Printer Friendly



The Ministry of Counter Narcotics (MCN) of Afghanistan was established in early 2005 to take a leading role in the development, coordination, monitoring and evaluation of government counternarcotics (CN) strategy (MCN, 2005). According to the Constitution and the Counter Narcotics and Intoxicants Law of the Islamic Republic of Afghanistan, the MCN plays the leading role in formulating and coordinating programs and policies with line ministries and organizations to combat narcotics and other drugs in Afghanistan. The MCN has developed three policies to target drugs from three different directions: alternative livelihoods, demand reduction, and law enforcement policy (MCN, 2005). Notably, the MCN has established the above policies and other action plans, including a campaign to eradicate cultivated poppy fields, every year through coordination and collaboration with other line ministries and concerned organizations. As the MCN itself lacks the operational capacity and authority to implement these plans and policies, proper coordination and collaboration among line ministries is essential for the successful implementation of CN policies, plans and programs.

Despite numerous efforts to combat drugs in Afghanistan, the cultivation, production, trafficking and use of illicit drugs remains a major challenge to Afghanistan's socioeconomic development. The MCN cannot achieve its goals and objectives without collaborating with other relevant governmental institutions and international agencies. Therefore, the MCN needs to enhance interagency collaboration and coordination, especially with key stakeholders, to effectively incorporate combating drugs into priority programs and to increase operational capacity to implement plans and strategies to achieve the goals of combating drugs and eliminating the cultivation, production, traffic and use of illicit drugs in Afghanistan. To enhance interagency collaboration, the existing patternized interactions in the collaborative process of combating drugs among CN stakeholders in Afghanistan need to be examined.

Notably, this study employs social network analysis to describe the importance and position of each CN stakeholder and the patternized collaborative affiliations among multisectoral actors, including governmental organizations (GOs), international organizations (IOs), and nongovernmental organizations (NGOs) at different levels and dimensions. These independent actors, which have their own goals and priorities, form networks in order to attain outcomes that they could not achieve alone (Isett et al., 2011, p. i161). To explain the distinct structural and institutional properties of connectedness and interdependency of actors, Provan and Kenis (2008) categorized the governance types of organizational networks into shared governance network, lead organization-governed network, and network administrative organization. Based on this classification, this study seeks to understand the patternized interactions of stakeholders in policy networks regarding seven main counternarcotics activities--i.e., influence in decision making, information and advice seeking, collaboration, resource sharing, funding provision, and goal congruence--and to identify the structure of each network based on the classification created by Provan and Kenis (2008) using a degree centrality measure and degree distributions. That is, the research question of this study is how the structures of the seven types of networks regarding Afghanistan counternarcotics activities relate to this given typology of network types. This paper will subsequently offer mechanisms and policy recommendations to enhance interagency collaboration between the MCN and other stakeholders, allowing them to better lead, collaborate, and coordinate CN activities in Afghanistan.

Interagency collaboration

Greater complexity has made the world smaller, but interconnected problems have increased in such a way that a single organization or, in some cases, even a country, is unable to solve some of them. One such problem is the cultivation, production, and trafficking of illicit drugs in Afghanistan. Despite government efforts to combat and eliminate drug-related activities in Afghanistan, the cultivation and production of drugs continues to increase. Additionally, we have not seen significant success in combating drugs over the past one and half decades as this multidimensional phenomenon has taken root in Afghanistan, the region and the world. Collaboration among different stakeholders, including regional and international institutions, is crucial in combating this multidimensional phenomenon. Numerous efforts have been made to develop conceptual frameworks guiding the assessment of interagency collaboration in public policy processes (Tseng, 2004). However, the changeability and complexity of studying collaboration, the nature of cooperative efforts, and diverse isotropic regularities make studying collaboration difficult (Knapp, 1995). To improve our understanding of interagency collaboration, interagency operation must first be clarified; that is, interagency operation "involves more than one agency working together in a planned and formal way rather than simply through informal networking (although the latter may support and develop the former). This can be at a strategic or operational level" (Warmington et al., 2004, p. 16). Similarly, interagency collaboration is multidimensional and developmental rather than static. Therefore, it may develop and change. However, considering the area and level at which scholars study interagency collaboration, its definition may also refer to those levels and arenas. For instance, Flynn and Harbin (1987, p. 35) suggested that at the agency level, "interagency collaboration is a general concept that describes a variety of efforts to reform the current categorical service delivery system."

Mattessich et al. (2001, p. 39) defined collaboration as "a mutually beneficial and well-defined relationship entered into by two or more organizations with a commitment to a set of common goals, a joint structure and shared responsibility, as well as mutual authority and accountability." Multisectoral or multiactor relationships can be maintained based on trust and a shared vision that potentially enhances the ability of the parties to achieve qualitatively better outcomes (Gray, 1989; Huxham, 1996). Other scholars, such as Melaville et al. (1993, p. 20), recognize collaboration as "a series of interrelated activities undertaken by stakeholders through five stages: [1] getting together, [2] building mutual trust, [3] developing a common strategic plan, [4] taking action, and [5] going to scale (i.e., implementing policies on public service delivery)."

Collaborative network structures

There are a variety of collaborative network structures given the different types of involved actors, network boundaries, and the presence or absence of various relational types. Moreover, studies have shown that what a network can achieve is indeed the outcome of its structural form (Baker & Faulkner, 1993; Burt, 2005; Cross et al., 2002). Provan and Kenis (2008) define three basic models of network governance: a shared governance network, a lead organization network, and a network administrative organization (NAO). Each of these models varies in its structure. None of these forms appears to be globally dominant; rather, each structure has specific capabilities, which means that each network varies in what it can best accomplish.

