Product Variety as a Barrier to Entry: Evidence from the...

Product Variety as a Barrier to Entry: Evidence from the

Post-Deregulation Korean Soju Market

Sungtak Hong∗ Jinhwa Chung†

This Version: June 2015‡

Abstract

Firms offer a variety of products not only to compete with firms already in the market

but also to preempt the profitable product space and deter potential entry. In this paper,

we investigate whether new product launches by the incumbent firms influenced competitors’

market entry decisions in the Korean soju market context. This market, previously composed

of independent geographic markets, each with a protected incumbent producer, became open

to nation-wide competition as a result of market deregulation in the early 1990’s. Across the

geographic soju markets, we look at the observed pattern of product proliferation by the local

firms and identify whether it had any impact on the number of competing firms in the markets.

We build an econometric model that accounts for causal structures surrounding the incumbent

firms’ product variety and the number of firms in the market, and the model also takes into

account both observed and unobserved heterogeneity across firms and regions. The model

parameters are estimated using instrumental variables techniques and we find that product

proliferation and dominant market leadership of the incumbent firm served as barriers to entry

following market deregulation.

Keywords: Product proliferation, entry deterrence, a system of simultaneous equations,

instrumental variables

∗Ph.D. candidate in marketing, London Business School, London, UK, NW1 4SA; [email protected]†Assistant Professor, Department of Economics and Finance, Keimyung University, Daegu, Korea.;

[email protected]‡The authors thank Bruce Hardie, Don Lehmann, Kanishka Misra, Naufel Vilcassim as well as conference

participants at INFORMS Marketing Science 2014, LBS Trans-Atlantic Doctoral Conference 2015 and EMACAnnual Conference 2015 for their helpful feedback.

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2 Hong and Chung: Product Variety as a Barrier to Entry

1. Introduction

This paper studies a multi-product firm’s use of product variety in its strategic interactions with

potential entrant firms. Theories of multi-product firm’s product differentiation generally predict

that firms will provide greater product variety in the presence of competing firms than monopolist

firms will do. The firm’s action of expanding the product variety may result from a rivalrous behav-

ior to serve better consumers with heterogeneous demand as the market becomes more competitive.

Alternatively, the action may be due to the firm’s market-preemptive behavior to deter further

entry (Hay 1976, Prescott and Visscher 1977, Eaton and Lipsey 1979, Bonanno 1987; review by

Lancaster 1990). For instance, in a contestable market with a threat of potential entry, even the

monopolist firm would respond to the threat by proliferating products and preempting the prof-

itable product space. This market-preemptive product proliferation then conveys a credible threat

to potential entrants and helps the firm monopolize the market by deterring future entry.

In a market with increasing product variety of the incumbent firms along with a few new com-

petitors over time, therefore, there are two distinct views to their expansion of product variety: it

could be the firms’ reaction to changes in competition intensity in the market with free entry, or

their market-preemptive action to deter new market entry. In the latter case, despite the observed

increase in product variety, the market may exhibit less than optimal product variety from a con-

sumer welfare perspective (in the presence of consumers with strong demand for variety). The

negative impact on consumers may take place later when the incumbent firms can offer less variety

without further entry by competitors who will have learned from their behavior of product prolifer-

ation. The question of whether the incumbent firms exercise preemptive power to inhibit the market

competition, and the implications of this on the firms’ profits and consumer welfare, has been of

central interest to policymakers and industrial economists. To answer this question, we need first

to investigate whether new product launches by the incumbent firms have influenced competitors’

market entry decisions. The identification of the causal relationship between the firms’ product

variety and the number of competing firms in the market is, therefore, an important empirical

task.

In this paper, we aim to document empirical evidence of the entry-deterring impact of an

incumbent firm’s product proliferation. Despite rich theories, there are few empirical studies that

demonstrate the role of product variety offered by the incumbent firm as a barrier to market entry.

Empirical researchers have studied how the firm’s product variety changes in accordance with the

Hong and Chung: Product Variety as a Barrier to Entry 3

level of market competition by looking at the observed product variety and the number of com-

peting firms in the market (Schmalensee 1978, Berry and Waldfogel 2001, Economides et al. 2008,

Watson 2009, Ren et al. 2011). Using this data, however, it is difficult to establish an explicit dis-

tinction between causes and effects of the variations in the product variety. A major challenge has

come from a potentially interactive causal relationship between the observed outcomes: the firms’

decisions on product variety and competitors’ decisions on market entry may affect each other.

Thus, even if we observe changes in outcomes from both of these decisions, it is often impossible

to identify whether the increase in product variety of the incumbent firm suppresses market entry

and results in fewer entrants than there would otherwise have been.

Given this empirical difficulty in identifying the causes and effects of a firm’s product variety in its

interaction with potential and actual competitors, previous empirical work looked at limiting cases

which enabled researchers to study one or the other. Most early work studying the entry-deterring

effect of the firm’s product proliferation studied industries where existing firms expanded their

product offerings while little market entry was made by competitors during the period. Focusing

on such industries that are characterized by a small number of firms and high profit margins,

the researchers sought evidence of market-preemptive product introductions by comparing the

observed pattern of product launches to theoretical predictions (Schmalensee 1978, West 1981).

A shortcoming of this approach is that the actual market entry rarely occurs and it is impossible

to know to what extent changes to the established firms’ product variety were in response to

the “threat” of new entrants.1 The evidence of entry-deterring impact of product proliferation

in this stream of research is, therefore, in large part obtained from conjecture, rather than from

quantification of the impact.2

Another stream of research examines how a firm’s product offerings changes depending on the

local market competition intensity by looking at an industry snapshot, where both the firms’

product choices and competition intensity vary across geographic markets. A central problem here

is that the observed geographic market structures are potentially endogenous due to unobserved

heterogeneity in demand across markets. The researchers have addressed this endogeneity issue

1 This problem has been also commonly acknowledged in the literature on the impact of new product introductions(Hausman 1997, Kadiyali et al. 1999, Petrin 2002, Draganska et al. 2009), where the timing of the new productintroduction has been assumed exogenous.

2 As a more recent study, Igami and Yang (2014) search for some direct evidence of multi-outlet firms’ marketpreemptive location choices by looking at a sequence of openings of hamburger chains across geographic markets inCanada. Although the competition in locations among multi-store chains serves as a spatial analogy of competitionin product space among multi-product manufacturing firms, the finding may not be translated into a context wherefirms can make prompt and less costly adjustments of their current product portfolio.


either by structurally modeling a firm’s market entry and product choice (Mazzeo 2002a,b, Seim

2006), or by estimating the reduced-form relationship through instrumental variable techniques

(Watson 2009, Ren et al. 2011).3 The findings from these studies provide insights into how a firm’s

product variety changes depending on the level of competition, yet do not account for how the

firm can in turn influence the competition by changing its product variety (i.e., the entry-deterring

effect).

Our approach differs from the above streams of research in that we quantify the entry-deterring

impact of product proliferation after accounting for changes in product variety due to actual

changes in the number of competing firms in the market. We do this by building an econometric

model that accounts for causal structures surrounding the incumbent firms’ product variety and

the number of firms in the market. We apply and estimate the model in the post-deregulation

Korean soju market. Soju is a traditional Korean liquor and serves as the cheapest and the most

widely consumed alternative in the overall alcohol beverage industry in Korea. According to the

Korean Alcohol and Liquor Industry Association, its market size by volume accounted for nearly

40% of the alcoholic beverage industry in 2011. The soju market under regulation in the 1980’s

was characterized by being divided into a group of geographic markets, each with a protected local

producer. Following a series of new government policies implemented in the early 1990’s, however,

the previously regulated geographic markets became open to nation-wide competition. While the

firm’s pricing remained regulated and monitored by the government throughout the periods, the

firms were allowed to produce a greater variety of products following market deregulation and this

led to product proliferation by the local (incumbent) firms.

The history of market regulation and deregulation in the Korean soju market serves as a good

empirical context to study the local incumbent firms’ product strategies as a response to market

entry. The government initially defined identities of incumbent firms in each geographic market,

and later the deregulatory policies generated incentives for the firms to enter all other markets.

As a consequence, each local firm had to react and protect their position as a market leader. The

government restricted the market competition strictly among the extant firms and thus the market

did not see any new firms nationwide after deregulation. Across the geographic soju markets, we

investigate whether the observed product proliferation by the local firms had any impact on the

number of competing firms entering that market.

3 Due to a high dimensionality problem, the structural approach has been applied mainly to a market context wheresingle product firms simultaneously make market entry and product choices.


