United States Diffusion Index: sa: Mfg: 3 Months Span data was reported at 67.100 Unit in Oct 2018. This records an increase from the previous number of 63.200 Unit for Sep 2018. United States Diffusion Index: sa: Mfg: 3 Months Span data is updated monthly, averaging 49.000 Unit from Jan 1991 (Median) to Oct 2018, with 334 observations. The data reached an all-time high of 82.200 Unit in Nov 1997 and a record low of 2.600 Unit in Mar 2009. United States Diffusion Index: sa: Mfg: 3 Months Span data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G041: Current Employment Statistics Survey: Diffusion Index.

The Weekly Economic Index (WEI) of the United States exhibited notable fluctuations between January 2021 and March 2025. Throughout this period, the WEI reached its lowest point at negative 0.98 percent in the third week of February 2021, while achieving its peak at 10.27 percent in the first week of May 2021. From 2021 through the initial half of 2023, the WEI demonstrated a gradual decline, interspersed with occasional minor upturns. This phase was succeeded by a period characterized by a modest overall increase. What is the Weekly Economic Index? The Weekly Economic Index (WEI) is an index of real economic activity using high-frequency data, used to signal the state of the U.S. economy. It is an index of 10 daily and weekly indicators, scaled to align with the four-quarter GDP growth rate. The indicators reflected in the WEI cover consumer behavior, the labor market, and production.

In 2022, tax revenues generated by Mexico accounted for 16.9 percent of the country's GDP, down from 17.3 percent reported in the previous year. Mexico is among the states with the lowest shares of tax revenue in GDP in Latin America.

In October 2024, the Sahm recession indicator was 0.43, a slight decrease from the previous month. The Sahm Rule was developed to flag the onset of an economic recession more quickly than other indicators. The Sahm Rule signals the start of a recession when the three-month moving average of the national unemployment rate rises by 0.50 percentage points or more relative to its low during the previous 12 months.

United States SBP: ND: Back to Usual Operations: 2-3 Mos data was reported at 94.300 % in 20 Sep 2020. This records a decrease from the previous number of 95.500 % for 13 Sep 2020. United States SBP: ND: Back to Usual Operations: 2-3 Mos data is updated weekly, averaging 27.150 % from Apr 2020 (Median) to 20 Sep 2020, with 14 observations. The data reached an all-time high of 96.600 % in 30 Aug 2020 and a record low of 14.200 % in 14 Jun 2020. United States SBP: ND: Back to Usual Operations: 2-3 Mos data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S038: Small Business Pulse Survey: by State: Midwest Region: Weekly, Beg Sunday (Discontinued).

Peru Macroeconomic Outlook Survey: Business Confidence Index: Economy: Next 3 Months: Trade data was reported at 52.083 Point in Feb 2025. This records an increase from the previous number of 51.020 Point for Jan 2025. Peru Macroeconomic Outlook Survey: Business Confidence Index: Economy: Next 3 Months: Trade data is updated monthly, averaging 52.222 Point from Jan 2010 (Median) to Feb 2025, with 182 observations. The data reached an all-time high of 75.926 Point in Jan 2010 and a record low of 14.167 Point in May 2020. Peru Macroeconomic Outlook Survey: Business Confidence Index: Economy: Next 3 Months: Trade data remains active status in CEIC and is reported by Central Reserve Bank of Peru. The data is categorized under Global Database’s Peru – Table PE.S001: Business Confidence Indicator.

In 2024, Brazil and Mexico were expected to be the countries with the largest gross domestic product (GDP) in Latin America and the Caribbean. In that year, Brazil's GDP could reach an estimated value of 2.4 trillion U.S. dollars, whereas Mexico's amounted to almost two trillion U.S. dollars. GDP is the total value of all goods and services produced in a country in a given year. It measures the economic strength of a country and a positive change indicates economic growth.

