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New measure of wealth accounts for resource depletion, environmental damage
Data Excerpts from a World Bank Report
Rhett A. Butler, mongabay.com
September 18, 2005


In September 2005, the World Bank published a report, Where Is the Wealth of Nations?, that introduced a new measure of wealth that takes into account the depletion of natural resources and damage to the environment. These factors are neglected under current indicators used to guide development decisions, notably Gross Domestic Product (GDP).

Below you will find the rankings for 118 countries. At the top of the list is Switzerland with wealth per capita of $648,241. At the bottom is Ethiopia at $1,965.

A related press release can be found here. Other related tables include:
Other related tables: Change in Wealth Per Capita | Genuine Savings Estimates by Country

All figures are from Where Is the Wealth of Nations?, a World Bank report. The figures are copyright the World Bank as is the explanatory text that follows. Further information, along with the full report in PDF form, is available at www.worldbank.org/sustainabledevelopment



World Bank 2005: Total wealth for selected countries of the world
Sort by Country | Wealth Rank (top) | Wealth Rank (bottom)

CountryPopulationSubsoil assets US$ per capitaTimber resources US$ per capitaNontimber Forest Resources US$ per capitaProtected Areas US$ per capitaCropland US$ per capitaPastureland US$ per capitaNatural capital US$ per capitaProduced capital + urban land US$ per capitaIntangible capital US$ per capitaTotal wealth US$ per capita
Albania3113000300387224716601574389217451167517312
Algeria30385000116706816161859426132008709-341818491
Antigua and Barbuda7231000280100346815003879691554131849
Argentina358500003253105219350363227541031219111109809139232
Australia19182000114917485511421436555902416758179288686371031
Austria8012000485829144241012982008717473118412789493080
Bangladesh131050000834298105296181742216000
Barbados267000988000190210138818168127181146737
Belgium-Luxembourg10690000202542005752161303060561388123451714
Belize2400000344127205201133695097103627552935
Benin6222000153219620760390133377157917895
Bhutan805000018888491291589328494526221807747
Bolivia842800093410014262321550541478321101124818141
Botswana1675000246172168129955730318389262848340592
Brazil170100000170860972440219981311675296437052886922
Bulgaria817000024412610221716501108344853031650525256
Burkina Faso112740000239142100547191121982130475087
Burundi680700042337113044121020614432859
Cameroon15117000914348357187274817947331749427110753
Canada3077000018566472412645756282916313477154226235982324979
Cape Verde435000004405858271139022832932942
Chad7861000031136680787316186128923074458
Chile15211000518898623110952443100110944106885609477726
China1262644992511106292714041462223295642089387
Colombia4229900030061342662531911978654748723324144660
Comoros5580000173087275967127057928030
Congo, Rep. of344700075360145033291393306343-121583516
Costa Rica3810000262911765758111310852783434474161611
Cote d'Ivoire1582700023671021125687231219971012514243
Denmark53400004173211251377218437751174680181483212575138
Dominica715300..146052745535973153103780259084
Dominican Republic835300028627374611980386317657232451133410
Ecuador1242000052053351931057526310651311728411778833745
Egypt63976000154400017050324938971473421879
El Salvador620900001054440439591241093145536476
Estonia13700003841382341490111425726283186854180266769
Ethiopia64298000063161673531977961779921965
Fiji81200077022701381522220841923848044880
Finland51720005861151259109084320811144561064346838419346
France588930008730777102627472091633557814403874468024
Gabon125800024656157084111480372858617797-321543168
Gambia, The1312000008343458151467251796365
Georgia52620006601296673780217995951064213036
