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Record W4317491743 · doi:10.1111/russ.12412

Oil in Putin's Russia: The Contests over Rents and Economic Policy By AdnanVatansever. Toronto: University of Toronto Press, 2021, 348 pp. $44.95. ISBN 978‐1‐4875‐2281‐0

2023· article· en· W4317491743 on OpenAlexaboutno aff
Michael De Groot

Bibliographic record

VenueThe Russian Review · 2023
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic rentCitationLibrary sciencePolitical scienceMedia studiesSociologyOperations researchLawEconomicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

The Russian invasion of Ukraine in February 2022 has exposed once again just how dependent on hydrocarbons the Kremlin remains. The oil sector is the backbone of the Russian economy, providing roughly $3 trillion in export revenue between 2000 and 2018. Oil earnings have broadly supported Russian social, economic, and political objectives, yet the mechanics of how Moscow has utilized the profits are complex. In Oil in Putin’s Russia, Adnan Vatansever pulls back the curtain to show how the Russian state and oil companies have competed for revenues and how they have decided to utilize them during the twenty-first century. Drawing on the secondary literature, the public record, and interviews with policymakers and oil industry officials, the book maps the politics of oil rent allocation onto the familiar history of President Vladimir Putin’s consolidation of executive power. At the heart of the book is a story of change over time. “When Putin was re-elected for the fourth time in March 2018, Russia’s oil rent generation model looked very different from the one in place in 1999,” Vatansever summarizes. “The ownership of the main players, and their respective rankings, had changed substantially” (p. 70). Explaining the non-linear trajectory is the central task of the book. The book begins by explaining how Putin managed to concentrate power after being elected president in 2000. The Russian state was famously weak under President Boris Yeltsin during the 1990s. Multiple “veto players” such as Duma legislators, oligarchs, and regional governors competed with the Kremlin for power. The government lacked the ability to collect taxes from the major oil companies and suffered from reduced oil revenue. The disruptions from the Soviet collapse caused Russian oil production to decline during the 1990s, and low prices on the world market compounded the problems. Echoing much of the literature that identifies Putin’s first presidential term as an inflection point in Russian political economy, Vatansever explains how Putin asserted executive power and reversed the state’s weakness during the Yeltsin years. He explains how Putin neutralized the political influence of such oil magnates as former Yukos chairman Mikhail Khodorkovsky. He shows how Putin built a loyal coalition in Duma that helped overhaul the tax regime and brought a windfall of revenue to the government. “Within a few years of his ascent to power,” he concludes, “President Putin was able to entirely transform taxation of the oil sector” (p. 103). By 2005, more than 80 percent of oil rents went to the state. Putin also established a Stabilization Fund for oil windfalls that would ensure that the financial disaster in 1998 could never happen again. After the Putin regime asserted preponderant control over oil rents, the question of how to distribute the funds brought different factions of the government into conflict. Putin pursued fiscally conservative policies during his first term as president. Cuts to public expenditure, ruble devaluation, and high oil prices pushed the budget into the black. “A budget surplus that kept growing was not a predetermined outcome,” Vatansever writes, “it was a choice” (p. 134). During Putin’s second term in office, however, the president gravitated toward an expansionary policy. Vatansever’s treatment of the early and mid-2000s is the strength of the book, but Putin’s third presidential term between 2012 and 2018 receives less sustained attention. The chapters are organized thematically. After Vatansever explains the rise of executive power, he turns to issues such as control of Russia’s rents, the relationship between the state and major oil companies, and the distribution of oil revenue. It might be difficult for non-specialists to keep track of the book’s multiple narrative arcs because neither the book nor the individual chapters is organized chronologically and Vatansever refers to multiple eras non-sequentially within each chapter. Oil in Putin’s Russia stays very close to its subject. It excels at explaining how the “sausage is made,” but the technical focus on turf battles sometimes obscures the environment in which the book’s protagonists acted. The importance of the social contract between the government and the Russian people and its impact on Putin’s political legitimacy are implicit in the book, for example, and more guidance about how rent-allocation schemes fit into the broader objectives of the Putin regime might have been useful for the reader. Economic data and strategies mean little until they are given political meaning. The stakes of oil rents and economic policy were incredibly high. A few strategically placed quotations sprinkled throughout the book that provided a human element to the narrative might have also given the reader a better sense of why the political battles mattered so much as well as a bit more texture in the writing. Because the book also engages the role of foreign companies on the Russian oil scene, an explanation of how Putin’s determination to limit their influence fit into his larger objectives might have been in order. Overall, Vatansever provides an illuminating account of rent allocation in Putin’s Russia. It is a welcome addition to the literature, and with Russian energy wealth playing a key role in supporting the Kremlin’s invasion of Ukraine, understanding Russian oil politics has never been so urgent.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.252
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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