Foreign Direct Investment and Productivity Spillovers: Firm-Level Evidence From Chilean Industrial Sector

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Foreign Direct Investment and
Productivity Spillovers: Firm-Level
Evidence From Chilean Industrial Sector
Leopoldo Laborda Castillo
a
, Daniel Sotelsek Salem
b
& Justo de
Jorge Moreno
b
a
World Bank and Institute of Latin American Studies , University of
Alcalá, Madrid , Spain
b
Institute of Latin American Studies, University of Alcalá , Madrid ,
Spain
Published online: 21 May 2014.
To cite this article: Leopoldo Laborda Castillo , Daniel Sotelsek Salem & Justo de Jorge Moreno (2014)
Foreign Direct Investment and Productivity Spillovers: Firm-Level Evidence From Chilean Industrial
Sector, Latin American Business Review, 15:2, 93-122, DOI: 10.1080/10978526.2014.905152
To link to this article: http://dx.doi.org/10.1080/10978526.2014.905152
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Foreign Direct Investment and Productivity
Spillovers: Firm-Level Evidence From
Chilean Industrial Sector
LEOPOLDO LABORDA CASTILLO
World Bank and Institute of Latin American Studies,
University of Alcala´, Madrid, Spain
DANIEL SOTELSEK SALEM and JUSTO DE JORGE MORENO
Institute of Latin American Studies, University of Alcala´, Madrid, Spain
ABSTRACT. Using firm-level panel data, this article examines
whether spillovers from foreign direct investment (FDI) make a
contribution to productivity growth in Chilean manufacturing
firms. The main contribution of this work is to apply a methodology
to estimate, in a consistent manner, the productivity impact
of investment climate variables, such as FDI. With this aim, the
spillover effects from FDI are analyzed using a stochastic frontier
approach (SFA). Productivity growth is decomposed using a gener-
alized Malmquist output-oriented index. The results show positive
productivity spillovers from FDI; higher competition is associated
with larger spillovers; and firms with high R&D effort gain more
spillover benefits compared to those with less R&D effort.
RESUMEN. En este trabajo se examina si los ‘‘derrames’’ o extern-
alidades positivas derivadas de la Inversioń Extranjera Directa
(IED) contribuyen al crecimiento de la productividad para un
panel de datos de empresas manufactureras chilenas. La principal
contribucioń de este trabajo es la aplicacioń de una metodologıá
para estimar, de manera consistente, el impacto en la productivi-
dad de variables relacionadas con el clima de inversioń, como
por ejemplo la IED. Para ello, los derrames de la IED han sido
analizados mediante un enfoque de frontera estoca´stica (SFA).
Received August 4, 2012; revised December 5, 2012; accepted November 12, 2013.
Address correspondence to Leopoldo Laborda Castillo, Institute of Latin American
Studies, University of Alcala´ c= Trinidad n
1, Colegio de Trinitarios, Alcala´ de Henares,
28801 Madrid, Spain. E-mail: llabordacastillo@gmail.com
Latin American Business Review, 15:93–122, 2014
Copyright # Taylor Francis Group, LLC
ISSN: 1097-8526 print=1528-6932 online
DOI: 10.1080/10978526.2014.905152
93
Downloadedby[danielsotelsek]at01:1728May2014

Adicionalmente, el crecimiento de la productividad es descom-
puesto mediante un ıńdice de produccioń de Malmquist orientado
al output. Los resultados muestran la existencia de derrames positi-
vos en te´rminos de productividad debidos a la IED; ası´ mismo,
mayor competencia se asocia con derrames ma´s grandes, y las
empresas con un esfuerzo elevado en I þ D obtienen mayores der-
rames en comparacioń con aquellas con menor esfuerzo en I þ D.
RESUMO. Este trabalho examina se os benefıćios econoˆmicos
indiretos, ou ‘spillovers,’ de investimento estrangeiro direto (IED)
contribuem para o aumento de produtividade nas empresas chile-
nas de manufatura que usam dados de empresas em painel. A
principal contribuiçaõ deste trabalho e´ a aplicaçaõ de uma meto-
dologia para avaliar de maneira consistente o impacto na produti-
vidade de varia´veis no ambiente dos investimentos, como o IED.
Para isso os efeitos do ‘spillover’ de IED saõ analisados usando-se
uma abordagem de fronteira estoca´stica (SFA). O aumento da
produtividade e´ decomposto usando-se um ıńdice Malmquist gen-
eralizado voltado para o ‘output.’ Os resultados mostram spillovers
positivos de produtividade que resultam do IED; uma maior compe-
tiçaõ eássociada com maiores ‘spillovers’ e empresas que fazem um
maior esforço em PD obteˆm mais benefıćios de ‘spillover’ do que as
que fazem menos esforço para desenvolver PD.
KEYWORDS. foreign direct investment, industrial sector, Malm-
quist index, productivity spillovers
INTRODUCTION
Foreign direct investment (FDI) is believed to provide recipient countries with
knowledge transfer as well as capital. The expectations to obtain productivity
spillovers through knowledge transfers have led to the development of
policies oriented toward the generation of favorable framework of FDI in
numerous countries. In this situation, it is important to ask ourselves, does
FDI lead to productivity spillovers? In order to answer this question, several
studies have been conducted in Latin American countries. For example,
Kokko, Tansini, and Zejan (2001) examined intra-industry spillovers from
FDI in Uruguayan manufacturing plants in 1988. Chudnovsky, Lo´pez, and
Rossi (2008) assessed the amounts of FDI inflows in Argentina during the
1990s; Waldkirch (2010) analyzed the FDI in Mexico since the inception of
the North American Free Trade Agreement; and Fernandes and Paunov
(2012) examined the impact of substantial FDI inflows of producer service sec-
tors in relation to the total factor productivity of Chilean manufacturing firms.
94 L. Laborda Castillo et al.

Taking into account these findings and motivated by additional results
in the extant literature, this article specifies the conditions under which
industries added efficiency (productivity of the factors) as well as whether
spillovers from FDI contribute to productivity growth. In others words, our
analysis extends this research and sheds light on the need to distinguish local
establishment characteristics when discussing potential benefits from FDI.
The main contribution of this work is to apply a methodology in order to
estimate, in a consistent manner, the productivity impact of investment climate
variables, such as FDI. To do this, we apply the methodology to the data col-
lected for investment climate assessments (ICA) surveys at firm level in Chile
(focusing on the industrial sector).1
We have panel data (T ¼ 3) for 2001, 2002,
and 2003 from Chile’s ICA survey. Methodologically, we make use of the
stochastic frontier analysis (SFA) as a robust parametric approach to estimate
FDI productivity spillovers in Chilean manufacturing firms. To measure Chile’s
IT services productivity we apply the method of data envelopment analysis
and compute the Malmquist index to decompose the total factor productivity
(TFP) growth into technical efficiency change (TEC), technological progress
(TP), and scale efficiency change (SEC). The results show positive productivity
spillovers from FDI—such that higher competition is associated with larger
spillovers, and firms with high research and development (RD) effort gain
more spillover benefits compared to those with less RD effort.
This research is organized as follows. First, we present the framework
proposed to analyze the relationship between technical FDI and productivity
spillovers in the context of the Chilean industrial sector. Following is a critical
review of the theoretical and empirical studies on productivity spillovers.
Next, we develop a methodology of analysis: we will discuss estimation tech-
niques followed by data sources and variable construction. The following
section presents the main empirical results obtained. The main conclusions
and a brief discussion end the article, which leads into an explanation of
certain policy implications.
THEORETICAL BACKGROUND AND HYPOTHESIS
Productivity—understood as the capacity an economy (company or business)
has to obtain the greatest advantage of inputs with respect to the generated
output—is a long-existing concern. A first approach was to try to understand,
in the most thorough way possible, the productivity of production factors,
since this allowed us to evaluate the quality of an economy’s growth or the
production of a company. In this context, the empirical literature studying
the relationship between FDI, productivity, and growth is voluminous and
constantly expanding. Recent works, such as Contessi and Weinberger
(2009) or Wooster and Diebel (2010), reviewed the empirical literature
on technology spillovers from FDI in developing countries. Contessi and
Foreign Direct Investment and Productivity Spillovers 95

