factors influencing migrants’ engagement with transnational economic activities

Factors Influencing Migrants’ Engagement with Transnational Economic Activities

in Post-Conflict Countries?

Work Package 4 Report

Linking Motives for Remittances and Investment from the Supply and the Demand Side

Nienke Regts, Marieke van Houte &

Ruerd Ruben

Radboud University NijmegenCentre for International Development Issues (cidin)

PO Box 9104, Nijmegen, The Netherlands

2010

Grant Agreement number: 210615Project acronym: INFOCONProject title: International Civil Society Forum on ConflictsFunding Scheme: Research for the Benefit of Specific Groups Research for Civil Society Organisations

Name, title and organisation of the scientific representative of the project’s coordinator: Stephan KAMPELMANNSecretary-General / Project ManagerStichting Internationalist Reviewpo box 75brussels 1040belgium+ 32-(0)‒26–08–24–[email protected]

Disclaimer 1The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7 / 2007–2011) under grant agreement Nr. 210615.

Disclaimer 2The views expressed in this document are purely those of the authors and may not in any circumstance be regarded as stating the official position of all partners in the INFOCON consortium.

3 | Factors Influencing Migrants’ Engagement

Table of Contents

List of tables · 5

Abstract · 7

1. Introduction · 9

2. Transnational Activities and Economic Networks · 10

3. Motives for Transnational Economic Activities: Some Key Hypotheses · 113.1. Supply Side Motives · 113.2. Demand Side Motives · 13

4. Research Design · 14

5. Results · 175.1. Determinants at the Supply Side · 175.2. Determinants for the Demand Side · 20

6. Conclusions and Implications · 21

Acknowledgements · 22

References · 22

Annex A · 25

A.1. Profiles · 27A.1.1. What Can Be Concluded from the Profiles

above? · 28A.1.2. Overall Picture · 28

A.2. CROSS-TABS · 29A.2.1. City of Settlement · 29A.2.2. City of Settlement · 30A.2.3. City of Settlement · 31A.2.4. City of Settlement · 32A.2.5. City of Settlement · 32A.2.6. City of Settlement · 33

A.3. TESTING HYPOTHESES · 34A.3.1. General Information · 34A.3.3. Testing Hypothesis 1, 4 and 5 · 37

Factors Influencing Migrants’ Engagement | 4

A.3.4. Comparing Outcomes of Different Dependent Variables · 40

Annex B · 41

B.1. PROFILES · 43Overall · 45

B.2. CROSS-TABS · 46B.2.1. Country of Origin · 46B.2.2. Country of Origin · 46B.2.3. Country of Origin · 47B.2.4. Country of Origin · 48B.2.5. Country of Origin · 48B.2.6. Country of Origin · 49B.2.7. Country of Origin · 50

B.3. Testing Hypotheses · 50B.3.1. General Information · 50B.3.2. Testing Hypotheses 1–5 with Dependent Variable

“Received Assistance” · 51Option 1: Outcomes Logistic Regression Using All

Independent Variables · 51Option 2: Outcomes Regression Leaving Out Political

Stability (Removed Outlier 31) · 52B.3.3. Testing Hypotheses 1–5 · 53

Dependent Variable “Received Remittances” · 53Option 3: Outcomes Logistic Regression Using All

Independent Variables · 53Option 4: Outcomes Regression Leaving Out Political

Stability (Removed Outlier 31) · 54Option 5: Outcomes Regression Single Analyses · 54

B.4. MULTIPLE IMPUTATION · 56B.4.1. Method · 56B.4.2. Step 1: Pattern of Missing Values · 57B.4.3. Step 2: Multiple Imputation · 57B.4.4. Step 3: The Analyses · 57

Codes Used from spss Files for Analyses · 591. City of Settlement · 592. Country of Origin · 59


List of Tables

Table 1. Sample Frame (Valid interviews) · 15

Table 2. Descriptives of Economic Diaspora Networks at Supply Side (n = 139) · 15

Table 3. Descriptives of Economic Diaspora Networks at the Receiving Side (n = 70) · 16

Table 4. Factors influencing Funding of Transnational Economic Activities (Supply Side) · 18

Table 5. Factors influencing Receipt of Assistance and Remittances (Demand Side) · 20

Perceiving of Conflict · 29

Chi-Square Tests · 29

View on integration · 30


Reason Migration · 31


Date of Arrival · 32


Frequent Travel · 32


CSO Linkages · 33


Table A.3.1. Significant determinants for involvement in activities toward economic development or poverty alleviation in country of origin (general economical involvement) · 35

Table A.3.2. Significant determinants for attracting investment / economic assistance · 37

Table A.3.3. Significant determinants for sending remittances · 38

Received Assistance · 46


Received remittances · 46


Perceiving of Conflict · 47


Tensions Felt in Europe · 48


Role eu community in country · 48


Cooperation with eu Community · 49


Political Stability · 50


Table B.3.1. Significant Determinants for Receiving Assistance · 51

Table B.3.2. Significant determinants for receiving assistance · 52

Table B.3.3. Significant Determinants for Receiving Remittances · 53







Imputation Models · 57

Table 4.1. Average Outcomes with Dependent Variable Received Remittances · 58


Abstract

It is increasingly acknowledged that poverty reduction should be considered part of conflict mitigation and post‒conflict reconstruction processes. Whereas transna-tional communities play a critical role in channeling remittances and investments resources towards their home countries, it is not fully understood what is their ac-tual impact on the regions of origin and to what extent their involvement contrib-utes to socio‒economic development. Since there are several (economical, political, social and cultural) activities undertaken by diaspora networks, this article focuses mainly on transnational economic networks. We discuss both the determinants of migrants organizations’ economic involvement towards their home country (supply side) and the factors for receiving and attracting economic assistance (demand side). Main attention is given to economic involvement in the form of sending / receiving remittances and engagement in co‒investment. Our analysis reveals that factors like the degree of integration, engagement with civil society organizations and European linkages, perception of conflict, travel frequency and arrival date play a significant role in shaping economic diaspora networks.

Keywords: transnational networks; remittances; investment; economic integration; post‒conflict reconstruction; european capital cities.


1. Introduction

I n an era of rapid developments in transport, communications and new technologies, people get more and more interlinked with each other. When borders seem to fade, international mi-gration becomes much easier and cheaper. Contacts between migrants and their home coun-

try are shaped within Transnational Communities (TCs) that operate throughout the world. It also becomes increasingly apparent that Civil Society Organizations (CSOs) representing these TCs of migrants play a crucial role in the domain of poverty reduction, especially in processes of conflict prevention and post‒conflict reconstruction. However, it is not clear what actual impact these TCs exercise on the regions of origin and to what extent they are effectively linked to economic reconstruction activities and political reconciliation processes. Transnational Communities are generally characterized by transnational practices that transcend national borders (Levitt, 2001). They can be classified in a variety of ways, both from the source as well as from the destination. This article explores the driving forces for engagement in transnational economic activities at the supply side and the likely implications for receiving assistance and remittances by individuals at the demand side.

Transnational economic activities as defined in this study include the activities that CSOs un-dertake which are directed at economic development or poverty alleviation in the country of ori-gin. These includes monetary remittances to the home country as well as their involvement in ac-tivities that are aimed at attracting investment or economic assistance from others to the country of origin. The study focuses on different TCs that are active in three distinct post‒conflict areas (Great Lakes region, Turkey and Kosovo) and related migrant communities located in major West-ern European cities (Berlin, Brussels, London and the Dutch Randstad).1

We examine these transnational economic activities in two directions. First, we focus on supply side factors, which include TC linkages with Civil Society Organizations (CSOs) and the degree of integration within the host country. We specifically explore factors and motives that determine participation in economic activities, such as the intrinsic characteristics of the migrants (e.g. ar-rival date, travel frequency) and the incentives derived from the situation in the home country (reasons for migration). Second, we explore the factors that determine the reception of economic assistance from the demand side. Here, individuals who receive assistance are addressed, looking at factors like the perceived conflict and political stability in European host countries and the role of the diaspora community in conflict resolution in the home country. In both cases, we include the perception of the conflict in the home country as a key control variable.

The structure of this article is as follows. Section two provides some background theories about transnationalism and transnational activities and discusses the determining factors for involve-ment and engagement in transnational economic activities. In § 3 we elaborate the hypotheses that are central to this study. In § 4, the research design and the profiles of the respondents are described. Hereafter, § 5 analyzes the results for the determinants of transnational economic ac-tivities at the supply side and at the demand side. The final section presents conclusions and impli-cations for research and policy.

1. The Dutch Randstad (Dutch: rim city, i.e. a city at the edge of a circle, with empty space in the centre) is a conurbation in the Netherlands. It consists of the four largest Dutch cities (Amsterdam, Rotterdam, The Hague and Utrecht), and the surround-ing areas. With its 7.5 million inhabitants, the Randstad hosts almost half of the population of the Netherlands.

http://en.wikipedia.org/wiki/Dutch_language

http://en.wikipedia.org/wiki/Conurbation

http://en.wikipedia.org/wiki/Netherlands

http://en.wikipedia.org/wiki/Amsterdam

http://en.wikipedia.org/wiki/Rotterdam

http://en.wikipedia.org/wiki/The_Hague

http://en.wikipedia.org/wiki/Utrecht_%28city%29


2. Transnational Activities and Economic Networks

Different disciplines and approaches use the term ‘transnationalism’ for analyzing international networks within the context of migration. Transnationalism is described in a variety of ways, but most social scientists agree that in a broad sense it contains the notion of multiple ties and interac-tions that link people or institutions across the borders of nation‒states (Vertovec 1999). Wayland (2004) uses transnationalism as identities and intra‒ethnic relations that transcend state borders. She states that a ‘diaspora’ is a form of transnational community that has been dispersed from its home country and whose members permanently live in a another host country. Others depict transnationalism as the activities or practices that are undertaken by migrants which contribute to the development of transnational communities (Al‒Ali et al. 2001). According to Brettell (2000), the idea of transnationalism emerged from the realization that immigrants maintain ties with their countries of origin, making home and host society a single arena for social action by moving back and forth across international borders and between different cultures and social systems, and by exploiting transnational relations as a form of social capital for their living strategies” (cited in: Dahinden 2005: 2). In this article we refer to transnationalism as a process of interactions between individuals, groups and institutions, where communities emerge and activities and interactions take place that transcend the borders of the nation‒state.

Transnational communities do not emerge in a vacuum. Development in transport and com-munication technology – like cheaper airline travel and internet – make it easier for migrants to maintain relations that transcend national borders (Levitt 2001). Transnational communities can thus emerge where countries of origin and settlement and migrants in different destinations are linked together (Bloch 2008). These communities are often characterized by transnational prac-tices that transcend borders. These practices or activities can be classified in a variety of ways. The usual distinction is between activities that are (a) political, such as lobbying; (b) economic, like remittances and investments; (c) social, like promotion of the human rights, and (d) cultural, such as delivering articles to newspapers. In addition, activities may take place at the individual level or through institutional channels (Al‒Ali et al. 2001). This article focuses on the economic practices that are undertaken by individuals supported through Civil Society Organizations (CSOs) that represent different transnational communities (TCs).

Transnationalism often appears related to migration (Levitt & Nyberg‒Sorensen 2004). Espe-cially when economic activities of migrant communities are subject of study, most current analy-ses focus on the role of remittances and their possible contributions to local poverty alleviation (Adams & Page 2005). It is commonly acknowledged that migrants are involved in sending money to their home country, and recent studies show that these remittances have become one of the most important factors contributing to economic development in post‒conflict countries (Ratha & Mohapatra 2007, Haas, 2007), representing up to 15–20% of GDP. International migration and related remittances streams can thus generate significant welfare benefits to the countries of ori-gin. Many families and individuals in developing countries depend on these money inflows and it helps them to move forward economically and to reinforce access to social services (Weiss Fagen and Bump 2006). Remittances are currently regarded as one of the most stable form of income and foreign exchange for developing countries, particularly in comparison with international aid that is often volatile and withdrawn before needs are fully met (Black et al. 2007; Weiss‒Fagen and Bump 2001). Remittances as a form of transnational economic activities can thus be considered as an important factor contributing to the development of the home country. In addition, transna-


tional linkages can be shaped through other types of economic involvement, particularly based on direct co‒investment in activities from migrants in their home country.

