Economics

■ 주택시장 동향 ㅇ 주택 매매시장 동향 ㅇ 주택 전월세시장 동향 ㅇ 주택 분양 ㅇ 주택 금융

♦ 6G 이동통신의 정체성 ♦ 6G가 이끌 미래 산업 변화 ♦ 주요국들의 준비와 경쟁 ♦ 6G 상용화의 핵심 변수 ♦ 전망 및 시사점

We study labor-market polarization by developing a framework with heterogeneous consumers, non-homothetic preferences, and endogenous labor supply a la Roy (1951). Because income-elastic sectors are intensive in high- and low-skilled occupations, shocks that raise either the average or the dispersion of income across households shift relative labor demand toward these occupations, generating an income channel to polarization. We quantify this channel by comparing the effect of shocks that affect the distribution of household income in our model and in a version with homothetic preferences in which the income distribution does not affect sectoral composition. The income channel is sizable: it..

This paper explores the relationship between standard labor market tightness (vacancies divided by unemployment) and generalized labor market tightness (vacancies divided by a measure of effective searchers that accounts for potential hires from all sources including those out of the labor force or currently employed). We show that much of what the standard model attributes to variation in matching efficiency reflects changes in the ratio of effective searchers to unemployment rather than changes in “true” matching efficiency. Generalized tightness outperforms standard tightness in Phillips curve equations, both in models with tightness entering linearly and in better-fitting models with..

We estimate the effect of PM₂.₅ pollution on migration between commuting zones in the United States from 2005-2019. To account for the correlation between origin and destination commuting zones’ pollution levels and potential endogeneity, we estimate a dyadic migration model and isolate permanent changes in origin and destination pollution emanating from distant coal-fired power plants. Annual panel and long-difference estimates indicate that air pollution plays a key role in relocation decisions. For the typical commuting zone, an isolated average 2005-2019 PM₂.₅ concentration decrease of 3.85 μg/m³ would avert out-migration and increase in-migration, totaling 2 percent of the p..

We examine the difference between two policies that target urban slums, relocation versus redevelopment on-site, on children’s future outcomes. We use evidence from a slum clearance program in Chile between 1979 and 1984, where two-thirds of slum-dwelling families were relocated to housing projects on the city’s periphery, and one-third received housing through on-site redevelopment at their original locations. We find that 40 years post-policy, displaced children receive 0.62 fewer years of schooling, earn 10.2% less, experience higher labor informality, and live in higher poverty areas compared to non-displaced children. Relocation to lower-opportunity areas and disruption of social ne..

How does uncertainty about future government policy affect households’ beliefs and subsequent borrowing, spending and debt payment behavior? We study these questions through the lens of student loan forgiveness in the United States, which following electoral promises, was announced in 2022 but never implemented due to judicial rulings. We conduct a customized information provision experiment embedded in a survey eliciting real-time beliefs about future debt forgiveness and repayment, which we link to credit bureau data, employment verification data, and nondurables consumption. Eligible borrowers who are more optimistic about forgiveness reduce payments on student loans by \$40 per month a..

We build a dynamic model of data-driven automation in which data (i) is heterogeneous and task-specific; (ii) accumulates endogenously as a byproduct of economic activity; and (iii) exhibits spillovers such that data generated by one task can augment the productivity of another. Along the transition path of automation, data plays a dual role in simultaneously augmenting the productivity of already-automated tasks and expanding the automation frontier. We derive tight conditions for the economy to be partially versus fully automated in the long-run. In the latter case, automation exhibits rich short-run dynamics that depend on the pattern of data spillovers but is always slow in the long-run:..

I study medical liability when artificial intelligence acts as a doctor rather than as a passive clinical tool. The central object is the legally usable medical record: the inputs, logs, warnings, prescriptions, follow-up instructions, and outcomes on which courts, contracts, insurers, and regulators can condition responsibility. I show that AI medical liability is an institutional design problem under imperfect legal information. If the record separates AI-controllable error from patient nonadherence and natural disease progression, high-powered AI-fault liability implements the standard accident-law ideal. If the record is coarse, the first best may be infeasible: the same transfer that di..