SOCIAL EXCLUSION,CHILDREN AND POVERTY: THE CASE OF EASTERN INDONESIA 2012

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Widi Laras Sari | Wisnu Harto Adi W

TNP2KSOCIAL EXCLUSION,CHILDREN AND POVERTY:

THE CASE OF EASTERN INDONESIA 2012



Social Exclusion, Children and Poverty: The Case of Eastern

Indonesia 20121

Wisnu Harto Adi Wijoyo2 and Widi Laras Sari3

Abstract

Using the Indonesia Family Life Survey-East dataset, conducted in 2012, covering 7

Provinces in Eastern part of Indonesia, the information on socio-economic condition,

demographic characteristics specifically related with accessibilities, social exclusion,

and poverty related in household and individual level was extracted. PCA and

Logistic regression being used to determined the weight and probabiliy magnitude

from the drivers of potential social exclusion. The result indicates that 46.9% children

are vulnerable to the social exclusion, with children living with household head lower

education and non - social participatory household condition weighted as significant

factor. Policies toward poverty reduction based on community (KUBE, KUR, etc.)

and education (BSM, PKH) should be prioritize in order to reduce the potential

children social exclusion.

keywords: children social exclusion, east Indonesia, Indonesia Family Life Survey, poverty,

logistic regression, principal component analysis

1 This paper is using the Pre-Eliminary Dataset of IFLS East (2012) that was being collected and

cleaned by SurveyMeter and RAND; funded by TNP2K and AusAID. The material in this paper

(Tables, Graphs, and Contents) is not for quotations as the data is on the finalization processes .2 Wisnu Harto is Researcher on TNP2K3 Widi Laras Sari is Researcher on LPEM FEUI



Introduction

Poverty has become a major issue in many countries that arise many tools to alleviateit. According to World Bank, poverty is defined as deprivation in well-being, whileUnited Nations defines poverty as a denial choices and opportunities. The existence of

poverty trap is also considered the most perplexing issue in poverty eradication. Poorfamilies would generate the children that seized in poverty hence exhibit poverty trap(Sachs, 2005).

Child poverty differs from adult poverty in that it has different causes and effects, andthe impact of poverty during childhood can have detrimental effects on childrenwhich are irreversible (Daly, et. al., 2008). Poverty impacts more acutely on childrenthan on adults because of their vulnerability due to age and dependency. For example,children are largely dependent on their direct environment for the provision of their

basic needs.

Children are essential in an economy as indicated by (Holzer, et. al, 2008) thatchildhood poverty costs about $500 per year to the US economy or equivalent fornearly 4% of Indonesia's gross domestic product (GDP). Moreover, it was alsoestimated that childhood poverty reduces productivity and economic output, raises thecosts of crime and raises health expenditures each year.

Axford (2003) described that at least there were three major issues of child well-beinghave contributed to greater interest in studying childhood. First, it viewed children as‘a minority group, whose wrong need righting’ since they require more protection,

know less, have less maturity, less strength and have moral inferiority (Mayall, 2002).

Second, children needs to be viewed as active and constructive members of societyinstead of as participants in society that means to allow them to participate their needshave to be fulfilled first and they can also be excluded (Mayall, 2002). Third, theindeterminate boundary between childhood and adulthood that arise from the massiveamount of activity demanded by schools, more access to sex and drugs and thedynamic in cultural and fashion taste has generated the current children’s quality of

life.

Through its publication, UNICEF conveys that problem in child poverty is morecomplicated than merely lack of resources. Child poverty is multidimensional(UNICEF, 2006). Therefore, this paper tries to explore more on a broader definition

of poverty that is called social exclusion on children.

Unlike the definition of poverty that agreed to be considered as lack of materialresources to meet needs, the definition of social exclusion is still not in harmonyamong the authors (Atkinson, 1998; Room, 1995; Tsakloglou & Papadopoulos,2002). However, they have in common understanding that social exclusion in notonly about material poverty, but also about the processes by which some individualsand group become marginalised in society (Millar, 2007).

The social problems such as poverty and social exclusion are more likely to occur indeveloping countries. These problems are usually characterised by a severe

deprivation of basic human needs (UN, 1995). An impoverished child growing up in adeveloping country suffers more hardship than most children living in poverty in



developed country (Gordon, et. al, 2003). When the social exclusion in developedcountries is mostly directed to minorities (Byrne, 2005), it occurs to the major bottomin social pyramid (Sen, 2000; Saith, 2001).