Weinberger reviewed the empirical literature that studies the relationship
between FDI, productivity, and growth using aggregate data; they focused
on two main questions: (i) whether there is evidence of a positive relationship
between FDI and national growth, and (ii) whether the output of ‘‘multina-
tional sectors’’ exhibits higher labor productivity. The authors discussed
how microeconomic evidence and a number of aggregation and composition
problems might help explain the ambiguous results obtained in this literature.
Wooster and Diebel used a sample of 32 studies to determine which aspects of
study design and data characteristics explain the magnitude, significance, and
direction of spillovers from FDI. Results suggest (i) that spillover effects are
more pronounced when studies measure the effect of FDI spillovers on out-
put, and they are more likely to be significant and positive for Asian countries;
(ii) and that the possibility that the documented spillover effects from FDI in
developing countries may be partly a product of model misspecification.
Most of the previous studies on FDI spillovers treat the specific mechan-
isms of productivity spillovers as occurring in a ‘‘black box’’ (Go¨rg Strobl,
2001). These studies often assume that productivity spillovers from FDI occur
automatically because of foreign firms’ presence in domestic markets. Chan-
nels of productivity spillovers are not explicitly taken into account in such
studies. However, some studies make serious attempts to take into account
the channels of productivity spillovers from FDI. According to Blomstro¨m
and Kokko (1998) and Kokko (1996), four fundamental mechanisms for
productivity spillovers have been derived:
1. Demonstration-imitation effect: Foreign firms in domestic markets can
create demonstration effects upon domestic firms through direct imitation
and reverse engineering (Das, 1987), or by means of innovation arising
from RD (Cheung, 2010). In the words of Ornaghi (2002),
demonstration-imitation effects occur if there are arms’ length relation-
ships between multinational corporations (MNCs) and domestic firms.
Domestic firms absorb more advanced production technologies and other
knowledge from MNCs. Ornaghi pleaded for the differentiation between
channels of technology spillovers in the case of process and product
innovations. The most important forms are imitation of managerial and
organizational innovation as well as imitation of technology.
2. Competition effect: The entry of MNCs may lead to greater competition in
domestic markets, which then forces domestic firms to use their resources
and technology in more efficient ways, thus leading to productivity
gains (Wang Blomstro¨m, 1992). According to Smarzynska (2003), the
competition effect is when competition from MNCs force domestic rivals
to update production technologies and techniques to become more pro-
ductive. The foreign linkage effect relates to export spillovers. Finally,
Smarzynska distinguished between knowledge (copying technologies
of foreign affiliates, observation, or hiring workers trained by foreign

subsidiaries) and competition spillovers (MNC entry leads to more severe
competition and forces domestic firms to higher efficiency and search for
new technologies).
3. Foreign linkage effect: Foreign firms in domestic markets may also create
productivity spillovers to domestic firms through foreign linkage effects.
According to Rodriguez-Clare (1996), foreign linkage effects may occur
because MNCs give access to new specialized intermediate inputs or
because domestic firms use local intermediate goods’ suppliers, whose
productivity has been raised through the expertise of the MNC.
4. Training effect: Knowledge may spillover to domestic firms via labor turn-
over; that is, when workers trained by multinational corporations move to
domestic firms and bring with them the knowledge and other crucial
intangible assets (Fosfuri, Motta, Rønde, 2001). Go¨rg and Greenaway
(2004) distinguished two mechanisms of the training effect: direct spil-
lovers through complementary workers, and indirect mechanism when
workers move and transfer knowledge between foreign and domestic
firms. According to Go¨rg and Strobl (2005), training effects take place if
there are movements of highly skilled personnel from MNCs to domestic
firms. These employees may take with them knowledge that may be
usefully applied in the domestic firm.
Heterogeneity of Domestic Firms and Industry Competition: Some
Hypotheses
Industry competition and heterogeneity of domestic firms as determinants of
knowledge and FDI spillovers relates primarily to their technological capacity,
human capital, and productivity. According to the empirical evidence, these
factors determine domestic firms’ absorption capacity for knowledge and
FDI spillovers. Our hypotheses are:
H1: High levels of competition (in terms of low Herfindahl-Hirschman
index of industry concentration), positively influences spillovers
from FDI (measured by the share of foreign firms’ output over total
output in the sector of activity).
Our first hypothesis relates to industry competition. According to
this hypothesis, higher competition is associated with larger spillovers
from foreign presence in the industry; that is, positive productivity through
competition.
Competition may result in either positive or negative productivity
spillovers for domestic firms. Aitken and Harrison (1999) argued that in the
short-run, the presence of foreign firms in an imperfect competition domestic
market might raise the average cost of production of domestic firms through
the ‘‘market stealing’’ phenomenon. Foreign firms with a lower marginal cost

have an incentive to increase production relative to their domestic compet-
itors. The productivity of domestic firms will fall, as they have to spread fixed
costs over a smaller amount of output.
However, in the end, when all costs can be treated as variable costs, there
is a possibility for domestic firms to reduce their costs by allocating their
resources more efficiently and imitating foreign firms’ knowledge (Wang
Blomstro¨m, 1992). If the efficiency effect from foreign presence is larger than
the competition effect, there can be positive productivity spillovers.
The predominance of liberalization-oriented policies over the past years
versus policies oriented toward engaging FDI have meant a huge decrease in
terms of market concentration. However, industry structure remains an
important control variable to be included in this study of FDI and productivity
spillovers.
Some studies found country-by-country empirical evidence supporting
this hypothesis in relation with industry competition. For example, in a study
conducted in Morocco, although Haddad and Harrison (1993) found no
evidence of technology spillovers, the increased competition by foreign
investors seemed to push local firms toward the best practice frontier in
industries with a low level of technology. On the other hand, Blomstro¨m,
Kokko, and Zejan (1994), in a study conducted in Mexico, found that local
competition correlates positively to imports of technology by MNEs.
H2: The level of RD effort (defined as the total expenditures on RD
divided by the total sales) positively influences spillovers from FDI
(measured by the share of foreign firms’ output over total output
in the sector of activity).
Our second hypothesis relates to the level of technological devel-
opment=technological capacity. According to this hypothesis, firms with
RD expenditure gain more productivity spillovers from FDI than those
without RD expenditure.
The mixed evidence of productivity spillovers leads to the argument that
firm-specific characteristics (or absorptive capacity) may influence the ability
of domestic firms to gain productivity spillovers from FDI (Findlay, 1978;
Glass Saggi, 1998; Wang Blomstro¨m, 1992). The most commonly used
measure of absorptive capacity is expenditure on RD. Kathuria (2000) found
evidence in a study on an Indian manufacturing firm that local firms that invest
in learning or RD activities receive high productivity spillovers, whereas the
non-RD local firms do not gain much from the presence of foreign firms.
This result indicates that productivity spillovers are not automatic conse-
quences of the presence of foreign firms; rather, they depend on the efforts
of local firms investing in RD activities. Kinoshita (2001) found similar evi-
dence in a study on Czech manufacturing firms, during 1995–1998. In a more
recent study of 12 Organisation for Economic Co-operation and Development