While the importance of remittances to the home country is fairly clear, questions can be raised regarding the underlying motives or incentives of migrants who are sending money home. In gen-eral, the remittances literature classifies three types of motivations for sending remittances: (a) altruism, (b) self‒interest and (c) mutually beneficial arrangements (Blue 2004, 64). While these motives reflect different behavioral intentions of migrants, they do not inform us about other contextual conditions that go beyond individual behavior, such as their perceptions regarding the political situation in the home and / or host country and the facilitating role of social networks. This article therefore focuses on some of these contextual factors that may influence engagement in economic assistance in general, focusing on motives and driving forces for sending and receiv-ing remittances and for attracting co‒investment.

3. Motives for Transnational Economic Activities: Some Key Hypotheses

The engagement of migrants with economic activities in their home countries can be analyzed both from the supply side and from the demand side. At the supply side, attention should be given to the motives for engaging in sending remittances or economic support. Specific factors at the supply side include the migrants’ perceptions of the domestic conflict (in the home country), their current degree of integration in the host country, the original motivation for migration, existing network linkages through Civil Society Organizations, travel frequency and date of arrival. At the receiving (demand) side, attention is given to the factors that influence the attraction of economic support. Key mediating factors include the perception of the conflict, the linkages with the send-ing community (living in European cities), the degree of cooperation with the migrant communi-ties living in Europe, and the political stability in the country of origin.

3.1. Supply Side Motives

Many people living in (post)‒conflict countries are highly dependent on transnational economic activities, especially in the form of remittances (Weiss Fagen and Bump 2001). This raises the question whether people living in (post‒)conflict countries receive more assistance than people living in other developing countries and whether migrants send more money to (post‒)conflict countries than to other countries. Hansen (2008) calculates that remittances inflows to post‒con-flict countries are globally equal to other poor countries. Ratha and Mohapatra (2007) find, how-ever, that migrants tend to send more money to their home country during hard times to help their families and friends. They even register that remittances increased following financial crisis and natural disasters in several countries. Within this line of reasoning, one might expect to find the same kind of outcome when countries are recovering from internal conflict. In this perspective, Mohamoud (2006) indeed acknowledges the importance of the situation in the country of origin. He found that if there is more stability in the home country, the diaspora tends to invest more in activities that contribute to socio‒economic development, such as community welfare projects and business investments. However, when the situation of the conflict in the home country is not yet stable, migrants tend to invest more in politically‒related activities. Based on a comparison of different situations in Ethiopia and the Democratic Republic of Congo (DRC), it appears that re-


turned migrants from Ethiopia were more likely to invest economically in the country because the political situation was considered rather stable. However, migrants from DRC did not yet engage in such economic activities as the situation in the home country was still considered to be too risky. As this study shows, the perceptions regarding the socio‒political situation in the home country affects the economic behavior of the senders. Related to the study of Mohamoud, we expect to find a positive relation between the perception of the senders and their economic involvement. We therefore test the hypothesis that the more one perceives the local conflict to be improving, the more this person is likely to become involved in economic activities towards the home country.

The second hypothesis that we are testing refers to the migrants’ attachment to the home coun-try. One of the most common assumptions that has been made regarding remittances is that when migrants establish themselves in the host country, remittances will decrease over time. The reason for this is that it is assumed that migrants, who are away for a longer time, eventually will get mar-ried, settle in the host country and become better integrated. For this reason their orientation will become more inclined towards the host country and consequently the amount of remittances to the home country will decrease over time (Blue 2004). Recent arrivals, on the other hand, are to be expected to maintain close family ties and are more knowledgeable about the actual condition in their home country. Therefore, the probability that they will send remittances is higher compared to migrants who have stayed for a longer period in the host country (ibid.). Bloch (2008: 296–297) found indeed that the category of migrants with least time in the host country was most involved in sending remittances. Within the same line of reasoning, one can assume that frequent traveling towards the home country is likely to result in more economic activities. We hypothesize that more attachment to ones home country will lead to stronger economic involvement with home country activities.2

The third dimension we highlight refers to the social networks to which migrants belong and that may influence their likelihood for economic involvement with the home country. Migration can be seen as a form of linkage that transcends national boundaries and where social networks and social relations play a significant role (Levitt and Schiller 2004). A typical example of this is that migrants maintain their social ties in the country of origin to safeguard social support net-works in case they need to return to their home country (Levitt and Jaworsky 2007). Social net-works thus link migrants with their families, but it also connect migrants together and with other individuals and organizations. It is acknowledged that social relations and networks are one of the key determinants for all transnational activities (Levitt and Nyberg Sorensen 2004). For example, the analysis made by Blue (2004) on family remittances to Cuba ascertained that, among others, social capital motives are a major driver for sending remittances, In a similar vein, the likelihood for sending remittances substantially increased in line with the social capital of Mexican migrants, considering the ties with relatives and organizations in the United States as key aspects of social capital. Finally, Bloch (2008) also found a strong positive relationship between (kinship) networks and transnational activities. In this article, we address migration as part of a social decision‒mak-ing process, considering the motivation to migrate to be induced by the social network linkages. Even though the act of migration may be an individual decision, the motivation belongs within the realm of social networks as it may influence people who stay at home to make the decision to migrate (Menjivar et al. 1998). Migrants who decide to move away to another country because of better economic prospects, still may share responsibilities for their parents or children at home. Otherwise, migrants that leave their home country because of political reasons tend to have less

2. Attachment is supposed to be influenced by the time one is away from the home country; frequent traveling back could en-hance the attachment to the home country.


opportunities to notice or involve family members or relatives. We therefore expect that when political motives for migration prevailed, migrants may feel morally more obligated to take care of the ones that stay behind and therefore are more likely to send remittances.

Finally, another aspect of the social network of migrants – representing an important pathway for economic involvement regarded to the home country – consists of the intensity of institutional linkages with Civil Society Organizations (CSOs). Even while sending remittances is an individual decision, the CSO network can be helpful to identify suitable investment opportunities. Moreover, CSO linkages contribute to reducer transaction and information costs and thus can enhance the effectiveness of transnational economic networks (Deans et al 2007). We therefore expect that stronger CSO linkages could enhance the economic network with home countries, but might also be helpful to deliver resources towards more community‒oriented programs.

3.2. Demand Side Motives

At the demand side we review several hypotheses regarding important determinants for receiving and attracting economic assistance. Some of these hypotheses are related to the social network dimensions outlined in the previous section. The first hypothesis to be tested contains the social networks of the receivers. As explained before, the social network of migrants is expected to be a major determinant for becoming economically involved in the home country. In this perspective one also expects to find a positive relation between social networks and economic assistance at the receiving side. De Janvry and Sadoulet (2000) find that membership of migration networks is re-garded as a key factor for receiving remittances incomes. These migration networks are helpful in providing information about how to invest or how to find employment in the home country. This results in an increase in remittances inflows and therefore the social network is acknowledged to be a major determinant for household income of the ones that stay behind. The social network at the receiving side is defined as the cooperation with migrant communities or organizations that are established in European cities. We expect to find a positive relation between the social net-works of the receivers and the delivery of economic assistance.

In the second place, transnational communities are recognized to play a significant role in the conflict of the home country. They may sustain (ethno‒political) conflicts as they provide resources that can influence the existing balance of economic, political and military power in the homeland. Group identities are no longer spatially or territorially bounded and the diaspora can become actively involved in the conflict in the home country, even though they live at another place (Demmers 2002). In a study on ethno‒nationalist networks and transnational opportunities, Wayland (2004) describes the role of ethnic networks in the Tamil diaspora. She argues that the Liberation Tigers of Tamil Eelam set up offices abroad and were engaged in several fundraising campaigns. This enabled the Tamil insurgents to sustain their quest for an independent homeland. Transnational communities can also influence the conflict in the home country by sending money, arms and equipment, or by influencing public opinion (Demmers 2002). This implies that the involvement of transnational communities in the conflict of their home country is apparent and important. In this study, we try to find an answer to the question whether and how the position of transnational communities in the local conflict influences the delivery and receipt of economic as-sistance. We hypothesize a positive relation between these variables: the more apparent the role of the migrant community living in Europe in the conflict, the more assistance is likely to be received.

The third key variable influencing the receipt of economic support from migrant communities refers to the role played by the European‒based migrant community in the conflict of the home country. Even while migrants do not physically live in the home country anymore, they still can in-


terference in the conflict of their homeland. Demmers (2002) calls this as sort of “virtual‒conflict”: migrants live their conflicts through communication and connections like internet and telephone without direct and physical risks or suffering. Migrants are separated from the direct (results of the) conflict and might therefore experience different emotions and behavior toward the conflict. Since the people who still live in the homeland might feel emotions like fear, stress and pain, the diaspora group might feel anger, frustration or alienation (Demmers 2002). This strongly influ-ences the perceptions of migrants regarding the conflict in their home country. Migrants might have different views on the conflict compared to the ones that stayed behind. In order to obtain a more in‒depth insight into this dimension, we included questions on the perception on the con-flict to both the migrants, but also to the receivers. We expect to find a positive relation between the perception on the conflict and the receipt of economic assistance.

Finally, the position of the migrant community in Europe is likely to influence the receipt of economic assistance. Given the current international economic crisis, it may be expected that migrants have less resources available to share with their family end relatives in the home country. Otherwise, upcoming tensions with (and between) diaspora communities in European cities are likely to enhance the feelings of inhospitality and could eventually reinforce the willingness to consider return. We expect a larger involvement in remittances in migrant networks that envisage future return prospects, relying on transnational economic activities to prepare their way back to the home country (Hagen‒Zanker, 2010). In a similar vein, engagement in economic assistance ac-tivities might be driven by efforts to enhance the role of the diaspora community in local conflict settlement and / or reconstruction processes. This factor is likely to increase if the perceived link-ages between home and host country communities are stronger.

4. Research Design

Several CSOs representing different TCs located in the four capital cities of European countries and related to three post‒conflict areas have been selected for this study. In total 74 interviews were conducted in the home countries with people from the Great Lakes region (Burundi, Demo-cratic Republic of Congo and Rwanda), Turkey and the Balkans (Kosovo) and 139 interviews were collected in the capital cities (Brussels, Berlin, London and the Randstad). These cities were se-lected to ensure general comparability of the field work and because they share major character-istic feature. A typical example of such characteristics is that big European cities often attract a multiplicity of TCs because of their economic opportunities.3 In addition, the choice for the three geographical areas was based on criteria of diversity and geographical scope and – more impor-tantly – inclusion was based on different dimensions of (post‒)conflicts setting.4

The sampling for data collection iss based on whether respondents were involved in any CSO representing one of the selected communities. The method of random sampling was used to select the CSOs. However, in some cases there was a limited number of CSOs available and snowball

3. Other reasons include: (a) clustering of TCs in different neighborhoods is often experienced in big cities; (b) political move-ment and lobbying is often most effective in the big cities; (c) organization, mobilization and density of TC networks become the most visible in big cities; and (d) confrontations between and within TCs become more visible in big cities.

4. The categories of the stage of the conflicts are (a) peaceful stable situations; (b) political tension situations; (c) violent political conflict; (d) low intensity conflicts; (e) high intensity conflicts. The dimensions of the conflicts were identified by (a) cultural dimensions; (b) socio‒economic and geographical dimensions; (c) political dimensions; (d) external dimensions.


sampling was applied. In these cases we decided to include all the CSOs that were present. Table 1 gives an overview of the collected data in the four host cities and three home countries.

Table 1. Sample Frame (Valid interviews)

Great Lakes Kosovo Turkey TotalHost cityRandstad 20 13 19 52Berlin – 5 20 25London 11 11 – 22Brussels 20 – 20 40

Home countryGreat Lakes 34 – – 34Kosovo – 17 – 17Turkey – – 23 23

Total 85 46 82 213

Data is collected through a method of semi‒structured, in‒depth interviews. These interviews also enabled to get an in‒depth understanding of the motives of respondents. It also helped to elucidate the complex details of people’s lived experiences. Some interviews were conducted with the assis-tance of interpreters and took between 30 minutes to several hours to complete.

