The impact of core labour standards on Foreign
Direct Investment in East Asia

Ritash Sarna
Visiting Researcher
The Japan Institute for Labour Policy and Training
Tokyo, Japan

October 2005
Draft

Table of contents
1. Introduction .................................................................................................................. 3
2. Core Labour Standards ................................................................................................. 6
2.1 Forced labour .......................................................................................................... 6
2.2 Child Labour ........................................................................................................... 7
2.3 Discrimination (Gender inequality) ........................................................................ 8
2.4 Freedom of association and collective bargaining (FACB) .................................. 10
3. Labour costs and FDI ..................................................................................................11
4. Theory and determinants of FDI ................................................................................ 14
5. Literature review......................................................................................................... 17
6. The model ................................................................................................................... 19
6.1 Variables for the benchmark model....................................................................... 21
6.2 Measuring core labour standards .......................................................................... 23
6.3 Model results......................................................................................................... 25
7. Conclusions ................................................................................................................ 27
8. References .................................................................................................................. 29
9. Appendices, Graphs and Tables.................................................................................. 32
Appendix 1: Comparison of US and Japanese FDI in sample countries................. 32
Appendix 2: Data sources........................................................................................ 36
Appendix 3: Results of the model ........................................................................... 37
Table A3-1: Results of the Benchmark model ..................................................... 37
Table A3-2: Results of the model, including the measures of CLS ..................... 38
Table A3-3: Results of the model, including the ratification index ..................... 39
Table A3-4: Correlation matrix of all the variables used ..................................... 40
Table 1: Types of labour standards .......................................................................... 41
Table 2: Ratification of Core Labor Standards Conventions................................... 41
Table 3: Labour force participation rates of ten to fourteen year olds .................... 42
Table 4: FDI location criteria................................................................................... 42
Table 5: Industrial breakdown available from U.S. and Japanese FDI data............ 43
Graph 1: Percentage of U.S. and Japanese inward FDI out of total FDI inflows fromthe world in sample countries.................................................................................. 44
Graph 2: Percentage of US and Japanese outward FDI in sample countries .......... 44

2

The impact of core labour standards on Foreign
Direct Investment in East Asia

1. Introduction
The recent decades have seen a surge in Foreign Direct Investment (FDI) flows in theface of rapid globalization. The potential role of FDI in a country’s development iswell-recognised as it also brings technology, managerial skills and employment alongwith capital and foreign exchange. Achievements of some of the East Asian countries insuccessfully employing FDI highlight its importance in a country’s overall development,
if used strategically. This perceived important role of FDI has sparked competition
among countries to attract investors by offering them various incentives. As this global
competition intensifies, some developed countries feel concerned about the “unfair
competitive advantage” in attracting FDI that some developing countries have overthem because of their low labour standards (poor worker rights). They feel that the low
labour standards of these countries may force them to lower their labour standards inorder to remain in competition. This can lead to a “race to the bottom” where countries
start weakening their regulations in order to gain a competitive edge. There can be a“prisoner’s dilemma” type of situation where everyone has an incentive to lower
standards but everyone would be better off if no one lowered standards. To avoid this,they claim some universal set of labour standards to be enforced on all countries.
Many of the East Asian countries were able to attract fair amounts of FDI when theirlabour standards were relatively weak. Were they able to attract this FDI because of
their weak labour standards or were there any other reasons specific to the region? Is
there a case to enforce some labour standards universally and what are its implications
on developing countries in terms of their competitiveness to attract FDI? These aresome questions I attempt to answer in this paper. The remaining part of this section
looks at the complex debate of international labour standards from the perspective ofboth who are in favour and against them. Section 2 focuses on core labour standards(CLS) which are considered to be basic worker rights which every country must respect.
Section 3 explores the relationship between labour costs and FDI. Section 4 looks
briefly at the theory and determinants of FDI. Section 5 reviews existing literature3

which explores the links between FDI and labour standards. In section 6, I apply thesame model specification and methodology as used by Daude et al. (for determining
FDI and labour standards linkages in Latin American and Caribbean countries) to thesample countries 1 in East Asia. The purpose of doing so is to simply test whether thesame model is applicable to a different set of countries or not. The conclusions of thispaper are presented section 8.
As manufacturing FDI moves to the emerging markets of the South, workers in the
North see it as unskilled, labour-intensive jobs moving out of their countries. They feelthat the main motivation for multinationals corporations (MNCs) to move abroad is to
exploit deliberately neglected weak labour standards combined with low wages of theSouth. They demand action (like trade sanctions or other punitive measures) to be taken
against the countries who might, on purpose, keep their labour standards low to attract
FDI. Increasing trade and investment in the South has been considered as one of themain reasons of falling wages, rising income inequality and unemployment (Wood1995). However, not everyone subscribes to this view just because the volume ofNorth-South trade is still not substantial enough to have such an impact (Krugman
1995). Although this maybe one of the reasons but there are other reasons like “tradeimbalances between developed countries themselves, cyclical movements in economic
activity and its slow long-term growth in advanced countries, technical change, andchanges in economic and social policy in these countries” (Singh and Zammitt, 2000).
Even if there are some losses in the unskilled manufacturing sector, they can be easily
offset and compensated by gains coming from cheap imported goods and wage-rise
experienced by the skilled sector.
Apart from the workers of the North, there are others (like activist NGOs and humanrights organizations) who feel concerned about poor labour standards of the South. It isvery common to hear about cases of MNCs exploiting workers in the South by paying
them too little or making them work under extremely poor conditions or making
children work. They make their case on a humanitarian ground that human beings
cannot be treated in this way to increase profits. They campaign against MNCs and raise
1 Sample countries used for this study referred to as East Asia are China, Hong Kong

(China), Indonesia, South Korea, Malaysia, Philippines, Singapore and Thailand.
Selection criteria were based on the availability of FDI data (from both US and Japan)
for these countries. However, China and Hong Kong (China) drop out of the sample
when regressions are run as strike data for China and political rights data for Hong
Kong (China) are not available.

4awareness among consumers to boycott their products. As these MNCs care about theirbrand image and not losing any customers, they often come up with codes of conduct
and accept some social responsibility for their actions. Elliott and Freeman (2003)
observe that overall these activists have been successful in creating a “market forstandards” in the North and in general consumers demand labour standards as much as
they demand other aspects of goods and services.
Developing countries often perceive labour standards as privileges of the rich North.
They argue that labour standards improve automatically after a certain level of
economic development has been achieved. Any attempt to improve their labour
standards before this level would raise average labour costs reducing their global
competitiveness and thus hindering their economic growth. So, for them, economic
growth comes first followed by improved labour standards, rather than the other way
around. They often criticise the North of being protectionist in the pretext of labour
standards to deny them free and fair access to their markets. They consider that theircomparative advantage lies in low-skilled labour-intensive manufacturing in the same
way that the North has a comparative advantage in high-skilled capital-intensive
manufacturing. The North is often accused of trying to destroy their comparativeadvantage by raising their labour standards which goes against free trade. They believethat the North itself went through this phase of weak labour standards during theprocess of industrialization and it is unfair to stop others in achieving growth in thesame manner.
Looking at both sides of the debate, one realizes that both sides have some valid
arguments and the solution is a complex one. Even if one accepts that workers from theNorth have some protectionism hidden in their agenda, concerns of the activists seem tobe genuine in improving the conditions of workers. This protectionism was also faced
by Japan in the 1950s while it was going through industrialization but it actually
resulted in improving working conditions. Japan’s high productivity and low labourcosts raised concerns in Europe and the USA about its competitiveness. Externalpressure was applied on Japan through the IMF and American trade unions to improve
its working conditions with the intention of raising its labour costs and discouraging
exports (Nakakita, unpublished paper). Regarding this debate, Elliot and Freeman(2003) observe that both sets of concerns are exaggerated as weak standards in one
country do not encourage race to the bottom and on the other hand observance of somebasic worker rights does not reduce comparative advantage of low wage countries.

52. Core Labour Standards
There is no strict definition of labour standards as they can range from very broad tovery specific issues related to work. Portes (1994) categorises them into four groups:
basic rights, survival rights, security rights and civic rights (see Table 1 for examples).
International Labour standards are set by the ILO through conventions, which once
ratified by the member states, become legally binding upon them. Ratification reflects
the commitment of the member states to respect these standards by law but does notnecessarily reflect how effectively these conventions are enforced in practice. Till date
(September 2005), there are 185 conventions. Not all of them are equally important for
all member states. Eight of these conventions are identified as fundamental principles
and rights at work. With the adoption of the ILO Declaration on Fundamental Principles
and Rights at Work in 1998, these conventions became universally binding on all
member states and cover the following four core labour standards (ILO, 2000).
(a) freedom of association and the effective recognition of the right to collective
bargaining;(b) the elimination of all forms of forced or compulsory labour;(c) the effective abolition of child labour; and(d) the elimination of discrimination in respect of employment and occupation.
Each of these core labour standards is covered by two conventions. Table 2 enlists theseconventions and their ratification dates by member states used for this study.
These core standards are considered to be a part of basic human rights (OECD, 1996)
and do not necessarily affect labour costs directly. Singh and Zammitt (2003) question
the criteria of choosing these rights as fundamental rights over other important rights
like health and safety at work and the right to a decent living. In this regard, Elliot andFreeman (2003) differentiate them from cash (or cost) standards (such as minimum
wages, safety at work) which can have a direct effect on labour costs and thus on theability to attract FDI. The following section discusses the effects of these standards on
FDI inflows in the context of East Asia.

