Mergers and Market Share: Evidence from the UK Financial ... V/V.A... · Michelle Haynes*† &...

Mergers and Market Share: Evidence from the UK Financial Mutual Sector

Michelle Haynes*† &

Steve Thompson*

*Nottingham University Business School Nottingham

NG8 1BB,UK

ABSTRACT

This paper presents an empirical investigation of the impact of acquisition activity on

sales and market share of firms in the UK financial mutual sector. The model is

estimated on an unbalanced panel of 93 UK building societies over the period 1982-

1993. In contrast to much of the existing merger literature, which finds no observable

benefit to the acquiring party, our results do indicate statistically significant gains

following acquisition.

Key words: mergers, market share, mutuals JEL Codes: G34, L11, G21

†Corresponding Author. Tel./Fax: 0115 9515483/9515262. E-mail: [email protected]

1

1. Introduction

It is a stylized fact of the merger literature that many – perhaps most – acquisitions

appear to fail, in as much as they apparently convey no observable net benefit to the

acquiring party. This is certainly not to conclude that on average mergers are value-

destroying, in the sense of either reducing overall shareholder wealth or worsening the

underlying joint performance of the participating firms, although a minority may be. It

is rather to say that the benefits of merger, if any, do not appear to be manifested in

either the profitability1 or the share price performance2 of the acquiring firm. The

gains, if any, that may follow an acquisition typically appear to be anticipated in the

bid price such that the acquirer’s shareholders enjoy no obvious benefit in share price

appreciation or improved profitability.

Three categories of explanation for the apparent failure of a high proportion of

acquisitions to yield financial benefits to the acquiring firm are found in the literature.

The first, derived from the managerial or agency theory approach (Mueller [1969],

Jensen [1986]), assumes that an acquisition represents some preferred expenditure of

managers able to exercise discretion whilst operating under a relaxed corporate

1 Most empirical work on the ex post profitability effects of acquisition – Ravenscraft and Scherer

[1987] for the US and Singh [1971], Cosh et al [1980] and Dickerson et al [1997] in the UK – presents

a consistent story of lowered profitability. Dickerson et al, for example, report that each acquisition

reduces profits by seven percent.

2 Event studies of the shareholder wealth effects of merger announcements typically reveal a small

overall gain, but one that accrues entirely to the target’s shareholders as a bid premium. Longer term

stock market studies, although not without methodological difficulties, tend to present a more

pessimistic assessment: see O’Sullivan [1997] for a survey of the stock market literature.

2

governance regime. Therefore it is not necessarily to be judged as a net present value

increasing investment. Second is the assumption that many managers simply overpay

for the target, with corresponding declines in the acquiring firm’s profitability and

share price. This may be a benign consequence of the bidding mechanism: namely the

potential acquirer’s valuation produces a random variable which triggers a bid if and

only if it exceeds the current share price, implying that positive forecast errors

generate take-overs whilst negative ones do not; the so-called “winner’s curse”. Roll

[1986] suggested this benign process is frequently supplemented by managers’

excessive self-confidence or hubris which he defined as: “the overbearing

presumption of bidders that their valuations are correct” (Roll [1986] p198).

The third possibility is that acquisition disappoints as a consequence of some

deterioration in aspects of post-acquisition performance, normally assumed to be that

of the acquired business. Unlike the two previous hypotheses, involving respectively

managerial motivations and confidential valuation that are each not directly

observable, it is in principle directly testable; although in practice the generation of a

counterfactual against which the observed outcome may be compared is frequently

problematic. While any post-merger deterioration may be considered as a form of

managerial failure, two broad categories of problem have been suggested in the

literature:

First, human resource management problems in the acquired company may cause a

decline in its performance. These might include the exit of more able managers and

staff (Cannella and Hambrick [1993]) and lowered commitment and morale

3

associated with increased uncertainty (Buono et al [1985], Buono and Bowditch

[1989]) and/or the expectation of job losses.

Second, assimilating the newly acquired business units into suitable configurations

may be difficult. Penrose [1959] developed the now-classic argument that the limited

availability of senior managerial resources constrains the efficient growth of firms,

with the ultimate constraint on growth-by-merger being the availability of executive

talent in the would-be acquirer.

In fact, the impact of acquisition activity on the acquiring party is, a priori,

ambiguous. Theory predicts that horizontal mergers have the potential to increase

market power and/or efficiency via increased economies of scale. An increase in

market power will result in higher profits and reduced sales. In contrast, an increase in

efficiency will lead to an expansion in both profits and sales. The net effect will

depend on the trade-off of market power for cost reduction (Williamson [1968]).

Therefore, it is possible to test whether a merger has increased market power or

efficiency by examining whether sales expand by more or less than is expected if the

two firms had not merged (Mueller [1980]). Thus, a reduction in sales of the merging

firms relative to the non-merging ones would indicate that the market power effect

dominates the efficiency gains. On the other hand, an increase in sales would be

consistent with the hypothesis that the efficiency effect dominates over the market

power effect.

The evidence on the underlying drivers of post-acquisition performance is limited and

somewhat ambiguous. Cowling et al [1980] developed a methodology for comparing

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pre-and post-merger unit costs which they applied to 11 large UK industrial mergers.

Applying an industry efficiency growth rate of 1.5 percent as a counterfactual, they

concluded that five out of 11 mergers showed post-merger gains, with the remainder

negative or broadly unaffected. However, the data requirements for this type of study

have precluded wider replication. Productivity analysis of the acquiring firms (e.g.

Lichtenberg and Siegel [1992], Conyon et al [2002]) has tended to produce a more

positive picture, but the relatively high incidence of post-merger divestment raises

issues of interpretation3.

Sales data has also been examined to evaluate post-merger success (see Mueller

[1980, 1985] and Gugler et al [2003]). The limited evidence from such work

generally suggests that sales show a ceteris paribus post-merger medium term

decline. This result is consistent with worsening performance through declining

incentives in the acquired firm and/or a reduction of the effectiveness with which

assets are managed in the merged firm, as has been widely conjectured since Penrose

[1959]. The difficulty is that such a result is also consistent with two alternative

explanations: First, if a horizontal merger raises market power, unit sales may fall;

although the overall impact on the firm’s revenue will depend on both the underlying

demand and the merger’s impact on costs. Second, as noted above, acquisition tends

to be followed by divestment leading to a decline in observed sales which may

obscure any merger-related impact on core activity performance. The absorption of

3 Divesting under-performing business units will appear to raise the productivity of those retained and

vice versa, without any necessary underlying changes.

5

large targets may generate substantial divestment, see Haynes et al. [2002], even

among horizontal acquisitions.

