Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The study investigates the impact of corporate tax incentives, specifically focusing on the phasing out of a significant income tax exemption for export-oriented firms in Tunisia, revealing that the reform led to a 20% decline in the entry of new offshore firms without affecting employment, revenue, or wage bills of existing firms [4][15][19] - The findings challenge the conventional belief that tax incentives are crucial for attracting investments, suggesting that other factors may play a more significant role in economic activity [4][20][24] Summary by Sections Introduction - Tax incentives are widely used to attract investment, with 87% of surveyed developing economies having at least one type of corporate income tax exemption [9] - In 2021, tax relief schemes accounted for 1.4% of global GDP and 7.8% of global tax revenues [9] Institutional Context and Policy Background - Tunisia's offshore regime provided significant tax benefits, costing up to 6.8% of GDP in foregone tax revenues in 2013 [27][28] - The 2014 corporate tax reform aimed to harmonize tax treatment between offshore and onshore firms, raising the CIT rate for offshore firms from 0% to 10% [29][33] Data and Descriptive Statistics - The analysis uses administrative records from Tunisian registered firms, focusing on approximately 198,000 firms, with 22,660 classified as offshore [40][42] - Offshore firms represent about 20% of total firms but account for a disproportionate share of economic activity, particularly in manufacturing [52] Empirical Strategy - A differences-in-differences approach is employed to assess the impact of the CIT reform, comparing outcomes of offshore and onshore firms before and after the reform [58][59] Effects of the 2014 Offshore Tax Reform - The number of offshore firms grew at a slower rate post-reform, with a significant drop in new entrants, while the onshore sector continued to expand [66][68] - Despite the decline in the number of offshore firms, there was no significant decrease in aggregate economic activity, as existing firms maintained their performance [70]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Social registries are essential tools for social protection, enabling the identification of individuals in poverty and vulnerability, facilitating resource targeting, and promoting access to services and benefits [13][15] - Over the past two decades, social registries in Latin America and the Caribbean (LAC) have significantly expanded in coverage, reaching over 80% in countries like Chile, Colombia, and Costa Rica [15] - The COVID-19 pandemic highlighted the importance of social registries, allowing countries with robust systems to respond effectively to citizens' needs [68][72] Summary by Sections I. Introduction - The World Bank aims to strengthen social registries in LAC to reduce knowledge gaps and improve social policy design and implementation [20][21] II. The Role of Social Registries in Social Policy - Social registries support the identification of individuals experiencing poverty, enabling participation in social protection programs and efficient resource targeting [27][31] - They serve as information systems that consolidate data for public policy decision-making [31][35] III. Evolution of Social Registries in LAC - Social registries have evolved significantly, with Colombia achieving 100% population coverage through innovations in interoperability during the COVID-19 pandemic [51][63] - The coverage of social registries has increased over time, with some countries experiencing fluctuations due to challenges in maintaining updated information [51][63] IV. The Role of Social Registries during the COVID-19 Pandemic - During the pandemic, 64% of the population in LAC was covered by some form of protection, with countries implementing innovations in their social registries [72][68] - Innovations included the incorporation of new data sources and the use of technologies like machine learning for better household classification [72][73] V. Social Registries in LAC Today - The report assesses social registries based on five dimensions: institutional arrangements, data collection mechanisms, socioeconomic targeting, information systems, and performance measures [80][81] - Institutional frameworks vary across countries, with most registries linked to ministries of social policy or planning [86][88] - The collection and updating of household information often remain static, but there is a shift towards hybrid models that combine self-declaration and administrative data [96][100]

Investment Rating - The report does not explicitly provide an investment rating for the industry or countries discussed [1]. Core Insights - The report emphasizes the need for Europe and Central Asia (ECA) middle-income countries (MICs) to transition from investment-driven growth to strategies that incorporate infusion of global capital and innovation to achieve high-income status [20][32]. - It highlights that ten ECA countries have successfully transitioned to high-income status since 1990, primarily through structural reforms and integration into EU markets [20][23]. - The report identifies a growing concern that many ECA countries may be caught in a middle-income trap, characterized by slow growth and challenges in achieving high-income status [31]. Summary by Sections Overview - The report outlines the economic transitions of ECA countries, noting that while some have achieved high-income status, many others face stagnation due to insufficient structural reforms and external economic pressures [20][25]. Investment, Infusion, and Innovation - The report advocates for a dual transition strategy for MICs: from investment to investment and infusion, and then to investment, infusion, and innovation [32][37]. - It stresses the importance of adopting new technologies and ideas from abroad to enhance domestic productivity and innovation [32][37]. Understanding Growth - The analysis employs a Schumpeterian lens, emphasizing the balance between creation, destruction, and preservation in economic growth [38][41]. - It notes that the forces of creation are currently weak in the ECA region, with many firms lacking innovation and productivity growth primarily driven by resource reallocation [41][43]. Drivers of Economic Growth - The report identifies three fundamental drivers of growth: enterprises, talent and social mobility, and energy efficiency [43]. - It highlights the inefficiencies in resource use among MICs, which significantly hampers their economic potential compared to high-income counterparts [43][44]. Talent and Social Mobility - The report discusses the declining quality of education and its impact on social mobility, emphasizing the need for reforms in vocational education and higher education systems [57][60]. - It points out that the quality of higher education is inadequate, which poses risks to long-term growth prospects in the region [60][64]. Energy - The report underscores the importance of addressing energy inefficiencies and transitioning to lower emissions to support economic growth [67][71]. - It notes that the dominance of state-owned enterprises (SOEs) in the energy sector hinders competition and innovation, necessitating policy reforms to facilitate market entry for new players [71][75].