2.1 Forced labour
This standard appears to be the most widely accepted out of the four. With the exception

6

of China 2 , the violation of this right does not appear to be linked with the sectors related
to FDI in East Asia. Most incidents related to forced or compulsory labour are in theform of domestic servants, farm workers, sex workers and prison labourers (ILO 2001).
Myanmar military has been accused by the ILO for using forced labour in a systematicmanner for agriculture and infrastructure development projects. However, these formsof forced labour are not linked to FDI in the region. Thus, the ban on forced labour will
not have any effect on global competitiveness. In this regard, Kucera (2001) observes
that “connections between forced labour and the formal manufacturing sector as well aswith FDI inflows appear tenuous at best”. Owing to the lack of consistent data
availability and weak links with FDI, this study is not able to use any alternative
measure of this standard apart from the ratifications

2.2 Child Labour
The prevalence of child labour increases the supply of unskilled labour, resulting in low
labour costs and thus increasing comparative advantage of a country in the unskilled
manufacturing sector. Labour costs could be also kept low by discriminating againstchildren and paying them less compared to adults. This is the main logic behind how
ban on child labour can affect FDI inflows. Even if foreign firms are not directly
involved in hiring child labour, their supply chains or associates could be involved in it.
However, Wood (1994) argues that workers with no schooling (including childlabourers) “work mainly in agriculture or other traditional activities, and are generally
unsuited to manufacturing and other modern activities, which require at least literacy or
primary schooling”. Following this logic, Kucera and Sarna (2004) argue that “a decline
in child labour will not have a negative effect on a country’s comparative advantage inunskilled labour-intensive manufactures because the unskilled labour market in whichchildren work is not, by and large, the unskilled labour market that matters for
manufacturing exports”.
Even if we consider that children work in manufacturing sectors related to FDI, thispercentage is very small 3 and firms’ losses by removing these children can becompensated by premium charged from the labour standards conscious consumers
2

ILO (2001) reports incidents related to the confinement of workers to the employer’s premises insome foreign-owned firms.
3 ILO (2002) estimates are that only 8.3 percent of the total child labour is in
manufacturing, so the percentage of children involved in FDI related manufacturing
will be even smaller.

7

(Elliott and Freeman, 2003). The problem in abolishing child labour does not lie withthe commitment of countries but in their poor economic conditions. In most cases, childlabour is a matter of survival and not choice. A sudden ban on child labour can deprive
families an important part of their incomes and children can end up being in worse
illegal activities (Basu, 1999). Realising this, the ILO introduced a new convention (no.
182) in 1999 to set its priorities on tackling the worst forms of child labour before otherforms. Thus, for an effective abolishment of child labour, measures should be taken tocompensate the losses of parents and firms while children should be given incentives tojoin schools.
Child labour also has an adverse impact on the human capital development of an
economy by depriving children of education. If children are well-educated, it adds on to
the future labour force being more skilled serving as an important determinant of FDI.
In order to achieve maximum benefits from foreign technologies, the importance of
having an educated labour force in the host economy is well established in the literature(Lim, 2001). The emphasis laid on education and human capital was one of the main
determinants of the success of the HPAEs (High Performing Asian Economies) (World
Bank, 1993). In this regard, Kucera and Sarna (2004) observe that the increase ineducational attainment in East Asia occurred along with sharp declines in the labour
force participation rates of ten to fourteen year olds reflected in Table 3.

2.3 Discrimination (Gender inequality)
Workers can face different types of discrimination based on their race, colour, gender,
religion, nationality, etc. This study deals only with discrimination based on genderbecause of its pervasive nature and data availability constraints for other types of
discrimination. The logic behind gender inequality being able to attract FDI is throughlow wages paid to women compared to men for equally productive work.
In most developing countries, the traditional role of women was confined to household
and childcare activities and men were responsible to earn for the family. In the face of
globalization, urbanization and more women getting educated, this pattern is changing
rapidly and more women are entering the labour force. MNCs take advantage of thisincreased supply by employing women in low-skilled labour-intensive manufacturingjobs such as garments, footwear and consumer electronics. MNCs prefer to hire womenas they have less bargaining power compared to men and accept lower wages. Womenare perceived as more productive in these sectors because of their “nimble fingers” and8are considered to be more flexible, patient, reliable, trainable, obedient and less prone tostrikes compared to men (Anker and Hein, 1985). It is worth noting that even if they arepaid less compared to men, MNCs tend to pay them much more than domestic firms
and they are better off with these jobs than they would be otherwise (Kabeer, 2000). On
the one hand these jobs have empowered women and increased their autonomy and on
the other hand their work burden has doubled along with their routine household work.
This has also segmented and branded women into particular types of low-paid jobs andincreased their chances of being discriminated against other types of jobs.
While income equality at the household level is considered the main determinant of theEast Asian success (World Bank 1993), Seguino (2000) argues that gender inequality
within the household also played an important role in the region’s success. She findsthat low female wages (and thus low labour costs) stimulated investment and exports,
which provided the foreign exchange to import capital goods, which raised productivity
and growth rates. This is in contrast to other studies which find positive relationshipbetween gender equality and economic growth. Klasen (1999) finds that genderinequality in education lowers the level of average human capital of workers, which
decreases investment and growth. Regarding the positive relationship between FDI andwomen’s employment, Braunstein (2005) observes that “there is mounting evidence thatwomen either lose these jobs to more qualified men as industries upgrade, or get pushed
down the production chain into subcontracted work as competition forces firms tocontinually lower costs. There is likely to be some short-term improvement in women’s
incomes as FDI expands, but the longer-term trajectory of women’s wages is lesspromising.”
In spite of this mixed and contrary evidence, one can question if it is a useful strategy toignore overall long-term development in order to attract short-term FDI. Braunstein(2005) rightly points out that “equity has its own merit in terms of development,
whether or not it is directly linked to economic growth”. World Bank (2001) considers
gender equality a development objective in its own right and its absence can harm thelevel of human capital and well-being of future generations through mothers’ lack of
education. In this regard, Fontana et al. (1998) observe that “growth and social
development which rests on the exploitation of women is a pattern that threatens humanresource development in the broadest sense: immediately, in terms of the attack on the
dignity and right to equality of leisure of women and, in the longer term, through its
impact on the well-being of children”.

92.4 Freedom of association and collective bargaining (FACB)This standard is concerned with workers rights to participate freely in forming unions,strikes and negotiating collectively with employers. This is the most disputed right outof all the four. Many employers resist enforcing this standard as it transfers some power
to workers. They believe unions can unnecessarily cause strikes, politics and corruption
hindering productivity and discouraging investment. Enforcement of this standard also
gives workers the right to negotiate for higher wages, thereby increasing average labourcosts and affecting FDI. Aidt and Tzannatos (2002) conclude that on average, union
members in both developed and developing countries are paid more than non-unionizedworkers. Freeman (1994) confirms these findings, even after controlling otherproductivity related determinants of wages. OECD (1996) argues that the observance of
FACB rights can offset negative effects of wages if they create a stable social climate
through better worker-employer relationship. This can improve workers’ motivation andproductivity, resulting in positive effects on FDI.
Singh and Zammitt (2003) question the inclusion of this right as a fundamental rightfrom a developing countries’ perspective where most of the workers operate in theinformal or agricultural sector. From the workers’ perspective this right is much more
important than others as it gives them access to a mechanism to negotiate other rights
with their employers. This right provides them a voice at work and its absence can makethem more exploitable and vulnerable to employers. Portes (1994) place these rights
under “civic rights” (Table 1) and consider them an important precondition fordevelopment. In this sense they can be thought of as an extension to civil liberties anddemocracy.
Concerns are often raised about some developing countries’ deliberate suppression of
FACB and other rights in EPZs (Export Processing Zones) to attract FDI. While thismight be true in many cases, Romero (1995) argues that unionization rates in EPZs
might be lower even in the absence of government restrictions because generally wages
and working conditions in EPZs are better compared to the rest of the economy and also
because of the difficulty in organizing workers.
Many of the East Asian countries experienced fair amounts of FDI in spite of theirrelatively weak FACB rights. Serious restrictions were placed on FACB rights in the10

NIEs 4 in the 60s and 70s during the period of their high growth (Brown 2000, Singhand Zammitt 2000). However, Singh and Zammitt (2000) observe that the reason why
Singapore and Korea placed these restrictions was not because they wanted to attract
FDI but to discourage the creation of communist trade unions and deny them any
political power. FACB violations are also common in other East Asian countries.
Malaysia restricted unionization in the highly foreign invested electronics sector and thePhilippines also banned strikes in certain important industries (Brown 2000). Significant
restrictions existed in Thailand as well (OECD 1996). Union activities in Indonesia
were restricted under Suharto until 1998 but it was able to attract a fair amount of FDI
until was hard hit by the financial crisis. Though FACB rights improved after Suharto’s
rule, FDI inflows declined to negative. Investor confidence has still not revived owing
to political unrest, restructuring and legal uncertainties. China does not allow any FACB
rights and also happens to be the world’s most favourite FDI destination. Again, someargue that motivation behind this is purely political and comes from the government’s
fear of giving power to trade unions. These examples do not necessarily mean that thesecountries were able to attract FDI only because of their weak FACB rights. One cannot
generalise this link by looking at only these countries.
There are two observations worth noting about the NIEs from their past experience: first,
even after their FACB rights improved, they continue to be competitive in receiving
FDI; second, even when they had weak FACB rights, their real wages continued to rise.
Can one conclude from this that rising wages and strong FACB rights do not pose athreat to FDI as long as countries are able to move their comparative advantage overtime from low value-added to high value-added industries as these countries did? Does
this mean that it is important to maintain low wages through weak FACB rights whencountries are still in the initial stage of this transition? The answers to these questions
depend on how much do foreign investors actually care about labour costs, which I
address to in the next section.

