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Rural-Urban Transition in Central Java: Population and Economic Structural Changes Based on Cluster Analysis


Abstract and Figures

In Central Java, in addition to the traditional view of urban transition as an aspect of urban industrialization, rural industrialization based on small- to medium-sized enterprises has become a concern, at least since the Indonesian economic crisis in 1997. Combinations of typical urban and rural activities have resulted in certain features of rural-urban transition as the urban population has continued to increase notably. The intention of this paper is to examine how rural-urban transition characterizes the industrialization of Central Java. Multivariate cluster analysis is applied to create a typology, with the district as the unit of analysis, to better understand the transition phenomenon in terms of the population and economic structure. The cluster solution shows that rural-urban transition occurs on at least two different paths. The first path could be described as industrialization from above, in which the transition takes place as a part of the urban growth process. The second path could be described as industrialization from below, in which rapid industrialization occurs far from the highest hierarchy of the urban center.
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Land 2013, 2, 419-436; doi:10.3390/land2030419
ISSN 2073-445X
Rural-Urban Transition in Central Java: Population and
Economic Structural Changes Based on Cluster Analysis
Wiwandari Handayani
Department of Urban and Regional Planning, Diponegoro University, Semarang 50275, Indonesia;
E-Mail:; Tel./Fax: +62-24-764-80-856
Received: 20 June 2013; in revised form: 12 July 2013 / Accepted: 1 August 2013 /
Published: 16 August 2013
Abstract: In Central Java, in addition to the traditional view of urban transition as an
aspect of urban industrialization, rural industrialization based on small- to medium-sized
enterprises has become a concern, at least since the Indonesian economic crisis in 1997.
Combinations of typical urban and rural activities have resulted in certain features of
rural-urban transition as the urban population has continued to increase notably. The
intention of this paper is to examine how rural-urban transition characterizes the
industrialization of Central Java. Multivariate cluster analysis is applied to create a
typology, with the district as the unit of analysis, to better understand the transition
phenomenon in terms of the population and economic structure. The cluster solution shows
that rural-urban transition occurs on at least two different paths. The first path could be
described as industrialization from above, in which the transition takes place as a part of
the urban growth process. The second path could be described as industrialization from
below, in which rapid industrialization occurs far from the highest hierarchy of the
urban center.
Keywords: rural-urban transition; urbanization; industrialization; Central Java;
cluster analysis
1. Introduction
Urbanization is credited with responsibility for various phenomena. Urbanization may be viewed
simply as a demographic phenomenon. Economic and social changes are also sometimes considered to
be consequences of urbanization. In general, urbanization can be equated with modernization, as well
as transformation in a development context [1]. Development associated with urbanization is
Land 2013, 2 420
inextricably related to the transitions between rural and urban areas or, in other words, the processes of
urban changes and rural changes [2]. From this perspective, transition can be viewed as a form of
urban development and rural development, involving transformation from rural to urban features.
Particularly in most of the developing countries in Asia, urban transition is strongly related to
international capitalism. Gilbert and Gugler [3] called this peripheral urbanization. In this form of
urbanization, multinational corporations play a significant role in forcing the development of large
urban areas. Evidence of this influence is the fact that manufacturing industries mostly based on
foreign investment have become engines of growth in many large cities in Asia, at least since the end
of World War II. These industries have attracted laborers to move either from rural or lower-hierarchy
urban centers to higher-hierarchy or larger urban areas. Moreover, to achieve good economic
performance, governments in most of Asia’s developing countries are likely to prioritize physical
infrastructure development in large urban centers over infrastructure development in smaller cities and
rural areas. As explained by the growth centers (unbalanced development) theory proposed by
Hirchman (1958), Myrdal (1957) and Perroux (1955), the development of these centers is expected to
spread to the surrounding regions [4]. However, what has happened is the reverse. The centers are
likely to absorb most potential resources, mainly laborers and natural resources, from their surrounding
rural areas and lower-hierarchy urban regions. Accordingly, disparity and equality have remained
central issues to be resolved, in addition to the existence of the informal sector in most urban areas in
Asia. These informal sectors have appeared mainly due to the lack of work opportunities in the urban
centers compared to the number of workers who seek jobs in these areas [5].
In Central Java, in addition to the traditional view of urban transition as an aspect of urban
industrialization, rural industrialization based on small- to medium-sized enterprises has become a
concern, at least since the Indonesian economic crisis in 1997. As discussed by Hill [6], high
dependency on foreign investment hit the Indonesian economy hard during the crisis. The government
had been providing ample space for foreign stakeholders to play a significant role in the
industrialization of the country. These open economic policies, however, were not matched by strong
performance in institutional development to maintain policies for enhancing necessary initiatives for
long-term industrialization. As a result, many large foreign-based enterprises located in urban areas
collapsed, while, as Tambunan [7] showed, small- to medium-sized enterprises played a dominant role
in the usage of local resources, and their flexibility in production processes produced the steadiest
growth during the crisis period.
