Families, labor markets, and public policies all structure a child’s opportunities and determine the extent to which adult earnings are related to family background. Cross-country comparisons and the underlying trends suggest that these drivers will most likely lower the degree of intergenerational earnings mobility for the next generation of Americans coming of age in a more polarized labor market, while the substantial rise in the income shares of the top 1 percent, their access to sources of high-quality human capital investment for their children, and the intergenerational transmission of employers and wealth will imply a much higher rate of transmission of economic advantage at the very top.
This study examines the impact of remittances from Moroccan living abroad on Morocco’s economic growth over the period 1974–2023. Based on a thorough review of the theoretical and empirical literature, it highlights the main transmission channels of remittances, particularly through consumption, productive investment, and human capital accumulation. The empirical analysis uses a time series approach, applying a bivariate VAR model between GDP and remittances. The results indicate that remittances have a positive and significant short-term effect on economic growth, as confirmed by impulse response functions and Granger causality tests. These findings emphasize the need for policies that better channel remittances into productive investments to enhance their contribution to sustainable economic development.
As insurers increasingly behave like financial intermediaries and actively participate in capital markets, understanding the dependence structure between insurance and financial risks becomes crucial for insurers' operations. This paper studies dynamic equilibrium insurance pricing when insurers face ambiguity about the correlation between insurance and financial risks and optimally choose underwriting and investment strategies under worst-case beliefs. Correlation ambiguity can generate multiple equilibrium regimes. Contrary to conventional intuition, we find ambiguity does not necessarily increase insurance prices nor reduce insurers' utility.
Neste ensaio, pretendo analisar discursos sobre a noção de código da vida torna-se basepara uma episteme que vê, no corpo, informação a ser decifrada, melhorada e governada. O ensaiose faz a partir de uma breve retomada histórica acerca dos conhecimentos sobre DNA e genética,novas políticas públicas envolvendo projetos genômicos e artigos no campo da economia e genética.Dentro desta perspectiva, a gestão da informação genômica do ser humano, passa por possibilidadesde investimento, produzindo novas formas de fomento para o Capital Humano.
The unequal distribution of resources and unjust utilization of the environment not only imperil the global ecological equilibrium but also undermine the sustainable development of the global economy. By delving into enterprises' offsite investment behavior amidst environmental inequality, insights into their environmental stewardship in the global arena can be gleaned. Drawing on data from Chinese non-financial enterprises spanning 2007–2020, our findings underscore that environmental inequality spurs offsite investments by enterprises, with capital flows predominantly directed towards other provinces rather than remaining localized. Mechanism analysis from economic perspectives reveals that environmental inequality adversely affects corporate income growth, and the resulting environmental governance pressure transfers to enterprises, reducing corporate competitiveness and prompting capital flight. Additionally, mechanism exploration from strategic perspectives reveals that environmental inequality leads to higher corporate financing costs, increased default risks, and a heightened risk of stock price collapse in the capital market. The financial pressure of addressing environmental inequality also transfers to enterprises within the jurisdiction, compelling them to seek sustainable development through offsite investment. Our analysis of heterogeneity indicates that the enhancing impact of environmental inequality is more pronounced among firms that receive low subsidies, are situated in the central and eastern regions of the country, contend with sluggish markets, possess non-state attributes, exhibit low technological advancement, and have high pollution intensity. Moreover, environmental inequality influences the industrial composition of the destination where capital flows. This study provides novel insights into evaluating the economic consequences of environmental equity and corporate capital management, furnishing a scholarly basis for advancing environmentally sustainable development.
This study examines the impact of technological capabilities across industries on venture capital (VC) inflows. Technological capabilities were proxied by industry-level patent applications and R&D expenditures. VC inflows were derived from annual investment statistics published by the Ministry of SMEs and Startups and the Korea Venture Capital Association. Multiple regression analysis shows that industries with more patent applications are more likely to attract venture investments. Moreover, the relationships among patents, R&D, and venture inflows vary significantly across industries. In the biomedical industry, VC inflows show strong positive correlations with patent applications (r = 0.762, <i data-eusoft-scrollable-element="1">p</i> < 0.001) and R&D investment (r = 0.900, <i data-eusoft-scrollable-element="1">p</i> < 0.001). In contrast, in the information and communication technology manufacturing sector, the association between patent applications and VC inflows is not statistically significant (R<sup data-eusoft-scrollable-element="1">2</sup> = 0.002, <i data-eusoft-scrollable-element="1">p</i> > 0.05), implying that the conversion efficiency of technological outputs into investment differs according to the industrial structure. This study provides evidence of how technological development translates into commercialization and private investment. The findings contribute to a nuanced understanding of success factors in technology-based startups by industry and may serve as a foundation for the formulation of effective policy measures and investment strategies to promote private capital inflows.
This paper develops a critical theory of artificial intelligence, within a historical constellation where computational systems increasingly generate cultural content that destabilises traditional distinctions between human and machine production. Through this analysis, I introduce the concept of the algorithmic condition, a cultural moment when machine-generated work not only becomes indistinguishable from human creation but actively reshapes our understanding of ideas of authenticity. This transformation, I argue, moves beyond false consciousness towards what I call post-consciousness, where the boundaries between individual and synthetic consciousness become porous. Drawing on critical theory and extending recent work on computational ideology, I develop three key theoretical contributions, first, the concept of the Inversion to describe a new computational turn in algorithmic society; second, automimetric production as a framework for understanding emerging practices of automated value creation; and third, constellational analysis as a methodological approach for mapping the complex interplay of technical systems, cultural forms and political economic structures. Through these contributions, I argue that we need new critical methods capable of addressing both the technical specificity of AI systems and their role in restructuring forms of life under computational capitalism. The paper concludes by suggesting that critical reflexivity is needed to engage with the algorithmic condition without being subsumed by it and that it represents a growing challenge for contemporary critical theory.
Sidney T. da Silva, Enrique C. Gabrick, Ana Luiza R. de Moraes
et al.
Climate change refers to substantial long-term variations in weather patterns. In this work, we employ a Machine Learning (ML) technique, the Random Forest (RF) algorithm, to forecast the monthly average temperature for Brazilian's states capitals (27 cities) and the whole country, from January 1961 until December 2022. To forecast the temperature at $k$-month, we consider as features in RF: $i)$ global emissions of carbon dioxide (CO$_2$), methane (CH$_4$), and nitrous oxide (N$_2$O) at $k$-month; $ii)$ temperatures from the previous three months, i.e., $(k-1)$, $(k-2)$ and $(k-3)$-month; $iii)$ combination of $i$ and $ii$. By investigating breakpoints in the time series, we discover that 24 cities and the gases present breakpoints in the 80's and 90's. After the breakpoints, we find an increase in the temperature and the gas emission. Thereafter, we separate the cities according to their geographical position and employ the RF algorithm to forecast the temperature from 2010-08 until 2022-12. Based on $i$, $ii$, and $iii$, we find that the three inputs result in a very precise forecast, with a normalized root mean squared error (NMRSE) less than 0.083 for the considered cases. From our simulations, the better forecasted region is Northeast through $iii$ (NMRSE = 0.012). Furthermore, we also investigate the forecasting of anomalous temperature data by removing the annual component of each time series. In this case, the best forecasting is obtained with strategy $i$, with the best region being Northeast (NRMSE = 0.090).
This study proposes a method for predicting startup inclusion, estimating the probability that a venture capital fund will invest in a given startup. Unlike general recommendation systems, which typically rank multiple candidates, our approach formulates the problem as a binary classification task tailored to each fund-startup pair. Each startup is represented by integrating textual, numerical, and structural features, with Node2Vec capturing network context and multihead attention enabling feature fusion. Fund investment histories are encoded as LSTM based sequences of past investees. Experiments on Japanese startup data demonstrate that the proposed method achieves higher accuracy than a static baseline. The results indicate that incorporating structural features and modeling temporal investment dynamics are effective in capturing fund-startup compatibility.
Paulo Roberto de Melo Barros Junior, Monica Alexandra Vilar Ribeiro De Meireles, Jose Luis Lima de Jesus Silva
Our work investigates the economic efficiency of the prevailing "ladder-step" investment strategy in oil and gas exploration, which advocates for the incremental acquisition of geological information throughout the project lifecycle. By employing a multi-agent Deep Reinforcement Learning (DRL) framework, we model an alternative strategy that prioritizes the early acquisition of high-quality information assets. We simulate the entire upstream value chain-comprising competitive bidding, exploration, and development phases-to evaluate the economic impact of this approach relative to traditional methods. Our results demonstrate that front-loading information investment significantly reduces the costs associated with redundant data acquisition and enhances the precision of reserve valuation. Specifically, we find that the alternative strategy outperforms traditional methods in highly competitive environments by mitigating the "winner's curse" through more accurate bidding. Furthermore, the economic benefits are most pronounced during the development phase, where superior data quality minimizes capital misallocation. These findings suggest that optimal investment timing is structurally dependent on market competition rather than solely on price volatility, offering a new paradigm for capital allocation in extractive industries.
Edward J. Oughton, Andrew Renton, Daniel Mac Marnus
et al.
Space weather events pose a growing threat to modern economies, yet their macroeconomic consequences still remain underexplored. This study presents the first dedicated economic assessment of geomagnetic storm impacts on Aotearoa New Zealand, quantifying potential gross domestic product (GDP) losses across seven conservative disruption and mitigation scenarios due to an extreme coronal mass ejection (CME). The primary focus is upon the damaging impacts of geomagnetically induced currents (GICs) on the electrical power transmission network. We support space weather mitigation investments decisions by providing a first-order approximation of their potential economic benefits, using best-in-class scientific models. In the absence of mitigation, a severe but realistic storm could result in up to NZ\$8.36 billion in lost GDP, with more than half stemming from cascading supply chain effects. Yet, even less severe scenarios incur losses exceeding NZ\$3 billion. Importantly, even with conservative impact estimates we find that research-led operational strategies, such as optimized switching and islanding, can avoid up to NZ\$370 million in losses for as little as NZ\$0.5 million in expenditure, delivering a benefit-cost ratio of 740 to 1. Equally, physical protections such as GIC blocking devices achieve benefit-cost returns up to 80 to 1, highlighting the strong case for investment in space weather mitigation. When also acknowledging additional unmodelled impacts, including multi-billion losses in capital equipment and long-term revenue, the economic rationale for pre-emptive mitigation becomes even more pertinent. Future research needs to integrate the modelling of capital and revenue losses for strategically important industrial facilities.
This paper considers a newly delayed reinsurance and investment optimization problem incorporating random risk aversion, in which an insurer pursues maximization of the expected certainty equivalent of her/his terminal wealth and the cumulative delayed information of the wealth over a period. Specially, the insurer's surplus dynamics are approximated using a drifted Brownian motion, while the financial market is described by the constant elasticity of variance (CEV) model. Moreover, the performance-linked capital flow feature is incorporated and the wealth process is formulated via a stochastic delay differential equation (SDDE). By adopting a game-theoretic approach, a verification theorem with rigorous proofs is established to capture the equilibrium reinsurance and investment strategy along with the equilibrium value function. Furthermore, analytical or semi-analytical equilibrium reinsurance and investment strategies, together with their equilibrium value functions, are obtained under the CEV model for the exponential utility and derived under the Black-Scholes model for both exponential and power utilities. Finally, several numerical experiments are conducted to analyze the behavioral characteristics of the freshly-derived equilibrium reinsurance and investment strategy.