Hasil untuk "Labor market. Labor supply. Labor demand"

Luigi Foscari, Emanuele Guidotti, Nicolò Cesa-Bianchi et al.

We study the emergence of tacit collusion in a repeated game between a market maker, who controls market liquidity, and a market taker, who chooses trade quantities. The market price evolves according to the endogenous price impact of trades and exogenous innovations to economic fundamentals. We define collusion as persistent overpricing over economic fundamentals and characterize the set of feasible and collusive strategy profiles. Our main result shows that a broad class of simple learning dynamics, including gradient ascent updates, converges in finite time to collusive strategies when the agents maximize individual wealth, defined as the value of their portfolio, without any explicit coordination. The key economic mechanism is that when aggregate supply in the market is positive, overpricing raises the market capitalization and thus the total wealth of market participants, inducing a cooperative component in otherwise non-cooperative learning objectives. These results identify an inherent structure through which decentralized learning by AI-driven agents can autonomously generate persistent overpricing in financial markets.

Mikayel Sukiasyan

What is the best macroprudential regulation when households differ in their exposure to profits from the financial sector? To answer the question, I study a real business cycle model with household heterogeneity and market incompleteness. In the model, shocks are amplified in states with high leverage, leading to lower investment. I consider the problem of a Ramsey planner who can finance transfers with a distortive tax on labor and levy taxes on the balance sheet components of experts. I show that the optimal tax on capital purchases is zero and the optimal policy relies mostly on a tax on deposit issuance. The latter redistributes between agents by affecting the equilibrium rate on deposits. The welfare gains from optimal policy are due to both redistribution and insurance and are larger the more unequal the initial distribution is. A simple tax rule that targets a level of leverage can achieve most of the welfare gains from optimal policy.

Umur Hasturk, Albert H. Schrotenboer, Kees Jan Roodbergen et al.

Green hydrogen has multiple use cases and is produced from renewable energy, such as solar or wind energy. It can be stored in large quantities, decoupling renewable energy generation from its use, and is therefore considered essential for achieving a climate-neutral economy. The intermittency of renewable energy generation and the stochastic nature of demand are, however, challenging factors for the dynamic planning of hydrogen storage and transportation. This holds particularly in the early-adoption phase when hydrogen distribution occurs through vehicle-based networks. We therefore address the Dynamic Inventory Routing Problem (DIRP) under stochastic supply and demand with direct deliveries for the vehicle-based distribution of hydrogen. To solve this problem, we propose a Constrained Reinforcement Learning (CRL) framework that integrates constraints into the learning process and incorporates parameterized post-decision state value predictions. Additionally, we introduce Lookahead-based CRL (LCRL), which improves decision-making over a multi-period horizon to enhance short-term planning while maintaining the value predictions. Our computational experiments demonstrate the efficacy of CRL and LCRL across diverse instances. Our learning methods provide near-optimal solutions on small scale instances that are solved via value iteration. Furthermore, both methods outperform typical deep learning approaches such as Proximal Policy Optimization, as well as classical inventory heuristics, such as (s,S)-policy-based and Power-of-Two-based heuristics. Furthermore, LCRL achieves a 10% improvement over CRL on average, albeit with higher computational requirements. Analyses of optimal replenishment policies reveal that accounting for stochastic supply and demand influences these policies, showing the importance of our addition to the DIRP.

Mike Zhang, Rob van der Goot, Barbara Plank

In Natural Language Processing, entity linking (EL) has centered around Wikipedia, but yet remains underexplored for the job market domain. Disambiguating skill mentions can help us get insight into the current labor market demands. In this work, we are the first to explore EL in this domain, specifically targeting the linkage of occupational skills to the ESCO taxonomy (le Vrang et al., 2014). Previous efforts linked coarse-grained (full) sentences to a corresponding ESCO skill. In this work, we link more fine-grained span-level mentions of skills. We tune two high-performing neural EL models, a bi-encoder (Wu et al., 2020) and an autoregressive model (Cao et al., 2021), on a synthetically generated mention--skill pair dataset and evaluate them on a human-annotated skill-linking benchmark. Our findings reveal that both models are capable of linking implicit mentions of skills to their correct taxonomy counterparts. Empirically, BLINK outperforms GENRE in strict evaluation, but GENRE performs better in loose evaluation (accuracy@$k$).

Frank Reyes, Federico Bono, Aman Sharma et al.

Java projects frequently rely on package managers such as Maven to manage complex webs of external dependencies. While these tools streamline development, they also introduce subtle risks to the software supply chain. In this paper, we present Maven-Hijack, a novel attack that exploits the order in which Maven packages dependencies and the way the Java Virtual Machine resolves classes at runtime. By injecting a malicious class with the same fully qualified name as a legitimate one into a dependency that is packaged earlier, an attacker can silently override core application behavior without modifying the main codebase or library names. We demonstrate the real-world feasibility of this attack by compromising the Corona-Warn-App, a widely used open-source COVID-19 contact tracing system, and gaining control over its database connection logic. We evaluate three mitigation strategies, such as sealed JARs, Java Modules, and the Maven Enforcer plugin. Our results show that, while Java Modules offer strong protection, the Maven Enforcer plugin with duplicate class detection provides the most practical and effective defense for current Java projects. These findings highlight the urgent need for improved safeguards in Java's build and dependency management processes to prevent stealthy supply chain attacks.

Hyung-il Ahn, Young Chol Song, Santiago Olivar et al.

Successful supply chain optimization must mitigate imbalances between supply and demand over time. While accurate demand prediction is essential for supply planning, it alone does not suffice. The key to successful supply planning for optimal and viable execution lies in maximizing predictability for both demand and supply throughout an execution horizon. Therefore, enhancing the accuracy of supply predictions is imperative to create an attainable supply plan that matches demand without overstocking or understocking. However, in complex supply chain networks with numerous nodes and edges, accurate supply predictions are challenging due to dynamic node interactions, cascading supply delays, resource availability, production and logistic capabilities. Consequently, supply executions often deviate from their initial plans. To address this, we present the Graph-based Supply Prediction (GSP) probabilistic model. Our attention-based graph neural network (GNN) model predicts supplies, inventory, and imbalances using graph-structured historical data, demand forecasting, and original supply plan inputs. The experiments, conducted using historical data from a global consumer goods company's large-scale supply chain, demonstrate that GSP significantly improves supply and inventory prediction accuracy, potentially offering supply plan corrections to optimize executions.

Saeed Valinezhad, َAhmad Salah manesh, Ebrahim Anvari

Extended AbstractPurpose: Monetary policy rules that are used to implement monetary policies by central banks are determined in a strategic framework in order to do trade-offs between goals such as inflation, unemployment and economic growth. Economic authorities use laws as a guide to implement their policies regarding the deviation of target variables from their desired goals or levels.The new Keynesian DSGE models in vogue in central banking have long ignored the insights of the economy information revolution to which Joseph Stiglitz made an important contribution. Furthermore, these models have not been successful in providing important research insights about alternative theories on model selection and the consequences of structural failures in the econometric literature, in which David Hendry plays a key role. By addressing the debates between the critics and advocates of New Keynesian DSGE models, Henry and Muelbauer show how evidence-based research can improve quantitative policy models and enable central banks to better understand financial stability and models.The fundamental difference between the neoclassical and Keynesian theories lies in the understanding of the labor market. In the neoclassical case, the nominal wage is understood as an adaptive variable. In the absence of labor market imperfections such as search and matching frictions or non-equilibrium wages due to the existence of asymmetric information, nominal wage adjustment settles the labor market for a given price level. In such a situation, Keynesian unemployment caused by the lack of aggregate demand cannot exist. The economy is supply-side determined because the factor of market settlement along with the aggregate production function requires a unique level of output and employment.Macroeconomic modeling has come under severe criticism since the Great Financial Crisis, when serious flaws in the methodology used to understand the economy as a whole became apparent. Criticisms have been directed toward the assumptions used in dominant models, especially in that economic factors are homogeneous and optimal and that the economy is in equilibrium. Some researchers seek to explore an interdisciplinary approach to macroeconomic modeling, with techniques drawn from other sciences (natural and social). In particular, they discuss agent-based modeling as an example of such a technique, which is used in a wide variety of disciplines. Agent-based models complement the existing approaches and are suitable for answering macroeconomic questions in which complexity, heterogeneity of economic agents and their expectations, networks, and discoveries play an important role. Based on the literature, this study uses a disequilibrium approach to investigate the effect of monetary policies on macroeconomic variables.Methodology: The purpose of this paper was to analyze the monetary policies under disequilibrium in Iran's economy. In order to analyze the results, the random dynamic disequilibrium method was used in the period of 1989-2022 based on the frequency of seasonal data. The approach used in this study is modeling in terms of the disequilibrium in economic markets and its spread to other markets as well as the reaction of macroeconomic variables to the shock. Disequilibrium in dynamic stochastic models occurs due to financial and real frictions in markets. The tools used in this study were the Hamilton-Jacobi-Belman method as well as Kolomogrof's forward-looking expectations in order to solve the model in disequilibrium conditions.Findings and Discussion: The results obtained from this study indicated that the currency deviation variable has increased in response to the monetary policy shock. Also, with the change in the same variable, this disequilibrium and deviation in the exchange rate has increased over time. It was observed that the shock of the monetary policy has led to the deviation in the exchange rate by creating disequilibrium in the prices. The response of consumer spending to monetary policy shocks has also been increasing over time. The results indicate that, with the introduction of the monetary policy shock, the situation and imbalance in the money and currency market has led to price pressure and an increase in household consumption expenditures, and this trend has been upward over time. The production deviation variable has also increased in response to the monetary policy shock.Conclusion and Policy Implications: Guiding monetary policies and designing a structure that increases the credibility and acceptability of both applied policies and monetary policymakers, on the one hand, and the realization of the main priority of monetary policies, i.e. achieving a low and stable inflation rate and making it possible to maintain it, on the other hand, in recent years have been the focus of attention of many economists. The results indicate that, with the introduction of the monetary policy shock, the disequilibrium in the money and currency market has led to price pressure and an increase in household consumption expenditures. This trend has been upward over time. The production deviation variable has also increased in response to the monetary policy shock. According to the results obtained from this study, it is suggested that the instability and disequilibrium in Iran's economy are due to the exchange rate and balance of payments, and this imbalance is transferred to all markets through economic policies. This is the basis for the country's monetary authorities to implement their policies, especially in the field of foreign exchange, by setting a fluctuation range and anchoring the nominal exchange rate to changes in foreign exchange reserves as well as the inflation rate to prevent the spread of disequilibrium in the currency and money market as the real sector of the economy.

Pascal Michaillat

Immigration is often blamed for increasing unemployment among local workers. This sentiment is reflected in the rise of anti-immigration parties and policies in Western democracies. And in fact, numerous studies estimate that in the short run, the arrival of new workers in a labor market raises the unemployment rate of local workers. Yet, standard migration models, such as the Walrasian model and the Diamond-Mortensen-Pissarides model, inherently assume that immigrants are absorbed into the labor market without affecting local unemployment. This paper presents a more general model of migration that allows for the possibility that not only the wages but also the unemployment rate of local workers may be affected by the arrival of newcomers. This extension is essential to capture the full range of potential impacts of labor migration on labor markets. The model blends a matching framework with job rationing. In it, the arrival of new workers raises the unemployment rate among local workers, particularly in a depressed labor market where job opportunities are limited. On the positive side, in-migration helps firms fill vacancies more easily, boosting their profits. The overall impact of in-migration on local welfare varies with labor market conditions: in-migration reduces welfare when the labor market is inefficiently slack, but it enhances welfare when the labor market is inefficiently tight.

Angelo D'Ambrosio, Raisa Kociurzynski, Alexis Papathanassopoulos et al.

For hospitals, realistic forecasting of bed demand during impending epidemics of infectious diseases is essential to avoid being overwhelmed by a potential sudden increase in the number of admitted patients. Short-term forecasting can aid hospitals in adjusting their planning and freeing up beds in time. We created an easy-to-use online on-request tool based on local data to forecast COVID-19 bed demand for individual hospitals. The tool is flexible and adaptable to different settings. It is based on a stochastic compartmental model for estimating the epidemic dynamics and coupled with an exponential smoothing model for forecasting. The models are written in R and Julia and implemented as an R-shiny dashboard. The model is parameterized using COVID-19 incidence, vaccination, and bed occupancy data at customizable geographical resolutions, loaded from official online sources or uploaded manually. Users can select their hospital's catchment area and adjust the number of COVID-19 occupied beds at the start of the simulation. The tool provides short-term forecasts of disease incidence and past and forecasted estimation of the epidemic reproductive number at the chosen geographical level. These quantities are then used to estimate the bed occupancy in both general wards and intensive care unit beds. The platform has proven efficient, providing results within seconds while coping with many concurrent users. By providing ad-hoc, local data informed forecasts, this platform allows decision-makers to evaluate realistic scenarios for allocating scarce resources, such as ICU beds, at various geographic levels.

Élida Cristina de Oliveira, Marcos Antônio Andrade da Costa, Wanderley Azevedo de Brito

O ordenamento jurídico brasileiro assegura direitos que buscam proteger os trabalhadores em face da situação de subordinação que esses sujeitos se encontram perante o empregador. Os direitos trabalhistas são frutos de uma luta intensa da classe trabalhadora contra as mazelas e péssimas condições de trabalho. Entretanto, a história recente mostra uma paulatina mitigação desses direitos decorrente da reestruturação produtiva, da globalização dos mercados consumidores e do avanço da informática e da comunicação. Nesse contexto, este artigo visa analisar como a educação jurídica, especificamente o conhecimento jurídico-trabalhista, pode contribuir para a formação integrada dos sujeitos trabalhadores diante do processo de precarização de direitos trabalhistas. Trata-se de um estudo de cunho teórico que busca articular os impactos da reestruturação produtiva do capital com o esvaziamento dos direitos trabalhistas, apresentando a educação jurídica como ferramenta contra hegemônica. Para tanto, fundamenta-se nos estudos de Antunes e Frigotto para compreender a reestruturação produtiva, nas análises de Rotondano, Siqueira, Silva e Leonardo para perceber a importância da educação jurídica e nas reflexões de Ramos, Moura, Ciavatta e Saviani para conceber novas possibilidades para uma formação integrada no âmbito da educação de jovens e adultos. Diante da conexão entre os temas, constata-se que a educação jurídica, ao desvelar para os trabalhadores a existência da disputa entre capital e trabalho presente no sistema capitalista, colabora com o desenvolvimento integral do indivíduo e com a transformação social desejada. Palavras-chave: educação jurídica; direitos trabalhistas; educação de jovens e adultos; formação integrada; precarização do trabalho.

Ivo Dickmann, Ana Maria de Oliveira Pereira

Este artigo estabelece uma crítica ao atual modelo de vida e produção capitalista, tendo como pano de fundo a Ecopedagogia. Para isso, discute os dilemas do desenvolvimento sustentável, sua história, limites e possibilidades, após apresenta a dialética do trabalho na relação entre seres humanos e natureza e, num último momento, trata a questão da formação humana e da práxis docente relacionado aos princípios da Ecopedagogia para repensar uma nova mentalidade socioambiental. Ao final, elenca um conjunto de considerações indicativas que se apresentam como resultado da reflexão ecopedagógica em diálogo com as teorias críticas da educação e da sociedade. Palavras-chave: Natureza; Capital; Trabalho; Ecopedagogia.

Mengxiao Zhang, Shi Chen, Haipeng Luo et al.

Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms' SCM strategies. In this work, we aim at designing online learning algorithms for this problem with an unknown demand distribution, which brings distinct features as compared to classic online optimization problems. Specifically, we consider the two-echelon supply chain model introduced in [Cachon and Zipkin, 1999] under two different settings: the centralized setting, where a planner decides both agents' strategy simultaneously, and the decentralized setting, where two agents decide their strategy independently and selfishly. We design algorithms that achieve favorable guarantees for both regret and convergence to the optimal inventory decision in both settings, and additionally for individual regret in the decentralized setting. Our algorithms are based on Online Gradient Descent and Online Newton Step, together with several new ingredients specifically designed for our problem. We also implement our algorithms and show their empirical effectiveness.

Arnfinn H. Midtbøen, Lincoln Quillian

Siden slutten av 1960-tallet har over 140 felteksperimenter i 30 land blitt gjennomført for å kartlegge omfanget av etnisk diskriminering i ansettelsesprosesser. Artikkelen bygger på en systematisk gjennomgang av denne litteraturen. Vi trekker fram tre hovedkonklusjoner: 1) Det er ingen tegn til endring i omfanget av diskriminering over tid; 2) diskriminering er en like stor barriere for etterkommere av innvandrere som for innvandrere; 3) omfanget av diskriminering mot jobbsøkere med bakgrunn fra Afrika, Asia, Midtøsten og Sør-Amerika er vesentlig større enn diskriminering mot jobbsøkere fra Europa. Konklusjonene drøftes i lys av teorier om assimilering og rasialisering.

Sânia Nayara da Costa Ferreira

A dissertação teve como objetivo evidenciar o sentido e a especificidade da educação profissional na Fundação Bradesco a partir da Lei da Aprendizagem, que se materializa na Fundação como Programa Adolescente Aprendiz, implementado no ano de 2004, em parceria com o Banco Bradesco, com o objetivo de oferecer formação profissional para jovens de 14 a 24 anos de idade e “fomentar oportunidades de ingresso no mercado de trabalho e contribuir para o desenvolvimento do país” (FUNDAÇÃO BRADESCO, 2014, p.28).

Francisco José da Silveira Lobo Neto

Há três fases no Projeto Rondon. A primeira da origem, em 1966, até 1985 com a extinção da Fundação Projeto Rondon. Assim o Projeto permaneceu em hibernação até 2003, quando a União Nacional dos Estudantes faz ao Presidente Lula o pedido de retomada das operações do Projeto. A formação de um grupo de trabalho em 2004 e o Decreto de 14 de janeiro de 2005 que cria o Comitê de Orientação e Supervisão do Projeto Rondon, representa bem uma segunda fase.

Andrea Biondi

This article focuses on the transformations and changes in the area of state aid control prompted by the COVID-19 pandemic. It attempts to provide a first assessment on the European Commission Temporary Framework on state aid measures to support the economy in the outbreak. It discusses whether the measures adopted have been effective and managed to guarantee on the one side the possibility of Member States to take swift and effective action as to ensure short and long term liquidity to undertakings affected by COVID 19 and on the other to preserve fair competition respect of state aid rules control.

M. H. Rahman, S. M. Rifaat

To reduce passenger waiting time and driver search friction, ride-hailing companies need to accurately forecast spatio-temporal demand and supply-demand gap. However, due to spatio-temporal dependencies pertaining to demand and supply-demand gap in a ride-hailing system, making accurate forecasts for both demand and supply-demand gap is a difficult task. Furthermore, due to confidentiality and privacy issues, ride-hailing data are sometimes released to the researchers by removing spatial adjacency information of the zones, which hinders the detection of spatio-temporal dependencies. To that end, a novel spatio-temporal deep learning architecture is proposed in this paper for forecasting demand and supply-demand gap in a ride-hailing system with anonymized spatial adjacency information, which integrates feature importance layer with a spatio-temporal deep learning architecture containing one-dimensional convolutional neural network (CNN) and zone-distributed independently recurrent neural network (IndRNN). The developed architecture is tested with real-world datasets of Didi Chuxing, which shows that our models based on the proposed architecture can outperform conventional time-series models (e.g., ARIMA) and machine learning models (e.g., gradient boosting machine, distributed random forest, generalized linear model, artificial neural network). Additionally, the feature importance layer provides an interpretation of the model by revealing the contribution of the input features utilized in prediction.

Bolong Zheng, Qi Hu, Lingfeng Ming et al.

We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) algorithm that predicts the service requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (DROP) algorithm that considers both the spatial-temporal predictions and the supplydemand state. We report on extensive experiments with realworld and synthetic data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.

Ravi Kashyap

This paper acts as a collection of various trading strategies and useful pieces of market information that might help to implement such strategies. This list is meant to be comprehensive (though by no means exhaustive) and hence we only provide pointers and give further sources to explore each strategy further. To set the stage for this exploration, we consider the factors that determine good and bad trades, the notions of market efficiency, the real prospect amidst the seemingly high expectations of homogeneous expectations from human beings and the catch-22 connotations that arise while comprehending the true meaning of rational investing. We can broadly classify trading ideas and client market color material into Delta-One and Derivative strategies since this acts as a natural categorization that depends on the expertise of the various trading desks that will implement these strategies. For each strategy, we will have a core idea and we will present different flavors of this central theme to demonstrate that we can easily cater to the varying risk appetites, regional preferences, asset management styles, investment philosophies, liability constraints, investment horizons, notional trading size, trading frequency and other preferences of different market participants.

Lia Tiriba, Maria Cristina Paulo Rodrigues, José Luiz Cordeiro Antunes

O Núcleo de Estudos, Documentação e Dados sobre Trabalho e Educação - NEDDATE, criado em 1985, por tempo indeterminado, está vinculado ao Programa de Pós-Graduação em Educação (Mestrado e Doutorado) e à Faculdade de Educação da Universidade Federal Fluminense. Edita a partir do ano de 2003 a revista online TrabalhoNecessário.