In a shared governance network, several administrations work together as a network without a specific governing organization. The administration of cooperative actions remains entirely with the participants in the network (see Figure 1a). In this model, the network members make every decision and control the activities of the network on their own without separate official administrative bodies. However, due to an excess of members in the network, a few members from the network may perform certain coordination and organizational actions.

The strengths of the shared governance form are its adaptability, its ability to act quickly based on member needs, and the inclusion and participation of all the network members. The disadvantage of this model is its moderate inefficiency. This is a form that appears to be perfect for networks that are geographically localized and where members of the network will likely have functional and complete direct participation (Provan & Kenis, 2008). According to Provan and Kenis (2008), shared network governance is most efficient in attaining network-level results when "[1] trust is widely shared among network participants (high-density, decentralized trust), [2] there are relatively few network participants, [3] network-level goal consensus is high, and [4] the need for network-level competencies is low" (ibid., p. 241).

The lead organization model can arise in horizontal multidimensional networks, especially when an organization has enough legitimacy and assets to take the leading role (see Figure 1b). This model primarily arises in state-funded services in which a central service provider with a central location in the network takes the leadership position (Provan & Kenis, 2008). For instance, according to the Counter Narcotics and Intoxicants Law, the MCN is the lead organization in making policies and coordinating CN activities in Afghanistan. In addition to preserving their own goals, all the affiliates in this network model have a shared purpose; they might also interact and work with each other.

However, one member of the group functions as the leading actor, coordinating every operation and making major decisions. That member administers the network to attain its goals and simplify the activities of the associated groups (Provan & Kenis, 2008). According to Provan and Kenis (2008), to attain network-level results, lead organization network governance is most efficient when "[1] trust is narrowly shared among network participants (low-density, highly centralized trust), [2] there are a relatively moderate number of network participants, [3] network-level goal consensus is moderately low, and [4] the need for network-level competencies is moderate" (ibid., p. 241).

An NAO is an alternative model that may be used to avoid the inefficiency of shared governance networks and the complications over supremacy and confrontation of lead organization-governed networks. The main concept behind an NAO is that a distinct managerial body is established specifically to manage and coordinate the network and its activities (see Figure 1c). The main goal of forming an NAO is to build network governance (Provan & Kenis, 2008). The NAO can be a GO or an NGO even if the network participants are largely for-profit corporations (Human and Provan, 2000; Provan & Kenis, 2008).

Groups and members of the network can cooperate with each other; however, a distinct and sovereign organization coordinates the actions and makes the important decisions. According to Provan and Kenis (2008), to attain network-level outcomes, network governance with an NAO is most effective when "[1] trust is moderately to widely shared among network participants (moderate density trust), [2] there are a moderate number to many network participants, [3] when network-level goal consensus is moderately high; and [4] when need for network-level competencies is high" (ibid., p. 241).

Multilevel and multisectoral networks

Hooghe and Marks (2004) classify multilevel governance from the perspective of governance levels into two types: federalism and monocentric governance. As federalism tends to share power between national and local governments, the most important concern in this type of governance is the correlation between the central government and local governments. By contrast, monocentric governance is an approach in which the government is the centre of authority and political power (Rhodes, 1997; Pierre, 2000; Kooiman, 2003). The government controls society and resources in three main steps. First, the state groups social problems and sets the agendas. Second, the state makes decisions concerning policy goals and means. Third, the state implements policies through a top-down approach. However, monocentric governance has been criticized from many recent governance perspectives (Hajer and Wagenaar, 2003; Hill and Lynn, 2004; Kooiman, 1993; Rhodes, 1997).

More recent studies (Henttonen et al., 2016; Hooghe & Marks, 2003; Raadschelders & Vigoda-Gadot, 2015) argue that networks are not single-level horizontal structures but rather multilayered and multidimensional structures. Rather than conceptualizing and measuring relationships in networks based on the premise that the nodes and hierarchy are vertically distinct from each other, and actors in different sectors--including state and non-state actors--are connected by inter-sector relationships (Bache and Flinders, 2004; Curry, 2015; Park and Lim, 2018; Zeleznik, 2016).


This paper studies interagency collaboration among CN stakeholders and the different line ministries of the MCN. Many scholars have used a social network analysis approach to study this kind of topic. For example, according to Rhodes and March (1992), policy network analysis has been recognized as an effective hypothetical instrument for understanding the complicated processes of making public policies. In this type of analysis, the multifarious, decentralized, and complicated facts of contemporary decision making and policy modification can be concisely explained, and the dominant policy stakeholders and diverse interactions among stakeholders in the policymaking processes can be classified by an SNA approach.


In order to consider the aspects of multisectoral and multilevel network governance structures where multiorganizational collaboration on Afghanistan CN activities in Afghanistan will be examined beyond a state-centric or monocentric governance style. Instead, data collection was conducted in Kabul City by focusing on three particular sectors--governmental actors, non-profits, and international organizations at multiple levels including ministries, deputy ministries, and departments from 18 June to 30 August 2016 (see Figure 2).

In this study, snowball sampling was used to /) select the relevant respondents and define the proper boundaries of the actors involved in the networks and //) examine their relationships with others. This approach starts with very carefully selected initial actors with the most reliable information on and knowledge of the issues in which researchers are interested and asks them first to nominate and recommend up to five other actors which are included within the boundary of the targeted networks. Then, the recommended actors are approached by the researchers and also given equal opportunities to list the names of other organizations actually and potentially engaged in the targeted networks.

Through several rounds of the repetitive referrals and nominations until the names of actors are saturated, the boundary of networks is expanded and finally determined (Knoke and Kuklinski, 1982; Scott, 2017). As the final step, all of the actors indicated as the involved actors are asked to reveal their relationships with the other actors within the targeted networks, which becomes the basis of constructing network/relational datasets for research. Snowball sampling is a typical method for detecting and specifying the boundary of networks--i.e., which actors compose the specific networks--in the research areas in the field of network studies (Prell, 2012; Robins, 2015), and this method renders the investigation of the targeted networking activities more feasible.

