The Gross Domestic Product (GDP) in North Korea was worth 18 billion US dollars in 2019, according to official data from the World Bank. The GDP value of North Korea represents 0.02 percent of the world economy. This dataset provides - North Korea GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2023, South Korea's nominal gross domestic product (GDP) reached approximately ***** trillion South Korean won, while North Korea's amounted to about **** trillion South Korean won. Consequently, South Korea's nominal GDP was approximately ** times larger than that of North Korea during that year. Moreover, North Korea's GDP growth has been notably slower than that of South Korea.North Korea's economic development North Korea's economy is centered around its capital city and military, with particular emphasis on the expansion of its nuclear capabilities in recent decades. Roughly ** percent of foreign trade has been with China in the past decade, from which it imports mainly intermediate goods and raw materials. Food shortages, exacerbated by the COVID-19 pandemic, are a recurring issue for North Korea, as poor harvests, international sanctions, and a downturn in inter-Korean trade have created sourcing problems. The full extent of this issue remains unknown, but it is estimated that almost **** the population is undernourished. Kaesong Industrial ComplexThe Kaesong Industrial Complex project began in 2000 and was a crucial part of South Korea's efforts to improve relations with North Korea. It aimed to foster cooperation between the two Koreas and promote stability in the region. The industrial park, located in Kaesong, North Korea, was intended to provide a platform for small and medium-sized South Korean companies. South Korea would provide the necessary capital and infrastructure, while North Korean workers would be tasked with manufacturing products, aiming to stimulate economic growth on both sides of the border. Unfortunately, the complex was affected by tensions between the two Koreas and shut down in 2016. It has not been reopened since.

GDP per person employed (constant 2017 PPP $) in North Korea was reported at 2745 USD in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. North Korea - GDP per person employed (constant 1990 PPP $) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

In 2023, South Korea's gross national income (GNI) per capita was approximately ***** million South Korean won, while North Korea's GNI per capita was about **** million won. South Korea's GNI per capita was almost ** times higher than that of North Korea.

North Korea: GDP per capita, constant dollars: The latest value from is U.S. dollars, unavailable from U.S. dollars in . In comparison, the world average is 0.00 U.S. dollars, based on data from countries. Historically, the average for North Korea from to is U.S. dollars. The minimum value, U.S. dollars, was reached in while the maximum of U.S. dollars was recorded in .

The Gross Domestic Product (GDP) in North Korea expanded 3.10 percent in the fourth quarter of 2023 over the same quarter of the previous year. This dataset provides - North Korea GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The gross domestic product (GDP) per capita in South Korea was forecast to continuously increase between 2024 and 2030 by in total 5,762.76 U.S. dollars (+15.95 percent). After the seventh consecutive increasing year, the GDP per capita is estimated to reach 41,891.75 U.S. dollars and therefore a new peak in 2030. This indicator describes the gross domestic product per capita at current prices. Thereby the gross domestic product was first converted from national currency to U.S. dollars at current exchange prices and then divided by the total population. The gross domestic products is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).Find more key insights for the gross domestic product (GDP) per capita in countries like Mongolia, Japan, and Taiwan.

In 2023, South Korea's gross domestic product (GDP) grew by about *** percent compared to the previous year. North Korea's GDP growth rate stood at about *** percent that year, achieving positive growth for the first time after experiencing a period of negative growth during the COVID-19 pandemic.

North Korea KP:(GDP) Gross Domestic Productper Person Employed: 2011 PPP data was reported at 2,752.026 Intl $ in 2017. This records an increase from the previous number of 2,720.361 Intl $ for 2016. North Korea KP:(GDP) Gross Domestic Productper Person Employed: 2011 PPP data is updated yearly, averaging 2,752.026 Intl $ from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 3,905.480 Intl $ in 1991 and a record low of 2,558.657 Intl $ in 1998. North Korea KP:(GDP) Gross Domestic Productper Person Employed: 2011 PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s North Korea – Table KP.World Bank: Employment and Unemployment. GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2011 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

North Korea: Capital investment as percent of GDP: The latest value from is percent, unavailable from percent in . In comparison, the world average is 0.00 percent, based on data from countries. Historically, the average for North Korea from to is percent. The minimum value, percent, was reached in while the maximum of percent was recorded in .

