MULTINATIONAL ENTERPRISES AND PRODUCTIVITY GROWTH:

FIRM-LEVEL EVIDENCE FROM CANADA

HYELIN CHOI

Abstract. Di�erences in income across countries are largely explained by productivity

variations, and a large variation in productivity is as much due to foreign as to domestic

innovation. Among others, foreign direct investment(FDI) and trade have long been

suspected to be major conduits of international technology transfer. In this paper, I

estimate FDI spillovers to Canada manufacturing �rms between 1994 and 2005. In order

to measure productivity more properly, I adopt the Olley-Pakes method, and I apply

instrumental variable estimation to solve endogeneity problem. My results suggest that

there is substantial positive within-industry spillovers by FDI. In addition, my results

show that larger �rms bene�t more from FDI spillovers and the spillovers mainly occur

within low-tech industries. Those results contrast with earlier work, indicating that

earlier work can not be generalized to other countries and periods.

Date: July.4.2012.1

1. Introduction

Di�erences in income across countries are largely explained by productivity variations,

and technology plays an important part in determining productivity.(William Easterly

and Ross Levine 2001, Robert Hall and Charles Jones 1999, Edward Prescott 1998).

However, only a small number of countries have committed to a large amount of R&D

expenditures, and so the overall pattern of world technology is determined by inter-

national technology di�usion. Hence, the technology spillovers are fundamental to the

economic growth in theories of endogenous economic growth.(Romer 1986, Aghion and

Howitt 1992, 1998, Grossman and Helpmen 1991).

Economists have had questions about mechanisms of international technology di�u-

sion and the extent to which each of them can account for it. Trade and foreign direct

investment(FDI) have long been suspected to be the main mechanisms of international

technology transfer. Trade introduces goods that embody advanced technology and do-

mestic �rms can learn new technology by imitating them. Multinational �rms open

their a�liates in the host country and produce the goods by hiring local workers or

sourcing intermediate goods from local suppliers. Here, local workers hired by multina-

tional enterprises(MNE) may be o�ered on-the-job-training and they would be attracted

to the domestic �rms once they quit the job in the MNE. Also, local suppliers connected

with MNE may be required to produce high standards of intermediate goods and MNE

hand down core technologies. Trade and FDI have been investigated by a number of

researchers. However, while the general pattern of international technology di�usion by

trade, a strong and positive role of imports and an insigni�cant role of exports, have

been shown by a large empirical literature, the technology transfer through FDI has not

come to a common conclusion. Despite plausible mechanisms of technology transfer from

MNE to domestic �rms, the empirical support for large positive externalities is weak.

Answering the question whether FDI contributes to the knowledge spillovers between

countries is important, in particular, for policy makers. For example, Alabama state in

the U.S. has spent about $230 million to attract a plant of Mercedes in 1994(Head 1998),

based on the belief that FDI brings positive externalities to domestic �rms. Can such

large subsidies be justi�ed?

Keller and Yeaple(2009) is the �rst to show strong positive externalities of FDI with

a sample of U.S. manufacturing �rms for the years 1987 to 1996. They also show that

small �rms bene�t more from FDI spillovers, and FDI spillovers are particularly strong

in high-tech industries. Their results are attributed to the Olley-Pakes method in com-

puting TFP, instrumental techniques(IV), and high-quality employment ratio data on

FDI. Their paper has raised two questions: are there strong FDI spillovers in Canada

that has very similar economic environment with the U.S., and is the pattern of FDI

spillovers in Canada the same as in the U.S.? In this paper, I estimate inward FDI

spillovers to Canada manufacturing �rms with a sample of 1,300 Canadian-owned �rms

for the years 1994 to 2005. I adopt the same production elasticities as in Keller and

Yeaple(2009) in order to properly measure TFP, and I employ IV estimation to solve

endogeneity. However, I use a di�erent measurement for FDI, production ratio data

from OECD. In some sense, I obtain similar results as in Keller and Yeaple(2009), but

di�erent results in another sense. First, large positive externalities from the a�liates

of multinational companies to host country �rms are observed in Canada. The result

coincides with Keller and Yeaple(2009). Second, I �nd that the large �rms are enjoying

the more positive externalities and the spillovers mainly occur within the low-tech indus-

tries. Interestingly, the results are not consistent with those in Keller and Yeaple(2009).

