21COE, University of Tokyo

MMRC Discussion Paper

No. 69MMRC
DISCUSSION PAPER SERIES

MMRC-F-69

Exports, FDI, and Productivity:
Dynamic Evidence from Japanese Firms
Fukunari Kimura
Faculty of Economics, Keio University
Kozo Kiyota
Faculty of Business Administration, Yokohama National University
Manufacturing Management Research Center (MMRC), the University of Tokyo
February 2006

21COE, University of Tokyo

MMRC Discussion Paper No. 69

Exports, FDI, and Productivity:
Dynamic Evidence from Japanese Firms *Fukunari Kimura †Faculty of Economics, Keio University

Kozo Kiyota
Faculty of Business Administration, Yokohama National University
Manufacturing Management Research Center (MMRC),
the University of Tokyo
February 2006*

The METI database employed in this analysis was prepared and analyzed in cooperation with the Japan
Center for Economic Research and the Research and Statistics Department, Ministry of Economy, Trade, andIndustry (METI). The authors would like to thank Kyoji Fukao, Keiko Ito, Masahito Kobayashi, Yoshiaki
Omori, Shujiro Urata, Ryuhei Wakasugi, seminar and conference participants at the Japan Society ofInternational Economics, METI, Research Institute of Economy, Trade and Industry (RIETI) and Yokohama
National University, an anonymous referee, and managing editor Harmen Lehment for their helpful comments.
Kozo Kiyota gratefully acknowledges the financial support of a grant-in-aid (B-2: 14330012) from Ministry of
Education, Culture, Sports, Science and Technology. The views expressed herein are those of the authors.
†
Corresponding author: Tel: +81-3-3453-4511 ext. 23215; Fax: +81-3-5427-1578; E-mail:fkimura@econ.keio.ac.jp; Address: Faculty of Economics, Keio University, 2-15-45, Mita, Minato-ku, Tokyo
108-8345, Japan.
1

Fukunari Kimura, Kozo Kiyota

Abstract

This paper examines the relationship between exports, foreign direct investment and firm
productivity. Using longitudinal panel data on Japanese firms, it is found that the mostproductive firms engage in exports and foreign direct investment, medium productive firms
engage in either exports or foreign direct investment, and the least productive firms focus
only on the domestic market. Moreover, exports and foreign direct investment appear to
improve firm productivity once the productivity convergence effect is controlled for. Firms
that retain a presence in foreign markets, either by exports or foreign direct investment, show
the highest productivity growth, which contributes to improvements in national productivity.
(103 words)
JEL Classification Codes: F10 (International Trade, General), F20 (International Factor
Movements and International Business, General), D21 (Firm Behavior)
Keywords: Multinational Enterprises, Panel Data, Firm Heterogeneity, Total Factor
Productivity, Firm Survival2

Exports, FDI, and Productivity

1.

Introduction

With the growing importance of firm heterogeneity, often represented as productivity
differences among firms, recent studies in international trade have begun to incorporate firm
heterogeneity. The pioneering works of Bernard et al. (2003) and Melitz (2003), for example,
theoretically clarify the relationship between exports and firm heterogeneity, represented as
productivity, in a general equilibrium framework. Although based on different models, theyboth conclude that the higher the productivity of the firm, the more it is likely to be anexporter.
This central prediction is confirmed in empirical studies utilizing firm/plant levellongitudinal panel data. 1 For instance, Bernard and Jensen examined the benefits of exports
at the plant level in the United States (Bernard and Jensen 1999, 2004a, and 2004b). They
found that exporters exhibited 4–18 percent higher productivity than nonexporters, andconfirmed this before the firms actually engaged in exports, although the impact of exports
on productivity was less clear.
Similar findings have been observed in other countries: for instance, Clerides et al.
(1998) in Colombia, Mexico, and Morocco; Bernard and Wagner (2001) in Germany;
Hallward-Driemeier et al. (2002) in Indonesia, Korea, Malaysia, the Philippines, andThailand; Baldwin and Gu (2003) in Canada. The results of these studies are summarized inTable 1. On average, previous work confirms that exporters have 10–15 percent higher
productivity than nonexporters. In fact, these differences are observed before firms become
exporters: put simply, good firms become exporters. However, it is not always the case that afirm improves its performance after entering the export market.
=== Table 1 ===In addition to exporting activities, foreign direct investment (FDI) has recently
become an important issue in the discussion on globalization. 2 Helpman et al. (2004) extend
Melitz’s study to incorporate FDI into a trade and firm heterogeneity model. Representing
firm heterogeneity with productivity differences, their model predicts that the least
productive firms serve only the domestic market. On the other hand, relatively more

1

The pioneering micro study on exports and productivity is Aw and Hwang (1995). Using cross-sectional
firm-level data on the Taiwanese electronics industry in 1986, they found significant differences in productivity
between exporters and non-exporters.
2
See Lewis and Richardson (2001) for an extensive survey of the related literature, as well as a heuristic
explanation for the benevolent effects of various channels of global commitment by firms, including FDI.
3

Fukunari Kimura, Kozo Kiyota

productive firms export, and the most productive firms engage in FDI. Empirical analysis
based on European firm data in 1996 partly confirms the theoretical prediction thatmultinational enterprises (MNE) are substantially more productive than non-MNE exporters.
Similarly, productivity differences between MNEs and non-MNEs, or between
foreign-owned firms and domestically owned firms have been extensively investigated inother countries: Globerman et al. (1994) in Canada; Doms and Jensen (1998) in the UnitedStates; Girma et al. (2002) in the United Kingdom; Hallward-Driemeier et al. (2002) inIndonesia, Korea, Malaysia, the Philippines, and Thailand; Kimura and Kiyota (2006) inJapan. All of these studies, with the exception of Globerman et al. (1994), confirm that
foreign-owned firms present higher productivity than domestic firms in the static sense.
The scope of most of these analyses is, however, limited to the static aspect, and the
dynamic aspects of FDI are not yet fully explored yet. That is, do good firms engage in FDI;
does FDI improve firm’s performance; or is it some combination of both? These questions
have not yet been addressed. Furthermore, the relationship between exports and FDI at thefirm level is still ambiguous. As Helpman et al. (2004) assume, the sunk costs of starting
global commitment can differ for exports and FDI. Further analysis is then needed to clarify
the relationship among trade, FDI and firm productivity.
In this paper, we investigate the dynamic aspects of FDI as well as exports. Questions
addressed are whether or not good firms conduct exports/FDI, and whether or notexports/FDI improve firm productivity. The data used is firm-level longitudinal panel data ofJapanese firms 1994–2000, which covers not only manufacturing firms but alsononmanufacturing firms, with approximately 22,000 observations each year.
The paper is organized as follows. Section 2 discusses the characteristics of thesample consisting of approximately 22,000 firms, and presents static differences between
exporters and nonexporters and between firms that engage in FDI and those that do not.
Section 3 examines the relationships among exports, FDI and productivity from a dynamic
perspective. Section 4 extends the analysis to link exports, FDI and aggregate productivity.
Section 5 concludes by providing the implications of the results for policy and future
research.
2.