As part of the snowball sampling in this study, the MCN--determined as the original seed actor that was the most relevant to administering counternarcotics issues after reviewing a variety of materials, including the Constitutional Law of Afghanistan, the Counter Narcotics and Intoxicants Law, CN policy documents, published literature, and publicly available documents--was initially contacted to nominate other stakeholders involved in the networks by conducting interviews with approximately 40 high- and mid-level bureaucrats in the MCN, the line ministries, and other stakeholders (such as NGOs and IOs).

The careful reviews of relevant literature and documents as well as the earlier stage interviews with public officials at multiple levels within the MCN and other stakeholders also enabled the multiplexity of relationships over seven primary activities of Afghanistan counternarcotics issues--influence in decision making, information and advice seeking, collaboration, resource sharing, funding provision, and goal congruence--to be recognized among the 93 organizations in total that were identified as being engaged in counternarcotics activities from our multiple rounds of referrals and nominations of the actors.

When the set of stakeholders was expanded to 93 actors, we returned to the previous actors and supplemented their previous responses with the added actors repeatedly. All of the interview respondents were the staff (mainly, high-ranking officials or spokespersons at the decision-making level and the heads of departments at the implementation level) in charge of counternarcotics issues representing these 93 actors. A survey questionnaire was developed to detect the relationships of the 93 actual stakeholders with the others by asking the interviewees to indicate their partner organizations (up to five) in the specific counternarcotics activities among the seven aforementioned (see Appendix 1 for survey questionnaire and Appendix 2 for the list of 93 actors and their acronyms used in this study).

Network methods

Through extensive data collection and relational surveys, network data on ties among 93 organizations were collected. As each actor was asked to indicate the most influential actors, the sources of information, advice, resources, or funds as well as the collaborative or goal-congruent partners, the links observed in our surveys are directional. Considering such a directionality of edges here, the in-degree centrality measure was employed to detect influential actors in the seven multiple networks regarding counternarcotics issues. In-degree centrality is a classical measure of influence: as other actors nominate a high-degree actor, the latter is classically interpreted as being prominent and respected and central in the network (Borgatti et al., 2013). The in-degree centrality of focal actor i can be systematically computed by counting the numbers of alters citing or referring to this 'ego' (i) as the source of specific resources or partner of targeted activities (Prell, 2012, p. 97), as below:

[C.sub.indegree] (i) = [n.summation over (j = 1)][[chi].sub.ji](i [not equal to] j)


[x.sub.ji] = 1 if actor i is mentioned by actor j (binary number); n = the number of nodes within the network.

To interpret the networks in relation to the categories provided by Provan and Kenis (2008), in-degree distributions were utilized. The characteristic differences among the three main types of network structure--shared governance, lead organization-overned network, and network administration organization--could be captured by distinct in-degree distributions. More specifically, i) the number of existing lead organizations--a single (or two) or multiple distributed central actors; ii) the differences in in-degrees between leading actors and the following actors--the skewedness of in-degree distributions; and iii) the institutional property of the central actor as a part of the substantial aspects of the central actors--whether it is an insider actor within a network or a intervener or coordinator from the outside of a network--are used for discerning the 3-type typology of network governance.


This study identified 93 actors at multiple levels and dimensions that are involved in seven different networks and different arenas of collaborative CN activities in Afghanistan. Table 1 summarizes the in-degree centrality measures for the top ten actors across the seven counternarcotics-related networks.

The seven network figures (Figures 3a through 9a) show the position of each actor in the specific network. To better understand the patternized interactions of the 95 actors within these seven networks, this paper applies a multilevel and multidimensional system of network structures and governance (Park & Lim, 2018) of collaborative CN activities in Afghanistan. Moreover, this study tries to assess each network structure based on the three types of network governance models identified by Provan and Kenis (2008).

Major actors in the influence network of CN policy in Afghanistan

The results in Table 1 indicate the ten most influential actors in the influence network according to their in-degree centrality measures. The Ministry of Interior Affairs (MoIA) is the most influential actor based on its highest in-degree centrality measure compared with all other actors in the influence network. Furthermore, its central position and number of ties in the network figure, as shown in Figure 3a, indicate that the MoIA is the most central actor in the influence network and in the CN policy process in Afghanistan. Due to its second rank for in-degree centrality, the Ministry of Agriculture, Irrigation and Livestock (MAIL) is another influential actor in this network.

As shown in Figure 3b, approximately four actors--located outside the right side of the normal curve with fewer gaps in in-degrees among these four, which have in-degrees that are significantly higher than the others--are the most important in this network and may have more influence over CN policies despite having their own priorities. The MoIA, the MAIL, and the Ministry of Public Health (MoPH) are the most influential actors followed by the MCN, which is responsible for leading and coordinating the CN activities of the government of Afghanistan according to the Counter Narcotics and Intoxicants Law. Based on the polycentric shape of the network and the multiple observed network members that have more influence and may perform coordination and organizational actions, this can be classified as a shared governance network.

Major actors within the information network of CN policy in Afghanistan

Governmental organizations were found to be the most reliable actors as the sources of information about CN activities. Table 1 lists the top ten actors in this network based on the in-degree centrality measure. The MoIA ranks first in in-degree centrality, indicating that most other actors depend on this organization for reliable information when making CN policies. The MCN, one of the most reliable members of this network and specifically established to lead, manage, and coordinate CN activities in Afghanistan, ranks second in terms of in-degree centrality. The MoPH, which ranks third in in-degree centrality, is also an important governmental source of reliable information on CN activities in this case.