This table contains information of the Regional accounts. Regional accounts give a description of the economic process in the regions of a country in conformity with the national accounts. Elements in the economic process distinguished in national accounts are production, distribution of income, spending and financing. Regional accounts focus on the description of the production processes in the various regions. The new Standard Industrial Classification 2008 (SIC 2008) is used in the National and Regional Accounts of the Netherlands. This code is based on the European classification Nomenclature générale des Activités économiques dans la Communauté Européenne (NACE Rev. 2) which is used in all Member States of the European Union.

Data available from: 1996

Status of the figures: The figures of year 1996 up to and including 2022 are final and the figures of the year 2023 are provisional.

Changes as of December 23rd 2024: The figures for the volume changes of gross value added in some branches, as well as the volume changes of total gross value added and GDP on regional level were incorrect. This is corrected in this publication. The national figures for the Netherlands remain unchanged.

Changes as of December 9th 2024: None, this is a new table. Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. This table contains revised data. For further information see section 3.

When will new figures be published? In April 2025 the flash estimates for the reporting year 2024 will become available.

In 2023, the gross domestic product (GDP) of China amounted to around 17.8 trillion U.S. dollars. In comparison to the GDP of the other BRIC countries India, Russia and Brazil, China came first that year and second in the world GDP ranking. The stagnation of China's GDP in U.S. dollar terms in 2022 and 2023 was mainly due to the appreciation of the U.S. dollar. China's real GDP growth was three percent in 2022 and 5.2 percent in 2023. In 2023, per capita GDP in China reached around 12,600 U.S. dollars. Economic performance in China Gross domestic product (GDP) is a primary economic indicator. It measures the total value of all goods and services produced in an economy over a certain time period. China's economy used to grow quickly in the past, but the growth rate of China’s real GDP gradually slowed down in recent years, and year-on-year GDP growth is forecasted to range at only around four percent in the years after 2023. Since 2010, China has been the world’s second-largest economy, surpassing Japan.China’s emergence in the world’s economy has a lot to do with its status as the ‘world’s factory’. Since 2013, China is the largest export country in the world. Some argue that it is partly due to the undervalued Chinese currency. The Big Mac Index, a simplified and informal way to measure the purchasing power parity between different currencies, indicates that the Chinese currency yuan was roughly undervalued by 31 percent in 2023. GDP development Although the impressive economic development in China has led millions of people out of poverty, China is still not in the league of industrialized countries on the per capita basis. To name one example, the U.S. per capita economic output was more than six times as large as in China in 2023. Meanwhile, the Chinese society faces increased income disparities. The Gini coefficient of China, a widely used indicator of economic inequality, has been larger than 0.45 over the last decade, whereas 0.40 is the warning level for social unrest.

In 2023, the gross domestic product (GDP) of Hong Kong amounted to around 381 billion U.S. dollars at current prices, equivalent to around 2.98 trillion Hong Kong dollars. The city’s GDP grew by 3.3 percent that year. Hong Kong’s GDP in comparison The GDP measures the total value of all goods and services produced in an economy over a certain period. Together with unemployment and inflation, it is one of the most observed economic indicators. While GDP figures in the local currency are sometimes more useful for analyzing internal economic developments, values in international currencies are important for regional comparison.Among economies in Asia-Pacific, Hong Kong’s nominal GDP is comparatively small. However, as an advanced economy and a global financial hub, the city’s per capita GDP is one of the highest in the region, only second to Singapore and Australia. Hong Kong’s economic development As an important international hub for finance and trade, Hong Kong’s economy is dominated by the service sector. Financial services contributed more than 20 percent to the city’s GDP and displayed one of the highest sectoral growth rates over the last decade. Hong Kong’s economic growth suffered severely during the COVID-19 pandemic but returned to sustained growth in 2023.