Lastly, IV estimation produces larger marginal e�ect of foreign �rms on the domestic

�rms' productivity than OLS estimation.

The following section brie�y reviews the previous literature about technology spillovers

associated with FDI. In section 3, I present the estimation framework for horizontal FDI

spillovers. Section 4 gives an overview of the data, and section 5 presents all estimation

results. Lastly, section 6 includes a summary and discusses future work.

2. Literature review

FDI has been emphasized as being a spillover channel both theoretically and em-

pirically. Some papers prove the presence of multinational �rms in the world econ-

omy.(Markusen 1984, Markusen and Venables 1998). Since �rm-speci�c activities such

as R&D, advertising, marketing, and management services have a characteristic of pub-

lic goods, MNEs does not need to duplicate them in a plant operation. Hence, they

take an advantage of the economies of multi-plant operation and market expansion by

establishing new a�liates in other countries. Also, Rodriguez-Clare(2007) argue that

the gains from openness, which includes not only trade but di�usion of ideas, are much

higher than the gains from only trade. In other words, there is another channel through

which countries interact and it has large positive impact on the economy. FDI could be

one of the potential channels.

Some theory papers on FDI spillovers model channels of spillover from multinational

�rms to domestic �rms. For example, multinationals give access to new specialized

intermediate inputs(Rodriguez-Clare 1996), they source intermediate inputs from local

suppliers whose productivity has been raised to meet the request of the higher stan-

dards of the multinational �rms, or domestic workers who have trained in the MNE are

attracted to domestic �rms(Fosfuri, Motta, and Ronde 2001).

There are a number of case studies on the FDI spillovers. These studies present mixed

evidence on the role of multinational �rms in generating technology transfer to domestic

�rms. In Bangladesh, the entry of foreign �rms led to a economic boom, in particular, in

the industry of textiles(Belot and Rhee 1989). However, the case study for 12 developing

countries found almost no evidence of technology transfer to local �rms.

An increasing number of authors have attempted to go beyond qualitative case studies.

A large body of empirical papers have examined mechanisms of international technology

di�usion and the role of each mechanism contributing to the FDI spillovers. However,

there is no general agreement on the technology spillovers through FDI. Among these

studies, Aitken and Harrison(1999) �nd that a foreign presence reduces productivity of

domestically-owned �rms by the so-called market-stealing e�ect. They point out the

identi�cation problem of previous studies because foreign investment might gravitate

toward more productive industries and the observed estimated coe�cient on the presence

of foreign �rms might be overstated. In order to �x this problem, Aitken and Harrison

re�ect the heterogeneity in productivity across industries by including industry-speci�c

�xed e�ects. Using panel data on Venezuelan plants, they get results of positive own-

plant e�ect, that TFP increases with the foreign equity participation within the �rm,

and negative horizontal spillovers within the industry, that productivity of domestic �rm

declines when foreign investment increases within the same industry. Gri�th, Redding,

and Simpson(2003) adopt an establishment's distance from the technology frontier as an

explanatory variable in an empirical speci�cation to measure the potential for technology

transfer. Even though the technology frontier is de�ned as an establishment with the

highest level of TFP within an industry in the UK regardless of whether it is foreign

or domestic, most of the technology frontier is taken up by foreign �rms. As a result,

the further is the distance from the technology frontier, the greater is the speed of

technology transfer, and foreign multinationals play an important role in the technology

transfer by pushing the technology frontier out and so increasing the speed of convergence

to the advanced technology. Ramondo(2009) investigates whether the increase in the

productivity of domestic �rms is due to the reallocation of production toward better

plants or to an increase in productivity of incumbent domestic �rms. Ramondo tests

the Melitz-type model with a panel data of the Chilean manufacturing sector and �nds

new evidences on foreign plants: The exit probability of a domestic plant has a positive

correlation with the foreign plant's entry, the exit from the market is more likely for

less productive domestic �rms, and there are positive spillovers from foreign to domestic

survival plants in the same industry. Lastly, Gorg, Hijzen, and Murakozy(2009) examine

whether the positive spillover e�ects or the negative competition e�ects dominate the

other one using panel data for Hungary. They conclude that �rms that relocate labor-

intensive activities to Hungary are unlikely to generate positive productivity spillovers

while productivity spillovers potentially increase in capital-intensive foreign a�liates,

that spillovers di�er between small and large domestic �rms, and that foreign presence

tends to a�ect the productivity of domestic �rms negatively whenever they compete in

the domestic market.