The Gross Domestic Product (GDP) in South Korea was worth 1712.79 billion US dollars in 2023, according to official data from the World Bank. The GDP value of South Korea represents 1.62 percent of the world economy. This dataset provides - South Korea GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, North Korea's military spending accounted for over 34.38 percent of its gross domestic product (GDP), ranking it first among countries with the highest share of military expenditure in GDP. Additionally, North Korea had the highest military expenditure per capita among these countries.

Analysis of ‘Winter Olympics Prediction - Fantasy Draft Picks’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ericsbrown/winter-olympics-prediction-fantasy-draft-picks on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Olympic Draft Predictive Model

Our family runs an Olympic Draft - similar to fantasy football or baseball - for each Olympic cycle. The purpose of this case study is to identify trends in medal count / point value to create a predictive analysis of which teams should be selected in which order.

There are a few assumptions that will impact the final analysis: Point Value - Each medal is worth the following: Gold - 6 points Silver - 4 points Bronze - 3 points For analysis reviewing the last 10 Olympic cycles. Winter Olympics only.

All GDP numbers are in USD

My initial hypothesis is that larger GDP per capita and size of contingency are correlated with better points values for the Olympic draft.

All Data pulled from the following Datasets:

Winter Olympics Medal Count - https://www.kaggle.com/ramontanoeiro/winter-olympic-medals-1924-2018 Worldwide GDP History - https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2020&start=1984&view=chart

GDP data was a wide format when downloaded from the World Bank. Opened file in Excel, removed irrelevant years, and saved as .csv.

Process

In RStudio utilized the following code to convert wide data to long:

install.packages("tidyverse") library(tidyverse) library(tidyr)

Converting to long data from wide

long <- newgdpdata %>% gather(year, value, -c("Country Name","Country Code"))

Completed these same steps for GDP per capita.

Primary Key Creation

Differing types of data between these two databases and there is not a good primary key to utilize. Used CONCAT to create a new key column in both combining the year and country code to create a unique identifier that matches between the datasets.

SELECT *, CONCAT(year,country_code) AS "Primary" FROM medal_count

Saved as new table "medals_w_primary"

Utilized Excel to concatenate the primary key for GDP and GDP per capita utilizing:

=CONCAT()

Saved as new csv files.

Uploaded all to SSMS.

Contingent Size

Next need to add contingent size.

No existing database had this information. Pulled data from Wikipedia.

2018 - No problem, pulled existing table. 2014 - Table was not created. Pulled information into excel, needed to convert the country NAMES into the country CODES.

Created excel document with all ISO Country Codes. Items were broken down between both formats, either 2 or 3 letters. Example:

AF/AFG

Used =RIGHT(C1,3) to extract only the country codes.

For the country participants list in 2014, copied source data from Wikipedia and pasted as plain text (not HTML).

Items then showed as: Albania (2)

Broke cells using "(" as the delimiter to separate country names and numbers, then find and replace to remove all parenthesis from this data.

We were left with: Albania 2

Used VLOOKUP to create correct country code: =VLOOKUP(A1,'Country Codes'!A:D,4,FALSE)

This worked for almost all items with a few exceptions that didn't match. Based on nature and size of items, manually checked on which items were incorrect.

Chinese Taipei 3 #N/A Great Britain 56 #N/A Virgin Islands 1 #N/A

This was relatively easy to fix by adding corresponding line items to the Country Codes sheet to account for future variability in the country code names.

Copied over to main sheet.

Repeated this process for additional years.

Once complete created sheet with all 10 cycles of data. In total there are 731 items.

Data Cleaning

Filtered by Country Code since this was an issue early on.

