We study the socially optimal level of illiquidity in an economy populated by households with taste shocks and present bias with naive beliefs. The government chooses mandatory contributions to accounts, each with a different pre-retirement withdrawal penalty. Collected penalties are rebated lump sum. When households have homogeneous present bias, β, the social optimum is well approximated by a single account with an early-withdrawal penalty of 1−β. When households have heterogeneous present bias, the social optimum is well approximated by a two-account system: (i) an account that is completely liquid and (ii) an account that is completely illiquid until retirement.
Recent literature suggests that both stock returns and economic growth are significantly higher under Democratic presidential administrations. This is a puzzle in that persistent differences in stock returns seem unlikely in efficient markets, and it is not obvious why Democrats should do better. Often these kinds of results go away upon further analysis or more data, and this appears to be true in the present case. In this paper the sample is extended to 28 administrations, fromWilson-1 through Biden. While the mean stock return under the Democrats is higher, none of the differences in means is significant at conventional significance levels. There is considerable variation in the mean return across administrations, which results in lack of significance. Similarly, while the mean output growth rate under the Democrats is larger, the difference is not significant. Again, there is considerable variation in output growth across administrations. Results are also presented with the nine administrations between Hayes and Taft added, a total of 37 administrations. While the added data are likely not as good, the conclusion is the same—no significant differences.
While the mechanism design paradigm emphasizes notions of efficiency based on agent preferences, policymakers often focus on alternative objectives. School districts emphasize educational achievement, and transplantation communities focus on patient survival. It is unclear whether choice-based mechanisms perform well when assessed based on these outcomes. This paper evaluates the assignment mechanism for allocating deceased donor kidneys on the basis of patient life-years from transplantation (LYFT). We examine the role of choice in increasing LYFT and compare realized assignments to benchmarks that remove choice. Our model combines choices and outcomes in order to study how selection affects LYFT. We show how to identify and estimate the model using instruments derived from the mechanism. The estimates suggest that the design in use selects patients with better post-transplant survival prospects and matches them well, resulting in an average LYFT of 9.29, which is 1.75 years more than a random assignment. However, the maximum aggregate LYFT is 14.08. Realizing the majority of the gains requires transplanting relatively healthy patients, who would have longer life expectancies even without a transplant. Therefore, a policymaker faces a dilemma between transplanting patients who are sicker and those for whom life will be extended the longest.
The recent artificial intelligence (AI) boom covers a period of rapid innovation and wide adoption of AI intelligence technologies across diverse industries. These developments have fueled an unprecedented frenzy in the Nasdaq, with AI-focused companies experiencing soaring stock prices that raise concerns about speculative bubbles and real-economy consequences. Against this background the present study investigates the formation of speculative bubbles in the Nasdaq stock market with a specific focus on the so-called ‘Magnificent Seven’ (Mag-7) individual stocks during the AI boom, spanning the period January 2017 to January 2025. We apply the real time PSY bubble detection methodology of Phillips et al. (2015a,b), while controlling for market and industry factors for individual stocks. Confidence intervals to assess the degree of speculative behavior in asset price dynamics are calculated using the near-unit root approach of Phillips (2023). The findings reveal the presence of speculative bubbles in the Nasdaq stock market and across all Mag-7 stocks. Nvidia and Microsoft experience the longest speculative periods over January 2017 – December 2021, while Nvidia and Tesla show the fastest rates of explosive behavior. Speculative bubbles persist in the market and in six of the seven stocks (excluding Apple) from December 2022 to January 2025. Near-unit-root inference indicates mildly explosive dynamics for Nvidia and Tesla (2017–2021) and local-to-unity near explosive behavior for all assets in both periods.
We explore the implications of ownership concentration for the recently concluded incentive auction that repurposed spectrum from broadcast TV to mobile broadband usage in the United States. We document significant multilicense ownership of TV stations. We show that in the reverse auction, in which TV stations bid to relinquish their licenses, multilicense owners have an inventive to withhold some TV stations to drive up prices for their remaining TV stations. Using a large-scale valuation and simulation exercise, we find that this strategic supply reduction increases payouts to TV stations by between 13.5 percent and 42.4 percent.