In 2024, Japan's gross domestic product (GDP) grew by three percent at current prices, according to the second preliminary announcement in March 2025. A year earlier, the highest growth rate of Japan’s nominal GDP in almost three decades was recorded. The nominal GDP measures the value of all goods and services produced in an economy, including price changes. GDP growth and inflation Japan’s real GDP growth, which is adjusted for inflation, was lower at 0.1 percent. After decades of struggling with deflation and attempts to reach a two percent inflation target with economic stimulus packages and monetary easing policies, consumer prices in Japan increased by almost 3.3 percent in 2023, led by global inflation. This development prompted the Bank of Japan to shift its monetary policy and raise the short-term interest rate for the first time in 17 years in 2024. Japan lost its status as the third-largest economy Many countries have raised interest rates in response to higher inflation in the past years. Since Japan’s central bank has done so at a much slower pace, a widening interest gap emerged between Japan and other major economies of the world. This is also one of the reasons for the depreciation of the yen against the dollar. Due to the weak yen, Japan’s GDP declined when converted into U.S. dollars, resulting in Japan losing its status as the third-largest economy in terms of GDP to Germany in 2023.

Ireland: Uneven economic development index, 0 (low) - 10 (high): The latest value from 2024 is 2.1 index points, an increase from 1.8 index points in 2023. In comparison, the world average is 5.28 index points, based on data from 176 countries. Historically, the average for Ireland from 2007 to 2024 is 2.31 index points. The minimum value, 1.3 index points, was reached in 2020 while the maximum of 3 index points was recorded in 2008.

The statistic shows gross domestic product (GDP) in Brazil from 1987 to 2023, with projections up until 2029. Gross domestic product denotes the aggregate value of all services and goods produced within a country in any given year. GDP is an important indicator of a country's economic power. In 2022, Brazil's gross domestic product amounted to around 1.95 trillion U.S. dollars. In comparison to the GDP of the other BRIC countries India, Russia and China, Brazil was ranked third that year.

Brazil's national finances

Brazil is one of the fastest growing economies in the world and the largest amongst all Latin American countries. Brazil is also a member of multiple economic organizations such as the G20 as well as one of the four countries in the BRIC economies, which consist of Brazil, Russia, India and China. Despite having one of the lower populations out of the four countries, Brazil maintained a relatively stable dollar value of all goods and services produced within the country in comparison to India, for example. This indicates that unemployment is low and in general business demand within the country has become relatively high.

Spending within the country has been relatively high, however is considered to be normal, especially for developing countries. It is expected that developing economies have a budget deficit of roughly 3 percent, primarily because spending is needed in order to fuel an economy at most times. However, most Brazilians still have faith in their country’s economic future and still believe that their own personal financial situation will improve along with the country’s economic position in the world.

In 2022, nearly 80 percent of the population in Colombia living in a household with three or more children under the age of 12 lived in poverty. Furthermore, about half of the families who had a woman household head lived in poverty as well.

This table shows the indicators of the macroeconomic scoreboard. Furthermore, some additional indicators are shown. To identify in a timely manner existing and potential imbalances and possible macroeconomic risks within the countries of the European Union in an early stage, the European Commission has drawn up a scoreboard with fourteen indicators. This scoreboard is part of the Macroeconomic Imbalance Procedure (MIP). This table contains quarterly and annual figures for both these fourteen indicators and nine additional indicators for the Netherlands.

The fourteen indicators in the macroeconomic scoreboard are: - Current account balance as % of GDP, 3 year moving average - Net international investment position, % of GDP - Real effective exchange rate, % change on three years previously - Share of world exports, % change on five years previously - Nominal unit labour costs, % change on three years previously - Deflated house prices, % change on one year previously - Private sector credit flow as % of GDP - Private sector debt as % of GDP - Government debt as % of GDP - Unemployment rate, three year moving average - Total financial sector liabilities, % change on one year previously - Activity rate, % of total population aged 15-64, change in percentage points on three years previously - Long-term unemployment rate, % of active population aged 15-74, change in percentage points on three years previously - Youth unemployment rate, % of active population aged 15-24, change in percentage points on three years previously

The additional indicators are: - Real effective exchange rate, index - Share of world exports, % - Nominal unit labour costs, index - Households credit flow as % of GDP - Non-financial corporations credit flow as % of GDP - Household debt as % of GDP - Non-financial corporations debt as % of GDP - Activity rate, % of total population aged 15-64 - Youth unemployment rate, % of active population aged 15-24

Data available from: first quarter of 2006.