Three studies of the effects of mergers on market share exist. An early study by

Goldberg [1973], using a sample of 44 mergers by advertising intensive firms in 15

different industries, observed insignificant changes in market shares over an average

of 3½ years after undertaking the merger. By contrast, Mueller [1985; 1986, Ch.9]

using a sample of 209 mergers by US manufacturing companies, reports a significant

decline in the market shares of merging firms in the US over an average of 11 years

following the merger. Moreover, significant declines in market shares were observed

for both horizontal and non-horizontal mergers, with larger falls reported for the

latter. Baldwin and Gorecki [1990] also found that horizontal mergers lead to a

significant reduction in market shares for the acquired firm. However, no significant

changes were reported for firms acquired in non-horizontal mergers. They conclude

that their results are consistent with an increase in market power following the

mergers. In all studies, the effects of mergers are examined by comparing the

performance of merging firms to a control group of non-merging firms.

One difficulty shared to a greater or lesser extent by the existing studies on the impact

of mergers concerns the heterogeneity of firms included. In any sample of firms,

acquisition activity tends to be concentrated among the larger firms, which also tend

to exhibit the higher levels of diversification. However, acquisition among diversified

firms is typically followed by divestment of unwanted businesses. Unless sales data

are available at a disaggregated level, this can be interpreted misleadingly as a sales

decline.

6

This paper seeks to circumvent the above difficulty by exploring the market impact of

merger activity across an industry characterised by extreme homogeneity of mergers

and institutions, namely the UK building societies. Structural regulation restricted the

societies to core activities and when limited diversification was eventually permitted,

this had to be separately reported and thus does not figure in the accounts of the

parent company, allowing us to continue to restrict attention to the core. Furthermore,

all acquisitions within the period were intra-sector acquisitions, implying that all were

horizontal and no post-merger divestments occurred, other than the disposal of assets

from activities rationalised after the merger. Therefore our data set provides a most

unusual degree of homogeneity across the institutions involved and the output

(deposits) observed. Also, since mutual acquisitions 4 involve no bid, the over-

payment hypothesis is irrelevant. Perhaps most importantly for a study of merger

effects, the complete lack of diversification removes the need for aggregating diverse

outputs and adjusting for any divestments in the subsequent post-merger adjustment

period5.

The paper also addresses directly the methodological problem encountered when

trying to assess merger outcomes from data which rela te to both parties before the

event and only the merged entity thereafter. Even a simple before- and after-

4 Nearly all sector acquisitions are "friendly" (see Section II).

5 One difficulty with using data from mutual firms during an era of regulation concerns the problem of

endogeneity. Prior work – see Thompson (1997) – suggests that a standard predicted model using

annual accounts data performed relatively weakly for this sector implying that instrumenting the actual

outcomes by predicted probabilities is unlikely to be very satisfactory. Furthermore, as the key

determinant of acquisition appeared to be size it seemed easier to address this directly. The endogeneity

problem is discussed in Gugler and Siebert (2004).

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comparison involves the explicit or implicit construction of a counterfactual about the

likely performance of each party had the merger not occurred. Unlike previous merger

studies, which typically make an implicit assumption that the acquired party’s

contribution would have remained static at the immediate pre-merger level, we

develop a methodology to explore the event’s impact under alternative assumptions

about the subsequent contribution of the acquired party.

Section 2 describes the UK building society sector and sample characteristics,

Sections 3 outlines the model to be employed. Section 4 summarises the results and a

brief conclusion follows.

2. UK Building Societies: Sector and Sample Characteristics

2.i Sector Characteristics

In their origins the UK building societies were very similar to the US Savings and Loans

associations, with a traditional specialization in savings deposit collection and mortgage

lending. In the UK, they remained very largely restricted to these core activities until

1987, since when the larger societies have enjoyed a limited, but gradually increasing,

freedom to diversify into other financial product markets [Drake (1989), Ingham and

Thompson (1995)]. Financial deregulation also permitted other financial institutions,

including commercial banks, to make a large-scale entry into the societies' formerly

exclusive preserve of mortgage lending. Since 1987, the societies have also enjoyed the

option of demutualizing their ownership structure and switching to the (generally less

8

restrictive) regulatory regime of the commercial banks. This option has been widely

exercised since 1995, but in the period of our study only one case occurred6.

In recent decades the building society sector has experienced a substantial decline in the

number of societies which, until recently, occurred exclusively through intra-sector

merger activity (see Figure 1). The number of independent societies has fallen by 75

percent in the past twenty years. For the most part these takeovers have been "friendly",

in the sense of being endorsed by the target's board of directors [see Thompson 1996)]

and not infrequently being initiated by the target itself. A minority of acquisitions have

been instigated by the sector's regulator, the Building Societies' Commissioner, as a

means of securing savers' deposits when a society faced financial instability. It is clear

that the process of acquisition among mutually owned firms, with no marketable equity

claims, is different to that involving stock firms. However, the identifiable

characteristics which distinguish targets from other societies, and these include smaller

size, lower recent growth, financial weakness and negative profitability [see Thompson

(1997)], appear very similar to those reported in the empirical literature on stock

acquisitions7.

What distinguishes mutual from stock mergers is the absence of a market in corporate

control. Mutuals, including UK building societies may be "owned" by their members, at

least in the sense that the latter have a legal claim on their net worth in the event of

liquidation, but in most other respects the ownership claim is severely attenuated. In

principle, new members may join on equal terms to existing ones, thus establishing new

6 Abbey National demutualised in 1989, however we have kept it in our sample subsequent to this

period in order to obtain reliable estimates of market share.

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claims on net worth. Thus it is infeasible to establish a secondary market in equity claims

through which a would-be acquirer could purchase control. Furthermore, in the building

societies' case the opportunity for near costless exit on demand by dissatisfied

depositors7, together with a one-person-one-vote decision rule, might be expected to

thwart effective attempts either to monitor management or to build up substantial voting

blocks [Thompson (1996)]. Moreover, the legislation covering society governance gives

the incumbent management team considerable discretion in deciding whether or not to

support a takeover approach. Since an unsupported takeover would not normally reach

the stage of a membership ballot, nearly all sector acquisitions are "friendly".

2.ii Sample Characteristics

Data was collected from the societies' annual reports and accounts using the Building

Societies Yearbook (various years), accounts held at the libraries of London, Manchester

and Warwick Business Schools and summaries produced by the Building Societies'

Association over the interval 1981 to 1992 inclusive. Comparability of data across

societies was straightforward since published accounts are standardized for regulatory

purposes. Until 1987, all societies were effectively restricted to the same core activities.

Since then, separate society and group accounts have been required to distinguish the

core and peripheral activities. The data below relate solely to the former.

The population of the sector fell from approximately 250 at the start of our period to

about 100 (see Figure 1). In every case bar one 8 this decline resulted from acquisition

within the sector. Since the research design involves first differences, inclusion in our

7 Palepu (1986) provides a comprehensive review of the general literature on merger target prediction.

10

sample required that at least three years of continuous data were available. The

exclusions reduced our sample size to an unbalanced panel of 93 societies. (The

number of firms in the sample and the balance of the panel are shown in Tables 1 and

2 in the Data Appendix.) Our sample represents on average approximately 84% of

total industry deposits. The omissions are principally drawn from those very small

societies that were acquired during the early part of our period and for which three

years of data were correspondingly unavailable. However, these do figure as targets

and hence impact upon the acquiring societies.

