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Short-term risk experience of involuntary resettled households in the Philippines and Indonesia
Habitat International 41 (2014) 165e175
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Short-term risk experience of involuntary resettled households in the Philippines and Indonesia Melissa Quetulio-Navarra a, *, Anke Niehof a, Hilje Van der Horst b, Wander van der Vaart c a
Sociology of Consumption and Households Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, The Netherlands Wageningen University, The Netherlands c Social Research Methodology, University of Humanistic Studies, Kromme Nieuwegracht 29, 3512 HD Utrecht, The Netherlands b
a b s t r a c t Keywords: Involuntary resettlement IRR model Resettlement risks Resettlement in the Philippines Resettlement in Indonesia
Involuntary resettlement often impoverishes the displaced households. Cernea argued that impoverishment can be avoided with his Involuntary Risks and Reconstruction Model (IRR). The IRR Model has been widely utilized in resettlement studies and identifies nine interlinked potential risks inherent to displacement. Nonetheless, in assessing the risks as well as the effectiveness of interventions, most attention is directed at the institutional context. This paper argues that factors that mitigate risks of impoverishment are much more wide ranging. It covers the period of a year before and a year after resettlement. It investigates the manifestation of eight risks brought about by involuntary resettlement episodes in the Philippines and Indonesia as well as the factors that increased or decreased them. The findings in the study of two different resettlement contexts show the multi-dimensionality of resettlement risk causes. The study furthermore shows that institutional context alone is not enough to explain the risk outcome. Culture, physical location, individual and household characteristics should be factored in during the examination of impoverishment risks. Ó 2013 Elsevier Ltd. All rights reserved.
Introduction Resettlement studies are in agreement that the displacement of impoverished families pushes them into worsening poverty situations (Cernea, 1985; Cernea & McDowell, 2000). Yet, involuntary resettlement still takes place rampantly. A decade-long review of the World Bank’s development projects entailing population movement revealed that 90e100 million individuals were reported to have entered the cycle of involuntary resettlement (MacDowell, 1996). Estimates reveal that approximately 10 million people per year enter the cycle of involuntary displacement and relocation due to dam- and transportation-related development programs alone (Cernea & McDowell, 2000). In the latest Indonesian ‘Centre on Housing Rights and Eviction Report’, more than 12,000 people were reportedly evicted in July and August 2008 to give way to the “green space” land reclamation project (COHRE, 2008). In the Philippines, 59,462 households relocated in the period 2001e2006 (HUDCC, 2008) due to various
* Corresponding author. Tel.: þ31(0)619 15 98 63. E-mail addresses: [email protected], [email protected] (M. QuetulioNavarra), [email protected] (A. Niehof), [email protected] (H. Van der Horst), [email protected] (W. van der Vaart). 0197-3975/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.habitatint.2013.07.013
infrastructure projects such as construction of railroads, cleaning up of waterways, road widening, etc. Though more recent data are lacking, there is no evidence that the pace of displacement is slowing down. Despite the evidence of negative effects of displacement on households’ livelihoods, the governments of the Philippines and Indonesia view involuntary resettlement as a development opportunity for both the poor resettlers and the public. It is believed to stimulate regional development, economic development, employment opportunities, and poverty alleviation, among others (Arndt & Sundrum, 1977; NEDA, 2011; WB, 2012). The Impoverishment, Risks and Reconstruction (IRR) model (Cernea, 2000), which identifies nine interlinked potential risks inherent to displacement, has been widely utilized in resettlement studies and projects. It is an analytical guide in mapping out the impoverishment risks incurred by involuntary displacement (Bang & Fewa, 2012; Cernea & Schmidt-Soltau, 2006; Dhakal, Nelson, & Smith, 2011; Muggah, 2000; Price, 2009) as well as a basis in generating an anti-poverty approach (Hong, Singh, & Ramic, 2009; Wilmsen, Webber, & Yuefang, 2011b; Wilmsen, Webber, & Yuefang, 2011a). In assessing the risks as well as the effectiveness of interventions, most attention is directed at the actions and frameworks provided by institutional actors. While institutional actors
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are important, the factors that mitigate risks of impoverishment are much more wide ranging. In addition to the institutional context, also cultural factors can be crucial. Furthermore, the IRR model does not adequately explain the differences across households. Household characteristics may cause great diversity in poverty outcomes, which are not explained when looking at the level of communities. In previous studies, differentiation of poverty risks in households was made by extrapolating from already disadvantageous poverty profile of the households. In other words, poverty before resettlement led to a higher risk of poverty after resettlement. In this article we evidence that such reasoning does not always hold true and that other household characteristics need to be taken into account. Through a comparative study of risks brought about by involuntary resettlement in the Philippines and Indonesia, at both household and community level this study sheds light on a wider range of factors that mitigate poverty at the level of communities and at the level of households during the first year of resettlement. If we understand the wider range of factors that mitigate poverty risks, as well as the underlying dynamics, we are in a better position to gear policies towards preventing impoverishment after the resettlement. Furthermore, the analytical strength of IRR model can be greatly improved with such knowledge. The present study uses the IRR lens to investigate the nature and magnitude of “risks” experienced by poor households a year after the displacement and resettlement episodes in two different cases, one in the Philippines and one in Indonesia, and tries to identify the factors (individual and household related characteristics and institutional interventions) that are instrumental in alleviating the resettlement impact and raise the resettlement “risk level” in each country. Moreover, the research looks into the factors that explain the differences in the risk experience between the two sites.
Approaching involuntary displacement and resettlement The most widely-adopted approach in resettling involuntary displaced populations is Cernea’s (2000) Impoverishment, Risks and Reconstruction (IRR) Model. The model highlights the intrinsic risks for impoverishment through displacement as well as the ways to alleviate the risks. The IRR model was conceptualized and developed in 1996, in a series of studies. Based on empirical findings, Cernea (2000) has identified the following nine interlinked potential risks that are inherent to displacement: 1. 2. 3. 4. 5. 6. 7. 8. 9.
Landlessness Joblessness Homelessness Marginalization Food insecurity Increased morbidity and mortality Loss of access to common property Social disarticulation Educational loss
Although these risks are inherent in a resettlement episode, the intensity of their manifestation can vary among individuals and households, and can be site-specific (Cernea & McDowell, 2000). Forced evictions affect invariably the poorest and most disadvantaged groups in any displacement context (Leckie, 1994; Stanley, 2000), while resettled individuals who have social, educational, and economic advantages can cope with and recover from the debilitating effects of the displacement faster than others without such benefits (Parasuraman & Cernea, 1999).
Parasuraman and Cernea (1999) stressed that the outcome of the resettlement is significantly influenced by the institutions’ approach to the displacement and resettlement activity through their resettlement policies and programs. In a dam-related resettlement in China, the “inputs” from the government resulted into maintaining or raising the income of the Chinese resettlers (McDonald, Webber, & Duan, 2008). The opposite transpired in a development-induced resettlement case in Indonesia (Nakayama, Yoshida, & Gunawan, 1999), in Xiaolangdi, China (Webber & McDonald, 2004), and in the Philippines (Quetulio-Navarra, 2007) where the inadequacy in the interventions (income restoration programs, resettlement policies, strategies, etc.) of the project implementers led to failure. Manifestation of the risks in the study Although Cernea (2000) cited nine risks in his model, in this paper only eight risks will be dealt with. We did not investigate the risk of “loss of access to common property” and the risk of “increased morbidity and mortality” is reduced to morbidity risk only. The manifestation of these eight risks (nature and extent) in the Philippines and Indonesia was examined by first establishing indicators for the eight risks followed by designing a risk index based on these identified indicators. Data on a year before the resettlement and a year after the resettlement were compared and a score system was applied: 1 if there is a positive change (less risk), 0 if there is no change, and 1 if the change is negative (more risk). Eventually all the scores (for each risk) were summed up to get the “total risk score”. Each of these eight risks was measured using the established indicators below: Landlessness. Landlessness was analysed by looking at the landownership situation of the households before and after the resettlement. Homelessness. The homelessness risk was looked into using housing-related data (housing ownership, house materials, floor size, number of bedrooms) before and after the resettlement. Joblessness. Based on the preceding qualitative investigation, the magnitude of joblessness that affected the household was determined based on the data regarding the employment status of only the household heads before and after the relocation. Marginalization. The characteristics and extent of marginalization were determined by utilizing data on household income, number of factors that divide the community, number of social services the households did not have or had limited access to, reasons for denied social services, and whether the resettlement site was perceived as peaceful and harmonious. Food insecurity. Level of food insecurity was looked into by comparing the before and after resettlement data on the percentage of low household income spent on food. Increased morbidity. Data on the number of adults and kids who got ill before and after the resettlement were compared along with the data on the number of basic services available in the previous and present communities. Social disarticulation. Social disarticulation in the two communities was measured by examining reports on the number of support ties, memberships in organizations, and relationship of the community with the local government, central government, NGOs and international organizations, and availability of public places before and after the involuntary displacement.
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Educational loss. The gravity of loss of education in the households after the relocation was based on the reports on the number of kids who stopped going to school after the resettlement. Methodological design In the Philippines, a household survey was undertaken from April to June 2011 in Kasiglahan Village 1 (KV1). KV1 is a government-managed urban resettlement community situated in Rizal Province, municipality of Rodriguez (see Fig. 1). It has a total land area of 85.70 ha with 9915 housing structures of 32 m2 each. The resettlement community was built for poor
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households that were evicted due to development projects, natural and man-made disasters. Resettlement of families started in 1999. In Indonesia, a survey was conducted from April to June 2012 in a rural community in Bantarpanjang Translok (BT). Bantarpanjang Translok is also a government-managed resettlement community situated in the district of Cilacap, in Central Java (see Fig. 2). Bantarpanjang Translok has a total land area of 3.1 ha with 97 housing structures of around 45 m2 each. The resettlement community was built for poor households that were displaced by landslides in nearby communities. It has been accommodating households since 2001.
Fig. 1. Philippine resettlement site.
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Fig. 2. Indonesia resettlement site.
