Geography and Acquirer Returns

Simi Kedia∗

and

Venkatesh Panchapagesan†

This Draft: September 2004

Preliminary. Comments Welcome.

Abstract We find evidence of “local bias” in the acquisition decisions of U.S public firms. Over the period 1990-2003, 18.8% transactions were local, where the acquirer and the target are within 100 kms of each other. The expected probability that a target would be acquired by a public firm that operates in the same industry and is located within 100 kms is, however, only 6.2 percent. Further, acquirer returns in local transaction are a significant 56% higher than that in non-local transactions. Our results also suggest that, information, rather than synergies, is more likely to be behind the superior performance of local acquirers. We add a new dimension – geography – to explain cross-sectional variation in acquirer returns.

∗ Rutgers Business School, 111 Washington Street, Newark, NJ 07102. (973) 353-1145, skedia@rbsmail.rutgers.edu

. We thank Phil Dybvig, Jean Helwege, Kewei Hou, Rene Stulz, Anjan Thakor, Ralph Walkling and seminar participants at Ohio State University and Washington University. All errors are ours.

. † Olin School of Business, Washington University, 1 Brookings Drive, St. Louis, MO 63130, (301) 978-8217, panchapagesan@olin.wustl.edu

Starting from Alfred Marshall, who tried to understand the workings of the so-called

“economies of agglomeration” in his neo-classical book, Principal of Economics, written in 1922,

economists have long highlighted the importance of geography to economic activity. Industries

could be geographically concentrated due to natural cost advantages of regions as well as due to

synergies caused by industry-specific spillovers. Geographic proximity, and the resulting

spatially constrained social networks have also been found to be important in facilitating

information and knowledge transfers.1 Though the effect of geography on the original location

decision of firms has been widely studied, we know little about its effect on the subsequent

investment decisions of firms. In this paper, we examine whether geography is an important

determinant of acquisitions made by publicly traded firms in the US. Also, we examine whether

geographical proximity increases the value created by these acquisitions.

Of a sample of all mergers and acquisitions of majority interest and assets by public firms

between 1990 and 2003, we find that 18.8 percent were categorized as “local” or geographically

proximate. As in prior studies (see, for example, Coval and Moskowitz (2001)), we define

transactions as “local” if they are between firms located within 100 kms of each other. In

contrast, the expected probability of a target (or its assets) being acquired by a public firm, in the

same industry, that is located within 100 kms is only 6.2 percent. Even if we consider all public

firms that operate in the same industry as either the target or the actual acquirer, the expected

probability of a local transaction is only 8.5 percent, much lower than the actual proportion of

18.8 percent.

Further, we find that acquirers earn a significantly higher return when they acquire

“local” targets as compared to “non-local” targets. The average 5-day cumulative abnormal

return, over [-2, 2] days around announcement, is 2.76 percent for local transactions as compared

to 1.77 percent for non-local transactions. This difference is not only statistically significant but

1 See Ellison and Glaeser (1997), Audretsch and Feldman (1996), Audretsch and Stephan (1996), and Baum and Sorensen (2003)

2

also economically significant. The increase in acquiring firm’s shareholder value created by local

transactions is 56% greater than the increase for non-local transactions.

A similar “local” bias has been documented in the investment decisions of mutual funds

(Coval and Moskowitz (1999,2001)) and in the portfolios of retail investors (See Huberman

(2001) and Ivkovich and Weisbennar (2003)). These studies also document a higher return to

local investments relative to non-local investments in the portfolio and attribute the superior

performance of local investments to informational advantage. An acquisition of another firm or

an acquisition of assets of another firm can be viewed simply as an addition to the existing

portfolio of projects for the acquiring firm. However, unlike investors who trade local stocks

mostly for information reasons, firms may prefer to transact with local firms to exploit synergies

as well. Synergies are likely to be higher when firms are closer to each other than when they are

separated by large distances. We investigate the relative contribution of synergies and

information advantages to the higher acquirer return documented in local transactions.

As industries tend to be geographically concentrated (for example, Hollywood and

Silicon valley), local acquisitions could proxy for same industry transactions, and their higher

return evidence of the higher synergies in these deals. However, we do not find that local

acquisitions are predominantly within the same industry. Approximately, 18.2 percent of all

deals in the same industry are local as compared to 18.9 percent when acquirers and targets are in

different industries. Moreover, acquirer return in local acquisitions within the same industry

(0.67%) is less than half of the acquirer return in local acquisitions across industries (1.44%).

This suggests that the higher local acquirer return is unlikely to be due to potentially higher

synergies associated with same industry transactions. However, there might be other operational

synergies associated with local transactions.

If local transactions are associated with higher synergies, then total returns should be

higher in local acquisitions (see Bradley, Desai and Kim (1983) and Lang, Stulz, and Walkling

3

(1989)). As the higher returns arise only when acquirers are local, local acquirers are likely to

capture most of the synergistic gains with target returns being the same or somewhat higher. In

contrast, as information asymmetry may prevent all potential buyers from bidding it is likely to be

associated with lower total returns and lower target returns. In a subset of our sample where the

targets are public, we examine target returns and total returns in local transactions to examine

whether local transactions are associated with higher synergies or information advantages. We

find no evidence that target returns or total returns are higher in local transactions suggesting that

local synergies are unlikely to account for the higher local acquirer return.

For asset markets to be competitive, all bidders and buyers of assets must be equally

informed about the asset. With information asymmetries, uninformed potential buyers may not

bid for the asset. In the absence of competitive bidding, the price at which the asset is sold will

not on average capture the gains from employing the asset in its most productive use.

Consequently, informed buyers of assets will earn positive returns from the purchase. If

geographic proximity is associated with information advantages, acquirers in local transactions

should earn higher returns in their acquisitions. Such an advantage could easily arise if

information travels slowly in spatially constrained social networks, as suggested in several studies

in economics and social science. (See, for example, Baum and Sorenson (2003).)

Using several proxies to capture the information asymmetry faced by target, we find that

the return to local acquirers is decreasing in the information availability of the target. Local

acquirer return is higher when the target is private than when it is public, indicating that physical

proximity helps mitigate non-availability of other public sources of information. We also find

that local acquirers have higher returns when the mode of payment involves some stock relative

to cash only acquisitions. As stock is more likely to be used when there is uncertainty about

target value (see Hansen (1987) and Eckbo, Giammarino and Heinkel (1990)), the results suggest

the role of geographic proximity in transactions with target uncertainty. Lastly, we use

geographic location to capture information availability of the target. Coval and Moskowitz

4

(2001), Malloy (2003), and Lougran and Schulz (2004) find that firms headquartered in non-

metro areas face information problems relative to metro firms. We find local acquirer return to

be higher when the target is located in a non-metro area than when it is in a metro area.

Aside of information and synergies, previous studies have identified agency costs as a

possible motive behind acquisitions. Acquisitions motivated by agency costs do not earn positive

returns in general (See Lang, Stulz and Walkling (1991)). Firms with high agency costs are less

likely to engage in acquisitions to exploit information advantages or synergies associated with

physical proximity. This would suggest that there should be little difference between local and

non-local acquisitions of high agency cost acquirers.

We use three proxies to capture agency costs in acquirers. Firstly, we estimate free cash

flow for acquirers in the year prior to the acquisition. Firms with free cash are more likely to

engage in value destroying acquisitions (See Jensen (1986), Lang, Stulz and Walkling (1991)).

Secondly, firms that undertake many acquisitions are less likely to be motivated by economic

gains. Lastly, large firms are more likely to undertake acquisitions that destroy value (See

Moeller, Schlingemann and Stulz (2003)). We find little difference in returns of local and non-

local acquisitions when acquirers have high agency costs as captured by all three proxies. On the

other hand, low agency costs acquirers earn significantly higher returns in their local transactions.

In summary, we find a “local bias” in the acquisition decisions of firms that seem to be

motivated more by informational advantages than by synergies arising from geographical

proximity. These information advantages might arise from social networks with owners and

employees of private targets, or from direct and indirect business interactions with target firms.

Presence of information asymmetries implies that some potential acquirers, facing information

disadvantages, may not bid for the assets of the target firm. This might potentially have

significant implications for efficient allocation of assets in the economy.

