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Informal carers provide the majority of care for people living with challenges related to older age, long-term illness, or disability. However, the care they provide often results in a significant income penalty for carers, a factor largely overlooked in the economics literature and policy discourse. Leveraging data from the UK Household Longitudinal Study, this paper provides the first robust causal estimates of the caring income penalty using a novel individual synthetic control based method that accounts for unit-level heterogeneity in post-treatment trajectories over time. Our baseline estimates identify an average relative income gap of up to 45%, with an average decrease of {\pounds}162 in monthly income, peaking at {\pounds}192 per month after 4 years, based on the difference between informal carers providing the highest-intensity of care and their synthetic counterparts. We find that the income penalty is more pronounced for women than for men, and varies by ethnicity and age.

This paper addresses how to assign a monetary value to scientific publications, particularly in the case of multi-author papers arising from large-scale research collaborations. Contemporary science increasingly relies on extensive and varied collaborations to tackle global challenges in fields such as life sciences, climate science, energy, high-energy physics, astronomy, and many others. We argue that existing literature fails to address the collaborative nature of research by overlooking the relationship between coauthorship and scientists productivity. Using the Marginal Cost of Production (MCP) approach, we first highlight the methodological limitations of ignoring this relationship, then propose a generalised MCP model to value co-authorship. As a case study, we examine High-Energy Physics (HEP) collaborations at the Large Hadron Collider (LHC) at CERN, analysing approximately half a million scientific outputs by over 50,000 authors from 1990 to 2021. Our findings indicate that collaborative adjustments yield monetary valuations for subsets of highly collaborative papers up to 3 orders of magnitude higher than previous estimates, with elevated values correlating with high research quality. This study contributes to the literature on research output evaluation, addressing debates in science policy around assessing research performance and impact. Our methodology is applicable to authorship valuation both within academia and in large-scale scientific collaborations, fitting diverse research impact assessment frameworks or as self-standing procedure. Additionally, we discuss the conditions under which this method may complement survey-based approaches., Comment: 38 pages, excluding annexes, 15 figures. Working paper to be submitted to "Scientometrics"

State capacity may shape whether natural resources generate prosperity, as it determines if windfalls are effectively turned into useful projects or wasted. We test this hypothesis studying the 2004-2011 mining boom in Peru, where mines' profits are redistributed as windfall transfers to local governments. Our empirical strategy combines geological data with the central government's mining windfalls allocation formula to identify the windfalls' effects on household incomes and other measures of economic development. Proxying local state capacity with the ability to tax and relying on a triple difference strategy we uncover significant variation in treatment response, with positive effects of windfalls limited to high state capacity localities. We find suggestive evidence that only localities with high state capacity succeed at transforming windfalls into infrastructure stocks, which in turns contributes to structural transformation and market integration. Lastly, social unrest increases in low state capacity localities that receive windfalls but fail to perceive their benefits. Our findings underscore important complementarities between investments in extractive industries and in state capacity.

To achieve sustainable development, there is widespread of the need to protect natural resource and improve government oversight in achieving China's economic security and ecological civilization. Compilation of natural resources balance sheet (NRBS) and enhancement of resources management are becoming an important topic in China. How to compile NRBS to affix the responsibility for government and officials for inadequate supervision is still not resolved satisfactorily. This paper proposes the NRBS to enable governments to identify the importance of natural resource restoration and to hold leading cadres accountable for a lack of adequate supervision. The NRBS consist of three accounts: natural resource assets, natural resource liabilities, and net worth. Important components of the NRBS for the liabilities account with a property rights regime are developed to measure and assign responsibility. The compilation of an NRBS is applied to the Chinese province of Shaanxi as an illustration to demonstrate that the accounting framework and the compilation steps are tractable using financial methods and available data. The accounting results of natural resource assets and liabilities unveil the threat to resource management and the policy implications to government and officials. Finally, the advantages and limitations of NRBS are discussed., Comment: An earlier version of this paper has been presented in International Conference on Economy and Finance in the Comprehensive Deepening Reform at Innovation Harbor of Xi'an Jiaotong University, October 16, 2021

InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. Supported by both PyTorch and GPU-accelerated JAX framework, the benchmark models an intertemporal social dilemma where companies balance short-term profit losses from climate mitigation efforts and long-term benefits from reducing climate risk, while ESG-conscious investors attempt to influence corporate behavior through their investment decisions. Companies allocate capital across mitigation, greenwashing, and resilience, with varying strategies influencing climate outcomes and investor preferences. Our experiments show that without ESG-conscious investors with sufficient capital, corporate mitigation efforts remain limited under the disclosure mandate. However, when a critical mass of investors prioritizes ESG, corporate cooperation increases, which in turn reduces climate risks and enhances long-term financial stability. Additionally, providing more information about global climate risks encourages companies to invest more in mitigation, even without investor involvement. Our findings align with empirical research using real-world data, highlighting MARL's potential to inform policy by providing insights into large-scale socio-economic challenges through efficient testing of alternative policy and market designs.