Rural industrialization has resulted in various typically urban activities taking place in an area
officially defined as rural. This industrialization is expected to generate strong economic links between
rural and urban areas, as well as with the world market. Qadeer [8] showed that there are many rural
regions in Asia with density and settlement systems with urban characteristics. He describes this as
urbanization by implosion”. From an urban hierarchical perspective, these areas are located relatively
far from large urban centers, and most of the time, they are classified as remote areas. The centers of
their development are obviously not large urban centers. McGee, in Forbes [9], explains these “wet
rice areas” as one form of ‘endogenous urbanization.’
Combinations of typical urban and rural activities have resulted in certain features of rural-urban
transition, as the urban population has continued to increase notably in various ways in Java.
Meanwhile, most research on rural-urban transition is focused only on particular aspects, such as the
Land 2013, 2 421
movement of people and the flow of natural resources. Migration and agricultural issues have become
the focus of matters in current rural-urban discourse [10–12]. There is still little discussion of
comprehensive features of the rural-urban transition process. The purpose of this paper is to examine
how rural-urban transition characterizes the urbanization of Central Java, using variables representing
various aspects of that transition. Multivariate cluster analysis is applied to create a typology to better
understand the transition phenomenon.
This paper is organized into four sections. Section 1 is an introduction that explains the rationale for
this study. Section 2 briefly explains the study area, data needs and the steps in the cluster analysis.
Section 3 presents the results and interpretation of the cluster analysis as the main statistical tool used
to classify rural-urban regions, followed by a discussion of rural-urban transition in terms of
population and economic structure. Section 4 concludes with some remarks concerning rural-urban
transition in the study area.
2. Data and Methods
2.1. Study Area and Data Needs
The study area is located within the administrative boundary of Central Java Province in Indonesia.
It is located on Java Island and consists of 35 districts/municipalities. The total area of the province is
3.25 million hectares, and the total population is 32.18 million persons or approximately 14 percent of
the Indonesian population (Central Bureau of Statistics (CBS), 2006 [13]). In general, urbanization in
Central Java Province has not been remarkable compared to other provinces on Java Island. Located
between two large provinces, namely, West Java and East Java, 35 percent of the area is moderately
hilly, and a significant portion of the area of the province is an agriculture protection zone. These
features have limited the development of the province. Figure 1 shows that the urban proportion of the
population in Central Java Province is the lowest of any province on Java Island, slightly lower than
that of East Java, which has reached 48.9 percent. Along with the urban proportion of the population,
the population density in Central Java Province is also reasonably low compared to other provinces on
the island (see Figure 2).
Figure 1. Urban population on Java Island, 2005. Source: CBS, 2005 [14].
Urban Population
Provinces in Java Island
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As the study area is within the provincial boundaries, one lower administrative level, i.e., the
district, was used as the analysis unit in the clustering process. All data employed in this research are
secondary data from annual statistical reports and relevant publications based on census and survey
data from the Central Bureau of Statistics (CBS). Variables were selected using a deductive approach
based strongly on the literature. In general, the variables are divided into three categories, namely,
physical environment, economic and sociopolitical variables. The variables are classified as static if
they are only based on one specific period (i.e., 2006) and dynamic if changes between two time series
are indicated (i.e., 1994 and 2006).
Figure 2. Population density on Java Island, 2006. Source: CBS, 2006 [13].
2.2. Methods: Applying a Cluster Analysis to Create a RuralUrban Typology for Central Java
Cluster analysis is a multivariate statistical approach for grouping data. Each group (cluster)
contains sets of cases that are similar in character. Cluster analysis was applied in creating a
rural-urban typology to better understand the rural-urban transition in the study area. To build the
typology, there are considerably large data sets (variables) that need to be classified into groups, based
on their similarity. Applying clustering algorithms helps to organize the data into groups efficiently,
based on a statistical formulation. This organizing process facilitates the discovery of the
characteristics of each group.
Several steps were necessary to create a typology using cluster analysis. A brief explanation of how
each step is applied in this research is presented below.
1. Assessment of the variables.
Calculation of the range, standard deviation, variance, skewness and kurtosis of the
variables. These statistical measures are used to analyze the distribution of the data and to
identify outliers.
This includes selection of variables and examination of data
quality. A stepwise analysis is used to examine the quality of the data. The analysis is mainly
derived from the following:
Matrix correlation to minimize multicollinearity.
