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Yayın Artificial Intelligence in Economic Modeling and Forecasting(S & B World Foundation, 2025) Kaytaz, Mehmet; Özmucur, Süleyman; Yürükoğlu, TanjuArtificial Intelligence (AI) originated from multiple disciplines, with Alan Turing’s work on the Turing Machine laying its foundation. Early developments such as neural networks and the Turing Test marked the beginning of AI’s evolution through cycles of enthusiasm and setbacks. Recent breakthroughs in big data, GPU computing, and deep learning have made AI a part of daily life, from healthcare and translation to robotics and gaming. However, its rapid expansion raises serious concerns regarding autonomous weapons, surveillance, labor displacement, and the existential threat posed by unsupervised superintelligent systems. Reflecting this surge, AI and machine learning publications have skyrocketed since 2019, with most research emerging only in the past five years and spanning diverse fields beyond computer science. In parallel, machine learning techniques have increasingly influenced modern economics, building on econometric tools such as regression, principal components, and ARIMA models. Concepts such as supervised learning and methods like logistic regression, LASSO, and neural networks have bridged the gap between traditional econometrics and data science, enhancing predictive accuracy and flexibility. Lawrence Klein’s Current Quarter Model (CQM), which leverages high-frequency indicators and bridge equations to nowcast GDP, exemplifies this integration. His approach, now echoed in MIDAS regressions and global modeling efforts like Project LINK, remains vital. The COVID-19 shock underscored the need for adaptive, interdisciplinary forecasting frameworks that incorporate health, behavioral, and environmental variables in an interconnected world. The use of AI, specifically machine learning (ML), in official statistics is very recent compared to other disciplines and areas. This may seem contradictory to the objectives of official statistics. At the same time, the digital revolution led to an abundance of all types of data and the demand for data has considerably increased. Several reasons may be specified for this delay. One reason may be the structure of official statistical organizations and the role of statisticians in these organizations. The breakthroughs in AI technology and the use of satellite imagery have disrupted the way official statisticians collect, process, and analyze data. There is skepticism among some official statisticians about employing new technological developments. Official statisticians are reluctant to work with data that does not rely on probability samples and legacy methods. Concerns about quality, ethics, and privacy are the major factors contributing to this unwillingness. There is also an insufficiency of both financial and human resources. Over the last seven years, the efforts of the United Nations Economic Commission for Europe (UNECE) within the framework of modernizing official statistics, along with its Machine Learning Group, have played a significant role in many national statistical offices adopting and applying machine learning methods. In two years, it grew from 120 statisticians from 23 countries to more than 400 statisticians from 35 countries. Currently, many NSOs and international organizations are involved in developing applications of ML in various areas of data collection. In various areas of data collection, many NSOs and international organizations are also involved in developing applications of ML. The IMF has developed the PortWatch Platform, utilizing satellite-based vessel data to provide real-time indicators of port and trade activity. Statistics Colombia is predicting poverty rates using daytime and nighttime satellite imagery. Statistics Indonesia has a similar project. Statistics Netherlands is using webscraping to identify different types of companies. The U.S. Census Bureau and the Bureau of Transportation Statistics (BTS) jointly produce the Commodity Flow Survey. They reduced manual workload by using machine learning methods. Federal Statistical Office of Switzerland developed StatBot, a chatbot for sharing statistical information, soon to provide services in three different languages. The Swedish Land Registry (SLR) is the government agency with the mission of securing the ownership of real estate and making geodata available for the society. SLR uses handwritten text recognition together with neural networks to get information from documents going back to 1850s. The Australian Bureau of Statistics is undertaking a comprehensive review of the Australian and New Zealand Standard Classification of Occupations using large language models. Statistics Canada also has explored the use of large language models to automate and enhance statistical report generation, aiming to improve efficiency and reduce manual workloads. The experience of statistical offices showed that machine learning has proven to contribute to producing data that is more relevant, with better quality, in a faster or more cost-efficient manner, without any significant reduction to any of these dimensions. Machine learning is advantageous particularly in processes that are labor intensive, repetitive and stable, such as in classification and coding. Another lesson from the activities of the Machine Learning group is that sharing and collaboration within and between statistical organizations are also essential to advance the use of machine learning based on lessons learned on where it adds value, where it shows promise and where it offers less value. Artificial intelligence (AI) is reshaping economic policymaking by enabling more dynamic, data-driven analysis and forecasting. Unlike traditional models, AI systems, especially those utilizing machine learning, can adapt to changing conditions and extract insights from massive