The 228 papers cluster into 11 substantive themes. Each theme below has its own scope, a modeling-vs-empirical split, and a "where it stands" line summarizing the consensus, the open questions, and the most-cited landmark papers. Numbers in parentheses are paper counts within the theme.

T1. Cryptocurrency as Asset Class (23 papers · 18 empirical, 5 modeling)

Central question: what kind of asset is crypto, and what determines its returns? The empirical core is Liu & Tsyvinski (RFS 2021) — crypto returns are NOT explained by traditional stock/commodity/currency factors but ARE explained by crypto-specific momentum and investor-attention factors. Liu-Tsyvinski-Wu (RFS 2022) identifies a 3-factor crypto-CAPM (market, size, momentum). On the modeling side, Biais-Bisière-Bouvard-Casamatta-Menkveld (J Finance 2023, 222 cites) provides the equilibrium asset-pricing model where Bitcoin's price reflects (i) a bubble component, (ii) a hedging value, and (iii) transactional benefits. Sockin-Xiong (NBER WP 2020) models cryptocurrency as a platform-token whose value reflects user demand. Empirical extensions: Borri (JEmpFin 2018) on tail risk, Brauneis-Mestel-Riordan-Theissen (JBF 2021) on liquidity measurement, Hinzen-John-Saleh (JFE 2022) on Bitcoin's limited-adoption problem driven by throughput constraints.

Where it stands: crypto is a distinct asset class; momentum + attention drive returns; standard portfolio-optimization principles apply but with crypto-specific factor models. Open: long-run equilibrium pricing under maturing institutional adoption; the role of stablecoins/DeFi in modulating crypto factor exposures.

T2. Blockchain Consensus & Economic Limits (18 papers · 12 empirical, 6 modeling)

Central question: what are the economic limits of the consensus mechanisms (PoW, PoS) that secure blockchains? Budish (NBER WP 2018, 210 cites) proves the most-cited result: the cost of attacking Bitcoin scales with miner revenue (not total wealth secured), implying a fundamental security ceiling. Leshno & Strack (AER 2020) formalize the blockchain trilemma — no protocol can simultaneously satisfy decentralization, security, AND scalability. On the PoS side, Saleh (RFS 2021, 631 cites) provides the first formal PoS model showing it can replicate PoW security without energy waste. Roşu & Saleh (Mgmt Sci 2020) studies long-run wealth dynamics under PoS. Pagnotta (RFS 2020) models the Bitcoin price-security feedback loop. Ma-Gans-Tourky (NBER WP 2018) analyzes mining market structure as a Cournot industry.

Where it stands: PoW has structural scaling limits (Budish bound); PoS is theoretically equivalent in security without energy cost; the blockchain trilemma is formalized as an impossibility result. Open: empirical security tests under partial-attack scenarios; PoS attack vectors when stake concentrates; sharding economics.

T3. Blockchain Disruption Theory (69 papers · 54 empirical, 15 modeling)

Central question: how does blockchain (smart contracts, distributed consensus) disrupt existing economic institutions? The most-cited paper in all of crypto research lives here: Cong & He (RFS 2019, 1,057 cites) on Blockchain Disruption and Smart Contracts. Catalini & Gans (NBER WP 2016) gives the original framework: blockchain reduces (i) verification costs and (ii) networking costs. Abadi & Brunnermeier (NBER WP 2018, 220 cites) "Blockchain Economics" formalizes the trade-off between decentralization, censorship-resistance, and consistency. Many empirical extensions in finance and business (firm performance, adoption, name changes), plus theoretical models of consortium / permissioned blockchains. Lumineau-Wang-Schilke (Org Sci 2020) formalizes blockchain governance as a distinct organizational mode.

Where it stands: blockchain has dual effects — it can intensify competition (by lowering verification costs) AND sustain market power (by enabling tacit collusion via transparency). The net effect is context-dependent. Open: empirical evidence on actual disruption magnitudes; long-run institutional adoption patterns; interaction with AI agents in smart-contract execution.

