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A Multi-Mechanism Model of Learning-By-Exporting:
Analysis of Export Induced Productivity Gains in Chinese Firms
Caleb H. Tse
Linhui Yu
JianJun (John) Zhu
Caleb H. Tse is a PhD candidate in marketing at the University of Hong Kong, School of Business, Hong
Kong ([email protected]). Dr. Linhui Yu is a research assistant professor in strategy and
international business at the University of Hong Kong, School of Business, Hong Kong
([email protected]). Dr. JianJun (John) Zhu is an assistant professor in marketing University of
Hong Kong, School of Business, Hong Kong ([email protected]).
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A Multi-Mechanism Model of Learning-By-Exporting:
Analysis of Export Induced Productivity Gains in Chinese Firms
ABSTRACT
This paper “opens the black box” in examining how and under what conditions do firms achieve
productivity gains through exporting, known as the learning-by-exporting (LBE) effect. We posit that
these firms leverage their learning, as the current LBE paradigm postulates, through improvement in
innovativeness, production capabilities and management emphasis. We test our hypotheses with panelized
data gathered from over 240,000 Chinese firms over a 7-year period (2001-2007).The results strongly
confirm a multi-mechanism LBE model. All three mechanisms show a parallel and significant mediation
between firm exports and productivity. We also show that the salience of these mechanisms is contingent
upon industry characteristics: firms in R&D-intensive industries as well as those in moderate levels of
export intensity demonstrate the most learning through all three channels. Hence, the multiple mechanism
model LBE offers useful implications for managers and policy-makers.
Key words: learning-by-exporting, innovativeness, organizational learning
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INTRODUCTION
International business studies have shown that firms, which expand abroad in order to source new
knowledge, can improve upon their capabilities (Almeida, 1996; Knight & Cavusgil, 2004). Through
exporting, the most prevalent form of internationalization, firms can acquire first-hand knowledge in
overseas markets as they serve diverse customers and compete with foreign firms. Labeled as “learningby-exporting” (LBE), this knowledge leveraging effect postulates that firms learn through conducting
exporting activities, leading to productivity gains (Aw, Chung, & Roberts, 2000). Although some studies
in developed economies have not found sufficient empirical support for this effect (Bernard & Jensen,
1999; Clerides, Lach, & Tybout, 1998), others have provided ample evidence using developing and
emerging economy contexts (Aw et al., 2000; Blalock & Gertler, 2004). Recently, this issue has emerged
as an important topic in international business (Lages, Jap, & Griffith, 2008; Salomon & Jin, 2008),
economics (Blalock & Gertler, 2004; Castellani, 2002 ) and firm strategy (Salomon & Jin, 2010). In the
highly competitive global market, the promise of productivity gains through enhanced knowledge has
strong implications for international business managers and policy makers.
Despite the importance of the topic, extant research has predominantly emphasized the direct
relationship between exporting and productivity, and few have made attempts to uncover the profound
mechanism of ‘learning’ (Castellani, 2002). This lack of comprehension is problematic and can seriously
affect subsequent managerial implications derived from this relationship. It will be misleading to build
our conventional wisdom simply on the direct causality between exporting and productivity gain. The
rationale for the LBE effect is rooted in organizational learning and international knowledge transfer
literature (Fiol & Lyles, 1985; Salomon, 2006), which suggests that it should not be the exporting
behavior in itself that is critical to productivity improvements, but instead should be the subsequent
knowledge acquired and experience accumulated from exporting.
Motivated by the emergence of this issue, our study ‘opens the black-box’ and extends from
previous efforts by postulating a multi-mechanism LBE model. Our objective is to go beyond the
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anecdotal evidences regarding the LBE effect and take a step further in probing the underlying
mechanisms that lead to a firm’s productivity increases, which are stimulated by exporting. While a
firm’s knowledge processing and exploitation is in itself unobservable to us, it does influence and is
manifested in more readily observable business practices (Schein, 1985). Drawing on organizational
learning literature, we propose that the LBE effect (i.e. exporting to productivity gains) is achieved, at
least partly, through the mediating roles of firm innovativeness, production capability improvements, and
managerial emphasis. A number of premises support the need for this expanded multi-mechanism view of
LBE.
First, a multi-mechanism model better fits the multi-dimensionality of knowledge (Huber, 1991)
that can be acquired and exploited by exporting firms. When firms serve customers and compete with
firms in overseas markets, they are exposed to a broad array of new and diversified knowledge (Griffith,
Huergo, & Mairesse, 2006), as well as to new challenges in quality standards and management efficiency
(Slater & Narver, 1995). This knowledge, whether experiential or vicarious, tacit or explicit, is not
restricted to just technological advancement. Learning and commercialization can occur within multiple
domains when firms are exposed to various types of new knowledge (Dierkes, Berthoin-Antal, Child, &
Nonaka, 2003). Improvements in product innovation (Liu & Buck 2007), production capabilities (Serti &
Tomasi, 2008) and managerial skills (Djankov & Hoekman, 2000) are among the most crucial areas and
objectives to fulfill in facilitating knowledge acquisition and exploitation. For firms internationalizing
their operations, multiple strategic intents and areas of improvement are common (Luo, Zhao, Wang, &
Xi, 2011); thus a multi-mechanism LBE model better captures the multi-faceted nature of firms’ learning
from exporting.
Second, the multi-mechanism model differentiates between the effectiveness of learning through
different conduits. The extent of learning is likely determined by the firm’s business activities and
resource allocations, which influence its learning capacity across various learning domains (such as
product innovation, production capabilities and management skills) of the firm (Dierkes et al. 2003). To
effectively learn in a specific domain, particular sets of resources of varying costs are essential. This issue
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is especially salient for emerging market firms, our study context, where the availability of resources and
associated costs differ across firms (Luo & Tung, 2007). A multi-mechanism LBE model would better
reflect the varying effectiveness of learning through different mechanisms and would provide insights
into the subsequent choices managers face in selecting which domain to improve upon.
Lastly, a multi-mechanism model of LBE is useful to policy makers. By now, the strong appeals
of LBE (i.e. engaging in exporting can lead to increases in productivity) have prompted growing numbers
of national (e.g., Brazil, Nigeria) and international bodies (e.g., United Nations, The World Bank) to
study the effects of LBE (Fernandes & Isgut, 2007). Based on their analyses, policy makers—
especially from emerging economies—are designing ways to stimulate their industries and firms for
growth (Luo, Xue, & Han 2010). Their strategic intents are wide, covering a broad spectrum of options.
