Vertical and Horizontal FDI Technology Spillovers:
Evidence from Thai Manufacturing*

Archanun Kohpaiboon
Faculty of Economics, Thammasat University,
Bangkok Thailandarchanun@econ.tu.ac.th

ABSTRACT: This paper examines Foreign Direct Investment (FDI) spillover, using an
unbalanced panel data set of the manufacturing survey of Thailand during the period
2001-03. In this paper, not only are both horizontal and vertical FDI technology
spillovers examined, but the former is also assumed to vary across industries. The key
hypothesis is that horizontal FDI spillovers depend on the trade policy regime as well as
the absorptive capability of locally owned plants. Our panel data econometric analysis
highlights the important role of the trade policy regime as a conditional gain ofhorizontal FDI spillovers. In particular, positive horizontal FDI spillovers are foundonly in an industry operating in a relatively liberal environment. Interestingly,
imposing an assumption of identical horizontal FDI spillovers across industry could
result in biased estimates of vertical FDI spillovers. The key policy inference highlights
the relative importance of the trade policy regime in harnessing the gain from foreign
presence. Liberalizing the foreign investment regime thus has to go hand in hand withliberalizing the trade policy to gain FDI technology spillovers. Our finding here gives awarning not to overemphasize the role of linkages. It is the quality rather thanmagnitude of linkages that should be used a proxy of the magnitude of vertical FDI
spillovers.
JEL Classification: F14, O24, D24*

I would like to thank Professor F. Kimura, Mr. S. Umezaki and all participants at theWorkshop ‘Deepening East Asia Economic Integration: Part II Firm-level Analysis, organized
by Economic Research Institute for ASEAN and East Asia (ERIA), December 23, 2008 andFebruary 9, 2009. The opinions expressed in this paper are the sole responsibility of the authorand do not reflect the views of the ERIA.

1. Issues
Enticing multinational enterprises (MNEs) to set up affiliations is placed high on thepolicy agenda in many countries, especially developing ones, as their entry would bringin much-needed capital, new production technologies, marketing techniques andmanagement knowhow.

While all of these potential benefits of Foreign Direct

Investment (FDI) are viewed as important, particular emphasis is placed ontechnological gains in the productivity and competitiveness of the domestic industry,
known as FDI technology spillovers (henceforth referred to as FDI spillovers). As aresult, the expectation of gaining from technology spillover persuades many developing
countries to offer various incentives in order to attract FDI. Nonetheless, only in someinvestment-receiving (host) countries are FDI spillovers empirically found.
While tangible efforts have recently been made to gain a better understanding of
the factors that determine the presence of FDI spillovers, they have not thus far borne
fruit (Crespo& Fontoura, 2007). The existing literature divides into two broad themes.
First, horizontal FDI spillovers are assumed not to be automatic but are hypothesized as
being a function of the economic environment and domestic policies in host countries.
In this literature, two determinants have been generally recognized as conditioning gains
from FDI. These are the trade policy regime and the absorptive capability of locallyowned enterprises.1

While both of these factors are acknowledged, most researchers

have examined only the role of absorptive capability. This may be because of the
difficulty of finding a reliable proxy for protection across industries. So far only a few
studies (e.g. Kokko et al., 2001; Kohpaiboon, 2006) have examined empirically the roleof thetrade policy regime. Additionally, there is a dearth of studies that bring absorptive
capacity and the trade policy regime together in examining FDI spillovers. A major
caveat of literature in this field is that it concentrates only on spillovers taking place
within a given industry, (i.e. horizontal FDI spillovers).

1

See the comprehensive survey in Görg & Greenaway (2004) Crespo & Fontoura(2007) Hayakawa et al. (2008).

1

In fact, a number of recent studies2 argue that it is more likely that FDI
spillovers would take place through backward and forward linkages (i.e. vertical FDI
spillovers) as opposed to horizontal ones. That is, where foreign investors involve
themselves with indigenous enterprises in upstream and/or downstream industries, it isvery likely that the latter will gain technological benefit from the former. MNEs would
have an incentive to prevent information leakage to their competitors, including local
enterprises, thereby reducing the possibility of horizontal spillover taking place. By
contrast, there would be incentive for them to transfer knowledge to their local suppliers
because such knowledge transfer would benefit the MNEs in terms of getting better
input quality and/or cheaper costs, and receiving inputs on time. It is also plausible thatspillovers from MNEs in upstream industries exist to provide inputs that either werepreviously unavailable in the country or to make them technologically more advancedor less expensive, or to ensure that they are accompanied by the provision ofcomplementary services (Javorcik, 2004).

Empirical studies examining the presence of vertical FDI technology spillovers
are sparse (Blomström et al. 2000; Lin & Saggi, 2005). The notable exception is
Javorcik (2004) and Blalock & Gertler (2008) which examined cases in Lithuanian andIndonesian manufacturing sectors, respectively. Their key finding supports the relative
importance of vertical against horizontal FDI spillovers. In particular, it was found thatvertical FDI spillovers were statistically significant. Nevertheless, a major caveat inthese two studies is that their empirical model contains the implicit assumption thathorizontal FDI spillovers are identical for all industries. As argued above such anassumption is rather restrictive. In addition, the correlation between protection and theextent of industries generating backward linkages tends to be positive, and omitting thetrade policy regime in examining FDI spillovers could create bias in the results.

Against this backdrop, this paper examines the presence of FDI technology
spillover in Thai manufacturing. Panel data econometric analysis is conducted, using
2

They are Rodŕigueze-Clare (1996), Markusen & Venables (1999), Javorcik (2004),
Lin & Saggi, (2005), Blalock & Gertler (2008)2

the Industrial Survey conducted by the Office of Industrial Economics, Ministry ofIndustry, during the period 2001-2003. This is the most up to date and reliable plantsurvey available so far. In the empirical model, we follow the general practice in thisresearch area, in which the productivity equation of locally owned plants in the
manufacturing sector is estimated and the statistical relationship between plants’productivity and the extent of foreign presence is examined. This paper contributes tothe existing literature in two ways. First, in our econometric analysis both horizontal
and vertical FDI spillovers are examined. So far there have been few studies (e.g.
Javorcik (2004) and Blalock & Gertler (2008) examining both spillovers
simultaneously. Additionally, our measure of backward and forward linkages takes into
consideration both direct and indirect (inter-sectoral) repercussions. This is different
from Javorcik (2004) and Blalock & Gertler (2008) in which only the direct linkage isincluded. Secondly, we allow horizontal FDI spillovers to vary across industries. Trade
policy regime and absorptive capability are included in the empirical model as the key
factors determining the extent of horizontal FDI spillovers.
Thai manufacturing is a good laboratory for the issue in hand for two reasons.
First, Thailand has been a large FDI recipient throughout the past three decades.
However, few studies have examined technology spillover in Thai manufacturing. So
far there have been two studies, Kohpaiboon (2006) and Kohpaiboon and Jongwanich(forthcoming), both of which are based on the Industrial Census of 1996. Hence, thispaper not only provides up-to-date evidence but also re-examine the relative importanceof spillover channels, and horizontal versus vertical spillovers.

Secondly, Thaimanufacturing is broad-based as opposed to neighbouring countries, covering a wide
range of industries from traditional labour- intensive industries like garment andfootwear to several key industries in the machinery and transport equipment sector suchas automotive, electronics, and electrical appliances. Hence, evidence drawn from Thaimanufacturing would provide an insightful lesson for other countries.
The paper is organized as follows: Section 2 provides an analytical framework
illustrating possible channels where FDI spillover could take place as well as the role ofkey determinants conditioning FDI spillovers.

3

In Section 3, patterns of labour

productivity across industries are discussed and related to the extent of the foreign
presence and the effective rate of protection. The following section explains theempirical model used in this paper (Section 4). Section 5 presents data and variable
construction and regression results are in Section 6. Conclusion and policy inferences
are in the final section.

2. Analytical Framework
While MNEs have the potential to generate considerable impact on host countries’
economies, it is often argued that spillovers are the most desirable benefit of all. In
general, there are at least three channels through which FDI spillovers can occur. The
first channel is the demonstration effect. The presence of foreign firms can have ademonstration effect that allows local firms to become familiar with superior
technologies, marketing and managerial practices used in foreign affiliates.

Thus,

spillover can take place in the form of imitating the foreign subsidiaries’ technology.
Over and above this, the presence of foreign affiliates can exert pressure on local firms
exhibiting technical or allocation inefficiencies to adopt more efficient methods. This
allows local firms to survive successfully or even compete with foreign firms. Sinceboth demonstration and competition effects are likely to occur simultaneously, these
two effects are regarded in the literature as a single channel of spillover.
Linkage is the second channel of FDI spillovers. Where foreign investors arelinked to upstream and downstream industries in host countries, the linked indigenous
firm has the possibility of gaining technological benefits. The former is referred to as
backward linkage and the latter as forward linkage. By backward linkage, foreign
investors establish an inter-firm relationship with local suppliers and create demand for
inputs from local suppliers in upstream industries. When these local firms are engaged
to supply certain raw materials, the high quality, reliability and speed of delivery that
MNE affiliates demand force them to enhance productivity. Moreover, in some cases,
local suppliers in upstream industries receive technical and managerial training in the
production of the required inputs.

This is likely to generate additional economic

4

activity and income, and to transfer technological and management skills to the hostcountry.
Similarly, forward linkage effects are created when one industry uses anotherindustry’s output as its inputs.