Table 2. Descriptives of Economic Diaspora Networks at Supply Side (n = 139)

Mean SD Min Max NDependent variablesEconomic involvement a 0.31 0.464 0 1 139Remittances sending a 0.15 0.359 0 1 139Attract investment a 0.08 0.271 0 1 114

Independent variables b Perception of conflict 1.34 0.477 1 2 139CSO linkages 1.25 0.432 1 2 130Degree of integration 1.85 0.800 0 3 123Date of arrival 1.85 0.597 1 3 133Travel frequency 0.67 0.470 0 1 135Reason for migration 2.42 1.047 1 4 135

Notes: (a) 1 = yes. (b) Perception in conflict (1 = improved, 2 = worsened); CSO linkages (1 = low / av-erage, 2 = high); Degree of integration (0 = no integration, 1 = language, 2 = adaptation and / or par-ticipation, 3 = accept and / or respect); date of arrival (1 = > 30 years ago, 2 = 10–30 years ago, 3 = <10 years ago); frequent travel (0 = no, 1 = yes); reason for migration (1 = family / born, 2 = socio‒economic perspective, 3 = political, 4 = violence / armed conflict).

We distinguish three different economic activities on the supply side (i.e. general economic in-volvement, sending remittances, attracting investment) and two on the demand side (i.e. receiving assistance, receiving remittances). Data about the involvement of the respondents, importation of conflicts, conflict policies, socio‒economic issues, organization strategies and other perceptions and migration history was collected on the supply side. On the demand side similar data was gath-


ered including some additional questions regarding the political stability in the country and the involvement with the community living in Europe (see Table 2).

Regarding the characteristics at the supply side, it becomes readily clear that almost one third of the respondents’ CSOs (31%) are involved in activities directed toward economic development in their country of origin. The CSOs of respondents from the Great Lakes seem to be the most active (44%), followed by CSOs from Kosovo (35%), while only 21% of CSOs by the Turkish / Kurds respondents are involved in activities towards economic development in their country of origin. In addition, results show that only 15% of all respondents are involved in sending remittances and an even smaller number (8%) is involved in attracting investment from others to their country of ori-gin. Remittances are most frequent amongst respondents that origin from the Great Lakes region (43%) and more reduced for people from Kosovo and Turkey (29%). On the other hand, Kosovars are most active in attracting investment (75%), while only 25% of respondents from the Great Lakes and a negligible share of respondents from Turkey appeared to be involved in these activities.

Even though only one third of the migrants’ CSOs are involved in any economic activities to-ward their home country, more than two third (70%) of the respondents on the demand side stated to have received any form of economic and non‒profit assistance from members of their commu-nity living abroad. Respondents from the Great Lakes received most assistance (almost 65%), while respectively 24.3% and 10.8% of the Kosovar and Turkish respondents received assistance. Globally 50% of the respondents report to have received remittances in the past year. More than half of all remittances (58%) was received by respondents from the Great Lakes. 30% was received by Koso-var respondents and 15% by the Turkish respondents. Table 3 provides the key descriptives for the respondents on the demand side.

Table 3. Descriptives of Economic Diaspora Networks at the Receiving Side (n = 70)

Mean sd Min Max nDependent variablesReceived assistance a 0.70 0.463 0 1 3Received remittances a 0.50 0.505 0 1 52

Independent variablesPerception of conflict (0–3) b 0.71 0.456 0 3 48Tensions in Europe a 0.52 0.505 0 1 69Role diaspora community a 0.59 0.495 0 1 70Cooperation migrant community a 0.66 0.478 0 1 65Political stability (0–3) c 1.91 0.805 1 3 45

Notes: (a) 1 = yes. (b) Perception in conflict (0 = no conflict, 1 = improved, 2 = no change, 3 = worsened). (c) Political stability (1 = not stable, 2 = average, 3 = stable).

Comparing the data from demand and supply (Tables 1 and 2) it becomes apparent that respond-ents at the receiving side are more positive on the development of the conflict in their home coun-try compared to the respondents at the supply side. More than half of the receivers (55.4%) perceive the conflict as improved (even though one third of the respondents feels that the political situation in their country is not stable) while only 47.1% of the latter states the same. This might indicate already important differences in perceptions amongst both groups.

Finally, it should be noted that the survey design was based on half‒open questions and topics that have be coded to enable comparison. Since the main focus of the research is related to the role


of migrant communities and CSOs in transnational economic activities, due attention is given to network dimensions. At the supply side, this is envisaged through variables that address the link-ages of migrants with local CSOs, while at the demand side some variables depicting the linkages with migrants communities in Europe are included. The latter variables reflect the information and perceptions regarding prevailing tensions that migrants experience in Europe, the role played by migrant communities in addressing the problems in their home countries, and the cooperative arrangements that exist between migrants in Europe and in the home country.

5. Results

Data analysis is based on statistical regression analysis for each of the economic linkages within diaspora networks, both from the demand and the supply-side persoective.

5.1. Determinants at the Supply Side

We first examine the determinants for general economic involvement from the supply side. As was stated before, these activities are undertaken partly through CSOs representing TCs. In the previous section we explained that the dependent variable was divided in one general variable (i.e. economic involvement) and two more specific variables (i.e. sending remittances and attracting investment). Table 4 shows the results obtained in the analyses for all three dependent variables. The determinants for general economic involvement will be examined first, following by an ex-amination of the two specific dependent variables. To examine the determinants Binary Logistic Regression analyses is used, applying a dummy for involvement for each of the dependent variables (i.e. economic involvement, sending remittances and attracting investment).

For the dependent variable general economic involvement, two specific hypotheses are consid-ered: (a) more intensive CSO linkages results in more economic involvement, and (b) the more the conflict in the home country is perceived as conflictive, the less involvement in economic activi-ties. We will first discuss the outcomes of these hypotheses following with the results for the other variables that are included.

The results in Table 4 show that the variable CSO linkages is positively related to general eco-nomic involvement. This implies that an increase in the intensity of CSO linkages will result in an increased probability of economic involvement. Also significant is the position in conflict. This variable shows to have a negative impact on general economic involvement, which means that if the local conflict situation is perceived as deteriorating, migrants will be less likely to become eco-nomically involved toward their home country.

Next to these main variables, the views on integration (in the host country), date of arrival and frequent travel appear to be significant. More recent arrival in the home country and frequent travel to the host country positively influence the economic involvement of respondents. For the variable ‘view on integration’ the outcomes are not very robust. The results show a trend toward more integration in the host country as a positive determinant for economic involvement in the home country. Finally, the reason for migration does not show any significant results. Before we give a more in‒depth explanation for these outcomes we discuss the determinants for sending remittances.

We start with the outcomes for the variables which are related to the hypotheses: (a) better inte-grated members of TCs are less involved in sending remittances; (b) less sending of remittances if


conflict is perceived as more conflictive and (c) more remittances sending with recent arrival and frequent travel. The independent variable ‘view on integration’ again shows a significant influence on the sending of remittances. However, without analyzing the separate categories we cannot con-clude anything definite about the nature or direction of this influence. Analyzing the separate cat-egories results in the following outcomes. Respondents who define integration as ‘language’ have the highest chance to be involved in sending remittances (compared to the other categories). Re-spondents who have the lowest chance of being involved in sending remittances are the ones who define integration as ‘accept and respect’. Thus, the outcome that ‘view on integration’ influences the sending of remittances mainly relies on the fact that the difference between the categories ‘no integration’ and ‘accept / respect’ is significant. Yet, no sequent order of B‒coefficients is found (the coefficient order of categories is 1,0,2,3). However, given the fact that the difference between cat-egory 1 and 0 is rather small, a major trend can be identified; better integration in the host country tends to lead to less sending of remittances to the home country.

Table 4. Factors influencing Funding of Transnational Economic Activities (Supply Side)

Indicator Econ. Involvement (n = 93)

Remittances(n = 95)

Attract Investment(n = 86)

Coeff. se sign. b Coeff. se sign. b Coeff. se sign. b

Perception of conflict in home country ‒1.341 0.745 ** ‒1.620 0.943 ** ‒0.319 0.956

CSO linkages 1.557 0.732 ** 0.965 0.729 * 1.208 0.849 *View on integration *** **No integration (0) a (ref) (ref)Language (1) a 2.939 1.579 ** 0.386 1.367Adapt / participate (2) a ‒0.626 1.119 ‒1.456 1.232Accept / respect (3) a ‒2.602 1.538 ** ‒3.250 1.724 **Date of arrival * **< 10 years ago (3) a (ref) 3.113 1.632 **> 30 years ago (1) a ‒2.014 1.319 * (ref)10–30 years ago (2) ‒0.488 1.050 2.776 1.269 **Frequent travel 1.145 0.811 * ‒1.453 0.813 ** 1.023 1.173Reason for migration **Family / born (0) a (ref) ‒0.257 1.011 ‒0.692 1.305Socio‒economic (1) a 0.722 0.902 (ref) ‒0.890 0.999Political situation (2) a ‒0.228 1.028 ‒2.885 1.047 *** ‒0.340 1.041Violence / war (3) a 0.233 1.039 ‒1.572 0.871 ** (ref)Constant ‒0.830 1.912 0.201 2.102 ‒3.843 2.207 **

Cox and Snell R2 0.363 0.280 0.056Nagelkerke R2 0.499 0.443 0.120

Notes: (a) reference category. (b) * = significant at 5%; ** = significant at 10%; *** = significant at 1%.

Also significant is the variable ‘position in conflict’. This variable shows a negative relation with remittances sending; respondents who define the conflict in their home country as deteriorating will be less likely to be involved in the sending of remittances. Furthermore, the date of arrival and frequent traveling now show significant influences. For the date of arrival, the same outcome


as for general economic involvement is found; more recent arrival results in a higher probability of outflow of remittances. Remarkable is the outcome for frequent traveling. While this variable is positively related to economic involvement, it is negatively related to remittances sending. This may be related to the fact that more direct control can be exercised on the use of remittances if this is combined with frequent travel.

Next to the main variables some other determinant factors were found. CSO linkages are posi-tively related to remittances sending. Also the variable ‘reason for migration’ shows a significant influence. But, due to the fact that the outcomes of B‒values are not fully sequential (i.e. the answer categories follow not logical order), no robust conclusions can be given. A global trend toward a difference between pull and push factors can be identified. Respondents who have migrated be-cause of their family or because of a better socio‒economic perspective in the host country (pull factors) have the highest chance of being involved in remittances sending. When migrants are be-ing pushed out of their country (forced migration due to the political situation for example) they will be less likely to be involved in remittances sending to their home country.

Finally, the results for the determinant factors for attracting investment are discussed. Because of the limited number of responses from the questionnaire (only 5.8% of the respondents stated to be involved in attracting investment from others to home country), we are not able to include ex-actly the same variables that were used in the previous analyses. Therefore, the matrix is reduced to four independent variables. The hypotheses to be tested on this matter are: (a) more invest-ment by TCs with more intensive CSO linkages, and (b) less investment by TCs if more conflict is perceived. As one can find out from the table above, there is just one variable which has a signifi-cant influence on attracting investment. The CSO linkages of migrants shows to be consistently positively related to attracting investment from others to home country. This is in line with the outcomes that were given before.

As stated before, social networks are one of the most important determinants for trans national activities. As the results from this study show, the variable CSO linkages is the only factor that influences all three dependent variables at the supply side. We can therefore state that social net-works is indeed an important factor for engagement in economic transnational activities; the stronger the CSO linkages, the more migrants are likely to be involved in economic activities in their home countries. In addition, it appears that the hypotheses on attachment to the home coun-try are partly confirmed. As expected, the outcomes for recent arrival are positively related to gen-eral economic involvement and remittances sending. However, the level of integration in the host country is not fully confirmed to be positively related with transnational economic activities. Even though a general trend was identified, the outcomes are not robust. The reason for this might lie in the fact that we do not have enough observations. It is expected that additional observations will result in a more valid statement regarding this trend. Furthermore, the factor frequent traveling to the home country did not show a steady result within the three dependent variables. While more frequent traveling results in higher general economic involvement, the exact opposite outcome was found for remittances sending. This can be attributed to the importance of supervising invest-ments made out of remittances.

