International NGOs and the Role of Network Centrality in Humanitarian Aid Operations: A Case Study...

Disasters, 2003, 27(4): 305–318

© Overseas Development Institute, 2003. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

International NGOs and the Role of Network Centrality in Humanitarian Aid Operations: A Case Study of Coordination During the 2000 Mozambique Floods

Spencer Moore Eugenia Eng University of Calgary University of North Carolina

Mark Daniel Université de Montréal

In February 2000, Mozambique suffered its worst flooding in almost 50 years: 699 people died and hundreds of thousands were displaced. Over 49 countries and 30 international non-governmental organisations provided humanitarian assistance. Coordination of disaster assistance is critical for effective humanitarian aid operations, but limited attention has been directed toward evaluating the system-wide structure of inter-organisational coordination during humanitarian operations. Network analysis methods were used to examine the structure of inter-organisational relations among 65 non-governmental organisations (NGOs) involved in the flood operations in Mozambique. Centrality scores were used to estimate NGO-specific potential for aid coordination and tested against NGO beneficiary numbers. The average number of relief- and recovery-period beneficiaries was significantly greater for NGOs with high relative to low centrality scores (p<0.05). This report addresses the significance of these findings in the context of the Mozambican 2000 floods and the type of data required to evaluate system-wide coordination. Keywords: Mozambique, disaster relief, social network analysis, inter-organisational coordination, floods.

Introduction

Improving the level of coordination among humanitarian aid organisations has been viewed as critical to optimise the flow of resources among agencies and increase the accountability, effectiveness and impact of aid operations (Rey, 1999). Yet, the growing number of humanitarian aid organisations has taxed the capacity of any one governmental agency to coordinate the resources and activities involved in humanitarian aid operations. Whether due to the sheer number of organisations or the range of resources and activities they offer, the difficulties in coordination have been viewed as a systemic problem for the ‘humanitarian aid regime’ (Rey, 1999). The Organisation for Economic Cooperation and Development (OECD) has called for evaluations of humanitarian aid operations to include an examination of the system of

S. Moore, E. Eng and M. Daniel

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Figure 1 Exploratory model of the relationship between network centrality and beneficiary numbers (based on author’s central hypothesis) organisations: ‘… the intervention of a single agency cannot be evaluated in isolation from what others are doing, particularly as what may seem appropriate from the point of view of a single actor, may not be appropriate from the point of view of the system as a whole’ (OECD, 1999). In other words, an effective evaluation of aid coordination requires that the evaluation be able to situate humanitarian aid operations within an inter-organisational network framework. Between 1970 and 1998, Mozambique experienced 11 flood and over 16 drought events. In those 11 flood events, it is estimated that over 1,585 people died (UNDP, 2002). In February 2000, Mozambique suffered what would be its worse flooding in almost 50 years. The Mozambican floods of 2000 resulted from heavy rainfalls that had been occurring in southern Africa since December 1999 and the later impact of tropical cyclones Connie and Eline in February 2000. In Mozambique, over 600 people died, hundreds of thousands of individuals were displaced and over 27 per cent of the population was affected in some manner by the disaster. In the health sector alone, the infrastructure for national health-care services was severely interrupted when the flooding partially destroyed a general hospital in Maputo City, four rural hospitals, eight large health centres and 31 small health centres in southern Mozambique (Mozambique, 2000a). Using a case study of the relief and recovery operations during the Mozambican floods of 2000, this paper provides a groundwork for further evaluation of the inter-organisational relations that influence the success or failure of coordination during humanitarian aid operations. Network analysis methods were used to examine the association between an organisation’s centrality in the inter-organisational network

Beneficiary numbers NGO network centrality

Degree centrality of an NGO

Eigenvector centrality of an NGO

Flow betweenness centrality of an NGO

Emergency projects

Recovery projects

Family beneficiaries

Individual beneficiaries

Individual beneficiaries

Family beneficiaries

Districts or provinces in which an NGO operates

Sectors in which an NGO operates

INGO or NGO

Network Centrality in Humanitarian Aid Operations 307

and the number of beneficiaries served. The concept of centrality measures the number and strength of ties that an actor or, in this case, an organisation, has with other organisations. Centrality has been used to capture the flow of information in a network and the potential level of coordination (Freeman, 1979; Faust, 1997). The central research question may thus be phrased as: Does an organisation’s ‘potential for communicative activity’ become reflected in its beneficiary numbers? By focusing on the structure of the humanitarian aid network, rather than the specific activities of individual agencies, the methods and findings from this study of centrality and inter-organisational relations offer a new lens for evaluating coordination and the flow of resources during humanitarian aid operations, in the spirit of improving the accountability, effectiveness and impact of relief and recovery assistance.

