Dynamic Measure of Competition and the Dispersion of

Corporate Policy and Performance

Margaret Rui Zhu1

City University of Hong Kong

November 2016

Preliminary, Please do not circulate

Abstract

This paper develops a dynamic measure of industry competition - Dynamic Market Share (DMS), which is the sum of absolute changes of market share for firms in an industry. A larger value of the DMS measure captures more intensified competition activities within the industry. The DMS measure has low correlation with the commonly used competition measures such as Herfindahl and Lerner Index. The DMS tends to increase when external competition is stronger, proxy by imports growth shock and tariff cuts. Generally, industries with high competition intensity have high cash holding and leverage, low profitability and valuation. More interestingly, based on the DMS measure, we find that the dispersion of corporate policies, such as cash holdings, leverage and cash flow, leads to greater competition activities and higher competition leads to larger performance dispersion in the industry.

1 Contact: Margaret.zhu@cityu.edu.hk. The author thanks the discussant, Jefferson Duarte, and conference participants at 2014 CityU Finance Conference for useful comments. The paper is preliminary and is part of the work in progress project with Tao Shu from University of Georgia and Sheridan Titman from University of Texas at Austin. Please do not cite or circulate this version.

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1 Introduction

There is long standing literature on how product market competition is related to innovation2,

financial constraints 3 , managerial incentives 4 and firm performance. 5 Generally, there are two

components in industry competition: entry barrier and competitive interactions between existing

firms. The existing measures of industry competition emphasize on the existing market power to

deter future entry (i.e. Herfindahl Index6 ) and current pricing strategies, such as Lerner Index (Price-

Cost Margin).

To complement the existing literature, the paper proposes a dynamic measure to capture the

competition activities inside of an industry: Dynamic Market Share (DMS). DMSj,t measure is

calculated as the sum of absolute7 change of market share for all firms in industry j from year t-1 to t.

This measure therefore captures how the market shares distribution changes from time t-1 to t,

directly evaluating market competition intensity inside of an industry. A larger value of the DMS

measure suggests a bigger change in how the market share is distributed within industry, thus

indicating a strengthened competition, and a greater pressure faced by managers from existing rivals.

The sample consists of all Compustat firms excluding commodity producers, Utilities, Financials,

Education and Public Administrative industry, covering 7,632 industry-year observations over the

period from 1977 to 2013. The DMS measures use segment-level data and adjust for market share

2 Aghion et al (2005) summarizes this line of literature. 3 See Fresard (2007), Phillips (1995) and others. 4 See Aggarwal et al (1999), Raith (2003) and others. 5 See Nickell (1996) and others. 6 Other less used competition measures include number of firms, four firm concentration ratio, and price elasticity, etc. Will be discussed in Section 5. 7 Sum of square term of market share changes is also used as robust. The results do not change significantly.

2

changes due to merger and acquisition. The time-series patterns of DMS measure vary across

industries. Some industries experience large waves over the time period of 1977 to 2013, such as

Machinery, Electronics and Communications in 1990s and 2000s. Other industries are relatively

stable over time, such as Chemicals, Rubber, Plastics and Leather.8 Overall, the DMS measure is

higher for the periods of 1985-1987 and 1998-2000, is relative low for the periods of 1978-1980,

1988-1991, and 2010-2013. The general patterns are consistent with the economic cycles. The DMS

measure does not have high correlation with Herfindahl index (-0.204) or Lerner index (-0.096). The

DMS measure is positively associated with imports growth and negatively correlated with the growth

of the tariff rate in the industry, which suggests that the competition between existing firms will

intensify when they face higher outside competition.

Generally, we find that industries with lower average ROA, but higher cash holding and leverage

are more likely to have higher competition dynamics. More interestingly, these dynamic industries

tend to have higher dispersion in financial policies, such as cash holding, leverage and cash flow at

previous period. In addition, we also find that high competition activities lead to future dispersion of

profitability and performance within the industry. Firms who just experience high level of

competition tend to have higher level of leverage, low level of cash holdings and cash flow. In order

to detect the dynamics between the DMS measure and the dispersion of corporate policies and

performance, we use panel vector autoregression technique. The impulse response functions show a

strong reaction of the dispersion of investment policies—capital expenditure, R&D and

advertising—to the impulse of DMS measure.

8 See Section 2 and Figure 2 for detailed industry definition and time pattern of the DMS measure.

3

There are many papers using measures of competition in the empirical IO literature. High

concentration, measured by Herfindahl index, is used as an indication of weak competition and leads

to high prices and high price-cost margins. 9 The Lerner index (Price-Cost-Margin) is another

traditional measure of competition. Papers like Aghion et al. (2005) and Nickell (1996) calculate it

directly as the profits-sales ratio. Others calculate the optimal PCM for each firm based on estimated

demand and cost functions.10 Corts (1999) criticize the PCM measure and shows that transitory

demand shocks could lead to overestimation of competition intensity. In economics literature,

people also use factor price elasticity (PE), first introduced by Panzar and Rosse (1987) to measure

industry competition. The idea is that if a firm can pass through the input price increase to sales

price, it has certain market power. The lower the price elasticity, the more competitive the market is.

However, similar to PCM, the PE measure only applies to single manufacturing product line with

identifiable input prices, which makes it hard to estimate using standard dataset. The paper

complements to the literature by providing a dynamic measure of competition interactions between

existing firms within an industry. The DMS measure can be adopted in any industry competing for

market shares, not limited to the manufacturing industries with defined input-output data as

required by Lerner index and price elasticity.

A large finance and economics literature analyzes how competition affects firm performance,

innovation and financial policies. Nickell (1996) shows that more intense competition, measured by

PCM, leads to more innovation and higher firm performance. Aghion et al. (2005) find an inverted-

U relationship between competition and innovation using U.K. data and PCM measurement. Giroud

9 See Scherer and Ross (1990) for an overview. 10 Examples here include Berry et al. (1995), Hausman et al. (1994) and Nevo (2001).

