Does Competition Lead to Customization?

Wen-Tai Hsu∗ Yi Lu† Travis Ng‡

December 2011

Abstract

This paper proposes a simple theory of competition and customization. When

firms allocate their production to both custom-made and generic products, the frac-

tion of sales from the former will increase in the face of increased competition. We

test this prediction using a World Bank survey of Chinese firms and find consistent

empirical results.

1 Introduction

Globalization has changed the global playing field substantially in the past few decades.

Significant reductions in tariff and non-tariff barriers and technological advances have

rendered the world’s markets more integrated and more competitive. To cope with the

increased competition they face, firms have experimented with a number of different

strategies, such as the flattening of firm hierarchies (e.g., Thesmar and Thoenig, 2000;

Guadalupe and Wulf, 2010), the geographic fragmentation of production (e.g., Hanson,

Mataloni, and Slaugher, 2005; Feenstra, 2010), innovation (e.g., Aghion, Bloom, Blundell,

∗Department of Economics, National University of Singapore†Department of Economics, National University of Singapore.‡Department of Economics, Chinese University of Hong Kong.

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Griffith, and Howitt, 2005), and the decentralization of the decision-making function (e.g.,

Bloom, Sadun, and Van Reenen, 2010).

In this paper, we explore another strategy that firms may adopt to cope with compe-

tition, that is, customization. More specifically, we ask whether competition leads firms

to seek the greater customization of their products. We define customization as the costly

alteration of a product to tailor it to customers’ needs or tastes. As the product is custom-

made to some customers, the firm exercises greater market power over these customers,

which constitutes the main source of the gains from customization. Intuitively, without

customization, when there is an increase in competition, the demands faced by individual

firms are necessarily reduced, as is their market power. In this context, customization is one

way to retain or increase market power and profits.

To formalize this intuition and derive formal predictions, we propose a simple theory

of competition and customization by adapting Loginova and Wang’s (2011) model of cus-

tomization, which builds upon Hotelling’s (1929) spatial competition framework. In our

model, the level of competition increases when there is an increase in the number of firms.

We show that the fraction of sales from customization increases when there is increased competi-

tion. Moreover, we show that if this increased competition is induced by a larger market

size, then firms have the incentive to reach out to more customers to accommodate their

needs/tastes.

The main task in this paper is to test our prediction that increased competition leads to

a larger fraction of customized sales. The effect of market competition on customization

has seldom been investigated empirically, primarily due to the difficulty of measuring

customization. Fortunately, a unique World Bank survey of Chinese firms, the Survey of

Chinese Enterprises (SCE), has opened up a window for such investigation. This survey

contains questions asking firms about the proportion of their competitors’ output that is

produced locally and the proportion of their own sales that is custom-made. Considerable

variations in both variables allow us to make inferences.

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Consistent with our model, we find increased competition to be significantly associ-

ated with a higher degree of customization. To ensure that this finding is not affected by

estimation problems, we estimate the association with the following series of specifica-

tions and robustness checks and find it to remain robust.es:

More Controls: Inclusion of a list of variables, including industry and city dummies, and

firm and CEO characteristics, to address the concern over omitted variables bias.

GMM: Generalized Method of Moments (GMM) estimation with two instruments for

competition to further deal with potential omitted variables bias, the reverse causal-

ity issue, and the measurement error problem.

Tobit Estimation: Alternative estimation to fit the censored data setting.

Outliers: Exclusion of outlying observations to ensure the finding is not driven by par-

ticular observations.

Alternative Measure: Alternative measure of competition to check whether the finding

is sensitive to subjective or objective measurement.

Self-Selection: Exclusion of firms recently relocated to the surveyed city to self-selection

concern.

In the theoretical literature, Loginova (2010) and Loginova and Wang (2011) are the

most closely related to ours. Note that although customization is closely related to hor-

izontal product differentiation, greater product differentiation in a spatial competition

framework is almost equivalent to greater entry, which reduces market power and is not

in line with the purpose of customization. Nevertheless, Shaked and Sutton (1982) have

gone in the vertical direction by showing that quality differentiation helps to relax price

competition.

As previously noted, due to the difficulty of measuring customization, there is rela-

tively little related empirical research. To the best of our knowledge, the studies closest

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to ours are Mazzeo (2002) and Holmes and Stevens (2010). Mazzeo (2002) shows that the

motels within larger clusters (thus facing greater competition) along interstate highways

in the U.S. tend to differentiate themselves by quality. This paper differs from Mazzeo

(2002) in two ways. First, we examine customization, which is conceptually very different

from vertical differentiation. Second, we go beyond a particular industry to demonstrate

that our prediction generally holds among several manufacturing industries. Holmes

and Stevens (2010) document the greater survival of small plants relative to large plants

in the face of fiercer competition. In particular, when competition becomes fiercer because

of an influx of foreign imports, it is the firms that specialize in custom-made goods that

have a greater chance of survival relative to those producing generic products. Our ob-

ject of examination differs from that of Holmes and Stevens (2010). This paper examines

the allocation of production to customized and generic products within firms rather than

comparing firms that do and do not customize.

The remainder of the paper is organized as follows. Section 2 presents a model that

formalizes our prediction that market competition leads to a larger proportion of custom-

made sales. Section 3 describes the data, specifies our empirical approach, and presents

the results. Section 4 concludes.

