The effect of mergers on employment and wages: Evidence from Japan

J. Japanese Int. Economies 26 (2012) 263–284

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Journal of The Japanese andInternational Economies

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The effect of mergers on employment and wages:Evidence from Japan

Katsuyuki Kubo a,⇑, Takuji Saito b

a Graduate School of Commerce, Waseda University, Japanb Faculty of Economics, Kyoto Sangyo University, Motoyama, Kamigamo, Kita-ku, Kyoto 6038555, Japan

a r t i c l e i n f o

Article history:Received 27 July 2009Revised 24 February 2011Available online 21 April 2011

JEL classification:G3J2J3L2

Keywords:MergerEmployment adjustmentWageJapan

0889-1583/$ - see front matter � 2011 Elsevier Indoi:10.1016/j.jjie.2011.04.001

⇑ Corresponding author. Address: Graduate SchoJapan.

E-mail addresses: [email protected] (K. Kubo),1 Result of the survey is published in Nikkei News

a b s t r a c t

Kubo, Katsuyuki, and Saito, Takuji—The effect of mergers onemployment and wages: Evidence from Japan

This study investigates the impact of mergers on employment andemployees’ wages in Japan, based on 111 mergers between listedfirms observed between 1990 and 2003. Typically, the number ofemployees decreases by 4.45% three years after a merger, evenafter changes in sales and other variables are controlled. Firms thatexperience related mergers, and rescue mergers are more likely todecrease the number of workers. At the same time, wages increaseby 5.46% per employee. These results suggest that the mainmotivation behind mergers is not to divest employees of theirwealth. J. Japanese Int. Economies 26 (2) (2012) 263–284. GraduateSchool of Commerce, Waseda University, Japan; Faculty of Eco-nomics, Kyoto Sangyo University, Motoyama, Kamigamo, Kita-ku,Kyoto 6038555, Japan.

� 2011 Elsevier Inc. All rights reserved.

1. Introduction

It is widely considered among employees that mergers and acquisitions result in job losses andwage cuts. A questionnaire survey by Nikkei Newspaper shows that more than 80% of employees inJapanese firms feel uncomfortable that their company is acquired by other firm.1 Employees feeluncomfortable because they expect that their chance of promotion in new firm will be smaller, for

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ol of Commerce, Waseda University Nishiwaseda Shinjuku, Tokyo 1698050,

[email protected] (T. Saito).paper on 7th August 2006.

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264 K. Kubo, T. Saito / J. Japanese Int. Economies 26 (2012) 263–284

example. In other words, they think that it is bad news for them that their firm will merge. To examinewhether employees do suffer after the merger, this study estimates the impact of mergers on employ-ment and wages in Japan.2

There have been several studies on the impact of mergers and acquisitions on employment andwages in the US and UK (Beckman and Forbes, 2004; Gokhale et al., 1995). Beckman and Forbes(2004) analyzed the influence of corporate acquisitions on employment and wages. The analysiswas done using UK companies between 1987 and 1995, during which period 62 acquisitions were exe-cuted. The total median employment of acquiring companies and acquired companies diminished by11% over 5 years following the acquisitions. They also analyzed the influences on wages to show thatthey increase after acquisitions. The wage increase after related mergers was 19%, larger than 8% forunrelated ones. Their result is consistent with Conyon et al. (2002), who examine the effect of 442takeovers and merger activities on employment in the UK using data from 1967 to 1996.

One of the few studies that utilize data from outside the US and UK is the one by Gugler and Yurto-glu (2004). By estimating an employment adjustment function, they examine the effect of M&As onlabor demand in the US and Europe using data from 10,282 firms in the period 1987–1998. They findthat the effect is insignificant in the US. In contrast, employment decreases after M&As by 12.4% in theUK and by 7.9% in continental Europe. This result is important because it shows that the effects ofmergers on labor vary among countries with different labor market environments. They suggest thatthe reason why the effect of mergers on employment conditions is weak in the US is that it is easier toreduce workforces under normal conditions, without mergers. In contrast, in countries where it is dif-ficult to dismiss workers, such as Germany and France, firms may attempt to adjust employment dur-ing mergers.

We analyzed changes in employment and wages following merger activities in Japan. There areseveral contributions from our analysis. One important advantage of our analysis over several previ-ous studies, which were based mainly in the US and the UK, is that the sample is of Japanese firms,where there are many distinctive features in firms’ employment policies. It should be noted thatemployment adjustment is slow in Japan, as shown in next section. According to Gugler and Yurtoglu,it is conjectured that the number of employees decreases in countries where it is difficult to dismissemployees under normal conditions. Mergers can provide a good justification for managers to changeemployment conditions. In Japan, firms have had difficulty dismissing their regular employees be-cause the courts have regarded such attempts as abuses of the right to dismiss employees. Tradition-ally, case law has played a significant role in constituting the ‘‘judicial principle of the abusive exerciseof dismissal rights.’’ According to this judicial principle, it is not possible for an employer to dismisstheir employees unless they produced strong justification.3 The OECD (1994, 1999) compared employ-ment protection legislation among OECD countries. In the ‘‘difficulty of dismissal’’ ranking of coun-tries, Japan was ranked 25 among 27 in the late 1990s, showing that it is difficult for employers todismiss workers, because higher numbers showed stricter legislation.

Another contribution of this study is that we examine changes in both wages and employmentsimultaneously. Most previous studies on the effect of M&As on labor focus on employment.4 How-ever, if the motivation behind mergers is to transfer wealth from employees, both wages and the num-ber of employees will change.5 Therefore, our analysis includes both the number of employees andwages as dependent variables.

Our results can be summarized as follows. Typically, the number of employees decreases by 4.454%three years following merger, even after we control for changes in sales and other variables. Wagesincrease by 5.459% per employee per year after merger. We classify mergers into subcategories:

2 Many studies have been conducted on the impact of merger activities on firm performance. Andrade et al. (2001), Bruner(2004), Holmstrom and Kaplan (2001) and Moeller et al. (2004) conduct excellent surveys on the efficiency effect of mergeractivities. Odagiri and Hase (1989) find little efficiency gain in mergers and acquisitions in Japan while some studies showperformance improvement after merger (Ikeda and Doi, 1983; Kruse et al., 2007).

3 Although this principle had no statutory foundation in labor law, it is now incorporated in the Employment Contract Law.4 There are a few studies on wages in the US and UK. However, there has been no such attempt outside Anglo-American

countries (Beckman and Forbes, 2004).5 Shleifer and Summers (1988) argued that outside investors can profit by acquiring a firm in which employees’ wages exceed

their productivity.

K. Kubo, T. Saito / J. Japanese Int. Economies 26 (2012) 263–284 265

related and unrelated mergers, rescue and nonrescue mergers. The firms that experience related andrescue merger are more likely to reduce their workforce. These relations persist even if we control forvariables such as sales and employees’ age. These results do not show that employees suffer after mer-ger, as employees remaining in the firm enjoy higher salaries.

The remainder of the paper proceeds as follows. We will describe the importance of long-termemployment and cooperative industrial relations in Japanese firms in next section. Section 3 describesthe data and presents basic statistics. Section 4 analyzes the effect of merger activities on employmentand wages without classifying merger types. The analysis in Section 5 classifies mergers into related/unrelated and rescue/nonrescue mergers. Section 6 concludes.

2. Seniority wage, long-term employment relationship, cooperative industrial relations and laboradjustment in Japan

There are several characteristics of the labor market in Japan, such as long-term employment andseniority-based pay. It is often considered that under seniority-based pay system, there is an implicitcontract under which a firm pays junior workers wages that are lower than their productivity wouldindicate, on the understanding that they would receive wages exceeding their productivity when theyare older. Although a positive correlation between age and wage can be observed in many countries,the correlation between length of service and wage has been stronger in Japan. In particular, the age-wage profile in Japan is steeper than in the US (Hashimoto and Raisian, 1985; Mincer and Higuchi,1988).

Another important feature of labor market in Japan is long-term employment. Typically, workersenter the firm after graduating and work for the same company for a long time. In addition to legalreasons, which make it difficult for firms to dismiss workers, it is often considered that there is an-other implicit contract between managers and employees that firms would keep long-term employ-ment. In contrast to popular perception that the effects of the long-term recession in the 1990s,many literature suggest firms still keep up the relationship (Chuma, 1998; Kato, 2001; Ono, 2010; Shi-mizutani and Yokoyama, 2009). For example, by examining Employment Trends Survey from 1991 to2003, Ono (2010) finds that separation rate for employees of age 25–59 is stable. Studies that estimateemployment adjustment functions also confirm that adjustment speed in Japan is slow (Abraham andHouseman, 1989; Hildreth and Ohtake, 1998; Kaufman, 1988).