Every activity that does not by its nature cater

exclusively to final demand induces attempts to utilize its outputs as inputs in otherindustries. Benefits for domestic suppliers resulting from the presence of MNEs may be
extended to other domestic firms that produce end-user consumer goods. The mostevident link is observed in the MNEs’ supply of higher quality inputs and/or at a lower
price to domestic producers of end-user consumer goods. The sum of the backward andforward linkages gives a total linkage effect, which can be seen as the growth in othernew industries induced by establishing an MNE affiliates.
The last channel is labour mobility. Foreign affiliates generally play a more
active role than local firms in educating and training local labour. Through this training
and subsequent work experience, workers become familiar with the foreign affiliates’
technologies and production methods. FDI spillovers through this channel occur whenemployees of foreign affiliates move on to local employers or set up their own business,using knowledge gained during their previous employment.
Empirically, most econometric studies have only examined the presence of FDI
spillovers through the demonstration and linkage channels simply because of data
availability. Analysis of labour mobility is very limited as researchers must have accessto information about top managers’ backgrounds. Unfortunately, such information is not
usually available.3 Secondly, in theory, FDI spillovers through the demonstration effectcan take place either within the same industry or across industries. In practice, it is very
difficult to measure the demonstration effect across industries so that spillovers throughdemonstration effects are usually referred to as horizontal FDI spillovers. On the other
hand, FDI spillovers through linkage occur when MNEs are located in a given industry,3

To the best of our knowledge so far, the only econometric analysis of spillovers
through labour mobility is undertaken by Görg and Strobl (2002), using firm level data in
Ghana.

5and benefit upstream and downstream industries. These are regarded as FDI vertical
spillovers.
The recent studies such as Rodŕigueze-Clare (1996); Markusen & Venables(1999); Lin & Saggi (2005); Javorcik (2004); and Blalock & Gertler (2008) highlightthe relative importance of vertical FDI spillovers as opposed to horizontal ones. In
particular, they argue that vertical FDI spillovers are likely. For example Blalock &Gertler (2008) argue that it is hard to believe that horizontal FDI spillovers are likely.
Firstly, the technology gap between foreign and domestic firms may often be wide.
Local firms may lack the absorptive capacity needed to recognize and adopt newtechnology. Similarly, the degree to which foreign and domestic firms actually compete
in the same market will also vary. It is possible, for example, that domestic firms may
produce for the local market while MNEs produce for export. Because of differences inquality and other attributes, exported and domestically consumed goods may entail
different production methods thereby reducing the potential for technology transfer. In
contrast, technological benefits to local firms through vertical linkages are much more
likely simply because MNEs have incentives to improve the productivity of their
suppliers with the expectation of input cost reduction and quality improvement inreturn. Moreover, MNEs are likely to procure inputs requiring less sophisticatedproduction techniques for which the gap is narrower.
The key finding of Javorcik (2004) and Blalock & Gertler (2008) supports thecore hypothesis, i.e. only vertical FDI spillovers through backward linkages are found.
Noticeably, the empirical model in both studies implicitly assumes that horizontal FDI
spillovers, if they exist, must be identical in all industries. In particular, locally owned
enterprises operating in two different industries (e.g. capital versus labour intensive
industries, restrictive versus liberal trade regime) would benefit identically from foreign
presence in their industries. This assumption seems to contradict a number of studies
pointing out the heterogeneity of spillovers (Görg & Greenaway, 2004; Crespo &Fontoura, 2007; Hayakawa et al. 2008).

6

In fact, the recent effort is to clarify what kinds of heterogeneity in MNEs and/orindigenous firms are crucial. So far there have been two factors identified, namely theabsorptive capability of indigenous firms and the trade policy regime. Whether a local
firm benefits from MNC presence depends on its capacity for assimilating knowledgeits absorptive capability (Kokko et al. 1996; Girma et al., 2001; Girma & Görg, 2003;
Kinoshita, 2001; Girma, 2005). The hypothesis in the literature points out that thehigher the absorptive capability, the greater the spillover the local firm in the hostcountry can expect.

Note that the absorptive capability is referred to as thetechnological gap between MNE affiliates and indigenous firms (Kokko, 1994;
Blomstrom & Sjohölm, 1999; Sjohölm, 1999).
The trade policy regime is another factor to be considered, although there arefew empirical studies examining its role in conditioning FDI technology spillovers. As
pioneered by Bhagwaiti (1973) as an extension to his theory of immiserizing growthand further developed by Bhagwati (1985, 1994); Brecher & Diaz-Alejandro (1977);
and Brecher & Findlay (1983), technology spillover tends to be smaller, or possibly
even negative, under a restrictive, import substitution (IS) regime compared with aliberalizing, export promotion (EP) regime (referred to as the ‘Bhagwati’s hypothesis’).
FDI inflows enticed by an import substitution (IS) trade regime tend to be marketseeking and are invested mostly in the industries where proprietary assets are important.
This creates barriers to entry for local firms and thus constrains technology andefficiency spillovers. In contrast, the export promotion (EP) regime is more conducive
to generating favorable spillover effects because, under such a regime, FDI is mostly
attracted to industries in which the country has comparative advantage, i.e. efficiencyseeking FDI. In such industries local firms have a greater potential to catch up withforeign firms and achieve productivity improvement. Additionally, domestic firms
already exposed to foreign competition will probably have a great capacity not only toabsorb foreign technology but also to counter the competition provided by MNEs in thelocal market, thereby precluding a negative impact through the competition channel(Crespo & Fontoura, 2007).

7

While recognizing the important role of absorptive capability, trade policy ishighlighted in this paper because it is highly policy relevant and there is room for
improvement in the context of developing countries. While progress on tariff reduction
has occurred as a consequence of the Uruguay Round, it is clear that much remains to
be done. There has been a considerable decline in average tariff rates in developing
countries, especially in Asia and Africa, but this has occurred in an uneven manner
thereby increasing tariff dispersion. This implies that countries with low average tariff
rates are likely to have very high tariff peaks and exhibit escalation at higher levels of
disaggregation (Jongwnaich & Kohpaiboon, 2007).
More importantly, ignoring these two key determinants from econometricanalysis of FDI spillovers studies could result in biased estimates as a consequence ofomitting relevant variables. This is especially true for the trade policy regime simply
because there is likely to be a positive correlation between protection and the extent ofindustries generating backward linkage.

This is in line with the infant industry

argument. Pioneered by Hirschman (1958), investible resources should be geared
toward industries that have maximum linkages with the rest of economy. Suchindustries are usually capital intensive and economies of scale still matter, so that
protection against foreign competition is always granted to give them time to gain moreproduction efficiency.

The widely cited example is the development strategy for

automotive industry in developing countries which are likely to be a combination
between restrictive local content requirement measures and a high cross-borderprotection.

Although industrial linkages were a part of import substitution

industrialization strategy that has became less important since the 1980s, promoting
linkages and policy-induced ones in particular have continued to linger in the minds ofpolicymakers and development analysts (Athukorala, 1998; Pursell, 2001).

8

3. Patterns of Labour Productivity and Foreign Presence in Thai
Manufacturing.
This section aims to illustrate productivity difference between foreign and indigenous
plants across industries disaggregated into 4 digit ISIC classification in the Thaimanufacturing sector. As well, the productivity difference is examined together withkey variables in the paper’s core analysis, namely capital-labour ratio, the extent offoreign presence (FOR), effective rate of protection (ERP), and backward linkages
4index (BLI).

Productivity here is measured by labour productivity, value added per

workers. Difference in labour productivity between foreign and locally owned plants as
5a per cent of the latter’s productivity is calculated.

The calculated productivity

difference is plotted together with difference in capital labour ratio between these two
types of firms as shown in Figure 1 to reveal whether the former is more productive
than the latter after accounting for difference in the capital-labour ratio. These indicators
are the average figure during the period 2001-03.
The scattered plot in Figure 1 suggests that foreign plants generally have higher
labour productivity than locally owned ones. Most of industries stay above thehorizontal axis implying the positive productivity difference.

The difference isaveraged out at 107 per cent with the maximum of nearly 400 per cent in dairy product(ISIC 1520) and the minimum of -61.8 per cent in alcoholic beverages (ISIC 1551).
Nevertheless, the positive productivity difference is largely due to the fact that foreign
plants tend to be more capital intensive than their local counterparts as indicated by theobserved positive relationship between productivity and capital-labour ratio differences.
A (Spearman) rank correlation between difference in labour productivity and capital4

See full detail in Appendix 1
We do not report absolute number of labour productivity simply because they varylargely across industries. For example, value added per worker of indigenous plants in 2001
was widely ranged from 95,891 baht/workers (ISIC 2029: other special purpose machinery) to
67,800,000 baht/workers (ISIC 1554: Soft Drink Industry). Since our interest here is to address
the issue whether foreign plants always exhibit higher labour productivity than indigenous ones
instead of explaining difference of labour productivity across industries, we decide to report
only the percentage difference. Absolute value added per workers is available upon the author’s
request.
5

9

labour ratio is about 0.44 and statistically significant at the conventional level (5 percent). Hence, the observed figure of positive labour productivity difference isinadequate to conclude that foreign plants are superior to local ones unless the capitallabour ratio is taken into consideration.
(Insert Figure 1 about here)
There are six industries experiencing a negative and significant (greater than 30
per cent) difference in labour productivity: i.e. locally owned plants have higher labour
productivity than foreign ones. They are alcoholic beverages (ISIC 1551), Tobacco(ISIC 2925), veneer sheets (ISIC 2021), Paper pulp and paperboard (ISIC 2101), Toys
(ISIC 3694) and animal feeds (ISIC 1533). A common pattern observed among them isthere are Thai conglomerates playing important roles. One obvious example is alcoholic
beverages (ISIC 1551) dominated by two Thai conglomerates such as Thai Beverages
Public Company, and the Singha Corporation. Similarly, in animal feeds and paper
pulp industries, there are two Thai MNEs, the Chareon Pokphand Group (CP Group)and Siam Cement Group, respectively.
6

We also examine foreign presence (FOR) measured in terms of output share ,effective rate of protection (ERP) and backward linkage index (BLI) in order to view
their correlation with the average of plant productivity. BLI here is constructed basedon the Leontief inter-industry accounting framework which provides for the capture ofboth direct and indirect (inter-sectoral) repercussions in the measurement process. It
shows the total units of output required, directly and indirectly, from all sectors(including the unit of output delivered to final demand by the given sector) when the
demand for the industry’s product rises by one unit.
Generally, foreign plants tend to locate in industries having a low effective rateof protection, as we found a negative correlation between FOR and ERP of -0.25

6

See further discussion on why output share is our preferable choice in this study in
Section 4.