Methods

Multiple methods were employed during the three phases of research development and analysis. The three phases of research were: the preliminary field observations and key-informant interviews in Mozambique during summer 2001; the construction of three sets of social network matrices from which three centrality measures were generated; and the quantitative hypothesis testing of the association between network centrality and beneficiary outcome numbers.

Phase One

During the first phase of the research, field observations were conducted in the provinces of Gaza, Inhambane and Maputo at various sites: Chokwe, Chibuto, Macia, Manhica, Marracuene, Xai-Xai and the resettlement area of Magoanine. Field observations provided the opportunity to assess the degree to which communities were affected by and have recovered from the flooding, and the level of non-governmental flood-related activity. Field interviews were also conducted with local officials and organisational representatives working in the various sites. Key-informant interviews were conducted with organisational representatives of UN system agencies, bilateral national development agencies and international and local non-governmental organisations. The sampling frame included all humanitarian and development aid organisations operating in Mozambique; the selection rule was whether a given organisation participated in the 2000 flood relief and recovery operations. Using snowball-sampling methods, organisations were identified and a single representative from those organisations was selected to be interviewed. A total of 19 formal interviews were conducted during the preliminary research phase. Although interviews were unstructured, all interviews required participants to address questions regarding the effects of the flood on the local populace, the projects and programmes put in place by their organisation, and the perceived degree of coordination among various organisations and between provincial and national authorities.

Phase Two

Network analysis methods were used to examine coordination of relief efforts during the 2000 humanitarian aid operations. Data for network analysis were provided


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through LINK, a Mozambican NGO that documented the relief and recovery work of both international and national non-governmental organisations. Information in the LINK database has been published in two primary documents: ‘Actividades Realizadas’ and ‘Capacidade de Resposta por ONG’ (LINK, 2000a, 2000b). The document ‘Actividades Realizadas’ provides information on NGOs involved in the relief and recovery efforts from February 2000 until September 2000, for organisations reporting data to LINK. Vital for network analysis, the documentation itemises the provinces, districts and sectors in which NGOs operated and their activities in those sectors at the time of the 2000 floods. The second document, called ‘Capacidade de Resposta por ONG’, provides information on the number of individual and family beneficiaries of the listed NGOs. Using data provided from LINK documentation, two two-mode (i.e., actor x event) matrices were created. Table 1 shows the two two-mode matrices created and the four one-mode affiliation matrices arising from those two two-mode matrices. Unlike one-mode networks which link a set of organisations or events to the same set of organisations or events, a two-mode network consists of a set of organisations and a set of events. The link between the two modes (actors and events) is through some shared membership. In other words, organisations are linked through the events that they share in common while events are linked to each other through the organisations that they have in common (Faust, 1997). From a list indicating particular districts in which organisations were active, an affiliation network capturing shared district activity was constructed. Two affiliation networks were constructed using the LINK documentation: an NGO network through shared district presence, and an NGO network through shared sector engagement. As shown in Table 1, the district- and sector-related matrices are considered ‘valued’. Rather than defining the presence of an NGO in a district as being either present or absent, a value was instead placed on an NGO’s involvement in a district according to the number of sectors in which the NGO was working there. Since there were a total of 10 sectors in which relief and recovery activities were classified, the maximum possible value for any NGO’s involvement in a district was 10. There were two reasons for ascribing a value to the relationship between districts and NGOs. First, to represent the importance of a given district to the goals and mission of a given NGO. Second, to represent the lesser or greater likelihood that an NGO would interact with other organisations in the district. In the NGO-sector matrices, relationships between NGOs and sectors were valued according to the NGO-specific number of projects operating in a particular sector. For NGO-district matrices, the maximum number of sectors in which an organisation could work was 10, hence the maximum possible value for NGO-sector matrices was also 10 (see Table 2). Table 1 Network matrices generated from LINK documentation Two-mode matrices One-mode matrices used Matrix characteristics 1) NGO x district (65 actors x 33 districts) LINK documentation