Found a number of N/A Country Codes:

Serbia and Montenegro FR Yugoslavia FR Yugoslavia Czechoslovakia Unified Team Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia

Appears to be issues with older codes, Soviet Union block countries especially. Referred to historical data and filled in these country codes manually. Codes found on iso.org.

Filled all in, one issue that was more difficult is the Unified Team of 1992 and Soviet Union. For simplicity used code for Russia - GDP data does not recognize the Soviet Union, breaks the union down to constituent countries. Using Russia is a reasonable figure for approximations and analysis to attempt to find trends.

From here created a filter and scanned through the country names to ensure there were no obvious outliers. Found the following:

Olympic Athletes from Russia[b] -- This is a one-off due to the recent PED controversy for Russia. Amended the Country Code to RUS to more accurately reflect the trends.

Korea[a] and South Korea -- both were listed in 2018. This is due to the unified Korean team that competed. This is an outlier and does not warrant standing on its own as the 2022 Olympics will not have this team (as of this writing on 01/14/2022). Removed the COR country code item.

Confirmed Primary Key was created for all entries.

Ran minimum and maximum years, no unexpected values. Ran minimum and maximum Athlete numbers, no unexpected values. Confirmed length of columns for Country Code and Primary Key.

No NULL values in any columns. Ready to import to SSMS.

SQL work

We now have 4 tables, joined together to create the master table:

SELECT [OlympicDraft].[dbo].[medals_w_primary].[year], host_country, host_city, [OlympicDraft].[dbo].[medals_w_primary].[country_name], [OlympicDraft].[dbo].[medals_w_primary].[country_code], Gold, Silver, Bronze, [OlympicDraft].[dbo].[gdp_w_primary].[value] AS GDP, [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita], Atheletes FROM medals_w_primary INNER JOIN gdp_w_primary ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[gdp_w_primary].[year_country] INNER JOIN contingency_cleaned ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[contingency_cleaned].[Year_Country] INNER JOIN convertedgdpdatapercapita ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[convertedgdpdatapercapita].[Year_Country] ORDER BY year DESC

This left us with the following table:

https://i.imgur.com/tpNhiNs.png" alt="Imgur">

Performed some basic cleaning tasks to ensure no outliers:

Checked GDP numbers: 1992 North Korea shows as null. Updated this row with information from countryeconomy.com - $12,458,000,000

Checked GDP per capita:

1992 North Korea again missing. Updated this to $595, utilized same source.

UPDATE [OlympicDraft].[dbo].[gdp_w_primary] SET [OlympicDraft].[dbo].[gdp_w_primary].[value] = 12458000000 WHERE [OlympicDraft].[dbo].[gdp_w_primary].[year_country] = '1992PRK'

UPDATE [OlympicDraft].[dbo].[convertedgdpdatapercapita] SET [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita] = 595 WHERE [OlympicDraft].[dbo].[convertedgdpdatapercapita].[year_country] = '1992PRK'

Liechtenstein showed as an outlier with GDP per capita at 180,366 in 2018. Confirmed this number is correct per the World Bank, appears Liechtenstein does not often have atheletes in the winter olympics. Performing a quick SQL search to verify this shows that they fielded 3 atheletes in 2018, with a Bronze medal being won. Initially this appears to be a good ratio for win/loss.

Finally, need to create a column that shows the total point value for each of these rows based on the above formula (6 points for Gold, 4 points for Silver, 3 points for Bronze).

Updated query as follows:

SELECT [OlympicDraft].[dbo].[medals_w_primary].[year], host_country, host_city, [OlympicDraft].[dbo].[medals_w_primary].[country_name], [OlympicDraft].[dbo].[medals_w_primary].[country_code], Gold, Silver, Bronze, [OlympicDraft].[dbo].[gdp_w_primary].[value] AS GDP, [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita], Atheletes, (Gold*6) + (Silver*4) + (Bronze*3) AS 'Total_Points' FROM [OlympicDraft].[dbo].[medals_w_primary] INNER JOIN gdp_w_primary ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[gdp_w_primary].[year_country] INNER JOIN contingency_cleaned ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[contingency_cleaned].[Year_Country] INNER JOIN convertedgdpdatapercapita ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[convertedgdpdatapercapita].[Year_Country] ORDER BY [OlympicDraft].[dbo].[convertedgdpdatapercapita].[year]

Spot checked, calculating correctly.

