Investment Rating - The report does not explicitly provide an investment rating for the industry analyzed Core Insights - The paper introduces a new dataset of high-frequency monetary policy shocks for 21 advanced economies and 8 emerging markets from 2000 to 2022, utilizing daily changes in interest rate swap rates around central bank announcements to identify unexpected monetary policy shocks [4][10] - A significant finding is that monetary policy decisions from small open economy central banks have substantial spillover effects on swap rates and bond yields in other countries, indicating that the impact of monetary policy is not limited to major central banks [4][16] Summary by Sections Introduction - The report discusses the reassessment of monetary policy effectiveness following the inflation surge post-COVID-19, emphasizing the need for identifying exogenous components of monetary policy [9][10] Data and Methodology - The dataset covers 29 countries, including 20 central banks, with a total of 3,545 monetary policy events, allowing for a comprehensive analysis of monetary policy shocks [19][20] - The methodology involves constructing monetary policy surprises using daily changes in interest rate swap rates around central bank announcements, ensuring consistency across countries [12][26] High-Frequency Impact of Monetary Policy Shocks - The report estimates the transmission of monetary policy to various interest rates and asset prices, highlighting the same-day effects of monetary policy announcements on sovereign bond yields and exchange rates [53][55] - It presents empirical results showing that a 100 basis point increase in monetary policy surprise significantly impacts sovereign bond yields in both advanced and emerging markets [56]

STAFF CLIMATE NOTES Harnessing Renewables in Sub-Saharan Africa: Barriers, Reforms, and Economic Prospects Kaihao Cai, Thibault Lemaire, Andrea Medici, Giovanni Melina, Gregor Schwerhoff, and Sneha Thube IMF STAFF CLIMATE NOTES 2024/005 ©2024 International Monetary Fund Harnessing Renewables in Sub-Saharan Africa: Barriers, Reforms, and Economic Prospects IMF Staff Climate Notes 2024/005 Kaihao Cai, Thibault Lemaire, Andrea Medici, Giovanni Melina, Gregor Schwerhoff, and Sneha Thube* DISCLAIMER: The IMF Sta ...

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The European Union (EU) aims for a significant transition to electric vehicles (EVs) to meet climate goals, with targets for CO2 emissions reductions from cars and vans set for 2030 to 2035 [7][3] - The shift towards EVs is influenced by the increasing dominance of Chinese manufacturers in the global EV market, which poses competitive challenges for the EU automotive sector [3][10] - The report analyzes the macroeconomic implications of this transition, indicating that the short-term GDP cost for the EU is relatively small, estimated at 0.2-0.3 percent, while long-term impacts are close to zero [3][17] Summary by Sections Introduction - The EU has set ambitious climate goals, including a rapid transition to EVs, with regulations requiring significant reductions in CO2 emissions from new vehicles [7][3] - Current EV sales in the EU are around 15 percent, with a target of 65 percent by 2030 and 100 percent by 2035 [7][3] Historical Context - The report draws parallels between Japan's rise in the automotive sector during the 1960s-1980s and China's current position in the EV market, suggesting that China's market penetration could occur at a faster rate [13][28] Model Specification and Scenarios - The report employs two models: the GIMF-GVC model for short- to medium-term dynamics and a dynamic quantitative trade model for long-run effects [30][34] - The models simulate the impacts of the dual shocks of the EV transition and increased Chinese EV imports on the EU economy [12][30] Simulation Results - The report finds that the transition to EVs will have varying impacts across EU countries, with significant losses for smaller economies reliant on the automotive sector, such as Hungary and Czechia [17][10] - Protectionist policies, such as tariffs on Chinese EVs, would exacerbate GDP losses, while increased Chinese foreign direct investment (FDI) in Europe could mitigate some negative impacts [17][10] Climate Implications - The transition to EVs is expected to have positive climate implications, but the report emphasizes the need for policies to support affected workers and regions during the transition [17][19] Conclusion - The report concludes that while the transition to EVs presents challenges, with appropriate policies, the economic impacts can be managed, and the long-term benefits of the green transition outweigh the costs of inaction [19][18]

