{"title":"Out of the Spotlight: A Long-Run Approach to College American Football Attendance in Division III","authors":"Paul A. Natke, Gregory A. Falls, Linlan Xiao","doi":"10.1007/s11293-025-09830-6","DOIUrl":"10.1007/s11293-025-09830-6","url":null,"abstract":"<div><p>American college football attendance in Division III, the lowest level of National Collegiate Athletic Association-sanctioned play, has been ignored. Attendance and associated financial flows are far lower than the highest levels of competition. Although the scale of operations is lower, the relative indirect benefits to Division III colleges can be substantial. This study takes a long-run approach using a balanced panel (208 teams over 20 years) to discover primary driving forces on average season attendance from among economic, team performance and demographic measures. Fixed- and random-effects models are estimated. The state’s monthly unemployment rate and real travel cost exert no impact. Attendance decreases as real incomes rise, suggesting these games are inferior goods. Winning games matters. More wins in the current season and the previous two seasons, and more playoff appearances in the last ten years all increase attendance. Habit persistence is present although its magnitude decreases as the lag increases. An increase in lifetime winning percentage also increases attendance in the random-effects model. Greater male undergraduate enrollment increases attendance. Higher county populations reduce attendance in the random-effects model. Findings suggest that building a successful team, as measured by winning games, is the most important determinant of fan attendance in Division III. This relationship holds in the short run (i.e., current season, recent previous seasons) and over longer periods (last ten years and lifetime of the program). Even at this lower level of competition, there is pressure on football coaches to win.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 3","pages":"183 - 195"},"PeriodicalIF":0.8,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Dynamics of Asset Interdependence in the Great Recession","authors":"Alan Chernoff","doi":"10.1007/s11293-025-09829-z","DOIUrl":"10.1007/s11293-025-09829-z","url":null,"abstract":"<div><p>Correlation among financial assets during the 2007–2008 financial crisis is a topic of interest among practitioners and academics, as the increase in correlation among previously considered uncorrelated classes during the crisis has negative implications for portfolio diversification. Previous research considered the rise in correlation throughout the crisis to be a result of liquidity. This paper aims to reexamine those results and test for evidence that a rise in correlation can be found by examining daily data from the Federal Reserve Economic Data database and Yahoo Finance. Correlation coefficients conditional on heteroskedasticity for stock and real estate returns are compared with each other, as well as with bill, note, and bond yields. By examining cross-asset correlations during the two years prior, during, and after the 2007–2008 financial crisis, the rise in cross-asset correlation during the crisis period can be attributed to a rise in volatility in financial markets at that time, and not due to a rise in the underlying linkage between asset classes. This rise in volatility can be linked to the lack of liquidity from the 2007–2008 financial crisis and supports a broader theoretical relationship between low liquidity, high volatility, and elevated measured correlations. These findings suggest that policy interventions aimed at restoring market liquidity during crises can help stabilize not only volatility but also investor confidence in diversification strategies. Perceived breakdowns in diversification may only be reflecting temporary volatility distortions, rather than structural failures in asset class separation.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 3","pages":"167 - 181"},"PeriodicalIF":0.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Revisiting the Effects of Regional Trade Agreements Using Staggered Adoption Design","authors":"Kaleb Abreha, Raymond Robertson","doi":"10.1007/s11293-025-09828-0","DOIUrl":"10.1007/s11293-025-09828-0","url":null,"abstract":"<div><p>Studies on regional trade agreements find heterogeneous trade-promoting effects that depend on the terms of the agreements, sectors and products covered, and the characteristics of signatory countries. The agreements also have different enforcement dates and implementation periods, affecting the identification, estimation, size, and interpretation of their effect on trade. This paper builds on recent advances in the difference-in-differences estimation to explicitly account for any heterogeneous and time-varying effects and multiple implementation periods of trade agreements. Using bilateral trade data between 1962 and 2020 from the <i>Centre d'Études Prospectives et d'Informations Internationales</i>, the estimates show an effect of about 25–30% 10 years after implementation, which is larger than the standard two-way fixed estimates. The estimates also show phasing-in effects, in which the effects become larger over time. Furthermore, accounting for anticipation of the agreements affects the timing, size, and significance of these phasing-in effects. Overall, these results highlight the adjustment process for trade policies to take effect.