Long-term neighborhood effects on integration of immigrants: The case of the 1951 Moluccan boatlift

Merve Nezihe Özera* Bas ter Weelb Karen van der Wielc**

January 31, 2017

Abstract

Integration of immigrants to their host countries has been much studied. However, evidence on how physical characteristics of the neighborhoods they live affect their integration is limited and ambiguous. This paper aims to estimate the impact of the physical neighborhood characteristics on immigrants’ long term education and labor market outcomes. We use administrative data on Moluccan immigrants in the Netherlands to exploit the random variation in their settlements after they had been boatlifted from Indonesia in 1951. Moluccan immigrants were assigned to residential areas called ‘woonoorden’, which differed in terms of their distance to the local native community, educational infrastructure, employment opportunities nearby, and housing structure. We analyze education and labor market outcomes of children born in these settlements after 45 to 60 years. We find that physical characteristics matter for these second generation immigrants but impacts differ between girls and boys. A kilometer increase in the distance to the local community results in 0.7% less likelihood of women having at least an upper secondary school degree. For men, the education level is not affected. Instead, we find that a kilometer increase in the distance to the local community decreases men’s income by 1.2% while having no significant effect on women’s. Our findings are instructive on the potential impacts of the location of refugee camps on further integration of refugees to host countries.

Key words: immigrant, neighborhood effects, integration, Moluccan, refugee

JEL classification: J15, J24, R23

a Research Centre for Education and the Labour Market (ROA), Maastricht University, The Netherlands b SEO Amsterdam Economics and University of Amsterdam, The Netherlands c CPB Netherlands Bureau for Economic Policy Analysis, The Netherlands * Corresponding author at: ROA, Maastricht University, P.O. Box 616 6200MD Maastricht, The Netherlands. T: +31 433883647, F: +31 433884914, E-mail: [email protected] ** Corresponding author at: CPB Netherlands Bureau for Economic Policy Analysis, Bezuidenhoutseweg 30 2594 AV Den Haag, The Netherlands. T: +31 652658469, E-mail: [email protected] 1. Introduction

Integration of immigrants has been a long debate in highly-immigrant-receiving Western European countries due to immigrants’ lagging behind in education and labor market outcomes compared to their native counterparts. Research has been showed that immigrant-native gap in labor market achievement is persistent across generations even though younger generations catch up their native counterparts in educational attainment (Algan, Dustmann, Glitz, & Manning, 2010). Neighborhood effects and ethnic enclaves have been addressed as one of the explanations of this poor immigrant performance as minorities tend to spatially cluster together in relatively-deprived neighborhoods (e.g. Clark & Drinkwater, 2002; Cutler & Glaeser, 1997). The hypothesis that physical characteristics of neighborhoods may have a potential impact on ethnic minorities’ achievements dates back to Kain’s (1968) spatial mismatch hypothesis. Nevertheless, research to date has been focused on the impacts of ethnic concentration rather than neighborhood characteristics. Hence, this paper aims to answer the question of whether neighborhood characteristics affect educational attainment and labor market achievement of second-generation immigrants.

Studies on ethnic enclaves dominate the literature of neighborhood effects on immigrant integration. Theoretical predictions on the impact of living in co-ethnically concentrated areas are ambiguous. On the one hand, immigrants can benefit from ethnic networks through information spillovers (Portes, 1998; Putnam, 1993). Support of a rooted ethnic community in the host country especially helps new- arriving immigrants find a job better matching their qualifications and so paying higher as empirically supported by many studies (e.g. Beckers & Borghans, 2011; Damm, 2009; Edin, Fredriksson, & Åslund, 2003; Patacchini & Zenou, 2012). On the other hand, living in high-density immigrant neighborhoods may impede immigrant integration in the long run. Constantly and frequently interacting with co-ethnic peers in enclaves may hamper acquisition of country-specific human capital (Borjas, 1995) such as fluency in local language (Chiswick, 1991; Chiswick, 1995; Lazear, 1999). Furthermore, values and norms’ being easily transferred in ethnic neighborhoods may lead to peer or role model effects. For instance, living in segregated neighborhoods where few adults have steady jobs may decrease an individual’s motivation to search for a job and to work (Wilson, 2012).

How physical characteristics of neighborhoods affect immigrants’ integration has got less attention in this strand of literature. One exception is Kain’s (1968) work on the impact of distance between ghettos and suburban jobs in the US on blacks’ employment opportunities. According to the spatial mismatch hypothesis of Kain (1968), minority groups are economically at a disadvantaged position since their access to information about the existing job opportunities is limited due to segregation. Moreover, living in a segregated neighborhood distant from job centers increases both search and commuting costs so minorities have less incentive to apply for jobs which would be a match otherwise. Furthermore, even if they apply, employers might not be willing to hire them as they tend to hire individuals living close by and not being reacted by the local community. Although Kain (1968) found a negative relationship between distance to job and share of nonwhites in employment supporting his hypothesis, evidence provided by later studies is mixed1.

Kain’s (1968) study provides a helpful starting point to hypothesize potential impacts of neighborhood characteristics on socioeconomic outcomes of immigrants. However, it does not say much about how location of neighborhoods may affect access to amenities which improve human capital and so future life outcomes of immigrants. It may be due to the concentration of the hypothesis on minorities living in inner-city ghettos, especially in the US. However, residential areas and living arrangements vary

1 See Jencks & Mayer (1990) and Ihlanfeldt & Sjoquist (1998) for a review.

1 across immigrant groups. For instance, refugee camps are often placed in the periphery of cities or in rural areas (Diken, 2004). Location of immigrant neighborhoods may affect availability of or access to facilities such as good-quality healthcare and education which are important for especially second- generation immigrants’ catching up their native counterparts. In this respect, neighborhood effects on the integration of second-generation immigrants intersect with another strand of literature on the impact of early childhood environment on later life outcomes. However, this literature also provides a mixed evidence on the presence of neighborhood effects. While several studies exploiting random assignment of families to different neighborhoods found significant improvements in children’s educational attainment (Gould, Lavy, & Paserman, 2004; Gould, Lavy, & Paserman, 2011), likelihood of being employed at later ages (Gould, Lavy, & Paserman, 2011), and health outcomes (Katz, Kling, & Liebman, 2001), others found only limited effects (e.g. Oreopoulos, 2003; Sanbonmatsu, Kling, Duncan, & Brooks-Gunn, 2006). In this regard, there is no consensus on the presence and magnitude of neighborhood effects on children’s later life outcomes and second-generation immigrants’ achievements.

Our study aims to fill this gap in the literature by providing further empirical evidence on whether or not neighborhoods matter for immigrant integration. We test whether physical characteristics of neighborhoods have any effect on second-generation immigrant children’s educational attainment and income in the long term. We use the administrative data of the children of Moluccan immigrants who arrived to the Netherlands in 1951 and randomly assigned to 90 residential areas called ‘woonoorden’. These residential areas differ from each other in their size, their distance to native towns and city centers, in type of the houses that new-arriving Moluccans lived, and in availability of other facilities such as primary school and work opportunity nearby. Second-generation Moluccans who were born in those settlements were traced in administrative records of Statistics Netherlands. Their education and labor market outcomes were obtained when they reached to ages of 45 to 62, which allow us to study the potential long-term impacts of neighborhoods on the integration of second-generation immigrants.

Our results show that a kilometer increase in the distance to local community results in 0.7% less likelihood of women’s having a upper secondary school degree while having employment opportunity nearby a settlement increases the likelihood of women’s being active in labor market at age 55 by 5.2%. We also find that a kilometer increase in the distance to local community decreases men’s income by 1.2%. We did not find robust significant results for the impact of housing structure and presence of primary school in settlements. We constructed a control group of Dutch natives who were born in the same time period in the same municipalities where Moluccan settlement were. Our findings show that second generation Moluccans are significantly worse off than Dutch natives in their education and labor market outcomes.

The rest of the paper is structured as follows: Section 2 provides a brief summary of the history of Moluccan community in the Netherlands and the characteristics of settlements they were assigned at their first arrival. Section 3 describes our data and provides summary statistics. Balance checks for treatment are provided in section 4. Section 5 presents our main findings. Section 6 provides results of our control group analyses and a discussion on our findings. Finally, section 7 concludes.

2. History of Moluccan community

2.1. Moluccan community in the Netherlands: The Moluccan boatlift in 1951

Moluccans refer to an ethnic, indigenous community living in Maluku Islands which have been a part of Indonesia since 1950. Indonesia had been a colony of the Netherlands until Japanese occupancy during World War II. The Netherlands attempted to restore its old hegemonic power on Indonesia after

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Japanese occupancy between 1942 and 1945. However, conflicts broke out as Indonesians who were against the restoration of Dutch colonial regime rebelled in 1945 in the leadership of Sukarno. The Netherlands reconstituted Royal Netherlands East Indies Army (KNIL) to quell the rebel and maintain order in Indonesia. Moluccan community served in KNIL as professional soldiers. Indonesia declared its independence in 1945. However, conflicts between Indonesians and KNIL lasted until 1949 when the Netherlands acknowledged Indonesia’s independence.

Figure 1: Moluccan settlements in the Netherlands in 1950s

When Indonesia got its independence, struggles between Moluccan community and other Indonesians, particularly Javanese who were the ruling ethnic group at that time, started as Indonesians accused Moluccans being traitors due to their collaboration with Dutch. As the social unrest deteriorated, Dutch government decided to ship Moluccan soldiers formerly served to KNIL to the Netherlands. More than 3,000 former KNIL soldiers together with their families, which constituted approximately 12,500 Moluccans, were shipped to the Netherlands (Oostindie, 2011, p.28). Although Dutch government’s initial plan was to provide a temporary settlement to Moluccans until alleviation of social upheaval, the unrest continued in Indonesia so hardly any Moluccan migrated back. Thus, initially temporary Moluccan community became a permanent part of the Dutch society (Oostindie, 2011, p.29).

Moluccan community arrived to the Netherlands had a homogeneous background. The community consisted of soldiers served to Dutch army under colonial rule and their families. Almost all of them were Christians, had limited knowledge of Dutch, and were poorly educated (Oostindie, 2011, p.28). Moluccans’ arrival was almost completely organized and financed by the Dutch government as done

3 for other immigrant communities from Dutch East Indies during 1950s and 1960s (Oostindie, 2011, p.40). Besides government, private organizations for the provision of some needs were mainly churches (Oostindie, 2011, p.40). Moluccans’ limited cultural capital and weak social status made them dependent on the government’s provision of housing. Dutch government placed them in settlements called ‘woonoorden’, some of which were previously used as public service buildings or military camps. There were 90 settlements in 73 municipalities scattered across the Netherlands as in Figure 1 established in the early years of Moluccans’ first arrival. A few of these settlements lasted until 1990s. Characteristics of these settlements are discussed in the next section in detail.