datasets. Central banks and institutions, such as the IMF and World Bank, now utilize AI for inflation tracking, labor analysis, and risk forecasting, while natural language processing aids in interpreting media and public sentiment. AI enhances forecasting accuracy for key indicators, such as GDP and inflation, by continuously updating projections. Deep learning and reinforcement learning further enhance real-time decision-making in an increasingly volatile global economy. AI is also transforming fiscal policy, trade, and regulation. Governments use predictive analytics for tax reform, compliance, and investment planning, while AI models assess trade shocks and climate risks. However, the rapid adoption of AI raises concerns about bias, transparency, and inequality, particularly in developing countries that lack data infrastructure and expertise. Ensuring AI systems are ethical, auditable, and inclusive is essential. Ultimately, AI's societal impact will hinge not just on innovation but on building governance frameworks that safeguard human rights and promote equitable outcomes. The global approach to AI regulation is fragmented. The EU leads with comprehensive laws, while countries like the U.S. favor sector-specific guidelines, and China pursues centralized, state-aligned control. International bodies such as the OECD promote ethical principles, but challenges remain, including cross-border enforcement, rapid innovation, and definitional ambiguity. To govern AI ethically, regulations must embed transparency, explainability, and oversight from the start. Independent audits, impact assessments, and robust privacy protections are crucial, especially in sensitive sectors such as healthcare and justice. Public trust depends on democratic participation and the inclusion of marginalized voices in shaping AI governance. Human-centered AI (HCAI) presents an alternative vision, one that supports rather than replaces human decision-making, and promotes usability, accountability, and equity. In fields such as education and healthcare, HCAI can enhance services while upholding ethical standards. However, AI’s labor market effects are concerning, as automation threatens jobs and exacerbates inequality. Without deliberate policies, such as reskilling and fair labor protections, especially in the Global South, AI could deepen global divides. Yet with inclusive governance, AI has the potential to reduce poverty, empower workers, and create a more equitable digital economy. The labor market implications of AI are profound. Cognitive automation threatens both lowskill and middle-income jobs while concentrating wealth among those who own the technology. These risks are widening income inequality and weakening social cohesion. Scholars such as Daron Acemoglu warn of "excessive automation" that replaces workers rather than empowering them, while others like Erik Brynjolfsson advocate for worker-augmenting AI and institutional reform to ensure inclusive innovation. Global disparities are stark—developed nations invest in reskilling and infrastructure, while developing economies face job displacement without adequate digital capacity. AI can be a force for upward mobility or social fragmentation, depending on how societies manage the transition. The impact of AI on poverty will depend heavily on policy choices. While it has already enabled life-saving advances in agriculture, healthcare, education, and microfinance in countries like Kenya, Colombia, and India, it also risks excluding low-income workers through automation and exploitative digital labor models. The rise of precarious gig work, digital piecework, and content moderation in the Global South underscores the need for inclusive labor protections, fair compensation, and recognition of data as a form of labor. Without intervention, the benefits of AI will continue to deepen global inequalities. With deliberate governance, however, AI can help build a fairer, more resilient, and more equitable digital economy.Yayın Global Inflation(S & B World Foundation, 2023-06) Kaytaz, Mehmet; Özmucur, Süleyman; Yürükoğlu, TanjuGlobalization led to the integration of more economies and more labor sources into the global economy. Thus, the availability of cheap labor helped accelerate economic growth and increase trade, particularly the expansion of global value chains. And the end of the cold war after the break of the Soviet Union also contributed to reducing inflationary pressures on a global scale. The global inflation rate tended to increase starting in 2019, and with COVID-19, it began to rise steadily. The pandemic caused further disruptions in economic activity and created supply chain issues, leading to higher commodity and consumer prices. Moreover, governments trying to cope with the pandemic had significant increases in government expenditures and Money supply. The average inflation rate reached the Great Recession levels in the middle months of 2020, while the median rate reached that level in October 2022. With output and employment, inflation is one of the most important macroeconomic variables. An inflation rate higher than moderate levels or a deflationary development creates instability in the economy. The instability leads to volatility in economic activities and economic inefficiencies. The result is lower rates of growth and social problems. Workers and pensioners whose wage rates are fixed for a period suffer the most because of inflation. To tame inflation, to reduce it to moderate rates requires reducing output. The cost of this is borne again by lowerincome groups. The Phillips curve has been at the center of the debates on inflation, economic growth, and monetary policy for over sixty years. It has been criticized and changed a lot from its original version of 1958. However, its modeling of the relationship between economic activity and inflation made it a useful and essential tool for policymakers. There are several