[INSERT FIGURE 1 HERE]

As shown in Table 1, the sample of 93 societies recorded 82 positive merger-year

observations, 54 of which were in the pre-1987 sub-period and 28 in the subsequent

post-deregulation sub-period9. Table 2 shows the extent to which firms in the sample

were involved in merger activity. Out of the sample of 93 companies, 34 firms (37%)

made at least one acquisition. Approximately 31% of the sample undertook between 1

and 4 mergers and 59 companies had no recorded mergers at all.

[INSERT TABLES 1 & 2 HERE]

Over the period of our investigation, 1981-1992, all acquisitions within the sector

were made by other members of the sector and were thus horizontal. Despite this

8 The exception was Abbey National who demutualised in 1989.

9 The actual number of acquisitions exceeded this figure since multiple acquisitions by a society in any

one year were treated as single mergers.

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intra-sector merger activity, concentration within the sector remained relatively low

(see Table 3) and no individual merger produced an increase in concentration

sufficient to merit “significant competitive concerns”, using for example the US

Department of Justice criteria 10. Table 3 also shows the yearly concentration ratios for

the four largest firms in our sample. As shown, the 4-firm concentration ratio is smaller

at the end of our study period than the beginning. Thus, both the number of firms and

concentration were falling over this period. This implies that the size distribution of

firms is getting more symmetric over time possibly as a result of mergers.

[INSERT TABLE 3 HERE]

Table 4 indicates that within our sample acquisitions are undertaken by larger firms. On

average, acquiring firms11 have almost double the value and number of deposits than

other firms in our sample. They also have the larger labour input, fixed and liquid assets.

These findings are consistent with results from other industries in the literature.

10 The within-sector Herfindahl, using our data, fell from a maximum of 1388 points in 1981 to 1098

points in 1992. To the extent that our sample falls short of the population, this represents an over-

estimate of true concentration. However, applying the US Department of Justice’s “Merger

Guidelines”, a widely used standard, results in the sector fluctuating between the “unconcentrated”

category and the lower reaches of the “moderately concentrated” category. No single merger in our

period appears to have added 100 points to the measured Herfindahl and thus no merger would “raise

significant competitive concerns” on the Department of Justice criteria. See US Department of Justice

and Federal Trade Commission Horizontal Merger Guidelines, April 2 1992, revised April 8 1997.

Subsequent mergers in the 1990s certainly did raise concentration quite sharply

11 A society is classified as an acquirer if they undertook an acquisition at any time over the sample

period.

12

(Definitions and methods of construction of these variables are given in the Data

Appendix.)


Table 5 shows the summary statistics for the acquired firms within our sample. They

are a fifth of the size of the average firm and their market share is approximately 0.2

percent.


3. Measuring the Impact of Merger

3.i Measuring the Impact of Merger on Sales

Measuring the impact of merger on sales is not as straightforward as it may first

appear. An obvious consequence of a merger is that the company grows in size as a

result of obtaining the sales of the acquired company. However the merger may have

an additional impact on sales. We would like to disentangle these two effects.

In order to measure the ‘merger impact’ on sales we need to look at how the

acquirer’s sales change, net of any increase that results simply from subsuming the

sales of the acquired firm. An obvious solution to this would simply be to subtract the

size of the acquired firm from the joint sales of the merged entity and see whether the

‘net sales’ are greater or smaller than that of the acquiring firm. If the net sales are

13

greater, then the merger has had a positive impact on sales. Figure 2 illustrates this

point:


Here, the pre-merger level of log sales of the acquiring firm is given by ASln . The

firm then acquires a target of size BSln at time t , with the size of the combined entity

being 'ln AS . Given this, the level of sales net of the size of the acquisition is simply

( BA SS lnln ' − )

If a merger that occurs at time t has no additional impact on the sales of the acquiring

firm, then the net sales should equal the pre-merger level ( ABA SSS lnlnln ' =− ). If net

sales are higher than the pre-acquisition level then the merger has had a positive

impact on the sales of the acquiring firm. Conversely, if net sales are below that of the

pre-acquisition level, then the effect of the merger on the acquirer’s sales is negative.

Note however that Figure 2 illustrates the simple case when neither the acquiring nor

the target firm grow, except as a consequence of merger. In the case illustrated in

Figure 3 however the acquiring firm (and presumably the acquired firm) is growing

prior to the merger. In such a case the above technique may need to be modified

depending on the investigator’s priors. In the year of merger the calculation of net

sales is unproblematic and we can observe the impact of the merger net of the size of

the acquiring firm.

14


However, in the years subsequent to the merger we may want to make additional

assumptions in order to calculate net sales. We might, for instance, want to assume

that the sales of the acquired firm would have grown subsequent to merger. We could

then net out from the combined sales those that result from the acquired firm and its

subsequent growth.

An accurate representation of the post merger net sales may be important for a

number of reasons. Firstly the ‘merger impact’ on sales may not be immediate, and

we would like to measure the impact on net growth in 1+t , 2+t etc. Secondly, we

would like to use the corrected net sales figures in our analysis. These datapoints

represent important information on sales within the sector that we do not want to

simply discard. Apart from reducing our degrees of freedom, it may induce bias in our

sample. It is important to note at this juncture that the net sales in the years after the

‘merger impact’ has been felt represent data points for non-merging firms (as the firm

is not merging in these years).

Figure 3 illustrates the consequences of two possible assumptions regarding the

growth of the acquired firm. Case 1 is the situation where we make the same

assumption as previously. That is, the component of sales deriving from the acquired

firm does not grow post merger. Hence, in order to get net sales, we subtract the

merger size from combined sales in all post merger years. In case 2 it has been

assumed that, post merger, the sales deriving from the acquired firm grow at the same

rate as that of the firm that acquired it. Note that the impact of these assumptions is

15

not innocuous. In the first case we are subtracting an amount that becomes

proportionately smaller over time. Hence the combined and net sales lines converge.

In the latter case this does not happen.

Since the impact of merger on net sales is made by comparing the mean change of

sales in years in which no merger takes place, to the mean change in years in which

one does, the measured impact of merger will differ depending on the counterfactual.

In the first case the mean of the non-merger years is higher than in the second, so the

measured impact of merger will be less. These are of course not the only possible

‘counterfactuals’. Below we discuss those used in this study.

3.ii Implementing the model in a regression framework

In order to ascertain the ‘merger impact’ on sales, the above framework involves

comparison of the mean change of net sales in merger and non-merger years. This can

be implemented within a regression framework via the estimation of the following

regression:

ititit MergeNetS εββ ++=∆ 10ln …(1)

Where Merge is a dummy equal to one in the year of a merger and zero otherwise,

itNetS is the level of sales in the firm, net of the size of any firms that have been

acquired and ε it is a stochastic error term. In such a specification 0β represents the

average growth rate of net sales for the acquiring firm and 1β represents the

percentage increase in sales as a result of the merger. The advantage of a regression

16

framework is that Equation (1) may be modified to include fixed effects, intended to

capture unobserved firm heterogeneity, and year dummies. Subsequent mergers may

also be straightforwardly handled.