Oostindie (2011, p.45-46) mentions that immigrants in the Netherlands from Dutch Indies had a relatively smooth integration process during 1960s and 1970s due to them having been educated in a system similar to Dutch schooling system, their affinity to Dutch culture, and being able to speak Dutch. It helped their children to successfully integrate into the Dutch society. However, it does not apply to Moluccans and their children (Oostindie, 2011, p.33,45-46). First generation Moluccans’ weaker cultural capital and poorer educational attainment made their own and their children’s integration more difficult (Oostindie, 2011, p.45-46) such that Moluccan community has been the only community with Indisch roots under Dutch minorities policy in 2000s (Oostindie, 2011, p.32). Hence, the process of Moluccan integration may be more similar to the case of later immigrants from countries like Morocco and Turkey. In this respect, our focus on second generation Moluccans to study immigrant integration is sensible and we think that our results are generalizable to a broader context.

2.2. Residential area characteristics

Moluccans were initially placed more than 50 settlements called ‘woonoorden’ when they first arrived in 1951 and numbers of these settlements increased to 90 in early years after their arrival. As Figure 1 shows, these settlements were scattered across the whole country. We collected information on characteristics of these settlements by doing research in archives of the Moluccan Historical Museum in Utrecht. We gathered information on the exact location of settlements, their opening and closure dates, and number of residents in years when the settlement was open. The list of settlements we identified are presented in Table A.1. As Table A.1 shows, more than 50 settlements were opened in 1951 and their numbers increased to 90 by 1960. Most of these settlements were closed by the end of 1960s possibly due to the relocation of Moluccans to nearby neighborhoods as a result of government’s housing projects (Tuynman-Kret, 1985). On average, 14,408 first and second generation Moluccans lived in these settlements during the time period that settlements were open.

We also obtained information on the location of the nearest church and hospital, availability of schooling facilities in settlements, availability of employment opportunity nearby the settlement, and the type of housing provided to immigrants. Settlements differed from each other in those characteristics, which are likely to affect second generation Moluccans’ later life outcomes as described in the next section. Since we were able to identify second generation Moluccans based on the municipality rather than the specific settlement that they were born and some municipalities consisted of more than one Moluccan settlement, we followed a rule for each characteristics to determine the features most of the residents in one municipality were exposed to. Hence, we aggregated characteristics of some settlements in municipalities where were more than Moluccan settlement. The list of these settlement characteristics at municipality level is presented in Table A.2.

We use distance between settlements and the nearest church and hospital as proxies of Moluccan immigrants’ distance to local native community, basic education and health care facilities, and labor

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market. We obtained latitude and longitude of the nearest church and hospital to each settlement and calculated the distance in kilometers between them2. If there was only one Moluccan settlement in a municipality, its distance to church and hospital is exactly reflected at municipality level. However, we took the average distance of settlements in municipalities where were more than one Moluccan settlement. As Table A.2 shows, the average distance to church was 6 and to hospital was 11 kilometers but there is substantial variation across settlements.

Table 1: Pairwise correlations between settlement characteristics at municipality level

Distance Distance Lower Type of Work Size of Share of Duration to church to hospital primary house opportunity the camp singles of the school camp Distance to church 1.0000

64 Distance to hospital 0.5702* 1.0000 0.0000 64 64 Lower primary school 0.0147 0.1419 1.0000 0.9080 0.2633 64 64 73 Type of house 0.0629 0.1129 -0.0995 1.0000 0.6243 0.3782 0.4092 63 63 71 71 Work opportunity -0.1351 0.0424 0.3150* -0.0837 1.0000 0.2871 0.7394 0.0066 0.4879 64 64 73 71 73 Size of the camp -0.0858 -0.1105 0.3371* -0.1535 0.4100* 1.0000 0.5001 0.3846 0.0035 0.2013 0.0003 64 64 73 71 73 73 Share of singles -0.1132 0.0586 -0.2058 0.3443* 0.1044 -0.1482 1.0000 0.3976 0.6624 0.0947 0.0050 0.4004 0.2312 58 58 67 65 67 67 67 Duration of the camp -0.1477 -0.1874 0.2716* -0.1073 0.2287 0.5941* -0.1975 1.0000 0.2442 0.1381 0.0201 0.3732 0.0517 0.0000 0.1091 64 64 73 71 73 73 67 73 Source: Authors’ estimation Notes: Pearson correlation coefficients, their significance level, and number of observations for each pair of settlement characteristics are presented in columns, respectively.

Some of the Moluccan settlements had on-site schooling facilities while children in other settlements had to attend to schools around settlements. We aggregated this feature at municipality level by considering the number of residents exposed to the condition at hand. For instance, suppose there were three Moluccan settlements in one municipality and two of them had lower primary school. Then, we considered that municipality as having on-site schooling facilities for Moluccan children if the total population of two of them exceeded the other. Otherwise, we considered that municipality as not having on-site schooling facilities for most of the Moluccan children lived in settlements there. As Table A.2 shows there were arrangements for lower primary education in more than 30 municipalities with Moluccan settlements. However, most of the Moluccan children lived in settlements had to go out of the settlement to access to higher level education. As Table 1 demonstrates presence of on-site lower primary school in a settlement is significantly and positively correlated with the size and the duration of the settlement.

2 We used geocode command in Stata to calculate the shortest distance based on the curvature of the earth. Thus, distance does not refer to travel distance here.

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Moluccan settlements also differed from each other in terms of the access to an employment opportunity. For instance, Moluccan residents in Kamp Q in Ridderkerk mainly worked in Machinefabriek Smit close by or some residents of Ijsseloord worked in flour mill nearby. However, other Moluccan residents did not have such a relatively easy access to labor market. We did the same aggregation for presence of employment opportunities nearby Moluccan settlements at municipality level as we did for the availability of schooling facilities. As Table A.2 illustrates, there were work opportunities only around 10 municipalities with Moluccan settlements.

Another difference across Moluccan settlements was the type of houses where Moluccans were placed. They were settled to former government buildings, old monasteries, military camps, or transit camps used during the World War II such as the ones Westerbork due to the housing shortage in the Netherlands in 1950s. Type of houses that Moluccans lived is a proxy for sanitary conditions that children were exposed to during their formative years. As Table A.2 shows most of the Moluccans in municipalities with settlements lived in houses in poor conditions such as wooden or metal barracks. Only few of them had the chance to live in stone buildings such as government buildings and old monasteries which probably had better sanitary conditions compared to barracks.

Finally, Table A.2 reveals that average size and duration of settlements differ from each other. Settlements in municipalities such as Vught or Westerbork hosted more than 2,000 first and second generation Moluccans on average during the time that they were open while there were much smaller settlements. We could not find information on the number of residents in some settlements as Table A.2 reveals as we could not for characteristics of some settlements which leads differences in our estimation samples. However, information that we gathered shows that more than 250 residents lived in a settlement for 11 years, on average. Table 1 reveals that larger settlements lasted longer. It may be the result of a government attempt to relocate residents in smaller settlements into larger settlements to better organize provision of needs. On the other hand, this positive correlation may be interpreted as that Moluccans had more children as the settlement lasted longer. In terms of our focal point in this study, this positive correlation may cause a doubt in randomization of Moluccan parents’ settlement if an early closure of a settlement is due to them leaving their settlement and made a choice on where to live in the same municipality or another municipality with a Moluccan settlement. To exclude this possibility, we provide balance checks and we run regressions on a Moluccan sample born in early years of their parents’ arrival in next sections.

3. Data

3.1. Data construction

Individuals with Moluccan origin are not easily distinguishable from other immigrants with Indonesian origin as they have similar names. In addition, Moluccans have started to be registered as Western immigrants in 2000s in Dutch administrative records. Hence, we started our data gathering first with the passenger lists obtained from social epidemiology professor Anton Kunst at The Academic Medical Center, University of Amsterdam. These passenger lists include names and surnames of Moluccans boatlifted from Indonesia to the Netherlands in 1951. Statistics Netherlands used surnames of these passengers to trace their children born in the Netherlands in municipal administrative records. Hence, we obtained registry records on second generation Moluccans born in the Netherlands including information on their birth date, birth place, and their parents’ birth date and origin (i.e. whether both parents are Moluccan or one of them is native Dutch).

We, then, restricted the individuals in the records to those who were born between 1951 and 1968 in the municipalities where Moluccan settlements that we identified in our archive search were. Birth

6 place restriction was required in order to identify second generation Moluccans who were most likely born in settlements and exposed to the conditions there. Time dimension was also included as another restriction on sample considering that many of the settlements were closed by the end of 1960s. After these restrictions, we ended up with almost 9,000 second generation Moluccans. We later merged these records with employment, income, and education registers of Statistics Netherlands by using the key identifier variable of each individual. Hence, we have information on their educational attainment, their employment history throughout their working life, their main income sources in 2014, their personal and household income in 2014, and where they currently live in the Netherlands. Finally, we merged this data file with the archive data on characteristics of municipalities with settlements where second generation Moluccans were born.

Considering the estimation of the Netherlands Interdisciplinary Demographic Institute (NIDI), we were expecting to find about 13,000 second generation Moluccans (Beets, et al., 2002) but we ended up with less than 9,000 individuals. Firstly, it might be due to the fact that Moluccans who came to the Netherlands were registered with different names or registered their children with different names. Secondly, we might be missing many women with Moluccan origin due to the surname change after marriage. Although Moluccan community has been closed and had strong cultural ties (Amersfoort, 1971), they had been getting married to native Dutch even in early years of their arrival (Oostindie, 2011, p.33). This may also cast doubt on whether children born in municipalities with Moluccan settlements were really exposed to the conditions there or not. Children of ethnically mixed couples are more likely to have lived outside the settlement even though they might have lived in the same municipality. Since we identified children who were likely to be born in Moluccan settlements only on the basis of the municipality that they were registered at the time of birth, we are not able to exclude the possibility of that there may have been Moluccans who were born in municipalities with Moluccan settlements but did not live in settlements.

Furthermore, we consider the time period of 1951-1968, when many of the settlements were open. However, this time period is relatively long and one may suspect that Moluccan parents might have moved and made a decision on where to live. This also casts doubt on random assignment of Moluccans to their neighborhoods. We think that it is unlikely considering Moluccans’ weak cultural capital, slow integration, and willingness to stay in their own community. Nevertheless, we run regressions on a sample of second generation Moluccans who were born in 5 years right after the first arrival to exclude this possibility. In addition, we merged our data with another register data which links parents to their children in order to identify siblings in the second generation Moluccan sample and to double check identity of parents. About half of the sample could not be matched with at least one of their parents. It may be due to the fact that such registry records linking children to their parents were not complete at that time as administrative data procedure started to be implemented in the Netherlands in 1950s. Still, we were able to find almost 9,000 second generation Moluccans in registry records together with the data on their later life outcomes. Hence, we believe that we cover an extensive part of this group and we provide balancing regressions, robustness checks, and control group regressions to exclude some of the potential problems mentioned above.

