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From Rank to Label: How Early Academic Rank Shapes Educational Diagnoses and Mental Health Outcomes
Get PDFThis study uses rich Canadian census and administrative data to examine the causal ef-
fects of early academic ranking on educational diagnoses and long-term mental well-being.
Leveraging within-classroom variation among students with similar abilities, I find that mov-
ing from the 0–5th to the 10–15th percentile reduces learning disability diagnoses by 34%
and mental health conditions by 16%. Conversely, shifting from the 85–90th to the 95–100th
percentile increases gifted diagnoses by 27%, showing that teacher perceptions and behaviors
are influenced by relative performance. Similar rank variation also lower adult mental health
challenges by 12% and boost learning-related self-esteem by 21%
Supermarket operating hours and distance to crime
Get PDFHousehold chores, particularly those related to food—such as meal preparation and grocery shopping—continue to reflect significant gender disparities. Supermarkets, by reducing the distance between consumers and food purchases while leveraging economies of scale to offer affordable and diverse options, are often associated with food security. However, it remains unclear how the establishment of these businesses impacts their surroundings, especially in comparison to other security measures, such as addressing crime.
This study examines how the operating hours and proximity of supermarkets affect local crime levels in Chicago, USA, over a one-year period (September 2023–August 2024). By combining three georeferenced datasets from the Chicago Police Department, Google Maps, and weather information to create a database and applying three negative binomial regression models.
Results indicate that open supermarkets are generally linked to slightly lower crime rates, though this effect fluctuates throughout the day—reducing crime in early hours but increasing it during peak periods. While proximity alone shows no strong correlation with crime, open supermarkets exhibit a localized deterrent effect
Chemins périlleux : la migration dangereuse des Éthiopiens vers l'Afrique du Sud
Get PDFSince the 1990s, Ethiopian youths and adults—primarily from the country’s southern and central regions—have been migrating to South Africa via the "southern route." Over the past 25 years, this male-dominated migration flow has grown increasingly irregular, relying on human smugglers and multiple transit countries. The Ethiopian immigrant population in South Africa has expanded significantly, with shifts in the demographics of migrants, including age, ethnicity, place of origin, gender, and socioeconomic status. Rural youth have increasingly joined this migration stream, and more women are now migrating for marriage. Migration brokers play a pivotal role in facilitating irregular migration from Ethiopia to South Africa. Upon arrival, most Ethiopian immigrants engage in the informal economy and remain socially segregated, with language barriers hindering integration. The profile of migrants has diversified in recent years, now including teenagers, college graduates, and civil servants. Concurrently, the smuggling and settlement processes have evolved, particularly due to stricter border controls—exacerbated by factors such as the COVID-19 pandemic—which have altered smuggling dynamics and exacerbated inequalities among Ethiopian migrants in South Africa. Social networks sustain this migration trend, fuelled by narratives of financial success shared by early migrants through remittances, material goods sent back home, and social media. Labour market demands shape migrant profiles, with varying skill levels (low-skilled, unskilled, high-skilled) and gendered labour roles influencing migration patterns. Religion, particularly evangelical Christianity, also plays a significant role, framing migration as a divine blessing, shaping risk perceptions, and providing spiritual support in navigating the challenges of settlement. Aspirations for economic advancement and self-improvement drive many migrants, often leading them into precarious journeys facilitated by smuggling networks operating from Hosanna (the capital of Hadiya Zone) and Nairobi. Corruption among law enforcement agencies further enables this transnational smuggling industry. However, rising xenophobia in South Africa and stricter enforcement in transit countries like Kenya, Tanzania, and Malawi have reduced migration along this route since 2015. Unauthorized Ethiopian migrants in South Africa face stigmatization. They are, often being perceived as criminals, informal economy operators, or threats to local employment opportunities. This perception exacerbates their marginalization and limits their integration into South African society
Monetary policy frameworks from 1999 to 2003: the less it changes the less it stays the same