In line with the discussion in the previous section, net deposits are given by:

*ititit acqsizeCombineSNetS −= …(2)

Where itCombineS is a the level of sales for the merged company and *itacqsize are

the sales deriving from the acquired firm in period t. The problem of finding a

counterfactual is thus a problem of specifying *itacqsize .

We adopt four alternative approaches involving different counterfactuals, which serve

to provide ‘bounds’ on the merger effect: First we assume that the target would have

experienced zero growth in sales after the merger year; Second, we assume that the

sales of the acquired party would have grown in line with the non-merging firms in

the sector; Third, we assumed that the sales of the acquired party would have grown

in line with the merging firms in the sector.

We would of course expect these counterfactuals to lead to different estimates of the

‘merger impact’. Figure 4 shows the difference in yearly growth rates between

merging and non-merging societies12. As can be seen, societies belonging to the

12 Average annual growth rates are calculated by estimating yearly growth rates for each society in our sample and then estimating the mean rate for non-merging and merging societies respectively. A society is classified as non-merging if they did not undertake any acquisitions over the entire sample period. The growth rates in the year of a merger were excluded when calculating the mean rate for merging societies, as these will show a huge increase in the merger year.

17

merging set have higher mean growth rates except in 1992. Thus the estimate of the

‘merger effect’ calculated under counterfactual 3 would be expected to be higher than

under counterfactual 2, since in the former case the mean of the observations in the

non-merging years will be lower. In contrast, counterfactual one will make the

smallest adjustment to the sales figures post merger and so would expect to yield the

lowest measured impact of merger.


Finally, we also adopt a more agnostic approach to the determination of the

counterfactual, and, in some senses, allow the data to decide the behaviour of the

firms post merger. We assume that the acquired firm would have grown at a constant

rate subsequent to merger ( 2β ) which may, or may not, be the same as that of the

acquiring or the non-acquiring firms. Hence m periods after the merger the size of the

acquired firm would be:

mitm

it acqsizeacqsize −= 2* β …(3)

Combining (1) (2) and (3) this gives:

ititmitm

it MergeacqsizeCombineS εβββ ++=−∆ − 102 ]ln[

Which we can rearrange as follows:

18

ititmitm

itmitm

it MergeacqsizeCombineSacqsizeCombineS εββββ ++=−−− −−−

−− 1011

212 ]ln[]ln[

])][[exp( 11

21102 −−−

−− −+=− mitm

ititmitm

it acqsizeCombineSMergeacqsizeCombineS ββββ

ititmit

mit

mitm

it MergeacqsizeCombineS

acqsizeCombineSεββ

ββ

++=

−−

−−−

−

−10

11

21

2ln

This leaves us with an expression for current combined sales as a function of

Merge ,CombineS , acqsize , and m , all of which are known.

mitm

mitm

ititit acqsizeacqsizeCombineSMergeCombineS −−−−

− +−+= 211

2110 ])][[exp( ββββ …(4)

The above framework can also be modified to account for firms undertaking more

than one merger and extended to include fixed effects and year dummies. We

implement it econometrically using non- linear least squares.

Note that this approach has advantages and disadvantages over counterfactuals 2 and

3. Although we do not make an assumption about which subset of firms the merged

entity is most similar to, for computation reasons we need to assume that the growth

rate of the acquired entity is constant post merger. This is not the case with

counterfactuals 2 and 3, where the counterfactual growth rate varies each year with

that of the comparison group. Which of the 4 counterfactuals is most appropriate is a

matter of judgement.

19

3.iii. Measuring the Impact of Merger on Market Share

In addition to estimating the impact of merger on sales, we also estimate the effect of

merger on market share.

In order to estimate the impact of merger on market share we use the following

estimating equation:

ititit MergeNetMS εαα ++=∆ 10 …(5)

Where Merge is a dummy equal to one in the year of a merger and zero otherwise,

itNetMS is the market share of the firm, net of the market share of any firms that have

been acquired and ε it is a stochastic error term. In such a specification 0α represents

the average growth rate of market share and 1α represents the percentage increase in

market share as a result of the merger. Equation (5) is modified to include fixed

effects intended to capture (unobserved) firm heterogeneity and lagged merger

dummies. Inter-yearly variation has no obvious economic interpretation in the market

share equation and hence the year dummies are dropped in the specifications.

We again need to make an assumption about what would have happened to the market

share of the acquired party had acquisition not occurred. We therefore adopt the

following counterfactuals: First we assume that the target would have experienced

zero growth in market share after the merger year; Second, we assume that the market

share of the acquired party would have fluctuated in line with the non-merging firms

20

in the sector; Third, we assumed that the market share of the acquired party would

have fluctuated in line with the merging firms in the sector13.

Figure 5 shows the difference in average annual growth rates of market shares

between merging and non-merging societies. As can be seen, societies belonging to

the merging set grow by more than the societies belonging to the non-merging set.

Thus the estimate of the ‘merger effect’ calculated under counterfactual 3 would be

expected to be higher than under counterfactual 2, since in the former case the mean

of the net observations in the non-merging years will be lower.


4. Results

4.i. Basic Results

Table 6 gives the results of our econometric specification for the impact of merger on

sales. Equations (1) and (4) were estimated using our panel of 93 building societies

across the years 1982-1992, involving a total of 888 observations 14. Table 7 gives the

estimates of our econometric specification for the impact of merger on market share.

Equation (5) was estimated over the same time period, involving a total of 990

13 We did not estimate the non linear least squares market share equation since the assumption of

constant growth rates for all firms in the sample is unsustainable.

14 One cross-section is lost from first-differencing.

21

observations 15. Columns (1), (2), (3) and (4) report the results using our four

counterfactuals. Columns (1) to (3) report the results from the fixed effects panel

model and the estimates from the non- linear least squares model are presented in

Column (4).

Column (1) in Table 6 shows that the effect of a merger is to increase deposits by

approximately 1.5 percent in the first post-merger year’s accounts though the effect is

statistically insignificant. Note that this is additional to the increase in size of the

acquiring firm as a result of obtaining the sales of the acquired company. There are no

significant effects reported in subsequent years: the coefficients on the lagged merger

terms are all insignificant 16. This is consistent with the hypothesis that the efficiency

effect dominates over the market power effect. The results in Column (1) are likely to

be an underestimate of the merger effect on sales since we have assumed that the

target would have experienced zero growth post-merger. This counterfactual makes

the smallest adjustment to the deposits figures post merger and so would expect it to

yield the lowest measured impact of merger. The results in Columns (2) and (3)

assume the target would of grown by the average rate of non-merging firms and

merging firms respectively. We would expect the coefficient on the merger dummy to

be larger for these counterfactuals since the adjustment to the post-merge deposits

figures will be larger than that for counterfactual one. These indicate the impact of the

merger is to increase deposits by between 2 and 2.3 percent in the first post-merger

15 See Section 4 for an explanation as to why we have two more observations in the market share

equations.