I show that U.S. incomes follow a one-parameter family of probability distributions over more than fifty years of data. I compare statistical models of income, and I highlight the inverse-gamma distribution as a parsimonious model that matches data particularly well and has straightforward theoretical interpretations. However, despite having relatively few parameters, the inverse-gamma distribution still overfits income data. I establish a linear relationship between parameter estimates, and a one-dimensional model emerges naturally when I exploit this relationship. I conclude with theoretical remarks about the model., Comment: 33 pages

We propose a machine learning algorithm for solving finite-horizon stochastic control problems based on a deep neural network representation of the optimal policy functions. The algorithm has three features: (1) It can solve high-dimensional (e.g., over 100 dimensions) and finite-horizon time-inhomogeneous stochastic control problems. (2) It has a monotonicity of performance improvement in each iteration, leading to good convergence properties. (3) It does not rely on the Bellman equation. To demonstrate the efficiency of the algorithm, it is applied to solve various finite-horizon time-inhomogeneous problems including recursive utility optimization under a stochastic volatility model, a multi-sector stochastic growth, and optimal control under a dynamic stochastic integration of climate and economy model with eight-dimensional state vectors and 600 time periods., Comment: arXiv admin note: substantial text overlap with arXiv:1611.01767

This paper presents findings from a web-experiment on a representative sample of the French population. It examines the acceptability of the Pigou-Dalton principle of transfers, which posits that transferring income from an individual to a relatively poorer one, reduces overall inequality. While up to 60% of respondents reject standard transfers, the three alternative transfers we test receive more approval, especially those promoting solidarity among lower-income recipients. The study then models respondents' preferences with two types of social welfare functions, utilitarian and Extended Gini. The Extended Gini model aligns better with individual preferences. Nevertheless, Extended Gini-type social welfare functions that adhere to the principle of transfers (including the one underlying the Gini index) poorly capture preferences of each individual. However, quite surprisingly, the preferences of the median individual align almost perfectly with the Gini-based function, using either parametric or non-parametric estimates.

Carbon credits are a key component of most national and organizational climate strategies. Financing and delivering carbon credits from forest-related activities faces multiple risks at the project and asset levels. Financial mechanisms are employed to mitigate risks for investors and project developers, complemented by non-financial measures such as environmental and social safeguards and physical risk mitigation. Despite these efforts, academic research highlights that safeguards and climate risk mitigation measures are not efficiently implemented in some carbon projects and that specification of environmental safeguards remains underdeveloped. Further, environmental and social risk mitigation capacities may not be integrated into financial mechanisms. This text examines how ecosystem capacities can be leveraged and valued for mitigation of and adaptation to physical risks by complementing carbon credits with biodiversity insurance and resilience value.

This study explores how refugees' destination preferences evolve during transit, with a focus on Central and Eastern Europe, particularly Romania. Using a mixed-methods approach, we analyse data from the International Organization for Migration's (IOM) Flow Monitoring Surveys and complement it with qualitative insights from focus group discussions with refugees. The quantitative analysis reveals that refugees' preferences for destination countries often change during transit, influenced by factors such as safety concerns, asylum conditions, education, and the presence of relatives at the destination. Our results support the application of bounded rationality and human capital theory, showing that while economic opportunities are important, safety becomes the dominant concern during transit. The qualitative analysis adds depth to these findings, highlighting the role of political instability, social networks, and economic hardships as initial migration drivers. Additionally, the study reveals how refugees reassess their destination choices based on their experiences in transit countries, with Romania emerging as a viable settlement destination due to its relative stability and access to asylum procedures. This research contributes to migration studies by challenging the traditional view of transit countries and offering new insights into the fluid nature of refugee decision-making., Comment: 22 pages, 2 figures

This study sought to better understand the causes of price disparity in cesarean sections, using newly released hospital data. Beginning January 1, 2021, Centers for Medicare and Medicaid Services (CMS) requires hospitals functioning in the United States to publish online pricing information for items and services these hospitals provide in a machine-readable format and a consumer friendly shoppable format. Initial analyses of these data have shown that the price for a given procedure can differ in a hospital and across hospitals. The cesarean section (C-section) is one of the most common inpatient procedures performed across all hospitals in the United States as of 2018. This preliminary study found that for C-section procedures, pricing varied from as little as \$162 to as high as \$115,483 for a single procedure. Overall, indicators for quality and whether or not the hospital was a teaching hospital were found to be significantly significant, while variables including median income and the gini coefficient for wealth inequality were not shown to be statistically significant.

We analyze the timing of NGO campaigns to shed light on NGOs' objectives and how their strategies evolve over time. Using data from 2,500 campaigns, we find that NGOs are eight times more likely to launch campaigns on Annual General Meeting (AGM) dates. These campaigns increase media exposure and stakeholder scrutiny, raising the likelihood of consumer boycotts and related shareholder proposals at the following AGM of the targeted firm but have no impact on current AGM votes. As NGOs build reputational capital, they adjust their timing to influence AGM outcomes, revealing the trade-offs they face to drive corporate change.

