Foreign Direct Investment and Productivity: - Ricerca sull'intelligenza artificiale specializzata al MIT

Foreign Direct Investment and Productivity:
A Cross-Country, Multisector Analysis
Rodolphe Desbordes and Loe Franssen∗

This paper adopts a cross-country, multisector approach to investigate the
intra- and inter-industry effects of foreign direct investment (FDI) on the
productivity of 15 emerging market economies in 2000 E 2008. Our main
ﬁnding is that intra-industry FDI has a large positive effect on total and
“exported” labor productivity. The effects of FDI on total factor productivity are
much more elusive, both in statistical and economic terms. This result suggests
that foreign ﬁrms raise the performance of their host economies through a
direct compositional effect. Foreign ﬁrms tend to be larger and more input
intensive and have greater access to foreign markets than domestic ﬁrms. Their
greater prevalence mechanically increases average labor productivity and export
performance.

Keywords: foreign direct investment, productivity, sector level, services
JEL codes: F23, O16

IO. introduzione

Many emerging market economies actively seek to attract foreign direct
investment
(FDI) because they believe that multinational enterprises will
contribute to economic growth by creating new job opportunities, enhancing
capital accumulation, and increasing total factor productivity (TFP).1 In practice,
these growth-enhancing effects have been difﬁcult to detect. Recent cross-country
studies, using a wide range of econometric techniques, do not generally ﬁnd
evidence that FDI affects gross domestic product (GDP) per capita (Carkovic
and Levine 2005; Herzer, Klasen, and Nowak-Lehmann 2008; Iamsiraroj and
Ulubasoglu 2015). Likewise, single-country, ﬁrm-level evidence on the inter-
and intra-industry effects of foreign ﬁrms on the TFP of domestic ﬁrms is

∗Rodolphe Desbordes (corresponding author): Professor of Economics, SKEMA Business School-Université
Côte d’Azur, Paris. E-mail: rodolphe.desbordes@skema.edu; Loe Franssen: Globalization Researcher, Statistics
Netherlands. E-mail: loefranssen@gmail.com. The authors are grateful to the team behind the International Trade
Centre’s Investment Map for data support. They would also like to thank the managing editor and two anonymous
referees for helpful comments and suggestions. The usual ADB disclaimer applies. ADB recognizes “China” as the
People’s Republic of China.

1Excellent surveys of the expected effects of FDI on host economies can be found in Navaretti and Venables

(2005), Caves (2007), and Dunning and Lundan (2008).

Asian Development Review, vol. 36, NO. 1, pag. 54–79
https://doi.org/10.1162/adev_a_00123

© 2019 Asian Development Bank and
Asian Development Bank Institute.
Pubblicato sotto Creative Commons
Attribuzione 3.0 Internazionale (CC BY 3.0) licenza.

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Foreign Direct Investment and Productivity 55

ambiguous (Havránek and Iršová 2011).2 Both these strands of the literature have
shortcomings. On the one hand, considering a country as the unit of analysis is
likely to lead to a signiﬁcant aggregation bias. D'altra parte, ﬁrm-level studies
are often country speciﬁc and tend to focus on the manufacturing sector.3 In their
quest for the indirect effects on the TFP of domestic ﬁrms, the latter studies also
neglect the direct contribution that foreign ﬁrms can make to sector-speciﬁc labor
productivity.4 There is therefore a need for a cross-country, multisector investigation
of the effects of FDI on various measures of host countries’ productivity.

This paper attempts to address this need. We use a sector-level database,
covering the years 2000 E 2008, of the FDI presence in 24 manufacturing and
service sectors of 15 emerging market economies. Our FDI proxy is the share of
the labor force employed by foreign ﬁrms. This is a direct and tangible indicator
of the prevalence of foreign ﬁrms. Our database includes detailed and high-quality
information for all sectors on output, inputs, inter-industry linkages, and export
indicators. Such a richness allows us to investigate the potentially heterogeneous
effects of intra- and inter-industry FDI on the TFP and labor productivity of the
manufacturing and service sectors. Lastly, our data are time varying. We can control
for a large number of unobserved effects at the country-sector level. Hence, while
we do not carry out the type of granular analysis found in ﬁrm-speciﬁc studies, our
empirical analysis offers more external validity than country-speciﬁc studies, more
internal validity than cross-country studies, greater coverage of sector-speciﬁc FDI
presence than many studies, an encompassing assessment of the potential effects
of FDI on productivity, and relatively high robustness against an omitted variable
bias.

We ﬁnd that a larger foreign presence tends to have a positive and statistically
signiﬁcant impact on TFP through manufacturing backward FDI linkages and
within-industry presence. The latter result only holds for service sectors and the
economic effects are modest. In the short run, doubling manufacturing backward
FDI linkages (intra-industry FDI in services) would increase TFP by about 2%
(5%). When we examine the determinants of labor productivity, a different picture
emerges. We no longer ﬁnd consistent inter-industry FDI effects. On the other
hand, the effect of intra-industry FDI is large, positive, and statistically signiﬁcant,

2In line with the rest of the literature, we use “industry” as a synonym for “sector.” Occasionally, we refer to

“broad industries,” which are the secondary and tertiary sectors.

3Fernandes and Paunov (2012) is one of the few exceptions. See their literature review for a list of studies on

FDI in services and manufacturing TFP.

4Lipsey (2004) and Navaretti and Venables (2005) report that foreign ﬁrms are usually found to be much
more productive than domestic ﬁrms, largely because they make much more intensive use (per worker) of physical
capital, human capital, and intermediates. They are also likely to be more export oriented. Criscuolo (2006) shows
that foreign afﬁliates, thanks to their higher labor productivity and growing share in total employment, accounted for
40% or more of labor productivity growth in the manufacturing and service sectors of Organisation for Economic
Co-operation and Development (OECD) countries in the late 1990s.

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56 Asian Development Review

and holds across broad industries. Doubling intra-industry FDI in either the
manufacturing or service sectors would increase value added per worker by about
20% in the short run. Part of this increase in labor productivity appears to be the
outcome of improved FDI-induced export performance. Overall, our results suggest
that the presence of foreign ﬁrms improves host countries’ average performance
simply because these ﬁrms are larger, unconditionally more productive, and more
integrated in the world economy than domestic ﬁrms.

The rest of this paper proceeds as follows. In section II, we describe the
effects that FDI can be expected to have on host economies’ development. In section
III, we present our empirical methodology. In section IV, we describe our key
variables and the data used. In section V, we provide our results. In section VI, we
investigate whether our results apply to the Asian countries of our sample. Finalmente,
in section VII, we conclude and discuss the ﬁndings and limitations of our study.

II. Conceptual Framework

Assume that the labor productivity q of a domestic ﬁrm in a given country
can be summarized as qN = βz(X), where β is an efﬁciency parameter and x is
a set of characteristics that determines its productivity.5 For a foreign ﬁrm, we
have qF = αz(X). Firms are heterogeneous as they do not share the same set of
characteristics. The distribution of domestic ﬁrms’ employment across domestic
ﬁrms with different characteristics x is n(X). The average productivity of domestic
ﬁrms is therefore qN = ∫ βz(X)N(X)dx, 1 = ∫ n(X)dx. The distribution of foreign
ﬁrms’ employment across foreign ﬁrms with different characteristics x is m(X).
The average productivity of foreign ﬁrms is therefore qF = ∫ αz(X)M(X)dx, 1 =
∫ n(X)dx. The overall average productivity is then ¯q = (1 − μ)qN + μqF , where μ
is the share of the total labor force employed by foreign ﬁrms.

Foreign ﬁrms may have characteristics that differ, on average, from those
of domestic ﬁrms and that allow the former to be generally more productive, cioè.,
xF > xn. It is also possible that foreign ﬁrms are technically more efﬁcient than
domestic ﬁrms, cioè., α > β. Finalmente, greater intra-industry or inter-industry FDI may
also inﬂuence β through externalities, cioè., β = β(μ).

This conceptual framework suggests two lines of enquiries within the
constraints of the data we have at hand. Primo, we can investigate whether the TFP
of an economy is higher when the share of the labor force employed by foreign
ﬁrms increases. Secondo, we can look for more general effects by exploring whether
a greater foreign presence is associated with higher labor productivity.

5This section heavily draws on Navaretti and Venables (2005).

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Foreign Direct Investment and Productivity 57

III. Empirical Methodology

UN.

Total Factor Productivity Estimation

To investigate the impacts of intra- and inter-industry FDI presence on TFP,

we adopt the following econometric model:

ln (GOsit ) = β B

2 ln (Lsit ) + β B

1 ln (Ksit ) + β B
+ β5 ln (BWFDIMsit ) + β6 ln (FWFDIMsit ) + β7 ln (BWFDISsit )
+ β8 ln (FWFDISsit ) + ρ ln (TFPsit−5) + αsi + αst + εsit

3 ln (Isit ) + β4 ln (HFDIsit )

(1)

where GOsit is gross output of sector s in country i at period t, Ksit is capital
services, Lsit is labor services, Isit is intermediate inputs, HFDIsit is intra-industry
FDI, BWFDIMsit is backward linkages from FDI in downstream manufacturing
is forward linkages from FDI in upstream manufacturing
sectors, FWFDIMsit
sectors, BWFDISsit is backward linkages from FDI in downstream service sectors,
FWFDISsit is forward linkages from FDI in upstream service sectors, TFPsit−5 is
5-year lagged TFP, αsi is a country-sector-speciﬁc effect, αst is a sector-time-speciﬁc
effect, and εsit is the error term. We allow β1 − β3 to differ across broad industries
B (manufacturing and services).

Equazione (1) is estimated in two distinct ways. We initially use a random
effects (RE) estimator, replacing αsi by αs and αi. This allows us to exploit
both the cross-sectional and time dimensions of our data to identify parameters
of interest. More information can crucially matter in the context of explanatory
variables measured with error. Tuttavia, the consistency of the RE estimator is
partly based on the assumption that the explanatory variables are not correlated with
an unobserved time-invariant, country-speciﬁc factor that is part of the composite
error term. This assumption could be too strong; Per esempio, foreign investors may
choose to locate in more structurally productive sectors. Hence, we also use a ﬁxed
effects (FE) estimator. By identifying our parameters solely on the basis of the time
series variation in our data, we control for the inﬂuence of an unobserved time-
invariant, country-sector-speciﬁc effect. D'altra parte, we no longer exploit the
information provided by the cross-sectional variation.

The input factors may also be correlated with unobserved time-invariant,
country-sector-speciﬁc factors. They may also be simultaneously determined with
produzione; in that case, an FE estimator would not help us to deal with this issue
(Van Beveren 2012). For this reason, we also generate indirect estimates of
TFP based on the use of an instrumental variables estimation. Exploiting all
the years available in our database, omitting the FDI variables, and applying
a system generalized methods-of-moments estimator (Blundell and Bond 1998),
we estimate equation (1) separately for the secondary and tertiary sectors. Nostro

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58 Asian Development Review

indirect estimates of
(cid:2)β B
2 ln(Lsit ) − (cid:2)β B
sensible to the choice of the internal instruments.6

ln(T F P) are then ln((cid:2)T F Psit ) = ln(GOsit ) − (cid:2)β B
1 ln(Ksit ) −
3 ln(Isit ). One drawback of this method is that estimates can be

We also include in our econometric models a 5-year lagged TFP term.7 We
do so for two reasons. Primo, we know from the literature on economic growth that
the evolution of TFP toward its equilibrium value may follow a partial adjustment or
convergence process (Solow 1956, Swan 1956). As such, emerging economies are
expected to grow faster than developed economies. One reason for this is that the
former are able to imitate new technology rather than having to innovate themselves,
which would be costlier (Gerschenkron 1962, Barro and Sala-i-Martin 1997).
Secondo, this lagged TFP term can capture unobserved country-sector factors.8

Overall, each estimation method has its pros and cons. To eliminate any
concern about cherry-picking our favorite estimates, we report all results. Standard
errors are clustered at the country-sector level.