Finally, we found that the position in the conflict appeared to be very important for all eco-nomic activities. When people perceive the local conflict as more conflictive, this will result in less engagement in economic activities toward the home country. This outcome was to be expected as we believed that the behavior of the migrants is very much influenced by the domestic prospects in their host country. It might also indicate that economic activities of migrant communities are most likely to follow upon the domestic political conditions, and are less influential in changing or modifying prevailing local conflicts.


5.2. Determinants for the Demand Side

This section contains an examination of the determinant factors for receiving economic assistance in general and for receiving remittances specifically. In order to examine this relationship, the method of Binary Logistic regression is used. Table 5 shows the determinant factors influencing the receipt of assistance. Due to a small amount of responses for the dependent variable receiv-ing remittances, we were initially not able to identify a solid matrix. Therefore, we relied on the method of Multiple Imputation before applying the logistic analyses. Multiple imputation is often used to deal with datasets with missing values. It is a technique that replaces each missing value with two or more acceptable values that represent a distribution of possibilities (Rubin, 2004: 1). In this study we imputed ten datasets which were further analyzed by using Binary Logistic regres-sion. The outcomes for receiving remittances in Table 5 are the combined (average outcomes for the ten datasets) outcomes from the analyses on the multiple imputation.

Table 5. Factors influencing Receipt of Assistance and Remittances (Demand Side)

Indicator Receiving assistance (n = 40) Receiving remittances (n = 72)Coeff. se Sign. b Coeff. se Sign. b

Perception of conflict **Worsened (3) a (ref) (ref)Improved (1) a 2.319 1.028 ** 0,089 0,771No change (2) a 2.817 1.453 ** 0,244 0,758

Tensions in Europe ‒2.068 1.013 ** ‒0,454 0,593Role Diaspora Community 1.642 0.980 ** 0,865 0,605 *Cooperation of migrant Community ‒0.162 1.062 ‒0,123 0,626

Political stability *Stable (3) a (ref)Unstable (1) a ‒1,111 0,761Middle (2) a 0,070 0,687

Constant ‒0.362 1.073 0,117 0,932

Cox and Snell R2 0.319 0.142Nagelkerke R2 0.446 0.190

Notes: (a) reference category. (b) * = significant at 5%; ** = significant at 10%.

We start with a description of the outcomes of the analyses on receiving assistance, followed by the outcomes for receiving remittances. For both dependent variables the following hypotheses are tested: (a) deteriorating conflict perception induces less receipt of economic assistance; (b) tensions felt in Europe lead to less receipt of assistance / remittances; (c) stronger involvement in diaspora community leads to more receipt of assistance / remittances; (d) stronger cooperation within migrant community leads to more delivery of economic assistance. For receiving of assis-tance we included a fifth hypothesis: (e) less political stability in country of origin leads to lower receipt of remittances.

As one can conclude from the results in Table 5, the perception on the conflict in the home country has a significant influence on the delivery of assistance. It is difficult to make hard as-sumptions on the direction of causality, since the following order of the coefficients of the separate


categories is not fully sequential. However, one can state that respondents who perceive the con-flict in their home country as deteriorating have the lowest change of receiving assistance com-pared to respondents who perceive the conflict as improving or not changed. This is in line with the outcomes from the supply side where less economic activities are undertaken if the conflict is perceived as more conflictive. Also significant is the factor ‘tensions in Europe’. When people in the home country know about tensions that are felt in Europe related to the conflict, it is less likely that they will receive any assistance from relatives outside the country. Finally, the role of the diaspora community in Europe (i.e. whether the community living in European cities played a significant role in the situation or the conflict in the respondents’ country) is positively related to the assistance one receives.

The results for the dependent variable receiving remittances also show a positive relation with the role of the diaspora community living in Europe. Moreover, the political stability in the home country influences the receipt of remittances. Respondents who perceive the conflict as unstable have the lowest chance of receiving remittances compared to respondents who perceive the con-flict as stable or somewhere in between (middle).

For the receiving side of economic activities there appears to be a very clear relation between the conflict in the home country and the receipt of any economic assistance, as the variables that show a significant relation are all somehow related to the conflict in the home country. As we expected before, tensions between communities in Europe results in a decrease of economic ac-tivities at the receiving side. Additionally, respondents who perceive the conflict as deteriorating are less likely to receive any assistance compared to respondents who are more positive about the conflict. This fact is in line with the outcomes on the sending side. More apparent even, are the outcomes for the role that the diaspora community living in Europe plays in the conflict; the more they are involved in the conflict, the more people are likely to receive assistance or remittances. This outcomes confirms the important role played by TCs in addressing the conflict of the home country and their contributions to economic recovery and development.

6. Conclusions and Implications

This article examined the engagement of migrants in transnational economic activities in their home countries, and tried to identify key factors influencing the receipt of economic assistance and remittances by individuals. We found that at the supply side, social networks (CSO linkages) represent the single most important factor influencing all kinds of economic activities in which migrants engage. In a similar vein, at the demand side linkages with diaspora communities rep-resent an important factor for attracting remittances and investments. Even while sending remit-tances remains essentially an individual decision, it takes place within a social‒cultural network.

Looking at the overall picture we can conclude that at the supply side people who consider the conflict in the home country as more conflictive will be less likely to be involved in economic ac-tivities and remittances sending. The date of arrival is also found to be important determinants for both activities: more recent arrival results in a higher probability of economic involvement and remittances. The views on integration (in Europe) and the reason for migration do not appeared to be strongly related to engagement in economic activities. At the demand side, a strong relationship between variables concerning the conflict in the home country and the economic activities was identified, pointing to an inverse impact of the conflict on the economic engagement of migrant communities.


As stated in the introduction, it is commonly believed that poverty reduction should be part of conflict prevention and post‒conflict reconstruction processes. Transnational communities might play a crucial role in this domain. Even though the focus of the survey did not contain specific questions regarding the way or the extend to which these communities can contribute to this is-sue, we discern some interesting outcomes. As was consistently found in § 4 while discussing the determinants at the supply side, variables related to the conflict (i.e. position in conflict) appear to be strongly, albeit negatively related to the likelihood of engagement in economic activities in the home countries. We cannot ascertain, however, whether the opposite relationship holds. CSO linkages and frequent travel to the home country can partly mitigate this relationship and tend to favour economic engagement. Similarly, all variables that were significantly related to the receipt of economic support are strongly related to the conflict perception in the home country: respond-ents who perceive the local conflict as deteriorating are less likely to receive economic assistance. Wherever the diaspora community is considered to play a role in local conflict mitigation, the likelihood of receiving remittances clearly increases. Consequently, migrant’ networks can act as an important linking pin between sending and receiving economic support.

Acknowledgements

The research for this study is funded through the International Civil Society Forum on Conflicts (infocon). infocon is a joint endeavour of several research institutes, including the Univer-sité Catholique de Louvain (research direction), University of Kent, Universität Duisburg‒Essen, Institut d’Études Politiques de Lille, cidin‒Radboud University Nijmegen, Université de Liège, Université Laval (Québec) and CSOs based in the Netherlands, Kosovo, Belgium, United Kingdom and Germany.

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Annex A

Data Analysis Cities of Settlement(Brussels, Randstad, Berlin and London)

Nienke Regts

December 2009


A.1. Profiles

Variables Economic Involved Remittances Attract Investment a

City of SettlementAmsterdam 58.1% 57.1% 55.6%Brussels 14% 19% 11.1%London 16.3% 9.5% 22.2%Berlin 11.6% 14.3% 11.1%

Country originGreat Lakes 44.2% 42.9% 22.2%Kosovo 34.9% 28.6% 77.8%Turkey 20.9% 28.6% –

Most important public roleLeading 53.5% 47.6% 55.6%Member 34.9% 42.9% 22.2%Other 9.3% 9.5% 22.2%

Type organization ngo 88.4% 95.2% 88.9%Political 9.3% – –Business 2.3% 4.8% 11.1%

View on conflictImproved 69.8% 76.2% 77.8%Worsened 23.3% 19% 22.2%No conflict 7% 4.8% –

Type of economic involvementPassive 65.1% nib niActive 20.9% ni niNot last year 14% ni ni

Type of economic involvement specificRemittances 48.8% ni niGoods 18.6% ni niKnowledge 16.3% ni ni

Motivation economic involvementEmotional attachment 23.3%Survival 32.6%Development 25.6% Future home country 14.00%

(a) Profiles are based on the three dependent variables that are used for the logistic regressions: (i) Economic in-volved = respondent involved in activities toward economic development or poverty alleviation. (ii) Remittanc-es = respondent sends remittances. (iii) Attract investment = respondent involved in activities aimed at attracting investment or economic assistance from others to country of origin. (b) ni = not involved.


A.1.1. What Can Be Concluded from the Profiles above?

More than half of the respondents who are involved in any economic activities in general (meaning combining the three dependent variables together) are settled in Amsterdam. For the respond-ents who declared their organization is involved in any activities towards the economic devel-opment / poverty alleviation in the country of origin and the respondents who declared to send remittances stem for more than 40% from the Great Lakes. Respondents who declared to be in-volved in activities aimed at attracting investment or economic assistance from others to country of origin mostly come from Kosovo (77.8%).

It becomes clear that almost everyone who is economically involved is a member of an NGO or private organization. In addition, more than 50% have an average intense CSO linkage, mean-ing they are involved on an average level in the CSO and have an average position into the CSO. Moreover, other NGOs (national and international) are for a great deal also the most important actors they interact with and with whom they defend their interest regarding the conflict in the country of origin.

When looking at how the respondents who are involved in any economic activity towards their country of origin look at the evolution of the conflict in the last 20 years than more than three third feels that is has improved – so most respondents who feel the conflict has improved over the last 20 years are also economical involved towards they country or origin.

The reason for leaving the country of origin is mostly when there was a poor socio-economical perspective for the respondents. And in all three cases far more than half of them arrived between 1990 and 2000 and did spent some time in the country of origin after they were migrated.

Looking at the identity card of these respondents, the ones who are involved in activities di-rected towards economic development / poverty alleviation and the ones who send remittances, more than half of them state they have the identity of their own community or country of origin. Of the respondents who are involved in activities aimed at attracting investment or economic as-sistance from others to country of origin, one third states to have the identity of their community or country of origin.

A.1.2. Overall Picture

Of the interviewees who stated to be economical active for their country of origin they seem to feel like they are still rather attached to their home country (look at identity and time spent in coun-try). In addition they are very much involved into their CSO, they have a rather high position into their own organization and they have an average CSO linkage. They also stand positive towards the evolution of the conflict, feeling that it has improved over the years. Apparent is also the date of arrival in the country of settlement. A large part of them are rather newcomers (comparing this to migrants who came around 1970–1980) (which might also explain their rather strong attachment to their home country).


A.2. CROSS-TABS

A.2.1. City of Settlement

Perceiving of Conflict

Perceiving Conflict New Total Improved Worsened

City of Settlement

Brussels Count 22 6 28Expected Count 18,4 9,6 28,0

Amsterdam Count 31 18 49Expected Count 32,1 16,9 49,0

Berlin Count 12 8 20Expected Count 13,1 6,9 20,0

London Count 13 9 22Expected Count 14,4 7,6 22,0

TotalCount 78 41 119Expected Count 78,0 41,0 119,0

Chi-Square Tests

Value df Asymp. sig. (2-sided)Pearson Chi-Square 2,895 a 3 ,408Likelihood Ratio 3,050 3 ,384Linear-by-Linear Association 2,025 1 ,155N of Valid Cases 119

Note: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 6,89.



