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J. ISSAAS Vol. 15, No. 2:81 -92 (2009)
81
EMPLOYMENT STRUCTURE IN A RICE FARMING VILLAGE IN MAL AYSIA：
A CASE STUDY IN SEBRANG PRAI
Rika Terano and Akimi Fujimoto
Tokyo University of Agriculture
(Received: April 3, 2009; Accepted: November 12, 2009)
ABSTRACT
Malaysia achieved dramatic economic growth through foreign investment in the
industrial sector from the 80s. This led to the creation and expansion of employment opportunities
in multi-national companies and factories built mostly on the west coast of Peninsular Malaysia.
People started to be employed full-time or part-time in the industrial sector located in rural or urban
areas. In the rural area this has created great impact on the employment structure of the traditional rice
farming villages. It is possible to assume a deagrarianization process in villages which lie closely
connected to the impact of industrialization. The impact of industrialization might have caused
deagrarianization of the traditional employment structure, especially among paddy farmers in the west
cost of Peninsular Malaysia.
This paper aims to examine the actual changes and influential factors in employment
structure, based on a case study in Kampung Permatang Tinggi Bakar Bata, Sebrang Prai. A survey
was conducted in 2006 among 42 Malay paddy farmers and 58 workers using a structured
questionnaire. Kampung Permatang Tinggi Bakar Bata, Sebrang Prai is located in one of the main
rice granaries and is adjacent to the industrial zone in the north of Penang. In order to discriminate
types of job, quantification method type II was used in the analysis to discriminate outside variables
by qualitative data. The results of the study indicated that there was a clear change in employment
structure among the paddy farmers. The number of full-time farmers decreased and the number of
part-time farmers increased in this area from the 1980s to 2000s. The study also revealed that age
was the most important factor in choosing between on-farm and off-farm work, and between full-time
and part-time work in the study village.
Key words: industrialization, deagrarianization, part-time farming, off-farm employment,
quantification method Type II
INTRODUCTION
In recent decades, the industrial sector in Malaysia has been growing rapidly through foreign
direct investment. During the 1970s-1980s, the Malaysian economy developed dramatically due to
export-oriented growth and import-substituting industrialization. Since 1986, outward
industrialization started with investment promotion measures (Ishizutu 1998). Penang has been in the
vanguard of development and industrialization due to electronics and electrical appliance companies
in Malaysia’s leading industrial zone. These companies have been supported by such incentives as
Pioneer Status and Free Trade Zone policies which had a strong capacity for labour absorption (Arai
1996). A large number of East Asian, European and American electrical and electronic companies
built their factories in Prai, Bayan Lepas Free Trade Zone and Mak Mandin industrial parks in Penang.
These multi-national companies provided job opportunities for rice growing villages. The
development of infrastructure such as highway, bridges and byways facilitated commuting between
urban and suburban areas. Thus it provided ample opportunities for villagers in Sebrang Prai to seek
jobs in the suburban area.
Employment structure in a rice farming village .....
82
While Penang has been well recognized as an urban area, it has played an important role in the
agricultural sector. The granary, called Sebrang Prai, has contributed with relatively high
productivity to domestic production of the staple food of the country. In fact, it is one of the eight
major rice granaries in Malaysia; Muda(MADA), Kemubu(KADA), Kerian-Sg.Manik, Barat Laut
Selangor, Sebrang Perak, Ketara(Benut), Sebrang Prai and Kumasin Semerak. Total cultivated area
was 4,666.9 hectares including 4,652.9 hectares of wetland and 13.8 hectares of dry land in Penang
state (Agriculture Census 2006). Paddy fields in Penang state were mostly irrigated, at the level of
98.5%. Since 1987, mini-estates became a popular system in northern and middle Sebrang Prai with
an average size of 499.9 hectares (Fujimoto 1994). According to PPK, currently 2,543.4 hectares of
fields are organized as mini-estates in 2005. Even though paddy area and the total production
seemed to be low, the yield is one of the highest among the eight granaries in Malaysia.
