Home > Does the shift of FDI from manufacturing to services accelerate economic growth
Does the worldwide shift of FDI from manufacturing to services accelerate economic growth?
A GMM estimation
study
Nadia Doytch
University of New Haven
and
Merih Uctum
Brooklyn College and the Graduate Center,
City University
of New York
August 2008
Abstract
We
examine the growth effect of manufacturing and service FDI inflows in
their own sector, their spillover to other sectors and the overall economy.
Evidence reveals that manufacturing FDI stimulates activity in manufacturing
in Latin America-Caribbean, Europe-Central Asia, middle to low-income
countries and industrial economies. Service FDI stimulates the
service industries but hurts manufacturing. Financial FDI enhances
growth in South-East-Asia and Pacific, high-income countries and service-based
economies by boosting activity in both sectors. However, nonfinancial-service
FDI drains resources and hurts manufacturing in the same group of countries.
Thus, a shift from manufacturing to service FDI may lead to deindustrialization
if it is spearheaded by nonfinancial FDI.
-----------------------------------------------
Key Words: Capital flows, sectoral FDI, manufacturing and service growth, GMM.
JEL Classification:
F2, F21, F43
*We would like to thank participants at the Midwest Finance Association Meeting, San Antonio, 2008, Eastern Economics Association Meetings, New York, 2007, seminars at the Graduate Center, University of New Haven, and in particular Robert Lipsey, John Devereux, Harvey Gram and Dhaval Dave for many useful suggestion and comments.
1. Introduction
The foreign direct investment (FDI) continues to tantalize researchers and governments because of its anticipated spillovers on economic growth, which make it a stable development engine. Growth oriented governments of emerging economies and developing countries have been competing to entice foreign capital with various attractive schemes. Now FDI stands as the most important foreign financing in these economies. A further recent development in international capital flows is the emergence of service FDI which has been gradually supplanting the traditional manufacturing FDI. The main issue at the present is whether this shift is beneficial to host countries or not.
Research on the economic impact of FDI has been two pronged. The macro approach looks at the cross-country growth effects of FDI and generally finds that foreign inflows overall benefit the host country’s economy. The micro approach examines plant-level productivity effects of FDI on firms in a single country and finds much less clear-cut results. Both approaches have obvious shortcomings. The first one is not able to control for industry-specific differences, which bias the findings and leave many questions unanswered. The second is country specific and therefore does not allow cross-country comparisons or a generalization of the findings. Moreover, none of the existing studies emphasize the growth or productivity impact of an inter-sectoral shift in FDI from manufacturing to services. Last but not least, all of the studies in the literature are based on cross-sectional or panel data analysis and take period averages. We argue that the time dimension of the data is essential in capturing the change in the growth effect caused by such a shift in trends. However, this dimension is entirely lost in the existing studies, which are all static.
In this study we address all of these issues. We examine the growth effect of the shift from manufacturing to service FDI, at the industry level and across countries. We do this by considering the impact of manufacturing and service FDI in both sectors, disaggregating the service FDI into financial and nonfinancial sectors, and by using an econometric methodology that controls for endogeneity, a problem prevalent in time series, while allowing us to preserve the time dimension of the data. We also control for additional effects that may otherwise bias results.
Since industry-specific FDIs differ in the technology they transfer to the host country, it is crucial that the analysis of the growth effects of FDI is conducted at the level of the absorbing sector1
. Moreover, due to a larger variation in capital intensity of production, service industries differ more in their “hard/soft” technology mixes than manufacturing industries which, in turn, requires further disaggregation of service FDI into financial and nonfinancial FDI.
This study is the first comprehensive industry analysis using the largest and the longest data span available (1990-2004 and 60 countries), which lifts the veil from aggregate growth studies. We disaggregate total FDI into manufacturing, services, financial and nonfinancial services and study the industries where growth is affected by different types of FDI flows. We partition the sample according to countries’ development levels, geographical location, and the relative size of the manufacturing and service sectors, and examine the sectoral impact on each sub-sample of a shift of FDI from manufacturing towards services.
The most important drawback of the traditional approach of the cross-sectional time-averaging methodology is that by its nature, it cannot capture the dynamic aspects of a shift in the sectoral flows of FDI. To remedy this, we conduct the analysis with the Blundell-Bond GMM estimator (Arellano and Bover, 1995; Blundell and Bond, 1998), which allows us to exploit both the time series dynamics and the pooled country characteristics of the data while controlling for endogeneity and omitted variable biases.
We find that in Latin America and the Caribbean and in Europe and Central Asia manufacturing FDI enhances aggregate growth by spurring manufacturing output. Aggregate growth in South-East Asia and the Pacific, however, is buoyed by financial FDI, which stimulates activity in both manufacturing and service sectors. Low and middle-income economies benefit from both manufacturing and non-financial service FDI. In contrast, high income countries benefit from financial FDI but are hurt from non-financial service FDI, which reduces growth through its spillovers in manufacturing industry. We also find that manufacturing FDI enhances growth in economies with high manufacturing share, financial FDI affects positively services-based economies, and non-financial service FDI reduces growth in services-based economies through negative spillovers in their manufacturing sectors. Our findings suggest that a rise in service FDI at the cost of manufacturing FDI is not all beneficial and may hurt some economies if the shift is biased towards nonfinancial FDI flows.
The organization of the paper
is as follows. After a brief review of the stylized facts and
the literature review (section 2), in section 3 we describe the model,
the data, and the empirical methodology. In section 4 we discuss
the results and conclude in section 5.
2. Stylized Facts and literature review
The gap between service and manufacturing FDI started to grow in 1970s when service FDI accounted for about a quarter of total FDI stock and continued to widen to the present. Service FDI stock share increased to 49% by 1990 and to 60% by 2002, reaching an estimated dollar amount of 4 trillion. At the same time during 1990-2002, the shares of both agriculture and manufacturing FDI stock have been continuously declining, from 9 to 6% and from 42 to 34%, respectively (UNCTC 1989a, p. 8, UNCTAD, WIR 2004).
The shares of the FDI net inflows (the difference between purchases and sales of domestic assets by foreigners) of FDI by sectors display very similar patterns. During 1990-2004, the share of the service FDI net inflows in the sample of 60 examined countries rose by 11 percent from 44 to 55 percent, while the share of manufacturing FDI net inflows fell by 12 percent from 33 to 21 percent (Figure 1).2
The shift away from agriculture and manufacturing towards services has been a long known phenomenon of the developed world.3
The share of service sector increased from 60 to 70% of GDP in the period 1990-2002 (World Bank, 2003) and in 2001 the service sector accounted on average for 72% of GDP in the developed countries and 52% of GDP in the developing countries (UNCTAD 2003f). Meanwhile the manufacturing sector share shrank in all high-income countries except for Japan from 25 to 20% between 1980 and1998 in a phenomenon sometimes called “deindustrialization”.
A voluminous literature examines the relation between total FDI and aggregate growth.4
Previous studies on
spillover effects of total FDI usually find a positive relation with
growth, if specific conditions such as skilled labor, high wealth and
developed financial markets are met (Borenstein, De Gregorio, Lee, 1998,
Blomstrom, Lipsey and Zejan, 1994, Alfaro, Kalemli-Ozcan and Volosovych,
2008). However, at the microeconomic level where all studies have been
conducted within the manufacturing sector, results are less clear-cut.