Labor Productivity Estimation

The estimation of equation (1) reduces the impact of FDI to an effect on TFP.
Tuttavia, as stressed in section II, foreign ﬁrms may also have broad positive direct
compositional effects on their host economies, leading to a rise in labor productivity
(higher real value added per worker). We estimate therefore, in a second stage, IL
following model:

ln (VAsit ) = γ1 ln (VAsit−5) + γ2 ln (HFDIsit ) + γ3 ln (BWFDIMsit )

+ γ4 ln (FWFDIMsit ) + γ5 ln (BWFDISsit ) + γ6 ln (FWFDISsit )
+ αsi + αst + εsit

(2)

where VAsit is value added per worker in sector s in country i at period t. We estimate
equation (2) using either an RE estimator or an FE estimator.

Finalmente, we explore whether the inﬂuence of FDI on labor productivity partly
occurs through the impact of FDI on export performance. Our outcome variables
in this third and ﬁnal stage are real gross exports per worker (X), real direct
domestic value added embodied in gross exports per worker (VAX), and the ratio

6We use the second to fourth lags of the potentially endogenous variables and we collapse the set of

instruments. For details, see Roodman (2009).

7Following Grifﬁth (1999), when we estimate equation (1), the 5-year lagged term is proxied by the 5-year

lagged input and output terms.

8It is well known that the dynamic RE and FE estimators are biased. Tuttavia, whereas the bias of the
estimator of the autoregressive parameter is large (and negative), the Monte Carlo simulations of Judson and Owen
(1999) show that this is not the case for the bias of the estimators of the coefﬁcients on the explanatory variables
(1%–3% of the true value). Hence, we focus on short-run effects. Unlike long-run effects, their calculations do not
involve the use of the estimated value of ρ.

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Foreign Direct Investment and Productivity 59

of the two preceding variables (VAX/X). We adopt an econometric model similar to
equation (2).

IV. Key Variables and Data

Our sector-level data on gross output, capital services,

labor services
(proxied by labor compensation per Fox and Smeets [2011]), employment,
intermediate inputs, and input–output tables come from the World Input–Output
Database.9 Data on value added, gross exports, and domestic value added embodied
in gross exports come from the Trade in Value Added Database created by the
Organisation for Economic Co-operation and Development (OECD).10 All values
are deﬂated using country-sector-speciﬁc gross output and value added deﬂators.

We deﬁne intra-industry FDI (HFDIsit ) as the share of workers employed by
foreign ﬁrms. Our data on the number of foreign workers come from the Investment
Map database provided by the International Trade Centre.11 Based on data originally
collected by Dun & Bradstreet, this website provides sector-speciﬁc data on the
latest number of foreign afﬁliates, the number of foreign afﬁliates established since
2000, and the total number of workers for a sample of the existing foreign afﬁliates.
We make the following assumptions to calculate HFDIsit: (io) we assume that
the latest year is 2008, (ii) we calculate the number of foreign afﬁliates in 2000 COME
the latest number minus the number of foreign afﬁliates established since 2000, (iii)
we calculate the average number of workers in the foreign afﬁliates for which we
have the data and consider that this average is reasonably close to the population
average, E (iv) we multiply the average number of workers by the number of
foreign afﬁliates in 2000 E 2008. As such, we ﬁnd that industries employ foreign
employees at an average rate of 19%, with industries such as electrical and optical
equipment; coke, reﬁned petroleum, and nuclear fuel; and ﬁnancial intermediation
employing signiﬁcantly more than that (see Tables A2 and A3 in the Appendix).

Our assumptions are unlikely to fully hold in practice. Nevertheless, given
that we have a large range of sectors and countries, as well as a large gap between
our 2 years, we expect HFDIsit to have a reasonably high signal-to-noise ratio
between and within country–partner pairs. Inoltre, the correlation coefﬁcient
between our intra-industry FDI variable and the share of ﬁnancial FDI stocks in
value added (for which we have very unbalanced data) È 0.5, which is signiﬁcant
at the 1% level.12 Lastly, to reduce the inﬂuence of extremely high observations,

9See http://www.wiod.org/home.
10See https://stats.oecd.org/index.aspx?queryid=66237.
11See http://www.investmentmap.org/.
12Inoltre, the correlation coefﬁcient between our intra-industry FDI variable and the share of workers
employed by foreign ﬁrms reported in the OECD Activity of Multinational Enterprises database for ﬁve countries
from our sample in 2008 È 0.7, which is signiﬁcant at the 1% level.

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60 Asian Development Review

we cap HFDIsit to 1 and transform it as HFDIsit ∗ 100 + 1. We adopt the same
transformation for the other FDI variables that we now describe.

To take into account the productivity effects of FDI in downstream sectors,

we construct the following backward FDI linkage variables:

BWFDIMsit =

BWFDISsit =

M(cid:3)

k=1

S(cid:3)

k=1

γskit ∗ HFDIkit

(3)

(4)

where γskit is the share of sector s’ gross output that is supplied to downstream
manufacturing (M) or service (S) sector k in country i at time t. As can be seen in
Table A3 in the Appendix, ﬁrms supply on average 31% of their output to domestic
downstream industries, con 11% going to downstream manufacturing industries
E 18% to service industries.

Likewise, to take into account the productivity effects of FDI in upstream

sectors, we construct the following forward FDI linkage variables:

FWFDIMsit =

FWFDISsit =

M(cid:3)

k=1

S(cid:3)

k=1

δskit ∗ HFDIkit

(5)

(6)

where δskit is the share of sector s’ total inputs supplied by upstream manufacturing
(M) or service (S) sector k in country i at time t.13 As can be seen in Table A3, ﬁrms
source on average 61% of their inputs from domestic upstream industries other than
their own. Inoltre, 18% of the inputs come from manufacturing industries and
36% from service industries.

Overall, matching the data that we have and focusing on emerging
economies, we end up with a sample of 15 countries, 13 manufacturing sectors, 11
service sectors, and the years 2000 E 2008.14 From a development perspective,
this is an interesting sample since none of the countries were classiﬁed as high
income in 2000. It includes large countries such as Brazil, India, the People’s
Republic of China (PRC), and the Russian Federation, as well as a group of
Central and Eastern European economies that were going through a period of
transitioning from a state-led to a market economy.15 During 2000–2008, these

13Javorcik (2004) has pioneered the use of these linkage measures in the FDI literature.
14For data availability reasons, the latest year in section V is 2010.
15See https://en.wikipedia.org/wiki/World_Bank_high-income_economy.

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Foreign Direct Investment and Productivity 61

Tavolo 1. Total Factor Productivity and Intra-Industry Foreign
Direct Investment

ln(GO)
RE
[1]

0.014**
(0.006)
702

ln(GO)
FE
[2]

0.029***
(0.010)
702

ln(TFPe)
RE
[3]

0.014**
(0.006)
702

ln(TFPe)
FE
[4]

0.023**
(0.009)
702

ln(HFDI)