Predictive regression models are often used to evaluate the predictive capability of economic fundamentals on bond and equity returns. Inferential procedures in these regressions typically employ parameter constancy or piecewise constancy in slope coefficients. Such formulations are prone to misspecification, more especially during periods of disturbance or evolution in prevailing economic and financial conditions, which can lead to size distortion and spurious evidence of predictability. To address these issues the present work proposes a semiparametric predictive regression model with mixed-root regressors and time-varying coefficients that allow for smooth evolution in the generating mechanism over time. For estimation and inference a novel variant of the self-generated instrument approach called Sieve-IVX is introduced, giving a robust approach to inference concerning time-varying predictability that is applicable irrespective of the degrees of persistence. Asymptotic theory of the Sieve-IVX approach is provided together with both pointwise and uniform inference procedures for testing predictability and model specification. Simulations show excellent performance characteristics of these statistics in finite samples. An empirical exercise is conducted to examine excess S&P 500 returns, applying Sieve-IVX regression coupled with pointwise and uniform tests to reveal evidence of time-varying patterns in the predictive capability of commonly used fundamental variables.
We empirically characterize how China is internationalizing its bond market by staggering the entry of different types of foreign investors into its domestic market and propose a dynamic reputation model to explain this strategy. Our framework rationalizes China's strategy as trying to build credibility as a safe issuer while reducing the cost of capital flight. We use our framework to shed light on China's response to episodes of capital outflows.
Cointegrating rank selection is studied in a function space reduced rank regression where the data are time series of cross section curves. A semiparametric approach to rank selection is employed using information criteria suitably modified to take account of the function space context, extending the linear cointegrating model to accommodate cross section data under general forms of dependence. A parametric formulation is employed analogous to recent work on cross section curve autoregression and cointegrating regression. Consistent cointegrating rank estimation is developed by the use of information criteria methods that are extended to the curve time series environment. The asymptotic theory involves two parameter Gaussian processes that generalize the standard limit processes involved in cointegrating regressions with conventional multiple time series. Simulations provide evidence of the effectiveness of consistent rank selection by the BIC criterion and the tendency of AIC to overestimate order as it does in standard lag order selection in autoregression as well as in reduced rank regression with multiple time series.
We test whether payments for ecosystem services (PES) can curb the highly polluting practice of crop residue burning in India. Standard PES contracts pay participants after verification that they met a proenvironment condition (clearing fields without burning). We randomize paying a portion of the money up front and unconditionally to address liquidity constraints and farmer distrust, which may undermine the standard contract's effectiveness. Incorporating partial up-front payment into the contract increases compliance by 10 percentage points, which is corroborated by satellite-based burning measurements. The cost per life saved is $3,600–$5,400. The standard PES contract has no effect on burning.
To safeguard economic and financial stability policymakers regularly take actions designed to increase resilience to systemic risks and curb speculative market behavior. To assess the effectiveness of such mitigation policies, we introduce a counterfactual approach tailored to accommodate the mildly explosive dynamics that occur during speculative bubbles. We derive asymptotics of the estimated treatment effect under a common factor structure that allows for explosive, I(1), and stationary factors, thereby having applicability to a wide range of prevailing economic conditions. An inferential procedure is proposed for the policy treatment effect that has asymptotic validity and demonstrates satisfactory finite sample performance. An empirical analysis examines the monetary policy of interest rate hikes implemented by the Reserve Bank of New Zealand, beginning in October 2021.This policy exerted a statistically significant cooling effect on all regional housing markets in New Zealand. Our findings show that this policy led to 20%-33% reductions in house prices in five out of six regions seven months after the enactment of the interest rate hike.