NGO x NGO (shared district activity)

Symmetric, i.e., non-directional Valued

2) NGO x sector (65 actors x 10 sectors) LINK documentation

NGO x NGO (shared sectoral involvement)

Symmetric, i.e., non-directional Valued


Table 2 Significant Mann-Whitney U test scores using district-affiliation centrality scores District-affiliation network matrix Degree Eigenvector Flow betweenness n Mean

rank

p and z Mean rank

p and z Mean rank

p and z

Emergency family benefici-aries

29 1–10.29 2–19.40

p=.004 z=-2.88

1–10.29 2–19.40

p=.004 z=-2.88

1–10.15 2–18.94

p=.005 z=-2.76

* Number 1 in mean rank means lower centrality ranking; 2 means higher centrality ranking. The unique characteristics of affiliation networks require careful selection of centrality measures. Three types of centrality measures were used in capturing the positional scores of the 65 organisations in the LINK-affiliated non-governmental network: Freeman degree centrality, eigenvector centrality and flow-betweenness (Wasserman and Faust, 1994; Borgatti and Everett, 1997; Faust, 1997). Ucinet 5 (Borgatti et al., 2002) was used to generate degree, eigenvector and flow-betweenness centrality measures for the 65 organisations common to the two one-mode matrices. Three measures were used to capture various dimensions of an organisation’s centrality in the network: degree, eigenvector and flow betweenness. Degree centrality measures highlight the actors with the most ties to other actors in the network. The higher an actor’s centrality value the more direct contact and ‘adjacency’ that actor has to other actors in the network (Wasserman and Faust, 1994) and, thus, may be seen as an ‘index of its potential communication activity’ (Freeman, 1979). Eigenvector centrality is meant to measure the strength of an organisation’s relationships to other network members and the centrality of those other organisations (Faust, 1997). Flow betweenness centrality measures the degree to which certain actors may be more central or exercise greater influence due to their location on the paths between various organisations (Wasserman and Faust, 1999; Faust, 1997). The flow betweenness measures, thus, represent the ‘potential for “flow” of information or resources between nodes’ (Faust, 1997). Following the generation of organisational centrality measures from the affiliation networks, inter-correlations between measures were assessed (see Table 3). Given the significant level of correlation between the two matrices on the three centrality measures (see Table 3), centrality scores generated from the district affiliation network alone were used in the final phase of analysis.

Phase 3

Degree, eigenvector and flow betweennness centrality measures were evaluated in relation to beneficiary outcome data, using non-parametric procedures. District affiliation network scores were dichotomised into ‘high’ and ‘low’ categories, using the mean as the cutting point. Mann-Whitney U tests were conducted to determine whether the ‘high’ and ‘low’ district affiliation categories differed in numbers of beneficiaries: statistical significance was at the .05 level. NGO beneficiary numbers


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Table 3 Mediating variables and centrality scores: significant Mann-Whitney U test INGC-relation network matrix Degree Eigenvector Flow

betweenness n Mean

rank p and z Mean

rank p and z Mean

rank p and z

NGO type 0=local NGO (n=33) 1=INGO (n=32)

65

0–28.38 1–37.77

p=.034 z=-2.121

0–28.27 1–37.88

p=.030 z=-2.168

0–28.50 1–37.64

p=.039 z=-2.065

Sectoral engagement water and sanitation 0=absent (n=49) 1=present (n=16)

65

0–28.08 1–48.06

p=.000 z=-3.890

0–27.96 1–48.44

p=.000 z=-3.985

0–28.40 1–47.09

p=.000 z=-3.638

Provincial operations Sofala 0=absent (n=49) 1=present (n=16)

