The Worldwide Shift of FDI to Services- How does it …...services FDI flows, such as financial...

0

The Worldwide Shift of FDI to Services- How does it Impact

Asia? New Evidence from Seventeen Asian Economies.

Nadia Doytch

CUNY Brooklyn College

and

Asian Institute of Management (Non-Resident Research Fellow)

September, 2014

Preliminary draft, please do not quote.

Abstract

We productivity spillovers of industry-level FDI on both, the sector of manufacturing and the sector of

services, in seventeen South and East Asian economies Using a dynamic panel GMM methodology, we

find significant productivity spillovers by several industry-level FDI: mining FDI and some specific

services FDI flows, such as financial services, trade, as well as transport and communications FDI. Services

FDI has a two-fold effect on the economy. Whiling having a positive impact of services growth, some

services FDI inflows have a negative effect on manufacturing growth. At the same time manufacturing FDI

has an impact only on its own sector.

-----------------------------------------------

Key Words: Capital flows, sectoral FDI, manufacturing and service growth, GMM.

JEL Classification: F2, F21, F43

1

1. Introduction

Emerging market and developing economies have been competing in attracting foreign

direct investment (FDI) because of its perceived positive productivity spillovers on

receiving countries. For many developing countries FDI stands as the most important

foreign source of financing as well. However, the traditional kind of FDI primarily

absorbed by the manufacturing sector has been gradually supplanted by services FDI with

financial services FDI emerging as one of the most substantial international capital flows

worldwide.

This process "deindustrialization" of FDI began in the 1970s, when services FDI

represented about a quarter of total capital stock and rose to more than 60% in 2007

preceding the Global Financial Crisis. The East Asia and the Pacific Region has been on

a path of steady recovery of FDI since the crisis with countries such as Vietnam,

Thailand, Indonesia and Malaysia completely rebound (Doytch, 2014).

This paper seeks to present new evidence about the growth implications of the

shift of FDI to services in the context of South and East Asia and the Pacific. Do the

sectors of manufacturing and services experience different productivity spillovers form

FDI? Does the effect of the shift of FDI to services depend on the type of absorbing

services industry- financial or non-financial? Different industry FDI transfers different

technology they transfer to the host countries. This technology varies in capital intensity

and “hard/soft” technology mixes. For example manufacturing FDI transfers equipment

and industrial processes, whereas service FDI tends to transfer technical, management

and marketing know-how, organizational skills and information in general.

This study employs a Generalized Method of Moments (GMM) dynamic panel

estimator (Blundell and Bond, 1998) to analyze sector level data on FDI for seventeen

South and East Asian Economies. The study is conducted by examining the growth

implications of the above described industry-level FDI inflows in separately in the sectors

of manufacturing and services, and also on aggregate economic growth. The GMM

estimator employed helps us correcting for endogeneity and omitted variable biases,

while exploring the time-variation of the data. The examined economies are: Australia,

Bangladesh, Brunei, Cambodia, China, Hong Kong, India, Indonesia, Japan, Rep. of

Korea, Lao, Malaysia, Pakistan, Philippines, Singapore, Thailand, Vietnam. Data of FDI

2

these economies is provided by ASEAN Secretariat, UNCTAD, OECD, as well

individual countries central banks. The data for most countries spans 1999-2011.

Although this topic has been explored before (Doytch and Uctum, 2011), this is

the first time a consistent balanced panel data set on Asian Economies is complied and

analyzed. The data set covers FDI absorbed by the sectors of extractive industries,

manufacturing, construction, finance, trade, business services, tourism and transportation

and communications services, in addition to aggregate services, and the aggregate

economy. The growth implications of some of this sectoral FDI have never been studied

before. The current study contributes by re-examining the evidence on FDI productivity

spillovers in Asia with current and significantly more detailed data than existing research.

In summary, we find an overall positive impact of FDI on growth. This positive

impact can be attributed to mining FDI and some specific services FDI flows, such as

financial services, trade, as well as transport and communications FDI. Services FDI has

a two-fold effect on the economy. Whiling having a positive impact of services growth,

some services FDI inflows have a negative effect on manufacturing growth. At the same

time manufacturing FDI has an impact only on its own sector.

The organization of the paper is as follows: section 1 gives a brief literature

review; section 2 some stylized facts about FDI in South and East Asia and the PAcific

region; section 3 describes the model, the data, and the empirical methodology; section 4-

the empirical results and then we conclude.

1. Brief Literature Review

A current systematic in-depth analysis of the theories underlining occurrence of

FDI can be found in Nayak and Chaudury, 2014). The treatment of FDI in international

finance has been transformed from regarding FDI as a subset of portfolio investment that

allocates fund to an use with highest rate of return (Kindleberger, 1969) to entirely re-

considering FDI in the light of the globalization that accelerated after the 1970s. The new

microeconomic view on FDI, which is not inconsistent the capital market theory,

emphasizes the role of competitive advantage of foreign firms. For a foreign firm to

successfully compete on domestic markets, where is has a number of disadvantages to

local firms, it should posses one of several competitive advantages to survive:

3

technological advantage supported by patents, brands etc., organizational expertise,

marketing channels, economies of scale or an unique source of cheap financing (Hymer,

1976; Caves, 1971; Dunning, 1974; and Nayak and Chaudury, 2014). The institutional

and other risk factors, on the other hand, determine the choice between FDI and

outsourcing. The possession of technological advantage may also be the reason for

internalizing the production and incorporation foreign subsidiaries rather than contracting

with foreign firms (Buckley and Casson, 1976; Nayak and Chaudury, 2014 ). Dunning

(1980) has added to the above two reasons for occurrence of FDI one more- the

existence of location advantages.