Summarizing, there are no common results for the FDI spillovers. Also, the evidence

for strong positive technology spillovers associated with FDI is insu�cient. This might

imply that the substantial subsidies to multinationals to facilitate technology transfer to

domestic �rms can not be justi�ed. I now return to my analysis to answer this question

with Canadian data.

3. Estimation framework

Some theoretical papers have suggested various mechanisms through which multina-

tional �rms can provide positive externalities for host country �rms. However, since

there is no consensus on which one is the most powerful, I take a broad view on how

multinational �rms a�ect the productivity of domestic �rms. The estimation is con-

structed to answer whether productivity of domestic �rm is higher in industries in which

foreign �rms are active. I will estimate the following equation to do so:

tfpijt = βX + γ1FDIjt + γ2IMPjt + εijt (1)

Here, tfpijt is a measure of total factor productivity of �rm i which belongs to industry

j at time t. FDIjt denotes a measure of foreign �rms in the industry to which �rm

i belongs at time t, and IMPjt analogously represents �rms i's exposure to industry

imports. In addition, I include control variables to better isolate the e�ect of foreign

�rms on the productivity of domestic �rms. X represents a vector of control variables,

for example, capital-labor ratio, �rm mark-up, industry mark-up, and market shares.

Lastly, ϵijt is a mean-zero error term.

There are various methods to calculate total factor productivity, but I adopt the

method that Keller and Yeaple(2009) used in their paper. They rely on the Olley-Pakes

method to avoid the problem of the simultaneity of input choice and selection bias.

Olley-Pakes(1996) develop a framework for dynamic industry equilibrium in which they

model optimal choice of sales and investment and include entry and exit decision of the

�rm. Due to the two aspects of the Olley-Pakes method, the simultaneity of input choice

is solved because input demand increases as productivity rises, and selection bias is also

considered because the �rms with very low productivity exit the market according to

their liquidation decision. Keller and Yeaple(2009) estimated the production function

elasticities in the following equation based on the work of Olley-Pakes:

tfpit = yit − βOPk kit − βOP

l lit − βOPm mit (2)

Here, yit denotes the logarithm of output of the �rm i at time t, and kit, lit, and mit

are the �rm i's logarithm of capital, labor, and materials, respectively, in the period

of t. Also, βOPk , βOP

l , and βOPm are the Olley-Pakes estimates of capital, labor, and

materials elasticities. As a result, they are computed as 0.188, 0.301, and 0.594 for each

elasticities, and they imply increasing returns to scale. For my sample of industries and

�rms, increasing returns is a plausible deviation from constant returns.

My measure of imports is the ratio of Canadian imports(denoted by m) to imports

plus total shipment of the industry(denoted by d) minus exports(denoted by e) to which

the �rm belongs.

IMPjt =mjt

mjt+djt−ejt(3)

for each period t, and industry j.

The most important variable, FDI, is de�ned as the share of the foreign-owned a�l-

iates' production(denoted by f) in foreign a�liates' production plus production of the

domestic �rms(denoted by d) by industry j to which �rm i belongs.

FDIjt =fjt

fjt+djt(4)

These two measures of imports and foreign direct investment show penetration of

foreign �rms in Canadian industry. If imports contribute to the international transfer

of the advanced technology, or if foreign a�liates have positive externalities for the

domestic �rms by vertical linkages, worker turnover, or advanced intermediate inputs

provided by foreign �rms, the coe�cient on imports and FDI would show higher positive

values. However, those estimates do not necessarily imply the causal relationship between

imports and FDI and domestic �rms' productivity. The multinational �rms might be

attracted to open their a�liates in a country in which the productivity of the industry

is relatively higher because they might be able to save transport costs by procuring

advanced intermediate inputs from the local suppliers, not from their parent �rms, and

also hire local workers who have been trained for higher labor productivity. Because of

this endogeneity problem, we can not conclude the import and FDI spillovers from the

above equation. Hence, I will employ instrumental variable estimation below to solve it.

