Data and Methodology
To understand the roles and relationships of stakeholders involved in migration pathways in the four study countries, relevant actors from government, civil society, embassies, private sector, academia, and donors were invited to take part in a facilitated network- mapping participatory process, known as the Net-Map process.6 The focus of the Net- Map workshops were used to identify key actors, and their interlinkages, involved in the migration of women workers from South Asia to the domestic care sectors in Lebanon and Jordan. The workshops considered the entire migration pathway and also the vulnerabilities of migrants who have returned to their countries of origin. Net-Map is a facilitation-based tool that combines network analysis with stakeholder mapping to help visualize how actors, interconnected formally and informally, can influence outcomes (Schiffer and Hauck 2010). We conducted five Net-Map workshops—one each in Bangladesh and Nepal as sending countries, and one in Lebanon and two in Jordan as destination countries.
In all these workshops, participants were invited from the government, civil society, international organizations, embassies, universities, and think tanks. Due to the COVID- 19 emergency, we organized the Bangladesh and Nepal workshops online via Zoom. The Jordan event had been conceptualized as a face-to-face event but faced challenges of government approval, partially linked to the COVID-19 pandemic, and was therefore moved to a quasi-online setting, with facilitators in Jordan engaging online with participants. All workshops were conducted in English except for Jordan, where one workshop was organized in Arabic and the other in English. In Lebanon, translation services from Arabic to English (and vice-versa) were used to generate a single Net-Map.
Conducting two workshops in Jordan helped overcome the language barriers of some participants who expressed difficulties understanding either of the languages. An English/Arabic interpreter accompanied one group, and the other was accompanied by a Bengali/Hindi/English interpreter.
The guiding question that framed these workshops was, “Who influences the migration of women from Bangladesh/Nepal to the domestic care and garment sectors in Lebanon and Jordan?” Participants in each workshop answered this question in multiple sessions.
During the first session, participants identified actors involved in women workers’
migration journey. Participants then drew connections between these actors and described the nature of their linkages, mapping which actors had authority or could exert informal pressure over others, who provided whom with finance and information, and who played an advocacy role. Finally, the third session focused on the most influential of these actors in terms of impact on the migratory process. Participants built an “influence tower” (Schiffer and Hauck 2010) by rating each actor’s influence from 0 to 5. Participants further discussed ways to improve women’s safe migration and reduce women’s
6 Our project timeline had envisioned implementing these workshops in early 2020; however, due to the late final project approval and COVID-19 onset, workshops were implemented with substantial delays. Moreover, three of the workshops were implemented virtually, applying, to our knowledge, Net-Map and ROAD for the first time in a virtual setting.
vulnerabilities to forced labour and trafficking. One Net-Map research paper, focusing on Bangladesh and Nepal, is currently undergoing peer review (Choudhury n.d.), while a second paper is readied for peer review (Choudhury et al. n.d.)
The Net-Map guidance can be found in Annex B.1.
Results
Figures 1.1 and 1.2 present a stylized version of the Net-Maps in each country. The Bangladesh Net-Map workshop identified 30 key actors linked in 66 ways, and the workshop in Nepal identified 48 key actors connected in 99 ways. The Lebanon workshop participants identified 29 stakeholders. In Jordan, the Arabic-language Net-Map identified 22 actors with 36 links, and the English workshop identified 30 actors with 58 links.
In the following sections, we discuss various distributions of these actors, their types, their relative influence in the networks, and the linkages that connect these actors. To help interpret these results, we use basic tools of network analysis to highlight the structural features of the empirical Net-Maps.
Figure 1.1 Observed Net-Maps from Bangladesh and Nepal
Bangladesh Nepal
Figure 1.2 Observed Net-Maps from Bangladesh and Nepal
Lebanon Jordan (Arabic) Jordan (English)
Source: Authors.
Countries of Origin: Bangladesh and Nepal
According to the 2022 Trafficking in Persons (TIP) ranking, Bangladesh and Nepal have made a significant institutional effort to eliminate trafficking in persons but have not yet achieved all minimum standards (U.S. Department of State 2022). Despite noticeable similarities at a global scale, these countries differ in terms of how their migration sectors are organized. Key actors, their relative ability to influence the sector, and the linkages among them that maintain the sector's current state are context-specific. Our analyses of Net-Maps capture this contextually dependent actor-level dynamic in each country.
As shown in Table 1.4, government agencies play a dominant role in Nepal, whereas private actors were as influential as the government in Bangladesh. Considering how these actors were linked, formal authority-type links (38%) dominated the Bangladesh migration sector, and advocacy or lobbying-type links (37%) dominated the Nepal migration sector. Information/advice-type links (24%) in Bangladesh were significantly higher (Chi-squared, p-value = .001) than in Nepal (18%). Formal authority with high information flow may indicate an authoritative system. Authority in Nepal is also a common linkage type (34%) after advocacy (37%), indicative of an advocacy network where various interests—including those representing female migrants—negotiate with the government.