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and Mueller (2011) find that weak governance firms have poor performance only in noncompetitive

industries. Fresard (2010) uses shocks from import tariff to measure competition and finds that large

cash reserves lead to future market share gains. The results is consistent with my findings that firms

tend of have higher cash holdings and large variation in cash holdings in industry with active

competition measures. Haushalter, Klasa and Maxwell (2007) argue that, when deciding upon their

optimal amount of cash, firms take into account the product market competition, measured

indirectly using proxy for predation risk. With the proposed dynamic measures of industry

competition, we can directly examine the relationship between firm performance, policies, stock

returns and the internal industry competition. While the literature mainly focus on how competition

affects the mean value of the firms’ investment and performance, the dispersion of corporate

policies and performance are less embraced. The paper also contributes to the literature by

providing new evidence on how industry competition is related to the dispersion of corporate

policies and performance.

The rest of the paper is organized as follows: section 2 describes the data; Section 3 develops the

measures; Section 4 examines the association between internal competition activities and external

competition shocks; Section 5 analyzes the relationship between the dynamic measures of

competition and the dispersion of corporate policies and performance; Section 6 concludes with a

discussion.

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2. Data

The paper uses standard data sources. The firm and industry characteristics data comes from

Compustat. The data used to develop the dynamic measures of competition are from Compustat

and Compustat-Segments data. I exclude firms with negative values in Total Assets, Total Liabilities,

Sales, COGS, or Cash to avoid data error. To identify industries with meaningful competition, I

excludes commodity producers (SIC 0100-1499), financial industries (SIC 6000-6999), Utilities (SIC

4900-4999), Public Administration industry (SIC 8800-9999) and industries with only one firm. To

obtain enough dynamics, I exclude firms with less than three years of data and firms make more

than two times of industry switches within five years.11 The final sample consists of 122,748 firm-

year observations and 7,556 industry-year observations from 1977 to 2013. The definitions of all

variables are reported in Appendix A.

The paper defines industries mainly at four-digit SIC level with adjustment based on business

nature and links in market competition. We excludes SIC industry code which is too broad and

consist of more than one competing industries, for example, Beverages (SIC 2080)12, Chemicals &

Allied Products (SIC 2800) and other Miscellaneous industries. We combine four-digit SIC

industries which have different SIC code but compete in the same market, for example, Eating and

Drinking Place (SIC 5810) and Eating Place (SIC 5812), Food Stores (SIC 5400), Grocery Store (SIC

5411) and Convenience Store (SIC 5412). The industry-by-industry report in Appendix B is

11 Industry switch is identified when firms report different three-digit SIC code at time t, compared to t-1. Multiple industry switches within five years indicate data error and add noises to dynamic competition measures. 12 Beverages (SIC 2080) contains both non-alcohol and alcohol beverages, which do not share the same consumer base.

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presented by two-digit SIC industries groups. 13 There are two popular alternative industry

classifications in the literature. Fama-French 48 industry classification defines industry based on the

product and correlated effects on asset prices. It classifies vertical industries together, such as

Forestry, Lumber products and Hard surface floor. It might be preferable in asset pricing analysis

because the vertical industries tend to experience same shocks and have correlated stock returns.

However, it is not desirable to examine industry competition effects because the vertical industries

do not compete with each other. For example, when Lumber industry experiences high level of

competition, its market share distribution changes and the margin variation decreases. But the

downstream industry - Hard Floor products may not have the same intensified competition. Instead,

the Hard Floor industry could possibly have decreased level of competition because the decreased

cost resulting from the increased competition of upstream lumber industry. Another alternative

industry classification is Text based industry classification (TNIC) developed by Hoberg and Phillips

(2013). The TNIC is a pair wise identification of how closely two firms’ products relate to each

other. It is useful to analyze, at the firm level, how one firm’s activities are related to its’ competitors’.

However, because TNIC is a pair wise classification, it does not have a clear definition of industry,

which makes it hard to calculate market share at industry level or analyze activities within or across

industry. For example, according to TNIC classification, firm A is a rival to firm B and firm B is a

rival to firm C, but firm A is not necessarily a rival to firm C.

13 See Appendix for details at industry groups.

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The imports tariff data and import penetration data is from Peter Schott website.14 The sample

period for Tariff growth data is from 1974 to 2005 and 1993 to 2007 for Imports growth data. The

paper also used merger and acquisition data covering 1978-2014 to adjust for market share changes

due to M&A. The data comes from Thomason ONE (SDC) database of Thomason Reuters.

3 Dynamic Measures of Competition

3.1 Adjust for Business Segments and M&As

When a sample firm has multiple segments, the firm’s SIC captures only its main segment. We

therefore use the segment-level sales data to calculate competition measures for an industry. That is,

to calculate DMS for an industry, we use all the single segment firms in this industry together with

the segment-level data of conglomerate firms’ segments in this industry.

Additionally, merger and acquisition activities can also lead to a mechanical change in market

share without actual competition activities. We adjust the merger and acquisition activities in SDC as

follows: when firm i in our sample acquired a private firm, or acquired a public firm in a different

industry in year y, then firm i'’s sales is contaminated, and therefore firm i was dropped from the

calculation of the industry competition measures in year y. For firm i that acquired a public firm j in

the same industry in year y, then we add firm j’s sales to firm i in year y-1. That is, we treat firms i

and j as one firm in the year prior to merger.

14 http://faculty.som.yale.edu/peterschott/sub_international.htm

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3.2 Dynamic Market Share (DMS)

The DMS measure is calculated as follows

DMSj,t=��|MSij,t-MSij,t-1|�n

i=1

where DMSj,t is the dynamic measure of competition for industry j at time t. MSij,t is the market

share for firm i in industry j at time t. The market share is defined as sales of firm i divided by total

sales of all the Compustat firms in the same defined industry.15

Table 1 defines the main variables used in this paper. The DMS measure captures the changes of

Market Share distribution from t-1 to t. A larger value of the DMS measure suggests a bigger change

in how the market share is distributed within industry, thus indicating an intensified competition.