Our econometric model comprises a system of simultaneous equations to quantify causal rela-

tionship between the incumbent firm’s provision of product variety and the number of competing

firms in the market. In the context of the Korean soju market, where the local producer faced a

new competition environment which encouraged new entry by competitors, an analysis focusing

on either side of the causal relationship between the product variety and the number of competing

firms will be mis-specified. In addition, the model takes into account both observed and unobserved

heterogeneity across geographic markets. Since each geographic market was also characterized by

its own local producer, we address heterogeneity due to market- and firm- specific factors in several

ways.

First, we include various socio-demographic characteristics of each market and firm-relevant

fixed effects in the model. Second, we take into account local market leadership of the incumbent

firm in each region. A distinct feature of the Korean soju market was that each region exhibited

heterogeneity with respect to market share of the local firm. If such variations in the local firms’

market shares across regions were partly due to a varying degree of consumer preference toward

their local products (e.g., consumers’ loyalty to the firm or inertia), any analysis ignoring this

feature could produce biased estimates of the causal relationship: consumers’ strong preference

toward local products could influence both the local firm’s provision of product variety and the

competitor’s decision to enter that region. We address this issue by endogenizing market share of

the local firm within each market. Having an additional equation that accounts for the local firm’s

market share in the region, the model also produces additional insights into how responsive the

market demand was to changes in product variety offered in the market.

The complete econometric system is estimated through the 3SLS regression and allows any other

unobserved shocks to the market variables to be correlated. In estimating the model, we make use of

various institutional background and sources of exogenous shocks which affected each endogenous

variable exclusively and thus serve as instrumental variables. The empirical results demonstrate

strong evidence of the role of product variety as a barrier to market entry after market deregulation:

the local firms were able to exercise a significant negative impact on potential entrants to the market

by offering greater product variety in the market. This implies that the local market competition

might have been inhibited despite the government’s deregulatory policies. On the other hand, the

impact of actual changes in the number of competing firms in the market on the local firm’s product

variety was not statistically significant. The results also show that the higher market share of the


incumbent firm makes market entry more difficult to potential entrants, and this emphasizes the

importance of accounting for heterogeneity in consumers’ preferences toward their local firms.

This paper proceeds as follows: Section 2 provides an overview of the Korean soju market and

the history of competition policy implementations. Section 3 describes the data and presents initial

evidence of the firm’s strategic use of product variety against new market entry after market

deregulation. The full model and our identification strategy are developed in Section 4, and the

empirical results are presented in Section 5. After a discussion of the implications of ignoring

simultaneity and endogeneity of the observed market variables, we conclude.

2. The Korean Soju Market Overview

Traditionally soju was made from rice through a distillation process. Due to rice shortages dur-

ing the 1960’s, however, the Korean government banned the use of rice for soju production and

provided soju producers with licenses for producing ethanol (the alcohol base made from barley

and tubers etc.) from which soju can be made by dilution instead. The unconditional provision of

such licenses, however, gave rise to more than 250 small-sized soju production sites nationwide,

and unhygienic operations and tax evasion by such small producers became a concern for the gov-

ernment. Consequently, the government became involved in the overall supply chain in order to

establish higher industry standards in product quality and to secure the government’s tax revenue

on alcohol.

The government initiated its control over the market by consolidating production sites in each of

11 regions based on which the production licenses were distributed (9 provinces and 2 metropolitan

cities, Seoul and Busan). By 1977, the Korean soju market was segmented into 11 regions, each

with one local producer. There were 10 producers nationwide; the leading producer, Jinro was

assigned to both Seoul and nearby Kyunggi province while the remaining 9 regions were allocated

to 9 other local producers. Figure 1 depicts the map of geographic soju markets and market sizes in

terms of adult population over 19 years, as of 1990. Producer 1 (P1) represents Jinro serving as a

local producer in both Seoul and Kyunggi province (Region 0 and 1 respectively), which accounted

for about 40% of the total adult population in Korea.

In consolidating the geographic soju markets, the government also strengthened the market lead-

ership of the producers in their base regions by implementing two other policies: first, in 1976,

the government mandated regional wholesalers to purchase at least 50% of products from their

designated local producers. By law, soju producers can distribute their products to consumers only


Figure 1 Map of Geographic Soju Market and Population by Region

via independent liquor wholesalers, and the wholesalers in each region are in charge of product

distribution to both on-premises (e.g., restaurants and bars) and off-premises (chain and individual

grocery retailers) in the region.4 This policy, therefore, protected the local producers from nation-

wide competition. Second, the government controlled price and quantity of ethanol to be supplied

to soju producers. Each year, the government determined the total quantity and allocated it to

each producer based on the previous year’s shipment volume. Price was also controlled in such a

way that the producers were required to report to the National Tax Services prior to any changes in

prices; only the increase due to the cost increase could be approved by the government. This control

over the quantity and price of the soju products prevented the total market size from fluctuations

and moreover, resulted in sticky market shares of the individual producers. Overall, the process of

market consolidation and protection of the local producers in the 1970’s reshaped the Korean soju

market structure: each geographic region had developed as a highly concentrated market for soju.

Although these changes enabled the government to monitor closely the price and quantity of

products, the lack of competitiveness of protected producers became a pressing issue facing the

new wave of trade liberalization in the late 1980’s.5 During this period, the government started

4 Until the 2000’s, the government also controlled the total number of wholesalers in each geographic market by limitingthe number of licenses to wholesale liquor. According to the Tax Office, there were 1,366 wholesalers nationwide asof 1990, which later increased to 3,536 as of 2008.

5 As related industries, the market for imported beer was opened in 1984, and the introduction of most foreign liquorproducts such as wine, vodka and whiskey followed in 1989.


lifting previous restrictions in a large number of industries. In 1990, they announced their plan for

market deregulation in 21 industries, and the soju market was one of them.

Deregulation of the soju market in the 1990’s can be summarized as three major policy changes:

1) The mandatory local product purchase policy imposed on the wholesalers, which guaranteed at

least 50% market share of local manufacturers in their base regions, was phased out in steps until

it was removed from all regions at the end of 1991. Following lobbying by small local producers,

several regions (except the three biggest region 0, 1 and 2) became regulated again in 1996 but

that did not last for more than a year. 2) The government’s central allocation of ethanol was ruled

unconstitutional in December 1992. Following the removal of this policy, the firms were able to

adjust quickly their shipment volume to market demand and thus, competition was promoted. 3)

The policy banning the use of rice and some other starch was relaxed as of July 1991. After this

change, the firms started developing new products using newly allowed ingredients. In fact, most

of the firms’ product differentiation at this time was conducted through blending distilled ethanol

from rice with traditional alcohol base.

In sum, by the end of 1991, every geographic soju market became open to national competition

and the formerly protected (i.e., having 50% local market share secured) local firms were encouraged

to produce a greater variety of products. As to the producers’ power as price setters, there was no

change during this period and their pricing was still monitored by the government.

3. Data and Motivating Analysis

The main source of data for this study is retail data from Nielsen Company Korea. The data

tracks bi-monthly product-level retail sales volume and value data across geographic markets for

the period 1994 - 1999. Price per unit volume sold can be computed from these two data. The

classification of the geographic markets in the data is consistent with that presented in the market

overview. Region 10 (Jeju island) and its local producer have been excluded due to their small

size, and thus the final data set spans 9 firms across 10 regions. In our main analysis, we exploit

this data to investigate how the local firm based in each region responded to competitors’ market

entry using product variety after market deregulation.

As a motivating analysis prior to the main study, we demonstrate that the Korean soju market

after 1994 makes a good empirical context where we could identify the causal relationship between

local incumbent firms’ product variety and market entry of competitors. The goal of this prelim-

inary analysis is twofold: 1) we show that the market exhibited rich variations in new market


entry and product variety offered by each local firm during the observed period, and that 2) the

variations are largely attributed to the new competitive environment brought about by market

deregulation. Overall, in this section, we document some initial evidence that the local incumbent

firms used product variety as a strategic tool after market deregulation. Since our retail data do

not contain data for the period under market regulation (i.e., before 1991), we obtained another

data set from the Korean Alcohol and Liquor Industry Association (KALIA) for this motivating

analysis. Notwithstanding some limitations, the data span the time period from 1986 to 1999 and

allow us to illustrate how market deregulation in the early 1990’s influenced the structure of the

local market competition and the local incumbent firms’ marketing strategies. We now describe

these data and a course of the analysis.

3.1. Shipment Data Description

Yearly and quarterly firm-level volume shipped to each geographic market was recorded by KALIA

for the initial time period of 1986-1993, and more detailed product-level shipment data are available

for the subsequent period until 1999. Although sales at the product level are not available before

1994, the data contain descriptions of product variants offered by every firm for the entire period.