16 We experimented with longer lags of the merger dummy, however the coefficients were still

insignificant. These results are available from the authors on request.

22

accounts. For the average acquiring firm, this is equivalent to increasing deposits by

approximately £43 million. Again, there are no significant merger effects reported in

subsequent years.


Finally, Column (4) reports our estimates from the non-linear least squares equation17.

This methodology suggests that a merger has an additional 3.9 per cent impact on

deposits post-merger. This suggests that the previous counterfactuals understate the

impact of the merger. It may be the case that reorganisation post-merger allows the

acquired firm to do much better than we have accounted for.

Table 7 shows the results for the market share equation. These indicate that the

reported increase in deposits from Table 5 translate into an increase in market share

for the merged firm. Column (1) shows that the impact of the merger is to increase

post-merger market share by approximately 0.043 percent in the first year. Note that

this increase is additional to the increase in market share from obtaining the market

share of the acquired firm. However, by the third post-merger year, there is a reported

significant decrease in market share by 0.038% which almost entirely wipes out any

earlier gains.


17 The estimated magnitude of the growth rate (β2) is 7.66%. This compares to an estimated growth rate

of 6.69% for the non-merging firms and 8.32% for the merging firms. Therefore the data indicates that

the target would grow at rates closer to the merged firms.

23

4.ii. Additional Experiments with the Data

In this section we run a number of tests of robustness of our results.

Merger Size: The merger literature has generally considered that the relative size of

an acquisition will affect its outcome.18 If the target is small relative to the acquirer,

they are unlikely to leave a large imprint on the consolidated firm (the potential

efficiency gains and/or market power effect could be easily obscured). In contrast,

large relative mergers are generally expected to contribute more to the efficiency

and/or market power of the combined entity resulting from the acquisition. Therefore,

the measured gains from large relative mergers should exceed those from small

relative mergers. However, in the UK building society sector during our period of

investigation there was an additional complication in that some acquisitions were

undertaken at the request of the regulator to ensure that potentially weak societies

could not become problematic. These transactions typically followed informal

representations and are not individually identifiable. However, industry analysts (e.g.

Drake [1989]) indicate that they generally involved the acquisition of small societies

by much larger ones. In consequence, the expected post-acquisition impact, if any, of

such transactions is potentially conflated with some preventative/rescue effects. To

investigate this we distinguished large (MERGEBIG) and small mergers

(MERGESMALL) on the basis of the relative size of target and acquirer. A merger

was classified as large if the target’s deposits as a proportion of the acquirer’s was

18 Previous studies which take into account the relative size of the target include Pilloff (1996) and

Rhoades (1998).

24

greater than the median value for the sample of targets as a whole19. The two sets of

binary variables were substituted for the single set of MERGER dummies in equation

(1). The ‘sales’ and market share results are presented in Tables 8 and 9.

As shown in Table 8, the results are consistent with the hypothesis that the gains from

large relative mergers exceed those from small relative mergers. Columns (1) through

to (3) in Table 8 show that the effect of a large relative merger (MERGEBIG) is to

increase deposits by between approximately 2.3 and 3.6 percent in the first post-

merger year’s accounts depending on the counterfactual adopted. There are no

significant effects of large mergers in subsequent years. In contrast, small relative

mergers have no significant impact on the sales of the acquiring party in the first post-

merger year (except for the non- linear estimates in Column (4) where a small positive

and significant impact is recorded). There is a significant impact of small relative

mergers in the third post-merger year though here the coefficient on

MERGESMALL2 is in fact negative20.


Table 9 reports the impact of small and large relative mergers on market share. The

increase in sales for large mergers translates into a statistically significant increase in

market share of approximately 0.08 percent in the first post-merger year. The

contemporaneous effects for small mergers are not significant. However, in the third

19 The median ratio is 5 percent.

20 Again, longer lags were included but none of the coefficients on the longer lag terms were

significant.

25

year post-merger, the market share of small relative mergers significantly declines by

approximately 0.05 percent.


Regulatory Environment: As discussed in Section 2, the sector as a whole underwent

substantial change to its regulatory and competitive environment over the period of our

investigation, especially after partial deregulation from January 1987. Regulatory

prohibitions on entry were removed for a whole range of financial products; the societies

were accorded the option of demutualizing to become commercial banks; whilst other

financial institutions made large-scale entries into the societies' core business of

providing mortgage loans. It has been widely conjectured [e.g. by Drake (1989)] that

such changes will have had the effect of increasing competitive pressures in the sector.

This, in turn, might be expected to impact on the merger process and encourage

acquirers to be more diligent in implementing cost savings. Furthermore, recent evidence

on US bank mergers, presented by Berger (1998), does suggest changing efficiency

consequences over the last decade or so. To investigate possible changes in their effects

over the period we distinguished mergers before and after January 1987. We then

generated two sets of dummy variables: the first, RMERGER, represents the pre-1987

regulated period mergers and the second, DMERGER, represents those in the

deregulated period.

The pattern of coefficients observed in Table 10 supports the above conjecture. As

shown in Columns (1) through to (3) in Table 10, the pre-1987 coefficients, although

positive for RMERGE were small and insignificant. By contrast, the post-1987 binary

26

variables DMERGE were significant, with an increase in sales of approximately 3

percent. There was no significant impact on deposits in subsequent post-merger years.

The non- linear estimates in Column (4) report a slightly larger increase in sales in the

deregulated period of 5 percent. Again, the pre-1987 coefficient is much smaller (1.6

percent), though here it is statistically significant.


Table 11 shows the impact of mergers on market share in the regulated and

deregulated periods. The increase in first year post-merger sales in the deregulated

period translates into a significant increase in market share for the acquiring firm of

approximately 0.06 percent. Mergers in the regulated period have a margina lly

significant but smaller impact on market share (0.03 percent) in the first post-merger

year. However, the impact of market share is negative by the third post-merger year in

the deregulated period.


4.iii Explaining the Observed Impact of Merger on Sales and Market Share

In the previous sections, we have shown that the effect of a merger is to increase both

the sales and market share of the acquiring firm. We have argued that this is

consistent with the hypothesis that the efficiency effect of consolidation dominates

over the market power effect. Previous work on the UK building society sector (see

Haynes and Thompson, 1999) indicates significant efficiency gains following

27

acquisition. Moreover, the post-merger gains appear to increase substantially in the post-

deregulation period, when pressures to cost minimize are widely considered to have

increased.