Germany8221000026926339111311761586444568678423323496447
Ghana1891208065290767855431336686834310365
Greece1056000031882101573424573455428973203445236972
Grenada101400000057267640161283854455312
Guatemala11385000301517571811697218297130982441130480
Guinea-Bissau1367000019536201180121185854915663974
Guyana75900011476802886125324252103013333217615810
Haiti7959000083366811279360168408235
Honduras645700024727189282118959530053064549711567
Hungary1002400053615242366272111314947154805664577072
India1015923008201591412213401921928115437386820
Indonesia206264992154934611516712455034722382801513869
Iran63664000113700261091989611141053336658124023
Ireland381300038522251172158381221053446542273414330490
Israel6289000100613501757877399944153246570294723
Italy5769000036105154326391083467851943316045372666
Jamaica2580000856157296098241522627101533501647796
Japan1268700002838563647103161513150258341470493241
Jordan488700091648958023493158752474031546
Kenya300920001235129113361529136886843746609
Korea, Rep. of47008000330304411241275202031399107864141282
Latvia237200001155279668150618775485129792873447198
Lesotho1744000042123926951532631169915477
Madagascar15523000017417136955345168139529445020
Malawi10311000018456264744578554238735200
Malaysia2327000069224381881611369249103130652452046687
Mali108400000121276441420295215762124635241
Mauritania2508159131114292111284802982103839387959
Mauritius1187000003057762642116334801060284
Mexico97966000607519912817611957218493189593442061872
Moldova427800003175224357523260433811738771
Morocco2870500010622247993453160434351792622965
Mozambique176910000340392926157105947826954232
Namibia1894000460962260204881235255742898136907
Nepal2304300002333881767111122960919643802
Netherlands15919000205327752710353090673962428352222421389
New Zealand385800035961648611117865824197614322636227163481242934
Nicaragua5071000947514618486741020921719940313214
Niger1074200019281521598187197528614343695
Nigeria12691000026392702461022784040667-19592748
Norway4491000498395735861339567192554828119650299230473708
Pakistan1380800002657494549448136897555297871
Panama2854000017622872632566645051110184159457663
Paraguay5270000088210057821931215537244802574735600
Peru25939000934153570981480341357555622990839046
Philippines7662700030901759130845154926731512919351
Portugal10130000414381073851724934362931011172837207477
Romania2243500012222906517516021154450884951611029113
Russian Federation1455550081177729212281317126213421721715593590038709
Rwanda7709000281927184998206654930555670
Senegal95300004238147786081961272975792010167
Seychelles811310084000842883696653125572
Singapore4018000000000079011173595252607
South Africa44000000111831046511238637340072704895959629
Spain4050000050811053602806971437439531217300261205
Sri Lanka18467000058241664858481727101120414731
St. Kitts and Nevis4428600000003571164457100167
St. Lucia1559960013033941083516135944909066199
St. Vincent1119920012021061092228104863651849232
Suriname42500044512931173762621132101586658182544447128
Swaziland104500003141130372467126736282284427739
Sweden88690002632434908154911201676795058331447143513424
Switzerland718000004935021958092396594399904542394648241
Syrian Arab Rep.161890006734060125573087253292-159810419
Thailand607280004699255855237096393676242429435854
Togo4562000716325216495091580053947109
Trinidad and Tobago12890003027942461124445430977144851208657549
Tunisia95640001610271281546736393962702632836537
Turkey674200001906434862270861350485803577447859
United Kingdom58880000473944144955831291716755239346347408753
United States282224000710613412381651275216651475279851418009512612
Uruguay33220000088223621554992791078798397118463
Venezuela24170000233020464179310865812722713627434245196
Zambia98860001342767167847798177969440916564
Zimbabwe12650000301211341703502581531137767049612




Column headers. What they mean.