The included product information is brand, ABV (alcohol-by-volume, %) and packsize, as well as

price which the manufacturers charged to wholesalers. Brand information was often missing and

we complemented the data by conducting online research on the history of brand launches by each

firm. The sources of complementary online research included news articles, company websites and

product blogs.

Table 1 summarizes the market size (in volume) of each product type included in the final data

set. The original data set covers sales from every channel including on- and off- premises, exports,

and non-taxed supply for the military. For the present analysis, we exclude the volume shipped to

channels other than on- and off- premises.6 Among the total volume shipped to these two channels,

roughly 99% of soju products contained alcohol between 20% and 25%. As to packsizes, the vast

majority of the shipment volume (i.e., more than 98%) came from either small sized products in a

range between 0.3 and 0.4 liter or 1.8 liter bottles. In fact, soju manufacturers offered their flagship

brands in both small and large sizes, and the industry standard for the small size lay around 350ml.

In addition, we note that products in packsizes larger than 1.8 liter (i.e., 2.7 and 3.6 liter) were

6 Although our retail data do not include on-premises sales, it is not possible to separate the on-premises volume fromthe shipment data. For the periods when both the retail and shipment data are available (1994-1999), we comparedmarket shares of each firm within each region computed from each data and confirmed that they were almost identical(the yearly correlations lay between 0.970 and 0.996).


Table 1 Soju Market Size in Volume by Product Type

Product Attribute Variant 1994 1995 1996 1997 1998 1999

ABV

15% 304 230 134 66 30 18

20-25% 752,453 728,633 759,956 834,231 869,031 999,906

30% 6,325 6,325 5,691 5,855 5,398 7,734

Packsize

0.2L 3,356 3,460 3,385 3,251 2,421 3,036

0.30˜0.38L 600,823 597,099 639,156 712,036 751,045 862,223

0.64L 16,111 13,237 12,966 11,876 11,678 13,844

1.8L 138,791 121,391 110,274 112,988 109,315 128,554

Total Volume 759,081 735,189 765,781 840,152 874,459 1,007,657

Notes: Market sizes in volume are in thousand liters.

designed and advertised to serve distinct consumer needs; these products often contained more

than 30% alcohol and the consumers used this type of soju products as an alcohol base from which

they created other variations of liquor for their own (e.g., fruit wine). We, therefore, exclude this

product type for the final data set.

We define product variety of a firm as the number of distinct product variants in terms of two

major product attributes; brand and ABV. We do not distinguish products of a given brand and

ABV offered in several sizes because the firms’ decisions on packsize largely followed the industry

standard and we do not observe that the firms changed or added a size of product under a given

brand following its launch. Instead, the firms attempted product differentiation by developing new

products which differed in ABV, either within a brand or as a new brand. As an illustration,

product variety of producer 9 during the observed period is presented in Figure 2. Before market

deregulation, the firm produced a limited line of products that varied mostly by ABV. This line of

products served as the firm’s flagship products and accounted for a majority of its sales throughout

the observation period. When the market became deregulated in the early 1990’s, the firm managed

a broader product portfolio. During this period, the restriction on the use of ingredients (rice

in particular) was relaxed and the firm introduced new products by blending the newly allowed

distilled ethanol from rice and adding other starches to the traditional alcohol base. The blending

determined the degree of ABV and flavors for these products. This pattern of expansion of the

firm’s product portfolio was consistently observed across the firms.

Both the number of firms operating in each geographic market and the incumbent firms’ product

variety can be obtained from the sales data. For example, firms or product variants that recorded


Figure 2 An Example of Product Variety of a Soju Producer

positive sales in each period can be counted. In the data, however, we observe that the firms

occasionally shipped negligible (yet non-zero) volume of products whose production had been

discontinued, possibly to clear out their inventory.7 To account for the presence of such a lagged

sales, we define active firms and product variants as ones whose sales exceeded certain thresholds:

we treat the firms whose market shares exceeded 0.2% in a region as active in that region. Similarly,

we count only the product variants of which the firms shipped more than 2,000 liters nationwide

during each period.8

Table 2 provides a snapshot of regional market structure in 1990 and 1994, which represent

before and after market deregulation respectively. As a measure for regional competition intensity,

we consider the Herfindahl-Hirschman Index (HHI) by region. A main feature of the market in 1990

is that each geographic market was characterized by the dominance of its local firm and a weak

presence of a few other firms. On the other hand, in 1994, most geographic markets became less

concentrated with more competing firms and the local incumbent firms offered significantly more

product variants to consumers. It is also noteworthy that there is heterogeneity across regions with

respect to the degree of the local firm’s dominance in the base region. In the following section,

we investigate further how these market variables changed continuously over time, and present

7 We identified such cases by comparing the sales record of major brands with its time of launch and delisting obtainedfrom other sources such as company websites and news articles.

8 The presented results remained constant using 1,000 liters as an alternative threshold.


Table 2 A Snapshot of Regional Market Structure: Pre- Vs. Post- Deregulation

Regional Market Structure Local Firm’s Performance

HHI # Active Firms Market Share # Product Variants

Region 1990 1994 1990 1994 1990 1994 1990 1994

R0 0.730 0.629 6 8 0.850 0.787 2 5

R1 0.642 0.609 6 7 0.795 0.767 2 5

R2 0.557 0.588 2 4 0.674 0.718 2 4

R3 0.325 0.254 7 8 0.482 0.307 1 4

R4 0.374 0.341 6 8 0.555 0.498 2 4

R5 0.600 0.492 4 5 0.758 0.668 2 6

R6 0.889 0.716 3 3 0.942 0.835 2 5

R7 0.699 0.505 4 6 0.824 0.653 2 8

R8 0.462 0.414 4 5 0.634 0.579 2 4

R9 0.491 0.455 5 6 0.626 0.577 3 5

some evidence that the variations after market deregulation were due to competitive interactions

between the local firms and competitors from other regions (i.e., potential entrant firms).

3.2. Motivating Analysis

In order to investigate changes of key market variables - the regional soju shipment volume, HHI

and the number of active firms in the regions - over time, we first regress yearly observations

of the variables with year fixed effects. The year 1991 is set as the base year throughout the

regressions to see if there was any significant change before and after that year. We also account

for unobserved sources of heterogeneity across regions by adding regional fixed effects. Figure 3

illustrates point estimates and 95% confidence intervals of the year fixed effects (in relation to

1991) in each regression.

Since the market volume and the number of competing firms may increase over time along with

the growth in population, we obtained data of regional adult population (i.e., population over

legal drinking age 19) from the Statistics Korea and included as a covariate in both regressions.

After accounting for the population growth, the regional market volume stayed at a constant

level until the end of the observed period. On the other hand, the regional market structure in

terms of concentration and the number of competing firms exhibited some significant fluctuations

during the 1990’s. Following the implementation of market deregulatory policies, we expect the

regional markets to become more competitive and their concentration (e.g., HHI) to decrease

significantly. Yet, the result suggests that market deregulation might have a small and temporary


Figure 3 Pre- and Post- Deregulation Market Trend

* Notes: The shaded area represents the middle 95% confidence intervals (base year=1991).

impact. Similarly, as to the number of active firms within regions, we do not observe any drastic or

permanent increase in the number despite some fluctuations since the early 1990’s. To investigate

whether the market deregulation indeed induced significantly more frequent market entry of the

competitors into each market, we conduct further analysis on the firm’s decisions on market entry.

The analysis also takes into account some more observed sources of heterogeneity across regions.

Analysis on market entry. We build a model for each firm’s entry decisions into 9 regions

outside its base region. This firm-level entry model incorporates heterogeneity across firms and

geographic markets that may account for profits from entry, thus the market’s attractiveness to

potential entrants. Formally, the model is written using latent profit from entry of firm j into region

r (∀r 6= j) at year t, πjrt and the observed binary entry decision (1 if entered and 0 otherwise),

Entryjrt:

πjrt = fj(RegionFactorsrt) +βj ·Firmj + γ ·Policyrt + εjrt, (1)

Entryjrt = 1[πjrt > 0]. (2)


fj(Region Factorsrt) refers to factors in each region r at year t that may affect demand for firm

j or the costs associated with entry. To account for the impact of the market size, regional adult

population and real GDP (per adult population) are selected. We also consider two location vari-

ables: 1) a measure of distance between producers and regions (defined as the number of regions to

pass in the shortest path to move from one region to the other region), and 2) the route from each

market to the “target” market. Acknowledging that more than 40% of the adult population resided

in Seoul and the surrounding Kyunggi region (Region 0 and 1), we treat the market covering these

regions as the target market. We then construct the path from each market to this target market

based on the Korean highways, to see if the firms are more likely to enter the regions that they pass

through on their way to the target market. Specifically, we operationalize a binary variable which

indicate the regions for each firm to pass through to reach the target market. Further descriptions

of the location variables based on the highway map are provided in Appendix A.