Density (People per Km2)
Provinces in Java Island
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2. Clustering of the algorithm selection
After comparing the methods of hierarchical clustering with single linkage, complete-linkage
clustering, group-average clustering, centroid clustering, median clustering, Wards’ clustering
and K-means (non-hierarchical) clustering, it was determined that Ward’s method, with its
squared Euclidian distance and z-score standardization, was the best suited to the purpose of this
research. A number of previous research projects using cluster analyses also highlight the
efficiency of Ward’s method compared to other methods [1518]. Figure 4a shows a dendrogram
of the final results of the cluster analysis, obtained using Predictive Analytics SoftWare (PASW)
Statistics 18. The z-score was used as the common method of standardization in applying Ward’s
hierarchical method. In principal, z-score standardization weighs each value of variable Xi
against the mean of variable X. The result is then divided by the standard deviation of each
variable, σ (see Equation (1)).
. Clustering was used to produce the most expected cluster.
As there is no agreement as to which is the best algorithm to be used, a comparison of all
possible algorithms with all possible distance measurements was applied.
Z = (Xi X)/σ (1)
One important assumption in applying z-score standardization is that the data should be
normally distributed [19]. This may also explain why the variable assessment in the previous
step includes assessment of parameters of the distribution of the data.
To determine how close or how far cases in the cluster are from each other, the squared
Euclidian distance method was applied. The squared Euclidian distance formula is the most
commonly used method for continuous data [16]. In addition, this calculation is highly
recommended for use with Ward’s hierarchical methods [16,19].
3. Validation
It was determined that 100 percent of the original grouped cases were correctly classified by
applying all of the variables and that 93.1 percent of the cases were correctly classified using
the stepwise approach. These percentages were convincing enough to conclude that the
dendrogram had produced its optimum result.
. To check the validity of the dendrogram, two types of discriminant analysis are
used. The first type involves applying all the variables to the analysis. The second type involves
using a stepwise approach in which only selected variables are used to validate the clustering
results. As Huberty [20] explains, to have a valid hit-rate estimation, the number of sample
sizes (case districts) should be large enough or at least n 5p , i.e., the number of cases
should be at least five times greater than the number of variables. The stepwise approach was
used to fulfill this criterion.
4. Interpretation
. In interpreting the clustering results, the urban and rural characteristics of each
district were differentiated. Therefore, as explained in the following section, subgroups in the
cluster are defined based on a combination of rural and urban characteristics.
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3. Results and Discussion
3.1. Assessment of the Variables
Initially, there were 35 districts, six urban and 29 non-urban, and 72 variables to be examined. To
produce robust and statistically convincing indicators, stepwise analysis was used to examine the
quality of the data. The examination consisted of two main steps. The first step involved calculation of
the ranges, standard deviations, variances, skewness and kurtosis of the variables. These statistical
measures were employed in analyzing the distribution of the data and the identification of outliers. The
second step involved using matrix correlation to minimize multicollinearity. In eliminating those
variables, the representation of each category is greatly improved. As z-score standardization was
applied, only variables found to be normally distributed were chosen. The mean values of the variables
ranged from a low of 3.4 (for annual growth of employment engaged in the non-primary sector) to a
high of 135.8 (for built-up areas); the skewness and kurtosis values of all the variables chosen were
close to zero (ranging from 0.99 to 0.99).
However, disparities between urban districts and non-urban districts do exist in the research area;
therefore, outliers were significant for several variables. In the first iteration, outliers were identified
due to six small areas (categorized as urban districts) contributing significantly to almost all
development indicators. These six urban districts were then excluded in the first iteration of variable
assessment. Nine variables related to Gross domestic product (GDP), employment and density were
eliminated in the second iteration, because of their non-normal distributions (as indicated by skewness
and kurtosis values >0.99 or <0.99) or because they contained outliers. Nevertheless, for the purpose
of comprehensive interpretation, the six urban districts that were excluded in the first iteration were
still considered in the final interpretation.
Figure 3. Selection of variables by stepwise analysis.
Initial date:
First iteration:
Second iteration:
72 variables
29 districts
6 districts defined as urban in
“formal” defination are eliminated
based on range, variance, skewness,
and kurtosis calculations.
21 var physical env
43 var economy
7 var social
1 var politic
Third iteration:
(Final result)
63 variables
9 variables are eliminated
based on range, variance,
skewness, and kurtosis
19 var physical env
37 var economy
6 var social
1 var politic
29 districts
30 variables
33 variables are eliminated
based on range, variance,
skewness, and kurtosis
13 var physical env
13 var economy
3 var social
1 var politic
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Another issue was multicollinearity, which was inevitable, because many of the variables initially
analyzed were likely to have been related. For example, the number of jobs in industries may be highly
correlated with the number of industries, and the GDP of the industry also may be highly correlated with
the number of large industries. It was also difficult to apply time series data, because, for some variables,
such as education attainment, the situation in 1994 was similar to the situation in 2006, as reflected in the
relatively high correlation between the two variables. Figure 3 explains the selection process of the
variables, and Table 1 shows the list of variables that were finally used in the cluster analysis.