To help guide these policy decisions, a broadened and thorough exploration of the LBE model is needed.
Hence, a multi-mechanism LBE model provides a better reflection of the required global strategy
for emerging market firms. We use Chinese firms as our study context because, relatively speaking, these
firms were known to lag behind global firms in various technological and managerial domains. However,
through internationalization, these firms have addressed some of their competitive disadvantages in
management expertise and technological capabilities (Child and Rodrigues, 2005), and have compensated
for their “latecomer” status in the global economy (Luo & Tung, 2007). In so doing, they eagerly acquire
foreign technologies, methods and practices. In sum, how Chinese firms achieve productivity gains
through LBE is an appropriate context to study this multi-mechanism model.
Challenged by their limited resources, business managers and public policy makers further find it
crucial to understand how firms of different industries learn, and how this learning can be effectively
stimulated. A related question is: Does industry heterogeneity moderate the effectiveness of learning
mechanisms so that firms in some industries learn more effectively and efficiently? To fill this gap, our
study investigates the effectiveness of the proposed learning mechanisms across industries varying in
export and R&D intensity.
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In sum, our study aims to contribute to the literature on LBE in three ways. First, it conceptually
delineates and empirically verifies a multi-mechanism LBE model, paying attention to the mediating
effects of three particular domains: innovativeness, production improvement and managerial emphasis.
By verifying the significance of these mediating mechanisms, the study helps establish them as learning
mechanisms for productivity gains as a result of exporting. Second, we examine the specific industry
conditions under which these learning mechanisms are significant. We do so by contrasting industries
with varying levels of export intensity and R&D intensity. This allows us to examine the issues related to
heterogeneity in learning and absorptive capacity at the industry level. Third, using a large panel dataset
of Chinese firms from 2001 to 2007, the study provides insights into the growth of these emerging market
firms and their global proliferation, a topic of significance in our world economy.
Literature on LBE
A plethora of studies have documented the existence of firms’ productivity gains from engaging
in exporting activities. Recent studies utilizing pre- and post- exporting data have documented the LBE
effect of firms experiencing an improvement in productivity as a result of exporting (Arnold & Hussinger,
2005). They attribute this trend to the acquisition of new technologies, product ideas, production methods,
etc., from foreign agents and through competition (Alvarez & Lopez, 2005; Serti & Tomasi, 2008). While
most studies are conducted in economics (Arnold & Hussinger, 2005; Serti & Tomasi, 2008) and some in
international business study literature (Salomon & Jin 2008; 2010), the basic tenet of this explanation has
roots in organizational learning literature, which purports that firms can learn and adapt in the face of new
customer demands (Clerides et al., 1998), technological advancement (Cassiman & Golovko, 2011) and
an external economic environment (Salomon and Shaver, 2005b), resulting in an improvement in
productivity.
One point in common for all these studies is that they are all phenomenon-oriented and have yet
to examine the underlying learning mechanism for the exporters (Lages et al., 2008; Salomon & Jin,
2008). To solve this puzzle, our study attempts to unveil the learning mechanisms leading to productivity
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gains. It posits that the LBE effect is multi-faceted and concurrent, involving a number of core
organizational processes for exporters to convert their acquired foreign knowledge to productivity gains.
Figure 1 summarizes our conceptual model. We discuss the mediating mechanisms one by one.
________________________________
Insert Figure 1 about here
________________________________
Productivity gain through firm innovativeness
Our first proposed mediating mechanism is firm innovativeness, which is defined as a firm’s
capacity and willingness to introduce novel and useful products or services through innovation processes
(idea generation, experimentation and commercialization) (Roger, 2003). Exporting firms expand and
diversify internationally, hence they benefit from acquiring new product ideas, designs, technologies from
knowledgeable buyers who can share and transfer product designs and production techniques (Djankov &
Hoekman, 2000; Zahra, Ireland, & Hitt, 2000). When these firms are exposed to new overseas markets as
well as to new technologies, products and/or process designs from foreign competitors, they are
encouraged to adapt and innovate accordingly (Zahra et al. 2000). Meanwhile, exporting firms need to
respond instantaneously to the changing demands from overseas customers and must either improve their
existing products or create new ones for them (Clerides et al., 1998; Salomon & Shaver,
2005a).Therefore, the technological knowledge and marketing competence gained through exporting will
lead to a higher level of output in innovation including increased patent applications (Salomon & Jin,
2008; 2010; Salomon & Shaver, 2005a) and to the development of new and better products (Sun & Hong,
2011).
As innovativeness is enhanced, firms not only incorporate new technologies, product designs,
and production methods into their operations due to foreign exposure, but they also creatively use
resources (capital and labor) to generate higher value outputs. Being innovative enables firms to
disseminate the technological and market knowledge throughout different divisions within themselves,
potentially spurring productivity gains via increasing operational efficiency, reducing production costs,
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renovating current goods and services, and capturing an increased demand for new products (Hall, 2011).
Subsequently, the current literature highlights the link between firm innovativeness and increased
productivity (Cassiman & Golovko, 2011; Griffith et al., 2006; Hall, 2011).
Thus, exporting exposes firms to more advanced technologies, increases their innovativeness, and
subsequently facilitates productivity gains within firms’ operations. We propose:
H1: Firm innovativeness will mediate the relationship between exporting and firm productivity.
Productivity gain through production capability improvement
Production capability improvement involves the process through which a firm upgrades its
capability in managing quality, capacity, process, logistics and its relevant workforce during the process
of transforming inputs into goods and service through effective investment and resource allocation
(David, 2005). Exporting firms are likely to improve their production capabilities as a response to foreign
market demands and characteristics, and to increase their capital investment after they enter into export
markets for two reasons (Serti & Tomasi, 2008).