As shown in Figure 4b, approximately five actors--located outside the right side of the normal curve with close distances in terms of in-degrees among these five--are the most pivotal in this network. Therefore, based on the classification of network governance types developed by Provan and Kenis (2008), this information network with multiple cores can be regarded as a shared governance network.

Major actors in the advice network of CN policy in Afghanistan

The ten key actors in the advice network are indicated in Table 1 according to the indegree centrality. These are the five actors that have been identified by other actors as the most influential organizations in delivering reliable advice about CN policy processes in Afghanistan. The MCN is recognized as the most dominantly influential actor in the advice network with seven more in-degrees compared with the MoIA and the United Nations Office on Drugs and Crime (UNODC), which is ranked second in in-degree centrality. According to the Counter Narcotics and Intoxicants Law, the MoIA is responsible for detecting and seizing drugs and arresting drug dealers. Additionally, the MoIA is the key law enforcement and security organization in Afghanistan. The UNODC is an IO providing technical and financial support for CN activities in Afghanistan and has been recognized as one of the most influential providers of reliable advice in this study.

Based on the distribution histogram of in-degree links observed in the advice network, as seen in Figure 5b, the advice network can be regarded as a lead organization network since the in-degree number of the MCN, as the predominantly leading actor within the advice network, is the highest and the subsequent degree metrics are significantly lower.

Major actors in the collaboration network of CN activities in Afghanistan

As indicated above, collaboration among all stakeholders is crucial to the successful implementation of CN policies in Afghanistan. Table 1 lists the top ten stakeholders in the collaboration network according to the in-degree centrality. These are the actors that were identified by the other actors as regularly collaborating partners in CN issues and activities. The MCN, which ranked first for in-degree centrality, is considered the most central actor collaborating on CN issues and activities. The UNOCD (ranked second for in-degree centrality), the MoIA (ranked third), and the MoPH (ranked fourth) are the other most influential actors in this network. Overall, the collaboration network comprises two groups of actors from the two-dimensional categories of governmental actors (the MCN, the MoIA, the MoPH, and the MAIL) and IOs (the UNODC and the INL). Governmental actors play a major role in decision making and the implementation of policies, while IOs collaborate to financially and technically support certain CN programs. Figure 6a also indicates that these collaborative interactions exist at all levels and dimensions in the collaboration network.

According to the in-degree distribution described in Figure 6b, multiple competing leading actors exist--such as the MCN, the UNOCD, the MoIA, and the MoPH--for which the in-degree numbers are similar. Thus, this collaboration network with several sub-leaders can be categorized as a shared governance network.

Major actors in the resource-sharing network of CN policymaking in Afghanistan

Table 1 shows the top ten actors in integrating staff and resources for achieving the purposes of CN activities. According to the in-degree centrality measure, the outstanding three actors in this network are the MoIA, the MCN, and the MoPH. As expected from the Counter Narcotics and Intoxicants Law, these are the key stakeholders of CN activities sharing specific responsibilities regarding CN activities in Afghanistan. The significantly higher in-degrees for these three actors can also be seen in the in-degree distribution (see Figure 7b), and this network can be considered a shared governance or participant governance network in which multiple leading organizations cooperatively work together.

Major actors in the financial resource network of CN policymaking in Afghanistan

Table 1 represents the top ten actors in providing funds for CN activities in Afghanistan. Based on in-degree centrality as a measure of the frequencies of 'tie nominations' from organizations--reflecting their financers--the two major financial supporters of CN activities in Afghanistan are IOs--the International Narcotics and Law Enforcement Affairs-Counter Narcotics (INL-CL) and the UNODC. Another interesting point within the financial network is that the connections among the actors from all three dimensions--governmental (such as the MoF), nongovernmental (such as the Colombo Plan), and international (such as the INL, the UNODC, and certain embassies)--are evident.

The INL works with the government of Afghanistan to support and address different law enforcement issues and CN activities. The UNODC is the global leader in the fight against crime and illegal drugs. That is, the leading actors in the financial resource network in CN activities in Afghanistan (the INL and the UNODC) are the international actors that work with the Afghan government to support CN activities and the rule of law. Therefore, the financial resource network is multidimensional, and many governmental and non-governmental actors rely on funds from outside of Afghanistan, i.e., from international organizations, to implement CN activities in Afghanistan. As this network is led by two outside international actors working to manage, organize, coordinate, and financially support CN activities, it can be said that they are the NAOs for this network.

Major actors in the goal-congruence network of CN policymaking in Afghanistan

Table 1 presents the top ten actors in the goal-congruence network, which indicates a shared vision and goals regarding CN in Afghanistan. The competing leading actors with the relatively highest in-degree centralities in this network include governmental as well as international actors, as expected. For example, the MCN, the MoIA, and the MoPH can be regarded the major governmental actors with specific responsibilities to combat drug-related activities in Afghanistan. They have also been found as the influential actors that have a shared vision. Another group of major nongovernmental international actors recognized in this network includes the UNODC and the INL-CN. Therefore, the goal-congruence network comprises multidimensional leading actors. Hence, among the three types of network governance, the goal-congruence network can be classified as a shared governance network.


This study has made a first exploratory attempt at applying descriptive SNA to determine the major influential actors in the actual networks of CN activities in Afghanistan and categorize the empirical networks into one of three types of network governance. That is, this study identified influential actors in the different types of networks and discussed the configurations and structures of each network to identify the collaboration structure, forms of network governance, and influential organizations collaborating in different networks and arenas of CN activities in Afghanistan. Furthermore, this study emphasizes the policymaking and implementation levels, as well as the governmental, nongovernmental and international dimensions, to determine their positions in the collaborative network structures for CN activities in Afghanistan.