measures of inflation, measured as the percentage change in prices over time. The most common measure is the Consumer Price Index (CPI), which reflects the changes in prices paid by consumers. The consumer should be a typical consumer. It requires a representative household and thousands of prices for the goods and services that comprise a typical consumer’s budget. Although gathering all needed data may be much simpler compared to earlier years, there may be issues with new commodities introduced and a pandemic where conducting the same surveys with the same accuracy may not be possible. Commodity prices, determined primarily by supply and demand, are influenced by various factors, such as speculators, producers' cartels, force majeure events, and disruptions in the supply chain, like the COVID-19 pandemic. Commodity price cycles have occurred throughout history, with increasing frequencies over time. The unprecedented swings in commodity prices during and after the pandemic had a significant impact on inflation dynamics worldwide. Energy prices, particularly natural gas, and hydrocarbon-based fertilizer prices, experienced the highest increases since 2018, affecting agricultural production costs and global food prices. The pass-through of commodity prices to domestic prices varies depending on the commodity type, inflation regime, exchange rates, and the level of competition in the market. Energy prices reached record highs due to geopolitical tensions, while food prices remained elevated, causing concerns about food insecurity. Metals and minerals experienced a rebound in demand, driven by the recovery of the global economy, but prices have since declined. The empirical studies show that supply chain disruptions, particularly shipping costs, influence import price inflation and domestic prices. These effects change from industry to industry and from economy to economy. Economies more integrated into the global economy are more affected than those less integrated. Island economies are affected more. In general, those economies with a strong central bank are less affected. These results suggest that the study of inflation and inflation policies should take globalization into account. Supply chains, especially global value chains, play an important role in forming inflationary expectations, the price formation behavior of firms, and the labor force. The Phillips curve would perform better with the inclusion of global variables into the model. A disequilibrium in demand and supply of goods & services is reflected in prices. A demand exceeding supply results in increases in prices. The percentage change in the general price level is the rate of inflation. This imbalance between supply and demand may start in the product market, as realized shortages during the pandemic, or it may originate in other markets and affect the product markets. The pandemic had a profound negative effect and created imbalances in labor markets, financial markets, government budget and foreign markets which led to more inflation. Some of these negative effects subsided, but most of them still linger and continue to have adverse effects on all the economies in the world. A very coordinated effort by world leaders and policy makers is the first step that is necessary to combat inflation and other issues that the world faces. Dealing with inflation, domestic or pass-thru global, requires the effective use of the combination of macroeconomic policy tools in a coordinated and judicious manner. Monetary policy, carried out by central banks, and fiscal policy, determined by the executive and legislative bodies of governments, played crucial roles in addressing the pandemic's economic and social effects. Monetary policy aimed to maintain macroeconomic stability, while fiscal policy interventions were targeted at specific problems arising from government-imposed restrictions. Monetary policy instruments such as interest rates, money supply management, inflation targeting, and expectations management were used by central banks worldwide. Interest rate reductions, quantitative easing, liquidity provisions, forward guidance, currency swaps, and targeted lending programs were common measures implemented during the pandemic. Quantitative easing proved to be a powerful tool, involving purchasing financial assets from the market to inject liquidity, lower borrowing costs, and stimulate lending and spending. However, the effectiveness of quantitative easing in stimulating inflation depends on various factors and the state of the economy. Following the formal ending of quantitative easing in major economies, monetary expansion started to slow down and even reverse in some cases in 2022. Policy rates were raised to manage the inflationary pressures. Fiscal policies adopted during the pandemic were a major inflationary shock, supported by the financial system and accommodated by monetary policies. These policies included direct income support, business support and stimulus packages, healthcare and vaccination spending, job protection and retraining programs, infrastructure investment, tax relief and deferrals, debt relief and financial sector support, and enhancements to social welfare programs. The extent to which these fiscal policy instruments were used varied across countries. Targeted interventions were found to have a lower inflationary effect compared to broad-based support. Providing targeted support to households through cash transfers was highlighted as the most cost-effective way to alleviate the burden on vulnerable families. In terms of the relationship between budget surplus and inflation, historical analyses show an inverse correlation in most periods, except during times of oil price hikes. Additionally, the relationship between the rate of inflation and changes in the assets of central banks showed long lags in their impact. The indicators suggest that the current inflationary process is