This note observes that the Cobb-Douglas function is uniquely characterized by the property that, if the labour share of cost for a constant-returns-to-scale firm remains constant when the firm minimizes its cost for any given output level, then the firm's production function must be Cobb-Douglas., Comment: 2 pages

This study explores the impact of the General Data Protection Regulation (GDPR), introduced on May 25th, 2018, on online trackers, vital elements in the online advertising ecosystem. Using a difference-in-differences approach with a balanced panel of 294 publishers, we compare publishers subject to the GDPR with those unaffected (the control group). Drawing on data from WhoTracks.me, which spans 32 months from May 2017 to December 2019, we analyze how the number of trackers used by publishers changed before and after the GDPR. The findings reveal that although online tracking increased for both groups, the rise was less significant for EU-based publishers subject to the GDPR. Specifically, the GDPR reduced about four trackers per publisher, equating to a 14.79% decrease compared to the control group. The GDPR was particularly effective in curbing privacy invasive trackers that collect and share personal data, thereby strengthening user privacy. However, it had a limited impact on advertising trackers and only slightly reduced the presence of analytics trackers.

We conducted a laboratory experiment involving human subjects to test the theoretical hypothesis that equilibrium selection can be impacted by manipulating the games dynamics process, by using modern control theory. Our findings indicate that human behavior consists with the predictions derived from evolutionary game theory paradigm. The consistency is supported by three key observations: (1) the long-term distribution of strategies in the strategy space, (2) the cyclic patterns observed within this space, and (3) the speed of convergence to the selected equilibrium. These findings suggest that the design of controllers aimed at equilibrium selection can indeed achieve their theoretical intended purpose. The location of this study in the knowledge tree of evolutionary game science is presented., Comment: 15 page, 11 firgure, keywors: evolutionary game theory, experimental economics, mordern control theory, equilibrium selection, game dynamics theory

We use multi-regional input-output analysis to calculate the paid labour, energy, emissions, and material use required to provide basic needs for all people. We calculate two different low-consumption scenarios, using the UK as a case study: (1) a "decent living" scenario, which includes only the bare necessities, and (2) a "good life" scenario, which is based on the minimum living standards demanded by UK residents. We compare the resulting footprints to the current footprint of the UK, and to the footprints of the US, China, India, and a global average. Labour footprints are disaggregated by sector, skill level, and region of origin. We find that both low-consumption scenarios would still require an unsustainable amount of labour and resources at the global scale. The decent living scenario would require a 26-hour working week, and on a per capita basis, 89 GJ of energy use, 5.9 tonnes of emissions, and 5.7 tonnes of used materials per year. The more socially sustainable good life scenario would require a 53-hour working week, 165 GJ of energy use, 9.9 tonnes of emissions, and 11.5 tonnes of used materials per capita. Both scenarios represent substantial reductions from the UK's current labour footprint of 68 hours per week, which the UK is only able to sustain by importing a substantial portion of its labour from other countries. We conclude that reducing consumption to the level of basic needs is not enough to achieve either social or environmental sustainability. Dramatic improvements in provisioning systems are also required., Comment: 36 pages, 5 figures, 1 table

In this paper, we present axiomatic characterizations of some simple risk-sharing (RS) rules, such as the uniform, the mean-proportional and the covariance-based linear RS rules. These characterizations make it easier to understand the underlying principles when applying these rules. Such principles typically include maintaining some degree of anonymity regarding participants' data and/or incident-specific data, adopting non-punitive processes and ensuring the equitability and fairness of risk sharing. By formalizing key concepts such as the reshuffling property, the source-anonymous contributions property and the strongly aggregate contributions property, along with their generalizations, we develop a comprehensive framework that expresses these principles clearly and defines the relevant rules. To illustrate, we demonstrate that the uniform RS rule, a simple mechanism in which risks are shared equally, is the only RS rule that satisfies both the reshuffling property and the source-anonymous contributions property. This straightforward axiomatic characterization of the uniform RS rule serves as a foundation for exploring similar principles in two broad classes of risk-sharing rules, which we baptize the $q$-proportional RS rules and the $(q_1,q_2)$-based linear RS rules, respectively. This framework also allows us to introduce novel particular RS rules, such as the scenario-based RS rules., Comment: 27

Air pollution has emerged as a serious challenge for China, posing a threat to public health and hindering the progress of sustainable economic development. In response to air pollution and other environmental issues, the Chinese government introduced a new Environmental Protection Law in 2015. This paper investigates the impact of the new Environmental Protection Law's implementation and corporate Environmental, Social, and Governance (ESG) investments on air pollution and public subjective well-being. Using panel data at the macro level, we employ a difference-in-differences (DID) model, with Chinese provinces and municipalities as units of analysis, to examine the combined effects of the new Environmental Protection Law and changes in corporate ESG investment intensity. The study evaluates their impacts on air quality and public subjective well-being. Findings indicate that these policies and investment behaviors significantly improve public subjective well-being by reducing air pollution. Notably, an increase in ESG investment significantly reduces air pollution levels and is positively associated with enhanced well-being. These results underscore the critical role of environmental legislation and corporate social responsibility in improving public quality of life and provide empirical support for promoting sustainable development in China and beyond.