countries, Griffith, Redding, and Van Reenen (2004) confirmed that RD
plays an important role in knowledge transfer, besides its role as a means
of innovation.
Some studies found country-by-country empirical evidence of this
hypothesis, in relation with the level of technological development=techno-
logical capacity. For example, Perez (1998), in a study conducted in the Uni-
ted Kingdom and Italy, found that firms with a lower technological gap than
their competitors experienced positive effects of increased foreign presence.
Girma, Gong, and Go¨rg (2006), in a study conducted in China, found that
firms that invest in RD have positive FDI spillovers. Halpern and Murakozy
(2007), in a study conducted in Hungary, found that firms with technology or
RD spending that is more advanced are likely to benefit more from the pres-
ence of foreign firms. Finally, Abraham, Konings, and Slootmaekers (2010), in
a study conducted in China, found that firms far away from the technological
frontier do not benefit from the presence of foreign firms, while firms operat-
ing close to the frontier enjoy positive spillovers (manufacturing).
H3: The level of human capital (defined as the percentage of the
workforce with some university or higher education level) positively
influences spillovers from FDI (measured by the share of foreign
firms’ output over total output in the sector of activity).
Our third hypothesis relates to the level of human capital. According to
this hypothesis, there is a positive productivity spillover from FDI. According
to Caves (1971), when MNCs establish subsidiaries overseas, they experience
disadvantages in the form of access to resources and domestic demand,
when compared to their local counterparts. In order to compete with dom-
estic firms, MNCs need to possess superior knowledge. With this superior
knowledge, MNCs are often assumed to have higher performance levels than
domestic firms, in particular being more efficient and productive.
Some studies found country-by-country empirical evidence of this
hypothesis, in relation to human capital. For example, Girma and colleagues
(2006), in a study conducted in China, found that firms that invest in human
capital experience positive FDI spillovers. Gorodnichenko, Svejnar, and
Terrell (2007), in a study conducted in Eastern Europe, found that firms with
a higher educated workforce gain from the presence of foreign firms in their
industry. Finally, Damijan, Rojec, Majcen, and Knell (2008), in a study conduc-
ted also in Eastern Europe, found that Spillovers substantially depend on the
absorptive capacity of local firms measured by the level of human capital.
H4: The level of spillovers from FDI (measured by the share of foreign
firms’ output over total output in the sector of activity) positively influ-
ences the change in the productivity growth and in their components
(technical efficiency, technological progress, and scale efficiency).

Finally, our fourth hypothesis relates to productivity level. According to
this hypothesis, there are positive FDI spillovers to each component of pro-
ductivity growth (TEC, TP, and SEC). According to Salim and Bloch (2009),
the empirical studies usually assume that productivity advantage from FDI
is exclusively contributed by technology transfers, because it is consistent with
the use of a conventional approach of production function. However, techni-
cal and scale efficiencies have scarcely been studied in relation to productivity
gains from FDI. In this context, Smeets (2008) argued that productivity
spillovers from FDI should be defined broadly, as they are the result of new
knowledge and not of new technology alone. Smeets defined knowledge as
including technological managerial and production skills, which may contrib-
ute to technical efficiency and the ability to exploit scale efficiency.
Some studies have found country-by-country empirical evidence of this
hypothesis in relation to level of productivity. For example, Haskel, Pereira,
and Slaughter (2007) in a study conducted in the United Kingdom, found that
less productive (and smaller) plants received, on average, stronger FDI spil-
lovers than more productive (and larger) plants. Castellani and Zanfei, (2003)
in a study conducted in Southern Europe, found that high productivity gaps
tended to favor positive effects of FDI. Examining Southern European firms,
Damijan and colleagues (2008) found that FDI spillovers depend on the
productivity level of individual firms. Finally, Keller and Yeaple (2009), in
a study conducted in the United States, found that relatively high productivity
is required for a firm to acquire FDI-related spillovers.
METHOD
Estimation Techniques: Stochastic Frontier Analysis and
Malmquist Index
This subsection proposes a brief assessment methodology for productivity
spillovers in order to examine when spillovers from FDI contribute to pro-
ductivity growth. The spillover effects from FDI will be analyzed using an
SFA approach (Kumbhakar Lovell, 2003). This approach uses the stochastic
frontier production function, following the guidelines set by Battese and
Coelli (1988, 1993, 1995), and a generalized Malmquist output-oriented index
to decompose productivity growth (Orea, 2002). For more detailed and
formal discussion, see Appendices A and B.
According to a number of authors (Aigner, Lovell, Schmidt 1977;
Meeusen Van den Broeck, 1977), the production frontier model without
a random component can be written as yi ¼ f(xi; b) Á TEi, where yi is the
observed scalar output of the producer i, xi is a vector of N inputs used by
the producer i, f(xi; b) is the production frontier, and b is a vector of tech-
nology parameters to be estimated. Finally TEi denotes the technical efficiency
defined as the ratio of observed output to maximum feasible output.2

A stochastic component that describes random shocks affecting the
production process is added. These shocks are not directly attributable to
the producer or the underlying technology. These shocks may come from
weather changes, economic adversities, or plain luck. We denote these
effects with expfvig. Each producer is facing a different shock, but we
assume the shocks are random and are described by a common distribution.
The stochastic production frontier will become: yi ¼ f(xi;b) Á TEi Á expfvig. If
we assume that TEi is also a stochastic variable, with a specific distribution
function, common to all producers, we can also write it as an exponential
TEi ¼ expfÀ uig, where ui ! 0, since we required TEi 1. As a result, we
obtain the equation: yi ¼ f(xi; b) Á expfÀuig Á expfvig.
Finally, if we also assume that f(xi;b) takes the log-linear Cobb-Douglas
form,3
the model can be written as ln yi ¼ b0 þ
P
nbn ln xi þ vi À ui, where vi
is the ‘‘noise’’ component, which we will almost always consider as a
two-sided normally distributed variable, and ui is the non-negative technical
inefficiency component. Together they constitute a compound error term,
with a specific distribution to be determined, hence the name of ‘‘composed
error model’’ to which it is often referred.
On the other hand, the Malmquist Productivity Index (MPI) is a bilateral
index that can be used to compare the production technology of two firms.
This index is also based on the concept of production function. In other
works, the MPI is a function of maximum possible production, with respect
to a set of inputs pertaining to capital and labor.
If we define Sa as the set of labor and capital inputs to the production
function of firm A, and Q as the production function of firm A, we could write
Q ¼ fa(Sa). To calculate the MPI of firm A with respect to firm B, we must sub-
stitute the labor and capital inputs of firm A into the production function of B,
and vice versa. The expression for MPI is MPI ¼
ffiffiffiffiffiffiffiffiffiffiffi
Q1Q2ð Þ
Q3Q4ð Þ
q
, where Q1 ¼ fa(Sa),
Q2 ¼ fa(Sb), Q3 ¼ fb(Sa), and Q4 ¼ fb(Sa).
Variables and Instruments
Table 1 presents a summary of the key variables used to empirically validate
the combined stochastic-inefficiency model for Chilean manufacturing
industries. Similar variables have been used by Escribano and Guasch
(2005), who compared cross-country performances in Guatemala, Honduras,
and Nicaragua. As shown in Table 1, the variables used in this study were
classified into three groups: (1) variables that define the production frontier,
(2) variables that define the term of inefficient production frontier, and (3)
control variables for the second stage analysis.
In the first group, value added was collected (in terms of value of gross
output minus intermediate inputs) as output and as inputs, capital stock (in
terms of Net book value of Machinery and equipment), and work (in terms
of total number of permanent workers). In the second group the existence