View on integration

Meaning of Term ‘Integration’

TotalNo Integration Language

Adaptation and / or

Participation

Accept and / or Respect

City of Settlement

Brussels

CountExpected Count

Adjusted Residual

8 0 17 6 31

2,8 4,3 18,9 5,0 31,0

3,8 ‒2,6 ‒,8 ,5

Amsterdam

CountExpected Count

Adjusted Residual

0 13 30 8 51

4,6 7,0 31,1 8,3 51,0

‒2,9 3,2 ‒,4 ‒,1

Berlin

CountExpected Count

Adjusted Residual

3 4 11 5 23

2,1 3,2 14,0 3,7 23,0

,8 ,6 ‒1,4 ,8

London

CountExpected Count

Adjusted Residual

0 0 17 1 18

1,6 2,5 11,0 2,9 18,0

‒1,4 ‒1,8 3,2 ‒1,3

TotalCount 11 17 75 20 123Expected Count 11,0 17,0 75,0 20,0 123,0

Chi-Square Tests

Value df Asymp. sig. (2-sided)

Exact sig. (2-sided)


Point Probability

Pearson Chi-Square 34,545 a 9 ,000 . b Likelihood Ratio 43,015 9 ,000 ,000 Fisher’s Exact Test 33,345 ,000 Linear-by-Linear Association 1,676 c 1 ,196 ,210 ,108 ,020

N of Valid Cases 123

Notes: (a) 9 cells (56,3%) have expected count less than 5. The minimum expected count is 1,61. (b) Cannot be com-puted because there is insufficient memory. (c) The standardized statistic is 1,294.



Reason Migration

Main Reason for Leaving Country of Origin

TotalFamily / Born

(Poor) Socio‒Economical Perspectives

Political Situation

Violence / Armed Conflicts /War

City of Settlement

Brussels

CountExpected Count

Adjusted Residual

10 24 5 1 40

8,6 14,2 8,9 8,3 40,0

,6 3,9 ‒1,8 ‒3,4

Amsterdam

CountExpected Count

Adjusted Residual

8 10 21 13 52

11,2 18,5 11,6 10,8 52,0

‒1,4 ‒3,1 4,0 1,0

Berlin

CountExpected Count

Adjusted Residual

10 7 3 3 23

4,9 8,2 5,1 4,8 23,0

2,8 ‒,6 ‒1,2 ‒1,0

London

CountExpected Count

Adjusted Residual

1 7 1 11 20

4,3 7,1 4,4 4,1 20,0

‒1,9 ‒,1 ‒2,0 4,1

TotalCountExpected Count

29 48 30 28 135

29,0 48,0 30,0 28,0 135,0

Chi-Square Tests

Value df Asymp. sig. (2‒sided) Exact sig. (2‒sided) Exact sig. (1‒sided)Pearson Chi‒Square 51,439 a 9 ,000 . b Likelihood Ratio 51,757 9 ,000 . b Fisher’s Exact Test . b . b Linear‒by‒Linear Association 8,635 1 ,003 . b . b


Notes: (a) 5 cells (31,3%) have expected count less than 5. The minimum expected count is 4,15. (b) Cannot be com-puted because there is insufficient memory.



Date of Arrival

Date of arrival new

TotalMore than 30 Years in

Country

Between 10 and 30 Years in Country

Less than 10 Years in

Country

City of Settlement

Brussels CountExpected Count

13 23 4 4010,5 25,0 4,5 40,0

Amsterdam CountExpected Count

8 38 6 5213,7 32,5 5,9 52,0

Berlin CountExpected Count

13 9 0 225,8 13,7 2,5 22,0

London CountExpected Count

1 13 5 195,0 11,9 2,1 19,0

Total CountExpected Count

35 83 15 13335,0 83,0 15,0 133,0

Chi-Square Tests

Value df Asymp. sig. (2-sided)Pearson Chi-Square 24,317 a 6 ,000Likelihood Ratio 25,517 6 ,000Linear-by-Linear Association 1,022 1 ,312N of Valid Cases 133

Note: (a) 3 cells (25,0%) have expected count less than 5. The minimum expected count is 2,14.


Frequent Travel

Times Spent in Country of Origin Total

No Yes

City of Settlement

BrusselsCountExpected CountAdjusted Residual

16 23 3912,7 26,3 39,01,3 ‒1,3

AmsterdamCountExpected CountAdjusted Residual

19 32 5116,6 34,4 51,0,9 ‒,9

BerlinCountExpected CountAdjusted Residual

4 20 247,8 16,2 24,0

‒1,8 1,8

LondonCountExpected CountAdjusted Residual

5 16 216,8 14,2 21,0‒,9 ,9


44 91 13544,0 91,0 135,0


Chi-Square Tests




Point Probability

Pearson Chi-Square 5,275 a 3 ,153 ,154 Likelihood Ratio 5,607 3 ,132 ,141 Fisher’s Exact Test 5,214 ,152 Linear-by-Linear Association 3,722 b 1 ,054 ,060 ,032 ,011


Notes: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 6,84. (b) The standardized statistic is 1,929.


CSO Linkages

CSO Linkage NewTotal

Low / Average Intensity High Intensity

City of Settlement

Brussels CountExpected Count

28 7 3526,4 8,6 35,0

Amsterdam CountExpected Count

30 19 4936,9 12,1 49,0

Berlin CountExpected Count

23 1 2418,1 5,9 24,0

London CountExpected Count

17 5 2216,6 5,4 22,0


98 32 130

98,0 32,0 130,0

Chi-Square Tests

Value df Asymp. sig. (2-sided)Pearson Chi-Square 11,147 a 3 ,011Likelihood Ratio 12,736 3 ,005Linear-by-Linear Association ,656 1 ,418N of Valid Cases 130Note: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,42.


A.3. TESTING HYPOTHESES

A.3.1. General Information

Three different dependent variables were used for testing the different hypotheses. The most gen-eral dependent variable is

1. “involvement in activities toward economic development or poverty alleviation in country of origin (economic involvement)”.

2. Next to this variable, two more specific dependent variables were used:

“sending remittances”,3. “attract investment or economic assistance”.4.

The hypotheses to be tested are the following:

1. Better integrated members of TCs are more / less involved in sending remittances. 2. More investment by TCs with more / less intensive CSO linkages.3. Less investment by TCs if more conflict is perceived. 4. More / less sending of remittances if conflict is perceived as more conflictive. 5. More remittances sending with recent arrival and frequent travel.

Deriving from the hypotheses above the following variables will be used in the regression:

Dependent variables:• Involvement in activities toward economic development or poverty alleviation (0 = no / 1 = yes)• Sending remittances (0 = no / 1 = yes)• Attract investment or economic assistance (0 = no / 1 = yes)

Independent variables: • Perceiving of conflict (1 = improved / 2 = worsened)• CSO linkages (1 = low or average intensity / 2 = high intensity)• View on integration (0 = no integration / 1 = language / communication / 2 = adapt and par-

ticipate / 3 = accept and respect)• Date of arrival (1 = more than 30 years ago / 2 = between 10 and 30 years ago / 3 = less than 10

years ago)• Frequent travel (0 = no / 2 = yes)• Reason for migration (0 = family or born / 1 = socio-economic / 2 = political situation / 3 = vio-

lence or war A.3.2. Testing Hypotheses 2 and 3Dependent variable:

• Involvement in activities toward economic development or poverty alleviation (0 = no / 1 = yes)Independent variables:

• Perceiving of conflict (1 = improved / 2 = worsened)• CSO linkages (1 = low or average intensity / 2 = high intensity)


Table A.3.1. Significant determinants for involvement in activities toward economic development or poverty alleviation

in country of origin (general economical involvement)

Variables (n = 93) c b se Wald Sign. (2-sided) c Sign. (1-sided) c

Perceiving of conflict (total) ‒1.465 0.694 4.452 0.035** 0.018**CSO linkages (total) 1.491 0.716 4.335 0.037** 0.019**View on integration (total) 12.746 0.005** 0.003***

No integration (0) b

Language (1) b 2.769 1.531 3.269 0.071* 0.036**Adapt / participate (2) b ‒0.839 0.993 0.713 0.398 0.199Accept / respect (3) b ‒2.793 1.456 3.678 0.055* 0.028**

Date of arrival (total) 4.901 0.086* 0.043**Less than 10 years in country (3) b

More than 30 years in country(1) b ‒2.311 1.125 4.216 0.040** 0.020**Between 10 and 30 years in country (2) b ‒0.718 0.896 0.643 0.423 0.212

Frequent travel (total) 1.074 0.796 1.823 0.177 0.089*Reason for migration (total) 1.582 0.663 0.335Family / born (0) b

Socio-economic (1) b 0.586 0.843 0.484 0.487 0.244Political situation (2) b ‒0.364 0.974 0.139 0.709 0.355Violence / war (3) b 0.140 1.011 0.019 0.890 0.445

Cox and Snell R2 0.414Nagelkerke R2 0.552

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) Reference category. (c) Removed outliers 115, 115, 54; constant excluded.

Interpretation of the Outcomes for Hypothesis 2:“More economic involvement by TCs with more / less intensive CSO linkages”.

The variable CSO linkages has a significant influence on activities toward economic develop-ment or poverty alleviation. Because the variable CSO linkages is a dichotome variable, a positive B indicates that an increase in the independents variable score will result in an increased prob-ability of the cases recording a score of 1 in the dependent variable. For this case it thus means that the higher the intensity of respondents’ CSO linkages, the more likely it is they will be involved in activities toward economic development or poverty alleviation.

Interpretation of the Outcomes for Hypothesis 3:“Less investment by TCs if more conflict is perceived”.

The variable perceiving of conflict has a negative influence on the economic involvement of the respondents. The B-coefficient is negative which means that respondents who perceive the conflict as more worsened have less chance of being economic involved compared to respondents who perceive the conflict as improved. To put it the other way around: the less conflict is perceived, the more one is likely to be involved in economic activities toward economic development or poverty alleviation.


Interpretation of Other Independent Variables:The variables view on integration, date of arrival and frequent travel have a significant influence on economic involvement.

For view on integration the category ‘no integration’ is used as reference category. The fact that view on integration has a significant influence on the dependent variable lies in the fact that the differences between 0 (no integration) and 1 (language / communication) and 0 (no integration) and 3 (accept and respect) are significant.

The category ‘language’ has the highest B-score, which means that respondents who define inte-gration as ‘language’ have the highest chance to score yes, they are economic involved (compared to the other categories). The second highest is the category ‘no integration’. The third is ‘adapt / par-ticipate’. The respondents who define integration as ‘accept / respect’ have the lowest chance of be-ing involved in economic involvement.

The ranking order of the B-coefficients (high-low): 1 0 2 3Original ranking order (most integrated till least integrated): 3 2 1 0

From these ranking orders one can find a trend towards the more integrated, the less one is in-volved in economic involvement.

The variable date of arrival also has a significant influence on economic involvement of respond-ents. In this case the category 3 (less than 10 years in country) is used for the reference category. The fact that the date of arrival has a significant influence on economic involvement, lies in the fact that the difference between 3 (less than 10 years) and 1 (more than 30 years in country) is significant. In addition, the category ‘less than 10 years in country’ has the highest B-score. This means that respondents who arrived less than 10 years in the country of settlement are the ones who have the highest chance of being economic involved (compared to the other categories). The second highest is the category ‘between 10 and 30 years’. The category which has the lowest chance to score a 1 on economic involvement is ‘more than 30 years in country’.

The ranking order of the B-coefficients (high-low): 3 2 1 Original ranking order: 3 2 1

Concluding can be stated: the more recent one arrived in the country of settlement, the more he / she is likely to be involved in economic involvement toward the country of origin.

Finally, the variable frequent travel has a significant influence on economic involvement. The B-coefficient is positive. This means that respondents who stated to travel back to their country of origin are more likely to be involved in economic involvement. Dependent variable:

• Attract investment (0 = no / 1 = yes)

Independent variables: • Perceiving of conflict (1 = improved / 2 = worsened)• CSO linkages (1 = low or average intensity / 2 = high intensity)


Table A.3.2. Significant determinants for attracting investment / economic assistance

Variables (n = 93) b se Wald Sign.(2-sided) a Sign.(1-sided) a

Perceiving of conflict (total) ‒2.834 1.248 5.152 0.023** 0.012**CSO linkages (total) 1.049 0.880 1.422 0.233 0.117Frequent travel (total) 0.724 1.140 0.403 0.525 0.263Reason for migration (total) 4.199 0.241 0.121Violence/war (3) b

Family/born (0) b ‒1.360 1.243 1.198 0.257 0.129Socio-economic (1) b ‒2.266 1.186 3.651 0.056* 0.028**Political situation (2) b ‒0.957 1.033 0.859 0.384 0.192

Cox and Snell R2 0.595Nagelkerke R2 0.794Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category.