Status of the figures: Annual and quarterly data are provisional.

Changes as of January 13th 2025: The figures for every indicator have been added for the third quarter of 2024. Additionally, Maastricht debt (EMU) has been revised from 2013 onwards due to an updated guideline for capitalised interest related to imputed European Financial Stability Facility (EFSF) loans. Furthermore, some indicator figures have been adjusted due to updated source data.

Adjustment as of July 17th 2024: Data of the private sector’s credit flow and debt were not correct. They have been adjusted in this version.

When will new figures be published? New data are published within 120 days after the end of each quarter. The first quarter may be revised in October, the second quarter in January. Quarterly data for the previous three quarters are adjusted along when the fourth quarter figures are published in April. This corresponds with the first estimate of the annual data for the previous year. The annual and quarterly data for the last three years are revised together with the publication of the first quarter in July.

According to preliminary data, the agricultural sector contributed around 6.8 percent to the gross domestic product (GDP) of China in 2024, whereas 36.5 percent of the economic value added originated from the industrial sector and 54.6 percent from the service sector, respectively. The total GDP of China at current prices amounted to approximately 134.91 trillion yuan in 2024. Economic development in China The gross domestic product (GDP) serves as a primary indicator to measure the economic performance of a country or a region. It is generally defined as the monetary value of all finished goods and services produced within a country in a specific period of time. It includes all of private and public spending, government spending, investments, and net exports which are calculated as total exports minus imports. In other words, GDP represents the size of the economy.With its national economy growing at an exceptional annual growth rate of above nine percent for three decades in succession, China had become the worlds’ second largest economy by 2010, surpassing all other economies but the United States. Even though China's GDP growth has cooled down in recent years, its economy still expanded at roughly two times the pace of the United States in 2024. Breakdown of GDP in China When compared to other developed countries, the proportions of agriculture and industry in China's GDP are significantly higher. Even though agriculture is a major industry in the United States, it only accounted for about one percent of the economy in 2023. While the service sector contributed to more than 70 percent of the economy in most developed countries, it's share was considerably lower in China. This was not only due to China's lower development level, but also to the country’s focus on manufacturing and export. However, as the future limitations of this growth model become more and more apparent, China is trying to shift it's economic focus to the high-tech and service sectors. Accordingly, growth rates of the service sector have been considerably higher than in industry and agriculture in the years before the spread of the coronavirus pandemic.

Business confidence is a sentiment indicator for the Dutch private sector, which indicates the direction in which the Dutch economy (gross domestic product, GDP) is expected to develop. When assessing the results, it can be assumed that the more optimistic or pessimistic the entrepreneurs are, the more the value of business confidence will deviate positively or negatively from the zero line and the greater the expectation is that the development of GDP will increase or decrease in the coming months. Business confidence in the total Dutch private sector is a weighted average of the confidence indicators of the underlying sectors/industries, which together form a representative reflection of the Dutch business community from a economic vieuwpoint. In addition to the quarterly series of business confidence, confidence indicators are also available on a monthly basis for the manufacturing industry and some underlying industries. These are published in a separate table under the name Producer confidence; sentiment indicator of manufacturing industry, branches. The results for the months of January, April, July and October correspond with the indicators manufacturing industry of Business confidence in quarters 1, 2, 3 and 4 respectively.

Data available from: 4th quarter 2008 - 2nd quarter 2023.

Changes as of July 27, 2023: This table has been discontinued. The reason for this is the renewed method for calculating the business confidence.

When will new figures be published? Does not apply. This table is followed by Business confidence; to sector/branches (active on August 15, 2023). See paragraph 3.

Statistics Netherlands is currently working on adjustments to the definition of the Business Confidence indicator. The aim is to improve the comparability between industries and between regional and national figures. The adjustments will come into effect when the results for the third quarter of 2023 are published. More information will follow at www.cbs.nl.