Number of observations

FE = ﬁxed effects estimator, GO = gross output, HFDI = intra-industry foreign
direct investment, RE = random effects estimator, TFPe = total factor productivity
estimates.
Notes: ***p-value < 0.01, **p-value < 0.05, and *p-value < 0.10. Cluster-robust standard errors are in parentheses. All regressions include 5-year lagged total factor productivity terms and sector-speciﬁc time effects. The GO columns include the log values of capital services, labor services, and intermediates, as well as their interactions with a dummy variable indicating whether the sector belongs to the broad services industry. The RE columns include country and sector ﬁxed effects. The FE columns include country-sector ﬁxed effects. Sources: International Trade Centre. Investment Map. http://www.investmentmap .org/; Organisation for Economic Co-operation and Development. 2015. Trade in Value Added Database. https://stats.oecd.org/index.aspx?queryid=66237; and World Bank. 2000–2014. World Input–Output Tables. http://www.wiod.org/home. countries experienced annual GDP growth rates of 5.6%, with the average for Asian countries reaching 7.4%. At the same time, these countries received signiﬁcant sums of FDI inﬂows that comprised 5.2% of GDP annually.16 As such, we believe this sample serves as an interesting case to examine the effects of FDI on productivity. The Appendix shows additional summary statistics. V. Results A. Total Factor Productivity We report four sets of estimates, reﬂecting the use of two different methodologies to estimate TFP (direct and indirect) and two different panel data estimators (RE estimator and FE estimator). We consider a ﬁnding to be relevant if the magnitude, sign, and statistical signiﬁcance of the coefﬁcient on a given variable are highly consistent across estimation methods. 1. Intra-Industry Foreign Direct Investment In Table 1, we only look at the impact of intra-industry FDI presence on TFP. In column 1, using direct TFP estimates and an RE estimator, a greater foreign presence in a given sector appears to be associated in a statistically signiﬁcant 16See https://data.worldbank.org/data-catalog/world-development-indicators. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 62 Asian Development Review Table 2. Total Factor Productivity, Intra- and Inter-Industry Foreign Direct Investment ln(GO) RE [1] 0.012** (0.006) 0.031*** (0.011) 0.025** (0.012) −0.015 (0.018) 0.023 (0.017) 702 ln(GO) FE [2] 0.024** (0.010) ln(TFPe) RE [3] 0.010* (0.006) 0.028* 0.031** (0.016) (0.012) 0.011 0.015 (0.012) (0.014) −0.046** −0.022 (0.019) (0.023) 0.033* 0.019 (0.017) (0.019) 702 702 ln(TFPe) FE [4] 0.024** (0.011) 0.028* (0.015) 0.005 (0.014) −0.054** (0.024) 0.019 (0.021) 702 ln(HFDI) ln(BWFDIM) ln(FWFDIM) ln(BWFDIS) ln(FWFDIS) Number of observations BWFDIM = manufacturing backward foreign direct investment linkages, BWFDIS = services backward foreign direct investment linkages, FE = ﬁxed effects estimator, FWFDIM = manufacturing forward foreign direct investment linkages, FWFDIS = services forward foreign direct investment linkages, GO = gross output, HFDI = intra- industry foreign direct investment, RE = random effects estimator, TFPe = total factor productivity estimates. Notes: ***p-value < 0.01, **p-value < 0.05, and *p-value < 0.10. Cluster-robust standard errors are in parentheses. All regressions include 5-year lagged total factor productivity terms and sector-speciﬁc time effects. The GO columns include the log values of capital services, labor services, and intermediates, as well as their interaction with a dummy variable indicating whether the sector belongs to the broad services industry. The RE columns include country and sector ﬁxed effects. The FE columns include country-sector ﬁxed effects. Sources: International Trade Centre. Investment Map. http://www.investmentmap .org/; Organisation for Economic Co-operation and Development. 2015. Trade in Value Added Database. https://stats.oecd.org/index.aspx?queryid=66237; and World Bank. 2000–2014. World Input–Output Tables. http://www.wiod.org/home. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 manner with higher TFP in the same sector. This result holds when we use the indirect TFP estimates (column 3) and when we apply an FE estimator (columns 2 and 4). Given the lack of evidence supporting the existence of intra-industry externalities (Iršová and Havránek 2013), the effect of intra-industry FDI is likely to be related to the greater TFP of foreign versus domestic ﬁrms. 2. Intra-Industry and Inter-Industry FDI In Table 2, we introduce in our initial model proxies for FDI linkages with manufacturing and services. We still ﬁnd that intra-industry FDI raises TFP. Furthermore, in line with the microeconomic FDI literature, we ﬁnd statistical evidence for a positive effect on TFP of an FDI presence in downstream manufacturing sectors. The economic effects are modest. In the short run, on the basis of the estimates reported in column 4, doubling intra-industry FDI from the Foreign Direct Investment and Productivity 63 Table 3. Total Factor Productivity and Broad-Industry-Speciﬁc Estimates for Intra-Industry Foreign Direct Investment ln(GO) RE [1] 0.012** (0.006) 0.000 (0.009) 0.031*** (0.011) 0.025** (0.012) −0.015 (0.018) 0.023 (0.017) 702 ln(GO) FE [2] 0.010 (0.011) 0.055** (0.023) 0.029* (0.017) 0.013 (0.015) −0.056** (0.023) 0.029 (0.019) 702 ln(TFPe) RE [3] 0.009 (0.006) 0.004 (0.009) 0.032*** (0.012) 0.015 (0.012) −0.023 (0.019) 0.019 (0.017) 702 ln(TFPe) FE [4] 0.011 (0.012) 0.050** (0.021) 0.032** (0.015) 0.006 (0.014) −0.063** (0.025) 0.012 (0.021) 702 ln(HFDI)*MAN ln(HFDI)*SERV ln(BWFDIM) ln(FWFDIM) ln(BWFDIS) ln(FWFDIS) Number of observations investment BWFDIM = manufacturing backward foreign direct linkages, BWFDIS = services backward foreign direct investment linkages, FE = ﬁxed effects estimator, FWFDIM = manufacturing forward foreign direct investment linkages, FWFDIS = services forward foreign direct investment linkages, GO = gross output, HFDI = intra-industry foreign direct investment, MAN or SERV = dummy variable indicating either manufacturing or services, RE = random effects estimator, TFPe = total factor productivity estimates. Notes: ***p-value < 0.01, **p-value < 0.05, and *p-value < 0.10. Cluster-robust standard errors are in parentheses. All regressions include 5-year lagged total factor productivity terms and sector-speciﬁc time effects. The GO columns include the log values of capital services, labor services, and intermediates, as well as their interaction with a dummy variable indicating whether the sector belongs to the broad services industry. The RE columns include country and sector ﬁxed effects. The FE columns include country-sector ﬁxed effects. Investment Map. http://www.investmentmap.org/; Sources: Organisation for Economic Co-operation and Development. 2015. Trade in Value Added Database. https://stats.oecd.org/index.aspx?queryid=66237; and World Bank. 2000–2014. World Input–Output Tables. http://www.wiod.org/home. International Trade Centre. current 19% to 38% would increase TFP in a given sector by ([20.024] − 1) ∗ 100% = 1.7%. Similarly, doubling backward linkages from FDI in manufacturing sectors, by either doubling the supply coefﬁcient from the current 11% (Table A3) or doubling the current share of foreign employees in downstream manufacturing industries (27%), would increase TFP by ([20.028] − 1) ∗ 100% = 1.96%. At the average sample values, the corresponding semielasticity terms are 0.10 and 0.66, the latter being in range of those reported in Havránek and Iršová (2011). In Table 3, we investigate whether our estimates for intra-industry FDI diverge across broad industries (manufacturing and services). The FE estimates suggest that the impact of intra-industry FDI may be much stronger in service sectors, as indicated by ln(HFDI) ∗ SERV. In the short run, on the basis of the estimates reported in column 4, doubling intra-industry FDI linkages would increase TFP in service (manufacturing) sectors by 3.5% (0.8%). l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 64 Asian Development Review Table 4. Speciﬁc Services-Forward Foreign Direct Investment Linkages ln(GO) RE [1] 0.012** (0.006) 0.031*** (0.011) 0.026** (0.012) −0.015 (0.018) 0.022 (0.018) 702 ln(GO) FE [2] 0.025** (0.010) 0.027* (0.016) 0.012 (0.015) −0.043* (0.024) 0.030 (0.019) 702 ln(TFPe) RE [3] 0.011* (0.006) 0.031*** (0.012) 0.016 (0.012) −0.023 (0.019) 0.022 (0.018) 702 ln(TFPe) FE [4] 0.025** (0.010) 0.028* (0.015) 0.006 (0.014) −0.053** (0.024) 0.019 (0.021) 702 ln(HFDI) ln(BWFDIM) ln(FWFDIM) ln(BWFDIS) ln(FWFDISS) Number of observations investment BWFDIM = manufacturing backward foreign direct linkages; BWFDIS = services backward foreign direct investment linkages; FE = ﬁxed effects estimator; FWFDIM = manufacturing forward foreign direct investment linkages; FWFDISS = services-forward foreign direct investment linkages related to the following upstream sectors: (i) electricity, gas and water supply; (ii) transport and communications; (iii) ﬁnancial intermediation; and (iv) real estate and business services; GO = gross output; HFDI = intra-industry foreign direct investment; RE = random effects estimator; TFPe = total factor productivity estimates. Notes: ***p-value < 0.01, **p-value < 0.05, and *p-value < 0.10. Cluster-robust standard errors are in parentheses. All regressions include 5-year lagged total factor productivity terms and sector-speciﬁc time effects. The GO columns include the log values of capital services, labor services, and intermediates, as well as their interaction with a dummy variable indicating whether the sector belongs to the broad services industry. The RE columns include country and sector ﬁxed effects. The FE columns include country-sector ﬁxed effects. Sources: Investment Map. http://www.investmentmap.org/; Organisation for Economic Co-operation and Development. 2015. Trade in Value Added Database. https://stats.oecd.org/index.aspx?queryid=66237; and World Bank. 2000–2014. World Input–Output Tables. http://www.wiod.org/home. International Trade Centre. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . In Table 4, we examine the distinct inﬂuence of speciﬁc forward linkages from FDI in service sectors. Following Fernandes and Paunov (2012), we focus on linkages with the following upstream service sectors: (i) electricity, gas, and water supply; (ii) transport and communications; (iii) ﬁnancial intermediation; and (iv) real estate and business services. As indicated by Fernandes and Paunov, these sectors are characterized by the facilitating and intermediating role they play for downstream ﬁrms. In addition, Table A2 shows that these are the service sectors that exhibit the highest foreign employment shares. However, we still fail to ﬁnd an impact of forward linkages from FDI in service sectors on TFP. f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 B. Labor Productivity In Table 5, we examine whether intra- and inter-industry FDI inﬂuences labor productivity (real value added per worker). This single factor productivity indicator is of great interest to policy makers and is frequently employed to make Foreign Direct Investment and Productivity 65 / X X A V / X X A V ) X A V ( n l ) X A V ( n l E F ] 6 [ E R ] 5 [ ) X ( n l E F ] 4 [ ) X ( n l E R ] 3 [ ) A V ( n l ) A V ( n l E F ] 2 [ E R ] 1 [ t n e m t s e v n I t c e r i D n g i e r o F r o t c e s i t l u M d n a y t i v i t c u d o r P r o b a L . 5 e l b a T E F ] 8 [ 8 1 0 0 − . ) 2 1 0 0 ( . 2 2 0 0 − . E R ] 7 [ ) 4 0 0 0 ( . 4 0 0 0 . 5 0 0 0 − . ) 7 1 0 0 ( . * 0 2 0 0 − . ) 1 1 0 0 ( . 1 3 0 0 . ) 8 2 0 0 ( . 1 1 0 0 − . ) 4 2 0 0 ( . 6 9 6 ) 6 0 0 0 ( . * * * 1 2 0 0 − . ) 6 0 0 0 ( . 8 0 0 0 . ) 9 0 0 0 ( . 0 0 0 0 . ) 8 0 0 0 ( . 6 9 6 * * * 8 4 3 . 0 * * * 7 1 2 . 0 * * * 1 2 4 . 0 * * * 7 1 2 . 0 * * * 8 5 2 . 0 * * * 4 7 1 . 0 ) I D F H ( n l ) 4 1 1 . 0 ( 2 8 0 . 0 − ) 4 5 1 . 0 ( 6 4 1 . 0 − ) 7 1 1 . 0 ( * * * 1 2 6 . 0 − ) 6 2 2 . 0 ( 3 4 1 . 0 ) 4 9 1 . 0 ( 6 9 6 ) 9 3 0 . 0 ( 0 7 0 . 0 − ) 4 7 0 . 0 ( 2 0 0 . 0 ) 8 7 0 . 0 ( 9 8 0 . 0 − ) 8 0 1 . 0 ( * * * 6 9 3 . 0 ) 0 1 1 . 0 ( 6 9 6 ) 6 3 1 . 0 ( 7 4 0 . 0 − ) 6 5 1 . 0 ( 6 8 0 . 0 − ) 1 2 1 . 0 ( * * * 0 2 7 . 0 − ) 3 3 2 . 0 ( 1 7 1 . 0 ) 2 0 2 . 0 ( 6 9 6 ) 7 3 0 . 0 ( 6 6 0 . 0 − ) 1 7 0 . 0 ( 4 4 0 . 0 ) 8 7 0 . 0 ( 1 2 1 . 