Likewise, this study showed how the same set of stakeholders engaged in the Afghanistan process of CN policymaking and implementation are connected through multiple types of relations across the governmental, nongovernmental, and international sectors in the form of multiactor and multilevel governance (van den Berg, 2011, p. 17). Therefore, this study provides an understanding of the ideal types of network governance (Kenis & Provan, 2009; Provan & Kenis, 2008) and their applications in the settings in which CN stakeholders are actually collaborating within seven networks in different areas of CN activities in Afghanistan.

Among the actors interacting to influence the CN policy process in Afghanistan, the MoIA, the MAIL, and the MCN are the most influential. The configurations of influence, information, collaboration, and goal-congruence networks can be classified as shared governance forms, as several stakeholders work together to form self-organizing networks without specific dominant governance organizations, and network members make decisions and control activities on their own without relying on a separate official administrative office for those collaborative activities. In other types of networks, including advice and resource-sharing networks, several competing central organizations such as the MCN, the MoIA, the MoPH, and the UNODC have been recognized the lead organizations. Lastly, in the financial resource network, two outside international actors, as NAOs, are the main providers of funds to CN activities in Afghanistan.

The empirical findings of this study indicate that GOs have generally been the most influential actors across the different networks of CN activities in Afghanistan. Additionally, IOs, including the UNODC and the INL-CN, also play significant and major roles in several networks. Furthermore, actors from multiple levels and dimensions have been involved in different aspects of Afghan CN activities. According to the laws, policies, and reported organizational structures, the form of governance tends to have hierarchical and vertical arrangements of organizations. However, this study empirically shows that the actors at different levels and across different dimensions interact with one another to achieve common policy objectives and goals regarding CN.

To address the complex and multidimensional issues of drug-related activities in Afghanistan, collaboration among different line ministries, especially among key stakeholders, is crucial. In particular, financial resources can play a critical role at various stages of decision making through the implementation of policy. Since Afghanistan lacks its own revenue sources to fund the expense of combating drug problems and related activities, CN activities rely on foreign aid. The financial resource network is mainly led and managed by outside international actors. Therefore, the government needs to maintain its own sustainable funding source for its policies. Above all, to enhance the interagency collaboration and governance of CN-related activities in Afghanistan, eliciting support from key decision makers and direct service providers is important. To gain such support, the government should establish a mechanism through which it can request support from and collaboration with actors from different levels and dimensions without disturbing the priorities of those actors.

As the empirical findings of this study show, CN stakeholders are collaborating in distinct areas of CN activities of interest to us in Afghanistan; however, the configurations or forms of those networks are not officially defined by law or determined by a specific policy or mechanism. Moreover, formal and informal interactions among stakeholders from the distinct levels and dimensions can be identified within each network. In fact, Article 7 of the Law on Campaign against Intoxicants, Drugs and their Control mandates the establishment of a "High Counter Intoxicants and Narcotics Commission" to better implement the provisions of the law and effectively combat narcotics. The 8th Article of that same law defines the "Duties and Authorities of the Commissions".

Beyond the establishment of "the High Counter Intoxicants and Narcotics Commission", the results of this exploratory and descriptive study recommend that the MCN and the Government of Afghanistan consider seven structures of collaborative networks achieved in different areas of CN activities to improve the coordination of CN activities and enhance interagency collaborations among CN stakeholders. This, we believe, would provide a better understanding of the actual networks around and beneath participating stakeholders to possibly improve the capacity of policymakers and implementers to achieve policy goals as planned and designed. Moreover, the roles, responsibilities, and authorities of each actor within the actual networks should be codified and defined based on its observed position and status as the centrality of each policy stakeholder in policy networks is directly related to its abilities to complete missions and tasks regarding counternarcotics.


Afghanistan National Development Strategy (ANDS). (2008--2013). A Strategy for Security, Governance, Economic Growth and Poverty Reduction. Retrieved from

Ansell, C. and Gash, A. (2008). Collaborative governance in theory and practice. Journal of Public Administration Research and Theory, 18(4), pp. 543-571.

Anti-drug Trafficking Policy. (May 2012). Retrieved from En.pdf

Bache, I. and Flinders, M. (Eds.) (2004). Multi-evel Governance, New York, NY: Oxford University Press.

Baker, W. E. and Faulkner, R. R. (1993). The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry. American Sociological Review, 58(6), pp. 837-860.

Borgatti, S. P., Everett, M. G. and Johnson, J. C. (2013). Analyzing Social Networks. Thousand Oaks, CA: Sage.

Burt, R. S. (2005). Brokerage and Closure: An Introduction to Social Capital. New York, NY: Oxford University Press.

Cross, R., Parker, A. and Borgatti, S. (2002). A Bird's-eye View: Using Social Network Analysis to Improve Knowledge Creation and Sharing. Somers, NY: IBM Corporation.

Counter Narcotics and intoxicants Law 2009. (2009). Retrieved from

Central Statistic Organization (CSO) Islamic Republic of Afghanistan. (n.d.). Retrieved from

Curry, D. (2015). Network Approaches to Multi-level Governance: Structures, Relations and Understanding Power between Levels, New York, NY: Palgrave Macmillan.

Flynn, C. C. and Harbin, G. L. (1987). Evaluating interagency coordination efforts using a multidimensional, interactional, developmental paradigm. Remedial and Special Education, 8(3), pp. 35-44.

Gray, B. (1989). Collaborating: Finding Common Ground for Multiparty Problems, San Francisco, CA: Jossey-Bass.

Hajer, M. and H. Wagenaar, (Eds.). (2003). Deliberative Policy Analysis: Understanding Governance in the Network Society. Cambridge, UK: Cambridge University Press.