65

0–27.12 1–51.00

p=.000 z=-4.648

0–27.08 1–51.13

p=.000 z=-4.678

0–27.36 1–50.28

p=.000 z=-4.461

were obtained from LINK documentation; the outcome data consisted of individual and family beneficiary numbers for NGO project activities. For purposes of comparison, averages were calculated for four categories of beneficiaries: • emergency-project individual beneficiaries; • emergency-project family beneficiaries; • recovery-project individual beneficiaries; and • recovery-project family beneficiaries. Since emergency projects had higher mean numbers of individual beneficiaries (29,888) and family beneficiaries (6,842) than recovery projects (9,838 individuals and 2,226 families, respectively), the separate identification and analysis of emergency period from recovery period NGO projects was necessary. This ensured that the analysis was not skewed towards NGOs with a higher number of emergency projects and thus perhaps a higher number of beneficiaries. Further analyses were conducted to assess possible confounding or mediating variables that might influence an NGO’s average beneficiary numbers. Such variables included whether an organisation was an international or national NGO (INGO), in which sectors of relief or recovery an organisation worked (sector), and in which


provinces the organisations worked (province). Dummy codes were used to represent a NGO’s type, and its presence or absence from a particular sector or province. Using the Mann-Whitney U test, these three variables were tested independently to examine if they had a significant association with average centrality scores and average beneficiary numbers. To evaluate possible confounding or mediator variables, a third affiliation network was constructed. This network used situation reports published by the INGC (Mozambican National Institute for Disaster Management). Network ties were based on an organisation’s reporting of joint activities or operations with other organisations during the relief and recovery period.

Results

The hypothesis that NGOs with higher centrality scores had greater average beneficiary numbers was upheld. As shown in Table 4, for the district-affiliation network, NGOs with higher degree, eigenvector and flow betweenness centrality values had a greater average number of emergency-project family beneficiaries. NGO type, sector engagement and provincial presence showed significant mediator or confounding influences on both organisational centrality values and beneficiary numbers. As shown in other work, INGOs had significantly higher average centrality scores than local, i.e. Mozambican, NGOs, thereby accounting for the greater visibility of international NGOs during humanitarian aid operations. In addition, organisations working in certain sectors, such as water and sanitation, or in provinces like Sofala showed higher on average centrality scores. In terms of beneficiary numbers, INGOs reported higher on average emergency-project individual beneficiaries than local NGOs. Further work also shows that certain sectors and provinces also showed higher on average beneficiary numbers than other sectors and provinces.

Discussion

Scholars have argued that the need to procure materials, resources or revenues to guarantee organisational survival has been the primary reason that organisations establish inter-organisational relations (Galaskiewicz, 1985). Within a humanitarian aid context, inter-organisational relations may be characterised by the exchange of information, staff, goods, cash and other items. Organisations may find themselves in an ambivalent position, recognising the need to enter into inter-organisational transactions while seeking to maintain their autonomy and independence from other organisations (Galaskiewicz, 1985). In the ‘normalistic’ vision of inter-organisational relations, organisations are linked together in inter-organisational networks through processes of voluntary exchange (Aldrich, 1979), while hierarchical models of resource allocation and movement emphasise the power and dependencies that develop during inter-organisational transactions (Aldrich, 1979; Galaskiewicz, 1985). Research within this latter field of resource dependency has argued that a ‘strong positive association exists between organisations’ network centrality and their supposed influence in community affairs’ (Galaskiewicz, 1985). The more central and visible actors in the network are more likely potential allies for other powerful actors, thereby appearing even more powerful (ibid.).


312

Table 4 Mediating variables and centrality scores: significant Mann-Whitney U test INGC-Relation Network Matrix Emergency project

individual beneficiaries Emergency project family beneficiaries

Recovery project family beneficiaries

n Mean rank

p and z Mean rank

p and z Mean rank

p and z

NGO type 0=local NGO (n=8) 1= INGO (n=8)

16

0–6.00 1–11.00

p=.036 z=-2.102

NS

NS

NS

NS

Sectoral engagement Food 0=absent (n=10) 1=present (n=19)

29

NS

NS

0–10.25 1–17.50

p=.027 z=-2.180

NS

NS

Infrastructure 0=absent (n=26) 1=present (n=3)