As one of developing countries, Indonesia also suffers from poverty problems. For

about 32.5 million Indonesians are currently live below the poverty line andapproximately half of all households remain clustered around the poverty line (WorldBank, 2009). However, Indonesian government has put poverty alleviation on its top

priority development by establishing some programs targeted at the poor andvulnerable such as subsidised rice (Raskin), health fee waivers (Jamkesmas),scholarships (BSM), and conditional cash transfers (PKH). According to NationalTeam for Accelerating Poverty Reduction (TNP2K) report, Indonesian has showed a

progressive decline in poverty rates over the past five years. Yet, many social problems still arouse from the poverty. Study of poverty in Indonesia is limited to itsdefinition based on income per capita, thus there is scanty examination of Indonesian

poverty through social exclusion.

Eastern Indonesia is the most remote region in Indonesian territory. This part ofIndonesia is left behind in terms of infrastructure development. Many social problemsarise within most of provinces in eastern part of Indonesia. Conflicts, separatism andsevere poverty are the issues that are common in this region which indicates there is adeteriorating social exclusion problem. This paper will focus on potential socialexclusion experienced by the children in eastern Indonesia which is the mostvulnerable group in the society. It will describe the condition of children in the region

based on descriptive data acquired from household data of Indonesia Life FamilySurvey (IFLS) East in 2012. There will be also policy recommendation to address the

problem and for the further studies.

The Concept of Social Exclusion

Poverty and Social Exclusion

Material poverty is not always the same with social exclusion. Exclusion can involvelack of resources in the form of goods, opportunities and standard of living, but it canalso be defined as being blocked form participating in key activities of society (LeGrand and Piachaud, 2002).

The term of ‘social exclusion’ (or relative deprivation / marginalization) is a conceptused in many parts of the world to characterise contemporary forms of socialdisadvantage and relegation to the fringe of society (De Haan, 2000). The term wasfirst utilized in France, and then being popular to be used in United Kingdom andother European part. The term of social exclusion itself is being used in many form ofdisciplines, e.g: education, sociology, psychology, politics and economics.

Partially, social exclusion refers to processes in which individuals or entirecommunities of people are systematically blocked from rights, opportunities andresources (e.g. housing, employment, healthcare, civic engagement, democratic

participation and due process) that are normally available to members of society and

which are key to social integration (Sen, 2000). While Beall & Piron (2004) describesthat social exclusion consists of exclusion from social, political and economic



institutions resulting from a complex and dynamic set of processes and relationshipsthat prevent individuals or groups form accessing resources, participation in societyand asserting their rights. Moreover, DFID (2005) defines social exclusion as a

process by which certain groups are systematically disadvantaged because they arediscriminated against on the basis of their ethnicity, race, religion, sexual orientation,

caste, descent, gender, age, disability, HIV status, migrant status or where they live.

Room (1995) suggests five key factors that are essential to the definition of socialexclusion. They are multidimensional, dynamic, collective, relational andcatastrophic. Moreover, Atkinson (1998) proposes three aspects of social exclusion,which are relativity, agency and dynamics. Oppenheim (1998) suggests that it isnecessary to focus on social exclusion rather than poverty for a number of reasons.

As explained above, social exclusion is multi-causal, relational, and it includes lesstangible aspects than poverty. Social exclusion causes the poverty in particular

people, leading to higher rates of poverty among affected groups. It also reduces the

productive capacity and rate of poverty reduction of society as a whole. Socialexclusion explains why some groups of people remain poorer than others, have lessfood, die younger, are less economically and politically involved, and are less likelyto benefit from services (DFID, 2005).

Study of social exclusion in Indonesia is still limited; one of the studies wasSudjatmiko (2011) who analysed social exclusion and social transition in Indonesia.He examined people’s access to welfare and furthering their aspiration and

representation in politics and social life. He concluded that historical and structuralfactors in Indonesia contributed to the persistence of exclusion policy of the lowerstrata.

Social exclusion causes poverty of particular people, leading to higher rates of poverty among affected people. For example, there is spatial exclusion, which is thedisadvantage of social exclusion on the basis of the location. People who live inremote areas may be prevented from fully participating in national economic andsocial life. The social ignominy of coming from ‘not-known’ area can make a person

find it more difficult to get a job. In the case of Indonesia, eastern part is known as themost remote and isolated area. For that reason, it is presumed that the society withinthe region is the most affected by social exclusion problem.

The Drivers of Social Exclusion

From the review of the literature on the driver of social exclusion from SPRU (TheSocial Policy Research, Universty of New York) that was commisioned by the SEU(Social Exclusion Unit, written by Bradshaw et-al) on 2004, showed that to associatethe drivers (drivers are factors/causes of social exclusion) with social exclusion is noteasy. There are difficulties in understanding the direction of the relationship betweendrivers because those drivers tend to interact and overlap. In a greater picture, thesocial exclusion mainly driven by the demographic condition, labor market and

also policy factors in the past (Bradshaw, 2004), they are defined as followed:



The key demographic factors have been large youth cohorts, ageing andincreased dependency ratios, and family change, particularly the increase inlone parent families.