0 − ) 5 0 1 . 0 ( * * * 5 2 3 . 0 ) 1 1 1 . 0 ( 6 9 6 ) 5 8 0 . 0 ( 6 6 1 . 0 ) 0 2 1 . 0 ( 8 1 0 . 0 − ) 7 7 0 . 0 ( * * * 8 7 6 . 0 − ) 5 8 1 . 0 ( 7 4 1 . 0 ) 5 3 1 . 0 ( 6 9 6 ) 0 3 0 . 0 ( 4 2 0 . 0 ) 4 5 0 . 0 ( 7 1 0 . 0 ) 3 5 0 . 0 ( 8 0 0 . 0 − * * 4 5 1 . 0 ) 8 6 0 . 0 ( ) 1 7 0 . 0 ( 6 9 6 s n o i t a v r e s b o f o r e b m u N ) S I D F W B ( n l ) S I D F W F ( n l ) I M D F W F ( n l ) I M D F W B ( n l r o t c e s e h t / = X X A V , r e k r o w r e p s t r o p x e n i d e i d o b m e d e d d a e u l a v c i t s e m o d = E F , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w k c a b s e c i v r e s = S I D F W B , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w k c a b g n i r u t c a f u n a m = M D F W B I t n e m t s e v n i t c e r i d n g i e r o f d r a w r o f s e c i v r e s r e h t e h w g n i t a c i d n i e l b a i r a v y m m u d = V R E S = S I D F W F , r o t a m i t s e s t c e f f e m o d n a r = E R , t n e m t s e v n i t c e r i d n g i e r o f y r t s u d n i - a r t n i , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w r o f g n i r u t c a f u n a m = M D F W F I , r o t a m = i t s e s t c e f f e d e x ﬁ I D F H , s e g a k n i l t c e r i d = X A V , r e k r o w r e p d e d d a e u l a v = A V , y r t s u d n i s e c i v r e s d a o r b e h t o t s g n o l e b . r e k r o w r e p s t r o p x e s s o r g = X , s t r o p x e s s o r g n i d e d d a e u l a v c i t s e m o d t c e r i d f o e r a h s r a e y - 5 e d u l c n i s n o i s s e r g e r l l A . s e s e h t n e r a p n i e r a s r o r r e d r a d n a t s t s u b o r - r e t s u l C . 0 1 . 0 < e u l a v - p * d n a , 5 0 . 0 < e u l a v - p * * , 1 0 . 0 < e u l a v - p * * * : s e t o N e d u l c n i s n m u l o c E F e h T . s t c e f f e d e x ﬁ r o t c e s d n a y r t n u o c e d u l c n i s n m u l o c E R e h T . s t c e f f e e m i t c ﬁ i c e p s - r o t c e s d n a s m r e t y t i v i t c u d o r p r o t c a f l a t o t d e g g a l . s e l b a T t u p t u O – t u p n I d l r o W . 4 1 0 2 – 0 0 0 2 . k n a B d l r o W d n a ; 7 3 2 6 6 = d i y r e u q ? x p s a . x e d n i / g r o . d c e o . s t a t s / / : s p t t h . t n e m p o l e v e D d n a n o i t a r e p o - o C c i m o n o c E r o f n o i t a s i n a g r O ; / g r o . p a m t n e m t s e v n i . w w w / / : p t t h . p a M t n e m t s e v n I . e s a b a t a D d e d d A e u l a V n i e d a r T . 5 1 0 2 . e m o h / g r o . d o i w w w w . / / : p t t h . e r t n e C e d a r T l a n o i t a n r e t n I : s e c r u o S . s t c e f f e d e x ﬁ r o t c e s - y r t n u o c l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 66 Asian Development Review international performance comparisons. It is also less sensitive to assumptions than TFP. Another advantage is that it allows us to account for the effects of FDI on domestic activity that are not mediated via TFP changes, e.g., different usage of factors of production. In columns 1 and 2, we ﬁnd that intra-industry FDI is strongly associated with higher labor productivity. In contrast with our previous ﬁndings, the economic effects are much larger. Using the estimates reported in column 2, in the short run, doubling intra-industry FDI would increase labor productivity by about 20%. Columns 3 through 6 show that part of this effect appears to be driven by FDI-induced integration via global value-added chains. Intra-industry FDI is consistently associated with improved export performance, measured either as exports per worker (X) or direct domestic value added embodied in exports per worker (VAX). In columns 7 and 8, we look at whether multisectoral FDI can induce value-added upgrading in the sense of increasing the share of domestic value-added content in gross exports (VAX/X). This does not appear to be the case, including for intra-industry FDI. On the other hand, greater manufacturing forward FDI linkages tend to induce value-added downgrading, possibly because ﬁrms make greater use of inter-industry intermediates that are produced by foreign ﬁrms. In Table 6, we investigate whether these results hold when we allow for a differential effect of intra-industry FDI across broad industry sectors by interacting ln(HFDI) with an industry dummy variable that indicates whether the host industry is a manufacturing sector (ln(HFDI) ∗ MAN) or a service sector (ln(HFDI) ∗ SERV). We still observe a strong effect of intra-industry FDI, particularly in the manufacturing sector. On the other hand, the effect is insigniﬁcant in the service sectors. In section VI, we will come back to this difference between broad industry classiﬁcations. Lastly, focusing on inter-industry FDI variables, as in the previous section, results are ambiguous as the sign, magnitude, and statistical signiﬁcance of the coefﬁcients on these variables vary widely across columns. For example, it is not clear how FDI in service sectors inﬂuences labor productivity or export performance, although our results suggest a positive role for forward linkages from FDI in service sectors. In addition, we do not ﬁnd a statistically signiﬁcant effect for backward linkages from FDI in manufacturing sectors anymore. To the extent that these externalities truly exist at the TFP level, they do not appear to be translated into greater labor productivity. VI. Regional Comparisons Table A1 shows that countries located in Central and Eastern Europe are predominant in our sample. In this last section, we explore how our results compare across the three regions mentioned in Table A1. To do so, we interact the FDI variables with three dummy variables, CEEU, LAC, and Asia, which take the value l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Foreign Direct Investment and Productivity 67 t n e m t s e v n I t c e r i D n g i e r o F y r t s u d n I - a r t n I r o f s e t a m i t s E c ﬁ i c e p S - y r t s u d n I - d a o r B d n a y t i v i t c u d o r P r o b a L . 6 e l b a T / X X A V / X X A V ) X A V ( n l ) X A V ( n l E F ] 8 [ E R ] 7 [ E F ] 6 [ E R ] 5 [ ) X ( n l E F ] 4 [ ) X ( n l E R ] 3 [ 6 1 0 0 − . 4 0 0 0 − . * * * 2 8 3 . 0 * * * 3 3 2 . 0 * * * 2 7 4 . 0 * * * 4 3 2 . 0 ) 3 1 0 0 ( . 5 0 0 0 − . ) 5 0 0 0 ( . 1 0 0 0 − . ) 1 2 0 0 ( . 3 2 0 0 − . ) 6 0 0 0 ( . 4 0 0 0 . ) 7 1 0 0 ( . * 0 2 0 0 − . ) 1 1 0 0 ( . 2 3 0 0 . ) 8 2 0 0 ( . 1 1 0 0 − . ) 4 2 0 0 ( . 6 9 6 ) 6 0 0 0 ( . * * * 1 2 0 0 − . ) 6 0 0 0 ( . 8 0 0 0 . ) 9 0 0 0 ( . 0 0 0 0 . ) 9 0 0 0 ( . 6 9 6 ) 9 3 1 . 0 ( 1 3 1 . 0 − ) 8 7 1 . 0 ( 5 9 0 . 0 − ) 5 5 1 . 0 ( 0 5 1 . 0 − ) 9 1 1 . 0 ( * * 5 9 5 . 0 − ) 1 3 2 . 0 ( 7 5 1 . 0 ) 3 9 1 . 0 ( 6 9 6 ) 3 4 0 . 0 ( 7 5 0 . 0 − ) 2 6 0 . 0 ( 1 8 0 . 0 − ) 4 7 0 . 0 ( 4 0 0 . 0 ) 8 7 0 . 0 ( 0 8 0 . 0 − ) 8 0 1 . 0 ( * * * 0 0 4 . 0 ) 1 1 1 . 0 ( 6 9 6 ) 4 6 1 . 0 ( 2 0 2 . 0 − ) 8 8 1 . 0 ( 7 6 0 . 0 − ) 7 5 1 . 0 ( 3 9 0 . 0 − ) 4 2 1 . 0 ( * * * 1 8 6 . 0 − ) 6 3 2 . 0 ( 2 9 1 . 0 ) 8 9 1 . 0 ( 6 9 6 ) 0 4 0 . 0 ( 7 5 0 . 0 − ) 9 5 0 . 0 ( 7 7 0 . 0 − ) 1 7 0 . 0 ( 7 4 0 . 0 ) 8 7 0 . 0 ( 2 1 1 . 0 − ) 6 0 1 . 0 ( * * * 0 3 3 . 0 ) 2 1 1 . 0 ( 6 9 6 ) A V ( n l ) A V ( n l E F ] 2 [ * * 7 4 2 . 0 ) 9 9 0 . 0 ( 5 4 0 . 0 ) 0 3 1 . 0 ( 0 7 1 . 0 ) 1 2 1 . 0 ( 7 1 0 . 0 − ) 7 7 0 . 0 ( * * * 6 8 6 . 0 − ) 6 8 1 . 0 ( 2 4 1 . 0 ) 4 3 1 . 0 ( 6 9 6 E R ] 1 [ * * * 4 9 1 . 0 ) 5 3 0 . 0 ( 9 6 0 . 0 − ) 0 5 0 . 0 ( 0 1 0 . 0 ) 4 5 0 . 0 ( 0 2 0 . 0 ) 4 5 0 . 0 ( 4 0 0 . 0 * * 9 5 1 . 0 ) 8 6 0 . 0 ( ) 1 7 0 . 0 ( 6 9 6 s n o i t a v r e s b o f o r e b m u N ) S I D F W B ( n l ) S I D F W F ( n l ) I M D F W F ( n l N A M * ) I D F H ( n l V R E S * ) I D F H ( n l ) I M D F W B ( n l , r e k r o w r e p d e d d a s t r o p x e s s o r g = X = E F , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w k c a b s e c i v r e s = S I D F W B , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w k c a b g n i r u t c a f u n a m = M D F W B I t n e m t s e v n i t c e r i d n g i e r o f d r a w r o f s e c i v r e s = S I D F W F , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w r o f e u l a v = A V , r o t a m i t s e s t c e f f e m o d n a r = E R , s e c i v r e s r o g n i r u t c a f u n a m r e h t i e g n i t a c i d n i g n i r u t c a f u n a m = M D F W F I , r o t a m i t s e s t c e f f e d e x ﬁ e l b a i r a v y m m u d = V R E S r o N A M , s e g a k n i l , s t r o p x e s s o r g n i d e d d a e u l a v c i t s e m o d t c e r i d f o e r a h s / = X X A V , r e k r o w r e p s t r o p x e n i d e i d o b m e d e d d a e u l a v c i t s e m o d t c e r i d = X A V r a e y - 5 e d u l c n i s n o i s s e r g e r l l A . s e s e h t n e r a p n i e r a s r o r r e d r a d n a t s t s u b o r - r e t s u l C . 0 1 . 0 < e u l a v - p * d n a , 5 0 . 0 < e u l a v - p * * , 1 0 . 0 < e u l a v - p * * * : s e t o N . r e k r o w r e p e d u l c n i s n m u l o c E F e h T . s t c e f f e d e x ﬁ r o t c e s d n a y r t n u o c e d u l c n i s n m u l o c E R e h T . s t c e f f e e m i t c ﬁ i c e p s - r o t c e s d n a s m r e t y t i v i t c u d o r p r o t c a f l a t o t d e g g a l . s e l b a T t u p t u O – t u p n I d l r o W . 4 1 0 2 – 0 0 0 2 . k n a B d l r o W d n a ; 7 3 2 6 6 = d i y r e u q ? x p s a . x e d n i / g r o . d c e o . s t a t s / / : s p t t h . t n e m p o l e v e D d n a n o i t a r e p o - o C c i m o n o c E r o f n o i t a s i n a g r O ; / g r o . p a m t n e m t s e v n i . w w w / / : p t t h . p a M t n e m t s e v n I . e s a b a t a D d e d d A e u l a V n i e d a r T . 5 1 0 2 . e m o h / g r o . d o i w w w w . / / : p t t h . e r t n e C e d a r T l a n o i t a n r e t n I : s e c r u o S . s t c e f f e d e x ﬁ r o t c e s - y r t n u o c l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 68 Asian Development Review of 1 when a country is located in Central and Eastern Europe, Latin America, or Asia, respectively. In Table 7, the variable ln(HFDI) ∗ SERV ∗ CEEU indicates that the effects of intra-industry FDI on TFP are especially present in the service sectors of the Central and Eastern European countries in our sample. On the other hand, Table 8 indicates that the impact of intra-industry FDI on total and exported labor productivity is much higher in the Asian manufacturing sectors than in the other region–sector combinations, especially when we only exploit the time series dimension of our data (the FE estimates). This ﬁnding is in line with the more general ﬁndings of Tables 3 and 6, which indicated that FDI is associated with higher TFP in services (Table 3) and higher labor productivity in manufacturing sectors (Table 6). In addition, there is some limited evidence that value-added upgrading took place (Table 8, columns 7 and 8). The fact that intra-industry FDI seems to particularly beneﬁt both European services as well as Asian manufacturing is surprising since they do not share many commonalities. In terms of skill endowments, for example, Table A4 shows that the median of average years of schooling in Europe during 2000–2010 is 10.8, while it is only 6.4 in Asia. In addition, European service sectors in Europe comprise signiﬁcantly more skilled workers (78%) than Asian manufacturing sectors (44%). Therefore, ﬁnding a single factor that can explain why horizontal FDI beneﬁts these region–sectors speciﬁcally seems unlikely. Instead, the explanation is more likely to be related to the underlying differences between our two measures of productivity: TFP and labor productivity. Since value added per worker is also affected by other factors of production, labor productivity can be considered only a partial measure of productivity. TFP, on the other hand, as a residual productivity measure, controls for such input factors that are captured by x in our conceptual framework. Then, an FDI-induced TFP premium suggests that foreign ﬁrms are better able to transform the same inputs into output, meaning that foreign ﬁrms’ technical efﬁciency exceeds that of domestic ﬁrms (α > β ). D'altra parte, evidence of a (signiﬁcantly higher)
FDI-induced labor productivity premium means that foreign ﬁrms tend to inhibit
more productive characteristics (xF > xN ), such as capital intensity, size, or skilled
employees. These more productive foreign ﬁrms will then mechanically increase the
overall productivity of a host economy, as per the equation ¯q = (1 − μ)qN + μqF ,
a process known as the compositional effect of FDI (Navaretti and Venables
2005).