Table 1.4 Actors and Linkages
Actor Types Bangladesh Nepal
Government 13 (43%) 24 (50%)
NGO 2 (07%) 9 (19%)
Private 13 (43%) 7 (14%)
Other 2 (07%) 8 (17%)
Types of linkage
Formal Authority 24 (38%) 34 (34%)
Information/Advice 15 (24%) 18 (18%)
Lobby/Advocacy 10 (16%) 37 (27%)
Money/Finance 14 (22%) 10 (10%)
Source: Authors.
The workshop participants were asked to rank (on a 0–5 scale) the actors’ abilities to influence the migration system in their country. Table 1.5 shows the top actors scoring in the 90th percentile or above on the 0–5 scale. In Bangladesh, actors from the government sector are the most influential. In contrast, governmental and nongovernmental entities were ranked equally influential in Nepal.
The workshop participants in Bangladesh perceived women migrants as having only average influence (scored 3 out of 5) in the network. In contrast, the Nepal workshop considered women migrants among the most influential actors. One participant in the Nepal workshop explained that the migrant is an important influencer because the migration process, including exposure to the risk of trafficking and forced labour, begins with her decision. Although the workshop participants rated the migrants as having a low capacity to influence the migration system, they placed them (the migrants) at the centre of the networks in both countries. The migrants receive (network authority) and send more arrows than most other actors. For example, the migrants were more central than government ministries in Bangladesh, including the Ministry of Expatriate's Welfare and Overseas Employment (MoEWOE) and Ministry of Foreign Affairs (MoFA). In Nepal, along with the migrant and the Ministry of Labor, Employment and Social Security (MoLESS) and Foreign Employment Board (FrEB), actors such as the migrant’s network (FFaC), ILO, and CSOs also have central places in the network.
Structurally, both countries’ networks are sparsely connected on network density measures, with only 4 percent of the Nepali and 7 percent of the Bangladeshi actors connected pairwise (Table 1.6). These networks also score similarly on the average degree score.7 This sparsity in Nepal may reflect its decentralized and federal governance structure instituted under the country’s new constitution. However, the measures of average network path and triangles (that is three actors connected, suggesting a specific network structure) indicate that only a few actors (through whom others are connected) hold the network together, thus possessing a disproportional controlling power over the network. This “network oligarchy” is also apparent in Bangladesh, although with slightly lesser intensity than in Nepal.
One can interpret the network oligarchy in terms of efficiency (Krebs, 2002). Nepal’s network is perhaps more efficient than Bangladesh’s, since it takes fewer paths for critical information and resources to flow from one point to another compared to the Bangladesh network. The measures of network modularity8 further attest to the efficiency argument. The Bangladesh network is more modular, thus fragmented, and less efficient than the Nepal network.
7 A degree score indicates the number of edges (arrows) a node or an actor has. The higher the degree, the more central the actor.
8 High modularity indicates clean, non-overlapping clusters.
Table 1.5 Top Influential Actors
Network Actor Perceived
Influence Score [0,5]
Network Degree Centrality
Network Authority Score
Actor Type
Bangladesh
Bureau of Manpower, Employment, and Training
(BMET); 5* 3 .00 Government
Bangladesh Overseas Employment Services Limited
(BOESL) 5* 2 .00 Government
Ministry of Expatriate's Welfare and Overseas
Employment (MoEWOE) ** 5* 10* .68* Government
Recruiting Agency in a Country
of Destination (RA-CoD) 5* 6 .07 Private
Women Migrant** 3 19* 1.00* Private
Ministry of Foreign Affairs
(MoFA)** 4 8* .48* Government
Nepal
Destination Countries
Employers (DsCE) 4.5* 2 .01 Private
Women Migrant** 4.4* 16* 1.00* Other
Parliamentary Committee on
Labor (PCoL) 4.4* 5 .17 Government
Local Intermediaries (LclI) 4.1* 9 .18 Government
Ministry of Foreign Affairs Destination Countries (MoFADC); Ministry of Labor,
Employment and Social Security (MoLEaSS)**
4.1* 12* .73* Government
Returnee Women Migrant
(RtrW) 4.1* 5 .14 Other
Civil Society Organizations
(CSO)** 3.5 22* .60* NGO
Foreign Employment Board
(FrEB) 3.9 19* .44 Government
International Labour
Organization (ILO) 3.5 12* .00 Donor
Family, Friends, and
Community (FFaC) 3.9 5 .45* Other
Ministry of Foreign Affairs Destination Countries
(MoFADC) 3.7 8 .52
Note: * indicates 90th percentile or above. ** indicates actors scoring 90th percentile or above on at least two measures.