The key labour market factors have been unemployment, flexibility in thelabour market, the dispersion of earnings and the concentration of work.

Policy issues could be a driver for social exclusion. Some particular problemsincluded where benefits had not been up-rated in line with the growth ofearnings, the abolition of some benefits, also lowering the expenditure of

particular services.

In general, the SEU study on the past social exclusion driver is similiar with thedimension of social exclusion defined by UNDP. Social exclusion is often perceivedas a vicious circle with three components: unemployment, poverty, and social

isolation / non-networked condition. From the graph 1 below, we could understandthe definition of social exclusion using those 3 components that has been mentionedabove. If an individual is living under the roof of the 3 element, they are defined associally excluded (UNDP, 2006).

Graph 2.1: Social Exclusion Dimension

Source: UNDP, 2006

In the present, social exclusion's driver is redefined by SEU (Bradshaw, 2004) inmore complex definition. The drivers of social exclusion covering the socio-economiccondition (low income, unemployment, social capital), education, health problem,housing / dwelling, transportation availability, neighborhood, and also the crimecondition.



Table 2.1: Summary of Social Exclusion Drivers (Bradshaw, 2004)

Drivers Definition

Low income Low income is without doubt a key driver of social exclusion today. It isassociated with a range of poor outcomes; many of these are long term.

Income poverty is mainly driven by/associated with family type andemployment circumstances.

Unemployment Inability to participate in the labour market is generally considered a keyindicator of social exclusion. Unemployment has knock on effects inother dimensions of social exclusion including homelessness, health,crime, and drug and alcohol problems.

Education Education has a pivotal role in the intergenerational transmission ofsocial exclusion. On balance the evidence suggests that education canreduce social exclusion.

Ill health Ill health is associated with social exclusion in a variety of ways. Healthstatus is a determinant of social position. Unhealthy behaviour can drivesocial exclusion, and social exclusion itself and the other drivers of itcan result in poor health.

Housing Here, social exclusion was treated as covering those sleeping rough orstaying in temporary and insecure forms of accommodation.

Transport Lack of affordable, reliable and safe transport can restrict access towork, education, services, food shopping and socio-cultural activities.

Social capital Social capital is a concept about which there is still a degree ofconfusion. Most of the writing on the subject is theoretical. Empiricalwork suggests social capital is lower in areas of poverty, though there issome evidence that employment may inhibit the development of

relationships and networks that enhance social capital at home.Neighbourhood There is a debate in the literature about whether there are independent

effects of living in a deprived neighbourhood. Poverty has become morespatially concentrated but it is much more difficult to investigate andconclude that neighbourhood per se impacts on social exclusion. Resultshave been found on educational outcomes and child development.

Crime The most powerful drivers of crime are community deprivation andincome inequalities resulting from unemployment. A criminal record isitself likely to lead to exclusion, having an impact on the chances ofobtaining employment in particular.

Fear of crime Fear of crime varies by neighbourhood and individual characteristics,

with a strong association with age, gender and ethnicity. However, poor people are more likely to fear crime.

Source: SEU, 2004

In summary, the social exclusion is driven by a complex interplay of demographic,economic, social and behavioural factors that are linked and mutually reinforcing(Bradshaw, 2004). It is cumulative and often intergenerational. The risks of socialexclusion are not evenly shared but concentrated in the poorest individuals andcommunities.



Dataset of IFLS East

Using the Indonesia Family Life Survey-East dataset, conducted in 2012, covering 7Provinces (Nusa Tenggara Timur, Kalimantan Timur, Sulawesi Tenggara, Maluku,Maluku Utara, Papua and Papua Barat) in Eastern part of Indonesia, the information

on socio-economic condition, demographic characteristics specifically related withaccessibilities, social exclusion, and poverty related in household and individual levelwas extracted.The total number of individual being observed on IFLS East are 10,745 person, withthe composition of 4,821 observation on the age of 18 or below (0 - 18 years old).Using UNICEF's definition of children as the individual of the age under 18 yearsold, we try to to understand the general composition and structure of children data onIFLS East.

Graph 3.1. IFLS East Population Pyramid, 2012

Source: Author's Estmation

From graph 3.1, we could see the population and gender structure from sample ofIFLS East data (all age), the percentage of observation under the age group of 18years old constructs around 44% of the overall population. Knowing that the IFLSEast observation having plenty of children observation, the analysis using the datasetof person under the age of 18 would be possible. From the dataset that has been

defined, some statistics and table related to the poverty and demographic condition being extracted from it as followings.