Investment Rating - The report does not explicitly provide an investment rating for the industry under analysis Core Insights - The issuance of retail central bank digital currencies (CBDCs) has significant implications for financial stability, which depend on the size of issuance, initial conditions, and banking sector reactions [13][25] - Adverse effects on financial stability can arise primarily when CBDCs replace deposits rather than cash, particularly if banks do not hold excess reserves [15][30] - The design of CBDCs is crucial; features that promote their use as a means of payment rather than a store of value can mitigate risks [21][33] Summary by Sections Executive Summary - The paper evaluates the financial stability implications of issuing retail CBDCs and emphasizes the need for policy measures to mitigate potential adverse impacts [13][25] Potential Benefits of Retail CBDC - CBDCs can reduce cash handling costs, foster financial inclusion, improve payment infrastructure, maintain monetary sovereignty, and reduce risks from private stablecoins [39][43][44] CBDC Issuance Scenarios - The report outlines various scenarios for CBDC issuance and their impact on financial sector balance sheets, highlighting that the effects depend on the amount of CBDC issued and the existing balance sheet conditions [52][54] Policy Options to Mitigate Adverse Effects - Central banks can employ macroprudential policies, expand lending operations, and design CBDCs to minimize financial stability risks [17][32][35]

Investment Rating - The report does not explicitly provide an investment rating for the industry but emphasizes the importance of climate policies in attracting green foreign direct investment (FDI) in emerging markets and developing economies (EMDEs) [5][6][17]. Core Insights - The report highlights that achieving COP28 goals necessitates a doubling of clean energy investment globally by 2030, requiring an additional $600 billion in EMDEs excluding China [5][9]. - It identifies that foreign direct investment (FDI) is crucial for EMDEs to bridge the renewable energy investment gap and finance green projects, especially in the context of low fiscal space and constrained domestic investors [5][6]. - The analysis indicates that a greater number of climate policies correlates with higher FDI flows into renewable energy, particularly in countries with solar potential and low fossil fuel dependence [6][17]. - The report notes that while climate policies positively influence green FDI in renewable energy, they do not show a significant relationship with FDI in electric vehicles (EVs) and green hydrogen [6][39]. Summary by Sections Green FDI and Climate Policies - Green FDI has accelerated since 2016, tripling as a share of global GDP from 2014 to 2022, with inflows increasing from approximately $40 billion in 2014 to over $200 billion in 2022 [26][28]. - The report emphasizes that investments in renewable energy have remained stable, while those in EVs and green hydrogen have surged recently [28][29]. Econometric Evidence - The econometric analysis shows that closing the climate policy gap between EMDEs and advanced economies (AEs) could triple the green FDI to GDP ratio in EMDEs and close 40% of the private renewable investment gap [6][17][36]. - The findings suggest that while climate policies are associated with increased green FDI, they do not adversely affect non-green FDI inflows [36][39]. Country-Specific Experiences - Successful countries in attracting green FDI in renewable energy have implemented a diverse set of climate policies and adapted their energy frameworks to technological changes [8][21]. - For EVs, countries with prior automobile manufacturing experience and national sectoral strategies have seen higher FDI inflows [22][23]. - In the case of green hydrogen, comprehensive national strategies and favorable renewable energy production conditions are key drivers of FDI [8][24]. Global Factors - The report discusses how global initiatives, such as the Just Energy Transition Partnership and EU strategies for green hydrogen, have positively impacted green FDI in EMDEs [24][25]. - It also highlights that geopolitical fragmentation may limit the ability of countries to attract green FDI, as investments are less likely between politically distant nations [20][44].