Sampling in the Philippines and Indonesia The 150 respondents in the Philippines were chosen through random sampling from a sampling frame of 6144 households who are either ‘original house and lot owners’ or ‘rights buyers’ (bought the house and lot rights from the original owner). The Project Office did not have the exact numbers of these two types of residents, but qualitative data yield that around 30e40% of the 6144 households are ‘rights buyers’. The 150 respondents are all original owners. In Indonesia, all 76 legitimate beneficiaries of the resettlement project in the Indonesia community were interviewed. Similar data gathering methods (quantitative and qualitative) were applied in both sites. A household survey was conducted that included a household composition sheet and a tailored-calendar tool. Qualitative methods included key informant interviews,
group interviews, participant and non-participant observations, indepth interviews and focus group discussions. The calendar tool is a graphical grid with domains that served as a visual aid as well as a data entry tool for both the interviewer and the respondent. The calendar collected histories (for this paper, only the data on a year before and a year after the resettlement) pertaining to the following six major life domains of the respondent: 1. 2. 3. 4. 5. 6.
Respondent’s profile Children’s information Household-related information Physical features of the community Respondent’s social engagement Respondent’s perception on the community
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Analysis of quantitative and qualitative data The quantitative data were entered into Excel and were analysed using SPSS version 19. The data on a year before and a year after resettlement were utilized and risk indexing was done for each potential risk. Multiple linear regression analysis was conducted on identified dependent and independent variables (see Table A.1). The dependent variable is the total risk score of a household and the independent variables are grouped into twothose that reflect the respondent’s socio-economic and demographic profile as well as the characteristics of his or her household, and the institutional context variables. The institutional context variables pertain to the factors that represent the interventions and influence of the government both local and central government level to the resettlement community. The qualitative data were recorded, transcribed and content analysis was applied. They were used to validate and complement the survey data.
Field results Involuntary displacees and the resettlement profile In the Philippines, 68% of the respondents were female and 32% male (Table B.1). More than half of the respondents were within the age bracket of 25e45, the ages ranging between 20 and 85. Only 27% of the respondents reached college or studied in technical school after high school, 47% finished or reached high school, and 25% only studied until elementary level. The average household size was 5.58 and the average yearly household income is Php 88,103.00 (US$2065.72). Thirty-three percent were housewives or husbands staying at home, 22% were labourers, 16% had a business in the community, while another 16% were said to be unemployed. The resettlers in KV1 were victims of development projects, natural disasters (like flooding along Pasig river), man-made disaster (garbage slide in Payatas) and wide-scale fire. The resettlement started in 1999. The resettlement program was a component of the Pasig River Environmental Management and Rehabilitation Sector Development Program funded by the Asian Development Bank amounting to US$100 million USD (PRRC, 2006). The program aimed to rehabilitate the Pasig River and part of it was relocating the poor families living along the river. The National Housing Authority, a government agency mandated to implement socialized housing program in the Philippines, undertook the resettlement program. The resettlement package includes a house and lot in a resettlement community that has basic services and primary public facilities. The house and lot is payable in 20 years at Php 250 (US$5.9) per month. The package was supposed to be prepared in accordance with the Urban Development Housing Act, which provides a systematic program for land use planning towards the allocation of lands for social housing for the underprivileged and homeless city dwellers. It covers a wide range of provisions the resettlers. In Indonesia, the majority of the respondents in Indonesia was male (92.1%) and 7.1% female (Table C.1). Most respondents (64.6%) belonged to the age bracket of 41e60 years old. High school was the highest education level reported by the respondents, while 71.1% of the respondents had only elementary-level education. More than half of the respondents were either doing elementary jobs (31.6%) or were farmers (23.7%). The average household size was 3.96 and the average household income was IDR 10,975,006.58 (US$1141.45). All were Muslims and were ethnic Sundanese. The community is divided into blocks called RT (rukun tetangga). Although the landslide took place in 2000, it took a year before the
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housing structures were in place. All households were victims of landslides. Their occupancy in Translok (the resettlement site) is in a lease-like agreement. The agencies primarily involved in the resettlement project were the Cilacap Provincial Government and the Department of Transmigration of Cilacap in particular. At national level the Ministry of Transmigration is a government agency tasked to undertake transmigration programs that aim at decongesting the densely populated area of Central Java. Mostly, but not in this case, families are relocated to other islands. Documents pertaining to the Bantarpanjang Translok project were difficult to get. According to the officials in Cilacap they were either lost or misplaced during the times that they transferred offices. An in-depth interview with a Transmigration officer in Cilacap who was directly involved in the supervision of the project, revealed that there was actually no budget earmarked for the Translok families affected by the 2000 landslide. However, given the urgency of the case, the Department reallocated some of their funds for the regular transmigration activities to the development of the Bantarpanjang Translok community. Subsequently, negotiations started about the land where the new community would be built. The land was originally under the administration of Perhutani, an Indonesian state-owned enterprises that has the duty and authority to administer the planning, management, exploitation and protection of forests. In order to acquire the land for the resettlement project, the Cilacap Provincial Government and Perhutani agreed to enter into a land switching contract in which the Cilacap Provincial Government compensates for the Perhutani land with a property twice the size of the target land. The land and the houses are still owned by the provincial government and there is no option for the resettlers to acquire it. Up until now, the Indonesian government still has no national policy for resettlement. The Transmigration Ministry follows the Keppres 55/1993 in the transmigration of families but the law only covers the aspect of land acquisition, not the provision of basic services and public facilities (Zaman, 2002). Hence, resettlement projects in Indonesia have been handled on an “ad hoc” basis. Involuntary resettlement risk index Table D.1 presents the resettlement risk indexes in the Philippines and Indonesia. Land ownership As shown on the mean score (1.00) for land ownership risk in Table D.1, KV1 resettlers did not experience any risk in losing land, rather the opposite happened. Before the involuntary resettlement, the respondents did not own the land where they were occupying (legally or illegally), which qualified them for the socialized housing program in the Philippines. After the relocation, each household was given a house and lot by virtue of an Award Certificate that is payable in 20 years at Php 250 (US$5.9) per month. After they have paid the full amount, they will be given a land title. The opposite was the case for the Indonesian respondents. Having owned their land, at least a small plot for their house and for farming, after the landslide the Translok relocatees became landless (mean risk score of 1.00). The resettlement project funds could not afford to replace their lost lands and they cannot purchase the land where they are presently residing. Series of negotiations between the Cilacap Transmigration Department and the Provincial Government did not result in a positive outcome. One leader in Translok said:
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“We have been working out with the RW leader and the village head for the possibility of acquiring the land where we are staying right now. But nothing is happening. This is really worse than when we were on the mountains. Before we owned the land where our houses were built and also had land for farming. But the landslide swallowed everything. We are scared now because anytime the Bupati (head of the district) wants to take back the land where we’re staying now, we have to leave”. (Interview with a male community leader, Translok Indonesia, June 2012)
Housing situation The Philippine resettled households are also in a better-off situation when it comes to housing, as evidenced by 2.03 risk score mean. Before the displacement, the households were renters, living with parents or relatives, or living in shanties under the bridge, on sidewalks, or along the railroad tracks. All of them, did not own a house but after their transfer to KV1 they were awarded with a concrete house structure with a toilet and gained security of tenure. When asked whether they were happy with their housing situation, one respondent said: “Now I don’t worry about strong rain or even storm because we now live in a house that has stone walls and strong roof. Previously, I was scared every time there was a storm or even a strong rain with strong wind because our roof and walls were made of very light materials and they could be just blown away at any moment”. (Survey interview with a female respondent in KV1 Plains, Philippines, June 2011) In contrast, the Indonesian resettlers are in a worse housing situation (risk score mean of 1.75). Before, they owned their houses, but when they transferred to Translok they were only given a “permission certificate” from the village head which gives them the right to use the housing structure but not own it. If before their houses were made of concrete, after the landslide they transferred to a wooden house with no divisions for bedroom and kitchen and no provision for toilet inside the house. Eight public toilets were built for the resettlers. Employment situation Employment risk score in the Philippines is very low (0.01), with only 5.4% of the respondents becoming jobless after the transfer. While most were able to keep their jobs or source of livelihood by re-establishing their small business like a small store or junk shop in KV1, some could not keep their jobs. Those who experienced a worse-off condition in terms of employment attributed this to the distance (50e70 km) of KV1 from their previous job, unrecoverable loss brought about by the garbage slide in Payatas, and the unavailability of employment opportunities in the new site. The Indonesian resettlers also scored very low on this risk (0.04), with 5.3% of the household heads losing their jobs. Since most of the resettlers are farm labourers it was not hard for them to find work in the rice fields after they transferred. Some even revealed that the resettlement got them closer to the rice fields; if previously they would walk for 2e3 h to reach their rice fields, now they only had to walk for 1 h from Translok. Some of them were also able to forge a special arrangement with Perhutani that allows them to plant some crops for own household consumption
or for selling on Perhutani land in the forest at a very minimal payment. Marginalization In aggregate, the KV1 relocatees in the Philippines are a little better off (0.03 mean risk score) after the transfer in terms of their level of economic, social, and psychological marginalization. The Indonesian Translok relocatees in Indonesia also scored positively in this respect (mean risk score of 0.28). However, in terms of access to social services during their first year in Translok, almost everybody expressed the same sentiments regarding their suffering from the absence or very poor basic services inside the resettlement site and the transition to public toilet system, one household head uttered: “On the mountains we did not have problems with the toilet. We just needed to find our own “toilet space” along the river and we didn’t need to keep it clean. When we came here we had a new system of using the toilet- we needed to line up, wait for our turn to use the toilet, and keep it clean which is difficult because you share the toilets with everybody here”. (Survey interview with a male household head in RT 2, Translok, Indonesia, May 2011) Food security The food security risk index for the Philippine case registers a very low score of 0.15 (mean), with 24.7% of the respondents adversely affected and 9.3% being better off. The risk score reflects the increase in the proportion of household income spent on food for the household, which in KV1 may be attributed to higher price of food items due to an increase in transactions costs coupled with the reported growth in number of children in the household after the resettlement. The food security situation of the Indonesian resettlers improved a little after the relocation as evidenced by the food security risk score of 0.09 (mean), with 11.8% of the households at “risk” and 9.3% of households doing better. While the resettlers lost their gardens to the landslide, after transferring to Translok some were able to arrange (with Perhutani or friends and relatives) a new garden lot where they could plant their crops, such as cassava, peanuts, and eggplants, again. Others who also had an increase in household income found food in Translok more accessible because of the stores around and the peddlers who come to the place to sell food. Morbidity Based on the morbidity index, the Philippine households are slightly at risk (0.53 risk score). However, this score is largely due to the reduction in the number of basic services provided in the resettlement site and does not reflect a higher incidence of illness in the household. Although the houses were already constructed when the families came to KV1, the basic services were provided only gradually, depending on the release of the budget from the central government. In the Indonesian case resettlement did not increase morbidity at all (mean risk score 0.33). Social articulation The KV1 residents did not suffer from social disarticulation risk after the move as shown by a 0.39 mean score. Similarly, the Translok residents seemed to have improved in terms of social
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articulation after the resettlement (mean score 1.25). A respondent remarked during an informal conversations:
transportation expenses took toll on their food and health budget. One respondent said:
“.one of the good things I like in Translok is that I can now borrow money from a money lender to buy some goods that I like or buy some stuff from a seller and pay them later with or without additional interest. We did not have this kind of arrangement before on the mountains”.