The results provide a new dimension to explain cross-sectional variation in bidder

returns. Several decades of research on merger activity (for surveys see Jensen and Ruback

5

(1983), Jarrell, Brickley and Netter (1988), and Andrade, Mitchell and Stafford (2001)) find that

acquirers earn, on average, a zero return in their acquisitions of public firms. However, more

recently, Fuller, Netter and Stegemoller (2002), and Moeller, Schlingemann and Stulz (2003)

document that average return to acquirers is significantly positive when the targets are not

publicly traded and for small acquirers respectively. We show that, conditional on the target

status and the size of the acquirer, local acquisitions earn significantly higher returns than non-

local acquisitions.

The rest of the paper is organized as follows. Section II discusses related literature,

section III discusses the data, section IV examines acquirer returns, section V relates acquirer

returns to proxies for information asymmetry, local synergies and agency costs and finally section

VI concludes.

II. Related Literature

There is an increasing and diverse literature examining the impact of geography and

physical distance. As mentioned before, Coval and Moskowitz (1999, 2001) document “local

bias” in mutual fund investments and show that these local investments earn higher returns.

Huberman (2001), Grinblatt and Keloharju (2001), Zhu (2002) and Ivkovich and Weisbennar

(2003) show a similar local bias in portfolios of individual investors. Petersen and Rajan (2002)

examine the effect of distance on commercial bank lending. Malloy (2003) and Orpurt (2004)

find that analysts are more accurate in their earnings forecasts of local firms. Loughran and

Schultz (2004) argue that information is first incorporated in the stock prices of firms located in

metro areas. Pirinsky and Wang (2004) find evidence of co-movement in the returns of firms

headquartered in the same geographic area. Kedia (2004) examines the effect of geographic

proximity on the incidence of financial misreporting.

The importance of geographic proximity has also been documented outside the context of

financial decision making. Audretsch and Feldman (1996) document the role of physical distance

6

in R&D spillovers and innovative activity. Audretsch and Stephan (1996) document the

importance of local scientists for biotechnology firms. Sah (1991) and Glaeser, Sarcedote and

Scheinkmen (1996) discuss the importance of physical proximity in influencing the perceived

cost of criminal activities and consequently crime adoption rates.

The paper also draws upon and contributes to the vast literature on merger activity (For

surveys see Jensen and Ruback (1983), Jarrell, Brickley and Netter (1988), and Andrade, Mitchell

and Stafford (2001). A common finding across these studies is that mergers create value, with

bidders making on average zero returns and most of the gains accruing to target companies. In

recent work Fuller, Netter and Stegemoller (2002) and Moeller, Schlingemann and Stulz (2003)

document that bidder returns are positive when the targets are non-public firms. Fuller, Netter

and Stegemoller (2002) suggest that these higher returns are due to a liquidity discount associated

with private targets. We find that some of the higher returns to acquirers, when targets are

private, are associated with information disadvantages of the target firms. Eckbo and Thorburn

(2000) find that in the acquisition of Canadian targets the returns to Canadian acquirers are on

average positive while those to US acquirers are on average zero. They find that product market

relationships, or foreign investment controls do not explain the higher return to Canadian

acquirers. Evidence in this paper suggests that information advantages arising from geographic

or cultural proximity might explain the higher return to Canadian acquirers.

The finding of on average zero returns to bidding firms is consistent with a competitive

acquisition market (See Mandelker (1974), Asquith (1983) and Ruback (1983)).2 In contrast to

mergers and acquisitions of majority interest, the evidence on the competitiveness of markets in

asset sales is weak. Jain (1985) finds significant positive gains to acquirers of assets and little

2 Other explanations for the on average zero bidder returns have been examined. Song and Walkling (2004) show that zero bidder returns are due to the market anticipating the acquisition programs of firms. Bhagat, Dong, Hirchleifer and Noah (2004) argue that the announcement period returns do not take into account the probability of the bid failing and also incorporate information about the stand alone value of the bidder.

7

evidence of multiple bids in the sale of assets. Lang, Poulsen and Stulz (1995) find that assets are

sold by poor performing firms for financing rather than for efficient redeployment of assets.

Pulvino (1998) shows that sale of assets by financially distressed firms takes place at prices well

below the fundamental value of the asset. Absence of information asymmetries is necessary to

ensure that all potential bidders emerge, and crucial to ensuring competitive acquisition markets.

By documenting the presence of information asymmetries, our paper is related to this literature.

III. Data

The sample consists of all completed transactions on Securities Data Corporation’s

(SDC) U.S. Mergers and Acquisitions Database from 1990 to 2003 for which the acquirer is

covered under CRSP, and data on city and state of acquirer and target is available on SDC. We

exclude transactions where either the acquirer or target firm is foreign. Only transactions that

were classified as a merger, an acquisition of majority interest, or acquisition of assets were

included. We exclude transactions where either the acquirer or target is a financial firm or utility

(SIC 6000 –6999 and SIC 4900 – 4999) as these firms and their acquisitions are likely to be

regulated. We also exclude transactions with value of less than a million dollars.

The distribution of transactions and their characteristics is displayed in Table 1. About

44.7% of all transactions involve mergers or acquisition of majority interest in the target and

55.3% of transactions involve asset acquisitions. In about 58% of the transactions the targets

were private firms, in 18% they were public firms and in the remaining they were subsidiaries.

The mean value of the transaction is $297 million while the median is much smaller at

$23 million (Table 2). Not surprisingly, the mean value of the transaction is much higher for

mergers and acquisition of majority interest ($562 million) in comparison to acquisition of assets

($83 million). Also not surprisingly, the mean value of transactions with public targets is

substantially higher ($1243 million) in comparison to private targets ($59 million) or subsidiaries

($160 million).

8

The mean relative value of the transaction, measured as the ratio of the value of the

transaction to the market value of the acquirer in the year prior to the announcement of the

transaction, is 0.29. The median is much lower at 0.09. The mean market value of acquirers of

private targets is $3.8 billion in comparison to $10.9 billion for acquirers of public targets.

Smaller firms are more likely to acquire private firms or assets from private firms.

About 60% of the transactions are in the same two-digit SIC as reported by SDC. There

is no difference in the percentage of same industry transactions by type of transaction or by the

organization form of the target. We group method of payment into three categories (See Martin

(1996) and Fuller, Netter and Stegemoller (2002)). The first category, cash, consists of all

payments made with cash, debt, liabilities or some combination of these. The second category,

stock, consists of all payments made with common stock, warrants and options or some

combination of these. The third category, hybrid, consists of all other combinations. About 44%

of all transactions are cash based, while 24% of transactions are stock based.

3.1 Estimation of Distance between Acquirer and Target

SDC reports the city and state for all acquirer and target firms. We match this data with

data from the U S Census Bureau Gazetteer to get latitudes and longitudes for each acquirer and

target. We then use the arc length between these two coordinates to estimate the distance

between the acquirer and the target. The distance d between place 1 and 2 was estimated using

the Haversine formula. With R the radius of the earth ( 6378 kilometers) the distance is

12

≈

)),1(arcsin(min212 aRd ××=

22 ))2/(sin()2cos()1cos())2(sin( dlonlatlatdlata ××+=

In the above expression and . Lat1 and lon1 are the latitude

and longitude of place 1 (acquirer’s location) and lat2 and lon2 are the latitude and longitude of

place 2 (target location).

12 latlatdlat −= 12 lonlondlon −=

9

A transaction was classified as a local transaction if the distance between acquirer and

target was less than 100km (See Coval and Moskowitz (2001), Malloy (2002)). Approximately,

18.8% of all transactions were categorized as local. Public firms and potential acquirers are not

distributed uniformly across the country. Consequently, the probability of a transaction being

local varies substantially. Firms located in areas with several public firm headquarters have a

higher likelihood of being acquired locally. We use several assumptions to estimate the expected

probability of being acquired locally. Firstly, we assume that all public firms could be potential

acquirers. For every transaction, we estimate the fraction of all public firms that are

headquartered within a 100km radius of the target firm in the year prior to the announcement of

the transaction. Data on company headquarters is obtained from Compustat. An average target

firm has only 6.9% of all public firms residing locally, i.e., the expected probability of being

acquired locally is 6.9%. This is in contrast to the 18.8% realized probability of being acquired

locally. This “local bias” is seen for all quintiles ranked by the fraction of public firms

headquartered locally (See Table 3, Panel A).

