Migration Magnet: The Role of Work Experience in Rural-Urban Wage Diff erentials in Mexico

This study estimates separate selectivity bias corrected wage equations for formal and informal workers in rural and urban Mexico using data from the Mexican Family Life Survey (MxFLS). We control for diff erent potential selection patterns using Probit and Multinominal logit models in the fi rst step in which health, personality traits and family characteristics serve as exclusion restrictions for working per se and working in the formal sector. Oaxaca-Blinder Decompositions show that rural-urban wage inequality in the formal and informal sector is determined by diff erences in observable human capital. In the informal sector, the wage diff erential is mainly explained by diff erences in returns to experience. Furthermore, we analyse rural-to-urban migrants‘ labour market performance. The fi ndings suggest that rural-to-urban migration will continue and the informal sector will further increase.Returns to experience; rural-urban wage diff erentials; informality; internal migration; Mexico

Migration Magnet: The Role of Work Experience in Rural-Urban Wage Differentials in Mexico

This study estimates separate selectivity bias corrected wage equations for formal and informal workers in rural and urban Mexico using data from the Mexican Family Life Survey (MxFLS). We control for different potential selection patterns using Probit and Multinominal logit models in the first step in which health, personality traits and family characteristics serve as exclusion restrictions for working per se and working in the formal sector. Oaxaca-Blinder Decompositions show that rural-urban wage inequality in the formal and informal sector is determined by differences in observable human capital. In the informal sector, the wage diff erential is mainly explained by diff erences in returns to experience. Furthermore, we analyse rural-to-urban migrants' labour market performance. The findings suggest that rural-to-urban migration will continue and the informal sector will further increase.Diese Studie beschäftigt sich mit Lohnunterschieden zwischen ländlichen und städtischen Regionen in Mexiko. Anhand des mexikanischen Family Life Survey (MxFLS/ENNVIH) werden für formelle und informelle Arbeitnehmer Lohngleichungen geschätzt, die für potentielle Verzerrungen durch Selektionsprozesse kontrollieren. Anhand von Oaxaca-Blinder-Dekompositionen wird gezeigt, dass die Lohnungleichheit zwischen ländlichen und städtischen Arbeitnehmern im formellen sowie im informellen Sektor durch Unterschiede im Humankapital erklärt werden können. Zusätzlich lässt sich im informellen Sektor die Lohnungleichheit durch unterschiedliche Renditen für Arbeitserfahrung erklären. Darüber hinaus untersuchen wir den Arbeitsmarkterfolg von Land-Stadt-Migranten. Die Ergebnisse weisen darauf hin, dass die Land-Stadt-Migration in Zukunft andauern wird und der informelle Sektor in Mexikos Städten weiter ansteigen wird

Nanostructure and properties of a Cu-Cr composite processed by severe plastic deformation

A Cu-Cr composite was processed by severe plastic deformation to investigate the role of interphase boundaries on the grain size reduction mechanisms. The as-deformed material exhibits a grain size of only 20nm. This gives rise to a dramatic increase of the hardness. Some deformation induced Cu super saturated solid solutions were clearly exhibited and it is shown that they decrease the hardness. The formation of such supersaturated solid solution and their influence on the mechanical properties are discussed

Warming of the Indian Ocean Threatens Eastern and Southern Africa, but could be Mitigated by Agricultural Development

Since 1980, the number of undernourished people in eastern and southern Africa has more than doubled. Rural development stalled and rural poverty expanded during the 1990s. Population growth remains very high and declining per capita agricultural capacity retards progress towards Millennium Development goals. Analyses of in situ station data and satellite observations of precipitation identify another problematic trend. Main growing season rainfall receipts have diminished by approximately 15% in food insecure countries clustered along the western rim of the Indian Ocean. Occurring during the main growing seasons in poor countries dependent on rain fed agriculture, these declines are societally dangerous. Will they persist or intensify? Tracing moisture deficits upstream to an anthropogenically warming Indian Ocean leads us to conclude that further rainfall declines are likely. We present analyses suggesting that warming in the central Indian Ocean disrupts onshore moisture transports, reducing continental rainfall. Thus late 20th century anthropogenic Indian Ocean warming has probably already produced societally dangerous climate change by creating drought and social disruption in some of the world's most fragile food economies. We quantify the potential impacts of the observed precipitation and agricultural capacity trends by modeling millions of undernourished people as a function of rainfall, population, cultivated area, seed and fertilizer use. Persistence of current tendencies may result in a 50% increase in undernourished people. On the other hand, modest increases in per capita agricultural productivity could more than offset the observed precipitation declines. Investing in agricultural development can help mitigate climate change while decreasing rural poverty and vulnerability

Is English the New Programming Language? How About Pseudo-code Engineering?