“Life was so hard here on our first year. We lost our source of income and my family had to rely on food rations and relief goods such as rice, noodle soup, canned sardines from the government, church, and NGOs. We would line up all the time for the food distribution and stock up food rations in our house which we will eat little by little.”
(Participant observation with women farm labourers in Translok, Indonesia, June 2012)
Education A low mean risk score (0.01) was found in the Philippine resettlement case for educational loss. In-depth interviews and desk research revealed that when the families relocated to the site, there were no schools. They had to send their children to schools located in nearby communities. However, they found this very inconvenient and costly (transport). They lodged strong complaints at the Project Management Office (National Housing Authority). Several months later (in 2000) some unoccupied housing units were converted into classrooms. In the Indonesian case the mean score is also low (0.07). The small number of children who stopped going to school may be due to the fact that the day care centre was only built much later. Total post resettlement risks As can be gleaned from Table D.1, the Philippine households are in a better-off condition (mean risk score of 2.75) compared with the Indonesian resettlers (mean risk score of 0.90). This is due to the fact that KV1 households gained house and lot assets after the displacement, while it is the opposite in the Indonesia case. In order to ascertain which among these dimensions yield the results shown in Table D.1, the total risk score was regressed against a number of variables that are grouped into three models presented in Table A.1. Regression results are shown in Table D.2 for the Indonesia case and Table E.1 for the Translok resettlement. Table D.2 shows that in Model 1 only the variables husband’s employment status and the number of membership in organizations can account for the level of involuntary resettlement risks experienced by the households in KV1 with an R-square of 12.8 percent. Model 2 (R2 ¼ 28%) which regressed the institutional context variables with the total risk scores yield four significant variables e the number of public places in KV1, community relationships with the local government, NGOs, and international organizations. However, the combination of these two dimensions (model 1 and 2 variables) explains best (at 38.9%) the variance in the postresettlement risks in KV1. Three of the variables that reflect socio-demographic and household characteristics are significant. Education of the household head, memberships in organizations after the resettlement, and the husband’s employment status, are the strongest predictor for risks. Surprisingly, in this resettlement case education is negatively related to risk, which challenges the usual claim of resettlement studies that “education” alleviates the harmful effects of involuntary resettlement. The result tells us that the higher the education level of the household head has the higher risks the household will face during resettlement. Compared to the lower educated, more educated household heads lost more after resettlement. Previously they had better jobs, better salaries and, therefore, could afford to rent a better house and had access to basic services. Some were able to keep their jobs in the city for a while, but the rent or the huge
(In-depth interview with a mother in KV1 Plains, Philippines, May 2011) An unemployed husband and a decrease of membership in organizations (voluntary and involuntary) increase the vulnerability level of a household after relocation. While the husband’s unemployment is self-explanatory, membership in organizations is about access to resources which may cushion an individual against risks. Six institutional context variables turn out to influence the households’ susceptibility to the risks inherent to involuntary resettlement. These are: living in Plains, number of public places in KV1, the community’s relationship with the local government and international organization (all reducing risk) and the community’s relationship with NGOs and church (both, surprisingly enhancing risk). Kasiglahan Village 1 is physically divided into two parts, the Plains and the Suburban. The Plains was the part developed first in the resettlement project, where the first groups of resettlers transferred. Although the Plains is more developed now than the Suburban, the situation was dismal during the early years, physically as well as socially. A community leader from Plains shared: “They (referring to NHA) resettled us all here to die. Housing was terrible, basic services were very minimal, and no jobs available. I don’t think they are really concerned with our welfare”. (In depth-interview with a KV1 community male leader, KV1 Plains, May 2011) The outcome on the number of public places (not the basic services) as influencing the level of risk a household might experience underscores the social and economic value of public places to the KV1 relocatees during their first year in the community. Public places like sidewalks, and markets functioned as venues for meeting new friends and acquaintances and as physical spaces for economic activities (e.g. in the market place). The risk level outcome in KV1 was likewise a result of the nature of the community relationship with the local, NGOs, international organizations and church. However, while a better relationship between the community and the local government as well as with the international organizations would mitigate the risks brought about by the resettlement, it is entirely the opposite in the case of relationships with the NGOs and church. A project manager admitted that basic services and basic facilities in KV1 were built late due to budgetary constraints. Households complained nonstop about the nature of service delivery in KV1. The NGOs intervened, but their lobbying efforts only exacerbated the social issues in KV1, eventually negatively influencing the cooperation of the households. Similarly, when the community or the households forge a good relationship with the church, most of the time this results in a patroneclient like relationship that limits connecting with other people who do not share the same religion or faith, thereby preventing them from expanding their social networks that are a potential well of resources. Table E.1 shows the regression results for the risk score of the Indonesian households. Three variables turned out to be significant
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in Model 1 with an R square of 18.60 percent: the education of the household head (by far with the strongest impact), the husband’s employment status, and membership in organizations. In Model 2 (R2 ¼ 19.50%), the variables number of public places and the relationship of the community with the local government turned out as predictors of risk level in Translok. Similar with the KV1 regression results, Model 3 provides the best explanation for the resettlement risk score in Translok (R2 ¼ 35.9%). Significant variables are education, husband’s employment status, and relationship with the local government. Like in the KV1 case, the higher the level of education of the household head the higher the resettlement risks score will be and the same explanation applies to this outcome. A good relationship between the Translok community and the local government decreases the risks since social programs that can mitigate impoverishment in the locality usually emanate from the level of the local government.
Discussion and conclusion This study about two different resettlement contexts in Southeast Asia presents robust evidence on the multi-dimensionality of resettlement risk causes. The study shows that, while the institutional context is important, also culture, physical location, and individual and household characteristics add to the explanation of risks experience during first year in the resettlement. The resettlement program policy of the country concerned proved to greatly influence the manifestation of eight risk factors identified by Cernea (2000). The resettlement program showed large differences between the two sites. The Philippine KV1 resettlers gained assets in the form of housing and land because there is a national policy on resettlement and socialized housing in the Philippines that protects right to housing and aims for humane relocation. The presence of the national policy made the KV1 residents aware of their rights, as apparent in the case of their lobbying for building a school in the site. On the other hand, the Indonesian Translok relocatees lost their houses and land to landslides and were never compensated for their loss due to the absence of a country policy on resettling natural disaster victims. Hence, they had no grounds to demand basic facilities and just compensations. While differences in level of educational level and age between the Philippine and Indonesian households could explain these dissimilar situations, the multivariate analyses showed that in both sites there is no significant impact of the respondents’ age, gender, income and household size on the total risk score. Both the Philippine and Indonesian households experienced four risks with two risks emerging in both cases (employment and education). Nonetheless, it is apparent that the risks manifested differently in the two cases. The role of the physical environment both in the previous and the resettlement residence, along with the households’ cultural background, influenced the emergence of risks related to food security and morbidity. The Indonesian Translok relocatees were able to prevent food insecurity after the resettlement because they continued their practice of planting crops in the mountain area through new arrangements with Perhutani, a relative, or a friend. It also helped that most men quickly found jobs and continued to work as farm labourers in the nearby rice fields after the transfer. In the Philippine KV1 case, however, households were unable to avoid food insecurity during their first year in the site. The KV1 resettlers were not accustomed to growing their own crops and had no access to land for farming. They relied on their income from nonagricultural activities to secure high-priced food items from peddlers coming to the site.
The multivariate analyses showed that in both the Philippine and Indonesian case the individual and household variables add substantively to the institutional ones in explaining risk. Moreover, in spite of the large differences, in both cases the same sociodemographic variables are influential (employment, membership in organizations and the unexpected negative impact of education) and their weight as relative to the institutional variables is about similar (12e18% versus 39e36% R-square). Regarding the institutional factors, the outcomes did show important differences between the Philippine and Indonesian case. Most consist of the absence of significant relationships in the Indonesian case. The findings also show that irrespective of the level of significance, the sign of all effects is similar (positive or negative) in both cases, except for the influence of NGOs and church which, unexpectedly, enhanced risk in the Philippines. The results of the research convey a new perspective on the concept of vulnerability in a forced resettlement context. Unlike the usual finding that education shields a resettler from risk, this study yielded a contradictory result. This may be due to the fact that a more educated household head tends to lose more during displacement and that resettlement in an impoverished community actually misplaces him or her in an employment pool, thereby eventually aggravating the risk level in other areas. In the Philippines, a good relationship with the church and NGOs during the first year in the resettlement site generated negative effects. We can infer from this that in an urban resettlement context like the Philippines, wherein you have a national policy on resettlement and community leaders who are aware of their rights, the intervention of the NGOs and perhaps the church may result in delays in service delivery and trigger disagreements among the project stakeholders. What should be nurtured first is the community relationship with the host local government (negative regression weight) who is in the best position to deliver the social services and interventions needed by the households and the project managers. This study demonstrates that features of the resettlement program, the institutional context, and individuals and households together cause and mitigate risk during the first year of resettlement. Taking these factors together proved to add substantially to the strength of the IRR model, even when applied to two quite divergent resettlement populations.