Secondly, we assume that only public firms in the same two-digit SIC as the target firm

are potential acquirers. An average target firm has only 6.2% of its industry located within

100km (Panel B, Table 3). Lastly, we extend the set of potential acquirers to also include all

public firms in the two-digit SIC of the actual acquirer. The expected probability of being

acquired locally is now estimated to be 8.5% (Panel C, Table 3). Under all three assumptions,

the expected probability of being acquired locally is well below the 18.8% local deals observed in

the data.

IV. Acquirer Returns

In this section, we examine the relation between acquirer returns and local transactions.

Acquirer returns are estimated using Brown and Warner’s (1985) methodology to calculate

cumulative abnormal returns (CARs) for the five-day period (-2,2) around the announcement of

10

the transaction. The abnormal returns are given as where is the daily return on

firm i and is the return on the CRSP value-weighted index. We do not estimate market

parameters as acquirers could complete multiple transactions over the year (See Fuller, Netter and

Stegemoller (2002)).

mii rrAR −= ir

mr

The average return to acquirers in local transactions was 2.76% ( See Table 4). This is

higher than the 1.77% return to acquirers in non-local transactions. This difference of 99 basis

points between returns to acquirers in local versus non-local deals is significant at the 1% level.

This difference between local and non-local returns is economically significant as well.

Acquirers earn 56% more in local transactions relative to non-local transactions. The returns to

acquirers in local transactions are somewhat higher for asset acquisitions relative to mergers and

acquisition of majority interest. Acquirers earn 114 basis points more in local relative to non-

local asset acquisitions. In comparison, they earn 92 basis points more in local mergers and

acquisition of majority interest. There are significant differences in acquirer returns for local and

non-local deals by the status of the target. Local acquirers returns are highest when the target is a

private firm. They earn 114 basis points more in these transactions. Acquirers also earn

significantly higher returns of 122 basis points in local transactions that involve subsidiaries.

When targets are public, average acquirer returns are negative for both local and non-local

transactions consistent with the results in prior literature. Though acquirer returns in local

transactions are higher (less negative), the difference is not statistically significant.

4.1What is Local and Non-Local?

Consistent with prior literature (See Coval and Moskowitz (1999, 2001) and Malloy

(2002)) we define local transactions as those where the acquirer and target firms reside within

100km of each other. However, there is no theoretical motivation for using 100km to define

11

local transactions. In this section, we examine the effect of distance on the distribution of

transactions and acquirer returns.

About 7.3% of all transactions occur when target and acquirer firms have zero distance

i.e., are located in the same city (See Table 5). Whereas 18.8% of all transactions occur when the

distance between target and acquirer is 100km or less, only 3.7% transactions occur in the next

100 km, i.e., when the distance is between 100km and 200km. Approximately, 3% of

transactions are added for every 100km for distance categories greater than 100km. This suggests

that a 100km radius captures a substantial fraction of the “local bias” observed in acquisitions.

A similar picture emerges when we examine the distribution of acquirer returns by

distance categories. The average acquirer return when targets and acquirers have zero distance is

2.68%. Average acquirer returns are somewhat higher for transactions in the next two distance

categories, that is when the distance is between zero and 50 km, and when the distance is between

50km and 100km. Average acquirer distance drops by a percentage (about 33%) when the

distance is between 100km and 200km and tends to be lower for distance greater than 200km as

seen in Figure 1. Any advantage from geographic proximity captured by the acquirer appears to

manifest itself for distances within 100km. We consequently, report all results using 100km to

define transactions that are local.

12

Figure 1: Distance and Mean Acquirer Returns

0.0000

0.0050

0.0100

0.0150

0.0200

0.0250

0.0300

0.0350

Ave

rage

Acq

uire

r Ret

urns

0

0

0 - 50km 50 -100 km 100-200 km 200 - 500 km 500 - 1000 km > 1000 km

4.2 Multivariate Analysis of Acquirer Returns

The univariate evidence on higher acquirer returns in local transactions does not control

for several important variables that affect acquirer returns. First, we control for the mode of

payment by including dummies that take the value one when the mode of payment is cash and

when it is a combination of cash and stock (hybrid). Fuller, Netter and Stegemoller (2002) report

that returns to acquirers are higher when the target is a subsidiary or a private firm. We control

for the status of the target by including a private dummy (takes the value one when the target is a

private firm) and a subsidiary dummy (takes the value one when the target is a subsidiary). We

also control for the relative value of the transaction and its interaction with the mode of payment.

To control for same industry deals and possibly higher returns associated with these deals, we

include a dummy for deals that are in the same two-digit SIC. Log of total assets in the year prior

to the announcement of the transaction is included to control for the size of the acquirer. Moeller,

Schlingemann and Stulz (2004) report that only small acquirers earn positive returns in their

13

acquisitions. We include a dummy for small acquirers to control for this effect. The small size

dummy takes the value one if the acquirer is in the bottom third by total assets. Lastly, we

include the local dummy that takes the value one when the transaction is local, i.e., the target and

the acquirer firm are headquartered within 100km of each other.

Controlling for these, the coefficient of the local dummy is positive and significant at the

1% level (See Table 6). 3 Local transactions are associated with higher acquirer returns of 95

basis points. This estimate is in line with the higher average return to local acquirers seen in

Table 4. Consistent with the results in Fuller, Netter and Stegemoller (2002) we find that acquirer

returns are significantly higher when the target is a private firm or subsidiary in comparison to

when it is a public firm. Returns to acquirers are higher in cash and hybrid deals in comparison

to stock deals. As expected, acquirer returns are increasing in the relative size of the transaction

and decreasing in acquirer size. Consistent with the results of Moeller, Schlingemann and Stulz

(2003) we find that the coefficient of small acquirer dummy is significant positive. Controlling

for merger waves by including year effects does not materially affect the estimated coefficient of

the local dummy (See Model 2).

Finally, we separately estimate the model for transactions that involve mergers and

acquisition of majority interest (model 3) and those that involve asset acquisitions (model 4).

Acquirers earn significantly higher returns in local transactions irrespective of the type of the

transaction, though the return to local acquirers is somewhat higher for asset sales than for

mergers in both the estimated magnitude and statistical significance.

V. Information Asymmetries, Synergies and Agency Costs

In this section, we explore the source of these higher returns to acquirers in local

transactions that has been documented.

5.1 Information Asymmetries

3 The extreme 1% of the cumulative abnormal returns have been windsorized.

14

Coval and Moskowitz (2001), Malloy (2003) and Ivkovich and Weisbennar (2003)

suggest that the geographic proximity is associated with information advantages. Any

information advantage possessed by the acquirer is likely to be more valuable the greater is the

information unavailability of the target firm. If the higher return to local acquirers is due to

information advantages, then it should be increasing in the severity of the information

unavailability of the target. We test for this by using four proxies for the severity of the

information problems faced by the target.

First, we use the status of the target to proxy for information availibility. Private firms,

unlike public ones, do not disclose financial details, have few outside investors, and do not have

analyst coverage. Given the inherently higher information asymmetries with private firms, the

information advantage arising from geographic proximity should be higher for private targets

than for public targets. Acquirers of public targets earn an insignificant 10 basis more in their

local transactions relative to their non-local transactions. In contrast, local acquirers of firms that

are not publicly traded earn a highly significant 110 basis points more (See Table 7).4

Hansen (1987) and Eckbo, Giammarino and Heinkel (1990) model the effect of

uncertainty with respect to target valuation on the mode of payment. They show that bidders

make cash offers when there is uncertainty with respect to their own value and stock offers when

there is uncertainty with respect to the target. This suggests that stock is more likely to be used

as a mode of payment when there is higher information asymmetry with respect to the target. As

seen in Model 2, Table 7, we find that local acquirers earn an insignificant 40 basis points more

when they acquire by cash only and a significant 127 basis points more when they use stock.

A source of information asymmetry that has been pointed by several papers is the

location of the firm. Firms in urban areas have fewer information problems relative to similar

firms in non-urban or remote areas (See Coval and Moskowitz (2001), Malloy (2002), and

4 Table 7 reports the results for the full sample of transactions, i.e., for both mergers and acquisitions of majority interest as well as asset acquisitions. Similar results are obtained when the model is estimated separately for assets acquisitions and for merges and acquisition of majority interest.

