The average for 2023 based on 196 countries was 656095 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2023. Below is a chart for all countries where data are available.

Algeria is the biggest country in Africa, with an area exceeding 2.38 million square kilometers as of 2020. The Democratic Republic of the Congo and Sudan follow with a total area of around 2.34 million and 1.88 million square kilometers, respectively. On the other hand, Seychelles is the smallest country on the continent, with an area of only 460 square kilometers. Overall, Africa’s total area exceeds 30 million square kilometers, being the second largest continent in the world after Asia. Nigeria and Ethiopia lead the ranking of the most populated countries in Africa.

How have the African countries been formed?

The political geography of Africa has been influenced by its colonial history. Between the 19th and 20th Century, the European colonizers have divided up Africa. The partition of the territories was merely driven by strategic purposes: Borders between countries were artificially created in the absence of a geographic border. Following the decolonization, most countries gained their independence in the second half of the 1900s. The newest country in Africa is South Sudan, which became independent in 2011.

Africa's physical geography

Geographically, the African continent is mostly constituted by plains and tablelands. Inner plateaus are prevalent in the sub-Saharan region. In the center-north, the arid Sahara Desert extends for around nine million square kilometers, being the largest subtropical desert in the world. The continent also has some of the biggest water basins worldwide, namely the Nile, Congo, and Niger rivers. East Africa has, instead, the highest summit on the continent, the Kilimanjaro. Peaking at 5,895 meters, the mountain dominates Tanzania’s landscape and attracts thousands of climbers each year.

This dataset is about books. It has 1 row and is filtered where the book is The big book of country music : a biographical encyclopedia. It features 7 columns including author, publication date, language, and book publisher.

Cuba is the largest island country or territory in the Caribbean, with a total area of almost 111 thousand square kilometers, followed by the Dominican Republic, with nearly 49 thousand square kilometers.

The average for 2023 based on 196 countries was 0.51 percent. The highest value was in India: 17.94 percent and the lowest value was in Andorra: 0 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Data Description: Weibo information with a country as the key word (such as "Egypt") in a period of time, including posting users, posting time, posting content, amount of comment, amount of thumb-up, etc. Time frame: 2016.10.1-2017.10.23. The amount of data: 340,000. Data Format: excel (Total 19).

At 7.99 U.S. dollars, Switzerland has the most expensive Big Macs in the world, according to the January 2025 Big Mac index. Concurrently, the cost of a Big Mac was 5.79 dollars in the U.S., and 5.95 U.S. dollars in the Euro area. What is the Big Mac index? The Big Mac index, published by The Economist, is a novel way of measuring whether the market exchange rates for different countries’ currencies are overvalued or undervalued. It does this by measuring each currency against a common standard – the Big Mac hamburger sold by McDonald’s restaurants all over the world. Twice a year the Economist converts the average national price of a Big Mac into U.S. dollars using the exchange rate at that point in time. As a Big Mac is a completely standardized product across the world, the argument goes that it should have the same relative cost in every country. Differences in the cost of a Big Mac expressed as U.S. dollars therefore reflect differences in the purchasing power of each currency. Is the Big Mac index a good measure of purchasing power parity? Purchasing power parity (PPP) is the idea that items should cost the same in different countries, based on the exchange rate at that time. This relationship does not hold in practice. Factors like tax rates, wage regulations, whether components need to be imported, and the level of market competition all contribute to price variations between countries. The Big Mac index does measure this basic point – that one U.S. dollar can buy more in some countries than others. There are more accurate ways to measure differences in PPP though, which convert a larger range of products into their dollar price. Adjusting for PPP can have a massive effect on how we understand a country’s economy. The country with the largest GDP adjusted for PPP is China, but when looking at the unadjusted GDP of different countries, the U.S. has the largest economy.

Data Description: WeiChat information with a country as the key word (such as "Egypt") in a period of time, including WeiChat number, article titles, abstracts, amount of reading, amount of thumb-up and so on. Time range: 2016.10.1-2017.10.23 Data volume: 1.84 million Data Format: excel (Total 19)

The average for 2023 based on 188 countries was 0.53 percent. The highest value was in the USA: 26.3 percent and the lowest value was in Andorra: 0 percent. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.

The North America Big Data as a Service Market would witness market growth of 18.8% CAGR during the forecast period (2024-2031). The US market dominated the North America Big Data as a Service Market by Country in 2023, and would continue to be a dominant market till 2031; thereby, achieving a mark

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This index compiles empirical data on AI and big data surveillance use for 179 countries around the world between 2012 and 2022— although the bulk of the sources stem from between 2017 and 2022. The index does not distinguish between legitimate and illegitimate uses of AI and big data surveillance. Rather, the purpose of the research is to show how new surveillance capabilities are transforming governments’ ability to monitor and track individuals or groups. Last updated February 2022.