Source: Authors.
Table 1.6 Descriptive Network Features
Network Attributes Bangladesh Nepal
Density (directed) 0.072 0.042
Average Degree 4.29 4.12
Average Path Length (geodesic) 2.79 2.16
Clustering Modularity 58% 39%
Total Number of Triangles 13 37
Source: Authors.
Destination Countries: Jordan and Lebanon
We also wanted to understand the institutional dynamics that shape the inflow of migrant workers to the West Asian countries that recruit women workers from South Asia, especially for the domestic care industry in Lebanon and the garment industry in Jordan.
With this goal, we conducted one Net-Map workshop in Lebanon and two in Jordan. The Lebanon workshop was conducted in English. In Jordan, we ran one workshop in local Arabic and the other in English.
The Lebanon workshop participants provided a list of 29 stakeholders in the country’s temporary labour immigration sector. About 45 percent of actors, as shown in Table 1.7, are from the government, and about 31 percent are from the private sector. The workshop participants used 69 in-and-out arrows (Table 1.8) to link these actors. About 41 percent and 36 percent of these linkages are authority and advocacy types, respectively. The heavy presence of both public sector and authority linkages indicate that the network is hierarchical in nature.
Table 1.7 Distribution of Actor Types
N Donors (%) GO (%) NGO (%) Private (%)
Lebanon 29 0.17 0.45 0.07 0.31
Jordan (English) 30 0.20 0.20 0.23 0.37
Jordan (Arabic) 22 0.05 0.36 0.31 0.27
Source: Authors.
Table 1.8 Distribution of Edges
N Advocacy (%) Authority (%) Finance (%) Information (%)
Lebanon 69 0.36 0.41 0.10 0.13
Jordan (English) 58 0.16 0.29 0.28 0.28
Jordan (Arabic)* 36 0.08 0.36 0.08 0.43
* others = 0.06 Source: Authors.
Unlike in Lebanon, the participants in Jordans’ two workshops put relatively little emphasis on the public sector. Although the Arabic-language workshop emphasized government actors more than its English counterpart, generally, both workshops highlighted the private sector and NGOs as key stakeholders. The Arabic-language workshop connected the actors authoritatively and, in most cases, by information
linkages. The English-language workshop, however, found its network balanced regarding types of linkages.
In Lebanon, the Net-Map indicates that a few heavyweight actors play a pivotal role in managing the network. This is evidenced by the distribution of the perceived influence score, which is skewed slightly positive, with a large standard deviation, and the distribution of the degree score (see Table 1.9). The average degree is distinctly higher than its median, with a large dispersion. Inequality in the network is high, as the most influential actor has as many as 20 linkages, while some actors are connected by only one link.
Table 1.9 Distribution of Participant Assigned Influence Score
Min. Median Mean SD Max
Lebanon 1 3 3.14 1.46 5
Jordan (English) 1 4 3.13 1.54 5
Jordan (Arabic) 1 3 3 1.35 5
Source: Authors.
Table 1.10 Distribution of Degree Score
Min. Median Mean SD Max
Lebanon 1 3 4.75 4.96 20
Jordan (English) 1 2 3.86 3.91 18
Jordan (Arabic) 1 3 3.27 4 6
Source: Authors.
In Lebanon, the influence score and degree statistic both identified the General Directorate of General Security (GDoGS) and Ministry of Labour (MoL) as having a high level of influence, as the only public sector actors with scores in the 90th percentile or above (Table 1.11). All the other actors in these categories are from the private sector, like the private recruiting agencies (PRA) and NGOs. Interestingly, the NGOs scored highest in authority,9 even higher than the Lebanese Parliament (LP), which came in second with about half the score. The other actors scoring high in authority are the GDoGS and the Ministry of Justice (MoJ). The Hub score parallels the authority score.10 Not surprisingly, the media scored high because they provide news and information and remain the only public source of information on abuse and malpractices, requiring the immediate attention of the local and international authorities. Donors and NGOs provide funding, advocate for best practices, provide expert opinion, and, in many cases, provide grassroots-level support.
9 Authority score measures network centrality based on the number of arrows each actor receives from others. It evaluates the importance of a node or an actor on a 0–1 scale, where 0 indicates no influence and 1 full authority.
10 While the authority measures how many arrows a node receives, the Hub score measures how it directs to the authorities.
