period cash to equity ratio. This might explain why an effect driven through the shareholderpayouts channel is statistically difficult to detect by examining financial leverage. Another reason
is that, all else equal, the JPX400’s positive effects on earnings may have a countervailing andpositive effect on total cash, lowering financial leverage.
Jointly, the analyses in Tables 7 and 8 show that JPX400-inclusion incentives drove firms to
improve ROE through the channels where they had the most slack. We find that the overall effects
are driven by operating changes—that is, by improving ROA. Nonetheless, we also find a significant
increase in shareholder payouts.

4.5

Accruals, Investments, R&D, and Employee Outcomes

The results we have presented so far demonstrate that the JPX400 index drove a significant,robust improvement in ROE. An interesting question is whether the index-inclusion incentives may
also have produced potentially undesirable effects. For example, managers could have improved
accounting earnings by manipulating accruals or by cutting productive investments, contrary to
the JPX400’s broader goals of improving capital efficiency and social welfare in Japan.14
Table 9 explores these possibilities by estimating the incentive effects of JPX400 inclusion on
six different outcome variables: Forward Accruals to Assets, Forward Log NOA, Forward R&D toSales, Forward Log Employees, and Forward Log Average Employee Pay. Each regression in thistable employs the main specification from column 4 of Table 3: DID in the dependent variable ofinterest with firm-level controls and a control for the lagged dependent variable.
Column 1 reports the results of the specification with Forward Accruals to Assets as the de14
A long literature in accounting, economics, and finance (e.g., Stein, 1989; Healy, 1985) has documented thatincentivizing managers using relatively short-term earnings measures can produce unintended, and potentially valuereducing, consequences.

Governance through Shame and Aspiration31

pendent variable. We use income-statement accruals as a proxy for earnings management. The
coefficient on T reat × P ost is not statistically significantly different from zero, suggesting that theincrease in ROE is not driven by accruals-based earnings management.
In order to examine the index-inclusion effects on “real” investments in net operating assets,column 2 reports the results of the specification with Log NOA as the dependent variable. Onceagain, we find no statistically significant treatment effect on Log NOA, suggesting that the JPX400
did not drive a statistically measurable change in investments in net operating assets.
Column 3 examines how firms alter R&D intensity (R&D-expense-to-sales ratio) in response to
the JPX400-inclusion incentives. R&D is often seen as having positive externalities and long-term
benefits that are not captured by the accounting system, but—as a discretionary expense item—
managers might cut R&D in order to boost reported earnings (e.g., Roychowdhury, 2006). R&D
expenditures could thus potentially entail a tradeoff between ROE and the macroeconomic andsocial-welfare goals of Japanese policy makers. The point estimate on T reat × P ost in column 3 of-.0011 is significant at the 10% level and indicates a 7.05% reduction in R&D intensity relative tothe treatment group’s pre-period mean of 1.56%. This suggests that the improvements in operating
margins documented in Table 7 are driven at least in part by cutting discretionary expenses like
R&D.
Next, we examine how firm-level employment responds to the incentives generated by the
JPX400. Japanese firms are well known for having historically had an implicit system of lifetime employment. This system has been weakened in recent years, but Japanese firms are still
more reluctant than Western firms to downsize employment. Some policy makers view these employment norms as a barrier to dynamism and growth; others see them as providing social-welfare
benefits to Japanese workers. Either way, any employment effects attributable to JPX400-inclusion
incentives are policy relevant.
Column 4 reports the result of the main DID specification with Forward Log Employees as
the dependent variable. We find no evidence of statistically or economically significant changes inGovernance through Shame and Aspiration

32

firm-level employment, suggesting that our main ROE result is unlikely to be driven by employee
downsizing.
Finally, we examine the impact of the index on average employee pay (column 5). The coefficients on T reat × P ost is insignificant, suggesting our main effects on ROE are unlikely to be
driven by a cut in mean employee compensation in treated firms. Collectively, the results in Table
9 suggest that the main ROE effect we document is not driven by significant accounting-based
earnings management or significant cuts to capital investments in operating assets, employment,or employee compensation. The ROE effects are driven at least in part by cutting discretionary
expenses such as R&D; however, we are unable to ascertain whether these cuts constitute a formof real earnings management or represent an improvement in R&D discipline.

4.6

Market Valuation

Our final analysis focuses on market-valuation outcomes associated with JPX400-inclusion incentives. Table 10 reports the results of our main DID specification using Forward Book to Market
ratio as the outcome variable of interest. Column 1 reports the results of a reduced-form OLS DID
with firm-level controls and time fixed effects. The negative, statistically significant coefficient on
T reat × P ost in this column indicates that treatment firms experienced a relative improvementin their valuations multiples. The point estimate of -.035 represents a 3.4% decrease in book-tomarket relative to the pre-period treatment-group mean of 1.023. This finding suggests that the
improvement in ROE for treatment firms led to an upward revision in the market’s expectations
about their future cash flows and that this revision was not compensated for by a commensurateincrease in expectations of firm risk.
Columns 2 and 3 supplement this analysis by implementing a two-stage least-squares estimationof the effect of Forward ROE on Forward Book to Market, using the DID interaction as the instrument for Forward ROE. Column 2 reports the results of the first stage of the estimation, which issimilar to our main result in Table 3. Column 3 reports the results of the second stage, which inGovernance through Shame and Aspiration33

effect regresses Forward Book to Market on the predicted values of Forward ROE.15 The negative,
statistically significant coefficient on Forward ROE indicates that a 1-percentage-point increase in
ROE due to the JPX400-inclusion incentives is estimated to yield a decrease in Forward Log Book
to Market of -1.612. In other words, a 2.4-percentage-point increase in Forward ROE —our mostrobust DID estimates in Table 3—is expected to yield a decrease in Forward Book to Market of
0.0387, which is a 3.8% decrease relative to the pre-period treatment-firm mean and consistent
with the reduced-form estimates in column 1. Overall, our evidence suggests that firms enjoyed an
expansion in multiples as a result of their on-average increase in ROE due to the JPX400.