Acknowledgement The authors are grateful to the Netherlands Fellowship Programme that funded the fieldwork in the Philippines and the Neys Van Hoogstraten Foundation that funded the fieldwork in Indonesia. Also, to the households and leaders in Kasiglahan Village Community and the Bantarpanjang Translok for their participation in the research, Engr. Abeth Matipo of the National Housing Authority, Mr. Candra Musi and Mr. Narso of Perum Perhutani for the logistical assistance, Dr. Ana Marie Karaos and Ms. Vangie Serrano of Action Group for the assistance at the community level in the Philippines, Dr. Sri Sunarti Purwaningsih and Dr. Herry Yogoswara of Indonesian Institute for Sciences (LIPI), Dr. Tyas Wulan and Dr. Suliyanto Jayakrama of Universitas Jenderal Soedirman, Yayasan Kusuma Buana led by Dr. Firman Lubis, Presidential Commission for the Urban Poor led by Chairman Hernani Panganiban, Dennis Sorino and Dulce Joy Sorino for database design and data processing, the Philippine Research Team led by Elizabeth Avila, the Indonesian Research Team led by Fanny Dwipoyanthi, Eden Frunt, Victor Allan C. Ilagan, Alicia S. Diaz, Martina Robles, Daniel Vincent, and in WUR Dr. Hester Moerbeek and Feng Zhao for statistical analysis.
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Appendices
Table B.1 (continued )
Table A.1 Dependent and independent variables. Dependent variable
Explanation
Total risk score
Sum of all scores on the eight different areas of potential risks
Independent Variables Demographic, socio-economic, & household characteristics Age Gender Dummy variable, female ¼ 1 (Philippines) Dummy variable, male ¼ 1 (Indonesia) (after resettlement) Husband’s employment status Dummy variable, where employed ¼ 1 Income (after resettlement) Household size Membership in organizations Number of support ties Institutional context (after resettlement) Living in plains (KV1) Dummy variable, Plains ¼ 1 Living in RT1 and RT 3 (Translok) Number of basic services Number of public places Number of denied social services Rate of community relationship Dummy variable, Very low ¼ 1; with: Low ¼ 2; Moderate ¼ 3; High ¼ 4; Very High ¼ 5 Central government Local government NGOs International organizations Church/mosque
Table B.1 Descriptive statistics of respondents in the Philippines N ¼ 150. Variables Gender Male Female Age 25e35 36e45 45e55 56e65 66 e more Civil status Single Married Separated Widowed Co-habiting Education Elementary school or less High school More than high school Occupation Entrepreneur Labourer Govt/private employee Housewife Retired Unemployed Household size 1e3 4e6
173
Percentage
Frequency
32.0 68.0
48 102
0.2 0.3 0.3 0.1 0.1
30 47 43 21 9
5.3 75.3 3.3 6.7 9.3
8 113 5 10 14
0.3 0.5 0.3
37 71 41
16.0 22.0 14.0 32.7 2.0 16.0
24 33 21 49 3 24
18.7 54.0
28 81
Variables
Percentage
7e9 10 or more Household income quartile <48,000 >¼48,000 and<90,000 >¼90,000 and<130,656 >¼130,656 Present address Plains Suburban
22.0 5.3
Frequency 33 8
25.0 25.0 25.0 25.0
36 35 36 35
60.7 39.3
91 59
Table C.1 Descriptive statistics of respondents in Indonesia N ¼ 76. Variables Gender Male Female Age 20e30 31e40 41e50 51e60 61 e more Civil status Single Married Widowed Education Never been to school Elementary school or less Junior high school High school Occupation Farmer Entrepreneur Elementary occupation Govt/private employee Housewife Others Household size 1e3 4e6 7e9 Household income quartile <4,560,000 >¼4,560,000 and<9,000,000 >¼9,000,000 and<14,850,000 >¼14,850,000 Present address RT 1 RT 2 RT 3 RT 5
Percentage
Frequency
92.1 7.9
70 6
4.0 23.7 34.2 30.3 7.9
3 18 26 23 6
2.6 93.4 4.0
2 71 3
4.0 71.1 19.7 5.3
3 54 15 4
23.7 13.2 31.6 7.9 5.3 15.8
18 10 24 6 4 12
34.2 63.2 2.6
26 48 2
25.0 27.6 22.4 25.0
19 21 17 19
26.3 36.8 32.9 4.0
20 28 25 3
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Table D.1 Risk index in the Philippines Philippines N ¼ 150 Indonesia N ¼ 76
Philippines
Indonesia
Philippines
Indonesia
Potential risks areas
Mean
SD
Mean
SD
Worse off%
Worse off%
Land ownership Housing situation Employment Marginalization Food security Morbidity Social Articulation Education Total risks score
1.00 2.03 0.01 0.03 0.15 0.53 0.39 0.01 2.75
0.00 1.16 0.46 1.51 0.56 1.03 2.09 0.16 3.55
1.00 1.75 0.04 0.28 0.09 0.33 1.25 0.07 0.90
0.00 1.13 0.26 1.09 0.57 0.57 1.29 0.25 2.28
0 1.3 5.4 32.7 24.7 57.3 34 2 15.3
100.0 88.2 5.3 19.7 11.8 5.3 5.3 6.6 53.9
Difference
100.0 86.9 0.1 13.0 12.9 52.0 28.7 4.6 38.6
Philippines
Indonesia
Same%
Same%
0 10.7 88 34.7 66 25.3 18.7 96.7 9.3
0.0 9.2 92.1 34.2 67.1 73.7 26.3 93.4 19.7
Difference
0.0 1.5 4.1 0.5 1.1 48.4 7.6 3.3 10.4
Philippines
Indonesia
Better off%
Better off%
100 88 6 32.6 9.3 17.3 47.2 0.07 75.3
0.0 2.6 1.3 46.1 21.1 21.1 68.4 0.0 26.3
Difference
100.0 85.4 4.7 13.5 11.8 3.8 21.2 0.1 49.0
Table D.2 Effects of socio-demographic, household characteristics and institutional context on the involuntary resettlement risks in the Philippines (pairwise deletion of missing values) N ¼ 150. MODEL 1
Age Female Education Husband’s employment status Income Household size Membership in organizations Number of support ties Living in Plains Number of basic services Number of public places Number of denied social services Rate of community relationship with Rate of community relationship with Rate of community relationship with Rate of community relationship with Rate of community relationship with R2
MODEL 2
B
SE
0.030 0.120 0.086 0.207** 0.030 0.087 0.215** 0.031
0.023 0.532 0.144 0.603 0.557 0.128 0.383 0.034
B
0.043 0.047 0.177** 0.158 0.043 0.327*** 0.317*** 0.282*** 0.106 28.0%
central government local government NGOs international organizations church 12.8%
MODEL 3 SE
0.471 0.073 0.092 0.080 0.305 0.300 0.249 0.267 0.216
B
SE
0.132 0.116 0.185** 0.215*** 0.089 0.040 0.014* 0.150 0.237*** 0.005 0.179** 0.125 0.032 0.259** 0.255*** 0.349*** 0.183** 38.9%
0.022 0.480 0.139 0.538 0.503 0.116 0.031 0.356 0.519 0.078 0.096 0.082 0.306 0.316 0.256 0.282 0.230
Significant at *p < 0.10,**p < 05 &, ***p < 0.01.
Table E.1 Effects of Socio-demographic, household characteristics and institutional context on the involuntary resettlement risks in Indonesia (pairwise deletion of missing values) N ¼ 76 MODEL 1
Age Male Education Husband’s employment status Income Household size Membership in organizations Number of support ties Living in RT1 Living in RT3 Number of basic services Number of public places Number of denied social services Rate of community relationship with Rate of community relationship with Rate of community relationship with Rate of community relationship with Rate of community relationship with R2
SE
0.045 0.069 0.262** 0.246** 0.059 0.008 0.208* 0.107
0.256 1.094 0.432 1.361 0.276 0.200 0.279 0.045
central government local government NGOs international organizations mosque
Significant at *p < 0.10,**p < 05 &, ***p < 0.01.
MODEL 2
B
18.6%
B
0.045 0.086 0.032 0.234* 0.096 0.134 0.349*** 0.215 0.071 0.021 19.5%
MODEL 3 SE
0.755 0.697 0.227 0.267 0.146 0.484 0.295 1.071 1.256 0.394
B
SE
0.101 0.016 0.247** 0.250* 0.003 0.060 0.133 0.162 0.044 0.010 0.051 0.166 0.167 0.116 0.324** 0.217 0.019 0.012 35.9%
0.274 1.201 0.451 1.454 0.347 0.216 0.301 0.051 0.782 0.715 0.236 0.281 0.168 0.524 0.296 1.053 1.316 0.391
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Muggah, H. (2000). Conflict-induced displacement and involuntary resettlement in Colombia: putting Cernea’s IRLR model to the test. Disasters, 198e216. Nakayama, M., Yoshida, T., & Gunawan, B. (1999). Compensation schemes for resettlers in Indonesian dam construction projects. Water International, 24, 348e355. NEDA. (2011). Philippine development plan 2011e2016. Pasig City: National Economic Development Authority Accessed 30.03.13 http://www.neda.gov.ph/PDP/20112016/FRONT%20MATTER.pdf. Parasuraman, S., & Cernea, M. M. (1999). The development dilemma: Displacement in India. Macmillan [etc.]. Price, S. (2009). Prologue: victims or partners? The social perspective in development-induced displacement and resettlement. The Asia Pacific Journal of Anthropology, 10, 266e282. PRRC. (2006). Adb loan nos. 1745/1746-Phi: Pasig river environmental management and rehabilitation sector development program: JanuaryeDecember 2006 report. Quezon City: Pasig River Rehabilitation Commission. Quetulio-Navarra, M. (2007). The Kamanava flood control resettlement project: As perceived by primary stakeholders (Unpublished Master’s Thesis). Japan: Graduate School of International Cooperation Studies, Kobe University. Stanley, J. (2000). Development-induced displacement and resettlement. forcedmigration.org. WB, & IEG. (2012). Indonesia transmigration program: A review of five bank-supported projects. Washington: World Bank. Webber, M., & McDonald, B. (2004). Involuntary resettlement, production and income: evidence from Xiaolangdi, PRC. World Development, 32, 673e690. Wilmsen, B., Webber, M., & Yuefang, D. (2011a). Development for whom? Rural to urban resettlement at the three Gorges Dam, China. Asian Studies Review, 35, 21e42 (22). Wilmsen, B., Webber, M., & Yuefang, D. (2011b). Involuntary rural resettlement: resources, strategies, and outcomes at the three Gorges Dam, China. The Journal of Environment & Development, 20, 355e380. Zaman, M. (2002). Resettlement and development in Indonesia. Journal of Contemporary Asia, 32, 255e266.
