Shareholder Perks, Ownership Structure, and Firm Value

Jonathan M. Karpoff Foster School of Business University of Washington

karpoff@uw.edu

Robert Schonlau Marriott School of Management

Brigham Young University robert.schonlau@byu.edu

Katsushi Suzuki

Graduate School of International Corporate Strategy Hitotsubashi University ksuzuki@ics.hit-u.ac.jp

First draft: November 11, 2015 Current version: September 21, 2016

Abstract Shareholder perks are in-kind gifts or purchase discounts made available to shareholders and are common at many firms. Shareholder perks return value to investors in a way that disproportionally rewards small shareholders. Using data from Japanese firms, we show that firms initiating perk programs attract small individual shareholders and decrease the concentration of share ownership. Firms that initiate perk programs experience an increase in value, an increase in share liquidity, and a decrease in the equity cost of capital. We infer that perk programs serve shareholder interests at the firms that adopt them.

JEL classification: G14, G30, G35

Keywords: Shareholder perks, investor base, shareholder clienteles, share liquidity

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1. Introduction

What do shareholders of public corporations get for their investments? Finance textbooks

emphasize that shareholders receive cash dividends, the prospect of capital gains, and certain control

rights. Missing from this list, however, are shareholder perks. Shareholder perks are in-kind gifts or

purchase discounts made available to shareholders. Shareholders of Ford Motor Co., for example, receive

“friends and neighbors” purchase discounts on the purchase of Ford automobiles. Berkshire Hathaway

shareholders receive price discounts on a variety of company products ranging from insurance to jewelry,

and Carnival Cruise Lines offers shareholders several hundred dollars of credits to spend while on a

cruise. Investment publications frequently highlight such perks as important considerations for small

investors.1

It may be tempting to dismiss shareholder perks as inconsequential. Indeed, the appearance of

triviality may explain the lack of much prior research in this area. We find, however, that firms initiating

perk programs experience an increase in shareholder base, increase in share liquidity, decrease in the

equity cost of capital, and increase in firm value. These findings indicate that shareholder perk programs

have meaningful effects on firms’ ownership structure, liquidity, cost of capital, and value.

We structure our analysis of shareholder perks around the following three questions: (i) Do firms

that initial perk programs experience a change in investor base? (ii) Does the initiation of a perk program

affect firm value, and (iii) if so, by what channels? To investigate these questions we use perk data from

the Japan Company Handbook for all publicly traded firms in Japan from 2001 to 2011. The Japanese

data are well suited for our research question because, unlike for most other markets in which perk

information is not uniformly collected, the Japan Company Handbook provides comprehensive data on

the companies that have adopted perks, the types of perks offered, and the minimum number of trading

1 For examples, see Moyer (2013) and Brumley (2014). We calculate that shareholder perks are provided by 28% of

all public companies in Japan, 17% of the FTSE 100 companies on the London Stock Exchange, and 12% of the

ASX 100 companies on the Australian Securities Exchange. Numbers for U.S. firms are difficult to attain, although

the financial press continues to publicize shareholder perks, e.g., Max (2015). For comparison, Fama and French

(2001) report that 20.8% of U.S. firms pay cash dividends.

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shares required to receive the perks. Nearly 30% of Japanese firms have offered perks in recent years,

including many new perk offerings. So the Japanese data allow us to examine both the short-term effects

around new perk initiations as well as the long-term effects for firms that adopted perks in prior years.

Shareholder perks can affect a firm’s investor base primarily because, unlike dividends or other

forms of payout, the same shareholder perk tends to be offered to each shareholder and does not scale up

with the size of the shareholding. Shareholder perks thus benefit small shareholders disproportionately.

Our tests show that small investors respond to such incentives in ways that significantly increase the

number of small shareholders and decrease ownership concentration in firms that initiate perk programs.

We consider two competing views about how shareholder perks can affect firm value. The

Shareholder Interest hypothesis holds that shareholder perks increase firm value by increasing investor

awareness or share liquidity, or both. Previous research finds that investors tend to invest in the securities

of firms that they know, either because of incomplete information (e.g., Merton 1987), familiarity bias

(Grinblatt and Keloharju, 2001; Grullon, Kanatas, and Weston, 2004), or attention constraints (e.g.,

Barber and Odean, 2008). As described in Merton (1987), increases in investor awareness could occur

via advertising or news coverage that attracts investor attention – as shareholder perk programs do – and

this increased awareness would be reflected in a broader investor base and result in a reduction in the

pricing of idiosyncratic risk, a reduction in the cost of capital, and higher firm value. Amihud,

Mendelson, and Uno (1999) note that increasing the number of shareholders also increases share

liquidity, thereby decreasing the cost of equity capital and increasing firm value.

The Managerial Interest hypothesis, in contrast, holds that shareholder perks help to entrench

managers and decrease firm value. As Grossman and Hart (1980) and Shleifer and Vishny (1986) argue,

ownership concentration and the presence of large shareholders can mitigate free-riding problems among

shareholders and managerial agency problems. Perk programs that decrease ownership concentration and

increase the number of small shareholders can offset such benefits and exacerbate managerial agency

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problems. In addition, previous research shows that an increase in entrenchment can decrease price

informativeness and firm disclosure, and increase information asymmetry (see, e.g., Eng and Mak, 2003;

Ferreira and Laux, 2007). Information asymmetry, in turn, is positively related to measures of illiquidity

and the cost of capital, implying that shareholder perks can reduce firm value by decreasing share

liquidity and increasing the cost of capital.2 Consistent with this argument, Brockman and Chung (2003)

and Diamond and Verrecchia (1991) find that decreases in investor protection and firm-level corporate

disclosures increase information asymmetries and decrease share liquidity.

Thus, the Shareholder Interest hypothesis predicts that shareholder perks will be associated with

higher firm value, lower required returns, and increased share liquidity. If these changes are due to

greater investor awareness, Merton (1987) predicts the decrease in the overall cost of capital will be

driven by a corresponding decrease in the shadow cost of capital. The Managerial Entrenchment

hypothesis, in contrast, predicts that shareholder perks will be associated with lower firm value, higher

required returns, and decreased share liquidity.

We examine these competing predictions using five sets of tests. To examine the impact of perk

programs on share value, we first measure share returns around the announcements of new perk

programs. Second, we utilize a matched sample difference-in-difference (DiD) approach to test whether

longer-term firm value increases following the initiation of new perk programs. Third, we test whether

higher share liquidity is associated with the use of shareholder perk programs using a panel regression

approach similar to that in Grullon, Kanatas, and Weston (2004), who model liquidity as a function of

general advertising. Fourth, we test for changes in the required return on equity around shareholder perk

announcements, and whether these changes are related to corresponding changes in Merton’s shadow cost

of capital. Fifth, we examine whether firms that use shareholder perks show evidence of managerial

agency problems, as evidenced by value-destroying acquisitions or lower CEO-turnover-performance

2 Glosten and Milgrom (1985) and Glosten and Harris (1988) show that information asymmetry is related to share

illiquidity. Kelly and Ljungqvist (2012), Choi, Jin, and Yan (2016) and Berkman, Koch, and Westerholm (2014) find

that an increase in information asymmetry is associated with an increase in the cost of capital.

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sensitivities.3 Table 1 summarizes the predictions of each test under each of the two hypotheses, along

with a summary of our empirical results.

[Insert Table 1]

The results of these tests are consistent with the Shareholder Interest hypothesis and are

inconsistent with the Managerial Interest hypothesis. The announcement of a new shareholder perk

program is associated with a 2.06% 3-day cumulative abnormal return. This result is corroborated in the

longer-horizon DiD tests in which we find that perk-initiating firms experience positive and significant

increases in their overall market value of equity (MVE) from the year before to the year following perk

initiations relative to a matched sample of firms that are observationally equivalent in the year before the

perk announcement.

To probe the robustness of the DiD result and to investigate the parallel trends assumption, we

repeat the DiD comparison in a placebo test, comparing the changes in the market value of equity for the

two groups of firms in the two years before the perk announcement and fail to find a significant

difference.4 The combination of the event study and DiD results help to mitigate the possibility that an

omitted factor might explain both the decision to use shareholder perks and the observed subsequent

increase in firm value. For an omitted variable at the perk-initiating-firms to explain our combined

results it must be the case that (1) the omitted variable was not a significant determinant of changes in the

market value of equity for the two groups of firms across the years leading up to the perk-program, (2)

yet the information content of the omitted variable became suddenly obvious to investors at the time of

the perk announcement, and (3) the omitted variable then became a driving force in the long-term change

in the market value of equity for just the perk-initiating firms in the year after the perk announcement.

Such an explanation seems unlikely.

3 This fifth test draws from Masulis, Wang, and Xie (2007) and Faleye (2007), who document that entrenched

managers tend to have worse acquirer announcement returns and weaker CEO-turnover sensitivity to firm

performance. 4 For a discussion of this approach see the discussion in Roberts and Whited (2012) Section 4.4.

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The liquidity and shadow cost of capital results further mitigate omitted variable concerns.

Consistent with the Shareholder Interest hypothesis, we find strong evidence that shareholder perk

programs are associated with higher share liquidity and a reduction in the required rate of return on

equity. Using either a matched-sample DiD framework focused on the subset of firms with new perk

programs, or using a panel data approach with the full sample of Japanese firms, we find that perk

programs are positively and significantly related to share liquidity. Additionally, we find that the

estimated required rate of return on equity decreases for perk initiating firms from the months before to

the months following the perk announcement. Furthermore, this change in the required rate of return is

positively and monotonically related to changes in Kadlec and McConnell’s (1994) measure of Merton’s

shadow cost of capital. Thus, not only does the equity cost of capital decrease after the perk

announcement, but this decrease is largest for the firms that the Merton (1987) model predicts would

experience the largest decreases. Together, these results provide strong collective support for the

Shareholder Interest hypothesis. In contrast, and contrary to the predictions of the Managerial

Entrenchment hypothesis, we fail to find evidence that perks are associated with low acquirer

announcement returns or a reduction in CEO-turnover sensitivity to firm performance.

This paper makes four contributions to the literature. First, we show that shareholder perk

programs work to broaden firms’ investor bases and to decrease the concentration of share ownership.

This implies that shareholder perks are not inconsequential oddities, but rather, a form of shareholder

payout that affects a firm’s ownership structure. Second, we find that the initiation of a shareholder perk

program is associated with a significant increase in share value. This implies that, even though they

violate the equal treatment of shares principle and they decrease ownership concentration, shareholder

perk programs increase value, on average, among the firms that initiate them. This result highlights some

potential advantages of increasing the number of small shareholders in a firm’s ownership structure, a

finding that stands in contrast to the typical focus in many papers on the monitoring advantages of large

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shareholders. Third, we find evidence consistent with the view that shareholder perks add value by

increasing liquidity and decreasing the firm’s cost of equity capital.

We are unaware of prior research on shareholder perks, but our paper relates to two other themes in

the finance literature. First, our results are consistent with the Merton (1987) incomplete information

model in which investors face meaningful costs to become knowledgeable about firms. This result is

consistent with Kadlec and McConnell (1994) and Grullon, Kanatas, and Weston (2004), but in a modern

setting with low-cost and widely available information about firms. We also tie shareholder perks not

only to increases in shareholder base and share liquidity, but also directly to firm value and to Merton’s

(1987) shadow cost of capital. Second, using a small sample of Japanese firms, Amihud, Mendelson,

and Uno (1999) find that increases in a firm’s shareholder base are associated with increases in firm

value. Our paper extends this result using a large sample. We also examine a specific firm policy that

causes a firm’s shareholder base to increase (shareholder perks) and examine the channels by which firm

value is affected (liquidity and the cost of capital).

The remainder of the paper is organized as follows. Section 2 describes the shareholder perk data

and provides information about firms that offer perks. Section 3 outlines our empirical strategy,

motivates the propensity-score matched sample, and documents changes in the shareholder base around

perk initiations. Section 4 reports our main empirical results for the tests related to the predictions

associated with the hypotheses described in Table 1. Section 5 discusses robustness tests, and Section 6

concludes.

2. Data on shareholder perks

2.1. Description of the shareholder perk data

To investigate the effects of shareholder perks, we collect shareholder perk data from the Japan

Company Handbook (in Japanese, Kaisha Shiki Hou), for all publicly traded firms in Japan from January

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2001 to December 2011. Our sample starts in 2001 because this is the first year that the EOL Esper

database is available for identifying the perk announcement dates. This data include information on the

companies that have adopted perks, the minimum number of trading shares required to receive

shareholder perks, the types of perks offered, the timing of perk payments, and in almost half of the cases

the value of the perk (the value is not reported in yen when the perk involves a discount). Figure 1 reports

on the number and percentage of Japanese companies with shareholder perks. The percentage of public

companies with shareholder perks averages 24.75% during the sample period, increasing to 28% in 2011

with a total estimated value of 17.2 billion yen in 2011 for the 46.53% of firms reporting the value of the

perks in 2011.

