Spiderweb Militarization: The Politics of Privatization Networks in Iran

• In Event: Poster Session: Racial and Ethnic Politics & Security and MENA Politics
  In Poster Session: Poster Session: Security and MENA Politics

Abstract

Objective
Governments worldwide claim that privatization leads to economic growth. However, not all privatization attempts are equally effective. In post-revolutionary Iran, scholars have documented pseudo-privatization, raising concerns about the influence of state-linked entities, particularly military forces, on the privatization of public assets. This paper seeks to tease apart the relationship between the military-industrial complex and privatization.
Previous Literature
Prior research has outlined two perspectives on the beneficiaries of Iranian privatization initiatives. One perspective reveals that pseudo-privatizations notably favor (para)military forces, contributing to a praetorian state. The argument is that the Iranian power elites have increasingly relied on armed forces for social mobilization and gaining control over the population. Initially driven by welfare concerns, the armed forces’ influence has expanded across all sectors of the economy.
Yet, some argue against reducing the issue merely as growing militarization. They contend that the privatization of state-owned enterprises (SOEs) occurs in a decentralized manner, suggesting that the post-revolutionary Iranian state operates as a subcontractor state. In this view, Iran’s subcontractor state can be seen as a consequence of how politics and society have shaped the form of capitalism that has taken root in the country.
Theoretical Framework
I argue that achieving a comprehensive empirical understanding of the case of privatization in Iran requires new theoretical efforts that can fully demonstrate who gets what and how. Such theoretical endeavor is only possible through a relational understanding of power. This paper brings the relational understanding of power back into the conversation in order to scale this literature into a larger network analysis.
Traditionally, when studying the global south, the definition of power emphasizes central actors’ attributes rather than their relationships, portraying them as individuals with unchecked authority. A network analysis of power elite relationships within the context of privatization in Iran shifts the focus away from the conventional attribute-based understanding of power held by centralized actors. Instead, it highlights a relational perspective on power, emphasizing the actors embedded within the network and the dynamics that emerge from their interactions. I have developed four network-based hypotheses to accomplish this objective.
Hypothesis A1: Actors associated with high-ranking officials in the Iranian state are more likely to own or control privatized assets than those without affiliations with such influential figures.
Hypothesis A2: Actors situated at a second-order network distance from high-ranking officials in the Iranian state are still more likely to own or control privatized assets than those without affiliations with such influential figures.
Hypothesis B1: Actors associated with the armed forces are more likely to own or control privatized assets than those without affiliations with such influential figures.
Hypothesis B2: Actors situated at a second-order network distance from the armed forces are still more likely to own or control privatized assets than those without affiliations with such influential figures.
Data
Data have been collected from various sources. First, I gathered the names of the SOEs that went through privatization from 1993 to 2023. This data was available publicly on the Iranian Privatization Organization website. Any company with over fifty percent of its shares privatized is part of this list.
After obtaining the core list, I gathered two data types for each company. The first, collected through Sayari.com and Rasm.io, is relational data, offering public records of entities and providing corporate networks of different companies. By cross-referencing compiled names with content from these websites, I identified boards, beneficial owners, managers, officers, shareholders, registered agents, and other pertinent figures of privatized companies.
The second data set, attribute data, is derived from archival research on news agencies for each privatized company and individual in the previous dataset. This data includes political and military involvements of individuals with identification details within each entity.
Conclusion
I used Exponential Random Graph Models (ERGMs) to test the hypotheses. ERGM is used to model the probability of observing a specific network structure based on a set of specified network features or characteristics to help understand the processes that generate the observed network. While the study is in its early stages, and data collection is ongoing, the preliminary findings exhibit promising support for all four hypotheses proposed.

Author

• Mohammad Khalilian, University of Kentucky