Cooperative industrial relations is another important characteristics of labor market in Japan. Thereare several mechanisms through which employee show their voice to top managers in addition totrade union (Kato and Morishima, 2002; Morishima, 1991). Among these, joint labor managementcommittee (JLMC) play an important role as a communication channel for management to informand consult with labor about the topics such as current business status of the firm and managementprograms or plans for future investment (Sugeno, 2002). Previous studies show that information shar-ing through joint labor management committee will lead to high productivity (Kato and Morishima,2002; Morishima, 1991). Using data of panel of 126 firms over 1973–1992, Kato and Morishima esti-mate the effect of participatory employment practice, such as JLMC, profit sharing, shop floor commit-tee in production function. It is found that introduction of these practices lead to 8–9% increase inproductivity. Morishima (1991) also shows the ROA of the firm with more information sharing is0.41% higher. These studies suggest that cooperative industrial relations, which is achieved throughparticipatory employment practices are essential for managers to keep firm profitable. Therefore,managers do not have incentives to change these industrial relations.

Traditionally, large Japanese firms have avoided dismissing their regular employees even whentheir business was slow. Firms do adjust labor cost by stopping hiring new employees, reducing work-ing hours, transfer employees to other firms or reduce the number of non-regular workers. Therefore,firm can protect implicit contract of long-term employment with its regular employees.

Kato (2001) shows that firms reduce their workforce do so by combination of limited hiring andtransfers of workers to related firms. Kester (1991) describes how a large steel company reducesthe number of employees but not dismissing them when it faces excess capacity problem. Severalhundred workers were dispatched to related companies or other automobile company. It is alsoemphasized that firms need to consult with unions to rely on these measures.


Nakata and Takehiro (2003) and Suruga (1998) estimate employment adjustment function usingfirm-level panel data in Japan. According to Suruga (1998), firms may change the speed of employ-ment adjustment according to their business performance. In particular, firms reduce the numberof employees in periods of large loss or 2 consecutive years’ losses. Firms may not adjust employmentif the loss is small, because of adjustment cost, such as negotiation costs with unions. It is suggestedthat information sharing between management and union through joint labor management commit-tee helps unions to understand the needs to reduce labor cost to reduce the bargaining cost.

Using firm-level panel data set for 33 large firms in automobile manufacturing, department stores,and supermarkets for 25 years, Nakata and Takehiro find that many Japanese firms experience largescale reduction in the number of employees. Twenty firms among 33 firms have undergone suchreductions at least twice in sample period. They also show that that partial adjustment model can ex-plain employment adjustment behavior of Japanese firms.

These studies suggest that workers in large Japanese firms are more involved in implicit contracts.Shleifer and Summers (1988) argued that outside investors can profit by acquiring a firm in whichemployees’ wages exceed their productivity through implicit contracts with the firm. The new man-agers would lower the wages of senior employees, because disaffirming the implicit contract allows anacquirer to gain benefits. New managers have little incentive to abide by an implicit contract made bytheir predecessors. If this is the case, declines in employment conditions will be observed after thecompletion of mergers and acquisitions. However, previous studies on labor market in Japan also sug-gest that managers do not have incentive to ‘breach’ implicit contract with worker of long-termemployment and seniority wage. It is essential for managers to have good relationship with workersbecause productivity of these firms will deteriorate once employees do not cooperate with managers.

3. Data

3.1. Sample selection

In this section, we discuss the selection of our sample of 111 mergers and show descriptive statis-tics. Our data are taken from listed firms in Tokyo, Osaka, Nagoya and other local stock exchanges.6 Wecollected data on mergers in the period 1990–2003. Only mergers between listed firms are included inthe sample. Mergers between listed and unlisted firms or between unlisted firms are not included be-cause we cannot obtain financial data and employment data for unlisted firms. We referred to ToshoYoran (TSE Factbook)7 and NihonKigyo no M&A Databook 1988–2002 (M&A data of Japanese firms) byRECOF to identify mergers. In most cases, one of the merging firms survives as the same company.Our sample includes only this type of merger.8 We call the surviving firm the Acquirer or Bidder, andthose delisted as Acquired, or Target.

Financial data is taken from the Development Bank of Japan database. The numbers of employeesand figures for wages, such as total labor cost are also taken from the database. Wage is calculated bydividing total labor cost by the number of employees. All variables are winsorized at 1% to control theeffect of outliers.

Table 1 shows the number of merger activities by year. It is shown that the number of mergers wasgreatest in 1999, when 22 mergers were reported. It also shows that the number of merger activity isincreasing. This is consistent with the idea that the behavior of Japanese firms have changed. Tradi-tionally, Japanese firms did not rely on merger and acquisition to extend their control over other firms.According to Kester (1991), when Japanese firms seek better coordination with their parts suppliers orcustomers, they build and maintain long-term relationship with them, instead of merger andacquisition.

6 Firms in emerging markets, such as MOTHERS, over-the-counter markets, JASDAQ, and HERCULES, are not included. Inaddition, mergers of financial institutions are not included in the samples because of the difference in accounting systems.

7 We look into reasons for delisted firms in Tosho Yoran and identified which delists were caused by merger activities. When twoor more listed firms merge, one (or more) firm is delisted from the stock exchange.

8 It is sometimes the case that merging firms create a new holding company and all merging firms come under its control. It isalso the case that merging firms form a new company. Our sample does not include these cases.

Table 1Number of Mergers by Year. This table shows the number of mergers between listed firms on Tokyo, Osaka, Nagoya and other localstock exchange completed during the period from 1990 to 2003. Mergers are categorized as related when the two-digit industrycode of an acquiring firm is the same as that of the acquired one, and otherwise as unrelated. Mergers are categorized as rescuewhen either an acquiring or acquired firm’s ordinary income is negative 1 year before the merger, and otherwise as nonrescue.

Year Merger Relatedness Previous performance

Unrelated Related Nonrescue Rescue

1990 2 2 0 2 01991 1 0 1 0 11992 4 1 3 3 11993 5 1 4 4 11994 5 1 4 2 31995 8 2 6 6 21996 3 0 3 3 01997 6 0 6 4 21998 13 4 9 9 41999 22 6 16 16 62000 18 4 14 13 52001 7 3 4 7 02002 4 1 3 3 12003 13 3 10 8 5

Total 111 28 83 80 31


Table 1 also presents a breakdown of mergers by some specific aspects, according to the industry towhich the acquiring and acquired companies belonged before merger, and in prior performance.

3.2. Basic statistics when mergers are not classified by type

Table 2 represents changes in the median of the variables from 2 years before to 3 years after themerger. It shows basic statistics of acquiring, acquired, and merged companies. Variables include thenumber of employees, wage per employee (thousand yen), total labor cost, sales (billion yen), sales peremployees (million yen), ROA (%), average age of employees and their average length of service. ROA isobtained by dividing before-tax before-interest profits by total assets.

We need to obtain values of each variable before a merger to examine the change from before toafter the event. Therefore, we calculate the values for ‘merged firm’ hypothetically before a mergerusing values for each firm. Consolidated sales and the number of employees are calculated by aggre-gating figures for acquiring and acquired companies. ROA is calculated as the weighted average of thetwo companies based on total assets. Wages per employee, average age, and average length of serviceis the weighted average based on the number of employees.

There are several significant characteristics in Table 2. First, the number of employees decreasesduring the merger process. The number of employees is 2923 two years before a merger and 2095three years after. It is also shown that wages after merger are higher than before the merger. Medianwage per employee increases from 7031.9 thousand yen (US$70,319) 2 years before merger to 7803.7thousand yen (US$78,037) afterwards.9 This table also shows the changes in total labor cost. It isshown that total labor cost decreases slightly before and after the merger.

It is important to examine the change in performance around a merger. In terms of ROA, thereseems little change when we compare the figures for 2 years before (3.9%) and 3 years after the merger(3.6%), showing that performance improvement is not observed in these mergers.