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Short-term risk experience of involuntary resettled households in the Philippines and Indonesia Melissa Quetulio-Navarra a, *, Anke Niehof a, Hilje Van der Horst b, Wander van der Vaart c a
Sociology of Consumption and Households Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, The Netherlands Wageningen University, The Netherlands c Social Research Methodology, University of Humanistic Studies, Kromme Nieuwegracht 29, 3512 HD Utrecht, The Netherlands b
a b s t r a c t Keywords: Involuntary resettlement IRR model Resettlement risks Resettlement in the Philippines Resettlement in Indonesia
Involuntary resettlement often impoverishes the displaced households. Cernea argued that impoverishment can be avoided with his Involuntary Risks and Reconstruction Model (IRR). The IRR Model has been widely utilized in resettlement studies and identifies nine interlinked potential risks inherent to displacement. Nonetheless, in assessing the risks as well as the effectiveness of interventions, most attention is directed at the institutional context. This paper argues that factors that mitigate risks of impoverishment are much more wide ranging. It covers the period of a year before and a year after resettlement. It investigates the manifestation of eight risks brought about by involuntary resettlement episodes in the Philippines and Indonesia as well as the factors that increased or decreased them. The findings in the study of two different resettlement contexts show the multi-dimensionality of resettlement risk causes. The study furthermore shows that institutional context alone is not enough to explain the risk outcome. Culture, physical location, individual and household characteristics should be factored in during the examination of impoverishment risks. Ó 2013 Elsevier Ltd. All rights reserved.
Introduction Resettlement studies are in agreement that the displacement of impoverished families pushes them into worsening poverty situations (Cernea, 1985; Cernea & McDowell, 2000). Yet, involuntary resettlement still takes place rampantly. A decade-long review of the World Bank’s development projects entailing population movement revealed that 90e100 million individuals were reported to have entered the cycle of involuntary resettlement (MacDowell, 1996). Estimates reveal that approximately 10 million people per year enter the cycle of involuntary displacement and relocation due to dam- and transportation-related development programs alone (Cernea & McDowell, 2000). In the latest Indonesian ‘Centre on Housing Rights and Eviction Report’, more than 12,000 people were reportedly evicted in July and August 2008 to give way to the “green space” land reclamation project (COHRE, 2008). In the Philippines, 59,462 households relocated in the period 2001e2006 (HUDCC, 2008) due to various
* Corresponding author. Tel.: þ31(0)619 15 98 63. E-mail addresses: [email protected], [email protected] (M. QuetulioNavarra), [email protected] (A. Niehof), [email protected] (H. Van der Horst), [email protected] (W. van der Vaart). 0197-3975/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.habitatint.2013.07.013
infrastructure projects such as construction of railroads, cleaning up of waterways, road widening, etc. Though more recent data are lacking, there is no evidence that the pace of displacement is slowing down. Despite the evidence of negative effects of displacement on households’ livelihoods, the governments of the Philippines and Indonesia view involuntary resettlement as a development opportunity for both the poor resettlers and the public. It is believed to stimulate regional development, economic development, employment opportunities, and poverty alleviation, among others (Arndt & Sundrum, 1977; NEDA, 2011; WB, 2012). The Impoverishment, Risks and Reconstruction (IRR) model (Cernea, 2000), which identifies nine interlinked potential risks inherent to displacement, has been widely utilized in resettlement studies and projects. It is an analytical guide in mapping out the impoverishment risks incurred by involuntary displacement (Bang & Fewa, 2012; Cernea & Schmidt-Soltau, 2006; Dhakal, Nelson, & Smith, 2011; Muggah, 2000; Price, 2009) as well as a basis in generating an anti-poverty approach (Hong, Singh, & Ramic, 2009; Wilmsen, Webber, & Yuefang, 2011b; Wilmsen, Webber, & Yuefang, 2011a). In assessing the risks as well as the effectiveness of interventions, most attention is directed at the actions and frameworks provided by institutional actors. While institutional actors
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are important, the factors that mitigate risks of impoverishment are much more wide ranging. In addition to the institutional context, also cultural factors can be crucial. Furthermore, the IRR model does not adequately explain the differences across households. Household characteristics may cause great diversity in poverty outcomes, which are not explained when looking at the level of communities. In previous studies, differentiation of poverty risks in households was made by extrapolating from already disadvantageous poverty profile of the households. In other words, poverty before resettlement led to a higher risk of poverty after resettlement. In this article we evidence that such reasoning does not always hold true and that other household characteristics need to be taken into account. Through a comparative study of risks brought about by involuntary resettlement in the Philippines and Indonesia, at both household and community level this study sheds light on a wider range of factors that mitigate poverty at the level of communities and at the level of households during the first year of resettlement. If we understand the wider range of factors that mitigate poverty risks, as well as the underlying dynamics, we are in a better position to gear policies towards preventing impoverishment after the resettlement. Furthermore, the analytical strength of IRR model can be greatly improved with such knowledge. The present study uses the IRR lens to investigate the nature and magnitude of “risks” experienced by poor households a year after the displacement and resettlement episodes in two different cases, one in the Philippines and one in Indonesia, and tries to identify the factors (individual and household related characteristics and institutional interventions) that are instrumental in alleviating the resettlement impact and raise the resettlement “risk level” in each country. Moreover, the research looks into the factors that explain the differences in the risk experience between the two sites.
Approaching involuntary displacement and resettlement The most widely-adopted approach in resettling involuntary displaced populations is Cernea’s (2000) Impoverishment, Risks and Reconstruction (IRR) Model. The model highlights the intrinsic risks for impoverishment through displacement as well as the ways to alleviate the risks. The IRR model was conceptualized and developed in 1996, in a series of studies. Based on empirical findings, Cernea (2000) has identified the following nine interlinked potential risks that are inherent to displacement: 1. 2. 3. 4. 5. 6. 7. 8. 9.
Landlessness Joblessness Homelessness Marginalization Food insecurity Increased morbidity and mortality Loss of access to common property Social disarticulation Educational loss
Although these risks are inherent in a resettlement episode, the intensity of their manifestation can vary among individuals and households, and can be site-specific (Cernea & McDowell, 2000). Forced evictions affect invariably the poorest and most disadvantaged groups in any displacement context (Leckie, 1994; Stanley, 2000), while resettled individuals who have social, educational, and economic advantages can cope with and recover from the debilitating effects of the displacement faster than others without such benefits (Parasuraman & Cernea, 1999).
Parasuraman and Cernea (1999) stressed that the outcome of the resettlement is significantly influenced by the institutions’ approach to the displacement and resettlement activity through their resettlement policies and programs. In a dam-related resettlement in China, the “inputs” from the government resulted into maintaining or raising the income of the Chinese resettlers (McDonald, Webber, & Duan, 2008). The opposite transpired in a development-induced resettlement case in Indonesia (Nakayama, Yoshida, & Gunawan, 1999), in Xiaolangdi, China (Webber & McDonald, 2004), and in the Philippines (Quetulio-Navarra, 2007) where the inadequacy in the interventions (income restoration programs, resettlement policies, strategies, etc.) of the project implementers led to failure. Manifestation of the risks in the study Although Cernea (2000) cited nine risks in his model, in this paper only eight risks will be dealt with. We did not investigate the risk of “loss of access to common property” and the risk of “increased morbidity and mortality” is reduced to morbidity risk only. The manifestation of these eight risks (nature and extent) in the Philippines and Indonesia was examined by first establishing indicators for the eight risks followed by designing a risk index based on these identified indicators. Data on a year before the resettlement and a year after the resettlement were compared and a score system was applied: 1 if there is a positive change (less risk), 0 if there is no change, and 1 if the change is negative (more risk). Eventually all the scores (for each risk) were summed up to get the “total risk score”. Each of these eight risks was measured using the established indicators below: Landlessness. Landlessness was analysed by looking at the landownership situation of the households before and after the resettlement. Homelessness. The homelessness risk was looked into using housing-related data (housing ownership, house materials, floor size, number of bedrooms) before and after the resettlement. Joblessness. Based on the preceding qualitative investigation, the magnitude of joblessness that affected the household was determined based on the data regarding the employment status of only the household heads before and after the relocation. Marginalization. The characteristics and extent of marginalization were determined by utilizing data on household income, number of factors that divide the community, number of social services the households did not have or had limited access to, reasons for denied social services, and whether the resettlement site was perceived as peaceful and harmonious. Food insecurity. Level of food insecurity was looked into by comparing the before and after resettlement data on the percentage of low household income spent on food. Increased morbidity. Data on the number of adults and kids who got ill before and after the resettlement were compared along with the data on the number of basic services available in the previous and present communities. Social disarticulation. Social disarticulation in the two communities was measured by examining reports on the number of support ties, memberships in organizations, and relationship of the community with the local government, central government, NGOs and international organizations, and availability of public places before and after the involuntary displacement.