not ending soon, although there is a decline in the global inflation rate. The effect of inflationary shock caused by fiscal policies adopted and implemented during the pandemic is continuing. The supply chain disruptions got weaker; however, the impact of supply shocks is longer lasting than expected. A significant problem is the labor market imbalance leading to sectoral price surges.Yayın Technology and Human Development(S & B World Foundation, 2024) Kaytaz, Mehmet; Özmucur, Süleyman; Yürükoğlu, TanjuUntil the last 250 years of human history the economic growth has been little. It is estimated that before 1750, per capita income in the world doubled every 6,000 years; since then, it has doubled every 50 years. There is a consensus that this growth is mainly due to the increase in productivity through technological progress. Population growth and capital accumulation by themselves cannot account for this level of economic growth. Technological progress, hence economic growth has not been equal across countries and regions, however it dramatically increased per capita incomes on average. Technological development did not just improve the average living standards but created important changes in social, economic, and political life. Historical studies show that technological progress, productivity growth and economic growth did not benefit everybody, at least in the beginning. During the Industrial Revolution productivity increased as well as working hours but the earnings did not increase until the second half o of the 19th century. Furthermore, working conditions deteriorated. The situation that some sections of society suffers with technological progress is not restricted to the Industrial Revolution. For example, increased mechanization may lead to unemployment. One approach to this problem is that technological progress increases living standards at the end and that is the important thing. There may be some casualties along the way, but that is an inevitable price to be paid for future gains. Since the Industrial Revolution there has been opposition to technological progress when it reduces the demand for labor. Historical experience shows that with the advance of technology some tasks were eliminated, however new tasks, new jobs were created. This does not eliminate the suffering of some workers or producers. The negative effects of technological progress are not limited to labor markets. These effects can be eliminated or reduced through economic policies. For example, innovations in medicine are more curative than preventative. Government policies may direct the research in this area to more preventative innovations. Technological progress gives more market power to innovators. With the market power they create barriers to entry, and the market moves towards a monopoly. Only government policies can put a check on the tendency to reduce competition. Technologies can be classified as worker enabling or worker replacing. The former increases the productivity of workers do not replace them. Policy makers can support enabling technologies. Abramovitz and Solow are the two economists who first estimated the direct contribution of technological progress to economic growth though using different approaches. Solow measured total factor productivity (TFP), that is, the portion of output that cannot be attributed to the measured inputs of capital and labor explicitly in a model of economic growth. In the United States the average annual growth rates for the 1947-2022 period were 3.09% for output, 1.77% for labor, 3.89% for capital, and total factor productivity 0.56. However, this model does not explain or rationalize technical progress. Endogenous growth models take technological progress and productivity growth into account. In the recent versions of these models new products make the older ones obsolete; the innovator of the new product has a monopoly power for a period of time which gives incentives for R&D and innovation. The period of 1921-2016 was the one with the highest growth rate in per capita real GDP in England (2.09%), followed by the 1816-1920 period (0.9%), and 1663-1736 (0.5%) and 1737-1815 (0.3%). Estimates suggest that growth in per capita real GDP were higher during 1870-1914 (second Industrial revolution) and 1947-2023 (digital revolution). On the other hand, there is no discernible evidence that the growth in per capita real GDP was higher during 1760-1830 (1st industrial revolution) compared with periods outside of above mentioned three periods. Growth in crop yields (as a measure of productivity in agriculture) for barley, wheat and oats were not significantly different during 1760-1830. There were significant differences in growth rates among countries during the 1820-2018 period. The spread in per capita GDP figures is getting bigger every year. This can easily be seen by the distribution of countries according to GDP per capita in 1820, 1920, 1970 and 2018. These differences among countries were a result of differences in average annual growth rates in GDP, population, and per capita GDP. In the United States, using data on 340 sectors during 1987-2022 period, average annual growth rate in real output was highest in semiconductor and related device manufacturing sector (16.97%), followed by wireless telecommunications carriers (except satellite) (15.93%), and electronic shopping and mail-order houses (13.46%). During the 1987-2022 period, average annual growth rate in labor productivity was highest in semiconductor and related device manufacturing sector (17.48%) among 326 sectors, followed by computer and peripheral xiv equipment manufacturing (12.99%), and wireless telecommunications carriers (except satellite) (12.49%). In fact, top growth rates in labor productivity were observed in sectors related to semiconductors, computers, and other electronics. There is a very close relationship between sectoral labor productivity and real