10

(Figure 2). The negative correlation is consistent with the trend of FDI inflows at themore aggregated level.

Up to the late 1970s, FDI was predominantly in import-

substitution industries such as textiles, automobiles, and chemicals. From then on, an
increasing share of FDI was directed to more export-oriented activities. To begin with,
export-oriented FDI went into light manufacturing industries such as clothing, textiles,
footwear and toys.

More recently, labour-intensive assembly activities in theelectronics and electrical goods industries have been the main attraction to foreign
investors. Interestingly, there is no clear relationship between FOR and BLI as theirsimple correlation approaches zero (Figure 3). This reconfirms the proposition that FDI
inflows in Southeast Asia including Thailand predominantly belong to the efficiencyseeking/export-oriented categories (Hill & Athukorala, 1998).
(Insert Figures 2 and 3 about here)

4. ModelTo examine the presence of technology spillover, we follow the standard practice in theliterature.

This begins with estimating the production function of locally owned

enterprises (Griliches, 1992; Javorcik, 2004; Crespo & Fontoura, 2007; Blalock &Gertler, 2008) A translog functional form is chosen to avoid the restriction imposed in
the Cobb Douglas forms that were popular in the previous empirical studies of Thaimanufacturing (e.g. Khanthachai et al., 1987; Tambunlertchai & Ramstetter, 1991), i.e.
unity of elasticity of substitution and log-linear relationship between inputs and outputs.
The translog function form also controls for input levels and scale effects on value
added. It is specified as equation (1);ln Yij = β 0 + β1 ln K ij + β 2 ln PLij + β 3 ln NLij + β 4 ln K ij ln PLij + β 5 ln K ij ln NLij ++β 5 ( ln PLij ) + β 6 ( ln NLij ) + β 7 ( ln K ij ) + β8 X ij222

where Yij = value added of plant i of industry j
PLij = number of production workers of plant i of industry j

11

(1)

NLij = number of non-production workers of plant i of industry j
K ij = fixed assets of plant i of industry j
X ij = controlling variables in affecting plant productivity of plant i of industryj.
In equation 1, there are three primary inputs, physical capital and two types oflabour (i.e. production and non-production workers).

The latter is done to allow

marginal products from them to be different. Controlling variables include both firmand industry-specific factors.
The first controlling variable is the plants’ market orientation nature ( MKTij ).
One clear-cut finding in the literature of the export-productivity nexus is that exporters
are found to have higher productivity than non-exporters as firms would expect more
intense competition in the global market than in the domestic market. In addition, there
are sunk costs induced by exports.7 Hence, the nature of market orientation is includedin the model with the theoretical expected positive sign. MKTij is measured a binary
dummy variable which equals to 1 if firms’ export-sale ratio exceeds 25 per cent andzero otherwise. The rationale of not using an actual export-output ratio is because therelationship between market orientation and productivity could be non-linear. Firms
planning to export must enhance their productivity to a certain level before export sothat a positive relationship between market orientation and productivity is expected
within a certain range of the export-output ratio only. In this study, 25 per cent isarbitrarily used so that sensitivity analysis is conducted by using 20 and 30 per cent asalternative cutting points. Nevertheless, the regression results are not sensitive to the
cutting points.8

7

Even though there is ongoing debate about whether firms become more productive
before export (self-selection) or experience productivity gains after export (learning fromexport). See the recent survey in Wagner (2007) and works cited therein.
8
Results are available upon author’s request.

12

As guided by the theory and previous empirical work on the determinants ofplant productivity differences, two industry-specific factors are taken into consideration.
These are producer concentration and trade protection. Because of its ease ofmeasurement, producer concentration is often used by policy makers to signal theintensity of product market competition and justify any action in preventing any
possibly anti-competitive behaviours. Here producer concentration is measured byoutput share of the four largest firms (CR4). The formulae to calculate CR4 are inequation (2).
4∑s

CR 4 j = i =n1

ij

∑s
i =1

(2)

ij

The impact of CR4 on plant productivity remains ambiguous nonetheless. On
the one hand, pioneered by Schumpeter (1942), productivity-enhancing activities
typically involve large fixed costs , are irrecoverable upon exit, and are subject to alarge degree of risk and uncertainty. Hence, the expectation of some form of transient
ex post market power is required for firms to have the incentive to invest in suchactivities. This is especially true in the context of developing countries whose domestic
market remains small (Roberts & Tybout, 1996). Perfect competition is not necessarily
conducive for productivity improvements.

On the other hand, the market power

required is not a sufficient condition for firms to commit to these activities as suggested
by a number of empirical studies (Symeonidis, 1996; Ahn, 2002). In fact, as these
activities are not costless, a certain degree of market competition is needed to force eachindividual firm to speed up the adoption of new technology (Porter, 1990; Aghion, et al.
1999). In many circumstance, the high level of producer concentration could retard
productivity improvement.
Protection is the second industry-specific variable controlled in the model. The
effect of protection on plant productivity has been long recognized in numerous
previous studies but is ambiguous (e.g. Corden, 1974: Hart, 1983; Martin & Page, 1983;

13

Scharfstein, 1988; Rodrik, 1991). While protection can create economic rents that canbe used for productivity improving activities, in practice an opposite effect can be seen.
By insulating firms from foreign competition, high protection tends to induce producers
to become ‘unresponsive’ to improved technological capability as well as to requests for
improvement in the quality and price of what they offer (de Melo and Urata, 1986;
Moran, 2001). This in turn results in a general deterioration of technological andmanagement skills

Hence, the sign of trade protection is theoretically ambiguous.

Protection is proxied by the effective rate of protection ( ERP ). Even though there is noconsensus between ERP and the nominal rate of protection (NRP) amongst economists
as to choice of one over the other (Corden, 1966; Cheh, 1974), political bargains in Thaimanufacturing are struck over ERP rather than NRP based on the econometric evidenceof Jongwanich & Kohpaiboon (2007).
An interaction term between CON j and ERPj is introduced to rectify the major
weakness of producer concentration in measuring the degree of product market
competition.

At best, producer concentration cannot capture dynamic aspects ofcompetition especially from imports. As mentioned above, competition is important forthe positive impact of concentration on productivity. In the competitive environment,
the less productive firms tend to be “weeded out”, so a highly concentrated industry
structure would be more conducive for firms to continue their innovative activities. By
contrast, in the absence of significant market competition, economic rents generated as aresult of high producer concentration are likely to be captured by its managers (andworkers) in the form of managerial slack or lack of effort. All in all, this suggests thatthe impact of producer concentration tends to be conditioned by the degree of market
competition so that the interaction term is introduced. The coefficient corresponding to
the interaction is expected to be negative.
The extent of foreign presence in an industry j ( FOR j ) is introduced to examine
horizontal technology spillovers, In some previous empirical studies, foreign presence
can be captured by either output, employment or capital shares. Expressing the foreign
presence as an employment share tends to underestimate the actual role of foreign14

affiliates because MNE affiliates tend to be more capital intensive than locally nonaffiliated firms.7 On the other hand, the capital share can easily be distorted by thepresence of foreign ownership restrictions. Such a restriction was in effect in Thailand
during the study period (Kohpaiboon, 2006). Hence, the output share is the preferredproxy.
As suggested in the previous studies, horizontal spillovers can be either positiveor negative, depending on the absorptive capability of local plants and the nature of the
trade policy regime. The absorptive capability of the local plant is measured by theratio of supervisory and management workers to total employment (QL) as supervisory
and management workers are regarded as skilled labour. The higher the ratio, thehigher the labour quality.

The expected sign of the corresponding coefficient is

positive. Trade policy regime is proxied by ERP. The higher the ERP, the less thehorizontal spillovers, so that the negative sign of the interaction term is expected.
As argued above, FDI can also generate vertical spillovers through the linkage
channel. To do so, inter-industry linkage is established according to the Leontief interindustry accounting framework. Consider an input-output framework of the‘complementary import’ type (i.e. the input-output table, in which the import content ofeach transaction is separately identified and allocated to an import matrix)9;

X = Ad X + Y d + E(3)where X = column vector of total gross output,
Ad = ⎡⎣ aijd ⎤⎦ , aijd = X ij X j = domestic input-output coefficient matrixn

Y d = column vector of domestic demand on domestically produced
goods.

E = column vector of export demand on domestically produced goods.

9

Another type of Input-output (I-O) table is a ‘competitive import’ type in which allimports (intermediate plus final) are treated as competing with domestic production and thus
imports are not separated from domestic transactions (Bulmer-Thomas, 1982).