Table A.3.2 only shows the outcomes for four of the six independent variables. This is because the variables date of arrival and view on integration showed to have 0 outcomes in some options. Re-ducing the number of categories does not make a difference. In that case, spss is not able to run the logistic regression.

Interpretation of the Outcomes for Hypothesis 2:“More attracting of investment / economic assistance by TCs with more / less intensive CSO link-ages”.

The results from Table A.2.1 show that the independent variable CSO linkages does not has a significant influence on attracting investment.

Interpretation of the Outcomes for Hypothesis 3:“Less attracting of investment / economic assistance by TCs if more conflict is perceived”.

The results from Table A.2.2 show that the independent variable perceiving of conflict has a significant influence on attracting investment. The B-coefficient is negative which means that re-spondents who perceive the conflict as worsened have less chance of being economic involved compared to respondents who perceive the conflict as improved. To put it the other way around: the less conflict is perceived, the more one is likely to be involved in activities aimed at attracting investment / economic assistance.

Interpretation of the Outcomes of Other Independent Variables:Frequent travel does not show to have a significant influence on attracting of investment. And the variable reason for migration also does not have a significant influence. However, between the different categories there appears to be 1 significant difference. The difference between 3 (vio-lence / war) and 1 (socio-economic) is significant. But, in total no significant influence of reason for migration on attracting investment is found.

A.3.3. Testing Hypothesis 1, 4 and 5

Dependent variable: • Sending remittances (0 = no / 1 = yes)


Independent variables: • View on integration (0 = no integration / 1 = language / communication / 2 = adapt and par-

ticipate / 3 = accept and respect)• Date of arrival (1 = > 30 years ago / 2 = between 10 and 30 years ago / 3 = < 10 years ago)• Frequent travel (0 = no / 2 = yes)•

Table A.3.3. Significant determinants for sending remittances

Variables (n = 95) b se Wald Sign. (2-sided) a Sign.(1-sided) a

Constant 0.201 2.102 0.009 0.924 0.462Perceiving of conflict (total) ‒1.620 0.943 2.951 0.086* 0.043**CSO linkages (total) 0.965 0.729 1.752 0.186 0.092*View on integration (total) 7.271 0.064* 0.032**

No integration (0) b

Language (1) b 0.386 1.367 0.080 0.778 0.389Adapt/participate (2) b ‒1.456 1.232 1.396 0.237 0.119Accept/respect (3) b ‒3.250 1.724 3.555 0.059* 0.030**

Date of arrival (total) 4.946 0.084* 0.042**More than 30 years in country (1) b

Between 10 and 30 years in country (2) b 2.776 1.269 4.789 0.029*** 0.015**Less than 10 years in country (3) b 3.113 1.632 3.641 0.056* 0.028**

Frequent travel (total) ‒1.453 0.813 3.198 0.074* 0.037**Reason for migration (total) 8.177 0.042** 0.021**Socio-economic (1) b

Family/born (0) b ‒0.257 1.011 0.065 0.799 0.400Political situation (2) b ‒2.885 1.047 7.599 0.006*** 0.003***Violence/war (3) b ‒1.572 0.871 3.260 0.071* 0.036**

Cox and Snell R2 0.28Nagelkerke R2 0.443

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category.

Interpretation of the Outcomes for Hypothesis 1:“Better integrated members of TCs are more / less involved in sending remittances”.

From the outcomes in Table A.2.3 can be found that ones view on integration has a significant influence on sending of remittances (sign. = 0.064). However, no further statements can be made about the nature of the influence. It is thus needed to look at the separately categories to find out in what way the view on integration has a significant influence. Looking at the categories separately it can be stated that respondents who define integration as ‘language / communication (1)’ have the highest chance to answer yes to the question whether they send remittances to their home country compared to the other categories (resulting from the B values). Secondly, come the respondents who define integration as ‘no integration’, third the respondents who define integration as ‘adapta-tion and / or participation’. Respondents who define integration as ‘accept and respect’ have the lowest chance of all groups to be involved in sending remittances.

The ranking order will be as follows (from highest till lowest B-coefficient): 1 0 2 3 The original ranking order (from high till low integration) is: 3 2 1 0


The difference between 0 (no integration) and 1 (language) and the difference between 0 (no inte-gration) and 2 (adapt / participate) is not significant. The difference between 0 (no integration) and 3 (accept / respect) is significant. So, concluded can be stated that the fact that the variable ‘view on integration’ has a significant influence on sending of remittances lies in the fact that the difference between 0 (no integration) and 3 (accept / respect) is significant. Herein, respondents who report integration as ‘language / communication’ are most likely to answer yes to the question whether they send remittances. In addition, because the ranking order of the B-coefficients is not in line with the original ranking order it cannot be stated that respondents with a more in-depth view on integration (or being more integrated) results in more / less sending of remittances. However, when taking category 0 and 1 together (the differences between the B-coefficients are rather small), a trend towards such a statement can be seen. The more one is integrated, the less this respondent is likely to be involved in the sending of remittances.

One remark about the method of logistic regression must be made. I think that for this par-ticular hypothesis, this method is not completely sufficient. Due to the outcomes, we cannot state that the higher the integration, the more sending of remittances. A trend towards this outcome is noticed, but in fact it is not a very sufficient conclusion.

Interpretation of the Outcomes for Hypothesis 4:“More / less sending of remittances if conflict is perceived as more conflictive”.

From the outcomes of Table A.2.2 it can be concluded that ‘perceiving of conflict’ has a sig-nificant influence on sending of remittances. Because the variable ‘perceiving of conflict’ is a di-chotome variable, a positive B indicates that an increase in the independents variable score will result in an increased probability of the cases recording a score of 1 in the dependent variable. In this case, the B-value is negative which means that the more conflict is perceived, the less one is likely to be involved in sending of remittances.

Interpretation of the Outcomes for Hypothesis 5:“More remittances sending with recent arrival and frequent travel”.

Frequent travel shows to have a significant influence on sending of remittances. The B-coeffi-cient is negative, which means that the more frequent one is traveling to the country of origin, the less likely one will be involved in the sending of remittances.

Recent arrival also shows a significant influence on sending of remittances. In this respect the referent category (1) is ‘more than 30 years in the country’. When we look at the category, which scores the highest B-coefficient (meaning the highest chance of scoring 1) we see that respondents who arrived less than 10 years ago have the highest chance of sending remittances. Respondents who are in the country between 10 and 30 years have the second highest B score. The category with the lowest chance of a score of yes is more than 30 years in country of settlement.

The ranking order from the highest B-coefficient till the lowest is as follows: 3 2 1 With an original ranking ranging from (high-low): 3 2 1

All differences with the reference category are significant. Thus, the fact that recent arrival has a significant influence on sending of remittances lies in the fact that the differences between 1 (be-fore 30 years) and 2 (between 10 and 30 years) and 3 (less than 10 years) are significant. Concluding can be stated that (regarding the B-order and the original order) the more recent one arrived in the country of settlement, the more likely he / she will be involved in the sending of remittances.


Interpretation of Other Independent Variables:The independent variable CSO linkages has a significant influence on the sending of remittances. The B-coefficient is positive. This means that the more intensive the CSO linkages, the more one is likely to be involved in the sending of remittances. This is in line with the influence of CSO linkage on economic involvement in general.

The independent variable reason for migration also has a significant influence on the sending of remittances. In this case category 1 (socio-economic reasons) is being used as the reference category. The fact that reasons for migration has a significant influence on the sending of remit-tances, lies in the fact that the differences between 1 (socio-economic) and 2 (political) and 1(socio-economic) and 3 (violence / war) are significant.

Respondents who mentioned socio-economic reasons for migration have the highest B-coeffi-cient, which means they have the highest chance in being involved in the sending of remittances compared to the other categories. The second highest is the category family / born, however the B-coefficient between these two is rather small. Third is category ‘violence / war’. Respondents who mention ‘political’ as the reason for migration have the lowest chance of being involved in the sending of remittances.

Ranking order B-coefficients (high-low): 1 0 3 2Original ranking order (high-low) 3 2 1 0

No final conclusions from these outcomes can be made. Perhaps a trend towards a difference be-tween pull and push factors can be found where pull factors have the highest chance in being in-volved in the sending of remittances. When respondents are pushed out of their country of origin (by war or political instability) they will be less likely to be involved in the sending of remittances. A reason for this can be that these people are more likely to go back to their country of origin when it is possible.

A.3.4. Comparing Outcomes of Different Dependent Variables

When comparing the three tables together the following can be concluded:• The variable “perceiving of conflict” shows to have a significant influence on all three de-

pendent variables. For all outcomes, the same conclusion is found: the more conflict is per-ceived, the less one is likely to be involved in any economic activities (in general, in remit-tances or in attracting investment). The strongest influence is on attracting investment. The influence on the other two variables is almost the same.

• The variable “view on integration” shows to have a significant influence on economic in-volvement in general and specific on sending remittances. For both dependent variables, the trend toward the higher the view on integration / the more integrated the less involvement in activities toward economic development or poverty alleviation or sending remittances is found.

• The variable “date of arrival” shows to have a significant influence on economic involve-ment in general and specific on sending remittances. Also here, the same outcome is found for both dependent variables: the more recent one arrived in the country of settlement, the more one is likely to be involved in economic involvement and sending of remittances.

• The variable “intensity of the CSO linkages” only influences economic involvement in gen-eral significantly.

• “Frequent travel” and “reason for migration” only influences sending of remittances signifi-cantly.

Annex B

Data Analysis country of origin(Great Lakes, Kosovo and Turkey)

Nienke Regts & Ruerd Ruben


B.1. PROFILES1

Variable Received Assistance

Received Remittances

Country of OriginRwanda 16.2% 19.2%Burundi 35.1% 26.9%Congo1 13.5% 11.5%Kosovo 24.3% 26.9%Turkey 10 / 8% 15.4%

Most Important Public RoleHead Organization 54.1% 46.2%Member / Employee 43.2% 53.8%Religious 2.70%

Type Organization One Is Member ofNGO 59.5% 65.4%Political Movement 5.4% 7.7%Business 13.5% 7.7%Public 2.7% –

Perceiving of ConflictImproved 56.8% 53.8%No Change 24.3% 19.2%Worsened 13.5% 19.2%

Tensions Felt in EuropeYes 29.7% 34.6%No 43.2% 38.5%

Role European Community in Situation CountryNone 27% 26.9%Share Knowledge / Skills etc. 29.7% 38.5%Socio-Economical Support 29.7% 30.8%Political Support 5.4% –

Influence European Community On International ActorsNone 16.2% 19.2%Share Knowledge 24.3% 23.1%Organizing Projects / Fund Raising 13.5% 15.4%Dialogue / Lobbying 24.3% 23.1%Create Network 2.7% 3.8%

Organization Cooperated with European Community in Those EffortsNo 21.6%Yes 78.4%

1. No. 95675 (Great-Lakes) and no. 84201 (Kosovo) were removed from the file country of origin and thus not used for any analy-ses. Answers were whether of the record or based on just some remarks.