Table 3.2. Average Agregated Provincial Level - Percapita Expenditures & Monthly

Expenditures on IFLS East, 2012 (Thousand Rupiahs)


To better understanding the expenditure data used to generate the table 3.1, the table3.2, with dataset of average percapita expenditure, poverty line, and generalhousehold level monthly expenditures (food, nonfood, schooling and housing) is

being generated. The mean / average of percapita expenditures under the poverty linealso being extracted. The highest average percapita expenditure is Papua (around1411 thousand rupiahs), and the lowest one is Maluku (Rp. 717,000.-) and NusaTenggara Timur (Rp. 768,000.-). The overall average number from sample of IFLSEast's percapita expenditure is lower than the BPS's national level on 2012, thereforethe agregated national level is 10,591 thousand rupiah (around 10 million, constantGDP percapita 2000). Eventough it's common knowledge that Eastern Indonesia iscostly comparable to Western part of Indonesia, the major role of economic still focuson Western part of Indonesia, especially in Java and Bali.

Table 3.3. Social Assistances on IFLS East (%), 2012


Province Code Dana Sehat (%) SKTM (%) BLT (%) BSM (%) JSLU (%) ASODKB (%) PKSA (%) Observation

NUSA TENGGARA TIMUR 53 22.54 24.64 40.84 16.35 0.06 0 0.39 1547

KALIMANTAN TIMUR 64 22.56 13.39 8.18 2.89 0.54 0 0.9 1113

SULAWESI TENGGARA 74 13.42 15.89 18.1 5.31 0.38 0 0 1595

MALUKU 81 3.48 11.67 31.4 13.81 0.49 0.88 0.33 1893

MALUKU UTARA 82 13.46 8.26 15.26 5.68 0.41 0.55 0 1751

PAPUA BARAT 91 14.37 10.88 34.17 12.26 1.43 0 0.34 1466

PAPUA 94 14.08 18.12 23.13 2.62 0.43 0.22 0.44 1380

Non Food Exp mean PCE mPCE<Povline BPS(2000) Type 1 Type 2 Type 3 Individual Children

Overall Obs / National 1031 212 10591 272 26757 10274 4603 387 3378 5800 10745 4821 NTT 768 170 218 18400 6680 2640 380 2235 3047 1547 680 KALIMANTAN TIMUR 1034 296 359 27100 15800 6643 383 3762 7926 1113 448 SULAWESI TENGGARA 1187 159 193 20200 8462 4398 444 3445 4242 1595 695 MALUKU 717 202 276 24200 8078 3250 235 3781 3806 1893 886 MALUKU UTARA 1102 178 241 26000 11400 5252 336 3539 6186 1751 827 PAPUA BARAT 1001 245 332 34000 11000 5444 280 3959 7232 1466 691 PAPUA 1411 233 287 37400 10500 4596 654 2921 8161 1380 594

Observation Monthly (in Thousand Rupiah)

PCE Housing Food Exp Province Povline Schooling



Table 3.4. Social Assistances on IFLS East (Number), 2012


Table 3.3 and 3.4 are showing the social assistance number and prevelance among allage in the IFLS East's provinces. The social assistances that are being displayed ontable 3 and table 4 are Dana Sehat (Health Fund), SKTM (Surat Keterangan TidakMampu / Poor Letter), BLT (Bantuan Langsung Tunai / Direct Cash Transfer), BSM(Bantuan Sekolah Miskin), JSLU (Jaminan Sosial Lanjut Usia / Social Assistance forElderly), ASODKB (Asistensi Sosial Orang Dengan Kecacatan Berat / SocialAssistance for Severely Disabled People), and PKSA (Program Kesejahteraan Sosial

Anak / Children Social Welfare Program). From Sen (2000), social assistances could become a solution toward inclusiveness. If one of the problem of social exclusion isemerging from being un-educated / having low education, BSM in theory could

become an alternative solution toward inclusiveness. BLT, BSM and Dana Sehat arehaving the higher number and percentage from both table, covering the basicnecessity due to poverty and increasing of fuel price on 2008 for BLT case, and basiceducation (BSM) or healthcare (Dana Sehat). Social assistance program which isspecifically targeting the children like PKSA is having low number, as well as the onethat targeting to help the elderly or disabled person (lower than 10%). Despite beingalmost 44% of the sampled data, children social assitance program is relatively low inthe IFLS East data.

In general, the dataset from IFLS East would be focused on generating the socialexclusion indicator for observation with age under 18 years old (children), from theUNDP and SEU definition of social exclusion on the chapter 2.

Methodology

Dataset Construction

Using dataset specific to children (age less equal 18 years old), author try to re-

construct social exclusion data based on both UNDP and SEU criterias: Based on dimension of social exclusion (unemployement status, poverty

condition, and socially networked status)

Based on social exclusion's drivers (low income, unemployement, education,ill health, housing condition, transportation, crime and neighboorhood)

Using Sen (2000) and Byrne (2005), authors try to include minority variableon the driver of social exclusion (being marginalized). The minority would bedefined using the share of respective religion or race by its total population.IFLS East having the data of religion and tribe / race on its questionaire, beingask to every household member.