TABLE1FunctionProductionVariables
VariableDefinitionMeasurementUnit
Frontier
Model
YNTx1:Value-AddedValueofgrossoutputminusintermediate
inputs.Forallfirms,valueofgrossoutput
andintermediateinputsfiguresinlocal
currencyaredeflated.
VariableexpressedinChileanpesos.
KNTx1:CapitalStockNetbookvalueofMachineryandequipment
(includingtransport).Forallfirms,netbook
valueofMachineryandequipmentfiguresin
localcurrencyaredeflated.
VariableexpressedinChileanpesos.
LNTx1:LaborTotalnumberofpermanentworkers(fullor
parttime).
Variableexpressedinnumberofworkers.
TNTx1:TimeCyclicalandHicksneutraltechnological
progress.
Dummyvariable:(int¼1)1ifyear2001and0
inyears20021and20032.In(t¼2)1ifyear
2002and0inyears20001and2003.Finally
(t¼3)1ifyear2003and0inyears2001and
2002.
Inefficiency
Model
S:SpilloverSpillovervariableismeasuredbytheshareof
foreignfirms’outputovertotaloutputinthe
sectorofactivity.
Variableexpressedin%.
HHI:Herfindahl–
Hirschmanindex
Herfindahl–Hirschmanindexforameasureof
concentration,whichiscalculatedfrom
H¼
Pm
i¼1S2
iismarketshareofeachfirms.
Variableexpressedin%.
RDNTx1:Researchand
Developmenteffort
RDeffortisdefinedasthetotalexpenditures
onRDdividedbythetotalsales.
Variable.Forallfirms,totalexpenditureson
RDandtotalsalespersonnelfiguresin
localcurrencyaredeflated.
SÃ
HHI:Productive
spilloversthrough
concentration
AninteractingvariableofspilloverandHHI,
whichisameasureofproductivityspillovers
throughconcentration.
Quantitativevariable.
SÃ
RDdummy:spillovers
absorptivecapacity
AninteractingvariableofspilloverandRD
dummy,whichisameasurewhetherRD
firmsreceivemoreorlessspillovers.
Quantitativevariable.
102

TNTx1:YearTime-varyinginefficiencyeffect.Dummyvariable.Dummyvariable:(int¼1)1
ifyear2001and0inyears20021and20032.
In(t¼2)1ifyear2002and0inyears20001
and2003.Finally(t¼3)1ifyear2003and0
inyears2001and2002.
Control
Variables
Fdummy:ForeignForeignownership,whichismeasuredbya
dummyvariable.
Dummyvariable:1iftheshareofforeign
ownershipisgreaterthan0%;and0if
otherwise.
A:AgeAgeoffirms,ismeasuredbythedifference
betweenyearofsurveyandyearofstarting
production.
Quantitativevariableexpressedinnumberof
years(age).
KH:HumanCapitalPercentoftheworkforcewithsomeuniversity
orhighereducationlevel.
Expressedin%.
TISdummy:Technological
Innovationsubsidies
ProjectoftechnologicalInnovation
(FONTEC-CORFO)Technological
Innovationsubsidies.
Dummyvariable.Whichismeasuredby1if
thefirmshaveatechnologicalinnovation
subsidy(byFONTEC-CORFO)and0
otherwise.
RDdummy:Researchand
Development
ExpenditureonRD.Dummyvariable.Whichismeasuredby1if
firmspendsonresearchanddevelopment
activitiesduringtheobservedyears,and0
otherwise.
KDNT:DepthofcapitalDepthofcapitalisestimatedbydividingthe
capitalstocksforeachindustrybylabor
input.
Quantitativevariable(thisratehasbeen
expressedin%).
RWNTx1:RealWageRealwageisdefinedasthetotalexpenditures
onpersonneldividedbythetotalnumberof
permanentworkers(fullorparttime).
Quantitativevariable.Forallfirms,total
expendituresonpersonnelfiguresinlocal
currencyaredeflated.
Note.Alltechnicalinformationaboutsampling,data,andquestionnaireareavailableathttp://www.enterprisesurveys.org/nada/index.php/catalog/379.Authors’
adaptationofdatafromtheInvestmentClimateSurveyDatabank,WorldBank,RD,ResearchandDevelopment.
103

of spillovers (in terms of share of foreign firms’ output over total output in
the sector of activity), the level of industrial concentration (in terms of
Herfindahl–Hirschman index), effort in RD (in terms of total expenditures
on RD divided by the total sales), and the temporary effect of these variables
are recorded. Finally, in the last set of control variables, the profile that char-
acterizes the companies in terms of Foreign ownership, Age, Human capital,
Expenditure on research and development, Depth of capital (estimated by
TABLE 2 Number of Firms and Percentage by Sector (2001–2003)
Sector Number of Firms %
Biotechnology 176 23.44
Chemicals 94 12.52
Farm and Fishing 96 12.78
Food and Beverages 41 5.46
IT Services 79 10.52
Machinery and Equipment 43 5.73
Metal Products 159 21.17
Paper Production 15 2
Wood and Cork Production 48 6.39
Total 751 100.00
Note. Authors’ elaboration from the Investment Climate Survey Databank,
World Bank.
TABLE 3 Descriptive Statistics of Variables: Entire Sample
Variable=Unit Year Obs. Mean Std. Dev. Min Max
Yt (Chilean pesos
constant 2000)
2003 751 5.66E þ 09 2.73E þ 10 178476.8 6.24E þ 11
2002 751 8.27E þ 09 4.90E þ 10 271033.9 9.14E þ 11
2001 751 5.83E þ 09 2.69E þ 10 232922.5 4.56E þ 11
Kt (Chilean pesos
constant 2000)
2003 751 2.23E þ 09 9.23E þ 09 72.3884 1.25E þ 11
2002 751 2.37E þ 09 1.02E þ 10 74.42492 1.62E þ 11
2001 751 2.31E þ 09 1.01E þ 10 76.27749 1.73E þ 11
Lt (Persons) 2003 751 132.0879 318.5573 1 4646
2002 751 129.6272 344.2935 1 6146
2001 751 132.4088 408.3376 1 8485
RWt (Ratio) 2003 751 1.05E þ 07 9.94E þ 07 4441.108 2.72E þ 09
2002 751 6873523 7567621 2198.118 7.13E þ 07
2001 749 1.81E þ 07 3.09E þ 08 2805.081 8.47E þ 09
KDt (Ratio) 2003 751 1.05E þ 07 9.94E þ 07 4441.108 2.72E þ 09
2002 751 6873523 7567621 2198.118 7.13E þ 07
2001 749 1.81E þ 07 3.09E þ 08 2805.081 8.47E þ 09
RDt (Ratio) 2003 703 0.007893 0.077297 0 1.819905
2002 697 0.00492 0.037606 0 0.488889
S (Ratio) 2003 751 0.233158 0.170991 0 0.889678
HHI (Ratio) 2003 751 0.144363 0.224908 0.051607 1
A (years) 2003 751 25.98935 23.00997 1 150
KH (Percentage) 2003 735 24.34126 28.02445 0 100
Note. Authors’ calculation from the Investment Climate Survey Databank, World Bank.