Some case studies indicate limited positive spillovers of FDI (Haskel,
Pereira and Slaughter, 2007, Blalock and Gertler, 2003), and other find
no or negative spillovers (Aitken and Harrison, 1999, Gorg and Strobl,
2001, Lipsey, 2003, 2004). Based on this inconclusive findings, Lipsey
and Sjoholm (2005) suggest a need for further industry level research
by arguing that “….the question shifts from how inward FDI affects
every host country and industry to which types of industries and host
countries are affected”. To this day, the only industry level
study we have been able to identify is Aykut and Sayek (2007) who examine
the effects of sectoral FDI on aggregate growth only. Their analysis
has the same drawbacks as the other studies in that it is a static framework
and addresses neither the industry-specific growth effects nor disaggregation
of service FDI.
3. Conceptual framework, empirical methodology and data
Conceptual framework
Productivity spillovers from FDI to domestic firms occur as externalities to the transfer of superior technology from foreign to domestic subsidiaries multinational enterprises (MNE). Due to the cross-country emphasis of our study, we consider the growth effects of different FDI flows in different sectors. As such, our analysis is on horizontal (inter-industry) spillovers. Because of the nature of our data, we are not examining vertical (inter-industry) spillovers, which occur thanks to technological knowledge provided by MNEs through vertical input-output linkages.
A voluntary or involuntary transfer of MNEs nontangible assets to domestically owned firms lowers the average cost curves of the latter and increases their productivity. This is a positive spillover. However, all spillovers are not positive and FDI can sometime harm domestic firms (Aitken and Harrison, 1997). This happens when imperfectly competitive domestic firms face competition from the foreign firm in the same market. The MNE can compete in quantity and capture some of the domestic market. The productivity of domestic firms declines as they move up their new average cost curve and spread their fixed costs over a smaller share of the market. A negative inter-industry spillover can also happen if the MNE that enters one industry drains resources from another industry, mainly in the form of skilled labor, attracted to higher compensations. In this case, the productivity of domestic firms in the other industry falls again because their cost curve shifts out. Both cases of negative spillovers would be translated into lower production, and dampened growth in the industry.
We should note that we use the term “spillover” loosely and do not distinguish between spillovers due to change in factor productivity, knowledge/technology diffusion or scale economies. We term spillover any such externalities that MNEs introduce in the host country, which affect sectoral growth rates.
We model these growth effects following the empirical growth literature based on the neoclassical Solow-Swan, Ramsey-Coopmans-Kass model. Unlike the typical study, we consider the interaction between different flows and sectors. The general representation of the model estimated using panel data (Islam, 1995; Caselli, Esquivel and Lefort, 1996; Durlauf and Quah, 1998; Durlauf, Johnson and Temple, 2004) adds up a time dimension to the cross-section growth equation and thus a dynamic aspect through a law of motion for output:
(1)
where is the growth rate of country i, is a vector containing the log of the “traditional” growth determinants suggested by the Solow growth model, such as population growth rate, technological progress and depreciation rate, human and physical capital (Mankiw, Romer, and Weil, 1992) and includes more recently developed determinants, such as FDI and institutional factors. The variables and are, respectively, a country-specific and a time-specific effect represented by year dummies. The country-specific effect that is most commonly used is a fixed (within-group) effect, because a random effect assumes an independent distribution of the explanatory variables from the individual effects, an assumption that is violated between and.
As we will argue below, the
correlation between the lagged dependent variables and the unobserved
residual is precisely the reason why panel data is to be preferred to
cross-sectional when analyzing growth effects. Cross-section estimates
produce a bias, caused by the correlation between and , which does not
disappear with time-averaging. Thus, if such a correlation exists, the
true underlying structure has a dynamic nature and time-averaging cross-section
techniques introduce a bias that cannot be removed by controlling for
fixed-effects. Therefore, to avoid these pitfalls, we stress the importance
of using the GMM methodology.
Empirical Methodology
The simplest methodology, which is more suitable for static cross-sectional data analysis, is the pooled OLS estimation. However, this method fails to account for the time-series dimension of data since it puts all observations together into a “pool” and creates two major flaws: (i) it fails to account for the unobserved country-specific (fixed) effects that cause an omitted variable bias, which then is picked up by the error term; (ii) it fails to control for the potential endogeneity problem. The correlation between some of the independent variables and country-specific effects is again picked up in the error term.
The method of fixed effects is designed to control for the unobserved country-specific time-invariant effects in the data. However, it corrects for the possible correlation between these effects and some of the independent variables, conditioning them out by taking deviations from time-averaged sample means. The result of applying such a procedure is that the dependent variable is stripped of its long-run variation – an approach that may be inappropriate for studying a dynamic concept. Growth episodes are more similar within than across countries and the within-country variation may not be enough to identify growth effects (Pritchett, 2000a). The lost long-run variation is alternatively captured by the “between” estimator.
A technical consequence of the within transformation is that it increases standard errors by exacerbating any measurement errors. This is especially problematic in the case of data with a small time dimension. Another technical issue is that the within approach is not informative when we deal with variables with little time variation or ones that are not measured frequently enough. Without an instrument, this approach does not address the problem of endogeneity either, and without time dummies it does not control for the unobserved common time effects among countries, which are then mistakenly picked up by a positive cross-sectional correlation. Overall, both cross-section approaches are not a good tool for analyzing a dynamic relationship between variables and where time-averaging is conceptually not sensible.
The most widely used alternative to the within estimation are the methods for dynamic panel estimation. Both dynamic panel GMM estimators- Arellano-Bond difference and Blundell-Bond system GMM are specifically designed to capture the joint endogeneity of some explanatory variables through the creation of a matrix of “internal” instruments. Arellano-Bond difference GMM uses lagged level observations as instruments for differenced variables. Blundell-Bond system GMM uses both lagged level observations as instruments for differenced variables and lagged differenced observations as instruments for level variables. Both estimators have one set of instruments to deal with endogeneity of regressors and another set to deal with the correlation between lagged dependent variable and the induced MA(1) error term.5
A necessary condition for both difference and system GMM is that the error term is not serially correlated, especially of second order, otherwise the standard errors of the instrument estimates grow without bound. For this reason Arellano and Bond (1991) have developed a second order autocorrelation test on which we base our analysis.6
A potential problem of the Arellano-Bond difference GMM estimator is that, under certain conditions, the variance of the estimates may increase asymptotically and create considerable bias if: (i) the dependent variable follows a random walk, which makes the first lag a poor instrument for its difference, (ii) the explanatory variables are persistent over time, which makes the lagged levels weak instruments for their differences, (iii) the time dimension of the sample is small (Alonso-Borrego and Arellano, 1996 and Blundell and Bond, 1998).
An additional necessary condition for the efficiency of the Blundell-Bond system GMM estimator is that, even if the unobserved country-specific effect is correlated with the regressors’ levels, it is not correlated with their differences. The condition also means that the deviations of the initial values of the independent variables from their long-run values are not systematically related to the country-specific effects.
The empirical model that we analyze is:
, (2)
,, and
where is real per capita growth, logis log of the lagged level of per capita, in constant year 2000 prices. 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 non-financial service FDI), and the subscripts i= 1,..,60 and t= 1,..,15 describe, the cross-sectional and time dimensions of the panel data, respectively. The row vector consists of the most commonly used control variables in the growth literature (Doytch 2005) comprising the investment share of GDP, the real lending interest rate, gross secondary school enrolment ratio, government consumption share of GDP and government stability.is a row vector of 15 year-dummy variables and is the FDI net inflows share of GDP.
The combinations between k GDP indexes and j FDI indexes give twelve distinct regressions. We regress three per capita growth rates (aggregate GDP, manufacturing value added and services value added) on four FDI measures (manufacturing, service, financial and non-financial service FDI). In addition, we examine the impact of total FDI on total GDP per capita growth as the thirteenth case, a benchmark most frequently used in the literature. We also conducted the same analysis with dummies instead of country sub-samples. The outcome was less clear-cut and often insignificant since this approach was not able to account for regional heterogeneity within subgroups as explicitly as the sub-samples we use here. Results are available upon request.
