HKS Mossavar-Rahmani Center for Business & Government

Private-sector development finance institutions (DFIs) have been asked over the past decade to scale climate finance dramatically while maintaining their traditional development mandate. The two objectives are linked but not the same, and little is known about how the institutions delivering both manage the trade-offs between them. This paper asks whether the two mandates are compatible in practice, what DFIs are doing to manage the tension, and what governance would make it more likely that they reinforce rather than displace each other. It draws on analysis of climate and development finance flows and on interviews with senior executives at seven bilateral DFIs and EBRD, alongside development finance experts.

The central finding is that the balance between the two mandates is unmanaged, struck by political instinct and institutional habit, and that the resulting incentives favor climate deals in richer markets over development deals in poorer ones. Climate mandates are set by shareholders without analysis and loosely specified, and a third mandate, the national interest, is now emerging through informal shareholder pressure. Rapid growth in climate investment has concentrated portfolios in energy and mitigation, while emitting but development-critical industries such as cement and steel have become harder to finance. Upper-middle-income countries take a larger share of DFIs' climate finance than of their other investment, and the private capital DFIs mobilize is more skewed still, because they typically mobilize less than 0.50 per dollar invested in low-income countries against more than 1 dollar in middle-income ones. The drive to mobilize is also misallocating concessional capital and turning DFIs into merchant banks, originating assets to sell them on.

DFIs are responding with portfolio floors, carbon-intensity budgets and structural separation, largely without direction from their shareholders. For DFI boards, the paper recommends deal-level tools to weigh climate against development, transition-finance frameworks for emitting sectors, more disciplined use of concessional capital, resilience screening across the whole portfolio, and structural separation of the two mandates where shareholders are aligned. For shareholders, it recommends setting the climate-development balance through analysis, not political negotiation, floors for low-income and least developed countries, new capital behind new climate commitments, and mobilization measured for quality, not volume alone. Taken together, these would put the balance between climate and development under active management.

This paper introduces a policy analysis framework designed to support systematic, transparent assessment of artificial intelligence (AI) governance proposals in a rapidly evolving and contested regulatory landscape. AI policy debates often collapse into binary positions that obscure underlying tradeoffs and normative assumptions. The framework structures policy analysis around multiple policy attributes, allowing users to surface priorities and tensions without prescribing outcomes. The research adopts a mixed-methods approach that integrates qualitative insights from subject matter experts with computational text analysis to inform the design of policy attribute indices and rubrics. The resulting approach quantifies the relative emphasis of different policy objectives and presents them through comparative visualizations that support interpretability and cross-policy comparison. The paper also examines the use of commercial large language models for rubric-based policy analysis, benchmarking their outputs against a domain-trained rubric-calibrated model with explicitly defined analytical assumptions. Rather than assessing policy effectiveness or desirability, the framework focuses on relevance and alignment across attributes. By making analytical assumptions explicit, including attribute selection, rubric construction, and weighting schemes, the framework enables users to evaluate whether its embedded priorities align with the users’ own normative commitments. The approach is jurisdiction-agnostic and intended to support policymakers, analysts, and researchers navigating complex AI governance environments.

The framework makes three contributions: (1) it operationalizes multidimensional policy assessment through empirically grounded rubrics that surface tradeoffs rather than resolving them; (2) it develops a transparent hybrid methodology combining feedback from subject-matter experts with computational validation; and (3) it demonstrates how domain-trained rubric-calibrated models can be used as a benchmark for comparing different general-purpose large language models. These methodological advances enable more systematic, reproducible policy comparison while maintaining transparency about embedded normative choices.

Digital assets and blockchain technology have undergone a speedy legitimization drive in 2025 and 2026 driven primarily by the vigorous pro-innovation stance of the second Trump Administration in the United States. Within six months of the beginning of President Trump’s second term in office, federal agencies rapidly dismantled Biden era guidance acting as impediments to the participation of banks, consumers and builders in the digital assets ecosystem and the US passed the Guiding and Establishing National Innovation for U.S. Stablecoins Act (“GENIUS Act”) setting up a pro-innovation regulatory framework to nurture the burgeoning market for stablecoins which is currently dominated by US dollar pegged stablecoins. In this new reality, the UK can and must find a way to carve out a leading role for itself and the promotion of sterling pegged stablecoins or risk being practically shut out of the digital assets market, left as a bit part player in a fast-growing global market and not capitalising on a potential source of demand that could marginally reduce the yield on UK government borrowing.