3.2. Cluster Solution and Dendrogram Interpretation
From examination of the dendrogram based on the explanation in Table 1, four main groups were
identified (see Figures 4 and 5). First, 16 districts were identified as being highly rural. With respect to
their physical environment, they are mainly characterized by relatively low proportions of their
population categorized as living in urban regions (<20 percent), low proportions of built-up areas
(<25 percent) and being mostly located in remote and relatively hilly areas, far from the closest growth
centers (16–20 km from the closest urban center) and airport facilities (71190 km from the closest
airport facility). Due to these physical limitations, the activity of these districts is dominated by
agriculture. In other words, the physical characteristics of these districts do not favor industrial
development. These districts also have very low proportions of people who have graduated from high
school or university (<20 percent). This low level of education has led to high average unemployment
growth (13.30 percent), as well as a low employment rate (<60 percent).
Table 1. Variables to explain the performance of the rural-urban interface in the
study area.
Physical Environment Economic Social-Politic
- Number of people living in urban areas
- Built-up area (km2)
- Average distance to the closest urban
center (km)
- Average distance to the closest airport
- Proportion of area (%) with zoning
limiting urbanization, including
environmental protection areas,
agricultural protection areas and
disaster-prone areas
- Contributions of the industrial and service
sectors to GDP (%)
- Contribution of the industrial and service
sectors to the provincial
GDP (%)
- Proportion of employment engaged in the
industrial and service
sectors (%)
- Proportion of employment engaged in the
industrial and service sectors at the
provincial level (%)
- Proportion of population that
has graduated from high
school and university (%)
- Employment rate (%)
- Political intervention, as
indicated by policy documents
by ranking (dummy variable)
- Growth of built-up area (km2) - Annual growth of the non-primary sector in
GDP (%)
- Growth of the GDP share of the
non-primary sector (%)
- Annual growth of employment engaged in
the industrial and service sectors (%)
- Growth of the proportion of employment
engaged in the industrial
and service sectors (%
- Unemployment growth (%)
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In addition to the 16 districts characterized above, there are six districts classified as Rural-Urban
Type C, which have a character very similar to that of the highly rural districts. These districts may be
classified as rural-urban, because of their physical characteristics. High proportions of the populations
of these districts are urban (2040 percent). These districts are not located in remote areas, which
means that they are closer to urban centers (1115 km from the closest urban center) and airport
facilities (5170 km from the closest airport facility) than the highly rural districts, and they are located
in flat areas. However, in terms of their economic features, these districts are highly rural and tend to
be dominated by agricultural activities.
Figure 4. (a) Cluster solution (final result from Predictive Analytics SoftWare (PASW)
18). (b) Cluster solution (final result from PASW 18).
Dendrogram Using Ward Linkage Rescaled Distance Cluster Combine
0 5 10 15 20 25
Demak 8
Grobogan 9
Pati 17
Sragen 24
Magelang 16
Boyolali 5
Karanganyar 11
Kendal 13
Semarang 23
Klaten 14
Sukoharjo 25
Jepara 10
Kudus 15
Banjarnegara 1
Wonosobo 29
Batang 3
Wonogiri 28
Purw orejo 21
Temanggung 27
Blora 4
Rembang 22
Pekalongan 18
Purbalingga 20
Pemalang 19
Tegal 26
Banyumas 2
Kebumen 12
Brebes 6
Cilacap 7
Highly rural
Rural-urban type A
Rural-urban type B
Rural-urban type C
Provincial roads
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The most significant characteristics of the rural-urban regions is evident in seven rural-urban
districts classified as Rural-Urban Type A and Type B. The two most obvious indicators are a high
contribution of the industrial sector to the GDP and a significant proportion of employment in the
non-primary sector. These districts are categorized further into two different groups.
Figure 5. Dendrogram interpretation.
The first group is Rural-Urban Type A, which is characterized by quite significant involvement of
smaller industrial activities. This group is mainly characterized by a relatively lower contribution of
the industrial sector to GDP (20–30 percent) and a higher proportion of employment in the industrial
sector (30–50 percent). These districts are not necessarily located on the urban fringe districts and are
not necessarily supported by the political will to develop these districts as centers of growth at the
provincial level. The second is Rural-Urban Type B, which is characterized by the dominant role of
large manufacturing industries. The dominant features of these districts are a high contribution of the
industrial sector to GDP (40–50 percent), with relatively low employment in the industrial sector
(15–20 percent), being located in an urban fringe area and being supported by the political will to push
for economic growth at the provincial level.