sectoral output. While economic growth is the expansion of the production capacity of an economy and the increase in output, development is the expansion of the capability of people to do things they have reason to value and choose to do. The expansion of capabilities have both direct and indirect effects on development. The expansion of human capabilities indirectly contributes to development by increasing productivity, raising economic growth, broadening development priorities, and bringing demographic changes in a more rational framework. The direct importance of the expansion of human capabilities in the achievement of development is its intrinsic value and its role in human freedom, well-being and quality of life. Technological progress leads to productivity increase which means higher rates of economic growth. This leads to higher income and higher levels of education and a healthier life. In turn, more educated and healthier workers are more productive. Furthermore, higher income leads to more research and development expenditures. Together with a more educated and healthier workforce there is more possibility of innovation and technological progress. Human Development Index (HDI) is a measure of development used widely. It is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living. There are empirical studies covering different countries and time periods indicating a strong relationship between productivity and economic growth and HDI or its components. Our estimates show that A one percent increase in labor productivity measured as GDP per employed leads to an increase of 0.75% of GDP in 175 countries for the period 1992-2022 taking country and time differences into account. If we use total factor productivity as the measure of technological progress, we find that one percent increase in the lagged TFP leads to a 0.5 percent increase in real GDP and a one percent increase in the growth rate of TFP causes a 0.15% increase in the growth rate real GDP. We find that the correlation between HDI and its components is very high, approximately 90%. While the relationship between HDI and education and health seems to be linear, the relationship with income is non-linear. We run various regressions where HDI is the dependent variable, and the past values of its components are regressors. We find that the relationship is significant. The rapid advancements in Artificial Intelligence (AI), 5G, cloud computing, cybersecurity, blockchain, and digital health are driving significant transformations in healthcare, finance, and urban management. These technologies are not just buzzwords but are making processes more efficient, integrated, and are impacting socio-economic factors. However, the progress and challenges in digital development also highlight the existence of the digital divide - the gap between those with access to information communication technology (ICT) and those with limited or no access. While the causality between the digital divide and socio-economic inequality goes both ways, the digital divide has become a significant determinant of income and wealth inequality both within a country and across countries globally. Mobile phone penetration has rapidly increased from 33 percent in 2005 to 110.6 percent in 2023. While 95 percent of the world population lived within mobile phone or broadband coverage, only 67 percent used the internet in 2023. Internet use by individuals skyrocketed from one billion in 2005 to 5.5 billion in 2023, with fixed broadband subscriptions increasing seven-fold and mobile phone subscriptions four times during the same period. The relationship between countries' per capita GNI and individual internet and mobile phone subscriptions is strong. As countries' income levels increase, mobile and internet use saturates the population, and the relationship becomes flat at the higher income levels. Gender inequality in the physical world is reflected in the digital world. The gap between genders from less than a percentage point in high-income countries jumps to 16 percentage points in low-income countries. Several studies show that women, limited by less expensive and sophisticated handsets, use mobile phones and the internet differently - often primarily voice and SMS- than men. The digital age gap, often called the digital divide across different age groups, primarily affects older adults who may not have grown up with current technology or who may not engage with digital tools as frequently as younger generations. This divide has significant implications for social inclusion, access to information, economic opportunities, and even health services, especially as more services move online Infrastructure quality, inclusivity of access, governmental policies, and digital proficiency are identified as major factors influencing the digital divide. Since commercial deployment began in 2019, 5G coverage has increased to 40 percent of the world population in 2023. Distribution, however, remains very uneven. Although mobile broadband deployment has been remarkably rapid, the bulk of the end-use internet traffic is still carried by fixed broadband. In 2022, 95.8 percent of internet traffic was handled by fixed broadband. While mobile internet subscriptions reached 87 per 100 people globally, only about 20 per 100 people had fixed broadband subscriptions in 2023. Fixed broadband is the more expensive service, even considering multiple users are accessing one subscription. In low-income countries, fixed broadband subscriptions can cost as much as 96 percent of per capita GNI as opposed to 7.4 percent for mobile subscriptions at about the same download speeds. In relative terms, fixed and mobile broadband are 25 to 30 times more expensive for people in low-income countries than in high-income countries. An important factor in expanding digital adoption in many countries has been the introduction of e-government to provide government services. While