Province Code Dana Sehat (num) SKTM (num) BLT (num) BSM (num) JSLU (num) ASODKB (num) PKSA (num) Observation

NUSA TENGGARA TIMUR 53 71 378 631 248 1 0 6 1547

KALIMANTAN TIMUR 64 60 149 91 32 6 0 10 1113

SULAWESI TENGGARA 74 71 252 288 84 6 0 0 1595

MALUKU 81 7 216 594 260 9 16 6 1893

MALUKU UTARA 82 44 138 261 81 4 5 0 1751

PAPUA BARAT 91 51 159 501 179 21 0 5 1466

PAPUA 94 59 250 319 36 6 3 6 1380



Mapping the Children with Potential Social Exclusion in East

Indonesia

Using the constructed dataset based on dimension and drivers of social exclusion,author would try to extract relevant descriptive statistics of children with potentialsocial exclusion on IFLS East observation area (IFLS is not being use to represent anygeographical location in Indonesia), such as: social exclusion by children's age group,family expenditure condition, gender, education of children and its caregiver(household head), religion and tribal, in order to explain the detailed picture ofchildren with potential social exclusion on Eastern Indonesia (2012).

Having the descriptive statistics to explain in detail the potency of social exclusion forchildren in Eastern part of Indonesia, the bigger picture would be explain using:

1. The two method would be further elaborated in the chapter 4, along withestimation result and table extraction specified for Potential Children with

Social Exclusion from IFLS East Dataset. Children Social Exclusion Ranking(derived from the driver), using the Principal Component Analysis to explainthe weight of each drivers from the dataset that has been defined before.

2. Basic Logit method is being used to explain the probability of Children SocialExclusion. In general, the logit estimation would provide the overall

probability of children social exclusion in eastern Indonesia.3. Mapping the Potentially Excluded Children in IFLS East sample data.

The PCA Method

In short, PCA (Principal Component Analysis) is a method / statistical technique that

allows us to derive one or more summary measures (PC / principal components) fromset of determinants / indicators. Each PC is a weighted average for a maximumamount of variance in the underlying indicators (Shepherd, 2009).Basically, PCA relies on some matrix of algebra. To explain further, let use the matrixoperation below. Theoretically, we have p variables observed across n cross-sectionalunits, for example, Provinces. From Shepherd (2009), Their correlation matrix would

be:

After that, using the spectral decomposition in the matrix algebra to turn the pindicators into r principal components, with the value of r < p. The algebra and matrix(spectral decomposition) would be:

with matrix of:



Therefore, the P is a matrix of r number of columns that could be con structed foreach of r on each PCs. Every element of P is the proportion of the variance for everyoriginal indicator that in particular PC accounts for (Sheperd, 2009). Each PC is aweighted average of the underlying indicators. Weight are chosen so as to maximizethe explained proportion of the variance in the original set of indicators (dimensional

reduction). When multiple PCA are produced, every PC is uncorrelated with theothers.

The Logit Methodology

The logistic function was invented in the 19th century for the description of thegrowth of populations and the course of autocatalytic chemical reactions, or chainreactions (Cramer, 2003). In general, logit is being used to estimates the probability achoice / decision is being made by several determinants as a factor.In short, the logit model is using the exponential equation in addition to extract the

probability on the interval if 0 and 1. The equation itself would be:

and could be simplyfied as:

in which Zi is the simplification of the α + xi’β+ε function (Cramer, 2003), and

functionate as the independent variable / determinant that would influencing the valueof the dependent variable. The Pi itself is the probability ratio. When Z ranged around-∞ to ∞. the Pi would generate the value in the interval of 1 and 0. In other word,these equations count the probability of Y resulted in the interval of 0 and 1. Theimplication of logit model itself is linear, but the Pi itself is not linear with the Z, sothe parameter that been resulted from the model estimation should be marginalizedfirst (marginal effect) using the odd-ratio. The odd-ratio would linearize the modelfrom the natural logarithm of its probability. The equation then would be define as:

After that, the equation above would be transformed into natural logarithm:

In short, the result from odd-ratio that has been linearized by natural logarithm would provide parameter that could be intepreted from its marginal effect.Using the PCA to rank and reduce the dimension of every driver of social exclusion,

and the logit method would provide the probability of social exclusion by the drivers



of social exclusion. Using both method, author would try to map the potentialchildren social exclusion on IFLS East Provincial level of survey area.

