We use a newly created measure of state-level capital per worker to test whether tax structures can explain long-standing differences in capital investment and productivity across U.S. states. A standard neoclassical growth model shows that differences in marginal tax rates on property, sales, capital gains, and corporate income will lead to persistent differences in capital per worker across states, predictions that are borne out in the data. We also show that the factors that drive state variation in capital-labor ratios are consistent with their impacts on per capita income and labor productivity, results that are also consistent with predictions from the theory. Persistent differences in capital-labor ratios across states are leading to rising income inequality between states.
The concept of resilience has now been widely adopted across the social sciences and has assumed prominence in regional science. While this growing attention is to be welcomed, it has also given rise to ambiguity and confusion within the existing regional science literature, especially regarding foundational questions and, consequently, to the policy implications of resilience. Using probability theory, this paper offers an analysis of three foundational and one policy-related issues associated with the use of resilience in regional science. The foundational issues address questions of definition, whether resilience should be understood as a process, and if resilience should invariably be regarded as desirable. The policy-related issue focuses on the related concepts of resilience and sustainability. The paper concludes with two recommendations about future research on resilience in regional science.
Nigeria has one of the highest numbers (over 20 million) of out-of-school children (OOSC) globally, underscoring the urgent need to address this crisis. While contemporary studies on OOSC are largely concerned with the determinants, a spatio-temporal analysis, which examines the spatial distribution of OOSC and how it evolves overtime, can offer additional insights and inform spatially explicit solutions to the challenge of OOSC in Nigeria. Using spatial statistical techniques, such as Global Moran’s I, Local Moran’s I, and Getis-Ord Gi*, we examine the geographical patterns of OOSC in Nigeria for 2016, 2018, and 2022. Findings suggest an increasing and strengthening trend of spatial concentration in the distribution of OOSC over time. Although significant clustering was present in 2016, it was more pronounced and spatially entrenched by 2022. Specifically, the Northwest and Northeast regions remain endemic to the crisis of OOSC. Southern states consistently maintained a low rate of OOSC relative to the North, however; the appearance of High Low (HL) outlier in Ogun and Osun suggest that Southern states are not totally free from this crisis. The study recommends that the government needs to implement targeted interventions that are context-specific to address the crisis of OOSC in Nigeria.
This study investigates stochastic income convergence across Italian regions at two spatial scales-NUTS-2 (regions) and NUTS-3 (provinces)-in a context of an affluent North and the less-developed South. Using dynamic panel data models, both non-spatial and spatial, for 2001-2021, the analysis explores the roles of persistence, unobserved regional heterogeneity, and spatial dependence in income gap dynamics. Models without fixed effects show near-unit-root behavior, indicating high persistence or divergence, whereas including fixed effects substantially reduces persistence, providing evidence of conditional stochastic convergence at both spatial scales. Spatial dependence is limited at the NUTS-2 level, with coefficients generally small and insignificant, but at the NUTS-3 level, spatial interactions are economically meaningful and significant, highlighting localized spillovers in provincial income disparities. Ignoring these interactions at the finer scale can overstate persistence. To correct for Nickell bias in dynamic fixed-effects models, System GMM estimators are employed. Spatial System GMM results show strong but stable persistence, with coefficients around 0.86-0.87. The implied half-life of income shocks is roughly five years at the regional level and four to four-and-a-half years at the provincial level, indicating faster adjustments locally. Overall, the study finds weak but robust conditional stochastic convergence, emphasizing the importance of accounting for both spatial dependence and unobserved heterogeneity in understanding regional income dynamics in Italy. Unlike previous studies, our results quantify the persistence and half-life of income shocks, highlighting the difference between NUTS-2 and NUTS-3.
Our study examines several factors affecting migration choice among US urban areas, with emphasis on a factor currently at the center of policy discussions: housing affordability. Aggregating county-to-county IRS migration data for core-based statistical areas in each of the 48 continental US states from 2014 through 2020, we implement a zero-inflated negative binomial model to estimate the effect of house price and rent differentials on migration choice. Our findings confirm that housing affordability is a relocation barrier that results in a loss of productivity and economic growth for the US economy. We also show that using an average measure of housing affordability masks important effects and reveal that differences in house prices greater than $50,000 and rents greater than $250 are thresholds for negatively impacting migration choice.