In order to better isolate the FDI spillovers, several variables are included in the

estimation equation. First, the capital-labor ratio is considered because it might be

associated with imports and FDI, and also it might signi�cantly a�ect the productivity

of the �rms. Next, the �rm mark-up, industry mark-up, and markets share are included

in the control variables. Aitken and Harrison(1999) argue that the presence of foreign

�rms changes the degree of market-competition by stealing the local �rms' shares. Also,

�rms with higher �rm mark-up relative to the industry mark-up, and larger market

shares are under less pressure of the productivity growth, so those �rm's productivity

might be relatively low. To capture the changes in the market competition from presence

of multinational �rms and impact of competitiveness on the productivity, �rm mark-up,

industry mark-up, and market shares are considered here. Firm mark-up is de�ned as

�rm sales over sales minus pro�ts, the industry mark-up is similarly de�ned as �rm mark-

up at the industry level, and market share is de�ned as �rm sales over total industry

sales.

4. Data

This study is based on the sample of manufacturing �rms in Canada from Standard

and Poor's Compustat database. The Compustat database includes publicly traded

companies, more importantly, most of large Canadian �rms. It means that the data

covers a signi�cant portion of Canadian economic activity. The sample consists of 1,530

Canadian domestic �rms that were operating between 1994 and 2005 after a great deal

of data cleaning. From the Compustat database, I obtain data on each �rm's output,

labor, materials, and capital inputs. First, the output is measured by net sales and it is

de�ated by industry-level price indexes from Statistics Canada. The labor is measured by

number of employees, and capital is measured by value of property, plant, and equipment,

net of depreciation. Lastly, materials follow the de�nition of cost of goods sold plus

administrative and selling expenses less wage expenditures, where wage is calculated by

multiplying number of employees with average industry wage. The former comes from

Compustat, while the latter is obtained from Statistics Canada.

Also, a great deal of data is supported by Statistics Canada. Statistics Canada is

a very good source to obtain a variety of data on the Canadian economy. Since they

classify the data by various criterion, it is easy to �nd data sets which are suitable for

our purpose. I obtain data on industry price indexes, average industry wage, total hours

of production workers, total industry sales, and total shipments classi�ed by four-digit

NAICS from Statistics Canada. They are used to de�ate output, and to compute wage

expenditures, capital-labor ratios, market shares, and import ratios, respectively.

The remaining data for import ratios, exports and imports, come from Industry

Canada. Industry Canada provides custom-based statistics on international trade in

goods, total imports, total exports, and trade balance. I obtain data on exports and

imports expressed in U.S. dollars for 76 industries classi�ed by four-digit NAICS for the

years between 1994 and 2005 from Industry Canada.

My primary interest is whether domestic �rm's productivity is related to the presence

of foreign �rms within the same industry. Hence, how to measure the presence of foreign

�rms in each industry is very important. The previous empirical papers for multina-

tional �rms have adopted the employment ratio, number of employees hired by foreign

�rms divided by total number of employees in the industry, or production ratio, goods

produced by foreign �rms divided by total goods in the industry, to measure the degree

of penetration of the foreign �rms in each industry. I use production ratios for foreign

presence here. The production ratio data is obtained from the OECD publications of

'Measuring globalization: the role of multinationals in OECD economies, volume 1' and

'Measuring globalization: activities of multinationals, volume 1' and OECD statistics.

Since the data of OECD is classi�ed by ISIC Rev.3, I convert ISIC Rev.3 into NAICS

referring to 'Concordances: 2002 NAICS US to ISIC Rev.3.1' from U.S. Census Bureau

in order to raise compatibility between OECD and previous data.

Lastly, I obtain data on the nominal exchange rate and producer price indexes from

Timothy Kehoe's website and they are used in generating an instrumental variable for

FDI.

Table 1 shows top �ve industries in which multinational �rms are the most active and

the last �ve industries that foreign �rms are the least active over the sample period. For

example, production by foreign �rms captures a large proportion of the total production

in the industry of motor vehicle manufacturing in Canada, while foreign activity in

furniture and kitchen cabinet manufacturing is very low. Also, �gure 1 shows that how

productivity of Canadian-owned �rms and foreign presence have changed over the sample

period. It indicates that the changes in productivity and foreign activity are strongly

correlated with each other in the Canadian economy.