Abstract

The World Bank and UNHCR in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform a targeted response. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets refugee household and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection, and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing. The data is uploaded in three files. The first is the hh file, which contains household level information. The 'hhid', uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'adult_id'. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the 'child_id'. The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 1,328 refugee households Wave 2: July 16 to September 18, 2020; 1,699 refugee households Wave 3: September 28 to December 2, 2020; 1,487 refugee households Wave 4: January 15 to March 25, 2021; 1,376 refugee households Wave 5: March 29 to June 13, 2021; 1,562 refugee households Wave 6: July 14 to November 3, 2021; 1,407 refugee households Wave 7: November 15, 2021, to March 31, 2022; 1,281 refugee households Wave 8: May 31 to July 8, 2022: 1,355 refugee households The same questionnaire is also administered to nationals in Kenya, with the data available in the WB microdata library: https://microdata.worldbank.org/index.php/catalog/3774

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Individual and Household

Universe

All persons of concern for UNHCR

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless, where sampling approaches differ across strata. For refugees in Kakuma and Kalobeyei, as well as for stateless people, recently conducted Socioeconomic Surveys (SES), were used as sampling frames. For the refugee population living in urban areas and the Dadaab camp, no such household survey data existed, and sampling frames were based on UNHCR's registration records (proGres), which include phone numbers. For Kakuma, Kalobeyei, Dadaab and urban refugees, a two-step sampling process was used. First, 1,000 individuals from each stratum were selected from the corresponding sampling frames. Each of these individuals received a text message to confirm that the registered phone was still active. In the second stage, implicitly stratifying by sex and age, the verified phone number lists were used to select the sample. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in. For the stateless population, all the participants of the Shona socioeconomic survey (n=400) were included in the RRPS, because of limited sample size. The sampling frames for the refugee and Shona stateless communities are thus representative of households with active phone numbers registered with UNHCR.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire included 12 sections Section 1: Introduction Section 2: Household background Section 3: Travel patterns and interactions Section 4: Employment Section 5: Food security Section 6: Income Loss Section 7: Transfers Section 8: Subjective welfare (50% of sample) Section 9: Health Section 10: COVID Knowledge Section 11: Household and Social Relations (50% of sample) Section 12: Conclusion

Cleaning operations

Variable names were kept constant across survey waves. For questions that remained exactly the same across survey waves, data points for all waves can be found under one variable name. For questions where the phrasing changed (even in a minimal way) across waves, variable names were also changed to reflect the change in phrasing. Extended missing values are used to indicate why a value is missing for all variables. The following extended missing values are used in the dataset: · .a for 'Don't know' · .b for 'Refused to respond' · .c for 'Outliers set to missing' · .d for 'Inconsistency set to missing' (used for employment data as explained below) · .e for 'Field Skipped' (where an error in the survey tool caused the question to be missed) · .z for 'Not administered' (as the variable was not relevant to the observation) More detailed data on children was collected between waves 3 and 7, compared to waves 1, 2 and 8. In waves 1 and 2, data on children, e.g. on their learning activities, was collected for all children in a household with one question. Therefore, variables related to children are part of the 'hh' data for waves 1 and 2. Between waves 3 and 7, questions on children in the household were asked for specific children. Some questions covered all children, while others were only administered to one randomly selected child in the household. This approach allows to disaggregate data at the level of the child household members, and the data can be found in the 'child' data set. The household level weights can be used for analysis of the children's data. In wave 8, detailed information on children was dropped, as the questionnaire focused on other topics. The education status of household members, except for the respondent, was imputed for rounds 1 and 2. For rounds 1 and 2, only the education status of the respondent was elicited, while for later rounds the education status for each household member was asked. In order to evaluate outcomes by the household member's education status, information on education was imputed for waves 1 and 2, using the information provided for all household members in waves 3, 4, and 5. This resulted in additional information on the education status for household members in round 1 and 2, which was not yet available for earlier versions of this data. Some questions are not asked repeatedly across waves such that their values were imputed. For some questions, answers are not possible or unlikely to change within two months between survey waves such that households were not asked about them in all waves. The questions on assets owned before March 2020 were only asked to households when they are interviewed for the first time. The questions on the dwelling's wall and floor material as well as the household's connection to the power grid was not asked for all households in wave 2 and 3, where only new households and those who moved were covered by these questions. Questions on the main source of electricity in the households and types of assets owned were not asked in wave 8. The missing values those variables have when they were not asked, are imputed from the answers given in earlier waves. Improved quality insurance algorithms lead to minor revisions to wave 1 to 5 data. Based on additional data checks, the team has made minor refinements to wave 1 to 5 data. The identification of the household members that were the respondent or the household head was refined in the rare cases where it was not possible to interview the same respondent as in previous waves for a given household such that another adult was interviewed. For this reason, for about 2 percent of observations the household head status was assigned to an incorrect household member, which was corrected. For <1 percent of households the respondent did not appear in adult level dataset. For about 1 percent of observations in wave 5 the respondent appeared twice in the adult level dataset. Data from questions on COVID-19 vaccinations from wave 7 was dropped from the dataset. Due to significantly higher self-reported vaccination rates compared to official administrative records, data on vaccinations was deemed unreliable, most likely due to social desirability bias. Consequently, questions on vaccination status and questions using the vaccination data as a validation criterion were dropped from the datasets.