Table 2 also shows that acquiring companies are approximately three to four times as large as ac-quired companies. For example, the median number of employees is 1856 two years before a mergerwhile it is 674 in acquired firms. The amount of sales is larger in an acquiring firm. In addition, ROA ofacquiring companies exceed that of acquired companies. Median ROA of an acquiring firm is 4.3% twoyears before a merger while it is 2.8% in the acquired firm.

9 We calculate figures in US dollars using the exchange rate of $1 = 100 Japanese yen.

Table 2Change in Variables around Mergers (Median). This table shows the changes in the median of the variables from 2 year before to 3years after the mergers between listed firms on Tokyo, Osaka, Nagoya and other local stock exchange completed during the periodfrom 1990 to 2003. For unified firms before mergers, sales and the number of employees are calculated by aggregating figures foracquiring and target companies. ROA is calculated as the weighted average of the two companies based on total assets. Wages peremployee, average age, and average length of service is the weighted average based on the number of employees.

�2 �1 0 1 2 3

Unified firmSample firms 111 111 111 111 111 110The number of employees 2923 2589 2458 2196 2103 2094.5Wage per employee (thousand yen) 7031.9 7053.9 7419.2 7096.3 7781.0 7803.7Total labor costs (billion yen) 20.2 20.4 20.1 18.7 20.1 19.3Sales (billion yen) 237 224 222 191 204 204Sales per employee (million yen) 73.2 71.4 69.2 63.7 76.7 75.5ROA (%) 3.9 3.7 3.7 3.7 3.4 3.6Average age of employees 38.0 38.5 38.6 38.7 39.1 39.6Average length of service of employees 15.8 16.0 16.1 16.6 16.6 16.8

Acquiring firmSample firms 111 111 111The number of employees 1856 1851 1830Wage per employee (thousand yen) 6870.2 6918.7 7317.2Total labor costs (billion yen) 13.7 14.3 13.7Sales (billion yen) 161 157 158Sales per employee (million yen) 71.9 73.0 71.4ROA (%) 4.3 4.3 3.9Average age of employees 38.2 38.3 38.6Average length of service of employees 15.7 15.9 16.1

Target firmSample firms 111 111 111The number of employees 674 680 606Wage per employee (thousand yen) 6751.8 6708.9 7131.6Total labor costs (billion yen) 4.8 4.4 4.5Sales (billion yen) 49 47 43Sales per employee (million yen) 65.6 65.5 68.4ROA (%) 2.8 2.5 2.4Average age of employees 38.2 38.2 38.5Average length of service of employees 14.6 14.8 15.5


3.3. Basic statistics when mergers are classified by type

It may be the case that the impact of a merger on employment conditions is not equal among var-ious types of mergers. Previous studies show that the impact on the number of employees variesaccording to merger types (Conyon et al., 2002). We first divided mergers into related and unrelated.They are categorized as related when the two-digit industry code of an acquiring firm is the same asthat of the acquired one.10 There are 83 related and 28 nonrelated mergers in our sample, showingthat many mergers are observed in the same industry. In other words, diversification may not bethe main motivation. This tendency is consistent with the results reported in Odagiri and Hase(1989) and Kang et al. (2000).

We also distinguish nonrescue mergers from rescue mergers. We classify a merger as rescue ifeither an acquiring or acquired firm’s ordinary income is negative 1 year before the merger.11

According to this criteria, out of 111 mergers, 80 mergers were classified as nonrescue mergers and31 as rescue mergers.

Changes in labor conditions may vary according to the type of merger. If the motivation behind arelated merger is to reduce excess capacity, then there would be a decrease in the number of

10 If multiple companies were acquired, we looked at the industry code of the acquired firm with the most employees.11 When multiple companies are acquired, we regard the transaction as rescue if at least one firm with negative ordinary income

is involved as the acquiring or an acquired firm.


employees. Firms that experience related merger can reduce average costs by concentrating their pro-duction in the most efficient plant and by closing less efficient ones down. The change in employmentconditions may not be the same between rescue and nonrescue mergers. Firms may find it essential tolower labor costs to reconstruct firms that are in financial distress.

Table 3 shows the change in variables of merged firms when mergers are classified based on busi-ness sectors and previous performance. One of the main differences between related and unrelatedmergers is the size of the firm. The size of the merged firm is larger in an unrelated merger than in

Table 3Change in variables around Mergers (Median) – Categorized by Relatedness and Previous Performance. This table shows thechange in variables of merged firms when mergers are classified based on business sectors and previous performance. Mergers arecategorized as related when the two-digit industry code of an acquiring firm is the same as that of the acquired one, and otherwiseas unrelated. Mergers are categorized as rescue when either an acquiring or acquired firm’s ordinary income is negative 1 yearbefore the merger, and otherwise as nonrescue. Before mergers, sales and the number of employees are calculated by aggregatingfigures for acquiring and target companies. ROA is calculated as the weighted average of the two companies based on total assets.Wages per employee, average age, and average length of service is the weighted average based on the number of employees.

�2 �1 0 1 2 3

A. Merger categorized by relatednessRelated merger–unified firm

Sample firms 83 83 83 83 83 83The number of employees 2216 2197 2062 1928 1922 1824Wage per employee (thousand yen) 7031.9 7058.7 7483.8 7114.8 7787.3 7836.0Total labor costs (billion yen) 19.7 19.2 18.4 16.0 16.3 16.4Sales (billion yen) 205 204 197 177 184 198Sales per employee (million yen) 76.5 73.2 73.1 63.7 78.0 77.7ROA (%) 4.0 3.7 3.7 3.7 3.3 3.7Average age of employees 38.0 38.6 38.8 38.8 39.3 39.8Average length of service of employees 15.4 15.6 16.1 16.7 16.7 16.9

Unrelated merger–unified firmSample firms 28 28 28 28 28 27Number of the employees 5739 5574 5527 4179.5 4368.5 4073Wage per employee (thousand yen) 6919.0 7037.7 7401.3 7030.7 7578.7 7781.3Total labor costs (billion yen) 39.9 39.4 38.2 28.8 34.3 37.0Sales (billion yen) 292 286 281 224 217 241Sales per employee (million yen) 60.6 64.9 65.9 62.8 68.3 67.8ROA (%) 3.6 3.8 3.5 3.8 3.6 3.5Average age of employees 37.9 38.0 38.2 38.7 38.9 39.4Average length of service of employees 15.9 16.5 16.3 16.3 16.3 16.3

B. Merger categorized by previous performanceNonrescue merger–unified firm

Sample firms 80 80 80 80 80 79The number of employees 2734.5 2487.5 2384 2181 2094 2056Wage per employee (thousand yen) 7066.7 7110.6 7401.3 7090.3 7783.4 7785.7Total labor costs (billion yen) 20.1 19.6 19.5 17.6 19.0 18.8Sales (billion yen) 201 214 217 186 181 182Sales per employee (million yen) 74.0 71.8 73.1 63.7 77.6 76.5ROA (%) 4.1 4.0 4.3 3.9 3.6 4.0Average age of employees 38.0 38.5 38.6 38.9 39.2 39.7Average length of service of employees 15.8 16.0 16.1 16.5 16.1 16.6

Rescue merger–unified firmSample firms 31 31 31 31 31 31The number of employees 3135 3057 2597 2236 2436 2483Wage per employee (thousand yen) 7023.4 7053.9 7483.6 7323.7 7781.6 7836.0Total labor costs (billion yen) 25.4 24.2 24.5 21.4 21.0 21.0Sales (billion yen) 303 270 222 191 251 272Sales per employee (million yen) 69.7 67.8 67.9 61.8 75.7 72.1ROA (%) 3.6 2.6 2.0 3.1 2.9 2.9Average age of employees 38.2 38.3 38.6 38.7 38.6 39.2Average length of service of employees 15.0 15.5 16.2 16.7 16.8 16.9


a related merger. For example, the median number of employees in a related merger is 2216 while it is5739 in an unrelated one 2 years before the merger. There is little difference in ROA, employees’ agesand their length of service.

The lower panel in Table 3 exhibits the change in variables when mergers are classified as rescue ornonrescue. It is shown that the number of employees is as large in a nonrescue merger as in a rescuemerger. ROA is lower in rescue mergers.

3.4. Comparison of growth of variables

To examine the effect of merger on employment conditions, it is essential to compare merged firmswith nonmerged firms. Even if we find significant declines in the number of employees of the firmsafter mergers, they may not be caused by merger events. The declines may be caused by other factors,such as a change in sales or in labor market regulation. To control for this possibility, we comparefirms that experience mergers and those that do not.