15

Loughran and Schulz (2004)). If analysts and other financial intermediaries, that are located in

urban areas, tend to focus on local and consequently urban firms, information problems will be

lower for urban firms. We use the location of the target firm’s headquarters to capture any

potential information disadvantages for non-urban firms. We follow Coval and Moskowitz

(2001) and classify the 20 largest cities in the country as metro areas. As our sample spans from

1990 to 2003, we use both the 1990 and 2000 census to classify the 20 largest cities. The metro

cities are those that are either on the 1990 or 2000 list of the 20 largest cities. The two lists are

the same with one exception. Our list of metro cities therefore includes 21 cities. A target firm is

classified as being located in a metro area if it is within 50km of the 21 largest cities. All other

targets firms are classified as residing in non-metro areas. Local acquirer returns are a significant

118 basis points when the target is in a non-metro area relative to an insignificant 63 basis points

when the target is in a metro area.

Lastly, we use the ratio of firm’s expenses on research and development scaled by sales

to capture growth opportunities and information asymmetries arising from it. However, since a

substantial part of our sample is not publicly traded we do not have data for research and

development expenditures at the firm level. We estimate the average ratio of research and

development expenses to sales at the two-digit SIC level. Target firms are classified as having

low R&D if their industry is in the bottom third of R&D expenditures in the year prior to the

announcement of the transaction. The return to local acquirers is not larger for the high R&D

group relative to the low R&D group (See Table 7, Model 4). As there is substantial variation in

the R&D expenditures within an industry, our industry level measure captures differences in

information asymmetry induced by growth opportunities only partially. This may account for the

absence of results obtained using R&D intensity to capture information asymmetries.

In summary, we find that returns to local acquirers are higher when the target faces

greater information asymmetry. Local acquirers earn significant positive returns when the target

is not publicly traded, when the acquirer uses stock to pay for the acquisitions and when the target

16

resides in a non-metro area. In comparison, local acquirers earn insignificant returns when the

target is publicly traded, when the acquirer uses cash to pay for the acquisition and when the

target resides in a metro area.

5.2 Industry Effects and Synergies

There is significant evidence in prior literature that industries tend to be geographically

concentrated. Ellison and Glaeser (1997) show that concentration in industries could arise due to

natural cost advantage of regions as well as to industry-specific spillovers. Audretsch and

Feldman (1996), and Audretsch and Stephan (1996) document the existence of industry specific

spillovers in economic activities like innovation that depend on new economic knowledge. This

causes these industries to be geographically concentrated.

If industries tend to be geographically concentrated, then local deals could be a proxy for

transactions within the same industry and the higher return to acquirers in local deals due to the

higher synergies associated with same industry transactions. However, we find no evidence that

local transactions are predominately in the same industry. About 18.2% of all deals in the same

two-digit SIC are local and 18.9% of all deals in different industries are local (see Table 8).

Further, we find no evidence that the return to acquirers in local deals is higher when it is in the

same industry. On the contrary, acquirer earn on average 67 basis points more in local deals in

the same industry while earning 144 basis points more in local deals in different industries. A

similar picture emerges with median acquirer returns. After controlling for other determinants of

acquirer returns, in a multivariate analysis, we find that local acquirers earn 76 basis points more

when the transaction is in the same industry and 110 basis points more when it is in different

industries. In summary, there is little evidence that local transactions proxy for same industry

transactions or that the higher return in local transaction is due to higher synergies associated with

same industry transactions.

17

However, synergies in local transactions could arise from sources unrelated to being in

the same industry. In the presence of these local synergies, the target firm’s assets are more

valuable to a local acquirer relative to a similar non-local acquirer. This implies that total returns

in local transactions should be higher relative to non-local transactions. Bradley, Desai and Kim

(1983) and Lang, Stulz, and Walkling (1989) show that total returns are higher in transactions

that create more value. As the synergies are specific to local acquirers, local acquirers are likely

to capture most of the higher value created from the redeployment of assets locally. Targets in

local transactions may also be able to share some of the gains if they have bargaining power. If

local transactions are associated with higher synergies then: a) total returns should be higher, b)

acquirer returns should be higher, and c) target returns should be the same or higher in local

transactions.

As discussed earlier, higher acquirer returns in local transactions are also consistent with

local acquirers having information advantages. However, the implication for target and total

returns are different in the presence of local synergies relative to the presence of local information

advantages. In the presence of information asymmetries the return to target firms in local

transactions should be lower than in non-local transactions. This is because information

asymmetry reduces the number of potential bidders and the offer price for the asset does not fully

capture the potential gains from employing the asset in its most productive use. If the local

informed acquirer is one of several productive users of the asset the total returns in the local

transaction should be no different than that of non-local transactions. However, to the extent that

local informed acquirers are not the most productive users of the asset, total returns in local

transactions should be lower than that in non-local transactions. Therefore, with local information

advantages a) acquirer returns should be higher, b) target returns should be lower, and c) total

returns should be the same or lower in local transactions.

Examining target returns and total returns in local transactions allows us to test for the

source of higher acquirer returns seen in local transactions. Data on target returns and total returns

18

however is available for only public targets. For this subset of transactions, where the target data

is available on CRSP, we estimated target returns and total returns. Consistent with the

estimation of acquirer returns we calculate [-2,2] day return for the target around the

announcement. The abnormal returns are given as where is the daily return on

the target firm i and is the return on the CRSP value-weighted index. Total return is weighted

average return to acquirers and targets with the weights being the market value of the acquirer

and target firms as of calendar year end prior to the announcement of the transaction.

mii rrAR −= ir

mr

A potential problem with using public firms is that there is little evidence that local

acquirers earn higher returns in this subsample (See Table 4). Lack of higher acquirer returns in

local transactions suggests the absence of both local synergies as well as local information

advantages. This makes it difficult to examine the importance of synergies relative to

information advantages. As seen in the prior section, returns to local acquirers are decreasing in

proxies for information asymmetry of the target. We use these proxies of information asymmetry

to isolate significant higher returns to local acquirers in this sample of only public targets.

We estimate returns to local acquirers when the target resides in a metro area relative to a

non-metro area. Consistent with the evidence in Table7, we find higher returns to local acquirers

when the target is in a non-metro area for this subsample of firms. To examine if the higher

return in local transactions, we had attributed to information asymmetry, is due to synergies we

examine target returns and total returns. Targets in non-metro areas earn 244 basis points lower

in local transactions though this is significant only at the 20% level (See table 9, Model 1). Total

return in local transactions where targets are in non-metro areas is not significantly different from

zero. The higher local acquirer returns are unlikely to be due to local synergies, as there is little

evidence of significantly higher total returns and higher target returns. There continues to be no

evidence of higher total returns when we use mode of payment to isolate higher local acquirer

returns (See Table 9, Model 2). As there is little evidence of higher total returns in local

19

transactions it is unlikely that the higher local acquirer returns are due to local synergies.

However, as this was documented in transactions more likely to be driven by information, the

result is suggestive and not conclusive for the absence of local synergies.

5.3 Agency Costs

Managers have incentives to grow their firms beyond their optimal size as this increases

their power and the resources under their control (Jensen (1986)). Lang, Stulz and Walkling

(1991) show that returns are significantly lower for bidders with free cash flow and low

investment opportunities. If bidders with agency costs are less likely to engage in local

transactions, then the higher return to local acquirers could reflect a lower probability of an

agency cost motivated acquisition. In this section, we examine whether a low agency cost firm is

more likely to undertake local transactions and whether this explains the higher returns observed

in local transactions.

We use three proxies to capture potential agency costs in firms. First, we calculate the

free cash in the firm (See Jensen (1986)). Free cash is the cash left after paying for all essential

expenses and investment. Free cash was estimated as lagged operating income before

depreciation (data13) – interest paid (data 15) – taxes paid (data 16) – changes in deferred taxes

(data 74) – capital expenditure (data 128) scaled by beginning of period total assets. Based on

the estimated free cash flow, firms are classified as having high agency costs if their free cash is

in the top quartile in the sample of acquirers. The second proxy to capture a higher probability of

undertaking value destroying acquisitions is firm size. Moeller, Schlingemann and Stulz (2004)

document that only small acquirers earn positive returns and the average return to large acquirers

is negative. We classify large firms, those with total assets in the year prior to the announcement

of the transactions in the top quartile, as being more likely to undertake value-destroying

acquisitions. The last proxy is the frequency of acquisitions. Acquirers that make several

acquisitions are more likely to be motivated by empire building considerations. We classify firms

20

as frequent acquirers if they undertake more than 15 acquisitions over our sample period of 1990-

2003. Frequent acquirers account for approximately 17% of the transactions in our sample.

All three proxies are significantly correlated with lower bidder returns (See Table 10)

Acquirers classified as having low agency costs earn significantly higher returns in both mergers

and acquisitions of majority interest, as well as, in asset acquisitions. We find that acquirers with

high agency costs make fewer local transactions. Acquirers with large free cash acquire locally in

17.6% of deals while acquirers with low free cash flow acquire locally in 18.4% of the deals.

