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.

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 July of 2025.

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 .

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.

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.

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.

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.

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.

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.

In 2023, North Korea's military spending accounted for over 36 percent of its gross domestic product (GDP), ranking it second in the list of countries with the highest share of military expenditure in GDP. Additionally, North Korea had the highest military expenditure per capita among these countries.

Coreia do Norte: O custo de iniciar um negócio, % do rendimento per capita: Para este indicador, fornecemos dados para Coreia do Norte de a . O valor médio por Coreia do Norte durante este período foi percent of Gross National Income per capita com o mínimo de percent of Gross National Income per capita em e o máximo de percent of Gross National Income per capita em . Os dados mais recentes de são percent of Gross National Income per capita. Para efeito de comparação, a média mundial em com base em países é 0.00 percent of Gross National Income per capita.

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)) +

Korea, Demokratische Volksrepublik: Kosten Unternehmensgründung, % Einkommen pro Kopf: Für diesen Indikator stellen wir Daten für Korea, Demokratische Volksrepublik von bis bereit. Der durchschnittliche Wert für Korea, Demokratische Volksrepublik in diesem Zeitraum lag bei percent of Gross National Income per capita mit einem Minimum von percent of Gross National Income per capita im Jahre und einem Maximum von percent of Gross National Income per capita im Jahre . Der neuste Wert aus dem Jahr liegt bei percent of Gross National Income per capita. Zum Vergleich: Der Weltdurchschnitt im Jahr , basierend auf Ländern, liegt bei 0.00 percent of Gross National Income per capita.

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.

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。

캐나다의 1인당 국내총생산은 2022년에 54,104.301 USD로 보고 되었습니다. 이는 2021년에 48,179.034 USD라는 이전 수치에 비 해상승한 기록입니다.캐나다의 1인당 국내총생산 데이터는 매년마다 업데이트 되며,52개의 관측으로 1971년부터 2022년사이에 평균 21,530.588 USD입니다. 이 데이터는2022년에 54,104.301 USD라는사상 최고치를, 1971년에 4,312.333 USD라는 최저치를 기록했습니다. 캐나다’의 1인당 국내총생산 데이터는 CEIC에 활성 상태로 남아 있으며CEIC Data에 의해 보고되는 정보입니다. 본 데이터는 World Trend Plus의 Global Economic Monitor - Table: GDP Per Capita: USD: Annual: North and South America하에 분류 됩니다.