Table 1.11 Most Important Actors and Coalitions
Criteria Lebanon Jordan (English) Jordan (Arabic) Participant
Assigned
Influence Score (>= 90th percentile)
GDoGs (GO)
PRA(Private)
HaR (Private)
HIE (Private)
TA (Private)
MoL (GO)
GAP (Private)
JCoI (Private)
MiGF (Private)
MoLOC (Foreign GO)
VS (Private)
Consumers (Private)
ILO (Donor)
MoInt (GO)
MoLiJ (GO)
Degree Statistics
(>= 90th percentile) GDoGS (GO)
Media (Private)
NGO (NGO)
MoL (GO)
Workers (Private) WCC(Private)
ILO (Donor) IOM (Donor) Media (Private)
Authority Score
(Rank) NGO(NGO) (1.00) LP(GO) (.55) GDoGS (GO) (.52) MoJ (GO) (.39)
WCC (Private) (1.00) Workers (Private) (0.46) ILO (Donor) (0.119)
Consumers (Private) (1.00) EmplrU (Private) (0.96) Factories (Private) (0.96) Hub Score (Rank) GDoGS (GO) (1.00)
Media (Private) (.80) INGO(NGO) (.70) UNAG(Donor) (.54)
ACfHR (Private) (1.00) TfLAaHR (Private) (1.00) UCLA (Private) (1.00)
ILO (Donor) (1.00) IOM (Donor) (1.00) MoLiJ (GO) (0.72) JGATE (Private) (0.59) Source: Authors.
In Jordan, when asked to weigh the actors’ relative influence on the immigration sector, the Arabic-language workshop provided a balanced view (Table 1.6). The most influential actor in the network has only six linkages. However, the English-language network is quite different and more comparable to the Lebanon network. The English workshop indicated that most stakeholders are influential, with a median stakeholder scoring 4 out of 5, and just a few actors in the network having very low influence. The most influential actor in the network has as many as 18 linkages, indicating high inequality in the system.
The Arabic-language workshop perceived Jordan’s Ministry of Interior (MoInt) and Ministry of Labor (MoLiJ) as influential, along with the ILO and private consumers.
However, the English workshop called attention to the role of the private sector and the Ministries of Labor in the countries of origin. Overall, the structural measures of centrality in both networks indicate that the private actors and donors are the controlling authorities of labour immigration in Jordan.
In the Lebanon network, the modularity of the actor coalition is only about 30 percent, less than one would expect given the sectarian nature of its social and political system (Acemoglu & Robinson 2020). One coalition stands out as large and significant. As reported in Table 1.12, the GDoGS is in a structural coalition with such private entities as private recruiting agencies, high-income employers, hotels and restaurants, and travel agencies. This analysis confirms that the GDoGS is the central authority in Lebanon’s labour immigration sector. However, it seems to manage the system with a group of private entities, as the actor coalition analysis reveals.
Table 1.12 Actors Coalitions
Criteria Lebanon Jordan (English) Jordan (Arabic)
Modularity 0.30% 0.46% 0.52%
Largest Structural Coalition (Community Score)
GDoGS (GO), PRA(Private), HIE(Private), HaR (Private), and TA(Private).
Mol (GO), MoLOC (foreign GO), MiGF (Private), JCol (Private), GAP (Private), VS (Private).
IOM (Donor), ILO
(Donor), Consumers
(Private)
Source: Authors.
Compared with the Lebanon network, the networks in Jordan have higher modularity and, thus, clearer boundaries of network coalitions. Consistent with the previous findings, the largest coalition should be expected in the Arabic-language network among donors and consumers. The English network emphasizes the close ties among the private actors.
It is noteworthy that Jordan’s Ministry of Labour is in the same network cluster as the labour ministries of the countries of origin, including high levels of communication between them.
The Migration System in the South Asia to West Asia Corridor
We identified eight actors that were mentioned in more than one workshop. In Figure 3, we treat these actors as network connectors in order to combine four Net-Maps to create an actor-level labour migration system map involving Bangladesh, Nepal, Lebanon, and Jordan (we dropped the Arabic version of the Jordan network for better visualization and analytic purposes). Two of these actors were from the country of origin—the labour ministries and the foreign ministries—and six were from the destination countries—
recruiting agents, Human Rights Commissions, interior ministries, labour ministries, and migrant workers.
All Net-Map workshops discussed above highlighted that the labour ministries, in both the countries of origin and destination, were critical players in the migration pathways between the South Asian and West Asian countries. Thus, labour ministries and their respective country’s foreign and interior ministries can play a vital role in changing the current state of migration in the pathways that have been marked by incidents of forced labour and trafficking in women.
The discussion in the previous sections shows that migrant women were also key actors, especially in their home countries. Their vulnerabilities begin at home, shaped not only by their immediate environment, where the informal recruiting agents dominate. Their problems were aggravated by the national policies that remain inadequate in providing a sufficient safety net against these vulnerabilities (Choudhury 2022). Our qualitative investigation in Lebanon shows that women migrants, when organized and tied to a social network, can also be critical actors in shaping the status of forced labour in a country of destination (Adra and Abdulrahim 2023).
Figure 1.3 Migration System in the South Asia to West Asia Pathway