A systematic analysis of productivity spillovers from FDI is available in Suyanto

and Bloch (2009). Productivity spillovers may occur, if as a result of the greater

competition domestic firms learn to utilize better their resources (Wang & Blomstrom,

1992); if knowledge spills over to domestic firms via labor turnover (Fosfuri, Motta, &

Ronde, 2001); or if there are demonstration effects, or new R&D innovation (Cheung &

Lin, 2004). Imperfect competition on domestic markets, however, can lead to negative

spillovers from FDI. If a transfer of superior technology lowers cost of production and

represents a positive spillover, in another situation foreign firms may take over a large

share of the product market or the resource markets and lead to productivity decline for

domestic firms declines (Aitken and Harrison, 1999). This is how FDI may bring

negative spillovers.

There are numerous studies exploring empirically the existence of spillovers. At

the firm level, studies have been largely inconclusive. Some case studies indicate limited

positive spillovers of FDI (Haskel, Pereira and Slaughter, 2007, Blalock and Gertler,

2003), and others find no or negative spillovers (Aitken and Harrison, 1999, Gorg and

Strobl, 2001, Lipsey, 2003, 2004). At the macroeconomic level, generally studies have

found positive productivity spillovers (Wei et al. 2001; Bende-Nabende and Ford, 1998).

FDI is viewed as an important source of financing (De Mello 1999; Eller et al. 2005) and

the positive spillovers are conditioned on factors of absorptive capacity (Borenstein, De

Gregorio, Lee, 1998, Balasubramanyam,1999; Blonigen and Wang, 2005; Blomstrom,

Lipsey and Zejan, 1994, Alfaro, Kalemli-Ozcan and Volosovych, 2008).

4

The trade literature also provides an opinion on the existence of productivity

spillovers. They are being viewed as horizontal and vertical transfer of knowledge via

buyer–supplier relationships of MNCs (Egan and Mody, 1992; Hobday, 1995; Radosevic,

1999). With increasing international fragmentation of production (Feenstra, 1998 and

Hummels et al., 2001) the FDI induced spillovers grow in importance.

Specifically in South and East Asia, Hsio and Hsio (2006) have tested the

relationship between GDP, exports, and FDI among China, Korea, Taiwan, Hong Kong,

Singapore, Malaysia, Philippines, and Thailand with a VAR methodology. They have

found that there is no universal rule of Granger causality in the region and instead, the

Granger causality relationships are individual. Petri (2012) shows that the intra-Asian

FDI flows, in contrast to other FDI flows, tend to go to economies with low technological

achievements and strong property rights, which facilitates technology flows among these

nations.

2. Stylized Facts and literature review

Please, see Appendix 1 Fig 1-12.

3. Model, Methodology and Data

The conceptual model for this study is based on classical growth theory and tests

the hypothesis of conditional convergence1:

ittiti Wyy Γ++= − )log()1(log 1,β (1)

Subscripts i and t describe the cross-sectional and time dimensions of the panel

data, respectively; �� - output the per capita of country i; iW is a vector containing the

log of the traditional growth determinants (technological progress, human capital,

physical capital and natural resources) and more recently developed determinants, such as

FDI and institutional factors. )1( β+ is that the parameter estimate that captures

convergence. Literature suggests that it should be negative, reflecting that fact that

countries that are further away from their steady-state level of output should grow faster

than those closer t to their steady-state levels.

1 See Islam (1995), Caselli, Esquivel and Lefort (1996), Durlauf and Quah (1998), Durlauf, Johnson and

Temple (2004).

5

The conceptual growth model translates into the following empirical model:

iti

tj

itit

k

ti

k

it fxyy εµηβββββ +++++++= − 4321,10 )log()1(log , (2)

),0(..~i

diii µσµ ),i.i.d.(0,~ εσε it .0][ =itiE εµ

where i= 1,..,17 and t= 1,..,12, the superscript k stands for a GDP index (k= GDP,

manufacturing value added, and services value added), the superscript j is an FDI index

(j= manufacturing FDI, service FDI, financial FDI, and nonfinancial service FDI).

Accordingly, k

ity is real per capita output in sector k, in constant year 2005 prices, k

tiy 1, − is

the lagged level of per capita output, j

itf is the GDP share of FDI net inflows into the jth

industry. The industries are as follows: (1)- the aggregate economy; (2)- extractive

industry (mining); (3)- manufacturing; (4)- aggregate services; (5)- financial services;

(6)- construction; (7)- wholesale and retail trade (trade); (8)- hotels and restaurants

(tourism); (9)- business services; (10) transport, storage and communications (transport).

The row vector itx consists of the most commonly used control variables in the

growth literature, such as domestic investment share of GDP, schooling (gross secondary

school enrolment ratio) as a proxy for human capital, natural resources rents share of

GDP (as a proxy for natural resource endowments); the Government Stability ICRG

Index and the Anti-Corruption ICRG Index, used interchangeably as proxies for

institutional quality. The variables iµ and tη are, respectively, a country-specific and a

time-specific effect. The combinations between output k indexes and the FDI j indexes

with a change of the institutional control variable translate into 60 different regression

models.

The method of the dynamic panel GMM estimator, known as Blundell-Bond

system GMM (Blundell and Bond, 1998; Arrelano and Bover, 1995) is designed to

capture the joint endogeneity of some of the explanatory variables through the creation of

a matrix of instruments based on lagged level observations and lagged differenced

observations. The estimator also has a matrix of instruments to deal with endogeneity of

lagged dependent variable and the induced MA(1) error term. This methodology has been

successfully applied in economic growth context in a number of studies (Caselli et al.,

1996; Easterly et al., 1997; Levine et al., 2000; Doytch and Uctum, 2011).