29 districs
defined as
16 Highly
High limitation of physical environment
Relatively bad performance in non-primary sectors (still
dominated by agricultural sector)
Low education level
13 Rural-
6 Rural-Urban
Urban form
mostly due to
their physical
Located in a relatively flat area
Small area categorized as environment protected or
Relatively bad performance in non-primary sector
activities (compared to Type A and B)
In particular, service activites exceed industrial
7 Rural-Urban
Urban form
mostly based on
industry and
service sectors
3 Rural-Urban
develop highly
based on big
'foot loose'
Perform very good in
GDP-industry, but not followed
by a good performance in
employment industry
Relatively bad performance
in GDP-service and employment
4 Rural-Urban
develop typically
based on local
Do not perform well in
GDP-industry (except Kudus) but
have a very good performance in
employment industry
Service sector activities also
develop well
Land 2013, 2 428
However, there are some similarities between the Rural-Urban Type A and Type B groups. They
both have relatively high proportions of people who have graduated from high school and university,
compared to highly rural regions and those in the Rural-Urban Type C group. They are also located in
relatively flat areas and have good access to the closest urban centers and airport facilities. Figure 5
summarizes the dendrogram interpretation.
3.3. Rural-Urban Transition in Central Java Based on an Examination of Population Changes
The rural-urban transition process is reflected in the shifting of the population that is defined as
living in rural or urban areas. In this research, urban and rural areas are defined on the basis of their
CBS classification, which is a function primarily of the population density, the percentage of farming
households and accessibility to urban facilities. For each variable, a score 10 is classified as urban
and 10 is classified as rural. For example, a district with a population density >8,500/km2, a
percentage of farming households <5 and all households served by electricity and a telephone network
and having access to some other main urban facilities would have the highest score. As Table 2 shows,
apart from the dominant contribution of the urban population living in urban districts, there is a
tendency toward significant urban population growth in Central Java Province as a whole.
The most rapid growth of the urban population took place in the districts in the Rural-Urban Type A
group. This group is characterized by areas whose development depends mostly on locally based
small- to medium-sized industries. These areas are not typically extensions of metropolitan regions
(EMRs), which are defined primarily as highly urbanized areas, like the urban districts in most
developing countries. The urban population of the Rural-Urban Type A group exceeded the rural
population in the middle of the 1990s, while during the same period, the Rural-Urban Type B group,
which actually represents the extended metropolitan regions, experienced a lower rate of urban growth,
and the inhabitants were still predominantly categorized as belonging to a rural population. The data in
Table 2 show that there have been more people living in urban areas in the Rural-Urban Type A group
since 1980 than in other subgroups, except urban districts. Moreover, Table 2 clearly shows that the
growth rate of the urban population in the Rural-Urban Type A group has been greater than that of
other subgroups. Up to the year 2000, there were still more people categorized as rural than there were
people categorized as urban in the Rural-Urban Type B group and, even more so, in the Rural-Urban
Type C group.
Table 2. Rural and urban populations in Central Java Province in 1980, 1990 and
2000 (percent). Source: CBS [21–24].
1980 1990 2000
Rural Urban Rural Urban Rural Urban
Central Java 81.05 18.95 73.01 26.99 59.82 40.18
Urban Districts 3.98 96.02 4.61 95.39 2.38 97.62
Rural Districts 87.11 12.89 80.97 19.03 69.73 30.27
Rural-Urban Type A 77.96 22.04 58.05 41.95 36.69 63.31
Rural-Urban Type B 88.59 11.41 80.04 19.96 60.40 39.60
Rural-Urban Type C 92.91 7.09 86.88 13.12 74.85 25.15
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The Jepara, Klaten and Sukoharjo districts, classified as Rural-Urban Type A, are famous for their
export-oriented wooden furniture industry. Klaten also has various marketable handicraft products, as
well as developed metal industries. In addition, Kudus has been developing based on its famous cigarette
industries, and its established textile and food industries have products that are marketed not only
nationally, but also on an international level. All these industries are characterized primarily as small- to
medium-sized industrial clusters that rely on local labor and local investment [25,26]. In contrast, the
Karanganyar, Kendal and Semarang districts are classified as Rural-Urban Type B and are characterized
by various types of large, foreign-based manufacturing industries.
The data in Table 2 show that rural-urban population transition in the research area takes place in
the regions with potentially endogenous resources, which are not necessarily located close to the
highest urban center in the hierarchy or supported by typical foreign-based industries. This is a
surprising finding, as population transition in Central Java follows a different pattern than that
observed in other large provinces in Indonesia. The pattern is also clearly unlike the phenomenon that
has generally been observed in developing countries, as reported by Jones [27] and Douglass [28] with
respect to the role of extended metropolitan regions (EMRs) in Asian countries.
Jones [27] argues that many large cities in Southeast Asia, including Indonesia, are growing very
rapidly and, therefore, have experienced very rapid growth in their urban populations in the areas
surrounding these cities, which are called EMRs. In these EMRs, populations are highly concentrated
only in particular urban centers, which has created an unbalanced form of development pattern. This
industrialization path could be described as industrialization from above. Four selected large cities in
Indonesia, as illustrated in Figure 6, experienced a common pattern of population growth that is typical
of most developing countries: a relatively higher population growth rate in the inner zone (closer to the
capital city) than in the outer zone. Only Bandung in West Java and Semarang in Central Java have
experienced relatively balanced growth in their cores, inner zones and outer zones.