Henttonen, K., Lahikainen, K. and Jauhiainen, T. (2016). Governance mechanisms in multi-party non-profit collaboration. Public Organization Review, 16(1), pp. 1-16.

Hooghe, L. and Marks, G. (2003). Unraveling the central state, but how? Types of multi-level governance. American Political Science Review, 97(2), pp. 233-243.

Human, S. E. and Provan, K. G. (2000). Legitimacy building in the evolution of smallfirm multilateral networks: a comparative study of success and demise. Administrative Science Quarterly, 45(2), pp. 327-365.

Huxham, C. (Ed.). (1996). Creating Collaborative Advantage. London, UK: Sage.

Isett, K. R., Mergel, I. A., LeRoux, K., Mischen, P. A. and Rethemeyer, R. K. (2011). Networks in public administration scholarship: understanding where we are and where we need to go. Journal of Public Administration Research and Theory, 21(suppl 1), pp. i157--i173.

Kenis, P. and Provan, K. G. (2009). Towards an exogenous theory of public network performance. Public Administration, 87(3), pp. 440-456.

Knapp, M. (1995). How shall we study comprehensive, collaborative services for children and families? Educational Researcher 24(4), pp. 5-16.

Knoke, D. and Kuklinski, J. H. (1982), Network Analysis. Newberry Park, CA: Sage.

Kooiman, J. (2003). Governing as Governance. London, UK: Sage.

Marks, G. W. and Hooghe, L. (2004). Contrasting visions of multi-level governance. In I. Bache, & M. Flinders (Eds.), Multi-level Governance, New York, NY: Oxford University Press.

Mattessich, P. W., Murray-Close, M. and Monsey, B. R. (2001). Collaboration: What Makes It Work. 2nd ed. Amherst H. Wilder Foundation.

Melaville, A., Blank, M. and Asayesh, G. (1993). Together We Can: A Guide for Crafting a Profamily System of Education and Human Services. United States Government Printing Office, Washington.

Ministry of Counter Narcotics of Afghanistan, (2005). The official website of the Ministry Retrieved from

Park, S. and Lim, S. (2018). Network Structures. In A. Farazmand (ed.), Global Encyclopedia of Public Administration, Public Policy, and Governance, Cham, Springer. DOI: 10.1007/978-3-319-31816-5_3206-1.

Pierre, J. (Ed.). (2000). Debating Governance: Authority, Steering, and Democracy. New York, NY: Oxford University Press.

Prell, C. (2012). Social Network Analysis: History, Theory, & Methodology Thousand Oaks, CA Sage.

Provan, K. G. and Kenis, P. (2008). Modes of nework governance: Strucutre, management, and effectiveness. Journal of Public Administration Research and Theory, 18(2), pp. 229-252.

Provan, K. G. and Milward, H. B. (1995). A preliminary theory of interorganizational network effectiveness: A comparative study of four community mental health Systems. Administrative Science Quarterly, 40(1), pp. 1-33.

Raadschelders, J. and Vigoda-Gadot, E. (2015). Global Dimensions of Public Administration and Governance: A Comparative Voyage. Hoboken, NJ: John Wiley & Sons.

Rhodes, R. A. W. (1997). Understanding Governance: Policy Networks, Governance, Reflexivity and Accountability. Milton Keynes, UK: Open University Press.

Rhodes, R. A. W. and March, D. (1992). New directions in the study of policy networks. European Journal of Political Research, 21(1-2), pp. 181-205.

Robins, G. (2015). Doing Social Network Research: Network-based Research Design for Social Scientists, Thousand Oaks, CA: Sage.

Scott, J. (2017). Social Network Analysis. 4th Edition. Thousand Oaks, CA: Sage.

The Constitution of Afghanistan 2004. (2004, January 3). Translated by: Sayed Shafi Rahel for the Secretariat of the Constitutional Commission. Retrieved from englis h3012201016726844.pdf.

Termeer, C. J., Dewulf, A. R. P. J. and Lieshout, M. V. (2010). Disentangling scale approaches in governance research: comparing monocentric, multilevel, and adaptive governance. Ecology and Society, 15(4), pp. 3-6.

Tseng, S. H. (2004). Interagency Collaboration in Early Intervention: Participants' Perspectives. Doctoral dissertation, University of Maryland, College Park.

Tomlinson, K. (2003). Effective Interagency Working: A Review of the Literature and Examples from Practice. Slough: National Foundation for Educational Research.

Warmington, P., Daniels, H., Edwards, A., Brown, S., Leadbetter, J., Martin, D. and Middleton, D. (2004). Interagency Collaboration: A Review of the Literature. Bath, UK: University of Bath.

United Nations Office on Drugs and Crime. (2015). World Drug Report 2015. United Nations Publications.

Van den Berg, C. (2011). Transforming for Europe: The Reshaping of National Bureaucracies in a System of Multi-Level Governance. Leiden: Leiden University Press.

Zeleznik, A. (2016). Explaining the ineffective implementation of constitutional rulings through the concept of policy networks: the case of public policy towards 'the Erased' in the Republic of Slovenia. Romanian Journal of Political Science, 16(2), pp. 86-120.