Our analysis is based on the Blundell-Bond system GMM with external instruments under two sets of conditions:
(i) The standard GMM conditions of no second order autocorrelation in the error term
for s≥2 and t=3,….T
for s≥2 and t=3,….T
for s≥2 and t=3,….T; (3)
where for external instruments we use log,,,,,, and
(ii) Additional conditions that the unobserved country-specific effect is not correlated with their differences even if it is correlated with the regressors’ levels, i.e. the deviations of the initial values of the independent variables from their long-run values are not systematically related to the country-specific effects:
(4)
The third condition allows using lagged first differences as instruments for levels.
In addition to system GMM estimation, we also ran pooled OLS with and the fixed country effects (FE) model with. We report them in the appendix and refer to these estimates when the system GMM results are not available because of second order autocorrelation in the error of the model.
A problem with System GMM estimator
can arise if the instruments are too many, leading to overfitting of
the model (Roodman, 2006). Unfortunately, there is little guidance in
the literature to determine how many instruments are “too many”
(Roodman 2006, Ruud 2000). A recommended rule of thumb by Roodman is
that instruments should not outnumber individuals (or countries).
We experimented both with different numbers of lags in the instrumental
matrix and results are largely consistent. We present here a set
of results based on the minimum optimum lags, an approach that we selected
to preserve the degrees of freedom.
Data
All variables, except the FDI net inflows, secondary school enrolment ratio and government stability, are from World Development Indicators (WDI), the World Bank web site and the reports of the Economic Intelligence Unit. The secondary school enrolment ratio is compiled from the web site of
,
United Nation Educational, Scientific and Cultural Organization and World Development Indicators (WDI). Government stability series are from the International Country Risk Guide reports. We compiled the FDI net inflow series from various sources, consisting of the Organization for Economic Cooperation and Development web site (all OECD countries), United Nations Conference on Trade And Development country profiles, Statistics of FDI in ASEAN (2005) and government institutions and investment agencies’ web sites7
.
The dependent growth variables - GDP, manufacturing value added, and services value added are percentage per capita annual growth
rates in constant local currency. 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 services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling.
Gross fixed capital formation as a share of GDP 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). According to the 1993 SNA, net acquisitions of valuables are also considered capital formation.
Real lending interest rate is the difference between the rate charged by banks on loans to prime customers and the annual inflation rate, measured by the GDP deflator. The latter is calculated as the ratio of GDP in current local currency to GDP in constant local currency (base year varies by country).
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.
General government final consumption expenditure as a share of GDP is government current expenditures for purchases of goods and services, including compensation of employees, and most expenditure on national defense and security, excluding government military expenditures, which are part of government capital formation.
Government stability is a variable compiled by the International Country Risk Guide. It has three components consisting of government unity, legislative strength and popular support. It assesses how well the government can carry out its declared programs and can stay in the office. It is an index from 0-12, where an increase reflects an improvement.
All FDI series are net inflows, accounting for the purchases and sales of domestic assets by foreigners in the corresponding year, and are in current USD. In order to match the definition of services from WDI, we subtracted FDI in Gas, Water and Electricity production and Construction from FDI in services obtained from other sources. The primary sources for data on FDI by industries are most often specialized investment government boards and agencies and sometimes general statistical agencies or ministries. The choice of the industry data to be compiled and reported is made locally. When the reported data is already aggregated at a certain level, it is difficult, if not impossible, to verify if all industries belonging to a sector are actually included in the data. The only way to assess the data quality is to explore the primary sources. For this study, we have cross-referenced the data with all available sources, including national statistical and investment agencies whenever available.
We divide the sample by geographical
regions and levels of development following the World Bank classifications
(Appendix 1). We group economies as manufacturing-based, services-based
and mixed economies as follows. We compare each observation of
manufacturing and services value added shares of GDP to the 60-country
average for each year. We classify the economy as manufacturing-based
(services-based) if it has both a share of manufacturing (services)
larger than the average for a particular year and
the share of services (manufacturing) smaller than the average for the
same year. Otherwise, the economy is defined as mixed.
4. Empirical results
Disparities within the service sector, combined with the differences across sectors make the analysis of total FDI growth effects opaque and misleading, since much of the FDI influence is revealed only at a sub-sectoral level. This could be the reason why previous studies on spillover effects of total FDI find “mixed evidence” and “no universal relationships” (Lipsey, 2004) and that “…studies do not individually find that wage or productivity spillovers do not exist. Mostly they find either positive or negative spillovers”(Lipsey and Sjolholm, 2005).
To address these concerns, in the following section we analyze the industry growth effects of sectoral FDI flows (manufacturing, services, financial, and nonfinancial services). In doing so, we consider own (or intra)-industry effects, intersectoral and aggregate growth effects of these flows, and distinguish between unbalanced sample that spans the period 1990-2004 and balanced sample covering the period 1998-2004.
We present in Tables 1 and 2 the results for the estimates of, the growth effects of FDI flows. All tables show the estimates from the GMM approach. The tables are divided into three panels corresponding to three ways of classifying the data: geographical regions, income groups, and relative sector shares. Table 1 shows the results for total FDI and manufacturing FDI. Table 2 displays the estimates of for aggregate service FDI and its components, financial and nonfinancial service FDI.
To save space, we do not report
the full regression results. However, to give an overall view
of the estimated regression equation, we provide in Appendix 2 the GMM
results for the benchmark model describing the aggregate growth-total
FDI relation and the remaining independent variables and in Appendix
3 Tables 3A-3D, we present the growth effects obtained by POLS and FE
approaches.
1) The effect of total FDI on real GDP per capita growth.
We start from the most general
level of aggregation by looking at the total FDI effect on overall growth
in ‘all countries’ (Table 1, top left cell). Both the unbalanced
(1990-2004) and the balanced (1998-2004) data panels reveal a significant
effect of total FDI on overall growth in the full sample. A 1 percent
rise in FDI flows increases growth by 0.15 to 0.18 percent. The
results from the GMM are confirmed by POLS and FE in both data panels,
with the three methods producing similar estimates (Appendix 3, Table
3A). This effect is mimicked in ECA, SEAP, high-income economies and
mixed economies (first column). Surprisingly, we find a negative
impact of total FDI on overall growth in the services-based economies.
We further inspect this finding below when we break down the FDI flows
into their sectoral components and examine their inter-industry growth
effects.
2) Growth effects of manufacturing FDI.
First consider the aggregate growth effect of manufacturing FDI flows (Table 1, last column). The most striking result with manufacturing FDI flows is their significant impact on growth rates in most categories and across industries, in contrast to service FDI as we will see later. Although for ‘all countries’ sample the estimate of the aggregate growth effect of manufacturing FDI is inconclusive due to second order autocorrelation (top right cell), significant estimates obtained from POLS and FE suggest a positive impact (Appendix 3, Table 3A). This impact is mostly noticeable in the aggregate growth of the LAC region. Aggregate growth in low and middle-income groups as well as in countries with large manufacturing bases also benefits from manufacturing FDI flows more than proportionally.
How does this effect permeate through manufacturing and service sectors? Evidence points to strong own industry effect in several geographical regions and to positive spillovers through the service sector in different categories (2nd column). Manufacturing FDI stimulates growth in manufacturing industry in LAC in both balanced and unbalanced samples and in ECA over the unbalanced sample. Yet, we also observe some spillovers when we look at the income distributions. Manufacturing FDI helps growth in manufacturing sectors in low income economies, but also spills over to service industry in middle-income economies and countries with relatively large manufacturing sectors (3rd column). The inter-industry spillovers of manufacturing FDI are thus stronger in less wealthy economies and manufacturing based economies.