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Educational loss. The gravity of loss of education in the households after the relocation was based on the reports on the number of kids who stopped going to school after the resettlement. Methodological design In the Philippines, a household survey was undertaken from April to June 2011 in Kasiglahan Village 1 (KV1). KV1 is a government-managed urban resettlement community situated in Rizal Province, municipality of Rodriguez (see Fig. 1). It has a total land area of 85.70 ha with 9915 housing structures of 32 m2 each. The resettlement community was built for poor
167
households that were evicted due to development projects, natural and man-made disasters. Resettlement of families started in 1999. In Indonesia, a survey was conducted from April to June 2012 in a rural community in Bantarpanjang Translok (BT). Bantarpanjang Translok is also a government-managed resettlement community situated in the district of Cilacap, in Central Java (see Fig. 2). Bantarpanjang Translok has a total land area of 3.1 ha with 97 housing structures of around 45 m2 each. The resettlement community was built for poor households that were displaced by landslides in nearby communities. It has been accommodating households since 2001.
Fig. 1. Philippine resettlement site.
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Fig. 2. Indonesia resettlement site.
Sampling in the Philippines and Indonesia The 150 respondents in the Philippines were chosen through random sampling from a sampling frame of 6144 households who are either ‘original house and lot owners’ or ‘rights buyers’ (bought the house and lot rights from the original owner). The Project Office did not have the exact numbers of these two types of residents, but qualitative data yield that around 30e40% of the 6144 households are ‘rights buyers’. The 150 respondents are all original owners. In Indonesia, all 76 legitimate beneficiaries of the resettlement project in the Indonesia community were interviewed. Similar data gathering methods (quantitative and qualitative) were applied in both sites. A household survey was conducted that included a household composition sheet and a tailored-calendar tool. Qualitative methods included key informant interviews,
group interviews, participant and non-participant observations, indepth interviews and focus group discussions. The calendar tool is a graphical grid with domains that served as a visual aid as well as a data entry tool for both the interviewer and the respondent. The calendar collected histories (for this paper, only the data on a year before and a year after the resettlement) pertaining to the following six major life domains of the respondent: 1. 2. 3. 4. 5. 6.
Respondent’s profile Children’s information Household-related information Physical features of the community Respondent’s social engagement Respondent’s perception on the community
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Analysis of quantitative and qualitative data The quantitative data were entered into Excel and were analysed using SPSS version 19. The data on a year before and a year after resettlement were utilized and risk indexing was done for each potential risk. Multiple linear regression analysis was conducted on identified dependent and independent variables (see Table A.1). The dependent variable is the total risk score of a household and the independent variables are grouped into twothose that reflect the respondent’s socio-economic and demographic profile as well as the characteristics of his or her household, and the institutional context variables. The institutional context variables pertain to the factors that represent the interventions and influence of the government both local and central government level to the resettlement community. The qualitative data were recorded, transcribed and content analysis was applied. They were used to validate and complement the survey data.
Field results Involuntary displacees and the resettlement profile In the Philippines, 68% of the respondents were female and 32% male (Table B.1). More than half of the respondents were within the age bracket of 25e45, the ages ranging between 20 and 85. Only 27% of the respondents reached college or studied in technical school after high school, 47% finished or reached high school, and 25% only studied until elementary level. The average household size was 5.58 and the average yearly household income is Php 88,103.00 (US$2065.72). Thirty-three percent were housewives or husbands staying at home, 22% were labourers, 16% had a business in the community, while another 16% were said to be unemployed. The resettlers in KV1 were victims of development projects, natural disasters (like flooding along Pasig river), man-made disaster (garbage slide in Payatas) and wide-scale fire. The resettlement started in 1999. The resettlement program was a component of the Pasig River Environmental Management and Rehabilitation Sector Development Program funded by the Asian Development Bank amounting to US$100 million USD (PRRC, 2006). The program aimed to rehabilitate the Pasig River and part of it was relocating the poor families living along the river. The National Housing Authority, a government agency mandated to implement socialized housing program in the Philippines, undertook the resettlement program. The resettlement package includes a house and lot in a resettlement community that has basic services and primary public facilities. The house and lot is payable in 20 years at Php 250 (US$5.9) per month. The package was supposed to be prepared in accordance with the Urban Development Housing Act, which provides a systematic program for land use planning towards the allocation of lands for social housing for the underprivileged and homeless city dwellers. It covers a wide range of provisions the resettlers. In Indonesia, the majority of the respondents in Indonesia was male (92.1%) and 7.1% female (Table C.1). Most respondents (64.6%) belonged to the age bracket of 41e60 years old. High school was the highest education level reported by the respondents, while 71.1% of the respondents had only elementary-level education. More than half of the respondents were either doing elementary jobs (31.6%) or were farmers (23.7%). The average household size was 3.96 and the average household income was IDR 10,975,006.58 (US$1141.45). All were Muslims and were ethnic Sundanese. The community is divided into blocks called RT (rukun tetangga). Although the landslide took place in 2000, it took a year before the
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housing structures were in place. All households were victims of landslides. Their occupancy in Translok (the resettlement site) is in a lease-like agreement. The agencies primarily involved in the resettlement project were the Cilacap Provincial Government and the Department of Transmigration of Cilacap in particular. At national level the Ministry of Transmigration is a government agency tasked to undertake transmigration programs that aim at decongesting the densely populated area of Central Java. Mostly, but not in this case, families are relocated to other islands. Documents pertaining to the Bantarpanjang Translok project were difficult to get. According to the officials in Cilacap they were either lost or misplaced during the times that they transferred offices. An in-depth interview with a Transmigration officer in Cilacap who was directly involved in the supervision of the project, revealed that there was actually no budget earmarked for the Translok families affected by the 2000 landslide. However, given the urgency of the case, the Department reallocated some of their funds for the regular transmigration activities to the development of the Bantarpanjang Translok community. Subsequently, negotiations started about the land where the new community would be built. The land was originally under the administration of Perhutani, an Indonesian state-owned enterprises that has the duty and authority to administer the planning, management, exploitation and protection of forests. In order to acquire the land for the resettlement project, the Cilacap Provincial Government and Perhutani agreed to enter into a land switching contract in which the Cilacap Provincial Government compensates for the Perhutani land with a property twice the size of the target land. The land and the houses are still owned by the provincial government and there is no option for the resettlers to acquire it. Up until now, the Indonesian government still has no national policy for resettlement. The Transmigration Ministry follows the Keppres 55/1993 in the transmigration of families but the law only covers the aspect of land acquisition, not the provision of basic services and public facilities (Zaman, 2002). Hence, resettlement projects in Indonesia have been handled on an “ad hoc” basis. Involuntary resettlement risk index Table D.1 presents the resettlement risk indexes in the Philippines and Indonesia. Land ownership As shown on the mean score (1.00) for land ownership risk in Table D.1, KV1 resettlers did not experience any risk in losing land, rather the opposite happened. Before the involuntary resettlement, the respondents did not own the land where they were occupying (legally or illegally), which qualified them for the socialized housing program in the Philippines. After the relocation, each household was given a house and lot by virtue of an Award Certificate that is payable in 20 years at Php 250 (US$5.9) per month. After they have paid the full amount, they will be given a land title. The opposite was the case for the Indonesian respondents. Having owned their land, at least a small plot for their house and for farming, after the landslide the Translok relocatees became landless (mean risk score of 1.00). The resettlement project funds could not afford to replace their lost lands and they cannot purchase the land where they are presently residing. Series of negotiations between the Cilacap Transmigration Department and the Provincial Government did not result in a positive outcome. One leader in Translok said:
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“We have been working out with the RW leader and the village head for the possibility of acquiring the land where we are staying right now. But nothing is happening. This is really worse than when we were on the mountains. Before we owned the land where our houses were built and also had land for farming. But the landslide swallowed everything. We are scared now because anytime the Bupati (head of the district) wants to take back the land where we’re staying now, we have to leave”. (Interview with a male community leader, Translok Indonesia, June 2012)
Housing situation The Philippine resettled households are also in a better-off situation when it comes to housing, as evidenced by 2.03 risk score mean. Before the displacement, the households were renters, living with parents or relatives, or living in shanties under the bridge, on sidewalks, or along the railroad tracks. All of them, did not own a house but after their transfer to KV1 they were awarded with a concrete house structure with a toilet and gained security of tenure. When asked whether they were happy with their housing situation, one respondent said: “Now I don’t worry about strong rain or even storm because we now live in a house that has stone walls and strong roof. Previously, I was scared every time there was a storm or even a strong rain with strong wind because our roof and walls were made of very light materials and they could be just blown away at any moment”. (Survey interview with a female respondent in KV1 Plains, Philippines, June 2011) In contrast, the Indonesian resettlers are in a worse housing situation (risk score mean of 1.75). Before, they owned their houses, but when they transferred to Translok they were only given a “permission certificate” from the village head which gives them the right to use the housing structure but not own it. If before their houses were made of concrete, after the landslide they transferred to a wooden house with no divisions for bedroom and kitchen and no provision for toilet inside the house. Eight public toilets were built for the resettlers. Employment situation Employment risk score in the Philippines is very low (0.01), with only 5.4% of the respondents becoming jobless after the transfer. While most were able to keep their jobs or source of livelihood by re-establishing their small business like a small store or junk shop in KV1, some could not keep their jobs. Those who experienced a worse-off condition in terms of employment attributed this to the distance (50e70 km) of KV1 from their previous job, unrecoverable loss brought about by the garbage slide in Payatas, and the unavailability of employment opportunities in the new site. The Indonesian resettlers also scored very low on this risk (0.04), with 5.3% of the household heads losing their jobs. Since most of the resettlers are farm labourers it was not hard for them to find work in the rice fields after they transferred. Some even revealed that the resettlement got them closer to the rice fields; if previously they would walk for 2e3 h to reach their rice fields, now they only had to walk for 1 h from Translok. Some of them were also able to forge a special arrangement with Perhutani that allows them to plant some crops for own household consumption
or for selling on Perhutani land in the forest at a very minimal payment. Marginalization In aggregate, the KV1 relocatees in the Philippines are a little better off (0.03 mean risk score) after the transfer in terms of their level of economic, social, and psychological marginalization. The Indonesian Translok relocatees in Indonesia also scored positively in this respect (mean risk score of 0.28). However, in terms of access to social services during their first year in Translok, almost everybody expressed the same sentiments regarding their suffering from the absence or very poor basic services inside the resettlement site and the transition to public toilet system, one household head uttered: “On the mountains we did not have problems with the toilet. We just needed to find our own “toilet space” along the river and we didn’t need to keep it clean. When we came here we had a new system of using the toilet- we needed to line up, wait for our turn to use the toilet, and keep it clean which is difficult because you share the toilets with everybody here”. (Survey interview with a male household head in RT 2, Translok, Indonesia, May 2011) Food security The food security risk index for the Philippine case registers a very low score of 0.15 (mean), with 24.7% of the respondents adversely affected and 9.3% being better off. The risk score reflects the increase in the proportion of household income spent on food for the household, which in KV1 may be attributed to higher price of food items due to an increase in transactions costs coupled with the reported growth in number of children in the household after the resettlement. The food security situation of the Indonesian resettlers improved a little after the relocation as evidenced by the food security risk score of 0.09 (mean), with 11.8% of the households at “risk” and 9.3% of households doing better. While the resettlers lost their gardens to the landslide, after transferring to Translok some were able to arrange (with Perhutani or friends and relatives) a new garden lot where they could plant their crops, such as cassava, peanuts, and eggplants, again. Others who also had an increase in household income found food in Translok more accessible because of the stores around and the peddlers who come to the place to sell food. Morbidity Based on the morbidity index, the Philippine households are slightly at risk (0.53 risk score). However, this score is largely due to the reduction in the number of basic services provided in the resettlement site and does not reflect a higher incidence of illness in the household. Although the houses were already constructed when the families came to KV1, the basic services were provided only gradually, depending on the release of the budget from the central government. In the Indonesian case resettlement did not increase morbidity at all (mean risk score 0.33). Social articulation The KV1 residents did not suffer from social disarticulation risk after the move as shown by a 0.39 mean score. Similarly, the Translok residents seemed to have improved in terms of social
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articulation after the resettlement (mean score 1.25). A respondent remarked during an informal conversations:
transportation expenses took toll on their food and health budget. One respondent said:
“.one of the good things I like in Translok is that I can now borrow money from a money lender to buy some goods that I like or buy some stuff from a seller and pay them later with or without additional interest. We did not have this kind of arrangement before on the mountains”.