6

The necessary conditions for the system GMM are: a) that the error term does not

have second order serial correlation and b) even if the unobserved country-specific effect

is correlated with the regressors’ levels, it is not correlated with their differences:

a. The standard GMM conditions of no second order autocorrelation in the error term

0)]([ 1,, =− −− tiit

k

stiyE εε for s≥2 and t=3,….T

0)]([ 1,, =− −− tiitstixE εε for s≥2 and t=3,….T

0)]([ 1,, =− −− tiit

j

stifE εε for s≥2 and t=3,….T; (3)

b. Additional conditions of no correlation of the unobserved country-specific effect with

their differences:

0)])([( 2,1, =+− −− iti

k

ti

k

ti yyE εµ

0)])([( 2,1, =+− −− itititi xxE εµ

0)])([( 2,1, =+− −− iti

j

ti

j

ti ffE εµ (4)

The regressions in this study are run with a minimum number of lags in the

instrumental matrix to preserve degrees of freedom.

The dependent variables - real per capita GDP, manufacturing value added, and

services value added are compiled from World Development Indicators (WDI).

Manufacturing refers to industries belonging to International Standard Industrial

Classification (ISIC), revision 3, divisions 15-37. Services correspond to ISIC divisions

50-99. Services include value added in wholesale and retail trade (including hotels and

restaurants), transport, and government, financial, professional, and personal services

such as education, health care, and real estate services2.

Gross domestic investment share of GDP is gross fixed capital formation share of

GDP, complied from World Development Indicators (WDI) that consists of plant,

machinery, and equipment purchases, construction of roads, railways, and the like,

including schools, offices, hospitals, private residential dwellings, and commercial and

industrial buildings, land improvements (e.g., fences, ditches).

2 Services also include the imputed bank service charges, import duties, as well as any statistical

discrepancies.

7

Gross secondary school enrollment ratio is the ratio of total enrollment,

regardless of age, to the population of the age group that officially corresponds to the

level of education shown.

Natural resources Rents share of GDP are also complied from WDI. The

indicator includes rents generated by coal, forest, mineral, natural gas, and oil resources

Estimates based on sources and methods described in "The Changing Wealth of Nations:

Measuring Sustainable Development in the New Millennium" (World Bank, 2011). We

hypothesize natural resources endowments are factor of economic growth that can have

both a positive and a negative impact on the economy. Potential negative impact may be

realized in the case of a "natural resource curse" situation.

Government stability and Anti-corruption that are ued interchangiby in the models

are compiled by the International Country Risk Guide. Government stability is defined to

have three components consisting of government unity, legislative strength and popular

support. The index, which ranges 0-12 assesses how well the government can carry out

its declared programs and can stay in the office. Anti-corruption, on the other hand, refers

to atual or potential corruption in the form of excessive patronage, nepotism, job

reservations, 'favor-for-favors', secret party funding, and suspiciously close ties between

politics and business. These sorts of corruption are potentially of great risk to foreign

business in that they can lead to popular discontent, unrealistic and inefficient controls on

the state economy, and encourage the development of the black market.

All FDI series are net inflows, accounting for the purchases and sales of domestic

assets by foreigners in the corresponding year. They are taken in proportion to GDP. The

sources for this variable are individual central banks web sites, United National

Conference on Trade and Development (UNCTAD) Statistics, ASEAN Statistics, and

OECD statistics.

FDI is defined by OECD Stat. as an investment that “reflects the objective of

obtaining a lasting interest by a resident entity in one economy (‘‘direct investor'') in an

entity resident in an economy other than that of the investor (‘‘direct investment

enterprise'')” (OECD, International direct investment database, Metadata). Direct

investment involves both the initial transaction between the two entities and all

subsequent capital transactions between them and among affiliated enterprises, both

8

incorporated and unincorporated (OECD, International direct investment database,

Metadata). A direct investment enterprise is define as an incorporated or unincorporated

enterprise in which a foreign investor owns 10 per cent or more of the ordinary shares or

voting power of an incorporated enterprise or the equivalent of an unincorporated

enterprise. A direct investment enterprise may be an incorporated enterprise - a

subsidiary or associate company - or an unincorporated enterprise (branch) (OECD,

International direct investment database, Metadata).

4. Empirical results

The empirical results are presented in six tables (Table 1-6). Tables 1-3 present

the results respectively of regressions of GDP per capita growth, manufacturing value

added per capita growth and services value added per capita growth with an institutional

control variable- government stability index from ICRG. Tables 4-6 present the results of

the same tee regressions controlling for anti-corruption. Each table consists of ten

columns presenting results of models with a different sectoral FDI inflow starting with

total FDI, and continuing with mining, manufacturing, aggregate services, financial

services, construction, trade, tourism, business services, and transport and

communications FDI. The rows of each table display model coefficients estimates of the

explanatory variables.

Productivity spillovers from FDI to domestic firms are attributed to transfer of

superior technologies from foreign to domestic subsidiaries’. They can, however, be

either positive or negative (Aitken and Harrison, 1999). Negative spillovers can occur, if

FDI in one industry depletes resources, such as skilled labor from another (Doytch and

Uctum, 2011). In this study, we find both positive and negative spillovers from FDI.

One of the robust results is the significance of domestic investment as a

determinant of growth, both at aggregate and sector level (Tables 1-6). This result is

expected and predicted by classical growth theory. Some of the more curious result relate

to the natural resource endowment, the schooling, and the institutional control indicators.