In addition to the regional factors, the model accounts for heterogeneity across firms using firm-

specific dummies, and Policyrt captures the impact of market deregulation on the firms’ entry

decisions after accounting for the observed and unobserved sources of heterogeneity. For the sake

of simplicity, we use a variable indicating the period after 1992, when all the major regulatory

policies were terminated. Further, to capture any impact of the temporary re-introduction of the

local product purchase policy in 1996, we add a year- and region- specific variable indicating the

regions that were regulated during 1996.

Combined with an assumption that idiosyncratic error term εjrt follows a standard normal dis-

tribution, the formulation becomes the Probit model, and the model is estimated based on yearly

observations of the firms’ entry decisions from 1986 to 1999. Firm 1 entered every region throughout

the period and was therefore excluded from this analysis. Table 3 presents the results from various

model specifications: first of all, we confirm that there was significantly more frequent entry after

market deregulation after accounting for heterogeneity across regions and firms. The population

and income mattered but population became less important as we accounted for the target market

and the paths to it separately. The negative effect of regional income can be attributed to the

fact that soju is the cheapest alternative among alcohol beverages that appeal to the population

with low income. Both distance measures turned out significant with expected signs; the further

the market is the less likely a firm is to enter the market, and the firm is more likely to enter the


Table 3 Parameter Estimates from The Probit Regression on Firm’s Entry

(I) (II) Correlations

Covariates Coefficients SD Coefficients SD Coefficients

Policy Variables:

Deregulation since 1992 0.271** (0.127) 0.507*** (0.140)

Temporary Regulation in 1996 0.031 (0.211) 0.100 (0.230)

Market Size and Location:

Adult Population (Mil.) 0.345*** (0.032) −0.085 (0.062) 1.000

Real GDP (per adult pop.) −0.036** (0.017) −0.042** (0.018) 0.357*** 1.000

Distance −0.411*** (0.065) −0.436*** (0.078) 0.406*** 0.104*** 1.000

Route to Target Markets 1.967*** (0.231) 0.656*** 0.187*** 0.269*** 1.000

Target Market (R0 and R1) 0.969** (0.385)

Firm Fixed Effects:√ √

Log Likelihood -515.191 -426.897

Observation 1,008 1,008

*** Significant at 1% level;** 5% level

Notes: The dependent variable in the model is a binary outcome of each firm’s market entry decision (1 if entered, and 0 otherwise).

Firm 1 entered every market throughout the period and was excluded from the analysis. The base year for real GDP is 2005.

markets they need to pass through on their way to the target market. The identified geographic

variables can serve as predictors for the number of competing firms in each market.9

Although the results from the firm-level entry model show that the market deregulation influ-

enced the firms’ decisions to enter the other markets, the earlier presented Figure 3 suggested that

it did not have either drastic or permanent impact. A possible explanation is that the local incum-

bent firms managed to deter some new market entry by competitors. We now turn to the local

firms’ marketing actions in their base regions. In particular, acknowledging that the firms’ pricing

was regulated both pre- and post- deregulation period, we look at how the local firms changed

their product variety after the market deregulation, and seek some evidence that they used product

variety as a strategic tool in competing with (potential) entrants from the other regions.10

Analysis on product variety. We begin by regressing the yearly observations of product

variety that each firm offered to their base markets on year- and firm- specific dummies. The

regression also includes adult population and income (per adult population) in the base region

9 We considered several alternative models to account for the firm’s performance outside its base region and confirmedthat they produced consistent results. See Appendix B.

10 A time-series regression on price of flagship product variants (which were available throughout the whole period)showed that their prices indeed remained constant after accounting for yearly inflation and changes in the amount ofalcohol (%). The results are available upon request.


Figure 4 Geographic Differences in Timing of Product Proliferation

1. All observations (1986 - 1999)

2. Regions based on the time of mandatory local product purchase policy removal (1986 - 1995)

Notes: Group 1 includes region 2, 5, 8 and 9 where local product purchase policy was removed between 1990 and 1991, and

group 2 includes region 3 and 4 where the policy remained in effect until the end of 1992.

as covariates. Figure 4 depicts the estimated time trend and it highlights a significant increase

in the number of product variants since 1992. Such a product proliferation, however, could have

been simply due to the new policy allowing the use of new ingredients in production in July 1991.

Seeking evidence that the degree of the product proliferation could also have been accounted for by

the degree of local competition each local firm faced, we exploit the fact that there were geographic

differences in terms of the time until which the local firms were able to secure their market shares.

Specifically, while the use of rice became allowed for every firm at the same time, the timing of

removal of mandatory local product purchase policy differed by region. The geographic coverage

of the policy decreased in steps: Region 0, 1, 6 and 7 whose local firms were the three largest

nationwide were exempt from the policy earlier in 1982. During the market deregulation in the


1990’s, the policy was removed from region 2, 5, 8 and 9 between 1990 and 1991 while it remained

in effect in region 3 and 4 until the end of 1992.11 In Figure 4, we also present the quarterly average

number of products in two groups of regions that differ in timing of the policy removal, and they

indicate that the incumbent firms in regions where they were able to secure their market shares

until a later period, introduced new products later.12 This finding serves as initial evidence that

the local incumbent firm and competitors from the other regions became involved in competitive

interactions with respect to their decisions on market entry and product variety after market

deregulation.

4. Full Model4.1. A System of Simultaneous Equations for Endogenous Market Outcomes

The primary objective of this paper is to identify the entry-deterring impact of an incumbent firm’s

product proliferation. In the post-deregulation Korean soju market context where a group of local

incumbent firms with limited pricing power faced new market entry from competitors based in

the other regions, we achieve this goal by establishing a causal relationship between two observed

market outcomes - how many firms competed in the market and how much product variety the

incumbent firms offered to local consumers.

The model comprises a system of simultaneous equations that represent causal structures of

region-level variables that are jointly determined. This analysis at the region level takes advantage

of the soju market context where these regions serve as separate geographic markets each with a

local firm that was exogenously assigned by government policy. We first write two equations for the

number of competing firms in the market and the local firm’s product variety. Another important

feature of the geographic soju markets is that there was heterogeneity across firms and regions.

In particular, we noted earlier that the market leadership of the local firms in their base regions

varied even before the market became deregulated. Such a variation in the local firms’ market

shares across geographic markets emphasizes the need to take into consideration the potential

heterogeneity in consumer preference for the local firm. Choi et al. (2013) show that the strong

market leadership by the local firm can be in part accounted for by the consumer’s choice of local

brands as an expression of their local identity. Acknowledging that the market shares of the local

11 It is noteworthy that this order of market deregulation was based on the national market share of the firms andwas not necessarily based on their capability of new product development. Annual reports published by the firmsoften acknowledged that the process of soju production varied little across the firms.

12 A firm-level ordered Probit model of product variety produced the consistent results. See Appendix C.


firms could be endogenously determined by the firms’ actions being targeted to such local demand,

we endogenize the variable in the complete system.

The full model, therefore, comprises three simultaneous equations to recover causal relationship

among the local firm’s market share and product variety, and the overall number of competing

firms at the region level:

ln(Nfirmrt) = α1 +β1ln(Incumb.Shrrt) + γ1ln(Incumb.Nprodrt) + θ1X +φ1Zfirm + ε1rt, (3)

ln(Incumb.Nprodrt) = α2 +β2ln(Incumb.Shrrt) + γ2ln(Nfirmrt) + θ2X +φ2Zprod + ε2rt, (4)

ln(Incumb.Shrrt) = α3 +β3ln(Incumb.Nprodrt) + γ3ln(Nfirmrt) + θ3X +φ3Zshare + ε3rt, (5)

εrt ∼MVN [0,Σ] ; Eεirtεjrt = σij , (6)

where Nfirmrt, Incumb.Nprodrt and Incumb.Shrrt refer to the number of firms, the local firm’s

number of product variants and its market share within their base region r at time t. These three

variables are included in logarithm for potential non-linear relationships, and the unit of time for

our analysis is bi-monthly.13 X contains various fixed effects common in the three equations while

{Zfirm,Zprod,Zshare} is a group of equation-specific explanatory variables which could also serve as

instrument variables for each endogenous variable. Lastly, Σ captures any unobserved correlations

among the three market outcomes.

For a system of equations to be structural and allow us to infer causality, each equation in

the system needs to have its own meaningful interpretation (Wooldridge 2010). Such “autonomy”

of each equation can be obtained by deriving the model from counterfactual reasoning based on

economic principles (e.g., demand and supply equations). Ideally, the system would account for

endogenous variables that are outcomes of decisions made by distinct economic units and our model

falls into this category.