The norms surrounding the seniority of official-sector creditors in sovereign debt restructurings contain significant ambiguities. As a result, stakeholders often disagree about the relative treatment of different institutions, delaying the restructuring process and potentially increasing the cost of official-sector financing for emerging market countries. This paper proposes a transparent and tractable framework for guiding the seniority of official claims. The framework would facilitate timely resolution of debt crises and incentivize concessional development finance.

A single seller offers one or more goods to a single buyer. The buyer’s values and the seller’s costs are private information. Each player has a commonly known prior over the other player’s value or cost, supported on a finite set. What is the optimal selling mechanism?

We argue that, despite this question’s importance and apparent simplicity, prior work offers no satisfactory answer. If the seller simply chooses an optimal menu given her realized costs, she fails to exploit her informational advantage. At the other extreme, the optimal trade mechanism that satisfies IC/IR constraints for both parties fails in practice, as it conditions prices on the seller’s unknown costs in an unenforceable way. The seller’s realistic capabilities lie somewhere in between: she may leverage private information but lacks unlimited commitment power.

To bridge this gap, we consider a solution concept built on the realistic assumption that the seller can commit to prices but nothing more. Similar—albeit technically distinct—solution concepts have been studied in the context of auctions with multiple buyers. Our concept proves surprisingly rich even with a single buyer. In our model, the buyer and seller engage in multiple rounds of cheap talk before the seller posts a menu of priced bundles. The buyer then purchases.

We measure value as profit for the seller and consumer surplus for the buyer. We prove that, when there is only one good, such cheap talk cannot improve the welfare of either party. We then demonstrate that cheap talk can be useful when there are (1) multiple goods with additive costs and values, (2) multiple units of a single good with constant marginal cost and diminishing marginal value, (3) interdependent values for a single good, or (4) repeated play of a one-good game. We also show that multiple rounds of communication can yield strictly higher expected profit than a single round.

Conceptually, these results show that in any extension beyond the canonical setting of one seller, one buyer, and one good, cheap talk creates value in bilateral trade. We discuss how realistic factors beyond our stripped-down model combine with cheap talk to enhance this value even further.

The rapid expansion of artificial intelligence infrastructure, including data centers and the energy, land, water, and labor systems that support them, presents regional policymakers with trade-offs that are poorly captured by the prevailing “innovation versus regulation” frame. This article develops the AI Infrastructure Triad as a conceptual framework for analyzing three competing priorities in regional AI infrastructure governance: Progress, Sustainability, and Equity. We argue that regions are unlikely to maximize all three simultaneously under current technological, institutional, and resource conditions. Drawing on prior work on the economic, physical, and moral limits of AI development, a previously coded dataset of 10,068 public comments submitted to the 2025 U.S. AI Action Plan and illustrative regional cases, the article interprets stakeholder and regional positions as different ways of prioritizing the triad’s frontiers. The evidence is used illustratively rather than as a full causal test. The paper’s contribution is to clarify the trade-offs that infrastructure decisions often obscure, distinguish deliberate triad governance from default allocation by market power or regulatory inertia, and propose a Deliberate Triad Choice Framework for policymakers considering AI infrastructure decisions of significant scale.

Fred Schauer showed how the law takes a messy, probabilistic world and uses rules to draw bright lines through it: liable or not, knowing or ignorant, guilty or innocent. Such neat categorical boxes are a feature, not a bug, for the law. They serve two critical purposes: (1) they determine an outcome, and (2) they fulfill the law’s need for predictability and administrability.