15

Solving equation (1) for X ,(X = 1 − Adcwhere 1 − Ad) ⎡⎣Y + E ⎤⎦
−1

d

(4)

h is the Leontief domestic inverse (LDI) matrix.
−1

Consider a row vector j, each element in the row, say bij , indicates amount ofindustry j’s output demand by an additional unit of industry i’s output produced, i.e.
derived demand for industry j’s output from industry i’s production. Note that bij
captures both direct and indirect (inter-sectoral) repercussions in the measurement
process. This is different from Blalock (2001), Schoors & van der Tol (2001) both citedin Javorcik (2004: 612) and Blalock & Gertler (2008) whose backward linkage proxy
captures only the direct demand for industry j, an element in input-output matrix. A
product between each element in row vector j and its corresponding degree of foreign
presence ( FOR j ) measures to a certain extent derived demand from foreign presence forindustry j’s output. Hence, the sum of the product from column 1 to n indicates total
derided demand for industry j’s products from foreign plants, backward linkages fromforeign plants. The higher the BACK j the greater the backward linkages. This implies
the greater vertical spillover through backward linkages and the positive sign ofcoefficient corresponding to BACK j is expected. Note that inputs supplied within theindustry j are excluded as they are already captured by FOR j .

In a column vector i in LDI matrix, each element, say bik , indicates demand forindustry k’s output to be used as inputs for producing a unit of industry i’s output.
When we multiply each element in column vector i with its corresponding foreign
share ( FORk ) , the product indicates intermediates of industry i supplied by foreign
plants located in in industry k. Hence, the sum of products would reflect a fraction totalintermediates used in industry i supplied by foreign plants, i.e. the forward linkage fromforeign presence. The greater the value of FORW j , the larger, the extent of foreign
presence in upstream industries. Hence, the corresponding coefficient is hypothesized16

to be positive. For the same reason as before, inputs purchased within the industry j are
not included.
Finally, two sets of binary dummy variables are included in the model. First,
two time dummy variables (t2002 and t2003) are included to capture time-specific fixed
effects, with 2001 as the base dummy. Secondly as argued in a number of studies suchas Cohen & Levin (1989) and Moulton (1990), studies of the firm size-innovative
activity relationship need to control for industry effects at a high level of aggregation,e.g. 2-digit level, especially when using a sample covering many industries.

In

particular, standard errors are corrected to take into account the fact that the measures ofpotential spillovers are industry-specific while the observations in the dataset are at thefirm level. Falling to make such a correction could lead to a serious downward bias in
the estimated errors thus resulting in a spurious finding of statistical significance of theaggregate variation of interest. It becomes even more important for those undertaken in
the context of developing countries where large firms are likely to be diversified andoperate in more than one industry.10 As a result, industry dummy variables at the 2 digit
ISIC industry classification are introduced.
All in all, the estimating equation of FDI technology spillover is as follows;(theoretical expected sign is given in parenthesis) ;lnYijt =γ0 +γ1 ln Kijt +γ2 ln PLijt +γ3 ln NLijt +γ4 ln Kijt ln PLijt +γ5 ln Kijt ln NLijt +γ6 ln NLijt ln PLijt +γ7 ( ln NLijt )2+γ8 ( ln PLijt ) +γ9 ( lnKijt ) +γ10MKTijt +γ11CR4j +γ12ERPj +γ13CR4j *ERPj +γ14FORj
2

2

(5)

+γ15ERPj *FORj +γ16QLijt *FORj +γ17BACKj +γ18FORWj +γ19t2002+γ20t2003+γ21IDj + μij

where
ln Yijt= Value added of plant i in industry j at time tln PLijt= Number of production workers of plant i in industry j at time tln NLijt= Number of non-production workers of plant i in industry j at time tln K ijt= Fixed assets of plant i in industry j at time t

CON i (+/-)= Producer concentration of industry j measured by the sum of market
share of top four plants

10

The conglomerate nature of large firms is very prominent in Southeast Asian
economies (Studwell, 2007).

17

ERPj (+/-)= Effective rate of protection in industry j

MKTijt (+)= Market orientation of plant i in industry j at time t measured

FOR j (+/-)

alternatively by binary dummy variable, which equals to 1 if theexport-output ratio exceeds 25 per cent and zero otherwise.
= Foreign presence in industry j measured by output share of foreign
plants to total sales captured horizontal spillovers.

FOR j * QLijt (+) = MNE technology spillover gain conditioned by QLijt(i.e. Absorptive capability hypothesis)
FOR j * ERPj (-) = MNE technology spillover gain conditioned trade policy regime(i.e. Bhagwati’s hypothesis)= Quality of labour of plant i in industry j at time t measured by the ratio

QLijt

of supervisory and management workers to total employment(+) = Backward linkages spillover from foreign presence to industry j

BACK jFORW j

(+) =

t2002

ID j

= Time dummy for 2002 which is one if observation is in 2002 and zerootherwises= Time dummy for 2003 which is one if observation is in 2003 and zerootherwises= Industry dummy at 2 digit ISIC classification

μijt

=

t2003

Forward linkages spillover of foreign presence to industry j

A stochastic error term, representing the omitted other influences.

5. Data and Variable Construction
In this study, the Industry Survey by the Office of Industrial Economics, Ministry ofIndustry (OIE Survey) during the period 2001-03 is used.11 The survey is available
from 2001 to 2006 but the quality of unpublished returns of the last three years survey(2004-6) is rather problematic. In particular, they are subject to inconsistency inindustry identification of samples, to a matching problem between sales figures andother plants’ basic information allocated in separated sheets, and to a sharp decline insample number.12 Hence, only the OIE survey during the period 2001-03 is used in thispaper.

11

The alternative data set is the 1997 industrial census that is quite dated and has been
empirically used in a number of studies (e.g. Kohpaiboon, 2006; Kohpaiboon & Ramstetter,
2008; Jongwanich & Kohpaiboon, 2009; Kohpaiboon & Jongwanich, forthcoming).
12
In particular, the number of plants covered in the OIE Survey 2006 dropped sharply
to less than 2,000 plants.

18

There are 4,365, 3,986, and 3,521 plants in the 2001, 2002 and 2003 Surveys,
respectively (Table 1). The survey was first cleaned up by identifying duplicatedsamples (i.e. plants belonging to the same firm which filled in the questionnaire using
the same records) in the survey. The procedure followed in dealing with this problem
was to treat as duplicates the records that report the same values of the five key
variables of interest in this study, namely registered capital, output value, domestic
sales, domestic raw materials, imported raw materials. As a consequence, nine samples
were identified and dropped. Secondly, plants were removed which had not responded to
one or more of the key questions and which had provided seemingly unrealistic informationsuch as the negative value added, no report of worker numbers, capital stocks, or the initial
capital stock of less than 10,000 baht. Finally, we excluded micro-enterprises which aredefined as plants with less than 10 workers. After the data cleaning above the number ofsamples dropped to 3,373, 3,328 and 3,153 samples for Survey 2001, 2002 and 2003.
On average, the coverage of the OIE survey accounted for around 40.1, 49.6, and 24.8
per cent of value added, gross output, and workforce, respectively, of the manufacturingsector.

Table 1 provides a summary of survey characteristics and the extent to which it

represents the whole manufacturing sector.
(Insert Table 1 about here)
All nominal variables are converted to real terms (1988 price) by thecorresponding producer price deflator at the 4-digit ISIC classification. Value added isdefined as the difference between gross output and raw materials net of changes ininventories, whereas capital stock is represented by the value of fixed assets at the initialperiod. The other information related to plant-specific variables (i.e. OWN and MKT )are reported in the survey.

CR4 is obtained from Kophaiboon & Ramstetter (2008) in which theconcentration is measured at the more aggregate level (e.g. many measured at the 4digit whereas some at the 3-digit ISIC classification) to guard against possible problems
arising from the fact that two reasonably substitutable goods are treated as two different
industries according to the conventional industrial classification at high level ofdisaggregation. Data on ERP estimates are from Jongwanich & Kohpaiboon (2007).

19

They are ERP 2003 estimates, reflecting the protection structure in 1997-2003 as there
was no major change in tariff during this period. In addition, the ERP series used is theweighted average of import-competing and export-oriented ERP. The latter is referred
to ERP estimates for exporters who are eligible for various tariff rebate programs.
Since ERP is based on the input-output (IO) industrial classifications, the official
concordance is used to convert them into 4-digit ISIC. In a case that there is not oneto-one matching in the concordance, the weighted average is applied using value added
as a weight.
The ideal dataset for measuring BACK j and FORW j is detailed information ofinter-industry relationship between local and foreign enterprises, how much the former
sells to or buys from the latter. Nevertheless, our choice is driven in part by data
limitations. Hence inter-industry relationship to measure BACK j and FORW j is based on
Thailand’s input-output table consisting of 180 economic activities (42 in agriculture
and primary sectors 93 in the manufacturing sector and the rest in the service sector).
One caveat when using Thailand’s input-output table is that car assembly and several
metallic parts manufactures such as body parts and inner panels are lumped into a single
category, (IO 125 motor vehicle) so that backward linkages measured would be to acertain extent underestimated. The same procedure applied for ERP is used to match
input-output (IO) industrial classifications to 4-digit ISIC.
To measure FOR using OIE survey would be problematic as the survey
coverage is rather limited. As discussed the surveys cover at most 50 per cent of the
manufacturing sector’s gross output and it is likely that foreign affiliates are covered in
the survey because of their relatively large firms. Hence, FOR measured from thesurvey tends to be overestimated and reflect the extent of foreign plants in the survey
rather their actual presence in the sector.