5

Discussion of the JPX400’s Overall Effect
The JPX400 has generated significant interest. Among regulators, policy makers, and investors

in Japan and globally, there is a lively discussion about the efficacy of this index. We conclude ouranalysis by assessing the overall impact of the index.
In our view, comparing the realized returns of the JPX400 to those of other Japanese indices,
as some policy makers and commentators do, does not reflect the success of the index, nor does
it appropriately capture the incentive effects of the index on corporate performance and value.
Such an approach has two conceptual flaws. First, as our analyses show, part of the effect of theindex resides in the performance effects on firms excluded from the index that are improving theirperformance to earn membership. Second, to the extent that valuation benefits accrue to such
firms prior to their inclusion in the JPX400—due to market participants either responding to or
anticipating firms’ performance improvements resulting from JPX400-inclusion incentives—theywould not be captured in the returns of the JPX400 index. Instead, the relative-return comparison
would attribute the beneficial effect of the JPX400 to the benchmark index. These two factors
could explain why the JPX400 has under performed the Nikkei225 over the three-year period from
15

These estimates are obtained in one step; thus the second-stage standard errors account for the first-stage
estimation noise.

Governance through Shame and Aspiration

34

June 2014 to June 2017, and why some stocks outperform after being excluded from the JPX400.
Our research design avoids both of these conceptual issues by leveraging differences in theintensity of JPX400-inclusion incentives and by focusing on a fundamental measure of performance(ROE). Using our revised approach, we find that the JPX400 index has been substantially more
successful than its portfolio returns would suggest.
To provide a conservative estimate of the JPX400’s overall impact on the aggregate Japanese
economy, we focus on the incremental income generated by the firms that we classify as “treated”
due to their index-inclusion incentives. In untabulated results, we estimate a simple DID in forward
net income using our baseline treatment and control groups, and find an on-average annual firm-level
improvement in net income of JPY6.0 billion. Aggregating across the 200 firms in the treatmentgroup yields a JPX400 effect on aggregate net income of JPY1.2 trillion per year. This represents
an 8.9% increase relative to pre-period average aggregate income across all public Japanese firms(JPY13.6 trillion per year). Moreover, relative to the change in average aggregate net income fromthe pre-period to the post-period (from JPY13.6 trillion to JPY21 trillion per year), the effects
attributable to the JPX400 account for 16%.16
To estimate the total wealth or valuation effects, we multiply the JPX400’s net-income effect bya range of plausible incremental price-to-earnings (P/E) multiples. A very conservative and lowerbound incremental P/E ratio on the new profits generated by the JPX400 would be 1: this would
be the appropriate incremental P/E on the new profits if the markets perceived the earnings boost
as completely unsustainable, and not likely to generate any internal alpha if reinvested. Under
this assumption, the wealth effect would be JPY1.2 trillion per year, for a total of JPY3.6 trillion,
or a 0.77% increase relative to the 2014 total Japanese market capitalization of JPY469 trillion.
A less conservative incremental P/E ratio would be the treatment firms’ cash-adjusted P/E ratio:(M Cap − Cash)/N etIncome.17 The average cash-adjusted P/E multiple for treated firms in the16Computations in this section uses the Datastream universe of public Japanese firms in computing aggregate
earnings and market capitalization.
17
This multiple is justified under the assumptions that (a) the market values Japanese corporations’ cash holdings
1-for-1, such that the cash-adjusted P/E ratio captures the market’s valuation of the companies’ earnings; and (b) theGovernance through Shame and Aspiration

35

post period is 17.07, implying a valuation impact of JPY61.45 trillion, or approximately 13.10%
of total market capitalization in June 2014. Taking the midpoint of these P/E multiple estimates,
our back-of-the-envelope estimation suggests that JPX400 improved the overall Japanese market’s
market capitalization by roughly 6.9%. Compared to the growth in market capitalization (measured
as of end-June) between 2014 and 2017 of JPY163 trillion, these estimates suggest that the JPX400
explains approximately 20% of the overall market capitalization growth since the introduction of
the index.
These estimates may also be conservative to the extent that firms further from the threshold
are still affected by JPX400-inclusion incentives, though to a lesser degree than firms nearer thethreshold of inclusion. We emphasize, however, that these back-of-the-envelope estimates are meant
to characterize the approximate magnitude of JPX400’s effect on Japanese corporate profitability
and market capitalization. At face value, these estimates would suggest that the JPX400 had ameaningful impact on aggregate corporate profitability and valuation.

6

Conclusion
Economists, sociologists, and psychologists have long recognized the role of prestige in human

motivation. As suggested in Goode (1978), throughout human history, prestige and status havebeen among the central mechanisms for motivating human action: from the Homeric Greeks andthe Vikings, who assigned honor and prestige to those with exception courage and skill in thebattlefield; to Britain’s Royal Society of Arts, that used honorary awards to encourage innovation(Khan, 2017); to developing economies that encouraged entrepreneurialism by assigning prestige toentrepreneurial activities (Steinhoff, 1980); to philanthropic organizations that report salient donor
categories to encourage more donations (Harbaugh, 1998a,b).
The analyses presented in this paper suggest that prestige-based mechanisms could also bemarket perceives the income effect as sustainable, so that expected growth of and discount rates on the incremental
profitability remain fixed.