In order to examine the effect of merger on employment condition, we need to compare thechanges in the number of employees, wages and total labor cost of merged firm with nonmerger firms.It is important to construct appropriate counter sample, as there would be some biases if we use allnonmerger firms as counter sample. It is because the characteristics of merger firms and nonmergerfirms would not be identical. We construct a sample of firms to control for changes in employmentcondition that is attributable to industry and economy wide shock. Mitchell and Mulherin (1996) findthat incidence of takeover activity is greatly influenced by industry shocks, such as deregulation andchanges in input costs.

The method of selecting control firms is as follows. We select three control firms for each mergedfirm using following criteria. First, the control firms must have the same two-digit industry code.12

Secondly, the control firms must have closest sales with merged firms 1 year before the merger (refer-ence year). Thirdly, the matching firms must not engaged in merger during corresponding years.Fourthly, we exclude firms for those we cannot obtain data during 6 years period around reference year.For example, firms that listed 1 year before the reference year, or firms that delist 3 year after, are ex-cluded. In addition, firms that become pure holding firms, are not included in the counter sample.

Tables 4A and 4B shows that merger firms are larger than nonmerger firms, though the difference isnot statistically significant. This might cause some biases because tendency to change employmentcondition may depend on size. For example, it may be easier for large firms to reduce the numberof employees. The difference in size can be explained by the fact that firms always become larger aftermerger. It is occasionally the case that the largest firm in the sector is involved in merger activity. It issometimes the case that unified firm becomes the largest firm in the industry. Among our 111 mergersample, 12 firms became largest firm in two-digit industry, and 8 become second largest.13 As thesefirms are largest firms in the sector, it is impossible to find three counter firms with similar size, i.e.matching firms must be smaller than merger firm. Therefore, merger sample is larger than counter group.Our sensitivity analysis suggests that the bias caused by the difference in size is not serious.14

12 There is only one case which we cannot find three matching firms within same two-digit industry code. It is KDDI that belongsto telecommunication industry. The firm is established after the merger between KDD and DDI in 2000. There are six firms thatbelong to same two-digit industry code in the same year. However, five of them, except NTT docomo, did not meet the criteria formatching firms. Other four firms are not included in control group because we cannot obtain data six years around reference year.It is because two firms went public around reference year, one firm transfer to pure holding company, and one firm delisted withinthree years around reference year. To examine the effect of including counter firm that match using one-digit industry code, weconduct all the remaining analysis excluding this case i.e. one merger firm and three matching firms. The results are almostidentical with those reported here.

13 Among our 111 sample, 65 firms are included in 10 largest firms in two-digit sector.14 We conduct sensitivity analysis to examine the effect of difference in size. In particular, we create subsample that excludes

these 20 firms which become 1st or 2nd largest firms in two-digit sector. Therefore the subsample includes 91 merger firms andcorresponding counter firms. The difference in size is much smaller in this subsample. In full sample, median sales of merger firmsis 224 billion yen while that of counter firms is 185. In a subsample, median sales of merger firms is 170 billion yen while that ofnonmerger firms is 166. We conduct all the analysis using this subsample. The results are qualitatively same to that uses fullsample, showing that the bias caused by the difference in size is not serious.

Table 4AGrowth Rate and Changes around Mergers. This table compares the growth of key variables around mergers between firms that experience mergers and those that do not. The method ofselecting control firms is as follows. Three control firms are selected for each merged firm following criteria. First, the control firms must have the same two-digit industry code. Secondly, thecontrol firms must have closest sales with merged firms 1 year before the merger (reference year). Thirdly, the matching firms must not engaged in merger during corresponding years.Fourthly, we exclude firms for those we cannot obtain data during 6 years period around reference year. Fifthly, firms that become pure holding firms, are not included in the counter sample.The last two columns show the results of the t test and Wilcoxon rank sum test to compare the difference of means and medians between merging firms and counter firms.

Variable name Merger sample Counter sample Merger vs. counter

Mean Median Standard deviation Mean Median Standard deviation t-Test Wilcoxon rank sum test

Salest,�1 (billion yen) 589 224 1090 426 185 695 1.84 1.51Employee growtht,1 (%) �9.88 �7.56 15.79 �4.88 �4.52 12.20 3.46 2.99Employee growtht,2 (%) �12.12 �11.21 19.21 �6.96 �7.15 17.33 2.62 2.39Employee growtht,3 (%) �14.13 �14.76 23.62 �7.12 �10.38 23.55 2.71 2.71Wage growtht,1 (%) �0.32 0.19 17.27 3.16 2.31 10.24 2.56 2.66Wage growtht,2 (%) 7.93 7.08 17.02 5.67 4.48 12.80 1.47 1.72Wagegrowtht,3 (%) 12.13 10.47 19.80 5.90 5.63 13.80 3.65 3.19Total labor cost growtht,1 (%) �10.23 �10.94 20.97 �2.17 �2.55 14.57 4.49 4.31Total labor cost growtht,2 (%) �4.70 �5.65 26.33 �2.01 �3.27 21.13 1.08 1.38Total labor cost growtht,3 (%) �4.12 �6.30 31.24 �2.36 �4.19 25.73 0.59 1.14Sales growtht,1 (%) �11.94 �9.91 17.95 0.00 �1.70 17.31 6.23 5.86Sales growtht,2 (%) �5.07 �8.29 27.10 0.73 �2.84 23.09 2.19 2.94Sales growtht,3 (%) �3.66 �9.40 32.66 2.38 �2.80 27.48 1.90 2.58Sales per employee growtht,1 (%) 0.18 �2.90 23.50 6.15 3.83 19.03 2.69 3.21Sales per employee growtht,2 (%) 10.65 6.59 29.90 10.06 7.64 23.29 0.21 0.60Sales per employee growtht,3 (%) 16.49 11.30 37.31 13.36 9.02 28.40 0.92 0.17Average age changet,1 (year) 0.43 0.54 1.04 0.75 0.50 3.19 1.03 0.09Average age changet,2 (year) 0.69 0.80 1.33 1.00 0.72 3.23 0.99 0.16Average age changet,3 (year) 1.07 1.18 1.45 1.23 1.00 3.35 0.48 0.93

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Table 4BGrowth Rate and Changes around Mergers – Categorized by Relatedness and Previous Performance. This table compares the growth of key variables around mergers between firms thatexperience mergers and those that do not. Mergers are categorized as related when the two-digit industry code of an acquiring firm is the same as that of the acquired one, and otherwise asunrelated. Mergers are categorized as rescue when either an acquiring or acquired firm’s ordinary income is negative 1 year before the merger, and otherwise as nonrescue. The method ofselecting control firms is as follows. Three control firms are selected for each merged firm following criteria. First, the control firms must have the same two-digit industry code. Secondly, thecontrol firms must have closest sales with merged firms 1 year before the merger (reference year). Thirdly, the matching firms must not engaged in merger during corresponding years.Fourthly, we exclude firms for those we cannot obtain data during 6 years period around reference year. Fifthly, firms that become pure holding firms, are not included in the counter sample.The last two columns show the results of the t test and Wilcoxon rank sum test to compare the difference of means and medians between merging firms and counter firms.