This index addresses three primary questions: Which countries have documented AI and big data public surveillance capabilities? What types of AI and big data public surveillance technologies are governments deploying? And which companies are involved in supplying this technology?

The index measures AI and big data public surveillance systems deployed by state authorities, such as safe cities, social media monitoring, or facial recognition cameras. It does not assess the use of surveillance in private spaces (such as privately-owned businesses in malls or hospitals), nor does it evaluate private uses of this technology (e.g., facial recognition integrated in personal devices). It also does not include AI and big data surveillance used in Automated Border Control systems that are commonly found in airport entry/exit terminals. Finally, the index includes a list of frequently mentioned companies – by country – which source material indicates provide AI and big data surveillance tools and services.

All reference source material used to build the index has been compiled into an open Zotero library, available at https://www.zotero.org/groups/2347403/global_ai_surveillance/items. The index includes detailed information for seventy-seven countries where open source analysis indicates that governments have acquired AI and big data public surveillance capabilities. The index breaks down AI and big data public surveillance tools into the following categories: smart city/safe city, public facial recognition systems, smart policing, and social media surveillance.

The findings indicate that at least seventy-seven out of 179 countries are actively using AI and big data technology for public surveillance purposes:

• Smart city/safe city platforms: fifty-five countries • Public facial recognition systems: sixty-eight countries • Smart policing: sixty-one countries • Social media surveillance: thirty-six countries

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

Four Big European Countries - Leading Indicators OECD: Leading indicators: CLI: Amplitude adjusted for Four Big European was 99.64537 Index in December of 2023, according to the United States Federal Reserve. Historically, Four Big European Countries - Leading Indicators OECD: Leading indicators: CLI: Amplitude adjusted for Four Big European reached a record high of 103.60961 in January of 1973 and a record low of 89.28487 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Four Big European Countries - Leading Indicators OECD: Leading indicators: CLI: Amplitude adjusted for Four Big European - last updated from the United States Federal Reserve on October of 2025.

Population and Housing Censuses: Population by municipality (with a population lower than or equal to 20,000 inhabitants), sex and country of birth (big groups). Municipalities with a population lower than or equal to 20,000 inhabitants.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

Four Big European Countries - Leading Indicators OECD: Leading indicators: CLI: Trend restored for Four Big European was 1.67682 Growth rate same period previous Yr. in January of 2024, according to the United States Federal Reserve. Historically, Four Big European Countries - Leading Indicators OECD: Leading indicators: CLI: Trend restored for Four Big European reached a record high of 14.39116 in April of 2021 and a record low of -9.08050 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Four Big European Countries - Leading Indicators OECD: Leading indicators: CLI: Trend restored for Four Big European - last updated from the United States Federal Reserve on October of 2025.

Table of INEBase Population by municipality (with a population lower than or equal to 20,000 inhabitants), sex and country of birth (big groups). Municipalities with a population lower than or equal to 20,000 inhabitants. Population and Housing Censuses