An alternative explanation is an improvement in service quality due to the target's

exposure to the greater range of services offered by the acquiring firms. One factor

which influences the quality of building societies’ services to its customers is the

coverage of the society’s ATM network. If the target firm is acquired by a firm with

an ATM network, then the target firm will be exposed to better services. To

investigate this we distinguished between acquisitions by firms with an ATM network

(MERGEATM) and mergers by firms without an ATM network21

(MERGENOATM). The two sets of binary variables were substituted for the single

set of MERGER dummies in equation (1).

The results in Table 12 indicate that our proxy for service quality has the expected

sign and is significant. The acquiring societies which have the potential to offer the

target’s customers the convenience of quick service via an extensive ATM network

have a larger effect on sales. The impact is between 3.5 and 4.5 percent depending on

the counterfactual adopted. By contrast, acquiring societies who are unable to offer

the target’s customers an improved service do not report any significant impact on

sales.


21 49 (60 percent) of the targets were acquired by firms with an ATM network.

28

Table 13 reports an equivalent experiment for market share. The acquiring societies

who offer improved services to the depositors of the acquired firm report an increase

in market share of approximately 0.07 percent in the first post-merger year. There are

no significant increases reported for mergers that offer no service improvements. In

fact, such mergers experience a significant decline in market share in the third post-

merger year.


5. Conclusions

While there has been a great deal of published research on the impact of acquisition

activity on the firm’s overall performance, as measured by profitability or share price

behaviour, relatively little is known about its effects on the proximate drivers of that

outcome, including sales and market share. Some researchers coming from a human

resource management perspective have hypothesised that human resource problems in

acquired firms, manifesting themselves in manager exit [Cannella and Hambrick

(1993)] or low employee morale [Buono (1985), Buono and Bowditch (1989)], cause

a post-acquisition deterioration. However, obtaining evidence on the impact of

acquisition on sales and/or market share is problematic for two reasons: First,

acquisitions frequently involve immediate product diversification and, especially

among diversified firms, subsequent divestitures, making market comparisons

difficult. Second, the act of acquisition typically removes subsequent data on the

acquired units from the public domain, forcing the researcher to construct a

counterfactual about how such units might have performed had the defining event of

29

the merger not occurred. This brief paper has utilised an industry characterised by an

unusual degree of homogeneity, in which all mergers were purely horizontal and in

which firms were constrained to the same core activity, to evaluate the impact of

acquisition activity as a means of growing output and market share.

Our results run counter to the human resource view and to the limited economic

evidence available from more diversified samples [e.g. Gugler et al (2003)]. Across a

range of counterfactual assumptions concerning the subsequent performance of the

target we find that, across our panel of UK building societies, net output (here

deposits net of acquired deposits) and net market share show a statistically significant

increase in the year following an acquisition. That is acquisition appears to have

facilitated significant gains in deposits and market share beyond those obtained from

the mere absorption of the target. Our subsequent experiments with the data suggested

that these were confined to larger acquisitions, were probably greater in the post-

deregulation era as demutualization to a stock bank entered boardroom agendas and

were greater for acquirers offering more comprehensive services. In contrast to much

of the received wisdom on merger outcomes, we find that acquisition was an

extremely effective way of growing output and market share.

Why should mergers produce gains beyond that from absorbing the acquired

company? We conjecture that this is consistent with efficiency gains post-merger

and/or the target’s exposure to the generally superior facilities offered by the

acquiring firm.

30

Of course, while the upside from using a data set of unusual homogeneity is an

obviation of the need for controls, the downside concerns the generality of the results.

Over the period of our analysis the UK building society sector was experiencing

deregulation and evolving towards the characteristics of the commercial (stock)

banks. In such a period it is entirely possible that the subset of acquiring societies

correlated strongly with the more market-orientated members of the industry. Our

findings that strong merger performance tended to be associated with the post-

deregulation sub-period and for acquirers offering more comprehensive services

would tend to reinforce this.

31

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34

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35

Data Appendix

Table 1. Number of Firms in Sample by Year Year No. of Companies 1981 48 1982 72 1983 85 1984 88 1985 89 1986 91 1987 92 1988 91 1989 87 1990 85 1991 77 1992 76

Table 2. Balance of the Panel No. of Years No. of Companies 3 1 5 2 6 2 7 5 8 2 9 5 10 15 11 22 12 39

Total 93

36

The variables used in the analysis were as follows:

Deposits (D) This is the book value of i‘s deposits in millions of £s at time t, as recorded

in the annual accounts, expressed in 1985 prices.

Labour (L) This is the number of full-time equivalent employees of society i at time t.

Each part-time employee is treated as half a full-timer.

Fixed Assets (K1) This is the book value of society i's fixed assets at time t, as recorded

in the annual accounts, expressed in 1985 prices.

Liquid Assets (K2) This is the total liquid assets (cash and deposits with other

institutions) as given in the society's accounts, expressed in 1985 prices.

Market Share (MS) This is the book value of i‘s deposits at time t, divided by total

industry deposits at time t, as recorded in the annual accounts.

MERGER This is a binary variable equal to one if year t represented the first year of

society i's accounting data subsequent to an intra-sector acquisition and zero otherwise.

37

Figure 1. Number of UK Building Societies, 1981-1992

population

0

50

100

150

200

250

300

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992

Year

Nu

mb

er o

f S

oci

etie

s

Figure 2. The Impact of Merger on Sales: Case 1

Time

tSln

lnSA

lnS’A

0 t

lnNet S (no merger impact)

lnNet S (negative merger impact)

lnNet S (positive merger impact)

38

Figure 3. The Impact of Merger on Sales: Case 2

Figure 4. Average Annual Growth Rates of Deposits for Merging and Non-merging Societies, 1982-1992

0

2

4

6

8

10

12

14

1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992

Year

Gro

wth

Rat

e (%

)

non-merging firms merging firms

Time

tSln

lnSA

lnS’A

0 t

lnNet S (case 2)

lnNet S (case 1)

39

Figure 5. Average Annual Growth Rates of Market Share for Merging and Non-merging Societies, 1982-1992

-0.07-0.06-0.05-0.04-0.03-0.02-0.01

00.010.020.030.04

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991

non-merging firms merging firms

40

Table 1. Frequency of Mergers Year Number 1981 6 1982 9 1983 9 1984 7 1985 11 1986 12 1987 7 1988 5 1989 3 1990 4 1991 4 1992 5 Total 82

Table 2. Number of Mergers by Company, 1981 to 1992 Number of Mergers No. of Companies 0 59 1 17 2 8 3 2 4 2 5 0 6 3 7 0 8 1 9 1 Total 93

41

Table 3. Herfindahl Index and 4-Firm Concentration Ratio by Year (Sample Data) Year Herfindahl 4-Firm CR 1981 0.1388 0.490473 1982 0.1226 0.488073 1983 0.1201 0.486364 1984 0.1207 0.488052 1985 0.1139 0.482439 1986 0.1097 0.469186 1987 0.1099 0.503121 1988 0.1096 0.518442 1989 0.1085 0.507115 1990 0.1039 0.494582 1991 0.1125 0.471006 1992 0.1098 0.459563