The higher the changes in the market share distribution, the more intensive competition the industry

has been through. Theoretically, the measure could range from 0 to 2. In the sample, the DMS

measure range from 0 to 1.423. The summary statistics of the measure are reported in Table 1.

Table 1 reports the summary statistics of different competition measures at industry level. Panel

A shows the statistics for the full sample and panel B for manufacturing industries only. There are

7,632 industry year observations in the main sample and 4,536 observations for manufacturing

industries only. The median number of firms in each industry is 11 for the main sample and 10 for

manufacturing industries. The mean value of DMS is 0.112. The magnitude is equivalent to the case

where one firm losing 11.2% in market share and the other firms gaining 11.2%. The measure shows

15 I use total industry sales from Bureau of Economic Analysis as a robustness check. The results do not differ significantly.

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a significant skewness of 3.40 with a median value of 0.087, which indicates that there are a small

number of industry-years experiencing especially high level of competition. The observation is

intuitively consistent with the notion that most of the industries at normal times are stable, while

some industries are more dynamic in certain periods.

Panel C and D in table 1 presents the summary statistics for subsample period of 1977-1995 and

1996-2013, respectively. DMS measures are not different in mean, but small increase in standard

deviations from early to later period.

The time-series patterns of DMS measure vary across industries. See Figure 2 for plots for

selected industries. Some industries experience large waves over the time period of 1977 to 2013,

such as Machinery, Electronics and Communications in 1990s and 2000s. Other industries are

relatively stable over time, such as Chemicals, Rubber, Plastics and Leather. Overall, the DMS

measure is higher for the periods of 1985-1987 and 1998-2000, is relative low for the periods of

1978-1980, 1988-1991, and 2010-2013. The general patterns are consistent with the economic cycles.

We examine the transition probabilities of the DMS measure in one-year, three-year and five-

year period. The results are reported in Table 3. It shows that the stable industries with the least

competition activities are sticky for several years, around 45% likely to stay as stable industry within

three years. Industries in the middle three quintiles are more likely to move around, but slightly tend

to stay in the middle quintiles. The dynamic industries with the most competition activities are less

sticky than stable industries, but more than 50% will stay in quintile 4 and 5 for one- and three-year

period.

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3.3 Comparison with Other Competition Measures

The dynamic competition measure, DMS, has three advantages compared to traditional

competition measures, namely, Herfindahl, Lerner index (PCM), and Price Elasticity (PE). First,

DMS captures the dynamic nature of industry competition and directly measures the effects from

firms’ competing actions. Herfindahl is a measure of organizational concentration, which does not

recognize the industry market share distribution change from {0.2, 0.3, 0.4, 0.1} to {0.3, 0.2, 0.1,

0.4}. PCM and PE are measures of product price margin, which varies across different industry. It is

difficult to compare competition across industries. Second, DMS can be calculated using standard

dataset where sales and cogs data are available. PCM and PE need estimation and detailed factor

input price data, which is not available in large scale. Third, PCM and PE are for product level data,

where input and output need to be separately identified. But firms could have a series of products

with different PCM and PE for each product line. For example, a machinery firm could produce

low-quality and high-quality machines with distinct PCM and PE for each lines. DMS, on the

contrary, can simply use total sales and cogs of the combined product line as long as industry can be

clearly defined. For similar reasons, DMS can be used for non-manufacturing industries, such as

financial and services, while PCM and PE can only be used for manufacturing industries.

Table 1 presents the summary statistics for different industry competition measures. Herfindahl

has a mean of 0.294 for the full sample and 0.297 for the manufacturing industries. Lerner index has

a mean of 0.067 and 0.065 for full sample and the manufacturing industries, respectively. Panel C

and D in table 1 presents the summary statistics for subsample period of 1977-1995 and 1996-2013,

respectively. DMS measures are not different in mean, but small increase in standard deviations

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from early to later period. The Herfindahl index increases from 0.266 to 0.324 and Lerner index

decreases from 0.074 to 0.061. Herfindahl indicates an increasing in concentration over time, but

Lerner index suggest an increasing in competition from early period to later period. Figure 1 plots

the time series of average value of the three competition measures over the period 1977-2013. It is

consistent with subsample statistics that Herfindahl is increasing over time. DMS and Lerner index,

on the other hand, have more dynamics than simple trend.

Table 2 reports the Pairwise Pearson correlation coefficient between different measures of

competition. The DMS measure is negatively correlated with Herfindahl index (-0.204) and with

Lerner index (-0.096).

To further analyze the correlation between different competition measures, we divide all the

industries in the sample into quintiles based on DMS measures each year. The mean values of

different competition measures for each quintile are reported in the top part of Table 4, panel A.

Herfindahl index shows a general decreasing pattern with DMS quintiles, while number of firms and

Lerner index is increasing. The observed relationships show that the dynamic competition

interactions within an industry (DMS) have a positive relationship with the external competition

pressure (Herfindahl) and a negative relationship with the ability to extract rent from customers in

the industry (Lerner index).

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4. Internal Competition Dynamics and External Competition shocks

The industry competition has two components: entry from outside competitors and competitive

interactions between existing firms. It is interesting to examine the relationship between the internal

and external competitions. We use two measures for outside competition: Imports penetration

growth and tariff rate growth.16 Large imports growth suggests an increase in outside competition

and tariff cuts indicates a potential entry from international competition. The imports growth shocks

are identified when imports penetration growth rate is two times higher than the median growth

rates. The tariff cut shocks are identified when tariff growth rate is two times lower than the median

growth rates.

The regression results of DMS, Herfindahl and Lerner index on lagged external competition

shocks are presented in Table 5. The tariff cut shocks lead to 0.010-0.012 increase in DMS measure

for the next period, which is about 10% increase at the mean level. The imports penetration shocks

lead to 0.032 growths in DMS (30% at the mean level). The coefficients are significant statistically

and economically. On the contrary, the external competition shocks do not affect the Herfindahl or

Lerner index significantly, shown in table 5, column (4)-(6). The effects are negative as expected, but

not statistically significant at 10% level. The results suggest that firms compete more aggressively

with each other when they face higher outside competition.