Governance through Shame and Aspiration

36

powerful in the realm of corporate governance, which has primarily focused on the role of formal
or pecuniary incentives or regulation. At minimum, our findings can inform corporate-governancereform efforts in capital markets with similar patterns of low capital efficiency and weak de-facto
shareholder rights, such as those of other East Asian economies like China, Korea, Singapore, orTaiwan. We stress, however, that our evidence does not necessarily speak to the sustainability ofthe documented incentive effects.
We believe that our findings are also important for developed markets, by highlighting thepotential of prestige or status incentives in corporate governance. Given the growing calls forlimiting executive compensation in the United States and in Europe, understanding non-pecuniary
mechanisms for eliciting meaningful changes in corporate behavior is useful in the evolving corporate
governance arena.
Indeed, attempts to use stock indexes as governance mechanisms are percolating across theglobal markets. Japan continues its efforts to use stock indexes to transform corporate norms.
In March 2017, TSE and Nikkei announced a new index, the JPX-Nikkei Mid and Small Cap
Index, extending profitability-based incentives to mid- and small-cap TSE-traded firms. Similarly,
MSCI announced the Japan Empowering Women index, backed by the GPIF and designed to shift
Japanese norms around gender diversity in the workplace. Similar efforts also exist in the U.S.
and the U.K. In July 2017, S&P Dow Jones in the and FTSE Russell announced their decisions to
exclude certain companies with multiple-voting class structures from their indexes. Globally, there
has been a growing number of ESG-based indexes over the last decade. Our findings highlight
managers’ concern for prestige as a channel through which indexes can motivate and influencechanges in corporate behavior. We look forward to further research on the role of non-pecuniary
incentives and the effectiveness of stock indexes in transforming corporate behavior.

Governance through Shame and Aspiration

37

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Fig. 1.
JPX400 Index Constituent Selection Time Line
Figure 1 is a schematic representation of the timeline of index selection, relative to firm-level information,for each year of the JPX400’s existence. Panel A is a timeline of the initial construction of the index in 2013;
Panel B is a timeline of the annual index rebalancing from 2014 onward. Vertical lines indicate important
dates.

A: 2013
Data for Initial
Construction of the Indexz}|{
Fundamentals
Data Fixed

Mcap and Liquidity
Data Fixed

Official
Announcementof
JPX400M archJune

N ovember

January, 2014

Selection

Index
Rebalance

Index
Creation

B: 2014+
Data forIndex Rebalancingz}|{
Fundamentals
Data Fixed

Mcap and Liquidity
Data Fixed

M archJune

AugustGovernance through Shame and Aspiration

42

Fig. 2.
Sample Construction
Figure 2 is a schematic representation of the process we follow to construct our baseline analysis sample.
Steps 1 and 2 mimic the JPX400 selection algorithm. At the end of June each year, we select the 1,200
most liquid firms trading in Japan and then winnow down this number to the 1,000 largest firms in terms ofmarket capitalization. Thereafter we follow the JPX400 ranking algorithm and compute a synthetic JPX400
rank for each of the 1,000 firms. Step 3 describes how we assign treatment status based on this synthetic
JPX400 ranking. The top 400 firms are our predicted JPX400 constituents. For our baseline analysis, weclassify firms with ranks of 301–500 as the treatment group, and firms with ranks of 501–800 as the control
group. We do not include firms with ranks of 1–300 or 801–1,000 in our baseline analysis, but include themin robustness tests.
Step 1: Each June, select top 1,000 firms by market capitalization and liquidity.

Step 2: Predict JPX400 rank using fundamental data available as of March and .

Step 3: Construct baseline treatment and control sample.

Predicted JPX400 Constituents
z
}|
{
1
|

100

200
{z

Dropped

300
}|

400
{z

500
}|

T reat = 1

600

700
{z

T reat = 0

800}|

900 1,000{z}DroppedFig. 3. Placebo Tests by Time
Figure 3 displays the results of placebo versions of our main analysis. We use historical data going back to 1991 to synthetically reconstruct
JPX400 ranks and implement our main difference-in-differences test for six sets of years prior to the launch of the JPX400 in 2013. We mimic
the temporal structure of our main analyses, and include seven years for each placebo test: three pre-treatment years and three post-treatment
years; the treatment year is excluded. The figure reports the point estimate and the 95% confidence intervals for six placebo tests. The y-axis
reports the magnitudes of the estimated treatment effect; the x-axis reports the mid-point or the dropout year of the seven-year window aroundwhich the treatment effect is estimated. The first placebo test uses data from 1994–2000, with 1994–1996 as the pre-placebo-treatment period,
1998–1999 as post-placebo-treatment, and 1997 as the midpoint and dropout year. The remaining placebo tests roll forward the window of
examination by three years so that the 3-year pre-placebo-treatment windows are non-overlapping.

.05
0
-.05
-.1

Coefficient on Treat x Post

.1

Governance through Shame and Aspiration

1997

2000

2003

2006

2009

Post Period Indicator in Time-Placebo Test
43

Governance through Shame and Aspiration

44

Table A1.
Description of Variables
This table presents definitions of variables used in our analysis. Nikkei membership data is taken fromhttps://indexes.nikkei.co.jp/en/nkave/index/component.
Average employee compensation andaverage executive officers and directors compensation are from a proprietary dataset collected by Toyo
Kezai. All other data are obtained from the Thomson Reuters Datastream database, and their variablenames are referred to in brackets in the computation column.
Variable

Description

Computation

Asset Turnover

Asset turnover ratio

Revenues [WC07240][WC02999]

Book to Market

Book-to-market ratio

Total Shareholders’ Equity [WC03995] / Market Value [MV]Dividends to Equity

Dividends to equity ratio

Cash Dividends Paid [WC04551] / Total
Shareholders’ Equity [WC03995]

Leverage

Financial leverage(Long Term Debt [WC03251] - Cash & ShortTerm Investments [WC02001]) / Total Shareholders’ Equity [WC03995]

Liquidity

Share turnover

12-month rolling average of Total Monthly Volume [VO] / Shares Outstanding [NOSH M]