Table 4. Descriptive Statistics for the Continuous Variables Variable Total (n=981) Acquirers (n=372) Non-acquirers (n=609) Mean S.D. Mean S.D. Mean S.D. Deposits – value 1294.58 3853.53 1953.09 3090.50 892.34 4204.74 Deposits – number 457656.6 1337212 691122.3 1088316 313537.1 1452203 Employment 717.34 1948.75 1109.16 1623.44 478.01 2088.46 Fixed Assets 22.12 92.64 45.73 111.51 7.74 75.54 Liquid Assets 272.20 776.87 428.40 671.08 177.05 820.85

Table 5. Descriptive Statistics for the Acquired Firms

Variable Total (n=82) Mean S.D. Deposits – value 221.33 614.8655 Deposits – number 80153.71 249324.2 Market Share (%) 0.17 0.47

42

Table 6. The Effect of a Merger on Deposits, 1982-1992 (1) (2) (3) (4) (a) (b) (a) (b) (a) (b) Constant 0.04875

(5.18)*** 0.0495 (5.22)***

0.0468 (4.80)***

0.0492 (5.01)***

0.0456 (4.54)***

0.0484 (4.76)***

0.0676 (14.19)***

MERGE 0.0151 (1.52)

0.0155 (1.55)

0.01907 (1.85)*

0.0203 (1.97)**

0.0220 (2.06)**

0.0233 (2.18)**

0.0386 (8.33)***

MERGE1 -0.0046 (0.48)

-0.0151 (1.44)

-0.0158 (1.55)

MERGE2 -0.0040 (0.43)

-0.0113 (1.17)

-0.0128 (1.27)

F 12.13

[11, 784] 10.28 [13, 782]

10.91 [11, 784]

9.58 [13, 782]

10.75 [11, 784]

9.47 [13, 782]

23658.19 [62, 826]

R2 0.13 0.12 0.12 0.11 0.12 0.11 0.99 No. of observations 888 888 888 888 888 888 888

Notes: The dependent variable is the first difference of logged net deposits. In Column (1) net deposits are calculated assuming a zero growth rate in the real deposits of the target. In Column (2) we assume the target’s deposits grow in line with the non-merging firms and in Column (3) we assume the target’s deposits grow in line with the merging firms in the sector. In Column (4) we have assumed the target’s deposits grow by a constant rate, which may or may not be the same as the acquiring or non-acquiring firms. This equation is estimated using non-linear least squares. F is an F- test of the overall significance of the regression. All regressions include firm fixed effects and year dummies. *** p=0.01, ** p=0.05, * p=0.1

Table 7. The Effect of a Merger on Market Share, 1982-1992 (1) (2) (3) (a) (b) (a) (b) (a) (b) Constant -0.012

(-3.32)*** -0.0083 (2.12)**

-0.0107 (3.00)***

-0.0072 (1.86)*

-0.0119 (3.35)***

-0.0082 (2.11)**

MERGE 0.0429 (2.75)***

0.0437 (2.80)***

0.0416 (2.69)***

0.0423 (2.74)***

0.0436 (2.82)***

0.0445 (2.88)***

MERGE1 -0.0038 (0.26)

-0.0032 (0.22)

-0.00434 (0.29)

MERGE2 -0.038 (2.63)***

-0.037 (2.58)***

-0.0393 (2.70)***

F 7.55

[1, 796] 4.95 [3, 794]

7.26 [1, 796]

4.74 [3, 794]

7.97 [1, 796]

5.23 [3, 794]

R2 0.14 0.13 0.15 0.13 0.15 0.14 No. of observations 890 890 890 890 890 890 Notes: The dependent variable is the first difference of market share. In Column (1) net market share is calculated assuming a zero growth rate in the market share of the target. In Column (2) we assume the target’s market share grows in line with the non-merging firms and in Column (3) we assume the target’s market share grows in line with the merging firms in the sector. F is an F- test of the overall significance of the regression. All regressions include firm fixed effects. *** p=0.01, ** p=0.05, * p=0.1

43

Table 8. The Effects of a Merger on Deposits Conditioned by Size, 1982-1992 (1) (2) (3) (4) (a) (b) (a) (b) (a) (b) Constant 0.0482

(5.12)*** 0.0495 (5.22)***

0.0462 (4.73)***

0.0487 (4.96)***

0.0451 (4.46)***

0.0497 (4.70)***

0.0672 (14.28)***

MERGEBIG 0.0226 (1.81)*

0.0258 (2.06)**

0.0281 (2.18)**

0.0315 (2.43)**

0.0331 (2.47)**

0.0364 (2.71)***

0.0576 (9.44)***

MERGEBIG1 -0.0074 (0.62)

-0.0025 (1.05)

-0.0026 (1.06)

MERGEBIG2 0.0161 (1.33)

0.0036 (0.27)

0.0016 (0.12)

MERGESMALL 0.0058 (0.43)

0.0093 (0.67)

0.0078 (0.55)

0.0102 (0.71)

0.0082 (0.56)

0.0105 (0.71)

0.0211 (3.69)***

MERGESMALL1 0.0034 (0.25)

0.0016 (0.12)

0.0016 (0.11)

MERGESMALL2 -0.028 (2.12)**

-0.0293 (2.12)**

-0.0299 (2.10)**

F 11.20

[12, 783] 8.90 [16, 779]

10.12 [12, 783]

8.23 [16, 779]

10.02 [12, 783]

8.15 [16, 779]

23847.56 [63, 825]


Notes: The dependent variable is the first difference of logged net deposits. A merger was classified as big if the target’s deposits as a proportion of the acquirer’s was greater than the median value for the sample of targets as a whole. In Column (1) net deposits are calculated assuming a zero growth rate in the real deposits of the target. In Column (2) we assume the target’s deposits grow in line with the non-merging firms and in Column (3) we assume the target’s deposits grow in line with the merging firms in the sector. F is an F- test of the overall significance of the regression. All regressions include firm fixed effects and year dummies. *** p=0.01, ** p=0.05, * p=0.1

Table 9. The Effects of a Merger on Market Share Conditioned by Size, 1982-1992 (1) (2) (3) (a) (b) (a) (b) (a) (b) Constant -0.0116

(3.25)*** -0.0084 (2.16)**

-0.000103 (2.93)***

-0.0073 (1.89)*

-0.0116 (3.27)***

-0.0083 (2.15)**

MERGEBIG 0.0803 (4.11)***

0.0825 (4.21)***

0.000783 (4.05)***

0.0805 (4.15)***

0.0814 (4.20)***

0.0835 (4.30)***

MERGEBIG1 -0.0151 (0.81)

-0.0137 (0.75)

-0.0163 (0.89)

MERGEBIG2 -0.0226 (1.19)

-0.0205 (1.09)

-0.0235 (1.26)