When aggregate growth is considered (Table 1 and 4), the natural resource

endowments proxied by natural resources rents, appear to have either neutral or non-

robust to the type of FDI flow negative impact on growth (Table 1, columns 1 and 9; and

9

Table 4, columns 1 and 8)3. When manicuring and services growth are considered

separately, natural resource endowments appear to be associated with a positive effect on

services growth rather than on manufacturing growth (Tables 2-3 and 5-6). The services

regressions controlling for mining FDI (Tables 3 and 6, column 2), total services (Tables

3 and 6, column 4), financial services (Table 6, column 5) and business services FDI

(Table 6, column 9) produce significant positive effects. At the same time natural

resources endowments show a negative impact on manufacturing growth in models with

business services FDI (Tables 2 and 5, column 9).

The inconclusive result about the contribution of natural resources to growth is

consistent with the conflicting hypotheses known from growth theory- on one hand

natural resources are a kind of capital and as such they are expected to contribute to

growth; on the other- natural resources can stall growth through a resource curse

mechanism that leads to over-appreciation of domestic currency and under exporting and

under investing in human capital growth. Thus, the impact of natural endowments can

either way.

Similarly to the natural resources result, the schooling variable also displays more

positive results in services growth rather than manufacturing growth regressions. The

gross secondary school enrollment ratio is significant in regressions controlling for total

services FDI (Tables 3 and 6, column 4). It is also positive and significant in aggregate

growth regressions with total services FDI (Tables 1 and 4, column 4). At the same time

it is negative and significant in manufacturing growth regressions that control for mining

FDI (Tables 2 and 5, column 2) or services FDI (Tables 2 and 5, columns Table 5,

columns 7-10). Thus, educated labor is more important for services growth than for

manufacturing growth. The fact that in some of the manufacturing regressions schooling

has a negative impact may be because it is more important for manufacturing for the

labor to be cheap than to be highly educated.

Another unexpected result is the lack of robust effect by the two institutional

variables explored- government stability and anti-corruption. Both produce mostly

neutral, but when significant- mixed results (Tables 1-6).

3These are the models controlling respectively for aggregate FDI and either business services or tourism

FDI.

10

Finally, the key explanatory variables of interest - the flows of FDI by industries

produce differential effects on the two explored sectors- manufacturing and services. The

models of aggregate GDP growth, with both government stability and anti-corruption as

controls for institutional quality, are consistent and show positive significant effects of

total FDI, mining FDI, total service FDI, financial service, trade, as well as transport and

communications FDI (Tables 1 and 4, columns 1, 2 4, 5, 7, and 10). When explored by

sectors, the positive effects of the described services FDI can be traced exclusively to

services growth (Tables 3 and 6, columns 4, 5, 7, and 10). The models with the two

different institutional controls yield consistent results for FDI.

To the contrary, FDI in extractive industry tends to slow down growth of the

services sector (Tables 3 and 6, column 2). Thus, the positive impact of mining FDI on

overall GDP growth cannot be traced to the services sector. There is no evidence in our

results that it is due to the manufacturing sector either (Tables 2 and 5, column 2).

Therefore, we suspect that it could be due to an impact on the primary sector.

Finally, the FDI flow that impacts positively the manufacturing sector is

manufacturing FDI (Tables 2 and 5, column 3). However, manufacturing growth is

impacted negatively by aggregate services FDI (Tables 2 and 5, column 4), and more

specifically business services FDI (Tables 2 and 5, column 9). This result is consistent

with previous studies on non-financial FDI (Doytch and Uctum, 2011) and casts more

light on which specific services flows acts as a de-industrialization factor in the Asian

economies. The hypothesis of this negative impact of business services FDI is that it

drains resources from manufacturing sector and therefore hurts manufacturing sector

growth.

6. Conclusion

This paper seeks to present new evidence about the growth implications industry-level

FDI with an emphasis on FDI in the services sector. It establishes productivity spillovers

form industry-level FDI in the region of South and East Asia and the Pacific, using a

GMM estimator.

In summary, we can say that this study that is based a total of sixty models, allows

us to differentiate between the effects of FDI at the industry level. We find a rich set of

11

results pointing out to an overall positive impact of FDI on growth for the group of South

and East Asian economies examined. The positive impact of total FDI can be mining FDI

and some specific services FDI flows- financial services, trade, as well as transport and

communications FDI. All of the described three flows work out their impact on the

aggregate economy via positive impact of the sector of services. Mining FDI, on the other

hand has a positive effect on the economy, although within the services sector its effect is

a negative one.

Services FDI, however, has a "double-edged" effect on the economy. Although it

tends to benefit its own sector, it depletes resources from manufacturing and hurts

manufacturing growth. This is specifically true for business services FDI. However, in

spite of being hurt by business services FDI, the manufacturing sector of South and East

Asian economies gets a boost from its own sector FDI. Although the effect of

manufacturing FDI is not visible at the aggregate growth level, it is significant enough to

spur growth in manufacturing.

12

References

Aitken, B. and Harrison, A. (1999), “Do Domestic Firms Benefit from Direct Foreign Investment?”

American Economic Review, 89, 605–18.

Alfaro, L., S. Kalemli-Ozcan and V. Volosovych (2008) “Why doesn’t capital flow from rich to poor

countries? an empirical investigation”, Review of Economic Studies, forthcoming

Arellano, M. and S. Bond, (1991), “Some Tests of Specification for Panel Data: Monte Carlo Evidence and

an Application to Employment Equations,” Review of Economic Studies, 58, 2, 277-97.

Arellano, M. and O. Bover, (1995), “Another Look at the Instrumental-Variable Estimation of Error-

Components Models,” Journal of Econometrics, 68, 29-51.