29

0–7.57 1–15.00

p=.039 z=-2.066

0–13.58 1–27.33

p=.008 z=-2.650

NS

NS

Water and sanitation 0=absent (n=23) 1=present (n=6)

29

NS

NS

0–13.35 1–21.33

p=.041 z=-2.046

NS

NS

Provincial presence Gaza 0=absent (n=15) 1=present (n=14)

29

0–5.80 1–13.00

p=.003 z=-2.931

0–11.97 1–18.25

p=.047 z=-1.986

NS

NS

Manica 0=absent (n=15) 1=present (n=2)

17

NS

NS

NS

NS

0–8.00 1–16.50

p=.025 z=-.236

Maputo Province 0=absent (n=4) 1=present (n=12)

16

0–13.50 1–6.83

p=.015 z=-2.427

NS

NS

NS

NS


Sofala 0=absent (n=22) 1=present (n=7)

29

0–6.92 1–13.25

p=.021 z=-2.306

0–12.36 1–23.29

p=.003 z=-2.956

NS

NS

While the results suggest that organisational centrality may affect average beneficiary numbers, they also show that international NGOs tended to be the more central organisations during the 2000 humanitarian aid operations in Mozambique. Seen in light of the comments of field interviewees, the findings of the network analysis suggest that inter-organisational relations during the relief and recovery period took on the characteristics of a hierarchical model of resource allocation and movement. Given the constraints under which both governmental agencies and local non-governmental organisations operated, national agencies and organisations tended to be dependent on not only the resources and expertise of international NGOs but also the positions that INGOs occupied in the humanitarian aid network. If, as argued, centrality represents ‘potential communicative activity’, then international NGOs when compared to local NGOs would appear on average to have been better positioned to oversee the flow of information or coordinate relief and recovery activities. The capacity and willingness of organisations to coordinate their activities with other organisations could be strained along several fault lines. Two particular fault lines can be considered: strains between governmental and non-governmental organisations; and strains between international and local NGOs. Mozambique is highly vulnerable to disasters such as droughts, cyclones and floods. From 1970–1998, Mozambique experienced 11 flood and over 16 drought events. In those 11 flood events, it is estimated that over 1,585 people died (UNDP, 2002). Ranked 169 out of 174 countries on the 1999 human development index, Mozambique’s geographical vulnerability to disasters is severely compounded by the country’s more general socio-economic vulnerability. The Mozambican floods of 2000 resulted from heavy rainfalls that had been occurring in southern Africa since December 1999 and the later impact of Cyclones Connie and Eline in February. In human terms, 699 people died, hundreds of thousands of individuals were displaced, and over 27 per cent of the country’s population was affected in some manner by the disaster. Particularly vulnerable among those displaced and affected by the flooding were women, children and the elderly. In the health sector alone, the infrastructure for national health-care services was severely interrupted when the flooding partially destroyed a general hospital in Maputo City, four rural hospitals, eight large health centres and 31 small health centres in southern Mozambique (Mozambique/INGC, 2000a). Foreign governments, international organisations and various local and international NGOs all provided financial aid, rescue support, relief supplies and recovery assistance to Mozambique during the 2000 floods. From 11 February 2000 to 11 August 2000, goods alone worth over US$71 million were imported into Mozambique as emergency assistance in various sectors: health, agriculture, water, education and mine clearance (Mozambique, 2000b). While goods and funds for aid were necessary, the international response to Mozambican emergency appeals placed significant pressure on the capacity of the INGC to manage relief operations (Christie, 2001). Mozambican government agencies were particularly susceptible to difficulties