Investment Rating - The report does not explicitly provide an investment rating for the residential property market in Ukraine. Core Insights - The authorities in Ukraine are committed to improving the House Price Index (HPI) to enhance transparency in the real estate market and strengthen systemic risk analysis [5][10] - A technical assistance mission was conducted to develop new processes for compiling the HPI, focusing on data cleaning and capacity building for staff [4][18] - The national HPI for Ukraine shows a price change of approximately 15% over six quarters from Q1 2023 to Q2 2024 [11] Summary by Sections Summary of Mission Outcomes and Priority Recommendations - A technical assistance mission took place from July 15-19, 2024, to assist Ukrainian authorities in developing new methods for HPI compilation [4] - The mission aimed to enhance data processing capabilities and improve the accuracy of property price indices [5][6] Data Sources - The SSSU currently uses data from real estate agents and the OLX platform for compiling the HPI [19] - OLX has agreed to provide data directly to the SSSU on a quarterly basis, which will improve data availability [19] Compilation Methods - The SSSU has implemented improved methods for HPI calculation, including the use of list prices and hedonic methods for quality adjustment [34] - A new stratification of data based on NUTS regional classification has been developed, enhancing the relevance of the data [36] Recommendations - The report outlines several priority recommendations, including upskilling staff in the R statistical package and automating data processing tasks [12][33] - It is recommended to extend data coverage to include houses and gather more detailed location information for properties [32][33]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Pakistan's economy has lagged behind regional peers for over a decade, with GDP per capita growth averaging only 1.9% from 2000 to 2022, compared to 4.5% for Bangladesh and 7.5% for China, indicating a need for urgent policy corrections [5][6] - The report highlights macroeconomic distortions and policy-related restrictions, including protectionist interventions and insufficient investment in human capital, as key factors contributing to underperformance [5][8] - Structural reforms are essential for creating fiscal space, building human capital, and enabling private sector growth, with potential growth increases of about 2% over five years from consistent reform implementation [39][58] Summary by Sections Economic Performance and the Road Ahead - Pakistan's living standards have declined, with weak contributions from human and physical capital and shrinking productivity [8] - Economic growth from 2000 to 2020 was primarily driven by physical capital accumulation and increased labor hours, while total factor productivity (TFP) and labor quality contributions were significantly lower [8][19] - The country has struggled with declining export performance and limited trade openness, which hampers development and external viability [8][11] Drivers of Decline in Living Standards - The report identifies persistent resource misallocation and policy-induced distortions as major contributors to the decline in living standards and competitiveness [14][19] - Agriculture, characterized by low productivity and government interventions, exemplifies how policies can trap resources in low-productivity sectors [16][18] - The regulatory environment and public investment management issues have deterred domestic and foreign direct investment, leading to low FDI inflows compared to peers [20][21] Supporting Private Sector Development - To shift Pakistan onto a new economic trajectory, it is crucial to remove protectionist policies and enhance competition, which would spur innovation and resource reallocation [30][39] - The report emphasizes the need for improved public investment efficiency and tax collection to create space for higher investment in physical and human capital [30][53] - Structural reforms could lead to a 7% increase in output and a 6% reduction in the public debt-to-GDP ratio over five years, highlighting the potential for significant macroeconomic gains [57][59]

Investment Rating - The report does not explicitly provide an investment rating for the industry under review. Core Insights - The paper examines the macroeconomic frameworks of IMF-supported programs in low-income countries from 2009 to 2022, focusing on the differences in macroeconomic targets and their achievements between fragile and non-fragile states [3][12][15] - Key findings indicate that program targets for fragile and non-fragile states are similar, with a notable optimism in projections except for inflation, and a weak correlation between targets and actual outcomes [3][15][16] - The study emphasizes the challenges in setting realistic, country-specific targets in IMF-supported programs, highlighting the need for better quantitative tailoring of these programs [3][12][15] Summary by Sections Introduction - The introduction outlines the study's focus on IMF-supported programs in low-income countries, particularly the differences in macroeconomic objectives between fragile and non-fragile states [12][13][18] Main Findings - Limited quantitative tailoring is observed, with no significant differences in program targets between fragile and non-fragile states [15][55] - There is considerable optimism in targets, which are often missed except for inflation [15][16] - Weak correlations exist between targets and outcomes, suggesting that country-independent targets could outperform program projections [16][55] Literature Review - The literature review highlights the scarcity of studies on IMF-supported programs in fragile states, noting that completion rates for lending programs with fragile states are significantly lower than those for non-fragile states [19][20] Methodology - The methodology section details the criteria for program inclusion, focusing on programs with a planned duration of 1.5 years or more, resulting in a sample of 84 programs across 43 countries [27][30] Quantitative Tailoring - The analysis of quantitative tailoring reveals that while absolute ambitiousness of targets is similar for both fragile and non-fragile states, relative ambitiousness may differ based on initial conditions [51][52][59] Optimism - The report assesses the extent of optimism in program targets, finding that targets are generally overly optimistic, particularly regarding growth projections [39][40] Correlation Between Targets and Outcomes - The correlation analysis indicates that for most variables, targets and outcomes are statistically independent, with the implication that better forecasting methods could improve program effectiveness [16][39] Conclusion - The conclusion reiterates the need for more realistic macroeconomic frameworks in IMF-supported programs, particularly for fragile states, to enhance the effectiveness of these programs [12][15][18]