TABLE4AveragebySector:2001–2003Considered
SectorMean(2001–2003)YtKtLtRWtKDtSHHIAKHRDt
Biotechnology3.17Eþ086718012711.5778968159855446280.8896780.5718599.5333366.646670.076589
Chemicals1.16Eþ103.87Eþ09142.5638820029253110340.3619730.07282532.361724.770970.003339
FarmandFishing1.16Eþ102.28Eþ09301.993516214286964600.3931780.07712216.104224.126670.003167
FoodandBeverages1.24Eþ103.36Eþ09205.55710918183201802250.0973760.05426136.465911.201530.001553
ITServices1.37Eþ092.66Eþ0880.47179311620111189340.3533660.05160711.962354.877770.01324
MachineryandEquipment8.39Eþ083.35Eþ0854.47154797639543060400.12416929.634116.955750.002957
MetalProducts1.40Eþ098.36Eþ0871.1319531140556690020.3114920.1044828.562510.458740.000995
PaperProduction6.09Eþ095.88Eþ09122.7215015281194984510.019353132.255810.488950.010118
WoodandCorkProduction5.78Eþ093.52Eþ0992.15616508029197377100.0607520.1698052410.618730.000137
Total6.59eþ092.31eþ09131.37461.18eþ071.51eþ070.23315750.144362625.9893524.341250.0064102
Note.Metalproducts(excl.ME);woodandcorkproduction(excl.furniture).Authors’calculationfromtheInvestmentClimateSurveyDatabank,WorldBank.
YNTx1,Value-added;KNTx1,Capitalstock;LNTx1,Labor;RWNTx1,Realwage;KDNT,Depthofcapital;S,Spillover;HHI,Herfindahl–Hirschmanindex;A,Age;KH,Human
capital;RDNTx1,Researchanddevelopmenteffort.
105

dividing the capital stocks for each industry by labor input), and Real wage
(defined as the total expenditures on personnel divided by the total number
of permanent workers) were included.
Statistical Source and Sample
These sections deal with the characteristics of the business and the investment
climate in which firms operate. The statistical source used for this analysis is
the World Bank’s Enterprise Surveys (WBES). The WBES collects data from
key manufacturing and service sectors in every region of the world. The
WBES use standardized survey instruments using a uniform sampling meth-
odology to minimize measurement error and to yield data that are comparable
across the world’s economies. It is worth noting that the sample covers estab-
lishments in nine industries, including six manufacturing sectors, information
technology, biotechnology, and fish farming. Furthermore, the sample
includes firms of all sizes: micro (16), small (16–49), medium (50–249)
and large (250þ workers) firms.
The Core instrument comprises 11 sections. The first eight sections
contain qualitative questions, which are based on a manager’s opinion on
the business environment and on motivation for business decisions. The next
section, only included in the Manufacturing Module, contains questions
about capacity (use of production capacity and hours of operation). The last
three sections of the questionnaire deal with data specific to the transactions
businesses make in order to operate.
From Chile’s ICA survey we are able to form a balanced data panel (see
Table 2). We have observations for 2001, 2002, and 2003.
Table 3 shows the descriptive statistics of the variables taken into
account in order to conduct the empirical analysis for the entire sample.
Table 4 shows the descriptive statistics of the variables considered in
order to conduct the empirical analysis by sectors. It presents important
differences in terms of value added during the period considered.
RESULTS
When observing the whole of the Chilean industry (Table 5), we observe
very similar values across sectors during the periods studied in terms of
technical efficiency levels.4
In this context, improvement in the use of their
productive inputs is close to 53%.
In terms of convergence the dynamic between the years considered is
shown in Figure 1, and it illustrates the trends in convergence (divergence)
and persistence (mobility) in the level of technical efficiency attained.
For Chilean industry technical efficiency over the whole period, we detect
a pattern of divergence and mobility.

Competition, Absorptive Capacity, and Productivity Spillovers
In this section hypotheses H1 and H2 are tested by estimating two alternative
technical inefficiency functions to avoid the possibility of multicollinearity.
The test of H1 includes a spillover variable, a competition variable (HHI),
and an interacting variable of spillover and HHI (see Table 6). In order to
evaluate the absorptive capacity of spillovers in the industry, the test of H2
includes a spillover variable, an RD effort variable, and an interacting
variable of RD effort and spillover (see Table 7).
FIGURE 1 Technical efficiency histogram. Note. Authors’ calculation from the Investment
Climate Survey Databank, World Bank.
TABLE 5 Estimates of Technical Efficiency: Average by Sector and Year
Production Function
Technical Efficiency
Average by sector 2001 2002 2003
Biotechnology 0.456 0.486 0.470
Chemicals 0.521 0.502 0.524
Farm and Fishing 0.447 0.454 0.468
Food and Beverages 0.473 0.474 0.469
IT Services 0.492 0.504 0.507
Machinery and Equipment 0.466 0.462 0.465
Metal Products 0.451 0.436 0.437
Paper Production 0.457 0.463 0.459
Wood and Cork Production 0.436 0.436 0.428
Total 0.473 0.473 0.475
Note. Authors’ calculation from the Investment Climate Survey Databank, World Bank.

TABLE6MaximumLikelihoodEstimatesofStochasticProductionFrontierWithInefficiencyCoefficientasFunctionofHHIandSpillovers
Variable1
ParameterModel1Model2Model3Model4Model5
ProductionFrontier1
Constantb016.832ÃÃÃ
16.831ÃÃÃ
16.813ÃÃÃ
11.785ÃÃÃ
11.835ÃÃÃ
ln(Lt)b10.1230.1230.1220.697ÃÃÃ
0.700ÃÃÃ
ln(Kt)b2À0.078À0.078À0.0780.361ÃÃÃ
0.358ÃÃÃ
[ln(Lt)]2
b11À0.058ÃÃÃ
À0.058ÃÃÃ
À0.058ÃÃÃ
ln(Lt)Ã
ln(Kt)b120.052ÃÃÃ
0.052ÃÃÃ
0.052ÃÃÃ
[ln(Kt)]2
b220.006ÃÃ
0.006ÃÃ
0.006ÃÃ
Ttbt0.0040.019
ln(Lt)Ã
Ttb1t0.008
ln(Kt)Ã
Ttb2tÀ0.000
T2
tbttÀ0.067À0.066
Equationuitconstantdu00.562ÃÃÃ
0.563ÃÃÃ
0.563ÃÃÃ
0.643ÃÃÃ
0.480ÃÃÃ
Sd1À0.933ÃÃ
À0.933ÃÃ
À0.933ÃÃ
À0.856ÃÃ
HHId13À0.228À0.228À0.227À0.231
SÃ
HHId142.286ÃÃ
2.284ÃÃ
2.284ÃÃ
2.093ÃÃ
Equationvitconstantdv00.0410.04080.0410.0350.0324
Sigmarv1.0201.0201.0201.0171.016
Waldchi25085.6905085.5205083.1705036.4505127.210
Probchi20.0000.0000.0000.0000.000
LogLikelihoodÀ3696.584À3696.648À3697.010À3727.206À3729.740
NumberofObs22532253225322532253
Note.Descriptioninnaturallogs.Model1isatranslogproductionfunction.Models2andModel3representaHicks-neutralandano-technologicalprogressproduction
functions,respectively.Model4isaCobb–Douglasproductionfunction.Model5representsano-inefficiencyproductionfunction(rucoef.1.271279andstd.err.
0650186;ccoef.1.25083andstd.err.0.0867159;sigma2coef.2.649112andstd.err.0.1343881).Likelihood-ratiotestofsigma_u¼0:chibar2(01)¼57.21Prob!
chibar2¼0.000.Standarderrorsareinparenthesesandpresenteduntiltwosignificantdigits,andthecorrespondingcoefficientsarepresenteduptothesamenumber
ofdigitsbehindthedecimalpointsasthestandarderrors.Authors’calculationfromtheInvestmentClimateSurveyDatabank,WorldBank.
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108

