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Abstract | PDF (911 kilobytes)

This study proposes a two-step estimator based on a multinomial probit model (MNP-TS) to correct sample selection bias arising in multinomial choice models. The conventional multinomial logit-based correction method relies on a correction term based on a single probability index, which may distort estimation when unobserved factors generate asymmetric bias across alternatives. To address this limitation, this study derives multiple correction terms in the form of generalized inverse Mills ratios using bivariate normal integration. Monte Carlo simulation results show that the proposed MNP-TS estimator performs comparably or better in finite samples under asymmetric selection bias. The estimator also demonstrates computational stability as an alternative to full-information maximum likelihood, which may suffer from convergence problems due to the instability of high-dimensional likelihood functions. An empirical analysis of donation amounts using the 2024 Giving Korea data confirms the practical usefulness of the MNP-TS model.

Abstract | PDF (1094 kilobytes)

We offer an accessible proof of the existence of utility representation for continuous and homothetic preferences. When restricted to the Euclidean setting, our result is applicable to both positive and negative quantities while the standard proofs based on monotonicity are limited to the non-negative orthant. Our analysis also clarifies the roles played by key assumptions in the classic results. Our utility representation embodies an inherent asymmetry between regions of the alternatives space which may be interesting for extensions and applications.

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Misinformation remains a persistent global challenge in modern media systems. We investigate whether and under what conditions fact-checking effectively deters misinformation and promotes truth, focusing on the strategic interaction where a biased outlet decides whether to misreport and a fact-checker decides whether to scrutinize the report. Our findings suggest that the deterrent effect of fact-checking hinges critically on the degree of media market polarization. Provided that the market is not excessively polarized, fact-checking strictly reduces misreporting by biased outlets, thereby improving information welfare---a result that echoes the classical views of John Milton and John Stuart Mill that the competition between opposing claims facilitates the discovery of truth. We also show that the presence of an active fact-checker can make biased outlets better off by serving as an external commitment device, thereby resolving the dynamic inconsistency problem.

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Grant allocation processes occasionally exhibit seemingly random outcomes: high-quality proposals are often rejected and weaker ones are sometimes funded. Existing explanations attribute such randomness to reviewer error, inconsistency, or institutional inattention. This paper presents a different interpretation. Even when project quality is perfectly observed, a funding agency may emph{optimally} introduce randomness into the allocation mechanism. Randomness broadens the applicant pool at the cost of average quality, and an agency that values breadth alongside quality may rationally accept it, particularly when high-quality projects possess positive outside options. I develop a simple equilibrium model in which the agency chooses a degree of priority for high-quality proposals, and applicants decide strategically whether to apply. Agency welfare is hump-shaped in the degree of randomness: pure lotteries raise adverse selection because high-quality applicants with valuable outside options opt out, while strict meritocracy maximizes quality but excludes the broad base of low-quality applicants. When the agency values participation sufficiently, the optimal mechanism involves an interior level of randomness, rationalizing partial lotteries, random tie-breaking, and other randomness observed in grant allocation.