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Export-oriented Industrialisation, Female Employment and Gender Wage Equity in East Asia

This paper investigates if export growth in manufacturing in east Asia led to a removal of labour market rigidities and the institutional biases of gender-based discrimination as commonly argued. It challenges the orthodox perspective by looking more closely at industrial employment in the region by gender. Gender discrimination in the region's labour markets seems to have survived economic liberalisation, with the large gender wage gaps characteristic of the region not closing despite rapid growth and full employment, and sometimes even becoming larger in some of the more developed economies in the region.

SPECIAL ARTICLE

Export-oriented Industrialisation, Female Employment and Gender Wage Equity in East Asia

Jomo Kwame Sundaram

This paper investigates if export growth in manufacturing in east Asia led to a removal of labour market rigidities and the institutional biases of gender-based discrimination as commonly argued. It challenges the orthodox perspective by looking more closely at industrial employment in the region by gender. Gender discrimination in the region’s labour markets seems to have survived economic liberalisation, with the large gender wage gaps characteristic of the region not closing despite rapid growth and full employment, and sometimes even becoming larger in some of the more developed economies in the region.

This article has been greatly expanded from my paper “Globalisation, Export-Oriented Industrialisation, Female Employment and Equity in East Asia” prepared for the United Nations Research Institute on Social Development project on “Globalisation, Export-Oriented Employment for Women and Social Policy”. I am appreciative of Shahra Razavi’s initial encouragement, as well as of Cristina Paciello’s assistance in preparing the tables on female manufacturing employment and Miriam Rehm’s help in updating them. Needless to say, I am solely responsible for this paper.

Jomo Kwame Sundaram (jomoks@yahoo.com) is with the United Nations Department of Economic and Social Affairs, New York.

I
t is often claimed that the rapid growth in east Asia in recent decades has been due to export-oriented manufacturing growth, which has enhanced women’s position within the economy. The assumption behind this assertion seems to be that with export growth (which is supposed to be facilitated by trade liberalisation), the demand for female labour increases faster than for male labour, so that female wages also rise faster than, and eventually converge with, male wages. These trends are presumed to eliminate labour market rigidities and remove the institutional foundations for gender-based discrimination in labour markets. Thus, globalisation is supposed to improve the condition of women by creating manufacturing employment opportunities for them while eliminating gender discrimination in labour markets.

This paper challenges this picture by looking more closely at industrial employment in the region by gender. Gender discrimination in the region’s labour markets seems to have survived economic liberalisation, with the large gender wage gaps characteristic of the region not closing despite rapid growth and full employment, and sometimes even becoming larger in some more developed economies.

The United Nations (1999) has noted that female employment in the developing world has generally been increasing more rapidly than male employment, and that export-oriented industries are more feminised. It also notes, however, that since the late 1980s, “in many middle-income countries the demand for women’s labour in manufacturing has been weakening, as export production became more skill- and capital-intensive” (United Nations 1999: 9), though it is not clear why de-feminisation necessarily follows from greater skill or capital intensity. As examples of this trend, it cites Singapore, Taiwan and South Korea. In South Korea specifically, it notes that “the composition of the workforce in the electronics industry has changed in favour of male workers, as production in this sector shifted to more sophisticated communication and computer products”, besides noting a similar trend with maquilas in Mexico.

Greater labour market flexibility has been brought about by institutional reforms enforced by governments which believed this to be desirable for attracting investments and thus enhancing growth. There has been relatively little resistance to such “casualisation”, as its negative consequences were partly offset by the post-1985 boom (after the appreciation of the yen and the currencies of the first-tier east Asian newly industrialising economies, the NIEs), which has been accompanied by declining unemployment as well as improved labour remuneration to

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retain employees. However, such casualisation undermines the likelihood of corporatism, and hence, of greater “commitment” by workers, e g, as “stakeholders” in Japan and Singapore. Labour market liberalisation as well as other developments are also likely to have weakened the bargaining power of workers in Malaysia, Thailand and Indonesia, and may thus have worsened income distribution.

Labour market liberalisation did not significantly reduce nominal wages or increase poverty in the region (except perhaps in Thailand) before the currency and financial crises of 1997 induced a regional recession in 1998. Until the crisis, growth continued to raise real incomes overall, and low unemployment and skill enhancement strengthened the bargaining power and remuneration of labour generally. To varying extents, governments introduced some “social safety nets” to reduce the dislocation caused by rapid structural changes and cyclical influences. However, such provisions were minimised on the presumption that full employment could be indefinitely assured – providing “workfare” and thus eliminating the need for “welfare” provisions.

It was often claimed that the unemployed could always count on “traditional” social safety nets provided by “families”, “communities” and informal sector participation – much of it heavily reliant on unpaid and poorly paid female labour. The higher unemployment and massive social dislocations due to the recessions following the 1997 east Asian financial crises have underscored the inadequacy of such provisions when most needed, and the disproportionate burden often thrown on the shoulders of poor women who often have had to try to increase their labour force participation in low-paying female-dominated sectors, while maintaining their “traditional” responsibilities for daily household reproduction (Francisco and Sen 1999).

East Asian Trends

Graph 1 shows the rising percentage of women in industrial/ manufacturing employment over time in the early stages of labour-intensive industrialisation, reflected in the rising proportion of the labour force in industry/manufacturing. As the east Asian NIEs achieved full employment, the tightening of the labour market triggered a rise in wages and other labour costs, and thereby encouraged greater structural change and shifts away from labour-intensive manufacturing employment which was predominantly female. The decline in manufacturing employment set in for the first-tier or first-generation east Asian NIEs by the mid-1980s, encouraging them to relocate low-skill labourintensive production to the rest of south-east Asia and China from the late 1980s. Such relocation was favoured by two other develop ments from the mid-1980s: first currency appreciation, and, second, retaliatory actions from countries that were the targets of the highly successful export drive from this region.

The currencies of east Asian NIEs, most notably South Korea and Taiwan, appreciated with the yen against the US dollar after the endaka or yen appreciation that followed the G7 Plaza Hotel accord of September 1985.1 The exceptions were the Hong Kong dollar, pegged to the US dollar from 1983, and the south-east Asian currencies, informally pegged to the US dollar, albeit at changing rates, at least from the mid-1980s.2 These currency appreciations had the effect of pushing the first wave of northeast Asian direct investment into south-east Asia (most notably Thailand, Malaysia, and Indonesia), while the low wages and currency depreciation in south-east Asia served as additional attraction for these investments.

The withdrawal of the General System of Preferences (GSP) privileges under the General Agreement on Tariffs and Trade (GATT) from the first-tier east Asian NIEs was another contributing factor that encouraged such relocation.3 The export success of the first-tier NIEs – which entailed continuously increasing international market shares – was at the root of this retaliatory measure. The ensuing south-east Asian boom in the late 1980s and early 1990s achieved near full employment, especially in Malaysia and Thailand, attracted labour immigration, especially from neighbouring countries, and raised wages and other labour costs as in the first-tier NIEs earlier. This resulted in similar relocations of foreign as well as domestically owned manufacturing capacities to Vietnam, China, India and elsewhere in the region.

Graph 1: Share of Women in Total Manufacturing Workers in East Asian Countries

(1957-2006, in %)

A: Hong Kong, Singapore, South Korea

55
50
45 40 Hong Kong
35 30 South Korea
25 20 Singapore
1957 15 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005
55 B: Malaysia, Thailand, Indonesia
50 Malaysia

45 Indonesia40 Thailand

35 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 For notes on individual time series, see Appendix Tables A1a-1f. Sources: ILO, Yearbook of Labour Statistics (various issues); ILO, LABORSTA database (http://laborsts.ilo.org/).

The tables on women’s employment in various industrial subsectors suggest that female labour shares have been highest in relatively low-skill, labour-intensive and often export-oriented manufacturing. Graph 1 shows manufacturing employment growing rapidly at different times in the different economies, and then declining in the more industrialised economies according to the ILO LABORSTA database. The tables also show the changing shares of women in the manufacturing labour forces. Graph 2 shows female wages as a share of male wages in the same economies except for Indonesia during the 1980s and 1990s. Appendix Tables A3a to A3d use data from a different source (UNIDO Industrial S tatistics) to show the percentages of women employed in various manufacturing sectors and the shares of the manufacturing workforces in these sectors from the 1970s. (The Appendix Tables A3a to A3d are posted on the EPW web site alongside this paper.)

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Graph 2: Female Wages as Shares of Male Wages in Five East Asian Countries

(1980-2006, in %)

90 80 70 60 50 South Korea Hong Kong Singapore Thailand

Malaysia

40

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 Sources: ILO, LABORSTA database (http://laborsts.ilo.org/).

In South Korea, manufacturing’s share of total employment rose from 6.3% in 1960 to 13.1% in 1970, 21.5% in 1980, reaching a peak of 27.8% in 1989 before declining to 20.3% in 1990, and 18.0% in 2006. During this period, the female share of manufacturing workers rose from 26.6% in 1960 to 32.9% in 1970, 39.0% in 1980, and 42.6% in 1990, before declining again to 35.8% in 2000, and to 32.9% in 2006. Female wages as a share of male wages in manufacturing rose almost continually from 45.1% in 1980 to a high of 57.9% in 2001, and, after a small downturn, reached 57.2% in 2006.

In Hong Kong, manufacturing’s share of total employment rose from 39.5% in 1961 to 46.1% in 1971, before declining to 42.1% in 1980, 27.6% in 1990, 10.4% in 2000, and 6.3% in 2006. During this period, the female share of manufacturing workers rose from 32.8% in 1961 to 41.7% in 1971, 45.2% in 1980, and peaked at 48.4% in 1982 and again 1984, before declining to 36.6% in 1995. Since then, the share of women in manufacturing employment has been fluctuating around 36%; in 2006, it was 35.5%. Female wages as a share of male wages in manufacturing have been closing the gap from 77.7% in 1982 to 92.5% in 2006.

In Singapore, manufacturing’s share of total employment rose from 13.9% in 1957 to 19.7% in 1970, 29.2% in 1980, reaching a peak of 30.3% in 1981 before declining to 16.8% in 2006. During this period, the female share of manufacturing workers rose from 18.2% in 1957 to 33.6% in 1970, 46.0% in 1980, 47.2% in 1987 before declining to 36.6% in 2006. Female wages as a share of male wages in manufacturing were essentially flat, rising from 61.5% in 1980 to 64.7% in 1984, then declining to 54.7% in 1990, and subsequently rising to 63.6% in 2006.

In Malaysia, manufacturing’s share of total employment rose from 16.0% in 1980 to 19.9% in 1990, reaching a peak of 23.3% in 1993 and again in 1997 and 2001, before declining to 20.3% in 2006. During this period, the female share of manufacturing workers rose from 38.2% in 1980 to a peak of 47.6% in 1990, before declining to 39% in 2006. Female wages as a share of male wages in manufacturing rose from 47.5% in 1983 to 62.9% in 1997.

In Thailand, manufacturing’s share of total employment rose from 3.4% in 1960 to 5.1% in 1971, 7.9% in 1980, 10.1% in 1990, 14.5% in 2000, and 14.6% in 2006. During this period, the female share of manufacturing workers rose unsteadily from 37.6% in 1960 to 53.3% in 2001. Female wages as a share of male wages in manufacturing rose from 63.8% in 1991 to 75.4% in 2003.

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In Indonesia, manufacturing’s share of total employment rose from 5.3% in 1961 to 6.5% in 1971, 9.0% in 1980, 10.1% in 1990, and to 13.2% in 1994. It remained around that level until 2006. During this period, the female share of manufacturing workers rose from 37.5% in 1961 to 42.6% in 1971, reached a peak of 47.8% in 1982, and then declined to 40.6% in 2006.

While the manufacturing share of total employment has begun to decline in the three first generation east Asian newly industrialised economies of Hong Kong, Singapore and South Korea from 1971, 1981 and 1989 respectively, the same cannot be said of the two second-tier south-east Asian newly industrialising countries of Thailand and Indonesia, in which the manufacturing sector expanded from 1986 and 1989 respectively, and have registered essentially stable shares of total employment since the mid-1990s. Malaysia appears to be in an intermediate position as its manufacturing sector began absorbing more labour earlier than the other two, but this has declined somewhat since the 1997-98 east Asian crisis.