2 A theory of competition and customization

We consider a spatial competition model with the possibility of customization by adapt-

ing the model developed by Loginova and Wang (2011). The main idea is that investing in

a customization technology allows firms to offer a subset of customers their ideal product

varieties. Firms can then price-discriminate across the customers who are offered these

customized products. The extra gains in profit due to such price discrimination consti-

tutes the main driving force for customization.1

1The three main differences between our model and Loginova and Wang’s (2011) are as follows. First,to focus on the impact of competition on customization, we do not consider the quality dimension. Second,

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2.1 Model

Consider a market in which each product i is characterized by xi ∈ (0, 1] on a circumfer-

ence. There are in total of mass D customers who are uniformly distributed in their ideal

variety x ∈ (0, 1]. A customer of type x derives utility v − t |x− xi| − pi from buying one

unit of product i, where v is a positive constant, t is a taste parameter, and pi is the price

of product i. We assume that v is sufficiently large that all customers find a product that

yields a positive payoff in equilibrium. The market includes a large number of ex ante

identical potential entrants. To enter, each entrant needs to pay an entry cost φ. As in

Salop (1979) and Syverson (2004), we assume that all entering firms are evenly spaced;

that is, if there are n firms, then each firm is 1/n distance away from its two neighboring

firms.2 For simplicity, assume that each firm operates with a zero marginal cost of pro-

duction.3 Suppose that the closest firms to a customer of type x are firm A to the left and

firm B to the right, and suppose that the customer’s distance to A is y. Hence the utility

that x enjoys from buying the non-customized, generic products of firm A’s and firm B is

v − ty − pGA and v − t(1/n− y)− pGB, respectively.

Investing in product-customization technology allows a firm to sell customers their

ideal variety beyond the firm’s location. Given an amount of investment by firm (denoted

by i), it can produce a customized product for every customer up to a distance of si away

(on both sides) from its location. Each firm i chooses si ≥ 0, which incurs an investment

cost of c (si). Assume that c (.) is differentiable, strictly increasing, and strictly convex and

that c (0) = 0. Hence, greater customization for more diverse customers is increasingly

costly. For customers beyond a distance of si away from firm i, however, the firm can sell

as data show that firms sell both customized and non-customized, generic products at the same time, weextend the notion of customization in Loginova and Wang (2011) to include customization for only a subsetof customers. Third, we opt for Salop’s (1979) circumference instead of a Hotelling interval, so as to modelthe endogenous increase in competition and the ensuing impact on customization.

2A micro-foundation for Salop’s even spacing is provided by Vogel (2008), who shows that mixed-strategy pricing in an auxiliary game can eliminate the possibility of firms’ undercutting their opponentson price. Under this setup, with the same marginal cost, firms choose to be equi-distant from neighboringfirms.

3It can be verified that assuming a positive marginal cost produces similar results.

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only a non-customized, generic product. Note that customization allows the firm to set

prices for the customized customers individually instead of applying a uniform price.

The game involves three stages, an entry stage followed by a customization stage and

then a pricing stage. In the entry stage, potential entrants decide whether to enter. In the

customization stage, firms simultaneously decide the amount of customization invest-

ments. These decisions become common knowledge in the pricing stage, in which firms

simultaneously choose prices. Customers decide which products to purchase, and profits

are realized. The subgame perfect equilibrium is solved using backward induction.

2.2 Analysis: market size, competition, and customization

In our SCE data, we observe that firms sell both customized and non-customized prod-

ucts. Thus, to focus our analysis on cases in which there is both customized and non-

customized production, we assume that c (s) increases in s sufficiently fast such that the

optimal si < 1/ (2n) for all i. Because si < 1/ (2n), no two competing neighboring firms’

customization areas overlap.

2.2.1 Pricing stage

Given n evenly spaced firms and customization investments si, i ∈ {1, 2, ..., n}, we can

solve the pricing decisions by looking at a Hotelling duopoly problem in which two firms,

A and B, are located at the two end points of [0, 1/n].4

Customer x ∈ [sA, 1/n−sB] chooses between the two firms in buying a non-customized

product, i.e., max{v − tx− pGA, v − t (1/n− x)− pGB

}. Then, a customer of type x who is

4d’Aspremont, Gabszewicz, and Thisse (1979) show that equilibrium may not exist in the pricing stageif the distance between two neighboring firms is so close that price undercutting neighboring firms is optimal.This issue arises in Salop’s setup if n is too large or t is too small. To avoid this problem, we assume asufficiently large t or a sufficiently large entry cost φ, such that n is small. An alternative is to resort tothe possibility of mixed-strategy pricing in the auxiliary game proposed by Vogel (2008), who proves theexistence of a pure-strategy price equilibrium under such a possibility.

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indifferent between the two choices is given by

x =1

2n− pGA − pGB

2t. (1)

Note that, for now, x in the above formula may be outside the interval [sA, 1/n− sB]. We

show that, indeed, x ∈ [sA, 1/n− sB] shortly.

For a customer x ∈ [0, sA], in addition to the choice of two non-customized products

from A and B, her additional choice is whether to buy the customized product offered

by firm A. Thus, she solves max{v − tx− pGA, v − t (1/n− x)− pGB, v − pxA

}. In this stage,

c (sA) is sunk, and firmAwould obviously choose to limit price pxA = min{tx+ pGA, t (1/n− x) + pGB

}to capture the sales from [0, sA]. We argue that it must be the case that pxA = tx + pGA, i.e.,

the customizing firm’s pricing of its customized product is constrained by its pricing of its

own non-customized product. To see this, suppose on the contrary that t (1/n− x)+pGB <

tx + pGA, which implies that x < sA, and all customers in (sA, 1/n] will purchase products

from firm B. Then, it is obviously to B’s benefit to increase pGB, as long as x < sA, because

the customers in (sA, 1/n] will continue to buy from it, regardless of the increase in price.

Thus, equilibrium price pGB must satisfy the condition that x ≥ sA, which, in turn, means

that t (1/n− x) + pGB ≥ tx+ pGA for all x ∈ [0, sA]. Hence, pxA = tx+ pGA. A similar argument

for firm B implies that x ≤ 1/n− sB. Thus, we have proved that x ∈ [sA, 1/n− sB].