In line with that framework, we expect the TFP premium to depend on host
economies’ absorptive capacity such as the level of human capital. We therefore
interact our FDI variables with the years of schooling, as a proxy for human capital,
in Table 9. Here, we would expect a positive interaction term, as the TFP spillover
from FDI should be higher for more levels of human capital.

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Foreign Direct Investment and Productivity 69

Tavolo 7. Total Factor Productivity and Region-Speciﬁc Estimates

ln(GO)
RE
(1)

0.012*
(0.006)
0.003
(0.009)
0.022*
(0.013)
0.040***
(0.015)
−0.016
(0.016)
0.016
(0.020)

0.004
(0.007)
0.004
(0.018)
0.019*
(0.011)
−0.007
(0.015)
−0.031
(0.030)
0.023
(0.018)

0.020
(0.019)
−0.009
(0.015)
0.042**
(0.019)
0.037
(0.034)
−0.004
(0.046)
0.071
(0.058)
702

ln(GO)
FE
(2)

ln(TFPe)
RE
(3)

0.010
0.004
(0.006)
(0.011)
0.066***
0.007
(0.009)
(0.025)
0.019
0.028
(0.013)
(0.020)
0.024*
0.008
(0.014)
(0.017)
−0.092*** −0.025
(0.017)
(0.028)
0.029
0.003
(0.019)
(0.029)

0.004
(0.029)
0.046
(0.068)
0.018
(0.019)
0.017
(0.028)
−0.022
(0.046)
0.028
(0.029)

0.074
(0.108)
−0.069
(0.192)
0.019
(0.043)
0.171
(0.168)
0.189
(0.146)
0.150
(0.154)
702

0.009
(0.007)
0.009
(0.019)
0.012
(0.012)
−0.007
(0.016)
−0.015
(0.032)
0.039*
(0.021)

0.017
(0.020)
−0.003
(0.016)
0.047**
(0.022)
0.045
(0.035)
−0.004
(0.048)
0.074
(0.059)
702

ln(TFPe)
FE
(4)

0.010
(0.013)
0.062***
(0.022)
0.036*
(0.021)
−0.009
(0.016)
−0.087***
(0.030)
0.035
(0.029)

0.009
(0.029)
0.053
(0.086)
0.018
(0.021)
0.031
(0.032)
0.013
(0.054)
0.012
(0.031)

0.170*
(0.100)
−0.300**
(0.142)
0.031
(0.046)
0.254
(0.159)
0.119
(0.157)
−0.095
(0.143)
702

ln(HFDI)*MAN*CEEU

ln(HFDI)*SERV*CEEU

ln(BWFDIM)*CEEU

ln(FWFDIM)*CEEU

ln(BWFDIS)*CEEU

ln(FWFDIS)*CEEU

ln(HFDI)*MAN*Asia

ln(HFDI)*SERV*Asia

ln(BWFDIM)*Asia

ln(FWFDIM)*Asia

ln(BWFDIS)*Asia

ln(FWFDIS)*Asia

ln(HFDI)*MAN*LAC

ln(HFDI)*SERV*LAC

ln(BWFDIM)*LAC

ln(FWFDIM*LAC

ln(BWFDIS)*LAC

ln(FWFDIS)*LAC

Number of observations

investment

Asia = dummy variable indicating whether the country is in Asia, BWFDIM =
linkages, BWFDIS = services
manufacturing backward foreign direct
backward foreign direct investment linkages, CEEU = dummy variable indicating whether
the country is in Central and Eastern Europe, FE = ﬁxed effects estimator, FWFDIM =
manufacturing forward foreign direct investment linkages, FWFDIS = services forward
foreign direct investment linkages, GO = gross output, HFDI = intra-industry foreign
direct investment, LAC = dummy variable indicating whether the country is in Latin
America, MAN or SERV = dummy variable indicating either manufacturing or services,
RE = random effects estimator, TFPe = total factor productivity estimates, VA = value
added per worker, VAX = direct domestic value added embodied in exports per worker,
VAX/X = share of direct domestic value added in gross exports, X = gross exports per
worker.

Continued.

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70 Asian Development Review

Tavolo 7. Continued.

