Using 1998 data, we show that the gender log wage gap in Sweden increases throughout the wage distribution and accelerates in the upper tail. We interpret this as a strong glass ceiling effect. We use quantile regression decompositions to examine whether this pattern can be ascribed primarily to gender differences in labor market characteristics or in the rewards to those characteristics. Even after extensive controls for gender differences in age, education (both level and field), sector, industry, and occupation, we find that the glass ceiling effect we see in the raw data persists to a considerable extent.
Abstract Precision agricultural technologies (PATs) allow more detailed management of in-field variability. Policy and advisory communities have championed PATs as a route to preserving natural capital whilst increasing productivity from agricultural land. A range of PATs are currently available for the agricultural producer but uptake varies by the type of technology and region. Whereas most studies on uptake have focused on US or Australia we empirically examine uptake of machine guidance (MG) and variable rate nitrogen technologies (VRNT) within European farming systems. Using primary information from 971 arable crop growers across five countries: Belgium, Germany, Greece, the Netherlands and the UK, a multilevel random intercept regression estimated a) the differences between adoption and non-adoption and b) the differences between VRNT and MG adoption. We find, aside from size and income differences, which reflect the economic cost barrier to adoption, an attitudinal difference, in terms of optimism towards the technology’s economic return leading to more probability of uptake. Moreover innovative and information seeking behaviour also proved significant when upgrading from machine guidance to variable rate technologies. Subsidy and taxation were considered positive drivers of uptake within the community. However, results suggest that more indirect interventions, such as informational support to counteract industry bias, and demonstration to prove the viability of economic return may be effective at meeting land manager and policy expectations towards PATs.
Groundwater recharge in mountain-front areas is a critical yet poorly constrained component of the water cycle in semiarid regions, particularly where traditional irrigation practices dominate. This study investigates the spatiotemporal dynamics of recharge induced by gravity-fed irrigation in the mountain-front of the Moroccan High Atlas, a key recharge zone for the Haouz aquifer. A simplified water balance approach, corrected for groundwater-based evapotranspiration, was applied to a 20-year dataset of irrigation diversions and remotely sensed evapotranspiration (MOD16A2), and validated against recharge estimates from the water table fluctuation (WTF) method. Results show strong spatial disparities, with upstream zones receiving disproportionately higher water allocations due to ancestral water rights, sustaining potential recharge in ∼90 % of months, while midstream and downstream zones consistently faced deficits. Despite local recharge events linked to flood years, statistically significant declining trends in recharge were observed across all zones, reflecting both reduced streamflow and intensified groundwater abstraction. Sensitivity tests revealed that neglecting rainfall and ΔS introduces only modest biases (≤12 % in upstream, ≤24 % in midstream zones), confirming the dominance of irrigation as the primary recharge driver. Potential recharge estimates aligned closely with WTF-derived values (differences of 5–14 %), further attesting to the reliability of the approach. These findings highlight the vulnerability of traditional irrigation systems under climate and human pressures and emphasize the urgent need for integrated water management strategies that safeguard ancestral irrigation practices while promoting adaptive measures such as managed aquifer recharge and climate-smart agriculture.
Simpson Zhang, Tennison Liu, Mihaela van der Schaar
Current labor markets are strongly affected by the economic forces of adverse selection, moral hazard, and reputation, each of which arises due to $\textit{incomplete information}$. These economic forces will still be influential after AI agents are introduced, and thus, agents must use metacognitive and strategic reasoning to perform effectively. Metacognition is a form of $\textit{internal reasoning}$ that includes the capabilities for self-assessment, task understanding, and evaluation of strategies. Strategic reasoning is $\textit{external reasoning}$ that covers holding beliefs about other participants in the labor market (e.g., competitors, colleagues), making strategic decisions, and learning about others over time. Both types of reasoning are required by agents as they decide among the many $\textit{actions}$ they can take in labor markets, both within and outside their jobs. We discuss current research into metacognitive and strategic reasoning and the areas requiring further development.
Since McCallum (1987), it is well known that in an overlapping generations (OLG) economy with land, the equilibrium is Pareto efficient because with balanced growth, the interest rate exceeds the economic growth rate ($R>G$), which rules out infinite debt rollover (a Ponzi scheme). We show that once we remove knife-edge restrictions on the production function and allow unbalanced growth, under some conditions an efficient equilibrium with land bubbles necessarily emerges and infinite debt rollover becomes possible, which is a markedly different insight from the conventional view derived from the Diamond (1965) landless economy. We also examine the possibility of Pareto inefficient equilibria.