Penang state is characterized by two contrasting dimensions: the important rice farming area in
Sebrang Prai and a developed industrial zone. Some studies in the past focused on technological
innovation in rice farming from the 1960s to 1980s (Purcal 1971;Fujimoto 1994), while one study was
directed to changes in employment structure caused by industrialization in Sebrang Prai (Fujimoto
1995). Employment structure was further affected by the opening of expressways and increase in
factories during the 1990s. It is therefore possible to assume the process of deagrarianization in this
village (Rigg 2001). This paper aims to clarify the details of the employment structure in a rice
farming village in Sebrang Prai by specifying the determinative factors on occupational choice,
examine particular groups which have been strongly affected by industrialization, and identify what
types of farmer tended to work in the off-farm sector. Specific aims of this paper are as follows: (1)
to identify the employment structure of paddy farmers and their family members; (2) to analyze the
determinant factors which influenced occupational choice between on-farm and off-farm work among
employed villagers; and (3) to clarify determinant factors which also affected occupational choice
between full-time and part-time farming among paddy farmers.
In order to clarify the difference between on-farm and off-farm work and between full-time
and part-time farming, the quantification method Type II was applied by setting these groups as
outside variables for the purpose of analyzing the contribution of each item to the choice of job
(Nagahama 1987). This method is based on the multidimensional data analysis developed by Chikio
Hayashi in Japan. The quantification aims to make numerical representation of intercorrelated
response pattern (qualitative data) with validity (Hayashi 1967). It quantifies outside variables and
items with canonical correlation analysis, and utilizes Lagrange's theorem resulting in eigenvalue
problem in the following manner.
When each item m has category of C1, C2, …, Cm, variable function on item y will become the
following linear equation.
y= a11x11＋…＋a1c1x1c1 + a21x21+…+ a2c2x2c2+…+ am1xm1+…＋amcmxmcm
xij=1 : i item and j category, xij=0 : others ( i =1,…,m : j =1,…,ci)
Category score aij normalizes to average out at 0 within each item. When outside variable is
discriminated into k groups, variable function on outside variable z will be the following linear
equation.
z =b1z1+b2z2+…+bkzk
zi=1:outside variable among groups , zi=0：others （i=1, …, k）
Category score of outside variable bi to each group equals the average of the item score on each
group. Correlation will indicate how much outside variable k groups were precisely discriminated.
Correlation r represents the range of values from 0 to 1 (Yanai 2005).
CHARACTERISTICS OF THE AREA AND RESPONDENTS STUDIED
J. ISSAAS Vol. 15, No. 2:81 -92 (2009)
83
Penang state consists of Penang island and a nearby region, Sebrang Prai of Peninsular Malaysia.
We focus on a rice farming village in Sebrang Prai. As shown in Figure 1, the study village is
located north of Kepala Batas, 2km from Muda River on the border of Kedah state, and approximately
20km from Butterworth. Kampung Permatang Tinggi area involves a total of four villages, which
are named Permatang Tinggi A, B, C and Bakar Bata. The survey was conducted in Kampung
Permatang Tinggi Bakar Bata (hereafter, abbreviated as Kg.PTBB) from May to July, 2006
Fig. 1. Map of the study village
Table1 presents the outline of the study village. The data were collected through interviews
with heads of households who all worked as paddy farmers in the village. Total number of
respondents was 42, consisting of 17 full-time, and 25 part-time farmers. It is clear that half of the
farmers, 21 respondents, only operated less than a hectare of paddy land. There were five farmers
cultivating more than two hectares, and the average operated area was 1.1 hectares per household, but
rented in land occupied 0.36 hectare on average. The interview with 42 heads of households
collected data on 100 family members who were engaged in some employment.
