Finance:GDP density

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Short description: Measure of economic activity per a given unit of area
Gridded GDP Density of the World 1990 and 2011

GDP density is a measure of economic activity by area. It is expressed as gross domestic product per square kilometer and can be calculated by multiplying GDP per capita of an area by the population density of that area. Amongst other uses it demonstrates the effects of geography on economy.[1]

GDP Density

What is GDP density

GDP density refers to the distribution or intensity of Gross Domestic Product (GDP) within a particular area or population. GDP density could be interpreted as a measure of economic activity or output relative to the size of a region or population, often expressed as GDP per capita or GDP per unit of area.

GDP per capita

The GDP per capita is a commonly utilized indicator to assess a nation's economic performance. It is calculated by dividing the total value of products and services produced in a nation during a given time period by the average population of that nation.[2] This measure, which is frequently used to compare the economic success of various countries, sheds light on the average economic output per person in a specific nation. GDP per capita typically denotes a higher standard of life because it shows that the nation's citizens have more resources and GDP density.

GDP per unit area

GDP per unit area refers to the geographical distribution of Gross Domestic Product within a country. It analyzes how economic output is spread across different regions, provinces, states, or territories within a nation. Understanding GDP geographic distribution is essential for policymakers, businesses, and researchers as it provides insights into regional disparities in economic development and helps identify areas that may require targeted interventions or investment. Policymakers can identify economically underperforming regions and may need to implement tailored policies to promote growth and development by analyzing the geographic distribution of GDP. By identifying the areas of concentrated economic activity, it also assists firms in making well-informed decisions regarding the allocation of resources and market prospects. Additionally, trends of urbanization, economic resiliency, and regional growth are studied by scholars using geographic GDP density data.

Fluctuations in GDP density

Academic discussions have highlighted two significant reasons why typical households might not benefit proportionally from economic growth. These reasons revolve around discrepancies between GDP per capita and median household income, shedding light on challenges in accurately measuring and interpreting economic indicators.[2] The first reason centers on differences in measurement between GDP per capita, derived from national accounts, and median household income, based on household surveys. Despite efforts by organizations like the OECD and EU to align income measures with national accounts concepts, disparities persist due to methodological variations and mismeasurement. Empirical studies reveal substantial disparities between mean per capita income from national accounts and household surveys across various countries. The second reason gaining attention is the widening income inequality, where a significant portion of national income growth concentrates at the top of the income distribution. This trend results in minimal increases in the median income. Empirical studies, primarily focused on countries like the USA and the UK, underscore the impact of income inequality on household income dynamics.

Effect on poverty levels

Geographical detectors have been used, especially in rural poverty contexts, to systematically measure the individual and interacting influences of numerous factors on changes in GDP density. This investigation has found the ideal qualities of important variables that support GDP density growth.[3] Furthermore, a better comprehension of the GDP density differentiation mechanism in rural poverty has been gained. Sufficient reasons for changes in GDP density in rural poverty have been established, based on variables like elevation, average yearly temperature, land use, normalized differential vegetation index (NDVI), and distance to main roadways. Crucially, it has been noted that these impact factors have interactive impacts on GDP density, with synergistic effects frequently producing nonlinear consequences and mutual enhancement.