Variable name Mean Median Standard deviation Mean Median Standard deviation t-Test Wilcoxon rank sum test

Related merger Unrelated merger Related vs. unrelated

Employee growtht,1 (%) �8.78 �6.59 16.26 �13.14 �9.68 14.06 1.27 1.22Employee growtht,2 (%) �10.68 �8.92 21.26 �16.40 �12.18 14.59 1.32 1.44Employee growtht,3 (%) �12.78 �14.76 25.73 �18.31 �14.76 15.08 1.06 0.96Wage growtht,1 (%) �0.01 1.72 17.97 �1.18 �4.06 15.27 0.31 0.22Wage growtht,2 (%) 9.20 7.88 17.25 4.15 5.32 16.00 1.36 1.57Wagegrowtht,3 (%) 13.27 11.64 20.11 8.63 7.98 18.71 1.06 0.99Total labor cost growtht,1(%) �9.74 �10.31 20.46 �11.68 �12.72 22.73 0.42 0.62Total labor cost growtht,2 (%) �3.28 �3.72 25.96 �8.89 �9.32 27.43 0.98 1.36Total labor cost growtht,3 (%) �2.84 �5.47 31.64 �8.04 �11.06 30.24 0.75 0.83

Nonrescue merger Rescue merger Nonrescue vs. rescue

Employee growtht,1 (%) �8.17 �5.90 15.97 �14.30 �11.21 14.62 1.86 2.26Employee growtht,2 (%) �9.84 �8.43 20.63 �18.02 �16.02 16.71 1.97 2.22Employee growtht,3 (%) �11.50 �13.21 24.78 �20.86 �22.17 19.13 1.89 2.06Wage growtht,1 (%) �0.41 �1.78 17.67 �0.01 2.32 16.46 0.11 0.43Wage growtht,2 (%) 8.27 8.02 17.32 7.05 5.31 16.45 0.34 0.75Wage growtht,3 (%) 12.93 11.64 20.51 10.09 9.66 18.00 0.68 0.68Total labor cost growtht,1 (%) �8.75 �10.58 21.30 �14.05 �12.26 19.89 1.20 1.04Total labor cost growtht,2 (%) �2.02 �2.37 28.31 �11.60 �12.38 19.03 1.74 1.68Total labor cost growtht,3 (%) �0.53 �4.28 34.10 �13.27 �15.90 20.08 1.95 1.64

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Panel A of Tables 4A and 4B compares the growth of key variables, such as the number of employ-ees, sales, wages and employees’ ages between firms that experience mergers and those that do not. Itshows growth of variables from 1 year before a merger to one, two, and three periods after the merger.This panel also shows whether the differences between merging firms and other firms are statisticallysignificant by t tests and Wilcoxon’s rank sum tests.

One of the most striking features in Panel A is that the reduction in the number of employees islarger in merger firms. For example, employee growtht,3, which shows the growth rate from 1 year be-fore the merger to 3 years after, is �14.76 (median), showing that the number of employees drops by14.76% around a merger. In contrast, it drops by 10.38% for nonmerger firms. The difference is statis-tically significant at the 1% level by both the t test and rank sum test. Considering that it is very dif-ficult to reduce the number of employees in Japan, 10.38% is not a small number. These figures reflectthe fact that many firms seek to reduce their number of employees during long recessions. However,firms that experience mergers shed more workers than those that do not.

Another important feature in Tables 4A and 4B is that wage growth is higher in the merger sample.For example, wage growtht,3, which shows the wage growth between 1 year before the merger to 3years after, is 10.47% (median) while it is 5.63% in the nonmerger sample. In other words, wages in-creases by 10.47% in a merging firm. The difference is statistically significant by 1% level by both t testand rank sum test. It is shown that wage increases is larger in merging firms even though employeesin nonmerging firms also receive higher salaries during corresponding period. This result also showsthat a merger is not bad news for employees who stay in the firm.

Panel B compares firms that experience mergers. It is shown that firms cut their labor force in alltypes of mergers. In particular, the reduction is larger in rescue than in nonrescue mergers. In rescuemergers, the labor force is 22.17% less 3 years after the merger and 13.21% less in a nonrescue one. Thedifference is statistically significant according to both t test and Wilcoxon’s rank sum test. Panel Bcompares the wage growth between firms that experience related merger and those with unrelatedone. Wage growth is slightly larger in related merger though the difference is not statistically signif-icant. In related merger, median wage growth is 11.64% while it is 7.98% in unrelated one. Panel B alsocompares between rescue merger and nonrescue firms. Wage growth 3 years after is higher in nonres-cue merger though the difference is not significant.

Results in Tables 4A and 4B clearly show that the number of employees decreases while wages in-crease after a merger. These results suggest that the motivation behind these mergers may not be toexpropriate employees. If this were the case, wages might decrease.

It should be noted that our results do not consider other variables, such as employees’ age. It isimportant to control these variables because changes in the number of employees and wages maybe attributable to changes in these variables. For example, employees’ wages increase whenemployees become older. To examine these possibilities, we conduct regression analyses in the nextsection.

4. Effect of merger on employment and wages without classifying mergers

4.1. Employment growth regression

In this section, we first examine the effect of mergers on employment and then proceed to analyzethe impact on wages and total labor cost. To examine the influence of mergers on employment, thefollowing model is estimated.

Employment growth ðaÞ ¼ a1 þ a2Sales growth ðaÞ þ a3Mergerþ a4Matching groupþ e1

Dependent variables are the growth rate of the number of employees from 1 year before the mergerand ‘a’ years thereafter. We examine the impact on a = 1, 2, and 3. Independent variables include salesgrowth, the merger dummy, in addition to matching group dummy. We include sales growth to con-trol changes in business conditions. Sales growth is also defined as the growth rate of sales from 1 yearbefore the merger and ‘a’ years thereafter. The merger dummy is defined as 1 if the firm experiences amerger. We include merged and unmerged firms to examine the effect of a merger. We define each

Table 5The Effects of Mergers on Employment Growth and Wage Change. This table shows the results of OLS regressions with 111merging firms and 333 control firms. The method of selecting control firms is as follows. Three control firms are selected for eachmerged firm using two criteria. First, the control firms have the same two-digit industry code. Secondly, the control firms musthave similar sales with merged firms 1 year before the merger. Thirdly, the matching firms must not engaged in merger duringcorresponding years. Fourthly, we exclude firms for those we cannot obtain data during 6 years period around reference year.Fifthly, firms that become pure holding firms, are not included in the counter sample. Merger is dummy variable which takes 1 ifthe firm experiences a merger. Matching group consists of each merged firm and three corresponding matching firms for themerged firm. In parentheses are robust standard errors.

Dependent variable= Employee growtht,a Wage growtht,a Total labor cost growtht,a

a= 1 2 3 1 2 3 1 2 3

Intercept 8.951** �18.189* �4.411 8.012*** 2.943 �15.350 12.747*** �9.597* 12.890(3.773) (9.390) (9.015) (2.916) (2.164) (9.518) (4.817) (5.443) (9.956)

Sales growtht,a 0.302*** 0.348*** 0.431*** 0.525*** 0.507*** 0.561***

(0.049) (0.048) (0.056) (0.069) (0.077) (0.069)

Sales per employeegrowtht,a

0.360*** 0.280*** 0.295***

(0.045) (0.044) (0.038)

Change inaverage age

0.135 0.064 0.286** 0.148 �0.011 0.003

(0.096) (0.176) (0.132) (0.164) (0.327) (0.353)

Merger �1.394 �3.141** �4.454** �1.275 2.111 5.459*** �1.747 0.239 1.661(1.365) (1.599) (2.106) (1.433) (1.576) (1.648) (1.618) (2.141) (2.369)

Matching groupdummy

Yes Yes Yes Yes Yes Yes Yes Yes Yes

R-squared 0.53 0.57 0.54 0.50 0.44 0.51 0.60 0.59 0.62Sample 444 444 444 444 444 444 444 444 444

* Significance at the 10% level.** Significance at the 5% level.*** Significance at the 1% level.


merged firm and three corresponding matching firms for the merged firm as same matching group.We assign dummy variables to each group to examine the difference between treatment and controlfirms, or to estimate matching group fixed effect. OLS is used to estimate.15

Table 5 shows the result. The three columns on the left show the impact of mergers on the growthrate of the number of employees. These columns confirm clearly that the number of employees de-crease after the merger. Coefficients for merger dummies are negative and significant in Columns 2and 3. In other words, firms reduce the number of their employees 2 and 3 years after the merger.In Column 2, the coefficient for merger is �3.141, showing that the number of employees decreasesby 3.141% following the merger, even after changes in sales are controlled for. The coefficient formergers in Column 1 is not significant, suggesting that it takes several years to reduce a firm’sworkforce.

4.2. Wage growth regression

Next, we examine the effect on wages. The following model is estimated. As in employment growthregression, we use OLS.

15 Weof OLS.

Wage growth ðaÞ ¼ b1 þ b2Sales per employee growth ðaÞ þ b3Mergerþ b4Change in Age

þ b5Matching Groupþ e2

also estimate using median regression for this and subsequent equations. The results are qualitatively similar to the resultsTherefore, we only report results based on OLS.


The dependent variable is wage growth (%), which shows the growth from 1 year before the mergerand ‘a’ years thereafter, where ‘a’ = 1, 2, 3. Independent variables include sales per employee growth,merger dummies, change in average age and matching group dummies.