Large acquirers acquire locally in 16% of the deals while small acquirers acquire locally in 18.8%

of the cases. Frequent acquirers acquire locally in only 14.2% of the deals in contrast to 19.3%

local transactions for less frequent acquirers.

As high agency cost acquirers are associated with lower returns and undertake fewer

local transactions, this might explain the higher returns documented for local transactions. When

proxies of agency costs are included in the OLS regressions, the coefficient of local transactions

continues to be positive and significant. For merger transactions, estimates of higher acquirer

returns in local transaction, controlling for agency problems, range from 88 basis points to 92

basis points with different proxies of agency costs. These higher returns in local transactions are

significant at the 5% level. For assets acquisitions, estimates of higher acquirer returns in local

transactions, controlling for agency problems, range from 100 to 105 basis points and are

significant at the 1% level. These results have not been displayed in tables for brevity. The higher

return to local acquirers is not explained away by the lower propensity with which high agency

cost acquirers undertake local transactions.

Since high agency costs acquirers are motivated to undertake acquisitions for reasons

other than value maximization, they are less likely to engage in acquisitions to exploit

information advantages or synergies. If the higher return in local transactions is due to

information advantages or synergies, then the local deals of high agency cost firms should be

associated with smaller or no positive returns. This is because these local transactions are

21

unlikely to be motivated by information advantages or synergies. To examine this we separately

estimate the return to local transactions for high agency cost and low agency cost acquirers.

When free cash is used to proxy for agency costs, we find that low agency cost acquirers

earn 101 basis points in local deals (significant at the 1% level) relative to an insignificant 70

basis point for high agency cost acquirer. With firm size as a proxy, we find low agency cost

acquirers earn 111 basis points in their local transactions (significant at the 1% level) relative to

an insignificant negative one basis point for high agency cost acquirers (see Table 11). Similarly,

less frequent acquirers earn a significant 112 basis points in local deals relative to a loss of 11

basis points for frequent acquirers. These results are not a function of the type of transactions

and hold for both mergers and acquisitions of majority interest as well as for asset acquisitions.

With all three proxies we find higher return in local transactions for low agency cost acquirers

relative to insignificant returns in local deals of high agency cost acquirers. This supports the

presence of local information advantages or local synergies as the source of the higher local

acquirer returns.

Coval and Moskowitz (2001) document that mutual funds in non-metro areas are more

likely to exploit the information advantages arising from geographic proximity. Malloy (2004)

also finds that analysts in non-metro areas are more likely to use local information advantages.

We examine whether a similar effect is observed in firm’s investment decisions. All acquirers

headquartered within 50km of the 20 largest cities are classified as metro acquirers and others as

non-metro acquirers. We find that metro acquirers earn an insignificant 75 basis points in local

transactions. In comparison, non-metro acquirers earn a significant (at the 1% level) return of 106

basis points in local transactions.

In summary, we find no evidence that the somewhat higher probability of local deals

being initiated by low agency cost acquirers explains the higher returns in local deals. We also

find little evidence that the higher average return to acquirers in local transactions is due to higher

synergies in local deals. Returns to acquirers in local deals are increasing in the proxies of

22

information asymmetry of target firms. Further, the higher return to acquirers in local

transactions is seen only for acquirers more likely to exploit local information advantages or local

synergies. This suggests that geographic proximity is associated with significant economic gains

to acquiring firms, which are likely to be arising from information advantages.

VIII. Conclusion

We find evidence of “local bias” in the acquisition decisions of U.S public firms over the

period 1990-2003. Target firms are acquired by public firms headquartered within 100km in

18.8% of the transactions, even though an average target firm has only 6.2% of public firms

headquartered locally that operate within the same industry. We also find that acquirers earn

significantly higher returns in their local transactions relative to their non-local transactions. The

return in local transactions is on average 56% higher than that in non-local transactions.

We examine the sources of these higher local acquirer returns. There is little evidence

that higher local acquirer returns are due to higher local synergies. There is also little evidence

that the higher acquirer returns are due to a higher probability that local transactions are initiated

by low agency cost acquirers. Lastly, we examine whether the higher local acquirer returns arise

from potential information advantages arising from geographic proximity. In support of this, we

find that the higher return in local transactions is increasing in proxies for information asymmetry

of the target. The returns are higher when the target is not publicly traded, when stock is used to

pay for the acquisition, and when the target is located in a non-metro area.

The evidence is consistent with the existence of information asymmetries in the market

for assets and with geographic proximity mitigating these information problems. The findings in

the paper support prior findings on the role of distance in resolution of information asymmetries.

The presence of information asymmetries in the acquisition market implies lack of competitive

bidding for targets that ensures efficient allocation of assets in the economy. The evidence in the

23

paper highlights the importance of further research that throws light on the precise nature of the

information advantages that arise from geographic proximity.

24

Reference

Andrade, G., M. Mitchell and E. Stafford, 2001, New Evidence and Perspectives on Mergers, Journal of Economic Perspectives, 15, 103-120. Asquith, P, 1983, Merger Bids, Market Uncertainty, and Stockholder Returns, Journal of Financial Economics, 11, 51-83. Asquith, P., B. R. Bruner and D. Mullins, 1983, The Gains to Bidding Firms from Mergers, Journal of Financial Economics, 11, 121-139. Audretsch, David and P. Stephan, 1996, “Company-Scientist Locational Links: The Case of Biotechnology,” The American Economic Review, Vol. 86 (3), pp. 641-652. Audretsch, David and M. Feldman, 1996, “R&D Spillovers and the Geography of Innovation and Production,” The American Economic Review, Vol. 86 (3), pp. 630-640. Baum, J. A. C, O. Sorenson, 2003, Geography and Strategy, Advances in Strategic Management, Volume 20, Elsevier Publishers. Bhagat, S., M. Dong, D. Hirchleifer, and R. Noah, 2004, Do Tender Offers Create Value? New Methods and Evidence, Working Paper, Ohio State University. Bradley, M., A. Desai, and E. Han Kim, 1983, The Rationale Behind Interfirm Tender Offers: Information or Synergy? Journal of Financial Economics 11, 183-206. Bradley, M., A. Desai, and E. Han Kim, 1988, Synergistic Gains from Corporate Acquisitions and their Division Between the Shareholders of Target and Acquiring Firms, Journal of Financial Economics, 21, 3-40 Brown, S and J. Warner, 1985, “Using Daily Stock Returns: The Case of Event Studies,” Journal of Financial Economics 14, 3-32. Coval, J., and T. Moskowitz, 1999, “Home Bias at Home: Local Equity Preference in Domestic Portfolios,” Journal of Finance, 54, 2045-2073. Coval, J., and T. Moskowitz, 2001, “The Geography of Investment: Informed Trading and Asset Prices,” Journal of Political Economy, 109, 4, 811-841 Eckbo, E, R. Giammarino and R. Heinkel, 1990, Asymmetric Information and the Medium of Exchange in Takeovers, Review of Financial Studies 3, 651-676. Eckbo, E. and K. Thorburn, 2000, Gains to Bidder Firms Revisited: Domestic and Foreign Acquisitions in Canada, Journal of Financial and Quantitative Analysis, Vol. 35, 1-25. Fuller, K. J. Netter and M. Stegemoller, 2002, “What do Returns to Acquiring Firms Tell Us? Evidence from Firms That Make Many Acquisitions,” Journal of Finance, 1763-1993. Glaeser, D., B. Sarcedote and J. Scheinkmen, 1996, “Crime and Social Interactions,” Quarterly Journal of Economics, Vol. 111 (2), pp. 507-548.