Appendix 1.1: Building the Wealth Estimates-Methodology

Energy and Mineral Resources

In this section, the methodology used in the estimation of the value of nonrenewable resources is described. At least three reasons lie behind the difficulties in such calculations. First, the importance of the inclusion of natural resources in the national accounting systems has only been recognized in the last decades, and although efforts to broaden the national accounts are being made, they are mostly limited to international organizations (such as the UN or the World Bank). Second, there are no private markets for subsoil resource deposits to convey information on the value of these stocks. Third, the stock size is defined in economic terms—reserves are “that part of the reserve base which could be economically extracted or produced at the time of determination,” —and, therefore, it is dependent on the prevalent economic conditions namely technology and prices (U.S. Geological Survey definition. It is clear that an increase in, say, oil price or a reduction in its extraction costs would increase the amount of “economically extractable” oil and therefore increase the reserves. Indeed, U.S. oil production has surpassed several times the proved reserves in 1950.).

Despite all these difficulties, dollar values were assigned to the stocks of the main energy resources (oil, gas, and coal [Coal is subdivided into two groups: hard coal (anthracite and bituminous) and soft coal (lignite and subbituminous).]) and to the stocks of ten metals and minerals (bauxite, copper, gold, iron ore, lead, nickel, phosphate rock, silver, tin, and zinc) for all the countries that exhibit production figures.

This section is explained in greater depth in Appendix 1.1 of Where Is the Wealth of Nations?
Tables courtesy of the World Bank


Timber Resources

The predominant economic use of forests has been as a source of timber. Timber wealth is calculated as the net present value of rents from roundwood production. The estimation then requires data on roundwood production, unit rents, and the time to exhaustion of the forest (if unsustainably managed).

The annual flow of roundwood production is obtained from the Food and Agriculture Organization of the United Nations database (FAOSTAT) (When data is missing, and if country’s forest area is less than 50 square kilometers, the value of production is assumed to be zero.). Calculating the rent is more complex. Theoretically, the value of standing timber is equal to the discounted future stumpage price received by the forest owner after taking out the costs of bringing the timber to maturity. In practice, stumpage prices are usually not readily available, and we calculated unit rents as the product between a composite weighted price times a rental rate.

The composite weighted price of standing timber is estimated as the average of three different prices (weighted by production): 1) the export unit value of coniferous industrial roundwood; 2) export unit value of non-coniferous industrial roundwood; and 3) an estimated world average price of fuelwood. Where country level prices are not available, the regional weighted average is used (After consultation with World Bank forestry experts, some country level prices were replaced by the regional average.).

Forestry production-cost data is not available for all countries. Consequently, regional rental rates ([price-cost]/price) were estimated using available studies and consultation with World Bank forestry experts.

Since we applied a market value to standing timber, it was necessary to distinguish between forests available and forests not available for wood supply as some standing timber is simply not accessible or economically viable. The area of forest available for wood supply was estimated as forests within 50 kilometers of infrastructure.

Rents were capitalized using a 4 percent discount rate to arrive at a stock of timber resources. The concept of sustainable use of forest resources is introduced via the choice of the time horizon over which the stream is capitalized. If roundwood production is smaller than net annual increments, that is, the forest is sustainably harvested, the time horizon is 25 years. If roundwood production is greater than the net annual increments, then the time to exhaustion is calculated. The smallest between this estimate and 25 is used. The time to exhaustion is based on estimates of forest volume divided by the difference between production and increment.

Roundwood and fuelwood production data is for the year 2000, and is from FAOSTAT forestry data online. Data on industrial roundwood (wood in rough) for coniferous and non-coniferous production were obtained from the United Nations Food and Agriculture Organization (UNFAO) yearbook: Forest products 1997–2001. Fuelwood price data is from FAOSTAT forestry data online. Roundwood export prices are calculated from data from FAO Forestry Products 1997– 2001. Studies used as a basis for rental rates were (Fortech 1997; Whiteman 1996; Tay 2001; Lopina 2003; Haripriya 1998; Global Witness 2001; Eurostat 2002).