This would also mitigate any possible

simultaneity bias in estimating the spillover equation (see below for further discussion).
Hence, in this study, FOR is constructed using the Industrial Census 1996 which
accounted for 76.2 per cent of the manufacturing sector’s gross outputs. In the census,
all plants with FDI (regardless of the magnitude of the foreign share in their capital

20

stock) are considered to be foreign rather than local plants. The cutting point (i.e. zero
per cent) seems to be slightly higher than what is widely used by the International
Monetary Fund (IMF) and other institutes such as the Organization for Economic Cooperation and Development (OECD), the US Department of Commence as well as
several scholars studying multinational firms (IMF, 1993; Lipsey, 2001), i.e. 10 per
cent. However, the choice is dictated by data availability. Information on foreign
ownership in the census is reported with a wide range, i.e. zero, less than 50, greater 50
and 100 per cent foreign shares. Tables 2 and 3 provide a statistical summary of all
variables discussed above and their correlation matrix.
(Insert Tables 2 and 3 about here)

6. Regression Results
To examine the presence of spillover from FDI, an unbalanced panel econometric
procedure is applied. We used the random effect estimator as our preferred estimation
technique. The alternative fixed effect estimator is not appropriate because our model
contains a number of time-invariant variables ( CON j , ERPj , FOR j , BACK j , and
FORW j ) all of which are central to our analysis. A major limitation of the random

effect estimator compared to its fixed effect counterpart is that it can yield inconsistentand biased estimates if the unobserved fixed effects are correlated with the remaining
component of the error term. However, this is unlikely to be a serious problem in ourcase because the number of explanatory variables is larger than the number of ‘within’observations (Wooldridge 2002, Chapter 10). The random effect estimator also has theadded advantage of taking caring of the serial correlation problem. The results arereported in Table 5. Nevertheless, the corresponding pooled cross-section estimations
are reported for the purpose of comparison. The random-effects and pooled crosssection estimates are remarkably similar, suggesting that unobserved effects would be
relatively unimportant in our model.
Studies of FDI spillovers are subject to a criticism about a possibility of asimultaneity problem. The positive relationship between foreign presence and plant21

productivity might be interpreted as reflecting the fact that foreign investment gravitates
towards more productive industries rather than representing any technology spilloverfrom FDI (Haddad & Harrison, 1993; Aitken & Harrison, 1999). The general responsein the literature is to undertake fixed-effect panel estimation. Nevertheless, ourestimation results are less likely to be subject to a simultaneity problem as FOR in thisstudy is a pre-determined variable obtained from the 1996 industrial census. In theory, it
is arguable that a pre-determined variable might contain expectations of future
outcomes hence the simultaneity problem remains unsolved. For example, current
investment of MNEs would be a result of their expectation of productivity gains in the
future. This argument is less likely to apply for this study since foreign presence here ismeasured by output share of current economic activities, and is unlikely to contain any
future expectation. Even though FOR reflects the distribution of foreign presence in
1996, as argued in Ramstetter (2003), the relative importance of foreign firms remains
unchanged during the past decade starting in 1996.

6.1 Is the foreign plant more productive than the locally-owned one?
Before we examine whether there are FDI spillovers and its relative importance between
horizontal and vertical spillovers, we ask a simple question; is the foreign plant is moreproductive than the locally owned one? Even though it is theoretically expected that
MNC affiliates should be more productive than locally non-affiliated firms (Caves,2007), it is not always true as suggested in several empirical studies such as Ramstetter(2006) in the case of Thai manufacturing. Menon (1998) and Oguchi et al. (2002) in thecase of Malaysian manufacturing.
To do so, Equation 5 discussed above is modified. First, the sample will coverboth foreign- and locally owned plants. Second, FOR and its related variables (itsinteraction terms with ERPj and QLijt as well as BACK j , and FORW j ) are replaced byownership variable (OWN) measured by a binary dummy variable which equals to 1 if
foreign ownership is greater than 10 per cent and zero otherwise. By definition, FDI
reflects the objective of an entity resident in one country to obtain a long-term
relationship between the direct investor and the host country enterprise, in which the22former has a significant degree of influence on the management of the latter. However,the significant degree does not necessarily mean majority ownership. Hence this study
follows the dominant current definition by the International Monetary Fund (IMF) andother institutes such as the Organization for Economic Co-operation and Development(OECD), the US Department of Commence as well as several scholars studying
multinational firms, which use 10 per cent.13

Nevertheless, we also use the actual

foreign ownership share (OWN1) as an alternative measure to examine the sensitivity of
results. . A statistical significance of OWN indicates the productivity difference.
The result of the productivity determinant equation is reported in Table 4. The
first and second columns are the results of pooled cross-sectional and random-effected
estimations, respectively. Our following discussion will be based on the latter becauseof the reasons discussed above. The estimated equation passes the Wald- test for overall
statistical significance at the 1 per cent level. The statistical significance of coefficients
corresponding to the primary inputs (capital, production workers and non-production
workers), their interactions, and some of their squared terms suggests that theassumption imposed in the Cobb-Douglas production function is not supported by plantlevel panel data of Thai manufacturing.

Even though translog functional form

specification is likely to be affected by the multicollinearity problem and standard error
is inflated, coefficients associated with the squared values of capital and production
workers are statistically significant at the one per cent level or better. It suggests thatsuch a multicollinearity problem would not create any severe effect on the regression
outcome. In particular, in the presence of the multicollinearity problem the effect stillshows up, simply because the true value itself is so large that even an estimate on thedownside still shows up as significant (Johnson, 1984: 249).
A coefficient corresponding to OWN is statistically significant. It suggests that
all other things (e.g. inputs level and scale effects) being equal, the foreign plant tends
to exhibit higher value added than the locally owned one. The coefficient of 0.21
indicates that the productivity difference between foreign and locally owned plants is
13

For example, the early Harvard studies under the direction of Raymond Vernon:
Vaupel & Curhan, (1969: p.3) and Wilkins (1970), both cited in Lipsey (2001)23

about 21 per cent on average after controlling input levels and scale effects. We alsofind that exporting firms tend to exhibit a higher level of productivity than nonexporting ones as the coefficient corresponding to MKT turns out to be positive andsignificant. Such evidence supports the consensus in the literature of the exportproductivity nexus that export-oriented plants tend to be more productive thandomestic-oriented plants.
.
Impacts of producer concentration and trade protection on plant productivity areto certain extent consistent with the findings of previous studies, i.e. Kohpaiboon &Jongwanich (forthcoming) using the , Industrial Census 1996 data set. That is, the net
impact of producer concentration on plant productivity is not automatic, but does depen
on the degree of tariff protection. Tariff reduction must reach a certain level before thepotential positive impact of producer concentration on productivity is observed.
Similarly, insulating firms from foreign competition is not sufficient to promote plant
productivity improvement. In a highly concentrated industry, high protection tends to
induce producers to become ‘unresponsive’ to improved technological capability and toretard productivity growth.14(Insert Table 5 about here)

6.2 Horizontal and Vertical FDI Spillovers
In this subsection the core hypothesis of this paper, namely the presence of horizontal
and vertical FDI spillovers, is addressed. Their regression results are reported in Table
14

Statistical significance of the interaction coefficient is very marginal at 15 per cent(one-tailed test). As seen in Section 6.2 when the sample covers only locally owned firms, theinteraction term turns out to be statistically significant at five per cent. This would be consistent
to the aggregate trend discussed in Section 3 that foreign plants in Thailand tend to be located inefficient-seeking industries especially electronics, electrical appliances and automobiles. In fact
FDI in automobile industry started with the traditional tariff-hopping style which aimed for ahighly protected domestic market. As argued in Kohpaiboon (2006b and 2007), FDI inflows
increased significantly in the 1990s with a shift in investment motivation to efficiency-seeking.
Such foreign plants are keen to improve their production efficiency and strengthen theirinternational competitiveness. This occurs even in a highly concentrated environment.
Therefore, when foreign plants are included, this could weaken the proposed non-linear
relationship among productivity, producer concentration and protection to some extent.

24

5. While both pooled cross-sectional and random-effect estimations are reported in thefirst two columns of Table 5 for the sake of comparison, our discussion will emphasise
random-effect estimations. The overall significance test (Wald test) is passed at the one
per cent level. In general, most of the firm- and industry-specific variables (i.e. K, NL,

PL, MKT, CR4, ERP and CR 4 * ERP ) turn out to be statistically significant and are inline with what are found in the productivity determinant equation in the previous
section.
(Insert Table 5 about here)
Regression results support the hypothesis that horizontal FDI spillovers can varyacross industry. The found negative coefficient of FOR*ERP fails to reject the‘Bhagwati hypothesis’. Given the extent of foreign presence, locally owned plants
operating in industries with more liberal trade regimes exhibit higher value added thanthose operating in the less liberal regimes. The evidence that the coefficient of FOR is
not statistically different from zero points out that foreign presence could either
negatively or positively affect the local plant’s productivity, depending on the nature of
the trade policy regime, i.e. ERP greater or less than zero. As shown in Figure 2, there
are many export-oriented industries experiencing negative ERP such as processed foods(ISIC 1511 and 1512), leather products (ISIC 1911). The negative figure is largely due
to the presence of cost in tariff drawback schemes (e.g. bank guarantees).

The

econometric findings in these studies are also in line with those in previous studies, i.e.
Balasubramanyam et al. (1996), Athukorala & Chand (2000), Kohpaiboon (2003:
2006a) and Kokko et al. (2001).
The interaction between foreign presence and absorptive capability is notstatistically different from zero. The statistic insignificance does not reject the role ofabsorptive capability in conditioning gains from horizontal FDI spillovers. The failure
to uncover its statistic significance could be due to a measuring problem. In particular,
the definition of non-production workers in the survey is wide, covering not only
supervisors and management workers but also clerical and administrative staff.
Interestingly when identical horizontal spillovers are relaxed, statistical significance ofvertical spillovers from both backward and forward linkages is not found.