First, the number of firms operating in the market is an outcome of market entry decisions

made by competitors from outside. Equation (3), therefore, contains covariates that influence the

competitors’ decisions of entry into that region. By including the local firm’s product variety as

an influential factor, we allow the model to identify the role of the local firm’s product variety

as a barrier to market entry (i.e., γ1 < 0). Second, Equation (4) accounts for causal effects of

explanatory variables on the local firm’s decisions on how much variety to offer to the market. The

set of explanatory variables include the number of firms in the market as an endogenous variable

13 According to the annual reports published by soju producers, the firms were able to develop and launch a newproduct in less than three months during the period of our study.


and γ2 captures the firm’s reactive product strategy depending on the degree of local competition.

Lastly, the market share of the local firm in the region represents local consumers’ choices over the

firm and Equation (5) will help us quantify the demand for product variety offered within the local

firm and demand for variety across firms; β3, if positive, captures the local firm’s potential share

gain from offering more products while γ3, if negative, accounts for the loss due to the presence of

the new firms.

The proposed model is similar to one in a related study by Bayus and Putsis (1999) in which they

build simultaneous equations to account for determinants and outcomes of a firm’s product line

decisions in the personal computer industry. By having market share, price and product line length

of an individual firm as dependent variables, however, the focus of their model lies in endogenizing

jointly the firm’s decisions on price and product variety. Although they observed that the number

of firms operating in the overall industry grew during the observation period, the scope of their

model is limited to the firm’s decisions conditional on market entry.14 In the Korean soju market,

firms lacked pricing power and we place an exclusive focus on the impact of the firm’s product

strategy on market entry.

It is noteworthy that our model not only treats the market share of the local firm as the outcome

of the market competition but also allows the market share of the firm to have a direct impact

on the firm’s product strategy (β2) and the competitors’ entry decisions into that region (β1).

There are two possible accounts that predict the direct impact of the firm’s market share on its

product variety. First, the higher market share could induce a firm to produce more variety because

the firm’s production capacity may grow with the market share. On the other hand, a firm as a

dominant market leader may have a weaker incentive to invest in new product development and

thus to offer less variety of products. While the former argument predicts the positive impact of

the market share on the product variety, the latter predicts the negative impact. The estimated

sign of the impact can inform us whether the effect of firm capacity (measured by the market share

in the base region) dominated the incumbency effect on provision of soju products.

We also expect the local firm’s market share to have an impact on the competitors’ entry deci-

sions. The higher market share of the local firm could indicate to the potential entrants how

receptive the local demand would be to new entrants (e.g., a degree of consumer inertia or loyalty

to a firm leading to high firm-switching costs), and thus induce fewer firms to enter. The negative

14 The study tests indirectly the market preemptive effect of product proliferation by investigating whether the firmcould charge a higher price when it offered greater variety of products, and do not find significant evidence.


sign of the estimated coefficient will support this reasoning. In sum, accounting for endogeniety of

the local firm’s market share in the system will uncover the role of consumer demand for the local

firm in the firm’s employment of competitive product strategies and competitors’ entry decisions.

Besides the three endogenous variables, each equation contains a set of exogenous covariates.

We consider time-specific dummy variables in X to capture the impact of any temporal shocks to

the soju market, common across regions. Among various specifications, we present a result using a

variable indicating the Korean economic crisis.15 In mid-1997, the East Asian economic crisis hit

Korea and after a chain of the conglomerates’ default on debts, Korea fell into the IMF bailout

program in November 1997. Thus we capture the impact of this economic crisis on the Korean soju

market using the variable indicating years after 1997. {Zfirm,Zprod,Zshare} represents each set of

exogenous variables included in the respective equation. We discuss the selected variables in the

subsequent section.

4.2. Identification Strategy

Empirical identification of the proposed model not only relies on variations in the selected variables

across geographic markets and over time but also on exclusion restrictions. The motivating analysis

confirmed that the market exhibited sufficient variations of the three endogenous variables. To

satisfy the exclusion restrictions, we need a set of exogenous variables which account for each

endogenous variable yet are uncorrelated with any unobserved factors that may affect the other

endogenous variables in the system. More specifically, for our system of three equations having all

three endogenous variables appearing in each equation, we need at least one instrumental variable

that is unique to each equation. In this section, we document the set of explanatory variables

and rationales for the selection in light of our empirical context. Figure 5 summarizes the set of

instrumental variables which help us identify the system.

The number of firms. To account for how many firms are competing in each geographic

market, we use regional characteristics that could potentially affect the profitability of the firms’

entry into that region. In particular, we use the variables that were identified from the firm-level

entry model presented earlier. The results of the model indicated that the level of population and

income of a region influenced the firms’ entry decision into that region. In addition, the results

indicated that the geographic locations of the regions with respect to the biggest target area (R0

and 1) would predict well the number of the firms in the regions; one of the identified predictors

15 We confirm that our main results do not depend on the selection of this time-specific control variable; e.g., theresults remained consistent when we used year fixed effects instead.


Figure 5 Instrument Variables for Endogenous Market Variables

for a firm’s entry decision to a certain region was whether that region was passed through by that

firm en route to the target area. In our equation for the number of firms at the regional level, we

include a modified version of this variable - the number of firms that used the region as a “through

route” to the target area.

The advantage of the use of these regional characteristics is that we do not expect them to have

any “direct” impact on the market share of the local firm; any impact on the market share will

be via either the number of competing firms or the product variety of the local firm. That said,

it is important to note that the population and the income level in a region could influence its

local firm’s product variety since the firm may determine how much product variety to offer to its

base market based on such characteristics of customers. Thus, these two variables are also used

to account for the local firm’s product variety. On the other hand, the location variable should

influence uniquely the number of the firms in the region and serve as a valid instrument variable

for the number of firms.

Product variety of the local firm. In addition to regional population and income, we consider

two variables unique to the equation for the local firm’s product variety. We look at agricultural

production volume in each region. Until 1992, the use of rice in soju production was banned and


thus the firms made the products by diluting ethanol and adding starch supplements such as those

from tubers. Since the implementation of a new policy allowing the use of rice in soju production,

the firms introduced new products utilizing distillation from rice. As indicated in Figure 2, most

of the new products launched during the observed period (1994-1999) were made from rice.

We, therefore, suspect that the firm’s decision of whether to produce new products using rice or

to stick to the flagship product line that relied on tubers was made with respect to their supply of

product variety. The two instrument variables to account for the product variety are: the annual

regional production volume of rice and tubers (V olrice + V oltuber), and the ratio between their

volume (V olrice/V oltuber). Although these variables are available only annually, the data varied by

region. We expect to observe greater product variety at times (and in regions) of abundance of

potential ingredients of rice and tubers. And given the total production volume of the ingredients,

the more rice was produced compared to tubers, the greater number of new products we expect

from the firms. According to annual reports of food grain consumption by the Korean Statistics

(2012), the rice consumption quantity for soju production varied indeed in accordance with the

level of the annual supply.

For these variables to satisfy the exclusion restrictions required to identify the model, we need

the variables to influence the other endogenous variables only indirectly through their impact on

product variety. For example, the restriction will be violated if the total amount of rice supplied

is correlated with the average price of soju products produced by the local firm and thus with the

firm’s performance (i.e., market share) and with the competitors’ decisions to enter the market

(i.e., the number of firms in the market). We checked this correlation and found that there was no

significant correlation between the relative abundance of rice production and the final soju price

at the firm level at least.16 This is possibly due to the fact that the rice served as an alternative

among other potential ingredients for soju and the use of rice in production is largely optional.

During the time of low supply of rice with possibly high price, the firms could choose to use less or

none of it for some products, hence there was not necessarily a strong correlation with the price at

the firm level. The decision on the level of rice consumption across product variants also supports

the hypothesized direct impact on the product variety of the firm.

16 We constructed the firm-level average price per volume (liter) sold and also obtained market price data of rice andtubers from Statistics Korea to check for a chain of correlations. As expected, the ratio between production volumeof rice and tubers were negatively correlated with their price ratio (−0.131; P=0.017). Neither the price ratio northe volume ratio was, however, correlated with the final price of soju products at the firm level (0.084; P=0.126and−0.023; P=0.683 respectively).