IMPLEMENTATION KNOW-HOW BRIEF Regulatory Sandboxes for Digital Health igital technology, applications, data, and information systems, as part of the ongoing transformation of health and health care can help ensure universal and equitable access to affordable, people-centered, and integrated quality care, contributing to the goal of reaching Universal Health Coverage (UHC). Intelligent use of data and digital technologies can elevate patient experience, improve clinician and staff satisfaction, drive operati ...

Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Economic, Trade, and Industry Implications of the Circular Economy Transition in Türkiye January 2025 © 2025 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of the staff of the World Bank. The findings, interpretations, and conclusions exp ...

Applying the Degree of Urbanisation A METHODOLOGICAL MANUAL TO DEFINE CITIES, TOWNS AND RURAL AREAS FOR INTERNATIONAL COMPARISONS 2021 edition Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized MANUALS A N D G U I D E L I N E S Public Disclosure Authorized Applying the Degree of Urbanisation A METHODOLOGICAL MANUAL TO DEFINE CITIES, TOWNS AND RURAL AREAS FOR INTERNATIONAL COMPARISONS 2021 edition 2021 edition Manuscript completed in December 2020 The designations employed ...

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The productivity of schooling is maximized when there is a match between a child's skill level and the complexity of the learning experiences offered at school, with mismatches in either direction being detrimental to learning outcomes [10][75] - The relationship between early childhood skill and the productivity of schooling follows an inverted-U shape, indicating that increasing early childhood skill enhances productivity up to a certain point, after which further increases can reduce productivity due to widening mismatches [10][76] Summary by Sections Introduction - The study emphasizes the importance of matching learning experiences to a child's understanding level to enhance learning outcomes, supported by various learning theories [2] Empirical Evidence - The research utilizes longitudinal data from the Young Lives Study, focusing on children from Peru, India, and Vietnam, to analyze the effects of schooling on child skill [8][12] - The findings indicate that the productivity of schooling is influenced by the difference between a child's existing skill and the complexity of the school curriculum [10][19] Methodology - A value-added specification is employed to account for individual-specific effects and to analyze the relationship between child skill and school complexity [9][41] - The study uses a non-linear dynamic panel model to estimate the effects of schooling, allowing for heterogeneity in productivity based on mismatches [9][50] Results - The main results reveal that a 1% increase in schooling can lead to a 0.55% increase in skill, with the productivity of schooling being highest when there is a match between child skill and school complexity [51][55] - The analysis shows that the effect of early childhood skill on schooling productivity is non-monotonic, with positive effects dominating in lower skill quartiles and negative effects in higher quartiles [56][60] Cross-Country Evidence - The study extends its findings to India and Vietnam, confirming similar patterns of heterogeneous effects of schooling based on the mismatch between child skill and school complexity [61][69] Conclusion - The research underscores the necessity of tailoring educational experiences to align with children's skill levels to optimize learning outcomes, providing external validity to existing educational interventions [75][76]

Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - The report emphasizes the importance of accurate and comprehensive measurement of household livelihoods for monitoring poverty alleviation and targeting social assistance programs. Traditional data collection methods are costly and often inadequate for local-level measurement, necessitating alternative approaches [5][10][12]. - The study evaluates satellite-based deep learning methods to enhance poverty measurement in data-scarce environments, demonstrating that transformer architectures can effectively measure local-level variations in household asset wealth and track changes over time [5][11][33]. - The research highlights the potential of combining satellite imagery, publicly available geo-features, and advanced deep learning techniques for hyperlocal and dynamic poverty measurement [5][11][35]. Summary by Sections Introduction - Accurate measurements of economic well-being are essential for achieving international poverty alleviation goals, including the UN's Sustainable Development Goal 1 [9]. - Traditional household surveys are often infrequent and spatially imprecise, creating a need for scalable alternatives [10]. Methodology - The study utilizes a large-scale dataset comprising over 12 million households across four African countries, leveraging both census data and multi-spectral satellite imagery [12][41]. - The research tests various deep learning models, including vision transformers and convolutional neural networks, to predict asset wealth index (AWI) [13][51]. Results - The transformer model outperforms other models in predicting country-level wealth, achieving R² values of 0.83, 0.70, and 0.62 for Malawi, Mozambique, and Madagascar, respectively [18]. - For wealth change prediction, the transformer model captures 52% of the variation in Malawi and 42% in Mozambique, outperforming traditional models [22][24]. - City-level wealth mapping demonstrates high accuracy, with R² values of 0.76 for Lilongwe and 0.67 for Blantyre, showcasing the effectiveness of high-resolution satellite imagery [32][34]. Discussion - The findings indicate that transformer models can effectively integrate geospatial features to enhance wealth predictions, particularly in data-scarce settings [35][37]. - The report underscores the necessity of having a critical mass of training data to ensure robust predictive performance, with accuracy deteriorating when training data falls below 10% of the population [36][38].

Investment Rating - The report does not explicitly provide an investment rating for the industry analyzed. Core Insights - The labor market impacts of artificial intelligence (AI) are expected to be more limited in low- and middle-income countries (LMICs) compared to high-income countries (HICs), with only 12% of workers in low-income countries and 15% in lower-middle-income countries experiencing high exposure to AI [3][74]. - AI exposure is higher among women, urban workers, and those with higher education levels, indicating a disparity in how different demographic groups are affected by AI advancements [3][12][75]. - The analysis suggests that while AI may enhance productivity and automate certain tasks, it does not necessarily lead to job losses, as it could also augment worker productivity [3][12]. Summary by Sections Introduction - The report highlights the rapid development of AI, particularly generative AI, and its potential to transform jobs and economic structures, similar to historical technological revolutions [8][9]. Measuring AI Exposure and Labor Market Impacts - The study employs the AI Occupational Exposure (AIOE) index to assess the potential impact of AI on various occupations across 25 countries, representing a population of 3.5 billion people [10][30]. - The AIOE index indicates that high-income countries have the highest exposure to AI, with an average score of 62, while low-income countries have an average score of 37 [11][41]. Stylized Facts about AI in Low-, Middle-, and High-Income Countries - The average AIOE across all countries is 47, with significant variations based on income levels. High-income countries show a right-skewed distribution of AI exposure, while low-income countries exhibit a left-skewed distribution [39][41]. - The report categorizes AI exposure into four levels and finds that high-skilled occupations are more exposed to AI than low-skilled ones, with white-collar industries being the most affected [61][64]. Conclusion - The findings emphasize the need for tailored policy responses to manage AI's impact on the workforce, particularly in LMICs, where infrastructure challenges such as lack of electricity may hinder AI adoption [74][76]. - The report concludes that fears of significant labor market disruptions in LMICs due to AI may be overstated, suggesting that the immediate effects may be more about improving access to services rather than widespread job losses [78].

Investment Rating - The report does not explicitly provide an investment rating for the industry analyzed. Core Insights - The analysis indicates that upward educational mobility is positively associated with GDP per capita in Europe and Central Asia, while relative mobility indicators show no correlation with country income levels [3][13][81] - In Latin America, higher relative mobility correlates with lower income, whereas higher absolute mobility is linked to higher income [13][14][81] - The study introduces a new measure of intergenerational mobility in education, termed the upward mobility gap, which enhances the understanding of educational mobility across different contexts [11][80] Summary by Sections Introduction - The report discusses the importance of social mobility in economic growth, emphasizing that talent allocation improves in socially mobile societies [7][8] Literature Review - Previous studies show significant variations in intergenerational educational mobility across countries, with high persistence in Latin America and greater mobility in Northern Europe [16][18] Measures of Educational Mobility - The report utilizes various measures to capture trends in intergenerational educational mobility, including oriented mobility measures and absolute mobility measures [20][33] Empirical Framework - The empirical analysis employs a framework to assess the relationship between educational mobility and economic development, using data from 68 countries over the period 2000-2020 [37][43] Educational Mobility Across Countries - The report identifies patterns of intergenerational educational mobility, noting that upward absolute mobility has declined in Europe and Central Asia, while South Asia has seen increases in both absolute and relative mobility [12][63] Educational Mobility and Economic Development - The analysis reveals a context-specific relationship between educational mobility and income levels, with upward mobility in higher education showing a positive correlation with GDP per capita across various regions [67][68][81] Conclusions - The findings suggest that the relationship between intergenerational educational mobility and economic development is complex and varies by region, indicating that certain aspects of mobility are more relevant for growth in specific contexts [81][82]