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with aid coordination because of the reorganisation in 1999 of its disaster management agencies. This reorganisation resulted in the creation of the INGC, which assumed the task of disaster management in place of the Department for the Prevention and Response to Natural Disasters (DPCCN). The INGC was concerned primarily with mitigation, preparedness and coordination instead of operation or response management. As part of the organisational restructuring, the national government sold off through privatisation policies much of its logistical capacity (warehouses, vehicles) to deal with natural disasters. It was assumed that the private sector had the capacity to address any future emergency (Simkin and Gottwals, 2000). The INGC was placed under the jurisdiction of the Minister for Foreign Affairs and Cooperation (Simkin and Gottwals, 2000). Although the newly formed INGC worked closely with the UN World Food Programme to devise new preparedness and response procedures, the size of the floods caught the INGC ill-prepared (Christie, 2001). Given the reorganisation of the INGC, its relative lack of experience in managing a disaster the size of the 2000 floods, and its own organisational capacity (Christie, 2001), the INGC at both the national and provincial level found itself unable to coordinate or even monitor the activities of all the NGOs that arrived in Mozambique during the emergency period. While NGOs were required to register with the INGC or various government ministries, they did not always do so. One incident stands out clearly as a result of a NGOs failure to register with government officials. On 24 March, it was reported that over five people died and 10 others injured when an NGO, which had not registered with officials, distributed food in a disorganised manner at the Chiaquelane accommodation centre (Mozambique, 2000b). Other incidents included NGOs telling people to return home when in fact there was still potential for further flooding in those locations. The failure of some NGOs to report their presence and activities to governmental authorities represents only one aspect of the frequently tested relations between governmental and non-governmental organisations. Field interviewees suggested that NGOs would occasionally become reluctant to work with provincial government authorities since NGO representatives sometimes felt that critical time and energy was lost during the political ceremony associated with certain aid-distribution events. In addition, as a result of the resources available to NGOs, particularly international ones, several NGOs might well have had more accurate information, greater logistical capacity and deeper financial coffers than did governmental ministries at the time. For example, a member of the municipial assembly of Chokwe reported that NGOs had started running the city of Chokwe: ‘when we came back, the NGOs were running the city — Chokwe was an NGO-occupied city. And the NGOs refused to recognize the existence of the municipal assembly and the municipal council’ (Christie, 2001). Despite some reports of close cooperation between national NGOs and international NGOs, relations were sometimes strained along several lines. First, different organisational demands influenced the work and operations of international NGOs. According to interviews, international NGOs were sometimes under significant pressure to spend money in a short period of time, thus leading to ‘short-term’ thinking and fewer relevant projects with long-term benefits. Second, the superior financial resources controlled by national and international NGOs meant that international NGOs sometimes hired away trained staff and personnel from local NGOs. Third, the primary language of the international relief community is English, which tended to exclude those national NGOs without staff proficiency in English. Coordination meetings held in English, thus prevented the full participation of NGOs without


personnel proficient in English. Fourth, the more frequently coordination meetings were held, the more strain that coordination efforts placed on organisational resources. Smaller sized NGOs often require personnel in the field leaving few staff available to attend meetings. Coordination activities might therefore exclude smaller NGOs, although those organisations might occupy important positions in the humanitarian aid network. The different attributes and capacities of the NGOs can thus affect their ability to participate equally in coordination activities. Whether the aid network assumed a hierarchical or an egalitarian form during the relief and recovery activities may not be a matter of concern as long as those activities were ‘well coordinated’. However, if more long-term issues are considered, such as developing local capacity for disaster preparedness, response and recovery, then the more appropriate question might be whether local agencies and organisations were well positioned in terms of ‘potential communicative activity’. It does not appear that this was the case in Mozambique. Indeed, the higher on average centrality of international NGOs over local NGOs suggests that local civil-society organisations on average remained peripheral to national-level coordination efforts (the major exception being the Mozambican Red Cross). While international NGOs might have sought out Mozambican NGOs with which to partner in particular sectors or locales, Mozambican NGOs nevertheless lacked the ties and links that international NGOs developed during the course of national-level coordination processes. As the emergency period ended and international NGOs began to reduce their aid and presence in Mozambique, one consequence may have been the collapse of critical links within the humanitarian aid network. This collapse could have left the network less cohesive, and Mozambican NGOs disproportionately disconnected from other organisations. The result may have been that the humanitarian aid organisations became less capable of coordinating recovery-period projects and implementing effective mitigation projects for future disasters. Indeed, field interviewees generally regarded the immediate relief period as the phase in which organisations coordinated their operations most effectively, while they reported a breakdown in coordination during the later recovery period.

