The present study is a quantitative study with a social network analysis approach. Based on legal obligations and conducted interviews with experts, the financial protection network for the poor was drawn. One of the important parts of the survey process was the creation of questions related to the arrangement and communication among mandated institutions. By doing that we tried to prepare a questionnaire of institutional options to draw accurately the optimal network.
The challenges in the legal obligations network, clarifying roles, avoiding overlapping tasks and rework, and optimizing relationships were the basis for designing the questions.
The research sample included 22 well-known experts who were purposefully selected from experts. They had high level of experience in financial support for the poor and had also relationships with different actors in organizational networks including Ministry of Health and Medical Education (MOHME), Ministry of Cooperatives, Labor and Social Welfare (MCLSW), Iranian Health Insurance Organization (IHIO), Iranian Red Crescent Society (IRCS), as insiders, and independent experts, as outsiders. We had equal number of insiders and outsiders in our expert body of research with eleven insiders from four network organizations and other experts from the outside.
Insiders provided detailed information about relationship among actors based on their direct experiences. Similarly, outsiders were beneficial due to their ability to view the entire network without direct involvement and, therefore, without conflict of interest biases. These two groups of informants, insiders and outsiders, provide us the opportunity of additional level of confirmation regarding network data, and also reducing possible biases. The combination of insiders and outsiders and relying on information from internal and external experts balances the biases, widens the perspectives, and validate methodology for incomplete SNA data (25).
To assess the validity of the questions, the questionnaire has been presented to several experts in network analysis and social welfare policy. Then, the questionnaire was given to five experts as a pilot. The accuracy and validity of the questions were reviewed and corrected based on experts’ recommendations.
The questionnaire had three sections: demographic information, proposed institutional options, and a commenting section; if desired. A summary of the objectives along with the institutional options for drawing up the optimal network was sent to thirty experts on health financial protection, and they were requested to submit their comments within a maximum of one week. The results were reviewed and analyzed, and the model was developed based on those comments.
A two-dimensional matrix was used to record the data and information collected from the questionnaire; the selected institutions by the experts formed the matrix rows, and the policies and support programs formed the columns. The value of matrix cells indicates the number of tasks each organization performs for different programs. A value of 2 was assigned to the institutions for implementing a program and a value of 1 was assigned to partner institutions. Then, a one-dimensional matrix of institutions was formed to determine which one has the most collaboration and which one has more centrality, power and position due to more collaboration with other institutions. UCINET software was used to enter data and data analysis and NetDraw software was used to draw networks and visual analysis.
Micro-indicators such as degree of centrality (identification of dominant actors in the network), betweenness centrality (identification of actors that link between others) (26), were used to show the position of institutions in the network. Several indicators of social network analysis contribute to understand the governance capacities of a system. These features of a system that can be translated into network governance indicators include density (ratio of the number of available links to the total number of possible links in the network), distance (the number of relationships in the shortest possible step from one actor to the another, which is in fact the most optimal or effective communication between two actors) and centralization (to the extent that communication in the network, instead of being evenly distributed among all members, focus on a small number of actors) (27), were calculated in this network.
Findings:
The amount of network indicators of the institutions proposed by the experts, which has been calculated using UCINET software, is presented in Table 1.
Table 1 - Indicators of the optimal network of institutions related to financial protection for the poor
Bonacich's power
|
Eigenvector
Centrality
|
Betweenness Centrality
|
Degree Centrality
|
Institution
|
|
1.611
|
0.486
|
0.825
|
0.372
|
Welfare Organization (WO)
|
1
|
.1.419
|
0.428
|
0.825
|
0.348
|
Imam Khomeini Relief Committee
|
2
|
1.684
|
0.508
|
0.825
|
0.338
|
Ministry of Cooperative, Labor and Social welfare (MCLSW
|
3
|
1.690
|
0.510
|
0.825
|
0.319
|
Iranian Health Insurance Organization (IHIO)
|
4
|
0.633
|
0.191
|
0.125
|
0.177
|
Charities
|
5
|
0.397
|
0.120
|
0.825
|
0.125
|
Ministry of Health and Medical Education (MOHME)
|
6
|
0.285
|
0.086
|
0.125
|
0.086
|
Municipalities
|
7
|
0.188
|
0.056
|
0
|
0.057
|
Supreme Council of Welfare and Social Security (SCWSS)
|
8
|
0.144
|
0.043
|
0.125
|
0.045
|
Iranian Red Crescent Society (IRCS)
|
9
|
0.059
|
0.018
|
0.500
|
0.024
|
Supreme Council of Health Insurance (SCHI)
|
10
|
0.028
|
0.008
|
0
|
0.009
|
Plan and Budget Organization (PBO)
|
11
|
Among 44 institutions identified in the network, the experts suggested only 11 institutions to participate in the optimal network. Legal collaboration between responsible and partner institutions regarding financial protection for the poor in two different optimal and legal networks is presented in Figure 1. The intensity and weakness of communication and the number of common tasks between the two entities are indicated by the thickness of the lines, and organizations with a higher degree centrality are represented by large squares.