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 3","pages":"113 - 124"},"PeriodicalIF":0.8,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Quality of Education and Economic Growth","authors":"Kalyan Chakraborty","doi":"10.1007/s11293-025-09826-2","DOIUrl":"10.1007/s11293-025-09826-2","url":null,"abstract":"<div><p>The conventional method of estimating the effect of education on economic growth is to estimate cross-country growth regressions using the annual growth of gross domestic product as a dependent variable and average number of years of schooling in a population and other macroeconomic factors as explanatory variables. Since students in different countries with the same number of years in school have different learning outcomes, cross-country studies that use quantity of schooling instead of quality of educational attainment and/or individual skills fail to capture the impact of human capital on economic growth. This study attempts to fill this gap using recently developed quality-adjusted educational attainment data from 53 countries over a 40-year period (1970–2010) and examines the significance of educational skills on economic growth. Several macroeconomic factors (e.g., government expenditure, growth of fixed capital, average longevity of the population, and trade as a percentage of gross domestic product) were used as control variables. The primary data source for the macroeconomic variables was the World Bank World Development Indicators. Cross country data on quality-adjusted educational attainment data came from three diverse sources: Lee and Lee, Barro and Lee, and Altinok and Diebolt. The empirical study used the dynamic generalized method of moments model that addresses the potential problem of endogeneity and causality, common issues in growth regressions. The study found that when human capital is measured by skill and learning, it is strongly associated with economic growth. Since educational institutions are the major source for developing cognitive skills, policies to improve educational attainment through investment in education would promote economic growth.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 3","pages":"125 - 141"},"PeriodicalIF":0.8,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Income Inequality and Water Quality in the U.S.: An Empirical Analysis of Economic Disparities and Environmental Outcomes","authors":"Hale Kirer Silva Lecuna, Billur Cohen","doi":"10.1007/s11293-025-09827-1","DOIUrl":"10.1007/s11293-025-09827-1","url":null,"abstract":"<div><p>This study analyzes the relationship between income inequality and water pollution in the United States. Unlike prior studies that rely on cross-country comparisons or primarily focus on air pollution, this study makes a novel contribution by examining how income inequality influences water pollution over time within the United States, providing insights relevant to national policy interventions. Using the autoregressive distributed lag model, both the short- and long-run effects of urbanization, income, and income inequality on water quality are examined. The analysis employs nationally aggregated data spanning from 1969 to 2020, using dissolved oxygen as the primary indicator of water quality. Data sources include the United States Census Bureau, the World Bank, the Federal Reserve Bank of St. Louis, and the Global Environmental Monitoring System. Our results indicate that a more equitable distribution of income has adverse effects on water quality in the long term. Conversely, the proportion of income accounted for by the top five percent of the population and urbanization both have adverse impacts on water quality, suggesting that income concentration at the top and fast urban growth are responsible for worsening water conditions. Increases in gross domestic product per capita improve water quality both in the short and long run. These findings highlight the importance of tailoring environmental policy to account for disparities in income structure. Improving water infrastructure in less affluent communities and regulating pollution-intensive activities in high-income areas could help mitigate inequality-driven environmental risks and support public health.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 3","pages":"197 - 212"},"PeriodicalIF":0.