T4. Token Offerings & Capital Raising (15 papers · 13 empirical, 2 modeling)

Central question: can ICOs / token sales work as a financing mechanism? Empirical anchor: Howell-Niessner-Yermack (RFS 2019, 498 cites) — successful ICOs feature credible disclosure, experienced teams, and active GitHub repositories; first-6-month returns +79% on average but mostly negative thereafter. Lyandres-Palazzo-Rabetti (Mgmt Sci 2022) extends to post-ICO performance: quality signals predict both. Catalini & Gans (NBER WP 2018) provides the theoretical model — tokens align incentives between entrepreneurs and early users, solving cold-start problems. Gan-Tsoukalas-Netessine (Mgmt Sci 2020) models how speculation generates positive externalities for platform adoption. Davydiuk-Gupta-Rosen (Mgmt Sci 2023) studies token retention as signaling. Fisch-Momtaz (JCF 2020) studies institutional-investor ICO participation.

Where it stands: ICOs worked as a new high-risk financing channel during 2017-2018 but largely collapsed after regulatory action. The fundamental insight — token-based incentive alignment — persists in modern IDOs and platform tokens. Open: long-run survival of token-financed projects; regulated security-token (STO) economics.

T5. Central Bank Digital Currency (11 papers · 3 empirical, 8 modeling)

Central question: should central banks issue digital currencies, and how should they be designed? Agenda-setting: Bordo & Levin (NBER WP 2017, 499 cites) — CBDC can enable negative interest rates by replacing physical cash. The dominant Top-5 paper: Chiu-Davoodalhosseini-Jiang-Zhu (JPE 2022) — structural model showing CBDC increases bank lending by 1.6% by reducing bank market power. Schilling-Fernández-Villaverde-Uhlig (NBER WP 2020) and Fernández-Villaverde-Sanches-Schilling-Uhlig (NBER WP 2020) are the main equilibrium models. Allen-Čapkun-Eyal-Fanti-Ford et al (NBER WP 2020) provides the technical-design framework. Raskin & Yermack (NBER WP 2016) is the early review.

Where it stands: theoretically rich; empirically thin (most countries are still in pilot phase). Net effect on banks depends on intermediation structure. Open: empirical evidence from China's e-CNY and other live pilots; interaction with stablecoins; cross-border CBDC payment systems.

T6. DeFi & Stablecoins (16 papers · 9 empirical, 7 modeling)

Central question: what are the economic functions and risks of decentralized finance? The canonical survey: Makarov & Schoar (NBER WP 2022, 144 cites) "Cryptocurrencies and DeFi" — maps DeFi protocols (lending, AMMs, derivatives, oracles) to traditional finance functions. Stablecoin work: Lyons & Viswanath-Natraj (NBER WP 2020) on what keeps stablecoins stable; Grobys-Junttila-Kolari-Sapkota (JEmpFin 2021) empirical stability analysis. Griffin & Shams (J Finance 2020) on Tether-Bitcoin manipulation. Chiu & Koeppl (RFS 2018) models blockchain-based settlement systems.

Where it stands: the DeFi taxonomy is established; lending/AMM/stablecoin economics is well-understood at the protocol level. Open: DeFi systemic risk (post-Terra/Luna, FTX); MEV (Maximal Extractable Value) economics; oracle manipulation; decentralized identity for unsecured lending.

T7. Crypto Market Microstructure (40 papers · 39 empirical, 1 modeling)

Central question: how do crypto markets actually trade? Foundational paper: Makarov & Schoar (JFE 2019, 754 cites) documents 1-5% persistent arbitrage across exchanges (Kimchi premium etc.), attributed to capital controls. Easley-O'Hara-Basu (JFE 2019, 578 cites) on the evolution of Bitcoin transaction fees. Brauneis-Mestel-Riordan-Theissen (JBF 2021) on crypto liquidity measurement. Foley-Karlsen-Putniņš (RFS 2019, 869 cites) on illicit transaction volume (also Theme T9). Gandal-Hamrick-Moore-Oberman (JME 2018, 733 cites) on Mt. Gox manipulation. Dimpfl-Peter (JFM 2020) on price discovery across exchanges. Bianchi-Babiak-Dickerson (JBF 2022) on trading volume and liquidity provision.