Log Avg EE Pay

Natural logarithm of the average employee pay

Log Avg Exec Pay

Natural logarithm of the average pay for executives anddirectors

Log Employees

Natural logarithm ofnumber of employees

Profit Margin

Net profit margin

Net income [WC07250] / Revenues [WC07240]

Repurchases

Estimated repurchases

Funds to Decrease Common or Preferred
Stock [WC04751] – Change in Preferred Stock[WC03451]

ROE

Return on equity

Net Income [WC07250] / Total Shareholders’Equity [WC03995]

ROA

Return on assets

Net Income[WC02999]

Shareholder Payouts

Shareholder payout ratio(Repurchases + Cash Dividends Paid) / Total
Shareholders’ Equity [WC03995]

Dependent Variables

the

/

Total

Assetsln(Employees [WC07011])[WC07250]

/

Total

AssetsGovernance through Shame and Aspiration

45

Table A1.
(Continued)

Control Variables
Log Book to Market

Natural logarithm of book-tomarket ratioln(Total Shareholders’ Equity [WC03995] / Market
Value [MV])Accruals to Assets

Ratio of total accruals to total
assets(Net Income [WC07250] – Funds from Operations[WC04201]) / Total Assets [WC02999]

Log NOANatural logarithm of net operating assetsln(Long-Term Debt [WC03251] – Cash & ShortTerm Investments [WC02001] + Total Shareholders’ Equity [WC03995])

R&D to Sales

R&D intensity

R&D Expenses [WC01201] / Revenues [WC07240]

Cash to Equity

Ratio of cash to total equity

Cash & Short-Term Investments [WC02001] / Total Shareholders’ Equity [WC03995]

Log Market Cap

Natural logarithm of market
capitalizationln(Market Value [MV])LT Debt to Equity

LT debt leverageLong-Term Debt [WC03251] / Total Shareholders’Equity [WC03995]

Sales Growth

Sales growth

Revenues [WC07240][WC07240]

Nikkei225

Nikkei225 indicator

Indicator for a company’s inclusion in the
Nikkei225 index at the time of the JPX400 selection in the given year/

LaggedRevenuesGovernance through Shame and Aspiration

46

Table 1.
Predicting JPX400 Membership
Columns 1 and 2 of this table reports estimates from OLS regressions of Actual Inclusion—an indicator forwhether a firm is selected for the JPX-Nikkei400—on Predicted Inclusion—an indicator for whether a firm’s
synthetic rank falls between 1 and 400. In columns 3 and 4, Predicted Inclusion is disaggregated into two
indicators, which specify whether a firm’s synthetic rank falls within 1–200 or within 201–400. Three otherindicators—for ranks within 400–600, within 600–800, and over 800—are also included in these regressions.
These indicators subsume the constant term in the regression. Columns 2 and 4 include other specified
firm-level controls. The sample consists of firms with synthetic ranks of 1–2000 in the years 2013–2016.
Standard errors, clustered at the firm-level, are reported in parentheses. Significance levels are indicated by∗ ∗∗ ∗∗∗, ,for 10%, 5%, and 1% respectively.

Predicted Inclusion(1)
Actual Inclusion
0.923∗∗∗(0.01)(2)
Actual Inclusion
0.864∗∗∗(0.01)

Rank 1–200
Rank 201 – 400
Rank 401 – 600
Rank 601 – 800
Rank outside 800
Log Market Cap
Log Book to Market
Sales Growth
LT Debt to Equity
Cash to Equity
Observations
R2

7,110
0.869

0.0217∗∗∗(0.00)-0.00180(0.00)-0.000275(0.01)-0.00120(0.00)
0.00134(0.00)
7,110
0.872

(3)
Actual Inclusion(4)
Actual Inclusion

0.991∗∗∗(0.00)
0.879∗∗∗(0.01)
0.0542∗∗∗(0.01)
0(.)
0.00488∗∗∗(0.00)

0.949∗∗∗(0.01)
0.857∗∗∗(0.02)
0.0457∗∗∗(0.01)
0(.)
0.0315∗∗∗(0.01)
0.0185∗∗∗(0.00)-0.000153(0.00)
0.00115(0.01)-0.000453(0.00)-0.0000954(0.00)
7,110
0.876

7,110
0.900

Table 2.
Summary Statistics
Table 2 reports summary statistics on the pre-treatment period (2010–2012) for the full sample used for the main regressions (firms
synthetically ranked 301–800). The rightmost three columns report the means for the treatment group (ranks 301–500) and thecontrol group (ranks 501–800) and T -statistic from the test of equality in means between the two groups. All continuous variables
are winsorized at the top and bottom 0.5% of the cross-sectional distribution.

ROE
ROA
Profit Margin
Asset Turnover
Leverage
Repurchases to Equity
Dividends to Equity
Log Book to Market
Lagged Annual Return
Accruals to Assets
Log NOA
R&D to Sales
Log Employees
Log Market Cap
Sales Growth
LT Debt to Equity
Cash to Equity
Nikkei 225

p25

M ean

p50

p75

SD

0.03000.00860.0136
0.6064-0.31750.00000.0115-0.1845-0.1453-0.0646
17.24690.00007.370217.47850.93650.01720.18680.00000.05410.02840.04690.95650.03010.00620.02050.08330.0355-0.0435
18.0358
0.01728.101518.0954
1.01270.40400.39180.14400.05000.02380.03010.8990-0.1108
0.00000.01700.1559-0.0091-0.042718.0206
0.00528.046717.9597
1.00410.16000.30360.00000.0821
0.04270.06051.24230.22060.00020.02410.42470.1555-0.0195
18.8100
0.02548.792118.6252
1.0741
0.49200.44110.00000.10070.03820.08550.63160.72990.02700.01500.53480.30820.0383
1.31700.0281
1.26700.86920.17110.67590.40780.3512

M ean
T reat = 1
0.05850.0311
0.04851.01210.09020.00650.02130.01740.0321-0.0453
18.3899
0.01568.452718.5053
1.00930.46320.40050.2060