Static Aspects of Exports, FDI and Firm Productivity

2.1. Data
We use the micro database of Kigyou Katsudou Kihon Chousa Houkokusho (The Results of4

Exports, FDI, and Productivity

the Basic Survey of Japanese Business Structure and Activities) by the Research andStatistics Department, Ministry of Economy, Trade and Industry (METI). This survey was
first conducted in the 1991 financial year (F/Y), then again in the 1994 F/Y and annually
thereafter. The main purpose of the survey is to statistically capture an overall picture ofJapanese corporate firms in light of their activity diversification, globalization, and strategies
on R&D and information technology.
One of the strengths of this survey is that it is based on firms, rather thanestablishments. Since the manufacturing census does not cover nonmanufacturing
establishments, including headquarters or sales branches, the aggregation of manufacturing
plants does not always truly depict the ‘firm’ unless it is a single-plant firm. In that sense,
our data captures the activity of firms more accurately than the manufacturing plant census.
A further strength is its coverage and reliability. The survey includes all firms withmore than 50 employees and more than 30 million yen in capital, covering bothmanufacturing and nonmanufacturing firms. 3 One limitation is the lack of some financial
information.
From this survey, we develop a longitudinal panel data set for the years 1994 to 2000,
based on each firm’s permanent number. We define FDI as having more than one affiliate inforeign countries. 4 Exporters are defined as firms whose sales are oriented towards foreigncountries. Other firms are defined as domestic firms. 5 We remove firms from our sample
where age (questionnaire-level year minus establishment year), total wages, tangible assets,value-added (sales less purchases) or the number of regular workers (including temporary
workers) were not positive and those firms that reenter the market. The number of firms
included amounts to over 22,000 each year. The panel data set therefore comprises anunbalanced panel.
2.2. Stylized Facts
Before turning to the dynamic aspects, we provide an overview of some basic statistics.
3

Some non-manufacturing industries, such as in finance, insurance and software services, are not included. The
industry is determined by the major sales products of the firm.
4
In this survey, Japanese foreign affiliate is defined as an affiliate with 20 percent of the equity coming fromthe Japanese parent firm. Note that our analysis includes both new and continuing investment. The METI
database can identify whether a firm has its first foreign affiliate, but cannot identify whether a firm has its firstforeign affiliate in a given country.
5
We did not distinguish between foreign-owned and non-foreign-owned firms in Japan. In Japan, inward FDI isstill very small and foreign-owned firms are in the minority. In terms of the total number of firms in 2000, only
1.2 percent was foreign-owned (Kiyota and Urata, 2005).
5

Fukunari Kimura, Kozo Kiyota

Figure 1 reports the number of exporters and firms engaged in FDI in 1994 and 2000.
Exporters are clearly in the minority. In 2000, of the roughly 22,000 firms included in thesample, only 20 percent (4,382 firms) reported exports. This is almost the same proportion as
the United States (Bernard et al. 2003). Firms that engage in FDI are even less. Only 13
percent (2,765 firms) engaged in FDI. Of all Japanese firms, 76 percent neither export nor
engage in FDI. Similar results are obtained for 1994.
=== Figure 1 ===FDI appears to have a close relationship with exports. In 2000, of the 2,765 firms
engaged in FDI, roughly two-thirds (1,988 firms) exported. On the other hand, exporters do
not always engage in FDI. Of the 4,382 exporters, just 45 percent (1,638 firms) engaged in
FDI. Firms that engage in FDI but not export are again in the minority, accounting for some
4 percent (777 firms) of our sample.
2.3. Do Firms That Engage in Exports and FDI Perform Better?
To make a comparison across firms and time, we employ deterministic (nonstochastic)
methods to compute total factor productivity (TFP) indexes. This follows pioneering workby Caves et al. (1982), extended in Good et al. (1983). This index employs a hypotheticalfirm that has the arithmetic mean values of log output, log input, and input cost shares overthe firms in each year. Each firm’s outputs and inputs are then measured relative to thishypothetical firm and the hypothetical firms are chain-linked over time. Hence, the index
measures the TFP of each firm in year t relative to the hypothetical firm in year 0 (theinitial year). A detailed explanation of the methodology and the data is provided in the
Appendix.
The advantage of the multilateral index is that we do not assume any specificproduction function. This is because the multilateral index only requires information on thequantity of output, as opposed to parametric methods that require the specification of a
production function. This is particularly useful when we cannot directly observe theproduction function for each firm. 6
Table 2 presents the export and FDI premia. 7 The premium is the productivity
6

The multilateral index method is also employed by Aw et al. (2000, 2003). Olley and Pakes (1996) discussalternative productivity measures that are consistent with a dynamic and stochastic model of industry
development. Levinsohn and Petrin (2003) extend Olley and Pakes (1996) such that intermediate input demand
functions play the same role as investment. However, due to the data availability (our data do not coverintermediate inputs except “purchases”), this method was not feasible.
7
Export/FDI premia are measured as the coefficients for export and FDI dummies in a regression of the form:
6

Exports, FDI, and Productivity

difference between exporter and nonexporter firms in the same industry and the year with thesame characteristics that engage in FDI. Two major findings are presented in this table. First,
firms engaging in exports and FDI tend to have higher productivity. Columns (1) and (2)present differences between exporters and nonexporters, while columns (3) and (4) show thedifferences between firms that engage in FDI and those that do not. The differences are
between 3.5–5.0 percent with statistical significance.
=== Table 2 ===Second, there is a systematic relationship among exports, FDI, and productivity. The
most productive firms engage in both exports and FDI, the second highest productive firms
engage only in FDI, while the third group engages only in exports. Columns (5) and (6)present differences among firms that export only, firms that engage in FDI only and firmsthat engage in both exports and FDI. The largest differences are observed for firms thatengage in both exports and FDI, presenting 6.4–7.9 percent higher productivity than firmsthat do not export or engage in FDI. The next largest coefficients are observed for firms thatengage in FDI only, followed by firms that export, but do not engage in FDI. The lowest
productive firms engage in neither exports nor FDI, and stay in the domestic market.
These findings support the theoretical predictions of Helpman et al. (2004). Their
model predicted that low productivity firms stay in the domestic market, firms with higher
productivity export, and highest productivity firms engage in FDI. However, we found yet
another outcome: highest productivity firms export and engage in FDI at the same time.
3.