The female proportion of manufacturing workers peaked at different times in the different economies: 1982-84 in Hong Kong, 1986-89 in Singapore, 1990 in South Korea and Malaysia, and 1993 in Indonesia. Except for Thailand, all other east Asian countries’ female manufacturing labour shares declined after 1993, with Indonesia remaining highest at 40.6% and South Korea l owest at 32.9% in 2006. Meanwhile, Thailand appears to have departed from the general east Asian trend as its female share of manufacturing workers has continued to rise since the 1980s.

According to the UNIDO Industrial Statistics, Thailand had a majority of women in the manufacturing labour force in 1986 and from 1990s. There was a female majority in Malaysia briefly from 1988 to 1992, while the female share in Indonesia reached almost 50% in 1999. In South Korea, the share of women in manufacturing has declined since 1982 and is now less than a third of total employment in manufacturing.

Table 1: Malaysia – Manufacturing Labour Skill Composition by Gender (1971-97)

1971 1979 1985 1990 1997
Females in total labour force (%) 32.1 46.7 46.1 52.4 45.5
Female skilled in total skilled (%) 34.2 52.5 52.6 59.4 51.4
Female unskilled in total unskilled (%) 40.3 54.7 55.3 60.0 51.6
Female skilled in female direct workers (%) 42.7 57.1 65.4 66.2 60.2
Male skilled in male direct workers (%) 49.3 55.7 60.0 54.9 61.8
Unskilled/skilled wage ratio 0.62 0.71 0.61 0.60 0.60
Skilled*/total direct workers (%) 46.8 54.5 58.6 54.6 61.0
Direct workers/total (%) 76.5 73.7 69.1 74.9 72.4
Technical, professional and skilled
in total labour force (%) 51.6 59.3 67.0 61.6 68.1

* includes semi-skilled workers. No data on skilled-unskilled decomposition of the labour force available after 1997. Sources: Computed from Malaysia, Industrial Survey, 1971, 1979, 1985, 1990, 1997. From Rasiah (2001: 26, Table 11).

UNIDO data permit a decomposition of the female manufacturing share into sub-sectors. In South Korea, Malaysia, Thailand and Indonesia, wearing apparel has consistently employed much more women than men. For Malaysia, Thailand and Indonesia, other sub-sectors largely dominated by female employees include textiles, footwear, tobacco, rubber products, professional scientific equipment, electrical machinery and other manufactures.

Table 2: Malaysia – Share of Female and Skilled Workers in Manufacturing (1983-2005, in %) The sub-sectors employing at least 5% of the total manufactur1983 1985 1990 1995 1999 2003 2005 ing workforce in all four economies in at least one year include Females in total labour force 44.6 45.5 50.8 45.6 46.8 43.5 46.4 food products, textiles, wearing apparel, rubber products and Full-time employees:

electrical machinery. This was also true of rubber products in

Professional 1.9 2.2 2.0 2.4 3.2 6.3 7.4 Non-professional managerial workers 2.3 2.4 1.8 2.3 2.8 n a n a

Malaysia. It is likely that food products were primarily for the

Technical and supervisory 8.5 9.1 8.6 9.6 10.6 12.4 11.7 domestic market, while varying portions of the other industrial Clerical and related 8.2 8.9 6.7 6.5 6.6 6.9 6.6

products are probably also for domestic consumption, though

General 5.7 5.6 4.0 3.9 3.2 4.3 4.5

they are more likely to be export-oriented. These industries also

Production workers 73.5 71.8 76.9 75.3 73.6 70.2 69.8 Skilled and semi-skilled/ seem to be characterised as primarily involving low-paying, total production workers 56.1 58.7 54.8 45.3 62.6 n a n a

low-skill, light manual work. It is not clear that export-oriented

Female skilled/

manufacturing per se has an especially strong connection with

female production workers 39.7 43.8 35.7 36.5 34.5 n a n a Male skilled/ female employment. male production workers 40.4 38.1 32.5 33.7 35.1 n a n a

Although the precise mechanisms and relations are still

Female workers/production workers:

poorly understood, there appears to have been a general

Skilled 50.5 54.8 59.1 51.9 52.0 n a n a Semi-skilled 43.8 36.7 50.6 45.6 52.0 n a n a r egional pattern of increased female employment in manufac-Unskilled 54.0 54.2 58.0 50.8 52.7 n a n a

turing during the period of early rapid labour-intensive industri-

Ratio of average wages:

alisation, p robably accelerated by the availability of export

Skilled: unskilled 1.6 1.7 1.6 1.7 1.7 n a n a Semi-skilled: unskilled 1.2 1.3 1.3 1.2 1.2 n a n a m arkets. With full employment, more sophisticated or skill- n a – not available.

intensive manufacturing and other related developments,

Sources: Industrial Surveys, 1983, 1985, 1990; Annual Survey of Manufacturing Industries, 1996, 2002, 2003. m anufacturing growth and industrial employment growth

This is true to a lesser extent for South Korea, which has a Appendix Tables A1: East Asian Six – Manufacturing Employment Table A1a: South Korea: Manufacturing Employment (1960-2006)

lower and declining female share of manufacturing employees

Year Total Total Women Men Share ofoverall. Sub-sectors that have a substantial share of female em- Manufacturing Manufacturing Employed in Employed in Women

Employment Employment Manufacturing Manufacturing Workers ployees, at least in some of these four countries, are “pottery, (in ‘000) (%) (in ‘000) (in ‘000) (%)

china and earthenware”, food products, leather, and “other” 1960 476 6.3 127 349 26.6 (non-industrial) chemicals. 1966 958 11.0 321 636 33.5 1970 1,284 13.1 423 861 32.9

In 2006, female wages as a share of male wages were lowest in

1975 2,205 18.6 755 1,450 34.2

Korea (57.2% from 45.1% in 1980), followed by Singapore (63.6%

1980 2,955 21.5 1,155 1,800 39.0

from 61.5% in 1980), Malaysia (62.9% in 19974 from 47.5% in

1981 2,859 20.3 1,112 1,747 38.8

1983), and Thailand (75.4% in 2003 from 63.8% in 1991). Hong

1982 3,033 21.0 1,167 1,866 38.4

Kong achieved the highest ratio of female-male wages in 2006 at

1983 3,266 22.5 1,242 2,024 38.0

92.5%, up from 77.7% in 1982 (Appendix Tables A2a to A2e, p 49).

1984 3,348 23.2 1,265 2,083 37.7

For the five economies with data between 1980 and 2006, only

1985 3,504 23.4 1,351 2,153 38.5 Singapore saw its gender wage gap return to its initial level after 1986 3,826 24.6 1,538 2,289 40.1 it deteriorated in the second half of the 1980s, while all other 1987 4,416 27.0 1,854 2,562 41.9 countries’ wage gaps closed slowly. 1988 4,667 27.6 1,966 2,701 42.1 South Korean, Malaysian, Thai and Hong Kong manufacturing 1989 4,882 27.8 2,075 2,807 42.5 1990 4,911 27.1 2,093 2,839 42.6

sectors thus paid significantly lower wages to women compared

1991 4,994 26.8 2,092 2,922 41.8

to men, but these gender wage gaps closed over time. Although

1992 4,860 25.5 1,940 2,920 39.9

the gender wage gap was smallest in Hong Kong at the beginning

1993 4,677 24.1 1,793 2,884 38.3

of the 1980s, it has continued to close over the last four decades,

1994 4,714 23.6 1,771 2,943 37.5

whereas the gender wage gaps remain largest in the other two

1995 4,797 23.4 1,762 3,035 36.7

first-tier east Asian NIEs, namely South Korea and Singapore.

1996 4,692 22.5 1,719 2,973 36.6

Culture alone does not seem to be able to explain the great dispa

1997 4,482 21.2 1,597 2,885 35.6 rity among the three first generation east Asian NIEs, often 1998 3,898 19.4 1,345 2,553 34.5 dubbed “confucianist”. 1999 4,006 19.7 1,443 2,563 35.5 There is little evidence that the closing of gender wage gaps 2000 4,293 20.3 1,535 2,758 35.8 was linked to the decline in female employment shares in 2001 4,267 19.8 1,519 2,748 35.6

2002 4,241 19.1 1,518 2,723 35.8

manufacturing. Rather, the descriptive data indicate that both

2003 4,205 19.0 1,475 2,730 35.1

trends took place independently of each other. In Singapore, the

2004 4,290 19.0 1,493 2,797 34.8

growing gender wage gap accompanied a stable share of female

2005 4,234.2 18.5 1,413.4 2,820.7 33.4

employment in the manufacturing sector in the years 1984 to

2006 4,167.1 18.0 1,372 2,795 32.9

1990. In the same period, Korea saw its participation rate of

From 1970 to 1991, classification of economic activities ISIC Rev 2. From 1992, classification of economic activities ISIC Rev 3.

women in the manufacturing sector rise, even as its gender wage

Sources: ILO, Yearbook of Labour Statistics (various issues). gap closed. ILO, LABORSTA database (http://laborsta.ilo.org/).

44 january 3, 2009

Table A1b: Hong Kong – Manufacturing Employment (1961-2006) manufacturing. This is true even if the industries concerned began

Year Total Total Women Men Share of

with import-substitution, as was the case in South Korea, for

Manufacturing Manufacturing Employed in Employed in Women Employment Employment Manufacturing Manufacturing Workers

example. And insofar as labour-intensive, low-cost manufact

(in ‘000) (%) (in ‘000) (in ‘000) (%)

uring for export has been the principal motive of industrial invest

1961 478.9 39.5 157.1 321.7 32.8

ments, such investments probably generated considerable direct

1966 553.1 38.0 238.4 314.6 43.1 1971 764.5 46.1 319.0 445.4 41.7 employment,5 and if the jobs created were female-typed, growing 1976 867.3 44.4 399.9 467.3 46.1 employment of women, at least initially.

1978 889.6 44.4 403.4 486.2 45.3 Joekes (1995) attributed the swing away from female intensity 1980 943.1 42.1 427.1 516.0 45.2 in Singaporean manufacturing, as the state pursued techno logical 1981 940.1 38.9 449.4 490.7 47.8

upgrading, to the fact that women workers with the needed

1982 903.1 37.5 437.7 465.4 48.4

technical qualifications were not available in sufficient numbers

1983 880.0 36.2 423.6 456.4 48.1

for recruitment to new technical and other skilled grades.

1984 927.7 37.0 449.3 478.4 48.4

However, better female educational achievements in Singapore,

1985 918.8 36.1 427.1 491.7 46.4

compared to their male counterparts, suggest that other influ

1986 919.2 35.0 424.3 495.0 46.1

ences may have been at play. These may include a gender-biased

1987 916.0 34.1 418.0 498.0 45.6

view of skilled labour requirements for the new industries and the

1988 870.9 31.9 387.3 483.6 44.4 1989 808.9 29.7 349.7 459.1 43.2 renewed emphasis on female maternal roles, especially for better 1990 751.0 27.6 314.4 436.5 41.8 educated women, since concerns about lower population growth

1991 717.0 26.0 292.2 424.8 40.7 arose in the 1980s (Chan and Chee 1985). The reduction of female 1992 650.5 23.7 255.2 395.3 39.2 labour shares in the manufacturing sector was a region-wide 1993 594.0 21.2 224.7 369.3 37.8

Table A1c: Singapore – Manufacturing Employment (1957-2006)

1994 562.6 19.5 202.4 360.2 35.9

Year Total Total Women Men Share of

1995 534.6 18.4 196.6 338.0 36.6

Manufacturing Manufacturing Employed in Employed in Women Employment Employment Manufacturing Manufacturing Workers

1996 489.9 15.9 179.9 310.3 36.7

(in ‘000) (%) (in ‘000) (in ‘000) (%)

1997 443.0 14.0 161.2 281.9 36.4

1957 66.7 13.9 12.2 54.4 18.2 1998 379.6 12.2 138.6 241.0 36.5

1965 n a n a n a n a n a 1999 353.9 11.4 131.5 222.3 37.2

1970 143.1 19.7 48.1 94.9 33.6 2000 333.7 10.4 120.4 213.3 36.1

1975 218.1 26.1 86.2 131.9 39.5 2001 324.1 10.0 115.6 208.6 35.7

1980 312.6 29.2 143.9 168.8 46.0 2002 287.0 8.9 106.3 180.7 37.0

1981 350.4 30.3 157.5 192.9 44.9 2003 268.3 8.4 93.4 175.0 34.8

1982 359.7 29.4 155.8 203.9 43.3 2004 231.2 7.1 84.4 146.8 36.5

1983 347.6 27.7 151.3 196.3 43.5 2005 223.5 6.7 77.0 146.6 34.5

1984 348.1 27.4 155.6 192.5 44.6 2006 216.5 6.3 76.9 139.6 35.5

1985 314.2 25.4 139.3 174.9 44.3 Classification of economic activities ISIC Rev 2.