16 Refer to Table 1 for detailed definition of the measures.

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5. The Dispersion of Corporate Policies, Performances and Competition Dynamics

In this section, we investigate how the DMS measure is related to corporate policies and

performance. Specifically, we test what kind of industries tend to have high competition dynamics in

section 5.1, how the dispersion of corporate policies and performances affects (section 5.1) and is

affected by (section 5.3) the dynamic competition measure and what is the effects of DMS on future

corporate policy and performance (section 5.4).

5.1 Industry Characteristics and Competition

Table 4, panel B presents the summary of average industry characteristics for the quintiles of

DMS intensity. Industries with high sales growth, lower ROA, less investment, high R&D and large

cash holdings are more likely to have higher competition dynamics. The regression results are shown

in Table 5 and Table 6. Consistent with the univariate test in table 4 and the literature, we find that

industries with higher competition dynamics at time t tend to have higher cash holdings (10%

higher), leverage (3-7% higher) and sales growth (4.5% higher) at the previous year. Those

industries also tend to have lower ROA (1.5-2.5% lower), but higher R&D (8% higher) at the

previous year. The observations are consistent with the literature arguing that firms increase cash

holding and leverage on expectation of future competition.17

5.2 Corporate Policy Dispersion and Competition

Table 6 reports how dynamic competition is affected by corporate policy dispersion. We find

that larger dispersion in cash holding, leverage and cash flow leads to higher dynamic competition.

17 See Freshad (2010), Haushaltera, Klasa and Maxwell (2007), Hoberg and Phillips (2014)

14

The results indicate that industries tend to have more dynamics in competition when the financial

situations are more diverse for different firms within the industry. The results are consistent with the

literature showing that deep-pocket firms tend to compete more aggressively when they face

shallow-pocket rivals.

In order to detect the dynamics between the DMS measure and the dispersion of corporate

policies and performance, we use panel vector autoregression technique. The results are displayed in

table 8 and figure 3. The impulse response functions show significant reactions of DMS measure to

the impulse of the dispersion of cash holding and leverage ratio.

5.3 Dynamic Competition and Corporate Performance Dispersion

Table 7 panel A reports how dynamic competition affects future corporate policy and

performance dispersion. We find that after experiencing strong competition interactions in the

industry, firms tend to have larger dispersion profitability (ROA) and valuation (Tobin’s Q). Firms

also have marginally higher dispersion of cash holding, leverage and R&D expenditures after

intensified competition. The results may have two indications. One is that deep-pocket competitors

choose to keep higher financial slack to prepare for the next round of competition or deter other

rivals to have further predatory or revenging competition. The other indication is that firms with

financial constraints became more tight-in-hand after a period of intensified competition. They just

cannot keep up with the cash flow even if they wanted to. The two indications could and need to be

tested in the next draft of the paper. The impulse response functions also show strong reactions of

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the dispersions of financial policies and investment policies—capital expenditure, R&D and

advertising—to the impulse of DMS measure. (See table 8 and figure 3 for details.)

5.4 Dynamic Competition and Future Corporate Policies and Performance

Table 7, panel B reports how dynamic competition affects future corporate policy and

performance average in the industry. We find that after experiencing strong competition interactions

in the industry, firms have lower profitability (ROA) and valuation (Tobin’s Q)18. On average, firms

also have lower cash holding, leverage and cash flow after intensified competition. The results

suggest that more intensified competition leads to decreased profitability, financial situation and

valuation. The results are consistent with the literature arguing that competition negatively affects

firm performance.

6. Conclusion

The paper complements the existing literature by proposing a dynamic measure to capture the

competition interactions inside of an industry: Dynamic Market Share (DMS). DMSj,t measure is

calculated as the sum of absolute19 change of market share for all firms in industry j from year t-1 to t.

This measure therefore captures how the market shares distribution changes from time t-1 to t,

directly evaluating market competition intensity inside of an industry. A larger value of the DMS

measure suggests a bigger change in how the market share is distributed within industry, thus

indicating a strengthened competition, and a greater pressure faced by managers from existing rivals. 18 contemporaneous results are reported in Table 6, column (1) and (2) 19 Sum of square term of market share changes is also used as robust. The results do not change significantly.

16

Using a full sample consisting of all Compustat firms, covering 7,632 industry-year observations

over the period from 1977 to 2013, we find that the DMS measure has large variation across

industries and over time. Overall, the DMS measure is higher for the periods of 1985-1987 and

1998-2000, is relative low for the periods of 1978-1980, 1988-1991, and 2010-2013. The DMS

measure does not have high correlation with Herfindahl index (-0.204) or Lerner index (-0.096). The

DMS measure is positively associated with imports growth and negatively correlated with the growth

of the tariff rate in the industry, which suggests that the competition between existing firms will

intensify when they face higher outside competition.

Generally, we find that industries with lower average ROA, but higher cash holding and leverage

are more likely to have higher competition dynamics. More interestingly, these dynamic industries

tend to have higher dispersion in financial policies, such as cash holding, leverage and cash flow at

previous period. In addition, we also find that high competition activities lead to future dispersion of

profitability and performance within the industry. Firms who just experience high level of

competition tend to have higher level of leverage, low level of cash holdings and cash flow. In order

to detect the dynamics between the DMS measure and the dispersion of corporate policies and

performance, we use panel vector autoregression technique. The impulse response functions show a

strong reaction of the dispersion of investment policies—capital expenditure, R&D and

advertising—to the impulse of DMS measure.

The paper complements to the literature by providing a dynamic measure of competition

interactions between existing firms within an industry. The DMS measure can be adopted in any

industry competing for market shares, not limited to the manufacturing industries with defined

17

input-output data as required by Lerner index and price elasticity. The paper finds positive

relationship between internal competition intensity with external competition shocks. The paper also

contributes to the literature by providing new evidence on how industry competition is related to the

dispersion of corporate policies and performance.

One puzzling fact, surprisingly, is that there is no clear pattern for the relationship between

dynamic competition and advertising expenditures or the dispersion of it. The summary statistics

across DMS quintiles (Table 4) shows a decreasing pattern of advertising as competition activities

intensified. But the relationship is not significant for the regression results for both mean value and

dispersion of advertising expenditures.

