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].

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 ...

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]

Investment Rating - The report indicates that enhancing data transparency can lead to increased sovereign bond returns, particularly in countries with medium to higher levels of institutional quality [3][10][25]. Core Insights - Improved data transparency can mitigate the negative impact of high debt levels on bond returns, suggesting that even highly indebted countries can attract global investors through enhanced transparency [3][10][25]. - The study identifies a threshold effect, indicating that countries need to achieve a certain level of institutional quality (ICRG score greater than 4.15) for international creditors to benefit from improved data transparency [3][10][25]. - The empirical analysis demonstrates that both creditors and debtors can benefit from increased data transparency, with significant implications for investment decisions in sovereign bonds [34][35]. Summary by Sections Introduction - The report explores the relationship between data transparency and sovereign bond returns, focusing on the benefits for global investors rather than just sovereign borrowers [7][8]. Estimation Technique and Data - The study employs Fixed Effect Instrumental Variables (FE-IV) for panel data analysis to estimate the impact of data transparency on sovereign bond returns, controlling for various macroeconomic and financial factors [12][14][18]. Empirical Analysis - The findings reveal that enhancing data transparency leads to higher bond returns in countries with medium to higher levels of institutional quality, while high levels of public debt generally correlate with lower bond returns [25][26][30]. - The analysis shows that improving data transparency can still attract investors even in highly leveraged countries, thus increasing bond returns [27][30]. Calculating Creditors' Benefits - The report quantifies the potential gains for international creditors from improving data transparency in borrowing countries, estimating significant increases in bond returns across various regions [32][63]. Conclusion - The study concludes that greater data transparency enhances sovereign bond returns, particularly in countries with adequate institutional quality, and highlights the mutual benefits for both creditors and debtors [34][35].

Investment Rating - The report does not explicitly provide an investment rating for the higher education sector in Azerbaijan. Core Insights - Azerbaijan prioritizes strengthening competitive human capital through enhanced education, aiming for higher education institutions to meet global economic demands [13][14] - Institutional development and state policies are essential for creating a competitive higher education system with quality research outputs [14] - The integration of AI into higher education is seen as a means to enhance competitiveness and improve educational outcomes [16][84] Summary by Sections Executive Summary - The report emphasizes the need for Azerbaijan to enhance human capital through education, focusing on the competitiveness of higher education institutions [13] - Work-integrated learning programs are highlighted as effective tools for bridging education and the labor market [14] - Concerns are raised about the quality of research outputs despite increased publication numbers, indicating a potential decline in research standards [15] Higher Education System of Azerbaijan - The higher education system consists of 52 institutions, with a majority being public universities located in Baku [24][25] - The system reflects a blend of Soviet heritage and European practices, having adopted the Bologna Process for standardization [22][23] Higher Education Student Population - In the 2022-2023 academic year, 222,809 students were enrolled in higher education, with a notable increase in master's degree enrollments [29][30] - Despite growth, Azerbaijan's tertiary education enrollment rates remain lower compared to similar countries [30] Governance of Higher Education and Research - The Ministry of Science and Education regulates the higher education system, with ongoing discussions about a new Law on Higher Education [33][34] - A significant shift in governance occurred in 2022, transferring research institutes from the Azerbaijan National Academy of Sciences to the Ministry of Science and Education [35] Funding of Higher Education & Research - Research funding has increased since 2005, but its proportion of GDP and public expenditure has declined, indicating chronic underfunding [38][40] - HEIs receive about 10% of the national annual research and development budget, limiting their research capabilities [41] Quality Assurance & Educational Programs in Higher Education - The quality assurance system is aligned with European standards, but state approval limits institutional autonomy in program development [44][46] - Continuous monitoring and periodic reviews are mandated to ensure quality, but greater autonomy is recommended for institutions [48] Alignment of Higher Education, Skills & the Labour Market - Graduate unemployment is a significant issue, with a mismatch between university training and labor market needs [50][51] - The report highlights the importance of aligning curricula with industry requirements to enhance graduate employability [17][63] Institutional Development for Increased Research & Technology Integration - Research incentives are in place, but the effectiveness of these incentives is questioned due to low research productivity and quality [72][79] - The integration of AI into education is seen as crucial for enhancing teaching and learning experiences [84][116] Recommendations - The report suggests establishing advisory boards to align higher education with labor market needs and integrating career development into curricula [92][94] - A National Graduate Survey is recommended to gather reliable data on graduates' employment outcomes [97][99] - Strengthening career centers and integrating career training into university curricula are emphasized as essential for improving student employability [100][101]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report emphasizes the increasing complexity of fragility, conflict, and violence (FCV) in low-income countries, predicting that by 2030, over half of the world's extreme poor will reside in such contexts [2][7] - Adaptive social protection (ASP) programs have shown positive impacts on poverty and vulnerability, but their effectiveness in FCV settings is less documented [3][8] - The study conducted by the Sahel Adaptive Social Protection Program (SASPP) involved nearly 400 qualitative interviews with beneficiaries in Burkina Faso and Cameroon to understand the impacts of ASP in FCV environments [10][11] Summary by Sections Key Recommendations - Longer-term and greater support should be provided for households facing significant conflict and insecurity, as beneficiaries often prioritize immediate needs over long-term investments due to security concerns [15][17] - Programs should incorporate flexibility in objectives to adapt to changing security environments, recognizing that insecurity varies across regions [19][21] - Designing programs to strengthen social cohesion is crucial, as beneficiaries reported forming new relationships and sharing resources, which can act as informal social insurance [24][25] - Strengthening communication is essential to boost trust in government and reinforce the social contract, as awareness of government involvement in ASP programs positively influences beneficiaries' attitudes [27][28] Channels of Impact - The study identified three key channels through which ASP programs impact individuals and communities: material, social, and political [13] - Material channel: Redistribution of resources improves economic wellbeing and resilience to shocks [13] - Social channel: Programs foster new relationships and community participation, acting as social insurance mechanisms [13] - Political channel: Resource allocation enhances trust in government and strengthens the social contract [13] Operational Recommendations - Providing longer-term support is necessary in FCV settings to address the limited access to markets and income-generating activities [17][18] - Programs should adapt to the specific contexts of insecurity, prioritizing safeguarding consumption and human capital over long-term productivity goals [21][22] - Communication strategies must be robust to manage perceptions and grievances among non-beneficiaries, ensuring transparency in program targeting [26][32]