Dynamic Aspects of Exports, FDI and Firm Productivity

3.1. Do Good Firms Become Exporters/Engage in FDI?
We now examine the pre- and postentry performance of Japanese firms entering foreign
markets. We focus on two channels for firms to enter the foreign market: exports and FDI.
This section investigates possible determinants of the decision to export and/or invest abroad,as suggested by Roberts and Tybout (1997).
ln θ it = α + βYit + γChar.s it + ε it , where θ is TFP, Y is a dummy variable indicating export/FDI
status that takes a value of one if the firm is an exporter/FDI and zero otherwise, Char.s is the vector of firm
characteristics and ε is the error term. Firm characteristics include the natural log of the capital-labor ratio,
firm age, the number of workers, R&D expenditure-sales ratio, TFP, foreign-owned dummy as well as year andindustry dummies. A foreign-owned dummy takes the value of one if foreign ownership is larger than ten
percent and zero otherwise. The coefficient β indicates the gap between exporters and non-exporters(between firms that engage in FDI and firms that do not). A fixed-effects model is employed for the estimationon the basis of the results of a Hausman specification test.
7

Fukunari Kimura, Kozo Kiyota

Suppose that firm i exports in year t if current and expected profits of exports are
greater than costs. Costs are defined as market-entry fixed costs (sunk costs) for exports Fit
plus variable costs. Denote current profit and current profit excluding fixed costs as π it andπ~ , respectively. Fixed costs are required only if the firm did not export in the previous year.
it

Exporting and non-exporting firms are distinguished by a variable Yit which takes a valueof one if firm i exports in year t and zero otherwise. 8 For simplicity, assume that this
fixed cost is the same across all firms and years ( Fit = F ). Thus the profit π it is described
as π = π~ − F (1 − Y ) .
it

it −1

it

Denote the discount rate of future revenue as δ . Assume that in year t , firms
choose an infinite sequence of values ( Yit , Yit +1 ,...) that maximizes the expected value ofrevenue. Denote the maximized revenue as:⎛ ∞⎞Vit (Ω it ) = max Et ⎜ ∑ δ τ −t π~iτ | Ω it ⎟ ,Yit⎝ τ =t⎠(1)

where Ω it is the firm-specific information set. Using Bellman’s equation, firm i ’s decision
to export is represented as Yit that satisfies:

Vit (Ω it ) = max Et (π~it (Yit , Yit −1 ,...) + δE[Vit +1 (Ω it +1 | Yit , Yit −1 ,...)]) .
Yit

(2)

In the dynamic framework, the firm exports if the present value of current and future
revenues of exporting is larger than the total costs (fixed plus variable). Denote the current
profit and discounted increase in the value of the firm in the future if the firm exports in yeart as:

π it* = π~it + δ (Et [Vit +1 (•) | Yit = 1] − Et [Vit +1 (•) | Yit = 0]) ,(3)

where E[Vit +1 (•)] is the expected value of maximized payoff conditioned by Yit . The
decision to export by firm i is represented as:⎧1 if π it* > F (1 − Yit −1 );Yit = ⎨
⎩0 otherwise.

(4)

In the empirical analysis, we specify the regression equation as follows:⎧⎪1 if β 0 + ∑ K β k Z ikt −1 − F (1 − Yit −1 ) + μ it > 0;
k =1
Yit = ⎨⎪⎩0 otherwise,(5)

where Z ikt −1 indicates firm-specific variables that may affect the probability of exporting in
8In our dataset, exports include sales by establishments abroad but exclude those by affiliates abroad.
8

Exports, FDI, and Productivity

period t ; μ it represents the error term.
There are several estimation strategies for this dynamic binary-choice model withunobserved heterogeneity, such as a probit model with random model (Roberts and Tybout
1997, and Bernard and Wagner 2001) or a linear probability model with fixed effects(Bernard and Jensen 1999, and Bernard and Wagner 2001). A linear probability model
requires instruments such as two-period lags of the right-hand-side variables (Bernard andWagner 2001). Since our sample period is not long enough to use such instruments, weemploy the probit model with random effects of the form:

Yit = β 0 + ∑k =1 β k Z ikt −1 + FYit −1 + μ it .
K

(6)

We introduce two-digit industry dummies for some of the regressions to control forindustry characteristics including comparative advantage and market conditions. 9 Additionalfirm characteristics Z it −1 include the capital-labor ratio, firm age, the number of workers,
the R&D expenditure-sales ratio, TFP, a foreign ownership dummy as well as year andindustry dummies. 10 In order to avoid problems with possible simultaneity, we lag all
explanatory variables by one year. 11
We also assume that the initial conditions Yi 0 are exogenous. If there are permanent
unobserved firm-specific characteristics in the error term, the coefficient of the initial status

Yi 0 will be biased. In order to fix this problem, however, we need appropriate instruments
such that the error term in estimating the initial status equation is to be freely correlated withthe error term μ it . 12 However, as was pointed out by Bernard and Wagner (2001), such
instrument is difficult to find. Given the lack of the appropriate instrument, following
Bernard and Wagner (2001), we estimate equation (6), assuming that the initial conditions
are exogenously determined. Summary statistics and the correlation matrix of variables areprovided in Table A1.
We also apply this analytical framework for the decision to engage in FDI. As for the9