Appendix 1: Survey questionnaire

a. Influence network: Which actors are particularly influential in decision making for countering narcotics policies? Please choose the names of organizations, up to five.

b. Information network: From which actors does your organization obtain information about the counternarcotics activities? Please choose the names of organizations, up to five.

c. Advice network: From which actors does your organization seek reliable advice about counternarcotics policy processes? Please choose the names of organizations, up to five.

d. Collaboration network: With which actors does your organization collaborate regularly concerning counternarcotics issues and activities? Please choose the names of organizations, up to five.

e. Resource-sharing network: Which actors are particularly integrating their staffs and resources for the purposes of counternarcotics activities with those of your organization? Please choose the names of organizations, up to five.

f. Financial resource network: Which actors are particularly providing funds for the purposes of counternarcotics activities to your organization? Please choose the names of organizations, up to five.

g. Goal-congruence network: Which actors in particular have shared visions and goals regarding counteringnarcotics in Afghanistan with your organization? Please choose the names of organizations, up to five.
Appendix 2: List of actors and acronyms in this study

Name of Organization                                   Acronym

Australian Agency for International Development        AAID
Afghanistan Civil Service Commission                   ACSC
Afghanistan Civil Service Commission-Afghanistan       ACSC-ACSI
Civil Service Institute
Afghanistan National Bureau of Environmental           ANBEP
British Embassy                                        British-E
Comprehensive Agriculture and Rural Development-       CARD-F
Criminal Justice Task Force                            CJTF
CJTF Court                                             CJTF-Court
Counter-Narcotic Commission of the House of            CNCHR
Central Statistics Organization                        CSO
Chinese Embassy                                        Chinese-E
The Colombo-Plan                                       Colombo-Plan
Decisive Support                                       DS
French Embassy                                         French-E
General Directorate of Physical Education and Sport    GDPhESA
of Afghanistan
Good Performers Initiative                             GPI
The Global-fund                                        Global-fund
Independent Directorate of Local Governance            IDLG
Independent Directorate of Local Governance-           IDLG-DPR
Directorate of Provincial Relations
International Narcotics and Law Enforcement Affairs    INL-CN
US Embassy-Counter Narcotics
International Narcotics and Law Enforcement Affairs    INL-DDA
US Embassy-DDA
Japan Embassy                                          Japan-E
Ministry of Agriculture, Irrigation & Livestock        MAIL
Ministry of Agriculture, Irrigation & Livestock-       MAIL-DAS
Department of Agriculture statistics
Ministry of Counter Narcotics                          MCN
Ministry of Counter Narcotics-Alternative              MCN-ALD
Ministry of Counter Narcotics-Drug Demand Reduction    MCN-DDR
Ministry of Counter Narcotics-Deputy Ministry of       MCN-DMPP
Planning and Policy
Ministry of Counter Narcotics-Finance and              MCN-FAD
Administration Directorate
Ministry of Counter Narcotics-General Directorate      MCN-GDPP
of Policy and Planning
Ministry of Counter Narcotics-Human Resource           MCN-HRD
Ministry of Counter Narcotics-Internal Inspection      MCN-IID
Ministry of Counter Narcotics-Law Enforcement          MCN-LED
Ministry of Counter Narcotics Monitoring and           MCN-MED
Evaluation Directorate
Ministry of Counter Narcotics-Minister's Private       MCN-MPO
Ministry of Counter Narcotics-Public Awareness         MCN-PAD
Ministry of Counter Narcotics-Procurement              MCN-PD
Ministry of Counter Narcotics-Provincial Relations     MCN-PRC
Ministry of Counter Narcotics Regional and             MCN-RIC

International Coordination
Ministry of Counter Narcotics-Research and Studies     MCN-RSD
Ministry of Counter Narcotics-Director of Survey       MCN-SD
Ministry of Haji and Religious Affairs                 MHRA
Ministry of Rural Rehabilitation and Development       MRRD
Ministry of Rural Rehabilitation and Development-      MRRD-AIRD
Ministry of Rural Rehabilitation and Development-      MRRD-DPI
Directorate of Public Information
Ministry of Borders, Tribal, Ethics Affairs            MoBTEA
Ministry of Borders, Tribal, Ethics Affairs-           MoBTEA-DJCTA
Directorate of Jirga's and Coordination Tribal
Ministry of Borders, Tribal, Ethics Affairs-           MoBTEA-HRB-RSD
Research and Study Directorate
Ministry of Borders, Tribal, Ethics Affairs-           MoBTEA-RSD
Research and Study Directorate
Ministry of Defence                                    MoD
Ministry of Defence-Detection and Intelligence         MoD-DI
Ministry of Defence-Directorate of Strategic           MoD-DSP
Ministry of Defence-Intelligence                       MoD-I
Ministry of Defence-Office on Drugs of Deputy          MoD-ODDMOS
Ministry of Policy and Strategy
Ministry of Defence-Office of Spokesperson             MoD-OS
Ministry of Education                                  MoE
Ministry of Energy and Water                           MoEW
Ministry of Finance                                    MoF
Ministry of Finance-Afghan Customs Department          MoF-ACD
Ministry of Foreign Affairs                            MoFA
Ministry of Higher Education                           MoHE
Ministry of Interior Affairs                           MoIA
Ministry of Interior Affairs-Border Police             MoIA-BP
Ministry of Interior Affairs-Counter Narcotic          MoIA-CNPA
Police Afghanistan
Ministry of Interior Affairs-Deputy Ministry           MoIA-DM
Ministry of Interior Affairs-Police 119                MoIA-P119
Ministry of Information and Culture                    MoIC
Ministry of Information and Culture-Deputy Ministry    MoIC-DMYA
of Youth Affairs
Ministry of Justice                                    MoJ
Ministry of Labour, Social Affairs, Martyrs and        MoLSAMD
Ministry of Labour, Social Affairs, Martyrs and        MoLSAMD-DSFA
Disabled-Directorate of Social Facilities Adjustment
Ministry of Public Heath                               MoPH
Ministry of Public Heath-Drug Demand Reduction         MoPH-DDR
Ministry of Public Heath-NACP                          MoPH-NACP
Ministry of Women's Affairs                            MoWA
Ministry of Women's Affairs-Directorate of             MoWA-DCSES
Coordination of Social and Economic Services
National Security Council                              NSC
National Directorate of Security                       NDS
United Nations Children's Fund                         UNICEF
Provincial Reconstruction Teams                        PRT
Regional Agricultural Development Program              RADP
Russian Embassy                                        Russian-E
Supreme Audit Office                                   SAO
Sunless Council                                        SC
United Nations                                         UN
United Nations Organization-Gender Equality and the    UN-Women
Empowerment of Women
United Nations Programme on HIV and AIDS               UNAIDS
United Nations Assistance Mission in Afghanistan       UNAMA
United Nation Development Programme                    UNDP
United Nations Office on Drugs and Crime               UNODC
United States Agency for International Development     USAID
World Bank                                             WB
World Health Organization                              WHO