M ean
T reat = 00.05120.02670.04590.9197-0.0097
0.00600.01990.12690.0378-0.042317.7994
0.0182
7.865517.82441.01510.36490.38610.1030

T-Stat ofDifference
1.3145
2.19980.57842.71982.46060.3315
1.8276-3.9403-0.3465-1.5218
7.9311-1.8103
8.637515.3226-0.6626
2.7037
0.63225.3076

Governance through Shame and Aspiration

Full Pre-Period Sample

47

Governance through Shame and Aspiration

48

Table 3.
Treatment Effects in ROE
Table 3 reports the estimates of DID regressions using Forward ROE as the dependent variable. Treat, an indicatordenoting the treatment group, is equal to one for firms ranked 301–500 under our replication of the JPX400 selection
algorithm and zero for firms ranked 501–800; Post is an indicator variable assuming a value one for the years
2014–2016 and zero for the pre-treatment period, 2010–2012. Column 1 reports a baseline specification with nocontrols or fixed effects; column two adds year-fixed effects; and column 3 adds industry-fixed effects, where industry
is defined by Datastream’s Industry Level 3 Name (INDM3), and firm-level controls. Column 4 reports results usingfirms’ contemporaneous ROE as a control, as well as specified controls and time fixed effects. Column 5 adds two
additional interaction terms—Treat x (Year = 2011) and Treat x (Year = 2012)—to the specification of column 4
to test differences in pre-treatment trends. All firm-level control variables are defined in Table A1. Standard errors,
clustered at the firm level, are reported in parentheses. Significance levels are indicated by ∗ , ∗∗ , ∗∗∗ for 10%, 5%,
and 1% respectively.

Treat x Post
Treat
Post

(1)
0.028∗∗∗(0.01)-0.006(0.01)
0.018∗∗∗(0.01)(2)
0.028∗∗∗(0.01)-0.006(0.01)(3)
0.025∗∗∗(0.01)
0.007(0.01)(4)
0.024∗∗∗(0.01)-0.005(0.01)(5)
0.022∗∗(0.01)-0.003(0.01)

0.384∗∗(0.15)-0.013∗∗(0.01)-0.045∗∗∗(0.01)-0.011(0.02)-0.007(0.00)-0.001(0.01)
YesNo
2,783
0.3031-0.005(0.01)-0.000(0.01)
0.384∗∗(0.15)-0.013∗∗(0.01)-0.045∗∗∗(0.01)-0.011(0.02)-0.007(0.00)-0.001(0.01)
YesNo
2,783
0.3026

Treat x (Year = 2011)
Treat x (Year = 2012)
ROE
Log Market Cap
Log Book to Market
Sales Growth
LT Debt to Equity
Cash to Equity
Time FE
Industry FE
Observations
R2

No
No
2,783
0.0221

Yes
No
2,783
0.0219-0.026∗∗∗(0.00)-0.069∗∗∗(0.01)
0.038∗∗(0.02)-0.013∗(0.01)-0.016(0.01)
Yes
Yes
2,783
0.2472Governance through Shame and Aspiration

49

Table 4.
JPX400 Index-Inclusion Incentives
Table 4 reports the regression results of various modifications to the main DID specification in column 4of Table 3. Each column reports a specification that considers different treatment and control definitions.
Column 1 partitions Treat—firms synthetically ranked 301-500—into two separate groups: firms that wereactually selected for the JPX400 index that year and firms that were excluded. For the pre-period, we
split Treat into those those ranked 300-400 (Treat x JPX-included ) and those ranked 401-500 (Treat x
JPX-excluded ). The F -statistic, and its corresponding p-value, from a test of equality between the twopartitioned DID coefficients are reported in the last two rows. Column 2 estimates a DID regression with an
expanded sample that includes firms ranked 1–300 and estimates a separate treatment effect for these firms(the coefficient on Rank 1-300 x Post). The F -statistic, and its corresponding p-value, from a test of equality
between the two interaction terms are reported in the last two rows. Column 3 estimates the main DID
specification using a sample of firms ranked 1301–1800, where the treatment firms are those ranked 1301–
1500 and control firms are those ranked 1501–1800. Column 4 expands the column 2 sample to include the
top 1000 firms; here, however, our treatment variable of interest is Quintile(Closeness), a variable indicating
the particular quintile in which a firm falls in terms of its closeness to the rank of 400. All firm-level control
variables are defined in Table A1. Standard errors, clustered at the firm level, are reported in parentheses.
Significance levels are indicated by ∗ , ∗∗ , ∗∗∗ for 10%, 5%, and 1% respectively.

Treat x JPX-included x Post
Treat x JPX-excluded x Post

(1)
Forward ROE
0.023∗∗∗(0.01)
0.025∗∗∗(0.01)(2)
Forward ROE(3)
Forward ROE

0.023∗∗∗(0.01)
0.006(0.01)

Treat x Post
Rank 1-300 x Post
Placebo Group Treat x Post

0.002
(0.01)
0.006∗∗(0.00)

Quintile(Closeness) x Post
Treat x JPX-included
Treat x JPX-excluded-0.000(0.01)-0.009(0.01)

Treat-0.009(0.01)
0.014(0.01)

Rank 1-300

0.020∗∗∗(0.01)

Placebo Group Treat
Quintile(Closeness)
ROE
Time FE
Firm Controls
Observations
R2
F-statp-value(4)
Forward ROE

0.381∗∗(0.15)
Yes
Yes
2,783
0.30300.050
0.823

0.439∗∗∗(0.12)
Yes
Yes
4,462
0.35327.719
0.006

0.171∗∗(0.07)
Yes
Yes
2,885
0.1196-0.000(0.00)
0.373∗∗∗(0.05)
Yes
Yes
5,546
0.2630Governance through Shame and Aspiration