3) Growth effects of service FDI
The analysis of growth effects of service FDI reveals some unexpected and surprising results, reflecting the complexities of inter-industry connections (Table 2, left panel). First, in contrast to manufacturing FDI, growth effects of these flows are unsystematic and some have negative cross-industry effects, as indicated by significant negative entries in various categories. The fall in growth following the entry of the MNE is an illustration of domestic firms’ cost curves shifting out, possibly as a result of resource drain by foreign firms. Furthermore, both positive and negative growth effects are mostly inter-industry rather than intra-industry, i.e. in the service sector.
The aggregate growth effect of service FDI is mostly insignificant, largely due to conflicting intersectoral effects that cancel each other’s impact. In the full sample (all countries), evidence suggests an unambiguous positive effect of service FDI in its own sector (top 2nd cell, left panel). A 1 percent rise in service FDI flows increases services output by 0.2 percent. This sharply contrasts with the significant negative spillovers reducing manufacturing output by 0.45 percent (top 1st cell, left panel).
The positive intra-industry
impact of service FDI in ‘all countries’ is reproduced primarily
in SEAP, middle income economies, and economies with relatively small
services share (2nd column, left panel). The
positive aggregate growth effect of 0.25 in SEAP suggests that service
sector growth generated by service FDI flows partially offsets the contraction
in the manufacturing caused by the same flows (top left panel).
The performance of firms operating in service and manufacturing sectors
in mixed economies is enhanced by service FDI and lead to a positive
aggregate growth effect (bottom left panel). Beyond these results,
evidence for LAC, ECA, manufacturing and services-based economies is
weak or negative, which call for further investigation of service flows.
In the next section we turn to disaggregation of the service sector
into its financial and nonfinancial components.
4) Growth effects of financial and non-financial service FDI.
A comparison of middle and right panels with the left panel in Table 2 gives a remarkable insight into the way service FDI works through to the economy. First, financial FDI contributes positively to aggregate growth in most categories (middle panels), while non-financial FDI’s contribution is predominantly negative often within the same categories (right panels). This negative impact accounts for most of the negative spillover of aggregate service FDI flows on manufacturing output. Second, although financial and non-financial FDI are components of service FDI, most of their influence spreads out to the economy via the manufacturing instead of the service sector.
We observe a strong aggregate growth effect of financial FDI, which is also found in ECA and particularly SEAP, mainly in high income economies, and economies with all type of industry shares (3rd column, middle panel). In SEAP, high income countries and economies with large services share both sectors benefit from financial FDI flows, corroborating the results for ‘all countries’. In low income countries and economies with high manufacturing shares, financial FDI spills over to the manufacturing industry and increases growth more than proportionally without any significant impact in its own sector (1st column, middle panel).
The right panel in Table 2 illustrates the impact of nonfinancial service FDI on industry growth rates. A comparison with left panel reveals a remarkable result about the drain that these flows cause in the manufacturing sectors in several categories. The negative spillover of nonfinancial service FDI on manufacturing almost entirely accounts for the negative growth rates caused by aggregate service FDI (left panel). ‘All countries’ manufacturing growth is hurt by nonfinancial service FDI that decreases output by 0.6 percent (top right panel). This contrasts with the 0.96 percent boost that the growth rate receives from 1 percent rise in financial service FDI (middle panel). Thus, the negative total growth effect of services FDI’s (top left panel) can, at least partially, be explained by the negative spillover from nonfinancial FDI. Further scrutiny of the data shows several negative spillover effects of aggregate service FDI in manufacturing growth in different categories (1st column, left panel). These can be partly traced to a contraction in manufacturing growth led by nonfinancial service FDI in the same categories, i.e., SEAP, high-income countries and services based economies (1st column, right panel). The negative estimates in the first column suggest that positive spillovers from financial service FDI are not able to defuse the negative ones by nonfinancial FDI (first column, middle panel).
Comparison across the three panels also explains the reasons behind insignificant aggregate growth effects of total FDI. In high income and services based economies, negative spillovers of nonfinancial service FDI flows on total growth are neutralized by positive spillovers of financial service FDI (3rd column, middle and right panels). Both effects account for the insignificant aggregate growth estimates of total service FDI flows in these categories (3rd column left panel). The insignificant impact of service FDI in service industry of high income countries (2nd column, first panel) can also be explained by negative spillovers of nonfinancial service FDI that cancel the positive spillovers of financial FDI (2nd columns, middle and right panels).
However, the impact of nonfinancial service FDI is not all harmful. In several instances these flows complement the financial service FDI and contribute positively to sectoral growth. For example, manufacturing growth in low-income countries, and in particular in mixed economies, gain from these flows (1st column, middle and right panels), accounting for the positive aggregate growth estimates of service FDI in these categories (1st column, left panel).
What do industry FDI flows
say about the channels through which the growth effect spills over to
the whole economy? Next, we turn to this question.
Discussion: sectoral flows and aggregate growth effect of total FDI
A comparison of total FDI in Table 1 and aggregate services in Table 2 (top panels) shows that the strong growth effect of total FDI on all the economies can be partly traced back to the significant impact of service FDI in the service sector, which outweighs its negative spillover in the manufacturing industry (1st raw). This pattern is replicated in SEAP. By contrast, in ECA the driving force behind the growth effect of total FDI is manufacturing FDI, which spurs growth in its own industry and dampens the negative impact of service FDI in manufacturing. Closer scrutiny of service FDI in Table 2, in turn, reveals that the negative spillovers are due to nonfinancial service FDI flows in “all countries” and SEAP and to financial FDI in ECA.
Although there is a less straightforward connection between sectoral FDI and aggregate growth among poor and rich countries, evidence suggests a compelling account for the category with industry shares. The favorable growth effect of total FDI in mixed economies (Table 1, bottom left panel) is mainly explained by the positive impact of service FDI in aggregate growth (Table 2, bottom left panel). Both components of service FDI contribute positively to aggregate growth (bottom middle and right panels). On the other hand, the unfavorable growth effect of FDI in services-based economies can be traced back to negative spillovers of service FDI in the manufacturing sector (Table 1, bottom panels), which outweigh the positive impact of manufacturing FDI in the same industry (Table 2, bottom left panel). These spillovers are entirely due to nonfinancial FDI that leads to sizeable contraction in manufacturing, offsetting the growth enhancing effect of financial FDI in both industries (Table 2, bottom middle and right panels).
At first blush we can tentatively
deduce that the benefits of a shift from manufacturing FDI to service
FDI is at best inconclusive if not detrimental to economic activity.
Manufacturing FDI is overall positively correlated with growth both
at the aggregate and the sectoral levels. However, while benefiting
the service sector, service FDI, and more specifically FDI into nonfinancial
service industry, often drains large resources from manufacturing sector.
Among some groups of countries, a rise in the service FDI at the expense
of manufacturing FDI is likely to hurt the manufacturing industry from
both directions. The first effect is due to the decline of manufacturing
FDI, and the second is due to draining effect of service FDI.
The total effect would be most painful in the ECA areas and in economies
with relatively large service sectors because of potential collapse
of the manufacturing industry without a significant compensating benefit
in the service industry. The shift would also lead to deindustrialization
in manufacturing of LAC and high income countries due to either falling
growth in manufacturing caused by the decline in manufacturing FDI or
negative spillovers of service FDI in this industry. The group
of countries that would clearly benefit from the change is the ones
with mixed industry bases, with equivalent shares of service and manufacturing
industries. Both industries benefit equally from financial and
nonfinancial FDI flows. In other categories of data, the net impact
of the shift is qualitatively more neutral for various reasons.