First, when firms export, they may face diverse customer groups who demand higher standards in
product quality and consistency. Whether or not quality is a concern, they will often experience fiercer
pricing competition pressuring them to explore better economies of scope and scale. In response,
exporting firms will increase their capital investments, expand existing assets (e.g. equipment, property,
buildings, and technology) and construct new production facilities. Second, when engaging in exporting
activities, firms are often challenged by the divergence between their existing production capability and
new business requirements. In response, they need to improve on their production facilities and upgrade
their physical assets (e.g., acquire new machinery as well as build new factories) in order to meet
potentially higher technical quality and safety standards, stringent logistics requirements and delivery
deadlines (Castellani, 2002).
At the same time, physical capital is a core input for a firm’s productivity. The improvements to a
firm’s production facilities and upgrades to its physical assets (e.g. equipment, building, and
technologies) can foster a higher level of firm productivity (Prescott, 1997). The upgrading of production
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integrates the firm’s industrial experience and its professional knowledge with its value chain. As
exporting firms increase their capital investments (i.e. upgrading production facilities, incorporating new
production processes and methods), they are more likely to achieve better economies of scope and scale,
higher capacity utilization, and higher levels of productivity (Castellani, 2002). The self-initiated
improvement of production capability, which comes in response to the higher demands of exporting,
consequently allows firms to experience productivity gains.
Therefore, as the knowledge collected from exporting filters to the firm, improved production
capability helps the firm to incorporate that knowledge into its production function, and to leverage it for
corporate growth. We hypothesize:
H2: Production capability improvement will mediate the relationship between exporting and firm
productivity.
Productivity gain through managerial emphasis
Managerial emphasis refers to a firm’s intensive investment in enhancing the management
functions of organizing, staffing, motivating, planning and controlling (David, 2005). Exporting firms
need to learn managerial best-practices (e.g. capital budgeting, strategic planning, government lobbying)
and develop management structures to deal with the challenges that arise in international trade (Djankov
& Hoekman, 2000). New market demands, which arise due to exports (higher quality standards, stringent
logistics requirements, and distribution channel issues), lead to heavier workloads that require new
management practices to facilitate learning (Slater & Narver, 1995). It becomes crucial for exporting
firms to invest more heavily in management, to adopt better management procedures, and to hire more
experienced management talents. As firms become more involved in exporting, hiring additional
management talents facilitates the integration of their own experience with the international best
practices. They become more skilled-labor intensive, and have on average a higher percentage of
management staff (Serti & Tomasi, 2008).
The enhancement of management talents by means of international and industry experience,
better communication skills, and leadership training is often both desired and necessary to bring firms in
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line with the stricter demands of embracing knowledge gained from an external environment. Managerial
emphasis is thus important for exporting firms who wish to maintain a strategic focus among competitive
and market forces, align resources, and manage outputs in dealing with the complexities of international
activities. These firms benefit from investing in management talents (e.g. managers with more
international exposure, and industry experience as well as higher levels of strategic thinking, and
leadership skills) to ensure that internal processes such as stricter quality controls and external
communication with agents are carried out efficiently. Consequently, better managerial emphasis
resulting from exporting experience improves productivity for the firm (Gomez-Mejia, 1988).
Thus, a strong managerial emphasis, gained from exporting, can further enhance a firm’s
capability in applying and integrating newly-acquired knowledge and increased productivity. We
hypothesize:
H3: Managerial emphasis will mediate the relationship between exporting and firm productivity.
Industry heterogeneity in LBE mechanisms
LBE represents an attractive developmental strategy to strengthen the productivity of firms across
different industries. Firms in the same industry share some similarities in their investment endowment,
technology adoption, knowledge acquisition, and production factors, etc., all of which contribute to
forming relatively stable comparative advantages over time (Porter, 1990). Hence, it is important to
understand the LBE effect at the industry level in order to guide and assist firms with their productivity
improvements. Regarding this, previous literature has only documented industry level differences in the
relationship between exporting and productivity (Aw et al., 2000; Salomon & Jin, 2008). In this study,
we extend our knowledge by investigating industry level heterogeneity in the functionality of underlying
learning mechanisms. Our study examines this question in two dimensions, by comparing industries with
different export and R&D intensities.
Learning in industries of different export intensities
In providing evidence of the LBE effects among Italian manufacturers, Castellani (2002)
uncovered a threshold effect of exporting. That is, firms need to engage in at least a moderate level of
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exporting activities in order to capture knowledge spillovers that improve productivity. Thus, learning
outcomes among industries which export little, may be minimal, as such exporting firms have a relatively
limited exposure to outside technologies and practices. It is documented that such firms experience less
pronounced productivity gains i.e. firms, which are new to exporting, readily exit or switch export
practices (Yasar, Nelson, & Rejesus, 2006).
On the other hand, there may also be a diminishing return effect of learning. Industries that have
gained much international experience from exporting and have established industry norms with
standardized international operations may have plateaued in their capacity for learning and productivity
gains. Thus, there may be a ceiling effect, in which firms exporting above a certain level may not achieve
significant productivity gains. These industries may be labor intensive (instead of technology oriented,
e.g., clothing, toys, furniture in China) in which firms specialize in Original Equipment Manufacturing
(OEM) business to secure overseas orders. These exporting firms tend to focus on selling and price
competition, paying little attention to what can be gained from abroad.
Taken together, we postulate that there is an “optimal” level of exporting intensity, which allows
for the greatest productivity gains from the related learning. Below a certain level of exporting, industries
may not have adequate exposure to sources of learning, whereas above this threshold, industries may have
already incorporated and exhausted the benefits of most of the learning effects or have too narrow a focus
on exporting. Thus, exporting intensity will display a curvilinear (an inverse U-shape) industry
heterogeneity effect. We propose that:
H4: Industries of a moderate level of export intensity demonstrate the most significant LBE
effect through the learning mechanisms.
Learning in industries of different R&D intensities
The ability to absorb technological advances differs across individual firms (Lages et al., 2008;
Salomon & Jin 2010), industries (Hill, 2003) and nations (Nelson & Rosenberg, 1993). Cohen and
Levinthal (1990) argue that a firm’s absorptive capacity lies in its ability “to recognize the value of new,
external knowledge, assimilate it, and apply it to commercial ends” (Levinthal, 1990: 128). This ability
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depends on prior knowledge and experience as well as the availability of trained technical staff.
Aggregately, this capacity also applies to the industry and national level.