High FDI Low FDI

Motor vehicle manufacturing Machine shops: screw, nut, and bolt

Motor Vehicle Body and Trailer Manufacturing Coating, engraving, heat treating, and allied activities

Electrical equipment and component manufacturing Furniture related product

Electric Lighting Equipment Manufacturing Leather and hide tanning and �nishing

Soap, Cleaning, and Toilet Preparation Manufacturing Furniture and kitchen cabinet

Table1. Top 5 industries with the most and the least FDI

Figure1. TFP and FDI changes over sample period

5. Results

This section presents the results of the paper. Before presenting the main results,

I discuss how the data is cleaned according to some criterion. Second, I lay out the

main results of the FDI spillovers in Canada, or OLS results of the main estimation

equation. Third, I report some interesting patterns of the FDI spillovers by �rm size and

technology. Lastly, I employ instrumental variable technique to solve an endogeneity

problem.

The entire data with which I have started out includes about 26,000 observations.

However, only about 1,300 observations are used to investigate the externalities of FDI

after a great deal of data cleaning. I �rst removed non-manufacturing �rms whose �rst

digits of NAICS do not begin with 3. After that, I delete �rms that have missing

values for variables needed for the regression, and I do not �ll in those missing values.

Lastly, I delete outliers according to some criterion. The observations that show negative

�rm or industry mark-up or a market share greater than one are deleted because only

positive mark-ups and market shares less than one are reasonable. In the end, this leaves

1530 observations in 76 industries. The table 2 below shows the descriptive statistics of

the main variables in the estimation equation. The mean of FDI and IMP imply that

almost half of total manufacturing production is produced by foreign �rms and half of

manufactured goods distributed in Canada �ow from other countries. This is consistent

with the fact that Canada is a large open economy.

Variable Mean Std. Dev Min Max

TFP 0.602 1.047 -5.40 3.871

FDI 0.492 0.217 0.112 0.887

Import 0.486 0.250 0.0001 1.482

Table.2 Descriptive Statistics

* p<0.05, ** p<0.01, *** p<0.001Standard errors in parentheses adj. R-sq 0.170 N 1283 _cons 0.0281 (0.138)nimp 0.120 (0.179)fdi 0.636** (0.228)sm 0.00129 (0.00421)fm -0.000873 (0.00118)ms 2.694*** (0.274)cl 0.00618 (0.00507) OLS (1) OLS results

Table 3. OLS results of the FDI spillovers

Table 3 shows the OLS results from the main estimation equation. The regression

equation is given by

tfpijt = β0 + β1CLijt + β2MSijt + β3FMijt + β4SMijt + β5FDIjt + β6IMPjt + ϵijt (5)

Here, while productivity(tfp), capital-labor ratio(CL), market share(MS), �rm mark-

up(FM), and industry mark-up(SM) are �rm-speci�c variables, foreign direct invest-

ment(FDI) and import ratio(IMP) are industry-speci�c variables. That is the reason

why the �rst �ve variables have subscripts of i, j, and t that represents �rm, industry,

and period, respectively, and the last two independent variables have subscripts of j and

t. Also, in the same vein, standard errors are clustered by industry-year combination

because �rms in the same industry are experiencing the same FDI and IMP innovation

in a give year. The standard errors are relatively large implying that there is not likely

to be the dependence of FDI and IMP shocks across �rms.

The OLS results are for the full sample of �rms, and the clustered standard errors are

shown in parentheses. I estimate a coe�cient on FDI of 0.636 and a coe�cient on IMP

of 0.120. However, while the FDI estimate is statistically signi�cant at the standard �ve

percent level, the IMP estimate are not statistically signi�cant. The results are consistent

with Keller and Yeaple(2009) and Rodriguez-Clare(2007) in some sense. Keller and

Yeaple(2009)'s estimation of international technology spillovers to U.S. manufacturing

�rms via imports and FDI shows strong and statistically signi�cant FDI spillovers, but

insigni�cant IMP spillovers. Their estimate of the total e�ect of FDI is 0.516, and

it is somewhat less than that estimated in my estimation. Also, my results line up

with Rodriguez-Clare(2007) that argues that there is another channel of international

technology transfer than trade.