Both channels may cancel each others’ effect on manufacturing growth
(low income) and services growth (manufacturing based). Alternatively,
the second channel may become uncertain because the negative spillover
of service FDI on manufacturing is tempered by higher growth in services
(SEAP).
5. Conclusion
This study is the first systematic cross-country analysis of the disaggregated growth-FDI relationship. It examines sectoral growth effects of industry FDI flows using a dynamic panel estimator of system GMM with external instruments, which overcomes several econometric pitfalls. We take advantage of the time series dimension of the data to examine the repercussions of the shift away from manufacturing FDI to service FDI and to study the impact of the industry flows into their own sectors and their spillovers to other sectors. Besides the traditional determinants of growth, we control for regional effects, income distribution and relative sector shares in the economies. We disaggregate total FDI into manufacturing and service FDI and the latter into financial and nonfinancial service FDI.
We find that manufacturing FDI is generally
growth enhancing, a finding that confirms those of previous studies. Evidence further suggests that this effect operates mainly through manufacturing sector and is strongest in the Latin America-Caribbean region, and Europe and Central Asia, low and middle income countries, as well as countries with large manufacturing bases. In contrast, at odds with what would be commonly expected, we find that service FDI is not always growth enhancing, and is likely to lead to deindustrialization in specific economies. Further disaggregation reveals that the culprit behind this result is the non-financial service FDI, which drains resources and hurts growth in manufacturing industry in the South East Asia and the Pacific region, high income countries and services-based economies. By contrast, financial service FDI spurs growth by stimulating economic activity in both manufacturing and service sectors in the same three groups.
Our findings have a clear policy implication concerning the incentives given by governments to attract FDI. In general, investment plans by MNEs in the manufacturing sector is likely to be a growth enhancing decision for the host country. However, decisions concerning service FDI should be based on the level of the development of the economy, the share of manufacturing and service industries, the geographical location of the country and whether the MNEs will invest in financial or nonfinancial service sector.
Appendix 1: Country list
Full Sample
Argentina, Armenia, Australia,
Austria, Bangladesh, Bolivia, Brazil, Bulgaria, Canada, Chile, China,
Colombia , Costa Rica, Cyprus, Czech Republic, Denmark, Ecuador, El
Salvador, Estonia, Finland , France, Germany, Honduras, Hong Kong, China,
Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Kazakhstan,
Korea, Rep., Malaysia, Mexico, Morocco, Myanmar, Netherlands, Norway,
Pakistan, Paraguay, Peru, Philippines, Poland , Portugal, Russian Federation,
Singapore, Spain, Sweden, Switzerland, Thailand, Tunisia , Turkey, Uganda,
United Kingdom, United States, Venezuela, RB, Vietnam
Latin America & the Caribbean
Argentina, Bolivia, Brazil,
Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador,
Honduras, Mexico, Paraguay, Peru, Venezuela, RB.
Europe & Central Asia
Armenia, Austria, Bulgaria,
Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany,
Ireland, Italy, Kazakhstan, Netherlands, Norway, Poland, Portugal, Russian
Federation, Spain, Sweden, Switzerland, Turkey, United Kingdom.
South & East Asia and the Pacific
Australia, Bangladesh, China, Hong Kong, China, Hungary, Iceland, India, Indonesia, Japan, Korea, Rep., Malaysia, Myanmar, Pakistan, Philippines, Singapore, Thailand, Vietnam.
Low Income group
Armenia, Bangladesh, Bolivia,
Brazil, Bulgaria, China, Colombia, Dominican Republic, Ecuador, El Salvador,
Honduras, India, Indonesia, Kazakhstan, Morocco, Myanmar, Pakistan,
Paraguay, Peru, Philippines, Russian Federation, Thailand, Tunisia,
Turkey, Uganda, Vietnam.
Middle Income group
Argentina, Chile, Costa Rica,
Czech Republic, Estonia, Hungary, Malaysia, Mexico, Poland, Venezuela,
RB.
High Income group
Australia, Austria, Canada, Cyprus, Denmark, Finland, France, Germany, Hong Kong, China, Iceland, Ireland, Israel, Italy , Japan, Korea, Rep., Netherlands, Norway, Portugal, Singapore, Spain, Switzerland, United Kingdom, United States.
Appendix 2: Determinants
of real GDP per capita growth
Panel 1990-2004:
| Real GDP per capita growth | All countries | LA&C | E&CA | S&EA | Low income | Middle income | High income | Man. Based | Mixed | Services-based |
|
log of lagged GDP
per capita level |
-0.020 (0.01) | -0.0538 (0.02) | -0.012 (0.26) | -0.016
(0.00) |
-0.048 (0.01) | -0.192 (0.08) | -0.110 (0.00) | -0.045 (0.06) | -0.008 (0.21) | -0.006 (0.48) |
|
Gross fixed capital
formation share of GDP |
0.402 (0.04) | 0.854 (0.00) | -0.081 (0.65) | 0.224 (0.02) | 0.590 (0.00) | 0.894
(0.00) |
-0.193 (0.22) | 0.538 (0.07) | 0.462 (0.01) | 0.424 (0.01) |
| Real lending interest rate | -0.077 (0.18) | -0.0385 (0.28) | -0.066 (0.58) | -0.132 (0.24) | -0.059 (0.25) | -0.032 (0.82) | 0.093 (0.50) | -.0419 (0.00) | 0.062 (0.46) | -0.105 (0.01) |
|
Gross secondary school
enrolment ratio |
0.0650
(0.23) |
0.066 (0.31) | 0.0151 (0.57) | 0.022 (0.77) | 0.133 (0.03) | 0.135 (0.58) | 0.051 (0.20) | 0.310 (0.06) | 0.039 (0.15) | 0.076 (0.12) |