This study proposes a unified multi-stage framework to reconstruct consistent monthly and annual labor indicators for all 33 Colombian departments from 1993 to 2025. The approach integrates temporal disaggregation, time-series splicing and interpolation, statistical learning, and institutional covariates to estimate seven key variables: employment, unemployment, labor force participation (PEA), inactivity, working-age population (PET), total population, and informality rate, including in regions without direct survey coverage. The framework enforces labor accounting identities, scales results to demographic projections, and aligns all estimates with national benchmarks to ensure internal coherence. Validation against official departmental GEIH aggregates and city-level informality data for the 23 metropolitan areas yields in-sample Mean Absolute Percentage Errors (MAPEs) below 2.3% across indicators, confirming strong predictive performance. To our knowledge, this is the first dataset to provide spatially exhaustive and temporally consistent monthly labor measures for Colombia. By incorporating both quantitative and qualitative dimensions of employment, the panel enhances the empirical foundation for analysing long-term labor market dynamics, identifying regional disparities, and designing targeted policy interventions.
Stephen Meisenbacher, Svetlozar Nestorov, Peter Norlander
Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demonstrate its reliability and accuracy in out-of-sample and LLM-as-a-Judge testing. We extract more than 10 billion data points from more than 155 million online job ads provided by the National Labor Exchange (NLx) Research Hub, including O*NET tasks, occupation codes, tools, and technologies, as well as wages, skills, industry, and more features. We describe the construction of a dataset of occupation, state, and industry level features aggregated by monthly active jobs from 2015 - 2025. We illustrate the potential for research and future uses in education and workforce development.
This paper develops a novel method for estimating the housing production function that addresses transmission bias caused by unobserved heterogeneity in land productivity. The approach builds on the nonparametric identification strategy of Gandhi et al. (2020) and exploits the zero-profit condition to allow consistent estimation even when either capital input or housing value is unobserved, under the assumption that land productivity follows a Markov process. Monte Carlo simulations demonstrate that the estimator performs well across a variety of production technologies.
Abstract Zero waste manufacturing (ZWM) is a concept to support countries transition to a circular economy by developing manufacturing technologies and systems that eliminate waste across entire waste value chains to the fullest extent possible through reuse and recycling. Implementation of ZWM, particularly in dense urban settings such as Singapore, presents challenges for stakeholders, which stem from issues related to land scarcity, productivity, and labor shortage. A framework to address these challenges is proposed comprising six themes of design for zero waste, smart waste audit and reduction planning, smart waste collection, high-value mixed waste processing, collaborative platform for industrial symbiosis, and waste to resource conversion and recycling. A systematic literature review is used to examine industry technologies and research across the six themes to determine how the technologies can support ZWM. The research reveals that a variety of mature waste measurement, collection, and conversion technologies can be integrated through internet-of-things applications and a collaborative platform for industrial symbiosis to support Singapore and other countries in developing a ZWM ecosystem. This research examines the technical limitations of implementing ZWM technologies in dense urban settings using Singapore as a case study. Future areas of research are then proposed to overcome the implementation barriers so that ZWM can be enabled.
This paper delves into the development and optimization of an intelligent agricultural monitoring system that makes use of drone technology to enhance agricultural productivity and sustainability. With the world's population on the rise and the scarcity of arable land, precision agriculture becomes essential in guaranteeing food security. By harnessing state-of-the-art sensors and imaging technologies, drone technology offers a new and innovative approach to agricultural monitoring. Efficiently collecting vital data on vegetation indices, soil moisture, and crop health. This study seeks to create a state-of-the-art drone-based monitoring system that uses advanced machine learning algorithms and data processing technologies to analyze agricultural data with exceptional precision. It explores the most efficient methods for operating drones, including flight planning and data collection protocols, with the aim of creating a comprehensive agricultural monitoring system. This platform provides the convenience of automating monitoring processes, leading to lower labor costs, improved resource allocation, and a beneficial impact on agricultural modernization. The article delves into the effects of the latest developments in drone technology and the subsequent decrease in costs on the agriculture industry worldwide. It highlights the ways in which these advancements are transforming conventional farming methods.
Junaid Mushtaq Lone, Shinsuke Agehara, Amr Abd-Elrahman
Commercial strawberry (Fragaria ×ananassa Duch.) production in Florida relies heavily on bare-root transplants, which typically have 3–5 leaves with partially desiccated roots. Successful establishment requires sprinkler irrigation during daylight hours for the first 10–14 days, leading to substantial water consumption. To address this issue, we evaluated the efficacy of intermittent sprinkler irrigation as a water conservation strategy. We conducted field experiments over two growing seasons [Season 1 (2021–22) and Season 2 (2022–23)] in west-central Florida using three major strawberry cultivars, ‘Florida127’, ‘Florida Brilliance’, and ‘FL 16.30–128’. Plants were subjected to four different intermittent irrigation programs during establishment: 10/0 (continuous irrigation), 10/10, 10/15, and 10/20 min (on/off) from 0800 to 1800 HR for 12 days after transplanting. The impact of intermittent irrigation on marketable yield was cultivar- and season-dependent. 'Florida Brilliance' exhibited a 27 % yield increase in Season 1 but no significant difference in Season 2. By contrast, the other two cultivars exhibited no significant yield response in either season. In ‘Florida Brilliance’, marketable yield was strongly correlated with early canopy growth, suggesting that the yield increase was due partly to accelerated canopy establishment. This surprising result could be explained by the role of stress-induced leaf senescence in enhancing acclimation to adverse environmental conditions. It is speculated that increased heat stress from intermittent irrigation promotes senescence of initial leaves, facilitating nutrient translocation to the crown and subsequently accelerating the formation of new leaves and roots. Our results demonstrate that, without significant yield loss, intermittent sprinkler irrigation can reduce water use by 50–67 % during the establishment of strawberry bare-root transplants, accounting for 322–429 mm of water saving (3.2–4.3 million liters per hectare). Importantly, this water-conservation practice is easy to implement and does not negatively impact fruit quality.