25

The

coefficient corresponding to BACK j is positive but not statistically different from zero.
The coefficient corresponding to FORW j turns out to be negative but insignificant.

In general, the key finding in this study (that there are only horizontal spillovers,
not vertical ones) run counter to that of Javorcik, (2004) and Blalock & Gertler (2008)
relating to Lithunian and Indonesian manufacturing sectors, respectively. They haveuncovered a statistically significant positive spillover through backward linkages but
not horizontal spillovers.

We suspect that the failure to appropriately control for

relevant explanatory variables may have biased the results of these studies.
Interestingly, our data set permits us to replicate their results through similar (arbitrary)variable choice. That is, equation 5 is re-estimated by dropping two interaction termswith horizontal FDI spillovers, i.e. imposing an assumption of identical horizontal
spillovers. The results are in line with Javorcik (2004) and Blalock & Gertler (2008).
Only the coefficient corresponding to BACK j is statistically significant at 10 per cent.

We rather argue that our model is more preferable as the results seem to be inline with the industrialization path in developing countries including Thailand. As
argued in Hugh (2001) several developing Southeast Asian economies pursue the socalled ‘dualistic approach’ in opening up international trade, i.e. they are still reluctant
to cut tariffs but opt for tariff drawback schemes as a key instrument to promote anexport-led industrialization strategy. For instance, Thailand has been conservative inopening the door for foreign made goods for the past three decades, as indicated in thefact that its applied tariff rates remain at the highest of the six original ASEAN
countries (Jongwanich & Kohpaiboon, 2007).
Under such a policy setup, two options are available for entrepreneurs, including
MNEs. In Option 1, entrepreneurs aim to be a part of the global economy in which
resource allocation is directed according to factor proportion consideration for neoclassical efficiency. Firms in this option tend to be more export-oriented. By contrast,
Option 2 encourages entrepreneurs to set up plants and supply highly protected local
markets in order to benefit from protection-induced economic rents. Even though

26

MNEs can occur in both options, MNEs existing in the first option (efficiency-seeking
MNEs) tends to be more beneficial than those in the second option (market-seeking
MNEs) argued in Athukorala and Chand (2000) based on US MNEs experience.
In this circumstance, backward linkages would hardly occur and nor would
vertical spillovers. Export-oriented firms including MNEs are unlikely to source local
intermediates because of the presence of intermediate tariffs so that they seem tooperate in ‘enclaves’ in isolation from local suppliers.

In the meantime, highly

protected domestic markets encourage indigenous suppliers to find their own nichemarkets that are not directly related to what exporting firms want. As long as the policyinduced incentive structure still creates the economic rents, it would be difficult to findqualified suppliers.
That would explain why MNEs which have played an important role in
Thailand’s industrialization generate limited backward linkages to indigenous firms.
Limited backward linkages are observed in several leading export-oriented industries inThailand such as the automotive, garment and hard disk drive industries (Kohpaiboon,
2006b; 2007 and 2008 and 2009). For example, while locally assembled vehicles inThailand are reliant largely on locally manufactured parts, as illustrated by the
proportion of imported parts to vehicle production, the number of purely Thai firms
must be around 10 suppliers, comparing to 287 MNE suppliers. Another example, theratio of imported fabric to garment production in Thailand has been increasing since1996 (Kohpaiboon, 2008: Figure 4). The same evidence is also found in the case of theHard Disk Drive industry (Kohpaiboon, 2009).

7. Conclusion and Policy Inferences
This paper examines FDI spillovers in Thai manufacturing, using industrial surveys
during the period 2001-03. A panel data econometric analysis of plant productivity
determinants of locally owned plants is undertaken. The paper goes beyond the existing
literature in two ways. First, both horizontal and vertical FDI spillovers are tested. In
addition, both direct and indirect (inter-sectoral) repercussions are captured in the27measurement process of industrial linkages. Secondly, horizontal FDI spillovers areallowed to be different from one industry to the other instead of assuming identicalvalues across industries.
The key finding is that advanced technology associated with MNE affiliates
does not always spill over to the local plants operating in the same industry. The extent
of spillovers depends on the nature of the trade policy regime. Only industries operating
under a liberal trade policy regime experience positive horizontal FDI spillovers.
Neither backward nor forward spillovers are found in our study. This seems to be incontradiction with the existing literature highlighting the relative importance of
backward linkages as a likely FDI spillover channel. Statistical significance of vertical
spillovers through backward linkages is found only if an assumption of identicalhorizontal FDI spillover is in place. Such an assumption seems to be restrictive. The
finding that export-oriented plants have higher productivity than domestic-marketoriented ones further highlight the role of trade policy regime on plant productivity
improvement process. Trade liberalization and its induced contestability environment
are an effective catalyst for firms to continue to improve their productivity. Besides,
only in low tariff environment, the positive impact of producer concentration on plant
productivity is observed.
Two policy inferences can be drawn from this study. First these results further
highlight the relative importance of the trade policy regime for productivity
enhancement and thus development policy. Liberalizing the foreign investment regime
thus has to go hand in hand with liberalizing trade policy to maximize gains from MNE
presence.

Trade liberalization itself also creates contestability environment that is

conducive for firms to continue improving their productivity. Secondly, while therelative importance of the linkage channel and its corresponding spillovers seems to bea convincing argument, our work here provides a warning for policymakers notoveremphasize it. The conducive role of the backward linkage channel is a result of
natural links that are driven by economic concerns and can be distorted by policy
measures. The ability of the policy domain to forge linkages seems to be limited.
Policy-induced linkages are not perfectly substitutes for natural linkages. This issue is28

increasingly important under a rising threat of the return of nationalism andprotectionism in the incoming global economic recession. The magnitude of linkages is
not a good proxy of the magnitude of vertical FDI spillovers. The quality of backward
linkages is a far better indication. Where quality is concerned, backward linkages
driven by economic concerns as well as motivated by capability of indigenous suppliers
are by far superior to that induced by policy measures.

29

Table 1
Sample Coverage of Office of Industrial Economics Survey
Year% of Thai Manufacturing Sector
Value Added

OutputEmployment

2001
2002
2003

45.3
41.1
33.8

52.6
53.7
42.4

24.5
25.5
24.5

Average

40.1

49.6

24.8

Number of Plants
BeforeAfter
Cleaning Cleaning
4,365
3,986
3,521

3,373
3,328
3,153

Source: Author’s compilation from OIE Survey whereas value added and outputof the manufacturing sector are from National Economics and Social
Development Board (NESDB). Labor force is from Key Indicators for Asia andthe Pacific 2008, Asian Development Bank.

Table 2
A Statistical Summary of the Key Variables
Unit

Mean

SD

Min

Max

VDijt

(ln) million baht

16.32

1.92

6.00

24.00

K ijt

(ln)million baht)

16.11

2.36

5.00

24.00

NLijt

(ln) workers

2.71

1.35

0.00

7.00

PLijt

(ln) workers

4.50

1.44

0.00

9.00

MKTijtzero-one dummy

0.29

0.45

0.00

1.00

CON j

0.44

0.11

0.23

0.69

ERPj

(ln) proportion(ln) proportion

0.12

0.14

-0.30

0.58

FOR j

(ln) proportion

0.36

0.15

0.00

0.69

QLijt

(ln) proportion

0.16

0.11

0.00

0.67

BACK j(ln) proportion

1.08

0.90

0.02

7.17

FORW j(ln) proportion

1.23

1.00

0.00

5.27

a

Notes: Mean = simple average; SD = standard deviation; Min = minimum; and Max =maximum;b
Estimates of VDijt , K ijt , NLijt and PLijt are the logarithmic transformation of theirvalue. The other variables are converted into logarithmic form as log(1+x) where x is thevariable
Source : Author’s computations based on data sources described in the text.

30

Table 3
Correlation Matrix of the Variables
VDijt

K ijt

NLijt

PLijt

MKTijt CON j ERPj

FOR j

QLijt

BACK j

VDijt

1.00

K ijt

0.75

1.00

NLijt

0.71

0.65

1.00

PLijt

0.77

0.66

0.72

1.00

MKTijt

0.28

0.19

0.27

0.40

1.00

CON j

-0.11

-0.13

-0.06

-0.07

0.05

1.00

ERPj

0.01

-0.05

0.01

0.09

0.12

0.15

1.00

FOR j

-0.03

-0.05

-0.07

-0.05

-0.01

0.11

-0.15

1.00

QLijt

-0.10

-0.04

0.30

-0.36

-0.22

0.00

-0.12

-0.02

1.00

BACK j

0.01

-0.01

-0.03

-0.01

-0.01

0.10

-0.03

0.25

-0.01

1.00

FORW j

-0.11

-0.05

-0.13

-0.20

-0.22

0.04

-0.23

0.19

0.09

0.09

Source: Author’s computations based on data sources described in the text.