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., Comment: 14 pages main text

We analyze the information embedded in forecasts by examining the moments of subjective probability distributions. Specifically, we propose that a forecast conveys a strong upward signal when the median exceeds a target, such as a central bank's 2% inflation threshold, coupled with positive skewness. Conversely, a median below the target with negative skewness signals strong downward expectations. In cases where the median and skewness diverge, the signal weakens, indicating mixed expectations. To formalize these insights, we develop a Signal Strength Indicator (SSI) that quantifies the consistency and directional alignment of forecast signals, assessing its predictive power within a Growth-at-Risk framework. Importantly, the SSI can be estimated without relying on parametric assumptions. Our findings indicate that the SSI offers valuable insights, suggesting it could serve as a practical tool for central banks to monitor expectations in real time.

We develop a theoretical framework analyzing responsible firms (REFs) that prioritize worker welfare alongside profits in labor markets with search frictions. At the micro level, REFs' use of market power varies with labor conditions: they refrain from using it in slack markets but may exercise it in tight markets without harming workers. Our macro analysis shows these firms offer higher wages, creating a distinct high-wage sector. When firms endogenously choose worker bargaining power, there is a trade-off between worker surplus and employment, though this improves with elastic labor supply. While REFs cannot survive with free entry, they can coexist with profit-maximizing firms under limited competition, where their presence forces ordinary firms to raise wages.

This paper combines a techno-economic energy system model with an econometric model to maximise electricity price forecasting accuracy. The proposed combination model is tested on the German day-ahead wholesale electricity market. Our paper also benchmarks the results against several econometric alternatives. Lastly, we demonstrate the economic value of improved price estimators maximising the revenue from an electric storage resource. The results demonstrate that our integrated model improves overall forecasting accuracy by 18 %, compared to available literature benchmarks. Furthermore, our robustness checks reveal that a) the Ensemble Deep Neural Network model performs best in our dataset and b) adding output from the techno-economic energy systems model as econometric model input improves the performance of all econometric models. The empirical relevance of the forecast improvement is confirmed by the results of the exemplary storage optimisation, in which the integration of the techno-economic energy system model leads to a revenue increase of up to 10 %.

We analyze the impact of the Reserve Bank of India's (RBI) monetary policy communications on Indian financial market from April 2014 to June 2024 using advanced natural language processing techniques. Employing BERTopic for topic modeling and a fine-tuned RoBERTa model for sentiment analysis, we assess how variations in sentiment across different economic topics affect the stock market. Our findings indicate that dovish sentiment generally leads to declines in equity markets, particularly in topics related to the interest rate policy framework and economic growth, suggesting that market participants interpret dovish language as signaling economic weakness rather than policy easing. Conversely, dovish sentiment regarding foreign exchange reserves management has a positive impact on equity market. These results highlight the importance of topic-specific communication strategies for central banks in emerging markets.

Surveys are an indispensable source of data for applied economic research; however, their reliance on self-reported information can introduce bias, especially if core variables such as personal income are misreported. To assess the extent and impact of this misreporting bias, we compare self-reported wages from the German Socio-Economic Panel (SOEP) with administrative wages from social security records (IEB) for the same individuals. Using a novel and unique data linkage (SOEP-ADIAB), we identify a modest but economically significant reporting bias, with SOEP respondents underreporting their administrative wages by about 7.3%. This misreporting varies systematically with individual, household, and especially job and firm characteristics. In replicating common empirical analyses in which wages serve as either dependent or independent variables, we find that misreporting is consequential for some, but not all estimated relationships. It turns out to be inconsequential for examining the returns to education, but relevant for analyzing the gender wage gap. In addition we find that misreporting bias can significantly affect the results when wage is used as the independent variable. Specifically, estimates of the wage-satisfaction relationship are substantially overestimated when based on survey data, although this bias is mitigated when focusing on interpersonal changes. Our findings underscore that survey-based measures of individual wages can significantly bias commonly estimated empirical relationships. They also demonstrate the enormous research potential of linked administrative-survey data.

Effective climate action depends on dismantling the assumptions and oversimplifications that have become the basis of climate policy. The assumption that greenhouse gases (GHG) are fungible and the use of single-point values in normalizing GHG species to CO2-equivalents can propagate inaccuracies in carbon accounting and have already led to failures of carbon offset systems. Separate emission reduction targets and tracking by GHG species are recommended to achieve long-term climate stabilization., Comment: 18 pages, 2 figures