“Life was so hard here on our first year. We lost our source of income and my family had to rely on food rations and relief goods such as rice, noodle soup, canned sardines from the government, church, and NGOs. We would line up all the time for the food distribution and stock up food rations in our house which we will eat little by little.”
(Participant observation with women farm labourers in Translok, Indonesia, June 2012)
Education A low mean risk score (0.01) was found in the Philippine resettlement case for educational loss. In-depth interviews and desk research revealed that when the families relocated to the site, there were no schools. They had to send their children to schools located in nearby communities. However, they found this very inconvenient and costly (transport). They lodged strong complaints at the Project Management Office (National Housing Authority). Several months later (in 2000) some unoccupied housing units were converted into classrooms. In the Indonesian case the mean score is also low (0.07). The small number of children who stopped going to school may be due to the fact that the day care centre was only built much later. Total post resettlement risks As can be gleaned from Table D.1, the Philippine households are in a better-off condition (mean risk score of 2.75) compared with the Indonesian resettlers (mean risk score of 0.90). This is due to the fact that KV1 households gained house and lot assets after the displacement, while it is the opposite in the Indonesia case. In order to ascertain which among these dimensions yield the results shown in Table D.1, the total risk score was regressed against a number of variables that are grouped into three models presented in Table A.1. Regression results are shown in Table D.2 for the Indonesia case and Table E.1 for the Translok resettlement. Table D.2 shows that in Model 1 only the variables husband’s employment status and the number of membership in organizations can account for the level of involuntary resettlement risks experienced by the households in KV1 with an R-square of 12.8 percent. Model 2 (R2 ¼ 28%) which regressed the institutional context variables with the total risk scores yield four significant variables e the number of public places in KV1, community relationships with the local government, NGOs, and international organizations. However, the combination of these two dimensions (model 1 and 2 variables) explains best (at 38.9%) the variance in the postresettlement risks in KV1. Three of the variables that reflect socio-demographic and household characteristics are significant. Education of the household head, memberships in organizations after the resettlement, and the husband’s employment status, are the strongest predictor for risks. Surprisingly, in this resettlement case education is negatively related to risk, which challenges the usual claim of resettlement studies that “education” alleviates the harmful effects of involuntary resettlement. The result tells us that the higher the education level of the household head has the higher risks the household will face during resettlement. Compared to the lower educated, more educated household heads lost more after resettlement. Previously they had better jobs, better salaries and, therefore, could afford to rent a better house and had access to basic services. Some were able to keep their jobs in the city for a while, but the rent or the huge
(In-depth interview with a mother in KV1 Plains, Philippines, May 2011) An unemployed husband and a decrease of membership in organizations (voluntary and involuntary) increase the vulnerability level of a household after relocation. While the husband’s unemployment is self-explanatory, membership in organizations is about access to resources which may cushion an individual against risks. Six institutional context variables turn out to influence the households’ susceptibility to the risks inherent to involuntary resettlement. These are: living in Plains, number of public places in KV1, the community’s relationship with the local government and international organization (all reducing risk) and the community’s relationship with NGOs and church (both, surprisingly enhancing risk). Kasiglahan Village 1 is physically divided into two parts, the Plains and the Suburban. The Plains was the part developed first in the resettlement project, where the first groups of resettlers transferred. Although the Plains is more developed now than the Suburban, the situation was dismal during the early years, physically as well as socially. A community leader from Plains shared: “They (referring to NHA) resettled us all here to die. Housing was terrible, basic services were very minimal, and no jobs available. I don’t think they are really concerned with our welfare”. (In depth-interview with a KV1 community male leader, KV1 Plains, May 2011) The outcome on the number of public places (not the basic services) as influencing the level of risk a household might experience underscores the social and economic value of public places to the KV1 relocatees during their first year in the community. Public places like sidewalks, and markets functioned as venues for meeting new friends and acquaintances and as physical spaces for economic activities (e.g. in the market place). The risk level outcome in KV1 was likewise a result of the nature of the community relationship with the local, NGOs, international organizations and church. However, while a better relationship between the community and the local government as well as with the international organizations would mitigate the risks brought about by the resettlement, it is entirely the opposite in the case of relationships with the NGOs and church. A project manager admitted that basic services and basic facilities in KV1 were built late due to budgetary constraints. Households complained nonstop about the nature of service delivery in KV1. The NGOs intervened, but their lobbying efforts only exacerbated the social issues in KV1, eventually negatively influencing the cooperation of the households. Similarly, when the community or the households forge a good relationship with the church, most of the time this results in a patroneclient like relationship that limits connecting with other people who do not share the same religion or faith, thereby preventing them from expanding their social networks that are a potential well of resources. Table E.1 shows the regression results for the risk score of the Indonesian households. Three variables turned out to be significant
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in Model 1 with an R square of 18.60 percent: the education of the household head (by far with the strongest impact), the husband’s employment status, and membership in organizations. In Model 2 (R2 ¼ 19.50%), the variables number of public places and the relationship of the community with the local government turned out as predictors of risk level in Translok. Similar with the KV1 regression results, Model 3 provides the best explanation for the resettlement risk score in Translok (R2 ¼ 35.9%). Significant variables are education, husband’s employment status, and relationship with the local government. Like in the KV1 case, the higher the level of education of the household head the higher the resettlement risks score will be and the same explanation applies to this outcome. A good relationship between the Translok community and the local government decreases the risks since social programs that can mitigate impoverishment in the locality usually emanate from the level of the local government.
Discussion and conclusion This study about two different resettlement contexts in Southeast Asia presents robust evidence on the multi-dimensionality of resettlement risk causes. The study shows that, while the institutional context is important, also culture, physical location, and individual and household characteristics add to the explanation of risks experience during first year in the resettlement. The resettlement program policy of the country concerned proved to greatly influence the manifestation of eight risk factors identified by Cernea (2000). The resettlement program showed large differences between the two sites. The Philippine KV1 resettlers gained assets in the form of housing and land because there is a national policy on resettlement and socialized housing in the Philippines that protects right to housing and aims for humane relocation. The presence of the national policy made the KV1 residents aware of their rights, as apparent in the case of their lobbying for building a school in the site. On the other hand, the Indonesian Translok relocatees lost their houses and land to landslides and were never compensated for their loss due to the absence of a country policy on resettling natural disaster victims. Hence, they had no grounds to demand basic facilities and just compensations. While differences in level of educational level and age between the Philippine and Indonesian households could explain these dissimilar situations, the multivariate analyses showed that in both sites there is no significant impact of the respondents’ age, gender, income and household size on the total risk score. Both the Philippine and Indonesian households experienced four risks with two risks emerging in both cases (employment and education). Nonetheless, it is apparent that the risks manifested differently in the two cases. The role of the physical environment both in the previous and the resettlement residence, along with the households’ cultural background, influenced the emergence of risks related to food security and morbidity. The Indonesian Translok relocatees were able to prevent food insecurity after the resettlement because they continued their practice of planting crops in the mountain area through new arrangements with Perhutani, a relative, or a friend. It also helped that most men quickly found jobs and continued to work as farm labourers in the nearby rice fields after the transfer. In the Philippine KV1 case, however, households were unable to avoid food insecurity during their first year in the site. The KV1 resettlers were not accustomed to growing their own crops and had no access to land for farming. They relied on their income from nonagricultural activities to secure high-priced food items from peddlers coming to the site.