We study the merits of regulating water pollution in the Ganges River caused by tanneries in Kanpur, India, by unitizing or merging the polluting tanneries. We first describe the n >= 2 polluting tanneries in Kanpur as a Cournot oligopoly in which all tanneries incur fixed and variable costs in producing leather. Second, we derive the Nash equilibrium output of leather and profits and discuss how many tanneries can survive in this equilibrium as a function of the fixed costs. Third, we permit m < n tanneries to merge and determine when this m-tannery merger is profitable to the unitized entity and to the non-unitized tanneries. Finally, we explain how the usefulness of unitization as a regulatory strategy depends on the magnitude of the tannery fixed costs and then comment on what our findings mean for improved water quality in the Ganges.
This study examines the impact of digital talent mobility on economic growth in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) using the spatial Durbin model and spatial mediation model. Based on panel data analysis from 2003 to 2022, our findings show that, first, digital talent flow significantly enhances economic growth and generates positive spatial spillovers through knowledge and technology diffusion. Second, industrial structure upgrading positively mediates this relationship, but its spatial mediation negatively affects surrounding cities by causing talent drain. Lastly, future industrial development potential exhibits only a limited local effect while displaying negative spatial mediation. As a policy conclusion, this study proposes that within the complex institutional context of the GBA, dynamic equilibrium between the radiative effects of core cities and balanced regional development should be achieved through cross-regional industrial coordination and integrated talent governance mechanisms.
Applying the time-varying Difference-in-Differences (DID) method, this study explores the impact of the quasi-natural experiment of the two-wave enlargements of the Yangtze River Delta urban agglomeration from 2003 to 2017 in China. We find a positive empirical effect of the enlargement as measured by the increase in a city’s consumption. The enlargement creates an increase in consumption by 18.1% in cities within the cluster relative to their non-cluster counterparts. For entrants and incumbents, we find an increase in consumption by 18.7% and 14.6%, respectively. The effect of the enlargement has temporal and city heterogeneities: the entrants benefit prior to the incumbents; cities with better infrastructure, larger scale, and proximity to regional central cities experience greater benefits from the enlargement. Our findings reveal that the enlargement enhances consumption by reducing the three types of administrative border barriers— city, province, and cluster—thereby increasing consumption in cities located along these borders. This study highlights the crucial role of integration in stimulating consumption and provides recommendations for policymakers to unify the domestic market by reducing administrative barriers and encouraging autonomous local practices.
Using Kansas as a case study and employing shift-share analysis, this paper examines the economic performance of a state's micropolitan areas, relative to that state's metropolitan areas. Employment and output data are examined for the period 2010 through 2022. To smooth out the various external shocks and cyclical fluctuations of this extended period, five-year estimated averages for 2006-2010 and 2018-2022 for industry-level employment were used to calculate the growth rates for the shift-share components. The results indicate that both micropolitan and metropolitan areas in Kansas grew more slowly than the overall national average due to the existing mix of industries. While this effect was considerably worse for micropolitan areas, the allocation effect indicates that micropolitan areas were more specialized in industries favorable for future growth, while metropolitan areas were specialized in unfavorable industries. This suggests that smaller cities in Kansas may be relatively more responsive to structural changes in local economies. The shift-share analysis also revealed that Kansas micropolitan areas are as diverse as the state's metropolitan areas, and, therefore, economic development efforts must be tailored to each area's specific economic circumstances.
Adopting a loanable funds model, this exploratory empirical study investigates the impact of economic freedom on the real interest rate yield on high-grade tax-exempt municipal bonds in the U.S. The AR/2SLS and FMOLS estimations imply that greater economic freedom leads to a lower ex post real interest rate yield on tax-exempt municipal bonds. Other interesting findings include the following: the real interest rate yield on tax-frees was an increasing function of both the ratio of the national debt to GDP and the ratio of the budget deficit to GDP while being a decreasing function of quantitative easing policies. Policy implications of these results include the need to limit the extent of the federal budget deficits in the U.S. lest there will be, among other things, significant limitations placed on the ability of towns, cities, counties, and states to finance outlays in response to changing demographic and economic circumstances and/or maintaining existing infrastructure.