expanding e-government can help to expand and accelerate digital adoption in countries where the ICT infrastructure is in place and affordable, it can also widen the digital divide and exclude people experiencing homelessness, people in poverty, older adults, and those who live in remote areas without broadband access. The digital literacy, defined as essential skills for engaging with digital media, processing information, and retrieving it, is strongly related to the years of schooling - number of years an individual spends in formal education, from primary school to higher education. Schools play a crucial role in laying the groundwork for digital literacy by teaching students basic skills and introducing them to digital tools. Digital literacy encompasses a broad array of Professional computing skills and, like traditional literacy, equips individuals to attain valued outcomes in life, particularly within the contemporary digital economy. Governments, particularly those authoritarian tendencies often employ internet shutdowns and censorship for political control and suppression of information with adverse effects on freedom of expression Frontier technologies refer to innovative and advanced technological developments at the cutting edge of research and development. These technologies can significantly impact and transform various industries and aspects of society. They can disrupt existing industries, create new markets, and drive significant economic and social change. Frontier technologies emerge from the convergence of multiple fields of science and technology led by advances in computing technologies and applications. There is no unique set of frontier technologies. Rapidly developing a broad spectrum of interrelated and interdependent technologies is transforming the world. Advances in one area trigger and spur breakthroughs in others. The Frontier Technology Readiness Index (FTRI) developed by the United Nations Conference on Trade and Development (UNCTAD) measures countries’ readiness in this respect and indicates how prepared countries are to adopt and adapt frontier technologies by combining data on information and communications technologies deployment (ICT), labor skills, research and development (R&D), industrial capacity, and availability of finance. The ICT deployment and skills are the two critical components of the FTRI. There is a strong relationship between per capita GDP and FTRI scores. High-income countries can more effectively integrate advanced technologies into their economies, enhancing competitiveness and creating high-value jobs. Similarly, the Human Development Index (HDI) shows a strong correlation with the FTRI. The impact of frontier technologies—such as artificial intelligence (AI), robotics, biotechnology, and advanced materials—on employment is profound and multifaceted. One of the most immediate impacts is the potential displacement of jobs through automation. Roles that involve routine tasks, whether physical or cognitive, are particularly vulnerable. The demand for high-skilled workers tends to increase with the adoption of frontier technologies, potentially widening the wage gap between highly skilled and less skilled workers. This shift can exacerbate income inequality unless robust educational systems and training programs help workers upskill or reskill.Yayın Economic Effects of COVID-19 Pandemic(S & B World Foundation, 2022) Kaytaz, Mehmet; Özmucur, Süleyman; Yürükoğlu, TanjuNow it is more than two years since the COVID-19 virus first appeared in China. Its overall effects are becoming clearer. It killed millions of people in a short time. It led to a deep recession. It disrupted the lives of many people all over the world. It pushed millions back into poverty. It is no wonder that the UN Deputy-Secretary-General characterized the situation as "we are facing a human crisis unlike any we have experienced". The severity and the depth of the economic effects of the pandemic are considered by many to be on the same level as the Great Depression or the Great Recession. The pandemic is essentially a health crisis. As of January 11, 2022 the number of cases has risen to about 312 million and the number of people died is more than 5.51 million. These are the official figures. The estimates of the excess deaths due to COVID-19 are about three times the official figures. The infection and death rates varied greatly across time and countries. The factors such as the age composition of population, the measures adopted, the strictness of the measures applied, the degree of readiness of the public institutions for a pandemic, and vaccination played an important role in the infection and death rates. For example, the countries which were affected by the SARS pandemic, seemed to be more ready for the COVID-19. The countries with an aged population, most of which are high income countries suffered higher death rates, because the incidence of death was significantly more on aged groups. The lockdowns, social distancing measures and travel restrictions caused an immediate drop in employment and production as well as a sharp drop in demand and some changes in the composition of demand. The recession hit all the countries. Though some recovered earlier than other countries. For example, in the United States, there were decreases in real GDP in all quarters of 2020. The major loss (decrease compared with the real GDP in the fourth quarter of 2019) in real GDP (10.1%) occurred in the second quarter of 2020. The general economic activity was lower in the five consecutive quarters following the last quarter of 2019. The second quarter of 2021 was the first time that the level of real GDP was higher than the level in the fourth quarter of 2019. In the first quarter of 2021, the cumulative loss had reached to 17.8% of real GDP of the fourth quarter of 2019. The cumulative loss decreased to 12.3% with improvements in the remaining three