[Insert Figure 1]

Table 2 reports on the distribution of perks across 32 industries based on 2-digit Nikkei industry

codes. 73.46% of firms’ perks in our sample involve their own products. For example, ANA, a Japanese

airline, provides a 50% discount coupon for its own airline tickets to shareholders who hold more than

1,000 shares (the minimum trade unit). But in many cases the perk involves an unrelated firm’s

products. For example, Takamatsu Construction Group Co., Ltd. gives five kilograms of an expensive

brand of rice to holders of 100 or more shares, and Suzuki Motor Corporation gives an assortment of

honey and rock salt to holders of 100 or more shares.5

[Insert Table 2]

In approximately 50% of all companies with shareholder perks, the value of the perk increases

slightly with the number of shares up to some maximum. But the rate of increase tends to be so low that

in almost all cases the highest perk value per dollar invested is achieved at the minimum stockholding

required to receive any perk award.6 For example, Toyo Suisan, a major food company, provides two

5 Appendix Table A.1 lists perks from 50 randomly chosen firms in the sample. The name of the company, number

of shares required to earn the perk, and the nature of the perk are listed. 6 Of the 1,023 firms offering perks in 2011, 634 of them offer some sort of tiered perk benefit. In 99.2% of these

cases the lowest number of shareholdings offers the highest yield. Even in the few cases where the higher perk yield

is achieved with a slightly higher number of shares the perk still offers disproportionate benefits to small

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levels of shareholder perks. The first is a gift of ¥3,000 of its food for shareholders who hold between

1,000 (the minimum trade unit) and 5,000 shares. The second is a gift of ¥5,000 of its food for

shareholders who hold more than 5,000 shares. So the maximum perk yield is available to shareholders

who hold exactly 1,000 shares. The perk yield is decreasing in the number of shares held above the

minimum and is nearly valueless on a relative basis for the largest shareholders.

Shareholders commonly receive their perks at the close of the company’s fiscal year. For firms that

pay perks twice per year, shareholders typically receive the perk at the close of the second and fourth

quarters. If a firm also pays a cash dividend, the ex-perk day is often the same as the ex-dividend day.

2.2. Characteristics of firms offering and initiating shareholder perks

After imposing the data requirements reflected in the control variables in Tables 4-8, our sample

contains 8,911 firm-years from 2001 - 2011 in which firms offered shareholder perks and 26,840 total

firm-years in which they did not.7 Across the sample period 1,311 unique firms offered perks, 544 for the

first time, and 4,329 never did. Table 3 provides descriptive information for perk and non-perk firms and

provides univariate tests of whether the two groups statistically differ in terms of firm and ownership

characteristics. The univariate description of these characteristics not only provides a better

understanding of the types of firms using perks but also helps motivate the types of observables we

include in the model in Section 3 when creating the propensity-score matched sample for which firms

initiate perk programs.

The firm characteristics included in Panel A of Table 3 provide information about the size,

leverage, profitability, and valuation of the firm as well as several characteristics that could plausibly

shareholders in that the perk value does not continue to scale with the number of shares held. 7 Data on the number of shareholders, number of individual shareholders, ownership, dividends, antitakeover

defenses, and industry classifications are obtained from the Nikkei NEEDS Financial Quest database, as are data

about the firms’ financial statements. We collect data related to stock prices, stock returns, and value weighted index

returns from the NPM portfolio master database. Information on the number of directors and the number of outside

directors is from Toyo Keizai, Inc. M&A data, i.e. name of acquiring company and acquired company, acquiring

date and price, and anti-takeover defense, are collected from the RECOF’s MARR database.

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relate to the managers’ decision to use shareholder perks. These additional characteristics include

measures of the firm’s excess cash,8 whether the firm pays a dividend, the percent of outside directors on

the board, and whether the firm has adopted a shareholder rights plan that can be used as an anti-takeover

provision. Each of these items could relate to the managers’ decision to use shareholder perks if, for

example, perks are used in place of dividends by cash-poor firms, or if perks are an approach to

entrenchment.

[Insert Table 3]

As noted in Panel A, perk firms are not statistically different than non-perk firms in terms of

size, leverage, excess cash, or the percent of outsiders on the board in the year before new perk programs

are initiated. Firms initiating new perk programs are, however, on average, more profitable, higher

valued, more likely to also pay a dividend, and less likely to adopt an anti-takeover provision than non-

perk firms. We exploit these differences when creating the matched sample in Section 3.

In Panel B we compare perk firms with non-perk firms in terms of their ownership structure

using shareholder information from the Nikkei Financial Quest database. This database classifies each

shareholder into one of six groups: individuals, financial institutions, financial instrument firms, foreign

investors, non-financial firms, or government holdings. The database also identifies the 10 largest

shareholders regardless of which of these groups they belong to. These comparisons are not identified in a

causal sense but provide simple descriptive intuition for perk and non-perk firms. We examine these

same six measures around new perk program announcements in later tests using better identified

empirical approaches.

Our first two measures are similar to measures used by Amihud, Mendelson, and Uno (1999).

The first measure captures the total number of unique retail shareholders. When calculating the number

of retail investors and their overall holdings we subtract the ownership of any individuals who are among

8 We use the residuals from a firm-level cash regression model to estimate the excess cash. In the regression we

regress (Cash/Book Asset) on (Cash flow/Asset), Volatility, Leverage, Dividend dummy, (Capital Expenditure/Book

Asset), Tobin Q, ln(Market value of Asset), Year dummy, and Industry dummy.

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the firm’s Top 10 shareholders from total individual ownership. The second measure divides the number

of individual retail shareholders by the total number of distinct shareholders. The third measure captures

board ownership and is measured as the percent of shares outstanding owned by the board.9 The fourth

measure captures the percent of shares owned by the Top 10 owners which can be individuals or

institutions. The fifth and sixth measures capture the percent of ownership held by retail owners versus

the percent held by institutional investors where institutional investors include holdings by financial

institutions and financial instrument firms.

We find that firms that initiate perks in the following year are not statically different from non-

initiators in terms of the number of unique individual retail shareholders. Firms that initiate new perk

programs tend to have higher board ownership, more ownership concentrated in the top 10 owners, lower

retail ownership, and lower institutional ownership than firms that do not initiate new perks. We use

these differences in the selection model for predicting which firms are likely to initiate perks.

3. Empirical strategy, shareholder perk determinants, and changes in shareholder base

Section 2 highlights the prevalence of shareholder perks across industries during our sample

period, and provides descriptive information and univariate comparisons for how perk firms differ from

non-perk firms. In this section we do three things. First, we outline the logic of our empirical approach

for testing the predictions associated with the Shareholder Interest and Managerial Interest hypotheses.

Second, in Section 3.2, we motivate a model for what factors affect whether or not firms adopt

shareholder perk programs. We use this model to identify a matched sample of firms that are

observationally similar to the firms that initiate new perk programs in terms of firm- and industry-level

characteristics in the year before perk initiation. And third, in Section 3.3, we use this matched sample to

explore changes in shareholder base that occur immediately around new perk initiations.

9 Unlike in the US, top Japanese executives are almost always also in top board positions. This means that

the %Board ownership measure includes the ownership of top executives as well.

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3.1. Empirical Strategy

As tabulated in Table 1, there are 6 empirical predictions associated with the Shareholder Interest

and Managerial Interest hypotheses. These 6 predictions link shareholder perks with such firm outcomes

as perk and acquisition announcement returns as well as the market value of equity, liquidity, required

returns, shadow cost of capital, and CEO-turnover-performance sensitivity. One challenge with studying

the effects of shareholder perks is that managers’ endogenously choose to initiate perk programs. Thus a

simple comparison of the above-mentioned firm outcomes between perk and non-perk firms potentially

suffers from omitted factors that might explain both the endogenous decision to use shareholder perks as

well as these specific outcomes. We adopt several approaches to mitigate this concern.

Our empirical strategy is multi-faceted and includes (1) announcement return tests, (2)

difference-in-difference tests focused on the subset of firms with newly initiated perk programs, (3)

multivariate regressions using the full sample of Japanese firms, (4) a test focused on changes in the cost

of capital in the year before to the year after perk initiations where the test is not simply looking for an

indication of change but is mapping the relative size of the change to predictions from Merton (1987), as

well as (5) two tests for evidence of perks leading to entrenchment but focused outside the perk-decision

time frame. Each of these approaches has different advantages and is based on different underlying

assumptions. We deliberately employ a diverse set of methodologies focused not only on multiple

outcome variables but also using subsets as well as the full sample with both market-based short-term as

well as long-term tests in order to exploit the argument that finding consistent results across this portfolio

of approaches should mitigate the concern than an omitted factor is explaining the results. In all tests we

find evidence consistent with the Shareholder Interest and not the Managerial Interest hypothesis. The

purpose and advantages of each of the empirical approaches is discussed in more detail below.

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3.1.1 Empirical Strategy: Perk Announcement Return Tests

As a first approach to testing for the expected wealth effects of shareholder perk programs we

investigate the market reactions to the announcements of these programs. One advantage of these tests

relative to the DiD tests is that the implications of the announcement returns –whether towards increased

or decreased value—are not in any way dependent on the selection of a matched control group. As

reported in Section 4 we find strong positive abnormal returns around perk announcements. In robustness

tests reported in the appendix (Table A.2) we confirm that these returns are not positively related to

advertising or future changes in sales suggesting that the mechanism of expected wealth creation is not

via a product advertising channel.

3.1.2 Empirical Strategy: Difference-in-Difference (DiD) Tests around the Subset of New Perk Initiations

We employ DiD tests around perk initiations modeling the new perk initiation as the “treatment”

to investigate how perks relate to changes in firm value and liquidity over time. DiD tests are used to

“recover treatment effects stemming from sharp changes in the… environment” (Roberts and Whited

(2012) pg. 34). As discussed by Roberts and Whited, the key identifying assumption in the DiD

framework is the “zero correlation” or “parallel trends” assumption requiring that “in the absence of

treatment, the average change in the response variable would have been the same for both treatment and

control groups.” Hence, in our application, the requirement is that the average changes in value and

liquidity over time would have been the same for the new-perk-initiating firms as for the matched non-

perk firms if not for the initiation of the new perks. This assumption is easy to defend when assignment

to treatment is random. But in our setting random assignment is unlikely thus requiring that we adjust the

specification to achieve conditional, rather than unconditional, independence by including covariates in

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the matched-sample model that plausibly account for the non-random assignment of firms to treatment

versus control groups making the error term conditionally independent.

To do this we start by including the 14 firm-level characteristics discussed as part of Table 3 as

control variables in the matching model. To this set of 14 we add 32 industry controls, annual controls,

as well as 2 additional variables chosen specifically because they could plausibly relate to a firm’s choice

of whether or not to initiate shareholder perks. We include industry and annual controls to ensure we

account for any industry-level or time-varying factors that might affect the decision to offer shareholder

perks.

The two new variables include a measure of general advertising expenses and a measure of the

monetary difference between the value of the current trade unit size versus the value of the minimum

allowable trade unit size. The rational for the inclusion of the advertising variable is to account for the

possibility that perks are a substitute or complement to general advertising. Thus, in the matching model,

assuming that the other firm-level characteristics as well as the combination of industry- and year-level

controls are not sufficient to account for the firm’s advertising environment, we also include a firm-level

control for its own advertising expenses.

The rational for the second variable is based on intuition from Amihud, Mendelson, and Uno

(1999). In that paper they document increases in both the number of small shareholders and liquidity

around events where managers lowered the minimum trade unit size in the early 1990s and thereby

facilitated trading by smaller shareholders. Assuming that one possible motive for shareholder perks

might be to increase the number of shareholders, and recognizing that trade unit sizes are also related to

these outcomes, we control for the value difference in the current trade unit size versus the value of the

minimum allowable trade unit size.10

10 In Japan investors have to trade above a specified minimum trade size. This minimum is calculated as the stock

price multiplied by the trade unit size. Firms can set the trade unit size at 1 or more shares up to 1,000 shares.

Many firms have trade unit sizes larger than 1. The value difference in the current trade unit size versus the value of

the minimum allowable trade unit size is calculated as the difference between (current stock price * current trade

unit size) and the (current stock price * 1).