R&D intensity has been used as a proxy for firms’ absorptive capacities (Mowery, Oxley, &
Silverman, 1996). In contrast to industries of lower R&D intensity (e.g. textiles and clothing), firms
within industries of higher R&D intensity (e.g., medicine manufacturing, transportation, electronics)
generally have larger capacities for absorbing technological knowledge, more technologically
experienced personnel, and greater resources to engage in knowledge exchanges. Learning can also occur
vicariously, as firms benefit from the accumulated knowledge and experience of their industry
counterparts (Argote, Beckman & Epple, 1990). With their greater emphasis on R&D investments, R&Dintensive industries reward talent more. Thus, they generate larger talent pools over time through
absorbing and assimilating the new knowledge gained from internationalization activities such as
exporting.
Moreover, industry R&D intensity levels can also signify an industry’s emphasis on or motivation
toward learning. Firms within relatively high R&D-intensive industries, exhibit a tendency toward
continuous innovation, improving on products and processes, and generating new knowledge to keep up
with developments in the industry (Aw, Roberts & Xu, 2008; Salomon & Jin, 2008).Whereas, firms in
low R&D-intensive industries, devote less attention to improvements and innovation and consequently
place less an emphasis on learning. Thus, when exposed to new technologies as well as to improved
production and managerial practices in export markets, firms in industries of varying R&D levels will
exhibit differential learning based on the emphasis they place on learning. We propose that:
H5: Industries of medium and high levels of R&D intensity demonstrate a significant LBE effect
through their learning mechanisms, while those with a low R&D intensity demonstrate a weak
LBE effect through their learning mechanisms.
DATA AND METHODS
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Our data is from the Annual Industrial Survey conducted by the National Bureau of Statistics of
China (NBS). This dataset provides a comprehensive set of operational and financial information of all
state owned enterprises (SOEs) and the “above scale” non-state owned enterprises in China, which have
annual sales above RMB 5,000,000 (or USD 620,000). This source has been proven to be reasonably
accurate and reliable in its data collection (Cai & Liu, 2009), and is widely used for research in
international business (e.g., Buckley, Clegg, & Wang, 2007), economics (e.g., Cai & Liu, 2009) as well as
in strategy studies (e.g. Zhang, Li, Li, & Zhou, 2010).
Our sample is an unbalanced panel of 249,326 domestic private firms, which spans a 7-year
period (2001 to 2007), and covers 29 two-digit SIC manufacturing industries (or 171 narrowly defined
three-digit manufacturing industries) and China’s 31 provinces and municipalities. We focus only on
domestic private firms for several reasons. First, domestic private firms participate most actively in
exporting, and their contribution to the total value of exporting increased from 7.3% in 2001 to above
30% in 2007 (Trade statistics from China Custom Administration). Second, compared to SOEs and
foreign owned enterprises, domestic private firms engage in relatively little processing trade (<7% of their
total exports) (Wang & Wei, 2010). Third, domestic private firms are purely market oriented in their
export behaviors, unlike SOEs whose major business decisions are known to be affected by government
intervention or political motivations (Eckaus, 2006).
Dependent variable
Following previous IB, economics and strategy research, we use firm total factor productivity
(TFP) as our dependent measure (e.g., Olley & Pakes, 1996; Siegel & Simons, 2010). TFP is defined as
the portion of output not explained by the amount of inputs used in production. We adopt the Olley and
Pakes’(1996) approach to estimate the firm-level TFP, which addresses the issue of simultaneity and
selection bias. It is assessed as the deviation of the observed output from the predicted value from a CobbDouglas production function.
௜௧ = ଴ + ௟ ௜௧ + ௞ ௜௧ + ௠ ௜௧ + ௜௧ + ௜௧ ,
(1)
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Where ௜௧ is the output (log transformed) of firm in year , ௜௧ , ௜௧ and ௜௧ are the input (log
transformed) of labor, capital, and material, respectively. ௜௧ is the firm-level productivity shock that is
observed by the firm. ௜௧ is an independently identical distributed (i.i.d.) unexpected productivity shock
that is unobserved by both the firm and the econometrician. After estimating βs in equation (1), TFP can
be calculated as follows:
௜௧ = ௜௧ − ௟ ௜௧ − ௠ ௜௧ − ௠ ௜௧
(2)
Independent variables
Export. Following Salomon and Jin (2008), we use export status to measure a firm’s export
behavior. We obtained the export information from the reported value of exported products by each firm.
For firms that do not export any of their products in a given year, export status appears as 0, or 1
otherwise.
Firm Innovativeness. We measure firm innovativeness by using a firm’s new product sales (log
transformed). The terms “innovation” and “new product development” have even been used
interchangeably (Iyer, LaPlaca, & Sharma, 2006). In our research context of manufacturing industry,
“innovation” refers primarily to the new product and the relevant business development. Specifically, a
firm adopts a new technology and/or design to develop new products, which demonstrates a significant
improvement in material quality, craftsmanship, and/or functionality. Existing research has validated the
use of new product performance as a proxy for the outcome measure of innovativeness (Gatignon &
Xuereb, 1997; Sengupta, 1998).1
Production Capability Improvement. We compute a firm’s capital investment (log transformed)
as a surrogate for its improvement in production capability. A firm’s capital investment captures the
extent to which a firm spends on their facility and equipment to improve production (Shaver, 2011), and
has been used to explain firm productivity and performance (Serti & Tomasi, 2008). This investment on
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facility and equipment enters into capital stock and depreciates according to accounting rules. The capital
stock evolves according to the following equation
௜,௧ = ௜,௧ିଵ − ௜,௧ିଵ + ௜,௧ (3)
Managerial Emphasis. We calculate a firm’s management expense (log transformed) as a
measure of its expenses for the management team. Effective human resource management helps select
and retain highly capable employees with unique managerial skill sets (Huselid, Jackson, & Schuler,
1997; Wright & McMahan, 1992). Following Koch and McGrath (1996), we use a firm’s investment in
human resources to capture its input to the strategic management of its human capital.
!"#௜௧ = !௜௧ − #, $, %"#௜௧ −
&&"#௜௧ − "'"#௜௧
(4)
Control variables
Firm size. Literature has long recognized the influence of firm size on firm’s productivity
(Schumpeter, 1950; Salomon & Jin, 2008). Firms of a larger size are generally more resourceful and
dominant in their respective industries. We use log transformed firm’s capital stock as a proxy for firm
size (Gulati, Lavie, and Singh, 2009).