1986 306.6 25.2 141.0 165.6 45.9

Sources: ILO, Yearbook of Labour Statistics (various issues). ILO, LABORSTA database (http://laborsta.ilo.org/). 1987 338.6 26.7 160.0 178.7 47.2

1988 379.1/313.1 28.4 111.7 201.4 20.4/35.7

1989 403.7 28.9 185.0 218.7 45.8

t apered off. This r esulted in a decline of the female share in such

1990 n a n a n a n a n a

employment, while the g ender wage gap continued to close

1991 429.6 28.1 189.4 240.2 44.0

e xcept in Singapore.

1992 434.1 27.5 190.5 243.6 43.8

Available data does not allow definitive claims regarding the

1993 429.5 26.9 181.7 247.8 42.3

role of export-oriented manufacturing as opposed to import

1994 422.5 25.6 174.8 247.8 41.3

substituting industrialisation. The case is especially intricate in

1995 408.0 23.9 151.6 256.4 37.1

north-east Asia where the two went hand in hand as the effective

1996 406.3 23.2 171.2 235.2 42.1 protection of import-substituting industries required, and was 1997 345.9 23.1 141 204.9 40.8 thus conditional upon, export expansion by the same industries. 1998 329.9 22.1 132.2 197.6 40.1 As the tables show, the fall in women’s share of manufacturing is 1999 329.2 21.7 132.5 196.8 40.2 not only true for the manufacturing sector as a whole, but for 2000 434.2 20.7 155.5 278.7 35.8

2001 307.8 19.5 116.6 191.2 37.9

e xport-oriented manufacturing as well. Joekes (1999) also notes

2002 299.0 19.0 115.1 184.0 38.5

that the share of women employed in export-processing zones

2003 303.6 18.9 115.0 188.6 37.9

d eclined between 1980 and 1990 in Malaysia, South Korea

2004 298.3 18.3 114.0 183.9 38.2

and the Philippines, with the sharpest decline from 75% to 54%

2005 n a n a n a n a n a

in Malaysia.

2006 301.7 16.8 110.4 191.3 36.6 However, insofar as exports implied that the manufacturing From 1970 to 1985, classification of economic activities ISIC Rev 2, from 1990 onwards, Classification of economic activities ISIC Rev 3, 2000 data from population census, all other years

growth rate was no longer limited by the (expanding) size of the

data from labour force survey.

n a = data not available.

Sources: ILO, Yearbook of Labour Statistics (various issues). alisation probably accelerated with successful export-oriented ILO, LABORSTA database (http://laborsta.ilo.org/).

domestic market – as for import-substituting industries – industri-

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Table A1d: Malaysia – Manufacturing Employment (1980-2006) the Multi-Fibre Arrangement (MFA) under the GATT, with

Year Total Total Women Men Share of

garments/apparel manufacturing especially labour-intensive,

Manufacturing Manufacturing Employed in Employed in Women Employment Employment Manufacturing Manufacturing Workers

with a very high proportion of women workers employed directly

(in ‘000) (%) (in ‘000) (in ‘000) (%)

on a “putting out” basis. Electrical and electronic manufacturing,

1980 768.8 16.0 294.1 457.6 38.2

testing and assembly have been relatively labour-intensive and

1981 813.5 16.0 328.0 485.5 40.3 1982 816.0 15.5 351.5 464.5 43.0 grew rapidly in some countries of the region in the last third of

1983 894.1 16.3 392.5 501.5 43.8 the 20th century. 1984 858.4 15.5 368.4 490.0 42.9 The socialisation of girls at home and school, especially in 1985 850.4 15.0 371.0 479.4 43.6 vocational training, deemed them especially suited to such work.

1986 874.0 15.1 394.1 479.9 45.0

When these industries went into decline, and as manufacturing

1987 928.9 15.5 428.1 500.8 46.0

production became more diversified and skill-intensive in the

1988 987.3 15.9 449.2 538.1 45.4

first tier or generation east Asian newly industrialised economies,

1989 1,171.1 18.3 543.7 627.4 46.4

the share of female manufacturing employment seems to have

1990 1,332.8 19.9 635.5 697.3 47.6

gone down.

1991 n a n a n a n a n a

While it is difficult to compare intra-household inequalities in

1992 1,639.6 23.2 767.4 872.2 46.8

income or consumption across countries due to the lack of robust

1993 1,726.9 23.3 766.3 960.7 44.3 1994 n a n a n a n a n a and comparable data sets, available evidence on gender wage 1995 1,780.5 23.2 761.4 1,019.1 42.7 gaps provides useful insights into gender inequalities. While

1996 1,912.1 22.7 796.6 1,115.5 41.6 there is no doubt that incomes and wages, including women’s 1997 2,002.5 23.3 806.8 1,195.7 40.2 wages, rose spectacularly over short periods, the gender gap in 1998 1,907.8 22.1 761.0 1,146.9 39.8

1999 1,990.7 22.5 802.9 1,187.9 40.3

Table A1e: Thailand – Manufacturing Employment (1960-2006) 2000 2,125.8 22.8 877.7 1,248.1 41.3 Year Total Total Women Men Share of Manufacturing Manufacturing Employed in Employed in Women

2001 2,184.1 23.3 907.8 1,276.3 41.6

Employment Employment Manufacturing Manufacturing Workers 2002 2,068.9 21.7 853.4 1,215.5 41.2 (in ‘000) (%) (in ‘000) (in ‘000) (%)

2003 2,131.0 21.6 875.1 1,255.9 41.1 1960 471.0 3.4 177.2 293.8 37.6

2004 2,023.0 20.3 817.3 1,205.7 40.4 1965 n a n a n a n a n a

2005 1,989.3 19.8 787.8 1,205.1 39.6 1971 852.2 5.1 420.0 432.3 49.2

2006 2,082.8 20.3 812.4 1,270.4 39.0 1975 1,657.4 10.2 745.3 912.1 44.9 From 1980 to 2000, Classification of economic activities ISIC Rev 2.

1980 1,788.9 7.9 752.7 1,036.0 42.0

From 2001 onwards, Classification of economic activities ISIC Rev 3. n a = data not available. 1981 1,915.0 9.1 843.0 1,071.9 44.0 Sources: ILO, Yearbook of Labour Statistics (various issues).

1982 2,205.7 10.2 962.4 1,238.7 43.6

ILO, LABORSTA database (http://laborsta.ilo.org/). 1983 2,189.6 9.5 981.5 1,208.0 44.8

1984 2,188.2 9.1 944.4 1,243.7 43.1

phenomenon; although the facilities for pre-employment indus

1985 2,280.0 8.8 1,013.7 1,143.9 44.4

trial vocational training in the rest of the region have been less

1986 2,068.8 7.7 931.1 1,137.7 45.0

well developed,6 they are likely to be as gender-biased. As the

1987 2,438.0 8.8 1,165.3 1,272.5 47.7

data discussed above show, Singapore was exceptional among

1988 2,460.6 8.3 1,114.3 1,346.2 45.2

the south-east Asian economies considered, as it was the only

1989 2,769.9 9.0 1,330.1 1,439.8 48.0

east Asian economy studied where the gender gap in

1990 3,132.5 10.1 1,563.7 1,568.8 49.9 manufacturing wages has actually widened in recent years. 1991 3,465.0 11.1 1,747.5 1,717.5 50.4 It is also possible that new industrial jobs have been male 1992 3,600.0 11.1 1,719.4 1,880.6 47.7 “gender-typed” adversely affecting female recruitment and 1993 3,961.0 12.3 1,929.5 2,031.5 48.7 promotion. Such gender typing might be reinforced by gender-1994 3,850.9 11.9 1,911.6 1,939.3 49.6 differentiated opportunities for on-the-job and other industrial

1995 4,376.6 13.4 2,172.2 2,204.4 49.6

1996 4,334.2 13.4 2,064.7 2,269.5 47.6

training. In the absence of conclusive evidence, it is difficult to be

1997 4,291.9 12.9 2,068.3 2,223.6 48.1

certain as to why employers exhibited a “preference” for male

1998 4,189.4 13.0 2,054.0 2,135.4 49.0

workers beyond a mere “taste for discrimination”. It is also possi

1999 4,394.6 13.6 2,169.6 2,224.8 49.3

ble that the early phases of labour-intensive industrialisation

2000 4,784.8 14.5 2,330.1 2,454.7 48.7

were more concerned with labour costs. Recruiting women

2001 4,750.4 14.2 2,546.7 2,203.6 53.6

probably enabled employers to save more on labour costs. Women

2002 5,039.8 14.7 2,656.3 2,383.5 52.7 may have been less unwilling to accept poorer working condi-2003 5,086.3 14.7 2,670.5 2,415.8

52.5 tions in terms of wages, non-unionisation and more casualised 2004 5,313.3 14.9 2,763.1 2,550.2 52.0

employment terms. 2005 5,350.1 14.7 2,842.9 2,507.3 53.1

Also, as elsewhere, late industrialisation often began with the 2006 5,306.6 14.6 2,827 2,479.6 53.3 From 1971 to 2001, Classification of economic activities ISIC Rev 2.

garments and apparel as well as electrical and electronic

From 2002 to 2006, Classification of economic activities ISIC Rev 3.

n a = data not available. Sources: ILO, Yearbook of Labour Statistics (various issues). half of the 20th century in the developing countries, thanks to ILO, LABORSTA database (http://laborsta.ilo.org/).

i ndustries. The historical role of textiles continued in the second

46 january 3, 2009

Table A1f: Indonesia – Manufacturing Employment (1961-2006)
Year Total Total Women Men Share of
Manufacturing Manufacturing Employed in Employed in Women
Employment Employment Manufacturing Manufacturing Workers
(in ‘000) (%) (in ‘000) (in ‘000) (%)
1961 1,856.1 5.3 697.3 1,158.7 37.5
1964-65 2,059.0 5.6 915.0 1,144.0 44.4
1971 2,681.9 6.5 1,143.4 1,538.5 42.6
1976 3,560.1 6.4 1,677.3 1,882.7 47.1
1980 4,680.1 9.0 2,112.5 2,537.5 45.1
1981 n a n a n a n a n a
1982 6,021.9 10.4 2,883.1 3,138.9 47.8
1983 n a n a n a n a n a
1984 n a n a n a n a n a
1985 5,795.9 9.2 2,625.8 3,170.1 45.3
1986 5,606.0 8.2 2,511.0 3,094.9 44.7
1987 5,818.5 8.2 2,634.3 3,184.2 45.2
1988 5,996.6 8.2 2,614.5 3,382.1 43.5
1989 7,334.9 8.1 3,409.6 3,925.3 46.4
1990 7,693.3 10.1 3,483.2 4,210.1 45.2
1991 7,946.4 10.3 3,535.7 4,410.7 44.4
1992 7,847.6 10.0 3,660.5 4,187.1 46.6
1993 8,784.0 11.0 4,165.0 4,619.0 47.4
1994 10,840.0 13.2 4,920.0 5,920.0 45.4
1995 10,127.0 12.6 4,323.0 5,804.0 42.7
1996 10,773.0 12.5 4,895.5 5,877.6 45.4
1997 11,215.0 12.8 5,026.0 6,189.0 44.8
1998 9,933.6 11.3 4,451.2 5,482.4 44.8
1999 11,516.0 13.0 5,034.7 6,481.0 43.7
2000 11,657.7 13.0 4,896.7 6,761.0 42.0
2001 12,086.1 13.3 5,132.6 6,953.6 42.5
2002 12,110.0 13.2 4,992.8 7,117.2 41.2
2003 10,927.3 12.0 4,388.3 6,539.0 40.2
2004 11,070.5 11.8 4,410.4 6,660.1 39.8
2005 11,652.4 12.3 4,792.3 6,860.1 41.1
2006 11,578.1 12.2 4,704.3 6,873.8 40.6

Classification of economic activities ISIC Rev 2. n a = data not available. Sources: ILO, Yearbook of Labour Statistics (various issues). ILO, LABORSTA database (http://laborsta.ilo.org/).

wages as well as the degree of occupational segregation in Taiwan, South Korea and Hong Kong remain large by international standards, and showed little sign of diminishing over time (Joekes 1995: Tables 3 and 7). Earlier data on female and male wage earnings in manufacturing for a broader range of developing countries suggest that South Korea, Singapore and even Hong Kong had some of the largest gender wage gaps compared to other economies, with little indication of amelioration over time (United Nations 1999).