3. Labour costs and FDI
Many countries oppose strict enforcement of labour standards as it raises labour costs
which can make them less attractive for foreign investors. In a regression analysis,
Kucera (2001) directly explores the effects of CLS on labour costs and does not find
4 NIEs- Newly Industrialized Economies (Hong Kong, Singapore, South Korea, Taiwan)

11any strong evidence on more child labour or greater gender inequality being associatedwith lower labour costs. However, he does find a fair amount of evidence suggesting
weaker FACB rights being associated with lower labour costs.
Labour costs might be an important factor in deciding location of FDI especially forsome labour-intensive manufacturing industries. Developing countries usually have anabundant supply of labour and thus can offer low labour costs to the investors but
investors care about other things as well, not just the labour costs. When consideringfactors related to labour market, investors care about the availability of skills, educationlevels and productivity levels rather than just labour costs (OECD 1996). Table 4 lists
the results taken from Hatem (1997) 5 of a survey of MNCs’ managers and international
experts in which they were asked to rank (from 0 to 5: 0- not important, 5- very
important) thirteen FDI location criteria. Labour costs rank ninth out of thirteen,showing that there are many other important factors investors take into account before
labour costs. “Political and social stability” and “Quality of labour” rank fourth and fifth,
respectively. The observance of labour standards can improve these two factors, in thelong run which can even displace the negative effects of labour costs on FDI. Educating
child labourers and female children (equally as male children) improves the quality oflabour of future generations. Giving equal rights and opportunities to women equallyqualified as men adds to the pool of human capital available to foreign investors. FACB
rights to workers can give them a sense of fair treatment at work which can improve
worker-employer relationship and creates a socially stable atmosphere at work. This canalso have a positive impact on workers’ motivation, productivity and quality of work.
Core labour standards need to be looked at from the broader context of development andnot just in terms of affecting labour costs. One needs to think about what role labourcosts can play in attracting FDI and what role FDI can play in the overall developmentof the economy. Labour costs might not rise substantially by enforcing core labour
standards and on the other hand, they can still rise even if these standards are notimposed, as we saw in the case of the NIEs. Moreover, there are other ways ofremaining competitive than through suppressing worker rights, for example, byincreasing productivity through improved technology. This is clear from the example of
NIEs that rising labour costs per se do not pose a problem to competitiveness as long as
countries are able to introduce superior technologies. However, this remains conditional
to the human capital being sufficiently developed to absorb new technologies. The NIEs
5 also cited in Kucera (2001), Braunstein (2005)

12

had a sufficient level of human capital to employ new technologies and were able tosucceed through ‘reverse engineering’ and ‘learning by doing’. Keeping labour costs
low through lax enforcement of standards might seem an attractive and easy option to
draw FDI in some industries and may result in only some short-term benefits. However,in the long run, these countries might find themselves trapped in a comparativeadvantage of only low-tech, labour-intensive industries and never be able to move up
the ladder without investing in technological changes and human capital. Worse would
be to experience a situation where labour costs and standards are deliberately kept low
and the FDI still shies away.
So far, I have only talked about how wages (or labour costs) can affect FDI inflows,
there are more than one ways in which FDI inflows can also affect wages. So, the causal
link exists both ways between FDI and wages. There are an overwhelming number ofstudies which show that MNCs pay higher wages, to equivalent workers, than theirdomestic-owned counterparts. Thus, the inflows of FDI itself can raise wages. Zhao
(2001) examines wages in a sample of 5345 state owned firms and 188 foreign owned
firms taken from the Chinese economy as a whole in 1996. He concludes that workers
in foreign-owned firms are paid twice as much as their counterparts with similar levelsof education and skills in state owned firms. In order to hire skilled workers from thestate-owned firms, foreign firms pay more to skilled workers, while less skilled workers
were available at a much lower or even negative wage premium. Lipsey and Sjöholm
(2004) examine host country wage effects of foreign firms in Indonesian manufacturingindustry using a cross-section dataset of plants for 1996. They control for different
characteristics of the workforce, as well as industries and enterprises. They conclude
that the wage premium in Indonesian manufacturing is about a quarter for blue-collar
workers and over half for white-collar workers. They also find that a higher foreign
presence in an industry is associated with a higher level of wages in domestic firms.
Velde and Morrissey (2004) use ILO data for wages and employment by occupation forthe period 1985-1998 to study the effects of FDI on wages in five East Asian countries(Hong Kong, Korea, Singapore, Thailand and Philippines). They find that overall, FDI
raised wages in all countries significantly, regardless of skill level. However, theyobserve some differences in the effects of FDI across these five countries. For instance,
FDI had a much larger effect on skilled workers than on low-skilled workers in
Thailand.
Whether foreign firms pay more or not could depend on a lot of factors related toworker or firm characteristics. The worker characteristics refer to their education, skill,13

age, gender and experience. It is possible that multinationals would pay more to get thebest workers available in the labour market based on these features. The firm
characteristics refer to firm size, industry, capital intensity, location, or othercharacteristics associated with multinationals that could belong to few domestic firms as
well. For example, a large size firm or a capital-intensive firm or a firm located in a big
city would tend to pay more irrespective of whether it is foreign or domestic firm. So,
these are the differentials that are not necessarily intrinsic to foreignness, although they
may be associated with it in practice. Multinationals could be paying premium to its
workers simply to avoid the costly labour turnover and to prevent workers from leaking
out the insights about firms’ specific assets (e.g. new products or technologies) tocompeting firms.
If MNCs pay more and if this can lead to domestic firms also paying more due toincreased competition, this can raise overall wages and thereby improve living standards
in an economy. However, it is also possible that this competition remains limited to only
one type of industry or MNCs pay more only to special types of workers or the amountof FDI is negligible for this overall “wage spillover” to happen. On the other extreme
and negative end, it could also be possible that with their superior and cost-reducing
technologies, foreign firms push local firms out of business and enjoy a monopoly inthose areas and are able to exploit local workers to their advantage. From the above
discussion, one can conclude that MNCs generally pay more to their workers (and much
more to skilled) compared to domestic firms showing their concern for the quality oflabour rather than just the labour costs.

4. Theory and determinants of FDI
The traditional Heckscher-Ohlin trade theory suggests that the two countries trade witheach other as they differ in their relative factor endowments. This implies that a country
will export the commodity that intensively uses its relatively abundant factor. This
theory assumes that countries may trade commodities but factors of production (capitaland labour) are immobile among countries. As we know, capital is mobile through FDI(and labour through migration), countries can benefit mutually by trading factors of
production in the same way as by trading commodities. Thus, the FDI can act both as a
substitute or a complement to trade. There are many theoretical models 6 explaining the
6 See Daude et al. (2003) for a review of different theoretical models of FDI

14

determinants of FDI but there is lack of consensus in recognizing one single theory thatis widely accepted (OECD 1996).
The OLI framework offered by John Dunning in 1977 (taken from Markusen et al.,1995) suggests that a firm decides to undertake FDI if three conditions are met:
ownership advantages (O), location advantages (L), and internalization advantages (I).
Ownership advantages refer to anything which foreign firms own and local firms do not.
These could include patent, blueprint, brand name, trade secret, goodwill, technicalknow-how, managerial skills etc. These assets are more likely to give rise to FDI thanphysical capital assets as they can be easily transferred to different locations at very low
cost. Location advantages refer to the attractiveness of the host country that makes
production more profitable compared to the source country. They could result from low
labour costs, high trade barriers, cheap transport, developed infrastructure, easy
availability of raw materials, customer access, growth and size of market or any
incentives offered by the host countries to attract FDI. They could also be non-economic
factors whether the host country shares the same language, culture or religion with thesource country. Internalization advantages exist where the product or a process is better
exploited internally within the firm rather than at arm’s length through markets. For
example, firms could also take into account that by selling blueprints or otherknowledge based assets, they run a risk of these secrets being leaked or sold out to otherfirms.
According to Shatz and Venables (2000), there are two main reasons for why a firm
would want to invest abroad. One is to better serve the local market, often calledhorizontal or market-seeking FDI, the other is to get lower-cost inputs known as verticalor efficiency-seeking FDI. Horizontal FDI (HFDI) takes place when firms can invest
abroad to duplicate the same stage of production process. This type of FDI acts as a
substitute for trade since firms replace their exports with production in the source
country and save on transport and tariff costs which otherwise would have been
involved in trade. Since the main purpose is to serve the local market, the growth andsize of the local market is very important for HFDI 7 . Vertical FDI (VFDI) takes placewhen firms split its activities by function and invest in different places for different
parts of the production process. The main purpose is not to produce to sell in the local
market but to export to the home country or to third countries. Low-cost inputs such as
7 This is also reflected in Table 4 where number one FDI location determinant is“Growth of market” and number two is “Size of market”.

15cheap labour costs and cheap raw material are more important here. VFDI acts as acomplement to trade since products at different stages of production need to be shipped
between different countries.
Thus, it is the VFDI which should get negatively affected with rising labour costs due tostrengthening of labour standards. Since different types of FDI is attracted by different
determinants, it is desirable to have a distinction between vertical and horizontal FDI
but this distinction becomes blurred as one plant can serve both functions (Shatz and
Venables 2000). Worth-mentioning is the example of the NIEs, which were once
important for receiving VFDI (due to their low labour costs), are now important
destinations for HFDI, reflecting their growing market opportunities and consumption
patterns of western economies (Athukorala and Hill, 2000). Kucera (2001) interprets thesurvey of MNC managers by Hatem (1997) mentioned earlier and concludes that HFDI
is more important in the services sector and VFDI in the manufacturing sector. He also
concludes from the survey findings that the Japanese investors attach more importanceto VFDI (thus, labour costs) compared to the investors from North America. This isslightly reflected for this region looking at Graph 2, which shows the percentage of US
and Japanese outward FDI (share of the region from total FDI flowing out of US andJapan) in the sample countries. Clearly, Japanese investors attach much more
importance to the manufacturing sector in the region compared to the US. The region
attracted 40% of Japan’s total manufacturing FDI in 1995 before declining to 25% in
2003. Since labour costs can have different effects on FDI depending on the sector andthe source country, this study does not just look at the total inflows of FDI, it splits FDI
into two broad categories- manufacturing and non-manufacturing, coming from two
different sources- the United States and Japan.
The efficiency-seeking FDI played an important role in the region’s increasing stock of
FDI through the emergence of vertically integrated global production networks. Liberal
FDI policy regimes, increasing capital mobility, declining communication costs andsuperior technologies helped MNCs to ‘slice’ their production into cross-bordermulti-plant operations. In stead of relocating entire industry to one host country, MNCs
distributed the production process to different countries within the region depending on
their skill and factor price differentials. This led to an intensification of intra-regional
trade and FDI (Athukorala and Hill, 2000). This regional concentration of FDI also
occurred due to what Krugman describes as ‘economic geography’ or ‘agglomeration
effects’. Production gets concentrated in one region as producers have an incentive tobenefit from each other through economies of scale. Agglomeration can also arise from16

reasons like concentration of skilled and specialized labour force, adequate
telecommunication and transport infrastructure and cultural similarities. These
agglomeration effects were important in attracting FDI in the region. Once a criticalmass of production was established in the region, more investors were attracted to theregion and FDI intensified through backward and forward linkages.
The overall dynamic growth of the region also played an important part in attracting
investors and FDI did not remain limited only to manufacturing. In other words, as theregion grew, it became an important recipient of HFDI as well. During the 90s, newsectors in non-manufacturing like finance, real estate, construction, retail trade andinfrastructure emerged as major recipients of FDI (ibid.). This is also reflected inAppendix 1 which compares US and Japanese manufacturing and total FDI inindividual countries. Since the total FDI roughly comprises of manufacturing andnon-manufacturing, bigger gaps in the curves of manufacturing and total FDI shows thedominance of non-manufacturing FDI. This gap is striking especially in the case of US
FDI in Hong Kong, Singapore and South Korea for the 90s pointing towards theimportance of non-manufacturing FDI in the NIEs.

5. Literature review
This section looks at the small but gradually increasing number of studies that examine
links between FDI and CLS. The main problems in studying these links stem not only
from the multiple and uncertain factors affecting FDI but also from the issues ofmeasuring CLS in a meaningful way. An OECD study (1996, updated in 2000) looks at
the relationship between the enforcement of FACB rights and FDI in 75 countries
between 1995 and 1998. Based on textual coding, a composite index was constructedreflecting the situation on enforcement of FACB rights in these countries. Countries
were ranked from 1 to 4, where 1 represented strongest rights and 4 represented weakest
rights. This study observes a small and positive correlation coefficient of 0.20 between
the FACB index and FDI inflows but other factors affecting FDI are not taken into
account.
Rodrik (1996) studies manufacturing FDI outflows from the US in 40 countries between
1982 and 1989 in a regression analysis. For the benchmark model specification, he
identifies three explanatory variables affecting FDI decisions as black-market premium
for foreign currency (proxy for government policy distortions), population and income

17

growth in the host country. He then adds various measures of labour standards to thisspecification. He measures labour standards as the total number of ILO conventions
ratified, the number of core conventions ratified, democracy index and the child labour
index. The democracy index is an average of civil liberties and political rights index
constructed by Freedom House and used as a proxy for FACB rights. The child labour
index is based on inadequacies in legislation or enforcement related to child labour
restrictions. He does not find any relationship between the number of conventions
ratified and FDI inflows. He finds a significantly positive relationship with the
democracy index, indicating more FDI inflows for countries with stronger FACB rights.
He also finds a significantly negative relationship with the child labour index, indicating
more FDI inflows for countries with less child labour. The main weakness of his model
specification is that he does not control for labour costs as a determinant of FDI.
Cook and Noble (1998) study the links between U.S. FDI outflows and labour standards
in 33 countries for 1993 in a regression analysis. Their determinants for FDI include
GDP per capita, education and labour costs. They find a positive relationship between
the number of conventions ratified and FDI on the one hand and a negative relationshipbetween FDI and other measures of labour standards like unionization rates, centralized
wage-setting structure and government restrictions on layoffs. The main shortcoming of
their model is that they do not take labour productivity into account.
Kucera’s (2001) study offers the most detailed and extensive analysis of the links
between FDI and CLS. He looks at the multiple measures of labour standards
constructed by him for up to 127 countries for the period between 1993 and 1997 in aregression analysis. He also controls for regional differences and differences in effects
on developed and developing countries through creating dummy variables. For thebenchmark FDI model specification, he uses explanatory variables as manufacturing
wages to value-added ratio, population, GDP per capita, openness, exchange rate
volatility, education and infrastructure. Dependent variable used is the inflows of FDI ina country as a share of world FDI inflows. Apart from using the available measures oflabour standards, he also constructs his own measures for FACB rights and child labourby coding textual sources related to violations of these rights. The overall and general
conclusion he draws from his study is that weak labour standards do not attract FDI. On
the contrary, the evidence found points in the opposite direction that countries withsound political and social environment (and hence better worker rights) offer a better
investment climate.

18

Daude et al. (2003) study the effects of CLS on FDI in the Latin America and the
Caribbean region through a panel data regression analysis for the period between 1989
and 2000. In order to study the differences in FDI inflows for manufacturing andnon-manufacturing sectors, they break FDI into these categories. They look at FDI
coming from US and Japan only, as sectoral breakdown of FDI data is available fromthese countries only. For the benchmark FDI model specification, factors affecting FDI
decisions taken into account are: source and host country GDP, distance between sourceand host, inflation, openness, illiteracy, political rights workers involved in strikes andmanufacturing wages to value-added ratio. Enforcement of labour standards is notmeasured through conventions ratified. The study uses labour force participation ratesof 10 to 14 years old to measure child labour, civil liberties index to measure FACB
rights and female to male illiteracy ratio as a measure for gender inequality. The study
concludes that better observance of FACB rights and greater gender equality areassociated with more, not less, FDI. Coefficient estimates on child labour are found to
be insignificant. I rely on the model specification and methodology used by Daude et al.
for this study.

6. The model
The way I look at the effects of CLS on FDI is by first estimating the benchmark modelfor the determinants of FDI and then adding on the measures of CLS to this model.
Owing to the multiple factors which can affect FDI decisions and lack of one widely
accepted theory, it is very difficult to come up with a model specification which
captures the determinants of FDI. For this reason, rather than coming up with my own
model specification, I rely on the specification (for both determinants of FDI andmeasures of CLS) already tried and tested by Daude et al (2003) in their study. I am notaware of any other study that tests the effects of CLS on FDI statistically andspecifically for the East Asian countries. For the sake of convenience, I refer to thisspecification as Daude et al model. Though their study is for the Latin American and
Caribbean countries, this study tests whether their model and findings hold for the East
Asian countries as well. I use the same methodology and variables for a different set ofcountries.
This model looks at FDI inflows from the MNCs of the United States and Japan only.
This approach has certain advantages and disadvantages as well. It is desirable to lookat the total FDI inflows but these data are not available in sectoral breakdown over time.

19

Since the effects of CLS on FDI can differ from manufacturing to non-manufacturing orfrom labour-intensive manufacturing to capital-intensive manufacturing, it is important
to be able to make this distinction for the FDI data. These data are available only for the
U.S. and Japan. They are considered to be leading investors and offer data broken down
by industry and country over time. This model also allows controlling for the source
country characteristics as the importance of CLS could differ for investment comingfrom different countries.
On the other hand, by only looking at the U.S. and Japanese FDI, it is hard to draw any
conclusions about the overall FDI, especially when these countries do not have asignificant share in the total FDI of the host countries. Graph 1 shows the declining
share of U.S. and Japanese FDI (63% in 1989 to merely 18% in 2003) in the sample
countries out of total FDI inflows from everywhere. This shows that the investment
attracted by this region is no more predominantly from the U.S. and Japanese MNCs
only. East Asian countries used for this study and for which both U.S. and Japanese data
are available are China, Hong Kong (China), Indonesia, South Korea, Malaysia,
Philippines, Singapore and Thailand 8 . Annex 1 shows the inflows of total andmanufacturing FDI from the U.S. and Japan in these countries.
This model pools the U.S. and Japanese data together in one equation but there are someissues related to comparability of the U.S. and Japanese data owing to different
methodologies used in collecting them. The U.S. FDI data used for this study arecollected by the Bureau of Economic Analysis (BEA) for the FDI already undertaken by
the U.S. firms. On the other hand, Japanese FDI data collected by the Ministry of
Finance (MOF) represent the planned and notified investments. These data sometimes
can be overstated as some firms do not undertake these notified investments or do so bylesser amount or much later after notifying (Huang, 1997). Where Japanese data reflect
only new investments, the U.S. data also take into account equity flows, inter-company
loans and reinvested earnings between the U.S. parent firms and their foreign affiliates.
Thus, the U.S. FDI inflows can also show negative values, for example when loans paidback by foreign affiliates increase the amount of investments; when parent firms reduceor withdraw equity from existing foreign affiliates; when dividend earned from foreign
firms exceeds the amount of reinvested earnings 9 . The Japanese data do not account for
8 FDI data for Taiwan are also available but other data for Taiwan is hard to find. Only

Japanese data are available for Vietnam. For these reasons, we did not include Taiwan
in the sample.
9 For details, refer to Bureau of Economic Analysis (1995).

20

these components and thus do not have negative values. Table 5 shows the industrial
breakdown offered by two datasets, which reflects the problem of comparability. For
example, it is interesting to know the effects of weak CLS on attracting FDI inlabour-intensive industries like textiles but such data do not exist for both countries.
This is the reason for looking at only the broad categories- manufacturing andnon-manufacturing, but even these categories are not completely problem-free. The U.S.
data do not have a category for non-manufacturing. I create this category by adding up
all other industries other than non-manufacturing. The problem arises when the U.S.
data do not report investment in certain sectors in order to conceal the individualcompany data for confidentiality reasons 10 . For this reason, sometimes manufacturing
and non-manufacturing do not add to the total. Due to all these reasons, the approach ofpooling the U.S. and Japanese data is doubtful.

6.1 Variables for the benchmark model
The years covered by this study are 1986 to 2003. All the variables are used as
three-year averages to smooth-out the data discrepancies 11 . Hence, the six periods
covered are 1986-88, 1989-91, 1992-94, 1995-97, 1998-2000 and 2001-03. Appendix 2
lists the sources used to collect data for these variables. Both OLS and panel data
random effects are employed with White’s heteroskedasticity-consistent standard errors
to run regressions. The benchmark model specification used is:
Log (1 + FDI-USJPijt) = β0 + β1 Log (GDP sourceit) + β2 Log (GDP hostjt) + β3 Log(Distanceij) + β4 Log (Inflationjt) + β5 Opennessjt + β6 Illiteracyjt + β7 Polrightsjt + β8
Log (1 + Strikejt) + Єijt
where subscript i refers to the FDI source country, j refers to the FDI host country, trefers to the time period, β0 is the intercept and Є is the error term. The explanation of
the variables used is as follows:
Log (1 + FDI-USJP): This dependent variable represents the natural log of FDI flows
from the source country (US or Japan) to the host country. 1 is added to the amount of
FDI in order to preserve the zero values, which would otherwise get eliminated when
the log of zero is taken. As already discussed, the US FDI data can sometimes be
negative and these observations get eliminated when logs are used for negative values.
10 Sometimes, data are omitted for major categories as well. For example, the figure fortotal manufacturing in China for the year 1986 is not disclosed.
11 In order to increase the number of observations, I use three-year averages instead offour-year averages used by Daude et al.

21

Three categories of FDI are looked at (total, manufacturing and non-manufacturing) inthree different equations. The Japanese FDI data were converted to US dollars using
average annual exchange rate for Japanese yen per US dollar 12 . Then, both US andJapanese data were converted into constant prices of the base year 2000, using the US
GDP deflator 13 .
Log (GDP source): GDP of the source country is represented by constant US dollars(base year- 2000) and is included to capture the source country characteristics. The
expected sign is positive, as the big economies tend to invest abroad more compared tothe small economies.
Log (GDP host): GDP of the host country is represented by constant US dollars (baseyear- 2000) and is included to capture the market size of the host economy. The
expected sign is positive as investors tend to go for countries with big markets. However,this can also depend on the type of FDI. For example, market size can be more
important for HFDI compared to VFDI.
Log (Distance): Distance is measured in miles as the great circle distance between
Chicago or Tokyo to the host country capital. Short distance can be of more importanceto vertical manufacturing FDI to save on transport costs.
Log (Inflation): Average inflation rate in the host country is included to look at theoverall economic and investment climate. High inflation rates can mean high levels of
uncertainties and can have a negative impact on FDI.
Openness: This is measured as the sum of exports and imports of goods and services asa share of GDP. A more open country may experience more FDI in the export-oriented
manufacturing sector to facilitate imports and exports of raw and finished products.
Illiteracy: This variable captures the level of skilled or unskilled labour force available
in the host country and is measured as the percentage of illiterate people of ages 15 andabove out of total. Illiteracy rates are constructed
Polrights: This variable is a Political Rights index constructed annually by Freedom
12 Exchange rates were obtained from the on-line version of International Financial

Statistics (IFS) database released by the International Monetary Fund (IMF) and theseries code used was 158..RF.ZF.
13 Data for GDP deflators were obtained from the on-line version of World Development
Indicators (2005) released annually by the World Bank.

22

House and captures the political environment of the host country. This index is based ona scale of 1 to 7, with 1 representing the highest degree of political rights available inthe country and 7 representing the lowest degree. Inclusion of this variable can bequestioned on the grounds of subjectivity involved in constructing it.
Log (1 + Strike): This represents the total number of workers involved in strikes andlockouts in a given year. Large number of strikes can create a negative investment
climate. Daude et al. warn about the interpretation of this variable as large number ofstrikes can mean a poor collective bargaining environment where workers are left withno choice but to strike. On the other hand, it also reflects that workers are able toorganise in large numbers and they can exercise their rights. However, the inclusion ofthis variable in the benchmark equation is questionable as it reflects more of a measureof worker rights and it eliminates those countries from the sample where strikes are notallowed (China, in the case of this study).
Wages to value-added: This variable represents wages as a share of value-added forthe entire manufacturing sector in a given year. This is used as a single measure tocapture labour costs relative to labour productivity 14 . This variable is not shown in themain equation as it is used only for the manufacturing sector since its relevance is very
sector-specific.

6.2 Measuring core labour standards
It is not only the multitude of factors affecting investment decisions but also the weakmeasures of labour standards available, which makes it difficult to examine linkages
between the two. Most of the studies measure labour standards through the number ofratifications of ILO conventions signed by a country. The ratification itself does notnecessarily mean that the standard is also actually enforced in practice and hence, is nota good measure of capturing the level of labour standard if used on its own. Relative
ease of availability of this measure makes it popular. Owing to lack of data availability,
direct measures to capture actual enforcement of labour standards are difficult to comeacross. Kucera (2001) overcomes this problem by constructing his own measures bycoding textual sources related to violations of CLS. These measures are of course
desirable and better than using mere ratifications or other proxy measures but theprocess involved in constructing them is time-consuming and lacks consistency related
14 See Kucera (2001) for a detailed explanation of why this measure is preferred overothers to control labour costs.

23

to textual sources over time as mentioned by Kucera himself. This study employs both
ratifications and proxy measures used by Daude et al. to capture the enforcement oflabour standards. These measures are to the benchmark model and are explained below.
Child Labour: The incidence of child labour is captured through the labour force
participation rates of 10 to 14 years old. The problem with this variable is that thesefigures are based on ILO estimates and projections from country surveys and their levelof accuracy is not uniform across countries. Many counties where minimum age forrecording economic activity is set at above 14 omit this category altogether (Mehran,2001). Another problem related to the use of this variable is that it does not take into
account child labourers below the age of ten.
Freedom of association and collective bargaining (FACB): There is no direct
measure available over time to quantify the enforcement of this standard. Though
unionization rates can capture this right partially but such data are not available in timeseries for many countries. A broader indicator, reflecting the general situation of civil
liberties in a country, is used. This measure is civil liberties index, constructed annually
by Freedom House in a similar way as the political rights index described earlier. It isbased on a scale of 1 to 7, with 1 representing the strongest civil liberties and 7
representing the weakest. This measure does not directly capture the FACB rights but
they are considered while constructing this index. Issues of whether formation of tradeunions are permitted and if they have collective bargaining rights are considered whileconstructing this index.
Gender inequality: This is captured by looking at the gaps of illiteracy rates between
males and females. The female-to-male illiteracy ratio for adults aged 15 and above isconstructed. Differences in male-female earnings are a better way to capture inequality
but due to limited availability of these data, differentials in their education are
considered. The working assumption for this variable is that the gaps in education will
also persist in the job market, that is, “labor market and pre-labor market discrimination
would occur in roughly similar proportions” (Daude et al., 2001)
Ratification index: This index is constructed as a percentage of core conventions
ratified by a country in a given year 15 . I add this measure in the benchmark model
separately without including the three variables mentioned above. Though Daude et al
15 Total number of fundamental conventions considered was seven till the year 1999,

when the eighth convention, no. 182 was introduced.

24

do not consider this de jure measure in their study, I look at it if it has any significancein the sample countries I use.

6.3 Model results
Results from this model are reported in appendix 3. Table A3-1 shows results from thebenchmark model for six equations- two each for total, manufacturing andnon-manufacturing US and Japanese FDI using OLS and random effects. China andHong Kong drop out of the sample as strike data for China and political rights data forHong Kong do not exist. The model is quite weak as the variables explain only 32 to 48
percent (compared to 59 to 82 for Daude et al.) of the variation in FDI inflows for the
sample countries and moreover most of the model estimates are statisticallyinsignificant. However, there are still some interesting differences observed between
manufacturing and non-manufacturing FDI. Random effects estimates do not differ
from OLS estimates for total and non-manufacturing FDI, perhaps suggesting that there
is no or very little cross-sectional variation left after controlling for all the explanatory
variables. The source country GDP is either insignificant or negatively significant(especially in manufacturing) in contrary to the expected sign. This could be driven by
Japan’s greater amount of FDI in spite of being a smaller economy compared to the US.
GDP of the host country is significantly positive at the level of 1% and as expected, inmost of the specifications confirming that the market size of the host economy isimportant. Distance is insignificant, except in the case of manufacturing FDI (random
effects) where it is negatively associated and significant at a 10% level. More openness
is associated with more FDI in manufacturing only and more illiteracy is associatedwith less FDI in manufacturing sector but both these variables are insignificant in all
specifications. Inflation and political rights are significant and positively associated with
non-manufacturing FDI only. Number of workers involved in strikes is significant at 5%and negatively associated but only in the case of non-manufacturing FDI. Thissuggests that investors are not concerned about rising inflation and worse political rights
and they do care about strikes in the non-manufacturing sector, however the samecannot be said about the manufacturing sector. Though wages to value-added ratio has anegative sign, it is insignificant. The correlations of these variables in Table A3-4, offer
an interesting insight into how these signs of association change when other variables
are taken into account in the regression equation. For example, all types of FDI inflows
are positively associated with source GDP, host GDP, openness and negatively
associated with inflation, strikes and political rights.

25

Adding CLS measures to the benchmark equation does not improve the model. These
results are shown in table A3-2 of appendix 3. The R-square increases by 3 to 9 percent(depending on the specification) but most of the variables remain insignificant. Politicalrights and strikes become insignificant for non-manufacturing as well. Openness
becomes significant and positively associated to the manufacturing FDI only. Measures
for FACB rights (civil liberties) and child labour are both statistically insignificant.
Measure for gender inequality for FDI in manufacturing is the only significant (at 5%
level for OLS) CLS measure. The female to male illiteracy ratio is negatively associatedwith FDI in manufacturing, implying that more gender inequality is associated with less,not more, FDI in manufacturing. The ratification index is added separately into thebenchmark equation (not together with other CLS measures) and results are reported intable A3-3 of appendix 3. It does not change the benchmark model results much exceptthat the wages to value-added ratio becomes significant at 10% level with a negative
sign for FDI in manufacturing (random effects). The only significant estimate for theratification index is for the manufacturing FDI (random effects) hinting towards more
ratifications being related to less FDI. The problem with adding this type of composite
index is that the effects on ratifications related to individual labour standards measures
cannot be isolated.
Since the predictive power of the model is weak, adding CLS measures in thisspecification can be questioned. Brown (2000) points out that since labour standards are
only one of several determinants of FDI, entering them as a dependent variable without
properly controlling for other key variables can lead to biased estimates. The weaknessof the model can be explained due to several reasons. First of all, the determinants of
FDI used in this model do not explain the variation in FDI inflows among the sample
countries as there could be some other more important variables (for example,
agglomeration effects) which are omitted and are specific to only these countries. In
other words, there could be the specificity of the sample countries which produces
different results from this model as compared to when used by Daude et al. Also, thesample size for this study is much smaller compared to theirs, indicating that a wider
and diverse range of countries can get better results for this specification. This study
offers a very basic and preliminary analysis of a model already used to test the linkages
between FDI and CLS. The results of this model should not be taken at face value andmore sensitivity analysis is required before reaching any definitive conclusion.
Given that these countries had relatively weak enforcement of some of the CLS andhigh inflows of FDI, an interesting future research topic would be to look at a different

26model specification for these countries. Also, a better way to study these countries
would be to look at them in the context of a broader set of countries. If one has togeneralize the effects of CLS on inflows of FDI, most of the countries can be lumped
into two broad groups. Group A contains most of the developed countries. Since most of
the FDI is exchanged within developed countries, these countries show big amounts of
FDI inflows. This group also happens to be relatively rich and can afford to have betterlabour standards. Group B contains most of the developing countries with relatively
small amounts of FDI and weak enforcement of labour standards. If a regression is run
between all countries to study the effects of CLS on inflows of FDI and if the factors
explaining the differences between these two groups are not controlled properly, it islikely that the results observed would be positive because of the influence of these two
big groups. Apart from these two big groups, there is also a small group C of countries
with relatively weak enforcement of labour standards and fair amounts of FDI inflows.
These are mostly East Asian countries used for this study. When this small group is also
included in a set of regressions for all countries, it is quite likely that their effectdisappears as outliers due to the influence of the other two big groups and the net result
remains positive. That is why it is not only important to study group C in a broader
context but also to isolate their effects from the general regression, for example throughdummy variables. By including as many countries as possible in the sample and thencontrolling for different regions, one can also study the different effects ofmanufacturing and non-manufacturing FDI in a more conclusive way. This can also
allow for studying the differences in patterns followed by the Japanese and US investors
in a better way. One can also come up with a more meaningful breakdown of FDI,
rather than just looking broadly at manufacturing and non-manufacturing. For instance,
manufacturing can be further broken down into FDI in labour-intensive andcapital-intensive industries.

7. Conclusions
This study contributes to the evolving body of literature examining the effects of CLS
on FDI. I investigate whether the relatively weak CLS in East Asia played any role inattracting FDI through regression analysis. I use the same model specification andmethodology as used by Daude et al. for Latin American and Caribbean countries. The
preliminary analysis shows that this model specification is not applicable to the sample
countries in East Asia. The explanatory variables used in the model fail to explain muchof variation in FDI inflows among the sample countries hinting towards other more27

important factors that are not accounted for in this model. Some ideas are suggested tofor future research to study these countries in a more conclusive way.
Though weak labour standards may not have been completely absent from investors’
minds while deciding about FDI location in East Asia, there were other more importantfactors affecting their decision. Important reasons for the region’s success in attracting
FDI were factors like maintaining competitiveness through superior technology, highlevels of human capital, skilled labour force, agglomeration effects, dynamic growth
and the role played by the state in strategically allocating FDI in the right sectors. Whilethe enforcement of CLS may increase labour costs and may have negative effects on
FDI in certain industries but in the long run these negative effects can be offset by the
positive effects gained through human capital development and stable environment if
CLS are enforced. The deliberate lax enforcement of CLS might seem an easy option
for developing countries to attract investors but this strategy may result in someshort-term benefits restricted to low-tech, labour-intensive industries. In the long-run,this strategy may harm the human capital development of LDCs and restrict theirtechnological upgrade.
It is generally well-accepted that labour standards and workers’ conditions improve by
themselves through economic growth and FDI brings this growth but these
improvements cannot be taken for granted. In the absence of enforcement of standards,
benefits coming from economic growth may remain restricted to only a small section ofprivileged workers, failing to improve conditions of majority of common workers. Lax
enforcement of standards can leave workers vulnerable to exploitation from the evergrowing powers of MNCs. With the increasing globalization and multinational
operations, the need to have some global and basic labour standards in order to protect
workers becomes more important than ever. Complying with these basic and minimumof labour standards, developing countries will not only improve conditions of theirworkers but will also gain legitimacy against the protectionist concerns (under the guiseof labour standards) raised by the North. Complying with CLS does not necessarilyhinder competitiveness of developing countries. Their search for FDI and improvement
in labour standards can be pursued hand in hand.

28

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Mehran, R. (2001). ILO labour force participation rates for 10-14 years old versus
UNESCO school enrolment ratios. ILO Bulletin of Labour Statistics, 3, xi-xxi.
OECD. 1996. Trade, Employment and Labour Standards: A Study of Core Workers’
Rights and International Trade. Paris: OECD.
Portes, Alejandro. 1994. “By-Passing the Rules: The Dialectics of Labour Standards andInformalization in Less-Developed Countries.” In: Werner Sengenberger and Duncan
Campbell (eds.), International Labour Standards and Economic Interdependence,
Geneva: International Institute for Labour Studies.
Seguino, S. 2000 “Accounting for Gender in Asian Economic Growth.” Feminist
Economics 6 (3): 22-58.