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We discuss the matching model in more detail in Section 3.2 but note here that applying this

matching model to the full sample of Japanese firms allows us to identify for each firm that does adopt a

perk the 5 nearest neighbor firms that are observationally equivalent in the year before the perk to the

firms that will adopt shareholder perks in the following year (i.e., the match is made in year t-1 assuming

the perk is announced in year t). The matched sample is as likely to have initiated a perk program as the

actual initiating firms based on all observables. We check the internal validity of our matching model by

investigating the parallel trends assumption in the years before the perk initiation in robustness checks as

well as discussing covariate balance preserved after the match.

The intuition for the robustness checks rely on the same logic as the parallel trends assumption

which is that the treatment group (perk-initiators) would have experienced similar trends in value and

liquidity across time as the control group (matched sample of non-perk firms) except for the initiation of

the perk program. This counterfactual is not observable across the perk-year but testing for evidence of

parallel trends in the years leading up to that year is possible; if each perk firm is successfully matched in

year t-1 with a set of control firms that, on average, really would have experienced similar changes in

value and liquidity going from year t-1 to year t+1 around the perk initiation then we would expect to find

that future perk firms experienced the same trends in value and liquidity as the control group going from

year t-2 to year t-1— both years occurring before the perk was actually initiated. Being able to show that

the trends in the two response variables for the treatment and control groups are identical in the years

leading up to the perk provides support for the idea of conditional independence achieved via the

inclusion of the many control variables in the matching model and provides support for the parallel trends

assumption.

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3.1.3 Empirical Strategy: Panel Regressions Using the Full Sample of Japanese Firms

One advantage of the DiD and announcement return tests is that changes in the outcome variables

(value and liquidity) are tracked over relatively short time horizons centered on the announcement of new

perk programs. Focusing over short time horizons in event time—and using a matched group in the case

of the DiD tests— helps mitigate concerns that unobservable factors might explain the changes in

outcomes; unobservable factors would have to systematically change suddenly and differently for

treatment firms than for control firms over short time periods and these changes would have to occur

exactly around perk programs announcements across 10 years of calendar time in order for unobservable

factors to explain the results. The potential disadvantage of these tests is the focus on the subset of firms

in our sample initiating new perks for which we can find the announcement date for the new perk

programs. Thus we supplement these event-focused tests with full sample panel regressions that include

all Japanese firms for which we can find financial information.

This approach utilizes the full sample and allows us to explore the relation between shareholder

perks and the liquidity and value outcome variables over longer horizons; if the implication from the

announcement return and DiD tests is that newly announced shareholder perk programs are associated

with increased value and liquidity then in the full panel over the long-term we would expect to find firms

with perks—regardless of whether they are new or not—are associated with higher value and liquidity.

This empirical approach is similar to the one used in Grullon, Kanatas, and Weston (2004) in modeling

measures of liquidity as a function of advertising. We note that these panel regression results are not

dependent on motivating a particular control group as in the DiD results but the implications are identical.

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3.1.4 Empirical Strategy: Changes in the Required Return and Shadow Cost of Capital around Perk

Initiations

Under the Managerial Interest hypothesis, shareholder perks contribute to entrenchment and a

higher required rate of return. Under the Shareholder Interest hypothesis, shareholder perks lead to

increased investor awareness which, per Merton (1987), leads to a lower required return. As part of

Merton’s incomplete information model, shareholders are underdiversified compared to the market

portfolio and as a result are subject to some idiosyncratic risk. In equilibrium, a security’s expected return

depends on the fraction of investors that purchase the security. Merton derives an expression for the

expected return for a security as a function of the shadow cost that arises from exposure to idiosyncratic

risk which in turn depends on the firm’s investor base which itself depends on overall investor awareness.

In Merton’s model, an increase in the size of the investor base—which would occur with greater investor

awareness of the firm—decreases the shadow cost of undiversified idiosyncratic risk and hence the

overall required return on equity.11

Thus for our purposes Merton’s model provides two specific predictions that would stem from

shareholder perk initiations if shareholder perks increase investor awareness in a way that leads to a

broader investor base: (1) we expect to find that the required return on equity decreases after shareholder

perk initiations, and (2) we expect that the changes in the required return on equity will be largest

(smallest) in the subset of firms that experience the largest (smallest) change in the shadow cost around

perk initiations.

To identify a change in the required return after the initiation of a shareholder perks program, we

estimate the following one-factor return model for each firm that adopted a perks program:

11 Merton notes that his model “provides a rationale for expenditure on advertising about the firm that is targeted for

investors…” if the advertising serves to increase the investor base thereby increasing firm value via decreases in the

shadow cost. Shareholder perks, even more than general advertising, seem to meet this description in that a reward

is offered only if the investor invests in the firm.

17

𝑟𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛼𝑖,0 + 𝛼𝑖,1𝐷𝑡 + (𝛽𝑖,0 + 𝛽𝑖,1𝐷𝑡)(𝑟𝑚,𝑡 − 𝑟𝑓,𝑡) + 𝜀𝑖,𝑡 (1)

Here, 𝑟𝑖,𝑡 is the weekly return for firm i at time t, and 𝑟𝑓,𝑡 is the risk-free rate at time t. 𝐷𝑡 = 1 if t is in the

post-adoption period and 𝐷𝑡 = 0 otherwise. 𝛽𝑖,0 and 𝛽𝑖,𝑖 are the pre-and post-adoption market risk

premium factor betas. 𝛼𝑖,0 is the pre-adoption abnormal return, and the sum of 𝛼𝑖,0 and 𝛼𝑖,1 is the post-

adoption abnormal return. Our main parameter of interest is 𝛼𝑖,1, which is the difference between the

post- and pre-adoption abnormal returns. If new shareholder perk programs cause greater investor

awareness then we would expect 𝛼𝑖,1 to be negative; in this approach, the firm itself acts as its own

comparison group from before the perk initiation hence 𝛼𝑖,1would only be negative post-adoption if

returns were systematically lower in the post-adoption period relative to the pre-adoption period.12

A negative change in initiating firms’ alphas is consistent with the argument that these firms’

equity cost of capital decreased, on average, as implied by the Shareholder Interest hypothesis and

opposite the implication in the Managerial Interest hypothesis. However, it is also possible to interpret

this result as indicating that firms initiating shareholder perks experienced superior stock price

performance before announcing their perks programs, which partially declined in the period after the

announcement. To rule out this explanation, as part of our empirical strategy we examine whether the

change in returns is related to the change in Merton’s (1987) shadow cost of capital. If the change in the

return is a function of the change in the shadow cost then we would expect a strong relation between an

expression for the shadow cost and the estimated alpha.

To measure the change in shadow cost, we follow a procedure in Kadlec and McConnell (1994)

and measure the change in shadow cost, Δλ, as:

12 To avoid contamination from a perk announcement effect, we run the regression for each firm for a 208-week

period using weeks –105 through –2 before the shareholder perk declaration week and weeks +2 through +105 after

the shareholder perk announcement week. The results are qualitatively similar when we use monthly data. We

winsorize each variable at its 1st and 99th percentiles.

18

∆𝜆𝑖 = [(𝑅𝑉𝐴𝑅𝑖∗𝑅𝐸𝐿𝐶𝐴𝑃𝑖

𝑁𝐼𝑁𝐷𝑖,𝑝𝑜𝑠𝑡) − (

𝑅𝑉𝐴𝑅𝑖∗𝑅𝐸𝐿𝐶𝐴𝑃𝑖

𝑁𝐼𝑁𝐷𝑖,𝑝𝑟𝑒)] ∗ 10,000 (2)

where NINDi,pre and NINDi,post are the number of individual shareholders at the end of the most recent

fiscal year before the perk initiation announcement date and at the fiscal year end in the year after the

announcement date for firm i.13 RVAR is the stock’s residual variance calculated from daily returns over

the 104 week post-adoption period. RELCAP is the firm’s market capitalization of its common stock at

the end of the month before the adoption announcement date divided by the contemporaneous level of the

TOPIX Index.

In Section 4 we report the estimated alpha from Equation (1) and show the change in the required

return is not only negative and significant but also that the change is monotonically related to the

expression for the change in shadow cost around perk initiations as measured in Equation (2). Testing for

evidence of the Shareholder Interest hypothesis by looking at changes in the required return and then

relating that change to the shadow cost of capital provides two new corroborating pieces of evidence in

favor of the Shareholder Interest hypothesis. This empirical approach is market-based, much like the

announcement return tests, but provides new information relative to the announcement returns in that the

4 weeks immediately around the perk announcement are excluded from the analysis. Unlike the DiD

results, this approach does not depend on the identification of a matched sample and in effect uses the

perk-initiating firms as their own matched sample.

13 See also Foerster and Karolyi (1999), Baker et al. (2002), and Chen et al. (2004). Kadlec and McConnell (1994)

use the total number of shareholders to compute the cost of capital instead of the number of individual shareholders.

We use the number of individual shareholders because shareholder perks are likely to have their primary impact on

the number of individual shareholders. We obtain similar results, however, using the total number of shareholder to

measure the change in the cost of capital.

19

3.1.5 Empirical Strategy: Two Additional Tests for Evidence of Entrenchment

We supplement the above tests with 2 additional tests. Our purpose in doing this is to broaden

the number of ways in which we can detect possible evidence of the Managerial Interest hypothesis

inasmuch as the predictions around the shadow cost were unique to the Shareholder Interest hypothesis.

Thus as an alternative way to test for evidence of entrenchment, we adopt the approaches and intuition

used in Masulis, Wang, and Xie (2007) and Faleye (2007) who document that entrenched managers tend

to have worse acquisition announcement returns and weaker CEO-turnover sensitivity to firm

performance.

The advantage in looking at the announcement returns for acquisitions made by perk and non-

perk firms is that it provides a setting separate from the announcement of the perk itself. The logic for

this test is that if shareholder perks increase the shareholder base in such a way that they lead to greater

entrenchment then we would expect to see evidence of relatively poorer acquisition decisions going

forward. Similarly, if perks lead to entrenchment then we would expect to see relatively weaker CEO-

turnover sensitivity to firm performance at perk firms. As reported in Section 4 we find evidence for

neither of these predictions.

3.2. Shareholder Perk Determinants – Matching Model

The univariate comparisons in Table 3 show that firms that initiate new shareholder perk

programs differ from other non-perk firms across multiple dimensions. In Table 4 we utilize these

relationships in a multivariate model to explain the likelihood that a firm initiates a new shareholder perk

program. Column 1 reports the pre-match relation between these variables, as well as those discussed in

Section 3.1.2, with respect to the likelihood of initiating a new shareholder perk. The data used to

estimate the results in the left column include all non-perk firm years and the dependent variable is set to

1 if the firm initiated a new shareholder perk in the following year. The significant results in column 1

20

underscore why we employ a matching procedure before comparing the non-perk firms that initiate perk

programs the following year with the other non-perk firms. The first column results also indicate that the

specification captures a reasonable amount of variation in the decision variable with a Pseudo R-square

value of 10.2%.

[Insert Table 4]

Using a nearest neighbor matching approach with replacement based on the propensity scores

from the first column, we identify the 5 non-perk-firms in year t-1 (control group) that are observationally

most similar to each firm that will initiate a new shareholder perk in year t (treatment group) in terms of

the variables in t-1 (for a discussion of the matching procedure see Rosenbaum and Rubin (1983) and

Smith and Todd (2005)). We purposefully make the match using pre-determined data from a year before

the treatment group initiates a perk to ensure that the covariates used in matching are not in turn affected

by the treatment (see discussion in Roberts and Whited (2012) and Caliendo and Kopeinig (2008)).

Column 2 in Table 4 provides information on the quality of the matching procedure by re-

estimating the probit model on the set of 544 treatment and 2,720 control firms. As intended, the

matching procedure identified a set of control firms that are not significantly different than the treatment

firms along the dimensions included in the matching model; both the control and treatment firms are

equally likely to initiate a new shareholder perk in year t based on the set of observables in year t-1. The

similarity between the control and treatment groups is shown not just by the lack of significant

coefficients in column 2 but also by the Pseudo R-square value which is indistinguishable from 0 after the

match (e.g. see discussion in Rosenbaum and Rubin (1983) and Caliendo and Kopeinig (2008)).

As discussed further in Section 5, when investigating the parallel trends assumption we show that

the same sets of control and treatment firms described above experience parallel trends in both their MVE

and liquidity going from year t-2 to year t-1 confirming that the control and treatment firms did in fact

experience parallel trends in the outcome variables of interest in the years leading up to t. This finding

21

supports the argument that the trends are not being materially affected by unobservable factors.