For now, we assume that the market size (or indeed the density) D = 1 for ease of

exposition. We will make D a general number when we discuss the impact of market

size. Using (1), we write firm A’s profit as a function of pGA and pGB5:

πA(pGA, p

GB) = [x− sA]pGA +

∫ sA

0

pxAdx

=

[1

2n− pGA − pGB

2t− sA

]pGA +

∫ sA

0

(tx+ pGA

)dx (2)

=

[1

2n− pGA − pGB

2t

]pGA +

∫ sA

0

txdx. (3)

5Note that c (sA) is sunk in this stage and hence does not show up.

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By observing (3), we can see that customization, or the lack thereof, has no effect on how

optimal price pGA is determined, given pGB; the first-order condition for pGA is the same as

that for a standard Hotelling problem, which is similarly the case for pGB. These solutions

are pGA(pGB)= t/ (2n) + pGB/2 and pGB

(pGA)= t/ (2n) + pGA/2. Thus, the equilibrium price

pair is (t/n, t/n), and x = 1/ (2n).

2.2.2 Customization stage

Plugging equilibrium price pair (t/n, t/n) into (2), the equilibrium profit of firm i, i =

A,B, from investing c (si) is thus

πi (si) =

[1

2n− si

]t

n+ts2i2

+tsin− c (si) (4)

=t

2n2+ts2i2− c (si) . (5)

Hence, optimal customization, s∗i , satisfies ts∗i = c′ (s∗i ). Recall that c (.) is strictly increas-

ing and strictly convex, and that c (0) = 0. To ensure that a unique s∗i ∈ (0, 1/ (2n))

maximizes firm i’s profit, it is sufficient that c′ (0) < t and that c increases fast enough in

s to ensure that c(1/ (2n)) > t/ (2n).

As our empirical measure of customization is the share of sales from customization,

we now show that greater competition (a larger n) leads to a larger such share. Divide the sum

of the second and third terms by that of the first three terms in (4), and we can see that

the share of sales from customization is

r =s2in

2 + 2nsis2in

2 + 1, (6)

which is strictly increasing in n. Although an increase in n decreases the sales of both

customized and the non-customized products due to the decrease in prices, the gains

from customization versus no customization, i.e., the second term in (5), is unaffected.

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These extra gains from customization arise because firms can price-discriminate each of

the customers offered customized products. As the gains from customization are robust

to an increase in competition, the optimal level of customization, s∗i , is unaffected by

increases in n. Meanwhile, as n increases, the number of customers still buying non-

customized products, 1/n − 2s∗i , necessarily decreases, or, put differently, the fraction of

customers offered customized products, 2s∗i /(1/n) = 2ns∗i , necessarily increases.

2.2.3 Market size and customization

What induces an increase in competition? Here, we offer a market size perspective and

examine the impact of such size on customization. Relax D = 1 to a general D > 0. It is

standard for an increase in D to weakly increase the number of firms n (weakly because

n is an integer number). We omit these uninteresting details, and simply denote such a

relation by n∗ (D), knowing that n∗ weakly increases in D.

As the magnitude of D has no impact on pricing decisions, the profit of firm i in the

second stage is

πi(si) = D

{[1

2n∗ (D)− si

]t

n∗ (D)+ts2i2

+tsin

}− c (si) (7)

= D

[t

2 [n∗ (D)]2+ts2i2

]− c (si) .

The first-order condition is thus

Dts∗i = c′ (s∗i ) .

Hence, regardless of the equilibrium entry n∗ (D), the optimal level of customization,

denoted by s∗i (D), must be strictly increasing in D. In terms of the share of sales from

customization, by inspecting (7), we can see immediately that the formula for this share

is the same as (6), except that now n = n∗ (D) and si = s∗i (D). Thus, as D increases, n∗

increases, which leads to a larger r if there is no change in s∗i , for the same reason as that

discussed previously. Nevertheless, because s∗i also increases in D, the increase in r is

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even larger. We summarize our results in the following proposition.

Proposition 1. An exogenous increase in competition, i.e., an increase in the number of firms n,

leads to a larger share of sales from customization, r, as defined by (6), whereas the level of each

firm i’s customization s∗i remains unchanged, which also implies that the fraction of customers

offered customized products, 2ns∗i , increases in n. An endogenous increase in competition induced

by a larger market size D leads to both a larger share of sales from customization, r, and more

customization activities, s∗i .

3 Empirical analysis

3.1 Data

Our empirical analysis draws on data from the Survey of Chinese Enterprises (SCE), which

was carried out by the World Bank in cooperation with the Enterprise Survey Organiza-

tion of China in early 2003. For balanced representation, the SCE covered 18 prefecture-

level cities in five geographic regions of China: Benxi, Changchun, Dalian, and Harbin in

the Northeastern region; Hangzhou, Jiangmen, Shenzhen, and Wenzhou in the Coastal

region; Changsha, Nanchang, Wuhan, and Zhengzhou in the Central region; Chongqing,

Guiyang, Kunming, and Nanning in the Southwestern region; and Lanzhou and Xi’an in

the Northwestern region.

In each of these cities, the SCE randomly sampled 100 or 150 firms from nine manufac-

turing industries (garments and leather products, electronic equipment, electronic parts

making, household electronics, auto and auto parts, food processing, chemical products

and medicine, biotech products and Chinese medicine, and metallurgical products) and

five service industries (transportation services, information technology, accounting and

non-banking financial services, advertising and marketing, and business services). The

total number of enterprises surveyed is 2,400.

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The SCE contains two parts. The first is a general questionnaire directed at senior

management that seeks information about the enterprise, such as degree of innovation,

product certification, marketing, relations with suppliers and customers, access to mar-

kets and technology, relations with government, labor force, infrastructure, involvement

in international trade, finance, and taxation, and the information on the CEO and board

of directors. The second questionnaire is directed at accountants and personnel managers

and covers ownership, various financial measures, and labor and training. Most of the

information in the first part of the SCE pertains to the survey year, 2002, whereas that in

the second part pertains to the 2000-2002 period.