|
Government consumption
share of GDP |
-0.003 (0.28) | 0.003 (0.17) | -0.007 (0.02) | 0.001 (0.77) | 0.000 (0.80) | -0.004 (0.09) | -0.000 (0.91) | -0.007 (0.10) | 0.001 (0.29) | -0.001 (0.71) |
| Government stability | 0.00 (0.98) | 0.009 (0.10) | -0.009 (0.00) | -0.00 (0.35) | -.0047968 (0.044) | 0.013 (0.04) | -0.002 (0.49) | 0.008 (0.23) | -0.000 (0.98) | -0.007 (0.00) |
| Total FDI share of GDP | 0.208 (0.07) | -0.240 (0.21) | 0.099 (0.03) | 0.254 (0.00) | -0.063 (0.72) | -0.034 (0.80) | 0.206 (0.00) | 0.104 (0.41) | 0.121 (0.24) | 0.011 (0.93) |
| Constant term | 0.115 (0.10) | 0.108 (0.38) | 0.351 (0.00) | 0.146 (0.03) | 0.199 (0.12) | 1.363 (0.06) | 1.1217 (0.00) | 0.087 (0.59) | -0.062 (0.20) | 0.013 (0.84) |
| Number of observations | 269 | 71 | 95 | 72 | 101 | 57 | 111 | 64 | 131 | 74 |
| Number of groups | 54 | 13 | 21 | 14 | 20 | 10 | 24 | 17 | 39 | 22 |
| Number of instruments | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 49 | 47 |
Panel 1998-2004
| Real GDP per capita growth | All countries | LA&C | E&CA | S&EA | Low income | Middle income | High income | Manufacturing
Based |
Mixed | Services-based |
|
log of lagged GDP
per capita level |
-.012
(0.00) |
-0.029
(0.11) |
-0.0180
(0.00) |
-0.012
(0.00) |
-0.025
(0.00) |
-0.082
(0.03) |
-0.0189
(0.01) |
-0.018
(0.02) |
-0.010
(0.00) |
0.001
(0.84) |
|
Gross fixed capital
formation share of GDP |
0.237
(0.00) |
0.503
(0.00) |
-0.057
(0.63) |
0.289
(0.00) |
0.263
(0.002) |
0.406
(0.01) |
-0.037
(0.67) |
0.186
(0.06) |
0.297
(0.00) |
0.597
(0.00) |
| Real lending interest rate | -0.051
(0.05) |
-0.071
(0.05) |
-0.082
(0.22) |
-0.007
(0.92) |
-0.069
(0.00) |
-0.140
(0.18) |
0.094
(0.24) |
-0.087
(0.54) |
-0.020
(0.65) |
-0.058
(0.18) |
|
Gross secondary school
enrolment ratio |
0.043
(0.00) |
0.083
(0.26) |
0.021
(0.08) |
0.036
(0.13) |
0.083
(0.00) |
0.100
(0.16) |
0.025
(0.02) |
0.082
(0.30) |
0.035
(0.02) |
0.007
(0.82) |
|
Government consumption
share of GDP |
-0.000
(0.88) |
0.001
(0.51) |
-0.002
(0.03) |
0.001
(0.01) |
0.000
(0.57) |
0.001
(0.25) |
-0.002
(0.01) |
-0.003
(0.08) |
0.001
(0.27) |
0.00
(0.73) |
| Government stability | 0.002
(0.30) |
0.009
(0.16) |
-0.003
(0.15) |
-0.008
(0.00) |
-0.002
(0.443) |
0.011
(0.02) |
-0.002
(0.32) |
0.001
(0.75) |
0.002
(0.40) |
-0.003
(0.32) |
| Total FDI share of GDP | 0.145
(0.06) |
-0.494
(0.23) |
0.154
(0.16) |
0.157
(0.00) |
0.072
(0.76) |
-0.622
(0.18) |
0.096
(0.24) |
0.259
(0.25) |
0.195
(0.01) |
-0.425
(0.05) |
| Constant term | 0.040
(0.18) |
0.056
(0.46) |
0.261
(0.00) |
0.119
(0.00) |
0.106
(0.08) |
0.516
(0.11) |
0.256
(0.01) |
0.126
(0.20) |
-0.010
(0.81) |
-0.038
(0.39) |
| Number of observations | 269 | 71 | 95 | 72 | 101 | 57 | 111 | 64 | 131 | 74 |
| Number of groups | 54 | 13 | 21 | 14 | 20 | 10 | 24 | 17 | 39 | 22 |
| Number of instruments | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 | 24 |
Appendix 3: Pooled
OLS (POLS) and Fixed Effect (FE) estimate results
Appendix Table 3A: Growth effect
of total FDI and Manufacturing FDI
| sample |
Total FDI |
Manufacturing FDI | |||||||
|
Manufacturing growth |
Service growth | GDP growth | |||||||
| POLS | FE | POLS | FE | POLS | FE | POLS | FE | ||
|
All countries |
1990-2004 | 0.128
(0.00)** |
0.087
(0.07)* |
0.611
(0.01)** |
0.346
(0.02)** |
0.082
(0.30) |
0.179
(0.01)** |
0.252
(0.01)** |
0.243
(0.00)** |
| 1998-2004 | 0.094
(0.03)** |
0.080
(0.08)* |
0.441
(0.03)** |
0.198
(0.19) |
0.094
(0.21) |
0.103
(0.07)* |
0.194
(0.05)* |
0.182
(0.01)** |
|
| Latin America & the Caribbean (LAC) | 1990-2004 | 0.004
(0.98) |
-0.047
(0.80) |
1.954
(0.04)** |
2.267
(0.05*)* |
0.261
(0.52) |
0.636
(0.18) |
0.629
(0.16) |
0.564
(0.28) |
| 1998-2004 | -0.114
(0.49) |
-0.229
(0.23) |
3.558
(0.03)** |
2.147
(0.33) |
1.278
(0.01)** |
1.235
(0.07)* |
1.439
(0.03)** |
0.245
(0.75) |
|
| Europe & Central Asia (ECA) | 1990-2004 | 0.181
(0.00)** |
0.111
(0.00)** |
0.373
(0.01)** |
0.173
(0.07)** |
-0.012
(0.84) |
0.059
(0.22) |
0.103
(0.04)** |
0.138
(0.00)** |
| 1998-2004 | 0.145
(0.00)** |
0.076
(0.10)* |
0.317
(0.10) |
0.122
(0.48) |
-0.012
(0.87) |
0.023
(0.74) |
0.088
(0.21) |
0.131
(0.01)** |
|
| South & East Asia and the Pacific (SEAP) | 1990-2004 | 0.075
(0.43) |
-0.045
(0.78) |
0.473
(0.35) |
0.688
(0.30) |
-0.167
(0.51) |
0.001
(1.00) |
0.097
(0.69) |
0.200
(0.61) |
| 1998-2004 | 0.074
(0.39) |
0.074
(0.54) |
0.169
(0.77) |
0.658
(0.25) |
0.134
(0.56) |
0.082
(0.81) |
0.110
(0.66) |
0.395
(0.25) |
|
|
Low income
economies |
1990-2004 | 0.108
(0.36) |
-0.111
(0.54) |
0.207
(0.80) |
0.479
(0.66) |
-0.268
(0.49) |
-0.308
(0.58) |
0.376
(0.38) |
-0.367
(0.52) |
| 1998-2004 | 0.096
(0.41) |
-0.011
(0.95) |
1.047
(0.27) |
0.972
(0.27) |
0.525
(0.23) |
0.688
(0.21) |
0.982
(0.06)* |
0.290
(0.52) |
|
|
Middle
income economies |
1990-2004 | 0.174
(0.33) |
0.103
(0.57) |
2.410
(0.01)** |
1.939
(0.02)** |
1.349
(0.00)** |
0.901
(0.03)** |
0.952
(0.04)** |
0.872
(0.02)** |
| 1998-2004 | 0.035
(0.87) |
0.145
(0.36) |
2.779
(0.01)** |
1.648
(0.11) |
0.961
(0.04)** |
0.856
(0.03)** |
0.823
(0.71) |
0.789
(0.02)** |
|
|
High income
economies |
1990-2004 | 0.114
(0.01)** |
0.086
(0.04)** |
0.354
(0.07)* |
0.124
(0.20) |
-0.009
(0.92) |
0.044
(0.43) |
0.111
(0.13) |
0.147
(0.01)** |
| 1998-2004 | 0.101
(0.02)** |
0.074
(0.08)* |
0.097
(0.65) |
-0.016
(0.91) |
-0.035
(0.63) |
0.018
(0.73) |
0.063
(0.41) |
0.112
(0.07)* |
|
|
Manufacturing
based economies |