Where it stands: rich empirical evidence; crypto markets are systematically less efficient than FX/equities at the cross-exchange level; manipulation is documented but harder to detect post-2018. Open: MEV economics; institutional vs retail flow dynamics; perpetual-futures and options market design.

T8. Blockchain in Operations & Supply Chain (16 papers · 15 empirical, 1 modeling)

Central question: when does blockchain create value in supply-chain and operations contexts? Agenda-setting OM paper: Babich & Hilary (M&SOM 2019, 755 cites) "OM Forum—Distributed Ledgers and Operations." Foundational empirical: Hastig & Sodhi (POMS 2019, 844 cites) on supply-chain traceability case studies. Chod-Trichakis-Tsoukalas-Aspegren (Mgmt Sci 2020, 624 cites) theorizes blockchain transparency lowers financing costs for upstream suppliers. Pun-Swaminathan-Hou (POMS 2021) and Shen-Dong-Minner (POMS 2021) study blockchain as counterfeit deterrent. Cui-Gaur-Liu (Mgmt Sci 2023) on supply-chain blockchain design trade-offs (transparency vs information leakage). Dong-Qiu-Xu (M&SOM 2022) on blockchain-enabled deep-tier supply-chain finance.

Where it stands: blockchain creates value in working-capital-constrained, high-information-asymmetry supply chains; less valuable when trust infrastructure already exists. Open: empirical evidence on actual cost savings from deployed systems; integration with IoT sensors for input-data verification.

T9. Illicit Activity & Regulation (6 papers · 5 empirical, 1 modeling)

Central question: how much crypto activity is illegal, and how should it be regulated? Headline empirical: Foley-Karlsen-Putniņš (RFS 2019, 869 cites) — ~25% of Bitcoin users and ~46% of transactions involve illegal activity. Yin-Langenheldt-Harlev-Mukkamala-Vatrapu (J MIS 2019) on ML-based de-anonymization for AML compliance. Auer-Tercero-Lucas (J Fin Stab 2022) on socioeconomic drivers of US crypto investment.

Where it stands: illicit share of crypto activity has declined over time as legitimate use grows; transaction-graph analysis is the standard methodology; KYC/AML rules at exchanges are the dominant policy tool. Open: privacy-preserving compliance (zero-knowledge KYC); cross-border regulatory coordination; impact of Travel Rule on cross-VASP transfers.

T10. Adoption & Investor Behavior (11 papers · 8 empirical, 3 modeling)

Central question: who adopts crypto and why? Álvarez-Argente-Patten (NBER WP 2022) on El Salvador's 2021 Bitcoin legal-tender natural experiment — finds limited consumer adoption despite government push. Mai-Shan-Bai-Wang-Chiang (J MIS 2018) on social-media impact on Bitcoin value (silent-majority hypothesis). Auer-Tercero-Lucas (J Fin Stab 2022) on US household crypto investment drivers. Schilling-Uhlig (JME 2019) on Bitcoin as alternative currency in equilibrium.

Where it stands: crypto adoption is bimodal — speculators and a small group of true believers; everyday transactional use remains limited even where legalized. Open: developing-country adoption patterns; intergenerational adoption (Gen-Z vs Boomers); CBDC vs private-crypto adoption substitution.

T11. NFTs, DAOs & Web3 (3 papers · 3 empirical, 0 modeling)

Central question: what economic value (if any) do NFTs and DAOs create? This is the newest and least-developed theme in the top journals. Whitaker & Kräussl (Mgmt Sci 2020) "Fractional Equity, Blockchain, and the Future of Creative Work" is the most-cited NFT-adjacent paper at 98 cites. Academic publication in top journals lags the NFT phenomenon by years (NFT mania peaked 2021-2022; rigorous papers are only starting to appear in 2024-2026).

Where it stands: empirical evidence is fragmented; most rigorous NFT analyses are in finance/computer-science workshop venues. Open: NFT pricing and bubble dynamics; DAO governance experiments; metaverse-economy primitives; intersection of NFTs with AI-generated content.