This paper explores the limitations of traditional econometric models, such as the Ricardian and profit-based approaches, in accurately quantifying the impacts of climate change on agriculture. While these models offer valuable insights, they often neglect spatial dependencies, heterogeneity, and spillover effects. We argue that spatial econometrics provides a more comprehensive and robust approach to analyzing climate change impacts. By explicitly incorporating spatial relationships between agricultural units, spatial econometric models capture the influence of factors such as proximity to markets, resource sharing, information diffusion, and spatial correlation of climate variables. We review pioneering studies employing spatial econometric models, including SAR, SEM, SLX, SARAR and SDM, which reveal significant discrepancies between spatial and non-spatial estimations. These studies demonstrate that neglecting spatial dependence can lead to biased estimates and inaccurate predictions of climate change impacts. Moreover, the incorporation of spatial effects often results in smaller marginal effects of climate variables, suggesting that traditional non-spatial models may overestimate negative consequences. This paper contributes to the ongoing research on climate change impacts on agriculture by highlighting the significance of spatial econometrics and emphasizing its potential to inform robust and effective adaptation strategies.
Brazil, a country with a great territorial dimension and relevant and heterogeneous agricultural production, faces the challenge of climate change, of increasing productivity in the countryside, and of developing an economy with low environmental impact. This paper is designed to assess the impacts of hypothetical 1% productivity increases in large-scale and in family farming within Brazilian biomes induced by public policies that promote the adoption of sustainable agricultural practices as a strategy to address climate change. Such analysis uses a Computable General Equilibrium model specially built for analysis of the rural sectors in the Brazilian biomes: TERM-Biomas. The study reveals that less developed regions could benefit from productivity increases, potentially leading to a reduction in regional inequality if such policies were directed toward them. The cattle industry demonstrates its capacity to drive national economic growth and significantly contributes to exports. The Large-Scale Soybean sector emerges as a key contributor to economic influence. Furthermore, Soybean cultivation exhibits significant ripple effects on non-agricultural sectors such as agricultural pesticides, public utilities, and freight transportation. Initiatives resembling Sustainable Rural Project (SRP) can potentially foster widespread productivity improvements across Brazilian biomes, benefiting large-scale and family farmers.
This research estimates long-term impacts of "redlining" maps on rates of home ownership. In the 1930s, the Home Owners' Loan Corporation drew maps that split US cities into zones with 4 categories of mortgage risk. We exploit the grade discontinuities at the borders between zones to estimate effects of the grade assignments. Using newly-created shapefiles for 1940 Census blocks in Birmingham, Alabama, we match 1940 and 2010 Census blocks in Birmingham to grades and then build small neighborhoods of blocks that are close together and lie along both sides of grade borders. We compare outcomes between higherand lower-graded blocks within neighborhoods. 2010 Households in lower-graded blocks are significantly less likely to live in an owner-occupied dwelling than their neighbors a couple blocks away in a higher-graded zone. However, the same is true in 1940, suggesting that estimates from 2010 severely overstate the effect of treatment. Extending the 2010 analysis to all US cities produces even smaller differences than in Birmingham.
We study water pollution in the Ganges River caused by tanneries in Kanpur, India. We analyze the merits of a recent claim that unitizing or merging the polluting tanneries can improve water quality in the Ganges. We first describe the n >= 2 polluting tanneries in Kanpur as a Cournot oligopoly and derive the equilibrium output of leather and profits. Second, we permit m < n tanneries to merge and determine the cost function, when the m tanneries can use their production facilities, and there are no other efficiency gains from unitization. Third, we examine when the m-tannery unitization is profitable to the unitized entity and to the non-unitized tanneries. Fourth, we discuss our conclusions about the profitability of the unitized and the non-unitized tanneries and comment on what our findings mean for improved water quality in the Ganges. Finally, we discuss some key regional dimensions of the Ganges water pollution problem caused by tanneries in Kanpur.
The structure of the Federal Reserve offers a unique opportunity for a "regional lens" to inform monetary policy. The regional lens, as I lay out in my presidential address, is a narrative collection of quantitative, qualitative, and anecdotal information from a particular region. I discuss the reasons fora regional lens, the potential inputs, the challenges of the lens, and the opportunities for members of professional associations, such as the Southern Regional Science Association, to participate.