Background: Transitioning from hospital to home presents significant challenges, particularly for patients aged 75 and above. The 'Your Care Needs You' (YCNY) intervention, evaluated through a cluster randomised controlled trial (cRCT), aims to empower these patients by providing essential information and skills to prepare them for being at home. This study evaluates the cost-effectiveness of the YCNY intervention compared to standard care within the UK National Health Service (NHS) over the trial's duration (90 days post-discharge). Methods: The economic evaluation utilised individual patient-level data from the YCNY cRCT. The analysis was conducted from the perspective of the NHS and Personal Social Services. The analysis calculated the adjusted mean differences in costs and Quality-Adjusted Life Years (QALYs) between the YCNY intervention and control groups. In our base case, Multilevel Mixed-Effects Generalised Linear Models (MME-GLM) were employed to account for the hierarchical data structure. Additional alternative scenarios and a probabilistic sensitivity analysis were conducted to assess the robustness of the findings. Results: The base case results indicated the YCNY intervention reduced costs ({\pounds}269) and improved outcomes (QALY gain of 0.0057). The net health benefit (NHB) of the YCNY intervention using a cost-effectiveness threshold of {\pounds}15.000/QALY was 0.0246. These findings suggest the YCNY intervention dominates usual care, with a high probability (89%) of the YCNY intervention being cost-effective at a {\pounds}15,000 per QALY threshold. The results remained robust across several alternative scenario analyses. Discussion: The YCNY intervention could represent a promising and potentially cost-effective approach for enhancing the safety and experience of older adults transitioning from hospital to home in the UK.

The development of artificial intelligence has made significant contributions to the financial sector. One of the main interests of investors is price predictions. Technical and fundamental analyses, as well as econometric analyses, are conducted for price predictions; recently, the use of AI-based methods has become more prevalent. This study examines daily Dollar/TL exchange rates from January 1, 2020, to October 4, 2024. It has been observed that among artificial intelligence models, random forest, support vector machines, k-nearest neighbors, decision trees, and gradient boosting models were not suitable; however, multilayer perceptron and linear regression models showed appropriate suitability and despite the sharp increase in Dollar/TL rates in Turkey as of 2019, the suitability of valid models has been maintained.

This paper introduces a definition of ideological polarization of an electorate around a particular central point. This definition does not assume a particular location or boundaries of the center. Using US survey data, the paper shows how this approach can be used to establish whether polarization is occurring, and find the position around which it is happening. I then show the ideological polarization as defined here is related to other phenomena, such as affective polarization and increased salience of divisive issues.

The goal of this paper is to develop a multilingual classifier and conditional probability estimator of occupation codes for online job advertisements according in accordance with the International Standard Classification of Occupations (ISCO) extended with the Polish Classification of Occupations and Specializations (KZiS), which is analogous to the European Classification of Occupations. In this paper, we utilise a range of data sources, including a novel one, namely the Central Job Offers Database, which is a register of all vacancies submitted to Public Employment Offices. Their staff members code the vacancies according to the ISCO and KZiS. A hierarchical multi-class classifier has been developed based on the transformer architecture. The classifier begins by encoding the jobs found in advertisements to the widest 1-digit occupational group, and then narrows the assignment to a 6-digit occupation code. We show that incorporation of the hierarchical structure of occupations improves prediction accuracy by 1-2 percentage points, particularly for the hand-coded online job advertisements. Finally, a bilingual (Polish and English) and multilingual (24 languages) model is developed based on data translated using closed and open-source software. The open-source software is provided for the benefit of the official statistics community, with a particular focus on international comparability., Comment: 51 pages

This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance exploitation (selecting from groups with proven track records) with exploration (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

Global food production and trade networks are highly dynamic, especially in response to shortages when countries adjust their supply strategies. In this study, we examine adjustments across 123 agri-food products from 192 countries resulting in 23616 individual scenarios of food shortage, and calibrate a multi-layer network model to understand the propagation of the shocks. We analyze shock mitigation actions, such as increasing imports, boosting production, or substituting food items. Our findings indicate that these lead to spillover effects potentially exacerbating food inequality: an Indian rice shock resulted in a 5.8 % increase in rice losses in countries with a low Human Development Index (HDI) and a 14.2 % decrease in those with a high HDI. Considering multiple interacting shocks leads to super-additive losses of up to 12 % of the total available food volume across the global food production network. This framework allows us to identify combinations of shocks that pose substantial systemic risks and reduce the resilience of the global food supply.

In this paper, I introduce a novel decomposition method based on Gaussian mixtures and k-Means clustering, applied to a large Brazilian administrative dataset, to analyze the gender wage gap through the lens of worker-firm interactions shaped by comparative advantage. These interactions generate wage levels in logs that exceed the simple sum of worker and firm components, making them challenging for traditional linear models to capture effectively. I find that these ``complementarity effects'' account for approximately 17% of the gender wage gap. Larger firms, high human capital, STEM degrees, and managerial roles are closely related to it. For instance, among managerial occupations, the match effect goes as high as one-third of the total gap. I also find women are less likely to be employed by firms offering higher returns to both human capital and firm-specific premiums, resulting in a significantly larger firm contribution to the gender wage gap than previously estimated. Combined, these factors explain nearly half of the overall gender wage gap, suggesting the importance of understanding firm-worker matches in addressing gender-based pay disparities.