MERGESMALL -0.00279 (0.13)

-0.0005 (0.20)

-0.000031 (0.15)

-0.0006 (0.30)

-0.0023 (0.11)

-0.0002 (0.10)

MERGESMALL1 0.0169 (0.80)

0.0167 (0.80)

0.0175 (0.83)

MERGESMALL2 -0.0547 (2.62)***

-0.0547 (2.65)***

-0.055 (2.66)***

F 8.79

[2, 795] 4.49 [6, 791]

8.54 [2, 795]

4.37 [6, 791]

9.17 [2, 795]

4.71 [6, 791]


44

Table 10. The Effects of a Merger on Deposits Conditioned by the Regulatory Environment, 1982-1992

(1) (2) (3) (4) (a) (b) (a) (b) (a) (b) Constant 0.0500

(5.30)*** 0.0501 (5.25)***

.0475658 (4.86)***

0.0494 (4.99)***

0.0465 (4.57)***

0.0483 (4.72)***

0.0679 (14.37)***

DMERGE 0.0298 (2.08)**

0.0307 (2.11)**

.027472 (1.86)*

0.0280 (1.86)*

0.0288 (2.05)**

0.0296 (1.90)*

0.0505 (9.19)***

DMERGE 1 -0.0147 (0.90)

-0.0136 (1.20)

-0.0133 (1.13)

DMERGE 2 -0.0181 (1.00)

-0.0045 (0.41)

-0.0062 (0.55)

RMERGE 0.0054 (0.44)

0.0029 (0.23)

.0134758 (1.07)

0.0125 (0.98)

0.01742 (1.34)

0.0163 (1.24)

0.0166 (2.24)***

RMERGE1 -0.0011 (0.10)

-0.0100 (1.18)

-0.0128 (1.29)

RMERGE 2 -0.0024 (0.23)

-0.0196 (1.38)

-0.0206 (1.48)

F 11.30

[12, 783] 8.60 [16, 779]

10.04 [12, 783]

7.91 [16, 779]

9.88 [12, 783]

7.81 [16, 779]

23670.57 [63, 825]



45

Table 11. The Effects of a Merger on Market Share Conditioned by the Regulatory Environment, 1982-1992

(1) (2) (3) (a) (b) (a) (b) (a) (b) Constant -0.01197

(3.34)*** -0.0085 (2.16)**

-0.0107 (3.02)***

-0.0074 (1.90)*

-0.0119 (3.36)***

-0.0083 (2.14)**

DMERGE 0.0629 (2.80)***

0.0591 (2.60)***

0.0624 (2.81)***

0.0579 (2.58)***

0.0640 (2.87)***

0.0600 (2.66)***

DMERGE1 0.00561 (0.22)

0.00958 (0.38)

0.0047 (0.19)

DMERGE 2 -0.0343 (1.22)

-0.0379 (1.36)

-0.0417 (1.49)

RMERGE 0.0307 (1.56)

0.0356 (1.88)*

0.0289 (1.58)

0.03375 (1.80)*

0.03126 (1.81)*

0.0354 (1.89)*

RMERGE 1 -0.0075 (0.44)

-0.00825 (0.49)

-0.0080 (0.48)

RMERGE 2 -0.0388 (2.38)**

-0.0362 (2.25)**

-0.0374 (2.31)**

F 4.54

[2, 795] 2.96 [6, 791]

4.48 [2, 795]

2.63 [6, 791]

4.79 [2, 795]

2.83 [6, 791]

R2 0.16 0.16 0.18 0.17 0.17 0.17 No. of observations 890 890 890 890 890 890 Notes: The dependent variable is the first difference of market share. In Column (1) net market share is calculated assuming a zero growth rate in the market share of the target. In Column (2) we assume the target’s market share grows in line with the non-merging firms and in Column (3) we assume the target’s market share grows in line with the me rging firms in the sector. F is an F- test of the overall significance of the regression. All regressions include firm fixed effects. *** p=0.01, ** p=0.05, * p=0.1

46

Table 12. The Impact of Mergers on Sales Conditioned by the Quality of Services, 1982-1991

(1) (2) (3) (4) (a) (b) (a) (b) (a) (b) Constant 0.0489

(5.21)*** 0.0492 (5.20)***

0.0470 (4.83)***

0.0483 (4.94)***

0.0460 (4.57)***

0.0473 (4.68)***

0.0674 (14.15)***

MERGEATM 0.0352 (2.81)***

0.0348 (2.76)***

0.0407 (3.14)***

0.0405 (3.11)***

0.0450 (3.35)***

0.0447 (3.31)***

0.0394 (8.43)***

MERGEATM 1 -0.0046 (0.36)

-0.0154 (1.17)

-0.0153 (1.12)

MERGEATM 2 -0.0102 (0.79)

-0.0190 (1.43)

-0.0144 (1.43)

MERGENOATM -0.0180 (1.12)

-0.0176 (1.09)

-0.0167 (1.00)

-0.0152 (0.91)

-0.0161 (0.94)

-0.0144 (0.83)

0.006 (0.21)

MERGENOATM1 -0.0034 (0.24)

-0.0145 (1.00)

-0.0161 (1.08)

MERGENOATM2 -0.0098 (0.71)

-0.0166 (1.16)

-0.0187 (1.26)

F 11.78

[12, 783] 8.89 [16, 779]

10.70 [12, 783]

8.42 [16, 779]

10.60 [12, 783]

8.37 [16, 779]

23296.88 [63, 825]



47

Table 13. The Effects of a Merger on Market Share Conditioned by the Quality of Services, 1982-1992

(1) (2) (3) (a) (b) (a) (b) (a) (b) Constant -0.0118

(3.31)*** -0.008 (2.04)**

-0.01058 (2.99)***

-0.069 (1.78)*

-0.0118 (3.34)***

-0.00787 (2.03)**

MERGEATM 0.0756 (3.85)***

0.0718 (3.65)***

0.0734 (3.78)***

0.0696 (3.57)***

0.0768 (3.94)***

0.07295 (3.74)***

MERGEATM1 0.0003 (0.20)

0.00102 (0.10)

0.0007 (0.05)

MERGEATM 2 -0.05 (0.79)

-0.056 (0.76)

-0.05965 (0.78)

MERGENOATM -0.01195 (0.47)

-0.0104 (0.79)

-0.01155 (0.46)

-0.01015 (0.51)

-0.01173 (0.47)

-0.01029 (0.41)

MERGENOATM1 -0.0117 (0.79)

-0.0111 (0.76)

-0.01146 (0.53)

MERGENOATM2 -0.017 (2.90)***

-0.0163 (2.85)***

-0.01688 (3.00)***

F 7.52

[2, 795] 4.10 [6, 791]

7.23 [2, 795]

3.93 [6, 791]

7.89 [2, 795]

4.32 [6, 791]


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Mergers and Market Share: Evidence from the UK Financial ... V/V.A... · Michelle Haynes*† &...

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