Foreign market conditions could also affect the decision to export and/or conduct FDI. However, because ofdifficulties in obtaining relevant data, we do not introduce foreign market variables in addition to the industry
dummies.
10
These firm characteristics are similar to those used in estimating export/FDI premia in Section 2.3.
11
For more details, see Bernard and Jensen (1999 p.12 and footnote 19).
12
Note that the dependent variable is not a continuous but a discrete variable. In case of a continuous
dependent variable, the Blundell and Bond (1998) GMM estimator may fix this type of problem as was shownin Görg, Hanley, and Strobl (2005), since it does not require the information on initial conditions. For moredetail about the initial conditions problem, see Hsiao (2003).
9

Fukunari Kimura, Kozo Kiyota

FDI analysis, variable Yit takes a value of one if a firm engages in FDI and zero otherwise.
In this paper, the firm that engages in FDI is defined as the firm with one or more affiliates
abroad.
Table 3 presents the regression results of equation (6) with random effects probit
estimation. Columns (1) through (3) present the results for exports while columns (4)through (6) indicate the results for FDI. For exports, three features stand out. First, goodfirms become exporters. The coefficients of TFP are significantly positive in all equations.
This implies that the higher the firm’s productivity, the higher the probability of firms
entering the export market. Second, sunk costs, as reflected by the coefficients for lagged
export dummies, also appear to be an important factor when a firm decides to enter theexport market. This finding is consistent with Roberts and Tybout (1997) and Bernard andJensen (1999). Third, the probability of exports increases with a firm’s size, researchintensity, and age. Foreign ownership is another significant factor in the decision to export.
=== Table 3 ===Similar results are observed for the decision to engage in FDI, with the exception of
the ownership structure: foreign-owned firms are not more likely to invest abroad. Sunk
costs for FDI, as reflected by the coefficients of the lagged FDI dummies, do not seem to bevery different from the coefficients for exports. However, once the sample firms are
separated by employment size, as presented in Table A2, we find that the sunk cost for FDI isslightly higher than that for exports for small-scale firms (less than 500 workers). This partly
supports the assumptions in Helpman et al. (2004).
3.2. Do Exports and FDI Contribute to Productivity Growth?
We next examine the reverse effect: whether or not exports and FDI contribute toproductivity growth. To test the effects of exports and FDI on productivity growth, following
Bernard and Jensen (1999), we run a simple regression of changes in TFP ( θ it ) on the initial
export/FDI status ( Yit ) and additional firm characteristics ( Char.s it ):%Δθ it = ln θ it − ln θ it −1(7)= α + βYit −1 + γChar.s it −1 + ε it .

The coefficient, β , represents the gap in the annual average growth rate of TFP
performance between exporters/firms that invest abroad and other firms. Additional firm
characteristics for the initial year are the same as those in Section 3.1.
10

Exports, FDI, and Productivity

Table 4 presents the regression results of equation (7) with the fixed-effects model. 13
Coefficients indicate the gap of annual average TFP growth rate between exporters/FDI
firms and others. Columns (1) through (3) indicate the results without controlling for the
initial TFP level, while columns (4) through (6) are the results controlling for the initial TFP
level. We control for the initial productivity level since the higher productivity, the lower thesubsequent growth if the productivity convergence effect over time holds. 14=== Table 4 ===The results of columns (1) through (3) do not strongly support the hypothesis thatexports and/or FDI improve productivity. However, once we control for the initial TFP level,a totally different story emerges. From columns (4) through (6), we can clearly see thepositive impact of exports and FDI on productivity growth. Exporters have 2.4 percent
higher growth than nonexporters. Firms that engage in FDI have 1.8 percent higher growththan those that do not engage in FDI. Firms that engage in both exports and FDI display thehighest productivity growth.
The coefficients of initial TFP level are significantly negative.15 These results suggestthat a productivity convergence effect exists: firms with higher productivity tend to havelower growth in subsequent years, and vice versa. Since we use annual panel data, macrobusiness cycles as well as annual idiosyncratic shocks at the firm level are leveled out overtime. Without controlling for the initial TFP level, the regression could generate an omitted
variable bias, which previous studies could not fully distinguish from the gains from trade.
However, once we control for the initial TFP level, or productivity convergence effect, we
can confirm the gains from trade: both exports and FDI improve firm productivity.

13

We select a fixed-effects model based on the results of a Hausman specification test.
The productivity convergence model assumes that productivity in firm i evolves according to:ln θ it = γ i + λ ln Dit + λ ln θ it −1 + ln ε it , where γ i is the asymptotic rate of productivity growth of firm

14

i , Dit represents the catch-up variable, and ε it stands for the firm-specific productivity shock. The
catch-up variable, Dit , is defined as a function of the productivity of firm i , θ it −1 , relative to the most*productive firm, θ : ln D = − ln(θ/ θ * ) = − ln(θˆ ) , where θˆ = θ / θ * . This formation
it −1

it

it −1

it −1

it −1

it −1

it −1

= α + β ln θˆleads to the productivity convergence equation: ln θˆ − ln θˆ
it

i

it −1

it −1

it −1

+ μ it with negative β .

See Bernard and Jones (1996), Wolff (1991) and Pascual and Westermann (2002) for the analysis of TFP
convergence.
15
Note that the dependent variable is percentage growth. The coefficients are one hundred times larger thanthose from traditional convergence regressions.
11

Fukunari Kimura, Kozo Kiyota

4.

Alternative Aspects of the Gains from Exports and FDI

4.1. Is ‘Survival’ in Foreign Markets Important?
To statistically test the importance of surviving in foreign markets, we run a similar
regression to equation (7), including the following three dummy variables:%Δθ it = ln θ it − ln θ it −1= α + β 1Start it + β 2 Both it + β 3Stop it + γChar.s it −1 + ε it ,(8)

where Start it = 1 if (Yit −1 = 0) * (Yit = 1)
Both it = 1 if (Yit −1 = 1) * (Yit = 1)
Stop it = 1 if (Yit −1 = 1) * (Yit = 0)
Additional firm characteristics are the same as those in Section 3.2, controlling for the
productivity convergence effect. If the continuation of exports and FDI, or the ‘survival’ inthe foreign market is important for the productivity growth of the firm, the coefficients β 2should be positive and larger than β1 and β 3 .
Table 5 presents the regression results. All coefficients for “Start,” “Both,” and“Stop” exhibit positive and statistically significant signs for both exports and FDI. The
coefficients “Both” are larger than those of “Start,” and the coefficients of “Stop” are thesmallest. These imply that firms that continue to export and/or conduct FDI have the highest
growth, while firms that just enter foreign market via exports and/or conduct FDI show
relatively modest growth. Firms that exit from foreign markets display relatively low TFP
growth, though their growth is still higher than that of firms which never entered foreign
markets. To continue to export and engage in FDI, or survive in the foreign market, istherefore important for the productivity growth of a firm.
=== Table 5 ===In Table 4, we confirmed that the interaction term between exports and FDI is an
important factor for analyzing TFP growth. A recent study by Kiyota and Urata (2005) foundthat firms do not make a choice between exporting and undertaking FDI, but exporters domake a decision on whether or not to undertake FDI. In order to control for the simultaneous
effects of exports and FDI, we introduce an interaction term between exports and FDI as
follows:

12

Exports, FDI, and Productivity%Δθ it = ln θ it − ln θ it −1= α + β 1 ExpStart it + β 20 ExpBoth it + β 21 ExpBoth_FDIStart it+ β 22 ExpBoth_FDIBoth it + β 23 ExpBoth_FDIStop it + β 3 ExpStop it(9)+ γChar.s it −1 + ε it ,where ExpStart it , ExpBoth it , and ExpStop it are the same as equation (8) for exports but
ExpBoth it is now decomposed into four parts. ExpBoth_FDIStart it is a dummy variable
that takes a value of one if exporters between years t − 1 and t commence FDI in year t ,ExpBoth_FDIBoth it is a dummy variable that takes a value of one if exporters between
years t − 1 and t commence FDI in the same period, and ExpBoth_FDIStop it is adummy variable that takes a value of one if exporters between year t − 1 and t quit FDI inyear t .
Column (3) in Table 5 indicates the estimation results of equation (9). The coefficientof ExpBoth it has a positive and significant sign, implying that the continuation of exports
contributes to productivity growth. The coefficient of ExpBoth_FDIBoth it also has apositive sign, but it is not significant. On the other hand, the coefficients of
ExpBoth_FDIStart it and ExpBoth_FDIStop it are negative, but not significant.
4.2. Do Exports and FDI Contribute to Survival in the Domestic Market?

To address this question, we estimate Cox’s proportional hazard model of the form:
hit = h0it exp( βYit + γZ it ),(10)

where hit is firm i ’s hazard rate and h0it is a baseline hazard rate.

16

The hazard rate isdefined as “the rate at which spells are completed after duration t , given that they last atleast until t ” (Greene 2003). Yit represents the export/FDI status in year t , and Z it is avector of corporate characteristics in year t and other variables used in Section 3.1.
The advantage of this analysis is that it requires no prior assumption on the
distribution of exit timing. Also, the hazard rate directly captures the probability that a firm
will exit in the next short interval of time given that it survives until time t . The estimatedcoefficients are interpreted as the ratio of hazards for a one-unit change in the corresponding
covariate (vector). For instance, suppose that we focus on the FDI status and β = 0.09 . This16

The equation does not have an intercept because the baseline hazard cannot be explicitly specified (and istherefore not estimated). Since hit = h0 it exp(α + βYit + γZ it ) = {h0it exp(α )} exp( βYit + γZ it ) , itsimply changes the baseline hazard from h0it to {h0it exp(α )} , both of which are not defined. Hence, any

value works for α .

13Fukunari Kimura, Kozo Kiyota

means that firms that engage in FDI face a hazard 10 percent greater than firms that do notsince exp(0.09) ≈ 1.10 . On the other hand, if β = −0.11 , firms that engage in FDI face ahazard 10 percent lower than firms that do not ( exp(−0.11) ≈ 0.9 ). If exports/FDI has apositive contribution on firm survival, the coefficient β must be significantly negative.
Table 6 presents the regression results of equation (10). Exports have positive
impacts on firm survival. The hazard rates of exports are 0.82–0.93, meaning that exporters
face a hazard rate 7–18 percent lower than nonexporters.
=== Table 6 ===On the other hand, FDI has negative impacts on firm survival. The hazard rates offirms that engage in FDI is between 1.09–1.23, meaning that FDI firms face hazard rates
9–23 percent greater than non-FDI firms. An explanation may be that FDI imposes highfinancial or managerial burdens on some firms, particularly small- and medium-sizedenterprises, such that the probability of exit is higher. 17
5.

Concluding Remarks

In this paper, we examine the relationship between exports, FDI and productivity using firmlevel longitudinal panel data for Japan between 1994 and 2000. Our major findings are as
follows: firms that engage in exports and FDI are in the minority. Most firms that engage in
FDI are exporters, while exporters do not always engage in FDI. The most productive firms
are those that engage in FDI and export. Medium productive firms engage in either exports
or FDI. The least productive firms neither export nor invest abroad.
These findings are consistent with theoretical predictions by Helpman et al. (2004).
Their model suggests that low productivity firms stay in the domestic market, higher
productivity firms export, and highest productivity firms engage in FDI. Our contribution isthat there is an additional choice. The highest productivity firms export as well as engage in
FDI. This may be caused by MNEs operating foreign affiliates in several countries, or by
intra-firm trade between headquarters and foreign affiliates. Exports and FDI do not seem tobe “substitutes.” Rather, both work as “complements” in designing global operations of
MNEs.
We also found that productivity was an important factor explaining the decision to
engage in FDI as well as export. Moreover, both exports and FDI do improve firm
17

Kimura and Fujii (2003) found that small firms could benefit from exports, but that conducting FDI
aggravated rather than improved the performance.
14

Exports, FDI, and Productivity

productivity, once we control for the productivity convergence effect. The positive impact ofexports and FDI on productivity growth has a potential policy implication: survival inforeign markets is likely to result in higher productivity growth for both the firm and theeconomy. Hence, the facilitation of exports or the enhancement of global commitment has
some validity, though more discussion on implementation and sequencing is required. We
should also keep in mind that FDI has a possible negative impact on firm survival.
Future research should include a more thorough investigation of the mechanics ofinteraction between global commitment and productivity. A clue may be found by examining
the differential effects of the various types of global commitment. For instance, various typesof international trade exist, including inter-industry, vertical and horizontal intra-industry,arm’s-length/intra-firm, and so on. There are also different types of FDI possible, including
horizontal, vertical, manufacturing/nonmanufacturing, etc. Although serious data limitations
exist, more thorough analyses would provide useful feedback for existing theoreticalinsights.