List of countries by GDP density

GDP forecast or estimate (million US$) by country
Country/Territory IMF GDP 2025 (million) Land area (km) GDP per km2
 United States 30,615,743 9,525,067 3,214,229
 China[n 1] 19,398,577 9,596,960 2,021,325
 Germany 5,013,574 357,581 14,020,806
 Japan 4,279,828 377,915 11,324,843
 India 4,125,213 3,287,263 1,254,908
 United Kingdom 3,958,780 244,376 16,199,545
 France 3,361,557 551,500 (metropolitan) 6,095,298
 Italy 2,543,677 302,068 8,420,875
 Russia 2,540,656 17,098,246 148,592
 Canada 2,283,599 9,984,670 228,711
 Brazil 2,256,910 8,510,346 265,196
 Spain 1,891,371 505,370 3,742,547
 Mexico 1,862,740 1,964,375 948,261
 South Korea 1,858,572 100,432 18,505,775
 Australia 1,829,508 7,741,220 236,333
 Turkey 1,565,471 783,562 1,997,890
 Indonesia 1,443,256 1,904,569 757,786
 Netherlands 1,320,635 41,865 31,545,085
 Saudi Arabia 1,268,535 2,149,690 590,101
 Poland 1,039,619 312,685 3,324,813
 Switzerland 1,002,666 41,291 24,282,919
 Taiwan 884,387 35,980 24,579,961
 Belgium 716,980 30,528 23,485,980
 Ireland 708,771 70,273 10,085,965
 Argentina 683,371 2,780,400 245,782
 Sweden 662,318 450,295 1,470,854
 Israel 610,752 21,937 27,841,182
 Singapore 574,185 740 775,925,676
 United Arab Emirates 569,097 83,600 6,807,380
 Austria 566,456 83,878 6,753,332
 Thailand 558,573 513,120 1,088,582
 Norway 517,102 386,224 1,338,866
 Philippines 494,158 300,000 1,647,193
 Vietnam 484,726 331,340 1,462,926
 Bangladesh 475,011 148,460 3,199,589
 Malaysia 470,572 330,621 1,423,297
 Denmark 459,612 42,947 10,701,842
 Colombia 438,121 1,138,910 384,684
 Hong Kong 428,233 1,114 384,410,233
 South Africa 426,383 1,219,090 349,755
 Romania 422,508 238,398 1,772,280
 Pakistan 410,495 882,363 465,222
 Czech Republic 383,384 78,871 4,860,899
 Iran 356,513 1,648,195 216,305
 Egypt 349,264 1,001,450 348,758
 Chile 347,174 756,102 459,163
 Portugal 337,936 92,225 3,664,256
 Peru 318,480 1,285,216 247,803
 Finland 314,724 338,145 930,737
 Kazakhstan 300,052 2,724,910 110,114
 Algeria 288,013 2,381,741 120,925
 Nigeria 285,003 923,768 308,522
 Greece 282,019 131,957 2,137,204
 Iraq 265,455 438,317 605,623
 New Zealand 262,909 268,838 977,946
 Hungary 247,759 93,025 2,663,359
 Qatar 222,119 11,586 19,171,327
 Ukraine 209,713 575,627 364,321
 Cuba 201,986 (2023) 109,884 1,838,175
 Morocco 179,612 446,550 402,221
 Kuwait 157,469 17,818 8,837,636
 Slovakia 154,587 50,310 7,625,265
 Uzbekistan 137,480 447,400 307,287
 Kenya 136,014 580,367 234,359
 Ecuador 130,529 283,561 460,321
 Dominican Republic 129,748 48,670 2,665,872
 Bulgaria 127,924 110,879 1,153,726
 Puerto Rico 126,546 9,100 13,906,154
 Guatemala 120,850 108,889 1,109,846
 Angola 115,167 1,246,700 92,377
 Ghana 111,963 238,537 469,374
 Ethiopia 109,492 1,104,300 99,151
 Oman 105,190 309,500 339,871
 Croatia 103,901 56,594 1,835,901
 Costa Rica 102,637 51,180 2,005,412
 Luxembourg 100,642 2,586 38,918,020
 Serbia 100,048 88,499 1,130,499
 Ivory Coast 99,207 322,462 307,655
 Sri Lanka 98,964 (2024) 67,240 1,471,802
 Lithuania 95,274 65,286 1,459,333
 Panama 90,408 75,320 1,200,319
 Tanzania 87,444 947,303 92,308
 Belarus 85,739 207,600 413,001
 Uruguay 84,986 176,215 482,286
 Venezuela 82,767 912,050 90,748
 Democratic Republic of the Congo 82,262 2,344,858 35,082
 Slovenia 79,221 20,273 3,907,710
 Azerbaijan 76,390 86,600 882,102
 Turkmenistan 72,119 488,100 147,755
 Uganda 64,993 241,550 269,066
 Cameroon 60,577 475,650 127,356
 Myanmar 60,561 676,578 89,511
 Tunisia 59,069 163,610 361,035
 Bolivia 57,086 1,098,581 51,963
 Jordan 56,157 89,318 628,731
 Zimbabwe 53,310 390,757 136,427
 Macau 52,379 33 1,587,242,424
 Cambodia 48,802 181,035 269,572
 Libya 47,941 1,759,540 27,246
 Latvia 47,880 64,594 741,245
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Notes

  1. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named China-THM

References

  1. ↑ "Gallup J. L., Sachs J. D., and Mellinger A. D. (1999): GEOGRAPHY AND ECONOMIC DEVELOPMENT". http://www.earthinstitute.columbia.edu/about/director/documents/irsr0899.pdf. 
  2. ↑ 2.0 2.1 Nolan, Brian; Roser, Max; Thewissen, Stefan (September 2019). "GDP Per Capita Versus Median Household Income: What Gives Rise to the Divergence Over Time and how does this Vary Across OECD Countries?" (in en). Review of Income and Wealth 65 (3): 465–494. doi:10.1111/roiw.12362. ISSN 0034-6586. https://onlinelibrary.wiley.com/doi/10.1111/roiw.12362. 
  3. ↑ Wang, Guangjie; Peng, Wenfu (March 2021). "Detecting influences of factors on GDP density differentiation of rural poverty changes" (in en). Structural Change and Economic Dynamics 56: 141–151. doi:10.1016/j.strueco.2020.10.004. https://linkinghub.elsevier.com/retrieve/pii/S0954349X20304057. 



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