The Statistics of Potential Children Social Exclusion on Eastern

Indonesia: Case of IFLS East 2012

In the previous chapter, authors has explained the methodologies to build the dataset,extract the tables and to rank the potential social exclusion driver from IFLS Eastdataset. Consistent with the explanation, authors try to disseminate the result on thischapter as follow: 1. Potential Children Social Exclusion; 2. Ranking of Determintant/ Drivers of Children Social Exclusion; 3. Logit Model to Children Social Exclusion;4. Mapping the Potential Children Social Exclusion in Eastern Indonesia (2012).

Table 4.1. Crime and Conflict Rates to Children Age Under 18 Years Old, by

Province

Province Code Crime&Conflict Rates(%) Crime&Conflict Numbers Observation

Nusa Tenggara Timur 53 39.12 266 680

Kalimantan Timur 64 47.1 211 448

Sulawesi Tenggara 74 23.88 166 695

Maluku 81 40.86 362 886

Maluku Utara 82 47.76 395 827

Papua Barat 91 46.45 321 691

Papua 94 61.11 363 594


Table 4.2. Descriptive Statistics of the Full Dataset

Variable Mean S.Deviation Max Min

Household Size 5.284411 2.276471 16 1

Live Under Poverty 0.0962308 0.2949212 1 0

Household Head being Unemployed 0.0321557 0.1764224 1 0

Household Having Social Partipation 0.4479293 0.4973044 1 0

Householder does not have social participation 0.7997208 0.4002279 1 0

Some of household member is illiterate 0.5355049 0.498761 1 0

Some of the householder does not have: Phone,

Internet or TV

0.3254537 0.4685659 1 0

Household does not have the right to vote /

does not vote in the last political activity

0.5291764 0.4991713 1 0

Children living under the roof of potential

social exclusion

0.2061424 0.4045528 1 0


Table 4.1 extracted to display the prevalences of conflict and crime on the provinciallevel under IFLS East dataset. Papua showing the unexpected result of 61.11% (or



363 cases of observation) case of conflict and crime happened to the respectiveobservation. With mean of 43.7% case over the 7 Provinces, Eastern Indonesiasomewhat displaying the harsh portrayal of living condition for Children in there.Sulawesi Tenggara showing the relatively low number of case and percentage ofconflict-crime happened to children on the observation.

As additional information for the dataset, descriptive statistic for IFLS East dataset(under 18 years old) being displayed. Around 20% of the observation showing to liveunder the potential social exclusion condition. As for the illiteracy rates, un-social andcrime rates, they all are over 40% being show in the dataset that has been compiled.Sen (2000) and Byrne (2005) defined that belief system (in this case is Religion)determined the basic right to be a part of political system. The voting paticipatory(vote over Presidential, and lower level election being compiled as one) showing thatMoslem and Christian (Catholic and Prostestant) are having the highest voting

particpatory. That's normal considered the number of population in Eastern Indonesiaare mostly Islam or Christian. The insteresting part is, children in most Provinces with

Protestant religion showing the relatively higher social exclusion percentages (mostlyis above 10%), like 26.4% in NTT, and 16.1% in Kalimantan Timur.



Table 4.3. Voting Participatory, Social Exclusion (Drivers), Social Exclusion

(Dimension) by Household Religion and Province Province Religion Voting Participatory Soc.Exclusion (Drivers)

Nusa Tenggara Timur Islam 0.502092 0.104603

Protestant 0.453936 0.264657

Catholic 0.50687 0.178626

Kalimantan Timur Islam 0.496739 0.047826

Protestant 0.515528 0.161491

Catholic 0.483871 0

Hinduism 0 0

Sulawesi Tenggara Islam 0.505291 0.109127

Protestant 0.5 0.75

Catholic 0 0

Hinduism 0.538462 0.064103

Maluku Islam 0.481195 0.061947

Protestant 0.481482 0.107527

Catholic 0.333333 0

Maluku Utara Islam 0.42093 0.065116

Protestant 0.411504 0.188053

Catholic 0.375 0.25

Buddhism 0 0

Papua Barat Islam 0.539924 0.051331

Protestant 0.418736 0.085779

Catholic 0.462963 0.037037

Papua Islam 0.565598 0.061224

Protestant 0.43579 0.195789

Catholic 0.419753 0.234568


The Principal Component Analysis

In order to be able to understant the weighting of each driver variables / indicators forchildren potential social exclusion,, author using the PCA method and ParallelAnalysis. Parallel analysis itself being used to select which components that will be

used when analyzing PCA. Most of the parallel analysis result are the same withselecting components only with Eigenvalues score higher than 1. Parallel Analysis(Graph 4.1) suggesting that we only component 1 until component 3, the Eigenvalueitself showing that we could use component 1 until component 4.