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Appendix A. Variable Definition

Variable Definition

Firm Characteristics Size Natural log of total asset of the firm i

Cash holding Cash and cash equivalent divided by total assets

Leverage Sum of short term debt and long term debt divided by total assets

Investment Capital Expenditure divided by total assets

Sales growth Total revenue at time t divided by total revenue at time t - 1 minus 1

ROA EBIT divided by total assets

R&D R&D expenses divided by total revenue

Advertising Advertising expenses divided by total revenue

Market to Book Market capitalization divided by book value of asset minus book value of debt

Industry Competition Measures DMS The sum of absolute change of market share in industry j at time t

Herfindahl Herfindahl index calculated by sum square of market share in defined industry

Lerner Index Mean Price-Cost Margin in industry j at time t, defined as sales minus cost of goods sold divided by total sales

Number of Firms Number of firms in industry j at time t.

Imports growth Growth rate of total Imports in four-digit SIC industry

Tariff growth Growth rate of average tariff rate on imported goods in four-digit SIC industry

19

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Schott, Peter K., 2008. The Relative Sophistication of Chinese Exports. Economic Policy January 2008.

Zingales, Luigi, 1998. Survival of the Fittest or the Fattest? Exit and Financing in the Trucking Industry, Journal of Finance

22

Table 1, Summary Statistics of Industry Competition Measures This table displayed the summary statistics of different industry competition measures. The statistics reported are mean, median, standard deviation, 25 percentile, 75 percentile. DMS is the sum of absolute change of market share in industry j from time t-1 to time t. Refer to section 3 for detail construction of the measures. The other variables are defined in Appendix A. The sample is all Compustat firms excluding commodity producers (SIC>1499), Utilities (SIC 4900-4999), Financials (SIC 6000-6999), Education and Public Administrative industry (SIC>8800) over the period from 1977 to 2013. The manufacturing industries in panel B. are industries with SIC between 2000-3999. Refer to section 2 for detailed definition of industries. Variable N Mean St. Dev. 25th Median 75th

Panel A. the whole sample DMS 7,632 0.112 0.101 0.052 0.087 0.139 Herfindahl 7,632 0.294 0.206 0.146 0.236 0.387 Lerner Index 7,559 0.067 0.142 0.029 0.079 0.129 Number of firms 7,632 21 41 6 11 21

Panel B. manufacturing industries only

DMS 4,536 0.110 0.098 0.052 0.086 0.137 Herfindahl 4,536 0.297 0.207 0.147 0.239 0.389 Lerner Index 4,505 0.065 0.134 0.034 0.087 0.129 Number of firms 4,536 20 41 5 10 20 Imports growth 1,219 0.108 0.250 -0.007 0.100 0.198 Tariff growth 1,985 -0.048 0.436 -0.161 -0.053 0.017

Panel C. sample 1977-1995

DMS 3,882 0.112 0.094 0.057 0.089 0.137 Herfindahl 3,882 0.266 0.188 0.132 0.212 0.349 Lerner Index 3,851 0.074 0.112 0.035 0.079 0.124 Number of firms 3,882 20 28 6 12 22 Imports growth 312 0.107 0.365 -0.087 0.094 0.207 Tariff growth 1,308 -0.028 0.371 -0.124 -0.044 0.013

Panel D. sample 1996-2013

DMS 3,750 0.112 0.107 0.047 0.084 0.140 Herfindahl 3,750 0.324 0.218 0.164 0.263 0.437 Lerner Index 3,708 0.061 0.167 0.021 0.078 0.133 Number of firms 3,750 23 51 5 10 21 Imports growth 907 0.108 0.195 0.010 0.100 0.194 Tariff growth 677 -0.085 0.539 -0.272 -0.083 0.026

23

Table 2, Correlation between different competition measures This table reports the pairwise correlation coefficients for different measures of competition: DMS, Herfindahl, Lerner Index, Number Firms, Imports growth and Tariff growth. DMS is the sum of absolute change of market share in industry j from time t-1 to time t. Refer to section 3 for detail construction of the measures. The other variables are defined in Appendix A. The sample is all Compustat firms excluding commodity producers (SIC>1499), Utilities (SIC 4900-4999), Financials (SIC 6000-6999), Education and Public Administrative industry (SIC>8800) over the period from 1977 to 2013. The manufacturing industries in panel B. are industries with SIC between 2000-3999. Refer to section 2 for detailed definition of industries.

DMS Herfindahl Lerner Number of Firms

Imports growth

Tariff growth

DMS 1.000

Herfindahl -0.204 1.000

Lerner -0.096 -0.038 1.000

Number of Firms

0.067 -0.335 -0.220 1.000

Imports growth 0.015 -0.006 0.003 0.038 1.000

Tariff growth -0.021 0.031 0.046 -0.050 -0.163 1.000

24

Table 3, Transition Probability of DMS measure categorization This table reports the transition probability for DMS quintiles. The rows are DMS quintiles at t and the columns are DMS measures in t+1 for Panel A, t+3 for Panel B, and t+5 for Panel C. DMS is the sum of absolute change of market share in industry j at time t. Refer to section 3 for detail construction of the measures. The other variables are defined in Appendix A. The sample is all Compustat firms excluding commodity producers (SIC>1499), Utilities (SIC 4900-4999), Financials (SIC 6000-6999), Education and Public Administrative industry (SIC>8800) over the period from 1977 to 2013.