25

Grinblatt, M., and M. Keloharju, 2001, Distance, Language, and Culture Bias: The Role of Investor Sophistication, Journal of Finance 56, 1053-1073. Heath, C., and A. Tversky, 1991. “Preferences and Beliefs: Ambiguity and Competence in Choice Under Uncertainty, Journal of Risk and Uncertainty, 4, 5-28. Huberman, G., 2001, Familiarity Breeds Investment,” Review of Financial Studies, Vol 14, 3, 659-680. Ivkovich, Z. and S. Weisbenner, S., 2002, Local Does as Local Is: Information Content of the Geography of Individual Investors Common Stock Investments, Forthcoming Journal of Finance. Jarell, G., J. Brickley and J. Netter, 1988, The Market for Corporate Control: The Empirical Evidence since 1980, Journal of Economic Perspectives, 2, 49-68. Jensen, M., and R. Ruback, 1983, The Market for Corporate Control: The Scientific Evidence, Journal of Financial Economics, 11, 5-50. Jensen, M., 1986 “Agency Costs of Free Cash Flow, Corporate Finance and Takeovers”, American Economic Review, Vol. 76, 2, 323-330. Kedia, S., 2004, “Geography and Misreporting,” Working Paper. Lang, L., R. Stulz, and R. Walkling, 1989, Managerial Performance, Tobin’s Q, and the Gains from Successful Tender Offers, Journal of Financial Economics, Vol. 24, 137-154. Lang, L., R. Stulz, and R. Walkling, 1991, “A test of Free Cash Flow Hypothesis: The Case of Bidder Returns, Journal of Financial Economics, Vol. 29, 315-335. Lang, L., A. Poulsen and R. Stulz, 1995, Asset Sales, Firm Performance, and the Agency Costs of Managerial Discretion, Journal of Financial Economics, 37, 3-37. Loughran and Schulz, 2004, Dissemination of Information: Urban versus Rural Stock Return Patterns, Working Paper, University of Notre Dame. Malloy, C., 2003, The Geography of Equity Analysis, Forthcoming Journal of Finance. Martin, K., 1996, The Method of Payment in Corporate Acquisitions, investment opportunities and, and management ownership, Journal of Finance, 51, 1227-1246. Mandelker, G., 1974, “Risk and Return: The Case of Merging Firms,” Journal of Financial Economics, 303-335. Moeller, S., F. Schlingemann, and R. Stulz, 2003, “Do Shareholder of Acquiring Firms Gain from Acquisitions, Dice Center Working Paper. Orpurt, S., 2004, Local Analyst Earnings Forecast Advantages in Europe, Working Paper, University of Chicago. Petersen, M., and R. Rajan, 2002, Does Distance Still Matter? The Information Revolution in Small Business Lending, Journal of Finance 57, 2533-2570.

26

Pirinsky, C., and Q. Wang, 2004, Does Corporate Headquarter Location Matter for Stock Returns, Working Paper, Texas A&M. Pulvino, T., 1998, “Do Asset Fire Sales Exist? An Empirical Investigation of Commercial Aircraft Transactions,” Journal of Finance, Vol. 53, No., 3, 939-978. Rao, R. 2000, Investment Bank Market Share, Contingent Fee Payments, and the Performance of Acquiring Firms, Journal of Financial Economics, 56, 293-324. Ruback, R., 1983, Assessing Competition in the Market for Corporate Control, Journal of Financial Economics, 11, 141-153. Sah, R,. 1991, “Social Osmosis and Patterns of Crime,” Journal of Political Economy, Vol. 99 (6), pp.1272-1295. Servaes, H. and M. Zenner, 1996, The Role of Investment Banks in Acquisitions, Review of Financial Studies, 9, 787-815. Song, M. and R. Walkling, 2004, Anticipation Acquisitions and the Bidder Return Puzzle, Working Paper, Ohio State University. Zhu, N, 2002, The Local Bias of Individual Investors, Working Paper, Yale University.

27

Table 1 Data Characteristics over time

The first column displays the percentage of all transactions during the year that were characterized as mergers or acquisition of majority interest. The remaining transactions primarily involved acquisition of assets. Columns 3-5 display the percentage of transactions during the year where the target firm was a private, public and subsidiary respectively.

Year Percentage of Deals that were Mergers and

Acquisitions

Percentage Deals with

Public Targets

Percentage Deals with

Private Targets

Percentage Deals with

Subsidiaries

Number of Deals

1990 42.18 22.27 44.55 32.23 2111991 52.34 17.87 52.34 28.94 2351992 45.16 11.17 55.09 33.50 4031993 41.68 10.29 53.17 36.36 5831994 43.32 14.58 53.41 30.79 7341995 47.36 17.39 54.08 28.30 8341996 46.20 16.19 57.95 25.67 10131997 38.25 14.35 63.54 21.72 12891998 43.32 20.75 58.59 20.51 12581999 47.99 21.93 60.07 17.73 11172000 53.59 19.86 63.57 16.18 10322001 45.22 25.93 51.22 22.53 6172002 38.59 19.39 58.94 20.72 5262003 40.49 18.61 60.53 20.45 489Total 44.72 17.98 57.87 23.75 10341

Table 2 Summary Statistics for the Data

Deal value is expressed in millions of dollars. Relative Value is the ratio of deal value and the market value of the acquirer in the year prior to the announcement of the deal. Size of the acquirer is the value of total assets, in millions of dollars, at the end of the year prior to the announcement of the deal. Market value of the acquirer, in millions of dollars, is the market value at the end of the year prior to the announcement of the deal. Acquirer and target firms are classified as being in the same industry if they belong to the same two digit SIC. Mode of payment is classified as cash if the payment consisted of cash, debt, liabilities or some combination of these. Mode of payment was classified as stock if the payment consisted of stock, warrants, options or some combination of these. All other model of payments were classified as hybrids.

Mean Median Number of Deals

Mean

Mergers and Acq.

Asset Acq.

Public Targets

Private Targets

SubsidiaryTargets

Deal Value 297.28 23.24 10341 562.39 82.78 1242.69 59.44 160.49Relative Value 0.29 0.09 9420 0.37 0.23 0.46 0.24 0.28Size of Acquirer 1996 189 9852 2674 1443 4863 1054 2026Market Value of Acquirer 5041 319 9421 7525 2988 10898 3850 3261Dummy for same industry deals

0.60 1 10341 0.59 0.61 0.61 0.58 0.63

Dummy for Cash Payments 0.44 0 10341 0.27 0.57 0.33 0.35 0.71Dummy for Stock Payments 0.24 0 10341 0.44 0.08 0.41 0.26 0.07Dummy for Hybrid Payments 0.32 0 10341 0.29 0.35 0.26 0.38 0.23

28

Table 3 Extent of Local Bias

Transactions are classified as local if the distance between the headquarters of acquiring and target firms is less than 100km. The table presents the average probability of being acquired locally by quintiles of firms ranked by the probability of being acquired locally. In Panel A, the probability of being acquired locally is estimated as the fraction of all public firms that reside locally. In Panel B, the probability of being acquired locally is estimated as the fraction of firms in the two digit SIC of the target that reside locally. Finally, in Panel C, the probability of being acquired locally is estimated as the fraction of public firms in the two digit SIC of the target and acquirer that reside locally. The second column in all panels is the fraction of targets in the group that were locally acquired.

Panel A Panel B Panel C Fraction of

all public firms that are local

Fraction of targets locally acquired

Fraction of all public firms in the two digit SIC of target that are local

Fraction of targets locally acquired

Fraction of public firms in two digit SIC of target & acquirer that are local

Fraction of targets locally acquired

Group 1 0.0152 0.1168 0.0054 0.0763 0.0072 0.0684Group 2 0.0292 0.1433 0.0205 0.1383 0.0269 0.1281Group 3 0.0454 0.1594 0.0404 0.1528 0.0546 0.1664Group 4 0.0763 0.2670 0.0711 0.2030 0.0994 0.2221Group 5 0.1645 0.2132 0.1426 0.3200 0.2076 0.3176 Full Sample 0.0690 0.1878 0.0619 0.1878 0.0851 0.1878

Table 4

Acquirer Returns for Local and Non-Local Deals The table displays average acquirer returns over [-2,2] day around announcement for local and non-local transactions. Transactions are classified as local if the target and acquirer are located within 100km of each other. Difference in mean returns is the difference in the average return to acquirers in local transactions and non-local transactions for the category. *,**,*** refer to significant at the 10%, 5% and 1% level. Mean Acquirer

Returns Median Acquirer

Returns Diff. in Mean Returns

% Local Deals

Local Non-Local Local Non-Local Full sample 0.0276 0.0177 0.0147 0.0102 0.0099*** 18.48Mergers and Acq. of Maj. Interest 0.0215 0.0123 0.0088 0.0057 0.0092** 19.98Asset Acquisitions 0.0334 0.0220 0.0181 0.0134 0.0114*** 17.26 Deals with Private Targets 0.0343 0.0229 0.0190 0.0141 0.0114*** 19.27Deals with Public Targets -0.0046 -0.0089 -0.0124 -0.0064 0.0043 19.28Deals with Subsidiaries 0.0375 0.0253 0.0190 0.0149 0.0122** 15.96