Aykut, D. and S. Sayek, (2007), “The role of the sectoral composition of FDI on growth”, in Do

Multinationals Feed Local Development and Growth?, L. Piscitello and G.D. Santangelo (eds.),

Elsevier.

Beck, T., R. Levine and N. Loayza (2000), “Finance and the source of growth”, Journal of Financial

Economics, 58, 1-2, 261-300.

Blalock, G. and P. J. Gertler, (2003) “Technology from Foreign Direct Investment Welfare Gains through

the Supply Chain”. Working paper, Department of Applied Economics and Mangement, Cornell

University.

Blomstrom M., R. Lipsey and M. Zejan (1992) "What explains developing country growth?", NBER

Working Paper No.4132

Blundell, R. and S. Bond, (1998), “Initial Conditions and Moment Restrictions in Dynamic Panel Data

Models,” Journal of Econometrics, 87, 1, 115-43.

Borensztein, E., De Gregorio, J. and Lee, J. (1998) "How does foreign direct investment affect economic

growth?", Journal of International Economics, 45, 115-135

Caselli, F., G. Esquivel, and F. Lefort, (1996), “Reopening the Convergence Debate: A New Look at Cross

Country Growth Empirics,” Journal of Economic Growth, 1, 3, 363-89.

Crespo, N. and M. P. Fontoura (2007), “Determinant Factors of FDI Spillovers- What do we really know?”,

World Development 35, No 3, pp.410-425.

Doytch, N.(2005), “Capital flows and growth: a survey of the empirical evidence”, CUNY Graduate

Center, Unpublished manuscript.

Durlauf, S, P. Johnson and J. Temple, (2004) Growth Econometrics, Wisconsin Madison-Social Systems,

WP 18.

Durlauf, S. and D. Quah, (1999), “The New Empirics of Economic Growth,” in Handbook of

Macroeconomics, J. Taylor and M. Woodford, eds., Amsterdam: North Holland.

Easterly, W., N. Loayza and P. Montiel (2007), “Has Latin America’s post-reform growth been

disappointing?”, Journal of International Economics, 43, 3-4, 287-311.

Haskel, J. E., S.C. Pereira and M.J., Slaughter, (2007) “Does Inward Foreign Direct Investment Boost

Productivity of Domestic Firms?” Review of Economics and Statistics, 89(3), 482-96.

Islam, N., (1995), “Growth Empirics: A Panel Data Approach,” Quarterly Journal of Economics, 110, 4,

1127-70.

Gorg, H. and E. Strobl, (2001) “Multinational Companies and Productivity Spillovers: A Meta Analysis.”

Economic Journal, 111, 475, 723-39.

Lim E.G., (2001) “Determinants of, and the relation between Foreign Direct Investment and Growth: A

Summary of the Recent Literature” IMF Working Paper No. 01/175.

Lipsey, R. E. (2003) “Foreign Direct Investment, Growth, and Competitiveness in Developping Countries.

In The Global Competitiveness Report, 2002-2003, ed., Peter Cornelius. New York: Oxford

University Press.

___________(2004) “Home- and Host-Country Effects of Foreign Direct Investment”. In Challenges to

Globalization, ed., Robert E. Baldwin and L. Alan Winters. Chicago:University of Chicago Press.

__________ and F. Sjoholm (2005) “The impact of Inward FDI on Host countries: Why Such Different

Answers?” in Does Foreign Direct Investment Promote Development?, Moran, T., Graham, E. and

Blomstrom, M. (eds), Institute for International Economics Center for Global Development,

Washington, DC.

Maddison, A., (1989), The World Economy In the 20th

Century, OECD: Paris.

13

Mankiw, N. G., D. Romer, and D. Weil, (1992), “A Contribution to the Empirics of Economic Growth,”

Quarterly Journal of Economics, 107, 2, 407-37.

Mont,O., (1999) “Strategic alliance between products and services”, Lund University, Unpublished

manuscript.

Mulder, N. (1999) “The economic performance of the service sector in Brazil, Mexico, ad the USA”.

Centre for International Economics, French Planning Agency (CEPII), Unpublished manuscript.

Pritchett, L. (2006), “The quest continues”, Finance and Development, 43, 1, 18-22.

Roodman, D. (2006) “How to do xtabond2: An Introduction to “Difference” and “System” GMM in Stata”,

Center for Global Development, Working Paper No.103.

Rudd, P.A., (2000) “Classical Econometrics” New York: Oxford University Press.

Solow, R., (1956), “A Contribution to the Theory of Economic Growth,” Quarterly Journal of Economics,

70, 1, 65-94.

Triplett J. and Bosworth T., (1999), “Productivity in the service sector”, Unpublished manuscript.

Van Den Berg H, (2001) Economic growth and development, McGraw Hill, NY.

UNCTAD, WIR (2004) The shift toward services (United Nations: New York and Geneva)

UNCTAD, (2003f), UNCTAD Handbook of Statistics (United Nations: New York and Geneva), United

Nations publication, Sales No. E/F.03.II.D.3.

UNCTC (1989a), Foreign Direct Investment and Transnational Corporations in Services (New York:

United Nations), United Nations publication, Sales No. E.89.II.A.1.

World Bank, (2003) World Development Indicators (Washington, D.C.: World Bank).

14

Appendix1: Figures

Fig. 1, 2 & 3:Total FDI by Income groups.

020

40

60

%

2000 2005 2010 2015year

Australia Brunei Darussalam

Hong Kong SAR, China Japan

Korea, Rep. Macao SAR, China

Singapore

Total FDI, High Income Economies

-20

24

6%

2000 2005 2010 2015year

China Indonesia

Malaysia Philippines

Thailand

Total FDI share of GDP, Middle Income Economies

05

10

%

2000 2005 2010 2015year

Bangladesh Cambodia

India Lao PDR

Pakistan Vietnam

Total FDI share of GDP, Low Income Economies

15

Fig. 4, 5 & 6:Manufacturing FDI by Income groups.