Get PDFThis paper, prepared as a background chapter for an edited book, considers the changes in monetary policy frameworks (MPFs) over the first quarter of the 21st century. Section 1 presents the overall global and regional trends in monetary policy frameworks from 1999 to 2023. That analysis shows that the distribution of MPFs has changed rather less in recent years. However, the paper then turns to analyse in more detail the important changes within the two most widespread MPFs, inflation targeting and loosely structured discretion, over the period. Section 2 examines the rise in inflation targeting and the changes made within that framework by a number of mainly advanced economies, which have put more emphasis on output and employment but retained the primacy of the quantitative inflation target. Section 3 examines the changes in the monetary policy instruments commonly used within the loosely structured discretion framework, which have involved substantial improvements in effectiveness over time, typically associated with developments in money and bond markets, which facilitate switches to alternative MPFs. Overall, the paper highlights what amounts to a useful refocus from the choice of monetary policy framework to the operation of monetary policy
Introduction to Generative AI: Its Impact on Jobs, Education, Work and Policy Making
Get PDFGenerative Artificial Intelligence (GenAI) has emerged as a transformative technology with significant implications for education and the workforce. This paper explores the opportunities and challenges posed by GenAI in these domains. We review recent studies and reports to analyze how GenAI is reshaping teaching and learning processes, as well as its impact on job markets. The paper highlights the potential of GenAI to enhance productivity, personalize education, and create new job opportunities, while also addressing concerns such as job displacement, ethical considerations, and the need for upskilling. We conclude with recommendations for policymakers, educators, and industry leaders to harness the benefits of GenAI while mitigating its risks. This paper also presents a comprehensive review of the impact of generative artificial intelligence (AI) on employment and education. We analyze recent developments in AI technology, its applications in various industries, and its implications for the future of work and learning. The review covers the potential benefits and challenges of AI integration in the workforce and educational systems, highlighting the need for adaptive strategies and policies to harness AI's potential while mitigating its risks. This paper further explores the multifaceted impact of Artificial Intelligence (AI) on the labor market and educational sectors. We examine the current trends and potential future scenarios, focusing on the replacement of traditional jobs, the creation of new opportunities, and the necessary adaptations in education to prepare for an AI-driven world. We analyze the perspectives of educators, industry professionals, and policymakers, highlighting the challenges and opportunities presented by the rapid advancement of AI technologies
Integrating Machine Learning and Hedonic Regression for Housing Price Prediction: A Systematic International Review of Model Performance and Interpretability
Get PDFIt is becoming increasingly important to predict property prices to mitigate investment risk, establish policies, and preserve market stability. To determine the practical utility and anticipated efficacy of the sophisticated statistical and machine learning models that have emerged, a comparative analysis is required.
The purpose of this systematic study is to assess the predictive effectiveness and interpretability of hedonic regression and complex machine learning models in the estimation of housing prices in a wide range of foreign scenarios.
In May 2024, a thorough search was conducted in Scopus, Google Scholar, and Web of Science. The search terms included "hedonic pricing models," "machine learning," and "housing price prediction," in addition to others. The inclusion criteria required the utilization of empirical research published after 2000, a comparison of at least two predictive models, and reliable transaction data. Research that utilized non-empirical methodologies or web- scraped prices was excluded. Twenty-three investigations met the eligibility criteria. The evaluation was conducted in accordance with the reporting criteria of PRISMA 2020.
Random Forest was the most frequently employed and consistently high-performing model, being selected in 14 of 23 studies and regarded as exceptional in five. Despite their lack of precision, hedonic regression models provided critical explanatory insights into critical variables, such as proximity to urban centers, property characteristics, and location. The integration of hedonic and machine learning models improved the interpretability and accuracy of the predicted results. Many of the studies included in this review were longitudinal, covered a diverse range of international contexts (specifically, Asia, Europe, America, and Australia), and demonstrated a rise in research output beyond 2020.
Even though hedonic models retain a significant amount of explanatory power, the precision of home price predictions is improved by machine learning, particularly Random Forest and neural networks. The optimal results for researchers, real estate professionals, and policymakers who aim to improve market transparency and enlighten effective policy decisions are achieved through the seamless integration of these techniques
An intergenerational welfare analysis in a small open economy model between social security systems
Get PDFThis study investigates the long-term macroeconomic and welfare impacts of transitioning from a Pay-As-You-Go to a fully funded pension
system, specifically within the Ecuadorian economic context. The study is motivated by financial and demographic challenges that threaten the
sustainability of the current pension structure. Understanding the effects of such a transition is essential for informed implementation.