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using Digital Devices for Fun: What are the Impacts on Academic Outcomes?","authors":"Fang Dong, Maria Cornachione Kula","doi":"10.1007/s11293-025-09825-3","DOIUrl":"10.1007/s11293-025-09825-3","url":null,"abstract":"<div><p>How does students’ time using digital devices on fun activities like gaming, social media, and watching videos for entertainment affect their academic outcomes? Data from the Organization for Economic Co-operation and Development’s 2015 Programme for International Student Assessment test of students’ math, science, and reading knowledge for 43 countries is used to investigate these questions. Estimation results of three-level hierarchical linear models with random intercept and random slope show that marginal increases in social media use frequency are associated with increases in science, math, and reading PISA scores (3.915, 4.506, and 5.705 points, respectively, all at the 1% statistical significance level). Marginal increases in gaming are associated with increases in science and math scores (2.216 and 1.972 points respectively, both at the 1% statistical significance level). Marginal increases in digital device use frequency for entertainment raise science (1.371 points at the 5% level) and reading (1.997 points at the 1% level) scores. These findings suggest that in general using digital devices for various fun uses is positively associated with academic outcomes, which should mitigate some parental and policymaker concerns over screen time.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 1-2","pages":"73 - 104"},"PeriodicalIF":0.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145170936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimating the Minimum Wage’s Spillover Effect on Below Median Local Housing Rents","authors":"Krieg Tidemann","doi":"10.1007/s11293-025-09824-4","DOIUrl":"10.1007/s11293-025-09824-4","url":null,"abstract":"<div><p>This paper offers empirical evidence on minimum wage spillovers into rental housing markets. Minimum wage spillovers may arise from two channels shifting housing demand: migration or minimum wage impacts on low-wage worker real income. Reduced-form estimation and a pooled event study exploit state-by-year variation from 1985—2023 in effective minimum wages to estimate the minimum wage’s impact on below median housing rents and low-wage worker population shares. The empirical strategies rely on rent data from the United States Department of Housing and Urban Development’s Fair Market Rents series and American Community Survey population shares data from the United States Census Bureau. The minimum wage has an inconclusive effect on low-wage worker population shares while reducing rents. A $1 minimum wage increase reduces below-median monthly rents by 0.7 – 2 percentage points. In contrast to the limited existing evidence for the minimum wage raising local housing prices, this paper’s evidence of the minimum wage decreasing below-median local housing rents contributes to the literature by suggesting a heterogeneous housing price response to increasing the minimum wage. It also offers new evidence affirming that higher minimum wages improve housing affordability.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 1-2","pages":"37 - 52"},"PeriodicalIF":0.8,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11293-025-09824-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Biden’s New Washington Consensus in a Trumpian World","authors":"Michael Lloyd","doi":"10.1007/s11293-025-09821-7","DOIUrl":"10.1007/s11293-025-09821-7","url":null,"abstract":"<div><p>In April 2023, Jake Sullivan, in a speech to the Brookings Institute, formally announced the replacement of what had become known since the1990s as the Washington Consensus with a New Washington Consensus. This paper defines and briefly describes the original Washington Consensus. It goes on to describe and discuss in detail the New Washington Consensus and its implications. Finally, the paper outlines the potential Trump approach to this specific Biden legacy, whether or not acknowledged. The broad geoeconomic and geopolitical implications of the unwinding of what is likely to transpire are explored briefly in the context of the Trump Presidency.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 1-2","pages":"19 - 35"},"PeriodicalIF":0.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145166836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring Macroeconomic Determinants of Housing Bubbles: New Evidence from Dynamic Panel Probit Models","authors":"Shu-hen Chiang, Chien-Fu Chen","doi":"10.1007/s11293-025-09820-8","DOIUrl":"10.1007/s11293-025-09820-8","url":null,"abstract":"<div><p>Since the 2008 global financial crisis, the detection of housing bubbles has attracted unprecedented attention. By using price-to-rent data collected from the Organization for Economic Co-operation and Development, this paper adopts the Phillips et al<i>.</i> right-tailed unit root tests to identify housing bubbles across the Group of Seven countries from the first quarter of 1970 to the second quarter of 2021. In addition, a novel estimation approach (the dynamic panel probit model) was employed to take account of the bubble persistence and explore the macroeconomic determinants driving the housing bubbles. The empirical results indicate that each country experienced episodes of housing bubbles and the bubble clusters appeared in two phases, namely, 2003–2008 and post-2016. More importantly, there is evidence that certain macroeconomic variables drove the housing bubbles, especially a low interest rate and rapidly-growing money supply. The policy implication of this study is that central banks implementing ultra-loose monetary policy need to take housing bubble risk into careful consideration.</p></div>","PeriodicalId":46061,"journal":{"name":"ATLANTIC ECONOMIC JOURNAL","volume":"53 1-2","pages":"3 - 17"},"PeriodicalIF":0.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}