The decision to annuitize wealth in retirement planning has become increasingly complex due to rising longevity risk and changing retirement patterns, including increased labor force participation at older ages. While an extensive literature studies consumption, labor, and annuitization decisions, these elements are typically examined in isolation. This paper develops a unified stochastic control and optimal stopping framework in which habit formation and endogenous labor supply shape retirement and annuitization decisions under age-dependent mortality. We derive optimal consumption, labor, portfolio, and annuitization policies in a continuous-time lifecycle model. The solution is characterized via dynamic programming and a Hamilton-Jacobi-Bellman variational inequality. Our results reveal a rich sequence of retirement dynamics. When wealth is low relative to habit, labor is supplied defensively to protect consumption standards. As wealth increases, agents enter a work-to-retire phase in which labor is supplied at its maximum level to accelerate access to retirement. Human capital acts as a stabilizing asset, justifying a more aggressive pre-retirement investment portfolio, followed by abrupt de-risking upon annuitization. Subjective mortality beliefs are a key determinant in shaping retirement dynamics. Agents with pessimistic longevity beliefs rationally perceive annuities as unattractive, leading them to avoid or delay annuitization. This framework provides a behavior-based explanation for low annuity demand and offers guidance for retirement planning jointly linking labor supply, portfolio choice, and the timing of annuitization.
The authors study the distributional consequences of the COVID-19 pandemic’s impact on employment, both during the onset of the pandemic and over subsequent months. Using cross-sectional and matched longitudinal data from the Current Population Survey, they show that the pandemic has exacerbated pre-existing inequalities. Although employment losses have been widespread, they have been substantially larger—and more persistent—in lower-paying occupations and industries. Hispanics and non-White workers suffered larger increases in job losses, not only because of their over-representation in lower-paying jobs but also because of a disproportionate increase in their job displacement probability relative to non-Hispanic White workers with the same job background. Gaps in year-on-year job displacement probabilities between Black and White workers have widened over the course of the pandemic recession, both overall and conditional on pre-displacement occupation and industry. These gaps are not explained by state-level differences in the severity of the pandemic nor by the associated response in terms of mitigation policies. In addition, evidence suggests that older workers have been retiring at faster rates.
Simone Figorilli, Lavinia Moscovini, Simone Vasta
et al.
Recently banana plantations have been affected by the Black Sigatoka Disease (BSD), producing streaks, lesions and yellow and brown spots on the leaves until the appearance of entire dead parts. The disease causes reductions in yield making it essential to assess infection by monitoring plants status and implementing agronomical measures. This work aims to develop a physical field device to identify the BSD presence. It consists in a 3D printed prototype embedding a smartphone acquiring and processing banana leaves images. An advanced Artificial Intelligence model was trained and implemented for real-time processing. The algorithm is a Convolutional Neural Network (CNN) able to classify the samples into 6 classes representative of different BSD stages infection. The trained model, showing an accuracy of 82 % in training and 78 % in validation, was integrated into a specifically developed mobile application for field use. The Android app allows to acquire, identify the georeferenced infection stage, sync all to a remote dedicated host from which the results can be mapped and exported to a .csv file for easy data management. The distinction between healthy and diseased leaves can be achieved using the Smart BSD device for real-time acquisition, establishing the right intervention strategy.
I examine whether the early but temporary availability of low-barrier employment opportunities in the hospitality sector affects the labor market integration of refugees. My identification strategy combines the quasi-exogenous allocation of refugees to Austrian regions with high seasonality in Austria's hospitality sector, where 25% of refugees find initial employment. Exploiting within region, within year variation, I find that receiving labor market access during high seasonal demand increases employment probability initially, with significant employment effects of up to 3 percentage points, or 9% of the mean, in the first months. Employment advantages diminish after the first year, indicating that such early employment opportunities do not serve as a stepping stone. Still, treated refugees have in total earned more in the first three years, with no significant differences in medium-term wages and job quality. One disadvantage of early employment in hospitality is the increased labor market segregation, as treated refugees are more likely to work in industries more typical for refugees and in firms with higher non-Austrian coworker shares.
Bahar (2025) argues that there is a long-term cointegrating relationship between US job vacancies and southwest border crossings. We show that this conclusion is based on a misspecified Engle-Granger test applied to first differences. Once the Engle-Granger test is correctly applied to levels, evidence for a cointegrating relationship vanishes, invalidating the paper's approach to estimating short- and long-run elasticities. Bahar's approach is therefore uninformative about the relationship between US labor market conditions and migration.
This paper investigates the economic feasibility of replacing human labor with robotics and automation in Qatar's manufacturing and service sectors. By analyzing labor costs, productivity gains, and implementation expenses, the study assesses the potential financial impact and return on investment of robotic integration. Results indicate the sectors where automation is economically viable and identify challenges related to workforce adaptation, policy, and infrastructure. These insights provide guidance for policymakers and industry stakeholders considering automation strategies in Qatar.