The Affordable Care Act was signed into law in 2010, expanding Medicaid and improving access to care for millions of low-income Americans. Fewer uninsured individuals reduced the cost of uncompensated care, consequently improving the financial health of hospitals. We hypothesize that this amelioration in hospital finances resulted in a marked improvement of quality measures in states that chose to expand Medicaid. To our knowledge, the impact of Medicaid expansion on the Medicare population has not been investigated. Using a difference-in-difference analysis, we compare readmission rates for four measures from the Hospital Readmission Reduction Program: acute myocardial infarction, pneumonia, heart failure, and coronary artery bypass graft surgery. Our analysis provides evidence that between 2013 and 2021 expansion states improved hospital quality relative to non-expansion states as it relates to acute myocardial infarction readmissions (p = 0.015) and coronary artery bypass graft surgery readmissions (p = 0.039). Our analysis provides some evidence that expanding Medicaid improved hospital quality, as measured by a reduction in readmission rates. Using visualizations, we provide some evidence that hospital quality improved for the other two measures as well. We believe that a refinement of our estimation method and an improved dataset will increase our chances of finding significant results for these two other measures.

We measure the association between generative AI (GAI) tool adoption and security operations center productivity. We find that GAI adoption is associated with a 30.13% reduction in security incident mean time to resolution. This result is robust to several modeling decisions. While unobserved confounders inhibit causal identification, this result is among the first to use observational data from live operations to investigate the relationship between GAI adoption and security worker productivity.

This study explores whether the Global Value Chain(GVC) participation of 16 emerging market economies (EMEs) from 1995 to 2018 in the manufacturing sector leads to a rise in carbon emissions embodied in trade. The study covers the ecological dimension of the Global Value Chain and validates the Pollution Haven Hypothesis in developing nations.To address the problem of cross-sectional dependence, autocorrelation, and heteroscedasticity panels, we estimate the above models using the feasible generalized least squares (FGLS) method. Our findings exhibit a continuous growth in carbon emissions of all the EMEs, there exists a positive association between GVC participation and domestic CO2 emissions embodied in gross exports. Also, EMEs' foreign carbon emissions embodied in gross exports directly correlate with backward GVC Participation, suggesting that the cleaner environment in developed countries comes at the expense of a dirtier environment in developing countries., Comment: 20 pages, 9 tables

In a study of 107 male Y Combinator founders, a surprising correlation between age-normalized testosterone and company stage was uncovered. Testosterone, a hormone associated with confidence, dominance, and drive, increased by 55.7% from pre-seed to seed funding, peaking at the Series B stage, where levels were 99.6% higher than pre-seed. After series B funding, testosterone was observed to drop by 42.2%, coinciding with a spike in cortisol levels. This age-normalized biomarker analysis supports the dual-hormone hypothesis, illustrating that early startup success fosters feelings of dominance and confidence, while later-stage pressures and stresses erode these feelings. An alternative interpretation of the data, which suggests the opportunity for a longitudinal study, is that male founders with higher testosterone are more likely to raise larger rounds of funding., Comment: 9 pages, 4 figures

This paper examines how a firm's performance can decline despite consistently implementing successful A/B test innovations -- a phenomenon we term "seesaw experimentation." While these innovations improve the measured primary dimension, they create negative externalities in unmeasured secondary dimensions that exceed the gains. Using a multivariate normal distribution model, we identify the conditions for this decline and propose positive hurdle rates as a solution. Our analysis shows how to set optimal hurdle rates to best mitigate these negative externalities and provides practical guidance for experimental design by demonstrating how these rates should vary with underlying parameters., Comment: 15 pages, 1 figure

Recent studies highlight economic complexity's role in mitigating fiscal crises, often measured via an economy's trade structure. Trade, however, is just one facet of an economy's structure and omits critical innovative activities like research. Here, we investigate how a multidimensional approach to economic complexity-including both trade and research structures-relates to fiscal instability. By using data on over 230 national fiscal crises from 1995 to 2021 and hazard duration analysis, we assess how measures of trade and research complexity combine to explain crisis likelihood. We find that the interaction of complexity dimensions significantly reduces crisis probability, whereas individual indexes alone are not robust predictors. This suggests that economies focusing on a single dimension may be more vulnerable, thus highlighting the importance of balanced development across multiple areas. These findings offer valuable insights for policymakers aiming to enhance economic resilience and mitigate fiscal risks.