As service industries are largely localized and customized, we focus here on the man-

ufacturing firms in the SCE. Our final sample thus contains 1,566 firms.

3.2 Empirical approach

In the spirit of Proposition 1, to test whether increased market competition (x) leads to a

larger fraction of sales from customized products (r), we start with the following linear

equation:

rfic = ζ + β · xfic + εfic, (8)

where f , i, and c index firm, industry, and city, respectively.

The measure of our dependent variable, rfic, comes from the SCE’s question concern-

ing about the percentage of a firm’s sales made to clients’ unique specifications (i.e., its

sales of products that cannot be sold to other clients), and is denoted as Custom-made in

the regression tables. Table 1 shows that of the manufacturing firms in the sample, ap-

proximately 40% of their output is custom-made. The large standard deviation of this

variable suggests that there are substantial variations from which to draw inferences.

The regressor of interest, xfic, concerns market competition. To capture the degree of

market competition, we use the percentage of output produced by a firm’s competitors’

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Table 1: Summary StatisticsVariable Obs Mean Std. Dev. Min MaxCustom-made 1511 0.406 0.419 0 1Local Competition 1507 0.273 0.337 0 1Firm characteristicsFirm Size 1563 5.040 1.453 0 9.899Firm Age 1566 2.494 0.777 1.099 3.970Private Ownership 1566 0.796 0.389 0 1Labor Productivity 1332 3.503 1.574 -3.761 11.150Skilled Labor 1542 0.026 0.060 0 1CEO characteristicsCEO Education 1553 15.359 2.511 0 19CEO Tenure 1548 6.240 4.580 1 33Deputy CEO Previously 1548 0.280 0.449 0 1Government Cadre Previously 1548 0.036 0.185 0 1Party Member 1524 0.648 0.478 0 1Government-appointed 1544 0.243 0.429 0 1Instrumental variableLocal Clients 1542 0.331 0.362 0 1Local Suppliers 1541 0.373 0.349 0 1Additional controlsClients’ Duration Dummies¡1 yr 1549 0.039 0.193 0 11 to 2 yrs 1549 0.073 0.260 0 12 to 3 yrs 1549 0.125 0.331 0 13 to 4 yrs 1549 0.126 0.332 0 1¿4 yrs 1549 0.637 0.481 0 1Custom-made Component 1444 0.068 0.221 0 1

in the same city. Focusing on the local rather than national market allows us to capture

the effect of market competition, as the product market may not be fully integrated due

to transportation costs and market friction. Recall that we employ the number of firms

to indicate the level of competition in our theoretical model for the sake of simplicity. In

reality, however, with heterogeneous firms, competitors’ output is a better measure. In

the presence of heterogeneous firm sizes in terms of output, excluding firm f itself in

constructing the measure of market competition allows us both to utilize firm-level varia-

tions even within the same city and same industry, and avoid the mechanical correlation

between the regressor and the outcome variable. For ease of exposition, xfic is denoted as

Local Competition in all of the regression tables.

Note that we use a subjective measure of market competition (that is, the firm’s per-

ceived percentage of competitors in the same city) rather than an objective measure (such

as the total number of firms in the same industry and city, as used in Holmes and Stevens

12

(2002) and Henderson (2003)). Our subjective measure has certain advantages over objec-

tive measures based on industry classification. Arguably, a firm’s decision making (with

regard to customization) is based on its perception of the competition. As long as firms

in the same industry and city potentially face different degrees of competition (e.g., large

versus small firms), our subjective measure is able to capture this heterogeneity in re-

sponses. However, it may suffer from the problem of idiosyncratic observational error or

misreporting. We provide details of how we deal with this potential measurement error

problem later in this section. Moreover, as a robustness check, we experiment with an

objective measure used in the literature (that is, the total amount of employment in the

same industry and same city).

To estimate equation (8), we primarily employ ordinary-least-squares (OLS) estima-

tion. However, as the dependent variable is left-side (at 0) and right-side (at 1) truncated,

we also use Tobit estimation as a robustness check. In addition, to deal with the possible

heteroskedasticity, we cluster the standard errors at the industry-city level in all of the

regressions.

Before proceeding to the results, we discuss several potential econometric problems

that may cause biases in estimating equation (8).

Omitted variables. It is plausible that xfic is correlated with the error term εfic in equa-

tion (8), thus biasing the estimation of β. One prominent set of omitted variables includes

industrial differences, such as differences in entry barriers (e.g., φ), customization tech-

nology (e.g., si), and taste (e.g., t). To address this concern, we include industry dummies

in the regression analysis. We also include city dummies to account for any potential city

differences. To further control for variations across industries within a city, we replace the

industry and city dummies with industry-city dummies.

Another prominent set of omitted variables encompasses those related to firm capa-

bility. Picone, Ridley, and Zandbergen (2009) show that firms with a greater ability to

differentiate their products are more likely to cluster strategically. To single out the effect

13

of local competition on customization, we control for a list of firm6 and CEO character-

istics commonly used in the literature.7 To avoid the “bad control” problem, we employ

the lagged values of these variables, as in the regression, wherever possible (Angrist and

Pischke, 2009).

As a result, the estimation equation becomes

rfic = α + β · xfic + Z′

ficγ + υfic, (9)

where Zfic is a vector of control variables.

Admittedly, it is impossible for us to control for all possible omitted variables in the

regression. However, as will be seen, controlling for the aforementioned prominent omit-

ted variables has almost no impact on our estimation of β, in terms of either statistical

significance or magnitude. As a further robustness check, we also resort to instrumental

variable estimation, which we discuss shortly.