29

Table 5 Distribution of Transactions and Returns by Distance

Mean acquirer returns is the average return to acquirers for all transactions where the distance between the acquirer and the target was estimated to lie in the distance range. The last column displays the fraction of transactions that lie in the particular distance category. The distance range 0 includes transactions where the acquirer and target were in the same city. The category 0-50km includes all transactions where the distance between acquirer and target was greater than zero but less than or equal to 50km. The remaining categories are similarly defined. Distance Range Mean Acquirer

ReturnsFraction of all transactions in the

distance category 1 0 0.0268 0.07272 0 – 50 km 0.0273 0.08883 50 km –100 km 0.0309 0.02634 100 km – 200 km 0.0209 0.03745 200 km – 500 km 0.0215 0.10166 500 km –1000 km 0.0146 0.15287 > 1000 km 0.0176 0.5204

30

Table 6 OLS Regressions for Acquirer Returns

The dependent variable is the [-2,2] day acquirer returns around announcement. Mode of payment is classified as cash if the payment consisted of cash, debt, liabilities or some combination of these. Mode of payment was classified as stock if the payment consisted of stock, warrants, options or some combination of these. All other model of payments were classified as hybrids. Relative value is the ratio of the value of the transaction to the market value of the acquirer at the end of the year prior to the announcement. Acquirers and targets were classified in the same industry if they belonged to the same two digit SIC code. Small size dummy takes the value one if acquirers assets are in the bottom third for the sample. Transactions were classified as local if the acquirer and target were within 100km of each other. Model 3 includes transactions that involved mergers and acquisition of majority interest. Model 4 includes transactions involving asset acquisitions. The errors are corrected for event clustering. P values are reported in parenthesis below. *,**, *** represent significance at the 10%, 5% and 1% level. Model 1 Model 2 Model 3 Model 4 Mergers Asset Acq. Constant 0.0062 0.0079 0.0023 0.0435 (0.40) (0.39) (0.87) (0.02)**Dummy for Local Transactions 0.0095 0.0094 0.0087 0.0105 (0.00)*** (0.00)*** (0.054)* (0.00)***Dummy if target is subsidiary 0.0333 0.0332 0.0316 0.0047 (0.00)*** (0.00)*** (0.00)** (0.75)Dummy if target is private firm 0.0277 0.0277 0.0308 -0.0037 (0.00)*** (0.00)*** (0.00)*** (0.80)Dummy if mode of payment is cash -0.0013 -0.0021 0.0134 -0.0113 (0.72) (0.55) (0.00)*** (0.11)Dummy if mode of payment is hybrid -0.0016 -0.0024 0.0030 -0.0079 (0.68) (0.54) (0.56) (0.28)Relative Value 0.0135 0.0134 0.0117 0.0680 (0.055)* (0.055)* (0.11) (0.03)**Relative value* Cash 0.0077 0.0079 -0.0049 -0.0402 (0.41) (0.40) (0.65) (0.22)Relative Value * Hybrid 0.0049 0.0049 -0.0022 -0.0400 (0.54) (0.53) (0.82) (0.21)Same Industry Dummy -0.0025 -0.0025 -0.0045 -0.0002 (0.26) (0.25) (0.18) (0.95)Acquirer Size (Log of total assets) -0.0033 -0.0032 -0.0029 -0.0035 (0.00)*** (0.00)*** (0.013)** (0.00)***Small size Dummy 0.0077 0.0078 0.0123 0.0034 (0.02)** (0.02)** (0.02)** (0.44) Year Effects No Yes Yes YesR-sqaure 0.0401 0.0422 0.0519 0.0448Num of observations 9119 9119 4134 4985

31

Table 7 Acquirer Returns in Local Transactions by Information Asymmetries

The dependent variable is the [-2,2] day acquirer return around announcement. Mode of payment is cash if payment is in cash, debt, or liabilities. It is stock if payment is in stock, warrants, or options. All other payments are hybrids. Relative value is the ratio of value of the deal to market value of the acquirer. Same Industry dummy is one if acquirers and targets are in the same two-digit SIC code. Small size dummy takes the value one if the acquirer’s assets are in the bottom third. Local dummy is one if the acquirer and target are located within 100km. Acquirer size is the log of the total assets in year prior. Public (Non-Public) dummy is one if target is a public (private or subsidiary) firm. Cash (Non-Cash) dummy is one if the mode of payment is cash (stock or hybrid). Metro (Non Metro) dummy is one if the target is located within (outside) 50km of the largest cities. Low R&D (High R&D) dummy is one if the target firm’s industry is in the bottom third (top two third) ranked by the industry ratio of R&D expenses to sales. The errors were corrected for clustering of events. P values are reported in parenthesis below. *,**, *** represent significance at the 10%, 5% and 1% level. Model 1 Model 2 Model 3 Model 4Constant 0.0072 0.0048 0.0058 0.0057 (0.42) (0.60) (0.52) (0.53)Local Dummy * Public Dummy 0.0010 (0.87) Local Dummy * Non Public Dummy 0.0110 (0.00)*** Local Dummy * Cash Dummy 0.0040 (0.28) Local Dummy * Non Cash Dummy 0.0127 (0.00)*** Local Dummy * Metro Dummy 0.0063 (0.1443) Local Dummy * Non Metro Dummy 0.0118 (0.00)*** Local Dummy * Low R&D Dummy 0.0130 (0.03)**Local Dummy * High R&D Dummy 0.0084 (0.011)**Dummy if target is subsidiary or private firm 0.0275 0.0294 0.0294 0.0294 (0.00)*** (0.00)*** (0.00)*** (0.00)***Dummy if mode of payment is cash -0.0007 0.0009 -0.0008 -0.0007 (0.83) (0.80) (0.82) (0.83)Dummy if model of payment is hybrid -0.0020 -0.0019 -0.0021 -0.0021 (0.59) (0.63) (0.59) (0.59)Relative Value 0.0139 0.0138 0.0139 0.0138 (0.046)** (0.048)** (0.046)** (0.047)**Relative Value *Cash 0.0078 0.0080 0.0079 0.0078 (0.40) (0.39) (0.40) (0.40)Relative Value * Hybrid 0.0048 0.0048 0.0047 0.0048 (0.54) (0.54) (0.55) (0.54)Dummy if same industry -0.0023 -0.0023 -0.0023 -0.0023 (0.30) (0.29) (0.28) (0.29)Acquirer Size -0.0030 -0.0030 -0.0030 -0.0030 (0.00)*** (0.00)*** (0.00)*** (0.00)***Small Size Dummy 0.0076 0.0076 0.0077 0.0077 (0.02)** (0.02)** (0.02)** (0.019)** Year effects Yes Yes Yes Yes Number of Observations 9119 9119 9119 9119R- Square 0.0419 0.042 0.0419 0.0417

32

Table 8 Industry, Synergies and Local Transactions

Transactions are classified as being in the same (different) industry if the target and the acquirer are in the same (different) two- digit SIC code. Local (Non-Local) transactions are those where the target and acquirer are located within (more than) 100km of each other. The multivariate analysis included a constant, dummy for targets that are non publicly traded, dummy when cash is used as a mode of payment, dummy when both cash and stock are used as a mode of payment, ratio of the value of the transaction to the market value of the acquirer (relative value), interaction of relative value with the mode of payment, log of total assets of the acquirer, and dummy when total assets of the acquirer are in the bottom third. The errors in the multivariate analysis are corrected for event clustering. *,**,*** denote significance at the 10%, 5%, and 1% level.