Due to the firm-specific nature of the decisions on product variety, we also consider a set of

additional fixed effects to capture some unobserved heterogeneity across the firms. Although the

local firm in every region was designated as part of the government policy at the same time (i.e.,

the length of the firm’s history is the same), we acknowledge that there could still be unobserved

heterogeneity with respect to their managerial resources and capacities in new product develop-

ment.17 Producer 1 and 2 are the two Korean conglomerates (“Chaebol”) which participated in

many other industries; Producer 1 (Jinro Corporation) was the national leader of soju produc-

tion and in addition to the soju market, the group invested in such diverse areas as convenient

stores, department stores and marine engineering to list a few. Another conglomerate, Producer

2 (Doosan Corporation) previously participated mainly in technology and infrastructure support

businesses, and entered the soju market by acquiring a local producer in region 2 in 1993. During

the period of our study (1994-1999), these two producers might have possessed distinct levels of

managerial resources or capacities compared to the other local producers solely focusing on the

soju industry. To account for any impact that these distinctions had on their decisions on product

variety, we include separate fixed effects for the two producers. Given the similar nature, these

conglomerate-specific fixed effects are also included in the equation for the firms’ market shares.

Market share of the local firm. As a unique variable for the firm’s market share, we consider

the manufacturer price of the firm’s flagship product line as a percentage of their retail price.

As noted, soju producers had limited power as price setters; their pricing was monitored by the

National Tax Office throughout the period of study. As a result, they set their manufacturer price

uniformly at the national level for a given product variant (Korea Fair Trade Commission 2006).

Given the uniform manufacturer prices, the firms could subsidize trade promotions occasionally to

lower their retail prices. To account for the impact of such changes in retail prices on the market

share, we computed the manufacturer price of a product variant as a percentage of its retail price;

(Pricemanu/Priceretail). This was done by matching product descriptions in our manufacturers’

shipment data and retail data, and comparing the observed national shipment price and regional

retail prices. The higher the percentage was the heavier trade promotion the firm might have

subsidized at the expense of its production margin and thus the higher market share the firm could

have gained.

17 The model already takes into account some of this aspect by including the market share of the firm in the region.The proposed fixed effects will thus quantify effects due to any firm-specific characteristics which did not correspondto their market shares.


The challenge of including this variable in the firm’s base region is that it is potentially endoge-

nous. That is, the estimate for the variable will be biased if the retail margin was correlated with

other unobserved factors affecting the market share of the firm such as advertising campaigns.

Also, for this variable to serve as a valid instrument for the market share, it should not affect the

firm’s product variety decisions or competitors’ entry decisions via any other channels. The firm

may adjust jointly product variety and retail margin for individual product variants, and the firm’s

aggressive trade promotions in its base region could also deter the competitors’ entry into that

region. We address these issues by; 1) using the average percentage across the regions outside the

firm’s base region and 2) using the percentage computed for the firm’s flagship product line only.

The rationale for the use of the average percentage outside the base region is similar to that for

the use of price in other markets in estimating the price effects on consumer demand (Hausman

1997, Nevo 2001); the unobserved characteristics (e.g., media advertising) are assumed independent

across the regions although the retail margin for the standard product line can be shared across

regions to some degree; in our context, this is also likely due to the uniform manufacturer prices. In

addition, we note that the firm’s decision on product variety is less likely to be influenced by this

variable computed for the flagship products outside the base region. This is based on an observation

that, the firms focused on their flagship product line and rarely expanded product variety outside

their base market, regardless of changes in their product portfolio in the base market.18 In sum,

we suspected that this variable outside the base region could have co-varied with that in the base

region due to the firm’s national trade policy, but have not necessarily varied in accordance with

the firm’s decision on product variety.

The proposed full model is applied to the bi-monthly sales data we obtained from Nielsen

Company and estimated using the 3SLS regression. Table 4 provides summary statistics of the

key variables in the data. Among the covariates, population, income and production volume are

transformed in logarithm in the estimation. We discuss additional details with respect to tests of

endogeneity and model specification in Appendix D.

18 The average number of the total product variants that the local firms offered to their own market during theobserved period (1994-1999) is 10.73 (SD : 2.81, Mode: 9) while the figure outside their base regions is significantlysmaller at 4.62 (SD : 2.80, Mode: 1) - excluding the regions the firms did not enter. Also, in 52.79% of the observationsoutside the base regions, the flagship products accounted for more than 80% of the total number of products offeredin the region.


Table 4 Summary Statistics of Market Variables

Local Firm-Specific Regional-Specific

Product Variety Market Share PriceM/PriceR # Firms Real GDP Population Rice/Tubers

R0 7.061 0.770 0.743 7.697 20,796,576 7.093 11.911

(1.197) (0.041) (0.043) (0.637) (806,199) (0.022) (3.998)

R1 6.879 0.714 0.743 8.061 15,728,614 7.814 11.911

(1.219) (0.042) (0.043) (0.788) (977,236) (0.491) (3.998)

R2 5.879 0.909 0.692 4.455 16,013,484 1.139 0.946

(1.269) (0.046) (0.044) (1.371) (912,476) (0.018) (0.095)

R3 3.879 0.377 0.873 7.273 16,949,924 1.110 10.855

(0.893) (0.054) (0.126) (0.517) (1,108,028) (0.032) (4.116)

R4 9.212 0.637 0.936 6.909 16,748,414 2.386 23.049

(1.410) (0.065) (0.054) (0.631) (888,083) (0.076) (3.570)

R5 5.545 0.719 0.850 5.000 13,621,942 1.465 9.527

(0.564) (0.032) (0.067) (0.500) (838,502) (0.017) (1.795)

R6 7.212 0.923 0.904 4.394 18,884,077 2.548 5.929

(1.083) (0.039) (0.068) (0.933) (1,050,918) (0.043) (0.735)

R7 8.000 0.705 0.722 5.151 15,471,990 3.991 9.941

(1.061) (0.164) (0.093) (0.667) (792,430) (0.080) (1.946)

R8 6.484 0.775 0.951 6.030 21,974,232 2.934 4.319

(0.834) (0.052) (0.092) (0.394) (2,109,320) (0.085) (0.569)

R9 6.061 0.666 0.778 6.121 12,665,488 2.928 29.820

(0.933) (0.194) (0.031) (0.484) (706,555) (0.021) (16.651)

* Notes: PriceM/PriceR for a producer is averaged across regions outside its base region.

Population is bimonthly population over age 15. Unit of population and real GDP per population are mil. and KRW.

Rice/tubers ratio is the ratio between the annual regional production volume of rice and tubers.

Real GDP (base year: 2005) and rice/tubers ratio are only available at the yearly level.

5. Empirical Results5.1. Interactions Between Local Firms and Competitors

Entry-deterring effects of product proliferation. Table 5 summarizes the main results from

the estimation. Most importantly, the model quantifies both directions of the causal impact between

the local firm’s product variety and the number of competing firms within the region. The results

indicate that the local firms’ observed increase in product variety was not due to the actual changes

of competitors in the market; the impact of the change in the number of firms in the market on the

incumbent firm’s product variety is not statistically significant at 10%. By offering greater variety

of products, however, the local firms were able to reduce significantly the number of competitors.


Table 5 Parameters from the Full Model

Dependent Variable

(I) Number of Firms (II) Product Variety (III) Market Share

Covariates Parameter SD Parameter SD Parameter SD

Endogenous Variables:

Number of firms −0.088 (0.159) −1.305*** (0.090)

Local firm’s product variety −0.143** (0.070) 0.339*** (0.066)

Local firm’s market share −0.436*** (0.066) −0.011 (0.119)

Instruments:

# firms passing through 0.123*** (0.018)

Adult population in the region 0.139*** (0.023) 0.539*** (0.040)

Income in the region −0.010 (0.047) −0.108 (0.071)

Total production volume of rice and tubers 0.111*** (0.013)

Production volume ratio between rice and tubers 0.004*** (0.001)

Ave. PriceM/PriceR outside the base region 0.294*** (0.106)

Fixed Effects:

Conglomerate 1 (P1) −0.600*** (0.064) 0.514*** (0.050)

Conglomerate 2 (P2) 0.310*** (0.056) 0.008 (0.042)

Economic Crisis (1998-1999) 0.072*** (0.022) 0.104*** (0.025) 0.090*** (0.027)

Observations 330 330 330


As an illustration of the effect, Producer 3 (P3) offered five product variants to their region where

there were six other competitors in the mid 1999’s. The estimated results suggest that P3 could have

had one fewer competitor if they had doubled the product variety from 5 to 10. This is the impact

uniquely due to the change in product variety, holding others constant, and this result implies that

the firm’s preemptive motives might have played a role in the observed product proliferation. The

magnitude of the entry-deterring impact of the product proliferation seems small yet it is only a

partial effect ignoring any corresponding chains of the effects. The estimated total effects via chains

of the effects are to be discussed with the role of the local firm’s market leadership.