Measurement error. Our measure of competition is a subjective measure, which raises

concerns over potential measurement error. Theoretically, it is difficult to say whether

and why firms in more competitive environments are more (or less) likely to misreport

the degree of market competition. However, the long list of controls in the regression

analysis may allow us to control for certain systematic patterns in the measurement error

across firms, although the existence of the white-noise type of measurement error may

drive the estimated coefficient β towards zero against any significant findings. We resort

6The variables related to firm characteristics include Firm Size (measured by the logarithm of 2001 totalemployment), Firm Age (measured by the logarithm of years of establishment), Private Ownership (measuredby the share of equity owned by private parties in 1999), Labor Productivity (measured by the logarithm ofoutput per worker in 2001), and Skilled Labor (measured by the share of workers in 2001 who dealt withadvanced technology).

7The variables concerning CEO characteristics are his or her human capital, including CEO Education(years of schooling), CEO Tenure (years of being CEO), and Deputy CEO Previously (a dummy variable in-dicating whether the CEO was the firm’s deputy CEO before he or she became its CEO), and politicalcapital, including Government Cadre Previously (a dummy variable indicating whether the CEO was a gov-ernment official before he or she became CEO), Party Member (a dummy variable indicating whether theCEO is a member of the Chinese Communist Party), and Government-appointed (a dummy variable indicat-ing whether the CEO was appointed by the government).

14

to the instrumental variable approach to further address this type of measurement error

problem.

More on omitted variables and measurement error. To further address concerns over omit-

ted variables and measurement error, we employ GMM estimation with two instruments

for the key explanatory variable, the measure of market competition.

Krugman (1991) shows that the clustering of manufacturers is positively correlated

with that of consumers due to the demand-supply linkage. As shown in Section 2.2.3, an

increase in the number of consumers leads to an increase in the degree of competition.

However, from equation (6), we can see that this increase in the number of consumers

does not lead directly to a larger fraction of sales from customized products. Hence, the

number of consumers constitutes a good instrumental variable for the degree of market

competition. One question in the SCE asks respondents about the percentage of the firm’s

clients (in terms of sales) that are located in the same city as the firm. We construct our

first instrument, Local Clients, accordingly.

Krugman and Venables (1995) and Venables (1996) further show that the clustering of

manufacturers is also positively correlated with that of their suppliers due to the vertical

linkage. Following these researchers’ insights, we construct our second instrument, Local

Suppliers, which is the percentage of a firm’s suppliers (in terms of sales) that are located

in the same city as the firm.

The validity of GMM estimation relies on the exclusion restriction, which means that

the two instruments can affect the outcome variable (Custom-made) only through the en-

dogenous variable (Local Competition). With regard to the exclusion restriction, note that

with the inclusion of industry and city dummies, the possible correlation between the

instrumental variables and the error term υfic in equation (9) stems largely from firm-

level characteristics. Given the small-sized nature of the sample firms, it is difficult to

see how an individual firm could influence the location decisions of its clients and sup-

pliers, which may be why the Hansen J statistic fails to reject the hypothesis that at least

15

one of our two instruments is valid and why the additional inclusion of firm and CEO

characteristics barely affects our estimated coefficient of β in the GMM regression.

We further conduct two sets of robustness checks for our GMM estimation. First,

we include two variables in the regression to control for the two oft-mentioned chan-

nels through which our instrumental variables may affect the outcome variable rather

than through market competition: Custom-made components (a dummy variable indicating

whether or not the firm’s two major components are uniquely supplied) and Client dura-

tion dummies (dummies of the average duration of the business relationship with clients

in the main business line: less than one year, one to two years, two to three years, three to

four years, and more than four years).

Second, we conduct two falsification tests. The first is based on a unique business

feature of China. Note that, in the SCE data, some firms are engaged in business with

the government (i.e., 301 of the 1,509 firms). In China, a firm’s engagement in business

with the government does not depend on its economic strength but rather on personal

connections (guanxi). Hence, market competition should not change the composition of

production for firms producing for the government. Following this argument, we divide

the entire sample according to whether or not a firm has business with the government,

and we check whether the estimate of β for the former subsample is smaller or even

insignificant.

Our second falsification test follows the spirit of Angrist and Pischke (2009): if some

variables are not supposed to be affected by the endogenous variable, then a reduced-

form regression of those variables on the instrumental variables should result in an in-

significant association. The SCE asks, “According to your tax reporting requirements do

you have to use a cash register or other electronic devices?” It is highly unlikely that

the degree of market competition in a given industry could influence the Chinese tax au-

thority to decide which tax reporting devices to use. Hence, conditional on the controls,

regressing the choice of cash register/electronic devices on our instrumental variables

16

should generate insignificant estimates of the instruments.

Self-selection issue. Even if we obtain a consistent and unbiased estimate of β, it is still

possible that the positive impact of market competition on customization simply reflects

the sorting of firms across locations, i.e., firms with a higher degree of customization

locate in more competitive areas.

Given the cross-sectional nature of our data, it is difficult for us to rule out this “dy-

namic” concern completely. By further exploring the data, however, we can compare

firms that recently moved to the surveyed city with those that have been there for a long

time. If the estimated coefficients of β are similar across these two samples, then self-

selection is unlikely to be a major concern in our analysis. One question in the SCE asks

whether the firm recently relocated from another city. However, as only a few firms an-

swered in the affirmative, it is not sensible to divide the full sample into two based on

firms’ answer to this question and to compare the two estimated coefficients of β. In-

stead, we compare the estimated coefficient of β for the subset of firms answering “no”

to this question with that for the whole sample.

3.3 Empirical results

3.3.1 Main results

Table 2 presents the OLS regression results. As Column 1 shows, Local Competition has a

positive and statistically significant association with the degree of customization, which

is consistent with our theoretical prediction. To gauge the economic significance of this

result, we calculate that a one standard deviation increase in Local Competition is asso-

ciated with an 0.112 × 0.415 = 4.65% increase in the percentage of custom-made prod-

ucts/services, or 11.45% relative to the mean of Custom-made.

To investigate whether the estimation is biased due to any omitted variables, we in-

clude industry and city dummies in Column 2 and industry-city dummies in Column 3.