Tiago Fávero de Oliveira, Breno Apolinário da Silva
O objetivo deste estudo é analisar como a mudança tecnológica altera processos produtivos e educativos. O texto aponta que, apesar do apelo de modernização e inovação, a difusão de tecnologias de inteligência artificial altera a relação entre linguagem e pensamento, produzindo uma educação tecnobancária cujos efeitos geram submissão, dominação, exploração e universalização de um pensamento único. O artigo parte das análises de Marx sobre a maquinaria e se desenvolve apontando alterações, contradições e desafios sobre o tema. Ao final, são apresentados caminhos para o enfrentamento da questão no sentido de gerar uma educação comprometida com os interesses de emancipação da classe dominada.
Palavras-chave: Educação tecnobancária; Inteligência Artificial; Educação.
Special aspects of education, Labor market. Labor supply. Labor demand
Do labor market policies initiated in periods of loose monetary policy yield different outcomes from those introduced when monetary tightening prevails? Using data from 11 euro-area members up to 2010 -- and extending to 17 countries up to 2020 -- we analyze three labor market policies: replacement rates, spending on active labor market policies (ALMPs), and employment protection. We find that these policies deliver different macroeconomic outcomes in low- and high-interest rate environments. In particular, ALMPs reduce unemployment if implemented under a loose monetary policy but not otherwise, whereas higher employment protection delivers expansionary effects under a tight monetary policy. These findings highlight that the effectiveness of labor market policies is significantly influenced by the monetary policy environment, emphasizing the need for coordinated policy design. Methodologically, we contribute by proposing to average local projections using Mallow's $C_{p}$ criterion, allowing for inferences that are robust to mis-specification and accommodate non-linearities.
What happens when employers value worker welfare in frictional labor markets? We show this "responsibility" creates an endogenous wedge in the marginal labor cost -- akin to a hiring subsidy -- altering wage and vacancy incentives rather than only changing the surplus split. The wedge is strongest when outside options are weak and separations rare, implying larger wage premia in slack, low-mobility markets. In a wage-posting model with on-the-job search, responsible firms may occupy the high-wage segment even when less productive. In a DMP model, responsible firms commit to higher worker bargaining power, raising the value of unemployment and thereby wages at regular firms.
Background: Open Source Software (OSS) fuels our global digital infrastructure but is commonly maintained by small groups of people whose time and labor represent a depletable resource. For the OSS projects to stay sustainable, i.e., viable and maintained over time without interruption or weakening, maintenance labor requires an underlying infrastructure to be supported and secured. Aims: Using the construct of human infrastructure, our study aims to investigate how maintenance labor can be supported and secured to enable the creation and maintenance of sustainable OSS projects, viewed from the maintainers' perspective. Method: In our exploration, we interviewed ten maintainers from nine well-adopted OSS projects. We coded the data in two steps using investigator-triangulation. Results: We constructed a framework of infrastructure design that provide insight for OSS projects in the design of their human infrastructure. The framework specifically highlight the importance of human factors, e.g., securing a work-life balance and proactively managing social pressure, toxicity, and diversity. We also note both differences and overlaps in how the infrastructure needs to support and secure maintenance labor from maintainers and the wider OSS community, respectively. Funding is specifically highlighted as an important enabler for both types of resources. Conclusions: The study contributes to the qualitative understanding of the importance, sensitivity, and risk for depletion of the maintenance labor required to build and maintain healthy OSS projects. Human infrastructure is pivotal in ensuring that maintenance labor is sustainable, and by extension the OSS projects on which we all depend.
In recent decades, the causes and consequences of climate change have accelerated, affecting our planet on an unprecedented scale. This change is closely tied to the ways in which humans alter their surroundings. As our actions continue to impact natural areas, using satellite images to observe and measure these effects has become crucial for understanding and combating climate change. Aiming to map land naturalness on the continuum of modern human pressure, we have developed a multi-modal supervised deep learning framework that addresses the unique challenges of satellite data and the task at hand. We incorporate contextual and geographical priors, represented by corresponding coordinate information and broader contextual information, including and surrounding the immediate patch to be predicted. Our framework improves the model's predictive performance in mapping land naturalness from Sentinel-2 data, a type of multi-spectral optical satellite imagery. Recognizing that our protective measures are only as effective as our understanding of the ecosystem, quantifying naturalness serves as a crucial step toward enhancing our environmental stewardship.