Urban areas in the US have been experiencing a rise in gentrification in recent years. Gentrification can revitalize a city center and increase tax revenues for the local government at the expense of displacing the poor from their homes, moving them further out from the city center. A large body of literature have extensively studied the causes and consequences of urban gentrification; however, most focused in the gentrification of downtown areas. Little is known about the relationship between rising income inequality and the rate of gentrification in the metropolitan areas of the US. Using county-and census tract-level data, I look at whether the rise in income inequality from the 1980s is associated with the rise in the rate of gentrification in US urban counties. I find that there is a positive association between the share of total income going to the richer segment of the population, and the subsequent rise in the rate of gentrification of an urban county. The rise in the share of total income received by the bottom quintile of income distribution does not correlate with gentrification rates.
A concerning consequence of climate change is the impact that reduced rainfall and increased temperatures are predicted to have on agricultural yields, with subsequent economic impacts across regions based on their reliance on agricultural production and knock-on effects through impacts on prices and trade. Several studies have used the relationship between climate variables and agricultural yields to project changes in agricultural productivity under climate scenarios as the input to Computable General Equilibrium models, however such studies are typically dominated by water-scarce countries. We extend this by considering the differential regional impacts and economic consequences within a water-abundant nation, permitting the analysis of climate-induced impacts on regional trade and production and consumption links between regions. Our framework estimates the historical relationship between climate variables and agricultural yields for four crop types (wheat, oats, winter barley and spring barley), calculates the future productivity of land under two climate scenarios, and then simulates the economy-wide impacts of these in a two-region computable equilibrium model of Western and Eastern Scotland. Our results suggest that there could be negative impacts of future changes in climate agricultural yields, with an overall reduction productivity by the end of the century which depends on climate scenarios. While the largest (absolute) negative economic impacts are in Eastern Scotland region, where cereal production is concentrated, impacts transmit through trade between the two regions, showing that policies to improve adaptation to climate change within the Agricultural sector would help to minimise overall economic losses across even water-abundant regions.
In this study, we examined the spatial distribution of subjective poverty across Turkey’s provinces. To accomplish this, we utilized the proportion of individuals who reported an inability to meet their basic needs, as reflected in the life index indicators computed by the Turkish Statistical Institute. After mapping the distribution of this variable by province, Moran’s I statistic, which is the spatial interaction statistic, was calculated It has been determined that the spatial distribution of subjective poverty is not random. It has been observed that the subjective poverty or perception of poverty in the context of the provinces of Turkey is similar and has a cluster. The existence of local clusters belonging to this statistic was investigated with LISA statistic and Getis-0rd statistic. According to the findings, the provinces that are high spots are generally the ones in the southeast and eastern regions of Turkey. Low spot areas are generally seen in the provinces in the western part of the country. According to the local spatial heterogeneity statistic , it is seen that the provinces in high and low spots are not different from their neighbors.
Quantifying climate adaptation is important from the standpoint of effectively assessing the extent to which economic agents make adjustments to their behavior, thereby reducing damages from climate change. We conduct such an investigation in the context of planting behavior in the U.S. corn belt by leveraging a climate econometrics framework. Our estimation method not only distinguishes the short-run response and the long-run response of planting behavior of corn to temperature and precipitation effects during the pre-growing season but also tests for climate adaptation as part of the short-run response. We find evidence of early and delayed planting in response to temperature and precipitation effects, respectively, across the pre-growing season. In addition, our findings confirm long-term climate adaptation in planting behavior and highlight the importance of accounting for a location’s advantage or disadvantage in terms of the extent of divergence between its climate normals and weather realization. These insights are informative for the policy efforts aimed at tailoring mitigation strategies to a region’s unique climate. Our study also generates insights of practical utility to agronomic advisory services, given that our findings confirm that the planting behavior (contingent upon a location’s climate) does leverage the flexibility provided by the pre-growing season window in response to temperature and precipitation effects.
The transition to lower-carbon energy sources has been described as the biggest social-technical challenge ever to face humans. This transition involves a roll-out of new technologies following decisions motivated by economic factors as well as political preferences. In this address I describe how this process has unfolded since 2000 using state-level energy data, while also examining the relationships among adoption, economic growth and political voting preferences. One key finding is the growing correlation since 1992 between the vote share of the Republican presidential candidate and CO2 emissions per capita, despite the growing adoption of green energy even in so-called red states. The paper concludes with a few recommendations for research, including the need to better understand growing community resistance to renewable energy sources and their community wide impacts.