Notes: ***p-value < 0.01, **p-value < 0.05, and *p-value < 0.10. Cluster-robust standard errors are in parentheses. All regressions include 5-year lagged total factor productivity terms and sector-speciﬁc time effects. The RE columns include country and sector ﬁxed effects. The FE columns include country-sector ﬁxed effects. Sources: International Trade Centre. Investment Map. http://www.investmentmap.org/; Organisation for Economic Co-operation and Development. 2015. Trade in Value Added Database. https://stats.oecd.org/index.aspx?queryid=66237; and World Bank. 2000–2014. World Input–Output Tables. http://www.wiod.org/home. On the other hand, the compositional effect depends on the difference between domestic (qN ) and foreign ﬁrms’ productivity (qF ). We therefore want to interact our FDI variables with a measure of domestic ﬁrms’ productivity. As a proxy, we take the host economies’ log labor productivity in the year 2000, ln (VA)2000, which is our earliest year of observation. The rationale is that, due to growing FDI over time, q is least affected by qF . Here, we expect a negative interaction term since the lower the initial labor productivity in the year 2000, the greater the gap between qF and qN and thus the larger the composition effect of FDI. Table A5 provides the descriptive statistics: the average labor productivity per region in the year 2000. As it shows, Latin America has signiﬁcantly higher value added per worker ($11,653) than both Central and Eastern Europe ($5,145) and Asia ($4,007). Tables 9 and 10 show evidence in line with our hypotheses. Table 9 shows a signiﬁcantly positive interaction coefﬁcient between intra-service-industry FDI and our proxy for human capital, ln(HFDI) ∗ SERV ∗ ln(HC), when using country- sector ﬁxed effects (columns 2 and 4). Apparently, the relatively large amount of years of schooling in Europe (Table A4) can partly explain why the European service sector has been able to achieve higher TFP spillovers from intra-industry FDI. The highly signiﬁcant interaction coefﬁcient ln(HFDI) ∗ ln(VA)2000 in Table 10, on the other hand, can explain why Asian manufacturing labor productivity beneﬁted disproportionally from intra-industry FDI. Namely, while horizontal FDI, ln(HFDI), is associated with signiﬁcantly higher labor productivity, this effect is signiﬁcantly larger for host economies that have lower initial labor productivity, as noted by ln(HFDI) ∗ ln(VA)2000. As we know from Table A5, Asia had the lowest labor productivity of all regions, partly providing an explanation for the ﬁndings of Table 8. All in all, these results contribute to the ﬁnding that developing economies in our sample beneﬁted from FDI largely through a compositional effect caused by the inﬂux of more productive foreign ﬁrms, rather than direct spillovers among foreign and domestic ﬁrms. In addition, our results suggest that the three large Asian countries in our sample—India, Indonesia, and the PRC—have disproportionately beneﬁted from the entry of foreign ﬁrms and the associated deployment of global value chains. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Foreign Direct Investment and Productivity 71 ) 5 1 0 0 ( . 3 0 0 0 − . ) 3 2 0 0 ( . 9 2 0 0 − . ) 5 3 0 0 ( . 0 2 0 0 − . ) 1 2 0 0 ( . 2 2 0 0 . ) 1 4 0 0 ( . 3 1 0 0 − . ) 2 4 0 0 ( . 4 0 0 0 − . ) 5 2 0 0 ( . 5 5 0 0 − . ) 8 5 0 0 ( . * * 2 4 0 0 − . ) 1 2 0 0 ( . 0 0 0 0 − . 9 0 0 0 − . 7 0 0 . 0 − * * 2 4 2 . 0 * * * 5 3 2 . 0 * * 3 3 3 . 0 * * * 4 4 2 . 0 * * 2 8 1 . 0 / X X A V / X X A V ) X A V ( n l ) X A V ( n l E F ) 8 ( E R ) 7 ( E F ) 6 ( E R ) 5 ( ) X ( n l E F ) 4 ( ) X ( n l E R ) 3 ( E F ) 2 ( ) A V ( n l ) A V ( n l s e t a m i t s E c ﬁ i c e p S - n o i g e R d n a y t i v i t c u d o r P r o b a L . 8 e l b a T ) 5 0 0 0 ( . 3 0 0 . 0 − ) 7 0 0 0 ( . 7 0 0 0 . ) 9 0 0 . 0 ( * * * 1 2 0 . 0 − ) 7 0 0 0 ( . 2 0 0 0 . ) 2 1 0 0 ( . 3 0 0 0 − . ) 1 1 0 . 0 ( ) 5 1 1 . 0 ( 1 5 1 . 0 − ) 3 8 1 . 0 ( * 6 2 3 . 0 − ) 7 8 1 . 0 ( 1 0 0 . 0 − ) 0 7 1 . 0 ( * * 8 4 5 . 0 − ) 4 5 2 . 0 ( 2 6 1 . 0 − ) 8 6 2 . 0 ( ) 8 4 0 . 0 ( 8 0 1 . 0 − ) 4 7 0 . 0 ( 3 2 1 . 0 − ) 8 9 0 . 0 ( 3 7 0 . 0 ) 3 9 0 . 0 ( 8 6 1 . 0 − ) 7 2 1 . 0 ( * * * 3 8 3 . 0 ) 3 3 1 . 0 ( ) 4 5 1 . 0 ( 9 4 2 . 0 − ) 5 9 1 . 0 ( 3 0 3 . 0 − ) 7 2 2 . 0 ( 9 3 0 . 0 ) 4 9 1 . 0 ( * * 0 7 5 . 0 − ) 5 6 2 . 0 ( 4 0 1 . 0 − ) 3 9 2 . 0 ( ) 4 4 0 . 0 ( 6 9 0 . 0 − ) 0 7 0 . 0 ( 3 2 1 . 0 − ) 3 9 0 . 0 ( 6 0 1 . 0 ) 0 9 0 . 0 ( 3 6 1 . 0 − * * 8 9 2 . 0 ) 9 1 1 . 0 ( ) 5 2 1 . 0 ( ) 1 9 0 . 0 ( 0 4 0 . 0 ) 6 2 1 . 0 ( 2 8 0 . 0 ) 7 4 1 . 0 ( 6 7 0 . 0 ) 4 0 1 . 0 ( * * * 3 8 8 . 0 − ) 8 5 2 . 0 ( 4 2 0 . 0 − ) 5 2 2 . 0 ( E R ) 1 ( * * * 5 8 1 . 0 ) 6 3 0 . 0 ( * * 3 4 1 . 0 − ) 7 5 0 . 0 ( 8 1 0 . 0 ) 6 6 0 . 0 ( * 4 1 1 . 0 ) 8 6 0 . 0 ( 1 6 0 . 0 − ) 7 8 0 . 0 ( 4 4 1 . 0 ) 6 9 0 . 0 ( U E E C * N A M * ) I D F H ( n l U E E C * V R E S * ) I D F H ( n l U E E C * ) M D F W B ( n l I U E E C * ) M D F W F ( n l I U E E C * ) S I D F W B ( n l U E E C * ) S I D F W F ( n l * * * 7 1 1 0 . ) 3 2 0 0 ( . ) 2 4 0 0 ( . 6 2 0 0 . ) 8 2 0 0 ( . . d e u n i t n o C ) 1 1 0 0 ( . 3 3 0 0 . ) 3 2 0 . 0 ( 1 1 0 . 0 ) 8 1 0 0 ( . 0 1 0 0 . * * * 6 9 0 . 1 * * * 5 2 3 . 0 * * * 2 1 1 . 1 * * * 9 9 2 . 0 * * * 6 8 8 . 0 * * * 8 0 3 . 0 a i s A * N A M * ) I D F H ( n l ) 6 0 0 . 0 ( 0 1 0 0 − . ) 2 1 0 . 0 ( 3 0 0 0 − . ) 0 1 0 . 0 ( 3 1 0 0 − . ) 0 3 3 . 0 ( 3 1 1 . 0 − ) 8 8 4 . 0 ( 8 7 2 . 0 − ) 6 7 2 . 0 ( 1 0 3 . 0 − ) 3 3 2 . 0 ( 6 7 8 . 0 − ) 3 3 5 . 0 ( 2 8 3 . 0 ) 1 3 3 . 0 ( ) 4 7 0 . 0 ( 9 5 0 . 0 − ) 3 1 1 . 0 ( 0 3 1 . 0 − ) 3 1 1 . 0 ( 3 3 0 . 0 ) 6 5 1 . 0 ( 2 2 2 . 0 − * * 4 1 4 . 0 ) 6 3 2 . 0 ( ) 7 8 1 . 0 ( ) 0 9 2 . 0 ( 9 1 1 . 0 ) 9 5 4 . 0 ( 7 7 1 . 0 − ) 1 8 2 . 0 ( 2 9 1 . 0 − ) 6 3 2 . 0 ( * * 4 8 2 . 1 − ) 7 7 5 . 0 ( 1 7 3 . 0 ) 7 4 3 . 0 ( ) 0 7 0 . 0 ( 9 4 0 . 0 − ) 7 0 1 . 0 ( 8 9 0 . 0 − ) 3 1 1 . 0 ( 4 7 0 . 0 ) 2 5 1 . 0 ( 1 2 3 . 0 − * * 8 8 3 . 0 ) 9 4 2 . 0 ( ) 0 9 1 . 0 ( ) 6 9 1 . 0 ( 8 5 1 . 0 − ) 7 9 2 . 0 ( 1 6 0 . 0 − ) 3 4 1 . 0 ( 7 8 0 . 0 − ) 9 4 1 . 0 ( 9 3 4 . 0 − ) 9 8 2 . 0 ( 0 6 2 . 0 ) 7 0 2 . 0 ( ) 8 5 0 . 0 ( 2 5 0 . 0 − ) 9 7 0 . 0 ( 5 8 0 . 0 − ) 7 8 0 . 0 ( 2 2 0 . 0 − ) 0 9 0 . 0 ( 5 2 0 . 0 ) 7 7 1 . 0 ( 2 1 0 . 0 − ) 8 2 1 . 0 ( a i s A * V R E S * ) I D F H ( n l I a i s A * ) M D F W B ( n l I a i s A * ) M D F W F ( n l a i s A * ) S I D F W B ( n l a i s A * ) S I D F W F ( n l l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 72 Asian Development Review / X X A V / X X A V ) X A V ( n l ) X A V ( n l E F ) 8 ( E R ) 7 ( 0 7 0 0 − . 9 0 0 0 − . * * 6 6 1 0 . ) 3 4 0 0 ( . ) 3 8 0 0 ( . * 7 3 0 0 . ) 9 1 0 0 ( . * 0 1 1 0 − . ) 0 6 0 0 ( . * * 3 5 1 0 − . ) 1 7 0 0 ( . 5 4 1 0 . ) 9 8 0 0 ( . 6 9 6 ) 6 0 0 0 ( . 1 0 0 0 − . ) 6 0 0 0 ( . 1 0 0 0 . ) 6 0 0 0 ( . * * * 2 2 0 0 − . ) 7 0 0 0 ( . 2 1 0 0 . ) 1 1 0 0 ( . 8 0 0 0 − . ) 3 1 0 0 ( . 6 9 6 E F ) 6 ( 9 4 0 . 0 ) 4 6 6 . 1 ( 1 9 9 . 1 ) 2 3 2 . 2 ( * 9 2 8 . 0 − ) 8 7 4 . 0 ( 6 8 2 . 0 − * * 4 5 5 . 3 ) 4 9 1 . 1 ( ) 3 0 8 . 1 ( * 4 6 0 . 3 ) 0 7 7 . 1 ( 6 9 6 E R ) 5 ( * * 5 0 2 . 0 ) 0 9 0 . 0 ( 8 8 0 . 0 − ) 8 7 0 . 0 ( 5 9 0 . 0 − ) 5 8 0 . 0 ( 6 8 0 . 0 − ) 7 9 0 . 0 ( 6 8 0 . 0 * * 7 0 5 . 0 ) 3 4 1 . 0 ( ) 2 5 2 . 0 ( 6 9 6 . d e u n i t n o C . 8 e l b a T ) X ( n l E F ) 4 ( 0 0 1 . 0 ) 8 6 3 . 1 ( 1 2 6 . 1 ) 9 4 9 . 1 ( * * 8 8 8 . 0 − ) 1 4 4 . 0 ( 0 6 2 . 0 − * * 8 6 6 . 3 ) 7 0 2 . 1 ( ) 4 0 7 . 1 ( 0 6 5 . 2 ) 6 6 7 . 1 ( 6 9 6 ) X ( n l E R ) 3 ( * * 0 9 1 . 0 ) 4 8 0 . 0 ( 4 8 0 . 0 − ) 6 7 0 . 0 ( 7 7 0 . 0 − ) 4 8 0 . 0 ( 6 4 0 . 0 − ) 4 0 1 . 0 ( 3 1 0 . 0 ) 4 4 1 . 0 ( 6 8 3 . 0 ) 2 6 2 . 0 ( 6 9 6 ) A V ( n l ) A V ( n l E F ) 2 ( 5 9 5 . 0 − ) 2 2 0 . 1 ( 7 3 7 . 1 ) 4 8 7 . 1 ( * 2 6 5 . 0 − ) 5 9 2 . 0 ( * 4 1 2 . 1 − ) 0 6 6 . 0 ( * * * 6 0 1 . 3 * * * 1 3 5 . 2 ) 7 8 0 . 1 ( ) 8 6 9 . 0 ( 6 9 6 E R ) 1 ( * * 3 4 1 . 0 C A L * N A M * ) I D F H ( n l ) 6 5 0 . 0 ( 5 6 0 . 0 − ) 9 5 0 . 0 ( 6 0 0 . 0 ) 8 6 0 . 0 ( 7 7 0 . 0 − ) 6 6 0 . 0 ( 1 1 0 . 0 ) 8 9 0 . 0 ( 1 9 1 . 0 ) 5 4 1 . 0 ( 6 9 6 C A L * V R E S * ) I D F H ( n l I C A L * ) M D F W B ( n l I C A L * M D F W F ( n l C A L * ) S I D F W B ( n l C A L * ) S I D F W F ( n l s n o i t a v r e s b o f o r e b m u N = X A V , r e k r o w r e p d e d d a e u l a v = A V , r o t a m i t s e s t c e f f e m o d n a r = E R , a c i r e m A n i t a L n i s i y r t n u o c e h t r e h t e h w g n i t a c i d n i e l b a i r a v y m m u d = C A L , s e c i v r e s r o g n i r u t c a f u n a m r e h t i e g n i t a c i d n i e l b a i r a v y m m u d = V R E S , t n e m t s e v n i t c e r i d n g i e r o f y r t s u d n i - a r t n i = I D F H , t u p t u o s s o r g = O G E F , e p o r u E n r e t s a E d n a l a r t n e C n i s i y r t n u o c e h t r e h t e h w g n i t a c i d n i e l b a i r a v y m m u d = U E E C t n e m t s e v n i t c e r i d n g i e r o f d r a w r o f s e c i v r e s = S I D F W F , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w r o f g n i r u t c a f u n a m = M D F W F I , r o t a m i t s e s t c e f f e , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w k c a b s e c i v r e s S I D F W B , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w k c a b g n i r u t c a f u n a m = M D F W B I , a i s A n i s i y r t n u o c e h t r e h t e h w g n i t a c i d n i e l b a i r a v y m m u d = a i s A , s e g a k n i l r o N A M d e x ﬁ = = r e p s t r o p x e s s o r g = X / = X X A V , s t r o p x e s s o r g n i d e d d a e u l a v c i t s e m o d t c e r i d f o e r a h s , r e k r o w r e p s t r o p x e n i d e i d o b m e d e d d a e u l a v c i t s e m o d t c e r i d d e g g a l r a e y - 5 e d u l c n i s n o i s s e r g e r l l A . s e s e h t n e r a p n i e r a s r o r r e d r a d n a t s t s u b o r - r e t s u l C . 