quarters of 2021. On the other hand, the total loss from the long-run trend is about 28%. The cumulative change from the final quarter of 2019 was negative in 80 out of 97 countries or regions. The negative change was the highest in Spain with 69.0%, followed by the Philippines (67.4%). In about 7 quarters, some countries started to turn the losses to minor gains. Serbia (cumulative change of positive 0.5%) and New Zealand (0.6%) are leading these countries. The changes were significantly positive in Ireland (76.7%), Turkey (30.6%), Taiwan (29.9%), China (25.7%), and Egypt (19.1%), among others. Comparisons with the level in December 2019 reveal that changes were even bigger for industrial production. For example, the Philippines had the biggest cumulative decrease, compared with the level in December 2019, in industrial production (347.0%), followed by Portugal (183.8%). These are much bigger decreases compared with decreases in real GDP. Since 33 countries/regions were on the positive side of the cumulative percentage change from December 2019, one can conclude that generally a relatively faster recovery was observed in industrial production compared with real GDP following large declines in economic activity due to the pandemic. As expected, export is one of the activities that was adversely affected the most by the pandemic. Cumulative decreases were quite large in some countries. On the other hand, there were large increases in exports in in some countries in 2020 and 2021 because of a very low starting point. Largest cumulative decreases in exports from December 2019 were observed in Nigeria (789.7%) and United Kingdom (394.8%). Largest cumulative increases in relation to the level in December 2019 were observed in Guyana (2201.7%) and Zambia (884.1%). One of the results of the pandemic was that sectors were affected in different ways and degrees. For example, since accommodation, restaurant, and hospitality sector required face to face interaction, it was affected much worse than some others. Educational services, transportation, utilities, retail trade, and mining were also among the sectors affected adversely both because of the working conditions and changes in the composition of demand. On the other hand, the demand increased for some services such as information and services which could be provided online did not suffer at all. For example, finance and insurance witnessed growth in real terms. The first visible impact of the COVID-19 on economic life was in the labor markets. The lock-down measures and social distancing rules led to an immediate decline in working hours and employment. The impact of the pandemic was deep and the recovery has progressed slowly. The global level of unemployment was 186 million in 2019. It is estimated to be 207 million in 2022, and it is expected that 2019 level will be reached in 2023 according to the ILO forecasts. Furthermore, the recovery varied with income levels of countries. Lowermiddle income countries performed worse than the others. Also many people have left the labor force. The 2022 labor force participation rate is forecasted to be still lower than the 2019 rate. The pandemic caused a very big declines in employment in the United States. Although, there were gains in employment after the second half of 2020, more than 20 million jobs lost due to the pandemic were not recovered, yet. The level of employment in March of 2022 is still below what it was in December 2019. In the United States, while the largest percentage decline in employment from December 2019 to March 2022 was observed in scenic and sightseeing transportation (28.3%), the largest percentage increase was observed in warehousing and storage (37.7%). The arithmetic average of the monthly rate of unemployment in the United States during January 1948 to March 2022 was 5.75%. The maximum unemployment rate was 14.7% (April 2020) , followed by 13.2% (May 2020), and 11.0% (June 2020). These are the three highest numbers since 1948. Among 65 countries/regions that data were released by the World Bank GEM, largest decreases in unemployment rate compared with the rate in December 2019 were observed in Greece (2.9%) and Turkey (2.2%). Largest increases in the rate of unemployment were realized in South Africa (5.5%) and Sub Saharan Africa (5.5%). The largest decreases in stock prices (in terms of US dollars) from December 2019 to December 2021 in 75 countries/regions were observed in Kenya (35.8%) and Brazil (31.6%), while the largest percentage increases from December 2019 were realized in Iran (275.1%) and Argentina (127.4). During 2020, the precious metals price index continued its increase compared with decreases in other indexes (energy, non-energy, fertilizers, and metals and minerals). A further look reveals that gold is the commodity deriving the increase in the precious metals index, and not silver nor the platinum. This is no surprise because gold has been seen as the key asset during periods of uncertainty. Comparisons of March 2022 price with December 2019 price reveal that the largest increases were seen in natural gas price in Europe (489%), while the largest decreases in prices were observed in tea (9.4%). The pandemic exacerbated the tendencies in global inflationary pressures. Largest percentage changes in GDP deflator from the final quarter of 2019 were observed in Argentina (92.8%) and Turkey (41.4%). There were only two countries in the group of 83 with decreases in GDP deflator (Ireland 3.0%, and Japan 0.4%). In the United States, the headline price index for consumer prices increased 11.4% from December 2019 to March 2022. Among countries, largest increases in the consumer price index from December 2019 to December 2021 were observed in Lebanon (588.7%) and Turkey (55.9). Bahrain, Fiji, and Japan had decreases in consumer prices from December 2019. The border closures, lockdown in supply markets, restrictions in vehicle