Variable Received Assistance

Received Remittances

Contribution European Migrant Community to Support Efforts in Home CountryNo 43.2% 50,0%Yes: Share Knowledge / Skills 24.3% 19.2% Organize Activities 13.5% 11.5% (Financial) Support for Development 8.1% 11.5%

European Community Role in Mitigation ConflictNo 35.1% 42.3%Share Knowledge / Skills 13.5% 15.4%Cooperation 2.7% 3.8%Return to Country 10.8% 7.7%Invest 21.6% 15.4%

Involved in Commercial Activities No 70.3% 65.4%Support / Set Up Business 10.8% 7.7%

Obstacles for Economic ActivitiesNone 24.3% 15.4%Communication 10.8% 11.5%Socio-Economical 29.7% 38.5%Political 18.9% 15.4%

Most Striking Event in Recent History (After ‘45)War / Rebellion 16.2% 19.2%Socio-Economical / Refugees 13.5% 11.5%Political Changes / Coups 37.8% 34.6%

Stability of Political SituationUnstable 29.7% 26.9%Not Unstable / Not Stable 27% 26.9%Rather Stable 18.9% 19.2%Stable 16.2% 19.2%

Identity CardEthnicity 8.1% 7.7%Nationality 21.6% 19.2%Profession / Education 29.7% 30.8%Civilian of The World 2.7% –

Personal Links with Organizations in EuropeNo 24.3% 23.1%Yes 62.2% 57.7%

Personal Contacts with Persons Who Live in EuropeNo 13.5% 11.5%Yes 73% 73.1%


In relation to the profiles of the migrants who were active in any economic activities towards their country of origin the profiles of the respondents who received any assistance are in line with each other. More than half of the respondents live in the Great Lakes and more than 70% received as-sistance in the form of money.

Respondents who have received assistance are most of the time member of an NGO or private organization. More than half of them have a leading position. And almost 80% of the organiza-tion they are involved in cooperates with the community in Europe. Most of them (more than one third) also have personal contacts with organizations and persons in Europe. For the respondents who receive remittances, almost the same outcomes are apparent.

Vision on the influence of community in Europe: A great deal of the respondents feels that the community in Europe has a significant role or influence related to the conflict and international actors. Sharing knowledge/skills and socio-economical support are the most common ways of influencing by the community.

About the conflict: more than half of the respondents perceive the conflict in their country as improved. However, looking at the political stability, only 35.1% and 38.4% of the respondents thinks that it is (rather) stable.

Overall

It can be stated that respondents who received assistance (which consist of around 70% mon-ey) from their community in Europe, have rather strong links with the European community (in terms of having contacts with people / organizations and cooperating with the community in Eu-rope). Moreover, it seems that the majority of these respondents are positive about the influence of the community in Europe on the conflict or international actors.

Regarding to their own involvement in the community, the leading position and being a mem-ber of an NGO are the most common categories. So it seems that these respondents are rather high involved in their community.

The conflict in their country is perceived as mostly improved (which is also in line with the view of the ‘senders’). But, a great deal does not feel that the political situation is very much stable.

So, it looks like that respondents who have received assistance and remittances, have rather strong links with their European community: their own organization cooperates with this com-munity and personally they know organizations and persons who live in Europe. Besides, most of these respondents feel that the European community is active in playing a mitigating kind of role in the conflict. In addition, the biggest part of these respondents are highly involved in their own community (the public role is rather high). Being involved in any commercial activities however, is not very common.


B.2. CROSS-TABS

B.2.1. Country of Origin

Received Assistance

Received Assistance?

TotalNo Yes

Country Of Origin Not Specific

Great Lakes CountAdjusted Residual

7 24 31‒1,4 1,4

Kosovo CountAdjusted Residual

2 9 11‒1,0 1,0

Turkey CountAdjusted Residual

7 4 112,7 ‒2,7

Total Count 16 37 53

Chi-Square Tests




Point Probability

Pearson Chi-Square 7,443 a 2 ,024 ,030 Likelihood Ratio 6,951 2 ,031 ,051 Fisher’s Exact Test 6,659 ,051 Linear-by-Linear Association 4,926 b 1 ,026 ,028 ,022 ,013N of Valid Cases 53

Notes: (a) 2 cells (33,3%) have expected count less than 5. The minimum expected count is 3,32. (b) The standardized statistic is ‒2,219.


Received remittances

Received RemittancesTotal

No Yes

Country of Origin Not Specific


16 15 31,3 ‒,3


3 7 10‒1,4 1,4


7 4 111,0 ‒1,0



Chi-Square Tests




Point Probability

Pearson Chi-Square 2,450 a 2 ,294 ,348 Likelihood Ratio 2,507 2 ,286 ,348 Fisher’s Exact Test 2,373 ,348 Linear-by-Linear Association ,114 b 1 ,735 ,866 ,433 ,126N of Valid Cases 52

Notes: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,00. (b) The standardized statistic is ‒,338.


Perceiving of Conflict

Situation / Conflict Worsened or Improved Last 20 Years Total

Improved No change Worsened



22 4 7 333,1 ‒3,5 ,2


5 7 2 14‒1,0 1,5 -,6


6 12 5 23‒2,5 2,4 ,3

Total Count 33 23 14 70

Chi-Square Tests




Point Probability


(a) 3 cells (33,3%) have expected count less than 5. The minimum expected count is 2,80. (b) The standardized statis-tic is 1,962.



Tensions Felt in Europe

Know about Tensions Happening in European Countries? TotalNo Yes



16 14 301,0 ‒1,0


5 1 61,9 ‒1,9


2 10 12‒2,5 2,5


Chi-Square Tests




Point Probability


Notes: (a) 2 cells (33,3%) have expected count less than 5. The minimum expected count is 2,88. (b) The stand-ardized statistic is 1,793.


Role eu community in country

Community in Europe significant role in situation in country? Total

Yes No



9 24 33‒2,2 2,2


3 11 14‒1,6 1,6


16 6 223,7 ‒3,7



Chi-Square Tests




Point Probability


Notes: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,68. (b) The standardized statistic is ‒3,176.


Cooperation with eu Community

Has Organization Cooperated with Community in Europe in

Any of the Efforts Total

No Yes



2 31 33‒4,7 4,7

Kosovo CountAdjusted ResidualCountAdjusted Residual

12 4 163,9 ‒3,9

Turkey 10 11 211,5 ‒1,5


Chi-Square Tests




Point Probability


Notes: (a) 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,49. (b) The stand-ardized statistic is ‒3,516.



Political Stability

Stability NewTotal

Not Stable Middle Stable



17 6 7 303,1 ‒2,4 -,7


2 4 7 13‒1,8 -,4 2,4


5 13 4 22‒1,7 2,9 ‒1,2

Total Count 24 23 18 65

Chi-Square Tests




Point Probability

Pearson Chi-Square 15,716 a 4 ,003 ,003 Likelihood Ratio 15,123 4 ,004 ,007 Fisher’s Exact Test 14,317 ,005 Linear-by-Linear Association 2,066 b 1 ,151 ,165 ,089 ,025N of Valid Cases 65 Notes: (a) 3 cells (33,3%) have expected count less than 5. The minimum expected count is 3,60. (b) The stand-ardized statistic is 1,437.

B.3. Testing Hypotheses

B.3.1. General Information

For the country of origin file I maintained two different dependent variables. The first dependent is a general one and the second is more specific:

Dependent variables:• Received assistance (0=no / 1=yes)• Receive remittances (0=no / 1=yes)

Independent variables which might be interesting for analyses:• Perceiving of conflict (C.1)• Tensions felt in Europe (C.3)• Community in Europe played significant role in situation country (D.1)• Organization cooperated with community in Europe (D.4)• Community in Europe role in mitigating the situation in country (D.5)• View on political stability in country (F.2)• Personal links with organization in Europe (F.8)

Tested hypotheses:1. More perceiving of conflict, more/less receiving assistance


2. Tensions felt in Europe leads to less receiving of any assistance/ remittances3. Involvement European community in country, more/less receiving assistance/ remittances4. Cooperation with European community leads to more assistance. 5. Less political stability in country of origin leads to less receiving of remittances. 6. Less receiving of remittances if more conflict is perceived.7. More/less receiving of remittances if eu communities mitigate more/less in conflict.8.

I tried to use all independent variables in one matrix. The problem was that when more variables were used, the total number of included case reduced (less than 40). I tried to find the best fitting model. These attempts are described in the following sections. This means that some variables (like F.8 and D.5) were not included in the analysis. I started with one model, using as much as pos-sible independent variables. For the dependent variable received remittances, this worked out. For the dependent variable received remittances, I also used single analyses and multiple imputation.

B.3.2. Testing Hypotheses 1–5 with Dependent Variable “Received Assistance”

Dependent variable:• Received assistance (0=no / 1=yes)

Independent variables:• C.1: Perceiving of conflict (1=improved / 2=no change / 3=worsened)• C.3: Knowing about tensions felt in Europe (0=no / 1=yes)• D.1: Community in Europe played significant role in situation country (0=no / 1=yes)• D.4: Has organization cooperated with community in Europe (in efforts like dialogue, peace

building etc.) (0=no / 1=yes)• Political stability (1=unstable / 2=middle / 3=stable)

Option 1: Outcomes Logistic Regression Using All Independent Variables

Table B.3.1. Significant Determinants for Receiving Assistance

Variables (N = 36) b se Wald Sign. (2-sided) a Sign.(1-sided) a

Constant 0.795 2.031 0.153 0.695 0.348Perceiving of Conflict (Total) 4.818 0.090* 0.045**Worsened (3) b

Improved (1) b 3.254 1.667 3.809 0.051* 0.026**No Change (2) b 4.335 2.151 4.059 0.044** 0.022**

Tensions in Europe (Total) ‒3.651 1.693 4.648 0.031** 0.016**Role eu Community (Total) 2.989 1.593 3.52 0.061* 0.031**Cooperation eu Community (Total) ‒0.545 1.398 0.152 0.696 0.348Political Stability (Total) 0.376 0.829 0.415Stable (3) b

Not Stable (1) b ‒0.709 1.824 0.151 0.697 0.349Middle (2) b ‒1.052 1.743 0.364 0.546 0.237

Cox And Snell R2 0.417 Nagelkerke R2 0.589

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category (removed outliers 31 and 21)


The number of selected cases becomes very small (N=36) when above 5 independent variables are used in one regression model. Just to show the outcomes I decided to depict this model here. In this model, political stability does not have a significant influence on the receiving of assistance. For a more detailed explanation of the outcomes I refer to the next pages.

Option 2: Outcomes Regression Leaving Out Political Stability (Removed Outlier 31)

In this option, the independent variable political stability is being left out. When this variable is left out, the total number of included cases runs from 36 to 40.

Table B.3.2. Significant determinants for receiving assistance

Variables (N = 40) b se Wald Sign.(2-sided) a Sign. (1-sided) a

Constant ‒0.362 1.073 0.114 0.736 0.368Perceiving of Conflict (Total) 5.833 0.054* 0.027**Worsened (3) b

Improved (1) b 2.319 1.028 5.089 0.024** 0.012**No Change (2) b 2.817 1.453 3.855 0.050* 0.025**

Tensions in Europe (Total) ‒2.068 1.013 4.166 0.041** 0.021**Role eu Community (Total) 1.642 0.98 2.807 0.094* 0.047**Cooperation eu Community (Total) ‒0.162 1.062 0.023 0.879 0.44Cox And Snell R2 0.319 Nagelkerke R2 0.446

Notes: (a) * = significant at 5%; ** = significant at 10%. (b) reference category (removed outliers 31 and 21).

Interpretation of the Outcomes For Hypothesis 1:“More perceiving of conflict, more/less receiving assistance”.

The independent variable ‘perceiving of conflict’ shows to have a significant influence on the receiving of assistance. Because no judgments can be made about the direction of this influence, it is needed to look at the separate categories. In this perspective, category 3 (worsened) is the refer-ence category. The outcomes in the table show that the separate categories 1 (improved) and 2 (no change) both have significant differences with the reference category. This means that the fact that the variable ‘perceiving of conflict’ has a significant influence on the receiving of assistance, lies in the fact that the differences between the categories 1 (improved) and 3 (worsened) and 2 (no change) and 3 (worsened) are significant.

Category 2 (no change) has the highest B-score. This means that respondents who define the conflict as ‘no change’ have the highest chance of a score on ‘yes, they do receive assistance’. The second highest B-score is for category 1 (improved). Respondents, who perceive the conflict as worsened, have the lowest chance of a score on ‘yes, they receive assistance’.

The ranking order from highest-lowest B-coefficient: 2 1 3Original ranking order (high-low): 3 2 1

Deriving from the outcomes above, no clear conclusion for the hypothesis can be found. Using the method of logistic regression cannot give an unambiguous outcome for the influence of ‘perceiv-ing of conflict’ on the receiving of assistance.


Interpretation of the Outcomes for Hypothesis 2:“Tensions felt in Europe leads to less receiving of any assistance / remittances”.