The multivariate analyses showed that in both the Philippine and Indonesian case the individual and household variables add substantively to the institutional ones in explaining risk. Moreover, in spite of the large differences, in both cases the same sociodemographic variables are influential (employment, membership in organizations and the unexpected negative impact of education) and their weight as relative to the institutional variables is about similar (12e18% versus 39e36% R-square). Regarding the institutional factors, the outcomes did show important differences between the Philippine and Indonesian case. Most consist of the absence of significant relationships in the Indonesian case. The findings also show that irrespective of the level of significance, the sign of all effects is similar (positive or negative) in both cases, except for the influence of NGOs and church which, unexpectedly, enhanced risk in the Philippines. The results of the research convey a new perspective on the concept of vulnerability in a forced resettlement context. Unlike the usual finding that education shields a resettler from risk, this study yielded a contradictory result. This may be due to the fact that a more educated household head tends to lose more during displacement and that resettlement in an impoverished community actually misplaces him or her in an employment pool, thereby eventually aggravating the risk level in other areas. In the Philippines, a good relationship with the church and NGOs during the first year in the resettlement site generated negative effects. We can infer from this that in an urban resettlement context like the Philippines, wherein you have a national policy on resettlement and community leaders who are aware of their rights, the intervention of the NGOs and perhaps the church may result in delays in service delivery and trigger disagreements among the project stakeholders. What should be nurtured first is the community relationship with the host local government (negative regression weight) who is in the best position to deliver the social services and interventions needed by the households and the project managers. This study demonstrates that features of the resettlement program, the institutional context, and individuals and households together cause and mitigate risk during the first year of resettlement. Taking these factors together proved to add substantially to the strength of the IRR model, even when applied to two quite divergent resettlement populations.
Acknowledgement The authors are grateful to the Netherlands Fellowship Programme that funded the fieldwork in the Philippines and the Neys Van Hoogstraten Foundation that funded the fieldwork in Indonesia. Also, to the households and leaders in Kasiglahan Village Community and the Bantarpanjang Translok for their participation in the research, Engr. Abeth Matipo of the National Housing Authority, Mr. Candra Musi and Mr. Narso of Perum Perhutani for the logistical assistance, Dr. Ana Marie Karaos and Ms. Vangie Serrano of Action Group for the assistance at the community level in the Philippines, Dr. Sri Sunarti Purwaningsih and Dr. Herry Yogoswara of Indonesian Institute for Sciences (LIPI), Dr. Tyas Wulan and Dr. Suliyanto Jayakrama of Universitas Jenderal Soedirman, Yayasan Kusuma Buana led by Dr. Firman Lubis, Presidential Commission for the Urban Poor led by Chairman Hernani Panganiban, Dennis Sorino and Dulce Joy Sorino for database design and data processing, the Philippine Research Team led by Elizabeth Avila, the Indonesian Research Team led by Fanny Dwipoyanthi, Eden Frunt, Victor Allan C. Ilagan, Alicia S. Diaz, Martina Robles, Daniel Vincent, and in WUR Dr. Hester Moerbeek and Feng Zhao for statistical analysis.
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Appendices
Table B.1 (continued )
Table A.1 Dependent and independent variables. Dependent variable
Explanation
Total risk score
Sum of all scores on the eight different areas of potential risks
Independent Variables Demographic, socio-economic, & household characteristics Age Gender Dummy variable, female ¼ 1 (Philippines) Dummy variable, male ¼ 1 (Indonesia) (after resettlement) Husband’s employment status Dummy variable, where employed ¼ 1 Income (after resettlement) Household size Membership in organizations Number of support ties Institutional context (after resettlement) Living in plains (KV1) Dummy variable, Plains ¼ 1 Living in RT1 and RT 3 (Translok) Number of basic services Number of public places Number of denied social services Rate of community relationship Dummy variable, Very low ¼ 1; with: Low ¼ 2; Moderate ¼ 3; High ¼ 4; Very High ¼ 5 Central government Local government NGOs International organizations Church/mosque
Table B.1 Descriptive statistics of respondents in the Philippines N ¼ 150. Variables Gender Male Female Age 25e35 36e45 45e55 56e65 66 e more Civil status Single Married Separated Widowed Co-habiting Education Elementary school or less High school More than high school Occupation Entrepreneur Labourer Govt/private employee Housewife Retired Unemployed Household size 1e3 4e6
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Percentage
Frequency
32.0 68.0
48 102
0.2 0.3 0.3 0.1 0.1
30 47 43 21 9
5.3 75.3 3.3 6.7 9.3
8 113 5 10 14
0.3 0.5 0.3
37 71 41
16.0 22.0 14.0 32.7 2.0 16.0
24 33 21 49 3 24
18.7 54.0
28 81
Variables
Percentage
7e9 10 or more Household income quartile <48,000 >¼48,000 and<90,000 >¼90,000 and<130,656 >¼130,656 Present address Plains Suburban
22.0 5.3
Frequency 33 8
25.0 25.0 25.0 25.0
36 35 36 35
60.7 39.3
91 59
Table C.1 Descriptive statistics of respondents in Indonesia N ¼ 76. Variables Gender Male Female Age 20e30 31e40 41e50 51e60 61 e more Civil status Single Married Widowed Education Never been to school Elementary school or less Junior high school High school Occupation Farmer Entrepreneur Elementary occupation Govt/private employee Housewife Others Household size 1e3 4e6 7e9 Household income quartile <4,560,000 >¼4,560,000 and<9,000,000 >¼9,000,000 and<14,850,000 >¼14,850,000 Present address RT 1 RT 2 RT 3 RT 5
Percentage
Frequency
92.1 7.9
70 6
4.0 23.7 34.2 30.3 7.9
3 18 26 23 6
2.6 93.4 4.0
2 71 3
4.0 71.1 19.7 5.3
3 54 15 4
23.7 13.2 31.6 7.9 5.3 15.8
18 10 24 6 4 12
34.2 63.2 2.6
26 48 2
25.0 27.6 22.4 25.0
19 21 17 19
26.3 36.8 32.9 4.0
20 28 25 3
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Table D.1 Risk index in the Philippines Philippines N ¼ 150 Indonesia N ¼ 76
Philippines
Indonesia
Philippines
Indonesia
Potential risks areas
Mean
SD
Mean
SD
Worse off%
Worse off%
Land ownership Housing situation Employment Marginalization Food security Morbidity Social Articulation Education Total risks score
1.00 2.03 0.01 0.03 0.15 0.53 0.39 0.01 2.75
0.00 1.16 0.46 1.51 0.56 1.03 2.09 0.16 3.55
1.00 1.75 0.04 0.28 0.09 0.33 1.25 0.07 0.90
0.00 1.13 0.26 1.09 0.57 0.57 1.29 0.25 2.28
0 1.3 5.4 32.7 24.7 57.3 34 2 15.3
100.0 88.2 5.3 19.7 11.8 5.3 5.3 6.6 53.9
Difference
100.0 86.9 0.1 13.0 12.9 52.0 28.7 4.6 38.6
Philippines
Indonesia
Same%
Same%
0 10.7 88 34.7 66 25.3 18.7 96.7 9.3
0.0 9.2 92.1 34.2 67.1 73.7 26.3 93.4 19.7
Difference
0.0 1.5 4.1 0.5 1.1 48.4 7.6 3.3 10.4
Philippines
Indonesia
Better off%
Better off%
100 88 6 32.6 9.3 17.3 47.2 0.07 75.3
0.0 2.6 1.3 46.1 21.1 21.1 68.4 0.0 26.3
Difference
100.0 85.4 4.7 13.5 11.8 3.8 21.2 0.1 49.0
Table D.2 Effects of socio-demographic, household characteristics and institutional context on the involuntary resettlement risks in the Philippines (pairwise deletion of missing values) N ¼ 150. MODEL 1
Age Female Education Husband’s employment status Income Household size Membership in organizations Number of support ties Living in Plains Number of basic services Number of public places Number of denied social services Rate of community relationship with Rate of community relationship with Rate of community relationship with Rate of community relationship with Rate of community relationship with R2
MODEL 2
B
SE
0.030 0.120 0.086 0.207** 0.030 0.087 0.215** 0.031
0.023 0.532 0.144 0.603 0.557 0.128 0.383 0.034
B
0.043 0.047 0.177** 0.158 0.043 0.327*** 0.317*** 0.282*** 0.106 28.0%
central government local government NGOs international organizations church 12.8%
MODEL 3 SE
0.471 0.073 0.092 0.080 0.305 0.300 0.249 0.267 0.216
B
SE
0.132 0.116 0.185** 0.215*** 0.089 0.040 0.014* 0.150 0.237*** 0.005 0.179** 0.125 0.032 0.259** 0.255*** 0.349*** 0.183** 38.9%
0.022 0.480 0.139 0.538 0.503 0.116 0.031 0.356 0.519 0.078 0.096 0.082 0.306 0.316 0.256 0.282 0.230
Significant at *p < 0.10,**p < 05 &, ***p < 0.01.
Table E.1 Effects of Socio-demographic, household characteristics and institutional context on the involuntary resettlement risks in Indonesia (pairwise deletion of missing values) N ¼ 76 MODEL 1
Age Male Education Husband’s employment status Income Household size Membership in organizations Number of support ties Living in RT1 Living in RT3 Number of basic services Number of public places Number of denied social services Rate of community relationship with Rate of community relationship with Rate of community relationship with Rate of community relationship with Rate of community relationship with R2
SE
0.045 0.069 0.262** 0.246** 0.059 0.008 0.208* 0.107
0.256 1.094 0.432 1.361 0.276 0.200 0.279 0.045
central government local government NGOs international organizations mosque
Significant at *p < 0.10,**p < 05 &, ***p < 0.01.
MODEL 2
B
18.6%
B
0.045 0.086 0.032 0.234* 0.096 0.134 0.349*** 0.215 0.071 0.021 19.5%
MODEL 3 SE
0.755 0.697 0.227 0.267 0.146 0.484 0.295 1.071 1.256 0.394
B
SE
0.101 0.016 0.247** 0.250* 0.003 0.060 0.133 0.162 0.044 0.010 0.051 0.166 0.167 0.116 0.324** 0.217 0.019 0.012 35.9%
0.274 1.201 0.451 1.454 0.347 0.216 0.301 0.051 0.782 0.715 0.236 0.281 0.168 0.524 0.296 1.053 1.316 0.391
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