The present paper shows that it can be advantageous for traders to publish their information on the true value of an asset even if they (i) cannot build a position in the asset prior to the publication of their information and (ii) cannot charge for the provision of information. The model also shows that the informational content of prices is U-shaped in the number of traders who publish their information. Put differently, information aggregation works best if either no trader, or if every trader publishes his information. Small groups of distinguished experts are, on the contrary, an obstacle to information aggregation. The model's key assumption is that the perception/interpretation of a given piece of published information differs slightly across traders., Comment: 10 pages, no figures

Bankruptcy prediction is an important research area that heavily relies on data science. It aims to help investors, managers, and regulators better understand the operational status of corporations and predict potential financial risks in advance. To improve prediction, researchers and practitioners have begun to utilize a variety of different types of data, ranging from traditional financial indicators to unstructured data, to aid in the construction and optimization of bankruptcy forecasting models. Over time, not only instrumentalized data improved, but also instrumentalized methodology for data structuring, cleaning, and analysis. With the aid of advanced analytical techniques that deploy machine learning and deep learning algorithms, bankruptcy assessment became more accurate over time. However, due to the sensitivity of financial data, the scarcity of valid public datasets remains a key bottleneck for the rapid modeling and evaluation of machine learning algorithms for targeted tasks. This study therefore introduces a taxonomy of datasets for bankruptcy research, and summarizes their characteristics. This paper also proposes a set of metrics to measure the quality and the informativeness of public datasets The taxonomy, coupled with the informativeness measure, thus aims at providing valuable insights to better assist researchers and practitioners in developing potential applications for various aspects of credit assessment and decision making by pointing at appropriate datasets for their studies.

This research employs the Alkire-Foster approach to measure multidimensional poverty between 2012 and 2020 in China, followed by examining the role of the three-pillar pension system in mitigating household multidimensional poverty. With the China Family Panel Studies data, our measurement uncovers the sustainable effects and mechanisms of household participation in the multi-pillar pension system on poverty mitigation. The results indicate that more participation in the pension system mitigates the probability of being trapped in multidimensional poverty. The findings reveal the significance of state social insurance, enterprise annuity, and individual commercial insurance. The mitigation effect of market-oriented pillars is achieved through more investment in and consumption for livelihood assets. Based upon the sustainable livelihoods framework, livelihood assets ameliorate household capabilities in human, natural, financial, and psychological capital against risks, shocks, and uncertainties. Our research contributes to the knowledge of how household participation in pension pillars sustainably mitigates multidimensional poverty through micro-level mechanisms and to the policy praxis of why a facilitating state is called for poverty mitigation from the perspective of new structural economics.

The document addresses the essential role of financial regulatory frameworks in mitigating climate-related risks within the financial sector. The assessment evaluates Canada's efforts to establish a regulatory framework for financial climate risk disclosure, compares it to international standards, and identifies areas for improvement. The regulatory framework, fiscal impact, and incentives to relinquish investments in fossil fuel projects and transition to renewable energies swiftly and seamlessly are the primary challenges that Canada faces in achieving energy net zero targets.

This study explores the impact of appearance discrimination in the labor market and whether education can mitigate this issue. A statistical analysis of approximately 1.058 million job advertisements in China from 2008 to 2010 found that about 7.7% and 2.6% of companies had explicit requirements regarding candidates' appearance and height, particularly in positions with lower educational requirements. Literature review indicates that attractive job seekers typically enjoy higher employment opportunities and wages, while unattractive individuals face significant income penalties. Regression analysis of 1,260 participants reveals a significant positive correlation between attractiveness scores and wages, especially in low-education groups. Conversely, in high-education groups, the influence of appearance on income is not significant. The study suggests that enhancing education levels can effectively alleviate income declines associated with appearance, providing policy recommendations to reduce appearance discrimination in the labor market., Comment: 13 pages

In recent years, large language models (LLMs) have attracted attention due to their ability to generate human-like text. As surveys and opinion polls remain key tools for gauging public attitudes, there is increasing interest in assessing whether LLMs can accurately replicate human responses. This study examines the potential of LLMs, specifically ChatGPT-4o, to replicate human responses in large-scale surveys and to predict election outcomes based on demographic data. Employing data from the World Values Survey (WVS) and the American National Election Studies (ANES), we assess the LLM's performance in two key tasks: simulating human responses and forecasting U.S. election results. In simulations, the LLM was tasked with generating synthetic responses for various socio-cultural and trust-related questions, demonstrating notable alignment with human response patterns across U.S.-China samples, though with some limitations on value-sensitive topics. In prediction tasks, the LLM was used to simulate voting behavior in past U.S. elections and predict the 2024 election outcome. Our findings show that the LLM replicates cultural differences effectively, exhibits in-sample predictive validity, and provides plausible out-of-sample forecasts, suggesting potential as a cost-effective supplement for survey-based research.

Business innovations are often arising from new combinations of pre-existing systems into new ones, coherently assembling features from their sources. We propose an abstract mathematical concept from category theory, the presheaf, to efficiently represent such coherent feature combinations. Moreover, operations on presheaves will allow us to formally describe and analyze business innovations that arise from novel mergings of systems from different domains. Equipped with such tools we provide an example by analyzing a successful case of such type of recombinant innovation, the digital hub concept proposed by Steve Jobs. The example shows how our framework can be used to bring formal rigor while preserving a fundamentally qualitative reasoning style., Comment: 29 pages