In digital advertising, the allocation of sponsored search, sponsored product, or display advertisements is mediated by auctions. The generation of bids in these auctions for attention is increasingly supported by auto-bidding algorithms and platform-provided data. We analyze the equilibrium properties of a sequence of increasingly sophisticated auto-bidding algorithms. First, we consider the equilibrium bidding behavior of an individual advertiser who controls the auto-bidding algorithm through the choice of their budget. Second, we examine the interaction when all bidders use budget-controlled bidding algorithms. Finally, we derive the bidding algorithm that maximizes the platform’s revenue while ensuring all advertisers continue to participate.
Caregiving is a service provided for children with the primary objective of taking care of them and ensuring that they are safe and have opportunities to learn and develop positive relationships with their caregivers and peers while their parents are away. Caregiving takes the forms of home-based care, centre-based care, school-based care, family child care and family, friend, and neighbour (FFN) care. The paper utilises preliminary findings on school attendance from a randomised controlled trial on the effects of a preschool intervention on child learning and women’s economic empowerment in Tharaka Nithi County in school-based care. The research sought to test whether a preschool-based intervention in a rural setting in Kenya influences child development and women’s labour market participation in a cost-effective manner. The project examines the impact of allowing three-year-old children to attend preschool versus the regular pre-primary education programming, which allows children aged 4 years and above to attend preschool. Implementation of the intervention started in January 2024 in 60 intervention schools where five three-year-old children were admitted to a playgroup (PG) in the pre-primary one (PP1) class. Twelve mentors and sixty caregivers were recruited and trained alongside sixty PP1 teachers from the sampled preschools to implement an adapted PP1 curriculum. The twelve mentors coached teachers weekly on the implementation of the curriculum in the five schools assigned to them. This paper presents preliminary findings on preschool attendance for the PG and PP1 children based on weekly attendance data from term one and term two of the 2024 school calendar year on the day the mentors visited the school. Findings reveal that school attendance was low during school openings, midterm breaks, and the last weeks before the schools closed. Public holidays, as well as extracurricular activities coupled with children being sent home for school levies, also contributed to children not attending school regularly. The findings further show that the attendance rate in term one was slightly higher than in term two.
We study mechanism design when agents have private preferences and private information about a common payoff-relevant state. We show that standard message-driven mechanisms cannot implement socially efficient allocations when agents have multidimensional types, even under favorable conditions.
To overcome this limitation, we propose data-driven mechanisms that leverage additional post-allocation information, modeled as an estimator of the payoff-relevant state. Our data-driven mechanisms extend the classic Vickrey-Clarke-Groves class. We show that they achieve exact implementation in posterior equilibrium when the state is either fully revealed or the utility is affine in an unbiased estimator. We also show that they achieve approximate implementation with a consistent estimator, converging to exact implementation as the estimator converges, and present bounds on the convergence rate.
We demonstrate applications to digital advertising auctions and large language model (LLM)-based mechanisms, where user engagement naturally reveals relevant information.
Welfare depends on the quantity, quality, and range of goods consumed. We use trade data, which report the quantities and prices of the individual goods that countries exchange, to learn about how the gains from trade and growth break down into these different margins. Our general equilibrium model, in which both quality and quantity contribute to consumption and to production, captures (i) how prices increase with importer and exporter per capita income, (ii) how the range of goods traded rises with importer and exporter size, and (iii) how products traveling longer distances have higher prices. Our framework can deliver a standard gravity formulation for total trade flows and for the gains from trade. We find that growth in the extensive margin contributes to about half of overall gains. Quality plays a larger role in the welfare gains from international trade than from economic growth due to selection.
We characterize the extreme points of multidimensional monotone functions from [0,1]^n to [0,1], as well as the extreme points of the set of one-dimensional marginals of these functions. These characterizations lead to new results in various mechanism design and information design problems, including public good provision with interdependent values; interim efficient bilateral trade mechanisms; asymmetric reduced form auctions; and optimal private private information structure. As another application, we also present a mechanism anti-equivalence theorem for two-agent, two-alternative social choice problems: A mechanism is payoff-equivalent to a deterministic DIC mechanism if and only if they are ex-post equivalent.