Table 2 shows the number of workers by main occupation and gender in Kg.PTBB. On-farm
employment is a category for paddy farmers, consisting of full-time farmers and part-time farmers.
There are only three women who helped their husbands in rice farming with limited working hours
among 42 households. Off-farm employment meant seasonal wage work in rice farming and other
jobs in the off-farm sectors such as the government, industrial, agricultural and informal sectors
(Table 2). More than half the workers in the off-farm sectors belonged to the industrial sector.
Employed workers in the off-farm sector included part-time farmers among household heads, their
wives, sons and daughters.
Table 1. The outline of the study village, 2006.
Penang State
Study village
Malaysian Peninsula
Employment structure in a rice farming village .....
84
Items
Total households
137
Number of households studied
42
Average family size (persons)
6
Total number of workers
100
Number of HHH
42
Full-time
17
Part-time
25
Number of farmers by tenurial status
Owner farmers
21
Owner-tenant farmers
16
Tenant farmers
5
Average farm size (relong)
2.1
Number of households by farm size (relong)
Less than 1.0
9
1.0-1.9
12
2.0-2.9
6
3.0-3.9
9
More than 4.0
6
Source: Survey, 2006
Note: 1relong in Sebrang Prai = 1.3acres = 0.4ha
Table 2. Number of workers by main occupation in the study village.
Type of job
Total
Men
Women
On-farm
Full-time farmer
16
1
17
Part-time farmer
25
-
25
Off-farm
Government sector
14
6
20
Industrial sector
30
21
51
Service sector
9
6
15
Side worker on farm
-
3
3
Source: Survey, 2006
Note: Multiple counting
EMPLOYMENT STRUCTURE IN PENANG S TATE , MALAYSIA 1970s-2005
J. ISSAAS Vol. 15, No. 2:81 -92 (2009)
85
Agricultural share of GDP declined from 37.8% in 1970 to 14.9% in 2005, while the
industrial sector became the fastest growing sector in the Malaysian economy. Changes in
agricultural structure in the various economic phases resulted in labour movement with rising demand
in the manufacturing sector. There was almost no change in the domestic labour force in the
agricultural sector, while the labor force in the industrial sector has continued to increase over two
decades. In comparison with 2004, the labour force in the industrial sector more than doubled in the
1980s.
This section focuses on the employment situation in Penang state in order to identify some
aspects of the chronological situation of labour utilization and actual employment. Since early times,
work in the rural area has consisted mostly of seasonal work in on-farm and off-farm jobs. In the
1960s, there was a period of transition from paddy single cropping to double–cropping with the
diffusion of irrigation facilities in the northern part of Penang State. Employment activities were
classified into on-farm and off-farm for both genders. Working patterns in on-farm and off-farm
employment related to paddy activities clearly depended on the season (Purcal 1971). The seasonal
variation of the cycle of activities centered around the harvesting months of July and January, and the
transplanting months of September and April. This made a difference in monthly work pattern and
working hours. In the case of farms smaller than the 0.5-1.1 hectares group, farmers were
underemployed up to 35% of their available time over the years (Purcal 1971). In the case of female
respondents, only a few of them helped in paddy farming and the rest did not take part in any farm
activities. Transplanting and harvesting were very heavy and important activities for women,
however this work was only temporary and seasonal, and women were underemployed for 57% of the
time in the whole year, as shown in Table 3. Main on-farm jobs were paddy work, rubber work, and
mat-making. Mat-making and other activities were generally low in productivity, although they were
important activities during the slack season of paddy farming. Off-farm activity for women was
paddy work in double-cropping area, but no activity in single cropping area. In the 1960s, women
were generally not allowed to seek jobs outside the village, which was a centrally important
environment for them. The exceptions were women from landless households or widows, who were
less inhibited by social constrictions on wage-work. This study shows that working pattern and
characteristics are connected to gender, season, landholding size and social custom.
Table 3 Under employment of men and women in a year.