Seguino (1997) argues that despite the high rate of growth of female employment in South Korea (which has been higher than the rate of growth of male employment), the gender wage gap has only marginally narrowed over the past 20 years, contrary to what neo-classical economic arguments predict. The data she cites suggest that the average ratio of female to male earnings in Korea’s manufacturing sector rose from 47.0% in 1975 to 50.5% in 1990, though the gap has continued to close in the decade since, as noted above. In the main female-dominated export-oriented industries – textiles, apparel and electronics – the female/male earnings ratio has declined, e g, in apparel, from 48.6% in 1977 to 58.4%

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in 1990, whereas the gap continued to grow in male- dominated industries.

According to Seguino (1997: 113-14), gender discrimination and differentiation in determining access to skill training provided by the state and by firms are part of the problem. She suggests that Korean chaebols have contributed to gender wage gaps since they dominate Korean manufacturing in both capital-intensive and labour-intensive industries. Chaebols, she suggests, are especially reluctant to hire female labour in their capital-intensive industries because of patriarchal norms that “reserve” preferred jobs for males. According to Seguino, chaebols rely on profits and foreign exchange earnings from exports to fund investments and technology imports in capital-intensive industries. “Limiting women’s job opportunities by segregating them in the labourintensive export industries ensures a cheap labour supply that promotes export sales and thus technology upgrading in other industries owned by the chaebol, and can lead to economic rewards from the state in return for meeting export targets” (Seguino 1997: 113).

Seguino then tries to explain the more or less persistent gender-based wage differentials in Korean manufacturing. She rejects surplus female labour as an explanation for gender-based wage differentials. Comparing productivity and wage increases in different industries, she concludes that productivity gains have been more equitably shared with workers in maledominated industries than in female-dominated industries, where wages have lagged behind productivity gains. Seguino argues that the failure of productivity gains in female-dominated industries to translate into commensurate wage gains cannot be explained by surplus female labour. Instead, she suggests that the explanation lies in labour market institutions, the role of the state and the “genderised” division of labour in Korean corporate culture.

Seguino (2000b) later noted that while the South Korean gender wage gap has been closing, the Taiwanese gap has been growing. She explains this difference as due to recent restructuring, economic liberalisation and different efforts to maintain export competitiveness, and notes that the gender wage gaps were more strongly associated with changes in intra-industry gender wage differentials rather than with employment shifts. Besides labour market influences, she finds factors influencing women’s relative bargaining power, particularly greater Taiwanese capital mobility (though partly offset by reduced female “crowding” in affected industries), to be important. Outward Korean FDI had the opposite effect, as more capital-intensive, male-dominated industries were affected. She argues that greater state intervention in South Korea – compared to Taiwan – limited outward FDI, increased minimum wages (which raised female earnings more) and favoured more export-oriented firms, which tended to make more (quantitative and qualitative) productivity-raising investments as wage rates rose.

Examining the determinants of economic growth in several newly industrialising economies where women constitute the bulk of the labour force in the export sector, Seguino (2000a) argues that gender wage inequality contributed to higher export-led growth during 1975-95. She found GDP growth in these economies positively related to the gender wage differences, and found

a positive link between gender wage inequality and growth via both channels [exports, and therefore technological change and productivity growth, as well as investment]. In particular,… gender inequality stimulates investment, but also enhances the productivity of investment, possibly through the effect that low wages for women has on exports and therefore technology imports (Seguino 2000a: 1223).

A review of manufacturing labour skill composition trends by gender in Malaysia is very suggestive. Table 1 (p 43) points to a sharp rise in the female share of the manufacturing labour force in Malaysia in the 1970s with the expansion of garments and electronics manufacturing, stagnation during the first half of the 1980s when the government emphasised import- substituting heavy industrialisation, and another sharp rise before peaking in the early 1990s. It then declined rather rapidly although the government continued to emphasise export- oriented manufacturing, pushed for greater technological sophistication and encouraged (mainly male) labour immigration. The female share of skilled workers rose sharply in the 1970s, and then again in the late 1980s, before dropping sharply in the early and mid-1990s. The skilled workers’ share of all female direct workers also moved in parallel, though the female share of unskilled workers also moved likewise. Yet, although women comprised 59.4% of all skilled workers and skilled workers comprised 66.2% of direct women workers in 1990, compared to 40.6% and 54.9% for male workers respectively, female workers in manufacturing earned only half of what males earned in that year.

Except in 1979, when unskilled workers earned 71% of what skilled workers earned at a time of close to full employment, they averaged around 61% in 1971, 1985, 1990 and even in 1997, when full employment was offset by a huge immigrant labour presence and the onset of the 1997-98 Asian financial crisis. The share of skilled workers among all direct workers rose steadily from 46.8% in 1971 to 61.0% in 1997, except for a sudden dip to 54.6% in 1990. Similarly, the share of technical, professional and skilled workers in the manufacturing labour force rose over the same period, but dipped in 1990. These parallel drops around 1990 probably reflect the sudden increase in industrial workers in the late 1980s with labour-intensive manufacturing investments from the first-tier east Asian newly industrialised economies such as Taiwan, Singapore and South Korea. The decline in the share of direct workers in manufacturing and the likely reduction of putting out work – mainly associated with garments manufacture – suggest a trend towards greater labour flexibility and casualisation around the same time. This trend was probably accelerated by increased unemployment in the mid-1980s and new government initiatives to attract new east Asian investors responsible for the rise in manufacturing labour recruitment from the late 1980s.

Table 2 (p 44), using data from different sources, suggests that the female share of the manufacturing labour force peaked at slightly over half in 1990, before declining to 43% in 2003. In 2005, it rose again to 46%. It also suggests a significant rise in the shares of professional as well as technical and supervisory workers between 1990 and 2003, mainly at the expense of production workers. The period since 1995, presumably after the 1997-98 crisis, has also seen a rising share of skilled and semiskilled workers. However, there have not been dramatic increases in the shares of skilled workers among both male and female production workers. However, the female share of skilled, semiskilled as well as unskilled workers fell between 1990 and 1995. There do not appear to be major changes in the ratios of skilled as well as semi-skilled to unskilled workers’ wages despite these gender ratio changes.

Conclusions

It seems that as manufacturing production has matured and diversified in the first-tier east Asian newly industrialised economies, women’s share of manufacturing employment has gone down. The available data point to rapid growth of female employment as industrialisation first accelerated – at different times – in the region, presumably around the garment and perhaps the electronic sector. The numbers of women in manufacturing employment were often almost at par with – if not in excess of – men at some point, before falling behind. However, contrary to Seguino’s (2000a) claims, there is no clear relationship between the timing of the peak in the female share of manufacturing employment and the “degree” or “maturity” of industrialisation, or even the achievement of near full employment.

Seguino (2000b) suggests that increased outward FDI reduced the gender wage gap in South Korea while increasing it in Taiwan, which she partly attributes to more pervasive state interventions in the former. However, the converse seems to have been true in the two municipal economies of Hong Kong and Singapore, where the state played a more s ignificant role in the industriali sation of the latter. The gender wage gap diminished in Malaysia and Thailand with near full employment in the early and mid-1990s, though there is little evidence of women workers’ bargaining power increasing otherwise.

The female share of manufacturing employment appears to have declined in east Asia with the deceleration of industrialisation and manufacturing export growth, especially involving garments and electronics, though available evidence does not allow a more careful and detailed examination of the processes at work. There is considerable evidence that the gender gap in wages as well as the degree of gendered occupational segregation remained large by international standards in Taiwan, South Korea, Hong Kong and Singapore, and only slow progress has been made over time. It seems likely that gender ideologies or cultural prejudice, including gender bias, perhaps embodied by popular and state understandings of “Confucianism”, as well as legal and normative requirements have served to perpetuate such differences over time. However, while manufacturing employment growth for women has fallen off, service employment opportunities have grown with structural change. An i nteresting venue for future research would thus be the impacts of this d evelopment on the wage gap and on the employment situation of women.

january 3, 2009

Notes and especially in terms of enrolment in courses and – (1999): “A Gender-Analytical Perspective on subjects with a strong technical content. Clearly, Trade and Sustainable Development”, Trade,

1 Following the second Plaza accord of September these economies’ public investments in human Gender and Sustainable Development (Geneva: 1985, Japan allowed its currency to appreciate resources went well beyond the primary school UNCTAD).

vis-à-vis the US dollar in order to reduce the limit recommended by the World Bank, with labour Rasiah, Rajah (1995): “Labour and Industrialisation in Japanese trade surplus with the US.

market interventions based on long-term consider-Malaysia”, Journal of Contemporary Asia, 25 (1),

2 Unlike the official peg of the Hong Kong dollar to ations (Rodrik 1994). The expansion of education pp 73-92.

the US dollar from 1983 through a currency board not only helped generate technical and professional – (2001): “Trade, Employment, Skills and Wages in system, the southeast Asian currencies were kept human resources for industrial upgrading, but also Malaysia” (Processed, IKMAS, UKM, Bangi).

within a fairly narrow range against the US dollar enhanced oppor tunities for upward socio-economic Rodrik, Dani (1994): “Getting Interventions Right: through central bank efforts. The Indonesian mobility, including skill enhancement and higher How South Korea and Taiwan Grew Rich”, rupiah devalued slightly every year while the remuneration (Deyo 1989).

Working Paper No 4964 (Washington DC: until July 1997. National Bureau of Economic Research). 3 The GSP was introduced in GATT in 1970. It gave Seguino, Stephanie (1997): “Gender Wage Inequality

Malaysian ringgit and Thai baht remained steady

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5 Indirect employment effects were often limited in Globalisation and Patriarchy in Symbiosis”, Paper Journal of Economics, 4 (4), July. south-east Asia where export-oriented industries presented for Social Watch: http://www.dawn. UNIDO (various years): International Yearbook of have been dominated by foreign direct investors org.fj/global/restructuring/wssd/asian.html. Industrial Statistics (Geneva: United Nations who have been more likely to be vertically Ghosh, Jayati and C P Chandrasekhar (2001): Crisis as Industrial Development Organisation). integrated with international production chains Conquest: Learning from East Asia (Hyderabad: United Nations, Division for the Advancement of

or networks. Orient Longman). Women (1999): “Employment and Displacement

6 While the gender gap in education is not large in Joekes, Susan (1995): “Trade-Related Employment for Effects of Globalisation” in The 1999 World east Asia compared to other regions, gender Women in Industry and Services in Developing Survey on the Role of Women in Development: inequalities are evident at the tertiary level Countries”, Occasional Paper No 5, Geneva, Globalisation, Gender and Work (New York:

(sometimes due to deliberate government policies), UNRISD. United Nations).

From 1980 to 1997, ISIC Rev. 2; from 1998

Appendix Tables A2a-A2e: East Asian Table A2b: Hong Kong – Table A2c: Singapore – Manufacturing Table A2d: Malaysia – Manufacturing Wages
Five – Manufacturing Wages by Gender Manufacturing Wages by Gender Wages by Gender (1980-2006) by Gender (1983-1997)
Table A2a: South Korea – Manufacturing (1982-2006) Female Male Female as Female Male Female as % of Male
Wages by Gender (1980-2006) Female Male Female as % of Male 1983 336 707 47.5
Female Male Female as % of Male 1980 1.66 2.7 61.5 1984 n a n a n a
1980 88.46 196.23 % of Male 45.1 1982 66.6 85.7 77.7 1981 1.94 3.14 61.8 19851986 407 400 825 844 49.3 47.4
1981 106.02 234.32 45.2 1983 73.7 92.9 79.3 1982 2.13 3.37 63.2 1987 401 838 47.9
19821983 120.52 136.81 267.33 295.98 45.1 46.2 1984 1985 84.4 91.2 104.2 115.1 81.0 79.2 19831984 2.37 2.66 3.72 4.11 63.7 64.7 198819891990 393 420 443 848 864 885 46.3 48.6 50.1
1984 149.72 317.27 47.2 1986 98.1 125.9 77.9 1985 2.74 4.32 63.4 1991 495 952 52.0
1985 162.71 346.85 46.9 1987 3,269 4,175.5 78.3 19861987 566.05 611.78 1,005.91 1047 56.3 58.4 19921993 558 612 1,037 1,082 53.8 56.6
1986198719881989199019911992 19931994 181.8 207.91 249.74 307.45 364.26 428.06 497.32 551.4 638.9 374.79 413.35 490.54 608.93 724.48 842.83 963.76 1,055.5 1,206.7 48.5 50.3 50.9 50.5 50.3 50.8 51.6 52.2 52.9 1988 1989 1990 1991 1992 1993 1994 1995 3,735.9 4,332.7 5,022.6 5,718.4 6,445.4 7,180.2 8,098.4 8,684.2 4,680.4 5,481.1 6,262.3 7,020.7 7,878.2 8,677.8 9,499.6 10,421.4 79.8 79.0 80.2 81.5 81.8 82.7 85.2 83.3 19881989199019911992199319941995 652.39 707.02 983.3 1096.8 1190.7 1294.5 1415.4 1541.2 1,118.07 1,228.57 1,797.5 1,970.1 2,127.4 2,266.2 2,473.8 2,644.0 58.3 57.5 54.7 55.7 56.0 57.1 57.2 58.3 1994 677 1,161 58.3 1995 719 1,242 57.9 1996 842 1,343 62.7 1997 912 1,449 62.9 1998 n a n a n a Data refer to earnings per month (ringgit) and employees. No data available since 1998. Source: ILO LABORSTA database (http://laborsta.ilo.org/) Table A2e: Thailand – Manufacturing Wages by Gender (1991-2003) Female Male Female as % of Male
1995 711.1 1,314.7 54.1 1996 9,390.4 11,260.0 83.4 1996 1674.4 2,815.2 59.5 1991 3,016 4,728 63.8
1996199719981999200020012002 795.6 852.0 820.1 933.1 1,055.8 1,121.3 1,211.0 1,463.4 1,527.2 1,467.3 1,686.3 1,826.0 1,936.0 2,177.0 54.4 55.8 55.9 55.3 57.8 57.9 55.6 1997 10,467.0 1998 10,915.9 1999 10,846.7 2000 11,101.4 2001 11,395.0 2002 11,123.2 12,165.2 12,555.7 12,893.2 12,697.1 12,929.7 12,810.2 86.0 86.9 84.1 87.4 88.1 86.8 1997199819992000200120022003 1,811 1,916 2,007 2,181 2,226 2,283 2,374 2,999.7 3,311 3,384 3,653 3,752 3,762 3,881 60.4 57.9 59.3 59.7 59.3 60.7 61.2 19921993199419951996 1997 1998 1999 2000 3,329 3,558 3,715 4,250 n a n a n a n a 5,052 5,159 5,145 5,205 6,234 n a n a n a n a 6,612 64.5 69.2 71.4 68.2 n a n a n a n a 76.4
200320042005 1,320.0 1,419.7 1,556.1 2,369.7 2,599.8 2,798.6 55.7 54.6 55.6 2003 11,021.1 2004 11,139.3 2005 11,015.0 12,082.7 11,880.7 12,248.6 91.2 93.8 89.9 200420052006 2,442 2,563 2,682 3,969 4,111 4,218 61.5 62.3 63.6 2001200220032004 5,122.4 6,143.7 5,538.8 n a 7,112.7 7,449.2 7,344.8 n a 72.0 82.5 75.4 n a

Data refer to wage rates per month (baht) and to employees; From 1991 to 1995, data come from labour-related

2006 1,675.6 2,931.9 57.2 2006 11,552.3 12,483.3 92.5 to 2006, ISIC Rev 3.

From 1980 to 1985, data refer to earnings From 1980 to 2006, data refer to earnings

From 1982 to 1986, data refer to wage establishment survey, from 2000 to 2003, from Labour

per hour (dollars); from 1986 to 2006, data per month (won) and employees.

rates per day (dollars) and wage earners. Force Survey.

From 1980 to 1992, Classification ISIC Rev. 2, refer to earnings per month; From 1980 From 1991 to 1995, and 2000, Classification ISIC Rev.2,

From 1987 to 2006, data refer to wages

and from 1993 to 2006, to 1989, data refer to wage earners; from from 2001 to 2003, Classification ISIC Rev 3.

Classification ISIC Rev 3. rates per month and salaried employees. 1990 to 2006, data refer to employees.

Data for 1994 and 1995 exclude public enterprises.No data Source: ILO LABORSTA database (http:// Source: ILO LABORSTA database Source: ILO LABORSTA database (http:// available since 2004. laborsta.ilo.org/). (http://laborsta.ilo.org/). laborsta.ilo.org/). Source: ILO LABORSTA database (http://laborsta.ilo.org/).

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Appendix Table A3: East Asian Four: Employment by Manufacturing Sub-Sector and Gender Table A3a: South Korea – Employment by Manufacturing Sub-Sector and Gender, 1963-2001

1963 1965 1970 1975 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Food products 311-12 F na na na na 39.1 42.2 41.1 43.9 44.2 47.3 46.4 48.2 48.8 47.9 47.7 48 49.2 49.6 49.5 49 49 50.3 50.5 53.6

T 8.6 9.6 8.6 7.4 6.3 6.6 6.5 6.4 6.2 5.9 5.9 5.8 6 6.3 6.4 6.2 6.2 6.3 6.1 6.2 6.6 6.2 6 6.2

Beverages 313 F na na na na 21 23.3 21.6 20.3 23.8 22.9 28.1 28.2 25.4 22.9 23.6 26.4 25.2 27.9 26.7 23.6 22.2 24 21.8 21.5

T 3.9 4.4 3.4 1.8 1.3 1.2 1.1 1 1 1 0.9 0.9 0.8 0.7 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.7

Tobacco 314 F na na na na 35.9 37.8 33.6 32.5 34.5 40.2 40.9 36.2 30.6 23.9 24 22 22.1 21.2 20.9 19.7 20.2 19.1 18.4 19

T 2.3 1.5 1.3 1.1 0.6 0.6 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.2 0.1 0.1 0.1

Textiles 321 F na na na na 63.9 65.7 66.3 64.7 64.5 63.4 61.6 59.7 57.4 57.6 54.3 53.5 52.2 51.4 50 48 47.8 46.8 45.5 46.3

T 27.9 27.1 24.7 22.8 18.3 17.3 16 15.3 14.7 14.1 13.4 12.7 11.7 11.8 11.5 10.9 10.3 9.5 8.8 8.6 8.9 9.0 8.7 8.0

Wearing apparel 322 F na na na na 77.6 75.8 77.8 76.3 75.7 75 73.1 73.4 72.7 72.7 74.2 70.7 70.6 70 70.9 71.1 72.5 73.6 73.5 76

T 3.4 3.9 5.9 10.7 10.3 10.1 10.3 10.2 9.8 9.2 9 8.7 7.8 7 6.8 7 6.6 6.5 6 5.8 5.4 5.5 5.4 5.2

Leather 323 F na na na na 40.1 40.2 37.2 36.4 35.9 36.9 34.4 35.6 37.3 39.5 40.4 40.9 40.1 42 42.6 41.3 41.2 42 40.9 42.7

T 0.4 0.5 0.4 1.4 1.2 1.3 1.2 1.2 1.4 1.6 1.4 1.5 1.3 1.4 1.2 1.1 1 1 1 0.8 0.9 0.9 0.8 0.7

Footwear 324 F na na na na 55.1 56.3 46.4 49.2 50.1 51.2 48.7 48 49.3 63.1 61.7 58.3 56.2 53 54.4 50.7 51.1 49.3 47.9 49.5

T 0.7 0.7 0.5 0.8 1.8 1.9 1.3 1.2 1.3 1.2 1.1 1.1 0.9 3.8 3.9 2.7 2.2 1.8 1.6 1.3 1.3 1.3 1.2 1.1

Wood/cork products 331 F na na na na 25 24.8 25.3 25.3 28.4 27 25.2 24.9 26.1 26.5 26.5 25.1 25 24.1 25.1 23.8 22.6 22.6 22 23.2

T 2.9 2.7 4.2 2.9 1.9 1.9 1.7 1.5 1.3 1.3 1.3 1.3 1.3 1.4 1.4 1.4 1.3 1.3 1.3 1.1 1 1.0 1.0 1.0

Furniture fixtures excl Met 332 F na na na na 24.1 22.2 23.6 23.7 21.9 24.5 25.9 26 28 28.9 29.2 28.8 28.3 27.2 25.3 24 23.5 23.1 21.9 24.5

T 1.3 1.4 1 0.7 0.9 1 1.1 1.1 1.1 1.1 1.2 1.4 1.4 1.6 1.6 1.7 1.6 1.6 1.8 1.6 1.3 1.4 1.5 1.5

Paper 341 F na na na na 26.8 24.9 24.3 23.6 22.6 22.6 23.1 23.3 23.6 23.9 24 23.6 22.5 22.3 23.3 21.5 20.1 19.8 20.3 21.9

T 2.7 2.3 2.2 2.2 2.2 2.1 2.1 2 2 2 2 2.1 2 2.1 2.2 2.1 2.2 2.2 2.3 2.3 2.3 2.2 2.1 2.2

Printing and publishing 342 F na na na na 28.5 28.1 25.9 26.3 26.4 26.1 27.5 27 28.9 29.8 30.1 29 29 30.4 29.9 30.5 29.5 31.1 31.8 33.6

T 4 4.2 3.5 2.7 2.3 2.3 2.3 2.2 2.1 2.1 2.2 2.4 2.4 2.6 2.8 3 3.1 3.1 3.1 3.1 2.9 3.1 3.2 3.4

Industrial chemicals 351 F na na na na 18.6 15.3 14.8 15.6 17.6 1.5 15.5 15.4 15.7 17.9 18.3 19.5 20.1 17.9 13.5 12 11.7 10.6 10.9 10.9

T 2 1.7 2.8 2.4 2 1.8 1.6 1.5 1.5 1.5 1.6 1.7 1.7 1.6 1.7 1.9 1.9 2 2.6 2.6 2.9 2.6 2.4 2.5

Other chemicals 352 F na na na na 39.2 37.8 37.2 37 36 0.2 39.1 36 37 37.3 35.1 33.7 33.2 34.3 32.2 29.9 28.1 29.6 28.9 29.4

T 4.5 3.8 3.1 3 2.6 2.5 2.4 2.4 2.5 2.5 2.5 2.5 2.5 2.7 2.8 2.6 2.6 2.6 2.8 2.9 3 2.8 2.9 2.9

Petroleum refineries 353 F na na na na 6.9 4.5 7 8.3 6.8 6.7 5.7 5 4.2 3.8 4.7 3 3.3 3 na 4.8 na 4.78 4.54 4.1

T 0 0.2 0.4 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.2 0.2 0.3 0.4 0.5 0.5 0.4 0.4 0.4

Prod Petrol/coal 354 F na na na na 7 9.2 10.1 7.8 8.4 9.9 9.7 10.9 11.8 11.8 12.6 13.9 13.3 13.3 na 11.1 na 15.3 0 1

T 3.6 3.1 1.4 0.9 0.6 0.6 0.5 0.5 0.5 0.4 0.4 0.4 0.3 0.4 0.3 0.2 0.2 0.3 na na na 0.0 0.0 0.0

Rubber products 355 F na na na na 55.6 58.7 59.4 61 60.1 59.3 58.6 57.3 58.7 23.5 22.6 19.9 20.1 20.8 20.2 19.8 18.9 19.5 20.6 20.8

T 4.9 5 3.3 4.6 5.1 5.5 5.6 6 6.3 6.4 6 6 6.1 1.1 1.1 1.1 1.1 1.2 1.2 1.3 1.4 1.3 1.4 1.3

Plastics products 356 F na na na na 45.7 30.3 26.3 25.3 24.9 26 25.8 27 28.2 25.7 2.5 24.9 25 25 26.7 25.5 25.4 26.3 27.2 29.2

T 0.2 0.4 1.1 1.7 2.6 2.2 2.3 2.5 2.8 2.9 2.9 3.2 3.3 4 4.1 4.2 4.3 4.4 3.7 3.7 3.9 4.4 4.6 5.0

Pottery,china,earthenware 361 F na na na na 59.7 51.8 51.8 53.6 52.2 52.8 51.1 51.7 50 51.1 51.2 51.7 52.5 52.7 51.9 49.3 50.5 51.1 52.1 53.3

T 1.5 1.6 0.8 0.5 0.9 0.8 0.7 0.6 0.7 0.7 0.6 0.6 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.4 0.3 0.4 0.3 0.3

Glass 362 F na na na na 26.7 24.3 26.1 24.9 25.1 26.2 25.4 24.5 22.4 21.9 21.2 22.6 20.9 21.1 19 17.8 15.7 16.6 16 17

T 0.4 0.7 1.1 0.9 0.9 0.8 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.9 0.9 0.8 0.8 0.9 0.9 0.9 0.8 0.8 0.7 0.7

Non-metallic products 369 F na na na na 16.8 15.5 16.1 15.4 15.8 15.8 16.9 17.4 18.7 1.9 18.3 17.2 16.6 16.2 15.5 14.8 13.7 14 14.1 15.1

T 3.8 4.6 3.9 2.9 2.7 2.9 2.9 2.9 2.6 2.5 2.5 2.6 2.8 3.2 3.3 3.2 3 3 3 2.9 2.5 2.26 2.16 2.1

Iron and steel 371 F na na na na 6.4 6.6 7.1 7.1 7 7 7.6 7.5 7.5 8.4 7.6 8.2 8.2 8.4 7.8 7.2 7.3 7 6.87 7.14

T 2.6 2.6 3.2 2.6 3.2 3.3 3.2 3 2.9 2.8 2.8 2.8 2.9 3.1 3.1 3 3 3.1 3.1 3.1 3.3 3.2 3.0 2.9

Non-ferrous metals 372 F na na na na 11.5 11.1 13.2 12 11.9 11.4 11.1 11.6 12.3 14 13.5 13.3 13.8 13.2 13.6 12.2 11.5 12 11.6 12.6

T 0.7 0.8 0.6 0.7 1.1 1 1.1 1 1 0.9 1.1 1 1 1.1 1.1 1 1 1.2 1 1 1.1 1.0 1.0 1.0

Metal products 381 F na na na na 16 10.1 17.2 17.8 17.6 17.8 17.5 18.9 19.8 21.9 22.1 21.5 21.1 20.6 20.1 19.5 18.9 19.8 20 21.5

T 3.7 3.9 4.1 3.7 5 5.2 5.2 5.2 5.3 5.3 5.8 5.7 5.9 6.1 6 6.6 6.9 7.2 6.9 6.7 6.6 6.8 6.8 7.1

Non-electrical machinery 382 F na na na na 11.8 15 15.6 16.7 18.3 16.2 17.4 16.2 16 18.9 17.4 18.4 18.4 18.3 19.2 18.7 18.3 19.4 18.9 19.4

T 3.6 3.3 3.1 3.3 4.2 4.5 4.8 5 5.4 5.7 6.1 6.7 7 9.2 9.1 9.7 10.2 10.4 11.6 12.8 11.7 12.1 12.5 12.4

Electrical machinery 383 F na na na na 71.2 57.1 53.5 50.9 56.3 55 50 47.9 49.1 46.4 45.3 44.6 45.4 45.5 45.7 41.3 38.4 41.3 42 39.7

T 2.6 2.8 4.7 9 10 10.8 11.9 12 13.5 15.1 15.5 15.3 15.1 14.3 14.3 14 14.3 14.9 14.5 14 14.6 15.0 16.2 16.0

Transport equipment 384 F na na na na 8.9 8.9 8.6 9.1 10.4 10.8 12.6 12.6 12.7 12.8 12.7 12.3 12.3 12.2 12.3 14.5 9.6 10.2 10.7 11.4

T 5 4.6 4.4 3.7 6.4 6.5 7 7.2 6.7 6.9 6.9 7.5 8.2 8.6 9 9.3 10.1 10.7 11.6 12.1 12.2 11.8 11.5 11.8

Prof Scientific Equipment 385 F na na na na 45.1 53.2 51.5 48.3 48.7 48.2 47.3 42.5 40.7 43.9 42.5 42.5 39.1 38.4 36.7 36 37 36.3 37.4 36.6

T 0.4 0.3 0.7 1.2 1.4 1.3 1.5 1.4 1.6 1.6 1.7 1.7 1.5 1.2 1.1 1.1 1.2 1.2 1.7 1.7 1.8 1.7 1.8 1.8

Other manufactures 390 F na na na na 55.8 53.5 57.1 56.6 56.7 43.7 51.2 49.6 49 48.5 46.5 45.2 44.5 43.9 42.6 42.8 41.4 42.9 41 40.7

T 2.3 2.3 5.8 4.1 3.8 3.9 4.1 3.9 4.3 4.3 3.9 3.5 3 2.7 2.4 2.1 1.9 1.9 1.8 1.7 1.8 1.8 1.7 1.6

Total manufactures 3 F na na na na 44.9 43 42.8 42.1 43.2 42.4 41 39.5 38.7 37 35.9 34.6 33.8 33.1 32.2 30.7 na 30.6 30.7 31

T 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

Percentages for employees calculated on the basis of UNIDO data. F = share of females in total manufacturing employment by branch; T = employees in total manufacturing by branch; na = data not available. Sources: UNIDO International Yearbook of Industrial Statistics (various issues) and UNIDO Industrial Statistics Database (3-digit level of ISIC Code 1963-99), 2001, 2004.

january 3, 2009

Table A3b: Malaysia – Employment by Manufacturing Sub-Sector and Gender, 1968-2000
1968 1970 1975 1983 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Food products 311-12 F na na na 32.8 33.1 33.6 33.9 33.8 34.1 34.7 35.3 36 34.8 34.1 34.7 33.7 37.9 na 31.7 33.1
T 15 12.6 12.9 12.5 12.9 13.4 13.1 11.8 10.5 8.8 8.1 7.5 7.1 6.8 6.7 6.7 6.4 6.6 7.7 7.7
Beverages 313 F na na na 29.3 29.8 29.6 29.4 31.3 30.2 33.3 34 34.1 30.2 33.3 32.7 32 38.2 na 34.7 35.7
T 2.1 2.1 1.4 1.2 1.2 1.1 1 0.8 0.6 0.5 0.4 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.4 0.3
Tobacco 314 F na na na 52.7 46.7 45.2 44.2 45.5 52 46.5 46.5 51.1 48.8 52.2 67.1 57.6 15.6 na 56.6 58.7
T 3 2.4 1.9 1.1 0.9 0.9 0.8 0.6 0.7 0.5 0.4 0.4 0.3 0.3 1.2 0.9 0.8 0.8 0.8 0.6
Textiles 321 F na na na 64.5 64.1 65.1 65.1 63.9 61.3 58.2 57.8 54.2 52.6 49.1 45.3 48.3 49.2 na 41.9 41.6
T 3.6 5.1 10 6.7 5.7 5.7 5.5 5.4 5 4.5 4.6 4.1 3.9 3.5 3.4 3.5 3.3 3.4 3.0 2.9
Wearing apparel 322 F na na na 88.4 89.4 89.1 88.8 87.7 87.4 85.3 86.3 86 84.3 81.2 83.4 79.3 73.4 na 76.5 74.2
T 2.5 3.1 3.7 5.6 6.5 7.2 7.7 7.8 8 7.7 7.2 6.9 6.2 5.4 5.3 4.1 4.5 4.8 4.3 4.7
Leather and products of leather 323 F na na na 66.7 66.7 50 52 57.1 71.4 61.1 66.7 66.7 63.6 60 58.6 62.1 70.8 na 65.0 59.4
T 0.2 0.3 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.3 0.2 0.2 0.2 0.2 0.2 0.1 0.2 0.1 0.2
Footwear 324 F na na na 61.5 55.6 55.6 55.6 50 68.8 50 57.1 57.9 52.4 52.9 48 46.2 54.5 na 40.0 41.8
T 0.5 0.5 0.5 0.3 0.1 0.2 0.2 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.4
Wood and cork products 331 F na na na 23.5 24.1 24.6 26 25.6 25.4 26 27.7 28.8 29.1 30.1 32.2 31.4 35.3 na 33.4 33.6
T 18.6 20.3 15.5 12.9 11.4 11 10.9 10.7 10.7 10.7 9.9 10.6 11.2 11.3 10.6 10.3 10.6 10 8.9 8.1
Furniture fixtures excl Metallic 332 F na na na 22.1 25.6 25.6 25.6 28.3 29.3 29.7 30.6 29.9 28.9 28.4 23.5 27.2 27.9 na 28.9 27.5
T 2.1 1.6 1.7 1.6 1.9 1.8 1.7 2 1.9 1.7 2.2 2.3 2.6 2.7 3 2.7 2.9 2.6 3.2 4.3
Paper 341 F na na na 42.4 38.6 39.4 33.3 32.7 33 33.3 32.7 31.8 31.9 32.8 31.9 31.1 34.1 na 31.0 33.2
T 1.2 1.1 1.2 1.3 1.4 1.5 1.7 1.7 1.6 1.6 1.6 1.7 1.6 1.6 1.7 1.6 1.7 1.6 1.7 2.1
Printing and publishing 342 F na na na 38.1 38.6 39 38.3 38.7 39.8 41.2 42.5 43.5 41.2 42.5 40.9 39.1 38 na 42.0 40.1
T 7.3 7.6 5 4 4.3 4.1 3.7 3.3 2.9 2.6 2.4 2.4 2.4 2.4 2.5 2.5 2.2 2.3 2.6 2.2
Industrial chemicals 351 F na na na 18.5 15.9 16.7 16.9 15.2 16.3 16.3 15.5 16.1 15.8 16.3 15.7 15.9 18 na 16.0 20.7
T 0.8 1 1.1 1.1 1.3 1.4 1.4 1.3 1.2 1.1 1.2 1.2 1.1 1 0.9 1 1.1 1.1 1.2 1.4
Other chemicals 352 F na na na 42.9 41.4 42.7 42.4 44 43.8 41.5 40.3 40.3 37.9 37.7 36.4 38.3 40.1 na 35.6 36.2
T 3.3 3.2 2.2 2 2 2 1.8 1.7 1.6 1.4 1.3 1.4 1.3 1.3 1.4 1.3 1.4 1.5 1.4 1.6
Petroleum refineries 353 F na na na 11.1 7.1 6.7 9.1 9.1 9.1 9.1 8.3 8.3 7.7 11.1 11.1 7.7 10.8 na 7.9 26.5
T 0.7 0.6 0.2 0.2 0.2 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.2 0.3 0.3 0.5
Products of petroleum and coal 354 F na na na 16.7 9.1 18.2 11.1 10 12.5 20 11.1 10 10 16.7 21.4 10 13.3 na 16.7 0.0
T 0 0 0.1 0.1 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0
Rubber products 355 F na na na 40.7 41.8 43 44.8 48.8 50.3 50.3 50.8 50.2 49.7 47.1 44.1 42.4 45.3 na 44.3 44.3
T 13.3 12.5 9.4 6.1 5.9 6.8 7.3 7.9 7.6 7.1 6.6 6.3 6 5.6 5.4 4.8 5.2 5.6 4.9 4.7
Plastics products 356 F na na na 53 53.9 54.3 55.2 54.7 53.2 53.4 53.6 52.1 49.4 45.9 46.3 42.7 43.4 na 44.9 46.1
T 1.6 2.6 2.4 3.1 3.2 3.4 3.5 3.4 3.9 4.3 4.7 4.8 4.7 5.1 5.2 5.2 4.3 4.8 5.9 6.1
Pottery, china and earthenware 361 F na na na 52.6 56.5 57.7 62.2 60.4 57.8 59.2 56.4 58.4 57.8 51.9 46.1 42.6 54.5 na 45.2 48.1
T 0.3 0.2 0.2 0.4 0.4 0.5 0.9 0.8 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.4 0.4 0.4 0.5 0.5
Glass 362 F na na na 13 13 16.7 14.3 14.3 16.1 13.9 13.9 13.5 11.9 13.5 12.1 26.2 28.4 na 14.1 15.4
T 0.5 0.6 0.5 0.5 0.4 0.5 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.5 0.7 0.6 0.6 0.7
Other non-metallic mineral prods 369 F na na na 21.2 21.5 21.7 20.2 21.2 20.3 19.6 19.4 20.1 18.8 18.1 17.6 16.5 16 na 16.7 17.0
T 4.9 4.4 3.9 4.4 4.7 4 3.4 3.1 3.1 3 2.9 2.8 2.6 2.6 2.7 3.1 3.3 3.5 2.5 2.5
Iron and steel 371 F na na na 13.1 13.2 12.1 11.3 11.9 11.7 11.7 12.3 11.9 11.6 11.4 10.5 12.3 12.8 na 12.7 16.0
T 2 1.8 2.2 2 2.2 2.1 2.1 1.7 1.6 1.6 1.6 1.5 1.5 1.5 1.6 1.7 1.9 2.1 1.6 1.8
Non-ferrous metals 372 F na na na 15.6 16.1 16 11.1 10 14.7 16.7 16.1 18.8 17.8 19.2 17.4 16.7 22.4 na 20.5 25.2
T 0.2 0.2 0.3 0.7 0.6 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.7 0.6 0.7 0.6 0.9
Metal products 381 F na na na 32.6 30.8 32.8 35.4 33.3 31.2 30.5 29.4 30.2 29.3 26.5 23.4 24 25.3 na 25.7 28.9
T 6.1 5.5 4.6 4.4 4.2 3.8 3.7 4 4 3.9 4 4.3 4 4 5.2 4.8 4.9 4.7 4.5 4.3
Non-electrical machinery 382 F na na na 13.4 17.3 16.7 17.6 19.6 19.2 25.2 33.7 34.2 35.8 31.3 29.7 46 41.7 na 41.7 50.8
T 4.5 4.2 3.2 2.9 2.8 2.8 2.8 2.7 2.6 3.2 3.5 3.4 3.9 3.8 4.6 3.8 5 4.5 4.8 8
Electrical machinery 383 F na na na 76.2 73.7 75.9 76.6 78 77.2 75.4 72.7 73.3 71.3 70.1 66.5 67 67.3 na 67.8 65.9
T 1.4 1.9 10.8 17.7 17.2 18.1 19.5 22.1 23.4 25.7 26.7 27.5 29.2 30.5 29 30.2 29.1 29.6 32.3 26.2
Transport equipment 384 F na na na 17.6 19.8 19.7 22.2 21.6 20.3 20.7 21.4 20.4 19.8 20 20.2 17.2 21 na 19.4 19.2
T 2.8 3.2 3.9 4.3 4 3.3 2.8 2.5 2.9 3 3.2 3.2 3.3 3.5 3.7 3.8 4.1 4.3 3.6 3.6
Professional scientific equipment 385 F na na na 67.9 70.7 70.9 71.4 75 74.2 72.6 71.1 69.7 72.4 69.6 72.2 70.7 90.5 na 68.5 69.1
T 0.5 0.6 0.3 1.1 1.2 1.2 1.2 1.3 1.4 1.7 1.9 1.8 1.7 1.8 1.8 1.5 1.6 1.7 1.5 1.9
Other manufactures 390 F na na na 67.1 69.5 75 73.3 73 66 62.8 62.7 61.1 59.4 59.4 52.1 57.8 70.7 na 57.6 53.3
T 1.1 0.9 1 1.5 1.7 2.1 2 1.9 2.1 2.1 2.1 2 1.7 1.6 1.4 1.6 1.5 1.6 1.1 1.7
Total manufactures 3 F na na na 44.8 44.8 46.9 48.6 50.2 50.6 50.7 50.8 50.9 49.6 48.4 46.3 45.7 47 na 47.2 46.2
T 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

Percentages for employees are calculated on the basis of data in thousands provided by UNIDO sources; F =s hare of females in total manufacturing employment by branch; T = employees in total manufacturing by branch; n.a. = data not available. Sources: UNIDO International Yearbook of Industrial Statistics (various issues) and UNIDO Industrial Statistics Database (3-digit level of ISIC Code 1963-99), 2001 and 2004.

Economic & Political Weekly

EPW
january 3, 2009

Table A3c: Thailand – Employment by Manufacturing Sub-Sector and Gender, 1968-1994, 1996-2000
1970 1975 1982 1984 1986 1988 1989 1990 1991 1993 1994 1996 1998 2000
Food products 311-12 F na na 37.5 30.6 34.8 36.3 35.1 30.9 48.3 58.2 51.8 Processed food 151 72.1 73.4 na
T 16.1 10.7 16.9 13.9 12.1 22.9 18.8 11.8 13.7 13.3 12.5 8.6 12.9 8.7
Beverages 313 F na na 28.2 21.8 9.12 4.38 9.47 4.81 33.5 23.7 21.6 Dairy 1520 44.2 39.9 na
T 3.83 3.71 6.6 6.88 6.15 4.16 3.75 5.49 1.31 2.09 0.94 0.6 0.4 0.4
Tobacco 314 F na na 47.3 49.9 47 61 62.2 49.9 48.4 58.9 57.6 Grain mill products 153 31.4 34.7 na
T 7.71 5.52 9.57 4.8 2.94 0.99 0.77 1.83 1.41 1.1 1.05 1.9 1.8 1.7
Textiles 321 F na na 75 75.1 74.7 75.7 74.6 66.6 70.8 70 67.1 Other food 154 42.1 45 na
T 20.8 38.7 24.9 21.3 30 19.9 22.6 23.7 17.3 15.5 16.5 3.4 2.4 na
Wearing apparel 322 F na na 90.5 93.2 87.6 82 70.8 84.1 83.1 87.4 92.1 Beverages 155 23.6 31.5 na
T 0.71 0.82 1.79 11.3 7.47 10.9 11.1 17.3 14.2 25.2 28.9 2.1 1.1 na
Leather and products of leather 323 F na na na 15.6 35.5 48.5 44.9 55.1 52.6 56.6 63.4 Tobacco 1600 54.3 65.3 na
T 0.45 0.46 0 0.5 0.45 1.05 0.71 0.7 0.84 0.77 0.95 0.7 0.4 0.5
Footwear 324 F na na 54.1 na 56.8 67 66.3 76.2 74.8 68.7 67.7 Textiles 171 65.6 70.1 na
T 0.05 0.05 0.17 0 0.34 1.79 1.05 1.02 3.96 2.54 0.92 6.9 7.3 7.2
Wood and cork products 331 F na na 26.8 26.2 27.4 30.4 30.3 37.1 35 42.5 39.2 Other textiles 172 66.1 60.5 na
T 9.81 5.95 4.29 3.35 3.44 2.73 2.27 1.53 2.63 2.22 2.07 1.9 2 2.2
Furniture fixtures excl Metallic 332 F na na 33.6 46.9 48.3 45.2 49.1 46 48.9 33.2 43.1 Knitted textiles 1730 72.7 82.7 na
T 0.5 0.38 0.17 0.95 3 3.12 2.73 1.85 1.81 1.4 1.28 0.5 0.7 0.7
Paper 341 F na na 27.2 43.9 20.3 32.6 29.6 35.6 44.1 38.4 37.4 Wearing apparel 1810 85.1 85.7 na
T 1.29 1.61 1.19 2.4 1.3 1.56 1.63 1.06 1.09 0.99 0.81 7.2 7 6.3
Printing and publishing 342 F na na 41.8 45.2 33.3 42.9 45 43.6 48.8 39 42.1 Fur 1820 69.5 53.2 na
T 3.4 2.79 0.88 1.49 1.89 2.6 2.3 1.47 2.03 1.11 1.3 0.1 0 0
Industrial chemicals 351 F na na 25.2 27.4 28.2 24.7 31 13.9 21.2 40.7 24.3 Leather 191 63 55.5 na
T 0.57 1.34 1.63 1.58 1.09 0.91 1.31 0.67 1.19 0.91 0.48 1.1 1.1 0.9
Other chemicals 352 F na na 52.6 51.8 54.1 51.9 52.4 55.9 53.7 50.9 54.3 Footwear 1920 67.2 65.6 na
T 5.11 4.36 4.5 2.98 1.87 2.4 2.41 1.27 2.11 1.76 1.08 2.6 2.8 3.6
Petroleum refineries 353 F na na na 13.2 na na 19 na 23.9 25 19 Sawmilling 2010 42.6 44.7 na
T na 0.38 0 0.29 na 0.19 0.35 na 0.15 0.63 0.25 0.9 2.1 2.3
Products of petroleum and coal 354 F na na 14.4 na 13.3 na 13 na 30.6 14.5 17.2 Wood, cork, straw, etc, 202 46.2 na na
T na 0.07 0.11 0 0.02 0 0.01 na 0.01 0.01 0.02 1.5 na na
Rubber products 355 F na na 48.5 37.3 43.4 45.9 46.6 40.6 51.3 48.1 49.5 Paper 210 38 36.5 na
T 5.47 1.86 3.38 5.14 4.62 3.14 2.61 2.18 3.42 2.79 2.33 1.8 1.4 1.9
Plastics products 356 F na na 47.4 36.1 59.8 50.8 43.5 33.4 58.6 48.8 51.1 Publishing 221 41.5 48.1 na
T 0.08 0.64 1.84 2.17 1.33 1.1 0.91 1.2 1.65 1.86 2.28 0.7 0.5 0.7
Pottery, china and earthenware 361 F na na 52.4 53.4 49.5 53.6 57.1 54.2 44.4 41.6 61.1 Printing 222 41.4 na na
T 1.56 1.27 0.49 0.79 0.88 1.09 1.62 1.08 1.15 0.96 1.24 1.2 na na
Glass 362 F na na 25.4 34 24.1 36.2 20.1 35.7 38.7 41 37.4 Recorded media 2230 65.2 na na
T 1.84 1.58 1.45 1.32 1.5 1.3 0.61 1.22 1.11 0.42 0.49 0.1 na na
Other non-metallic mineral prods 369 F na na 17.4 20 23.5 26.8 22 16.8 28.7 22.9 22 Coke oven 2310 17.9 13 na
T 4.92 2.99 5.41 3.52 3.32 2.75 3.33 3.88 3.93 2.62 3.12 0 0 0
Iron and steel 371 F na na 10.1 10.4 10.7 7.47 6.84 18 10.2 10.2 18.7 Petroleum refineries 2320 23 56.3 na
T 1.54 0.27 1.01 2.76 1.69 1.63 1.86 1.61 2.17 2.5 1.2 0.3 0 0.3
Non-ferrous metals 372 F na na 50.2 19.4 14 11 12 8.18 11.3 31.4 14.9 Nuclear fuel 2330 18.2 43.4 na
T 0.38 0.16 0.5 0.08 0.34 0.43 0.27 0.16 0.76 0.29 1.55 0 0 0
Metal products 381 F na na 36.9 32.5 39.6 34.4 31.7 33.5 29 41.2 42.5 Basic chemicals 241 28.7 30.4 na
T 3.57 4.25 2.91 4.56 4.67 3.87 3.28 3.08 4.05 3.42 5.69 1.1 1 0.9
Non-electrical machinery 382 F na na 15.4 13.7 17 22.7 10.6 16 29.9 26.7 24.5 Other chemicals 242 53.2 52.4 na
T 1.85 1.29 0.47 0.79 0.75 0.54 0.45 3.81 3.69 2.98 1.46 2.6 2 2.6
Electrical machinery 383 F na na 56.3 40.4 51.9 43.1 58.4 40.6 46.4 63 56.4 Man-made fibres 2430 35.1 38.2 na
T 2.24 6.15 4.69 2.85 3.11 3.3 6.26 4.89 4.61 5.79 5.25 0.2 0.1 0.2
Transport equipment 384 F na na 18.3 20.4 24.3 20.3 27.4 19.6 15.8 21 21.7 Rubber 251 50.4 48.5 na
T 4.42 1.69 2.91 2.46 4.14 3.67 4.23 2.53 4.89 3.86 3.15 3.3 3.5 3.3
Professional Scientific Equipment 385 F na na 58.6 61.9 82 76.5 77.8 na 72.5 69.6 67.9 Plastic 2520 57.9 59.4 na
T na 0.1 0.35 0.31 0.55 0.5 0.45 0.32 0.65 0.33 0.38 4.7 5.2 5
Other manufactures 390 F na na 58.2 51.4 38.5 56.7 66.3 69.3 60.4 67 68.2 Glass 2610 39.1 36.3 na
T 1.75 0.9 1.89 1.47 1.08 1.49 2.36 4.38 4.11 2.75 2.78 0.7 0.5 0.5
Total manufactures 3 F na na 47.9 48.6 50.1 49 49.8 50 53.2 60 62.2 Non-metallic mineral products nec 269 35.2 31.5 na
T 100 100 100 100 100 100 100 100 100 100 100 5.7 5.8 na
Basic iron and steel 2710 17.5 22.9 na
1 1.6 1.7
Basic precious and non-ferrous metals 2720 24.2 na na
0.2 0 0
january 3, 2009 Economic & Political Weekly
EPW
1970 1975 1982 1984 1986 1988 1989 1990 1991 1993 1994 1996 1998 2000
Total manufactures 3
Casting of metals 273 20.7 na na
0.6 0 0
Structured metal products 281 20.8 26.9 na
1.9 1.3 0
Other metal products 289 36.9 43.1 na
3.5 4 0
Gen purpose machinery 291 40.3 na na
1.8 0 0
Special purpose machinery 292 47.4 na na
1.6 0 0
Domestic appliances nec 2930 52.7 na na
1.3 0 0
Office, comp 3000 86.6 84.6 na
3.3 1.8 2.6
Electric motors 3110 48.8 69.6 na
0.5 4.3 4.9
Electricity distr 3120 62 na na
1 0 0
Insulated wire 3130 69.9 na na
0.4 0 0
Accumulators and batteries 3140 77.6 na na
0.1 0 0
Lighting 3150 70.9 na na
0.3 0 0
Other electrical equipment 3190 60 na na
1.2 0 0
Electronic valves 3210 69.4 80.5 na
2.9 6.4 6.7
TV/radio transm 3220 73.7 na na
0.5 0 0
TV and radio receivers 3230 70.7 na na
1.4 0 0
Med, testing appl 331 62.3 66.6 na
0.3 0.3 0.3
Optical instruments 3220 70 71 na
0.3 0.4 0.9
Clocks 3330 80.5 na na
0.4 0 0
Motor vehicles 3410 8.4 28.4 na
1.5 3.3 4
Automobiles 3420 15.5 14.9 na
1 0.6 0.4
Parts for auto 3430 37.1 na na
2 0 0
Ships 351 16 20.8 na
0.2 0.1 0.2
Railway 3520 1.2 na na
0 0 0
Aircraft 3530 69.6 na na
0 0 0
Transport equipment nec 359 38.5 33.1 na
0.8 0.7 0.8
Furniture 3610 40.9 41.2 na
3.6 3.6 3.2
Manufacturing nec 369 69.6 69 na
4.4 3.8 0
Rec of metal waste 3710 17 37.4 na
0 0 0.1
R of non-met waste 3720 44.8 na na
Percentages for employees are calculated on the basis of data in thousands provided by UNIDO sources; F = share of females in total manufacturing employment by branch; T = employees in total manufacturing by branch; 0 0 0
n.a. = data not available. Total manufactures D 54.3 57.9 na
Sources: UNIDO International Yearbook of Industrial Statistics (various issues) and UNIDO Industrial Statistics Database (3-digit level of ISIC Code 1963-99), 2001, 2004. 100 94.2 75.7