Saved result as winter_olympics_study.csv.

We can now see that all relevant information is in this table:

https://i.imgur.com/ceZvqCA.png" alt="Imgur">

RStudio Work

To continue our analysis, opened this CSV in RStudio.

install.packages("tidyverse") library(tidyverse) library(ggplot2) install.packages("forecast") library(forecast) install.packages("GGally") library(GGally) install.packages("modelr") library(modelr)

View(winter_olympic_study)

Finding correlation between gdp_per_capita and Total_Points

ggplot(data = winter_olympic_study) + geom_point(aes(x=gdp_per_capita,y=Total_Points,color=country_name)) + facet_wrap(~country_name)

cor(winter_olympic_study$gdp_per_capita, winter_olympic_study$Total_Points, method = c("pearson"))

Result is .347, showing a moderate correlation between these two figures.

Looked next at GDP vs. Total_Points ggplot(data = winter_olympic_study) + geom_point(aes(x=GDP,y=Total_Points,color=country_name))+ facet_wrap(~country_name)

cor(winter_olympic_study$GDP, winter_olympic_study$Total_Points, method = c("pearson")) This resulted in 0.35, statistically insignificant difference between this and GDP Per Capita

Next looked at contingent size vs. total points ggplot(data = winter_olympic_study) + geom_point(aes(x=Atheletes,y=Total_Points,color=country_name)) +

The statistic shows gross domestic product (GDP) of South Korea from 1987 to 2024, with projections up until 2029. GDP or gross domestic product is the sum of all goods and services produced in a country in a year; it is a strong indicator of economic strength. In 2024, South Korea's GDP was around 1.87 trillion U.S. dollars. See global GDP for a global comparison. South Korea’s economy South Korea is doing quite well economically. It is among the leading export countries worldwide, it mainly exports electronics, automobiles and machinery. South Korea is also one of the leading import countries worldwide. Additionally, it is one of the leading countries with the largest proportion of global domestic product / GDP based on Purchasing Power Parity (PPP). Its GDP has been increasing for the last few years, while the gross domestic product / GDP growth in South Korea has not been steady but increasing since 2009. South Korea is an OECD member and a member of the G20 states. Among the latter, its GDP growth was higher than that of the United States or the European Union in 2013. South Korea is one of the fastest-growing economies worldwide. Its standard of living is also considered to be quite high, the unemployment rate, which is one key factor, has been at around 3 percent, give or take a few percentage points, for the past decade. As a comparison, the United States’ unemployment rate was almost twice, sometimes three times as high as in South Korea during the same period. As for employment, South Korea’s rate is almost the same as that of the United States or France, with more than 60 percent of employed persons in the population.

The Centre for the Study of Public Policy (CSPP) has conducted a series of Barometer surveys in Eastern Europe to gauge mass response to democratization. This project extends this research to the Republic of Korea. Because democratization has become a global phenomenon in the past two decades along with economic liberalization and marketization, this raises fundamental questions about the primacy of universalist theories of change. Much attention has focused on Post-Communist European countries (PCE) experiencing the double transformation to democracy and a market economy and comparisons with prior, albeit less radical changes in Southern Europe and Latin America. The Republic of Korea can add to our understanding of worldwide change, for it has experienced rapid economic success in the global economy, and subsequent rapid democratization and then economic crisis.
While no country is 'typically' Asian, the Republic of Korea has an indubitable status as an Asian country - many centuries of history as an independent state, a Confucian and Buddhist tradition, and in its location between Japan and China in a non-geographical as well as a geographical sense. Korea began industrialization and entry into the global economy after Japan, but it has been spectacularly successful up to the 1997 economic crisis, becoming a member of OECD with a GDP per capita higher than any PCE country. It is a new democracy, as a military dictatorship ruled for decades before democratization began in the mid-1980s, and the first opposition candidate won a presidential election in 1997. Korea has parallels with Germany before 1990, as its neighbour North Korea still has an unreconstructed Communist regime.
This survey is the sixth in a series of New Korea Barometer surveys, which have been conducted by Professor Doh Chull Shin since 1988. The previous five surveys are not held at the Data Archive. More information about the survey series may be found on the CSPP website at http://www.cspp.strath.ac.uk/.