Background: The integration of artificial intelligence (AI) into daily life, particularly through chatbots utilizing natural language processing (NLP), presents both revolutionary potential and unique challenges. This research is motivated by the intricacies of human-computer interaction within the context of conversational AI, focusing on the role of structured inputs from pseudo-code engineering in enhancing chatbot comprehension and response accuracy. Objectives: This study investigates the comparative effectiveness of natural language versus pseudo-code engineering generated inputs in eliciting precise and actionable responses from ChatGPT, a leading language model by OpenAI. It aims to delineate how different input forms impact the model's performance in understanding and executing complex, multi-intention tasks. Design: Employing a case study methodology supplemented by discourse analysis, the research analyzes ChatGPT's responses to inputs varying from natural language to pseudo-code engineering. The study specifically examines the model's proficiency across four categories: understanding of intentions, interpretability, completeness, and creativity. Setting and Participants: As a theoretical exploration of AI interaction, this study focuses on the analysis of structured and unstructured inputs processed by ChatGPT, without direct human participants. Data collection and analysis: The research utilizes synthetic case scenarios, including the organization of a "weekly meal plan" and a "shopping list," to assess ChatGPT's response to prompts in both natural language and pseudo-code engineering. The analysis is grounded in the identification of patterns, contradictions, and unique response elements across different input formats. Results: Findings reveal that pseudo-code engineering inputs significantly enhance the clarity and determinism of ChatGPT's responses, reducing ambiguity inherent in natural language. Enhanced natural language, structured through prompt engineering techniques, similarly improves the model's interpretability and creativity. Conclusions: The study underscores the potential of pseudo-code engineering in refining human-AI interaction, advocating for its broader application across disciplines requiring precise AI responses. It highlights pseudo-code engineering's efficacy in achieving more deterministic, concise, and direct outcomes from AI systems like ChatGPT, pointing towards future innovations in conversational AI technology

Is English the New Programming Language? How About Pseudo-code Engineering?

Background: The integration of artificial intelligence (AI) into daily life, particularly through chatbots utilizing natural language processing (NLP), presents both revolutionary potential and unique challenges. This intended to investigate how different input forms impact ChatGPT, a leading language model by OpenAI, performance in understanding and executing complex, multi-intention tasks. Design: Employing a case study methodology supplemented by discourse analysis, the research analyzes ChatGPT's responses to inputs varying from natural language to pseudo-code engineering. The study specifically examines the model's proficiency across four categories: understanding of intentions, interpretability, completeness, and creativity. Setting and Participants: As a theoretical exploration of AI interaction, this study focuses on the analysis of structured and unstructured inputs processed by ChatGPT, without direct human participants. Data collection and analysis: The research utilizes synthetic case scenarios, including the organization of a "weekly meal plan" and a "shopping list," to assess ChatGPT's response to prompts in both natural language and pseudo-code engineering. The analysis is grounded in the identification of patterns, contradictions, and unique response elements across different input formats. Results: Findings reveal that pseudo-code engineering inputs significantly enhance the clarity and determinism of ChatGPT's responses, reducing ambiguity inherent in natural language. Enhanced natural language, structured through prompt engineering techniques, similarly improves the model's interpretability and creativity. Conclusions: The study underscores the potential of pseudo-code engineering in refining human-AI interaction and achieving more deterministic, concise, and direct outcomes, advocating for its broader application across disciplines requiring precise AI responses

Brief communication:getting Greenland’s glaciers right – a new data set of all official Greenlandic glacier names

Place names in Greenland can be difficult to get right, as they are a mix of Greenlandic, Danish, and other foreign languages. In addition, orthographies have changed over time. With this new data set, we give the researcher working with Greenlandic glaciers the proper tool to find the correct name for glaciers and ice caps in Greenland and to locate glaciers described in the historic literature with the old Greenlandic orthography. The data set contains information on the names of 733 glaciers, 285 originating from the Greenland Ice Sheet (GrIS) and 448 from local glaciers and ice caps (LGICs)