Figure 6. Zones of extended metropolitan regions (EMRs) in selected large cities
in Indonesia. Source: the Jakarta, Bandung, Medan and Surabaya figures are taken from
Mamas et al. (2001) in Jones [27]; the Semarang figures are based on the cluster
analysis results.
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Semarang, the capital city of Central Java, has experienced relatively lower growth than other large
cities in Indonesia. Furthermore, as shown in Figure 6 and Table 3, the inner zone in Central Java also
experienced a lower urban population growth rate than the inner zone in four other selected large cities
in Indonesia.
There are at least two factors that explain this extraordinary population growth pattern in the study
area. First, the role of foreign-based manufacturing industries that are mostly located in EMRs and
have led to typical industrialization from above is less dominant in Central Java than in other
developed provinces in Indonesia. As a result, less labor has migrated to the surrounding core regions
in the province than in most cases, because foreign-based industries attract significant numbers of
laborers. This has resulted in more gradual population growth in the area. Second, endogenous
resources in particular regions in the study area have acted as potential engines of growth. In this
sense, it is not always true that rapid development can only occur as part of the growth of a core
region, as, in fact, it has resulted in unbalanced development in most regions in developing countries.
Table 3. Rate of population growth of selected large cities in Indonesia. Source: the
Jakarta, Bandung, Medan Surabaya data are taken from Mamas et al. (2001) in Jones [27];
the Semarang data are taken from CBS [21,23].
Capital City and Zone Average Annual Rate of Increase (%)
Core Inner Zone Outer Zone
Jakarta 2.08 6.01 1.84
Bandung 2.75 2.93 2.77
Surabaya 1.73 2.50 0.89
Medan 1.91 5.46 0.54
Semarang 1.67 1.05 0.95
Note: the average values for Jakarta, Bandung, Medan and Surabaya are taken from data from 1990 and
1995; the average value for Semarang is taken from data from 1990 and 2000.
Figure 6 clearly illustrates that the development of Rural-Urban Type A areas is mostly based on
more endogenous resources located relatively far from Semarang, the capital city. As the
industrialization that has been taking place in these rural-urban areas has employed mostly local
resources and has been located quite far from the large urban center (i.e., Semarang), the path could be
defined as industrialization from below. Accordingly, the existence of this particular rural-urban
region, along with the fact that Central Java has a relatively balanced average annual rate of population
growth, indicates that endogenous urbanization or urbanization from below should be considered a
potential form of the rural-urban transition.
3.4. Rural-Urban Transition in Central Java Based on an Examination of Economic
Structural Changes
Central Java Province has experienced a noteworthy economic transition over the last three decades.
In 1976, the non-primary GDP exceeded the primary GDP. As shown in Table 4, in 1976, the primary
GDP contributed approximately 40 percent of the total GDP, but the proportion declined significantly
to only approximately 20 percent in 2006. This shows that Central Java has developed considerably
Land 2013, 2 431
from being dominated by agriculture to being a more industrialized province, at least as indicated by
GDP contribution.
Further examination of the performance of the subgroups revealed quite different population and
economic transition paths. Economic transformation has occurred at similar rates in the Rural-Urban
Type A and Rural-Urban Type B areas. In fact, the GDP contribution of the non-primary sector in the
Urban, Rural-Urban Type A and Type B areas surpassed the contribution of the primary sector to GDP
in 1976. This indicates that rapid industrialization, as an indicator of development from an economic
perspective, had taken place to a notable degree in these areas. These were the only groups of rural
districts to achieve transformation in the middle of the 1970s. The Rural-Urban Type C groups, which
actually were characterized as rural, did not achieve transformation until the late 1980s.
Table 4. Share of Gross domestic product (GDP) based on groups identified in the cluster
analysis (percent).
1976 1986 1994 2006
Central Java Primary 41.60 33.86 24.52 22.20
Non-Primary 58.40 66.14 75.48 77.80
Urban Districts Primary 6.50 6.30 6.46 5.86
Non-Primary 93.50 93.70 93.54 94.14
Rural Districts Primary 50.03 41.70 38.07 37.28
Non-Primary 49.97 58.30 61.93 62.72
Rural-Urban Type A Primary 38.65 33.47 23.90 18.06
Non-Primary 61.35 66.53 76.10 81.94
Rural-Urban Type B Primary 49.46 41.95 25.16 19.73
Non-Primary 50.54 58.05 74.84 80.27
Rural-Urban Type C Primary 59.91 50.79 42.49 37.62
Non-Primary 40.09 49.21 57.51 62.38
Note: % of GDP represents the average value of each group.
Comparing Rural-Urban Type A, which represents local potential support, and Rural-Urban Type
B, which represents the dominant role of multinational companies in the New International Division of
Labor (NIDL) type of industries, it is interesting to note that the two types of regions have had similar
economic transition paths. As illustrated in Table 4, between 1994 and 2006, the contributions of the
non-primary and primary sectors to GDP were similar for the Rural-Urban Type A and Type B areas.