Promoting the development of eco-industries plays a significant role in achieving the harmonious symbiosis between economic growth and environmental protection as well as enhancing the comprehensive effectiveness of ecological and economic benefits. Due to their unique nature, cooperatives may play a crucial role in facilitating the integration between farmers and the development of eco-industries. To investigate whether cooperatives possess the capacity to enhance the income-generating effects for farmers involved in eco-industries, this study selected the Crested Ibis National Nature Reserve (CINNR), a representative area for eco-industry development, as the research site. Data were gathered through face-to-face interviews, and this research empirically analyzed the impact of cooperatives on the income-generating effect of farmers using endogenous switching regression (ESR). The findings are threefold. First, cooperatives indeed enhance the income-generating effects for farmers engaged in eco-industries. Second, variables such as the distribution of agroforestry materials, premium capacity, soil quality, and status of village cadres have a positive impact on farmers joining cooperatives, whereas punishment initiatives discourage their participation. Third, for farmers who have joined cooperatives, factors such as the distribution of agroforestry materials, premium capacity, low-cost conservation initiatives, land area, status of village cadres, the proportion of labor force, technical training, soil quality, and land area positively affect their income from eco-industries. Conversely, punishment initiatives, age, and land location negatively impact their income. The results of this study provide new ideas for farmers to participate in the development of eco-industries, new evidence showing co-operatives can improve farmers’ income, and new directions for coordinating conflicts between conservation and development in protected areas.
: Specialized villages (SVs), as a special form of economic development in agricultural areas, are considered an important lever for rural revitalization. In recent years, many SVs have shown a highly intensive development trend, with the emergence and increase of large specialized households, causing some small specialized households to abandon characteristic production projects. The proportion of specialized households engaging in specialized production in SVs has decreased, and labor outflow has become apparent. Based on the data of two censuses of SVs in Henan Province and the data of field investigation of SVs in typical case areas conducted from 2022 to 2023, this study used the spatial autocorrelation and hot spot analysis methods to analyze the characteristics of labor outflow from different types of SVs. Geographic detectors were used to detect, identify, and analyze the dominant factors of labor outflow from SVs. This study found that: 1) Overall, the outflow of labor from SVs in various regions of Henan Province has significantly increased. 2) The spatial agglomeration characteristics of labor outflow in agricultural SVs were obvious, while the labor outflow in industrial SVs did not show spatial agglomeration characteristics, and the labor outflow in service and other SVs showed a spatial agglomeration trend. 3) The outflow of labor force from agricultural SVs were increasing in various regions, while the outflow of labor force from industrial SVs were most obvious in the eastern region of the province. The outflow of labor force from service industry and other SVs were relatively complex. 4) The quantitative analysis results indicate that the rate of specialized households, location characteristics, arable land resources, and human resources are the main factors affecting the outflow of the labor force from SVs. 5) This study indicates that the mechanism of labor outflow in SVs is relatively complex, and villages and industries are important material carriers that attract labor force. The interaction of centrifugal force generated in the process of industrial development and centripetal force generated by policies and norms leads to a dynamic state of labor loss and return.
This study proposes mechanisms for firms to thrive in a turbulent business environment by investigating the mediating role of frugal innovation in the relationship between competitive intelligence and value creation. The study also examined absorptive capacity as a critical boundary condition that influences the impact of competitive intelligence on frugal innovation. A survey questionnaire was administered to 302 SMEs, and the data were analysed using Amos. Structural equation modelling (SEM) was employed to test the hypothesis. The findings demonstrate a significant positive impact of competitive intelligence on SME value creation. The study also found that frugal innovation acts as a mechanism to amplify the impact of competitive intelligence on value creation, and this relationship is anchored by a high level of absorptive capacity. The study demonstrates how competitive intelligence and frugal innovation synergistically drive value creation amid turbulence under the auspices of absorptive capacity, thereby increasing SMEs’ resilience and competitiveness. The study sheds light on previously unexplored aspects of value creation in a turbulent business environment, offering novel perspectives on dynamic capabilities.
Gender inequality is a significant concern in many cultures, as women face significant barriers to asset acquisition particularly land ownership and control. Land acquisition and land tenure security are complex issues that affect various cultural groups differently, leading to disparities in access and ownership especially when superimposed with other socio-economic issues like gender inequality. Measuring the severity of these issues across different cultural groups is challenging due to variations in cultural norms, expectations and effectiveness of the measurement framework to correctly assess the level of severity. While nominal measures of gender asset gap provide valuable insights into land acquisition and tenure security issues, they do not fully capture the nuances of cultural differences and the impact of governmental and corporate policies that influence gender disparity in land ownership and control. The proposed framework aims to fill this gap by incorporating cultural and policy factors in developing a new measurement framework equipped with a more robust, comprehensive metric to standardize the approach to assessing the severity of gender asset disparity in a general sense but with a focus on land acquisition and tenure security to engender more effective interventions and policy recommendations.