Appendix: Construction of the Multilateral TFP Index
Methodology
The multilateral index measures the TFP of each firm in year t relative to that of ahypothetical firm in year 0 (the initial year). The hypothetical firm is the firm that has thearithmetic mean values of log output, log input, and input cost shares over firms in each year.
Suppose that the TFP of this hypothetical firm is equal to one. The TFP index for firm i inyear t relative to that of the hypothetical firm in the initial year is defined as

(

)

(

ln θ it ≈ ln Qit − ln Qt + ∑τ = 2 ln Qτ − ln Qτ −1
t

(

)

)

(

)

1
(sijt + s jt ) ln X ijt − ln X jt − ∑τt =2 ∑ Jj=1 1 (s jτ + s jτ −1 ) ln X jτ − ln X jτ −1 ,22where ln Qit , ln X ijt , and s ijt are the log output, log input of factor j , and the cost share− ∑ j =1
Jof factor j for firm i , respectively. ln Q t , ln X jt , and s jt are the values of the
hypothetical firm in year t and are equal to the arithmetic means of corresponding
variables over all firms in year t . The first term on the right hand side is the deviation of thefirm’s output from the output of the hypothetical firm in year t , and the second term is the
cumulative change in the output the hypothetical firm between year t and year, t = 0 . The
same operations are applied to each input j , weighted by the average of the cost shares.
15

Fukunari Kimura, Kozo Kiyota

Data
Output is defined as value-added, while the inputs are capital and labor. The main advantage
of value-added is its aggregation property. This is because it is difficult to aggregate firmlevel productivity to the whole economy level if we use gross output: value-added is directlycomparable across industries. For other data and their manipulation, we adopt themethodology described in the Appendix in Nishimura et al. (2005).

16

Exports, FDI, and Productivity

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Affiliates in Canada,” Canadian Journal of Economics, 27(1): 143-156.
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Exports, FDI, and Productivity

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Roberts, M.J. and J.R. Tybout. 1997. “The Decision to Export in Colombia: An Empirical
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American Economic Review, 81(3): 565-579.

19

Figure 1: Exports and FDI of Japanese Firms, 1994 and 2000
1994 (21,232 firms)

Exports andFDI (1,638)
7.7%

2000 (21,661 firms)

FDI only
(664)
3.1%

Exports only
(2,485)
11.7%

FDI only
(777)
3.6%

Exports only
(2,394)
11.1%

Domestic
firms(16,445)
77.5%

Source: METI database.

Exports andFDI (1,988)
9.2%

Domestic
firms(16,502)76.2%

Table 1: Export, FDI and Productivity of Firm: Summary Results of Previous Studies
Exports and productivity
Authors

Roberts and Tybout (1997)

Country

Colombia

Unit
PeriodPlants
1981-1989

Industry
Do exporters perform better?
With control?
Do good firms (plants) become exporters?
Do exports improve productivity?
Authors

Manufacturing
Not examined
Not examinedYes
Not examined
Bernard and Wagner (2001)

Clerides, Lach, and Tybout(1998)
Colombia, Mexico, andMoroccoFirms and plants
1981-1991 (Colombia); 19861990 (Mexico); 1984-1991(Morocco)
Manufacturing (Table 7)
Yes
Yes
Yes
No
Baldwin and Gu (2003)

Country

Germany

Unit
PeriodIndustry
Do exporters perform better?
With control?
Do good firms (plants) become exporters?
Do exports improve productivity?
FDI and productivity
Authors

Plants1978-1992
Manufacturing
Not examined
Not examined
Yes a)
Not examined

Country

Bernard and Jensen (1999)

Aw, Chung, and Roberts (2000) Bernard and Jensen (2004a)

United States

Korea and Taiwan

Plants1984-1992

Firms and plants
Plants
1981-1991 (Taiwan); 1983-1993 1983-1992(Korea)CanadaManufacturing3.5-18.1% (Table 1)
Yes
Yes
No
Bernard, Eaton, Jensen, andKortum (2003)
United States

Plants1974-1996
Manufacturing4.0-15.0% (p.641)
Yes
Yes
Yes

Plants1992
Manufacturing
15-33% (Table 2) a)
Yes
Not examined
Not examined

Manufacturing
3.9-31.1% (Table 2)No
Yes for Taiwan
No for both countries
Hallward-Driemeier, Iarossi,
and Sokoloff (2002)
Indonesia, Korea, Malaysia,
Philippines, and Thailand
Plants
1996-1998
Manufacturing
Yes
Yes
Not examined
Not examined

Globerman, Ries, and Vertinsky Doms and Jensen (1998)(1994)
CanadaUnited States

Unit
PlantsPlantsPeriod1986
1987
Industry
Manufacturing
Manufacturing
Do foreign-owned firms perform better than Not significant difference (Table 2.3-2.4% (Table 7.4)
domestic firms?
2) a)With control?
Yes
Yes
Notes:a) Labor productivityb) Except some non-manufacturing industries such as agriculture, finance and insurance, software.

Girma, Thompson, and Wright(2002)
United Kingdom

United States

Manufacturing
8-9% (p.9)
No
Yes
No
Bernard and Jensen (2004b)
United States
Plants1984-1992
Manufacturing
11-12% (Table 3) a)
Yes
Yes (but not robust)Not examined
Kimura and Kiyota (2006)

Firm
1989-1994
Manufacturing
14% (Table 2)

Hallward-Driemeier, Iarossi,
and Sokoloff (2002)
Indonesia, Korea, Malaysia,
Philippines, and Thailand
Plants
1996-1998
Manufacturing
Yes

Yes

Yes

No

Japan
Firms
1994-1997
All industry b)
67.2-101.8% (Table 3)Table 2: Export and FDI Premia
Dependent variable: TFP level(1)(2)
Export dummy
3.95
3.54
[7.05][6.37]
FDI dummy
FDI & Export dummy
FDI only dummy
Export only dummy

Table 3: Determinants of the Decision to Export and to Engage in FDI, 1994-2000

(3)