It is only the growth of the non-primary sector’s contribution to GDP in the Rural-Urban Type A areas
that is more stable than that of the Rural-Urban Type B areas. The Rural-Urban Type B areas
experienced quite rapid growth in non-primary GDP contributions between 1986 and 1994. The rate of
growth then decreased slightly between 1994 and 2006, mostly due to the Asian economic crisis in the
middle of the 1990s. Another finding is that the growth of the non-primary sector’s GDP contribution
of the rural districts was very slow, as shown in the Table 4; the non-primary GDP contribution of this
subgroup was stagnant between 1994 and 2006.
With respect to the patterns observed, there are at least two important points that should be taken
into account. The first point pertains to the growth pattern. The Rural-Urban Type A group
experienced the steadiest growth, which indicates that external circumstances do not have a strong
Land 2013, 2 432
influence on local economic performance. In contrast, the growth of the Rural-Urban Type B group,
which was highly dependent on external resources, was relatively unsteady. At least, a considerably
slower growth rate was observed during the crisis (middle of 1990s) than during the previous period
(1976–1986) and during the period after the crisis (the 2000s). The second point pertains to the
contributions among the subgroups. There was a significant gap between the contribution of developed
rural-urban regions (Rural-Urban Types A and B) and that of underdeveloped regions (Rural-Urban
Type C and rural districts). These differences are evidence of a pattern of unbalanced development in
the region.
The pattern of GDP contribution is likely to be somewhat congruent with economic performance
based on employment in medium and large industries. As illustrated in Figure 7, the Rural-Urban Type
A groups have absorbed the largest number of employees in medium- and large-sized industries and
have experienced the highest growth rates of employment in these types of industries, compared to
other subgroups. During the period from 1993 to 2006, employment in medium- and large-sized
industries in these rural-urban regions increased at the fastest rates, leaving behind other subgroups
quite significantly. The Rural-Urban Type C group, which consists of six districts, had considerably
less employment in medium- to large-sized industries than the Rural-Urban Type A group, which
consists of four districts. However, it is important to note that 16 of the districts analyzed were rural,
and therefore, the levels of employment in medium- and large-sized industries are higher in these
districts than in Rural-Urban Type B and Rural-Urban Type C districts. In fact, the average number of
people employed in these industries was only 9,600 per district in 2006, but was 31,440 for the
rural-urban areas (Types A, B and C). Nevertheless, all subgroups experienced positive growth rates,
which suggests that industrialization was quite significant at the provincial level.
Figure 7. Number of jobs in medium- and large-sized industries.
A similar trend was also observed for small industries (see Figure 8). Both the number of industries
and the number of jobs indicate significant growth in Rural-Urban Type A compared to the other
subgroups. Although rural districts have contributed significantly to employment in small industries,
the trend is likely to decline. The number of industries in rural districts decreased from 184,360 to
160,164, and the number of jobs decreased from 436,715 to 385,215. A similar pattern was observed
for Rural-Urban Type B. The number of industries decreased from 17,948 to 6,322, and the number of
Urban 58404 55883 61710 107648 164008
Type A 60891 67807 90355 121781 202170
Type B
Type C 15217 14008 21352 52488 79617
Rural 42083 39767 64388 92319 153585
Number of jobs
Land 2013, 2 433
jobs decreased to a quarter of the 1993 level, from 40,911 to 14,501. It is clear that only the
Rural-Urban Type A districts, followed by the urban districts and Rural-Urban Type C districts,
exhibited relatively stable performance.
The facts reported above revealed that the Rural-Urban Type A group exhibited the steadiest growth
and the smoothest economic transition, compared to the other rural-urban subgroups. One important
point that should be acknowledged as a key performance factor is the dominant role of local resources,
which may reduce the influence of uncontrollable external situations. Zhu [29] has reported quite
similar results for Rural-Urban Type A areas in Fujian Province in China. The development of
township and village enterprises (TVEs) in the province has led to what he calls “in situ urbanization”.
Qadeer [8] has also discussed emerging settlement systems characterized as urban being located
relatively distant from urban centers in Pakistan, India and Bangladesh.
Figure 8. Numbers of industries and jobs in small industries.
With respect to Rural-Urban Type A areas, studies by Yu Zhu [29] and Qadeer [8] have also shown
that small- and medium-sized enterprises (SMEs), which are mostly centered in rural areas, should be
regarded as potential generators of new growth that reduce inequality among regions. Considering that
SMEs are characterized as labor-intensive and that the distribution of income among enterprises is
relatively more even, the dominant role of SMEs can be seen as a good sign, because it indicates a
more equal income distribution. Weiss [30], Weijland [26], and Tambunan [31] believe that SMEs
play a strategic function in most developing countries, as they are very important to the survival of
most rural households.