-20

24

68

%

2000 2005 2010 2015year




Singapore

Manufacturing FDI, High Income Economies

-2-1

01

23

%

2000 2005 2010 2015year

China Indonesia


Thailand

Manufacturing FDI share of GDP, Middle Income Economies

02

46

%

2000 2005 2010 2015year

Bangladesh Cambodia

India Lao PDR

Pakistan Vietnam

Manufacturing FDI share of GDP, Low Income Economies

16

Fig. 7,8 & 9: Business Services FDI by Income groups

05

10

15

20

25

%

2000 2005 2010 2015year




Singapore

Business Services FDI, High Income Economies

-.2

0.2

.4.6

%

2000 2005 2010 2015year

China Indonesia


Thailand

Business FDI, Middle Income Economies

0.5

11.5

22

.5%

2000 2005 2010 2015year

Bangladesh Cambodia

India Lao PDR

Pakistan Vietnam

Business Services FDI, Low Income Economies

17

Fig. 10,11 & 12: Trade Services FDI by Income groups

02

46

%

2000 2005 2010 2015year




Singapore

Trade Service FDI, High Income Economies

-.5

0.5

1%

2000 2005 2010 2015year

China Indonesia


Thailand

Trade Services FDI, Middle Income Economies

0.0

5.1

.15

.2.2

5%

2000 2005 2010 2015year

Bangladesh India

Lao PDR Pakistan

Vietnam

Trade Services FDI, Low Income Economies

18

Table: Sectoral FDI Data Availability

countries Data Coverage

Australia 1985-2011

Bangladesh 1995-2011

Brunei 1999-2011

China 1997-2008

Hong Kong 1998-2008

India 1995-2010

Indonesia 1995-2011

Japan 1980-2011

Korea, Rep. 1980-2011

Malaysia 1999-2011

Pakistan 1985-2009

Philippines 1995-2011

Singapore 1999-2011

Thailand 1980-2011

Vietnam 1999-2011

Table 1: GDP per Capita Growth, Institutional Control- Government Stability

Independent

variables

(1)

Total

FDI/GDP

(2)

Extractive

Industries

FDI/GDP

(3)

Manufacturing

FDI/GDP

(4)

Total Services

FDI/GDP

(5)

Financial

FDI/GDP

(6)

Construction

FDI/GDP

(7)

Trade

FDI/GDP

(8)

Tourism

FDI/GDP

(9)

Business

FDI/GDP

(10)

Transport

FDI/GDP

Log of Lagged

Real GDP per capita

.998***

(219.24)

1.001***

(258.04)

.993***

(322.15)

.985***

(249.47)

.996***

(266.04)

.995***

(262.46)

.992***

(248.26)

.994***

(450.74)

.998***

(251.47)

.996***

(259.35)

Fixed Capital share

of GDP .002***

(6.19)

.002***

(8.61)

.002***

(6.06)

.002***

(6.37)

.002***

(7.52)

.002***

(7.76)

.002***

(8.21)

.002***

(6.49)

.002***

(7.12)

.002***

(8.03)

Natural Resources

Rents share of GDP

-.0002***

(-2.98)

-.0002

(-1.42)

-.0002

(-1.46)

4.06e-06

(0.03)

-.0003

(-1.13)

-.0001

(-1.01)

-.0002

(-1.00)

-.001

(-1.28)

-.0003***

(-2.84)

-.001

(-1.26)

Gross Sec. School

Enrollment Ratio

(%)

-.0001

(-0.81)

-.0002

(-1.54)

.00006

(0.40) .0005**

(2.36)

-.0002

(-0.82)

-6.61e-06

(-0.04)

.00006

(0.30)

-.00003

(-0.39)

-.0002

(-1.12)

-.0001

(-0.78)

Government

Stability Index

-.0002

(-0.10)

.001

(0.82)

.001

(0.68)

.0007

(0.45)

.001

(0.79)

.0003

(0.20)

.002

(0.96)

.001

(0.87)

.003**

(2.10)

.0006

(0.48)

FDI/GDP

.068**

(2.22)

.021**

(2.22)

.148

(0.93)

.170***

(4.04)

.443*

(1.75)

.160

(0.13)

.455*

(1.86)

-.162

(-0.08)

.025

(0.49)

.235*

(1.65)

Observations 449 248 297 262 239

249

261

86

226

174

Number of countries 18 15 16 15 14

16

16

7

15

9

* Z-values in brackets. Level of significance: *-10%, **-5%; ***-1%.

Table 2: Manufacturing Value Added per Capita Growth, , Institutional Control- Government Stability

Independent

variables

(1)

Total

FDI/GDP

(2)

Extractive

Industries

FDI/GDP

(3)

Manufacturing

FDI/GDP

(4)

Total Services

FDI/GDP

(5)

Financial

FDI/GDP

(6)

Construction

FDI/GDP

(7)

Trade

FDI/GDP

(8)

Tourism

FDI/GDP

(9)

Business

FDI/GDP

(10)

Transport

FDI/GDP

Log of Lagged

Real Manufacturing

Value Added per

capita

.988***

(196.36)

1.0003***

(211.10)

.992***

(282.90)

1.0003***

(188.85)

1.0004***

(241.72)

.995***

(218.36)

1.005***

(177.94)

.996***

(257.19)

.999***

(197.75)

1.002***

(117.04)

Fixed Capital share

of GDP .003***

(6.27)

.004***

(7.21)

.003***

(3.76)

.003***

(5.00)