The emergence of new and disruptive technologies makes the economy and labor market more unstable. To overcome this kind of uncertainty and to make the labor market more comprehensible, we must employ labor market intelligence techniques, which are predominantly based on data analysis. Companies use job posting sites to advertise their job vacancies, known as online job vacancies (OJVs). LinkedIn is one of the most utilized websites for matching the supply and demand sides of the labor market; companies post their job vacancies on their job pages, and LinkedIn recommends these jobs to job seekers who are likely to be interested. However, with the vast number of online job vacancies, it becomes challenging to discern overarching trends in the labor market. In this paper, we propose a data mining-based approach for job classification in the modern online labor market. We employed structural topic modeling as our methodology and used the NASDAQ-100 indexed companies' online job vacancies on LinkedIn as the input data. We discover that among all 13 job categories, Marketing, Branding, and Sales; Software Engineering; Hardware Engineering; Industrial Engineering; and Project Management are the most frequently posted job classifications. This study aims to provide a clearer understanding of job market trends, enabling stakeholders to make informed decisions in a rapidly evolving employment landscape.
This paper examines the impact of racial discrimination in hiring on employment, wages, and wealth disparities between black and white workers. Using a labor search-and-matching model with racially prejudiced and non-prejudiced firms, we show that labor market frictions sustain discriminatory practices as an equilibrium outcome. These practices account for 57% of the racial unemployment gap, 48% of the average wage gap, and 16% of the median wealth gap. Discriminatory hiring also increases unemployment and wage volatility for black workers, increasing their labor market risks over the business cycle. Eliminating prejudiced firms reduces these disparities and improves the welfare of black workers as well as the overall economic welfare.
[Introduction] With the development of energy storage technology, technical schemes and application scenarios become more and more complex. For energy storage research, the conventional classification method based on technical route has certain limitations in some cases. [Method] A classification method of energy storage research based on object hierarchy was proposed: according to the hierarchy order of objects from micro to macro, energy storage researches were divided into material-level, device-level, system & power plant-level and power system-level. This method was applied to the quantitative analysis of 5 397 articles published in journals of energy storage research retrieved by CNKI. [Result] The results show that the number of power system-level research literatures in 2022 is obviously more than other types of literatures, and the number and cumulative citation times of power system-level research literatures in the top 200 literatures cited from 2001 to 2022 are absolutely superior. Before 2010, material-level and device-level research literatures account for a relatively high proportion. Since 2010, the proportion of system & power plant-level and power system-level research literatures has gradually increased, and the growth rate of power system-level research literatures is more obvious. The newly installed capacity of new energy storage from 2011 to 2022 is positively correlated with the number of literatures. Since the system & power plant-level research often directly focuses on actual projects, the number of such literatures is most closely related to the newly installed capacity. [Conclusion] A new way to summarize the research results of energy storage is provided, which could provide support for industrial policy formulation, industrial chain layout and other work.
The paper focuses on some labour law questions which arise from work in the metaverse. The first question is whether meta-work could be the next new type of work as standard employment relationship, which is going through a transformation in general. Indeed, the idea of personal work – as a main pillar of the employment relationship – was challenged by platform work in the recent years, but metaverse seems to further question the old paradigms. The article shortly examines the question of wages, for instance, as the metaverse generally relies on cryptocurrency (CC) to pay for transactions and purchases. Subsequently, the paper mainly concentrates on the analysis of health and safety at work and of the discrimination ban in metaverse.
Law in general. Comparative and uniform law. Jurisprudence, Labor. Work. Working class
A central question in economics is whether automation will displace human labor and diminish standards of living. Whilst prior works typically frame this question as a competition between human labor and machines, we frame it as a competition between human consumers and human suppliers. Specifically, we observe that human needs favor long tail distributions, i.e., a long list of niche items that are substantial in aggregate demand. In turn, the long tails are reflected in the goods and services that fulfill those needs. With this background, we propose a theoretical model of economic activity on a long tail distribution, where innovation in demand for new niche outputs competes with innovation in supply automation for mature outputs. Our model yields analytic expressions and asymptotes for the shares of automation and labor in terms of just four parameters: the rates of innovation in supply and demand, the exponent of the long tail distribution and an initial value. We validate the model via non-linear stochastic regression on historical US economic data with surprising accuracy.
When land becomes more connected, its value can change because of network externalities. This idea is intuitive and appealing to developers and policymakers, but documenting their importance is empirically challenging because it is difficult to isolate the determinants of land value in practice. We address this challenge with real estate in The Sandbox, a virtual economy built on blockchain, which provides a series of natural experiments that can be used to estimate the causal impact of land-based of network externalities. Our results show that when new land becomes available, the network value of existing land increases, but there is a trade-off as new land also competes with existing supply. Our work illustrates the benefits of using virtual worlds to conduct policy experiments.
We study the dynamic effects of fires on county labor markets in the US using a novel geophysical measure of fire exposure based on satellite imagery. We find increased fire exposure causes lower employment growth in the short and medium run, with medium-run effects being linked to migration. We also document heterogeneous effects across counties by education and industrial concentration levels, states of the business cycle, and fire size. By overcoming challenges in measuring fire impacts, we identify vulnerable places and economic states, offering guidance on tailoring relief efforts and contributing to a broader understanding of natural disasters' economic impacts.