Male
Female
Time available for productive work(hrs) (A)
2,200
1,650
Working time (hrs) (B)
1,463
713
Available time (%) (C=B/A*100)
67
43
Underemployed workers (% of villagers)
33
57
Source: J.T.Purcal 1971, pp.26 and 57
After an investment promotion measure was established, the characteristics of employment
structure on the west coast have recently been transformed, with factories being built in
manufacturing quarters. The increase of companies and factories in free trade zones accelerated the
influx of labour force from the agricultural to the manufacturing sectors, from 1986. Furthermore, in
the 1970s and 1980s, there was a new trend in labour saving technology in paddy work. The
introduction of direct-seeding instead of transplanting by hand, and mechanization with combine
harvesters transformed the work of crop establishment, harvesting, threshing and transportation.
These were very important technological changes in the rice farming sector. Traditional contract
activities and exchange labour custom could not persist in the face of labour-saving technology, which
deprived women of employment opportunities in rice farming (Fujimoto 1994). From 1978 to 1987,
Employment structure in a rice farming village .....
86
the number of full-time rice farming households decreased, and family members increasingly tended
to work in the off-farm sector. One of the key changes was that women became supplementary
workers after the introduction of labour-saving technology, while men played a vital role in
mechanized rice farming. From 1986 and specifically in the 1990s, expanded employment
opportunities promoted the employment of the younger generation in the industrial sector.
Employment structure in the study area
Table 4 shows the time-series data of two different villages in Seberang Prai: Kg.PTBB and
Kampung Guak Tok Said (Kg.GTS). Since both villages are rice growing villages, located only 3
km from each other, these data were accepted to examine changes in rice farm occupation in Sebrang
Prai. It then becomes clear that the ratio of full-time farmers decreased from 83% in 1978 to 67% in
1987, and drastically to 31% in 2006. On the other hand, the ratio of part-time farmers has gradually
increased from 12% in 1978 to 55% in 2006. It is possible to assume the number of part-time
farmers increased rapidly due to the introduction of the commuting bus from company and factory to
the neighboring villages on the highway and its junction. The percentage of part-time farmers has
gradually escalated in both villages by this transformation in the surrounding environment. At the
same time, as Table 4 indicates, there is a clear trend of aging of farmers, from 40 years old to 49
years old in 1978, and to 56 years old in 2006. In the case of full-time farmers, the trend is more
obvious: 30 years in 1978 to 62 years in 2006. This implies the lack of successors in rice farming in
the area.
DETERMINATION OF OCCU PATIONAL CHOICE
Occupational Choices and Determinants
This section is devoted to a quantitative analysis of determinants of occupational choice in
Kg.PTBB, based on Analysis of Accidents at Railroad Crossing by The Quantification Method. It
should be noted that although this method is unrelated to agriculture, it is actually highly relevant in
revealing the determinant factors affecting the choice of job. The quantification method Type II is
able to analyze the contribution of each item to job choice by setting the following two groups to the
outside variable. Let us consider two kinds of grouping, firstly on-farm and off-farm work. The
group of on-farm work includes full-time and part-time farmers, and the other group involves off-farm
workers in the non-agricultural sector. The second grouping of 42 farms is determined by whether
he/she is a full-time or part-time farmer.
Through these two sets of outside variable, influential factors having an impact on occupational
choice will be analyzed for 100 workers for on-farm and off-farm work, and for 42 farmers to be
full-time or part-time farmers. Off-farm workers consist of hired workers and self employed
workers, including part-time farmers. Possible determinant factors taken into consideration are
gender, age, educational background and landholding.
Between On-Farm and Off-Farm Work as Outside Variable
Independent variable of on-farm work is 1, and off-farm work is 2. The items are as follows:
variable k1 is a dummy variable for sex (man=1, woman=2). Variable k2 is a categorical data for
educational background level (no schooling =1, elementary school=2, junior high school=3, high
school=4, University=5). Variable k3 is also categorical data for age (18-39 =1, 40-69=2, more than
70=3), and k4 is a dummy variable for land tenurial status with or without land ownership (no land
holding=1, land holding=2).