TABLE7MaximumLikelihoodEstimatesofStochasticProductionFrontierWithInefficiencyCoefficientasFunctionofResearchandDevelopment
andSpillovers
VariableParameterModel1Model2Model3Model4
ProductionFrontier1
Constantb016.431ÃÃÃ
16.595ÃÃÃ
16.586ÃÃÃ
11.813ÃÃÃ
ln(Lt)b10.1160.1330.1330.720ÃÃÃ
ln(Kt)b2À0.046À0.058À0.0580.351
[ln(Lt)]2
b11À0.051ÃÃÃ
À0.051ÃÃÃ
À0.051ÃÃÃ
ln(Lt)Ã
ln(Kt)b120.050ÃÃÃ
0.050ÃÃÃ
0.050ÃÃÃ
[ln(Kt)]2
b220.0060.0060.006
Ttbt0.341À0.020
ln(Lt)Ã
Ttb1t0.042
ln(Kt)Ã
Ttb2tÀ0.027
Equationuitconstantdu00.1780.1790.1760.227
Sd1À0.193À0.183À0.185À0.068
RDtd137.874ÃÃÃ
7.825ÃÃÃ
7.817ÃÃÃ
8.557ÃÃÃ
SÃ
RDtd14À11.235Ã
À11.172Ã
À11.137Ã
À14.175Ã
Equationvitconstantdv00.0580.0580.0590.063
Sigmarv1.0291.0291.0301.032
Waldchi23333.4303331.0703330.2803299.020
Probchi20.0000.0000.0000.000
LoglikelihoodÀ2262.129À2262.505À2262.554À2279.915
N.ofobs1400140014001400
Note.Descriptioninnaturallogs.Model1isatranslogproductionfunction.Models2andModel3representaHicks-neutralandano-technologicalprogressproduction
functions,respectively.Model4isaCobb–Douglasproductionfunction.Standarderrorsareinparenthesesandpresenteduntiltwosignificantdigits,andthecorre-
spondingcoefficientsarepresenteduptothesamenumberofdigitsbehindthedecimalpointsasthestandarderrors.Authors’calculationfromtheInvestmentClimate
SurveyDatabank,WorldBank.
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109

The estimation results of a translog stochastic production frontier (see
Table 6) show that the coefficients of labor and capital are expected to have
positive signs (in models 4 and 5). The positive and highly significant coeffi-
cients confirm the expected positive and significant output effects of labor
and capital. On the other hand, if we take into account the squared variable
of labor [ln(Lt)]2
in models 1, 2, and 3 we will observe a negative and statisti-
cally significant 1% level, which indicated a decrease regarding labor perfor-
mance. This cannot be confirmed in the case of the variable squared capital.
However, the same is not true for the squared capital. The squared variable
of capital [ln(Kt)]2
in models 1, 2, and 3 is positive and statistically significant
at a 5% level, which indicates an increasing return to capital. Furthermore,
the estimated coefficient of the interacting variable between labor and capital
ln(Lt)Ã
ln(Kt) in models 1, 2, and 3 is positive and significant at a 1% level, sug-
gesting a substitution effect between labor and capital. The last finding is
consistent with classic works from Arrow, Chenery, Minhas and Solow
(1961). These authors found empirical evidence that the elasticity of substi-
tution between capital and labor in manufacturing may typically be less than
unity. However, these authors also found that there are weaker indications
that this conclusion is reversed in primary production.
An interesting result of this study is the estimated coefficients of the inef-
ficiency function in the second part of the models in Table 6. The negative
and significant coefficient on the spillover variable (spillover) in Models 1,
2, 3, and 4 in Table 6 implies a positive and significant efficiency spillover
in the Chilean industrial sectors. This result suggests that in the Chilean
industrial sectors foreign investments result in domestic firms utilizing their
resources in a more efficient way, which then leads to gains in productivity.
The positive coefficient of the interacting variable between concen-
tration and spillovers SÃ
HHI in Models 1, 2, 3, and 4 suggests that a lower con-
centration is associated with larger spillovers from foreign presence. From
these findings, it may be inferred that domestic firms operating in a non-
concentrated subsector of the Chilean industrial sectors may gain spillover
benefits from foreign firms.
According to Salim and Bloch (2009), higher concentration is an inverse
measure of static competition that can protect inefficient firms. However, a
higher concentration can also be the result of dynamic competition among
firms of differential efficiency that removes inefficient firms from the industry
as argued by Demsetz (1973) and Peltzman (1977). The first argument
suggests that HHI is associated with greater inefficiency, while the latter
argument suggests that HHI is associated with lower inefficiency.
Table 7 presents the estimated parameters of productivity spillovers and
absorptive capacity.
In Table 7, the estimated parameters of production functions have a
similar sign and significance as in the baseline models shown in Table 6.
The coefficient of the RD dummy is positive and significant at the 1% level,

suggesting that firms with high RD effort, on average, have lower efficien-
cies compared to those with low RD effort. The negative coefficient of the
interacting variable between RD and spillovers (SÃ
RDt) suggests that firms
with high RD efforts gain more spillovers from foreign firms. Given this
result, it is possible to infer that firms with high RD effort can reap greater
benefits from foreign firms’ presence by upgrading their knowledge and cre-
ating innovation. This finding confirms that firms’ absorptive capacity (or
firms’ specific characteristic) determines productivity spillovers from FDI,
as argued in some previous studies, for example, by Kathuria (2000).
Sources of Productivity Growth and FDI Spillovers
In this section, hypotheses H3 and H4 are tested estimating the indices of
TEC, TP, SEC and G0 using Equations [7] and [14] (see Appendix B); the
average of these indices for the selected period (2001–2003) is presented
in Table 8.
Table 8 shows that a major contribution to productivity growth in the
Chilean industrial sectors comes from TP and from TEC—with the exception
of the biotechnology sector. In contrast, the SEC indices are relatively low,
suggesting that this component does not contribute much to productivity
growth.
After obtaining the indices of Malmquist total productivity growth index
(G0), TEC, TP, SEC, the next step is to estimate the contribution of FDI
spillovers on the total factor productivity growth and its sources. Using the
indices of TEC, TP, SEC, and G0 obtained from the decomposition, we then
estimate the impact of FDI spillovers on total factor productivity growth and
its sources (see Table 9).
Similar to Salim and Bloch (2009), we propose a second step analysis
proposed for these indices. To conduct the analysis, we include several
TABLE 8 Sources of Productivity Growth by Sector: 2001–2003
Sector Mean TEC TP SEC G0
Biotechnology À8.819 0.417 0.803 À7.598
Chemicals 3.273 2.030 À0.137 5.165
Farm and Fishing 6.179 2.448 À0.991 7.636
Food and Beverages 0.749 2.290 À0.586 2.453
IT Services 6.496 1.052 0.077 7.625
Machinery and Equipment À0.910 1.642 0.802 1.533
Metal Products À4.166 1.761 À0.362 À2.766
Paper Production 0.854 2.079 0.836 3.770
Wood and Cork Production À1.141 1.794 0.840 1.492
Total 1.526 1.801 À0.052 3.275
Note. TEC ¼ technical efficiency change; TP ¼ technical progress; SEC ¼ scale efficiency change.
Authors’ calculation from the Investment Climate Survey Databank, World Bank.