3.2. Explanation of variables

We derived three groups of dependent variables from the registers of Statistics Netherlands to analyze long-term impact of neighborhood characteristics on later life outcomes of second generation immigrants. Our first outcome variable is educational attainment. We have two education variables: having at least and upper secondary school degree and having at least a tertiary degree. Our second outcome variable is income, for which we also have two variables: personal income and household

7 income. Our final outcome variable is being employed in 2013 to see how neighborhood variation affects labor market attachment of immigrants. All outcome variables except personal and household income are dummies.

Our treatment is the random assignment of first generation Moluccans to settlements across the Netherlands. Each settlement has different characteristics as explained in Section 2.2 and represented in Table A.2. We exploit the random exposure to different neighborhood characteristics of Moluccans who had a homogeneous background. First of all, we use settlements’ distance to the nearest church measured in kilometers as a proxy of the distance to local native community and provision of some basic needs organized by churches3. Availability of lower primary school is a dummy and a proxy of having immediate access to education facilities in settlements. Having employment opportunity nearby settlements is another dummy variable that we use to see whether Moluccans had immediate access to labor market or not. Type of housing is a categorical variable taking 1 for metal barracks and 2 for stone building. Base group is Moluccans living in wooden barracks. Type of housing is a proxy of health conditions in which Moluccan children lived during their formative years. We assume that metal barracks might be better in terms of durability and endurance to weather conditions and stone buildings are probably the best among them as they may have access to basic facilities such as running water or sewage, and are probably better sheltered. Finally, we include size of the settlement and duration of settlement which are measured by number of people lived in a settlement and years that a settlement was open, respectively. We categorize these features into three: pull factors are distance to church and employment opportunity, which pull immigrants outside of own community; push factors are availability of primary school, size and duration of settlement, which push immigrants to stay in their own community; health conditions include type of housing that immigrants lived in.

We control for age and sex of second generation Moluccans in our regressions. We also include number of income earners in a household in household income estimations. In addition to these standard controls, we include mothers’ birth year as a balancing control. Since we use Dutch administrative records, we do not have extensive data on background characteristics of Moluccan parents who came from Indonesia. Hence, we were able to run balancing regressions only on the age of parents. Although age distribution of parents across settlements look like balanced, we still included mother’s age to control for a potential tendency in assignment of parents to certain neighborhoods. We included only mothers’ birth year since it is highly correlated with fathers’ birth year and less balanced compared to fathers’ birth year as presented in Section 4.2.

3.3. Summary statistics

Descriptive statistics of our sample is provided in Table 2. We ended up with 8,675 second generation Moluccans after identifying individuals with Moluccan origin in administrative records and restricting that sample to those who were born in municipalities with settlements in years 1951-1968. As described earlier, we then merged this file with education and labor market registers and settlement characteristics. We could not find information on distance to church and hospital and type of housing for some of the settlements that we identified during our archive search. In addition, there were no records for age of five individuals’ mothers. We also lose more than 500 observations in income regressions as we could not find information on these individuals’ income in administrative records4.

3 Since distance to church and distance to hospital are highly correlated, we use distance to church to test our hypothesis in our first of regressions. 4 For the time being, we cannot provide summary statistics of personal income as our tables are checked by Statistics Netherlands for their compatibility with confidentiality rules on presenting administrative data to public.

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We lose more than half of our sample in regressions for educational attainment. Administrative data on individuals’ educational attainment in the Netherlands is not available for most of the population. More reliable records are available for those who are younger and who have higher educational attainment. Hence, our results for educational attainment should be interpreted with caution.

Table 2: Summary statistics for outcome and independent variables

Outcome variables N Mean Std.Dev. Min Max Educational attainment* 3,410 1.59 0.84 Primary 396 0.12 Lower secondary 1,017 0.30 Upper secondary 1,604 0.47 0 1 Tertiary 393 0.11 0 1 Gross annual HH income** 8,101 65,833.87 37,975.05 Employed at age 55 5,318 0.75 0.43 0 1 Parents’ characteristics Mother’s birth year 8,670 1927.57 6.70 1903 1949 Father’s birth year 8,554 1922.14 7.16 1886 1951 Individual controls Age 8,675 55.80 4.17 45 62 Female 8,675 0.50 0.50 0 1 Income earners in HH 8,101 1.78 1.11 0 7 Duration in residential area (years) 8,675 11.59 5.30 0.15 26.83 Residential area characteristics Distance to church 8,377 4.98 5.29 0.3 29.1 Distance to hospital 8,377 9.64 7.07 1.15 33.1 Lower primary school 8,675 0.66 0.48 0 1 Type of housing* 7,238 0.19 0.46 0 2 Wooden barrack 6,115 0.85 Metal barrack 896 0.12 Stone building 227 0.03 Work opportunity 8,675 0.37 0.48 0 1 Size of residential area 8,675 997.73 934.58 0 2,407 Duration of residential area (years) 8,675 18.03 10.96 1.26 41.28 Source: Authors’ tabulation Notes: * Mean values for each category of educational attainment and type of housing represent the share of sample in each sub-category. It should be noted that educational attainment is not a categorical dependent variable; we created two separate dummies for having at least upper secondary education and having at least tertiary education. It is represented with all categories in this table to provide information on educational attainment of the sample. ** We cannot provide minimum and maximum values for income variables due to the confidentiality rules of Statistics Netherlands.

Figure 2: Distribution of the birth years of Moluccans’ parents

Panel (a): Mothers’ birth year Panel (b): Fathers’ birth year

Source: Authors’ tabulation

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Our sample consists of second generation Moluccans who were born between 1951 and 1968. Hence, individuals in our sample were 45 to 62 years old in 2013. Average age in our sample is around 56. Half of our sample is female. These second generation immigrants lived in their settlements for 12 years on average, which is expected considering many of the settlements were closed by the end of 1960s. Mean year of birth for second generation Moluccans’ mothers is 1927 while that of fathers is 1922. This means that first generation Moluccan women arrived to the Netherlands were 24 and men were 29 on average. Hence, they were in their reproductive ages. Figure 2 represents distribution of the birth years of mothers’ and fathers’ of individuals in our sample. Histograms reveal that parents of few individuals in our sample were born around 1950. It may be due to the mistakenly recorded registers. Still, these people can have children in ‘woonoorden’ during their settlement period as they would reach their reproductive ages by 1968 if we assume that these records are fully correct.

Second generation Moluccans in our sample were born and lived in settlements in about 5 kilometer- distance to the nearest church and 10 kilometer-distance to the nearest hospital, on average; however, these characteristics substantially vary across individuals who were born in different settlements as Table 2 illustrates. 66% of them lived in settlements with facilities for lower primary education and 37% of them were born in settlements close to work opportunities. There is relatively less variation in housing conditions they were exposed to since 85% of them lived in wooden barracks. Almost half of these second generation Moluccans for whom we could find information have attained an upper secondary school diploma. However, it should be kept in mind that this ratio might be inflated due to the missing information in education registers for older and lower educated individuals. Mean annual gross household income was around €65,000 in 2013. 75% of those who reached at least 55 by 2013 were active in labor market when they were 55.

4. Methodology

4.1. Estimation strategy

Our aim is to estimate the effects of physical characteristics of neighborhoods on immigrants’ integration. To do this, we estimated the following regression.

푦푖푗 = 훼 + 훽푆푒푡푡푙푒푚푒푛푡푗 + 훾푋푖 + 휀푖푗 where 푦푖푗 represents outcome of individual 푖 born in municipality with Moluccan settlement 푗.

푆푒푡푡푙푒푚푒푛푡푗 is characteristics of each municipality with Moluccan settlements. 푋푖 is individual controls such as age and sex. In order to exclude any potential relocation decision of government based on parents’ age, we also include mothers’ birth year as a balancing control. Finally, 휀푖푗 is the error term.

We argue that 훽 shows causal effect of each neighborhood characteristics on a certain outcome of interest as we exploit the random assignment of Moluccans who arrived to the Netherlands in 1951 by Dutch government to residential areas. These residential areas differ from each other as explained in previous sections in detail. Thus, Dutch government led to an unintentional variation in conditions that Moluccans and their children were exposed to. First generation Moluccans had a homogeneous background. They were soldiers of KNIL and their families, were poorly educated, and did not have a good command of Dutch. Hence, they mainly relied on government’s guidance for housing. In this respect, we believe in that it is difficult for immigrant families in these conditions to have their own decision on the places they would like to settle. Since families were not able to self-select themselves

10 into certain locations, the impact of physical neighborhood characteristics on their children’s integration to the Dutch society must be determined by the variation that Dutch government led to.

Our first neighborhood characteristics is distance to church, which is a proxy of the distance of Moluccan community to native Dutch community. Churches were also centers of organization for provision of some basic services that were left by the government to private organizations (Oostindie, 2011). Hence, it is also a proxy of easiness of the access to some basic needs. Hence, we hypothesize that increase in the distance between Moluccan settlements and the nearest churches has a negative impact on educational attainment, labor market attachment, and earnings of second generation Moluccans. Similarly, distance to hospital is a proxy of being able to access higher education and healthcare facilities in better quality, also other amenities associated with city life which may increase human and social capital. Thus, we expect to find a negative relationship between distance to hospital and second generation Moluccans’ education and labor market outcomes.

Some settlements had on-site facilities to provide basic education to children and larger settlements were more likely to have these facilities. Our expectation is ambiguous for the effect of this variable on later life outcomes of second generation Moluccans. On the one hand, it may have a positive effect as such an on-site facility provides an immediate access to education. On the other hand, it may have a negative effect as, unlike children born in settlements without these facilities, children having access to on-site schooling did not travel to go to schools to which their native Dutch peers attended. Hence, their encountering with native Dutch community might be postponed which may cause a disadvantage in their early affinity to Dutch community.

Another variation across settlements comes from the type of housing. As shown in Table 2, 85% of Moluccans in our sample lived in wooden barracks which we assume to have the worst sanitary conditions compared to metal barracks and stone buildings. In this respect, type of housing is a proxy of health conditions that second generation Moluccans were exposed to during their formative years of human capital. We assume that metal barracks are better than wooden ones in terms of durability and endurance to weather conditions. Stone buildings were probably the best among them as they might have access to basic facilities such as running water or sewage, and were better sheltered. We expect to find a positive effect of living in a better shelter on later life outcomes of interest.