Limitations

There were a number of limitations involving the data used for the present study. First, as discussed earlier, NGOs listed in the LINK documentation were used as the sampling frame. Therefore, the study sample only includes those organisations that were willing to report their activities to governmental and non-governmental sources. While the 65 NGOs found in the documentation may be representative of all NGOs working in the Mozambique 2000 floods, this may not necessarily be the case, particularly because of the fact that undocumented NGOs were known to have participated in relief and recovery operations. As a result, a random sample of the actual network population was not possible. Second, limited data existed on the degree to which various organisations worked together during the relief and recovery operations. Some such data were constructed using the LINK and INGC sources, namely through shared district and sector affiliation and INGC relations. However, these data could be improved through documents or surveys detailing the types of exchange relations that occurred among NGOs during the relief and recovery operations. Third, there was a limitation involving outcome data on the work of the NGOs surveyed. The average number of beneficiaries per project were the only types


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of outcome data maintained across the 65 organisations listed in the LINK sources. These data were not highly consistent, however, for the various NGOs involved in the operations — as the reporting coverage percentages indicate. It is important to note, however, that beneficiary numbers are notoriously difficult to calculate and maintain with certainty. Not only does a disaster situation imply that an organisation’s efforts are placed primarily on rescuing or assisting individuals or families rather than recording such efforts, but, in addition, the movement of people during a disaster means that numbers can change quickly (Rey, 1999). Another factor that reduces the reliability of beneficiary numbers is the politics that surround those numbers. As an interviewee commented, the prestige of an organisation in relief and recovery situations becomes tied to the number of beneficiaries that the organisation has assisted, which sometimes results in the over-inflation of numbers. Other types of outcome data, such as donor contributions or number of organisations with which an organisation worked, would benefit the network analysis suggested herein. Due to data limitations, statistical analyses were limited to non-parametric methods. As a result of such limitations, more powerful statistical tests that might examine the causal relations among the variables were not possible. The conclusions presented are speculative and tentative, and therefore require further investigation.

Conclusion

By examining the influence of the humanitarian aid structure on the coordination of relief and recovery operations, the present study explored the use of centrality measures to evaluate those operations. There appears to be a positive association between higher on average centrality scores and higher on average beneficiary numbers, specifically during the emergency period immediately following the flooding. This association seems to be mediated or confounded by other variables, such as NGO-type, sectoral engagement and provincial presence. The important role of such other influences requires further investigation. Nevertheless, this association did not hold during the recovery period; whether due to data limitations or decreased coordination among organisations is not clear. As noted, however, interviewees did express the opinion that coordination worked better during the emergency period than during the recovery period. Further research and improved data are required to examine whether the lack of an association between more central organisations and higher recovery period beneficiaries accounts for this reported decrease in coordination. The Mozambican Red Cross consistently held the highest degree and eigenvector centrality scores in district- and sector-affiliation networks. The results nevertheless illustrate that a positive association existed between international NGOs and average degree, eigenvector and flow betweenness centrality scores. With the exception of the Mozambican Red Cross, which has extensive external connections, local NGOs remained on average peripheral to the coordination processes. The hierarchical characteristics associated with this specific aid network suggest that local civil society capacity for responding to future disasters has not been developed and may in fact result in its increased dependence on international NGOs. The success of humanitarian aid operations ultimately depends on the ability of organisations to work together. Whether ‘working together’ means information-sharing or joint operations and projects, inter-organisational coordination is not simply the product of two organisations choosing to share resources, personnel or projects. It


is also a product of the inter-organisational structure in which those organisations exist. How does the humanitarian aid network situate certain organisations to be in better or worse positions to work with other organisations? How does an organisation’s position affect various relief and recovery outcomes? Which types of organisations occupy or play key ‘broker’ or mediating roles during humanitarian aid operations? By first identifying how the network structure affects inter-organisational coordination and humanitarian aid outcomes, practitioners and emergency managers will be better able to identify key organisations for specific types of relief or recovery activities. Before policies that influence the aid network can be made, policymakers, emergency managers and public health practitioners need to know more about the humanitarian aid structure and its effects on aid operations. This study sought to take some initial steps in this direction.

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