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 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.

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) 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.

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.

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.

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.

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 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.

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)) +