Finally, availability of employment opportunity nearby settlements creates another source of variation. Having such an opportunity nearby settlement is expected to increase the likelihood of having labor market attachment for residents living there. If parents of second generation Moluccans had such an easy labor market attachment, it may positively affect the conditions that their children lived in. Parents’ having higher income due to the jobs they found nearby their settlements is expected to have a positive impact on their children’s educational attainment and health conditions.

4.2. Balance checks

As we cover a long period of time for our analyses of second generation immigrants (i.e. 17 years between 1951-1968), it can be argued that some Moluccans were likely to leave their initial residential areas. If it is so, who left becomes an important question to answer whether there is a sample selection problem or not. In order to eliminate this concern, we provide balancing regressions in this section. As our data is based on administrative records, we do not have extensive information about background characteristics of first-generations Moluccans who were shipped to the Netherlands. The only information available is parents’ birth year. Hence, we regress mothers’ and fathers’ age on settlement characteristics to check whether there is a homogeneous distribution Moluccans across settlements.

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Table 3: Balance check of mother’s birth year w.r.t. settlement characteristics

Identified Moluccan mothers giving birth in years All mothers All 1951-1955 1951 1954 1968 Variables (1) (2) (3) (4) (5) (6) Distance to church 0.064 0.093 0.017 -0.311*** -0.016 -0.244 (0.060) (0.059) (0.079) (0.097) (0.056) (0.213) Distance to hospital -0.140*** -0.149*** 0.005 0.277*** 0.016 0.336 (0.049) (0.048) (0.067) (0.076) (0.060) (0.241) Lower primary school -2.543*** -2.836*** -1.069 1.304 -1.635* 2.403 (0.622) (0.615) (0.968) (0.972) (0.909) (3.569) Type of house (base: Wooden barrack) Metal barrack 0.051 0.076 -0.223 0.955 -0.098 5.321*** (1.517) (1.671) (1.213) (1.091) (0.929) (1.709) Stone building -1.019 -0.327 -1.028 2.212 -0.855 (1.765) (1.803) (1.215) (2.243) (1.769) Work opportunity 4.001*** 4.151*** 1.582 -1.647 2.558** 3.714* (1.227) (1.207) (1.582) (1.706) (1.094) (1.904) Size of residential area -0.002*** -0.002*** -0.000 0.000 -0.001** -0.002 (0.001) (0.001) (0.000) (0.000) (0.000) (0.002) Duration of residential area 0.239** 0.282** -0.002 0.441*** -0.005 -0.632** (0.101) (0.106) (0.128) (0.122) (0.119) (0.257) Constant 1,928.489*** 1,927.765*** 1,925.015*** 1,916.318*** 1,925.950*** 1,944.422*** (1.232) (1.283) (2.046) (1.922) (2.191) (3.474) Observations 1,477 1,339 438 334 558 69 R-squared 0.085 0.098 0.007 0.090 0.026 0.114 F-stat F(8,51) F(8,51) F(8,41) F(8,26) F(8,40) F(7,12) 18.58 21.48 0.60 9.01 1.64 8.90 Prob>F 0.0000 0.0000 0.7714 0.0000 0.1435 0.0006 Source: Authors’ estimation Notes: Dependent variable is mother’s birth year. OLS coefficients are presented in columns. Cluster-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 4: Balance check of father’s birth year w.r.t settlement characteristics

Identified Moluccan fathers having child in years All fathers All 1951-1955 1951 1954 1968 Variables (1) (2) (3) (4) (5) (6) Distance to church -0.054 -0.054 -0.048 0.001 0.004 -0.055 (0.070) (0.083) (0.076) (0.113) (0.071) (0.099) Distance to hospital -0.111* -0.063 0.106 0.066 0.017 0.251*** (0.058) (0.072) (0.067) (0.083) (0.065) (0.037) Lower primary school -1.446* -1.467 -1.255 -0.190 -1.417* 11.426*** (0.773) (0.931) (0.892) (1.834) (0.790) (1.134) Type of house (base: Wooden barrack) Metal barrack -0.459 -1.162 -0.541 -1.301 -0.339 -3.865*** (1.456) (1.326) (0.647) (1.311) (1.067) (1.225) Stone building -2.146 -2.450* -0.424 2.247* -1.369 (1.724) (1.240) (1.051) (1.294) (1.794) Work opportunity 2.940* 4.147** 1.350 1.886 0.861 -5.048*** (1.479) (1.808) (1.126) (2.108) (1.059) (0.830) Size of residential area -0.002*** -0.003*** -0.000* -0.001** -0.000 -0.004*** (0.001) (0.001) (0.000) (0.000) (0.000) (0.001) Duration of residential area 0.197 0.142 -0.017 0.319** 0.115 -1.004*** (0.139) (0.153) (0.063) (0.127) (0.084) (0.088) Constant 1,925.023*** 1,924.974*** 1,920.993*** 1,914.021*** 1,918.755*** 1,947.381*** (1.787) (2.114) (0.907) (2.021) (1.074) (1.365) Observations 1,116 733 252 242 398 54 R-squared 0.084 0.085 0.019 0.048 0.014 0.229 F-stat F(8,52) F(8,50) F(8,38) F(8,29) F(8,38) F(7,12) 9.08 4.78 1.45 3.20 1.03 95.05 Prob>F 0.0000 0.0002 0.2066 0.0100 0.4340 0.0000 Source: Authors’ estimation Notes: Dependent variable is father’s birth year. OLS coefficients are presented in columns. Cluster-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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Table 3 and 4 illustrates balancing regressions for mothers’ and fathers’ birth year, respectively. Column 1 shows results for parents available in the first Moluccan file that we constructed by tracing individuals with the same surnames with passengers boatlifted to the Netherlands. As mentioned earlier, we then merged this file with another register linking parents to their children in order to identify siblings. Column 2 shows results for the sample of parents identified in that register. We also run balancing regressions for parents’ who had children in different years in order to see whether there is a pattern in the age distribution across settlements. Year 1954 is selected as many of the births happened in that year; hence, it may reflect age distribution of more parents.

Column 5 in Table 3 shows that parents in settlements with a primary school were older. Similarly, parents in larger settlements were older too. The explanation might be that individuals in larger settlements stayed longer as those settlements lasted longer and lower primary school facilities were provided in those settlements as there were many more children needed education. Hence, the relationship that we observe here might be related to the dynamics after government’s initial assignment since there is no reason to believe that government had a deliberate plan to assign older individuals to larger settlements with schooling facilities.

Column 5 in Table 3 also shows that younger mothers tend to be in settlements with employment opportunities nearby. A similar association is also present for fathers in Column 5 of Table 4 although it is not significant. This relationship might not be incidental. There might have been a deliberate effort government to locate younger individuals to settlements nearby factories or wind mills in order to utilize their manpower. Still, these two tables reveal that there is no clear significant pattern in distribution of parents to settlements according to their age. Hence, there is no clue of a selection mechanism which may affect random assignment. In case, we include mother’s birth year as a balancing control in our regressions to take into account a potential selection based on one of the characteristics of parents. Our reason to choose mother’s birth year is that fathers’ age is distributed in a relatively more balanced way compared to mothers and mothers’ and fathers’ age is highly correlated.

We ran another set of balancing regressions of the characteristics of population placed into Moluccan settlements. We found number of singles and families in each settlement during the time that settlements were open during our archive search in the Moluccan Historical Museum. The logic behind these regressions is that singles might be more likely to leave a settlement to find a job or due to marriage. In order to test it, we use the average share of singles throughout the period that settlements were open in Column 1 and share of singles in a settlement in 1955 in Column 25. Then we compared our results presented in Table 5. As can be seen, many of the coefficients are very similar; hence, it decreases the possibility of majority of people having a tendency to leave their settlements. We observe that share of singles was higher in settlements with work opportunity nearby. It may indicate that government had a deliberate effort to locate single and young individuals to settlements near factories to utilize their manpower. Hence, this finding support to include mother’s birth year as a balancing control into our regressions.

Some residential areas were closed earlier than others. As can be seen in Table 1, larger residential areas stayed longer. The negative relationship between share of singles and duration of a settlement may be driven by the positive relationship between size and duration of settlement as size of the camp is in denominator of the dependent variable. Singles may be more likely to leave the camp as they can be more mobile due to marriage or employment. This may decrease the size of a settlement and lead to

5 We could find this information only for a few number of settlements in 1951. Hence, we used information on population in 1955 which is available for more settlements.

13 its closure. However, Column 2 in Table 5 shows that coefficient of duration of residential area is insignificant. Thus, we conclude that moving out of the camp is not such a major issue that can lead to a selection bias in our results at least in early years of settlements. Still, it indicates a necessity to check robustness of our results on a sample born in early years of settlements.

Table 5: Balance checks for share of singles in settlements

Share of singles Share of singles in 1955 Variables (1) (2) Distance to church -0.005 0.000 (0.004) (0.004) Distance to hospital 0.002 -0.003 (0.003) (0.003) Lower primary school -0.076 -0.086** (0.046) (0.042) Type of house (base: Wooden barrack) Metal barrack 0.068 0.051 (0.055) (0.047) Stone building 0.166** 0.002 (0.079) (0.078) Work opportunity 0.119* 0.149** (0.062) (0.059) Duration of residential area -0.009* -0.008 (0.005) (0.005) Constant 0.175** 0.230*** (0.079) (0.082) Observations 57 45 R-squared 0.275 0.272 F-test 2.660 1.980 Prob>F 0.0205 0.0847 Source: Authors’ estimation Notes: Dependent variable is average share of singles in residential areas in Column 1 and share of singles in residential areas in 1955. OLS coefficients are presented in the column. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

5. Results

5.1. Educational attainment

Our first outcome variable is having at least an upper secondary school diploma. The results are provided in Table 6. We found an insignificant negative relationship between distance to church and having at least an upper secondary degree. Our results seem robust for those born in early years of settlements. It seem that the insignificant effect we found is driven by heterogeneous results for men and women. Column 7 shows that a kilometer increase in distance to church causes 0.7% less likelihood of a woman born in a Moluccan settlement to have upper secondary degree. However, we do not observe this significant negative impact for men. This finding is in line with the findings of earlier research. In traditional communities, resource allocation in family may favor boys more than girls since boys can be seen as a means of support for family by parents during their old age. Hence, having more resources nearby residential areas might make women get education at a lower cost and improves resource allocation in favor of girls. Considering that churches were centers of provision of some basic needs for Moluccan settlements, being distant to churches might have led to a negative impact on girls who may be more sensitive to the conditions around in their formative years. Our second outcome variable is having at least a tertiary degree. The results are provided in Table 7. We could not find any significant effect of physical neighborhood characteristics except having lived in metal barracks. Column 7 in Table 7 shows that girls who lived in better sheltered houses had higher likelihood to have a tertiary degree. Again, we do not observe this effect for boys.