movements, interruptions in trade, labor shortages, and maintaining of physical distance in manufacturing created multidimensional negative impacts on supply chains. The trade as share of world GDP fell from 56.3% in 2019 to 51.6% in 2020. The export of goods and services declined by 8.9%. The net inflow of FDI fell from 1.7% of GDP in 2019 to 1.4% in 2020. In the decline of trade and FDI the disruption of supply chains and hence GVCs played an important role. The impact of COVID-19 has been more severe in comparison to recent epidemics such as SARS 2003 and H1N1 2009. Furthermore, its impact has been more diversified and dynamic. In the case of COVID-19 all the nodes (enterprises in the chain) and edges (relationship between enterprises) were affected simultaneously. GVCs both propagated and mitigated the impact of COVID-19 lockdowns. GVCs also played an important role in the rapid recovery of trade observed in the second half of 2020 in USA. As long as governments do not try to decrease dependency on other countries and firms do not go into vertical integration GVCs continue to grow. It is possible that firms will shift their input sources towards some other developing countries where prices are more favorable and the supply reliable. They will not reshore, near-shore or The immediate impact of COVID-19 was on output and employment. The sharp drop in output and increase in unemployment led to increasing inequality of income distribution and poverty. Furthermore, disruption of educational activities created conditions for exacerbating the inequality in the longer term. The COVID-19 pandemic brought about one of the largest fiscal and monetary policy responses compared to previous crises seen in the world. The U.S. which is by far the most affected country in terms of infections and the number of deaths has allocated significant budgetary resources, around 25 percent of GDP to dealing with the pandemic in 2020. The average fiscal cost for the high-income countries was around 10 percent of GDP and less than half of that for the emerging markets. Some countries chose to make credit accessible to corporate and household sectors while spending limited amounts of budgetary resources for direct support. Because of the multitude of factors involved in dealing with the pandemic, it is difficult to attribute outcomes to financial resources expended particularly in the middle- and low-income countries. Regardless of the type of the dominant instrument, fiscal or financial, large sums of liquidity were injected into the economies. This helped a faster recovery than most expected and reduce the suffering from supply shortages due to logistics breakdowns. These large outlays limited the fiscal space for many governments and reduced the margins of maneuverability for monetary policy in the coming years. Only time will tell the trade-off between forgoing high priority spending in the future and the outcomes of the pandemic-induced expenditures. This includes the impact of the global inflation faced today, some of it can be traced back to monetary expansion to deal with the pandemic, but there are other aggravating factors like the Russian invasion of Ukraine, sanctions, and the vagaries of the energy markets. According to UNESCO the COVID-19 pandemic has been the worst shock to education systems in a century, with more than 1.6 billion children and youth not being able to attend school for months. The pandemic hit the education system all over the world. All levels of education were affected. Most damaging effect was felt by all the children at risk, marginalized, and children with disabilities. It affected 99% of students in low and lower-middle income countries. Some of the impact of school closures on students were in short–term, but some will be felt in the long-term. Furthermore, this impact was uneven and unequitable across countries and within countries. The global learning crisis has grown by even more than previously feared: this generation of students now risks losing $17 trillion in lifetime earnings in present value as a result of school closures, or the equivalent of 14 percent of today’s global GDP, far more than the $10 trillion estimated in 2020. In low- and middle-income countries, the share of children living in Learning Poverty—already over 50 percent before the pandemic—will rise sharply, potentially up to 70 percent, given the long school closures and the varying quality and effectiveness of remote learning. The international inequality of income distribution (inequality between countries in terms of GDP per capita) tended to increase with the pandemic. The lower income countries faced a higher drop in output than in relatively higher income countries. International inequality takes each country as a unit at per capita income. If this measure is weighted by the population, that is, each person earns the same per capita income then the deterioration in the distribution becomes more clear. Indeed, China and India dominates the distribution. If they are excluded both pre- and post-pandemic inequality increases. The relatively quick recovery of China played a role in this development. Still there are very few household data to measure the effects of the COVID-19 on national inequality (within country distribution of income). The available data and studies suggest that in many countries income inequality increased or the potential for more inequality got stronger. The countries which supported the unemployed with various subsidies slowed or reversed the worsening in inequality. International and national distribution of income constitute the global distribution. It is not wrong to forecast that the overall effects of the COVID-19 pandemic on income inequality is negative. The effects of the pandemic on poverty is related to its effects on income inequality. It seems that the worst effect of the pandemic is felt by the poor. Millions of people fell back into poverty.