From an economic perspective, though, information comes in shades of gray, not black and white. Indeed, sometimes those grays include dots and waves and not merely tones, such as when potential states of the world are poorly defined. Beliefs update by degree, and uncertainties in the law are rarely resolved to one or zero before a decision must be made. Finally, information may be written in invisible ink, as when ignorance hits and individuals do not even recognize the existence of some states of the world.

This paper introduces strategic information asceticism. A decision maker deliberately forgoes acquiring valuable information because possessing it may enable others to obtain it through direct monitoring, leaks, or compulsory disclosure and then use it to take harmful actions such as entering her market. To prevent such competitive responses, Apple tightly limited external market testing ahead of the first iPhone, and OpenAI chose a sudden public release of ChatGPT over more extensive testing. The price of market surprise: quality suffered.

Strategic information asceticism (SIA) arises when the decision maker would strictly prefer to acquire the information absent others' strategic reactions; the information is forgone because others may exploit it once it exists. SIA occurs in a two-sided strategic environment. The ascetic equilibrium holds because the decision maker’s restraint preserves uncertainty among sophisticated counterparts or unawareness among naïve ones.

The analysis (i) defines SIA and distinguishes it from non-disclosure and strategic ignorance; (ii) develops a general model that yields testable comparative statics in leakage probability and opponent responsiveness; (iii) applies SIA to market testing as a canonical case; (iv) extends its application to espionage, test-optional college admissions, and (v) discusses governments countering SIA in regulatory settings.

SIA differs sharply from well-known forms of information non-acquisition. It is not driven by internal costs, cognitive limitations, or preferences for ignorance. Neither does it involve selective disclosure or persuasion.

The return of industrial policy to the center of US economic policy has renewed questions not only about state capacity and economic effectiveness, but also about distributional implications. Both the Biden and Trump administrations have embraced increasingly interventionist strategies to support strategic sectors, but a question remains underdiscussed: who captures the gains when publicly-supported innovation succeeds?

This paper examines whether governments that assume substantial private-sector innovation risk receive returns commensurate with their contribution. Adopting a novel venture-capital perspective on public intervention with respect to previous articles published on the topic, it evaluates industrial policy not only in terms of its effects on innovation and production, but also in terms of whether the public sector (and the stakeholders it represents) captures returns proportional to the risks it assumes.

To organize the analysis, the paper develops two analytical frameworks: a three-stage taxonomy of public support covering upstream research, scale-up finance, and mature demand creation and diffusion, and a four-part typology of value-capture instruments consisting of grants and subsidies, repayment, royalties, and equity participation.

The analysis focuses in particular on the second stage – scale-up finance – where public intervention most closely resembles risk capital and where the choice among value-capture instruments is especially consequential.

These frameworks are applied to two corporate case studies, Tesla and Moderna, both of which received significant public support and were able to create significant private value. The case comparative shows that distributional outcomes vary systematically with the stage of intervention, the instrument used, and the institutional mechanisms through which public returns are defined. To wit, Tesla illustrates a case of highly concentrated private gains with limited direct public return, whereas Moderna shows a more balanced outcome achieved alongside ex-post negotiation.

The paper concludes that industrial policy is more likely to be politically durable and distributionally sustainable when value-capture instruments are matched to the form and timing of public risk-taking.

What does it mean in practice for a company to respect human rights? The paper examines the operational implications of a "corporate responsibility to respect human rights", a pillar of the recent framework put forward by UN SRSG John Ruggie under the title "Protect, Respect and Remedy." The authors define an approach to due diligence based on assessing the risk of company involvement in human rights violations. The authors draw on the work of Statoil Hydro in developing a human rights risk assessment method for its global operations and argue that due diligence and risk are proven approaches to guarding against social harm by companies. The paper outlines a practical approach to human rights due diligence based on the assumption that the nature of a company's business activities and relationships decides the environment of human rights risk in which the company must operate. The authors argue that companies are able to exercise greater control over their exposure to human right related risk than generally assumed possible and that an empirically-grounded, risk-based approach to due diligence permits companies to more effectively manage their responsibility to respect human rights and their responsibility to turn a profit.