Translation (Htoi San) I am telling a story of an intelligent man. Once upon a time, a king had many male and female servants in a country. He had a brilliant, talented, and well-educated male servant among all his helpers. That manservant talked about himself, "There is no one educated like me." He spoke about this again and again so that the king heard him. Therefore the king asked him to come. The king told him, "As you are so smart and wise, I will test you." "Yes, sure, My King. Please go ahead." The king tested him, "I will examine you as the first one, Catch the wave from the sea over there." The competent servant responded, "My King, if you want me to catch the wave, please make me wings which are made of sand." The king was amazed by his servant's answer and did not know how to reply to his request. "Alright, never mind. I will give another task," the king continued. The king caught a baby duck and gave it to him. The king ordered him, "Take this and make five dishes with this duckling." The manservant replied, "Alright. My King. Please listen to my request as well. Please prepare a stove and a pot for cooking for me." He also brought a needle for another request. "Please help me to make this needle into a knife big enough for cooking." The king thought his servant was insulted for making such a small needle into a big knife, so the king was so angry and chased him away, "I don't want to talk to you anymore. Get out of my sight. Get out of my palace. Don't even step on my ground." The following day, that manservant rode a small pony, carried the soil from his house, and then placed it at the entrance of the gate outside the palace. He placed the soil from his house, and he rode the pony to and fro in front of the gate for the entire morning. Then the king saw this and ordered his men, "Kill that man. I ordered him not to stay in my sight, but he let me see him. I commanded him not to step on my ground." That manservant responded, "My King, I do not step on your belonging ground. This soil is from my own house that I brought and stepped on." The king did not know how to respond to him. Then the king said, "If you say so, I will test you with one thing." The manservant asked, "My King, if I cannot answer you this time, what are you going to do to me?" The king answered, "It depends on you and how you answer." "If so, ask me," the manservant responded. The king talked about a kettle and a fruit, and then he gave the kettle first. "Put the fruit into the hole of the kettle. It must fit into it." "When you put the fruit, it should not destroy, should not be flaccid, and it must be put into the kettle nicely." The king asked him to do that. The king gave that kettle to the manservant. The servant went home and thought about how to do it as the king gave him a month to think it over. He would be tortured and killed if he could not get the solution within a month. So he thought about it seriously. After a week, he went to an orchard. He saw a passionate tree bearing fruits. The passionate fruits were still young, and they fit exactly into the hole of the kettle which the king gave him. He put the fruit into the kettle, and the fruit went into it easily. "This will be bigger after three weeks. It will go in without any bruises and marks. It will be inside the kettle beautifully." The man thought this way, and after that, he put the fruit into the pot and hung it on the tree, On the third week morning, he went to see the kettle and found the fruit inside was bigger. Then there was no bruise, and it was perfectly nice. So the manservant plucked the fruit from the orchard and brought it to the king. The king was so amazed when he received it. "Oh my! How can you put this fruit into the kettle? This big fruit was put into a small hole in the kettle without any marks, and nothing was wrong. You are so bright." "That is why I congratulate you." The king praised that man with gold, jade, and jewelry. Furthermore, the king placed him in the palace and gave him a bodyguard position. Transcription (Lu Awng) Hpaji chye ai la a lam tsun na re. Moi shawng de da mungdan langai kaw hkawhkam wa kaw gaw mayam num mayam la grai lu ai da. Dai hkawhkam wa gaw dai shaloi she, dai mayam ni kaw na baw nu grai byin ai grai hpaji grai rawng ai mayam la langai ma nga ai da. Dai mayam la gaw, ngai na ram hpaji chye ai kadai nnga na sai ngu na dai hku ngu tsun hkrai, tsun ai shaloi she hkawhkam wa na mat ai da. Hkawhkam wa na mat na she hkawhkam wa dai la hpe shaga shangun ai da. Shaga shangun rai yang she, e nang hpaji grai chye nga ai majaw nang hpe ngai chyam yu na ngu tsun ai da. Dai shaloi she, e mai ai hkawhkam wa e tsun rit ngu tsun ai da. Dai shaloi hkawhkam wa gaw,e (nam bat) langai hku na nang hpe ngai chyam na ga gaw oh hka kaba kaw na hka leng sa rim wa rit ngu tsun ai shaloi she, hkawhkam wa hpe bai tsun ai da, e hkawhkam wa e hka leng hpe rim na