J. ISSAAS Vol. 15, No. 2:81 -92 (2009)
87
Table 4. Changes in farm occupation status, 1978-2006.
19781)
19872)
20063)
Employment pattern
No.
％
Paddy
land
(relong)
Average
age of
HHH
No.
％
Paddy
land
(relong)
Average
age of
HHH
No.
％
Paddy
land
(relong)
Average
age of
HHH
Rice farming
43
83
2.9
39
35
67
3.7
52
13
31
3.0
62
Rice + other crop farming
1
2
2.3
55
0
0
-
-
3
7
2.2
60
Rice farming + wage labour
6
12
2.1
31
14
27
2.7
41
23
55
2.4
52
Rice farming + self employed
2
4
1.9
53
3
6
1.2
49
3
7
5.1
52
Total
52
100
2.8
40
52
100
3.3
49
42
100
2.73
56
Source:1) and 2) Fujimoto,1994
3) Survey, 2006
Note: Figures for 1978 and 1987 refer to Kg. G.T.Said
Employment structure in a rice farming village .....
88
The results of the estimation are presented in Table 5. As correlation coefficient r = 0.7661
indicates, the discrimination of 100 workers in the analysis is reasonably good. The positive value
of the category score represents the extent of contribution of the category to on-farm work, and the
negative value to off-farm work. In Table 5, the extent of the contribution of each item is shown by
a score range, while the order for the score ranges is indicated in parentheses. This method was used
to obtain category scores and score ranges for two groups (on-farm work and off-farm work), so as to
elucidate the dependence of those groups on particular items.
Table 5. Values of category scores for employment choice factors obtained for two groups
(on-farm and off-farm workers).
Number of workers
Items
On-farm
Off-farm
Category
score
Score
range
Gender
Man
41
26
0.2657
Woman
1
32
-0.5395
0.8053
(2)
Education
No schooling
3
2
-0.3093
Elementary school
20
8
0.1921
Junior high school
7
2
0.0885
High school
11
31
-0.0747
University
1
15
-0.0933
0.5013
(4)
Age
18-39
1
43
-0.5758
40-59
25
13
0.3955
More than 60
16
2
0.5725
1.14837
(1)
Tenurial status
Tenant
17
58
-0.1989
Owner
25
0
0.5967
0.7956
(3)
Source: Survey, 2006
Note: Owner includes tenant-owner
r = 0.7661
The largest score range was obtained for the factor of “age”, followed by “gender”, “tenurial
status” and “educational background”. The category giving the largest positive score was “tenurial
status” because land owner has the character of being occupied in rice farming as on-farm worker.
The second highest score was for the category “generation: younger than 40”, but with a negative sign,
indicating that the younger generation preferred to be employed in off-farm work. The largest score
range is seen for the generation, and interestingly the category score increases with the increase in age.
The generation from 18 to 39 had a maximum category score, indicating that membership in this age
group clearly affected choice of job between on-farm and off-farm works.
The score range for gender is the second largest. It is clear that the effect of gender is an
important factor in job choice with men tending to work on-farm and women off-farm. The score
range for tenurial status was the third largest among the four score ranges. It is interesting to note that
the score range for educational background is the smallest, and so are the category scores. However,
J. ISSAAS Vol. 15, No. 2:81 -92 (2009)
89
there is a clear tendency for the workers who studied at high school and university to prefer working
in the off- farm sector. In this case, off-farm work involved governmental or private companies. On
the other hand, the workers with no-schooling tended to work in self-employed sector or in temporary
wage work in the off-farm sector.
The Outside Variables between Full-Time and Part-Time Farmers
In order to clarify the choice between full-time and part-time work from factors including
“education”, “age”, “pension”, “farming experience” and “size of planted area”, an analysis was also
conducted by the quantification method Type II with these two groups being the outside variables.