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Table 6: Results for having at least an upper secondary school degree

Full sample Born in 1951-1956 With controls With controls Without Whole Female Male Without Whole Female Male controls sample controls sample Variables (1) (2) (3) (4) (5) (6) (7) (8) Pull factors Distance to church -0.002* -0.001 -0.004** 0.002 -0.003 -0.003 -0.007** 0.002 (0.001) (0.001) (0.002) (0.002) (0.002) (0.003) (0.003) (0.005) Employment opportunity 0.013 0.018 0.031 0.011 0.033 0.043** 0.062 0.034 (0.018) (0.013) (0.024) (0.020) (0.023) (0.021) (0.040) (0.030) Observations 3,232 3,228 1,578 1,650 1,454 1,453 688 765 R-squared 0.001 0.023 0.045 0.029 0.002 0.027 0.050 0.040 Push factors Lower primary school -0.004 0.003 0.028 -0.016 0.005 0.018 0.015 0.011 (0.019) (0.018) (0.031) (0.031) (0.038) (0.039) (0.063) (0.071) Size of settlement -0.000 -0.000 -0.000 0.000 -0.000 -0.000 -0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Duration of settlement -0.000 -0.001** -0.000 -0.002*** -0.000 -0.001 -0.001 -0.001 (0.000) (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Observations 3,348 3,344 1,630 1,714 1,520 1,519 717 802 R-squared 0.001 0.021 0.042 0.028 0.000 0.023 0.044 0.035 Health conditions Type of housing Metal barrack 0.021 0.015 -0.018 0.039 -0.011 -0.004 -0.073 0.039 (0.031) (0.023) (0.047) (0.034) (0.028) (0.025) (0.066) (0.072) Stone building 0.048 0.064 0.060** 0.060 0.021 0.008 -0.029 0.020 (0.040) (0.040) (0.027) (0.083) (0.042) (0.049) (0.042) (0.076) Observations 2,819 2,817 1,385 1,432 1,218 1,217 585 632 R-squared 0.000 0.023 0.045 0.032 0.000 0.027 0.056 0.047 Source: Authors’ estimation Notes: Dependent variable is at least having an upper secondary school degree. Control variables are sex, birth year dummies, and mother’s birth year dummies. Cluster-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 7: Results for having at least a tertiary degree

Full sample Born in 1951-1956 With controls With controls Without Whole Female Male Without Whole Female Male controls sample controls sample Variables (1) (2) (3) (4) (5) (6) (7) (8) Pull factors Distance to church -0.000 -0.000 0.001 -0.001 0.000 0.001 0.000 0.000 (0.002) (0.002) (0.001) (0.003) (0.003) (0.003) (0.002) (0.005) Employment opportunity 0.005 0.002 0.002 0.001 -0.001 -0.003 -0.010 0.002 (0.014) (0.014) (0.019) (0.017) (0.013) (0.015) (0.024) (0.025) Observations 3,232 3,228 1,578 1,650 1,454 1,453 688 765 R-squared 0.000 0.017 0.037 0.037 0.000 0.030 0.038 0.041 Push factors Lower primary school 0.009 0.005 0.026 -0.016 -0.006 -0.005 0.031 -0.046 (0.016) (0.016) (0.019) (0.026) (0.023) (0.024) (0.027) (0.044) Size of settlement -0.000* -0.000** -0.000* -0.000 -0.000 -0.000 -0.000** 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Duration of settlement 0.001** 0.001* 0.001 0.001 0.000 0.000 0.000 0.000 (0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.001) (0.001) Observations 3,348 3,344 1,630 1,714 1,520 1,519 717 802 R-squared 0.001 0.019 0.038 0.036 0.000 0.031 0.039 0.042 Health conditions Type of housing Metal barrack 0.027 0.019 0.036 -0.001 0.041 0.030 0.049* 0.003 (0.017) (0.019) (0.023) (0.028) (0.033) (0.037) (0.026) (0.060) Stone building 0.035 0.037 0.069 -0.007 0.026 0.019 0.112 -0.070 (0.048) (0.047) (0.063) (0.084) (0.022) (0.018) (0.080) (0.047) Observations 2,819 2,817 1,385 1,432 1,218 1,217 585 632 R-squared 0.001 0.020 0.045 0.036 0.002 0.037 0.045 0.047 Source: Authors’ estimation Notes: Dependent variable is at least having a tertiary degree. Control variables are sex, birth year dummies, and mother’s birth year dummies. Cluster-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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5.2. Employment

Our third outcome variable is being employed in 2013. Results are provided in Table 8. We found a negative impact of distance to church on women’s employment as expected but this effect is not robust. Unexpectedly, we found a positive robust effect of distance to church on men’s employment. This may be due to the fact that men’s being more likely to be employed in manual factory jobs which tend to agglomerated further away from town and city centers. It might be likely that men’s being located settlements distant to town centers have increased their likelihood of finding a job in factories outside town and city centers.

Table 8: Results for being employed in 2013

Full sample Born in 1951-1956 With controls With controls Without Whole Female Male Without Whole Female Male controls sample controls sample Variables (1) (2) (3) (4) (5) (6) (7) (8) Pull factors Distance to church -0.001 -0.000 -0.002** 0.002* 0.001 0.001 -0.001 0.002** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Employment opportunity 0.018 0.012 0.031* -0.008 0.025* 0.025** 0.052*** -0.003 (0.016) (0.011) (0.017) (0.013) (0.013) (0.011) (0.017) (0.014) Observations 7,805 7,802 3,913 3,889 3,668 3,667 1,794 1,873 R-squared 0.001 0.027 0.044 0.025 0.001 0.026 0.042 0.026 Push factors Lower primary school 0.022 0.021 0.023 0.019 0.017 0.019 0.016 0.018 (0.017) (0.015) (0.024) (0.018) (0.020) (0.019) (0.032) (0.023) Size of settlement -0.000*** 0.000 0.000 -0.000 -0.000 0.000 0.000 -0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Duration of settlement -0.000 -0.001*** -0.001*** -0.000 -0.002*** -0.001*** -0.003*** -0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Observations 8,069 8,066 4,043 4,023 3,824 3,823 1,864 1,959 R-squared 0.001 0.027 0.042 0.025 0.001 0.024 0.041 0.024 Health conditions Type of housing Metal barrack 0.016 0.015 0.023 0.009 0.031 0.021 0.036 0.007 (0.016) (0.020) (0.031) (0.026) (0.019) (0.019) (0.035) (0.028) Stone building -0.046*** 0.006 0.011 0.007 -0.002 0.017 0.046 -0.009 (0.015) (0.024) (0.054) (0.044) (0.018) (0.026) (0.062) (0.054) Observations 6,732 6,730 3,399 3,331 3,044 3,043 1,503 1,540 R-squared 0.000 0.026 0.040 0.026 0.001 0.024 0.049 0.028 Source: Authors’ estimation Notes: Dependent variable is being employed in 2013. Control variables are sex, birth year dummies, and mother’s birth year dummies. Cluster-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

We find a significant positive effect of having work opportunity nearby settlement on females’ labor market attachment. This effect is robust across two sample of interest. This finding can be interpreted in two ways. Having work opportunity nearby a settlement may increase parents’ likelihood of being employed and improve resources of families to educate their children in favor of girls. This effect is obvious in Column 6 of Table 6. Hence, better educated Moluccan women had better chance to find a job in labor market. Another way of interpreting this finding is that having an employment opportunity nearby might improve access of women to labor market in earlier years. As they have had more job experience, they might have stayed attached to the labor market in their future life.

5.3. Personal and household income

Our fourth outcome variable is personal income. Results provided in Table 9 illustrate that being distant local native community and town center has a negative impact on personal income but it is usually insignificant except for the male sample in Column 8. Being distant to local community and

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Table 9: Results for personal income

Full sample Born in 1951-1956 With controls With controls Without Whole Female Male Without Whole Female Male controls sample controls sample Variables (1) (2) (3) (4) (5) (6) (7) (8) Pull factors Distance to church -0.008** -0.005 -0.006 -0.004 -0.006 -0.006 -0.001 -0.012*** (0.004) (0.003) (0.007) (0.003) (0.004) (0.004) (0.006) (0.004) Employment opportunity 0.027 0.019 0.061 -0.024 0.039 0.064 0.051 0.068 (0.039) (0.037) (0.069) (0.052) (0.043) (0.046) (0.075) (0.046) Observations 7,805 7,802 3,913 3,889 3,668 3,667 1,794 1,873 R-squared 0.000 0.042 0.025 0.013 0.000 0.058 0.029 0.018 Push factors Lower primary school 0.025 0.039 0.099 -0.008 0.029 0.088 0.123 0.026 (0.059) (0.055) (0.103) (0.056) (0.081) (0.082) (0.137) (0.088) Size of settlement -0.000 0.000 0.000 0.000 0.000 0.000 -0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Duration of settlement -0.002 -0.004** -0.003 -0.006*** -0.003** -0.005*** -0.006** -0.003* (0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.003) (0.002) Observations 8,069 8,066 4,043 4,023 3,824 3,823 1,864 1,959 R-squared 0.000 0.042 0.024 0.014 0.000 0.058 0.029 0.017 Health conditions Type of housing Metal barrack -0.012 -0.031 -0.046 -0.015 0.122* 0.057 0.225** -0.092 (0.070) (0.061) (0.128) (0.064) (0.073) (0.068) (0.107) (0.092) Stone building -0.159*** 0.002 -0.080 0.113 0.081 0.134 0.205 0.030 (0.054) (0.103) (0.112) (0.137) (0.057) (0.127) (0.142) (0.154) Observations 6,732 6,730 3,399 3,331 3,044 3,043 1,503 1,540 R-squared 0.000 0.044 0.026 0.018 0.000 0.061 0.034 0.027 Source: Authors’ estimation Notes: Dependent variable is log personal income in 2013. Control variables are sex, birth year dummies, and mother’s birth year dummies. Cluster-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 10: Results for household income

