The Determinants and Dimensions of Armed Group Taxation
Bibliographic record
Abstract
Why do some armed groups tax while others do not? When they do tax, how do they develop their taxation systems, and what determines whether taxation is enforced coercively or more contractually? This dissertation investigates the prevalence, institutionalization, and nature of armed group taxation (AGT) to understand its implications for governance, legitimacy, and conflict outcomes. While taxation has long been viewed as a defining function of the state, armed groups frequently engage in taxation as part of their broader governance practices, particularly in contexts where the state is fragile, contested, or absent. Taxation is the most common governance practice of armed groups, yet it remains underexplored as a distinct wartime institution despite its potentially transformative effects. This dissertation argues that AGT varies along three key dimensions: its prevalence, its degree of institutionalization, and the extent to which it is enforced coercively or contractually. The argument is structured around three interrelated questions: (1) When do armed groups tax? (2) When do they institutionalize taxation? and (3) When do they tax in more contractual ways? Drawing on a global quantitative analysis and a comparative case study of the Moro Islamic Liberation Front (MILF) and the Communist Party of the Philippines-New People’s Army (CPP-NPA), this research demonstrates that AGT is shaped by three primary factors: revenue imperatives, feasibility (military and organizational capacity), and community embeddedness. The findings show that while financial pressures drive armed groups to tax, their ability to do so is constrained by military strength and organizational capacity, which are necessary for enforcing compliance. When groups lack these capabilities, taxation remains infeasible, regardless of financial need. However, even among groups that successfully institutionalize taxation, the mere existence of tax systems does not automatically lead to more contractual or reciprocal relationships with taxpayers. Instead, community embeddedness and the conditional role of non-tax revenues shape the nature of taxation. Embedded groups with access to alternative revenue sources are more likely to allocate resources toward public goods and services, reinforcing taxation as a governance tool rather than mere extraction. In contrast, groups that lack embeddedness and face acute financial desperation—particularly those without non-tax revenues—are more likely to apply more coercive taxation to wealthier taxpayers, such as foreign businesses or political actors, while engaging minimally with the broader populations. As a result, taxation by non-embedded groups is more likely to be perceived as coercive, whereas embedded groups with financial stability are better positioned to cultivate reciprocal fiscal relationships with taxpayers, towards a more contractual approach to taxation. This dissertation makes three key contributions. First, it develops a framework for conceptualizing AGT as a distinct phenomenon, challenging the common conflation of taxation with extortion or looting. Second, it provides empirical evidence on the prevalence and institutionalization of armed group taxation, showing how taxation systems evolve over time in response to revenue pressures and feasibility. Finally, it examines the role of AGT in shaping wartime political orders, highlighting its potential to foster governance legitimacy or deepen coercion, with implications for state-building and post-conflict transitions. By treating taxation as a central component of rebel governance, this research advances our understanding of how armed groups interact with local populations and the conditions under which they develop state-like institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".