Table A3c: Thailand (Continued)

Economic & Political Weekly

EPW
january 3, 2009

Table A3d: Indonesia – Employment by Manufacturing Sub-Sector and Gender, 1970-2001
1970 1975 1980 1985 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Food products 311-12 F na na na na na na na 39.3 40.3 40.3 40.5 na 39.6 41 40 40
T 19.1 20.3 15.9 18 14.8 14.7 14.2 14.4 13 12.5 13.2 na 14.4 13.3 13.5 13.1
Beverages 313 F na na na na na na na. 38 35.2 31.2 35.3 32.5 34 34.8 34.7 30.8
T 0.8 0.9 0.7 0.7 0.4 0.5 0.5 0.6 0.5 0.5 0.6 0.8 0.56 0.57 0.56 0.58
Tobacco 314 F na na na na na na na 79.9 81.3 58.3 80.8 na 81.5 82.1 82.5 82.2
T 27.4 18.7 16.5 12.2 7.7 6.1 5.5 5.2 5.6 8.2 5.2 7 6.05 6.02 5.86 6.2
Textiles 321 F na na na na na na na 53.3 53.3 52.9 53.5 na 54.6 52.1 50.8 50.1
T 29.5 26.2 23.9 17.7 15.6 16 16.5 16.4 16 15 15 19.4 15 15.7 15.8 14.2
Wearing apparel 322 F na na na na na n.a na 76.7 76.3 76.1 76.1 76.7 75.8 77.6 78.2 77.5
T 0.2 0.6 1.6 4.1 9.1 9.2 9.6 9.9 9.3 8.9 9.3 12.2 8.66 10.7 11.5 11.9
Leather and products of leather 323 F na na na na na na na 38.1 41.5 42.4 47.2 48 63.3 55.4 57.5 57.5
T 0.3 0.4 0.3 0.3 0.4 0.6 0.7 0.7 0.5 0.5 0.6 0.8 0.84 0.56 0.51 0.79
Footwear 324 F na na na na na na na 74 76.2 76.8 76 76 73.9 73.3 73.1 74
T 0.7 0.8 0.8 0.5 2.2 4.3 5.9 6.5 6.9 7 7.1 8.9 6.64 6.52 6.25 5.89
Wood and cork products 331 F na na na na na na na 37.4 36.2 35.7 35.9 na 36.6 36 36 36.7
T 1.5 4.7 6.1 10.1 12.2 11.4 11.2 10.6 10.3 9.4 9.6 11.9 10.2 10.2 9.37 9.62
Furniture fixtures excl metallic 332 F na na na na na na na 32.4 31.6 29.4 30.1 31.6 33.8 31.7 31.8 31.3
T 0.4 0.7 0.6 0.7 2.9 3.4 3.1 3.5 3.4 3.4 3.7 4.8 5 4.64 4.61 4.26
Paper 341 F na na na na na na na 24 22.1 21.1 20.6 19.8 18.2 23 21.7 21.6
T 0.9 1.1 1.2 1.3 1.6 1.9 2.2 2.1 2 2.1 2.2 3.3 3.04 2.46 2.57 2.76
Printing and publishing 342 F na na na na na na na 31 31.9 30.7 30.9 31.2 32.2 32.7 31.1 32.2
T 2.5 2.5 2.1 2.1 1.6 1.4 1.4 1.4 1.4 1.4 1.6 2 1.29 1.33 1.37 1.1
Industrial chemicals 351 F na na na na na na na 19.9 20 18.9 18.1 na 23.5 17.3 15.8 24.7
T 0.8 1.3 1.4 1.8 1.8 1.6 1.6 1.7 1.6 1.6 1.6 na 3.19 2.02 1.94 2.59
Other chemicals 352 F na na na na na na na 51.2 50.6 51.7 49.5 50.4 47.1 46.9 48.2 48.9
T 4.2 4.1 4.2 4.5 3.1 2.9 2.9 2.8 2.7 2.7 2.7 3.6 2.81 2.73 2.77 2.71
Petroleum refineries 353 F na na na na na na na na 2.4 13.3 14.4 4.8 10.7 12.6 11.3 13.6
T na na na na na na na na 0 0 0 0 0.13 0.13 0.09 0.06
Products of petroleum and coal 354 F na na na na na na na 10.8 33.2 26.3 20.2 25.5 19.7 9.95 17.6 14
T na na na na na 0 0 na 0 0 0 0.1 0.03 0.02 0.02 0.02
Rubber products 355 F na n.a na na na na na 24.9 24.7 23.4 24.2 na 22.8 22.7 21.8 28.1
T 1.2 1.3 3.8 5.6 6.2 4.5 4.5 3.4 3.4 3 3 na 3.3 3.18 3.33 3.6
Plastics products 356 F na na na na na na na 50.9 51.5 50.3 49.1 47.6 48.9 48.7 48.5 51
T 1.1 2 1.8 2.9 3 3.4 2.8 3.4 3.7 3.7 4 5.1 3.38 3.73 3.82 4.47
Pottery, china and earthenware 361 F na na na na na na na 37.4 34.9 35.6 32.3 25.7 na na na na
T 0.2 0.3 0.7 0.7 0.8 1 1 1 1 1.1 0.9 1.2 na na na na
Glass 362 F na na na na na na na 24 17.8 16.5 16.6 20 na na na na
T 0.7 0.9 0.9 0.6 0.5 0.6 0.6 0.6 0.5 0.5 0.6 0.7 na na na na
Other non-metallic mineral prods 369 F na na na na na na na 22.4 22.1 23.1 23.9 na na na na na
T 2.2 3.5 3.2 3.9 2.8 2.6 2.5 2.5 2.4 2.6 2.8 na na na na na
Iron and steel 371 F na na na na na na na 5.3 5.3 4.7 5.1 5.3 5.46 4.34 5.35 5.38
T na 0.2 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.7 0.7 1.1 0.85 0.89 0.93 1
Non-ferrous metals 372 F na na na na na na na 21.7 9.8 8.5 9.3 9.3 12.9 13.7 15.1 16.8
T na na na na 0.2 0.3 0.3 0.3 0.3 0.3 0.4 0.6 0.56 0.51 0.51 0.46
Metal products 381 F na na na na na na na 23.9 23.8 22.1 21.1 23.1 23.7 25.5 25.4 25.1
T 2.8 3.2 4.2 3.5 3 3.2 3.6 3.3 3.4 3.5 3.8 4.4 2.74 2.71 2.64 2.8
Non-electrical machinery 382 F na na na na na na na 12.4 11.5 11 11.3 10.3 12.6 17.1 19.3 48
T 0.9 1.2 1.2 1 1.1 1.1 1.1 1 0.9 1 1 1.6 1.14 1.21 1.1 2.97
Electrical machinery 383 F na na na na na na na 51.9 54.8 52.5 54 55.8 60.1 61.8 61.9 50.4
T 0.6 1.5 3.9 2.6 2.2 2.4 2.6 3 3.7 3.9 3.9 5.8 4.55 5.41 5.5 3.32
Transport equipment 384 F na na na na na na na 11.9 12.1 12.5 12.3 11.5 11.8 11.1 11.1 12.1
T 0.8 2.7 3.1 3.4 3.2 3.3 2.9 2.8 3 3 3.1 3.9 2.68 2.67 2.84 2.78
Prof scientific equipment 385 F na na na na na na na 48.4 54.7 60.7 62.4 61.8 63.5 63.4 65.7 48.6
T 0 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.5 0.44 0.49 0.51 0.3
Other manufactures 390 F na na na na na na na 68.9 69.9 69.2 67.4 na 68.8 70.9 68.9 65.9
T 1.3 0.6 0.6 0.7 1.1 1.5 1.7 2 1.9 1.8 1.7 na 2.49 2.28 2.13 2.46
Total manufactures 3 F na na na na na na na 47.3 47.6 46.1 46.8 na 47.9 49.1 48.9 48.8
T 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

Percentages for employees are calculated on the basis of UNIDO data. F = share of females in total manufacturing employment by branch; T = employees in total manufacturing by branch; n.a. = data not available. Sources: UNIDO International Yearbook of Industrial Statistics (various issues) and UNIDO Industrial Statistics Database (3-digit level of ISIC Code 1963-1999), 2001 and 2004.

january 3, 2009

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