Least

Competition Quintile 2 Quintile 3 Quintile 4 Most competition

Panel A. One-Year Transition probabilities

Least Competition 0.446 0.158 0.102 0.085 0.147

Quintile 2 0.121 0.282 0.207 0.181 0.119

Quintile 3 0.111 0.230 0.269 0.208 0.156

Quintile 4 0.095 0.169 0.227 0.277 0.201

Most competition 0.148 0.135 0.168 0.222 0.293

Panel B. Three-Year Transition probabilities Least Competition 0.349 0.148 0.101 0.111 0.156

Quintile 2 0.181 0.243 0.225 0.165 0.105

Quintile 3 0.128 0.223 0.230 0.200 0.138

Quintile 4 0.121 0.181 0.199 0.240 0.169

Most competition 0.155 0.131 0.170 0.210 0.234

Panel C. Five-Year Transition probabilities Least Competition 0.283 0.151 0.109 0.111 0.154

Quintile 2 0.178 0.224 0.193 0.165 0.105

Quintile 3 0.135 0.194 0.216 0.202 0.123

Quintile 4 0.132 0.170 0.214 0.199 0.142

Most competition 0.160 0.139 0.146 0.200 0.190

25

Table 4, Industry Characteristics of DMS Intensity This table reports the firm and industry characteristics for DMS quintiles, measured each year. Panel A. summarizes Competition Measures, Panel B summarizes the mean value of industry characteristics and Panel C summarizes the dispersion measure of industry characteristics. DMS is the sum of absolute change of market share in industry j at time t. Refer to section 3 for detail construction of the measures. Disp(x) is the standard deviation of variable x divided by the mean value of x in defined industry at time t. The other variables are defined in Appendix A. The sample is all Compustat firms excluding commodity producers (SIC>1499), Utilities (SIC 4900-4999), Financials (SIC 6000-6999), Education and Public Administrative industry (SIC>8800) over the period from 1977 to 2013. *, ** and ***indicates 10%, 5% and 1% significance, respectably.

Least Competition Quintile 2 Quintile 3 Quintile 4 Most

Competition

T-stat of Most-Least

Competition Panel A. Competition Measure DMS 0.029 0.060 0.089 0.128 0.254 64.88*** Herfindahl 0.474 0.283 0.239 0.223 0.250 -28.95*** Lerner Index 0.085 0.075 0.067 0.061 0.049 -6.12*** Number of firms 9.630 18.800 24.500 30.200 23.200 12.60*** Imports growth 0.114 0.112 0.092 0.115 0.110 -0.17 Tariff growth -0.043 -0.038 -0.059 -0.049 -0.051 -0.29 Panel B. Mean value of industry characteristics Sales growth 0.122 0.115 0.130 0.158 0.196 6.88*** ROA 0.032 0.031 0.016 0.008 -0.011 -7.29*** Tobin's Q 1.650 1.610 1.610 1.650 1.670 0.77 Investment 0.064 0.060 0.060 0.058 0.060 -2.46** R&D 0.030 0.034 0.040 0.047 0.053 8.72*** Advertising expenses 0.047 0.039 0.038 0.035 0.037 -4.22*** Cash holding 0.110 0.111 0.116 0.130 0.137 7.97*** Leverage 0.290 0.296 0.296 0.294 0.295 0.96 Panel C. Dispersion measure of industry characteristics Disp(Sales Growth) 0.272 0.129 0.181 0.247 0.546 2.08** Disp(ROA) 0.194 0.094 0.125 0.147 0.305 2.13** Disp(Q) 0.512 0.457 0.496 0.598 1.030 5.37*** Disp(Investment) 0.046 0.027 0.033 0.040 0.072 2.07** Disp(R&D) 0.035 0.020 0.033 0.036 0.079 3.68*** Disp(Advertising) 0.040 0.017 0.017 0.024 0.041 0.06 Disp(Cash) 0.105 0.066 0.071 0.089 0.160 2.59** Disp(Leverage) 0.193 0.139 0.120 0.172 0.285 2.16* Disp(Cash Flow) 0.188 0.100 0.121 0.145 0.331 2.61**

26

Table 5, Competition Measures and External Competition Shocks

This table reports the regression results for competition measures—DMS, Herfindahl and Lerner Index—on external competition measures: Imports shocks and Tariff Cuts. The dependent variables are DMS at t+1 for column (1)-(3), Herfindahl at t+1 for columns (4)-(5) and Lerner Index for column (6). DMS is the sum of absolute change of market share in industry j at time t. Imports shocks are dummy variable which equals to 1 when imports growth rates are at least twice as the median growth rate. Tariff cut shocks are dummy variable which equals to 1 when tariff negative growth rates are twice as the median growth rate. Refer to section 3 for detail construction of the measures. The other variables are defined in Appendix A. The sample is all Compustat firms excluding commodity producers (SIC>1499), Utilities (SIC 4900-4999), Financials (SIC 6000-6999), Education and Public Administrative industry (SIC>8800) over the period from 1977 to 2013. Heterogamous robust T-statistics are reported in parenthesis *, ** and ***indicates 10%, 5% and 1% significance, respectably. (1) (2) (3) (4) (5) (6) DMS t+1 DMS t+1 DMS t+1 Herfindahl t+1 Herfindahl t+1 Lerner t+1 Tariff cut shocks 0.010** 0.012** -0.015 -0.003

(2.22) (2.47) (-1.61) (-0.70) Imports shocks 0.032** -0.011

(2.07) (-0.43) Average sales growth 0.022 0.002 0.004 -0.006 -0.056 -0.053***

(1.61) (0.11) (0.27) (-0.15) (-1.07) (-3.17) ROA -0.048* -0.069** -0.028 -0.010 0.028 0.405***

(-1.82) (-2.28) (-1.30) (-0.17) (0.40) (10.55) Tobin's Q -0.004 -0.001 -0.004 -0.027*** -0.032** -0.008

(-1.07) (-0.14) (-0.98) (-2.63) (-2.09) (-1.09) Investment -0.174** -0.141* -0.126 -0.771*** -0.671** 0.030

(-2.43) (-1.99) (-1.14) (-4.09) (-2.55) (0.34) Cash 0.066* 0.051* 0.111** -0.046 -0.081 -0.352***

(1.96) (1.85) (2.17) (-0.43) (-0.76) (-5.93) Leverage 0.048* 0.048* 0.036 -0.063 -0.174** 0.054

(1.87) (1.89) (1.17) (-0.96) (-2.48) (1.57) Advertising -0.002 0.346***

(-0.03) (3.27) Constant 0.105*** 0.097*** 0.110*** 0.374*** 0.462*** 0.092***

(7.89) (5.98) (6.23) (12.24) (12.06) (4.76)