The result is shown in Columns 4–6 in Table 5. One striking characteristics is that wages do in-crease in merger firms. Coefficient of merger dummy in Column 6 is 5.459 and statistically significant.In other words, wage growth is 5.459% higher in merger firms compared with nonmerging sampleafter controlling growth of sales per employee and change in average age.

This result is particularly important because it is not consistent with the idea that managers willlower employees’ wage to increase profit. However, our result indicates that, on the contrary, employ-ment conditions do improve after a merger.

It should be noted that the coefficients are significant after controlling for age. It is important tocontrol for age because firms may cease hiring new graduates after a merger to decrease labor costs.If this is the case, average age increases. Then the average wage increases because of the positive age-wage relationship. However, our results show that the increase in wage cannot be attributed to theageing of a workforce.

4.3. Total labor cost growth regression

In addition to employment and wage growth, we examine the determinants of total labor costgrowth. As in previous regressions OLS is used. The following equation is estimated.

16 In p

The depMergerthe valMergerdummiinterprSupposyears bfactors.counter

Total labor cost growth ðaÞ ¼ c1 þ c2Sales growth ðaÞ þ c3Mergerþ c4Change in Age

þ c5Matching Groupþ e3

The dependent variables are the growth rate of total labor cost from 1 year before the merger and‘a’ years thereafter. As before, we examine the impact on a = 1, 2, and 3. Independent variables includesales growth, merger dummies, change in average age and matching group dummies. Columns 7–9show the result. The coefficients for merger dummies are not significant. In other words, there is noevidence that total labor cost decreases significantly in merger firms.

4.4. Change in the number of employees, wages and total labor cost: A graphical illustrations

We now exhibit change in employment condition of merger firms graphically. We show thechanges in deviation of merged firms’ employment level from that of nonmerger firms, from two yearsbefore to 3 years after the merger. We obtain these deviations by estimating the determinants of thelevel of employment to control other factors, such as sales.16

The results are shown in Fig. 1. This figure shows the change in relative level of employment inmerged firms, compared with nonmerger firms. The horizontal axis is year: 0 represents merger yearand �2 shows the year 2 years before the merger. The vertical axis shows the difference in the numberof employees between merged and nonmerged firms.

articular, we estimate the following regression.

ln ðthe number of employeesÞ ¼ d1 þ d2 ln ðSalesÞ þ d3Mergert�2 þ d4Mergert�1 þ d5Mergert þ d6Mergertþ1

þ d7Mergertþ2 þ d8Mergertþ3 þ d9Z1 þ e4

endent variable is the log of the number of employees. By including Mergert�2, Mergert�1, Mergert, Mergert+1, Mergert+2,t+3, dummies, we are able to examine the change of employment around a merger. Mergert�2 dummy, for example, takesue of 1 if the observation is two years before the merger. If the coefficient of Mergert+3 were smaller than that oft�2, it would show that the number of employees decreases. We include size (log of sales), year dummies, and industryes as control variables, which is shown as Z1 in the above equation. Therefore, the coefficients of merger dummies can beeted as the difference in employment level of merger firms, compared with firms of the same size in the same industry.e that the coefficient of the merger dummy Mergert�2 is 0.1. This shows that the number of employees of a firm twoefore a merger is typically 10% larger than that of other firms that do not merge, controlling for sales, industry and otherIn this regression, we use all non-merging listed firms in Tokyo, Osaka, Nagoya and other local stock exchanges assample. Figures are based on coefficients of the results.

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Panel A. Employment around mergers

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Panel B. Wage around mergers

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Panel C. Total labor cost around mergers

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Fig. 1. Change in the Level of Employment, Wage and Total Labor Cost around Merger. This figure shows the change in relativelevel of employment, wage level and total labor cost in merged firms, compared with nonmerger firms. We estimate thefollowing model using OLS and median regression: ln (the number of employees, wage per employee, total laborcost) = d1 + d2ln(Sales) + d3Mergert�2 + d4Mergert�1 + d5Mergert + d6Mergert+1 + d7Mergert+2 + d8Mergert+3 + d9Z1 + e4. We includesize (log of sales), year dummies, and industry dummies as control variables, which is shown as Z1 in the above equation foremployment and total labor cost regression. For wage regression, independent variables include log of sales per employee,average age in addition to year dummies and industry dummies. All nonmerging firms in Tokyo, Osaka, Nagoya and other localstock exchanges is used as counter sample. The coefficient of each merger dummy is shown in figure.


Fig. 1 clearly shows that the number of employees decreases steadily around a merger event. Mer-ger firms are 15.7% (median) larger than the nonmerger sample in terms of the number of employ-ment 2 years before the event. This becomes 8.4% two years after. In other words, the number ofemployees decreases by around 7.3% (=15.7–8.4). These results are consistent with the results in Table5 that the number of employees decreases.

In addition to employment, we show the change in the level of wages graphically. As in previoussubsection, we estimate the determinants of the level of wages in the first place and use the coeffi-cients to draw a figure.17

The results are shown in Fig. 1. As in change in employment, the horizontal axis shows years whilethe vertical axis is the relative wage level of merged firm, compared with nonmerger firms after con-trolling industry and other variables.

17 The dependent variable is the log of employees’ average wage. Independent variables include log of sales per employee,average age, merger dummies, industry dummies and year dummies.

Table 6The Effects of Related and Unrelated Mergers on Employment Growth and Wage Change. This table shows the results of OLSregressions with 111 merging firms and 333 control firms. The method of selecting control firms is as follows. Three control firmsare selected for each merged firm using two criteria. First, the control firms have the same two-digit industry code. Secondly, thecontrol firms must have similar sales with merged firms 1 year before the merger. Thirdly, the matching firms must not engaged inmerger during corresponding years. Fourthly, we exclude firms for those we cannot obtain data during 6 years period aroundreference year. Fifthly, firms that become pure holding firms, are not included in the counter sample. Merger is dummy variablewhich takes 1 if the firm experiences a merger. Mergers are categorized as related when the two-digit industry code of an acquiringfirm is the same as that of the acquired one, and otherwise as unrelated. Matching group consists of each merged firm and threecorresponding matching firms for the merged firm. In parentheses are robust standard errors.


a= 1 2 3 1 2 3 1 2 3

Intercept 9.195** �18.392* �4.420 7.678** �8.377** �15.456 12.161** 13.240 13.041(3.865) (9.391) (9.049) (3.062) (3.955) (9.427) (5.033) (12.952) (10.222)

Sales growtht,a 0.300*** 0.351*** 0.433*** 0.532*** 0.514*** 0.569***

(0.050) (0.050) (0.057) (0.067) (0.074) (0.066)


0.361*** 0.280*** 0.297***

(0.045) (0.044) (0.037)

Change inaverage age

0.135 0.064 0.288** 0.147 �0.010 0.015

(0.095) (0.176) (0.133) (0.169) (0.331) (0.355)

Related merger �0.995 �3.494* �4.927** �1.775 2.156 4.773** �2.704 �0.934 �0.239(1.501) (1.846) (2.475) (1.693) (1.794) (1.878) (1.832) (2.319) (2.580)

Unrelated merger �2.708 �2.038 �2.950 0.241 1.977 7.536** 1.408 3.901 7.721(2.837) (3.006) (3.846) (2.557) (3.223) (3.257) (3.731) (4.782) (5.398)

Matching group dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes

R-squared 0.53 0.57 0.55 0.50 0.44 0.51 0.61 0.59 0.62Sample 444 444 444 444 444 444 444 444 444

* Significance at the 10% level.** Significance at the 5% level.*** Significance at the 1% level.


Again, it is confirmed that wages do increase around the merger. For example, the wage in mergerfirm is 1.4% (median) higher 2 years before the merger. It is 5.2% higher three years after. This resultshows that wages increase by 3.8% (=5.2–1.4) during the merger process. It should be noted that thisresult is obtained after controlling for employees’ age and other variables.

Furthermore, we illustrate changes in total labor cost graphically.18 It is shown that there is noclear tendency in total labor cost. The results in Fig. 1 clearly show that firms reduce labor whileincreasing their wages. Importantly, these results hold after controlling for various factors. We cannotfind evidence that employees suffer after merger in this section.