50

Table 5.
Compensation Incentives
Table 5 reports the results of DID regressions examining executive compensation and pay-performancesensitivity. Column 1 reports the results of a regression following the specification in column 4 of Table 3,
but using Forward Log Avg Exec Pay as the outcome variable. Columns 2 and 3 resemble standard payperformance regressions, i.e., a regression of the contemporaneous annual change in Log Avg Exec Pay on
Annual Return, but also reflect the DID nature of our analysis through the interaction of Annual Returnwith Treat and Post. Treat is an indicator variable assuming a value of one for firms ranked 301–500 under
our replication of the JPX400 selection algorithm and zero for firms ranked 501–800. For the specification
reported in column 1, Post is an indicator variable assuming a value one for the years 2014–2016 and zerofor the pre-treatment period, 2010–2012. As our outcome variable switches to a contemporaneous variable,from a year-ahead one in column 1, the relevant pre-period for the specifications reported in columns 2 and3 spans 2011–2013. Consequently, for these specifications, Post assumes a value of zero for 2011–2013 anda value of one for the years 2015–2017. Column 1 includes firm-level controls as in column 4 of Table 3,
and all columns include time fixed effects, but coefficient estimates are unreported. All firm-level control
variables are defined in Table A1. Standard errors, clustered at the firm level, are reported in parentheses.
Significance levels are indicated by ∗ , ∗∗ , ∗∗∗ for 10%, 5%, and 1% respectively.

(1)
Treat x Annual Return x Post
Treat x Annual Return
Annual Return x Post
Treat x Post
Treat

0.032
(0.02)-0.026*(0.01)

Lagged Annual Return
Log Avg Exec Pay
Log Avg EE Pay
Time FE
Industry FE
Firm Controls
Observations
R2

0.818***(0.02)
0.028(0.03)
Yes
No
Yes
2,560
0.7136

(2)

(3)

0.014
(0.05)
0.006(0.03)-0.030(0.03)
0.023(0.02)-0.007(0.02)
0.044**(0.02)

0.019(0.05)
0.003(0.03)-0.036(0.03)
0.021(0.02)-0.005(0.02)
0.046**(0.02)

Yes
No
No
2,667
0.0100

Yes
Yes
No
2,667
0.0102Governance through Shame and Aspiration

51

Table 6.
Prestige vs. Other Index-Inclusion Incentives
Table 6 examines the differential liquidity gains, from inclusion in the JPX, for Nikkei225 firms vs nonNikkei225 firms and thereafter examines the differential ROE response, to JPX-related incentives, for these
two groups of firms. Column 1 reports OLS regressions of ∆Liquidity, the forward change in share turnover,on two separate indicators, denoting the change in JPX400 membership status for Nikkei225 firms (∆JPX400
Membership x Nikkei225 ) and non-Nikkei225 firms (∆JPX400 Membership x non-Nikkei225 ). This test
spans the post-JPX period of 2013–2017 and uses our full sample of firms ranked 1–1,000, conditional on
not being in the JPX in the prior year. The last two rows of column 1 report the F -statistic and its p-value
from a Wald test of equality between the two interaction coefficients. We include time fixed effects andthe set of firm-level controls used in column 3 of Table 3. The remaining columns report the OLS resultsfrom estimation of equations 4 (columns 2 and 3) and 5 (columns 4 and 5), using the full sample of firms
ranked 1–1,000. In all columns, Nikkei225 indicates contemporaneous membership in the Nikkei 225 index;
Treat is an indicator variable assuming a value of one for firms ranked 301–500 under our replication of the
JPX400 selection algorithm and zero for all other firms ranked between 1 and 1,000; Quintile(Closeness)
is a quintile ranking, ranging from the values of 0 to 4, of a firm’s distance from the rank of 400, where
the highest (lowest) quintile reflect those firms whose ranks are closest to (farthest from) 400; and Post is
an indicator variable assuming a value one for the years 2014–2016 and zero for the pre-treatment period,
2010–2012. For columns 2 and 3,the last two rows report the F -statistic and its p-value from a Wald test ofequality between the two triple-interaction coefficients. For columns 2–5, we include time fixed effects andfirm-level controls as in column 4 of Table 3, but coefficient estimates are unreported. All firm-level control
variables are defined in Table A1. Standard errors, clustered at the firm level, are reported in parentheses.
Significance levels are indicated by ∗ , ∗∗ , ∗∗∗ for 10%, 5%, and 1% respectively.

Governance through Shame and Aspiration

52

Table 6. Continued
Splitting Treatment(1)∆Liquidity∆ JPX400 Membership x Nikkei225∆ JPX400 Membership x non-Nikkei225(2)Forward
ROE(3)
Forward
ROE

Triple Diffs(4)
ForwardROE-0.052(0.07)
0.106**(0.05)

Treat x Nikkei225 x Post

0.066***(0.02)
0.011**(0.01)

Treat x non-Nikkei225 x Post
Quintile(Closeness) x Nikkei225 x Post

0.050**(0.02)

0.013***(0.00)
0.004(0.00)

Quintile(Closeness) x non-Nikkei225 x Post

0.015*(0.01)

Quintile(Closeness) x Post
Nikkei225 x Post

0.006
(0.01)
0.012**(0.01)-0.055**(0.02)

Treat x Post
Treat x Nikkei225-0.059***(0.02)
0.003(0.00)

Treat x non-Nikkei225
Quintile(Closeness) x Nikkei225-0.009**(0.00)
0.002(0.00)

Quintile(Closeness) x non-Nikkei225

0.004(0.05)Treat
ROE
Time FE
Firm Controls
Observations
R2
F-statp-value

Yes
Yes
2,488
0.10644.131
0.042

0.373***(0.05)
Yes
Yes
5,546
0.26576.315
0.012

0.367***(0.05)
Yes
Yes
5,546
0.2661
6.531
0.011

0.003
(0.00)-0.018(0.02)-0.017***(0.01)