The FDI estimate suggests a large magnitude of the economic impact of foreign

spillovers on productivity growth in Canada. From the FDI estimate obtained here,

and changes of the average production share and productivity over the sample period,

we can see how much the FDI spillovers account for Canada manufacturing productivity

growth for the years of 1994 to 2005. Table 4 shows a brief summary of how to get an

accountability of FDI for the productivity growth. The FDI spillovers account for about

16 percent of Canada manufacturing productivity growth for the sample period. This is

a bit larger than earlier literature, and above all, Keller and Yeaple(2009)'s 13.5 percent

of the accountability of FDI spillovers for the productivity growth.

Turning to the control variables, their signs come in as expected, but they are not al-

ways statistically signi�cant. The relatively large and statistically signi�cant coe�cient

on market share implies that market share accounts for large part of productivity inno-

vation. For the mark-up variables, negative �rm estimate and positive industry estimate

are consistent with �rms with large �rm mark-up relative to the industry mark-up being

under less pressure to attain productivity growth.

year Avg. FDI Avg. TFP Accountability

1994 0.480 0.604

2005 0.525 0.784

△( FDI, TFP) 0.045 0.18 15.93%

Table 4. Accountability of FDI spillovers for productivity growth

I raise one important question for the patterns of the FDI spillovers: does FDI a�ect

equally the small and large �rms in the host country? Both of arguments that support the

strong FDI spillovers among small �rms and among large �rms have been suggested by

the existing literature. The small �rms may bene�t more from multinational �rms in the

same industry since they have the most to learn technologically, or the large �rms may

bene�t more from FDI since they have more ability to absorb the advanced technology.

To investigate this question, I add an interaction term of FDI with an indicator of the

�rm size. The estimation equation is given:

tfpijt = β0+β1CLijt+β2MSijt+β3FMijt+β4SMijt+β5FDIjt+β6IMPjt+β7(FDIjt∗

ind(sizeijt)) + ϵijt (6)

where ind(sizeijt) denotes an indicator of the �rm's size, and the de�nition of the

remaining terms are the same as above. First, I create the indicator of the �rm's size by

ranking log sales and normalizing them by the total number of �rms, for each industry

and each year. The indicator is de�ned on (0,1] with a value of 1 for the largest �rm and

the smallest value for the �rms with the lowest sales. Then, I generate an interaction

term by multiplying FDI with the indicators of the �rm size.

Table 5. shows the results for the above equation. I estimate a negative coe�cient

on FDI and a positive coe�cient on the interaction term. The p-value of the former

is 0.12 which implies statistical insigni�cance at 10 percent level, but it is not a large

deviation from the 10 percent, and the p-value of the latter are very close to zero. These

suggest that the FDI estimate for the largest �rms is 1.5, while that for the smallest

�rms is about -0.452. It can be interpreted that large �rms bene�t from foreign �rms,

but the small �rms are hurt by them. The results are not consistent with the Keller

and Yeaple(2009). Their estimation suggests the opposite results, a positive impact of

multinational �rms on the small �rms, but negative impact on the large �rms. In the

view of that the U.S economy and Canada economy are very similar, these contrasting

results are very interesting and are worth further study.

* p<0.05, ** p<0.01, *** p<0.001Standard errors in parentheses adj. R-sq 0.245 N 1283 _cons -0.0110 (0.139)fdi_size 1.949*** (0.278)nimp 0.229 (0.191)fdi -0.452 (0.295)sm -0.00148 (0.00474)fm 0.000228 (0.000988)ms 2.019*** (0.266)cl -0.000174 (0.00455) Size (1) Differential Spillover Estimates by Size

Table 5. Di�erential spillover estimates by size

The next important question of the patterns of the FDI spillovers is whether FDI

a�ects equally Canada-owned �rms in high-technology industry and low-technology in-

dustry. To answer the question, I divide the entire sample into two groups, referred to as

high- and low-tech industries, according to the criterion de�ned by the U.S. Bureau of

Labor Statistics. The high-tech industries include computer-related or chemical-related

industries. The classi�cation of �rms by technology is di�erent from the previous classi�-

cation by size. Since the former has been done based on the R&D expenditures, the ratio

of the workers with advanced skills to the total number of workers, etc, even small �rms

can be included in the high-tech industries. The high-tech industries cover 17 four-digit

NAICS and 726 observations fall on those NAICS. The remaining NAICS are included

in the low-tech industries, and the corresponding number of observations are 609.

