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The Consolidation Paradox in Labor Markets: Network Fragility and Spatial Wage Spillovers
No full textSpatial econometrics lacks principled methods for measuring minimum wage spillovers. Existing approaches assume arbitrary functional forms without theoretical justification, preventing researchers from answering basic questions: How far do effects reach? Through which channels? At what speed? This paper derives spatial treatment effects from first principles using Navier-Stokes equations. Three theoretical predictions emerge and are validated empirically. First, treatment boundaries exhibit self-similar scaling, growing proportional to the square root of elapsed time as predicted by diffusion theory (estimated exponent: 0.500, standard error: 0.001). Second, spatial weights follow Modified Bessel K-zero functions, the exact Green's function solution to the two-dimensional Helmholtz equation. This theoretically-derived specification fits observed spillover patterns substantially better than exponential, Gaussian, or power-law alternatives commonly assumed in applied work (R-squared: 0.99 versus 0.35). Third, network consolidation paradoxically increases rather than dampens wage volatility during stress periods, with consolidation-volatility correlation rising from near-zero to positive 0.0067 following COVID-19. Using 64,421 county-quarter observations from 2018 to 2023, I estimate characteristic spillover distance of 100 miles with cumulative effects reaching 0.44 log points over four quarters. Economic network linkages dominate geographic proximity by factor of eight, demonstrating that institutional connections matter more than physical distance. Spatial decay parameters increased 27 percent during COVID-19 (from 0.0155 to 0.0196), shrinking effective spillover radius from 65 to 51 miles and confirming time-varying dynamics predicted by perturbation theory. The framework provides concrete policy guidance. Regional minimum wage coordination should encompass 100-mile radius under normal conditions, contracting to 65 miles during crises. For Japan's minimum wage reform targeting 1,500 yen per hour by 2030, spillovers from Tokyo will substantially affect surrounding prefectures within 160 kilometers. Self-similar scaling implies effects reach half of final magnitude within one year but continue expanding indefinitely, requiring multi-year coordination frameworks
Dynamic Spatial Treatment Effects as Continuous Functionals: Theory and Evidence from Healthcare Access
No full textI develop a continuous functional framework for spatial treatment effects grounded in Navier-Stokes partial differential equations. Rather than discrete treatment parameters, the framework characterizes treatment intensity as continuous functions over space-time, enabling rigorous analysis of boundary evolution, spatial gradients, and cumulative exposure. Empirical validation using 32,520 U.S. ZIP codes demonstrates exponential spatial decay for healthcare access ( per km, ) with detectable boundaries at 37.1 km. The framework successfully diagnoses when scope conditions hold: positive decay parameters validate diffusion assumptions near hospitals, while negative parameters correctly signal urban confounding effects. Heterogeneity analysis reveals 2-13 stronger distance effects for elderly populations and substantial education gradients. Model selection strongly favors logarithmic decay over exponential (), representing a middle ground between exponential and power-law decay. Applications span environmental economics, banking, and healthcare policy. The continuous functional framework provides predictive capability (), parameter sensitivity (), and diagnostic tests unavailable in traditional difference-in-differences approaches
Nonparametric Identification and Estimation of Spatial Treatment Effect Boundaries: Evidence from 42 Million Pollution Observations
No full textThis paper develops a nonparametric framework for identifying and estimating spatial boundaries of treatment effects in settings with geographic spillovers. While atmospheric dispersion theory predicts exponential decay of pollution under idealized assumptions, these assumptions—steady winds, homogeneous atmospheres, flat terrain—are systematically violated in practice. I establish nonparametric identification of spatial boundaries under weak smoothness and monotonicity conditions, propose a kernel-based estimator with data-driven bandwidth selection, and derive asymptotic theory for inference. Using 42 million satellite observations of NO concentrations near coal plants (2019-2021), I find that nonparametric kernel regression reduces prediction errors by 1.0 percentage point on average compared to parametric exponential decay assumptions, with largest improvements at policy-relevant distances: 2.8 percentage points at 10 km (near-source impacts) and 3.7 percentage points at 100 km (long-range transport). Parametric methods systematically underestimate near-source concentrations while overestimating long-range decay. The COVID-19 pandemic provides a natural experiment validating the framework's temporal sensitivity: NO concentrations dropped 4.6\% in 2020, then recovered 5.7\% in 2021. These results demonstrate that flexible, data-driven spatial methods substantially outperform restrictive parametric assumptions in environmental policy applications
Risk Aversion, Exposure, and Management; organizational aspects in Emerging Markets
No full textThis article aims to study risk aversion, risk exposure and value, risk exposure management, and organizational aspects, with the objective of analyzing the relationship between risk aversion, risk exposure, and organizational value in Mozambique, highlighting effective risk exposure management strategies for the sustainability of companies in the local context. The article discusses aspects of the different perceptions that reveal the organization's degree of vulnerability to adverse events, both external, such as economic and political changes, and internal, such as operational failures and problems in the supply chain. It deepens the understanding of the relationship between risk aversion, risk exposure, and value creation, highlighting how companies can use advanced management practices to mitigate risks and transform uncertainties into opportunities. It is important to mention that effective risk exposure management is essential for Mozambican companies to balance risk aversion with the pursuit of opportunities that drive growth. It was found that, with regard to risk mitigation practices, some companies use insurance, currency hedge contracts, and market diversification strategies to protect their assets and ensure financial predictability. Strengthening governance and promoting an organizational culture focused on innovation and risk mitigation are fundamental to transforming uncertainties into competitive advantages