As is indicated by correlation coefficient r = 0.5440, the discrimination in the analysis may be
adequate. The positive value of the category score represents the extent of contribution of the category
to being full-time farmer, and the negative value part-time farmers.
Independent variable of full-time farmer is 1, and part-time farmer is 2. The items are as
follows: variable k1 is a dummy variable for education (no schooling and elementary=1, secondary=2,
high school and university=3). Variable k2 is a categorical data for age (18-49 years old=1, 50-69
years old=2, elder than 70 years old=3). Variable k3 is also categorical data for pension (no pension
=1, pension=2). Variable k4 is categorical data for farming experience (0-19 years=1, 20-39 years=2,
more than 40 years=3), and k5 is categorical data for planted area (0.0-0.9 relong=1, 1.0-1.9 relong=2,
2.0-2.9 relong=3, 3.0-3.9 relong=4, more than 4.0 relong=5). For all of these items, positive
contributions are expected on the choice of being full-time farmers.
Table 6 presents the results of the analysis, from which the following points deserve mentioning.
First, the largest score range was obtained for the factor of “age”, followed by “size of planted area”,
“pension”, “farming experience” and “education”. The category giving the largest positive score
was “older than 70 years old”, indicating that older farmers were likely to be full-time farmers.
The second highest score for the category was “pension”, suggesting that farmers older than
56 years old who had previously worked in the governmental sector were most likely to be full-time
farmers. It should be noted that only workers in the governmental sector can receive pension after
retirement at 56 years old. Second, the score range of age was the largest and the category score
increases with the increase in age. Age of more than 70 years old gave a maximum category score,
indicating that the choice of becoming full-time farmers was most strongly affected in this age group.
Third, the score range of the planted area was the second largest, and farmers in the size of
0.0-0.9 relong, 2.0-2.9 relong and 3.0-3.9 relong tended to be full-time farmers. On the other hand,
farmers in the size of 1.0-1.9 relong and more than 4.0 relong tended to be part-time farmers with
negative category scores. Most of the farmers in both groups worked in the informal sector as
self-employed workers, by which they could manage the time schedule for daily work of on-farm and
off-farm labour hours. Fourth, the score range of pension was the third largest, and indicated that
farmers with pension tended to be full-time farmers, which is consistent with the earlier discussion.
Lastly, the score range of the period of farming experience revealed that farmers with more
than 40 years of experience in rice farming tended to be full-time farmers, while farmers with less
experience tended to be part-time farmers. This again is consistent with the earlier finding that the
older farmers tended to remain as full-time farmers.
Employment structure in a rice farming village .....
90
Table 6. Values of category scores for employment choice factors obtained for two groups.
(full and part time farmers).
Number of farmers
Item
Full-time
Part-time
Category
score
Score
range
Education
No schooling and
elementary
11
12
-0.0888
Junior high school
3
4
0.1398
High school and university
3
9
0.0887
0.2287
(5)
Age (years)
18-49
0
11
-1.0911
50-70
14
13
0.2747
More than 70
3
1
1.1463
2.2374
(1)
Pension
None
10
24
-0.2676
Have
7
1
1.1375
1.4051
(3)
Farming experience (years)
Less than 20
6
10
-0.1087
20-40 years
6
13
-0.0065
More than 40
5
2
0.2662
0.3749
(4)
Planted land (relong)
Less than 1.0
4
5
0.2815
1.0-1.9
4
8
-0.8679
2.0-2.9
3
3
0.7868
3.0-3.9
5
4
0.4470
More than 4.0
1
5
-0.1438
1.6547
(2)
Source: Survey, 2006
Note: r = 0.544
CONCLUSION
Concerning the employment structure of paddy farmers and their family members, there was an
interesting pattern in the study village. First, we can point out a large increase in the number of
part-time farmers during the past decades. At the same time, there was a clear trend of the increase in
aged farmers. Second, four factors of age, sex, land holding and educational background were
discovered to be the determinants of the occupational choice of the head of the farm households to be
engaged in on-farm or off-farm employment. Third, five factors involving age, size of planted area,
with or without pension, period of rice farming experience, and educational background were seen to
affect the occupational choice of being full-time or part-time farmers.