Observation 1887 1522 1201 1522 1201 1883 Adjusted R-square 0.014 0.018 0.003 0.027 0.030 0.385

27

Table 6, OLS Regression of DMS Measures on lagged Corporate Policy Dispersion This table reports the regressions of DMS measures on lagged corporate policy dispersion. The dependent variables are DMS at t+1. DMS is the sum of absolute change of market share in industry j from time t-1 to time t. Column (1) and (2) reports the contemporaneous results. Column (3) and (4) reports the results for DMS regression on one lagged dispersion measures of corporate policies. Column (5) and (6) reports the results for DMS regression on two lagged dispersion measures of corporate policies. Refer to section 3 for detail construction of the measures. The other variables are defined in Appendix A. The sample is all Compustat firms excluding commodity producers (SIC>1499), Utilities (SIC 4900-4999), Financials (SIC 6000-6999), Education and Public Administrative industry (SIC>8800) over the period from 1977 to 2013. Heterogamous robust T-statistics are reported in parenthesis *, ** and ***indicates 10%, 5% and 1% significance, respectably. (1) (2) (3) (4) (5) (6) DMS t DMS t DMS t+1 DMS t+1 DMS t+2 DMS t+2 Disp(Cash) -0.016 -0.044* 0.004** 0.008** -0.002 -0.014

(-1.29) (-1.65) (2.32) (2.22) (-0.14) (-0.61) Disp(Leverage) 0.004 0.001 0.011** 0.005* 0.012* 0.014

(0.65) (0.11) (2.44) (1.97) (1.65) (1.09) Disp(Cash Flow) 0.020*** 0.040*** 0.013** 0.019** -0.001 -0.014

(3.97) (4.37) (2.32) (2.25) (-0.23) (-0.74) Disp(ROA) -0.013*** -0.007* -0.005 0.002 -0.001 0.009

(-3.26) (-1.69) (-1.06) (0.20) (-0.16) (0.38) Disp(Q) 0.005*** 0.007*** 0.002* 0.003* 0.003** 0.004*

(2.96) (3.12) (1.67) (1.66) (2.04) (1.86) Disp(Investment) -0.012 -0.007 -0.024* -0.054* 0.005 0.058

(-0.65) (-0.22) (-1.77) (-1.90) (0.27) (1.25) Disp(Advertising) -0.065* -0.051 -0.052

(-1.78) (-1.10) (-1.21) Disp(R&D) -0.007 0.006 0.053**

(-0.19) (0.20) (2.26) Sales Growth 0.072*** 0.061*** 0.045*** 0.045*** 0.027*** 0.045***

(7.51) (5.83) (5.22) (3.15) (3.18) (3.34) ROA -0.061*** -0.023** -0.025** -0.015* -0.011 0.004

(-5.28) (-2.47) (-2.18) (1.92) (-0.87) (0.17) Tobin's Q -0.015*** -0.017*** -0.004* -0.006* -0.001 -0.007*

(-5.75) (-4.87) (-1.82) (-1.68) (-0.44) (-1.78) Investment -0.070* 0.034 -0.020 0.069 -0.004 0.035

(-1.93) (0.56) (-0.56) (1.25) (-0.11) (0.67) Cash holding 0.107*** 0.189*** 0.115*** 0.145*** 0.124*** 0.169***

(4.84) (5.76) (4.97) (4.20) (5.28) (5.54) Leverage 0.039*** 0.070*** 0.043*** 0.069*** 0.049*** 0.069***

(3.57) (4.29) (3.81) (4.12) (4.10) (4.17) Advertising 0.011 -0.026 -0.004

(0.23) (-0.69) (-0.10) R&D 0.077** 0.082** 0.055

(1.99) (2.18) (1.53) Constant 0.104*** 0.078*** 0.085*** 0.067*** 0.079*** 0.066***

(17.49) (9.29) (14.48) (8.16) (12.34) (7.66)

Observation 6,836 3,832 6,624 3,738 6,416 3,637 Adjusted R-square 0.055 0.092 0.054 0.060 0.047 0.069

28

Table 7, OLS regression of corporate policy dispersion on lagged DMS competition This table reports the regressions of corporate policy dispersion and future firm performance on lagged DMS measures. All independent variables are lagged at t-1. Panel A reports the results of the dispersion of corporate policy and performance on DMS. Panel B reports the results of the mean value of corporate policy and performance on DMS. Refer to section 3 for detail construction of the measures. The other variables are defined in Appendix A. The sample is all Compustat firms excluding commodity producers (SIC>1499), Utilities (SIC 4900-4999), Financials (SIC 6000-6999), Education and Public Administrative industry (SIC>8800) over the period from 1977 to 2013. Heterogamous robust T-statistics are reported in parenthesis *, ** and ***indicates 10%, 5% and 1% significance, respectably. Panel A. The Dispersion of Corporate Policy and performance on DMS (1) (2) (3) (4) (5) (6) (7) (8) Disp(ROA) Disp(Q) Disp(Investment) Disp(R&D) Disp(Advertising) Disp(Cash) Disp(Leverage) Disp(Cash Flow) DMS 0.291* 1.684*** 0.062 0.053* 0.007 0.145** 0.265* 0.255

(1.85) (3.64) (1.51) (1.94) (0.15) (2.17) (1.93) (1.52) ROA -0.279 -0.963 0.014 -0.049 0.082 0.018 -0.220* -0.219

(-0.89) (-1.56) (0.18) (-0.94) (0.61) (0.14) (-1.75) (-0.71) Tobin's Q 0.063** 0.499*** 0.009 0.011 0.007 0.006 -0.032 0.043

(2.00) (6.31) (1.55) (1.46) (1.05) (0.56) (-0.71) (1.19) Investment 1.395*** 4.322*** 0.641*** 0.336*** 0.249** 0.642*** 0.647*** 1.341***