Abstract copyright UK Data Service and data collection copyright owner. The Centre for the Study of Public Policy (CSPP) has conducted a series of Barometer surveys in Eastern Europe to gauge mass response to democratization. This project extends this research to the Republic of Korea. Because democratization has become a global phenomenon in the past two decades along with economic liberalization and marketization, this raises fundamental questions about the primacy of universalist theories of change. Much attention has focused on Post-Communist European countries (PCE) experiencing the double transformation to democracy and a market economy and comparisons with prior, albeit less radical changes in Southern Europe and Latin America. The Republic of Korea can add to our understanding of worldwide change, for it has experienced rapid economic success in the global economy, and subsequent rapid democratization and then economic crisis. While no country is 'typically' Asian, the Republic of Korea has an indubitable status as an Asian country - many centuries of history as an independent state, a Confucian and Buddhist tradition, and in its location between Japan and China in a non-geographical as well as a geographical sense. Korea began industrialization and entry into the global economy after Japan, but it has been spectacularly successful up to the 1997 economic crisis, becoming a member of OECD with a GDP per capita higher than any PCE country. It is a new democracy, as a military dictatorship ruled for decades before democratization began in the mid-1980s, and the first opposition candidate won a presidential election in 1997. Korea has parallels with Germany before 1990, as its neighbour North Korea still has an unreconstructed Communist regime. This survey is the sixth in a series of New Korea Barometer surveys, which have been conducted by Professor Doh Chull Shin since 1988. The previous five surveys are not held at the Data Archive. More information about the survey series may be found on the CSPP website at http://www.cspp.strath.ac.uk/. Main Topics: The questionnaire covers attitudes toward democracy, employment, social capital, trust and distrust, and expectations of fair and honest treatment by public officials. The questionnaire combines questions regularly included in interviews in Central and Eastern Europe and Russia, and also many questions asked in Professor Shin's previous five Korean surveys. Multi-stage stratified random sample Face-to-face interview

KP：雇员的人均国内生产总值（GDP）：2011年购买力平价在12-01-2017达2,752.026Intl $，相较于12-01-2016的2,720.361Intl $有所增长。KP：雇员的人均国内生产总值（GDP）：2011年购买力平价数据按年更新，12-01-1991至12-01-2017期间平均值为2,752.026Intl $，共27份观测结果。该数据的历史最高值出现于12-01-1991，达3,905.480Intl $，而历史最低值则出现于12-01-1998，为2,558.657Intl $。CEIC提供的KP：雇员的人均国内生产总值（GDP）：2011年购买力平价数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的朝鲜 – 表 KP.世界银行：就业和失业。

Snapshot img

Automotive Hydraulics System Market Size 2024-2028

The automotive hydraulics system market size is forecast to increase by USD 5.45 billion at a CAGR of 2.8% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The increasing emphasis on advanced hydraulic equipment, such as smart hydraulic systems, is driving market expansion. Additionally, the electrification of mechanical components is gaining traction, leading to the development of more efficient pumps and motors. However, the high manufacturing and maintenance costs associated with hydraulic components and equipment pose a challenge to market growth. In the waste management industry, hydraulic systems are crucial for rubbish compactors and concrete pumping applications. Similarly, in agriculture, hydraulic systems design plays a vital role in the operation of agricultural machinery. Motion pictures also utilize hydraulic equipment for various effects. In the energy sector, hydraulics are utilized in oil and gas exploration and production. In the aerospace industry, hydraulic systems are essential for aircraft control systems and landing gear.