Table 6. shows results for the high-tech and low-tech samples separately. I estimate

very large and signi�cant FDI spillovers for the low-tech industries. The FDI estimate

is 0.98 and it is statistically signi�cant at 0.1% level. However, the FDI estimate for

high-tech industries is not statistically signi�cant. The results suggest that the FDI

spillovers mainly occur in the low-tech industries. It may be associated with the tech-

nology transfer cost. Since the di�culty of transferring know-how and technology is

increasing in technological complexity, �rms in high-tech industries import most of in-

termediate goods that embody their core complex technology from their parent �rms,

and there is little connection with the domestic �rms. In contrast, the �rms in low-

tech industries face relatively low transfer cost of the technology, they source much of

intermediate goods from local suppliers or in their own a�liates. In this sense, foreign

a�liates in the low-tech industries have very active connections with the domestic �rms

and the mechanisms suggested above, for example, vertical linkages or worker turnover,

can be actively engaged in the FDI spillovers.

The results is not consistent with the Keller and Yeaple(2009). They also divide

the entire U.S. manufacturing �rms into two groups, high-tech and low-tech industries,

according to the R&D expenditures. They show that strong positive externalities from

FDI in high-tech industries, and an insigni�cant FDI estimate for low-tech industries.

Again, when considering very similar economic environment of the U.S. and Canada, the

contrasting result is a puzzle.

* p<0.05, ** p<0.01, *** p<0.001Standard errors in parentheses adj. R-sq 0.153 0.233 N 726 609 _cons 0.141 (0.366) -0.0367 (0.156)nimp -0.0535 (0.284) -0.206 (0.183)fdinaics4 0.265 (0.356) 0.980*** (0.258)sm 0.149 (0.152) -0.00116 (0.00427)fm 0.111 (0.0788) -0.00237*** (0.000554)ms 3.085*** (0.654) 2.255*** (0.271)cl -0.00584 (0.00942) 0.0209* (0.00870) High-Tech Low-Tech (1) (2) FDI Spillovers in High-Technology versus Low-Technology Industries

Tables 6. FDI spillovers in high-tech versus low-tech industries

One of the major concerns in the estimation here is an endogeneity problem. The large

positive coe�cient on FDI can be interpreted in two ways. The correlation of FDI with

domestic �rms' productivity could re�ect strong spillovers from foreign multinationals to

Canadian �rms, or it could imply that foreign �rms are attracted to the industries with

higher productivity or to the industries with lower productivity. If foreign �rms are more

likely to invest in industries with higher productivity, the FDI estimate would be biased

upward. On the contrast, if foreign �rms are mainly concentrated in the industries with

lower productivity, it would be biased downward. In order to be clear the direction of

causation between FDI and productivity, I employ instrumental variable(IV) estimation.

I choose the real exchange rate interacted with industry dummies as an instrumental

variable for FDI, which is theoretically supported by Froot and Stein(1991). They argue

that the rise in the real exchange rate makes assets become cheaper for foreigners and

increases their real wealth, leading to an increase in investment of foreigners.

Based on their argument, I �rst generate interaction terms by multiplying the real

exchange rate with industry dummies, and regress FDI on control variables, IMP, and

the interaction terms because each industry responds to the changes in real exchange

rate di�erently. The foreign presence in some industries is more likely to be sensitive to

the changes in real exchange rate, and some industries do not show sensitive reaction to

them. Therefore, coe�cients on the interaction terms show that how much real exchange

rate a�ect FDI in each industry. Hence, the following equation is estimated:

fdi = β1+β2cl+β3ms+β4fm+β5sm+β6imp+β7exrp313+β8exrp315+· · · β72exrp3399 (7)

where exrp*number denotes the interaction terms generated above and the number

behind exrp indicates each industry. Next, I generate an instrumental variable for FDI

by multiplying the coe�cients on the interaction terms with real exchange rate. They

are all have di�erent values for each industry in a given year.