.004***

(6.35)

.004***

(7.10)

.004***

(5.75)

.005***

(10.87)

.003***

(3.57)

.004***

(5.04)

Natural Resources

Rents share of GDP

.0003

(1.10)

-.0003

(-1.22)

.0002

(0.57)

-.0003

(-1.21)

.0001

(0.32)

.0004

(0.91)

-.0001

(-0.35) -.001**

(0.40)

-.0003

(-1.52)

.002

(1.20)

Gross Sec. School

Enrollment Ratio

(%)

.00007

(0.33) -.0005***

(-3.35)

-.00004

(-0.22)

-.0005

(-1.61)

-.0005

(-1.68)

-.0002

(-1.25)

-.0006***

(-3.31)

-.00008

(-0.42)

-.0004***

(-2.84)

-.0006**

(-2.03)

Government

Stability Index

.0007

(0.22)

.002

(0.75)

-.004

(-1.46)

.002

(0.63)

-.001

(-0.62)

-.004

(-0.89)

-.0003

(-0.12)

-.016***

(-4.89)

.003

(1.32)

-.007**

(-2.85)

FDI/GDP

.033

(0.31)

.018

(0.73)

.681***

(2.99)

-.170*

(-1.86)

.341

(0.46)

-2.133

(-0.92)

-.685

(-1.00)

1.747

(0.39)

-.439***

(-5.69)

-.155

(-0.49)

Observations 380 232 267 232 211

220

232

72

205

148


15

15

6

14

8


Table 3: Services Value Added per Capita Growth, , Institutional Control- Government Stability

Independent

variables

(1)

Total

FDI/GDP

(2)

Extractive

Industries

FDI/GDP

(3)

Manufacturing

FDI/GDP

(4)

Total Services

FDI/GDP

(5)

Financial

FDI/GDP

(6)

Construction

FDI/GDP

(7)

Trade

FDI/GDP

(8)

Tourism

FDI/GDP

(9)

Business

FDI/GDP

(10)

Transport

FDI/GDP

Log of Lagged

Real GDP per capita

.993***

(315.97)

.999***

(268.58)

.994***

(342.17)

.987***

(217.97)

.997***

(331.35)

.997***

(289.98)

.993***

(265.72)

.998***

(213.11)

.997***

(274.39)

.998***

(323.12)

Fixed Capital share

of GDP .002***

(7.00)

.002***

(9.02)

.002***

(5.98)

.002***

(5.46)

.002***

(7.51)

.002***

(6.48)

.002***

(7.74)

.002***

(5.67)

.002***

(7.09)

.002***

(17.72)

Natural Resources

Rents share of GDP

-.0001

(-1.05)

.0003**

(2.21)

.0001

(0.89)

.0001**

(0.97)

.00009

(0.34)

.0001

(0.93)

.0001

(0.81)

.0002

(0.19)

.00003

(0.24)

-.0002

(-0.20)

Gross Sec. School

Enrollment Ratio

(%)

.00001

(0.06)

-.0002

(-1.09)

.00003

(0.26) .0004*

(1.70)

-1.34

(-1.23)

-.00004

(-0.30)

.00006

(0.33)

-.0002

(-1.19)

-.0001

(-0.86)

-.0002

(-1.38)

Government

Stability Index

.003**

(2.03)

.001

(0.69)

.0008

(0.53)

.0004

(0.23)

.0008

(0.45)

.0007

(0.38)

.001

(0.82)

.001

(1.22)

.002

(1.42)

-.0003

(-0.27)

FDI/GDP

.055

(1.52)

-.071***

(-3.76)

.209

(1.16)

.167***

(2.61)

.401*

(1.62)

.232

(0.21)

.530*

(1.87)

-.602

(-0.29)

.027

(0.43)

.372**

(2.12)

Observations 409 248 279 244 226

236

248

83

221

164


16

16

7

15

9


Table 4: GDP per Capita Growth, Institutional Control- Anti-corruption

Independent

variables

(1)

Total

FDI/GDP

(2)

Extractive

Industries

FDI/GDP

(3)

Manufacturing

FDI/GDP

(4)

Total Services

FDI/GDP

(5)

Financial

FDI/GDP

(6)

Construction

FDI/GDP

(7)

Trade

FDI/GDP

(8)

Tourism

FDI/GDP

(9)

Business

FDI/GDP

(10)

Transport

FDI/GDP

Log of Lagged

Real GDP per capita

.997***

(187.81)

1.001***

(248.20)

.996***

(350.86)

.981***

(171.98)

.991***

(317.90)

.996***

(239.47)

.992***

(248.26)

.991***

(511.38)

.994***

(249.92)

.996***

(259.35)

Fixed Capital share

of GDP .002***

(7.33)

.002***

(9.48)

.002***

(5.98)

.002***

(5.47)

.003***

(7.34)

.002***

(8.05)

.002***

(8.21)

.002***

(8.72)

.002***

(6.14)

.002***

(8.03)

Natural Resources

Rents share of GDP

-.0002*

(-1.66)

-.0001

(-0.58)

-.0002

(-1.56)

.0001

(0.60)

.00001

(0.06)

-.0001

(-1.00)

-.0002

(-1.00)

-.001*

(-1.78)

.00004

(0.28)

-.001

(-1.26)

Gross Sec. School

Enrollment Ratio

(%)

-.0001

(-0.66) -.0003**

(-2.07)

.00002

(0.14) .0005**

(2.22)

-.00006

(-0.28)

3.30e-06

(0.02)

.00006

(0.30)

-.0001

(-0.99)

-.0002

(-1.59)

-.0001

(-0.78)

Anti-corruption

Index

-.00006

(-0.02)