Graph 4.1. Parallel Analysis of PCA Result: Selecting The Maximum Component

(PC)


Table 4.4. Principal Component Analysis of Children Data on IFLS East (Age under

18 years)Components Components with >=1 Eigenvalues

Component

1

Component

2

Component

3

Component

4

Eigenvalues 1.57 1.166 1.068 1.004

Cumulatives 17.50% 30.40% 42.26% 53.42%

Variables of PCA

1. Low Household Head Highest

Education

0.5322 -0.1991 0.0986 0.0683

2. Poor Household 0.4981 -0.1142 -0.2634 -0.1377

3. Unemployed Household Head 0.1972 0.1289 -0.7719 0.188

4. Crime / Conflict Victim

Household

-0.2264 -0.105 0.2675 0.4591

5. Good Condition of Dwelling 0.3729 0.0359 0.2781 0.2704

6. Non Voter Household 0.0425 0.6642 -0.0624 0.305

7. Non Social Participatory

Household

0.1056 0.6253 0.1859 0.0474

8. Non Indirect Social

Participatory Household

0.4716 -0.027 0.3429 0.06

9. Disabled Household Member 0.0642 0.2924 0.1422 -0.7472




The first principal component is strongly correlated with nine of the original

variables. The first principal component increases with increasing of low household

head education scores. Furthermore, we see that the first principal component

correlates most strongly with the Low household head education state. In fact, we

could state that based on the correlation of 0.532 that this principal component is primarily a measure of the low level of education. It would follow that communities

with many uneducated household would tend to have a lot of (in this case) possible

social exclusion available.

The second principal component increases with two of the values, non-voter

household and non-socially participated household. These component can be viewed

as a measure of how unsocial the children living in household with those terms would

face higher probability of unsociallable life, which leads into social exclusion itself.

The third principal component descrease with increasing on household employement

status (unemployed). This suggests that unemployement status of householdsomewhat tends to decrease the potential social exclusion, yet it's only has relatively

low Eigenvalue score (lower weight) relatively to the previous 2 had.

The Logit Model

Last, the logit model would be used to explain the probability of social exclusion forchildren based on 9 or 6 selected drivers from the dataset. Replicating the PCAvariables, with 9 drivers as independent variable, the logistic model explain arelatively low probability of social exclusion over Eastern Indonesia, or around 0.3%

is the probability of social exclusion with 9 driver as a independent variable. With 552observation, the first model result showing insignificant effect across the odd-ratio,with no variable having significant correlation with dependent (potential socialexclusion) data.

The second result of 6 drivers as independent variable are giving promosing result.Most variables are showing significant and explainable result. Poor condition, lowereducation of household head, Non - networked (both not in social partitipation, not inindirect social network via technology or not a household of voter) giving significantresult over the potential social exclusion for children. Sen (2000) explained that

poverty are a main trigger of social exclusion, as well as Sachs (2005) recalled that

lower education would leads into poverty, and the poverty itself having direct andsignificant effect over social exclusion condition. Housing condition and SocialParticipatory, defined by SEU (Bradshaw, 2004) as drivers of social exclusion.



Table 4.5. The Logit Model of Potential Social Exclusin: 9 and 6 Drivers for age

<=18 Years old

Dependent Variable:

Potential Social Exclusion(Age under 18 Years)

9 Drivers of Social

Exclusion (dy/dx)

6 Drivers of Social Exclusion (Un-

networked = non Social

Participatory, non Indirect SocialParticipatory, non Voter household

(dy/dx)

Household Head HighestEducation

0.94 0.43***

(6.779) (0.042)

Poor Household 0.99*** 0.33***

(0.194) (0.077)

Unemployed Household Head 0.99* 0.2

(0.581) (0.144)

Crime / Conflict VictimHousehold

Omitted

Good Condition of Dwelling 0.99*** 0.44***

(0.361) (0.043)

Non - Networked 0.47***

(0.089)

Non Voter Household 0.02

(4.951)

Non Social ParticipatoryHousehold

0.91

(10.293)

Non Indirect SocialParticipatory Household

0.99***

(0.047)

Disabled Household Member 0.001 0.12

(-0.377) (0.315)

LR chi-squared (prob)585.6*** 247.6 ***

N 552 599

Overall MFX 0.003 0.469

* = significant at 10% ** = significant at 5% *** = significant at 1%


Eventough the prevalence of social exclusion for children under age of 18 years old inEastern Indonesia is relatively low (around 20% from survey's population of IFLSEast), using the significant drivers being displayed in the logistic model, we couldidentify the cause of children social exclusion in the eastern part of Indonesia with

better interpretation. The probability of children being socially excluded is 46.9%using the given 6 selected driver.



Mapping the Social Exclusion

Graph 4.2. The Map of Potential Children Social Exclusion: Case of IFLS East


Using the generated dataset, the final result showing up the result from table 4.6.