17

Table 2: Customization and Competition1 2 3 4 5

Dependent variable Custom-madeLocal Competition 0.098*** 0.101*** 0.115*** 0.115*** 0.112***

[0.034] [0.031] [0.031] [0.033] [0.036]Firm characteristicsFirm Size -0.010 -0.004

[0.010] [0.011]Firm Age 0.018 0.007

[0.018] [0.020]Private Ownership 0.039 0.043

[0.030] [0.033]Labor Productivity 0.005 0.004

[0.009] [0.010]Skilled Labor -0.031 -0.056

[0.234] [0.232]CEO characteristicsCEO Education 0.003

[0.006]CEO Tenure 0.004

[0.003]Deputy CEO Previously 0.049*

[0.029]Government Cadre Previously 0.060

[0.073]Party Member -0.055*

[0.033]Government-appointed 0.024

[0.032]Industry dummies - Yes - - -City dummies - Yes - - -Industry-city dummies - - Yes Yes YesObservations 1459 1459 1459 1246 1191R-squared 0.006 0.088 0.153 0.171 0.180p-value for F-test 0.005 0.000 0.000 0.000 0.000Mean[standard deviation]Custom-made 0.404 0.404 0.404 0.403 0.405

[0.418] [0.418] [0.418] [0.415] [0.415]Local Competition 0.275 0.275 0.275 0.267 0.269

[0.337] [0.337] [0.337] [0.329] [0.330]

White-robust standard errors clustered at the industry-city level are reported in brack-ets. ∗, ∗∗, and ∗ ∗ ∗ represent statistical significance at the 10%, 5%, and 1% level. Aconstant term is included in all regressions, but the results are not reported to savespace.

18

Clearly, the estimation coefficients of Local Competition remain positive and highly signif-

icant. Further, the magnitude of the estimated coefficients barely changes across these

specifications.

In Columns 4 and 5, we further include a list of commonly used firm and CEO char-

acteristics to account for any firm heterogeneity that may cause bias in our estimation.

More specifically, we include firm size and age to control for economies of scale and the

seniority effect; the percentage of private ownership to take care of the crucial differences

between state-owned enterprises (SOEs) and private firms in China; and labor produc-

tivity and the skilled labor ratio to deal with technological differences among firms. For

CEO characteristics, we include three proxies of a CEO’s human capital and another three

for his or her political capital. It is clear that the estimation coefficients are always positive

and statistically significant, and their magnitude is almost the same as the corresponding

values in the regressions without these controls.

3.3.2 GMM estimates

Given that the controls used in Columns 2 through 5 largely account for important dif-

ferences across firms, the concern over potential omitted-variable bias is alleviated. To

address the remaining concern that some unobserved heterogeneity may still bias our

results via correlation with Local Competition, we conduct GMM estimation with the two

instruments proposed in Section 3.2, Local Clients and Local Suppliers.

The GMM estimation results are reported in Table 3. As Panel B of Column 1 shows,

both instruments are positively and statistically significantly correlated with the key ex-

planatory variable (Local Competition). The Kleibergen-Paap rk Lm statistic further con-

firms that the instruments are relevant, and the Kleibergen-Paap Wald rk F statistic rules

out the concern over weak instruments.

With regard to the central issue, after being instrumented, Local Competition still has a

positive and statistically significant association with the degree of customization, which

19

Tabl

e3:

Cus

tom

izat

ion

and

Com

peti

tion

:Ins

trum

enta

lVar

iabl

eEs

tim

atio

n1

23

45

6Es

tim

atio

nG

MM

OLS

Sam

ple

Full

Busi

ness

wit

hgo

vern

men

tN

obu

sine

ssw

ith

gove

rnm

ent

Full

Dep

.var

.:C

usto

m-m

ade

Dep

.var

.:C

ash

Reg

iste

rLo

calC

ompe

titi

on0.

200*

0.24

2*0.

253*

-0.1

540.

275*

[0.1

18]

[0.1

37]

[0.1

42]

[0.2

91]

[0.1

57]

Cus

tom

-mad

eC

ompo

nent

0.11

3[0

.081

]C

lient

Dur

atio

nD

umm

ies

Yes

Firm

Cha

ract

eris

tics

-Ye

sYe

sYe

sYe

sC

EOC

hara

cter

isti

cs-

Yes

Yes

Yes

Yes

Indu

stry

-cit

yD

umm

ies

Yes

Yes

Yes

Yes

Yes

Pane

lB,fi

rsts

tage

Dep

.var

.:Lo

calC

ompe

titi

onLo

calC

lient

s0.

375*

**0.

350*

**0.

365*

**0.

280*

*0.

359*

**-0

.039

[0.0

34]

[0.0

35]

[0.0

39]

[0.1

13]

[0.0

44]

[0.0

31]

Loca

lSup

plie

rs0.

122*

**0.

106*

**0.

083*

**0.

113

0.11

5***

0.03

3[0

.029

][0

.032

][0

.031

][0

.086

][0

.033

][0

.032

]C

usto

m-m

ade

Com

pone

nt-0

.023

[0.0

43]

Clie

ntD

urat

ion

Dum

mie

sYe

sFi

rmC

hara

cter

isti

cs-

Yes

Yes

Yes

Yes

Yes

CEO

Cha

ract

eris

tics

-Ye

sYe

sYe

sYe

sYe

sIn

dust

ry-c

ity

Dum

mie

sYe

sYe

sYe

sYe

sYe

sYe

sPa

nelC

Var

ious

econ

omet

ric

test

sfo

rfir

stst

age

Kle

iber

gen-

Paap

rkLM

stat

isti

c64

.96*

**50

.87*

**50

.39*

**15

.24*

**38

.04*

**K

leib

erge

n-Pa

apW

ald

rkF

stat

isti

c10

7.49

65.4

764

.35

9.03

47.2

7H

anse

nJs

tati

stic

2.45

41.