31

FORW j1.00

INTP
ln K ij

ln Kij 2
ln NLijln NLij 2
ln PLijln PLij 2
ln K ij ln NLij
ln K ij ln PLijln NLij ln PLijt 2002t 2003MKTij
OWN ijTable 4
Regression Results of Productivity Determinants
OLS
RE
11.99
11.88
(48.70)***(48.32)***
-0.17
-0.14
(-6.01)***(-5.21)***
0.01
0.01
(8.71)***(8.76)***
0.41
0.41(8.50)***(9.25)***-0.0020.005(0.20)(0.62)
0.40
0.36(10.35)***(10.35)***
0.024
0.02
(2.74)***(2.43)**
0.02
0.01(4.08)***(3.73)***
0.01
0.01(1.8)**(2.67)***
-0.09
-0.09
(-10.20)***(-10.94)***
-0.04
-0.04
(-1.76)*(-2.06)**-0.037-0.03(-1.50)(-1.42)0.07
0.08(3.11)***(2.62)***
0.21
0.21(8.82)***(6.51)***

Industry-specific
CON j
ERPj
CON j ERPj

# Observations
F-stat
R-sq
RESET

0.63
(3.53)***
0.79(2.11)**-1.01(-1.05)δ
9,815
1132.9 ***0.78
1.50 (p=0.21)

0.72(3.36)***
0.88(1.94)**-1.09(1.02) δ
9,815 (3,963 groups)
19788.5 ***0.78

Notes: OLS = Ordinary Least Squares whereas RE = Random Effect Estimation; The number inthe parenthesis of OLS is t-statistics constructed from robust standard error whereas that of RE
is z-statistics,. RESET is the RESET- functional form misspecification tests; ***,**, * and δ
indicates a statistical significance at 1, 5, 10 and 15 per cent level, respectively.
Sources: Author’s estimation

32

Table 5
Regression Results: Horizontal and Vertical FDI Technology Spillover
Heterogenous Horizontal
Identical Horizontal Spillovers
Spillovers
Pooled-crossRE
Pooled-crossRE
SectionSection
11.92
11.92
12.03
12.08
INTP
(39.39)***(38.56)***(39.32)***(39.48)***ln K ij
-0.15
-0.13
-0.15
-0.14
(-4.19)***(-4.01)***(-4.25)***(-4.14)***
0.009
0.01
0.009
0.009
ln K ij 2(5.06)***(5.51)***(5.22)***(5.64)***
ln NLij
0.37
0.36
0.37
0.36
(5.81)***(6.38)***(6.00)***(6.46)***-0.01-0.002-0.01-0.002ln NLij 2(-0.85)(-0.18)(0.91)(-0.23)ln PLij
0.36
0.32
0.36
0.32
(7.43)***(7.64)***(7.47)***(7.65)***
2
0.01
0.01
0.01
0.01
ln PLij(1.19)(1.33)(1.07)(1.18)
ln K ij ln NLij
0.02
0.018
0.02
0.02
(4.21)***(3.84)***(4.14)***(3.93)***
ln K ij ln PLij
0.02
0.02
0.02
0.02
(2.52)***(2.77)***(3.02)***(3.27)***
ln NLij ln PLij
-0.08
-0.08
-0.08
-0.08
(-6.76)***(-7.50)***(-7.08)***(7.63)***
t 2002
-0.04
-0.04
-0.04
-0.39
(-1.42)(-1.75)*(-1.41)(-1.74)*t 2003
-0.04
-0.03
-0.04
-0.03
(-1.42)(-1.42)(-1.43)(-1.42)
MKTij
0.10
0.10
0.10
0.10
(3.66)***(2.71)***(3.62)***(2.65)***
Industry-specific
CON j
0.90
0.99
0.88
0.95
(3.77)***(3.56)***(3.70)***(3.41)***
ERPj
2.07
2.14
1.66
1.51
(4.50)***(3.68)***(3.40)***(2.71)***CON j ERPj
-2.85
-2.11
-3.66
-2.98
(-2.12)**(-1.57)*(-2.86)***(-2.25)**
FOR j
0.25
0.26
-0.75
-0.18
(1.28)*(1.09)(-0.57)(-1.13)
FOR j ERPj
-2.55
-3.53
(-2.85)***(-3.65)***
FOR j QLijt
-0.18
-0.16
(-0.27)(-0.23)(Cont.)

33

Table 5 (Contd.)

BACK jFORW j# Observations
F-statWald-test ( χ 2 )Overall R-sq
WithinBetweenRESETHeterogenous Horizontal
Spillovers
Pooled-crossRE
Section0.02
0.02(0.66)(0.82)-0.01-0.01(-0.67)(-0.50)6,907
6,907(2,843 groups)
565.3***
11194.6***
0.74

0.74
0.02
0.80

0.55
(p=0.65)

Identical Horizontal Spillovers
Pooled-crossSection
0.03(1.29)*-0.01(-0.54)
6,907

RE
0.04
(1.77)*-0.01(-0.35)
6,907(2,843 groups)

597.2***
11122.52***
0.74

0.74
0.02
0.80

0.82
(p=0.48)

Notes: OLS = Ordinary Least Squares whereas RE = Random Effect Estimation; The number inthe parenthesis of OLS is t-statistics constructed from robust standard error whereas that of RE
is z-statistics,. RESET is the RESET- functional form misspecification tests: ***, **, * and δ
indicates a statistical significance at 1, 5, 10 and 15 per cent level, respectively.
Sources: Author’s estimation34% Difference in capital labour ratio

Figure 1
Correlation between productivity gap and difference in capital-labour ratio between
foreign establishment and indigenous plants during the period 2001-03
700.0
600.0
500.0
400.0
300.0
200.0
100.0
0.0

-100.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

% Difference in labour productivity⎛ VA ⎞
Notes: Productivity gap %Δ ⎜⎟ is measured as the difference in labour productivity between⎝ L ⎠foreign establishment and indigenous plants as a per cent of labour productivity of the latter. %Difference in capital labour ratio between foreign establishment and indigenous plants⎛K⎞%Δ ⎜ ⎟ is measured in the similar way as productivity gap. Linear line here is based on the⎝L⎠⎛ VA ⎞
simple ordinary least square estimation in which % Δ ⎜⎟ is a dependent variable and
⎝ L ⎠
⎛K⎞
%Δ ⎜ ⎟ as the explanatory variable. This is to draw general statistic inference.
⎝L⎠⎛ VA ⎞⎛K⎞%Δ ⎜⎟ = 77.8 + 0.14 %Δ ⎜ ⎟
L
⎝
⎠
⎝L⎠
(7.01)*** (5.86)***(t-stat is in parenthesis)
Sources: Author’s compilation. See the full data in Appendix 1.

35

Figure 2
Correlation between foreign presence (FOR) and effective rate of protection (ERP)
0.70

ERP

0.60
0.50
0.40
0.30
0.20
0.10
0.00
-0.100.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

-0.20
-0.30
-0.40
FOR

Notes: Linear line here is based on the simple ordinary least square estimation in which FOR isa dependent variable and ERP as the explanatory variable. This is to draw general statistic
inference.
FOR= 0.44 - 0.33 ERP(18.7)*** (-2.66)***(t-stat is in parenthesis)
Sources: Author’s compilation. See the full data in Appendix 1.

36

Figure 3
Correlation between foreign presence (FOR) and backward linkage index (BLI)
1.20
1.00

BLI

0.80
0.60
0.40
0.20
0.00
0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

FOR

Notes: The statistical relationship between FOR and BLI is not significantly different from zero
based on the simple ordinary least square estimation in which FOR is a dependent variable and
BLI as the explanatory variable.
FOR= 0.41 - 0.004 BLI(12.9)*** (-0.08)(t-stat is in parenthesis)
Sources: Author’s compilation. See the full data in Appendix 1.

37

⎛ VA ⎞
⎛K⎞⎟ ), Capital-Labour Ratio( %Δ ⎜ ⎟ ), Foreign Presence, Backward Linkage Index and
⎝ L ⎠
⎝L⎠

Appendix 1: Patterns of Labour Productivity( %Δ ⎜Effective Rate of Protection of Thai Manufacturing

ISIC
1511
1512
1513
1514
1520
1531
1532
1533
1541
1542
1543
1544
1549
1551
1552
1553
1554
1600
1711
1712
1721

Description
Production, processing and preserving of meat and meat products
Processing and preserving of fish and fish products
Processing and preserving of fruit and vegetables
Manufacture of vegetable and animal oils and fats
Manufacture of dairy products
Manufacture of grain mill products
Manufacture of starches and starch products
Manufacture of prepared animal feeds
Manufacture of bakery products
Manufacture of sugar
Manufacture of cocoa, chocolate and sugar confectionery
Manufacture of macaroni, noodles, couscous and similar farinaceous products
Manufacture of other food products n.e.c.
Distilling, rectifying and blending of spirits; ethyl alcohol production from fermented
materials
Manufacture of wines
Manufacture of malt liquors and malt
Manufacture of soft drinks; production of mineral waters
Manufacture of tobacco products
Preparation and spinning of textile fibres; weaving of textiles
Finishing of textiles
Manufacture of made-up textile articles, except apparel

38⎛ VA ⎞⎛K⎞%Δ ⎜⎟ %Δ ⎜ ⎟
L
⎝
⎠
⎝L⎠
13.1
105.6
10.2
-46.2
0.8
42.8
30.9
-27.2
391.7
24.2
42.6
-61.5
160.9
277.7
-36.2
14.8
80.1
104.4
16.0
47.4
72.2
295.5
40.4
64.4
122.0
-43.8
-61.8
n.a.
249.1
84.4
217.4
102.2
n.a.
8.1

-36.7
n.a.
281.2
111.8
-57.1
121.0
n.a.
-68.4

FOR
0.32
0.29
0.27
0.13
0.21
0.13
0.39
0.23
0.12
0.21
0.32
0.27
0.51

BLI
0.91
0.72
0.47
0.42
0.58
0.66
0.57
0.59
0.70
0.84
0.66
0.84
0.59

ERP
-0.14
-0.08
0.15
0.39
0.12
0.14
0.12
-0.11
0.25
0.42
0.12
0.42
0.05

0.00
0.67
0.02
0.48
0.04
0.47
0.34
0.54

0.61
0.65
0.34
0.51
0.19
0.63
0.58
0.71

0.42
0.57
0.58
0.02
0.55
0.15
0.22
0.36
(Contd.)Appendix 1 (contd.)
ISIC
1722
1723
1729
1730
1810
1911
1912
1920
2010
2021
2022
2029
2101
2102
2109
2221
2320
2411
2413
2421
2422
2423
2424
2429