Following the guidelines in Caliendo and Kopeinig (2008), as tabulated in Appendix tables A.3 and A.4,

we perform two additional robustness tests to ensure (1) common support between the treatment and

control groups by comparing the distribution of the propensity scores for the treatment and control

groups, and (2) covariate balance is preserved after the match by testing whether the means of the

matching variables statistically differ between the treatment and control groups in year t-1. In both cases

we find that our matching approach works well according to the guidelines in the literature.

3.3. Changes in the Shareholder Base around New Shareholder Perk Programs

As noted in the introduction, perk yields tend to be largest for small shareholders. If investors

care about perks then the number of small shareholders should increase with shareholder perks. In Table

5 we report evidence of this increase. Panel A reports the direct change in these measures from the year

before the perk was initiated (year t-1) to 1 and the 2 years after (t+1 and t+2). The single differences

capture actual changes at the firms involved and are not interpreted relative to a matched sample. The

results show that the number of unique individual retail shareholders, the ratio of these shareholders to the

overall number of distinct owners, and the percent of ownership held by retail investors increased from

the year before to the year after the perk initiations in a significant manner. The Panel A results also

show a corresponding decrease in the board ownership, in the ownership by the Top 10 shareholders, and

in the ownership of institutional investors.

The Panel A results are consistent with the incentives provided by shareholder perks but, as is

true of single difference results in general, it is possible that unobservable factors influence the

shareholder base across this same time period. Hence, in Panel B we account for the effect of such

unobservable factors by calculating the DiD in these measures relative to the matched control group. A

DiD approach accounts for the unobservable factors if such factors would have affected the control group

22

over the same time period in the same way. The DiD results, in general, corroborate the single difference

results showing a strong increase in small shareholders from the year before to the year after the perk

initiations relative to the control group.

[Insert Table 5]

4. Results

In this section we tabulate and discuss the main empirical results following the empirical strategy outlined

in Section 3.1.

4.1. Evidence of value creation – announcement returns

For the announcement return tests, we focus on the subset of 307 new perk announcements that are

not contaminated by simultaneous announcements of stock splits, earnings, changes in the trading unit

size, or are announced within 150 days of their IPO.14 To measure abnormal stock returns we use a one-

factor market model. Abnormal returns are computed as follows:

𝐴𝑅𝑖,𝑡 = 𝑅𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 − 𝛼�̂� − 𝛽�̂�𝑅𝑀𝑡 (3)

𝐶𝐴𝑅𝑖(−1,1) = ∑ 𝐴𝑅𝑖,𝑡1𝑡=−1 (4)

where Returni,t is the stock return on day t for firm i, and RM is the value-weighted return for all listed

firms. ARi,t is the abnormal return for firm i on day t. Coefficient estimates are obtained using an ordinary

least squares regression using returns from days -150 through -11 relative to the announcement day.

CARi(-1,1) is the cumulative abnormal return for firm i from day -1 through day 1 relative to the

14 We could find announcement dates for 429 of the 544 new perk programs using the eol ESPer database. Of these,

122 of the perk announcements co-occur with announcements of stock splits, earnings, changes in the trading unit

size, or are announced within 150 days of their IPO. So the announcement return tests are based on the 307 pure

perk announcements. To ensure focusing on the sample of 307 announcements does not introduce selection bias in

our tests we compare the ownership characteristics for the 307 observations with the other 237 observations in

Appendix Table A.5. The two subgroups do not differ significantly in 5 of the 6 ownership measures but are slightly

different in terms of the #Individual/#total shareholders measure.

23

announcement. Over the three-day period centered on the announcement day, the cumulative average

abnormal return is 2.06% with a t-statistic of 5.02. These results are consistent with the Shareholder

Interest hypothesis and inconsistent with the Managerial Interest hypothesis.

4.2. Evidence of value creation – difference-in-difference tests

The announcement returns suggest that shareholder perk announcements are associated with

expected value creation. In this section we explore this relation further using annual data in a DiD

framework. In the DiD tests, the value-related outcome variable of interest is the firm’s MVE. The

identifying assumption is that the change in the MVE across years would have been similar at the

treatment and control firms across event time except for the effect of treatment. The advantage of using a

propensity score matched sample in a DiD framework is that it allows us to control for both observable

and unobservable factors. The observable factors are accounted for in the selection of the matched group

(e.g., Table 4). The unobservable factors are accounted for because the DiD estimate compares the

change in MVE at the treatment firms with the change at the control firms over the same time periods.

Although the DiD estimates are calculated in event time, it is worth noting that each treatment

firm, and its set of control firms, are actually being drawn from various underlying calendar years across

a 10 year period. Having event time observations occur across an underlying 10-year calendar period in a

DiD framework helps mitigate concerns that macro-level-related unobservable factors might be

explaining observed differences in the changes in MVE because such unobservables would have to

systematically affect treatment firms specifically over narrow ranges of event time differently than for

control firms despite (1) the fact that treatment and control firms are matched on observables at the

beginning of the event time range and (2) the macro and industry environments vary widely over calendar

time across the 10 years in the sample.

[Insert Table 6]

24

Table 6 Panel A reports the DiD estimates spanning the treatment period. Whether we look at

changes in the MVE or changes in the ln(MVE) we find evidence that firm value increased more at

treatment firms than at control firms over the same time horizons. These results corroborate the

announcement return results and are consistent with the Shareholder Interest hypothesis. Table 6 Panel B

reports the results from a robustness test related to the parallel trends assumption where the same

treatment and control firms’ MVE is compared across the two years leading up to the perk. As expected

with the parallel trends assumption, the DiD results in Panel B are insignificant. We talk more about

these results in Section 5.

4.3. Evidence of value creation –panel regression tests

Both the announcement return results and the DiD results show that firms that initiate shareholder

perk programs experience increases in firm value. Both of these tests are focused on the subset of firms

that initiate new perk programs during our sample period. We check this conclusion using the full sample

of public Japanese firms from 2001 – 2011. If shareholder perks are associated with increases in value,

as suggested by both the prior tests, then in the cross section of the full sample we would expect to

observe, all else equal, that firms with shareholder perks are associated with higher valuations. Thus in

Table 7 we model each firm’s MVE and Tobin’s Q as a function of control variables as well as an

indicator for whether the firm utilizes shareholder perks. We note that these regressions document

correlations only and are intended, as such, to simply corroborate the intuition from the announcement

return and DiD tests using the full sample. We model firm value using MVE in year t and year t+1 in the

first two columns and Tobin’s Q in years t and t+1 in the second two columns. The Tobin’s Q measure

has the advantage in this setting that the valuation is scaled by assets. In these specifications we control

for firm size, profitability, age, and advertising as well as firm, industry, and year fixed effects.

25

Following Anderson and Reeb (2003) we also control for return volatility in these regressions. The errors

are clustered both by firm and year. We find corroborating evidence in both the MVE and Tobin’s Q

models for the implications of the earlier tests in that firms that use shareholder perks are associated with

higher valuations.

[Insert Table 7]

4.4. Evidence of changes in liquidity – difference-in-difference tests

For the reasons discussed in Sections 3.1.2 and 4.2, we use a DiD framework to investigate the

effects that shareholder perks have on liquidity. Because we don’t have access to either the historical bid-

ask spread data or quoted depth, we follow Amihud (2002) and use a measure of relative price impact as

a measure of illiquidity. The illiquidity measure (ILLQ) is calculated as follows:

𝐼𝐿𝐿𝑄𝑖,𝑦 = 1/𝐷𝑖,𝑦 ∑|𝑅𝑖𝑦𝑑|

𝑉𝑂𝐾𝐷𝑖𝑦𝑑

𝐷𝑖𝑦𝑡=1 ∗ 1,000,000 (5)

Riyd is the return on stock i on day d of year y and VOLDiyd is the daily volume in yen. D is the number of

days for which data are available for stock i in year y. This ratio gives the absolute (percentage) price

change per dollar of daily trading volume, or the daily price impact of the order flow. We limit the sample

to those firm-year observations with at least 100 days of data in a given year. As shown in Table 8 Panel

A the illiquidity measure decreases more (i.e., liquidity increases) at the treatment firms than the control

firms and the differences are significant at the 1% level. This finding is consistent with the Shareholder

Interest hypothesis. Table 8 Panel B reports the results from a robustness test related to the parallel

trends assumption where the treatment and control firms’ ILLQs are compared across the two years

leading up to the perk. As expected for the parallel trends assumption the DiD results are not significant

in Panel B. We talk more about these results in Section 5.

[Insert Table 8]

26

4.5. Evidence of changes in liquidity – panel regression test

The DiD results in Section 4.4 indicate that firms that initiate perk programs experience a

significant increase in liquidity compared to the control firms over the same time horizon. This finding

implies that in the full sample of public firms we would expect to find corroborating evidence of

shareholder perks being associated with higher liquidity. To test this we follow Grullon, Kanatas, and

Weston (2004) in modeling liquidity using a panel regression. We follow their choice of control

variables and control for advertising, firm age, ROA, ln(MVE), 1/share price, ln(share turnover), a

measure of return volatility, as well as year and firm fixed effects. Our results show that shareholder

perks are associated with higher liquidity (lower illiquidity) as predicted by the Shareholder Interest

hypothesis and as shown using the DiD tests.

[Insert Table 9]

4.6. Evidence of changes in required returns – time series regression, shadow costs

The results in Sections 4.1 – 4.3 show that shareholder perks are associated with higher firm

value. Based on intuition from Merton (1987), as described in Section 3.1.4, the Shareholder Interest

hypothesis predicts that the increased firm value is driven via a reduction in the required rate of return

and that the reduction in the required return corresponds with a reduction in the shadow cost of capital.

Thus in this section we do two things: First, we use Equation (1) to test for evidence of a reduction in the

required rate of return following a perk initiation using the set of firms that initiate new perks during our

sample period. Second, we then categorize these firms into quartiles according to Kadlec and McConnell

(1994)’s expression for the change in shadow cost as described in Equation (2) and show that the change

in required return is monotonically related to the corresponding change in shadow cost expression. The

combination of these findings is strongly supportive of Merton’s (1987) predictions.

27

We estimate the return model described in Equation (1) using weekly data from 105 to 2 weeks

before the shareholder perk announcement as well as data from 2 to 105 weeks following the

announcement. As discussed in Section 3.1.4, the coefficient of interest for our purposes in this model is

𝛼1 representing the difference in the required returns for firms that initiate perks going from before to

after the perk initiation. In Table 10 we report the overall sample 𝛼1estimate. Consistent with the

Shareholder Interest hypothesis we find that the average weekly required return is .157% lower in the

post-perk period than in the pre-perk period.

[Insert Table 10]

In the lower portion of Table 10 the 𝛼1estimate is reported for each quartile of change in shadow

cost. As predicted the largest changes in the required returns occur in the quartiles that experience the

largest change in the shadow cost of capital.

4.7. Evidence of entrenchment? – acquisition announcement returns and CEO-performance-turnover

Thus far all of the evidence has been consistent with the predictions of the Shareholder Interest

hypothesis. As an alternative way to test for evidence of entrenchment, we adopt the approaches and

intuition used in Masulis, Wang, and Xie (2007) and Faleye (2007) and test whether perks are associated

with worse acquisition announcement returns or weaker CEO-turnover sensitivity to firm performance.

In Table 11 we report the results from these tests. In columns 1 and 2 we model the acquisition

announcement returns as a function of whether the firm uses shareholder perks as well as a set of control

variables. Following Masulis et al. (2007) but adapted for Japanese data we focus on (1) completed

acquisitions, (2) where the acquirer controlled less than 50% of the target shares prior to the

announcement and owns 100% of share after the transaction, (3) the deal value disclosed in the Recof

Japanese merger database is more than 100 million yen and is at least 1% of the acquirer’s market value

28

of equity measured on the 11th trading day prior to the announcement date, and (4) the acquirer has

financial data available via the Nikkei Financial Quest database. The sample includes 868 acquisitions

during our sample period of which 227 acquiring firms had shareholder perks. We do not find that firms

that use shareholder perks have lower acquisition announcement returns. This is inconsistent with the

Managerial Interest hypothesis.

[Insert Table 11]

In columns 3-5 of Table 11 we report on whether there is an interaction effect at firms that use

shareholder perks and a reduction in CEO turnover sensitivity to performance. In these regressions we

control for accounting and market performance, CEO age and tenure as well as industry and year effects.

The key variables of interest are the interactions in the second and third rows and are not significant;

there is no evidence that the sensitivity of CEO turnover to firm performance is different at perk firms

than at non-perk firms. These results are not consistent with the Managerial Interest hypothesis.