Full sample Born in 1951-1956 With controls With controls Without Whole Female Male Without Whole Female Male controls sample controls sample Variables (1) (2) (3) (4) (5) (6) (7) (8) Pull factors Distance to church -0.006** -0.005** -0.005* -0.005* -0.003 -0.003 -0.000 -0.005 (0.003) (0.002) (0.003) (0.003) (0.002) (0.002) (0.002) (0.003) Employment opportunity -0.019 -0.028 -0.026 -0.039 0.010 0.003 0.030 -0.023 (0.033) (0.027) (0.045) (0.024) (0.017) (0.021) (0.040) (0.038) Observations 7,834 7,831 3,930 3,901 3,696 3,695 1,808 1,887 R-squared 0.001 0.010 0.016 0.019 0.000 0.011 0.021 0.019 Push factors Lower primary school 0.000 -0.003 0.002 -0.012 0.037 0.044 0.029 0.044 (0.034) (0.032) (0.045) (0.041) (0.037) (0.036) (0.050) (0.057) Size of settlement -0.000*** -0.000** -0.000** -0.000 -0.000 -0.000 -0.000 -0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Duration of settlement 0.002** 0.002* 0.003** 0.000 0.001 0.001* -0.001 0.002*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Observations 8,101 8,098 4,060 4,038 3,855 3,854 1,878 1,976 R-squared 0.001 0.010 0.018 0.018 0.000 0.011 0.024 0.019 Health conditions Type of housing Metal barrack -0.008 -0.009 0.072* -0.074 -0.002 -0.004 0.067 -0.067 (0.040) (0.037) (0.041) (0.056) (0.025) (0.024) (0.046) (0.044) Stone building 0.086** 0.123*** 0.124** 0.129*** 0.123** 0.137*** 0.108 0.165*** (0.042) (0.031) (0.055) (0.027) (0.055) (0.047) (0.069) (0.040) Observations 6,761 6,759 3,413 3,346 3,071 3,070 1,514 1,556 R-squared 0.000 0.011 0.021 0.020 0.001 0.012 0.030 0.020 Source: Authors’ estimation Notes: Dependent variable is log personal income in 2013. Control variables are sex, birth year dummies, and mother’s birth year dummies. Cluster-robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

17 city center might have negatively affected integration of Moluccan male which in turn caused an increase in probability of finding low paying jobs possibly in manufacturing outside town centers. We also find that growing up in better sheltered houses increases future income of women who may be more sensitive to immediate environmental conditions than men. We also run set of regressions on household income. The results are provided in Table 10. We did not find any significant of physical neighborhood characteristics on household income.

6. Discussion

We find limited but mostly expected results for the physical characteristics of neighborhoods that we tested. Our results show that increase in distance to church, a proxy of distance to provision of basic needs and local native community, decreases the likelihood of women having higher education, and men having higher earnings. Unexpectedly, we found a positive effect of increase in distance to church on men’s probability of being employed. We interpret this finding as that being located in settlements distant to town centers might increase probability of finding a manual job in manufacturing sector in which factories tend to locate outside town and city centers. However, these jobs are usually low paying jobs. Furthermore, distance to native community may negatively affect integration which in turn lead to being less knowledgeable about higher paying jobs and lower returns to skills due to weak cultural capital. We also find that having grown up in settlements with employment opportunities increases labor market attachment of women. It can be either due to having the opportunity of an earlier access to the labor market or due to having better education thanks to increasing family resources.

We believe that our findings are causal. It is because first generation Moluccans had a homogeneous background and weak cultural and human capital when they arrived to the Netherlands. Hence, they relied on government to provide a shelter to them. Their likelihood of moving to another place or choosing the neighborhood that they would like to live was very low especially in the early years of their arrival. We provided balancing regressions on age distribution of parents and share of singles across settlements. These regressions signal that there was no deliberate attempt of government to place Moluccans to certain locations based on their characteristics, except the possibility that young and single first comers were located to settlements with work opportunities nearby probably in order to utilize their manpower. We included mother’s birth year in our regressions as a balancing control to solve the potential effects of such a selection. In addition, we provide robustness checks on a restricted sample of second generation Moluccans born in the following five years right after their arrival. We believe that results of these regressions eliminate any suspicion on the exogeneity due to parents’ leaving their settlements and making their own location choice in later years of their arrival.

A majority Moluccans are still living in places very close to their initial settlement. 56% of second generation Moluccans born in settlements are currently living in a sphere of at most 50 kilometers away from their birth place. Still, our analysis might be upward biased due to the possibility that there can be Moluccans who were born in the same municipalities but were not exposed to the conditions in settlements. It can be likely especially for children of inter-ethnically married couples. In addition, it should be kept in mind that we may not be able to trace some of the Moluccan women due to their name change after marriage. Furthermore, we note that our results for educational attainment may be biased due to the missing records in education registers of Statistics Netherlands. Despite of all these potential problems, it is obvious that settlement location had an effect on Moluccans’ integration in the long run. We constructed a control group of Dutch individuals who were born in the same municipalities in the same time period to strengthen our standpoint.

18

6.1. Comparison of control group and Moluccan group

We obtained registers of whole Dutch population from Statistics Netherlands. Then, we restricted the sample to native Dutch who were born in the municipalities where Moluccan settlements were in years 1951-1968. We followed the same process as we did for Moluccan data file. We merged Dutch sample with education, employment history and address registers. Then, we merged this file with our Moluccan data file to compare outcomes of Dutch individuals with Moluccans who were born in the same places and in the same time period. We ran regressions for each outcome variable by including municipality fixed effects, a dummy for being Moluccan or Dutch, interaction of Moluccan dummy and municipality dummies, and standard controls such as sex and birth year.

Results for having at least an upper secondary degree are presented in Appendix B. We only presented baseline coefficient for being Moluccan and interaction terms due to the limited space. As this table shows, being a Moluccan is associated with less likelihood of attaining a upper secondary degree. However, this relationship disappears when controls are included. In addition, this relationship does not exist for Moluccan males. Despite of this, we observe that Moluccans born in 50 of 62 municipalities with settlements have less likelihood to obtain an upper secondary degree compared to Dutch natives born in the same municipalities. We have similar results for personal income too. This shows that even though Moluccans were exposed to the same period effects that Dutch individuals were exposed to, they did not share the same environment conditions with them although they were born in the same municipalities.

7. Conclusion

Our results show that physical neighborhood characteristics are important for the integration second generation immigrants. How neighborhood characteristics such as ethnic concentration can affect outcomes of immigrants have been much studied but the role of physical characteristics haven’t been examined in detail. Our study contributes to the literature by filling this gap. We provide causal evidence by exploiting the random assignment of first generation Moluccans to settlements across the Netherlands when they were boatlifted from Indonesia in 1951. Since this case has similarities with the treatment of refugees in host countries, our results have policy implications especially on the integration of refugees.

Firstly, we find that distance to local native community matters. As distance to local community increases, encountering with local native culture becomes difficult. In turn, it affects the accumulation of cultural capital necessary to catch up natives in labor market and education. Hence, the closer refugee camps to cities and towns are, the faster the integration of immigrants will be. Secondly, having easy access to basic needs is important in terms of immigrant children’s health and education. However, we should note that we did not find any significant positive impact of having on-site lower primary education facilities on later life outcomes of second generation Moluccans. This means that easy access to education and health facilities is important for health and human capital development of children but the way of provision should not restrain their encountering with the local community. For instance, education being provided on-site leads to immigrant children’s having peers from their own ethnic community; hence, it may postpone their encountering with the local culture and restrain their accumulation of country-specific capital. Thirdly, refugees being able to access to local labor market is also important especially for women. Having an easy access to work opportunities nearby camps may enable women to reconcile their home duties with labor market and increase their labor market attachment. It also provides an opportunity to encounter with the natives working there so it positively affects cultural capital. Our findings reveal that these effects are heterogeneous. Women are more

19 affected by the immediate environmental conditions than men. It may be because resource allocation among family members in traditional societies may favor men more than women. Hence, any intervention which increases the resource pool might more positively affect women’s outcomes. This finding suggests that women can be more targeted in policy interventions to improve integration of immigrants.

References

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Katz, L., Kling, J., & Liebman, J. (2001). Moving to Opportunity in Boston: Early Results of a Randomized Mobility Experiment. The Quarterly Journal of Economics, 116(2), 607-654. Lazear, E. (1999). Culture and Language. Journal of Political Economy, 107(6), S95-S126. Oostindie, G. (2011). Postcolonial Netherlands: Sixty-five years of forgetting, commemorating, silencing. Amsterdam: Amsterdam University Press. Oreopoulos, P. (2003). The Long-Run Consequences of Living in a Poor Neighborhood. The Quarterly Journal of Economics, 118(4), 1533-1575. Patacchini, E., & Zenou, Y. (2012). Ethnic Networks and Employment Outcomes. Regional Science and Urban Economics, 42(6), 938–949. Portes, A. (1998). Social Capital: Its Origins and Applications in Modern Sociology. Annual Review of Sociology, 24, 1-24. Putnam, R. (1993). The Prosperous Community. The American Prospect, 4(13), 35-42. Sanbonmatsu, L., Kling, J., Duncan, G., & Brooks-Gunn, J. (2006). Neighborhoods and Academic Achievement: Results from the Moving to Opportunity Experiment. The Journal of Human Resources, XLI(4), 649-691. Tuynman-Kret, M. (1985). Molukkers van Huis uit - De Huisvestingssituatie van Molukkers in Nederland. Den Hague: Ministerie van Volkshuisvesting, Ruimtelijke Ordening en Milieubeheer. Wilson, W. (2012). The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy (2nd ed.). Chicago: The University of Chicago Press.