The problems in the network of legal obligations, including the density of communication between several central institutions and the isolation of other institutions, have been solved in the optimal network, and relationship has been distributed in a balanced and orderly manner among all institutions present in the network. Network density in a circular arrangement shows better situation in terms of involvement of all institutions. Strengthening and balancing relationship of coordinating bodies (including Supreme Council, Ministries and the Planning and Budgeting Organization) with executive bodies (Imam Khomeini Relief Foundation, Welfare Organization and Iranian Health Insurance Organization (IHIO) on the one hand, and adequate relationship among executive bodies with non-governmental institutions (NGO) on the other hand, shows that in the proposed model relationship is seen in the form of a network. The strong presence of NGOs reflects the institutional approach to policy-making for financial protection. Balanced distribution of power between these institutions reflects a network of relationships in financial protection policies.
Network's Micro-Indicators
As it shows in Table 1, according to the experts, Welfare Organization should have the most authority in the network and with a very small difference, Relief Foundation, MCLW and the IHIO stand in the next ranks. A noteworthy point is the high centrality of charities, which indicates the increased power of these institutions due to change their position and relations and a democratic approach to policy-making. The position of the Welfare Organization in the optimal network shows a greater tendency towards centrality than the Relief Foundation, which was the most powerful institution in the network of legal obligations.
Betweenness centrality of the institutions is also of balanced distribution, and the Welfare Organization, the Relief Foundation, the MCLW, the MOHME, and the IHIO have been proposed with the most betweenness centrality. The high betweenness centrality of the Supreme Council of Health Insurance has made this council more accessible and more executive than the Supreme Council of Welfare and Social Security. Betweenness centrality distribution is shown in Figure 2.
The IHIO and the MCLW have the most special eigenvector centrality (meaning connection to authority sources in the network) due to the neighborhood with more central institutions, and the Welfare Organization is in the next rank. In the proposed network, the IHIO has the highest Bonacich power due to the centralities of the connected points, and the MCLW and the Welfare Organization are in the next ranks. It is understood that these institutions should have the most important role in formulating strategies as well as executive activities of financial protection.
In an optimal network, there are no cut-off points (entities whose removal from the network causes the network to become two separate parts and can cause or prevent relationship between other entities) and this indicates that the network is optimal in terms of more balanced distribution of power between institutions and better state of relationship between institutions. In general, due to the change in the position of institutions and increasing organizational relationships between them, the deep difference between the maximum and minimum number of relationships and the difference in the centrality of actors in the network of legal obligations has decreased. The cohesion has increased and the distribution of power and relationships has become more rational (figure 3).
Having more centrality means better position of accessibility in the network, which in turn increases collaboration power compared to others. In an optimal network of financial protection for the poor, the Welfare Organization, the Relief Foundation, the MCLW and the IHIO were recognized as the most powerful institutions. The majority of experts have chosen the following tasks and responsibilities for different institutions: the Supreme Council of Welfare and Social Security for policy-making and supervising financial protection policies; the MCLW and the Welfare Organization to identify the poor; the IHIO for basic insurance of the poor and cost-sharing coverage; the Welfare Organization and the Relief Foundation to cover the costs of services outside the benefit package and referring to health centers; and the MCLW as a single window for division of tasks among institutions. More network connections have been suggested between the Welfare Organization, the Relief Foundation, the MCLW and the IHIO.
Network's Macro-Indicators
Network governance indicators including density, centralization and distance between actors in the optimal network were calculated. The density of the legal obligations network of financial protection was 32.7%, which shows that the density among the 44 institutions present in the network is at a weak level and the relationship and collaboration between the institutions is not favorable. On the other hand, this index has reached 90% in the optimal network, which shows that the optimal network has a very high density.
The centralization in the optimal financial protection network is 0.11, which indicates that the network is decentralized and the flow of information is not restricted to a limited number of actors. It also shows that the network is managed by a shared-governance model of the members. The average geodesic distance in the network of legal obligations is 2.2. It indicates that the organizational unity in the field of financial protection for the poor among the institutions involved in the network is weak. When all the actors are directly connected, the average distance will be 1 and the flow of information is expected to be fast. In the optimal network with fewer actors, the distance is 1.09, which indicates that the actors are all in direct contact with each other.