4. Conclusions
Urbanization, which is highly correlated to the nature of rural-urban transition, has led to particular
features of the rural-urban interface in Central Java. Cluster analysis shows that industrialization is a
prominent indicator of the transition process, which follows at least two different paths. The first path
can be described as industrialization from above, in which the transition takes place as a part of the
urban growth process and is mostly the result of the development of foreign-based manufacturing
Numbe r of
Emplo yment
Numbe r of
Emplo yment
Urba n
Type A
Type B
Type C
Land 2013, 2 434
industries. The second path can be described as industrialization from below, in which rapid (mostly
locally based) industrialization leads to a particular type of urban transition that occurs far from the
highest hierarchy of urban centers and is dominated by smaller-scale industries.
Districts that are categorized as rural-urban regions are located in urban fringes or as a part of the
development of extended metropolitan regions. The economic activity of these districts is dominated
by large manufacturing industries. Because the districts are located in favorable areas for foreign and
local large investors, mostly due to the infrastructure provided and the supply of blue-collar workers,
these industries do not significantly generate backward and forward linkages within the local economy.
The existence of this particular type of industry has proven to be very effective in accelerating
economic growth, because they contribute significantly to the regions’ GDP, although they provide
only a relatively small amount of employment. Indeed, the results of the cluster analysis may provide
more evidence that the rural-urban transition in most developing countries does not proceed smoothly.
Douglass [28] and Gilbert and Gugler [3] have expressed the belief that major cities have dominant
roles, in terms of population and economic activities derived from the existence of multinational
industries, in rural-urban disparities.
In contrast, industrialization from below may provide evidence that industrialization in developing
countries does not necessarily always take place as part of the urban growth process or as part of
extended metropolitan regional development and does not always depend on the presence of large
manufacturing industries. As Zhu [29] observed in the case of in situ urbanization in Fujian Province
in China and as Qadeer [8] discussed with respect to urbanization in Pakistan, India and Bangladesh,
rural-urban regions, in the form of urbanization from below, are mostly located quite far from urban
districts. Accordingly, the economic activity of these districts is dominated by locally based small-to
medium-sized industries. This implies that these particular industries form strong local bonds. In
general, these industries absorb significant numbers of workers, but unfortunately, they contribute only
a small amount to the regional GDP. This means that the industries perform at a relatively low level
of productivity.
Rural-urban regions characterized by industrialization from below should be acknowledged as a
promising spatial form that may lead to more balanced development patterns in the future. These
regions have strong local potential, which implies a stronger foundation for the economy, in addition to
their ability to absorb significant numbers of people into the labor force. The form of urbanization
from below observed in this research indicates that the concept of endogenous growth for developing
countries is essentially down-to-earth.
This research was funded by the Directorate General of Higher Education (DGHE) of Indonesia,
under its postgraduate scholarship scheme. The authors would also like to acknowledge
Stefan Siedentop from the Institute of Regional Development Planning of the University of Stuttgart),
and Jochen Monstadt from Spatial and Infrastructure Planning of the Technische Universität
Darmstädt, for their constructive insights that improved the quality of this research.
Land 2013, 2 435
Conflict of Interest
The authors declare no conflict of interest.
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We test the null hypothesis that municipalities defined as central cities by the US Bureau of the Census in 1990 are homogeneous-a hypothesis we reject. Rather, we find that US central cities consist of 2 distinct subsets of municipalities that are aggregated from 13 cluster groupings. The article has two purposes. The first is methodological. We develop a method that uses cluster analysis to group US central cities; then we employ discriminant analysis to establish the statistical validity of those groups. We also develop techniques to minimise the role of judgement in selecting the appropriate cluster solution. The second purpose of the article is to test the substantive null hypothesis. Our rejection of the homogeneity assumption raises the spectre of specification error in research and public policies that assume homogeneity among central cities.
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." textemdash}Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." textemdash{Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." textemdashTechnometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.
Partial table of contents: Discriminant Analysis in Research. PREDICTION. Basic Ideas of Classification. Multivariate Normal Rules. Classification Results. Hit Rate Estimation. Nonnormal Rules. Reporting Results of a PDA. Applications of PDA. DESCRIPTION. Group Separation. Assessing Effects. Describing Effects. Selecting and Ordering Variables. Reporting Results of a DDA. Applications of DDA. ISSUES AND PROBLEMS. Special Problems. Appendices. Answers to Exercises. References. Index.
This article begins with a review of the government's policy framework, together with some conceptual and analytical issues as they relate to SMEs. Next, the empirical evidence on SMEs is examined, referring both to aggregate, macro-level evidence and case studies, and including some of the emerging material on the impact of the recent crisis. The article then asks whether there is any special case for intervention in favor of SMEs and points to areas where government policy has and has not worked. Finally, it closes by highlighting gaps in the database and research material. Following much of the literature, the discussion focuses primarily on the industrial sector, although where relevant the net is cast more widely.