0 1 . 0 < e u l a v - p * d n a , 5 0 . 0 < e u l a v - p * * , 1 0 . 0 < e u l a v - p * * * : s e t o N . r e k r o w - y r t n u o c e d u l c n i s n m u l o c E F e h T . s t c e f f e d e x ﬁ r o t c e s d n a y r t n u o c e d u l c n i s n m u l o c E R e h T . s t c e f f e e m i t c ﬁ i c e p s - r o t c e s d n a s m r e t y t i v i t c u d o r p r o t c a f l a t o t . s e l b a T t u p t u O – t u p n I d l r o W . 4 1 0 2 – 0 0 0 2 . k n a B d l r o W d n a ; 7 3 2 6 6 = d i y r e u q ? x p s a . x e d n i / g r o . d c e o . s t a t s / / : s p t t h . t n e m p o l e v e D d n a n o i t a r e p o - o C c i m o n o c E r o f n o i t a s i n a g r O ; / g r o . p a m t n e m t s e v n i . w w w / / : p t t h . p a M t n e m t s e v n I . e s a b a t a D d e d d A e u l a V n i e d a r T . 5 1 0 2 . e m o h / g r o . d o i w w w w . / / : p t t h . e r t n e C e d a r T l a n o i t a n r e t n I : s e c r u o S . s t c e f f e d e x ﬁ r o t c e s l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Foreign Direct Investment and Productivity 73 Table 9. Total Factor Productivity and Human Capital ln(GO) RE (1) ln(GO) FE (2) ln(TFPe) RE (3) ln(TFPe) FE (4) ln(HC)*MAN ln(HC)*SERV ln(HFDI)*MAN 0.294*** (0.110) 0.002 (0.058) 0.033 (0.029) 0.014 (0.058) ln(HFDI)*MAN*ln(HC) −0.010 (0.013) ln(HFDI)*SERV*ln(HC) −0.006 (0.026) ln(HFDI)*SERV ln(BWFDIM) ln(BWFDIM)*ln(HC) ln(FWFDIM) ln(FWFDIM)*ln(HC) ln(BWFDIS) ln(BWFDIS)*ln(HC) ln(FWFDIS) ln(FWFDIS)*ln(HC) Number of observations 0.012 (0.050) 0.007 (0.023) −0.037 (0.064) 0.029 (0.029) 0.061 (0.094) −0.034 (0.041) 0.168* (0.093) −0.068 (0.043) 702 0.339*** (0.091) −0.033 (0.055) 0.057* (0.030) −0.003 (0.060) −0.022 (0.013) 0.003 (0.026) 0.118 (0.104) −0.275* (0.147) −0.047 (0.043) 0.146** (0.064) −0.033 0.017 (0.135) (0.055) 0.031 0.005 (0.062) (0.025) 0.150 0.009 (0.114) (0.070) −0.060 0.004 (0.052) (0.031) 0.541*** 0.122 (0.102) (0.181) −0.279*** −0.066 (0.044) (0.085) −0.150 0.160 (0.098) (0.146) −0.066 0.087 (0.045) (0.070) 702 702 0.173* (0.103) −0.278* (0.167) −0.071 (0.044) 0.146* (0.075) −0.024 (0.131) 0.027 (0.060) 0.241** (0.114) −0.103** (0.051) 0.484** (0.198) −0.252*** (0.091) −0.062 (0.147) 0.038 (0.071) 702 BWFDIM = manufacturing backward foreign direct investment linkages, BWFDIS = services backward foreign direct investment linkages, FWFDIM = manufacturing forward foreign direct investment linkages, FWFDIS = services forward foreign direct investment linkages, GO = gross output, HC = log of years of schooling to proxy human capital, HFDI = intra-industry foreign direct investment, MAN or SERV = dummy variable indicating either manufacturing or services, RE = random effects estimator, TFPe = total factor productivity estimates, VA = value added per worker, VAX = direct domestic value added embodied in exports per worker, VAX/X = share of direct domestic value added in gross exports, X = gross exports per worker. Notes: ***p-value < 0.01, **p-value < 0.05, and *p-value < 0.10. Cluster-robust standard errors are in parentheses. All regressions include a 5-year lagged total factor productivity term, sector-speciﬁc time effects, and a dummy indicating broad industry classiﬁcations. The GO columns include the log values of capital services, labor services, and intermediates, as well as their interaction with a dummy variable indicating whether the sector belongs to the broad service industry The RE columns include country and sector ﬁxed effects. The FE columns include country-sector ﬁxed effects. Sources: International Trade Centre. Investment Map. http://www.investmentmap.org/; Organisation for Economic Co-operation and Development. 2015. Trade in Value Added Database. https://stats.oecd.org/index.aspx?queryid=66237; Wittgenstein Centre for Demography and Global Human Capital. 2015. Wittgenstein Centre Data Explorer Version 1.2. http://witt.null2.net/shiny/wic/; and World Bank. 2000–2014. World Input–Output Tables. http://www.wiod.org/home. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 74 Asian Development Review / X X A V / X X A V ) X A V ( n l ) X A V ( n l ) X ( n L E F ) 8 ( * * 8 2 1 0 − . ) 4 6 0 0 ( . * 4 1 0 0 . ) 8 0 0 0 ( . ) 2 5 1 0 ( . 0 1 0 0 . ) 9 1 0 0 ( . 5 9 0 0 . 5 0 1 0 − . ) 3 1 1 0 ( . 4 1 0 0 − . ) 4 1 0 0 ( . 4 1 0 0 − . E R ) 7 ( 0 3 0 0 . ) 1 2 0 0 ( . 8 1 0 0 . ) 7 1 0 0 ( . 3 0 0 0 − . ) 2 0 0 0 ( . 3 0 0 0 . ) 6 3 0 0 ( . 1 0 0 0 . ) 4 0 0 0 ( . 6 3 0 0 − . ) 6 3 0 0 ( . 2 0 0 0 . ) 4 0 0 0 ( . 7 2 0 0 − . ) 1 6 2 0 ( . 7 0 0 0 . ) 3 3 0 0 ( . 1 3 0 0 . ) 9 2 2 0 ( . 5 0 0 0 − . ) 1 3 0 0 ( . 6 9 6 ) 3 6 0 0 ( . 4 0 0 0 . ) 7 0 0 0 ( . * * 7 3 1 0 − . * * 8 1 0 0 . ) 4 6 0 0 ( . ) 8 0 0 0 ( . 6 9 6 E F ) 6 ( * * * 3 5 8 . 1 ) 8 4 4 . 0 ( * * * 4 9 1 . 0 − ) 5 5 0 . 0 ( 7 0 3 . 0 − ) 2 1 2 . 1 ( 8 1 0 . 0 ) 4 5 1 . 0 ( 3 7 7 . 0 ) 7 0 1 . 1 ( 9 1 1 . 0 − ) 2 4 1 . 0 ( 5 8 1 . 2 − ) 3 2 7 . 1 ( 9 2 2 . 0 ) 8 2 2 . 0 ( 9 2 3 . 2 − ) 9 5 5 . 1 ( 5 3 3 . 0 ) 5 0 2 . 0 ( 6 9 6 E R ) 5 ( * * * 6 6 7 . 0 * * * 5 3 7 . 0 ) 7 6 2 . 0 ( ) 3 2 2 . 0 ( * * 5 6 0 . 0 − ) 7 2 0 . 0 ( 4 6 6 . 0 ) 8 0 4 . 0 ( * 1 8 0 . 0 − ) 7 4 0 . 0 ( 1 9 5 . 0 ) 7 7 4 . 0 ( 3 7 0 . 0 − ) 6 5 0 . 0 ( 4 5 7 . 0 − ) 5 0 6 . 0 ( 0 7 0 . 0 ) 2 7 0 . 0 ( 2 5 1 . 0 ) 8 0 6 . 0 ( 5 2 0 . 0 ) 7 7 0 . 0 ( 6 9 6 E F ) 4 ( * * * 5 9 5 . 2 ) 3 5 4 . 0 ( * * * 1 8 2 . 0 − ) 7 5 0 . 0 ( 9 1 3 . 0 − ) 4 7 1 . 1 ( 3 2 0 . 0 ) 0 5 1 . 0 ( 3 4 2 . 0 ) 6 0 1 . 1 ( 6 4 0 . 0 − ) 2 4 1 . 0 ( 0 1 7 . 2 − ) 9 4 7 . 1 ( 1 8 2 . 0 ) 4 3 2 . 0 ( 3 5 6 . 1 − ) 3 7 5 . 1 ( 9 4 2 . 0 ) 9 0 2 . 0 ( 6 9 6 ) X ( n l E R ) 3 ( * * 4 6 6 . 0 ) 9 5 2 . 0 ( * * * 5 8 6 . 0 ) 6 2 2 . 0 ( * * 9 5 0 . 0 − ) 7 2 0 . 0 ( * 7 6 6 . 0 ) 8 9 3 . 0 ( * 3 8 0 . 0 − ) 6 4 0 . 0 ( 3 8 5 . 0 ) 2 6 4 . 0 ( 9 6 0 . 0 − ) 5 5 0 . 0 ( 5 8 7 . 0 − ) 3 8 5 . 0 ( 0 7 0 . 0 ) 9 6 0 . 0 ( 5 2 8 . 0 ) 2 8 5 . 0 ( 0 7 0 . 0 − ) 4 7 0 . 0 ( 6 9 6 ) A V ( n l ) A V ( n l E F ) 2 ( * * * 1 5 3 . 1 ) 2 5 3 . 0 ( * * * 4 4 1 . 0 − ) 5 4 0 . 0 ( ) 1 1 0 . 1 ( 9 1 0 . 0 ) 9 2 1 . 0 ( 3 3 0 . 0 − * * * 3 1 8 . 2 ) 9 9 7 . 0 ( * * * 8 6 3 . 0 − ) 4 0 1 . 0 ( 8 3 7 . 0 − ) 0 6 2 . 1 ( 2 3 0 . 0 ) 6 6 1 . 0 ( * * * 7 5 1 . 4 − * * * 8 6 5 . 0 ) 6 7 1 . 1 ( ) 3 5 1 . 0 ( 6 9 6 E R ) 1 ( * * * 3 7 5 . 0 * * * 1 3 4 . 0 ) 3 0 2 . 0 ( ) 1 5 1 . 0 ( * 4 3 0 . 0 − ) 9 1 0 . 0 ( 3 7 0 . 0 ) 1 8 2 . 0 ( 1 0 0 . 0 − * * 1 3 7 . 0 ) 3 3 0 . 0 ( ) 5 5 3 . 0 ( * * 8 8 0 . 0 − ) 3 4 0 . 0 ( 5 4 3 . 0 ) 3 2 4 . 0 ( 8 4 0 . 0 − ) 2 5 0 . 0 ( 7 3 3 . 0 − ) 1 9 4 . 0 ( 7 5 0 . 0 ) 2 6 0 . 0 ( 6 9 6 y t i v i t c u d o r P r o b a L l a i t i n I d n a y t i v i t c u d o r P r o b a L . 0 1 e l b a T 0 0 0 2 ) A V ( n l * ) M D F W B ( n l I 0 0 0 2 ) A V ( n l * ) M D F W F ( n l I 0 0 0 2 ) A V ( n l * ) S I D F W B ( n l ) S I D F W F ( n l 0 0 0 2 ) A V ( n l * ) S I D F W F ( n l s n o i t a v r e s b o f o r e b m u N ) S I D F W B ( n l ) I M D F W F ( n l ) I M D F W B ( n l 0 0 0 2 ) A V ( n l * ) I D F H ( n l 0 0 0 2 ) A V ( n l ) I D F H ( n l s s o r g n i d e d d a e u l a v c i t s e m o d t c e r i d f o e r a h s / = X X A V , r e k r o w r e p s t r o p x e n i d e i d o b m e d e d d a e u l a v c i t s e m o d t c e r i d = X A V , r e k r o w r e p d e d d a s t c e f f e d e x ﬁ = E F , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w k c a b s e c i v r e s = S I D F W B , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w k c a b g n i r u t c a f u n a m I = M D F W B - a r t n i = y t i v i t c u d o r p r o t c a f l a t o t = e P F T , r o t a m i t s e s t c e f f e m o d n a r = E R I D F H , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w r o f s e c i v r e s , 0 0 0 2 r a e y e h t n i r e k r o w r e p d e d d a e u l a v f o g o l 0 0 0 2 ) A V ( n l , t n e m t s e v n i t c e r i d n g i e r o f y r t s u d n i = = S I D F W F , s e g a k n i l t n e m t s e v n i t c e r i d n g i e r o f d r a w r o f g n i r u t c a f u n a m = M D F W F I , r o t a m i t s e . r e k r o w r e p s t r o p x e e u l a v = A V , s e t a m i t s e s s o r g = X , s t r o p x e t n e d n e p e d d e g g a l r a e y - 5 a e d u l c n i s n o i s s e r g e r l l A . s e s e h t n e r a p n i e r a s r o r r e d r a d n a t s t s u b o r - r e t s u l C . 0 1 . 0 < e u l a v - p * d n a , 5 0 . 0 < e u l a v - p * * , 1 0 . 0 < e u l a v - p * * * : s e t o N n i e d a r T . 5 1 0 2 . t n e m p o l e v e D d n a n o i t a r e p o - o C c i m o n o c E r o f n o i t a s i n a g r O ; / g r o . p a m t n e m t s e v n i . w w w / / : p t t h . p a M t n e m t s e v n I . e r t n e C e d a r T l a n o i t a n r e t n I : s e c r u o S . e m o h / g r o . d o i w w w w . / / : p t t h . s e l b a T t u p t u O – t u p n I d l r o W . 