Investment Rating - The report does not explicitly provide an investment rating for the industry under discussion. Core Insights - The productivity growth in Latin America and the Caribbean (LAC) has stagnated since 1980, with income levels showing no convergence with the US, unlike emerging Asia and Europe [8][21] - The sluggish productivity growth is attributed to limited technology diffusion and a large informal sector, which hampers overall economic growth [9][10][19] - Artificial intelligence (AI) has the potential to enhance productivity in the formal sector, reduce informality, and facilitate economic convergence with advanced economies [14][20] Summary by Sections Low Productivity Growth in Latin America and the Caribbean - Labor productivity growth in LAC has been only 0.5 percent annually since 1980, compared to 4.2 percent in East Asia and 1.4 percent in the US [21] - The informal sector remains large, with informal firms being only 15 percent as productive as formal firms, limiting overall productivity growth [25][31] The Important Role of Technology Diffusion in Growth - The limited diffusion of technology is a key reason for low productivity growth in LAC, with IT adoption lagging behind advanced economies [12][11] - Historical patterns show that technology diffusion can account for significant income disparities between countries [41] The Difference AI Can Make - AI is expected to boost labor productivity growth similarly to past IT innovations, with the potential for rapid adoption due to lower capital requirements [13][14] - AI could enable LAC to leapfrog technologies, as seen in Brazil's digital payment system, Pix [14] Labor Market Implications of AI - AI may not necessarily reduce overall employment; instead, it could create new jobs while also leading to job polarization [15][16] - Jobs at high risk of automation are primarily in sectors like call centers, while jobs that benefit from AI are in areas like healthcare [17][18] Adoption of AI in LAC - LAC's exposure to AI is below 40 percent of the labor force, compared to 60 percent in advanced economies, indicating a risk of falling behind in AI adoption [19] - Factors hindering AI adoption include weak competition, regulatory issues, and a large informal sector [19] Policies to Achieve the Full Potential of AI - Policies should focus on enhancing technology diffusion, supporting workforce transitions, and addressing skills gaps through education and training [20] - Investments in digital infrastructure and measures to reduce labor market informality are essential for maximizing AI benefits [20]

Industry Investment Rating - The report does not explicitly provide an investment rating for the industry, as it focuses on the development of a Global Soft Power Index (GSPI) rather than direct investment recommendations [3][9] Core Viewpoints - The report introduces a new comprehensive Global Soft Power Index (GSPI) composed of six dimensions, which allows for comparisons at both the headline level and sub-indices level, enabling granular analysis of soft power across countries [3][9] - The GSPI is constructed using a three-step approach: normalization of variables, aggregation into sub-indices, and aggregation into the final index, providing a systematic framework for measuring soft power [9][25] - The report highlights the macro-financial relevance of the GSPI, particularly its impact on exchange rate volatility, with the culture and global reach dimensions being the most significant in explaining real exchange rate volatility [10][60][62] Data Overview - The GSPI is based on 29 indicators across six dimensions (commercial, culture, digital, education, global reach, and institutions) for a broad set of countries from 1990 to 2021 [9][15] - Data availability constraints limit the most comprehensive version of the GSPI to a balanced panel of 66 countries from 2007 to 2021 [20] Index Methodology - The GSPI is constructed using a standard three-step approach: normalization of variables, aggregation into sub-indices, and aggregation into the final index [25] - Principal Component Analysis (PCA) is used to determine the weights for each sub-index, ensuring that the highest possible variation in the indicator set is accounted for with the smallest number of factors [27][30] Index Results - There is significant variation in the level of soft power across countries, with the GSPI ranging from -0.59 in the Dominican Republic to 1.68 in South Korea as of 2021 [11][39] - Advanced economies generally have higher levels of soft power, but this is not always the case, as seen in the evolution of soft power in China and the United Kingdom, where China's soft power increased significantly while the UK's declined [11][40] - The GSPI and its sub-indices allow for the classification of countries into four groups based on their soft power levels, with Japan and South Korea forming a distinct group due to their high commercial prowess and lower performance in the culture dimension [43][46] Macro-Financial Application - The GSPI is used to test the impact of soft power on exchange rate volatility, with results showing that low soft-power countries exhibit a different dynamic between soft power and real exchange rate volatility compared to medium and high soft-power countries [10][49] - The culture and global reach dimensions of soft power are particularly relevant in explaining real exchange rate volatility, with global reach being the most significant sub-index [60][62]