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Shatz, H. and A.J. Venables, 2000, "The Geography of International Investment," WorldBank Policy Research Working Paper No. 2338.
Singh, A. and Zammitt, A. (2003) 'Globalisation, labour standards and economic
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York: Oxford University Press.
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in China Economic Review Volume 12, Issue 1, pp. 40-57.

31

9. Appendices, Graphs and Tables
Appendix 1: Comparison of US and Japanese FDI in sample countries
Source for US FDI data- Bureau of Economic Analysis website:
http://www.bea.doc.gov/bea/di/di1usdbal.htm
Source for Japanese FDI data- Ministry of Finance website:
http://www.mof.go.jp/english/e1c008.htm

Current USD(Millions)

US and Japanese FDI in China
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
-500
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Japanese total FDI

Japanese mfg. FDI

US total FDI

US mfg. FDI

32

Current USD(Millions)

US and Japanese FDI in Hong Kong, China
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
-500
-1000
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Japanese total FDI

Japanese mfg. FDI

US total FDI

US mfg. FDI

Current USD(Millions)

US and Japanese FDI in Indonesia
2750
2500
2250
2000
1750
1500
1250
1000
750
500
250
0
-250
-500
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Japanese total FDI

Japanese mfg. FDI

US total FDI

US mfg. FDI

33

Current USD(Millions)

US and Japanese FDI in Korea, Rep.
2750
2500
2250
2000
1750
1500
1250
1000
750
500
250
0
-250
-500
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Japanese total FDI

Japanese mfg. FDI

US total FDI

US mfg. FDI

Current USD(Millions)

US and Japanese FDI in Malaysia
2000
1750
1500
1250
1000
750
500
250
0
-250
-500
-750
-1000
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Japanese total FDI

Japanese mfg. FDI

US total FDI

US mfg. FDI

34

Current USD(Millions)

US and Japanese FDI in Philippines
1250
1000
750
500
250
0
-250
-500
-750
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Japanese total FDI

Japanese mfg. FDI

US total FDI

US mfg. FDI

Current USD(Millions)

US and Japanese FDI in Singapore
6500
6000
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
-500
-1000
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Japanese total FDI

Japanese mfg. FDI

US total FDI

US mfg. FDI

35

Current USD(Millions)

US and Japanese FDI in Thailand
2250
2000
1750
1500
1250
1000
750
500
250
0
-250
-500
-750
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Japanese total FDI

Japanese mfg. FDI

US total FDI

US mfg. FDIAppendix 2: Data sources
Literacy data: This taken from Statistical Indicators for Asia and the Pacific 2004
Compendium, Volume XXXIV and World Development Indicators CD-ROM, 2004.
Distance: This is calculated as a great circle distance by taking geographic coordinatesof the cities involved from the website- http://www.timeanddate.com/worldclock/Wages to value-added ratio: This ratio is calculated from the ‘Total Manufacturing’category from UNIDO Industrial Statistics Database CD-ROM (3-digit ISIC, Revision2), 2003.
Civil Liberties and Political Rights indices: These rankings are taken from theFreedom House website- http://www.freedomhouse.org
Strike: Data for ‘Number of workers involved in strike’ are taken from Table 9B ofyearly data form the ILO labour statistics website- http://laborsta.ilo.org.
Ratification index: This is constructed by using information from the ILO websitehttp://www.ilo.org/ilolex/english/docs/declworld.htm
All other data: World Development Indicators on-line version, 2005 is used for allother data.

36

Appendix 3: Results of the model
Table A3-1: Results of the Benchmark model
Dependent variable (3 equations)- US and Japanese FDI for all, manufacturing andnon-manufacturing industries
Total FDI
OLS
RandomEffects
Constant

Manufacturing FDI Non-manufacturing FDI
OLS
RandomOLS
RandomEffects
Effects

Political Rights (1=highest, 7= lowest)
Log (no. of workers
in strikes + 1)
Wages to valueadded

42.313 *1.833-1.590-1.5210.875 ***
2.831
0.346
0.820
0.085
0.504
0.000
-0.108
0.007
0.164
0.130
0.834
-0.140-1.412-42.313 *1.833-1.590-1.5210.875 ***
2.831
0.346
0.820
0.085
0.504
0.000
-0.108
0.007
0.164
0.130
0.834
-0.140-1.412-66.179 **2.416-2.032 *-1.766
0.379
1.164
0.469
1.044
-0.198-0.9020.007
1.378-0.080-1.114
0.053
0.215
0.050
0.399
-4.212-1.457-50.082-1.3542.798
1.638-0.039-0.066-1.433 *-1.758-0.069-0.3790.003
0.797-0.0002-0.0030.168
1.012-0.014-0.174-1.950-1.15912.4340.700-0.422-0.556
0.736 ***
3.909
0.220
0.688
0.236 **
2.124
-0.003-0.9150.008
0.267
0.187 *
1.771
-0.222 **-2.484-12.4340.700-0.422-0.556
0.736 ***
3.909
0.220
0.688
0.236 **
2.124
-0.003-0.9150.008
0.267
0.187 *
1.771
-0.222 **-2.484-

Observations
R-squared

60
0.332

60
0.332

57
0.320

57
0.434

55
0.479

55
0.479

Log (GDP source)
Log (GDP host)Log (Distance
between source andLog (Inflation)
Openness
Adult Illiteracy rates

Bold numbers are regression coefficient estimates and numbers below are their associated t-statistics*, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

37

Table A3-2: Results of the model, including the measures of CLS
Dependent variable (3 equations)- US and Japanese FDI for all, manufacturing andnon-manufacturing industries
Total FDI
OLS
RandomEffects
Constant

Manufacturing FDI Non-manufacturing FDI
OLS
RandomOLS
RandomEffects
Effects

Female to male
illiteracy ratio

27.526
1.227-2.075 *-1.8191.916 ***
2.778
0.478
1.122
0.169
0.857
0.009
1.370
0.054
1.100
-0.130-0.555-0.038-0.319
0.106
0.475
0.056
1.539
-0.414-1.25027.526
1.227-2.075 *-1.8191.916 ***
2.778
0.478
1.122
0.169
0.857
0.009
1.370
0.054
1.100
-0.130-0.555-0.038-0.319
0.106
0.475
0.056
1.539
-0.414-1.25065.983 **2.174-3.200 **-2.5571.666 ***
3.072
0.871 *
1.885
-0.266-1.0500.012 **
2.107
-0.040-0.544
0.041
0.134
0.049
0.383
-1.793-0.666-0.310-1.2320.024
0.689-0.784 **-2.318-0.736-0.017
0.326
0.164
0.636
0.891
-0.519-0.588-0.153-0.9200.006
0.999-0.019-0.3160.165
0.841-0.007-0.061-1.850-0.611-0.212-1.0190.010
0.150-0.397-0.925

0.287
0.014
0.094
0.104
0.615
1.171
0.010
0.027
0.268 **
2.365
0.000
-0.019
0.026
0.585
0.119
0.780
-0.159-1.497
0.065
0.341
0.195
0.757
0.033
1.146

0.287
0.014
0.094
0.104
0.615
1.171
0.010
0.027
0.268 **
2.365
0.000
-0.019
0.026
0.585
0.119
0.780
-0.159-1.497
0.065
0.341
0.195
0.757
0.033
1.146

Observations
R-squared

60
0.397

60
0.397

57
0.417

57
0.492

55
0.500

55
0.500

Log (GDP source)
Log (GDP host)Log (Distance between
source and host)Log (Inflation)
Openness
Adult Illiteracy rates
Political Rights (1=highest, 7= lowest)
Log (no. of workers instrikes + 1)
Wages to value-added
Civil Liberties
Child Labour

Bold numbers are regression coefficient estimates and numbers below are their associated t-statistics.
*, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

38

Table A3-3: Results of the model, including the ratification index
Dependent variable (3 equations)- US and Japanese FDI for all, manufacturing andnon-manufacturing industries
Total FDI
OLS
RandomEffects
Constant
Log (GDP source)
Log (GDP host)Log (Distance
between source andLog (Inflation)
Openness
Adult Illiteracy rates
Political Rights (1=highest, 7= lowest)
Log (no. of workers
in strikes + 1)
Wages to valueadded
Ratification index

Observations
R-squared

Manufacturing FDI Non-manufacturing FDI
OLS
RandomOLS
RandomEffects
Effects

47.761 **
2.106-1.980 *-1.802
1.055 ***
2.588
0.496
1.182
0.102
0.579
0.000
0.013
0.010
0.244
0.106
0.653
-0.143-1.4760.005
0.732

37.3161.378-1.600-1.222
1.056 **
2.295
0.362
0.713
0.103
0.583
0.001
0.230
0.024
0.532
0.084
0.491
-0.117-1.1200.004
0.522

69.854 **2.507-2.308 *-1.902
0.510
1.280
0.572
1.239
-0.195-0.8720.006
1.281-0.082-1.114
0.055
0.224
0.038
0.285
-3.271-0.9920.005
0.676-82.834 **-2.0904.174 **
2.197
-0.155-0.256-1.897 *-1.895-0.163-0.903
0.003
1.020
0.018
0.213
0.162
1.002
-0.002-0.029-3.583 *-1.936-0.018 *-1.752