This study focuses on predicting the Human Development Index (HDI) trends for GCC countries Saudi Arabia, Qatar, Kuwait, Bahrain, United Arab Emirates, and Omanusing machine learning techniques, specifically the XGBoost algorithm. HDI is a composite measure of life expectancy, education, and income, reflecting overall human development. Data was gathered from official government sources and international databases, including the World Bank and UNDP, covering the period from 1996 to 2022. Using the Edit Distance on Real sequence (EDR) method for feature selection, the model analyzed key indicators to predict HDI values over the next five years (2023-2027). The model demonstrated strong predictive accuracy for in-sample data, but minor overfitting issues were observed with out-of-sample predictions, particularly in the case of the UAE. The forecast results suggest that Kuwait, Bahrain, and the UAE will see stable or slightly increasing HDI values, while Saudi Arabia, Qatar, and Oman are likely to experience minimal fluctuations or slight decreases. This study highlights the importance of economic, health, and educational indicators in determining HDI trends and emphasizes the need for region-specific predictive models to improve accuracy. Policymakers should focus on targeted interventions in healthcare, education, and economic diversification to enhance human development outcomes., Comment: arXiv admin note: text overlap with arXiv:2410.21516, arXiv:2410.21511

Social media platforms have led to online consumption communities, or fandoms, that involve complex networks of ancillary creators and consumers focused on some core product or intellectual property. For example, video game communities include networks of players and content creators centered around a specific video game. These networks are complex in that video game publishers often sponsor creators, but creators and publishers may have divergent incentives. Specifically, creators can potentially benefit from content that builds their own following at the expense of the core game. Our research investigates the relationship between consuming live-streamed content and engagement with a specific video game. We examine the causal effect of viewing live-streamed content on subsequent gameplay for a specific game, using an unexpected service interruption of the livestreaming platform and time zone differences among users. We find live-streamed content significantly increases gameplay as a 10% increase in live-streamed viewing minutes results in a 3.08% increase in gameplay minutes. We also explore how this effect varies by user loyalty to different types of streamer channels (firm-owned, mega, and micro). The positive effects of live-streamed content are greatest for micro-streamers and smallest for mega-streamers. These findings are salient for firms allocating sponsorship resources., Comment: 51 pages, 9 Tables, 6 Figures

As generative AI (GAI) tools become increasingly integrated into workplace environments, it is essential to measure their impact on productivity across specific domains. This study evaluates the effects of Microsoft's Security Copilot ("Copilot") on information technology administrators ("IT admins") through randomized controlled trials. Participants were divided into treatment and control groups, with the former granted access to Copilot within Microsoft's Entra and Intune admin centers. Across three IT admin scenarios - sign-in troubleshooting, device policy management, and device troubleshooting - Copilot users demonstrated substantial improvements in both accuracy and speed. Across all scenarios and tasks, Copilot subjects experienced a 34.53% improvement in overall accuracy and a 29.79% reduction in task completion time. We also find that the productivity benefits vary by task type, with more complex tasks showing greater improvement. In free response tasks, Copilot users identified 146.07% more relevant facts and reduced task completion time by 61.14%. Subject satisfaction with Copilot was high, with participants reporting reduced effort and a strong preference for using the tool in future tasks. These findings suggest that GAI tools like Copilot can significantly enhance the productivity and efficiency of IT admins, especially in scenarios requiring information synthesis and complex decision-making., Comment: Speed results were off by a few decimal points. Qualitative results are unchanged

This paper constructs a novel intellectual property (IP) protection strategy using differential equation theory, aiming to analyze and optimize the effectiveness of IP protection. By developing a mathematical model, it explores the dynamic impact of IP protection intensity on both innovative enterprises and infringement activities. The study finds that a well-designed IP protection strategy can effectively reduce infringement while promoting technological innovation. The paper also discusses the effects of strategies under varying parameter conditions and verifies the model's rationality and effectiveness through numerical simulation. The findings provide theoretical support and references for formulating IP protection policies.

Policy efforts have primarily focused on expanding variable renewable energy sources (vRES) to meet carbon emission reduction targets. The integration of high shares of renewables into the energy system is central to both policy making and research, focusing on the need for balancing options between vRES and demand. In this work we analyze and compare three key integration measures: grid expansions, electricity storage, and the role of production, storage and transport of low-carbon hydrogen. We focus on their potential to reduce emissions and energy system costs, individually and in combination. We take the North Sea as an exemplary region with ambitious 2030-2040 targets for offshore wind developments. The projections on installed generation and grid capacities, along with demand estimates from the Ten Year Network Development Plan (TYNDP) 2022, serve as a starting point for our energy system model. This starting model can then be further expanded with the three integration measures. Our findings show that grid expansions across the North Sea are a no-regret measure lowering costs, emissions and required renewable. The production of hydrogen and its direct use in industry has a lower cost reduction potential and emission reduction potential, while hydrogen storage and transport have little to no additional value. In the short term (2030), electricity storage can help to reduce emissions, but it is not cost competitive. In the longer term (2040), storage can help to balance investments in vRES assets by providing additional flexibility to the system. Combining the three integration measures provides additional benefits. The highest emission reductions can be achieved by combining electricity storage with an expansion of the grid. The highest economic benefits can be achieved with a combination of grid expansions and hydrogen production for direct use in industry.