Same Industry

Deals Different Industry Deals

Mean Acquirer Returns Local Transactions 0.0235 0.0336 Non-Local Transactions 0.0168 0.0192 Difference in Means of Local and Non-Local Deals 0.0067 0.0144 T-stat for difference in means 1.76* 3.15*** Median Acquirer Returns Local Transactions 0.0112 0.0188 Non-Local Transactions 0.0107 0.0087 Difference in Medians of Local and Non-Local Deals 0.0005 0.0101 Z-stat for difference in medians 1.01 3.53*** Estimates from Multivariate Analysis Local dummy & with Same (Different) Industry Dummy 0.0076 0.0110 T-stat for Estimated Coefficient 1.96** 2.42** Percentage of Deals that were Local 18.2 18.9

33

Table 9 Total Returns and Target Returns for Public Targets

Acquirer (target) return is the [-2,2] day acquirer (target) return around announcement of the transaction. Total returns are the weighted average return for acquirers and target. Mode of payment is cash if payment is in cash, debt, or liabilities. It is stock if payment is in stock, warrants, or options. All other payments are hybrids. Relative value is the ratio of value of the deal to market value of the acquirer. Same Industry dummy is one if acquirers and targets are in the same two-digit SIC code. Small size dummy takes the value one if the acquirer’s assets are in the bottom third. Local dummy is one if the acquirer and target are located within 100km. Acquirer and target size is the log of the total assets in year prior. Metro (Non Metro) dummy is one if the target is located within (outside) 50km of the largest cities. The errors were corrected for clustering of events. P values are reported in parenthesis below. *,**, *** represent significance at the 10%, 5% and 1% level.

Model 1 Model 2 Acquirer

Returns Target

ReturnsTotal

ReturnsAcquirer Returns

Target Returns

Total Returns

Constant 0.0482 0.4998 0.0419 0.0452 0.5080 0.0406 (0.07)* (0.00)*** (0.12) (0.09)* (0.00)*** (0.13)Dummy if mode of payment is cash 0.0243 0.0982 0.0335 0.0282 0.0959 0.0361 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)***Dummy if model of payment is hybrid 0.0047 -0.0046 0.0069 0.0059 -0.0080 0.0074 (0.55) (0.81) (0.35) (0.46) (0.68) (0.32)Relative Value -0.0064 -0.0313 -0.0106 -0.0062 -0.0322 -0.0105 (0.48) (0.00)*** (0.07)* (0.50) (0.00)*** (0.07)*Relative Value *Cash 0.0069 0.0293 0.0123 0.0067 0.0302 0.0122 (0.45) (0.00)*** (0.039)** (0.47) (0.00)*** (0.041)**Relative Value * Hybrid 0.0142 0.0308 0.0269 0.0139 0.0316 0.0268 (0.18) (0.00)*** (0.00)*** (0.19) (0.00)*** (0.00)***Dummy if same industry -0.0007 0.0050 -0.0007 -0.0004 0.0043 -0.0006 (0.89) (0.73) (0.88) (0.93) (0.76) (0.90)Acquirer Size (Log of Total Assets) -0.0105 -0.0268 0.0009 -0.0106 -0.0267 0.0009 (0.00)*** (0.00)*** (0.62) (0.00)*** (0.00)*** (0.63)Target Size (Log of Total Assets) 0.0057 0.0096 -0.0078 0.0059 0.0089 -0.0077 (0.02)** (0.15) (0.00)*** (0.014)** (0.18) (0.00)***Small Size Dummy 0.0078 -0.0555 0.0020 0.0079 -0.0569 0.0020 (0.35) (0.012)** (0.80) (0.34) (0.01)*** (0.81)Local Dummy * Metro Dummy -0.0042 0.0407 0.0017 (0.64) (0.13) (0.87) Local Dummy * Non Metro Dummy 0.0157 -0.0244 0.0092 (0.07)* (0.21) (0.28) Local Dummy * Cash Dummy -0.0097 0.0126 -0.0052 (0.32) (0.72) (0.64)Local Dummy * Non-Cash Dummy 0.0118 0.0054 0.0095 (0.155) (0.79) (0.26) Year effects Yes Yes Yes Yes Yes Yes Number of Observations 1432 1445 1432 1432 1445 1432R- Square 0.0735 0.1191 0.0682 0.0730 0.1163 0.0687

34

Table 10 Proxies of Agency Cost, Returns and Propensity for Local Deals

The table displays average acquirer returns which are [-2,2] day return around announcement. Free cash flow is is the cash left after paying for all essential expenses and investment scaled by total assets. Acquirers with free cash flow in the top quartile are classified as having high free cash flow. The rest are classified as low free cash flow. Acquirers with total assets in the top quartile are classified as large size and the rest as low size. Frequent acquirers are those that make more than 15 acquisitions over 1990-2003. P values are reported in parenthesis below. *,**, *** represent significance at the 10%, 5% and 1% level. Mergers & Acquisition of

Majority Interest Asset Acquisitions Full Sample

Mean Return Fraction of Local Deals

Mean Return

Fraction of Local Deals

Fraction of Local Deals

High Free Cash Flow 0.0078 0.1895 0.01677 0.1626 0.1761Low Free Cash Flow 0.0160 0.2022 0.0263 0.1703 0.1842T-Statistic -2.45** -3.18*** Large Acquirer Size -0.0098 0.1762 0.0065 0.1413 0.1603Small Acquirer Size 0.0195 0.2055 0.0269 0.1740 0.1877T-Statistic -9.54*** -7.28*** Frequent Acquirers 0.0054 0.1648 0.0168 0.1208 0.1422Infrequent Acquirers 0.0161 0.2076 0.0253 0.1821 0.1934T-Statistic -3.12*** -2.81***

35

36

Table 11 Acquirer Returns in Local Transactions by Acquirer Agency Costs

The dependent variable is the [-2,2] day acquirer return around announcement. Mode of payment is cash if payment is in cash, debt, or liabilities. It is stock if payment is in stock, warrants, or options. All other payments are hybrids. Relative value is the ratio of value of the deal to market value of the acquirer. Same Industry dummy is one if acquirers and targets are in the same two-digit SIC code. Small size dummy takes the value one if the acquirer’s assets are in the bottom third. Local dummy is one if the acquirer and target are located within 100km. Free cash flow is is the cash left after paying for all essential expenses and investment scaled by total assets. Acquirers with free cash flow in the top quartile are classified as having high free cash flow. The rest are classified as low free cash flow. Acquirers with total assets in the top quartile are classified as large size and the rest as low size. Frequent acquirers are those that make more than 15 acquisitions over 1990-2002. Acquirer Metro is a dummy that is one if the acquirer is within 50km of the 20 largest cities. The errors were corrected for clustering of events. A constant was included but has not been displayed. P values are reported in parenthesis below. *,**, *** represent significance at the 10%, 5% and 1% level. Model 1 Model 2 Model 3 Model 4Acquirer Free Cash Flow 0.0010 (0.78) Local Dummy * High Free Cash Flow 0.0070 (0.16) Local Dummy * Low Free Cash Flow 0.0101 (0.00)*** Local Dummy * Large Size -0.0001 (0.9847) Local Dummy * Small Size 0.0111 (0.00)*** Infrequent Acquirer Dummy -0.0046 (0.08)* Local Dummy * Frequent Acquirers -0.0011 (0.83) Local Dummy * Infrequent Acquirers 0.0112 (0.00)*** Local Dummy * Acquirer Metro Dummy 0.0075 (0.11)Local Dummy * Acquirer Non Metro Dummy 0.0106 (0.00)***Dummy if target is subsidiary or private firm 0.0291 0.0293 0.0293 0.0294 (0.00)*** (0.00)*** (0.00)*** (0.00)***Dummy if mode of payment is cash -0.0005 -0.0007 -0.0005 -0.0007 (0.87) (0.84) (0.87) (0.83)Dummy if model of payment is hybrid -0.0027 -0.0020 -0.0019 -0.0020 (0.48) (0.60) (0.61) (0.59)Relative Value 0.0135 0.0139 0.0139 0.0138 (0.06)* (0.04)** (0.04)** (0.046)**Relative Value *Cash 0.0077 0.0079 0.0080 0.0079 (0.42) (0.39) (0.39) (0.39)Relative Value * Hybrid 0.0052 0.0048 0.0048 0.0048 (0.51) (0.53) (0.53) (0.53)Dummy if same industry -0.0019 -0.0023 -0.0024 -0.0023 (0.39) (0.28) (0.28) (0.29)Acquirer Size (Log of Total Assets) -0.0031 -0.0027 -0.0032 -0.0030 (0.00)*** (0.00)*** (0.00)*** (0.00)***Small Size Dummy 0.0068 0.0083 0.0075 0.0076 (0.04)** (0.012)** (0.02)** (0.02)**Year effects Yes Yes Yes Yes Number of Observations 8955 9119 9119 9119R- Square 0.0419 0.042 0.0421 0.0417