The use of product variety as a strategic tool in the soju market was also supported by a positive

consumer response to product variety. Offering a greater variety of products to the local consumers

generated additional market share for the firm. Despite the positive impact of the product variety

on the market share, a negative impact due to new entrants implies that the consumers also

maintained strong demand for variety at the firm level. In fact, the new entrant attracted more

demand than a new product variant introduced by the local firm. As noted earlier, in many cases,


the new entrants did not bring to the market their complete selection of products from their

base regions. In general, each new entrant offered only a small number of products (if not one),

often from its flagship product line. We can conclude, therefore, that the consumers in the soju

market valued standard products from various producers more than additional variety offered by

their local firm, and this finding suggests a potentially negative impact of entry-deterring product

proliferation on consumer welfare.

The role of market leadership of the local firms. In addition to bringing insights into

the local consumer’s demand for variety, our model also reveals the role of the firm’s local market

leadership in the interactions with potential competitors. First, it quantifies a direct impact of such

leadership on the firm’s decision on product variety. We were agnostic about the sign of the impact

ex-ante. On one hand, we conjectured that the impact could be negative if the firm exploited the

dominant position in the region and produced less variety, all else being equal. On the other hand,

we also expected that the firms could grow with a higher market share and thus become more

capable of producing greater variety. The estimated impact is not statistically significant at 10%,

possibly due to these diverging effects. In addition, we allowed the model to identify a direct impact

of the local firm’s market share on competitors’ entry decisions (thus the resulting number of firms).

This impact is significant and negative; the higher the market share that the local firm has, the

fewer entrants into the market they could expect. This implies that the potential entrants might

have taken into account how strong the incumbent firm was in their base region to learn about

how receptive the local demand would be to new entrants. Together with product variety of the

incumbent firm, therefore, the strong market leadership of the firm served as a barrier to market

entry. Returning to the illustration of Producer 3 above, by offering 10 product variants instead of

5, it could have also captured higher market share. And taking into account the complementary

entry-deterring impact due to this higher market share, it could have had two fewer competitors

by offering greater product variety.

The impact of regional characteristics. The impact of various market characteristics on the

three endogenous variables are mostly significant and of expected signs. First, the findings from the

equation of the number of firms replicated those we obtained from the firm-level entry model; an

attractive market to enter was characterized by large population with lower income (although not

significant at 10%), and its location which required frequent through passage by the other firms en

route to the capital region. Second, the results from the equation of the local firm’s product variety

show that the firms produced greater variety as the market became more populated. They also did


so in years and regions of rich production of potential ingredients and more so when the availability

of rice dominated that of traditional ingredients, tubers. Product strategies employed by the two

conglomerates differed; while Doosan corporation (P2) managed a larger selection of products than

that of the other small producers, Jinro corporation (P1) did the opposite. Lastly, the manufacturer

price of the flagship product line as a percentage of the retail price was a significant predictor for

the local firm’s market share. The impact of this variable was significant and positive indicating

that the more the producer was able to set the retail price closer to the manufacturer price (e.g.,

through subsidizing trade promotions etc.) the higher market share the firm was able to obtain.

5.2. The Importance of Accounting for Local Consumer Demand

Our empirical results have identified both the incumbent firm’s product variety and the current

market leadership as barriers to market entry in the post-deregulation Korean soju market. To

highlight the importance of accounting for the role of the local firm’s market share in understanding

the entry-deterring effects of product proliferation, we also estimate a pairwise model omitting the

market share completely from the system. The model still identifies the causal relationship between

the incumbent firm’s product variety and the number of competing firms in the market, but do not

take into account how the incumbent and potential entrant firms incorporate the consumer demand

for their local products into their decisions. The results from the pairwise model are presented in

Table 6. The model produces the results consistent with those from the full model in terms of the

impact of exogenous variables. Yet, the results differ significantly in terms of the implications on

the firms’ product strategies.

Without accounting for the market share, the model leads to an upward bias of the magnitude

of the effect of entry-deterrence through product proliferation. Also, one may conclude that new

market entry actually reduced significantly the incumbent firm’s product variety. The reason behind

these biases in identification is twofold: 1) the pairwise model does not take into account the impact

of the signal to the potential entrants through the local firm’s dominance in the region. The results

from the full model indicates that the consumers in the soju market exhibit demand for variety

and firms offering greater variety generally enjoyed the higher market share. The higher market

shares of the firms then made market entry more difficult to potential entrants. The pairwise model

does not distinguish this impact of the signal to the potential entrants through the local firm’s

dominance in the region. Instead, it captures the aggregate impact of offering a large selection of

products on the entrant firms’ market entry. 2) The model does not recognize the mechanism that

the competitors, once entered, could weaken significantly the market share of the local firm. In


Table 6 Parameters from the Pairwise Model without Market Share

Dependent Variable

(I) Number of Firms (II) Product Variety

Covariates Parameter SD Parameter SD

Endogenous Variables:

Number of firms −0.282*** (0.097)

Local firm’s product variety −0.427*** (0.078)

Instruments:

# firms passing through 0.176*** (0.021)

Adult population in the region 0.129*** (0.031) 0.470*** (0.032)

Income in the region −0.072 (0.067) −0.175** (0.076)

Total production volume of rice and tubers 0.130*** (0.014)

Production volume ratio between rice and tubers 0.006*** (0.001)

Fixed Effects:

Conglomerate 1 (P1) −0.418*** (0.065)

Conglomerate 2 (P2) 0.280*** (0.049)

Economic crisis (1998-1999) 0.065*** (0.024) 0.113*** (0.022)

Observations 330 330


the presence of consumer demand for variety, the negative impact of the entrants could be larger

on local firms that managed less variety in the market. Thus, in regions where the local firms

were damaged badly by the new entrants, the model would mis-attribute the firm’s lower level of

product variety to the firm’s reactive adjustment against the market entry.

6. Conclusion

In a market with frequent new market entry, empirical identification of the incumbent firms’ strate-

gic use of product variety and its impact on market entry is a challenging task largely due to the

potentially interactive causal relationship between the two market outcomes. Depending on the

direction of the causality, the incumbent firms’ action of product variety expansion can be viewed

as a competitive outcome corresponding to the growing number of competitors, or as an entry-

deterring one which could be detrimental to consumers. Our empirical results in the Korean soju

market demonstrate the role of product variety as a significant barrier to entry and suggest that


Table 7 Parameter Estimates from Regressions of the Number of Firms

(I) OLS (II) 2SLS (III) 2SLS (IV) 3SLS Full Model

Endogeneity (Instrumented) None Product Variety Product Variety; Share Product Variety; Share

Correlations in system errors No No No Yes

Coefficients SD Coefficients SD Coefficients SD Coefficients SD

Covariates

Local firm’s product variety −0.072 (0.043) −0.325*** (0.073) −0.241*** (0.075) −0.143** (0.070)

Local firm’s market share −0.344*** (0.047) −0.308*** (0.049) −0.467*** (0.068) −0.436*** (0.066)

# firms passing through 0.135*** (0.020) 0.110*** (0.022) 0.080*** (0.024) 0.123*** (0.018)

Adult population 0.119*** (0.025) 0.182*** (0.029) 0.200*** (0.030) 0.139*** (0.023)

Income 0.007 (0.060) 0.024 (0.063) 0.070 (0.064) −0.010 (0.047)

Economic Crisis (1998-1999) 0.057*** (0.021) 0.079*** (0.022) 0.084*** (0.022) 0.072*** (0.022)

Observation 330 330 330 330


Notes: Presented coefficients are from the regression of the number of firms as a dependent variable

the firm’s entry-deterring motives could have been a driver of the observed product proliferation

after market deregulation.

Table 7 presents the entry-deterring effect of product proliferation estimated from various model

specifications and highlights the contribution of our approach. The OLS specification disregard-

ing the endogeneity of the market outcomes cannot identify the entry-deterring power that the

incumbent firms could exercise in the market. Taking into account only the simultaneity between

the incumbent and entrant firms’ actions can still produce a biased estimate by missing the com-

plementary entry-deterring impact due to the strong market leadership. Allowing any unobserved

sources of correlation between market variables also improves the results.

Our study motivates further work in the field of strategic interactions between incumbent and

potential entrant firms that compete in a given set of geographic markets. In the Korean soju

market, a fixed set of firms competed in multiple geographic markets. Such multi-market contacts

among the firms in repeated competition might have generated a new environment for collusion

(Bernheim and Whinston 1990). For example, firms located in adjacent markets could have been

involved in more cooperative interactions. Although we do not observe any obvious pattern of such

collusion with respect to market entry (e.g., firms from close regions are more likely to enter each

other’s market), it would be an interesting avenue for future research to investigate whether there

were any sustainable collusive behaviors in the presence of the multi-market contacts.