Quintile(Closeness)
Nikkei225(5)
ForwardROE-0.007(0.01)
0.001(0.00)
0.373***(0.05)
Yes
Yes
5,546
0.26560.003(0.00)
0.019(0.01)

0.366***(0.05)
Yes
Yes
5,546
0.2663Governance through Shame and Aspiration

53

Table 7.
Drivers of ROE
Table 7 reports the results of DID regressions following the specification in column 4 of Table 3, butusing as dependent variables the drivers of ROE —Forward ROA, Forward Profit Margins, Forward Asset
Turnover, and Forward Leverage. As in Table 3, Treat, an indicator denoting the treatment group, is equal
to one for firms ranked 301–500 under our replication of the JPX400 selection algorithm and zero for firms
ranked 501–800; Post is an indicator variable assuming a value one for the years 2014–2016 and zero for the
pre-treatment period, 2010–2012. Panel A reports the results estimated on the full sample; Panel B reports
results estimated on subsamples. The starting sample for all analyses is one for which all variables used in
the baseline analysis—columns 3-5 of Table 3—are available. All firm-level control variables are defined inTable A1. Standard errors, clustered at the firm level, are reported in parentheses. Significance levels areindicated by ∗ , ∗∗ , ∗∗∗ for 10%, 5%, and 1% respectively.

Panel A: Full Sample Analysis

Treat x Post
Treat
ROA

(1)
Forward
ROA
0.0053∗∗(0.002)-0.0020(0.002)0.5307∗∗∗(0.058)(2)
ForwardProfit Margins
0.0074∗∗(0.004)-0.0147∗∗∗(0.003)

Asset Turnover

Log Book to Market
Sales Growth
LT Debt to Equity
Cash to Equity
Time FE
Observations
R2(4)
Forward
Leverage-0.0048(0.016)0.0077(0.013)0.9564∗∗∗(0.007)0.0058∗∗(0.003)-0.0035(0.006)-0.0109(0.026)0.0006(0.003)-0.0090(0.007)
Yes
2,784
0.95380.0173∗∗(0.007)0.0331∗∗∗(0.012)0.0728∗∗(0.034)0.9456∗∗∗(0.021)-0.8277∗∗∗(0.032)Yes
2,783
0.88750.8153∗∗∗(0.063)

Profit Margin

Log Market Cap(3)
ForwardAsset Turnover
0.0047(0.008)-0.0161∗∗∗(0.006)-0.0026∗∗(0.001)-0.0201∗∗∗(0.003)-0.0167∗∗(0.007)-0.0052∗∗∗(0.001)-0.0047∗∗(0.002)Yes
2,784
0.50020.0058∗∗∗(0.001)-0.0175∗∗∗(0.005)-0.0402∗∗∗(0.010)0.0026(0.002)-0.0013(0.004)Yes
2,785
0.6261

Table 7. Continued

Panel B: Subsample Analysis
Forward Profit Margin

Treat
Profit Margin(2)
Above-MedianProfit Margin
0.0010(0.005)-0.0104**(0.004)0.9318***(0.025)

Asset Turnover
Log Market Cap
Log Book to Market
Sales Growth
LT Debt to Equity
Cash to Equity
Time FE
Observations
R2-0.0038**(0.002)-0.0148***(0.004)-0.0067(0.013)-0.0051**(0.002)0.0060(0.006)Yes
1,255
0.07000.0096***(0.002)-0.0113***(0.004)-0.0344**(0.015)-0.0052(0.006)-0.0082*(0.005)Yes
1,530
0.7175

Forward Net Debt Leverage(3)
Below-Median
Asset Turn
0.0180***(0.007)-0.0249***(0.005)(4)
Above-Median
Asset Turn
-0.0104(0.015)-0.0078(0.010)(5)Below-Median
Leverage
0.0254(0.020)-0.0142(0.014)(6)
Above-Median
Leverage-0.0213(0.026)0.0242(0.021)

0.9919***(0.006)0.0069***(0.002)-0.0192***(0.004)-0.0239(0.024)0.0012(0.002)-0.0003(0.004)Yes
1,355
0.93200.9442***(0.011)0.0044(0.005)0.0062(0.011)0.0083(0.049)0.0213(0.014)-0.0291(0.022)Yes
1,429
0.90360.0063(0.007)0.0232**(0.011)0.0733**(0.037)0.6923***(0.105)-0.7505***(0.049)Yes
1,281
0.72930.0234**(0.011)
0.0518**(0.024)0.0667(0.061)
0.9515***(0.029)-0.8910***(0.061)Yes
1,502
0.8546

Governance through Shame and Aspiration

Treat x Post

(1)
Below-MedianProfit Margin
0.0158***(0.006)-0.0090*(0.005)0.0910(0.112)

Forward Asset Turnover54

Governance through Shame and Aspiration

55

Table 8.
Shareholder Payouts
Table 8 reports the results of DID regressions following the specification in column 4 of Table 3, but using
Shareholder Payouts as the dependent variable. As in Table 3, Treat, an indicator denoting the treatmentgroup, is equal to one for firms ranked 301–500 under our replication of the JPX400 selection algorithm andzero for firms ranked 501–800; Post is an indicator variable assuming a value one for the years 2014–2016
and zero for the pre-treatment period, 2010–2012. Column 1 reports results for the regression estimated on
the full sample; columns 2 and 3 report results estimated on the subsample of firms firms with above-medianand below-median contemporaneous cash-to-equity ratios. The starting sample for all analyses is one forwhich all variables used in the baseline analysis–columns 3-5 of Table 3—are available. All firm-level control
variables are defined in Table A1. Standard errors, clustered at the firm level, are reported in parentheses.
Significance levels are indicated by ∗ , ∗∗ , ∗∗∗ for 10%, 5%, and 1% respectively.