1990-2004 | 0.295
(0.00)** |
0.013
(0.90) |
3.125
(0.01)** |
1.550
(0.20) |
1.020
(0.02)** |
0.783
(0.14) |
1.696
(0.00)** |
0.840
(0.09)* |
| 1998-2004 | 0.204
(0.03)** |
0.006
(0.96) |
2.146
(0.09)* |
-0.262
(0.85) |
0.536
(0.37) |
0.571
(0.50) |
1.058
(0.15) |
0.504
(0.39) |
|
|
Mixed
economies |
1990-2004 | 0.129
(0.02)** |
0.103
(0.23) |
0.455
(0.02)** |
0.133
(0.19) |
0.047
(0.54) |
0.093
(0.12) |
0.191
(0.02)** |
0.077
(0.28) |
| 1998-2004 | 0.114
(0.05)* |
0.095
(0.18) |
0.228
(0.07)* |
0.035
(0.83) |
0.131
(0.07)* |
0.052
(0.34) |
0.172
(0.02)** |
0.026
(0.71) |
|
|
Services
based economies |
1990-2004 | -0.066
(0.36) |
-0.023
(0.72) |
0.277
(0.66) |
-0.048
(0.95) |
-0.210
(0.47) |
0.130
(0.72) |
-0.169
(0.52) |
0.287
(0.24) |
| 1998-2004 | -0.100
(0.19) |
-0.117
(0.09)*) |
0.326
(0.70) |
0.312
(0.79) |
-0.199
(0.44) |
0.186
(0.49) |
-0.278
(0.42) |
0.568
(0.15) |
|
Appendix Table 3B: Growth Effects
of Services FDI
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Appendix Table 3C: Growth effects
of financial services FDI
| Manufacturing growth | Service growth | GDP growth | |||||
| sample | POLS | FE | POLS | FE | POLS | FE | |
|
All countries |
1990-2004 | 0.694
(0.00)** |
0.791
(0.00)** |
0.163
(0.17) |
0.291
(0.00)** |
0.374
(0.00)** |
0.300
(0.00)** |
| 1998-2004 | 0.718
(0.01)** |
0.924
(0.00)** |
0.205
(0.09)* |
0.251
(0.00)** |
0.386
(0.00)** |
0.287
(0.00)** |
|
| Latin America & the Caribbean (LAC) | 1990-2004 | 0.564
(0.62) |
-0.472
(0.76) |
0.213
(0.76) |
0.283
(0.69) |
0.040
(0.96) |
0.283
(0.73) |
| 1998-2004 | 1.207
(0.53) |
-3.291
(0.09)** |
0.505
(0.60) |
-0.932
(0.23) |
-0.087
(0.93) |
-1.930
(0.08)* |
|
| Europe & Central Asia (ECA) | 1990-2004 | 0.503
(0.12) |
0.693
(0.05)* |
0.074
(0.49) |
0.181
(0.20) |
0.384
(0.00)** |
0.181
(0.02)** |
| 1998-2004 | 0.239
(0.59) |
1.324
(0.08)* |
-0.083
(0.45) |
0.031
(0.88) |
0.333
(0.00)** |
0.215
(0.09)* |
|
| South & East Asia and the Pacific (SEAP) | 1990-2004 | 0.843
(0.01)** |
1.043
(0.00)** |
0.199
(0.22) |
0.209
(0.02)** |
0.363
(0.03)** |
0.274
(0.03)** |
| 1998-2004 | 0.790
(0.02)** |
0.969
(0.00)** |
0.290
(0.00)** |
0.172
(0.02)** |
0.407
(0.01)** |
0.335
(0.00)** |
|
|
Low income
economies |
1990-2004 | 0.373
(0.81) |
-0.398
(0.78) |
-1.022
(0.42) |
-1.012
(0.28) |
-0.953
(0.50) |
-1.105
(0.28) |
| 1998-2004 | 2.569
(0.00)** |
2.978
(0.00)** |
0.361
(0.45) |
0.279
(0.55) |
0.474
(0.44) |
0.662
(0.17) |
|
|
Middle
income economies |
1990-2004 | 0.422
(0.72) |
0.373
(0.78) |
0.647
(0.23) |
-0.066
(0.87) |
0.625
(0.19) |
0.140
(0.78) |
| 1998-2004 | 3.704
(0.08)** |
-0.192
(0.90) |
0.910
(0.32) |
-0.216
(0.83) |
0.809
(0.34) |
-0.669
(0.34) |
|
|
High income
economies |
1990-2004 | 0.678
(0.01)** |
0.913
(0.00)** |
0.180
(0.10)* |
0.286
(0.00)** |
0.339
(0.00)** |
0.270
(0.00)** |
| 1998-2004 | 0.677
(0.04)** |
0.886
(0.00)** |
0.161
(0.16) |
0.271
(0.00)** |
0.383
(0.00)** |
0.294
(0.00)** |
|
|
Manufacturing
based economies |
1990-2004 | 2.211
(0.25) |
-1.099
(0.42) |
0.120
(0.89) |
0.700
(0.55) |
0.714
(0.00)** |
0.303
(0.20) |
| 1998-2004 | 2.597
(0.19) |
-4.172
(0.05)* |
-0.505
(0.61) |
0.253
(0.86) |
0.553
(0.00)** |
0.200
(0.16) |
|
|
Mixed
economies |
1990-2004 | 0.477
(0.13) |
0.317
(0.407) |
0.076
(0.70) |
0.150
(0.53) |
0.264
(0.12) |
0.098
(0.68) |
| 1998-2004 | 0.629
(0.09)* |
1.330
(0.107) |
0.264
(0.24) |
0.028
(0.92) |
0.373
(0.02)** |
0.263
(0.34) |
|
|
Services
based economies |
1990-2004 | 0.763
(0.01)** |
0.673
(0.00)** |
0.145
(0.30) |
0.191
(0.02)** |
0.268
(0.14) |
0.244
(0.01)** |
| 1998-2004 | 0.730
(0.04)** |
0.834
(0.00)** |
0.102
(0.46)_ |
0.092
(0.24) |
0.297
(0.11) |
0.232
(0.02)** |
|
Appendix Table 3D: Growth effects of nonfinancial services FDI
| Manufacturing growth | Service growth | GDP growth | |||||
| sample | POLS | FE | POLS | FE | POLS | FE | |
|
All countries |
1990-2004 | -0.165
(0.41) |
-0.105
(0.70) |
0.044
(0.67) |
-0.089
(0.25) |
0.039
(0.74) |
-0.088
(0.39) |
| 1998-2004 | -0.296
(0.13) |
-0.182
(0.53) |
0.050
(0.65) |
-0.106
(0.21) |
0.006
(0.96) |
-0.110
(0.34) |
|
| Latin America & the Caribbean (LAC) | 1990-2004 | 0.678
(0.38) |
0.850
(0.32) |
0.879
(0.04)** |
0.450
(0.27) |
0.698
(0.16) |
0.606
(0.21) |
| 1998-2004 | 0.287
(0.79) |
-0.350
(0.78) |
1.337
(0.12) |
0.785
(0.285) |
0.737
(0.41) |
0.437
(0.59) |
|
| Europe & Central Asia (ECA) | 1990-2004 | 0.139
(0.54) |
0.187
(0.49) |
0.052
(0.56) |
0.001
(0.99) |
-0.078
(0.31) |
-0.016
(0.80) |
| 1998-2004 | 0.073
(0.72) |
0.117
(0.61) |
0.052
(0.51) |
-0.019
(0.82) |
-0.089
(0.27) |
-0.107
(0.03)** |
|
| South & East Asia and the Pacific (SEAP) | 1990-2004 | -0.871
(0.00)** |
-0.683
(0.02)** |
-0.042
(0.82) |
-0.143
(0.16) |
-0.095
(0.65) |
-0.323
(0.00) |
| 1998-2004 | -0.980
(0.00)** |
-0.713
(0.00) |
-0.064
(0.68) |
-0.144
(0.08)* |
-0.164
(0.37) |
-0.280
(0.00) |
|
|
Low income
economies |
1990-2004 | 0.858
(0.04)** |
0.573
(0.25) |
0.566
(0.07)* |
-0.156
(0.59) |
0.661
(0.01)** |
0.088
(0.81) |
| 1998-2004 | 0.508
(0.26) |
0.430
(0.24) |
0.476
(0.16) |
0.040
(0.86) |
0.629
(0.03)** |
0.388
(0.32) |
|
|
Middle
income economies |
1990-2004 | 1.283
(0.07)** |
0.703
(0.30) |
0.547
(0.13) |
-0.078
(0.72) |
0.643
(0.05)* |
0.124
(0.69) |
| 1998-2004 | 1.198
(0.14) |
0.410
(0.49) |
0.318
(0.44) |
-0.073
(0.77) |
0.403
(0.20) |
-0.037
(0.84) |
|
|
High income
economies |
1990-2004 | -0.476
(0.00)** |
-0.376
(0.13) |
-0.058
(0.55) |
-0.115
(0.09)* |
-0.125
(0.20) |
-0.199
(0.01)** |