Firm age. Productivity and performance levels generally evolve as firms mature and become
more experienced (Soh, 2010; Zahra & George, 2002). We control for this age effect on a firm’s overall
productivity. Age is measured as the number of years since a firm’s registration date.
Subsidies. For firms potentially engaged in an exporting business, special treatment such as
government subsidies may have an impact on their export (Eckaus, 2006), and may increase the level of
exports (Miyagiwa & Ohno, 1995). They help a firm improve its cost structure, daily operations, business
strategies, and competitive advantages, which in turn contributes to its productivity output. We include
the reported government subsidies in our analyses as a control variable.
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Other Controls. We also include three types of dummy variables in all of our models: year,
industry (defined with 3-digit SIC code), and region/province (Zhang et al., 2010). This step helps to
capture the potential longitudinal, cross-sectional and geographical variations of productivity.
Industry characteristics
Export intensity. We define the export intensity of an industry as the average export intensity (i.e.
the ratio of its total export value to its total sales) of all firms in the industry. We calculate the industry
level (defined by 3-digit SIC) export intensity for the study period of 2001-2007. We then rank all 171
manufacturing industries in ascending order according to their export intensity. We define industries in
the first quartile (0-25%) as those of low export intensity, in the second and third quartiles (25%-75%) as
those of medium export intensity, and in the fourth quartile (75%-100%) as those of high export intensity.
R&D intensity. We define the R&D intensity of an industry as the average R&D intensity (i.e. the
ratio of an industry’s R&D expenses to its total sales) of all firms in the industry. Likewise, we define
industries in the first quartile (or, 0-25%) in R&D intensity as those of low R&D intensity, in the second
and third quartiles (25%-75%) as those of medium R&D intensity, and in the fourth quartile (75%-100%)
as those of high R&D intensity.2
Statistical method
We develop a series of equations to test the proposed hypotheses. First of all, we examine direct
effect of exporting on productivity free of mediators using
'௜௧ = ଵ଴ + ଵଵ × '௜,௧ିଵ + ଵଶ × "#௜,௧ିଵ + ∑௝ୀଵ )ଵ௝ × ௜௝௧ +
௃
-ଵ௧௦ ≠ .
*ଵ௜௧ ; *ଵ௜ ~MVN(0, +); E(*ଵ௜௧ ) = 0; and cov(*ଵ௜௧ , *ଵ௜௦ ) = , ଶ
-ଵ = (5)
where controlijt includes control variables such as firm size, firm age, subsidies, and dummy variables for
time, industry, and province. We take four steps to improve the model control and minimize possible
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misspecifications. First, we form a fixed effect model to control for the potential variation of dependent
variable across time, industries and provinces (Arellano, 2003). Second, we use the lag term of key
variables (e.g. Exportt-1) to control for their lagging effects on the dependent measures (e.g. Productivityt)
(e.g. Salomon & Jin, 2008; 2010). Third, we include the lag term of the dependent variable (e.g.
Productivityt-1) to capture the potential path dependence of the dependent measure (e.g. Productivityt)
(e.g. Salomon & Jin, 2008; 2010). Lastly, we adopt a within firm correlation matrix structure to capture
other possible firm-specific factors that cause outcomes to be systematically correlated across time
(Ballinger, 2004). Our rich dataset allows us to adopt an unstructured form for the correlation matrix,
which is the least restrictive in revealing the true correlation structure (Ballinger, 2004). We use a similar
model setup for Equation 6-10.
We test the direct effects of three mediators on productivity free of the variable, Export, using
'௜௧ = ଶ଴ + ଶଵ × '௜,௧ିଵ + ଶଷ × $௜௧ + ଶସ ×
'/$௜௧ + ଶହ ×
!"ℎ௜௧ + ∑௝ୀଵ )ଶ௝ ×
௃
௜௝௧ + *ଶ௜௧
(6)
Next, we study exporting’s effect on three such proposed mediators as
$௜௧ = ଷ଴ + ଷଵ × $௜,௧ିଵ + ଷଶ × "#௜,௧ିଵ + ଷସ ×
'/$௜௧ + ଷହ ×
!"ℎ௜௧ + ∑௝ୀଵ )ଷ௝ ×
௃
௜௝௧ + *ଷ௜௧
(7)
To capture the possible correlation between innovativeness and the other two firm actions of
production capability improvement and managerial emphasis, we include them in the model. The similar
formulation is adopted for Equation 8-9.
18
'/$௜௧ = ସ଴ + ସଵ × '/$௜,௧ିଵ +
ସଶ × "#௜,௧ିଵ + ସଷ × $௜௧ + ସହ ×
!"ℎ௜௧ + ∑௃௝ୀଵ )ସ௝ ×
௜௝௧ + *ସ௜௧
(8)
!"ℎ௜௧ = ହ଴ + ହଵ ×
!"ℎ௜,௧ିଵ + ହଶ × "#௜,௧ିଵ +
ହଷ × Innovativeness௜௧ + ହସ × '/$௜௧ + ∑௝ୀଵ )ହ௝ ×
௃
௜௝௧ + *ହ௜௧
(9)
Lastly, we model the mediators’ effect on productivity after controlling exporting as
'௜௧ = ଺଴ + ଺ଵ × '௜,௧ିଵ + ଺ଶ × "#௜,௧ିଵ + ଺ଷ × $௜௧ +
଺ସ × '/$௜௧ + ଺ହ ×
!"ℎ௜௧ +
∑௃௝ୀଵ )଺௝ × ௜௝௧ + *଺௜௧
(10)
This model formulation allows us to examine the direct and indirect LBE effects, and test for the
existence of the three learning mechanisms (Mackinnon, Lockwood, Hoffman, West, & Sheets, 2002;
Preacher & Hayes, 2008).
We use a generalized estimating equation (GEE) with maximum likelihood estimation for our
proposed model (Equation 5-10). GEE is used to estimate a generalized linear model for the crosssectional longitudinal data, which assumes and deals with within-subject error dependence (Hardin &
Hilbe, 2003). GEE performs a more consistent, robust, and efficient parameter estimation when withinsubject error dependence is present (Burton, Gurrin, & Sly, 1998).