5. Effect of merger on employment and wages when mergers are classified by type

5.1. The impact of related and unrelated mergers on employment conditions

5.1.1. Growth regressionIn this section, we examine the effect of mergers when mergers are divided by their characteristics.

If the motivation behind a merger is different according to merger type, then the effect on employ-ment conditions may not be identical.

18 Again, we estimate the determinants of log of total labor cost to control the effect of variables. Independent variables includelog of sales, merger dummies, industry dummies and year dummies.


First, we analyze the impact of mergers by dividing our sample into related and unrelated. As in theprevious section, we show the result of employment growth regression, wage growth regression andtotal labor cost growth regression. Then we proceed to show the changes around merger graphically.

Table 6 shows the impact on growth rate of employment, wage and total labor cost. The three left-hand columns exhibit the effect of related and unrelated mergers on the growth of the number ofemployees. One striking feature is that the coefficients for related mergers are significant, while thosefor unrelated mergers are not; that is, the reduction in workforce in unrelated mergers is not

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PanelA. Employment around related and unrelated mergers

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Panel B. Wage around related and unrelated mergers



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Palnel C.Total labor cost around related and unrelated mergers

Fig. 2. Change in the Level of Employment, Wage and Total Labor Cost around Related and Unrelated Mergers. This figure showsthe change in relative level of employment in firms that experience related and unrelated merger, compared with nonmergerfirms. We estimate the following model using OLS and median regression: ln (the number of employees, wage per employee,total labor cost) = d1 + d2ln(Sales) + d3Mergert�2 + d4Mergert�1 + d5Mergert + d6Mergert+1 + d7Mergert+2 + d8Mergert+3 + d9Z1 + e4.We include size (log of sales), year dummies, and industry dummies as control variables, which is shown as Z1 in the aboveequation for employment and total labor cost regression. For wage regression, independent variables include log of sales peremployee, average age in addition to year dummies and industry dummies. All nonmerging firms in Tokyo, Osaka, Nagoya andother local stock exchanges is used as counter sample. The coefficient of each merger dummy is shown in figure.


significantly different from that in nonmerger firms. In addition, the absolute values of the coefficientsfor related mergers are larger than those of unrelated mergers. These results are consistent with theidea that firms attempt to improve their efficiency through scale economy in related mergers.

Columns 4–6 in Table 6 show the effect of merger on wage growth. It is shown in Column 6 thatcoefficient for both related and unrelated mergers are positive and significant, showing that wage in-creases in both type of mergers. Columns 7–9 exhibit the result of regression on total labor cost. Wefind no evidence that total labor cost in related and unrelated merger firms are significantly differentfrom that of nonmerger firms.

5.1.2. Level regression with graphical illustrationAs in Section 4.4, we show in Fig. 2 changes in employment, wages and total labor cost in related

and unrelated merger graphically. This figure is based on regression analyses that estimate the deter-minants of the number of employees, wages and total labor cost. It is shown that the number ofemployee decreases and that wages increase in a related merger. In particular, firms that experiencerelated merger have 15.9% (median) more employees compared with nonmerger firms 2 years beforemerger. The difference decreases to 1.2% three years after. In other words, these firms reduce employ-ment by 14.7% (=15.9–1.2). It is also consistent with the idea that firms can restructure their assets andlabor to utilize economies of scale after a related merger.

Fig. 2 also shows the changes in wages around related and nonrelated merger. Wages are 0.9%(median) higher 2 years before a related merger and 4.9% higher 2 years after. In other words, wagesrise around 4% (=4.9–0.9) relative to nonmerging group firms. In related mergers, the productivityimprovement through restructuring may explain wage increases.

Table 7The Effects of Nonrescue and Rescue Mergers on Employment Growth and Wage Change. This table shows the results of OLSregressions with 111 merging firms and 333 control firms. The method of selecting control firms is as follows. Three control firmsare selected for each merged firm using two criteria. First, the control firms have the same two-digit industry code. Secondly, thecontrol firms must have similar sales with merged firms 1 year before the merger. Thirdly, the matching firms must not engaged inmerger during corresponding years. Fourthly, we exclude firms for those we cannot obtain data during 6 years period aroundreference year. Fifthly, firms that become pure holding firms, are not included in the counter sample. Merger is dummy variablewhich takes 1 if the firm experiences a merger. Mergers are categorized as rescue when either an acquiring or acquired firm’sordinary income is negative 1 year before the merger, and otherwise as nonrescue. Matching group consists of each merged firmand three corresponding matching firms for the merged firm. In parentheses are robust standard errors.


a= 1 2 3 1 2 3 1 2 3

Intercept 8.548** �18.593** �4.617 8.012*** �8.307** �15.420 12.544** 13.113 12.664(3.757) (9.333) (9.014) (2.925) (3.935) (9.544) (4.855) (13.275) (9.733)

Sales growtht,a 0.305*** 0.347*** 0.429*** 0.526*** 0.505*** 0.558***

(0.049) (0.048) (0.056) (0.069) (0.077) (0.069)


0.360*** 0.281*** 0.295***

(0.045) (0.044) (0.038)

Change in average age 0.135 0.064 0.285** 0.148 �0.013 �0.004(0.096) (0.176) (0.132) (0.161) (0.323) (0.351)

Nonrescue merger 0.531 �1.662 �3.019 �1.275 2.255 5.710*** �0.777 1.408 3.076(1.668) (2.003) (2.549) (1.601) (1.890) (1.975) (1.939) (2.766) (3.087)

Rescue merger �6.248*** �6.980*** �8.167** �1.276 1.737 4.818 �4.194 �2.801 �2.003(2.015) (2.181) (3.516) (2.953) (2.811) (2.993) (2.798) (2.734) (2.966)

Matching group dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes

R-squared 0.54 0.57 0.55 0.50 0.44 0.51 0.61 0.59 0.62Sample 444 444 444 444 444 444 444 444 444

⁄ Significance at the 10% level.** Significance at the 5% level.*** Significance at the 1% level.


Wage in unrelated merger increases as well; it is 1.9% (median) higher 2 years before and is 7.6%higher 3 years after. Fig. 2 also exhibits the changes in total labor cost. It is shown that total labor costdecreases in related merger while it is not in unrelated one. In particular, total labor cost is 12.3%(median) higher in related merger 2 years before the event while it becomes only 2.2% three yearsafter. In other words, firms can reduce total labor cost in related merger. This result is consistent withthe idea that firms can improve their productivity through restructuring in related mergers.

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Panel A. Employment around nonrescue and rescue mergers

Nonrescue-OLSNonrescue-Median

Rescue-OLSRescue-Median

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Panel B. Wage around nonrescue and rescue mergers

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Nonrescue-OLS

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Panel C. Total labor cost around nonrescue and rescue mergers

Fig. 3. Change in the Level of Employment, Wage and Total Labor Cost around Nonrescue and Rescue Mergers. This figure showsthe change in relative level of employment in firms that experience rescue and nonrescue merger, compared with nonmergerfirms. We estimate the following model using OLS and median regression: ln (the number of employees, wage per employee,total labor cost) = d1 + d2ln(Sales) + d3Mergert�2 + d4Mergert�1 + d5Mergert + d6Mergert+1 + d7Mergert+2 + d8Mergert+3 + d9Z1 + e4.We include size (log of sales), year dummies, and industry dummies as control variables, which is shown as Z1 in the aboveequation for employment and total labor cost regression. For wage regression, independent variables include log of sales peremployee, average age in addition to year dummies and industry dummies. All nonmerging firms in Tokyo, Osaka, Nagoya andother local stock exchanges is used as counter sample. The coefficient of each merger dummy is shown in figure.


5.2. The impact of rescue and nonrescue mergers on employment conditions

5.2.1. Growth regressionWe now proceed to examine the impact of rescue and nonrescue mergers. The results of regres-

sions of these mergers on employment, wage and total labor cost are shown in Table 7. An importantfeature in this table is that the number of employees decreases in rescue mergers but not in nonrescuemergers. If the firm is in financial distress because of excess labor, it may gain profit by cutting thenumber of employees. Column 3 shows that the number of employees decreases by 8.167% in rescuemergers. The result suggests that in a rescue merger, firms attempt to improve performance by reduc-ing the number of employees.

Column 6 of Table 7 shows that wage increases in nonrescue merger but not in rescue one, aftercontrolling sales per employee growth and employees’ age. The coefficient for nonrescue merger is5.71% and significant at 1% level. In other words, employees in firms that experience nonrescue mergerenjoy 5.71% higher salaries after merger. Coefficients for rescue and nonrescue merger dummies arenot significant for total labor cost.