TABLE9SourcesofProductivityGrowthandSpillovers
DependentVariable
SECTPTECG0
SourcesproductandFDIspilloversModel1Model2Model1Model2Model1Model2Model1Model2
IndependentvariablesCoef=E.St.Coef=E.St.Coef=E.
St.
Coef=E.
St.
Coef=E.StCoef=E.StCoef=E.StCoef=E.St
S:Spillover2.8361.38757.59561.820
HHI:Herfindahl–Hirschmanindex2.0950.4099.18211.687
RDNTx1:ResearchandDevelopment
effort
66.101ÃÃÃ
74.497ÃÃÃ
À.548À1.431À150.977À79.949À85.423À6.883
SÃ
HHI:Productivespillovers
concentration
À1.911À5.232ÃÃ
À267.855ÃÃ
À275.000ÃÃ
SÃ
RD:Spilloversabsorpt.CapacityÀ75.222ÃÃÃ
À85.592ÃÃÃ
0.5541.587212.598126.457137.93042.451
ControlVariables
Fdummy:Foreign2.9972.5620.852ÃÃ
0.797Ã
À50.065Ã
À54.502ÃÃ
À46.215Ã
À51.141ÃÃ
A:AgeÀ0.015À0.0160.011ÃÃ
0.011ÃÃ
À0.087À0.164À0.090À0.169
KH:HumanCapital0.0040.027À0.005À0.006À0.0010.172À0.0020.193
TISdummy:Technolog.Innovation
subsidies
À2.8143À3.094Ã
0.3080.3340.502À3.672À2.002À6.431
KDNT:CapitalDeeping5.51e–092.97e–096.80e–101.55e–098.08e–09À4.05e–091.43e–084.75e–10
RWNTx1:RealWageÀ3.03e–07Ã
À0.23e–07À4.56e–08À4.24e–08À8.57e–07À6.07e–07À1.21e–06À8.73e–07
IndustrialSectors
ChemicalsÀ1.4781.466ÃÃ
77.800Ã
77.788Ã
FarmandFishingÀ3.2661.919ÃÃÃ
86.840ÃÃ
85.494ÃÃ
112

FoodandBeveragesÀ3.1821.659ÃÃÃ
76.647ÃÃ
75.124ÃÃ
ITServices0.3351.489ÃÃ
108.891ÃÃÃ
110.716ÃÃÃ
MachineryandEquipmentÀ3.5940.54670.05167.003
MetalProductsÀ4.2151.230ÃÃ
76.221Ã
73.236Ã
PaperProductionÀ1.0851.690ÃÃ
79.99980.604Ã
WoodandCorkProductionÀ3.8621.09275.31572.545
Constant4.5330.2370.7752.009ÃÃÃ
À63.909Ã
7.229À58.599Ã
9.476
Numberofobs8787878787878787
F1.7702.0902.9403.5101.0801.5301.0701.480
ProbF0.0530.0310.0010.0000.3900.1390.3960.157
R-squared0.2880.2340.4010.3390.1970.1830.1970.178
AdjR-squared0.1250.1210.2650.2420.0140.0630.0130.057
RootMSE4.7034.7130.9550.97059.66258.16759.51458.164
Note.OmittedDummies:Biotechnology;Model1withindustrialsectordummies.TEC¼technicalefficiencychange;TP¼technicalprogress;SEC¼scaleefficiency
change;FDI¼foreigndirectinvestment.Authors’calculationfromtheInvestmentClimateSurveyDatabank,WorldBank.
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p.01.
113

control variables, such as Foreign, Age, Human Capital, Technological
Innovation Subsidies, Depth of Capital, Real Wage, and Industrial sectors.
Table 9 reveals the effects of ‘‘spillovers’’ over different productivity
components in association with other factors such as market concentration
or RD. For example, it can be seen how the interaction between market
concentration and spillovers has a negative and statistically significant effect
on TEC and (G0). Along the same lines, the interaction between RD activity
and spillovers has a negative and statistically significant effect on SEC. The
negative and statistically significant estimate of the interacting variable
between spillover and RD effort indicates that firms with high RD effort
tend to gain less technological spillovers on SEC. The estimate for an
interacting variable between spillover and concentration is negative and
statistically significant, suggesting that competition is associated with higher
spillovers on TP, TEC, and G0.
DISCUSSION
At least two reasons make Chile an interesting case study: First, because from
1994 to 2007 the country’s average rate of economic growth was close to 5%,
with most of this growth experienced in the decade before 1994. Examining
Chile is also relevant due to its relative importance in the subcontinent. In
fact, most of the economic variables have presented good behavior (inflation,
public deficit, external balance, finances, etc.). In this sense, this economy
appears to be an excellent candidate to test the hypothesis concerning the
existence of productivity spillovers in the manufacturing sector because of
FDI. This study examines the productivity spillovers from FDI in the Chilean
industrial sector by using unique and extensive firm-level panel data cover-
ing 2001–2003. This article uses the stochastic frontier production function
following the technique used by Battese and Coelli (1995) and a generalized
Malmquist output-oriented index to decompose productivity growth.
The intra-industry productivity spillovers are examined through the
spillover variable, and the roles of competition and RD in extending spil-
lovers from FDI are evaluated to test a channel of productivity spillovers.
The empirical results show that intra-industry productivity spillovers are
present in the Chilean industrial sector. Firms with RD expenditure receive
more productivity spillovers than those without RD expenditure. These find-
ings support our second hypothesis H2 and, in line with works by Girma and
colleagues (2006) for the Chinese industry, we can add empirical evidence
that Chilean firms that invest in RD have positive FDI spillovers. Results also
show that technological progress is the major driver of productivity growth in
the Chilean industrial firms. FDI spillovers have been found to be positive but
not significant for scale efficiency change, technological progress change,
technical efficiency change, and total productivity growth change.

In addition to the empirical evidence found, we think that the main
contribution of our study is to have applied a parametric methodology,
which allows performing robust estimates and using the firm as the unit of
analysis. We believe that the limited empirical literature on this topic in the
context of Latin America and the Caribbean gives value to this work.
On the other hand, the main limitation of this work stems from restricted
data. This circumstance makes it necessary to be cautious with the results, but
also opens the door to future studies that use other methodologies based on
dynamic panel models. (See, for example, Arellano Bond, 1991; Arellano
Bover, 1995; Bover Arellano, 1997; Blundell Bond, 1998.) These meth-
odologies allow the use of time series with a number of shorter periods and
set multiple lagged endogenous variables among the explanatory variables.
Alternatively, albeit less robust, possibly helpful in validating our results is
the use of nonparametric techniques to assess efficiency and productivity.
Policy Implications
Despite the presence of positive spillovers from FDI, the policy implications
of these findings are not straightforward. However, certain policy reflections
do follow from the findings. The findings of positive productivity spillovers at
the aggregated manufacturing level suggest that the government should con-
tinue to provide FDI-friendly environment and deregulation policies related
to FDI. This could include simplifying the administrative processes for FDI
inflows, additional incentives for foreign firms that are willing to transfer their
knowledge to domestic firms, and further trade policy reforms to promote a
more competitive environment in the manufacturing sector. Outcomes from
the decomposition analysis suggest that incentives should be provided to FDI
that generate advanced managerial knowledge as well as those that provide
up-to-date technology transfer. Results suggest the importance of improving
the capacity of domestic firms in order to update the spillover benefits from
FDI. For instance, it is worth noting that Salim and Bloch (2009) suggested
that policies for strengthening the absorptive capacity of domestic firms
through investing in knowledge and human capital formation might be
superior to policies that provide concessions for FDI.
Preferential policies that solely consist of opening up some selected
regions are not optimal for Chile. In order to reap more benefits from foreign
presence, coordinated industrial policies that reinforce regional comple-
mentarities are needed. In addition, the removal of restrictions to the free
movement of production factors across regional borders appears to be
crucial to improve productivity levels.
Such policies also reveal the importance of controlling for those local
capacities related to the macroeconomic and institutional environment. Thus,
host country governments should develop a set of policies that are not
only focused on inward FDI promotion but also on the improvement of their