| 1998-2004 | -0.533
(0.01)** |
-0.393
(0.11) |
-0.092
(0.27) |
-0.099
(0.25) |
-0.165
(0.07)* |
-0.185
(0.04)** |
|
|
Manufacturing
based economies |
1990-2004 | 1.282
(0.04)** |
0.013
(1.00) |
0.098
(0.84) |
-0.104
(0.83) |
0.551
(0.20) |
0.114
(0.56) |
| 1998-2004 | 1.453
(0.04)** |
-0.389
(0.65) |
-0.179
(0.73) |
-0.430
(0.35) |
0.303
(0.50) |
-0.213
(0.27) |
|
|
Mixed
economies |
1990-2004 | 0.181
(0.39) |
0.084
(0.70) |
0.081
(0.54) |
-0.262
(0.06)* |
0.262
(0.01)** |
-0.006
(0.97) |
| 1998-2004 | 0.107
(0.61) |
0.175
(0.47) |
0.149
(0.30) |
-0.145
(0.11) |
0.286
(0.01)** |
0.109
(0.59) |
|
|
Services
based economies |
1990-2004 | -0.541
(0.04)** |
-0.221
(0.45) |
-0.044
(0.69) |
-0.058
(0.50) |
-0.144
(0.30) |
-0.183
(0.04)** |
| 1998-2004 | -0.794
(0.00)** |
-0.618
(0.01) |
-0.075
(0.41) |
-0.113
(0.01)** |
-0.184
(0.16 |
-0.256
(0.00)** |
|
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.
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.
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.
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.
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).
Table 1: Growth effects of total FDI and Manufacturing FDI†
| Total FDI | Manufacturing FDI | ||||
| sample | Aggregate growth | Manufacturing growth | Service growth | GDP growth | |
|
All countries |
1990-2004 | 0.176
(0.00)** |
0.049
(0.84) |
-0.047 (0.73) | n/a |
| 1998-2004 | 0.145
(0.06)* |
0.058
(0.93) |
0.034
(0.90) |
0.049 (0.87) | |
| Latin America & the Caribbean (LAC) | 1990-2004 | 0.068
(0.73) |
3.047
(0.02)** |
0.326
(0.67) |
1.243 (0.13) |
| 1998-2004 | -0.494
(0.23) |
7.762
(0.00)** |
n/a | 3.206 (0.00)** | |
| Europe & Central Asia (ECA) | 1990-2004 | 0.115
(0.09)* |
0.353
(0.00)** |
-0.026 (0.75) | 0.060 (0.48) |
| 1998-2004 | 0.154
(0.16) |
-0.315
(0.44) |
-0.157 (0.30) | -0.063 (0.76) | |
| South & East Asia and the Pacific (SEAP) | 1990-2004 | 0.037
(0.72) |
1.130
(0.52) |
-0.473 (0.14) | -0.131 (0.54) |
| 1998-2004 | 0.157
(0.00)** |
0.174
(0.81) |
-0.090 (0.65) | 0.199 (0.35) | |
|
Low income
economies |
1990-2004 | 0.145
(0.28) |
2.987
(0.05*) |
-0.825 (0.52) | -0.715 (0.68) |
| 1998-2004 | 0.072
(0.76) |
4.425
(0.00)** |
1.105
(0.11) |
1.800 (0.08)* | |
|
Middle
income economies |
1990-2004 | 0.184
(0.55) |
0.231
(0.04)** |
2.037 (0.00)** | 2.284 (0.00)** |
| 1998-2004 | -0.622
(0.18) |
2.904
(0.24) |
2.074
(0.01)** |
2.671 (0.03)** | |
|
High income
economies |
1990-2004 | 0.141
(0.01)** |
0.355
(0.35) |
-0.125 (0.35) | 0.036 (0.47) |
| 1998-2004 | 0.096
(0.24) |
0.347
(0.32) |
-0.136 (0.58) | 0.095 (0.60) | |
|
Manufacturing
based economies |
1990-2004 | n/a | n/a | 1.730 (0.00)** | 2.456 (0.01)** |
| 1998-2004 | 0.259
(0.25) |
3.395
(0.27) |
1.258
(0.22) |
2.241 (0.10)* | |
|
Mixed
economies |
1990-2004 | 0.241
(0.00)** |
0.134
(0.34) |
-0.026 (0.80) | 0.059 (0.56) |
| 1998-2004 | 0.195
(0.01)** |
-0.048
(0.91) |
0.080
(0.75) |
-0.195 (0.32) | |
|
Services
based economies |
1990-2004 | -0.344
(0.03)** |
0.711
(0.36) |
-0.465 (0.42) | 0.202 (0.24) |
| 1998-2004 | -0.425
(0.05)* |
2.305
(0.02)** |
-0.203 (0.60) | -0.224 (0.57) | |
†The
first entry in each cell is the estimate of the FDI coefficient on growth.
Figures in parentheses are p-values. The “n/a” results are due to
second order autocorrelation in the error of the model. The coefficients
and the p-vales are robust to heteroscedasticity and obtained from one-step
Blundell-Bond System GMM with external instruments. ** and * represent
marginal significance levels with less than 5% , and with equal or less
than 10%, respectively.
Table 2: Growth
effects of Services FDI, Financial and Nonfinancial Services
FDI†
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
†See footnote in Table 1; ** and * represent marginal significance levels with less than 5% , and with equal or less than 10%, respectively.
Figure 1
Source: UNCTAD, OECD,
government websites.
1 Manufacturing FDI transfers predominantly “hard” technology (equipment and industrial processes), whereas service FDI typically transfers “soft technology” (technical, management and marketing know-how, expertise, organizational skills and information).
2 These figures are based on the UNCTAD’s classification of services, which differs by two industries from the one used by the World Bank. UNCTAD includes Gas, Water and Electricity production and Construction, while the ISIC’s classification followed by the World Bank does not.
3 In a study of 16 developed countries from 1870 to 1987, Maddison (1989) reports an almost ten-fold decrease in the share of agriculture in GDP, which declined form 39% to 4 %, a substantial increase in the share of industry (manufacturing, mining, construction, and utilities) from 26 to 36%, and an even larger increase in the share of services, which rose from 35 to 60%.
4 See Lim, 2001, Doytch, 2005 and Crespo and Fontoura, 2007 for a survey of the literature.
5 For an application to growth regression of Arellano Bond methodology see Caselli, Esquivel and Lefort (1996) and Easterly, Loayza and Montiel (1997) and that of Blundell and Bond see Levine, Loayza and Beck (2000)
6 By construction, the differenced error term is first-order serially correlated even if the original error term is not.
7 We would like to acknowledge L.-M. Saavedral for her help with data collection at the UN Statistical Library.
All Rights Reserved Powered by Free Document Search and Download
Copyright © 2011