The independent variable ‘tensions’ has a significant influence on the receiving of assistance. The B-coefficient is negative, which means that the more respondents know about tensions hap-pening in Europe (regarding the conflict), the less they are likely to receive any assistance.

Interpretation of the Outcomes for Hypothesis 3:

“Involvement European community in conflict, more / less receiving assistance / remittances”.The independent variable D.1 (community in Europe played significant role in situation country) also has a significant influence on the receiving of assistance. The B-coefficient is positive, which means that the more involvement of the European community in the situation of the country of origin, the more the respondents are likely to receive any assistance.

Interpretation of the Outcomes for Hypothesis 4:“Cooperation with European community leads to more assistance”.

The independent variable D.4 (has organization cooperated with community in Europe) does not has a significant influence on receiving of assistance.

B.3.3. Testing Hypotheses 1–5

Dependent Variable “Received Remittances”For the testing of the hypotheses 1–5 with the dependent variable received remittances, I follow the same procedure in section 3.2.

Option 3: Outcomes Logistic Regression Using All Independent Variables

Table B.3.3. Significant Determinants for Receiving Remittances

Variables (n = 38) b se Wald Sign. (2-sided) Sign.(1-sided)Constant 0.104 1.181 0.008 0,930 0.465Perceiving of Conflict (Total) 0.919 0.46Worsened (3) a

Improved (1) a 0,271 0.908 0.089 0.765 0.383No Change (2) a ‒0.093 1.019 0.008 0.928 0.464

Tensions in Europe (Total) ‒0.4 0.728 0.302 0.583 0.292Role eu Community (Total) 0.614 0.759 0.653 0.419 0.21Cooperation eu Community ‒0.199 0.929 0.046 0.831 0.416Political Stability (Total) 0.462 0.794 0.397Stable (3) a

Unstable (1) a ‒0.635 0.986 0.415 0.519 0.26Middle (2) a ‒0.187 0.982 0.036 0.849 0.425

Cox and Snell R2 0.064 Nagelkerke R2 0.086Note: (a) reference category.


As can be concluded from the table above, there is no single variable which has a significant influ-ence on the receiving of remittances. Also when excluding the variable political stability (which results in N=41) does not give any significant outcomes (see option 4). spss did not find any outli-ers. None of the independent variables have a significant influence on the receiving of remittances.

Option 4: Outcomes Regression Leaving Out Political Stability (Removed Outlier 31)

In this option, the independent variable political stability is being left out. When this variable is left out, the total number of included cases runs from 38 to 41.


Variables (n = 41) B SE Wald Sign. (2-sided) Sign. (1-sided)Constant ‒0.609 0.963 0.4 0.527 0.264Perceiving of Conflict (Total) 0.9 0.638 0.319Worsened (3) a

Improved (1) a 0.695 0.789 0.778 0.378 0.189No Change (2) a 0.152 0.982 0.024 0.877 0.439

Tensions in Europe (Total) ‒0.152 0.652 0.054 0.816 0.408Role Eu Community (Total) 0.447 0.699 0.41 0.522 0.261Cooperation Eu Community ‒0.184 0.805 0.052 0.819 0.41Cox And Snell R2 0.044 Nagelkerke R2 0.058Note: (a) reference category.

Also here there are no significant outcomes. None of the independent variables have a significant influence on the receiving of remittances.

Because there are no significant outcomes, I tried to find some with single analyses (using just one predictor). See tables below. The tables show that only the variables role of the eu community in the country of origin and the political stability have significant influences on the receiving of remittances.

Option 5: Outcomes Regression Single Analyses


Variables (n = 50) B SE Wald Sign. (2-sided) Sign.(1-sided)Constant ‒0.47 0.57 0.68 0.41 0.205Perceiving of Conflict (Total) 1.295 0.523 0.262Worsened (3) a

Improved (1) a 0.711 0.698 1.038 0.308 0.154No change (2) a 0.134 0.817 0.027 0.87 0.435

Cox and Snell R2 0.026Nagelkerke R2 0.035Note: (a) reference category.

Table B.3.5 shows that the independent variable perceiving of conflict has no significant influence on the receiving of remittances.



Variables (n = 41) B SE Wald Sign. (2-sided) Sign.(1-sided)Constant 0 0.447 0 1 0.5Tensions in Europe (Total) ‒0.288 0.628 0.21 0.647 0.324Cox and Snell R2 0.005 Nagelkerke R2 0.007

Table B.3.6 shows that the independent variable tensions felt in Europe has no significant influence on the receiving of remittances.


Variables (n = 52) B SE Wald Sign.(2-sided) Sign.(1-sided) a

Constant ‒0.619 0.469 1.744 0.187 0.094*Role eu Community (Total) 0.999 0.591 2.854 0.091 0.046**Cox and Snell R2 0.055 Nagelkerke R2 0.074

Note: (a) * = significant at 5%; ** = significant at 10%. Table B.3.7 shows that the independent variable role of eu community in the country of origin has a significant influence on the receiving of remittances. The B-coefficient is positive, which means that an increase of the role of the eu community leads to an increase of the receiving of remit-tances.


Variables (n = 52) B SE Wald Sign.(2-sided) Sign.(1-sided)Constant 0 0.535 0 1 0.5Cooperation eu Community (Total) 0 0.625 0 1 0.5Cox and Snell R2 0 Nagelkerke R2 0

Table B.3.8 shows that the independent variable cooperation with the eu community does not has a significant influence on the receiving of remittances.


Variables (n = 48) B SE Wald Sign.(2-sided) Sign. (1-sided) a

Constant 0.693 0.548 1.602 0.206 0.103Political Stability (Total) 3.422 0.181 0.091*Stable (3) b

Unstable (1) b ‒1.312 0.721 3.313 0.068 0.034**Middle (2) b ‒0.539 0.781 0.477 0.49 0.245

Cox and Snell R2 0.072 Nagelkerke R2 0.096



Table B.3.9 shows that the independent variable political stability has a significant influence on the receiving of remittances (spss tests 2-sided). The fact that this variable has a significant influence lies in the fact that the difference between unstable (1) and the reference category (stable) is sig-nificant. In this case, respondents who define the political stability in their country as stable, have the highest chance of receiving of remittances. Second are the respondents who define the politi-cal stability as middle. Respondents who define the political stability as unstable have the lowest chance of receiving of remittances (compared to the other groups).

Order of B-coefficient (highest-lowest): 3 2 1Original order (high-low): 3 2 1

Concluding: the more stable the political situation in the country (according to the respondents), the more chance for receiving of remittances.

B.4. MULTIPLE IMPUTATION

B.4.1. Method

Multiple imputation provides a useful strategy for dealing with data sets with missing val-ues. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple im-putation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analysis. No matter which complete-data analysis is used, the process of combining results from different imputed data sets is essentially the same. This results in statistically valid inferences that properly reflect the uncertainty due to missing values.

In § B.3, the options for the dependent varaibel received assistance gave some nice outcomes. Es-pecially, option 2 was rather ok because the number of included cases was just enough and some significant outcomes were found. Using the method of multiple imputation is new for me. It is also a rather new method in spss (included upward from version 17). Because I do not have version 17, I was not able to try out all possible options (it takes a lot of extra time because average numbers have to be calculated). I decided to try and find a better fitting model for the dependent variable receving remittances. In the sections below I explain the steps which are use in multiple imputa-tion. § B.4.4 gives an overview of the final outcomes. It is argued that the independent variables role of eu communty in country of origin and the political stability have significant influences on the receivng of remittances.


B.4.2. Step 1: Pattern of Missing Values

B.4.3. Step 2: Multiple Imputation

This step imputes missing values 10 times based on the incomplete (with missing values) matrix. In the table below, you can find how many times values are imputed for each independent variable.

Imputation Models

Model Missing Values

Imputed ValuesType Effects

C.1.impwor Situation / Conflict Worsened or Improved Last 20 Years

Logistic RegressionD.4cooporg, D.1rolecomm,

stabilitypolit, remit, C.3tensions

2 20

D.4cooporg Has Organization Cooperated with Community in Europe in Any of the Efforts

Logistic RegressionC.1.impwor, D.1rolecomm,


2 20

D.1rolecomm Community in Europe Significant Role in Situation in Country?

Logistic RegressionC.1.impwor, D.4cooporg,


3 30

Stabilitypolit Stability New Logistic RegressionC.1.impwor, D.4cooporg, D.1rolecomm, remit, C.3tensions

7 70

Remit Received Remittances Logistic RegressionC.1.impwor, D.4cooporg, D.1rolecomm, stabilitypolit, C.3tensions

20 200

C.3Tensions Know About Tensions Happening in European Countries?

Logistic RegressionC.1.impwor, D.4cooporg, D.1rolecomm, stabilitypolit, remit

24 240

B.4.4. Step 3: The Analyses

Running the analyses with multiple imputation will give you, in this case, the outcomes in tenfold. The total number of included cases is as much as the total number of respondents, because no missing values are apparent anymore. So, in this case the total number of included cases is not 38


(see Table B.3.3) but 72. Because the outcomes are in tenfold, the first table shows the outcomes of the average numbers (B, average sign and min/max sign). The second table is the original one.

Table 4.1. Average Outcomes with Dependent Variable Received Remittances

Variables (n = 72) B Sign. (2/1-sided) Sign. max Sign. minConstant 0,117 0,703 / 0.352 0,993 0,360 Perceiving of Conflict (Total) 0,688 / 0.344 0,965 0,217Worsened (3)1 Improved (1) 0,089 0,736 / 0.368 0,985 0,305No Change (2) 0,244 0,513 / 0.257 0,820 0,147

Tensions In Europe (Total) ‒0,454 0,413 / 0.207 0,819 0,090Role Eu Community (Total) 0,865 0,198 / 0.099* 0,580 0,046Cooperation Eu Community (Total) ‒0,123 0,632 / 0.316 0,991 0,172Political Stability (Total) 0,183 / 0.092* 0,515 0,030Stable (3)1 Unstable (1) ‒1,111 0,213 / 0.101 0,861 0,026Middle (2) 0,070 0,567 / 0.284 0,988 0,078

Cox And Snell R2 0.064 Nagelkerke R2 0.086


Interpretation Outcomes for the Hypotheses: The variables ‘perceiving of conflict, tensions felt in Europe and cooperation eu community’ do not have significant influences on the dependent variable.

Based on the hypotheses, the significance can be divided by two (spss tests 2-sided). For this reason, political stability has a significant influence on the receiving of remittances (sign.= 0.092). However, the independent categories do not show a significant difference with the reference cat-egory. So, how to interpretate this?

For the same reason, the role of the eu community on average has a significant influence on the sending of remittances (sign. = 0.099), with a between sign.= 0.580 and sign.= 0.046 (both 2-sided). Because the difference between the max and min is rather large, it means that the influence of the missing values also is large. The B-coefficient is positive, which means that (on average) the more respondents feel that the eu community plays a role in the country, the more they are likely to receive remittances.


Codes Used from spss Files for Analyses

1. City of Settlement

Dependent Variables Name in spssEconomic Involvement E.1econdev (row 34)Remittances remittances (row 59)Attract Investment E.4invest (row 39)Independent Variables Name in spssPerceiving of Conflict: impwornew (row 61)CSO Linkages: intensitynew (row 60)View on Integration: C.7meanint (row 23)Date of Arrival: G.2datearrivalnew (row 53)Frequent Travel: G.3back (row 54)Reason for Migration: G.1reason (row 51)

2. Country of Origin

Dependent Variables Name in spssReceiving Assistance: E.1receive (row 30)Receiving Remittances: remit (row 51)Independent Variables Name in spssPerceiving of Conflict C.1.impwor (row 13)Know about Tensions Felt in Europe C.3tensions (row 18)European Community Played Significant Role D.1rolecomm (row 20)Has Organization Cooperated with Community In Europe. D.4cooporg (row 26)

Political Stability stabilitypolit (row 58)

factors influencing migrants’ engagement with transnational economic activities

Documents

annex b

country of origin

city of settlement

testing hypotheses

outcomes logistic regression

transnational activities

key hypotheses

testing hypothesis