4 1 0 2 – 0 0 0 2 . k n a B d l r o W d n a ; 7 3 2 6 6 = d i y r e u q ? x p s a . x e d n i / g r o . d c e o . s t a t s / / : s p t t h . e s a b a t a D d e d d A e u l a V . s t c e f f e d e x ﬁ r o t c e s - y r t n u o c e d u l c n i s n m u l o c E F e h T . s t c e f f e d e x ﬁ r o t c e s d n a y r t n u o c e d u l c n i s n m u l o c E R e h T . s t c e f f e e m i t c ﬁ i c e p s - r o t c e s d n a m r e t e l b a i r a v l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Foreign Direct Investment and Productivity 75 VII. Conclusions In this paper, we have investigated how intra- and inter-industry FDI inﬂuences the average productivity of a sample of emerging market economies. Overall, we ﬁnd a large positive effect of intra-industry FDI on total and export- related labor productivity. The fact that this effect is much harder to detect, in economic and statistical terms, when we examine the determinants of TFP suggests that foreign ﬁrms raise the performance of their host economies through a compositional effect. Foreign ﬁrms tend to be larger than domestic ﬁrms; they make more intensive use of (possibly better) physical capital, human capital, and intermediates; and they have greater access to foreign markets. Hence, their greater prevalence in a given sector mechanically increases average labor productivity and export performance. As stressed by Lipsey (2004) and Navaretti and Venables (2005), this FDI-induced composition effect can be crucial for host countries’ economic development. It should not be discounted in favor of potential foreign externalities for which we have not found robust evidence and whose existence is often conditional on domestic absorptive capacities. In addition to improving their FDI attractiveness, governments should also ensure that they adopt policies that increase the quantity, quality, and technological level of local producers. This will leverage the beneﬁts of FDI and sustain long-run economic development. These considerations led the Government of the PRC to introduce in 2006 and 2015, respectively, the Indigenous Innovation and Made in China 2025 policy packages to upgrade domestic manufacturing. Finally, it should be acknowledged that our results need to be interpreted with caution. Our estimates are likely to be affected by an endogeneity bias that is related, at the very least, to measurement error. 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Grifﬁth, Rachel. 1999. “Using the ARD Establishment Level Data to Look at Foreign Ownership and Productivity in the United Kingdom.” The Economic Journal 109 (456): 416– 42. Havránek, Tomáš, and Zuzuna Iršová. 2011. “Estimating Vertical Spillovers from FDI: Why Results Vary and What the True Effect Is.” Journal of International Economics 85 (2): 234–44. Herzer, Dierk, Stephan Klasen, and Felicitas Nowak-Lehmann. 2008. “In Search of FDI-Led Growth in Developing Countries: The Way Forward.” Economic Modelling 25 (5): 793– 810. Iamsiraroj, Sasi, and Mehmet Ali Ulubasoglu. 2015. “Foreign Direct Investment and Economic Growth: A Real Relationship or Wishful Thinking?” Economic Modelling 51: 200– 13. International Trade Centre. Investment Map. http://www.investmentmap.org/. Iršová, Zuzuna, and Tomáš Havránek. 2013. “Determinants of Horizontal Spillovers from FDI: Evidence from a Large Meta-Analysis.” World Development 42: 1–15. 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Solow, Robert. M. 1956. “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics 70 (1): 65–94. Swan, Trevor S. 1956. “Economic Growth and Capital Accumulation.” Economic Record 32 (2): 334–61. Van Beveren, Ilke. 2012. “Total Factor Productivity Estimation: A Practical Review.” Journal of Economic Surveys 26 (1): 98–128. Wittgenstein Centre for Demography and Global Human Capital. 2015. Wittgenstein Centre Data Explorer Version 1.2. http://witt.null2.net/shiny/wic/. World Bank. 2000–2014. World Input–Output Tables. http://www.wiod.org/home. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Foreign Direct Investment and Productivity 77 Appendix. Descriptive Summary Statistics Table A1. List of Emerging Market Economies in the Database Latin America Brazil Mexico Asia India Indonesia People’s Republic of China Central and Eastern Europe Bulgaria Czech Republic Hungary Latvia Lithuania Poland Romania Russian Federation Slovakia Turkey Source: Authors’ compilation. Table A2. Manufacturing and Service Sectors in the Database Broad Industry ISIC rev 3.1 Sector Name Median Median HFDI HFDI 2008 (%) 2000 (%) M M M M M M M M M M M M M S S S S S S S S S S S Machinery, nec Coke, reﬁned petroleum, and nuclear fuel Chemicals and chemical products Rubber and plastics Other nonmetallic minerals D15t16 Food, beverages, and tobacco D17t19 Textiles, leather, and footwear D20 Wood, of wood, and cork D21t22 Pulp, paper, printing, and publishing D23 D24 D25 D26 D27t28 Basic metals and fabricated metals D29 D30t33 Electrical and optical equipment D34t35 Transport equipment D36t37 Manufacturing nec; recycling E F G H I J K L M N O Electricity, gas, and water supply Construction Wholesale and retail trade; repair of goods Hotels and restaurants Transport, storage and communications Financial intermediation Real estate, renting, and business activities Public administration and defense Education Health and social work Other community, social, and personal service activities 8 6 5 6 25 14 15 8 9 20 41 30 18 1 1 6 4 5 28 11 0 0 0 4 8 10 12 8 46 29 14 13 21 42 57 42 17 4 2 7 6 5 42 10 0 0 0 6 HFDI = intra-industry foreign direct M = manufacturing, nec = not elsewhere classiﬁed, S = services. Source: Authors’ compilation. investment, ISIC = International Standard Industrial Classiﬁcation, l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 78 Asian Development Review Variable Mean Std. Dev. Min. Max. Obs. Table A3. Summary Statistics Value added ($) per worker (VA) Real gross exports ($) per worker (X) Real direct domestic VA ($) in gross 8,980.15 10,062.6 2,542.46 14,377.95 35,841.95 6,247.34 12.5 0 0 183,240.38 547,825.31 72,482.41 exports (VAX) Domestic use coefﬁcient (δ) Domestic supply coefﬁcient (γ ) Domestic use coefﬁcient from manufacturing sectors Domestic supply coefﬁcient to manufacturing sectors Domestic use coefﬁcient from service sectors Domestic supply coefﬁcient to service sectors Share of foreign persons employed (HFDI) HFDI in manufacturing sectors HFDI in service sectors Backward FDI linkages (BWFDI) BWFDI from manufacturing sectors (BWFDIM) BWFDI from service sectors (BWFDIS) Forward FDI linkages (FWFDI) FWFDI to manufacturing sectors (FWFDIM) FWFDI to service sectors (FWFDIS) 0.61 0.31 0.18 0.11 0.36 0.18 19.89 27.78 10.28 5.45 4.09 2.25 9.75 5.83 4.56 702 686 684 702 702 702 0.17 0.2 0.13 0.17 0 0 0.97 0.92 0.71 0.1 0 0.51 698 0.16 0.06 0.83 702 0.14 25.29 18.01 6.15 5.33 4.54 1.52 7.72 5.75 0 1 2.8 2.24 1 1 1 1.38 1.05 0.78 701 101 67.23 22.56 37.97 35.29 11.7 56.23 47.84 702 702 702 702 702 702 702 702 3.54 1.15 35.43 702 Notes: Use (supply) coefﬁcients are calculated as the share of inputs supplied by upstream (to downstream) industries. Foreign direct investment (FDI) variables have been transformed using FDI (cid:2) 100 + 1. BWFDI (FWFDI) variables are the product of the supply coefﬁcient γ (use coefﬁcient δ) and HFDI in the downstream (upstream) sector. Sources: Authors’ calculations and International Trade Centre. Investment Map. http://www.investmentmap.org/; Organisation for Economic Co-operation and Development. 2015. Trade in Value Added Database. https://stats .oecd.org/index.aspx?queryid=66237; and World Bank. 2000–2014. World Input–Output Tables. http://www.wiod .org/home. Region Schooling (Years) Broad Industry Share of Skilled Workers Table A4. Skill Proxies Latin America Asia Central and Eastern Europe 7.2 6.4 10.8 Manufacturing Services Manufacturing Services Manufacturing Services 0.57 0.65 0.44 0.63 0.68 0.78 Notes: The share of skilled workers equals the share in total hours worked by high- and medium- skilled workers. Schooling represents the median of average years of schooling per country over the period 2000–2010. Sources: International Trade Centre. Investment Map. http://www.investmentmap.org/; Organisation for Economic Co-operation and Development. 2015. Trade in Value Added Database. https://stats .oecd.org/index.aspx?queryid=66237; Wittgenstein Centre for Demography and Global Human Capital. 2015. Wittgenstein Centre Data Explorer Version 1.2. http://witt.null2.net/shiny/wic/; and World Bank. 2000–2014. World Input–Output Tables. http://www.wiod.org/home. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Foreign Direct Investment and Productivity 79 Table A5. Region-Speciﬁc Labor Productivity, 2000 Region Value Added per Worker ($, VA2000) Latin America Asia Central and Eastern Europe 11,652.87 4,007.16 5,144.67 Source: International Trade Centre. Investment Map. http://www.investmentmap.org/. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 6 1 5 4 1 6 4 4 1 6 3 a d e v _ a _ 0 0 1 2 3 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3
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