11.2300.584-0.334-0.3670.695 **
2.342
0.187
0.492
0.231 **
2.102
-0.003-0.9200.008
0.244
0.192 *
1.765
-0.221 **-2.457-0.001-0.20411.1520.577-0.330-0.3600.694 **
2.331
0.185
0.486
0.229 **
2.083
-0.003-0.9100.008
0.248
0.191 *
1.761
-0.220 **-2.440-0.001-0.208

60
0.340

60
0.535

57
0.325

57
0.470

55
0.480

55
0.480

Bold numbers are regression coefficient estimates and numbers below are their associated t-statistics*, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

39

Table A3-4: Correlation matrix of all the variables used
Host
Source Distance Inflation Openne Illiteracy Political No. of
Total
country country betweenss
rates
Rightsworkers FDI
GDP
GDP
source(1=best, in strikes
and host
7= worst)

Host country GDP
Source country GDP
Distance between source and hostInflation
Openness
Illiteracy rates
Political Rights (1=best, 7= worst)
No. of workers in strikes
Total FDI
Mfg. FDI
Non-mfg. FDI
Ratification index
Wages to value added
Civil Liberties (1=best, 7= worst)
Child Labour
Female to male illiteracy ratio

1.00
0.03
0.01
-0.18
0.72
-0.30
0.00
-0.09
0.47
0.35
0.46
-0.40
0.61
-0.10
-0.68
0.63

1.00
-0.55
-0.24
0.11
-0.19
-0.11
-0.25
0.22
0.28
0.12
0.38
-0.29
-0.16
-0.06
-0.07

1.00
-0.02
0.13
0.22
0.19
-0.18
0.07
-0.10
0.06
0.10
0.06
0.17
0.06
-0.17

1.00
-0.23
0.03
0.30
0.41
-0.20
-0.24
-0.04
0.35
-0.22
0.41
0.31
-0.22

1.00
0.14
0.30
-0.45
0.39
0.31
0.37
-0.13
0.38
0.33
-0.48
0.15

1.00
0.72
-0.23
0.00
-0.03
0.12
0.14
-0.23
0.71
0.14
-0.41

1.00
-0.09
0.33
0.20
0.46
-0.03
-0.15
0.86
0.11
-0.15

1.00
-0.09
-0.13
-0.002
-0.40
-0.03
-0.19
-0.21
0.43

1.00
0.79
0.78
-0.14
0.08
0.23
-0.09
0.22

Mfg. FDI NonRatificati Wages Civil
Child
mfg. FDI on index to value Liberties Labour
added (1=best,
7= worst)

1.00
0.59
-0.16
0.00
0.15
-0.02
0.15

1.00
-0.21
-0.03
0.34
-0.09
0.31

1.00
-0.41
0.01
0.24
-0.71

1.00
-0.22
-0.53
0.34

1.00
0.21
-0.30

40

1.00
-0.54

Femaleto maleilliteracy
ratio

1.00

Table 1: Types of labour standards
Type
I. Basic rights

II. Survival rights

III. Security rights

IV. Civic rights

ExamplesRight against use of child labour
Right against involuntary servitude
Right against physical coercion
Right to a living wage
Right to accident compensation
Right to a limited work week
Right against arbitrary dismissal
Right to retirement compensation
Right to survivors' compensation
Right to free association
Right to collective representation
Right to free expression of grievances

Source: Portes (1994)

Table 2: Ratification of Core Labor Standards Conventions
Freedom of association Elimination of forced andElimination of
Abolition of child labourand collective
compulsory labour
discrimination in
bargaining
respect of employment
and occupation
Country
China

Conv. 87

Conv. 98

Conv. 29

Conv. 105 Conv. 100 Conv. 111 Conv. 138 Conv. 182
02:11:1990
28:04:1999 08:08:2002

Hong Kong, China 15:10:1963 06:09:1975 06:03:1931 25:11:1959
Indonesia
Korea, South
Malaysia
Philippines
Singapore
Thailand

28:04:1999 08:08:2002

09:06:1998 15:07:1957 12:06:1950 07:06:1999 11:08:1958 07:06:1999 07:06:1999 28:03:2000
08:12:1997 04:12:1998 28:01:1999 29:03:2001
05:06:1961 11:11:1957

09:09:1997

09:09:1997 10:11:2000

29:12:1953 29:12:1953 15:07:2005 17:11:1960 29:12:1953 17:11:1960 04:06:1998 28:11:2000
25:10:1965 25:10:1965

30:05:2002

14:06:2001

26:02:1969 02:12:1969 08:02:1999

11:05:2004 16:02:2001

Source: ILO website- http://www.ilo.org/ilolex/english/docs/declworld.htm*Both Malaysia and Singapore ratified but later denounced the convention no. 105 due to problems
related to compulsory prison labour (ILO, 2001).

Convention names:
Convention no. 87: Freedom of Association and Protection of the Right to Organise
Convention, 1948
Convention no. 98: Right to Organise and Collective Bargaining Convention, 1949
Convention no. 29: Forced Labour Convention, 1930
Convention no. 105: Abolition of Forced Labour Convention, 1957
Convention no. 100: Equal Remuneration Convention, 1951

41

Convention no. 111: Discrimination (Employment and Occupation) Convention, 1958
Convention no. 138: Minimum Age Convention, 1973
Convention no. 182: Worst Forms of Child Labour Convention, 1999
Table 3: Labour force participation rates of ten to fourteen year olds
Country
1960 1965 1970 1975 1980 1985 1990 1995 2000 2003
China
43.17 41.10 39.03 34.76 30.48 22.86 15.24 11.55 7.86 5.50
Hong Kong, China 7.44 6.96 6.48 6.23 5.97 2.99 0.00 0.00 0.00 0.00
Indonesia
22.11 20.38 18.64 16.09 13.53 12.42 11.30 9.56 7.82 6.77
Korea, Rep.
14.41 11.89 9.37 4.87 0.36 0.18 0.00 0.00 0.00 0.00
Malaysia
10.12 9.25 8.38 8.18 7.97 5.98 3.98 3.16 2.33 1.63
Philippines
21.39 19.53 17.67 15.92 14.17 12.42 10.66 8.05 5.44 3.81
Singapore
5.58 4.51 3.44 2.58 1.71 0.86 0.00 0.00 0.00 0.00
Thailand
35.19 32.71 30.22 27.73 25.24 22.74 20.24 16.23 12.22 10.04
Vietnam36.22 31.24 26.26 24.01 21.76 17.40 13.04 9.13 5.22 3.65
Source: World Development Indicators (2005)- on-line version

Table 4: FDI location criteria
FDI Location Criteria
Score
1. Growth of market
4.2
2. Size of market
4.1
3. Profit perspectives
4
4. Political and social stability
3.3
5. Quality of labour
3
6. Legal and regulatory environment3
7. Quality of infrastructure
2.9
8. Manufacturing and services environm
2.9
9. Cost of labour
2.4
10. Access to high technologies
2.3
11. Fear of protectionism
2.2
12. Access to financial resources
2
13. Access to raw materials
2
Source: Hatem (1997)

42

Table 5: Industrial breakdown available from U.S. and Japanese FDI data.
Industrial breakdown of US FDI data

Industrial breakdown ofJapanese FDI data
Format changed to the following since 1999
Food
Food and kindred products
Food
TextileChemicals and allied products
Chemicals
Lumber&Pulp
Primary and fabricated metals
Primary and fabricated metals
ChemicalIndustrial machinery and equipment
Machinery
Metal
Electronic and other electric
Computer and electronic products
MachineryTransportation equipment
Electrical equipment, appliances, and components Electrical
Other manufacturing
Transportation equipment
Transport
Manufacturing Total
Manufacturing TotalothersPetroleum
MiningManufacturing Total
Wholesale trade
Utilities
Farming&Forestry
Banking
Wholesale trade
Fishery
Finance, insurance and real estate
Information
MiningServices
Depository institutions
Construction
Other industries
Finance (except depository institutions) and insurance Trade
All industries (Total)
Professional, scientific, and technical services
Finance&Insurance
Other industries
Service
All industries (Total)
Transportation
Real EstateothersNon-Manufacturing Total
Branches
TOTAL

Source for US FDI data- Bureau of Economic Analysis website:
http://www.bea.doc.gov/bea/di/di1usdbal.htm
Source for Japanese FDI data- Ministry of Finance website:
http://www.mof.go.jp/english/e1c008.htm

43Graph 1: Percentage of U.S. and Japanese inward FDI out of total FDI
inflows from the world in sample countries
70
60
50
40
30
20
10
0

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Share of JP FDI

Share of US FDI

Share of JP+US FDI

Source for US FDI data- Bureau of Economic Analysis website
Source for Japanese FDI data- Ministry of Finance website
Source for total FDI inflows- World Development Indicators (2005) - on-line version

Graph 2: Percentage of US and Japanese outward FDI in sample countries
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
-5.00

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
US- All industries

US- Mfg

US- Non-mfg

JP- All industries

JP- Mfg

JP- Non-mfg

Source for US FDI data- Bureau of Economic Analysis website
Source for Japanese FDI data- Ministry of Finance website

44