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Appendix A: Characteristics of Moluccan settlements

Table A.1: Moluccan settlements

Residential area Old municipality New municipality Province Region Opening Closure Residents Marum Marum Marum Groningen North 1951/06 1966/04 336 Oranje Ooststellingwerf Ooststellingwerf Friesland North 1951/05 1961/07 143 Ybenheer Ooststellingwerf Ooststellingwerf Friesland North 1951/05 1968/07 221 Geesbrug Oosterhesselen Coevorden Drenthe North 1951/04 1968/07 144 Stuifzand Ruinen Hoogeveen Drenthe North 1951/04 1963/10 68 St. Joseph Enschede Enschede Overijssel East 1951/04 1954/05 313 Eerde Ommen Ommen Overijssel East 1952/09 1961/09 136 Laarbrug Ommen Ommen Overijssel East 1951/04 1966/12 266 Beugelen Staphorst Staphorst Overijssel East 1951/04 1963/06 249 Conrad Staphorst Staphorst Overijssel East 1951/05 1965/11 136 Vosschenbosch Wierden Wierden Overijssel East 1951/05 1975/07 649 Golflinks Arnhem Arnhem Gelderland East 1956/09 1968/01 175 Onderlangs Arnhem Arnhem Gelderland East 1955/05 1955/09 De Biezen Barneveld Barneveld Gelderland East 1951/04 1973/06 632 De Schaffelaar Barneveld Barneveld Gelderland East 1951/04 1955/10 De Kemp Wehl Doetinchem Gelderland East 1953/05 1965/02 86 De Bruynhorst Ede Ede Gelderland East 1954/05 1963/12 116 De Zwaluwenburg Elburg Elburg Gelderland East 1951/05 1955/11 Vaassen (Berkenoord) Epe Epe Gelderland East 1958/05 1981/11 813 De Hogehorst Groesbeek Groesbeek Gelderland East 1952/01 1962/07 122 Schutsluizen Tiel Tiel Gelderland East 1951/05 1962/01 167 Elzenpasch Tiel Tiel Gelderland East 1951/05 1961/06 79 Teuge Voorst Voorst Gelderland East 1951/05 1962/10 465 Vosseveld Winterswijk Winterswijk Gelderland East 1959/11 1969/11 530 Almere Huizen Huizen Noord-Holland West 1951/03 1965/06 326 Medemblik Medemblik Medemblik Noord-Holland West 1959/10 1961/06 292 Coehoorn Muiden Muiden Noord-Holland West 1951/05 1962/12 284 IJsseloord Capelle aan den IJssel Capelle aan den Ijssel Zuid-Holland West 1958/04 1976/06 715 Kamp Q Ridderkerk Ridderkerk Zuid-Holland West 1952/01 1958/09 21 Singel Woerden Woerden Utrecht West 1951/05 1967/06 165 Utrechtse Straatweg Woerden Woerden Utrecht West 1951/05 1965/06 198 Kazerne Woerden Woerden Utrecht West 1951/05 1965/06 172 Middelburg II Middelburg Middelburg Zeeland West 1954/04 1961/11 73 Middelburg I Middelburg Middelburg Zeeland West 1955/02 1965/12 101 Kruiningen II Kruiningen Reimerswaal Zeeland West 1954/10 1963/07 154 Kruiningen I Kruiningen Reimerswaal Zeeland West 1954/10 1960/10 167 Koudekerke Koudekerke Veere Zeeland West 1951/06 1962/12 108

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Serooskerke Serooskerke Veere Zeeland West 1951/06 1963/12 99 Grijpskerke Grijpskerke Veere Zeeland West 1951/06 1952/05 Westkapelle Westkapelle Veere Zeeland West 1951/09 1960/12 142 Vlissingen I Vlissingen Vlissingen Zeeland West 1954/07 1962/11 248 Vliegveld Souburg Vlissingen Vlissingen Zeeland West 1955/05 1965/06 77 Vlissingen II Vlissingen Vlissingen Zeeland West 1951/04 1956/08 Vierlingsbeek Vierlingsbeek Boxmeer Noord-Brabant South 1953/03 1968/07 51 Villheide Mill en Sint Hubert Mill en Sint Hubert Noord-Brabant South 1951/04 1961/03 116 Baarschot Oost-, West- en Middelbeers Oirschot Noord-Brabant South 1955/06 1959/11 159 Lunetten Vught Vught Noord-Brabant South 1951/03 1992/06 2,407 Oude Molen Bergen Bergen Limburg South 1952/06 1968/01 129 Genapium Gennep Gennep Limburg South 1951/05 1968/12 134 Plasmolen Mook en Middelaar Mook en Middelaar Limburg South 1951/06 1960/09 63 Maashaven Roermond Roermond Limburg South 1951/06 1963/12 105 Blerick Venlo Venlo Limburg South 1953/12 1968/12 183 Vlakwater Venray Venray Limburg South 1954/05 1968/01 138 Tungelroy Weert Weert Limburg South 1951/04 1968/12 166 Tienray Meerlo Horst aan de Maas Limburg South 1952/02 1962/12 58 Heythuysen Heythuysen Leudal Limburg South 1951/04 1961/04 40 Lage Mierde Hoge en Lage Mierde Reusel-De Mierden Noord-Brabant South 1954/09 1962/12 151 Montfort Montfort Roerdalen Limburg South 1951/11 1963/12 66 Wouw Wouw Roosendaal Noord-Brabant South 1952/12 1963/12 57 Kerkwerve Kerkwerve Schouwen-Duiveland Zeeland West 1951/07 1951/11 Burghsluis Burgh Schouwen-Duiveland Zeeland West 1952/02 1956/12 60 Brijdorpe Duivendijke Schouwen-Duiveland Zeeland West 1951/06 1952/09 Noordwelle Noordwelle Schouwen-Duiveland Zeeland West 1951/06 1951/09 Ruinen Ruinen De Wolden Drenthe North 1960/03 1962/12 58 De Fledders Norg Noordenveld Drenthe North 1951/05 1956/06 Klein Baal Bemmel Lingewaard Gelderland East 1952/05 1968/06 141 Beenderribben Steenwijk Steenwijkerland Overijssel East 1951/05 1960/12 134 Eind van 't Diep Steenwijk Steenwijkerland Overijssel East 1951/05 1951/11 Pikbroek Steenwijkerwold Steenwijkerland Overijssel East 1951/04 1951/11 Lillbosch Echt Echt-Susteren Limburg South 1951/05 1952/06 Op de Loop Echt Echt-Susteren Limburg South 1951/11 1965/12 70 Rodanborgh Aardenburg Sluis Zeeland West 1951/04 1959/11 71 De Haven Breskens Sluis Zeeland West 1952/01 1962/12 105 Wilgenhof Oostburg Sluis Zeeland West 1951/04 1963/03 104 Duinoord Groede Sluis Zeeland West 1954/05 1958/12 36 Donzel Nistelrode Bernheze Noord-Brabant South 1958/05 1968/02 409 Pieterberg Westerbork Midden-Drenthe Drenthe North 1951/05 1962/11 130 Mantinge Westerbork Midden-Drenthe Drenthe North 1953/09 1960/03 100 Schattenberg Westerbork Midden-Drenthe Drenthe North 1951/03 1971/06 1,999 Snodenhoek Elst Overbetuwe Gelderland East 1951/05 1968/06 283

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De Haar Heteren Overbetuwe Gelderland East 1954/05 1967/12 127 Lingebrug Kesteren Neder-Betuwe Gelderland East 1953/05 1965/12 65 Overbroek Echteld Neder-Betuwe Gelderland East 1951/05 1969/11 144 Graetheide Geleen Sittard-Geleen Limburg South 1951/04 1963/01 470 Carel Coenraad Polder Finsterwolde Oldambt Groningen North 1953/09 1961/12 298 Capuc. Klooster Eijsden Eijsden-Margraten Limburg South 1951/04 1962/01 238 Rijckholt Rijckholt Eijsden-Margraten Limburg South 1952/11 1967/09 166 Oude Zeug Wieringermeer Hollands Kroon Noord-Holland West 1954/08 1962/12 59 Wite Pael Haskerland De Friese Meren Friesland North 1951/12 1960/12 78 Wyldemerck Gaasterland De Friese Meren Friesland North 1954/12 1968/06 254 Total 14,408 Source: Authors’ tabulation, Moluccan Historical Museum Notes: Provinces are assigned based on new municipality codes of Statistics Netherlands. Total number of residents represent the average of the stock of Moluccan population in the period when settlements were open.

24

Table A.2: Characteristics of municipalities with Moluccan settlements

Mean distance to Availability of school Common Residential area Church Hospital Lower primary Upper primary Common type of work Mean number Mean duration house opportunity of residents of the camp Finsterwolde 14.20 14.10 Yes No Wooden barrack No 297 8.25 Marum 1.60 22.10 No No Wooden barrack No 335 14.84 Gaasterland 5.30 32.80 Yes No Wooden barrack Yes 253 13.51 Haskerland 8.10 10.40 No No Wooden barrack No 79 9.01 Ooststellingwerf 21.50 21.60 No No Wooden barrack No 364 13.68 Norg 4.10 11.70 No No Wooden barrack No 0 5.09 Oosterhesselen 10.80 11.30 No No Wooden barrack No 144 17.26 Ruinen 5.10 5.40 No No Wooden barrack No 126 7.63 Westerbork 6.93 15.23 Yes Yes Wooden barrack Yes 2,229 12.76 Enschede 1.80 7.70 Yes No Stone building No 313 3.08 Ommen 4.15 23.45 Yes No Wooden barrack Yes 403 12.34 Staphorst 7.80 7.60 Yes No Wooden barrack No 385 13.35 Steenwijk 8.90 21.20 Yes No Wooden barrack No 134 5.05 Steenwijkerwold 6.00 19.10 Yes No Wooden barrack No 0 0.59 Wierden 3.40 9.90 Yes No Wooden barrack Yes 649 24.18 Arnhem No No Wooden barrack No 176 5.84 Barneveld 1.60 1.95 Yes No Wooden barrack Yes 633 13.34 Bemmel No No Wooden barrack No 141 16.10 Echteld 5.30 10.20 Yes No Wooden barrack No 144 18.52 Ede 5.20 7.70 No No Wooden barrack No 117 9.59 Elburg 1.90 19.30 No No Stone building No 1 4.51 Elst 1.40 9.80 No No Wooden barrack No 283 17.10 Epe 2.10 9.00 Yes No Wooden barrack Yes 813 23.52 Groesbeek 0.50 9.30 No No Wooden barrack No 123 10.50 Heteren 4.50 4.10 No No Wooden barrack No 126 13.59 Kesteren 0.30 12.20 No No Wooden barrack No 65 12.59 Tiel 2.55 1.75 Yes No Wooden barrack No 245 10.39 Voorst 3.90 8.30 Yes No Metal barrack Yes 466 11.43 Wehl 3.60 9.50 No No Wooden barrack No 86 11.76 Winterswijk 2.20 2.00 No No Wooden barrack No 530 10.01 Huizen 1.90 4.70 No No Wooden barrack No 326 14.26 Medemblik 11.10 17.50 No No Wooden barrack No 292 1.67 Muiden Yes No Wooden barrack No 284 11.59 Wieringermeer 9.30 29.40 No No Metal barrack No 59 8.34 Capelle aan den IJssel 4.30 4.90 Yes No Wooden barrack Yes 714 18.18 Ridderkerk 0.90 8.90 No No Metal barrack Yes 22 6.67 Woerden 0.80 1.30 No No Wooden barrack No 535 14.76 Aardenburg 7.80 33.10 Yes No Wooden barrack No 71 8.59