(4)

5.04
[7.79]

3.79[5.89]

(5)

(6)

8.00
6.43
[9.38][7.57]
5.96
4.82[6.81][5.54]
4.16
3.86[6.83][6.37]
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
153,147 153,147 153,147
0.050
0.040
0.050

Year dummy
No
Yes
No
Industry dummy
No
Yes
No
Firm characteristics
Yes
Yes
Yes
N
153,147 153,147 153,147
R2
0.040
0.050
0.040
Notes: a) Fixed-effect model is used for estimation.
b) All differences are significant at 1% level and figures in brackets indicate t-statistics.
c) Estimated coefficients indicate the gap (%) of TFP level between exporters/firms
engages in FDI and other firms
Source: METI Database

Dependent variable:

All firms
Export dummy (t)(1)(2)
3.10*** 3.01***[216.98] [204.35]

FDI dummy (t)
(3)
(4)
(5)
(6)
Export dummy
2.91***
0.62***[192.44][34.40]
FDI dummy
0.59***
3.05*** 3.00*** 2.82***[28.77][176.54] [171.73] [155.89]
TFP
0.18*** 0.16*** 0.13***0.17*** 0.14*** 0.10***[14.64][12.52][10.43][12.82][10.16][6.60]
Number of workers
0.09*** 0.13*** 0.06***
0.22*** 0.26*** 0.24***[12.37][17.69][7.67][28.42][31.45][28.95]
Capital-labor ratio (millions of yen, 1994 prices)
0.02*** 0.02*** 0.01
0.07*** 0.07*** 0.07***[3.95][3.30][1.05][10.02][9.47][9.07]
R&D expenditure-sales ratio (%)
0.05*** 0.04*** 0.04***
0.03*** 0.02*** 0.01**[17.76][12.06][10.98][11.90][7.36][2.47]
Age
0.11*** 0.09*** 0.07***0.12*** 0.10*** 0.05***[8.28][6.61][5.20][7.37][5.88][3.05]
Foreign ownership dummy
0.27*** 0.24*** 0.27***-0.08-0.09*-0.21***[6.28][5.46][6.28][1.53][1.88][4.43]
Constant-2.79*** -3.19*** -2.76***-3.75*** -4.12*** -3.89***[51.88][40.34][34.14][58.30][46.37][43.86]
Year dummy
No
Yes
Yes
No
Yes
Yes
Industry dummy
No
Yes
Yes
No
Yes
Yes
N
121,825 121,825 121,825
121,825 121,825 121,825
AIC
0.323
0.312
0.305
0.244
0.240
0.230
Log-Likelihood-19682.0 -18971.9 -18554.2-14878.1 -14571.6 -13994.0
Notes:a) Random-effect probit model is used for estimation.
b) ***, **, and * indicate level of significance at 1%, 5%, and 10% and figures in brackets indicate z-statistics.

Source:c) All independent variables are at period t-1. We take natural log for TFP, number of workers, capital-laborratio and age.
METI DatabaseTable 4: Effects of Exports and FDI on TFP Growth
Dependent variable: annual average TFP growth (%)
All firms
Without controlling forWith controlling for"convergence" effect"convergence" effect
(1)
(2)
(3)
(4)
(5)
(6)
Export dummy-0.20
2.41***[0.24][3.86]
FDI dummy-0.69
1.83**[0.74][2.57]
FDI & Export dummy-0.31
3.91***[0.25][4.14]
FDI only dummy-2.19*
1.84*[1.74][1.91]
Export only dummy-0.76
2.39***[0.86][3.52]
ln TFP (initial TFP level)-87.87***-87.86***-87.88***[263.69] [263.66] [263.69]
Year dummy
Yes
Yes
Yes
Yes
Yes
Yes
Industry dummy
Yes
Yes
Yes
Yes
Yes
Yes
Firm characteristics
Yes
Yes
Yes
Yes
Yes
Yes
Initial TFP level
No
No
No
Yes
Yes
Yes
N
121,825 121,825 121,825
121,825 121,825 121,825
R2
0.02
0.02
0.02
0.43
0.43
0.43
Notes: a) Fixed-effect model is used for estimation.
b) ***, **, and * indicate level of significance at 1%, 5%, and 10%,respectively. Figures in brackets indicate t-statistics.
c) Estimated coefficients indicate the gaps of the growth rate between
exporters/firms that engage in FDI and other firms.
Source: METI Database

Table 5: Effects of Exports and FDI on TFP Growth: Extended Analysis
Dependent variable: annual average TFP growth (%)

Exports, Start
Exports, Both(1)
2.19***[2.67]
3.89***[4.74](2)

Exports, Both & FDI, Start
Exports, Both & FDI, Both
Exports, Both & FDI, Stop
Exports, Stop

2.50***[2.85]

FDI, Start
FDI, Both
FDI, Stop
Year dummy
Industry dummy
Firm characteristics
Initial TFP level
N
R2
For notes and sources, see Table 4.

Yes
Yes
Yes
Yes
121,8250.43

2.42**[2.45]
4.38***[4.60]
0.44[0.40]
Yes
Yes
Yes
Yes
121,8250.43(3)2.19***[2.67]
3.58***[4.01]-0.02[0.02]
1.29[1.20]-1.61[1.08]
2.48***[2.83]

Yes
Yes
Yes
Yes
121,825
0.43

Table 6: Determinants of Firm Survival in Domestic Market

Export dummy(1)(2)-0.20*** -0.07*[5.41][1.78]

FDI dummy
TFP
Number of workers
Capital-labor ratio (millions of yen, 1994 prices)
R&D expenditure-sales ratio (%)
Age
Foreign ownership dummy-0.20***[10.04]-0.64***[35.85]-0.13***[14.19]-0.01[1.19]-0.75***[38.20]0.56***[5.80]
NoNo-0.19***[9.07]-0.65***[35.45]-0.09***[9.73]
0.00[0.53]-0.72***[29.60]0.48***[4.96]
Yes
Yes

(3)

(4)

0.09*
[1.85]-0.22***[11.07]-0.66***[36.25]-0.14***[14.52]-0.03**[2.49]-0.76***[38.35]0.50***[5.19]
NoNo