What will the size of the market be during the forecast period?

Request Free Sample

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Passenger cars
  Commercial vehicles


Geography

  APAC

    China
    Japan
    South Korea


  North America

    US


  Europe

    Germany


  South America



  Middle East and Africa

By Application Insights

The passenger cars segment is estimated to witness significant growth during the forecast period.

The market in the United States is witnessing significant growth due to the increasing integration of advanced features and enhanced safety standards in passenger vehicles. Hydraulic systems are essential components in various applications, such as braking, steering, and suspension, to improve vehicle performance and driving comfort. Strict safety regulations, including the requirement for Anti-Lock Braking Systems (ABS) and Electronic Stability Control (ESC), are driving the adoption of hydraulic systems in the passenger cars segment. Technological innovations are playing a crucial role in the growth of the market. For instance, adaptive suspension systems and precision braking mechanisms are becoming standard features in modern passenger vehicles, catering to consumer demand for smoother rides and superior handling.

In addition, the waste management industry in the US also relies heavily on hydraulic equipment, such as hydraulic pumps and hydraulic motors, for rubbish collection and concrete pumping in construction sites. Furthermore, technological advancements are a significant catalyst for market growth. Hydraulic systems design focuses on efficient fluid movement and maintaining hydraulic pressure to ensure optimal performance. Hydraulic components, including hydraulic pumps, hydraulic motors, and hydraulic valves, are crucial in maintaining the hydraulic pressure required for various applications. The hydraulic systems market in the US is expected to continue its growth trajectory due to the increasing demand for hydraulic equipment in various industries, including automotive, waste management, agriculture, and construction.

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The Passenger cars segment was valued at USD 21.61 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 40% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

In the market, countries in the Asia Pacific region, specifically China, India, and Japan, hold a significant share of the revenue. The high adoption rate of vehicles in these nations, driven by increasing disposable incomes, is a primary factor fueling market growth. For instance, China and India's economies have seen substantial growth, leading to an increase in the number of consumers purchasing vehicles. In Singapore, the GDP per capita rose from USD 47,236.96 in 2010 to USD 59,797 in 2021. This economic expansion has resulted in a wave in sales of luxury cars. The automotive industry in the region is a substantial contributor to the demand for automotive aftermarket services due to the high volume of vehicle sales.

Furthermore, industries such as construction equipment, Paper mills, Logging, Manufac

******* had the highest level of the Human Development Index (HDI) worldwide in 2023 with a value of *****. With a score of ****, ****** followed closely behind *********** and had the second-highest level of human development in that year. The rise of the Asian tigers In the decades after the Cold War, the four so-called Asian tigers, South Korea, Singapore, Taiwan, and Hong Kong (now a Special Administrative Region of China) experienced rapid economic growth and increasing human development. At number eight and number 13 of the HDI, respectively, *********************** are the only Asian locations within the top-15 highest HDI scores. Both locations have experienced tremendous economic growth since the 1980’s and 1990’s. In 1980, the per capita GDP of Hong Kong was ***** U.S. dollars, increasing throughout the decades until reaching ****** in 2023, which is expected to continue to increase in the future. Meanwhile, in 1989, Singapore had a GDP of nearly ** billion U.S. dollars, which has risen to nearly *** billion U.S. dollars today and is also expected to keep increasing. Growth of the UAE The United Arab Emirates (UAE) is the only Middle Eastern country besides Israel within the highest ranking HDI scores globally. Within the Middle East and North Africa (MENA) region, the UAE has the third-largest GDP behind Saudi Arabia and Israel, reaching nearly *** billion U.S. dollars by 2022. Per capita, the UAE GDP was around ****** U.S. dollars in 1989, and has nearly doubled to ****** U.S. dollars by 2021. Moreover, this is expected to reach over ****** U.S. dollars by 2029. On top of being a major oil producer, the UAE has become a hub for finance and business and attracts millions of tourists annually.