Table 4.6. Social Exclusion Rates and Number, by Province Province Code Social Exclusion(%) Social Exclusion Numbers Observation

Nusa Tenggara Timur 53 7.06 48 680Kalimantan Timur 64 3.57 16 448Sulawesi Tenggara 74 7.05 49 695Maluku 81 6.43 57 886Maluku Utara 82 6.17 51 827Papua Barat

91 4.78 33 691Papua 94 4.21 25 594


Based on 9 drivers of social exclusion (household head education, living under poorhousehold, unemployed household head, living under victim of conflict / crimecondition, not ideal dwelling, children with disability condition, non-social

participation household, non-voter household, and non-networked household in theterm of having phone, TV and internet access), author defined the children as havingthe potential of being socially excluded if they have more than 5 criterias of driver.Table 4.6 explain the prevalence and number of children social exclusion in the

provincial level. Surprisingly, NTT and Sulawesi Tenggara provide the highest rateson children social exclusion, with more than 7% from its children population are



potentially excluded socially. The lowest number is on Kalimantan Timur, as weknow that Kalimantan is obviously not as remote as the other eastern part of thesurvey.

Resume and Policy Implication

In the case of Eastern Indonesia, our findings using logistic model, 46.9% probabilityof getting socially excluded for children with characteristics of: living in the poor

household, unsocially networked family, unfitted dwelling / housing condition,

and lower education household head (intergenerational poverty and social

exclusion). As for the PCA weighting result, household head education and social

network / community and social participatory weighted more than other factorsderiving the social exclusion for children. Direct policy aiming toward povertyreducation and education could lower the probability of getting socially excluded forchildren.

As a conclusion, the SEU report proven to have the same direction as the drivers ofsocial exclusion in the eastern part of Indonesia, the indicators positively correlatedwith social, eventough the magnitude of probability would be vary (Bradshaw, 2004).From those results, we emphasize that the social exclusion reducation is in the samedirection with poverty reduction program. Policies related with infrastructure andinformation/communication development is needed, yet multidimensional approachestoward poverty reduction could be a direct solution to decrease the probability ofgetting socially excluded for children, in the short-term. To emphasize, the socialsecurity and social assistance is created to open the opportunity of reducing the

poverty and social exclusion.

To be specific, 3 Clusters of Policy toward development of lagging behind region andrelated institution, such as: Kementerian Pembangunan Daerah Tertinggal,Kementrian Sosial, and TNP2K (National Team for Accelerating Poverty Reduction)could focused on the first (PKH, Jamkesmas, Raskin, BSM, etc - Direct aid) and thirdCluster (KUR, PNPM, etc - Community based aid) in order to reduce the poverty inthe Eastern region of Indonesia.

1. The community participation program s are needed in order to liven up thesocial participatory in the 7 region of IFLS East, especially the one that havinghigher rates of social exclusion, such as: Nusa Tenggara Timur, Sulawesi

Tenggara, Both Maluku and Maluku Utara, also to be sceptical includingPapua and West Papua in the program. Kementrian Sosial and TNP2K Cluster2 and 3 could implement some community based program in those area,

KUBE (Kelompok Usaha Bersama) and KUR (Kredit Usaha Rakyat)

which are based on entepreneurship and community partnership

hopefully would reduce both poverty, unemployement, and social

exclusion. 2. Conditional Cash Transfer program, PKH (Program Keluarga Harapan)

would provide both health and educational tuition for poor and eligible

household. This program could lower the high number of low

intergenational education, and prevent the children to be uneducated

enough (could achieve 9 years of study) .



3. Direct aid on education (BSM - Bantuan Siswa Miskin), and Jamkesmas /Jamkesda (Health Insurance for poor), and also basic necessity program likeRaskin (Rice for Poor) would provide short-run solution to reduce the loweducation, low health condition, and provide basic necessity for the householdto be healthy. Author emphasize that BSM should be a priority program,

in order to reduce the intergenerational low education attainment, andalso provide 9 years basic education to the children.

4. Kementerian Pembangunan Daerah Tertinggal and Kementrian Teknologi danInformasi could provide some access to increase the networking condition inmost remote area in the eastern Indonesia. Technological literacy and public

facilities (healthcare, road, school, etc) would be needed in order to

reduce both poverty and social exclusion.

Innovative programs from government or non-government entities should besupported, especially the one that related to social exclusion reducation. As asuggestion, author is trying imply that Indonesia need a Social Exclusion Unit, or

Special Taskforce of Social Inclusion Program in order to reveal its true potential byextending the funcionality of the programs related to social exclusion, specifically tothe children social exclusion. This paper is far from perfect, so future study would beneeded in order to perfecting the social exclusion theme and related.

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SOCIAL EXCLUSION,CHILDREN AND POVERTY: THE CASE OF EASTERN INDONESIA 2012