(3.19) (3.98) (5.44) (2.78) (2.00) (3.79) -2.61 (3.53) R&D -0.219 -1.227** -0.124 0.347*** -0.170* -0.245* 0.287 0.019

(-0.57) (-2.27) (-1.41) (3.39) (-1.69) (-1.72) -0.65 (0.04) Cash holding 0.388 1.261** 0.083 0.011 0.045 0.578*** 0.889** 0.488

(1.30) (2.06) (1.32) (0.12) (0.61) (5.20) -2.19 (1.35) Leverage 0.261** 0.082 0.050** -0.004 0.034 0.120*** 0.403*** 0.208**

(2.25) (0.30) (2.20) (-0.11) (1.33) (3.12) -4.96 (2.00) Cash Flow -0.700 -0.352 -0.151 -0.106 -0.226 -0.221 -0.16 -0.735

(-1.11) (-0.76) (-0.94) (-1.62) (-0.89) (-0.85) (-0.94) (-1.18) Constant -0.166*** -0.795*** -0.043*** -0.022 -0.015 -0.070*** -0.090** -0.129**

(-2.73) (-4.54) (-3.33) (-1.05) (-1.52) (-3.49) (-2.06) (-2.06) Observation 6056 5955 6057 5058 4268 6059 6055 6029

Adjusted R-square 0.037 0.058 0.021 0.067 0.010 0.026 0.022 0.035

29

Panel B. Mean value of Corporate Policy and performance on DMS (1) (2) (3) (4) (5) (6) (7) (8) ROA Tobin's Q Investment R&D Advertising Cash Leverage Cash Flow DMS -0.048** -0.224*** -0.002 0.015 -0.012 -0.016* 0.041*** -0.046**

(-2.17) (-2.66) (-0.50) (1.60) (-1.50) (-1.93) -3.36 (-1.96) ROA 0.446*** 0.221 -0.001 -0.027 0.006 -0.000 0.008 0.265***

(6.46) (1.49) (-0.22) (-1.12) (0.41) (-0.01) -0.33 -4.71 Tobin's Q -0.010 0.640*** 0.003*** 0.004*** 0.009*** 0.003* 0.002 -0.010*

(-1.58) (21.86) (4.02) (3.02) (6.40) (1.73) -0.52 (-1.68) Investment -0.063 -0.088 0.761*** 0.009 0.064*** -0.014 0.135*** 0.215***

(-1.09) (-0.45) (46.52) (0.43) (2.71) (-0.70) -3.65 -3.63 R&D -0.342*** 0.772*** -0.025*** 0.654*** -0.092*** 0.134*** -0.022 -0.323***

(-4.79) (4.33) (-4.10) (15.21) (-5.77) (7.17) (-0.71) (-3.99) Cash holding -0.133*** 0.460*** 0.021*** 0.086*** -0.006 0.782*** -0.081*** -0.113**

(-2.97) (2.92) (3.80) (4.98) (-0.44) (49.84) (-3.18) (-2.34) Leverage -0.050** -0.008 0.010*** -0.017** -0.034*** -0.019*** 0.778*** -0.054**

(-2.13) (-0.12) (3.09) (-2.46) (-5.51) (-2.93) -52.4 (-2.33) Cash Flow 0.092 -0.397*** 0.021*** -0.009 -0.028** -0.012 0.006 0.248***

(1.56) (-3.24) (3.29) (-0.48) (-2.18) (-0.94) -0.24 -4.79 Constant 0.073*** 0.548*** 0.004** 0.000 0.037*** 0.026*** 0.060*** 0.045***

(5.34) (11.51) (2.56) (0.04) (11.84) (5.97) -8.25 -3.35

Observation 6196 6180 6196 6020 5138 6196 6196 6191 Adjusted R-square 0.455 0.520 0.633 0.586 0.025 0.728 0.62 0.4

30

Table 8, Panel Vector Autoregression of DMS and Corporate Policy and Performance Dispersion

This table reports the panel vector auto regression of DMS and corporate policy and performance dispersion. Column (1) reports the coefficient of lagged variables response to DMS. Column (2) reports the variables’ response from lagged DMS. DMS is the sum of absolute change of market share in industry j at time t. Active is a dummy variable, which equals to 1 when DMS measure is above its median. Refer to section 3 for detail construction of the measures. The other variables are defined in Appendix A. The sample is all Compustat firms excluding commodity producers (SIC>1499), Utilities (SIC 4900-4999), Financials (SIC 6000-6999), Education and Public Administrative industry (SIC>8800) over the period from 1977 to 2013. Heterogamous robust T-statistics are reported in parenthesis *, ** and ***indicates 10%, 5% and 1% significance, respectably.

(1) (2)

Variables Response to DMS t+1

Response from DMSt-1

DMS 0.313*** (10.58) Dispersion of Cash 0.013* 0.527***

(1.94) (3.74) Dispersion of Leverage 0.005* 0.935***

(1.88) (4.10) Dispersion of Cash Flow 0.003 1.463***

(0.99) (2.46) Dispersion of ROA 0.003 1.375**

(0.85) (2.18) Dispersion of Q 0.003*** 0.520***

(2.77) (4.77) Dispersion of Investment 0.014 0.314***

(1.44) (4.30) Dispersion of R&D 0.010 0.296

(0.39) (1.55) Dispersion of Advertising 0.016 0.175*** (1.18) (5.79)

31

Figure 1. Average Competition Measures over time 1977-2013

32

Figure 2. Plot of DMS measure for selected industries

2-digit SIC 26, 27 paper and printing 2-digit SIC 32. Stone and concrete

2-digit SIC 35,36 Machinery and Electronics 2-digit SIC 48 communications

2-digit SIC 51, wholesale non-durable 2-digit SIC 70, Hotels

33

Figure 3, Impulse Response of DMS Measure and Corporate Policy and Performance Dispersion Dispersion of Cash Holding and DMS Dispersion of Leverage and DMS

Dispersion of Cash Flow and DMS Dispersion of ROA and DMS

Dispersion of Tobin’s Q and DMS Dispersion of Investment and DMS

Dispersion of R&D and DMS Dispersion of Advertising and DMS