Investment Rating - The report does not explicitly provide an investment rating for the health sector in Pakistan Core Insights - The Government of Pakistan has prioritized Universal Health Coverage (UHC) and primary health care access as central goals in its health policy agenda, as outlined in the National Health Vision 2016–2025 [3] - The implementation of the Essential Package of Health Services (EPHS) and the Sehat Sahulat Program (SSP) are significant initiatives aimed at expanding health coverage and improving public health financing efficiency [4][5] - Despite recent increases in public health expenditures, Pakistan's per capita health spending remains low compared to regional averages, indicating a need for further investment and reform [7][40] Summary by Sections Health Financing and UHC - The report emphasizes the need for financing reforms to support the expansion of provincial social health insurance programs and district-level EPHS packages [5] - Current per capita public health expenditures in Pakistan are approximately US$14, significantly below the estimated US$28 needed to finance a comprehensive EPHS [40] Public Health Expenditures - Per capita public health spending varies across provinces, with Sindh at US$10.0, Punjab at US$10.3, Balochistan at US$8.7, and Khyber Pakhtunkhwa at US$7.8 [7] - The South Asia Region average for per capita public health expenditures was US$17.5 in 2017, highlighting that no province in Pakistan meets this benchmark [7] Efficiency and Resource Allocation - The report identifies a historical trend of stagnant public health financing growth, which has recently shifted, with real per capita public health expenditures increasing by approximately PKR 1,000 between FY2011 and FY2018 [8] - There is a significant disparity in health outcomes across provinces, suggesting opportunities for efficiency improvements in health financing [15][16] Health Equity and Out-of-Pocket Expenditures - Out-of-pocket (OOP) expenditures account for 60% of current health expenditures in Pakistan, with rates varying by province, indicating a critical equity concern [33][34] - The cost of medicines and vaccines is identified as the primary driver of OOP expenditures, necessitating targeted health financing interventions [37] Recommendations for Improvement - The report recommends increasing the share of health expenditures allocated to primary health care (PHC) and implementing the EPHS to improve health financing efficiency [22][45] - It suggests conducting feasibility studies on health taxes and soft earmarking to enhance funding for health initiatives [42]