5. Robustness considerations

In this section we discuss several additional tests that provide information on the robustness of

our results and rule out several plausible alternative explanations. The associated tables are tabulated in

the appendix but referenced here.

5.1. Alternative explanations for link between perks and increased firm value?

In Section 4 we report evidence that shareholder perks are associated with increased firm value.

This conclusion is supported by positive significant perk announcement returns, in single-difference and

DiD tests that track changes in the MVE in the year before to the years following the perk with and

without a matched sample, and in full-sample panel regressions. Consistent with intuition from Merton

(1987) and Amihud, Mendelson, and Uno (1999) we also find a corresponding reduction in the cost of

29

capital and an increase in liquidity around perk events. All of these findings are consistent with the

Shareholder Interest hypothesis but are there other explanations for the results?

One possible alternative explanation could focus on the possible advertising nature of shareholder

perks. If perks also serve as product advertisements then the increase in firm value could be partially

driven by increased future sales stemming from increased advertising. This alternative explanation need

not contradict the motivation of the Shareholder Interest hypothesis but it might represent a separate

channel through which perks lead to increased firm value. In some ways we have tried to guard against

this alternative in the earlier tests by including advertising as one of the matching variables in Table 4 and

by controlling explicitly for advertising in Tables 7 and 9; the effect that perks have in Tables 7 and 9 are

above and beyond the effect that general advertising might have on these outcomes.

But to be thorough we propose two additional tests related to this alternative explanation. First,

we model the perk announcement returns as a function of whether the firm uses its own product as the

perk. Arguably, firms that use their own product as the perk achieve more targeted product-market

advertising, so if the announcement returns are reflecting the expected effect of advertising then the

announcement returns should be higher for the subset of firms who use their own product. This test is

tabulated in the Appendix Table A.2. We do not find evidence that the CARs are higher for firms using

their own product as the perk.

For the second robustness test we model the perk announcement returns as a function of the

change in sales (Sales t+2/Sales t-1). If perks enhance value via an effective product market advertising

channel then we would expect a relation between the announcement CARs and a corresponding change in

sales. As reported in Appendix Table A.2 we do not find this outcome and if anything find a slight

negative correlation.

30

5.2. Checking the parallel trends assumption as part of a larger argument that unobservable factors are

unlikely to explain our results

Our conclusions that shareholder perks are associated with both increased firm value and higher

liquidity are not solely dependent on the DiD framework given the supporting evidence found using other

methodologies including announcement returns, panel regressions, and in the monotonic relation between

changes in required returns and shadow costs. Nevertheless, the DiD framework provides some of the

main results in the paper and we discuss here some robustness tests we use to check the internal validity

of our model and thereby to provide some corroborating evidence that the parallel trends assumption has

been satisfied.

As discussed in Sections 3.1.2 and 3.2, we match the treatment and control firms using

observables in year t-1 relative to the perk initiation. For identification we have to assume that the

response variable(s) (MVE and ILLQ in our tests) at both the treatment and control firms would have

experienced parallel trends going from year t-1 to year t+1 if not for the initiation of the perk programs.

This assumption is not testable. But what is testable is whether the same response variables experienced

parallel trends going from year t-2 to year t-1. We tabulate these tests in Panel B of Tables 6 and 8 and

find that the parallel trends assumption was satisfied in the years leading up to the perk.

The advantage of performing these robustness tests is not just to find evidence supportive of the

parallel trends assumption, but in combination with the main DiD results and the announcement returns

these additional results provide a strong argument that unobservable factors are not driving changes in our

main outcome variables. To see this consider that if the perks themselves are not driving the

announcement CAR results then the information content of the omitted variable would have to become

suddenly obvious to investors at the time of the perk announcement in order to explain the observed

market reaction to the perk-announcements. Yet, the combination of the main DiD results (Panel A in

Tables 6 and 8) with the parallel trends tests (Panel B in Tables 6 and 8) show that the changes in the

31

response variables (MVE and liquidity) of perk-initiating firms from the year before to the year after the

perk initiation are statistically different than the changes in the same variables across the same years for

the matched sample but that the changes in these variables were not statistically different between the two

groups of firms in the years leading up to the perk program. Together these results require that for an

omitted variable at the perk-initiating-firms to explain either the value or liquidity DiD results it must be

the case that (1) the omitted variable was not a significant determinant of changes in either MVE or

liquidity across the years leading up to the perk-program, (2) yet, despite not having been an influence for

either MVE or liquidity in the past, the information content of the omitted variable must become suddenly

obvious to investors at the time of the perk announcement to explain the announcement returns, and (3)

then the omitted variable must become a driving force in the changes in both MVE and liquidity for just

the perk-initiating firms in the years after the perk announcement. This explanation seems unlikely.

6. Conclusion

In this paper we call attention to shareholder perks as a way that managers return value to

shareholders that disproportionately benefits small investors. Although roughly equal numbers of firms

pay shareholder perks as cash dividends, this appears to be the first empirical research into how perks

affect firms’ ownership structure and value. We find that firms that initiate new perk programs

experience a significant increase in their shareholder base and decrease in the concentration of their

ownership structure. Initiating firms also experience an increase in firm value, on average, as measured

in both short-term returns and longer-term value DiD tests. We do not find evidence that the use or

initiation of shareholder perk programs exacerbates managerial agency problems, as measured by

acquisition returns and CEO turnover sensitivity to firm performance.

We further explore the channels by which perks create value. Firms initiating perks experience

an increase in share liquidity and decrease in the cost of capital. Similarly, in panel regressions, perk-

32

paying firms also have relatively liquid shares and low costs of capital. These results imply that

shareholder perks tend to serve shareholder interests by attracting small shareholders, increasing share

liquidity, and decreasing the cost of capital. The decreases in firms’ cost of capital are mirrored by

decreases in Merton’s (1987) shadow cost of capital, further suggesting that perks help to mitigate an

information problem that causes small investors to underdiversify and bear idiosyncratic risk. These

results belie any suspicion that shareholder perks have insignificant effects. To the contrary, perks appear

to represent a private contracting solution that offsets some of the negative consequences of the real

information costs that affect shareholders’ portfolio choices and returns.

33

References

Amihud, Y., Mendelson, H., & Uno, J. (1999). Number of shareholders and stock prices: Evidence from

Japan. The Journal of Finance, 54(3), 1169-1184.

Anderson, R., & Reeb, D. (2003). Founding-Family Ownership and Firm Performance: Evidence from

the S&P 500. The Journal of Finance, 58(3), 1301-1328.

Baker, H. K., Nofsinger, J. R., & Weaver, D. G. (2002). International cross-listing and visibility. Journal

of Financial and Quantitative Analysis, 37(03), 495-521.

Berkman, H., Koch, P., & Westerholm, P. (2014). Informed Trading through the Accounts of Children.

Journal of Finance, 69, 363-404.

Brumley (2014). http://investorplace.com/2014/08/shareholder-perks/#.Ve3eNbR5IWQ))

Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity

Score Matching. Journal of Economic Surveys, 22(1), 31-72.

Chen, H., Noronha, G., & Singal, V. (2004). The price response to S&P 500 index additions and

deletions: Evidence of asymmetry and a new explanation. The Journal of Finance, 59(4), 1901-

1930.

Choi, J., Jin, L., & Yan, H. (2016). Informed Trading and Expected Returns. Working paper available at

SSRN: http://ssrn.com/abstract=2193733 or http://dx.doi.org/10.2139/ssrn.2193733

Dhaliwal, D., & Li, O. Z. (2006). Investor Tax Heterogeneity and Ex‐ Dividend Day Trading Volume.

The Journal of Finance, 61(1), 463-490.

Eades, K. M., Hess, P. J., & Kim, E. H. (1984). On interpreting security returns during the ex-dividend

period. Journal of Financial Economics, 13(1), 3-34.

Elton, E. J., & Gruber, M. J. (1970). Marginal stockholder tax rates and the clientele effect. The Review of

Economics and Statistics, 52(1), 68-74.

Eng, L. L., & Mak, Y. T. (2003). Corporate governance and voluntary disclosure. Journal of Accounting

and Public Policy, 22(4), 325-345.

Fama, E. F., & French, K. R. (2001). Disappearing dividends: changing firm characteristics or lower

propensity to pay? Journal of Financial Economics, 60(1), 3-43.

Foerster, S. R., & Karolyi, G. A. (1999). The effects of market segmentation and investor recognition on

asset prices: Evidence from foreign stocks listing in the United States. The Journal of Finance,

54(3), 981-1013.

Glosten, L. R., & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist market with

heterogeneously informed traders. Journal of Financial Economics, 14(1), 71-100.

Glosten, L. R., & Harris, L. E. (1988). Estimating the components of the bid/ask spread. Journal of

Financial Economics, 21(1), 123-142.

Grossman, S. J., & Hart, O. D. (1980). Takeover bids, the free-rider problem, and the theory of the

corporation. The Bell Journal of Economics, 42-64.

34

Grullon, G., Kanatas, G., & Weston, J. P. (2004). Advertising, breadth of ownership, and liquidity.

Review of Financial Studies, 17(2), 439-461.

Kadlec, G. B., & McConnell, J. J. (1994). The effect of market segmentation and illiquidity on asset

prices: Evidence from exchange listings. The Journal of Finance, 49(2), 611-636.

Kelly, B. & Ljungqvist, A. (2012). Testing Asymmetric-Information Asset Pricing Models. Review of

Financial Studies, 25(5), 1366-1413.

Lakonishok, J., & Vermaelen, T. (1986). Tax-induced trading around ex-dividend days. Journal of

Financial Economics, 16(3), 287-319.

Max, S., (2015). Do shareholders still get perks from companies? Time.com Money, online at

http://time.com/money/4006977/do-shareholders-still-get-perks-from-companies/, accessed on

September 21, 2015.

Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information. The

Journal of Finance, 42(3), 483-510.

Michaely, R. (1991). Ex-dividend day stock price behavior: The case of the 1986 Tax Reform Act. The

Journal of Finance, 46(3), 845-859.

Michaely, R., & Vila, J. L. (1995). Investors' heterogeneity, prices, and volume around the ex-dividend

day. Journal of Financial and Quantitative Analysis, 30(02), 171-198.

Moyer, Liz. 2013. Stocks with benefits, The Wall Street Journal, December 13, 2013. Accessed online at

http://www.wsj.com/articles/SB10001424052702303330204579250763759331636.

Poterba, J. M., & Summers, L. H. (1984). New evidence that taxes affect the valuation of dividends. The

Journal of Finance, 39(5), 1397-1415.

Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for

causal effects. Biometrika 70(1), 41-50.

Shleifer, A., & Vishny, R. W. (1986). Large shareholders and corporate control. The Journal of Political

Economy, 461-488.

Smith, J., & Todd, P. (2005). Does matching overcome LaLonde’s critique of nonexperimental

estimators? Journal of Econometrics 125(1-2), 305-353.

35

Figure 1: Number and percentage of Japanese firms paying shareholder perks,

2001-2011.

The figure shows the number and percent of public Japanese firms offering shareholder

perks from 2001-2011. The shareholder perk data is from the Japanese Company

Handbook.

0

0.05

0.1

0.15

0.2

0.25

0.3

400

500

600

700

800

900

1000

1100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

# of firms with shareholder perks

% of firms with shareholder perks

Number

36

Table 1: Empirical predictions for the Shareholder Interest and Managerial Interest hypotheses. The empirical results in the last column are based on the analysis summarized in Tables 5-11. The empty spaces in the last column for prediction 6 indicate that there was no significant difference in acquisition announcement returns or in CEO-turnover-performance between perk and non-perk firms.

Shareholder Interest

Hypothesis Predicted

Signs

Managerial Interest

Hypothesis Predicted

Signs

Empirical

Results

(1) perk announcement returns

(2) change in firm value

(3) change in liquidity

(4) change in required return

(5) change in shadow cost of capital

(6) a. acquisition announcement returns

b. CEO-turnover-performance

sensitivity

+

+

+

-

-

-

-

-

+

-

-

+

+

+

-

-

37

Table 2: Distribution of the sample across Nikkei small size industry classifications

The prevalence of shareholder perks is shown by industry for the 2-digit Nikkei industry classifications. Financial industries are not included in the sample.