rai yang ngai hpe zai bru hte galaw ai singawn ngai hpe galaw ya rit ngu dai hku ngu tsun ai da. Hkawhkam wa mau mat ai da, shi dai hku ngu tsun dat ai hpe kara hku bai nchye tsun mat na she, e rai sai langai bai shangun na ngu bai tsun ai da. Dai shaloi she, shi hkai pyet kasha langai mi rim ya ai da. Maw ndai hpe tsi htu shat mai myu hpan manga shi lu hkrsa shadu u ngu tsun ai da. Dai shaloi she, e mai ai hkawhkam wa e rai yang ngai hpyi shawn ai ma madat ya u yaw, dai majaw shat shadu na wandap langai ma galaw ya u langai mi gaw dai tsi htu shadu na shat mai di langai mi jaw rit ngu tsun ai da. Ngu na langai mi gaw shi samyit la sa ai da. Ndai samyit hpe dai shat mai shadu na nhtu ram kaba hkra galaw ya rit ngu tsun na she, samyit kachyi sha law hpe wa nhtu ram kaba hkra galaw ya rit ngu gaw ngai hpe roi ai nan re ngu na hkawhkam wa grai pawt mat na she, e nang hpe ngai grai nkam shaga sai dai majaw pru wa nu ndai nye hkawhkam wang kaw na i pru wa sanu, ndai ngai nga ai lamu ga ndai hku hkum kabye ngu na gawt kau ai da. Dai la hpe gawt kau rai shaloi she, dai la wa hpang jahpawt du shaloi she grai kadun ai gumra kaji dai hpan hpe jawn na she shi gaw kaga shi na nta wang na ga hpe she htaw sa wa na she dai hkawhkam wa na wang shin gan kaw na chyinghka lam kaw she dai shi nyap da. Shi na nta na ga dai hpe wa nyep da na she, dai gumra leng hte she sa sa wa wa ning hkrang dan ai da jahpawt ting, dai yang hkawhkam wa tsun ai da oh ra la wa hpe sat kau mu ngai mu ai shara kaw hku nga she ngu yang me ngai hpe mu shangun ai gaw ngai nga ai lamu ga gaw hkum kabye ngu tsun yang me ngu tsun na she, dai la wa gaw tsun ai da e hkawhkam wa e ngai nang madu ai lamu ga kaw ngai nkabye ai ndai gaw ngai na nta wang kaw na nan hte sa wa ai ga she re dai kaw she ngai kabye ai ngu dai hku ngu tsun ai da. Dai shaloi hkawhkam wa hpa nchye tsun na, dan nga yang nang hpe ngai lama mi naw taun na ngu tsun ai da. Dai shaloi she hkawhkam wa nang ya lang ngai bai htai na nlu tsun mat yang nang ngai hpe hpa baw galaw na ngu tsun yang she, dai gaw i nang na ntsa kaw she seng ai nang htai ai de she seng ai ngu tsun ai da. E rai yang shangun rit ngu tsun ai da. Dai shai she, (hka ya) di langai ma namsi langai ma tsun ai da, ndai (hka ya) di shawng jaw dat ai da. E ndai (hka ya) di kaw ndai (hka ya) di na ndai ahku kaw hkrat re namsi bang u. Bang na i namsi dai dai hku hten mat ai ma nmai byin ai, dai hku anyuk anyap rai ma nmai byin ai dai majaw kata kaw grai tsawm hkra rawng ra ai ngu dai hku ngu tsun ai da. Dai shaloi (hka ya) di jaw dat ai da, dai shaloi she shi wa myit sai da nta kaw wa myit yang she ga ngai ndai kara hku di na ta shata mi ahkang jaw dat ai da. Dai shaloi she shata mi kaw nlu myit yang sat kau na ari dai hku jaw na ngu myit ai she, shi wa myit sai da dai shaloi she bat mi ngu na shaloi shi wa she shi na namsi sun de sa mat wa ai da. Hkai sun de sa mat wa shaloi she, (hpyaw ye) si si taw ai hpe sa mu ai da (hpyaw ye) si wa hkalung wa she shi na dai hkawhkam wa jaw dat ai (hka ya) di hte (koit ti) da dai hku ram daw re hpe she shawng bang dat yang she dai kaw shang mat wa ai da. Dai namsi wa shang mat wa shaloi she, ndai gaw bat masum ram nga yang gaw loi mi kaba mat jang gaw (hka ya) di kaw shang jang gaw i hpye ma nhpye agut agat rai ma nre sai grai tsawm hkra rawng sai ngu na shi gaw myit na she, dai (hka ya) di hpe dai (hpyaw ye) si ma dai hku shawng bang kau da na she noi kau da ai da. Dai hpun kaw noi kau da rai shaloi bat masum ngu na jahpawt she (hka ya) di hpe sa yu yang she nam si wa she kata kaw grai kaba mat taw ai da. Rai na hpye ma nhpye ai grai tsawm taw ai jang she, dai hpe she shi gaw nam si hkin dai kaw na di na she (hka ya) dai hpe la sa mat wa ai da. Hkawhkam wa hpe sa jaw ai shaloi hkawhkam wa grai mau sai da, aga nam si ndai gaw kara hku wa bang ai ta ahku kachyi sha law kaw nam si ndai kaba law ya hpye mung nhpye hpa mung nbyin re gaw nang na baw nu grai ram ai. Dai majaw ngai nang hpe shagrau ai ngu na dai la hpe ja ma, lung seng dai ni shagrau na she dai hkawhkam wamg kaw shi hpe shi na sak sin hku na shi hpe ahkang jaw kau ai da. . Language as given: Jinghpaw

Four Big European Countries - Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Original series for Four Big European was 108.65530 Index 2015=100 in January of 2024, according to the United States Federal Reserve. Historically, Four Big European Countries - Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Original series for Four Big European reached a record high of 108.65530 in January of 2024 and a record low of 25.49468 in February of 1960. Trading Economics provides the current actual value, an historical data chart and related indicators for Four Big European Countries - Leading Indicators OECD: Reference series: Gross Domestic Product (GDP): Original series for Four Big European - last updated from the United States Federal Reserve on October of 2025.