368

1.74

30.

102

0.40

8H

ausm

ante

st0.

505

0.87

20.

902

1.38

21.

345

Obs

erva

tion

s14

3711

8211

1224

891

312

41

Whi

te-r

obus

tsta

ndar

der

rors

clus

tere

dat

the

indu

stry

-cit

yle

vela

rere

port

edin

brac

kets

.∗,∗∗,

and∗∗∗

repr

esen

tsta

tist

ical

sign

ifica

nce

atth

e10

%,5

%,a

nd1%

leve

l.A

cons

tant

term

isin

clud

edin

allr

egre

ssio

ns,b

utth

ere

sult

sar

eno

trep

orte

dto

save

spac

e.

20

is consistent with our OLS estimation results. In terms of magnitude, although the coef-

ficient now rises to 0.200, which is larger than the corresponding OLS estimate (0.115 in

Column 3 of Table 2), the Hausman test (reported in Panel C of Table 3) shows that the

GMM estimated coefficient is not statistically different from the OLS estimated coefficient.

To further verify the validity of our GMM estimation with regard to the exclusion

restriction, we conduct the following four sets of tests.

First, with two instruments for our endogenous variable, we report the over-identification

test by the Hansen J statistic, for which the default hypothesis is that at least one of the

two instruments is valid. As shown in Panel C of Table 3, in all of our GMM estimations,

the Hansen J statistic is always statistically insignificant, which suggests that at least one

of our instruments is valid.

Second, we include firm and CEO characteristics in Column 2. Although we believe

that the small-sized nature of the firms in our data renders it difficult for an individual

firm’s characteristics to influence the location choice of its clients and suppliers, the ad-

ditional control of firm and CEO characteristics can help us to check the validity of our

argument and improve estimation efficiency. As can be seen in Column 2, the estimation

coefficient of Local Competition remains positive and statistically significant. Although the

magnitude increases from 0.200 to 0.242, much of the increase can be explained by the

reduction in sample size.8

Third, we further control for two other oft-mentioned channels in Column 3. One

possible failing of our GMM estimation is that the clustering of suppliers, which is likely

to be correlated with the clustering of firms, might positively affect firms’ incentives to

produce customized goods, because there may be more customized components avail-

able. The SCE contains a question asking each firm whether its two major components

are uniquely supplied to it, which allows us to construct a control variable to address

this concern. Another possibility is that the number of years that a firm has done busi-

8The estimation coefficient for the same sample in Column 2, but without firm and CEO characteristics,is 0.231.

21

ness with its clients may affect both its location and customization strategies. From the

SCE data, we construct five dummies to account for five different business durations with

clients (i.e., less than one year, one to two years, two to three years, three to four years,

and more than four years). As shown in Column 3, controlling these two additional sets

of variables has virtually no impact on our estimation results.

Finally, we conduct two falsification tests. First, as discussed in Section 3.2, we expect

the subsample of firms that have some business dealings with the government to have a

smaller or even insignificant coefficient of β. The GMM estimation results are reported

in Columns 4-5 of Table 3.9 Consistent with our intuition, the estimated coefficient of

Local Competition for the subsample of firms with government business dealings loses its

statistical significance and becomes negative, whereas it remains positive and statistically

significant for those without any such dealings. Second, as market competition is un-

likely to influence the Chinese tax authority’s decision over which tax reporting device to

employ, the regression of the use of a cash register/electronic devices on the instrumental

variables is expected to generate insignificant estimates. Indeed, we find in Column 6 of

Table 3 that our two instrumental variables have no significant association with the use

of a cash register/electronic devices.

Overall, the results in Tables 2-3 confirm our theory that competition increases the

degree of customization. These findings are unlikely to be driven by omitted variables,

measurement error, and/or reverse causality.

3.3.3 Robustness checks

In this subsection, we provide a further series of robustness checks. For ease of com-

parison, we copy the corresponding OLS regression results from Column 5 of Table 2 to

Column 1 of Table 4.

Tobit regression. Our dependent variable varies from 0 to 1, which may raise concern

9The OLS estimation results, which are available upon request, exhibits a similar pattern.

22

Table 4: Customization and Competition, Robustness Checks1 2 3 4 5 6

Estimation OLS Tobit Robust OLS OLS GLS OLSDependent variable Custom-madeSample Full Full Full Local firmsLocal Competition 0.112*** 0.172*** 0.126*** 0.353*** 0.113***

[0.036] [0.002] [0.043] [0.109] [0.037]Log Total Employment 0.058***

[0.015]Firm Characteristics Yes Yes Yes Yes Yes YesCEO Characteristics Yes Yes Yes Yes Yes YesIndustry-city Dummies Yes Yes Yes Yes Yes YesObservations 1191 1191 1190 1227 1191 1167Pseudo R2/R-squared 0.180 0.096 - 0.1067 - 0.181p-value for F-test 0.000 0.000 0.000 0.000 - 0.000

White-robust standard errors clustered at the industry-city level are reported in brackets. ∗, ∗∗,and ∗∗∗ represent statistical significance at the 10%, 5%, and 1% level. A constant term is includedin all regressions, but the results are not reported to save space.

about the validity of OLS regression for this censored data setting. As a robustness check,

we thus employ Tobit regression with a left-side truncation at 0 and a right-side trunca-

tion at 1. The Tobit regression results are reported in Column 2 of Table 4. The estimated

coefficient of Local Competition remains positive and statistically significant, and the esti-

mation becomes more precise.

Outliers. There may also be concern that our estimation results are driven by or biased

due to some outlying observations. To address this concern, we re-estimate our results

using robust OLS regression, which first performs an initial screening based on Cook’s

distance > 1 to eliminate gross outliers and then performs Huber iterations followed by

biweight iterations (Li, 1985). As shown in Column 3 of Table 4, only one observation is

dropped in this robust OLS regression, and our main findings remain robust, thus imply-

ing that outliers are not a major concern in our analysis.