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Breskens 22.70 22.90 No No Metal barrack No 105 10.92 Groede 29.10 29.20 No No Metal barrack No 36 4.59 Kruiningen 1.50 11.45 Yes No Wooden barrack No 321 7.38 Middelburg 1.30 1.15 No No Wooden barrack No 173 9.22 Oostburg 28.50 28.60 Yes No Metal barrack No 105 11.92 Vlissingen 2.33 2.03 No No Wooden barrack No 325 7.93 Westkapelle 5.80 16.00 No No Wooden barrack No 142 9.26 Hoge en Lage Mierde 3.90 19.80 Yes No Wooden barrack No 151 8.25 Mill en Sint Hubert No No Wooden barrack No 115 9.92 Nistelrode 4.30 7.30 No No Wooden barrack Yes 408 9.76 Oost-, West- en Middelbeers 3.90 15.70 Yes No Metal barrack No 159 4.42 Vierlingsbeek 0.70 9.10 No No Wooden barrack No 51 15.35 Vught 3.30 6.10 Yes Yes No 2,407 41.28 Wouw 6.10 7.10 No No Wooden barrack No 57 11.01 Bergen 7.00 12.80 Yes No Wooden barrack No 129 15.59 Echt 4.90 12.00 No No Stone building No 71 7.59 Eijsden 1.40 10.00 No No Stone building No 238 10.76 Geleen Yes No No 471 11.76 Gennep 1.00 6.40 Yes No Wooden barrack No 134 17.60 Heythuysen No No Wooden barrack No 40 10.01 Meerlo 0.60 12.20 No No Metal barrack No 57 10.84 Montfort Yes No Metal barrack No 67 12.09 Mook en Middelaar Yes No Wooden barrack No 64 9.26 Roermond 0.90 1.60 No No Metal barrack No 105 12.51 Venlo 1.70 2.80 No No Metal barrack No 183 15.01 Venray 1.90 1.50 Yes No Wooden barrack No 138 13.68 Weert 5.70 5.10 Yes No Metal barrack No 166 17.68 Burgh No No Wooden barrack No 60 4.84 Duivendijke 9.20 6.40 No No Wooden barrack No 0 1.25 Grijpskerke 6.10 6.00 No No Wooden barrack No 0 0.92 Kerkwerve 3.20 3.90 No No Wooden barrack No 0 0.34 Koudekerke 4.60 4.10 Yes No Wooden barrack No 108 11.51 Noordwelle 4.60 12.50 No No Wooden barrack No 0 0.25 Rijckholt 1.90 9.30 No No Stone building No 166 14.84 Serooskerke 6.90 6.70 No No Metal barrack No 99 12.51 Mean 5.56 11.38 258 11.03 Source: Authors’ tabulation, Moluccan Historical Museum Notes: Distance to church and hospital in kilometers indicates the mean distance to the nearest church and hospital of settlements in the municipality. Availability of school indicates whether there is any school in settlement in the municipality which most of the residents can benefit. Type of house represents the structure of the barracks that most of the residents in municipalities lived in. Work represents whether there is any employment opportunity around the settlement which is available to most of the residents. Mean duration of the camp shows how many years settlement were present in the municipality.

26

Appendix B: Comparison of Moluccans with control group

Table B.1: Comparison of educational attainment

Without controls With controls Female Male Variables (1) (2) (3) (4) Moluccan -0.043*** -0.004 -0.061*** 0.063*** (0.000) (0.003) (0.003) (0.004) Interaction Moluccan*birth place Marum -0.132*** -0.137*** 0.026*** -0.290*** (0.000) (0.002) (0.002) (0.003) Gaasterland -0.169*** -0.208*** -0.079*** -0.370*** (0.000) (0.003) (0.004) (0.004) Haskerland -0.027*** -0.048*** 0.409*** -0.310*** (0.000) (0.003) (0.006) (0.004) Ooststellingwerf -0.103*** -0.121*** -0.129*** -0.124*** (0.000) (0.002) (0.003) (0.003) Norg 0.336*** 0.449*** 0.536*** (0.000) (0.006) (0.010) Oosterhesselen -0.131*** -0.138*** -0.070*** -0.201*** (0.000) (0.003) (0.005) (0.004) Ruinen -0.021*** -0.039*** 0.131*** -0.261*** (0.000) (0.002) (0.004) (0.004) Westerbork -0.109*** -0.104*** -0.078*** -0.139*** (0.000) (0.002) (0.003) (0.003) Enschede -0.023*** 0.031*** 0.161*** -0.093*** (0.000) (0.006) (0.009) (0.006) Ommen -0.036*** -0.037*** 0.062*** -0.137*** (0.000) (0.002) (0.003) (0.002) Staphorst -0.057*** -0.053*** 0.124*** -0.245*** (0.000) (0.002) (0.004) (0.002) Steenwijk -0.271*** -0.258*** -0.391*** -0.030*** (0.000) (0.002) (0.004) (0.004) Wierden -0.049*** -0.074*** 0.025*** -0.171*** (0.000) (0.002) (0.003) (0.003) Arnhem -0.036*** -0.066*** -0.172*** 0.102*** (0.000) (0.003) (0.003) (0.005) Barneveld 0.037*** 0.009*** 0.143*** -0.127*** (0.000) (0.002) (0.003) (0.003) Bemmel -0.048*** -0.079*** 0.166*** -0.306*** (0.000) (0.002) (0.004) (0.003) Echteld 0.181*** 0.171*** 0.129*** 0.258*** (0.000) (0.002) (0.003) (0.003) Ede -0.021*** -0.025*** 0.126*** -0.202*** (0.000) (0.002) (0.003) (0.003) Elst -0.049*** -0.092*** -0.045*** -0.151*** (0.000) (0.003) (0.003) (0.004) Epe -0.061*** -0.123*** -0.045*** -0.220*** (0.000) (0.004) (0.006) (0.005) Groesbeek -0.155*** -0.158*** -0.031*** -0.326*** (0.000) (0.001) (0.002) (0.003) Heteren -0.090*** -0.127*** 0.000 -0.220*** (0.000) (0.002) (0.003) (0.003) Kesteren 0.026*** 0.006* 0.038*** 0.002 (0.000) (0.003) (0.003) (0.006) Tiel -0.123*** -0.092*** 0.076*** -0.415*** (0.000) (0.002) (0.003) (0.004) Voorst -0.078*** -0.081*** 0.016*** -0.183*** (0.000) (0.002) (0.003) (0.002) Wehl -0.412*** -0.413*** -0.288*** -0.550*** (0.000) (0.003) (0.004) (0.006) Winterswijk -0.045*** -0.106*** -0.025*** -0.235*** (0.000) (0.005) (0.006) (0.007) Huizen -0.133*** -0.133*** 0.022*** -0.322*** (0.000) (0.002) (0.002) (0.002) Medemblik 0.055*** 0.087*** -0.010** 0.276*** (0.000) (0.003) (0.004) (0.006) Muiden -0.002*** 0.034*** 0.078*** -0.028*** (0.000) (0.005) (0.006) (0.006) Wieringermeer -0.233*** -0.279*** -0.345*** (0.000) (0.003) (0.004) Capelle aan den Ijssel -0.066*** -0.117*** -0.075*** -0.169*** (0.000) (0.003) (0.005) (0.004) Ridderkerk 0.342*** 0.335*** 0.218***

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(0.000) (0.004) (0.007) Woerden -0.088*** -0.079*** -0.078*** -0.086*** (0.000) (0.002) (0.003) (0.002) Aardenburg -0.707*** -0.738*** -0.882*** (0.000) (0.004) (0.008) Groede 0.119*** 0.157*** 0.505*** -0.720*** (0.000) (0.004) (0.005) (0.010) Kruiningen -0.185*** -0.237*** -0.318*** (0.000) (0.004) (0.006) Middelburg -0.149*** -0.170*** -0.206*** -0.150*** (0.000) (0.002) (0.003) (0.002) Oostburg -0.155*** -0.141*** -0.187*** -0.099*** (0.000) (0.002) (0.003) (0.002) Vlissingen -0.087*** -0.106*** -0.046*** -0.175*** (0.000) (0.002) (0.003) (0.003) Westkapelle -0.747*** -0.783*** -0.871*** (0.000) (0.006) (0.007) Hoge en Lage Mierde -0.030*** -0.045*** 0.003 -0.127*** (0.000) (0.002) (0.003) (0.003) Mill en Sint Hubert -0.002*** -0.011*** 0.259*** -0.214*** (0.000) (0.002) (0.003) (0.002) Nistelrode -0.068*** -0.116*** -0.081*** -0.161*** (0.000) (0.004) (0.005) (0.005) Oost-, West- en Middelbeers -0.061*** -0.095*** -0.021*** -0.171*** (0.000) (0.002) (0.003) (0.004) Vierlingsbeek -0.051*** -0.111*** -0.088*** -0.158*** (0.000) (0.003) (0.005) (0.005) Vught -0.174*** -0.177*** -0.089*** -0.274*** (0.000) (0.002) (0.003) (0.003) Bergen -0.208*** -0.229*** -0.316*** -0.183*** (0.000) (0.002) (0.004) (0.003) Echt 0.084*** 0.075*** 0.108*** 0.073*** (0.000) (0.003) (0.004) (0.003) Eijsden -0.130*** -0.112*** 0.051*** -0.300*** (0.000) (0.002) (0.003) (0.003) Geleen -0.062*** -0.118*** -0.175*** (0.000) (0.004) (0.005) Gennep -0.175*** -0.181*** -0.071*** -0.336*** (0.000) (0.002) (0.003) (0.003) Heythuysen 0.309*** 0.382*** 0.460*** (0.000) (0.008) (0.011) Meerlo -0.281*** -0.312*** -0.453*** -0.266*** (0.000) (0.002) (0.005) (0.003) Montfort 0.110*** 0.109*** 0.324*** -0.116*** (0.000) (0.002) (0.005) (0.004) Mook en Middelaar -0.458*** -0.404*** -0.039*** -0.774*** (0.000) (0.003) (0.008) (0.005) Roermond -0.074*** -0.080*** -0.162*** -0.038*** (0.000) (0.001) (0.003) (0.002) Venlo 0.041*** -0.013*** 0.070*** -0.101*** (0.000) (0.003) (0.004) (0.004) Venray -0.035*** -0.062*** 0.187*** -0.276*** (0.000) (0.002) (0.002) (0.004) Weert -0.165*** -0.185*** -0.111*** -0.262*** (0.000) (0.002) (0.004) (0.003) Koudekerke 0.309*** 0.256*** 0.164*** (0.000) (0.004) (0.005) Serooskerke -0.755*** -0.763*** -0.856*** (0.000) (0.005) (0.006) Observations 122,580 122,580 61,482 61,098 R-squared 0.010 0.032 0.037 0.026 Source: Authors’ estimation Notes: Dependent variable is having at least upper secondary school degree. Sex, birth year dummies, and birth place dummies are included. Due to the limitation, only baseline effect of being a Moluccan and interaction terms are presented in the table. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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