Description
Manufacture of carpets and rugs
Manufacture of cordage, rope, twine and netting
Manufacture of other textiles n.e.c.
Manufacture of knitted and crocheted fabrics and articles
Manufacture of wearing apparel, except fur apparelTanning and dressing of leather
Manufacture of luggage, handbags and the like, saddlery and harness
Manufacture of footwear
Sawmilling and planing of wood
Manufacture of veneer sheets; manufacture of plywood, laminboard, particle board and otherpanels and boards
Manufacture of builders' carpentry and joinery
Manufacture of other products of wood; manufacture of articles of cork, straw and plaiting
materials
Manufacture of pulp, paper and paperboard
Manufacture of corrugated paper and paperboard and of containers of paper and paperboard
Manufacture of other articles of paper and paperboard
Printing
Manufacture of refined petroleum products
Manufacture of basic chemicals, except fertilizers and nitrogen compounds
Manufacture of plastics in primary forms and of synthetic rubber
Manufacture of pesticides and other agro-chemical products
Manufacture of paints, varnishes and similar coatings, printing ink and mastics
Manufacture of pharmaceuticals, medicinal chemicals and botanical products
Manufacture of soap and detergents, cleaning and polishing preparations, perfumes andtoilet preparations
Manufacture of other chemical products n.e.c.

39⎛ VA ⎞⎛K⎞%Δ ⎜⎟ %Δ ⎜ ⎟
⎝ L ⎠
⎝L⎠
n.a.
n.a.
n.a.
n.a.
118.9
244.6
-0.6
37.9
18.0
-11.4
65.2
161.9
25.9
196.6
-8.7
-16.0
27.8
186.0

FOR
0.58
0.34
0.63
0.39
0.31
0.24
0.34
0.29
0.15

BLI
0.74
0.64
0.64
0.65
0.68
0.89
0.49
0.64
0.29

ERP
0.06
0.12
0.18
0.13
0.37
-0.30
0.23
0.06
0.02

-49.0
61.3

-10.3
49.4

0.37
0.06

0.35
0.35

0.03
0.03

n.a.
-44.6
53.2
112.3
23.3
370.3
87.0
81.0
n.a.
97.8
276.1

n.a.
106.5
78.5
100.3
-20.6
817.6
160.7
88.7
n.a.
164.2
56.0

0.21
0.52
0.16
0.50
0.10
0.44
0.37
0.46
0.64
0.60
0.17

0.54
0.33
0.35
0.41
0.46
0.14
0.35
0.51
0.44
0.52
0.41

0.45
0.03
0.13
0.15
0.17
0.04
0.07
0.15
0.03
0.01
0.00

284.8
n.a.

424.7
n.a.

0.52
0.53

0.49
0.54

0.02
0.06
(Contd.)Appendix 1 (contd.)
ISIC
2430
2511
2519
2520
2610
2691
2692
2693
2694
2695
2696
2710
2720
2731
2811
2812
2891
2892
2893
2899
2911
2912
2913
2914
2915
2919
2922

Description
Manufacture of man-made fibres
Manufacture of rubber tyres and tubes; retreading and rebuilding of rubber tyres
Manufacture of other rubber products
Manufacture of plastics products
Manufacture of glass and glass products
Manufacture of non-structural non-refractory ceramic ware
Manufacture of refractory ceramic products
Manufacture of structural non-refractory clay and ceramic products
Manufacture of cement, lime and plaster
Manufacture of articles of concrete, cement and plasterCutting, shaping and finishing of stone
Manufacture of basic iron and steel
Manufacture of basic precious and non-ferrous metals
Casting of iron and steel
Manufacture of structural metal products
Manufacture of tanks, reservoirs and containers of metal
Forging, pressing, stamping and roll-forming of metal; powder metallurgy
Treatment and coating of metals; general mechanical engineering on a fee or contract basis
Manufacture of cutlery, hand tools and general hardware
Manufacture of other fabricated metal products n.e.c.
Manufacture of engines and turbines, except aircraft, vehicle and cycle engines
Manufacture of pumps, compressors, taps and valves
Manufacture of bearings, gears, gearing and driving elements
Manufacture of ovens, furnaces and furnace burners
Manufacture of lifting and handling equipment
Manufacture of other general purpose machinery
Manufacture of machine-tools

40⎛ VA ⎞⎛K⎞%Δ ⎜⎟ %Δ ⎜ ⎟
⎝ L ⎠
⎝L⎠
75.2
120.0
282.2
156.0
-3.8
38.1
45.8
70.1
188.9
404.5
111.0
140.7
205.8
444.0
249.5
110.1
100.3
154.5
143.1
53.5
-24.4
-71.8
154.2
175.8
386.3
3501.8374.61223.0
76.1
53.6
159.0
161.2
n.a.
n.a.
32.3
219.0
162.1
188.8
187.0
264.9
265.9
491.4
129.9
252.5
311.3
640.6
113.7
52.0
285.9
589.8
207.8
82.2
157.4
625.8

FOR
0.63
0.57
0.29
0.31
0.49
0.39
0.52
0.03
0.13
0.27
0.08
0.23
0.40
0.63
0.45
0.48
0.54
0.64
0.40
0.37
0.64
0.43
0.65
0.63
0.64
0.54
0.46

BLI
0.63
0.58
0.61
0.57
0.30
0.31
0.57
0.50
0.48
0.54
0.18
0.49
0.42
1.13
0.35
0.34
1.13
1.13
0.37
-2.70
0.44
0.45
0.33
0.39
0.36
0.42
0.40

ERP
-0.10
0.29
0.15
0.14
0.03
0.02
0.11
0.07
0.00
0.05
0.04
0.06
-0.01
0.00
0.11
0.12
0.00
0.00
0.16
0.00
0.01
0.05
0.20
0.00
0.14
0.03
0.00
(Contd.)Appendix 1 (contd.)
ISIC
2924
2925
2929
2930
3000
3110
3120
3130
3140
3150
3190
3210
3220
3230
3311
3312
3320
3410
3420
3430
3591
3592

Description
Manufacture of machinery for mining, quarrying and construction
Manufacture of machinery for food, beverage and tobacco processing
Manufacture of other special purpose machinery
Manufacture of domestic appliances n.e.c.
Manufacture of office, accounting and computing machinery
Manufacture of electric motors, generators and transformers
Manufacture of electricity distribution and control apparatus
Manufacture of insulated wire and cable
Manufacture of accumulators, primary cells and primary batteries
Manufacture of electric lamps and lighting equipment
Manufacture of other electrical equipment n.e.c.
Manufacture of electronic valves and tubes and other electronic components
Manufacture of television and radio transmitters and apparatus for line telephony and line
telegraphy
Manufacture of television and radio receivers, sound or video recording or reproducing
apparatus, and associated goods
Manufacture of medical and surgical equipment and orthopaedic appliances
Manufacture of instruments and appliances for measuring, checking, testing, navigating andother purposes, except industrial process control equipment
Manufacture of optical instruments and photographic equipment
Manufacture of motor vehicles
Manufacture of bodies (coachwork) for motor vehicles; manufacture of trailers and semitrailers
Manufacture of parts and accessories for motor vehicles and their engines
Manufacture of motorcycles
Manufacture of bicycles and invalid carriages

41⎛ VA ⎞⎛K⎞%Δ ⎜⎟ %Δ ⎜ ⎟
⎝ L ⎠
⎝L⎠
n.a.
n.a.
-59.2
338.2
n.a.
n.a.
64.7
128.8
9.5
368.5
114.2
43.2
79.7
151.6
219.7
469.6
234.1
372.8
48.8
87.1
17.1
29.5
32.1
-24.1

FOR
0.16
0.00
0.55
0.62
0.69
0.45
0.64
0.62
0.60
0.40
0.57
0.68

BLI
0.37
0.40
0.44
0.44
0.44
0.30
0.20
0.42
0.48
0.36
0.23
0.26

ERP
0.14
0.00
0.00
0.05
0.00
0.00
-0.01
0.06
-0.07
0.04
0.04
0.02

45.3

23.5

0.57

0.15

0.00

-16.0
35.5

51.4
117.1

0.62
0.52

0.15
0.43

0.00
-0.02

99.3
198.9
235.7

10.0
333.2
1.4

0.64
0.65
0.67

0.21
0.38
0.33

0.00
0.00
0.20

9.5
86.3
226.4
201.8

285.4
126.2
553.3
259.8

0.53
0.43
0.48
0.00

0.33
0.37
0.62
0.62

0.20
0.14
0.39
0.39
(Contd.)Appendix 1 (contd.)
ISIC
3610
3691
3693
3694
3699

Description
Manufacture of furniture
Manufacture of jewellery and related articles
Manufacture of sports goods
Manufacture of games and toys
Other manufacturing n.e.c.
Average
Max
Min
Sources: Author’s compilation. See details of variables construction in the text.

42⎛ VA ⎞⎛K⎞%Δ ⎜⎟ %Δ ⎜ ⎟
⎝ L ⎠
⎝L⎠
49.6
23.5
11.5
94.0
22.7
56.5
-39.7
162.8
68.0
62.9
106.93204.87391.673501.79-61.79-71.76

FOR
0.26
0.40
0.67
0.26
0.50
0.40
0.69
0.00

BLI
0.50
0.42
0.48
0.59
0.64
0.46
1.13
-2.70

ERP
0.16
0.06
0.31
0.07
0.33
0.11
0.58
-0.30

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