The quantification method Type II revealed that age was a crucial factor for determining the job
J. ISSAAS Vol. 15, No. 2:81 -92 (2009)
91
choice between on–farm and off-farm employment, as well as being full-time or part-time farmers.
Gender also appeared to be a very important factor in determining whether to be engaged in on-farm
or off-farm employment, while farm size played a key role in determining to be full-time or part-time
farmers. It was clearly shown that the younger generation preferred working off-farm, and younger
farmers chose to be part-time farmers. This tendency is attributed to the characteristics of the area,
which is located within commuting distance of an industrial zone.
It is clear that employment structure in the study village has been affected by its geographical
advantage of location within commuting distance of the industrial zone. In addition, improved
infrastructure such as roads and highways brought about a huge impact to the study village by
commuting bus of companies and factories. This traditional rice village on the west coast of Malaysia
demonstrated a typical case of the deagrarianization in employment structure caused by
industrialization.
REFERENCES
Arai, T. 1996. “The Industry and Dynamics of the Labour Market in Northwest Malaysia: How Does
Penang’s Industrialization Influence the Surrounding Area?,” Journal of International
Relations, Asia University, 6(1): 63-38.
Department of Statistics, Government of Malaysia (DOSM). 1971. Mid-Term Review of Malaysia
Plan, 1971-1975, Kuala Lumpur.
____. 1998. Third National Agricultural Policy, 1998-2010, Kuala Lumpur.
____. 2006. Ninth Malaysia Plan, 2006-2010, Kuala Lumpur.
____. 2000. Population and Housing Census in Malaysia, Population Distribution and Basic
Demographic Characteristics, Kuala Lumpur.
____. 2005. Malaysia Economic Statistics - Time Series 2005, Kuala Lumpur.
____. 2006. Agriculture Census 2005, Kuala Lumpur.
____. 2006. Labour Force Survey Report, 2006, Kuala Lumpur.
Department of Agriculture, Government of Malaysia (DOA). 1999. Paddy Production Survey Report
in Off Season 1999, Kuala Lumpur.
____. 2000. Paddy Production Survey Report in Main Season 1999-2000, Kuala Lumpur.
Fujimoto, A. 1994. Malay Farmers Respond, World Planning, Tokyo, 268 pp.
____. 1995. “Structure and Changing Patterns of Rural Employment in Malaysia: A Study of Rice
Growing Village,” pp 211-243, In Mizuno, K. (ed.), Rural Employment in Southeast Asia,
Institute of Developing Economies, Tokyo (in Japanese).
Employment structure in a rice farming village .....
92
Hayashi, C. 1967. Quantification: theory and method, Asakura publishing, 233 pp. (in Japanese).
Ishizutu, S. 1998. “Infrastructure and Geographical Positioning of Manufacturing Firms in Malaysia,”
Annuals of the Association of Economic Geographers, 44(1):18-34.
Nagahama,T. 1987. “Analysis of Accidents at Railroad Crossings by the Qualification Method,”
Behaviormerika, 21:65-80.
Purcal, J.T. 1971. Rice Economy: A Case Study of Four Villages in West Malaysia, Universiti Malaya
Press, Kuala Lumpur, 248 pp.
Rigg, J. 2001. “Embracing the Global in Thailand: Activism and Pragmatism in an Era of
Deagrarianitzaion,” Journal of World Development, 29(6):945-960.
Yanai, H. 2005. Excel Statistics: Practical Multiple Classification Analysis, OMS Publisher, Saitama,
174 pp. (in Japanese).