(1)
All
Treat x Post
Treat
Repurchases to Equity
Dividends to Equity
Log Market Cap
Log Book to Market
Sales Growth
LT Debt to Equity
Cash to Equity
Time FE
Observations
R2

0.0059**(0.003)-0.0039***(0.001)0.0147(0.033)0.9491***(0.070)-0.0001(0.001)-0.0077***(0.002)0.0071(0.007)-0.0010(0.001)0.0051(0.003)Yes
2,781
0.2611(2)
Below-MedianCash-to-Equity
0.0023(0.002)-0.0011(0.001)0.1370*(0.083)0.8850***(0.057)0.0004(0.001)-0.0055***(0.001)0.0003(0.004)-0.0027***(0.001)-0.0037(0.006)Yes
1,548
0.3026(3)
Above-MedianCash-to-Equity
0.0095*(0.005)-0.0083***(0.002)-0.0315(0.028)0.9872***(0.105)0.0009(0.001)-0.0081***(0.003)0.0127(0.011)0.0001(0.002)0.0025(0.005)Yes
1,233
0.2449Governance through Shame and Aspiration

56

Table 9.
Accruals and Investments
Table 9 reports the results of DID regressions following the specification in column 4 of Table 3, but using
alternative outcome variables: Forward Accruals to Assets, Forward Net Operating Assets, Forward R&D
to Sales, Forward Log Employees, Forward Log Average Employee Pay, and Forward Log Average Executive
Pay. As in Table 3, Treat is equal to one for firms ranked 301–500 under the JPX400 selection algorithm
and zero for firms ranked 501–800; Post is an indicator variable assuming a value of one for years 2014–2016
and zero for the pre-treatment period, 2010–2012. The starting sample for all analyses is one for which all
variables used in the baseline analysis—columns 3-5 of Table 3—are available. Observations vary acrossspecifications depending on the availability of specific new variables used for the analysis. All firm-level
control variables are defined in Table A1. Standard errors, clustered at the firm level, are reported inparentheses. Significance levels are indicated by ∗ , ∗∗ , ∗∗∗ for 10%, 5%, and 1% respectively.

Treat x Post
Treat
Accruals to Assets(1)
Forward
Accruals
to Assets
0.0017(0.002)0.0003(0.002)0.4042***(0.034)Log NOA(2)
ForwardLog
NOA
0.0329(0.022)0.0038(0.014)(3)
Forward
R&D toSales-0.0011*(0.001)0.0011**(0.000)(4)
ForwardLog
Employees
0.0050(0.011)0.0044(0.009)0.6881***(0.094)R&D to Sales

0.9805***(0.018)Log Employees

0.9817***(0.005)

Log Avg EE Pay
Log Avg Exec Pay
Log Market Cap
Log Book to Market
Sales Growth
LT Debt to Equity
Cash to Equity
Time FE
Observations
R2(5)
Forward
Log Avg
EE Pay0.0045(0.005)-0.0117***(0.004)-0.0007(0.001)0.0048***(0.002)0.0250***(0.006)0.0003(0.001)0.0125***(0.002)Yes
2,779
0.22950.3007***(0.097)0.2935***(0.101)0.1630***(0.050)0.2083***(0.068)-0.2537***(0.098)Yes
2,710
0.9618-0.0003(0.000)0.0003(0.000)0.0035***(0.001)-0.0001(0.000)0.0005(0.000)Yes
2,785
0.92600.0060(0.007)-0.0449***(0.009)0.0505**(0.020)0.0110**(0.006)-0.0185**(0.009)Yes
2,654
0.98830.9515***(0.008)-0.0021(0.003)0.0077***(0.002)-0.0063**(0.003)0.0049(0.010)-0.0024(0.002)-0.0036(0.004)Yes
2,560
0.9209Governance through Shame and Aspiration

57

Table 10.
Market Valuation
Table 10 reports the results of a reduced form and instrumental-variables (IV) analyses using Forward
BM as the dependent variable. The endogenous variable of interest in the IV analysis, Forward ROE, isinstrumented for by our DID estimator, Treat x Post. As in Table 3, Treat is equal to one for firms ranked
301–500 under the JPX400 selection algorithm and zero for firms ranked 501–800; Post is an indicatorvariable assuming a value of one for years 2014–2016 and zero for the pre-treatment period, 2010–2012.
Column 1 reports the results of a reduced-form DID effect estimate on Forward BM, similar to our main
DID specification used in column 4 of Table 3. Columns 2 and 3 report the first and second stages of a IV
regression. The last row of column 2 reports the first-stage F -statistic for the instrument. All firm-level
control variables are defined in Table A1. Standard errors, clustered at the firm level, are reported inparentheses. Significance levels are indicated by ∗ , ∗∗ , ∗∗∗ for 10%, 5%, and 1% respectively.

(1)
OLS

(2)
IV 1st Stage(3)
IV 2nd Stage

Forward Log Book to Market

Forward ROE

Forward Log Book to Market-1.612*(0.91)-0.035*(0.02)
0.032**(0.01)-0.093(0.09)
0.854***(0.02)-0.026***(0.01)
0.089**(0.04)-0.007(0.01)-0.046***(0.01)
Yes
2,725
0.75400.022***(0.01)-0.004(0.01)
0.407***(0.15)-0.038***(0.01)-0.013**(0.01)
0.009(0.03)-0.006(0.00)
0.012*(0.01)
Yes
2,725
0.28459.4818Forward ROE
Treat x Post
Treat
ROE
Book to Market
Log Market Cap
Sales Growth
LT Debt to Equity
Cash to Equity
Time FE
Observations
R2
First Stage F

0.025(0.02)
0.563(0.40)
0.793***(0.04)-0.047***(0.02)
0.104**(0.05)-0.017(0.01)-0.027(0.02)
Yes
2,725
0.6954