Another possible extension of the study is the entrant firm’s strategic choice of product variety.

Multi-product firms may customize their product offerings across geographic markets depending


on the competitive environment in each market, and this could complicate the dynamics with the

incumbent firm in the market. In the present context, conditional on entry, most entrant firms’

product offerings were limited to their flagship product line, and thus we abstract away from the

entrant firms’ joint decisions of market entry and product variety. In some other markets where

we may observe large variations in product variety of both the incumbent and entrant firms, this

dynamic between the firms in product space could be an important topic to explore.


Appendix A: Construction of Location Variables

In this appendix, we present a map of the Korean highways connecting geographic regions. We located

soju production factories of local firms (P2 - P9) in the map and identified the shortest path to Region

1 (Kyunggi, which surrounds Seoul) from each production site. Each factory was located with easy access

to the highways. In the model accounting for a firm’s decision to enter a certain region, we accounted for

whether the firm must pass through that region en route to the target market of R1 as a binary measure.

In our full model at the region level, for each region, we counted the number of competitors that used each

region as a through passage. The map and the order of destinations are presented below.

The Map of Major Korean Highways and Local Production Sites

* Notes: The location of each producer’s factory (P2 - 9) is marked on the map.

The Order of Destinations in the Shortest Route to Region 1


Appendix B: Complementary Analysis on Market Entry

This appendix presents the complementary evidence that market deregulation induced new market entry

and that some socio-demographics (e.g., population and income) and location characteristics of a region

can predict the number of competing firms in that region. Alternative analyses we considered are the Logit

regression on the binary variable of the firm’s market entry decision and the Type I Tobit regression on

the actual volume that the firm shipped to each region outside its base region. For the Logit regression,

producer 1 (P1) was excluded from the analysis for the sake of identification because it entered every market

throughout the period of study. The regression on the shipment volume included the volume shipped by P1

and thus used 1,120 observations from 1986 to 1999. The main result from the alternative models remained

consistent with that obtained from the Probit regression.

Parameter Estimates from Regressions on Firm’s Market Entry and Shipment Volume

Logit Type I Tobit

Dependent Variable = 1{Volume>0} D V = Shipment Volume (Mil. Liter)

(I) (II) (III) (IV)

Covariates Coefficients SD Coefficients SD Coefficients SD Coefficients SD

Policy Variables:

Deregulation since 1992 0.418** (0.217) 0.850*** (0.237) 0.794 (0.558) 1.319** (0.562)

Temporary Regulation in 1996 0.080 (0.357) 0.222 (0.384) −0.700 (0.957) −0.632 (0.951)

Market Size and Location:

Adult Population (Mil.) 0.576*** (0.056) −0.153 (0.103) 1.725*** (0.125) 0.727*** (0.244)

Real GDP (per adult pop.) −0.057** (0.029) −0.077** (0.030) 0.006 (0.076) 0.004 (0.075)

Distance −0.711*** (0.111) −0.775*** (0.136) −2.008*** (0.265) −1.574*** (0.272)

Route to Target Markets 3.593*** (0.458) 4.926*** (0.686)

Target Market (R0 and R1) 1.826** (0.676) 0.887 (1.295)

Firm Fixed Effects:√ √ √ √

Log Likelihood -515.562 -424.823 -2309.603 -2274.035

Observation 1,008 1,008 1,120 1,120


Notes: For entry model regressions, we exclude producer 1 which entered every market.

Appendix C: Complementary Analysis on Product Variety

This section presents the results from analyzing the firms’ provision of product variety following the soju

market deregulation. In particular, we consider two policy changes which could have affected the firms’

decisions. The first change is the implementation of new policy which allowed the use of rice. Such a change

in availability of new ingredients could lead to an increase in product variety. The other change is the removal

of policy which guaranteed the firms at least 50% market share in their local markets. While the increase


in the product variety due to the former change is accounted for by relaxed production constraints, the

increase due to the latter change suggests that the firm’s provision of product variety was led by the regional

competition intensity. Taking advantage of the fact that the above two policy changes did not take place at

the same time, we seek some evidence of the firm’s adjustment of product variety depending on the local

market competition. We build an ordered Probit model of the firm’s product variety as the following:

Ujt = f(RegionalDemographicsjt, Policyjt, F irmj),

Njt =

1 if Ujt ≤ α1 = 0

j if αk−1 <Ujt ≤ αk

......

11 if α10 ≤Ujt

Ujt represents the latent profit condition of firm j at quarter t, as a function of regional demographic

characteristics - adult population and income (per population) -, the current policy status for the region and

firm-level fixed effects. Then firm’s choice over the number of product variants to offer, Njt is determined by

the above rule. Assuming Ujt contains a stochastic component which follows the standard normal distribution,

we estimate parameters of the model together with boundary parameters αk (j = 2,...,10).

The results demonstrate that after accounting for the role of demographics, the policy allowing the use

of rice increased the product variety of the firms. However, when we add an interaction term with whether

some local market shares were secured by the other policy, the explanatory power of the interaction term

dominates. This indicates that the impact of the policy allowing the use of rice had a significantly bigger

impact in the regions where the market share of the local firm is no longer protected.

Parameter Estimates from Ordered Probit Regression on Firms’ Product Variety

(I) (II) (III)

Covariates Parameter SD Parameter SD Parameter SD

Base market population (Mil .) −0.687*** (0.175) −0.774*** (0.179) −0.781*** (0.179)

Real GDP (per adult population) 0.735*** (0.046) 0.503*** (0.055) 0.493*** (0.055)

Use of rice deregulated (1991 3Q) 1.809*** (0.247) 0.726 (0.247)

Use of rice×local product purchase deregulation 1.175* (0.212)

Fixed Effects:

Firm√ √ √

Log Likelihood -345.444 -316.542 -315.049

Observation 351 351 351

*** Significant at 1% level;** 5% level; * 10% level


Appendix D: Tests of Model Specification

Tests of validity of instruments. We performed several tests to confirm the validity of our model spec-

ification and identification strategies. First, we investigated if the three market variables – the number of

competing firms and the local firm’s product variety and market share within the local market – are indeed

endogenous in the system by employing the (Durbin-Wu-) Hausman test of endogeneity for each equation.

The tests rejected the null hypothesis that the two variables on the right hand side are jointly exogenous

in the equation, emphasizing the need for instrumental variables in estimating the system: the IV estimator

is preferred over the OLS estimator at 1% level of significance for equation (3) and (5), and at 10% level

for equation (4). In our final results from the full model, the coefficients for the two endogenous variables in

equation (4) were not statistically significant.

We also tested the validity of the selected instrument variables by employing two types of tests: 1) one on

whether they are correlated sufficiently with corresponding endogenous variables and 2) the other on whether

they satisfy the exclusion restrictions. First to check the relevance of the instrumental variables, we considered

the goodness-of-fit of the “first-stage” regressions. In the presence of more than one endogenous variable in

the equation, we followed a procedure proposed by Stock and Yogo (2005). It tests weak instruments by

investigating Cregg-Donald F -statistic based on two criteria: the level of bias of the IV estimator relative to

that of the OLS estimator, and the rejection rate of the standard Wald test for the endogenous variables.

We compared the F -statistics with the tabulated critical values at different levels of bias and rejection rates.

The F -statistics exceeded the critical values of a 5% relative bias (i.e., the IV estimators produced at most

5% bias compared to that produced by the OLS estimator) and 10% rejection rate, and thus the test rejected

the null hypothesis that our instruments are weak.

Wherever applicable, the test of overidentifying restrictions was also performed. The Sargan test was

employed for 2SLS estimators from each equation and confirmed that our instrument variables satisfy the

exclusion restrictions required for the model identification.

Robustness checks using control variables. In order to understand how robust our results from the

proposed model are when different control variables are used, we considered some other variables. In the

proposed model, we account for any unobserved sources of variations over time using a variable of “the

national economic crisis,” which captures any changes in the market outcomes since 1998 (i.e., periods under

the IMF bailout programme). We confirm that our main results do not depend on the selection of this time-

specific control variable, and the main results remained consistent when we used year fixed effects instead. In

addition, we noted that consumer demand for soju during the observed period might also have evolved due

to other factors which our current model does not capture. To investigate whether there were any factors

that drove the growth of the overall alcohol beverage market, we tested whether an addition of variables for

the market size of relevant product categories could change our results. In particular, we complemented the

model by obtaining market data from beer and cheongju (Korean fermented wine). We ran a model with

the number of beer brands and the regional market size of cheongju as additional covariates that might have

affected the soju product variety and the number of soju producers in the region. We found that none of

these had any significant impact thus on our main results.


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