RESULTS
Table 1 shows the descriptive statistics and the correlation matrix among the key variables. The
correlations among the key variables are in line with our expectations.
________________________________
Insert Table 1 about here
________________________________
Regression results: main effects
19
In Table 2a, we display estimation results of 6 models corresponding to equation 5-10.
________________________________
Insert Table 2a about here
________________________________
The results of Model 1 demonstrate the positive and significant effect of exporting on
productivity, which strongly supports the LBE theory. The results of Models 3, 4 and 5 show that firms
increase their inputs to innovativeness, production capability improvement, and managerial emphasis if
involved in exporting. Meanwhile, the results of Models 2 and 6 show that the estimates of these three
variables are all positive and significant, which means that a firm’s inputs to them eventually lead to
enhanced firm productivity.
Mediating effects
We use two tests to examine the mediation effects. Baron and Kenny (1986) proposed three
necessary conditions for the presence of a mediation effect. The mediation effect exists when we can
establish: first, that the key variable (export) impacts on the dependent variable (productivity); second,
that the key variable influences the mediators, and third, that the mediators affect the dependent variable
after controlling for the influence of the key variable. The results shown in Table 2a satisfy all three
conditions. Therefore H1, H2, and H3 are supported.
There are other statistical methods to test for the mediation effects, using the difference in the
coefficients or the product of coefficients, and most of these methods actually lead to similar conclusions
(Mackinnon et al., 2002). Due to the fact that the product approach has a higher statistical power while
maintaining acceptable control over the Type I error rate (Preacher & Hayes, 2008), we further adopted
the Sobel (1982) test for the mediation effect (Ndofor, Sirmon, & He, 2011). The results of the mediating
effect tests are shown in Table 2b. The mediation effects of the three mediators are all statistically
significant. Hence, the results of the Sobel (1982) test provide further support for H1, H2, and H3. We
also calculated the effect ratio for each of the three mediators. According to Jose (2008), it is a full
20
mediation when the effect ratio is no lower than 0.8; otherwise, it is a partial mediation. According to the
results, all three mediators are partial mediation effects.
________________________________
Insert Table 2b about here
________________________________
Industry differences
We performed two tests to examine differential learning mechanisms for industries with different
levels of export and R&D intensity. The results are reported in Table 3a, and Table 3b, respectively.
_________________________________________
Insert Table3a and Table 3b about here
_________________________________________
Export intensity. Table 3a Columns 1-3 summarize the estimates of key variables in the above 6
models for firms with low, medium, and high export intensity. For firms with low export intensity, using
the rules by Baron and Kenny (1986), we conclude that there is learning through production capability
improvement and managerial emphasis, but not through innovativeness, since exporting does not have a
significant and positive influence on innovativeness as shown in Model 3. For firms with a high level of
export intensity, the results shown in Model 4 suggest that they learn through innovativeness and
managerial emphasis, but not through production capability improvement. The overall learning effect is
very trivial because the estimate of the export dummy in Model 1 is insignificant. In comparison, firms
with a medium level of export intensity learn from all three sources. This supports H4. In the left panel of
Table 3b we present the results of the Sobel (1982) test for mediation effects and find consistent results.
R&D Intensity. Similarly, Table 3a Columns 4-6 summarize the estimates of key variables in the
above 6 models for firms with low, medium, and high R&D intensity. For firms with a low R&D
intensity, we conclude that there is learning through innovativeness and managerial emphasis, but not
through production capability improvement. This is because the positive relationship between production
capability improvement and productivity is not significant as shown in Model 2 and 6. In comparison,
21
firms with medium and high levels of R&D intensity learn from all of the proposed mechanisms. This is
in support of H5. In the right section of Table 3b we also report the results of the Sobel (1982) test for
mediation effects and find consistent results.
Robustness checks
To assess the robustness of our findings, we run several variants of our proposed model. First, our
study consists of a system of equations (Equation 5 to 10). It could be argued that the dependent variables
(e.g. innovativeness, production capability improvement, and managerial emphasis) are endogenously
determined because some of them appear on both sides of the system of equations. To rule out this
concern, we performed a 2-stage least square (2SLS) estimation on the simultaneous equation system
(Greene, 2003). Second, we propose an unstructured variance-covariance option, but it is be possible that
a firm’s productivity levels and decisions (e.g. new products sales, capital investment, and management
expenses) are highly and systematically correlated across time. We therefore tested our model with a firstorder autoregressive (AR1) error correlation structure. Third, we adopted the most parsimonious variancecovariance structure and assumed an independently identical distributed –i.i.d. error term, i.e. ordinary
least square (OLS) estimation for each of the equations. The results are consistent with our findings, and
available for checking upon request.
DISCUSSION AND CONCLUSION
Drawing from the literature and theories in international business and strategy, we postulated that
the learning mechanisms underlying the LBE effect are multifaceted based on the tenets presented in the
organizational learning literature (Huber, 1991; Dierkes et al., 2003). That is, firms that export, especially
those from a developing or emerging economy like China, can acquire and exploit diverse knowledge
within technological, production, and managerial domains, leading to productivity gains for the whole of
organization. Through this study, we find significant and robust empirical evidence that innovativeness,
production capability improvements, and managerial emphasis serve to mediate the relationship between
22
exporting and productivity, thus contributing to the literature by ‘opening the black box’ on the LBE
effect.
This expanded LBE learning perspective is consistent with existing organizational learning
literature. Knowledge transfer can be achieved when firms export (Aw et al., 2000), acquire or merge
with other firms (Bresman, Birkenshaw, & Nobel, 1999), form strategic alliances (Mowery et al., 1996),
or establish subsidiaries in foreign markets (Minbaeva et al., 2003). However, knowledge acquisition,
transformation and exploitation are integral steps in making a facility fully operational, and their role
extends beyond the simple process of knowledge transfer. Our model highlights the importance of the
later stages of absorbing and leveraging the acquired knowledge. Our study further demonstrates that
there is industry heterogeneity in the underlying learning mechanism with respect to the different export
and R&D intensities. Our results show that firms in industries of non-low R&D and medium export
intensities benefit the most from the LBE effect. Our findings are in congruence with the absorptive
capacity (Cohen & Levinthal, 1990; Zahra & George, 2002) and export threshold (Castellani, 2002)
views, which postulate R&D-intensive industries have a greater capability and motivation to learn, and
that learning would occur in accordance with an optimal level of exporting.