The research has two primary objectives: first, to simulate this reform under various economic shocks, particularly changes in oil income and
interest rates given that variability in oil revenues directly affects the economy as oil is
Ecuador’s main source of income; and second, to evaluate how the timing of the
changes influences welfare outcomes across generations. The analysis is based
on a transition from a Pay-As-You-Go system to a Fully Funded system, allowing for a more flexible response to demographic and fiscal pressures. To achieve this, a calibrated Overlapping Generations model is employed, integrated with a Small Open Economy framework and tailored to
Ecuadorian data. This model allows for simulation of the pension reform under different macroeconomic conditions and transition scenarios.
Findings suggest that while a fully funded system may increase welfare in the new steady-state equilibrium relative to a PAYG system reflecting the right timing for replacing the social security system under a general equilibrium model positive economic shocks can produce
large welfare gains. However, welfare outcomes during the transition period remain highly sensitive to shocks, which in some scenarios can
cause net losses for certain generations. The impact varies depending on the type of shock and the timing of reform implementation.
These results highlight the importance of timing and economic context when designing pension policy. A poorly timed reform could reduce
expected benefits, even if long-term outcomes appear favorable
Saving Motives over the Life-Cycle
Get PDFThree key drivers of savings are life-cycle, precautionary, and bequest motives. What is their relative quantitative importance? We revisit this question focusing on the role of preferences and institutions. We address the challenge of disentangling the effects of different saving motives on one’s decisions by considering many aspects of people’s behavior both before and after retirement. We illustrate why this approach is informative about the underlying preference parameters, and hence allows us to uncover the relative strength of different motives. Our decomposition exercises reveal that bequest motive is the key driver of savings starting from the middle-age and long before retirement. We also find that life-cycle motive and precautionary motive due to medical expense shocks play a minor role. The former result is due the crowding out effect of Social Security. The latter is due to the combined effect of health insurance and the means-tested transfers
Does commercial farming protect the environment? Evidence from chemical input use in Haryana, India
Get PDFPurpose
This study investigates the impact of contract farming (CF) on chemical input usage (fertilisers, pesticides and herbicides) in wheat farming in Haryana, India, weighing on environmental risks from unsustainable chemical input usage under CF.
Design/methodology/approach
The research employs an endogenous switching regression (ESR) model using data from 754 farm households, enabling a comparative analysis between contract and non-contract farmers.
Findings
The results show that farmers who adopted CF would have reduced chemical input usage by 26.8% if they did not adopt it. Conversely, non-adopters would have increased chemical input usage by 54% if they adopted CF. While CF enhances farm productivity and income, it also increases chemical input usage, posing risks such as soil fertility loss and water contamination.
Originality
This study addresses the overlooked topic of chemical input usage in CF research. Leveraging household data and using an endogenous switching regression model provides unique comparative analysis and counterfactual scenarios. The findings contribute to understanding the environmental implications of CF and propose actionable recommendations for sustainable agricultural practices.
Managerial or Policy implications
The study recommends promoting organic farming and minimal chemical usage in CF agreements. Government intervention is needed to reduce the environmental impact of CF. Policies should promote environment-friendly fertilisers and provide guidelines on chemical usage based on crop variety, seed quality and soil fertility.
Research limitations/implications
The geographic focus on Haryana may limit generalisability. Reliance on cross-sectional data from a single season might not capture variability across different seasons. Future research could expand to other regions, use longitudinal data and investigate a broader range of crops
Macroeconomic outcomes of trade facilitation reform: a productivity growth-based analysis in some selected African countries
Get PDFThe article investigates the contribution of trade facilitation to productivity growth in Sub-Saharan African (SSA) countries. We include four trade facilitation indicators (i.e., physical infrastructure, ICT, business and regulatory environment, border, and transport efficiency) as explanatory factors for productivity growth measured by both total factor productivity and labor productivity. The empirical evidence is based on both Pooled Ordinary Least Squares (POLS) and the Instrumental Variable Two-Stage Least squares (IV-2SLS) in a sample of 29 SSA countries over the period 2004-2017. The main results from the study show that trade facilitation contributes positively and significantly to total factor productivity as well as labor productivity in SSA. Based on this finding, SSA countries need to improve border procedures as well as the business and regulatory environment to generate substantial productivity gains and boost the competitiveness of micro, small and medium-sized enterprises (MSMEs), given the job creation potential of MSMEs