* p<0.05, ** p<0.01, *** p<0.001Standard errors in parentheses adj. R-sq 0.169 0.170 N 1229 1283 _cons -0.117 (0.171) 0.0281 (0.138)nimp 0.153 (0.184) 0.120 (0.179)sm 0.0235 (0.0354) 0.00129 (0.00421)fm -0.000854 (0.00118) -0.000873 (0.00118)ms 2.733*** (0.280) 2.694*** (0.274)cl 0.00448 (0.00488) 0.00618 (0.00507)fdi 0.826** (0.274) 0.636** (0.228) IV OLS (1) (2) Instrumental Variable Estimation

Table 7. Instrumental variable estimation

Table 7 shows the IV results. The instrument for FDI is valid because the F-statistic

is much greater than the commonly used threshold of 10 and p-value is almost zero in

the �rst-stage regression. The coe�cient on FDI is positive at 0.826, which is greater

than FDI estimates in OLS speci�cation. Hence, the IV estimation shows clear evidence

of positive spillovers from foreign multinationals to domestic �rms. However, the larger

estimate in the IV estimation than those in the corresponding OLS estimation might

be because of measurement error. I use foreign �rms' production share of the total

production within one industry as an proxy for the knowledge transferred from the

multinational �rms to domestic �rms. If it is an imperfect proxy, it causes measurement

error. Nevertheless, I keep it as my FDI variable because it is associated with the various

mechanisms through which spillovers occur between �rms. Also, the proxy has been used

as FDI variable in many previous literature.

6. Summary and discussion

The question whether the multinational �rms generate positive externalities, or spillovers,

to domestic �rms is very important, in particular, for policy makers because they have

to make a decision whether to spend government money to attract the foreign �rms to

their country. Actually, governments all over the world have o�ered special incentives to

foreign �rms, for example, lower income taxes, income tax holidays, import duty exemp-

tions, and subsidies for infrastructure, based on the assumption that foreign �rms bring

positive spillovers to the host country. However, the econometric evidence on this have

not come to the common conclusion.

In this paper, I revisit the question and estimate international technology transfer to

Canadian domestic manufacturing �rms via FDI between the years of 1994 and 2005.

I �nd that there is a strong positive spillovers from foreign �rms to domestic �rms in

Canadian manufacturing sector. FDI had contributed to about 27 percent on average

of the growth of productivity of Canadian �rms for the sample years. This implies that

foreign �rms play an economically important role in Canada. Also, I �nd some patterns

of FDI spillovers: the larger �rms bene�t more from FDI than smaller �rms, and the

FDI spillovers are more active in low-tech industries.

The results are contrasted to those in the previous literature. In particular, even

though Keller and Yeaple(2009) estimate the FDI spillovers in the U.S. manufacturing

sector, which has very similar economic environment with Canada, they show that the

smaller �rms and �rms in the low-tech industries bene�t more from FDI. However,

the contrasting results of Keller and Yeaple(2009)'s and mine can be all supported by

plausible arguments. First, small �rms bene�t from multinational �rms because they

have the most to learn from MNE. Also, since large �rms have more ability to absorb

the advanced technology, they can enjoy more positive FDI spillovers. Second, high-tech

industries are more likely to have knowledge that foreign a�liates impart on domestic

�rms, so they bene�t more from FDI. In contrast, foreign a�liates in low-tech industries

complete most parts of their goods in the host country due to relatively low technology

transfer cost, and, in this process, they are actively connected with the domestic �rms.

In other words, FDI spillover mechanisms are actively involved here. However, there is no

generalized theory or empirical evidence to show how each of them is working in which

economic environments. It remains to �nd the patterns of FDI spillovers in di�erent

economic environment for the future work. It should be helpful for policy makers when

making a decision about investment in attracting the foreign �rms.

Keller and Yeaple(2009) and my paper show a strong FDI spillovers in the U.S. and

Canada. The next important question is whether the results can be extended to other

countries, in particular to middle- and low-income countries. If so, they can achieve

an economic growth by o�ering various incentives to attract the multinational �rms.

Another important question is that how �rm, industry, and country dimensions together

a�ect international technology transfer. If one country or region has an appropriate

condition for all of three dimensions, the investment in attracting foreign �rms would be

justi�ed. More research needs to be done to better understand which condition would

make the FDI spillovers to be the most positive. It would help poor countries to promote

their economic growth in the globalized world economy.

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