Findings from our study offer a number of strategic insights to firm managers. First, we establish
that it is not the exporting behavior in itself that drives productivity gain, but it is the underlying diverse
experience and knowledge obtained through exporting. Firms that exhibit learning are those who are the
most adept at their own organizational processes and who can fully integrate and exploit the benefits of
their international experience (Zahra & George, 2002). Our multifaceted learning model suggests that
productivity gains can be achieved in multiple ways and that learning capabilities are dynamic (Teece,
Pisano, and Shuen, 1997). Second, the three mediating mechanisms are in themselves highly desirable
organization outcomes. It is likely that each mechanism requires different resource sets and top
management attention. Our findings underscore the benefits that exporting can bring to firms, especially
those in emerging markets, e.g., diverse technological, production, and managerial knowledge that can
increase firm productivity. Furthermore, our findings highlight the importance and benefits of
23
management’s learning capabilities and processes within the firm: acquiring new knowledge from abroad,
transforming it and exploiting it within a firm’s operations. Depending on the effectiveness of each
learning mechanism and the availability of resources, a firm’s top management needs to assess the
potential trade-offs between these learning mechanisms, deciding which mechanism or combination
would be most beneficial to the firm’s international activities or overall productivity.
Our study also has implications for policy makers. Firstly, as we have shown, learning through
exporting is multidimensional. Thus, it can be seen that encouraging exporting not only provides new
technological knowledge, but it also can introduce new production and managerial knowledge to firms in
different industries. Secondly, since we have found that R&D-intensive industries learn more from
exporting, more should be done to encourage R&D investments in low R&D industries in order to
increase their absorptive capacity at the industry level, which can further facilitate international
knowledge transfer and learning. Government policies that subsidize and reward R&D spending (such as
China’s recent indigenous innovation policy) may in fact be effective not only in boosting domestic
innovation, but may also have the additional benefit of spurring learning by exporting effects at both the
firm and industry levels. Thus, specific policies to encourage R&D investments in low R&D industries
may be constructive. Thirdly, our findings with respect to export intensity show that policy makers should
be aware of the optimal (moderate) level of exporting, and appreciate the fact that the learning by
exporting effects are less salient in industries for which export levels are either too high or low. Thus,
more emphasis should be put on encouraging exporting in low export industries. Eliminating trade
barriers or promoting economic policies, which encourage trade in protected or low export industries,
may encourage knowledge transfer, organizational learning and higher levels of productivity in these
industries.
This study has caveats that invite future research efforts. First, we have identified three learning
mechanisms pertinent to the LBE effect. It is possible that other core processes (e.g. resource alignment)
may also mediate a firm’s productivity gain. Second, we initially proposed that it is not the exporting
behavior itself, but that instead it is the accumulated experience and knowledge obtained from exporting
24
that lead to productivity enhancement. By acquiring measures of the contents of experience and
knowledge, the details of the learning process can be examined. Therefore, it would be useful to examine
and validate the relevant explanations through executive surveys in a future study. Third, besides export
level and R&D intensity, it is also likely that other industry characteristics (e.g. competition) can
moderate the LBE effect. Future research would be well served to delve more deeply into this domain.
Lastly, we empirically test our model with Chinese private firms from 2001 to 2007. While China may be
an exemplary country among emerging market economies, the generalizability of our results is yet to be
established through future studies of different contexts.
Notes
1
We repeat our investigation by replacing new product sales with R&D expenses as the measure for
firm’s level of innovativeness (e.g., Gulati et al., 2009; Soh, 2010). The results are consistent and
available for checking upon request.
2
We repeat our test by using new product sales as the outcome measure for firm’s commitment to
technology improvement and innovation (e.g. Gatingon and Xuereb, 1997; Sengupta, 1998). The results
are consistent and available for checking upon request.
25
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29
Figure 1. Multi-mechanism learning-by-exporting
exporting model
30
Table 1. Correlation and sample statistics
N=249,326
Mean
Std.
Dev.
Min
Max
1
1.
Productivity (t)
1.18
0.33
-7.38
11.49
1.00
2.
Productivity (t-1)
1.17
0.33
-4.91
10.39
0.42
1.00
3.
Export (t)
0.19
0.40
0.00
1.00
0.12
0.12
4.
Export (t-1)
0.21
0.41
0.00
1.00
0.11
0.12
0.84
1.00
5.
Innovativeness (t)
0.61
2.27
0.00
15.83
0.16
0.16
0.43
0.40
1.00
6.
Innovativeness (t-1)
0.65
2.31
0.00
15.52
0.15
0.16
0.42
0.42
0.85
7.
Production capability improvement (t)
6.12
2.31
0.00
15.30
0.18
0.17
0.31
0.30
0.33
0.33
1.00
8.
Production capability improvement (t-1)
6.01
2.26
0.00
15.30
0.16
0.17
0.32
0.32
0.33
0.34
0.69
1.00
9.
Managerial emphasis (t)
2.03
2.99
-0.69
12.66
0.17
0.18
0.31
0.30
0.35
0.35
0.51
0.52
10.
Managerial emphasis (t-1)
1.24
2.45
0.00
11.73
0.18
0.19
0.34
0.34
0.38
0.38
0.58
0.60
0.72
1.00
11.
Firm age
6.43
9.39
0.00
30
0.07
0.09
0.16
0.16
0.16
0.17
0.24
0.22
0.21
0.20
1.00
12.
Firm size
8.03
1.44
0.00
15.53
0.18
0.18
0.37
0.37
0.39
0.39
0.78
0.74
0.61
0.69
0.28 1.00
13.
Subsidies
0.46
1.56
0.00
14.21
0.08
0.08
0.20
0.20
0.22
0.21
0.27
0.28
0.28
0.30 0.09 0.32 1.00
2
3
4
5
6
7
8
9
10
11
12
13
1.00
1.00
1.00
31
Table2a. Estimation results: GEE (unstructured)
Model1
Productivity
Lag export
Productivity
0.010***
(0.001)
Innovativeness
Production cap.
improvement
Managerial
emphasis
Lag productivity
Model 2
0.372***
(0.002)
0.005***
(0.000)
0.003***
(0.000)
0.003***
(0.000)
0.368***
(0.002)
Lag innovativeness
Lag production cap.
improvement
Lag managerial
emphasis
Firm size
0.010***
(0.000)
Firm age
0.000**
(0.000)
Subsidy
0.002***
(0.000)
Constant
0.645***
(0.007)
Number of obs.