political and economic framework. According to Alguacil, Cuadros, and Orts
(2011), more general policies such as building modern infrastructure or
increasing and strengthening the institutions should be pursued, which not
only attract FDI but also benefit domestic firms.
ACKNOWLEDGMENTS
We appreciate the efforts of the two anonymous reviewers and their useful
comments and suggestions for improving the article. We want to thank the
support and kindness of the Manager Editor of LABR. Finally, we appreciate
the comments of the colleagues of the Columbia University (SIPA).
NOTES
1. These data have become the standard way for the World Bank to identify key obstacles to country
competitiveness, in order to prioritize policy reforms for enhancing competitiveness.
2. When TEi ¼ 1, the i-th firm obtains the maximum feasible output, while when TEih1 we have a
measure of the shortfall of the observed output from maximum feasible output.
3. In Appendix I we also present the more complex translog production function used in the
empirical analysis.
4. The estimated efficiency indices have values between 0 and 1, where the most efficient companies
are those closest to 1.
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APPENDIX A
DETERMINISTIC FRONTIER PRODUCTION FUNCTIONS:
THE STOCHASTIC FRONTIER-INEFFICIENCY MODEL
The approaches linked to the analysis of the productivity are well known: on
one hand, we have a descendent vision of the analysis of the productivity
associated with the concept of residual factor that this tied to the questions
raised by Solow in his well-known article of 1957. On the other hand, we have
an ascending approach that attempts a measurement of the productivity from
a heterogeneity that is inherent to the different production units (companies).
This work line finds its basis in the pioneer works of Farrel, who
searched for a measurement of the efficiency through the decomposition
of the productivity’s growth. Both approaches have resulted in multiple
works and important technical developments that allow, in one way or
another, one to get to know the bases on which the change in productivity
of the factors stand.

Following Battese and Coelli (1995), the stochastic frontier approach
is used to estimate a production function and an inefficiency function
simultaneously. The Battese–Coelli model can be expressed as follows:
yit ¼ f xit; t; bð Þ Á exp vit À uitð Þ ð1Þ
where yit implies the production of the ith firm (i ¼ 1, 2, . . . , N) in the tth
period (t ¼ 1, 2, . . . , T), xit denotes a (1 Â k) vector of explanatory variables,
and b represents the (k Â 1) vector of parameters to be estimated. The error
term consists of two components: vit and uit, which are independent of each
other. In addition, the vit denotes the time-specific and stochastic part, with
idd N 0; r2
v
À Á
, and the uit represents technical inefficiency, which is normal
distribution, but truncated at zero with mean zitd and variance r2
u.
The technical inefficiency effects, uit, are assumed as a function of a
(1 Â j) vector of observable nonstochastic explanatory variables, zit, and a
(j Â 1) vector of unknown parameters to be estimated, d. In a linear equation,
the technical inefficiency effects can be specified as follows:
uit ¼ zitd þ wit ð2Þ
where wit is an unobservable random variable, which is defined by the
truncation of the normal distribution with zero mean and variance, r2
u, such
that the point of truncation is Àzitd.
Equation (1) shows the stochastic production function in terms of the
original production value, and Equation (2) represents the technical inef-
ficiency effects. The parameters of both equations can be estimated simul-
taneously by the maximum-likelihood method. The likelihood function is
expressed in terms of variance parameters r2
s r2
v þ r2
u and c r2
u=r2
s e. If
c equals zero, then the model reduces to a traditional mean response function
in which zit can be directly included into the production function.
Based on the theoretical model in Equations (1) and (2), we start with a
flexible functional form, namely, a translog production function. By adopting
a flexible functional form, the risk of errors in the model specification can be
reduced. Moreover, the translog form is useful for decomposing the total
factor productivity growth. The functional form of the translog production
function is as follows:
ln yit ¼ b0 þ
XN
n¼1
bn ln xnit þ
1
2
XN
n¼1
XN
k¼1
bnk ln xnit ln xkit
þ btt þ
1
2
but2
þ
XN
n¼1
bnt ln xnitt þ vit À uit ð3Þ
where y implies output, x represents variables that explain output (labor and

capital, so N ¼ 2), t is time, i is firm. uit is defined as:
uit ¼ d0 þ
XJ
j¼1
djzit þ wit ð4Þ
where z is the set of explanatory variables that explain technical inefficiency.
Given the specifications in Equations (3) and (4), the technical efficiency of
production for the ith firm at the tth year is defined as the ratio of the actual
output of firm i, ln yit, to its potential output, ln y
p
it:
TE ¼
ln yit
ln yp
it
¼ E Àuit vit À uitð Þj½ Š ¼ E Àzitd À witð Þ vit À uitð Þj½ Š ð5Þ
where
ln y
p
it ¼ b0 þ
XN
n¼1
bn ln xnit þ
1
2
XN
n¼1
XN
k¼1
bnk ln xnit ln xkit
þ btt þ
1
2
but2
þ
XN
n¼1
bnt ln xnitt þ vit ð6Þ
APPENDIX B
DECOMPOSING PRODUCTIVITY GROWTH:
A GENERALIZED MALMQUIST INDEX
According to Orea (2002), if firm i’s technology in time t can be represented
by a translog output-oriented distance function D0(yit, xit, t) where yit, xit, and
t are defined previously, then the logarithm of a generalized output-oriented
Malmquist productivity growth index, Gt;tþ1
0i , can be decomposed into TEC,
TP, and SEC between time periods t and t þ 1:
Gt;tþ1
0i ¼ TECt;tþ1
i þ TPt;tþ1
i þ SECt;tþ1
i ð7Þ
where
TECt;tþ1
i ¼ ln D0 yi;tþ1; xi;tþ1; t þ 1
À Á
À ln D0 yi;tþ1; xi;tþ1; t
À Á
ð8Þ
TPt;tþ1
i ¼
1
2
@ ln D0 yi;tþ1; xi;tþ1; t þ 1
À Á
@ t þ 1ð Þ
þ
@ ln D0 yi;tþ1; xi;tþ1; t
À Á
@t
!
ð9Þ

SECt;tþ1
i ¼
1
2
XN
n¼1
ei;tþ1 À 1
ei;tþ1
ei;tþ1;n þ
eit À 1
eit
eitn
!
Á ln
xi;tþ1;n
xitn
!
ð10Þ
where eit ¼
PN
n¼1 eitn is the scale elasticity such that eitn ¼ @ ln D0 yit;xit;tð Þ
@ ln xitn
.
If the output is only one, then a translog output-oriented distance
function can be defined as
ln D0 yit; xit; tð Þ ¼ ln yit À ln yp
it À vit ð11Þ
Given the technical efficiency measure in Equation (5), the technical
efficiency change between periods t þ 1 and t can be estimated by following
Coelli and colleagues (2005):
TECt;tþ1
i ¼ ln TEi;tþ1 À ln TEit ð8Þ
The technical progress index can be obtained from Equations (6), (9),
and (11) as follows:
TPi;tþ1;t
¼
1
2
XN
n¼1
btn ln xi;tþ1;n þ
XN
n¼1
btn ln xitn þ 2bt þ 2btt t þ 1ð Þ½ Š þ t
#
ð13Þ
From Equation (3), the scale elasticity can be written as
enit ¼ bn þ
1
2
XK
k¼1
bnkxnit þ bntt ð14Þ
The index of scale efficiency change then can be calculated by using
Equations (10) and (14).

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