Mohammad Haroon Ebrahimi (1), Seunghoo Lim (2)

(1) Mohammad Haroon Ebrahimi is Head of Supervision and Tracking of Property Confiscation in the Ministry of Counter Narcotics, Kabul, Afghanistan. He was also a former Japanese Grant Aid (PEACE) Scholar at the International University of Japan. His specializations are network governance, policy process, and international relations.

(2) Seunghoo Lim (Corresponding Author) is Associate Professor in the Public Management and Policy Analysis Program at the International University of Japan. His specializations are policy process, collaborative and participatory governance, and public/nonprofit management. Email:

Caption: Figure 1a: Shared Governance Network

Caption: Figure 1b: Lead Organization

Caption: Figure 1c: Network Administrative Organization:

Caption: Figure 1. Data collection design considering multilevel and multidimensional network structures comprising of Afghanistan CN stakeholders and their relationships

Caption: Figure 3a: Influence Network

Caption: Figure 3b: In-degree distribution histogram for the Influence Network:

Caption: Figure 4a: Information Network

Caption: Figure 4b: In-degree distribution histogram for Information Network

Caption: Figure 5a: Advice Network

Caption: Figure 5b: In-degree distribution histogram for Advice Network

Caption: Figure 6a: Collaboration Network

Caption: Figure 6b: In-degree distribution histogram for Collaboration Network

Caption: Figure 7a: Resource-sharing Network

Caption: Figure 7b: In-degree distribution histogram for Resource-sharing Network: Figure 2. The resource-sharing network of CN policymaking in Afghanistan:

Caption: Figure 8a: Financial Resource Network

Caption: Figure 8b: In-degree distribution histogram for Financial Resource Network

Caption: Figure 9a: Goal-congruence Network

Caption: Figure 9b: In-degree distribution histogram for Goal-congruence Network
Table 1. Key actors across the seven collaborative networks regarding
counternarcotics activities in Afghanistan

Ranks        Influence    Information   Advice       Collaboration
             Network      Network       Network      Network

1            MoIA         MoIA          MCN          MCN
             (25, .272)   (18, .196)    (19, .207)   (20, .109)
2            MAIL         MCN           MoIA         UNODC
             (21, .228)   (16, .174)    (12, .130)   (16, .087)
3            MoPH         MoPH          UNODC        MoIA
             (19, .207)   (14, .152)    (12, .130)   (12, .065)
4            MCN          MAIL          MoPH         MoPH
             (17, .185)   (11, .120)    (11, .120)   (12, .065)
5            MoE          UNODC         MAIL         INL-CN
             (13, .141)   (10, .109)    (6, .065)    (8, .043)
6            MRRD         MRRD          MRRD         MAIL
             (12, .130)   (6, .065)     (4, .043)    (4, .022)
7            MHRA         INL-CN        INL-CN       MoE
             (9, .098)    (4, .043)     (3, .033)    (4, .022)
8            MoD          MoE           MoLSAMD      British-E
             (6, .065)    (4, .043)     (3, .033)    (3, .016)
9            MoLSAMD      MoLSAMD       MoD          Colombo-Plan
             (6, .065)    (4, .043)     (2, .022)    (3, .016)
10           MoIA-DM      MCN-PAD       MoE          MHRA
             (4, .043)    (3, .033)     (2, .022)    (3, .016)
Density      .019         .016          .012         .015
In-centra-   .255         .182          .196         .051

Ranks        Resource-    Financial      Goal-
             sharing      Resource       congruence
             Network      Network        Network

1            MCN          INL-CN         UNODC
             (17, .185)   (24, .130)     (16, .087)
2            MoIA         UNODC          MCN
             (17, .185)   (22, .120)     (15, .082)
3            MoPH         MoF            MoIA
             (13, .141)   (13, .017)     (15, .082)
4            UNODC        MoIA           MoPH
             (8, .087)    (9, .049)      (15, .082)
5            MAIL         British-E      INL-CN
             (7, .076)    (8, .043)      (11, .060)
6            MoD          Colombo-Plan   MAIL
             (7, .076)    (8, .043)      (10, .054)
7            IDLG         MCN            MRRD
             (6, .065)    (7, .038)      (6, .033)
8            MRRD         MoPH           Colombo-Plan
             (6, .065)    (2, .011)      (5, .027)
9            MoIA-DM      WB             MHRA
             (6, .065)    (2, .011)      (4, .022)
10           INL-CN       CARD-F         MoE
             (4, .043)    (1, .005)      (4, .022)
Density      .015         .013           .015
In-centra-   .171         .062           .040

Notes: The first value in the parentheses indicates the in-degree
centrality of each individual actor, and the second value is its
normalized in-degree centrality value in each network.

Source: Authors' own calculations
COPYRIGHT 2018 Romanian Academic Society
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Ebrahimi, Mohammad Haroon; Lim, Seunghoo
Publication:Romanian Journal of Political Science
Article Type:Report
Geographic Code:9AFGH
Date:Jun 22, 2018

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters