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

Peilin Rao, Randall R. Rojas

This paper provides robust, new evidence on the causal drivers of market troughs. We demonstrate that conclusions about these triggers are critically sensitive to model specification, moving beyond restrictive linear models with a flexible DML average partial effect causal machine learning framework. Our robust estimates identify the volatility of options-implied risk appetite and market liquidity as key causal drivers, relationships misrepresented or obscured by simpler models. These findings provide high-frequency empirical support for intermediary asset pricing theories. This causal analysis is enabled by a high-performance nowcasting model that accurately identifies capitulation events in real-time.

Rahman Khorramfar, Dharik Mallapragada, Saurabh Amin

Implementing economy-wide decarbonization strategies based on decarbonizing the power grid via variable renewable energy (VRE) expansion and electrification of end-uses requires new approaches for energy infrastructure planning that consider, among other factors, weather-induced uncertainty in demand and VRE supply. An energy planning model that fails to account for these uncertainties can hinder the intended transition efforts to a low-carbon grid and increase the risk of supply shortage especially during extreme weather conditions. Here, we consider the generation and transmission expansion problem of joint power-gas infrastructure and operations planning under the uncertainty of both demand and renewable supply. We propose two distributionally robust optimization approaches based on moment (MDRO) and Wasserstein distance (WDRO) ambiguity sets to endogenize these uncertainties and account for the change in the underlying distribution of these parameters that is caused by the climate change, among other factors. Furthermore, our model considers the risk-aversion of the energy planners in the modeling framework via the conditional value-at-risk (CVaR) metric. An equivalent mixed-integer linear programming (MILP) reformulation of both modeling frameworks is presented, and a computationally efficient approximation scheme to obtain near-optimal solutions is proposed. We demonstrate the resulting DRO planning models and solution strategy via a New England case study under different levels of end-use electrification and decarbonization targets. Our experiments systematically explore different modeling aspects and compare the DRO models with stochastic programming (SP) results.

David Ellerman

The usual formulas for the fair market valuation of a firm at time $t$ include the profits accruing to the shares at time $t$ from the use of wage or salaried labor in the future. But in employee-owned firms or partnerships, the future worker-members or partners are the residual claimants at those future times, so in those cases, the future residuals do not accrue to the current shareholder/residual-claimants. Hence any `fair market valuation' of an employee-owned firm or partnership that assumes those future residuals accrue to the current shareholder/residual-claimants is inappropriate. Keywords: fair market valuations, residual claimants, property rights, personal rights, Miller-Modigliani valuations.

Johnny So, Michael Ferdman, Nick Nikiforakis

The web continues to grow, but dependency-monitoring tools and standards for resource integrity lag behind. Currently, there exists no robust method to verify the integrity of web resources, much less in a generalizable yet performant manner, and supply chains remain one of the most targeted parts of the attack surface of web applications. In this paper, we present the design of LiMS, a transparent system to bootstrap link integrity guarantees in web browsing sessions with minimal overhead. At its core, LiMS uses a set of customizable integrity policies to declare the (un)expected properties of resources, verifies these policies, and enforces them for website visitors. We discuss how basic integrity policies can serve as building blocks for a comprehensive set of integrity policies, while providing guarantees that would be sufficient to defend against recent supply chain attacks detailed by security industry reports. Finally, we evaluate our open-sourced prototype by simulating deployments on a representative sample of 450 domains that are diverse in ranking and category. We find that our proposal offers the ability to bootstrap marked security improvements with an overall overhead of hundreds of milliseconds on initial page loads, and negligible overhead on reloads, regardless of network speeds. In addition, from examining archived data for the sample sites, we find that several of the proposed policy building blocks suit their dependency usage patterns, and would incur minimal administrative overhead.

Andrey Fradkin

This paper documents three stylized facts about the demand for Large Language Models (LLMs) using data from OpenRouter, a prominent LLM marketplace. First, new models experience rapid initial adoption that stabilizes within weeks. Second, model releases differ substantially in whether they primarily attract new users or substitute demand from competing models. Third, multihoming, using multiple models simultaneously, is common among apps. These findings suggest significant horizontal and vertical differentiation in the LLM market, implying opportunities for providers to maintain demand and pricing power despite rapid technological advances.

Anna Piszcz

The traditional institutional model of a competition agency has assumed entrusting competition protection to a single authority. Over time, more and more often national competition authorities are being entrusted not only with competition enforcement but also with other functions (tasks). And the tendency to increasingly entrust competition authorities with additional roles not related to competition policy may seem worrying, as there may be a risk of pushing competition protection to the background. This article is going to reflect upon the relevant changes after the enlargement of the European Union (2004) and the current situation of selected competition authorities in this context. This contribution will explore the topic in relation to the main models of multifunctional competition authorities entrusted with sectoral regulation and examine authorities which combine competition enforcement with sectoral regulation in infrastructure industries. The article will also take into account that public enforcement of the prohibition of unfair trading practices provided for in Directive 2019/633 (“UTP Directive”), and limited to the agri-food market, was assigned to competition authorities (in line with the European Commission’s suggestion), which is the case of almost half of the EU Member States. It will attempt at the identification of advantages and disadvantages of entrusting competition authorities with additional sectoral regulation functions and discuss whether Directive 2019/1 (“ECN+ Directive”) may serve as a cure for disadvantages of adding not related objectives, such as overwhelming numbers of tasks and lacking resources, including human resources or a relatively small part of budget dedicated to competition enforcement. The research methods employed in this article include, first, a doctrinal legal method, second, systemic and teleological approaches, and, last, a comparative analysis.

Esrat Farhana Dulia, Syed A.M. Shihab

Electric vertical takeoff and landing (eVTOL) aircraft manufacturers await numerous pre-orders for eVTOLs and expect demand for such advanced air mobility (AAM) aircraft to rise dramatically soon. However, eVTOL manufacturers (EMs) cannot commence mass production of commercial eVTOLs due to a lack of supply chain planning for eVTOL manufacturing. The eVTOL supply chain differs from traditional ones due to stringent quality standards and limited suppliers for eVTOL parts, shortages in skilled labor and machinery, and contract renegotiations with major aerospace suppliers. The emerging AAM aircraft market introduces uncertainties in supplier pricing and capacities, eVTOL manufacturing costs, and eVTOL demand, further compounding the supply chain planning challenges for EMs. Despite this critical need, no study has been conducted to develop a comprehensive supply chain planning model for EMs. To address this research gap, we propose a stochastic optimization model for integrated supply chain planning of EMs while maximizing their operating profits under the abovementioned uncertainties. We conduct various numerical cases to analyze the impact of 1) endogenous eVTOL demand influenced by the quality of eVTOLs, 2) supply chain disruptions caused by geopolitical conflicts and resource scarcity, and 3) high-volume eVTOL demand similar to that experienced by automotive manufacturers, on EM supply chain planning. The results indicate that our proposed model is adaptable in all cases and outperforms established benchmark stochastic models. The findings suggest that EMs can commence mass eVTOL production with our model, enabling them to make optimal decisions and profits even under potential disruptions.

Sandra M. Leitner, Oliver Reiter

Abstract This paper analyses changes in the speed of labour demand for new hires in response to the lockdowns that were repeatedly put in place to contain the spread of the COVID-19 pandemic. It tests whether the uncertainty-reducing effect of similar lockdowns occurring in quick succession increased the responsiveness of the labour market, thereby allowing for more rapid adjustment, both at the beginning and at the end of subsequent lockdowns. It uses high-frequency online job-posting data and applies an event study approach to the beginning of three national lockdowns and the subsequent reopening in Austria between 2020 and 2022. In view of the importance of progress in vaccination for labour market recovery, it also looks at vaccine roll-out as an additional COVID-19 containment measure, with 2021 as the main roll-out period. The results indicate very different responses to the three lockdowns, with a decline in job-posting activity of between 47 and 50% during the first lockdown and of between 29 and 31% during the second; but an increase of 23% to 28% during the last lockdown. Moreover, responses to the first lockdown were sluggish, with a slow decline at the beginning and a very slow recovery after it was lifted; but over subsequent lockdowns the responses were more rapid and more symmetrical. Responses to the various events differed by occupation and industry: the strongest responses were to be observed in the highly skilled and more-teleworkable occupations of technicians, and managers and professionals, who were badly affected during the first lockdown; the leisure and hospitality industry, which was the hardest hit on account of the mandatory closures and the widespread travel restrictions and bans, and which recovered only very slowly; and the IT, internet and telecommunications industry, where posting activity developed in a direction opposite to that seen in the other industries. Finally, there is little robust evidence of a differentiated effect of vaccinations during lockdowns, suggesting that vaccination roll-out did not have an additional demand-generating effect, over and above the lockdowns.

Haoyang Chen, Peiyan Sun, Qiyuan Song et al.

Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate them deterministically, assuming compliance with the reposition. In this paper, we consider a more realistic and driver-centric scenario where drivers have unique cruising preferences and can decide whether to take the recommendation or not on their own. We propose i-Rebalance, a personalized vehicle reposition technique with deep reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on accepting reposition recommendations through an on-field user study involving 99 real drivers. To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order. This sequential learning strategy facilitates more effective policy training within a smaller action space compared to traditional joint-action methods. Evaluation of real-world trajectory data shows that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%.

Hunter Ng

Wash trading, the practice of simultaneously placing buy and sell orders for the same asset to inflate trading volume, has been prevalent in cryptocurrency markets. This paper investigates whether wash traders in Bitcoin act deliberately to exploit market conditions and identifies the characteristics of such manipulative behavior. Using a unique dataset of 18 million transactions from Mt. Gox, once the largest Bitcoin exchange, I find that wash trading intensifies when legitimate trading volume is low and diminishes when it is high, indicating strategic timing to maximize impact in less liquid markets. The activity also exhibits spillover effects across platforms and decreases when trading volumes in other asset classes like stocks or gold rise, suggesting sensitivity to broader market dynamics. Additionally, wash traders exploit periods of heightened media attention and online rumors to amplify their influence, causing rapid but short-lived spikes in legitimate trading volume. Using an exogenous demand shock associated with illicit online marketplaces, I find that wash trading responds to contemporaneous events affecting Bitcoin demand. These results advance the understanding of manipulative practices in digital currency markets and have significant implications for regulators aiming to detect and prevent wash trading.

Mengjia Zhen, Junlan Yu, Siyi Chen et al.

Utilizing panel data from 264 prefecture-level cities in mainland China between 2009 and 2017, this study employs a multi-period difference-in-differences model and propensity score matching to assess the effects of county-to-district transformation (CDT) on the scale, proportion, and price of the urban residential land supply. The findings reveal the following details: (1) CDT led to a short-term increase in the overall proportion and price of this land, whereas its influence on the scale of the supply exhibited a time lag; (2) the policy’s impact on residential land supply varied across different types of cities, with a more pronounced effect on the scale, proportion, and price in large cities; and (3) the current implementation of CDT primarily modified the urban land’s supply–demand relationship through the expansion of built-up space, conversion of spatial function, and agglomeration of population and the labor force, consequently affecting the supply of the aforementioned land. Finally, this paper puts forward relevant policy suggestions on how to adjust land supply and effectively regulate the land market during the process of promoting the withdrawal of counties in the future.

Roseny de Almeida

Poeta, carioca, antropólogo, amante da cultura popular, defensor do meio ambiente, andarilho… no seu país e fora dele. Tinha um pé na academia, outro na educação popular, escreveu dezenas de livros, lecionou em diversas universidades brasileiras e estrangeiras, foi amigo e companheiro de Paulo Freire, veja que dupla perfeita! Foi assim que o nosso gigante da educação popular fez pousada por aqui por oitenta e três anos, um “plantador de sonhos”. 

Adriana D'Agostini, Célia Vendramini, Mauro Titton

O entrevistado deste número é o sociólogo italiano Pietro Basso que concedeu a entrevista na sua casa, em Mogliano Veneto, no dia 19 de junho de 2023, tendo sido revisada pelo mesmo após a transcrição. Pietro Basso tem larga experiência acadêmica e militante. Lecionou sociologia no Instituto Universitário Oriental de Nápoles e na Universidade Ca’Foscari Veneza, Itália. Atualmente está aposentado e contribui como editor da revista “Il Cuneo rosso” e do blog internacionalista “Il pungolo rosso”. Foi por muitos anos diretor, na Ca’Foscari, do Master Sull’Immigrazione, a primeira experiência italiana de formação no âmbito da pós-graduação sobre o fenômeno migratório, que teve entre seus palestrantes estudiosos de alto nível de todo o mundo.  É autor e organizador de muitos livros, edições de revistas e ensaios sobre temas da mundialização e do mercado de trabalho, desemprego, organização do trabalho e do tempo de trabalho, “raça” e racismo de Estado, islamofobia, imigração internacional, lutas do proletariado, história do movimento comunista. Algumas de suas obras foram traduzidas em vários idiomas. No Brasil, além de artigos e capítulos de livros, publicou o livro “Tempos modernos, jornadas antigas: vidas de trabalho no início do século XXI”, pela editora da UNICAMP, em 2018. A sua produção acadêmica e ativismo político concentram-se na crítica marxista do capitalismo e nesta entrevista Pietro nos fala sobre a reprodução social do proletariado hoje no contexto do capital, global e nacional, indicando tendências gerais e contrastando com situações específicas regionais. Sobre a reprodução social da massa de trabalhadores imigrantes que compõem o proletariado, Pietro analisa como “o destino das trabalhadoras e dos trabalhadores imigrantes é o destino de todos”, ou seja, como a inferiorização dos imigrantes fomenta divisões na classe trabalhadora e funciona como alavanca para piorar a condição do proletariado como um todo. Ao mesmo tempo, as lutas dos imigrantes contra a discriminação e o racismo incidem também nas lutas dos trabalhadores em geral. Nosso entrevistado aborda ainda as dificuldades de organização dos trabalhadores italianos, recorrendo a elementos históricos, ao contexto social e político atual e indicando os setores e organizações que, de forma limitada, vêm se constituindo como vanguarda das lutas. Por fim, Pietro é desafiado a pensar sobre um novo Manifesto do Partido Comunista, analisando alguns aspectos, como o nível atual de destrutividade do capitalismo plenamente realizado, a atual composição do proletariado cada vez mais multinacional e multirracial, a crescente composição feminina do proletariado internacional e o vigor da concepção de Marx e Engels sobre a auto-organização da classe, necessária para orientar o protagonismo de massa dos trabalhadores diante das condições atuais. A entrevista é acompanhada pela tradução do texto original em italiano Quarenta anos de ataques capitalistas: como mudaram a condição e o modo de pensar dos trabalhadores, publicado originalmente na revista Il cuneo rosso.

Kevin X. D. Huang, Guoqiang Tian, Xiaowen Wang

Abstract The Chinese economy upheld a frail recovery in 2022 under the triple superposition of contraction of demand, disruption in supply, and weakening expectations, aggravated by unanticipated adverse shocks in the midst of global turmoil. Over the year, rising income uncertainty set off by the pandemic shock continued depressing household consumption and housing demand. Trade also saw slowing growth, along with consumption and investment, with sluggish residential investment awaiting policy stimulus to take force. Unemployment rate remained high, and was much higher for youth engendered by severe structural imbalances in the labor market. Local government debt burden worsened while revenue shrinking, only to exacerbate the local fiscal financial risk. It fared better on the price side. While growth in producer price index kept falling, consumer price index maintained steady growth. Renminbi depreciated against USD through fluctuations with larger swings, but the exchange rate remained in a manageable band. The Institute for Advanced Research‐China Macroeconomic Model projects the baseline growth rate in real gross domestic product to be 5.4% in 2023. We have also used the model to conduct alternative scenario analyses and policy simulations to assess the impacts of potential downside risks or favorable situations. Our findings call for a focus on economic construction with deepening reform and opening up more comprehensively and initiatively. Only by doing so can China spur market vitality, strengthen business confidence, and forge competitive advantages.

Mahdi Ebrahimzadeh-Afrouzi, Masoud Asadpour Ahmadchali

Due to the growing concerns for sustainable development, supply chains seek to invest in social sustainability issues to seize more market share in today's competitive business environment. This study aims to develop a coordination scheme for a manufacturer-retailer supply chain (SC) contributing to social donation (SD) activity under a cause-related marketing (CRM) campaign. In the presence of consumer social awareness (CSA), the manufacturer notices consumers through some activities (i.e. labelling) that he participates in a CRM campaign by donating a proportion of the retail price to a cause whenever a consumer makes a purchase. In this study, the market demand depends on the retail price, the retailer's stock level and donation size. The proposed problem is designed under three decision-making systems. Firstly, a decentralized decision-making system (traditional structure), where the SC's members aim to optimize their profits regardless of the other member's profitability, is investigated. Then, the problem is designed under a centralized decision-making system to obtain the best values of the retail price and replenishment decisions from the entire SC perspective. Afterwards, an incentive mechanism based on a cost and revenue-sharing (RCS) factor is developed in the coordination system to persuade the SC members to accept the optimal results of the centralized system without suffering any profit loss. Moreover, the surplus profit obtained in the centralized system is divided between the members based on their bargaining power. The numerical investigations and the blocked decision-making on SD activity are presented to evaluate the proposed model. Not only does the proposed coordination model increase the SC members' profit, but it is also desirable in achieving a more socially responsible SC.

Catarina Vieira Peres de Fraipont

Sihong He, Zhili Zhang, Shuo Han et al.

Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the long charging time and high recharging frequency of EVs pose challenges to efficiently managing EV AMoD systems. The complicated dynamic charging and mobility process of EV AMoD systems makes the demand and supply uncertainties significant when designing vehicle balancing algorithms. In this work, we design a data-driven distributionally robust optimization (DRO) approach to balance EVs for both the mobility service and the charging process. The optimization goal is to minimize the worst-case expected cost under both passenger mobility demand uncertainties and EV supply uncertainties. We then propose a novel distributional uncertainty sets construction algorithm that guarantees the produced parameters are contained in desired confidence regions with a given probability. To solve the proposed DRO AMoD EV balancing problem, we derive an equivalent computationally tractable convex optimization problem. Based on real-world EV data of a taxi system, we show that with our solution the average total balancing cost is reduced by 14.49%, and the average mobility fairness and charging fairness are improved by 15.78% and 34.51%, respectively, compared to solutions that do not consider uncertainties.

Musaab Mousa, Saeed Nosratabadi, Judit Sagi et al.

Analyzing the financial benefit of marketing is still a critical topic for both practitioners and researchers. Companies consider marketing costs as a type of investment and expect this investment to be returned to the company in the form of profit. On the other hand, companies adopt different innovative strategies to increase their value. Therefore, this study aims to test the impact of marketing investment on firm value and systematic risk. To do so, data related to four Arabic emerging markets during the period 2010-2019 are considered, and firm share price and beta share are considered to measure firm value and systematic risk, respectively. Since a firm's ownership concentration is a determinant factor in firm value and systematic risk, this variable is considered a moderated variable in the relationship between marketing investment and firm value and systematic risk. The findings of the study, using panel data regression, indicate that increasing investment in marketing has a positive effect on the firm value valuation model. It is also found that the ownership concentration variable has a reinforcing role in the relationship between marketing investment and firm value. It is also disclosed that it moderates the systematic risk aligned with the monitoring impact of controlling shareholders. This study provides a logical combination of governance-marketing dimensions to interpret performance indicators in the capital market.

Gourav Saha, Alhussein A. Abouzeid, Zaheer Khan et al.

This paper addresses the following question which is of interest in designing efficient exclusive-use spectrum licenses sold through spectrum auctions. Given a system model in which customer demand, revenue, and bids of wireless operators are characterized by stochastic processes and an operator is interested in joining the market only if its expected revenue is above a threshold and the lease duration is below a threshold, what is the optimal lease duration which maximizes the net customer demand served by the wireless operators? Increasing or decreasing lease duration has many competing effects; while shorter lease duration may increase the efficiency of spectrum allocation, longer lease duration may increase market competition by incentivizing more operators to enter the market. We formulate this problem as a two-stage Stackelberg game consisting of the regulator and the wireless operators and design efficient algorithms to find the Stackelberg equilibrium of the entire game. These algorithms can also be used to find the Stackelberg equilibrium under some generalizations of our model. Using these algorithms, we obtain important numerical results and insights that characterize how the optimal lease duration varies with respect to market parameters in order to maximize the spectrum utilization. A few of our numerical results are non-intuitive as they suggest that increasing market competition may not necessarily improve spectrum utilization. To the best of our knowledge, this paper presents the first mathematical approach to optimize the lease duration of spectrum licenses.