Alternative measure of market competition. In the analysis thus far, we have focused on

a subjective measure of market competition, that is, the perceived percentage of a firm’s

competitors (in terms of output) located in the same city. For a robustness check, we

employ an objective measure used in the literature, that is, the total amount of employ-

ment in the same industry and same city. As can be seen in Column 4 of Table 4, the

alternative, objective measure of market competition still has a positive and statistically

23

significant estimated coefficient.

Self-selection? Finally, it could be argued that the positive coefficient may reflect the

self-selection by firms with more customized goods into more competitive areas rather

than the impact of competition on customization. To alleviate this concern, we re-run our

estimation on a sub-sample that excludes firms that recently relocated from another city

to the survey city. As shown in Column 6 of Table 4, the estimated coefficient of Local

Competition remains positive and statistically significant, and its magnitude is almost the

same as that of the estimated coefficient for the full sample. This result implies that self-

selection is not the main explanation for our findings.

4 Conclusion

As empirical measures of customization are rare, this paper’s main contribution is its an-

swer to the question of whether competition leads to customization based on a unique

data set from a World Bank survey on Chinese firms. Before carrying out a number of

tests, we use a simple Hotelling/Salop model with customization activities to formal-

ize the intuition that competition leads to a larger fraction of sales from custom-made

products, which is precisely our measure of customization. We find this prediction to

withstand the tests, as our results are robust to a variety of empirical specifications.

References

[1] Aghion, Philippe, Nick Bloom, Richard Blundell, Rachel Griffith, and Peter

Howitt. 2005. “Competition and Innovation: An Inverted-U Relationship,” Quarterly

Journal of Economics, 120: 701-728

[2] Angrist, Joshua, and Jorn-Steffen Pischke. 2009. Mostly Harmless Econometrics.

Princeton, NJ: Princeton University Press.

24

[3] Bloom, Nicholas, Raffaella Sadun, and John Van Reenen. 2010. “Does Product

Market Competition Lead Firms to Decentralize?” American Economic Review Papers

and Proceedings, 100: 434-438.

[4] d’Aspremont, Claude, Jean J. Gabszewicz, and Jacques-Francois Thisse. 1979. “On

Hotelling’s ‘Stability in Competition’,” Econometrica, 47(5): 1145-50.

[5] Feenstra, Robert C. 2010. Offshoring in the Global Economy. Cambridge, MA: MIT

Press.

[6] Guadalupe, Maria, and Julie Wulf. 2010. “The Flattening Firm and Product Market

Competition: The Effect of Trade Liberalization on Corporate Hierarchies,” American

Economic Journal: Applied Economics, 2(4) : 105-127.

[7] Hanson, Gordon H., Raymond J. Mataloni, and Matthew Slaughter. 2005. “Verti-

cal Production Networks in Multinational Firms,” Review of Economics and Statistics,

87(4): 664-678.

[8] Henderson, J. Vernon. 2003. “Marshall’s Scale Economies,” Journal of Urban Eco-

nomics, 53: 1-28.

[9] Holmes, Thomas J., and John J. Stevens. 2002. “Geographic Concentration and Es-

tablishment Scale,” Review of Economics and Statistics, 84(4): 682-690.

[10] Holmes, Thomas J., and John J. Stevens. 2010. “An Alternative Theory of the Size

Distribution with an Application to Trade,” Working paper, University of Minnesota.

[11] Hotelling, Harold. 1929. “Stability in Competition,” Economic Journal, 39(153): 41-57.

[12] Krugman, Paul. 1991. “Increasing Returns and Economic Geography,” Journal of Po-

litical Economy, 99(3): 483-499.

[13] Krugman, Paul, and Anthony Venables. 1995. “Globalization and the Inequality of

Nations,” Quarterly Journal of Economics, 110(4): 857-880.

25

[14] Li, Guoying. 1985. “Robust Regression,” in Exploring Data Tables, Trends, and Shapes,

eds. D. C. Hoaglin, F. Mosteller, and J. W. Tukey, 281-340. New York: Wiley.

[15] Loginova, Oksana. 2010. “Brand Familiarity and Product Knowledge in Customiza-

tion.” International Journal of Economic Theory, 6(3): 297-309.

[16] Loginova, Oksana, and X. Henry Wang. 2011. “Customization with Vertically Dif-

ferentiated Products,” Journal of Economics and Management Strategy, 20(2): 475-515.

[17] Mazzeo, Michael J. 2002. “Product Choice and Oligopoly Market Structure,” RAND

Journal of Economics, 33(2): 221-242.

[18] Picone, Gabriel A., David B. Ridley, and Paul A. Zandbergen. 2009. “Distance

Decreases with Differentiation: Strategic Agglomeration by Retailers,” International

Journal of Industrial Organization, 27(3): 463-473.

[19] Salop, Steven C. 1979. “Monopolistic Competition with Outside Goods,” Bell Journal

of Economics, 10(1): 141-56.

[20] Shaked, Avner, and John Sutton. 1982. “Relaxing Price Competition through Prod-

uct Differentiation,” Review of Economic Studies, 49(1): 3-13.

[21] Syverson, Chad. 2004. “Market Structure and Productivity: A Concrete Example,”

Journal of Political Economy, 112(6): 1181-1222.

[22] Thesmar, David, and Mathias Thoenig. 2000. “Creative Destruction and Firm Or-

ganization Choice,” Quarterly Journal of Economics, 115(4): 1201-1237.

[23] Venables, Anthony. 1996. “Equilibrium Locations of Vertically Linked Industries,”

International Economic Review, 37(2): 341-359.

[24] Vogel, Jonathan. 2008. “Spatial Competition with Heterogeneous Firms,” Journal of

Political Economy, 116(3): 423-466.

26