.002

(0.54)

-.002

(-0.83)

.005

(1.47)

.008**

(2.56)

-.0008

(-0.20)

.002

(0.96)

.009

(1.33)

.009**

(2.35)

.0006

(0.48)

FDI/GDP

.071**

(2.13)

.011**

(1.12)

.163

(1.12)

.167***

(3.52)

.478***

(3.11)

.158

(0.13)

.455*

(1.86)

-1.034

(-0.39)

.053

(1.02)

.235*

(1.65)

Observations 450 248 298 263 240

249

261

86

226

177


16

16

7

15

9


Table 5: Manufacturing Value Added per Capita Growth, , Institutional Control- Anti-corruption

Independent

variables

(1)

Total

FDI/GDP

(2)

Extractive

Industries

FDI/GDP

(3)

Manufacturing

FDI/GDP

(4)

Total Services

FDI/GDP

(5)

Financial

FDI/GDP

(6)

Construction

FDI/GDP

(7)

Trade

FDI/GDP

(8)

Tourism

FDI/GDP

(9)

Business

FDI/GDP

(10)

Transport

FDI/GDP

Log of Lagged

Real Manufacturing

Value Added per

capita

.989***

(164.10)

1.001***

(176.22)

.994***

(333.10)

1.002***

(151.26)

.999***

(146.74)

.997***

(218.24)

1.005***

(150.37)

.994***

(301.32)

1.00005***

(191.67)

1.008***

(124.12)

Fixed Capital share

of GDP .003***

(7.39)

.004***

(6.69)

.002***

(3.92)

.003***

(5.26)

.004***

(5.96)

.004***

(8.40)

.004***

(6.59)

.005***

(10.26)

.003***

(3.04)

.004***

(6.08)

Natural Resources

Rents share of GDP

.0003

(1.24)

-.0001

(-0.80)

-.0001

(-0.61)

-.0003

(-1.39)

.0001

(0.54)

.0001

(0.41)

-.00005

(-0.29) -.001*

(-1.84)

-.0001

(-0.45)

.003

(1.54)

Gross Sec. School

Enrollment Ratio

(%)

.0001

(0.46) -.0006***

(-3.35)

.0001

(0.52)

-.0004

(-1.32)

-.0003

(-1.09)

-.0001

(-0.82)

-.0006***

(-3.00)

.0006**

(2.46)

-.0005**

(-2.28)

-.0005**

(-1.23)

Anti-Corruption

Index

-.002

(-0.39)

.001

(0.20)

-.011

(-1.61)

-.003

(-0.43)

-.003

(-0.36)

-.006

(-0.67)

-.001

(-.001)

-.036***

(-4.70)

.004

(0.56)

-.012

(-1.14)

FDI/GDP

.047

(0.52)

.007

(0.29)

.652***

(2.97)

-.133**

(-2.11)

.436

(0.68)

-2.193

(-1.00)

-.600

(-0.95)

2.270

(0.42)

-.431***

(-3.38)

-.280

(-0.84)

Observations 381 232 268 233 212

220

232

72

205

148


15

15

6

14

8


Table 6: Services Value Added per Capita Growth, , Institutional Control- Anti-corruption

Independent

variables

(1)

Total

FDI/GDP

(2)

Extractive

Industries

FDI/GDP

(3)

Manufacturing

FDI/GDP

(4)

Total Services

FDI/GDP

(5)

Financial

FDI/GDP

(6)

Construction

FDI/GDP

(7)

Trade

FDI/GDP

(8)

Tourism

FDI/GDP

(9)

Business

FDI/GDP

(10)

Transport

FDI/GDP

Log of Lagged

Real GDP per capita

.993***

(317.04)

.999***

(244.62)

.995***

(346.92)

.982***

(159.76)

.991***

(290.71)

.997***

(274.58)

.988***

(197.44)

.993***

(403.70)

.993***

(301.79)

.995***

(324.82)

Fixed Capital share

of GDP .002***

(7.83)

.002***

(9.36)

.002***

(5.23)

.002***

(4.69)

.002***

(6.83)

.002***

(6.21)

.002***

(6.55)

.002***

(6.80)

.002***

(5.53)

.002***

(16.25)

Natural Resources

Rents share of GDP

.00008

(0.96)

.0004**

(2.58)

.0001

(1.28)

.0002**

(1.24)

.0003*

(1.80)

.0001

(1.32)

.0003*

(1.94)

-.0008

(-0.67)

.00003**

(2.30)

-.0007

(-0.64)

Gross Sec. School

Enrollment Ratio

(%)

-.0001

(-0.69)

-.0002

(-1.50)

-8.10e-06

(-0.05) .0004*

(1.89)

-.0001

(-0.50)

-.00004

(-0.26)

00009

(0.57)

-.0002

(-1.56)

-.0002*

(-1.72)

-.0002**

(-2.01)

Anti-Corruption

Index

.004

(1.48)

.002

(0.54)

.0003

(0.10)

.009*

(1.81)

.011***

(3.03)

.0002

(0.07)

.007

(1.37)

.011

(1.78)

.011***

(3.31)

.005

(1.18)

FDI/GDP

.050

(1.44)

-.078***

(-5.11)

.165

(0.90)

.154**

(2.56)

.328**

(2.11)

.178

(0.16)

.575**

(2.06)

-1.590

(-0.55)

.031

(0.50)

.354**

(2.22)

Observations 410 248 284 249 227

236

248

83

221

164


16

16

7

15

9


The Worldwide Shift of FDI to Services- How does it …...services FDI flows, such as financial...

Documents

Transcript of The Worldwide Shift of FDI to Services- How does it …...services FDI flows, such as financial...