Industry name # of firms in each industry % of firms in industry with

perks

Air Transportation 58 32.8%

Chemicals 2,191 15.4%

Communication Services 336 30.7%

Construction 2,217 10.1%

Drugs 538 16.7%

Electric & Electronic Equipment 3,166 5.3%

Fish & Marine Products 107 38.3%

Foods 1,515 69.5%

Iron & Steel 610 4.4%

Machinery 2,631 6.2%

Mining 94 8.5%

Motor Vehicles & Auto Parts 901 13.9%

Non-ferrous Metal & Metal Products 1,466 10.6%

Other Manufacturing 1,241 29.2%

Petroleum 114 1.8%

Precision Equipment 546 7.3%

Pulp & Paper 272 15.8%

Railroad Transportation 334 97.9%

Real Estate 1,098 23.0%

Retail Trade 2,728 67.0%

Rubber Products 244 14.3%

Sea Transportation 197 25.4%

Services 6,806 30.8%

Shipbuilding & Repairing 69 0.0%

Stone, Clay & Glass Products 754 10.7%

Textile Products 669 14.6%

Transportation Equipment 149 20.8%

Trucking 395 29.4%

Utilities - Electric 117 0.0%

Utilities - Gas 131 0.0%

Warehousing & Harbor Transportation 453 11.9%

Wholesale Trade 3,979 25.2%

38

Table 3: Univariate Descriptive Statistics This table reports summary statistics for the 2001 to 2011 sample. The sample contains 8,911 (26,840) firm-years in which firms offered (did not offer) shareholder perks. Panels A and B report on descriptive firm characteristics as well as measures of the ownership structure using data from the end of the previous fiscal year. The first two columns report information for the total sample. The second and third groups of columns report on firm-years with shareholder perks (Perk firms), and firm-years without perks (Non-perk firm years). The fourth and fifth groups of columns divide the non-perk firm years according to whether the firm initiates a new perk in the following year (Initiation) or not (Non-initiation). In Panel A, Market value of assets is the sum of the market capitalization and the book value of debt. Leverage is the sum of short- and long-term interest bearing debt divided by the book value. ROA is the operating profit divided by the book assets. Excess cash is the residual from regressing cash holdings on firm-specific characteristics and represents the firm’s excess cash holdings. Tobin’s Q is the sum of market capitalization and book debt over book assets. Dividend is a dummy variable for whether the firm pays a dividend.% of outside directors is the percent of directors that outsiders based on the outsider classifications in the Toyo Keizai database. Anti-takeover is a dummy variable for whether the firm adopts a rights plan that can be used as an anti-takeover defense. In Panel B, the various ownership variables are defined using data from the Nikkei Financial Quest database. Within this database each shareholding is classified as pertaining to one of six groups: individuals, financial institutions, financial instruments firm, foreign investors, non-financial firms, or government holdings. The 10 largest shareholders are also identified and can be part of any of the 6 investor classifications. #Individual shareholders is the total number of unique individual retail shareholders. #Individual/#total shareholders is the proportion of distinct shareholders that are retail investors. %Board ownership is the percent of shares outstanding owned by board members. %Top 10 Ownership is the percent of shares outstanding owned by the largest 10 shareholders whether they be individuals or institutions. Individual retail ownership is calculated by subtracting the ownership of any individuals who are among the firm’s Top 10 shareholders from total individual ownership and is intended to provide an aggregate measure of small shareholdings. %Retail ownership is the proportion of shares outstanding held by this group. %Institutional ownership is the proportion of shares outstanding held by financial firms and financial instruments firms. Statistical significance levels are based on cross-sectional t-statistics. * and *** indicate significance at the 10% and 1% levels, respectively, in two-tailed tests.

Total

(N = 35,751)

Perk firms

(N = 8,911)

Non-perk firm-years

Total non-perk

(N = 26,840)

Initiation

(N = 544)

Non-initiation

(N = 26,296)

Diff

(A) -

(B)

t-statistics

Mean Median

Mean Median

Mean Median Mean (A) Median

Mean (B) Median

Panel A: Firm characteristics

Market value of assets (million yen) 197,474 26,989

168,144 30,846

207,212 25,623

156,536 27,032

208,261 25,582 -51,725 -1.11

Leverage 0.218 0.183

0.237 0.201

0.212 0.177

0.213 0.178

0.212 0.177 0.00 0.15

ROA 0.046 0.041

0.052 0.045

0.044 0.040

0.067 0.059

0.043 0.039 0.02 7.96 ***

Excess Cash 0.000 -0.012

-0.010 -0.018

0.003 -0.009

0.002 -0.009

0.003 -0.009 0.00 -0.23

Tobin’s q 1.166 0.973

1.155 1.007

1.170 0.962

1.387 1.051

1.166 0.961 0.22 6.52 ***

Dividend 0.821 1.000

0.874 1.000

0.803 1.000

0.875 1.000

0.801 1.000 0.07 4.29 ***

% of outside director 0.265 0.250

0.272 0.250

0.262 0.250

0.263 0.250

0.262 0.250 0.00 0.19

Anti-takeover 0.065 0.000

0.084 0.000

0.058 0.000

0.028 0.000

0.059 0.000 -0.03 -3.09 ***

Panel B: Ownership information

#Individual shareholders 9,822 3,098

10,765 3,897

9,509 2,862

7,317 1,923

9,555 2,893 -2,237 -1.32

#Individual/#total shareholders 0.940 0.955

0.945 0.961

0.939 0.953

0.929 0.946

0.939 0.954 -0.01 -5.71 ***

%Board ownership 0.097 0.023 0.115 0.048 0.091 0.018 0.160 0.092 0.090 0.017 0.07 11.46 ***

%Top 10 ownership 0.543 0.537 0.550 0.553 0.540 0.532 0.584 0.588 0.539 0.530 0.04 6.40 ***

%Retail ownership 0.317 0.308

0.306 0.295

0.320 0.313

0.278 0.266

0.321 0.314 -0.04 -7.04 ***

%Institutional ownership 0.191 0.162

0.178 0.150

0.195 0.165

0.177 0.149

0.195 0.165 -0.02 -3.03 ***

39

Table 4: Propensity Score Matching Model and Diagnostics The sample starts with the full set of firm-years for public Japanese firms from 2001 – 2010 not offering shareholder

perks. The Pre-match columns report parameter estimates from a probit model using the full sample prior to

matching where the dependent variable is set to 1 if the firm in question initiates a shareholder perk program in the

following year. The propensity scores from the Pre-match model are used to identify the 5 nearest neighbor matches

for each treatment firm with replacement. The same model is then re-estimated in the Post-match column but using

only the matched subsample as opposed to the full sample. The matched subsample includes the 544 treatment

firms and their 2,720 respective matched control firms.

Pre-match Post-match

(1) (2)

ln(# of individuals) -0.147*** 0.03

(-3.84) (0.64)

#Individual/#total shareholders 0.327 -0.91

(0.70) (-1.37)

%Retail ownership -0.502 0.22

(-1.63) (0.51)

%Board ownership 0.770*** -0.03

(5.27) (-0.14)

%Institutional ownership -0.308 0.03

(-1.25) (0.09)

%Top 10 ownership -0.604** 0.27

(-2.18) (0.69)

Outside director ratio 0.206 -0.08

(1.36) (-0.37)

Diffence -0.003 0.07

(-0.02) (0.44)

ln(Market Asset) 0.082*** -0.00

(2.59) (-0.03)

ln(Advertising) 0.036*** 0.00

(4.17) (0.06)

Leverage -0.008 0.02

(-0.06) (0.12)

ROA 0.953** -0.21

(2.35) (-0.40)

ExCash -0.430** -0.10

(-2.52) (-0.41)

TobinQ -0.018 0.02

(-0.58) (0.50)

Dividend 0.088 0.02

(1.39) (0.19)

DiffMTU -0.008 -0.00

(-1.40) (-0.49)

Constant -2.329*** -0.47

(-2.80) (-0.40)

Year indicator variables Yes Yes

Industry indicator variables Yes Yes

Control 26,296 2,720

Control (unique observations) 26,296 2,424

Treatment 544 544

Observations 26,840 3,264

Pseudo R-Square 0.102 0.004

40

Table 5: Changes in Shareholder Base around New Perk Initiations

The table reports the single difference (Panel A) and difference-in-difference (Panel B) changes in 6 measures of the

shareholder base that occur around the 544 firm-years in our sample where firms initiated new shareholder perk

programs. In the table year t-1 is the fiscal period immediately before perk initiation which occurs in year t. Years

t+1 and t+2 represent the 2 fiscal periods immediately following the perk initiations. The control sample in Panel B

is comprised of the propensity score matched sample from Table 4. The variables are defined using data from the

Nikkei Financial Quest database. Within this database each shareholding is classified as pertaining to one of six

groups: individuals, financial institutions, financial instruments firm, foreign investors, non-financial firms, or

government holdings. The 10 largest shareholders are also identified and can be part of any of the 6 investor

classifications. #Individual shareholders is the total number of unique individual retail shareholders.

#Individual/#total shareholders is the proportion of distinct shareholders that are retail investors. %Board

ownership is the percent of shares outstanding owned by board members. %Top 10 Ownership is the percent of

shares outstanding owned by the largest 10 shareholders whether they be individuals or institutions. Individual retail

ownership is calculated by subtracting the ownership of any individuals who are among the firm’s Top 10

shareholders from total individual ownership and is intended to provide an aggregate measure of small

shareholdings. %Retail ownership is the proportion of shares outstanding held by this group. %Institutional

ownership is the proportion of shares outstanding held by financial firms and financial instruments firms.

%Institutional ownership is the proportion of shares outstanding held by financial firms and financial instruments

firms.

Panel A: Single Difference t-1 t+1 t+2 Difference

(t+2) -(t-1) t-stat

(1) #Individual shareholders Mean 7,314 9,450 10,927 3,500 6.56 ***

Median 1,919 3,573 4,088

(2) #Individual/#total shareholders Mean 0.926 0.950 0.956 0.031 12.46 ***

Median 0.943 0.964 0.967

(3) %Board ownership Mean 0.162 0.140 0.131 0.014 -8.84 ***

Median 0.091 0.060 0.058

(4) %Top 10 Ownership Mean 0.584 0.564 0.557 0.006 -5.91 ***

Median 0.588 0.566 0.559

(5) %Retail ownership Mean 0.278 0.297 0.309 0.014 7.15 ***

Median 0.266 0.283 0.299

(6) %Institutional ownership Mean 0.177 0.169 0.166 -0.012 -4.63 ***

Median 0.149 0.145 0.142

Panel B: Difference-in-Difference Difference

period Treatment Control DiD t-stat N

(1) #Individual shareholders (t+1) - (t-1) 2,064 650 1,414 4.87 *** 536

(t+2) - (t-1) 3,500 1,054 2,446 4.57 *** 522

(2) #Individual/#total shareholders (t+1) - (t-1) 0.024 0.006 0.019 18.34 *** 536

(t+2) - (t-1) 0.031 0.009 0.022 23.05 *** 522

(3) %Board ownership (t+1) - (t-1) -0.021 0.001 -0.022 -1.31 536

(t+2) - (t-1) -0.033 0.029 -0.062 -1.72 * 522

(4) %Top 10 Ownership (t+1) - (t-1) -0.019 -0.006 -0.012 -2.38 ** 536

(t+2) - (t-1) -0.023 -0.013 -0.010 -1.40 522

(5) %Retail ownership (t+1) - (t-1) 0.018 0.011 0.007 2.12 ** 536

(t+2) - (t-1) 0.029 0.020 0.008 3.23 *** 522

(6) %Institutional ownership (t+1) - (t-1) -0.008 -0.007 -0.001 -0.60 536

(t+2) - (t-1) -0.013 -0.012 0.000 -1.31 522

41

Table 6: Difference-in-difference estimates for changes in market value of equity

The table below reports the difference-in-difference (DiD) estimates for the change in the market value of

equity (MVE) at treatment firms (perk initiating firms) versus control firms. The sample spans 10 years

in calendar time (2001-2011). The control firms are selected according to the model discussed in Table 4.

Period (t-1) is the fiscal year prior to the announcement of the new perk.