0.16***[3.18]-0.20***[9.62]-0.66***[35.84]-0.10***[10.00]0.00[0.00]-0.73***[29.66]
0.46***[4.76]
Yes
Yes(5)-0.12***[2.94]
0.21***[4.00]-0.19***[9.27]-0.66***[35.63]-0.09***[9.95]
0.00[0.36]-0.73***[29.62]0.50***[5.09]Yes
Yes

Year dummy
Industry dummy
Hazard rate
Exports
0.82
0.93
0.89
FDI
1.09
1.17
1.23
N
118,415 118,415 118,415 118,415 118,415
AIC
0.907
0.871
0.871
0.907
0.871
Log-Likelihood-53690.1 -51514.2 -51506.5 -53703.7 -51510.9
Notes:a) Cox's proportional hazard model is used for estimation.
b) ***, **, and * indicate level of significance at 1%, 5%, and 10%, respectively. Figures inbrackets indicate z-statistics.
Source: METI DatabaseTable A1: Summary Statistics and Correlation Matrix
Summary statistics
N
Mean
S.D.
Variable
FDI dummy
Export dummy
FDI & Export dummy
FDI only dummy
Export only dummy
L
Capital-labor ratio (millions of yen, 1994 prices) (KL)R&D expenditure-sales ratio (%) (RDS)
Age
Foreign ownership dummy (FOD)
Total factor productivity (TFP, natural log)
Variable
FDI dummy (t-1)
Export dummy (t-1)
TFP (t-1)
L (t-1)
Capital-labor ratio (millions of yen, 1994 prices) (KL) (t-1)
R&D expenditure-sales ratio (%) (RDS) (t-1)
Age (t-1)
Foreign ownership dummy (FOD) (t-1)
Note: For the definition of variables, see main text.
Source: METI database.

153,147
153,147
153,147
153,147
153,147
153,147
153,147
153,147
153,147
153,147
153,147
N
131,486
131,486
131,486
131,486
131,486
131,486
131,486
131,486

0.12
0.20
0.09
0.03
0.12
5.17
1.65
0.51
3.47
0.02
-0.01
Mean
0.12
0.20
-0.02
5.17
1.65
0.57
3.47
0.02

0.32
0.40
0.28
0.18
0.32
0.99
1.31
1.81
0.57
0.15
0.62
S.D.
0.32
0.40
0.60
0.98
1.28
1.86
0.56
0.14

Correlation matrix
FDID
EXPD FDIEXP FDIonly EXPonly L
KL
RDS
Age
FOD
TFP
D
D
D
1.000
0.470
1.000
0.829
0.608
1.000
0.511 -0.095 -0.058
1.000-0.1340.722 -0.111 -0.068
1.000
0.367
0.231
0.328
0.151
0.003
1.000
0.140
0.103
0.117
0.070
0.027
0.101
1.000
0.206
0.268
0.229
0.017
0.136
0.198
0.087
1.000
0.153
0.137
0.145
0.051
0.046
0.129
0.300
0.056
1.000
0.103
0.164
0.104
0.023
0.114
0.159
0.030
0.123 -0.030
1.000
0.148
0.193
0.144
0.043
0.116
0.065 -0.118
0.096 -0.014
0.139
1.000
FDID
EXPD TFP
L
KL
RDS
AGE
FOD
1.000
0.467
1.000
0.148
0.191
1.000
0.370
0.236
0.066
1.000
0.141
0.103 -0.096
0.108
1.000
0.220
0.287
0.105
0.207
0.094
1.000
0.151
0.135 -0.010
0.129
0.292
0.062
1.000
0.093
0.162
0.133
0.152
0.030
0.132 -0.030
1.000

Table A2: Determinants of the Decision to Export and to Engage in FDI, by Employment Scale

Dependent variable:
Export dummy
FDI dummy
TFP
Number of workers
Capital-labor ratio (millions of yen, 1994 prices)
R&D expenditure-sales ratio (%)
Age
Foreign ownership dummy
Constant
Year dummy
Industry dummy
N
AIC
Log-Likelihood
Note:
See Table 3.
Source: METI Databaseless than 100 workers
ExportFDI
dummy (t) dummy (t)
(a1)
(a2)
3.07***[109.12]3.12***[77.49]0.18***
0.15***[7.50][4.87]
0.08
0.31***[1.41][4.11]
0.01
0.05***[1.15][3.41]
0.03***
0.03***[3.88][3.15]
0.08***
0.04[3.26][1.11]
0.41***-0.22[3.95][1.31]-2.96***-4.03***[9.76][10.52]
Yes
Yes
Yes
Yes
39,837
39,8370.268
0.153-5299.8-3022.2

100-500 workers
ExportFDI
dummy (t) dummy (t)
(a3)
(a4)
2.97***[150.50]
3.00***[126.69]
0.16***
0.13***[9.07][6.89]
0.14***
0.27***[6.95][11.91]0.03***
0.07***[3.01][6.36]
0.04***
0.02***[9.20][4.84]
0.09***
0.08***[4.77][3.69]
0.30***
0.04[4.68][0.51]-3.15***-4.15***[22.70][25.71]
Yes
Yes
Yes
Yes
64,39764,3970.330
0.252-10601.8-8086.9500-1000 workers
ExportFDI
dummy (t) dummy (t)
(a5)
(a6)
3.04***[60.39]2.86***[61.67]0.14***
0.13***[3.30][3.28]
0.09
0.22***[0.96][2.62]
0.05*
0.12***[1.83][4.82]
0.06***
0.04***[4.63][3.35]
0.19***
0.25***[3.64][4.83]
0.16-0.22*[1.15][1.66]-3.35***-4.21***[5.29][7.19]
Yes
Yes
Yes
Yes
9,675
9,675
0.343
0.402
-1623.1-1908.4

more than 1000 workers
ExportFDI
dummy (t) dummy (t)
(a7)
(a8)
3.04***[53.65]
2.91***[55.52]0.06
0.16***[1.32][3.54]
0.00
0.26***[0.12][6.39]
0.05*
0.15***[1.82][5.17]
0.06***
0.03***[5.06][2.99]
0.09
0.21***[1.59][3.76]
0.14-0.14[1.53][1.49]-2.23***-4.53***[6.51][12.49]
Yes
Yes
Yes
Yes
7,916
7,916
0.349
0.378
-1348.6-1462.5