266,649
0.004***
(0.000)
0.000*
(0.000)
0.001***
(0.000)
0.652***
(0.007)
Model3
Model4
Model5
Model6
Production
Managerial
Innovativeness Capability
Productivity
Emphasis
Improvement
0.080***
0.049***
0.204***
0.005***
(0.010)
(0.009)
(0.011)
(0.001)
0.007***
0.021***
0.005***
(0.001)
(0.002)
(0.000)
0.014***
0.037***
0.003***
(0.002)
(0.002)
(0.000)
0.024***
0.029***
0.003***
(0.002)
(0.002)
(0.000)
0.367***
(0.002)
0.621***
(0.002)
0.188***
(0.002)
0.102***
(0.002)
0.077***
0.842***
0.311***
0.004***
(0.004)
(0.003)
(0.004)
(0.000)
0.001**
0.001***
-0.000
0.000*
(0.000)
(0.000)
(0.000)
(0.000)
0.060***
0.028***
0.083***
0.001***
(0.002)
(0.002)
(0.002)
(0.000)
-0.472***
-1.980***
2.485***
0.653***
(0.048)
(0.044)
(0.057)
(0.007)
266,649
266,649
266,649
266,649
* p<0.10; ** p<0.05; *** p<0.01; all two-tailed tests; Standard errors listed in (parentheses).
Key estimates are in BOLD; estimates for industry, time, and provincial dummies are omitted.
Table 2b. Sobel (1982) test of mediation effect
c
a
Mediators
Innovativeness
0.01 0.080
Production capability improvement
0.01 0.049
Managerial emphasis
0.01 0.204
σa
0.010
0.009
0.011
b
0.005
0.003
0.003
σb
0.000
0.000
0.000
Z
8.01***
5.44***
18.54***
266,649
Effect ratio
0.040
0.015
0.061
Note: Z = a × b/aଶ σଶୠ + b ଶ σଶୟ ; Effectratio = a × b/c; * p<0.10; ** p<0.05; *** p<0.01; all two-tailed tests.
a is the effect of Export on each mediator; b is the effect of each mediator on Productivity; and c is the effect of
Export on Productivity.
32
Table 3a. Estimation results for industries with different levels of export and R&D intensity
Model
Model 1
Dep. Var.
Productivity
Ind. Var.
Lag export
Model 2
Productivity
Innovativeness
Production cap.
improvement
Managerial emphasis
Model 3
Innovativeness
Lag export
Model 4
Lag export
Model 5
Production cap.
improvement
Managerial emphasis
Lag export
Model 6
Productivity
Lag export
Innovativeness
Production cap.
improvement
Managerial emphasis
Number of obs.
Low
0.029***
(0.005)
0.008***
(0.001)
0.004***
(0.001)
0.003***
(0.001)
-0.042
(0.028)
0.085***
(0.029)
0.204***
(0.035)
0.016***
(0.005)
0.008***
(0.001)
0.004***
(0.001)
0.003***
(0.000)
66,767
Export intensity
Med
High
0.016*** -0.001
(0.002)
(0.002)
0.005*** 0.003***
(0.000)
(0.000)
0.003*** 0.003***
(0.000)
(0.001)
0.003*** 0.004***
(0.000)
(0.000)
0.102*** 0.076***
(0.015)
(0.016)
0.058*** 0.019
(0.013)
(0.013)
0.193*** 0.128***
(0.016)
(0.017)
0.010*** -0.003
(0.002)
(0.002)
0.005*** 0.003***
(0.000)
(0.000)
0.002*** 0.003***
(0.000)
(0.001)
0.003*** 0.004***
(0.000)
(0.001)
133,329 66,553
* p<0.10; ** p<0.05; *** p<0.01; all two-tailed tests; standard errors listed in (parentheses).
Estimates for control variables are omitted.
Low
0.015***
(0.003)
0.009***
(0.001)
0.001
(0.001)
0.004***
(0.001)
0.143***
(0.019)
0.073***
(0.020)
0.279***
(0.024)
0.007**
(0.003)
0.008***
(0.001)
0.001
(0.001)
0.004***
(0.001)
61,041
R&D Intensity
Med
High
0.008***
0.008***
(0.002)
(0.002)
0.005***
0.004***
(0.000)
(0.000)
0.004***
0.003***
(0.000)
(0.001)
0.003***
0.003***
(0.000)
(0.000)
0.076***
0.069***
(0.015)
(0.018)
0.048***
0.043***
(0.015)
(0.013)
0.210***
0.163***
(0.017)
(0.017)
0.003
0.004*
(0.002)
(0.002)
0.005***
0.004***
(0.000)
(0.000)
0.004***
0.003***
(0.000)
(0.001)
0.003***
0.003***
(0.000)
(0.000)
110,879
94,729
33
Table 3b. Sobel (1982) test of mediation effects for industries with different levels of export and R&D intensity
Mediators
Innovativeness
Production capability improvement
Managerial emphasis
Low
Z
Effect ratio
Z
Effect ratio
Z
Effect ratio
-1.47
N.S.
2.36**
0.012
2.67***
0.021
Export intensity
Med
high
6.8***
0.032
4.46***
0.037
12.06***
0.036
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
Low
5.48***
0.076
0.96
N.S.
3.78***
0.074
R&D intensity
Med
High
5.07***
0.048
3.2***
0.024
12.35***
0.079
3.83***
0.035
2.22**
0.016
9.59***
0.061
Note: Z = a × b/aଶ σଶୠ + b ଶ σଶୟ ; Effectratio = a × b/c; * p<0.10; ** p<0.05; *** p<0.01; all two-tailed tests.
a is the effect of Export on each mediator; b is the effect of each mediator on Productivity; and c is the effect of Export on Productivity.
‘N.S.’ stands for ‘Not Significant’; ‘N.A.’ stands for ‘Not Applicable’, because there is no learning effect based on non-significant effect of Export on
Productivity in Model 1.