This table contains information on the balance sheet of the general government sector. The information is limited to financial assets and liabilities. For each reporting period the opening and closing stocks, financial transactions and other changes are shown. Transactions are economic flows that are the result of agreements between units. Other changes are changes in the value of assets or liabilities that do not result from transactions such as revaluations or reclassifications. The figures are consolidated which means that flows between units that belong to the same sector are eliminated. As a result, assets and liabilities of subsectors do not add up to total assets or liabilities of general government. For example, loans of the State provided to social security funds are part of loans of the State. However, these are not included in the consolidated assets of general government, because it is an asset of a government unit with a government unit as debtor. Financial assets and liabilities in this table are presented at market value. The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date.

Data available from: Yearly figures from 1995, quarterly figures from 1999.

Status of the figures: The figures for the period 1995-2022 are final. The figures for 2023 and 2024 are provisional.

Changes as of 24 December 2024: Figures on the third quarter of 2024 are available. The figures for the second quarter of 2024 have been adjusted.

When will new figures be published? Provisional quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter may be revised, in December the figures on the second quarter may be revised and in March the first three quarters may be revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the most actual figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to the three revised years at the end of June. More information on the revision policy of Dutch national accounts and government finance statistics can be found under 'relevant articles' under paragraph 3.

In 2023, China's gross domestic product amounted to approximately 17.7 trillion U.S. dollars, which was the highest GDP across the Asia-Pacific region. Japan followed with a GDP of around 4.2 trillion dollars.  China, Asia-Pacific's titan The significance of the Asia-Pacific region to the world is multifaceted, ranging from geopolitical importance to being home to more than half of the world's population. Characterized by emerging countries and dynamic economic activities, the region plays a key role in the global economy. China, the most populous country after India, and the second largest economy on the planet, accounted for about half of the total gross domestic product (GDP) in APAC as of 2023. The GDP growth in China was characterized by high rates for decades. Following the COVID-19 pandemic, the country has struggled to catch up with the previous level of growth rates and was forecast to stay at more modest real GDP growth rates in the coming years.  A new paradigm of development in the Asia-Pacific region Even though the Asia-Pacific region has made significant economic improvements in the last decades, from a developmental perspective, tackling existing socio-economic issues will be critical for future growth. An aspect worth mentioning is the GDP per capita in the region. EU countries, for example, had about three times as much GDP per capita compared to East Asia and the Pacific region in 2022. China has been working towards changing its economic focus to high-tech and service sectors while reducing its concentration on agriculture.