Panel A – DiD estimates for MVE

Difference

period

Difference at

Treatment Firms

Difference at

Control Firms DiD z-stat N

MVE (t+1) - (t-1) 20,922 -3,330 24,252 2.19** 536

(t+2) - (t-1) 23,137 3,100 20,038 1.95* 522

ln(MVE) (t+1) - (t-1) 0.004 -0.042 0.045 2.08** 536

(t+2) - (t-1) 0.017 -0.059 0.076 2.78*** 522

Panel B – Parallel trends related robustness test

Difference

period

Difference at

Treatment Firms

Difference at

Control Firms DiD z-stat N

MVE (t-1) - (t-2) -10,399 940 -11,339 -0.52 475

lnMVE (t-1) - (t-2) 0.053 0.023 0.030 0.20 475

42

Table 7: Firm value as a function of shareholder perks

The table reports OLS coefficients from panel regressions where in columns 1-3 the dependent variable is

ln(MVE) and in columns 4-6 the dependent variable is Tobins Q. The sample includes all public

Japanese firms from 2001-2011 for which the control data is available. The control variables are

measured as of the end of the prior fiscal year. Return volatility is measured as the standard deviation of

ROA across the prior 5 years. The control variables are described in Table 3. t-statistics are reported in

parenthesis below the coefficients. Significance is shown at the 10%, 5%, and 1% levels using *, **, and

***, respectively. The errors are clustered by firm and year.

MVE t MVE t+1 Tobin’s Q t Tobin’s Q t+1

(1) (2) (3) (4)

Perk indicator variable 0.085*** 0.100*** 0.054** 0.055***

(4.92) (5.40) (2.33) (3.36)

ln(Advertising) 0.009** 0.004 0.008** 0.003

(2.25) (1.08) (2.45) (1.36)

ln(Age) -1.371*** -1.029*** -1.835*** -1.246***

(-6.11) (-4.90) (-4.18) (-3.54)

ln(Asset) 0.653*** 0.273*** -0.310*** -0.368***

(25.85) (4.32) (-5.55) (-7.17)

ROA 2.915*** 2.142*** 2.745*** 0.994***

(13.00) (7.86) (8.09) (4.80)

Leverage -1.288*** -0.782*** 0.187 0.361***

(-13.39) (-5.06) (1.41) (4.76)

ln(Return volatility) 0.201*** 0.081*** 0.406*** 0.133***

(3.50) (2.79) (6.31) (3.54)

Constant -1.336 6.661*** 14.867*** 14.404***

(-1.16) (3.30) (5.38) (5.98)

Year indicator variables Yes Yes Yes Yes

Firm indicator variables Yes Yes Yes Yes

Observations 35,751 35,052 35,751 35,090

Adjusted R-squared 0.961 0.952 0.702 0.695

43

Table 8: Difference-in-difference estimates for changes in liquidity

The table below reports the difference-in-difference (DiD) estimates for the change in a measure of

illiquidity (ILLQ) at treatment firms (perk initiating firms) versus control firms. The illiquidity measure

follows Amihud (2002) and is calculated as:

𝐼𝐿𝐿𝑄𝑖,𝑦 = 1/𝐷𝑖,𝑦 ∑|𝑅𝑖𝑦𝑑|

𝑉𝑂𝐾𝐷𝑖𝑦𝑑

𝐷𝑖𝑦

𝑡=1

∗ 1,000,000

where Riyd is the return on stock i on day d of year y and VOLDiyd is the daily volume in yens. D is the

number of days for which data are available for stock i in year y. Panel A – DiD tests for ILLQ

Difference

period

Difference at

Treatment Firms

Difference at

Control Firms DiD z-stat N

ILLQ (t+1) – (t-1) -0.221 -0.042 -0.179 -3.40*** 521

(t+2) – (t-1) -0.276 -0.038 -0.238 -3.34*** 507

Panel B – Parallel trends related robustness test

Difference

period Treatment Control DiD z-stat N

ILLQ (t-1) - (t-2) -0.092 -0.162 0.070 1.53 427

44

Table 9: Liquidity as a function of shareholder perks

The dependent variable in the OLS regressions shown below is a measure of relative price impact, or

illiquidity, discussed in Amihud (2002). The illiquidity measure follows Amihud (2002) and is calculated

as:

𝐼𝐿𝐿𝑄𝑖,𝑦 = 1/𝐷𝑖,𝑦 ∑|𝑅𝑖𝑦𝑑|

𝑉𝑂𝐾𝐷𝑖𝑦𝑑

𝐷𝑖𝑦

𝑡=1

∗ 1,000,000

where Riyd is the return on stock i on day d of year y and VOLDiyd is the daily volume in yens. D is the

number of days for which data are available for stock i in year y. Perk is an indicator variable for the use

of perks in the firm in that year. The sample includes public Japanese firms from 2001-2011 for which

the data is available. We require a minimum of 100 days each year of price impact data to estimate the

ILLQ measure each year. t-statistics are reported in parenthesis below the coefficients. Significance is

shown at the 10%, 5%, and 1% levels using *, **, and ***, respectively. (1) (2)

Perk -0.114***

(-5.54)

ln(Advertising) -0.002 -0.002

(-0.59) (-0.58)

ln(Age) 0.541*** 0.546***

(6.72) (6.79)

ROA -0.743*** -0.753***

(-7.12) (-7.22)

ln(MVE) -0.933*** -0.929***

(-80.20) (-79.67)

1/price -0.907 -0.921*

(-1.62) (-1.65)

ln(Turnover) -4.718*** -4.725***

(-43.94) (-43.97)

ln(Return volatility) 0.743*** 0.739***

(32.65) (32.43)

Constant 4.910*** 4.881***

(14.81) (14.73)

Year indicator variables Yes Yes

Firm indicator variables Yes Yes

Observations 34,634 34,634

Adjusted R-squared 0.957 0.957

45

Table 10: Change in required returns as a function of change in shadow cost quartile

The table reports the 𝛼0 and 𝛼1 estimate from the weekly return model:

𝑟𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛼𝑖,0 + 𝛼𝑖,1𝐷𝑡 + (𝛽𝑖,0 + 𝛽𝑖,1𝐷𝑡)(𝑟𝑚,𝑡 − 𝑟𝑓,𝑡) + 𝜀𝑖,𝑡

where, 𝑟𝑖,𝑡 is the weekly return for firm i at time t, and 𝑟𝑓,𝑡 is the risk-free rate at time t. 𝐷𝑡 = 1 if t is in

the post-perk-adoption period and 𝐷𝑡 = 0 otherwise. 𝛽𝑖,0 and 𝛽𝑖,𝑖 are the pre-and post-adoption market

risk premium factor betas. 𝛼𝑖,1 is the post-adoption abnormal return which is the difference between the

post- and pre-adoption abnormal returns. The model is estimated using weekly return data from 105 to 2

weeks before the perk announcement and from 2 to 105 weeks following the perk announcement.

Following Kadlec and McConnell (1994) the change in shadow cost is modeled as:

∆𝜆𝑖 = [(𝑅𝑉𝐴𝑅𝑖 ∗ 𝑅𝐸𝐿𝐶𝐴𝑃𝑖

𝑁𝐼𝑁𝐷𝑖,𝑝𝑜𝑠𝑡) − (

𝑅𝑉𝐴𝑅𝑖 ∗ 𝑅𝐸𝐿𝐶𝐴𝑃𝑖

𝑁𝐼𝑁𝐷𝑖,𝑝𝑟𝑒)] ∗ 10,000

where NINDi,pre and NINDi,post are the number of individual shareholders at the end of the most recent

fiscal year before the perk initiation announcement date and at the fiscal year end in the year after the

announcement date for firm i. RVAR is the stock’s residual variance calculated from daily returns over

the 104 week post-adoption period. RELCAP is the firm’s market capitalization of its common stock at

the end of the month before the adoption announcement date divided by the contemporaneous level of the

TOPIX Index.

N

Mean

ΔShadowcost

Cost of capital

Pre-adoption

α0

Difference

α1

Total sample 307 -2.193 *** 0.281 *** (6.05) -0.157 *** (-2.70)

Shadow cost quartile

Quartile 1 (Largest Δλ） 77 -7.659 *** 0.647 *** (6.08) -0.601 *** (-4.90)

Quartile 2 77 -1.192 *** 0.361 *** (4.23) -0.217 * (-1.79)

Quartile 3 77 -0.242 *** 0.097 (1.29) 0.045 (0.45)

Quartile 4 (Smallest Δλ） 76 0.355 *** 0.014 (0.17) 0.150 (1.48)

Quartile 1 - Quartile 4 -8.014 *** 0.633 *** (4.63) -0.751 *** (-4.71)

46

Table 11: Acquisition announce returns and CEO turnover

For the tests in columns 1 and 2 we identify 868 acquisitions in the Recof Japanese merger database during sample period.

Following Masulis et al. (2007) we focus on (1) completed acquisitions, (2) where the acquirer controlled less than 50% of

the target shares prior to the announcement and owns 100% of share after the transaction, (3) the deal value disclosed in

the database is more than 100 million yen and is at least 1% of the acquirer’s market value of equity measured on the 11th

trading day prior to the announcement date, and (4) the acquirer has financial data available via the Nikkei Financial Quest

database. The dependent variable in columns 1 and 2 is the CAR(-1,1) around the acquisition announcement return. The

dependent variable in columns 3-5 is set to 1 if the CEO is replaced.

CAR(-1,1) CEO turnover

(1) (2) (3) (4) (5)

Perk dummy -0.073 -0.212 -0.122*** -0.121*** -0.121**

(-0.15) (-0.43) (-2.83) (-2.80) (-2.33)

Prior return x Perk dummy -0.071

(-0.72)

ROA x Perk dummy -0.033

(-0.04)

Prior return -0.132*** -0.121** -0.132***

(-2.84) (-2.44) (-2.84)

ROA 2.223 3.169 -4.269*** -4.266*** -4.263***

(0.61) (0.79) (-14.46) (-14.46) (-13.22)

ln(Market asset) -0.194* -0.226 0.038*** 0.038*** 0.038***

(-1.66) (-1.58) (3.46) (3.46) (3.46)

TobinQ -0.169 -0.254

(-1.05) (-1.36)

Relative Size 6.438*** 6.902***

(2.85) (3.08)

Debt ratio 0.093 -0.345

(0.08) (-0.27)

CEO-Chairman indicator 0.192*** 0.192*** 0.192***

(4.74) (4.73) (4.74)

ln(CEO Tenure) 0.011 0.011 0.011

(0.69) (0.70) (0.69)

ln(CEO Age) 4.130*** 4.129*** 4.130***

(25.14) (25.13) (25.13)

Constant 5.322* 8.967* -19.620*** -19.616*** -19.620***

(1.79) (1.65) (-28.05) (-28.04) (-28.04)

Year indicator variables No Yes Yes Yes Yes

Industry indicator variables No Yes Yes Yes Yes

Observations 868 868 34,303 34,303 34,303

Adjusted R-squared 0.042 0.029 0.0563 0.0563 0.0563

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

by Jonathan Karpoff, Robert Schonlau, and Katsushi Suzuki

Brief description of appendix tables and figures:

A.1 – Summary of shareholder perks at 50 randomly chosen firms in the sample.

A.2 – Regression of perk announcement returns on advertising and other variables.

A.3 – Comparison of distribution of propensity scores for the treatment and control

groups.

A.4 – Test of covariate balance after the match.

A.5 – Test of whether the ownership characteristics are different between the included

versus excluded observations.

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

Table A.1 Summary of shareholder perks at 50 randomly chosen firms in sample

Firm name Industry Perks Condition Stock price

(yen)

Perk yield

for minimum

shareholder

AEON CO., LTD. Supermarket Chains

3% cash back card Holders of 100 to 499 shares 1518.5 -

4% cash back card Holders of 500 to 999 shares 1518.5 -

5% cash back card Holders of 1,000 to 2,999 shares 1518.5 -

7% cash back card Holders of 3,000 or more shares 1518.5 -

AGS Corp. Miscellaneous Services 2,000 yen of gift card Holders of 100 or more shares 990 2.02%

AIT Corp. Harbor Transportation 2,000 yen of product assortment Holders of 100 or more shares 888 2.25%

ASICS Corp. Manufacturing, NEC 15% discount coupon * 5 at Asics online store Holders of 100 to 999 shares 1,900 -

20% discount coupon * 5 at Asics online store Holders of 1,000 or more shares 1,900 -

AUTOBACS

SEVEN CO., LTD.

Miscellaneous

Wholesales

3,000 yen of discount coupon Holders of 100 to 299 shares 1,489 2.01%

7,500 yen of discount coupon Holders of 300 to 999 shares 1,489 1.68%

10,000 yen of discount coupon Holders of 1,000 to 2,999 shares 1,489 0.67%

15,000 yen of discount coupon Holders of 3,000 or more shares 1,489 0.34%

Coca-Cola East

Japan Co., Ltd. Foods, NEC

1,440 yen of drink assortment Holders of 100 to 499 shares 2,050 0.70%

2,880 yen of drink assortment Holders of 500 or more shares 2,050 0.28%

DEAR LIFE CO.,

LTD. Real Estate - Sales 1,000 yen of gift card Holders of 100 or more shares 325 3.08%

DUSKIN CO.,

LTD. Miscellaneous Services

1,000 yen of gift card Holders of 100 to 299 shares 1,767 0.57%

2,000 yen of gift card Holders of 300 or more shares 1,767 0.38%

EDION Corp. Wholesale - Electric

Goods 3,000 yen discount coupon Holders of 100 or more shares 863 3.48%

G Three Holdings

Corp. Miscellaneous Services

1,000 yen of gift card Holders of 1,000 to 4,999 shares 56 1.79%

5,000 yen of gift card Holders of 5,000 or more shares 56 1.79%

HIOKI E.E.