Analyzing Nigeria’s unemployment problem: evidence from the quantile regression approach
No full textMost studies on Okun’s Law focus on mean effects using ordinary least squares or dynamic models, with limited use of quantile regression. This leaves unexplored how output growth impacts unemployment across different points of the unemployment distribution. By applying a static quantile regression framework, this study fills this gap in analyzing the unemployment problem and its key determinants in Nigeria from 1991 to 2024. The findings showed that aggregate GDP growth reduces unemployment, with stronger effects at higher quantiles. However, results showed a weaker-than-expected Okun’s Law coefficient, reflecting the non-inclusive nature of Nigeria's economic growth. Sectoral analysis reveals that while Agriculture and Industry exert limited effects, the Construction and Services sectors significantly reduce unemployment, particularly through key activities such as Professional & Scientific Services, Trade, Real Estate, and Health. Government consumption largely worsens unemployment, whereas net FDI inflows foster job creation. The results highlight the need for structural transformation and policy realignment towards productive investments that strengthen the employment intensity of growth
Meet Karel Engliš (1880–1961), A Prominent Czech Economist, Logician, Politician and Scholar: Introduction and his Bibliography in Foreign Languages
No full textDo you want to know more about an economist who before Mises conceived of economics as a science of human action? Someone who at the same time was an ordoliberal sooner than Eucken? Someone who added politics to his many talents and who saved Czechoslovakia from hyperinflation then raging in Mises' Vienna, as well as in Budapest, Berlin and other major European cities? A Neoliberal deprived of his liberties by the Nazis and communists alike? Someone who then didn't give in to the dictatorship but fought for democracy with his mighty pen? Then feel free to read my publication titled Meet Karel Engliš (1880-1961), A Prominent Czech Economist, Logician and Scholar. Introduction and his Bibliography in Foreign Languages. The publication contains an international bibliography of the works of Karel Engliš, founder of Masaryk University, a prominent Czech economist whose work was also appreciated by representatives of the Austrian school such as L. von Mises, F. A. von Hayek and I. M. Kirzner. Karel Engliš was one of the most prominent figures in European economics in the interwar period, but after 1948 the communist regime banned his publishing, lecturing and foreign activities, and his books were withdrawn from library collections. An overview of his works in English, German, French and other languages, supplemented by links to online resources and secondary literature, provides a valuable basis for a new research of his work and significance. The aim of the publication is to renew international awareness of Engliš's teleological economics and to facilitate further research into his legacy
Identifying the Shocks also Identifies the Constants: Implications for VAR analysis
No full textRestrictions on the contemporaneous effects matrix used to identify fundamental shocks in a structural VAR, also determine the mapping from the structural constant terms to the reduced form constant terms. In some models one will have priors about these structural constant terms and these should therefore be included in a Bayesian estimation procedure. We illustrate the significance of this using a standard 3 variable VAR estimated in Baumeister and Hamilton (2018). We show that imposing priors over the structural constant terms can lead to a more intuitive estimated monetary policy rule and a larger role for monetary policy in describing the evolution of the data, particularly for inflation
Asignando Recursos a fuerzas de venta, según performance
No full textEstudiamos el problema dinámico que enfrenta una firma para asignar recursos que facilitan
el trabajo de sus vendedores. La eficiencia requiere que se le asignen más recursos a los mejores
vendedores, pero si al principio de sus carreras ellos muestran sus cartas, serán explotados en el
futuro. ¿Cómo es el diseño óptimo de asignación? ¿Qué tan eficientes son las formas habituales
de asignar recursos? En este trabajo analizamos esas preguntas
اولویت بندی واگذاری بنگاه¬های اقتصادی زیر مجموعه صندوق¬های بازنشستگی با تاکید بر مدیریت سرمایه¬گذاری: شواهدی جدید از رویکرد DCC-GARCH R2 decomposed connectedness
No full textIn recent years, due to the resource deficits and financial imbalances of pension funds, the issue of structural reform and optimization of their investment portfolios has gained greater importance. Among the key solutions proposed is the divestment of underperforming enterprises to the private sector in line with Article 44 of the Constitution and within the framework of implementing the Seventh National Development Plan. The main challenge in this context lies in establishing a scientifically sound and efficient prioritization for such divestments. Accordingly, adopting modern portfolio management approaches—such as the DCC GARCH R² decomposed connectedness model recently introduced by Cocca et al. (2024)—can offer a more comprehensive perspective for decision making regarding whether to retain or divest these enterprises. Based on this approach, the optimal weight of each enterprise in the investment portfolio, the efficiency of risk hedging, the beta coefficient, and the Sharpe ratio should be evaluated under various portfolio management frameworks, including MVP, MCP, MCOP, MRP, and MPG. Ultimately, the model yielding the highest Sharpe ratio is selected as the optimal approach.
In such a setting, it becomes feasible to prioritize enterprises for earlier or later divestment based on two key dimensions—return and risk. This priority setting aligns with the mandates of the Seventh National Development Plan; however, to date, this has not been examined using the aforementioned approaches, which could be of significant value to policymakers
Multibrand price dispersion
No full textWe study a market in which firms each might supply a number of variants, or "brands", of fundamentally the same product. Consumers differ in the sets of brands they consider, and firms compete using (multi-dimensional) mixed pricing strategies. We show when firms apply uniform pricing across their brands, and when they use segmented pricing so that one "discount" brand is priced below another "premium" brand. We study the case of symmetric brands in particular, and discuss the impact of a firm introducing a new brand, of imposing a requirement to set uniform prices across brands, and of mergers between firms