5.2.2. Level regression with graphical illustrationNext, we show the change in employment, wages and total labor cost around rescue and nonrescue

mergers graphically. The results are shown in Fig. 3. It is shown that the number of employeesdecreases in rescue merger. The number of employees in firms that experience rescue merger is2.8% (median) higher compared with nonmerging firms 2 years before while it is 8.2% lower 3 yearsafter, showing that these firms typically decrease employment by 11% (=2.8–(�8.2)).

As for wages, wages appear to increase around mergers both in rescue and nonrescue cases. It lookslike there is no clear tendency that firms in rescue and nonrescue merger decreases total labor cost.

The results in this section show that the impact of a merger varies according to type. Important re-sults in these tables are that, again, we find no evidence that wages decrease in merging firms.

6. Conclusion

This paper analyzed the impact of corporate mergers on employment and wages in Japan. The re-sults are summarized below. First, we examine the impact without classifying merger types. The num-ber of employees decreases significantly after a merger. Typically, the number of employees decreasesby 4.454% three years after a merger, even after we control for changes in sales and other variables.Typically, average wage increase is 5.459% higher in merger firms. The decrease in employment is sig-nificant in related and rescue mergers.

Our results also show that it takes several years for firms to reduce the number of employees. Manycoefficients for merger dummies are significant 2 or 3 years after a merger, but not in the regressionfor 1 year after the merger. This may be because firms try to decrease their workforce without dismiss-ing any employees. Instead, companies attempt to reduce the number of employees after a mergeronly moderately by pausing the recruitment of new employees, or by soliciting voluntary retirement.

One of our motivations in this paper is to examine whether employees suffer during merger pro-cess. Our results show that employment conditions improve for those who stay in the firm. Workersenjoy higher wages after related and nonrescue mergers, in particular.

Our results raise a number of interesting questions. One of the most fundamental is why wages in-crease after merger. There are several possible interpretations. Firstly, it can be the case that employ-ees who receives relatively lower wages leave the firm after the merger. As shown above, the numberof employees decreases during the process. If, for example, young workers are more likely to leave, itmight be the case that the average wage increases as they earn lower wages. Even when middle-agedemployees leave, if those who leave the firm had received relatively lower wages because they belongto lower rank, the average wage decreases as well. Although it is beyond the scope of this paper toinvestigate the change in the composition of workforce, previous literature shows that might be thecase. Kubo (2004) has analyzed which type of employees leaves the firm after the merger, usingemployees’ individual data of one merger case between large Japanese firms in 2000. He shows that


there are two types of employees who leave after merger. They are either young workers who spendless than 10 years after they enter the firm. As they are young and there is a positive correlation be-tween age and wage, they receive lower wages. Kubo also shows that another type of employees wholeave is relatively older employees but belong in lower rank within corporate hierarchy. As there is astrong correlation between rank and wages, they receive relatively lower wages. In either case, wagesof those who leave the firm after the merger is considered to be relatively lower. If employees withlower wages leave the firm, then the average wage might increase. Although Kubo has analyzedone merger case, if similar tendency were observed in other mergers, the average wage mightincrease.

Secondly, wage increase can be explained by the wage setting behavior of managers. When twofirms merge, wage levels are not same. There are firms with higher wages and those with lower wages.Merged firm would need to adjust wage level after the merger to unify wage system. Unified firm candecrease the wages of employees in a firm with relatively higher wages. However, it is not easy to doso because employees would not cooperate with managers. Alternatively, firms can increase the wagelevel of employees in a firm with lower wages. If this is the case, average wage of unified firm wouldincrease.

Relatedly, it is important to gain support from employees to succeed in the merger process. If a newmanagement attempts to expropriate employees by lowering wages, then they may not cooperatewith them. Recent experiences in large Japanese firms suggest that employees’ cooperation is essentialfor successful merger. For example, the merger agreement between top managers of SEGA, video gamedeveloper and Bandai, toy maker, are abandoned because of the opposition by the employees of Ban-dai.19 In particular, middle managers of Bandai opposed the agreement. It is reported that employeesof Bandai fear that their chance of future promotion will be smaller after the merger because the mer-ger is led by Sega. Considering that employees’ cooperation is essential in successful merger process, itis more appropriate for new managers to increase wages rather than cutting wages.

Lastly, wage increase can be explained by improvement in productivity. If each worker’s productiv-ity improves after a merger by scrapping excess factory, then wages may increase. Kruse et al. (2007)analyzed the effect of merger on operating performance of the manufacturing firm in Japan from 1969to 1999, finding performance improvement after merger. Ikeda and Doi (1983) examined performanceconsequences of 48 mergers that occurred in 1964–1975 in Japan. By examining various performancemeasures, such as return on equity, return on assets and sales per employee, they find that perfor-mance improved after merger in many cases. These results suggest that performance improves aftermerger. Although rigorous analysis on the performance after the merger is beyond the scope of thispaper, if performance improves in our sample, wage increase can be attributable to the improvementin productivity. This interpretation is consistent with the fact that the growth of sales per employee ishigher in merger sample, as shown in Tables 4A and 4B, though the difference with nonmerger firms isnot statistically significant. The growth of sales per employee is 11.3% (median) in merging firms be-tween 1 year before and 2 years after while it is 9.02% in counter sample.

While we have shown some facts on the effect of mergers on employment conditions, several morequestions need to be answered to distinguish these alternative interpretations in future research.Firstly, can wage increase be attributed to productivity improvement? The difficulty in analyzing thisquestion is that productivity and employment conditions are jointly determined. One might argue thatchange in performance can be attributed to change in employment condition. Beckman and Forbes(2004) and Kruse et al. (2007) include change in employment as independent variable when theyexamine the determinants of performance change around mergers and acquisitions. These studiesimplicitly assume that performance change is caused by the change in employment condition. In con-trast, we analyze the determinants of employment condition using change in sales as independentvariable. It would be of important to extend our analysis by making both performance and employ-ment condition as endogenous variables. If we could find good instrumental variable, then we cananalyze using simultaneous equations systems, for example.

19 Details of this process is shown in Nikkei Newspaper Article ‘‘Bandai, Sega to GappeiKaisho (Bandai and Sega Abandon theirMerger)’’, on 28th May 1997.


Second question that could be answered in future research is whether there is a variation in changein employment condition among employees. In this research we focus on the change in average wageof a firm. However, it might be the case that some employees’ wages increases after the merger andothers’ decreases even in the same firm. If a new manager tries to strengthen the link between pro-ductivity and wages, then wages of those with high contribution would increase. This question canbe addressed by using individual data of employees, or by using insider econometrics (Shaw, 2009).Shaw shows that more researches have been made using individual data within the firm. There areonly a few studies that examine individual employees data in merger cases. Tsuru et al. (2005) exam-ine the change in wages of individual employee analyzing the data in a firm that experience merger.Unfortunately, they did not show explicitly whether wage level increases or whose wages increasesaround merger, as the goal of their paper is to examine the change in the determinants of wage struc-ture. They find that the link between wages and performance evaluation. Researchers might be able toexamine various questions regarding the change in wages by utilizing individual data. For example, itwould be interesting to examine whether the number of employees with pay increase is larger thanthose without.

Acknowledgments

The authors would like to thank the editor of the Journal, Takeo Hoshi, two anonymous referees,Daiji Kawaguchi, Hideaki Miyajima, Hiroshi Osano, Masatoshi Kato for their helpful comments andsuggestions on an earlier version of this paper. Useful discussion by seminar participants at the KansaiLabor Economics Workshop, Trans-Pacific Labor Seminar, Business Economics Workshop, CorporateGovernance seminar at RIETI (Research Institute of Economy, Trade and Industry), Seminar at WasedaUniversity and conference attendees at the Japanese Economic Association Annual Meeting is alsogratefully acknowledged. Authors acknowledge the financial support of a Grant-in-Aid for ScientificResearch (19530292, Katsuyuki Kubo, 19830093, Takuji Saito) from the Japanese Ministry of Educa-tion, Science, Sports and Culture, and Waseda University Grant for Special Research Projects (Projectnumber: 2009A-502, Katsuyuki Kubo).

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The effect of mergers on employment and wages: Evidence from Japan