CORPORATIOM

Electric Industrial

Controls

3.5 kilogram of brand apple Holders of 100 to 999 shares 1,903 -

5 kilograms of brand apple Holders of 1,000 or more shares 1,903 -

Japan Pulp and

Paper Company

Ltd.

Miscellaneous

Wholesales 24 rolls of toilet paper Holders of 1,000 or more shares 359 -

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

Japan Exchange

Group, Inc.

Other Financing

Business 3,000 yen of gift card Holders of 100 or more shares 1,392 2.16%

J. FRONT

RETAILING Co.

Ltd.

Department Stores 10 % of discount card Holders of 100 or more shares 1,140 -

KFC Holdings

Japan, Ltd. Miscellaneous Services

500 yen of meal coupon Holders of 100 to 299 shares 1,833 0.27%

1,500 yen of meal coupon Holders of 300 to 499 shares 1,833 0.27%

2,500 yen of meal coupon Holders of 500 to 999 shares 1,833 0.27%

5,000 yen of meal coupon Holders of 1,00 or more shares 1,833 0.27%

KIKKOMAN

Corp. Flavoring Extracts 2,500 yen of own products Holders of 1,000 or more shares 3,895 0.06%

KOMEDA

Holdings Co., Ltd. Wholesale - Foods 1200 yen of e-money or product assortment Holders of 100 or more shares 1,825 0.66%

KONICA

MINOLTA, INC.

Measuring Devices,

NEC Original calendar Holders of 100 or more shares 796 -

KYOKUYO CO.,

LTD. Foods 5,000 yen of canned assortment Holders of 1,000 or more shares 264 1.89%

LEOPALACE21

Corp. Real Estate - Sales 2 nights free hotel voucher Holders of 100 or more shares 757 -

MAC HOUSE

CO., LTD. Retail Stores, NEC

1,000 yen of discount coupon Holders of 100 to 499 shares 761 1.31%

3,000 yen of discount coupon Holders of 500 to 999 shares 761 0.79%

5,000 yen of discount coupon Holders of 1,000 or more shares 761 0.66%

MAEZAWA

KASEI

INDUSTRIES

CO., LTD.

Plastics 3 kilogram of brand rice Holders of 100 or more shares 1,026 -

Masuda Flour

Milling Co., Ltd. Grain Mill Products 3,000 yen of Ibonoito noodle Holders of 1,000 or more shares 310 -

MARUZEN CO.,

LTD. Metal Products, NEC

3,000 yen of meal coupon Holders of 1,000 to 9,999 shares 969 0.31%

5,000 yen of meal coupon Holders of 10,000 or more shares 969 0.05%

Maruzen CHI

Holdings Co., Ltd. Retail Stores, NEC 1,000 yen of gift card Holders of 100 or more shares 380 2.63%

McDonald's

Holdings Company

(Japan), Ltd.

Miscellaneous Services Discount meal ticket Holders of 100 or more shares 3,200 -

MINISTOP CO.,

LTD. Supermarket Chains

5 free tickets of ice cream Holders of 100 to 199 shares 1,744 -

5 (3) free tickets of ice cream (coffee) Holders of 200 to 999 shares 1,744 -

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

20 (3) free tickets of ice cream (coffee) Holders of 1,000 or more shares 1,744 -

MORESCO Corp. Oil & Coal Products 1,000 yen of gift card Holders of 100 or more shares 1,184 0.84%

Morinaga Milk

Industry Co., Ltd. Dairy Products 12 brand tofu Holders of 1,000 or more shares 739 -

NIPPON

PARKING

DEVELOPMENT

CO., Ltd.

Real Estate - Rental 30% discount coupon of 1 day parking fee * 5 Holders of 100 or more shares 140 -

NIPPON PILLAR

PACKING CO.,

LTD.

Machinery, NEC 1,000 yen of gift card Holders of 100 or more shares 1,039 0.96%

Nippon Yusen

Kabushiki Kaisha Shipping - Nucleus Discount cruise coupon Holders of 1,000 or more shares 195 -

NISSEI PLASTIC

INDUSTRIAL

CO., LTD.

Machinery, NEC 1,500 yen of local specify goods Holders of 100 to 499 shares 667 2.25%

3,000 yen of local specify goods Holders of 500 or more shares 667 0.90%

NISSIN KOGYO

CO., LTD.

Auto Parts &

Accessories

3,000 yen of meal assortment Holders of 300 to 999 shares 1,430 0.70%

5,000 yen of meal assortment Holders of 1,000 or more shares 1,430 0.35%

Oisix Inc. Retail Stores, NEC Brand rice Holders of 100 or more shares 2,042 -

SAIZERIYA CO.,

LTD. Miscellaneous Services

1,000 yen of pasta assortment Holders of 100 to 499 shares 2,279 0.44%

10,000 yen of candy assortment Holders of 500 or more shares 2,279 0.44%

Sony Corp. Household Appliances Discount coupon at Sony store Holders of 100 or more shares 3,236 -

Suzuki Motor

Corp. Motor Vehicles Assortment of honey and rock salt Holders of 100 or more shares 3,134 -

Takamatsu

Construction

Group Co., Ltd.

Home & Pre-Fabs 5 kilograms of an expensive brand rice Holders of 100 or more shares 2,419 -

TBK Co., Ltd. Auto Parts &

Accessories

1.3 kilograms of an expensive brand rice Holders of 100 to 499 shares 382 -

2 kilograms of an expensive brand rice Holders of 500 to 999 shares 382 -

5 kilograms of an expensive brand rice Holders of 1,000 or more shares 382 -

Tea Life Co., Ltd. Retail Stores, NEC

1,000 yen of discount coupon Holders of 100 to 499 shares 1,014 0.99%

2,000 yen of discount coupon Holders of 500 to 999 shares 1,014 0.39%

3,000 yen of discount coupon Holders of 1,000 or more shares 1,014 0.30%

TEIKOKU SEN-I

Co., Ltd.

Miscellaneous Textile

Products 1,000 yen gift card and 3,000 yen own product Holders of 100 or more shares 1,386 2.89%

The Yamanashi Regional Banks Personal loan interest rates - 0.2% Holders of 1,000 or more shares 406 -

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

Chuo Bank, Ltd.

Tokai Tokyo

Financial Holdings,

Inc.

Securities 2,000 yen of original gift card Holders of 1,000 to 2,999 shares 495 0.40%

4,000 yen of original gift card Holders of 3,000 or more shares 495 0.27%

Torikizoku co., ltd. Miscellaneous Services

2,000 yen of food ticket Holders of 100 to 299 shares 2,045 0.98%

6,000 yen of food ticket Holders of 300 to 499 shares 2,045 0.98%

10,000 yen of food ticket Holders of 500 or more shares 2,045 0.98%

Toyo Tire &

Rubber Co., Ltd. Tires 1,000 yen of gift card Holders of 100 or more shares 1,178 0.85%

Tri-Stage Inc. Miscellaneous Services 2,000 yen of gift card Holders of 100 to 499 shares 1,975 1.01%

10,000 yen of gift card Holders of 500 or more shares 1,975 1.01%

West Japan

Railway Company Railroad (Major)

One 50% discount coupon per 100 shares Holders of 100 to 1,000 shares 6,716 -

Ten 50% discount coupon and one 50 % discount

coupon per 200 share Holders of 1,000 to 10,100 shares 6,716 -

Fifty-five 50% discount coupon + one 50%

discount coupon per 300 share Holders of 1,000 to 10,100 shares 6,716 -

One hundred 50% discount coupon Holders of 20,000 or more shares 6,716 -

Yamaha Corp. Musical Instrument

1,500 yen of original gift item, 1,500 yen of

discount coupon, or 1,500 yen of donation Holders of 100 to 999 shares 3,015 0.50%

3,000 yen of original gift item, 3,000 yen of

discount coupon, or 3,000 yen of donation Holders of 1,000 or more shares 3,015 0.10%

YOSHINOYA

HOLDINGS CO.,

LTD.

Miscellaneous Services

300 yen discount coupon * 10 Holders of 100 to 999 shares 1,448 2.07%

300 yen discount coupon * 20 Holders of 1,000 to 1,999 shares 1,448 0.41%

300 yen discount coupon * 40 Holders of 2,000 or more shares 1,448 0.41%

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

A.2 – Regression of perk announcement returns on advertising, sales, and other variables

The dependent variable in all columns is the CAR(-1,+1) around the perk announcement.

(1) (2) (3) (4) (5)

Own product 0.422 -0.257

(0.43) (-0.27)

Advertising/Asset 13.558 15.796

(0.64) (0.73)

ROA_t+2 - ROA_t-1 0.421 5.782

(0.06) (0.99)

Sales_t+2/Sales_t-1 -2.810* -3.910***

(-1.95) (-2.71)

ln(Market asset) -0.915***

(-2.99)

TobinQ 0.804

(1.14)

ROA_t-1 -3.091

(-0.34)

Leverage 3.697*

(1.72)

Constant 1.909*** 1.848*** 2.083*** 5.200*** 14.524***

(4.79) (3.84) (5.58) (3.09) (3.80)

Observations 307 307 300 301 300

Adjusted R-squared -0.002 0.000 -0.003 0.010 0.045

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

A.3– Comparison of distribution of propensity scores for the treatment and control groups

Following Caliendo and Kopeinig (2008) we confirm common support by comparing the distribution of propensity scores for the treatment and

control firms associated with Table 4 in the main paper.

0

20

40

60

80

100

120

Treatment

0

100

200

300

400

500

600

Control

Appendix for Shareholder Perks, Ownership Structure, and Firm Value

A.4 – Test of covariate balance after the match.

Following Caliendo and Kopeinig (2008) we confirm covariate balance following the match by t-tests of

whether the variables listed in Table 3 of the main paper are statistically different between the control and

treatment groups identified using Table 4 in the main paper. As intended, none of the differences are

significant even at the 10% level.

Treatment

(N = 544)

Control

(N = 2,720) Difference

(A) - (B) t-statistics

Mean (A) Mean (B)

Panel A: Firm characteristics

Market value of assets (million yen) 156,536 229,819 -73,283 -1.46

Leverage 0.213 0.213 0.000 0.04

ROA 0.067 0.066 0.001 0.38

ExCash 0.002 0.004 -0.002 -0.34

Tobin’s q 1.387 1.386 0.001 0.03

Dividend 0.875 0.880 -0.005 -0.34

% of outside director 0.263 0.262 0.001 0.13

Anti-takeover 0.028 0.027 0.001 0.10

Panel B: Ownership information

#Individual retail shareholders, 7,317 8,682 -1,365 -0.60

#Individual/#total shareholders, 0.929 0.927 0.002 0.66

%Board ownership 0.160 0.162 -0.002 -0.23

%Top 10 ownership 0.584 0.582 0.002 0.31

%Retail ownership 0.278 0.281 -0.003 -0.58

%Institutional ownership 0.177 0.175 0.002 0.35

9

A.5– Test of whether the ownership characteristics are different between the included versus excluded observations.

As described in the main paper we could find announcement dates for 429 of the 544 new perk programs using the eol ESPer database. Of these,

122 of these perk announcements co-occur with announcements of stock splits, earnings, changes in the trading unit size, or are announced within

150 days of their IPO. In the table below we test whether the mean ownership measures are different between the 307 observations used in CAR

tests and the 237observations not included either because we could not identify the announcement dates or because of other material news being

announced at the same time as the perk.

Initiation

(N = 544)

Announcement sample

(N = 307)

Non announcement

sample (N = 237)

Difference t-statistic

Mean Median Mean Median Mean Median

#Individual retail shareholders 7,317 1,923 6,509 2,072 8,365 1,870 -1,856 -0.98

#Individual/#total shareholders 0.929 0.946 0.935 0.946 0.922 0.946 0.013 2.02 **

%Board ownership 0.160 0.092 0.169 0.096 0.147 0.087 0.022 0.68

%Top 10 ownership 0.584 0.588 0.583 0.591 0.585 0.591 -0.002 -0.98

%Retail ownership 0.278 0.266 0.278 0.266 0.278 0.261 0.000 0.22

%Institutional ownership 0.177 0.149 0.176 0.154 0.178 0.143 -0.002 0.12