Nontimber Forest Resources

Timber revenues are not the only contribution forests make. Nontimber forest benefits such as minor forest products, hunting, recreation, watershed protection, options, and existence values are significant benefits not explicitly accounted for. This leads to forest resources being undervalued. A review of nontimber forest benefits in developed and developing countries reveals that returns per hectare per year from such benefits vary from $190 per hectare in developed countries to $145 per hectare in developing countries (based on Lampietti and Dixon 1995 and Croitoru and others 2003 and adjusted to 2000 prices). Assuming that only one-tenth of the forest area in each country is accessible, this per hectare value is multiplied by one-tenth of the forest area in each country. Non-timber forest resources are then valued as the net present value of benefits over a time horizon of 25 years (When data is missing, and if country’s forest area is less than 50 square kilometers, the value of non-timber forest benefits is assumed to be zero.).

Cropland

Country- level data on agricultural land prices are not widely published and even if local data were available, it is arguable that land markets are so distorted that a meaningful comparison across countries would be difficult. We have therefore chosen to estimate land values based on the present discounted value of land rents, assuming that the products of the land are sold at world prices.

The return to land is computed as the difference between market value of output crops and cropspecific production costs. Nine representative crops were taken mainly based on their production significance in terms of sowing area, production volume, and revenue. With these three aspects taken into consideration the following nine representative crops were considered: maize, rice, wheat, banana, grapes, apples, oranges, soybean, and coffee. Maize, rice, and wheat were calculated individually as they occupy most of the world’s agricultural land resources. Banana, grapes, apples, and oranges were used as proxies for the broader category of fruits and vegetables. Soybean and coffee were used as proxies for the broader categories of oil crops and beverages respectively. Roots, pulses, and other crops were calculated as the residual of total arable and permanent cropland minus the sowing areas of the above nine categories.

The annual economic return to land is measured as a percentage of each crop’s production revenue, otherwise known as the rental rate. The calculated rental rates were obtained from a series of sector studies. So, for example, the rental rate for rice uses information on rental rates for Lao (67.6 percent), Egypt (30.6 percent) and Indonesia (56.1 percent) to obtain a world rental rate for rice of 51 percent. The other rental rates used are: 30 percent for maize (from China, Egypt, Yemen), 34 percent for wheat (from Egypt, Yemen, Mongolia, Ecuador), 27 percent for soybean (from China, Brazil, Argentina), 8 percent for coffee (Nicaragua, Peru, Vietnam, Costa Rica), 42 percent for bananas (from Brazil, Colombia, Costa Rica, Cote D’Ivoire, Ecuador, Martinique, Suriname, Yemen), 31 percent for grapes (from Moldova and Argentina), 36 per cent for apples and oranges (the value is based on the average for banana and grapes, as no sector studies were found).

The crop-specific ratios are then multiplied by values of production at eorld prices. This has the effect of assigning higher land rents to more productive soils. However, applying average cropspecific ratios in this manner probably understates the value of the most productive lands and overstates the value of the least productive land within a country.

A country’s overall land rent is calculated as a weighted average (weighted by sowing areas) of rents from ten crop categories. Return to land for the tenth category (roots, pulses, and other crops) is calculated differently. Since there is no representative crop for it, the land rent is calculated as 80 percent of the weighted average (weighted by sow area) of the three major cereals. This is based on the assumption that roots, pulses, and other crops yield lower returns to land per hectare.

In order to reflect the sustainability of current cultivation practices, the annual return in 2000 is projected to the year 2020 based on growth in production (land areas are assumed to stay constant). Between 2020 and 2024, the vale of production was held constant. The growth rates are 0.97 percent and 1.94 percent in developed and developing countries respectively (Rosengrant 1995). The discounted present value of this flow was then calculated using a discount rate of 4 percent.

Pastureland

Pasturelands are valued at the opportunity cost of preserving land for grazing. The returns to pastureland are assumed to be a fixed proportion of the value of output. On average, costs of production are 55 percent of revenues, and therefore, returns to pastureland are assumed to be 45 percent of output value. Value of output is based on the production of beef, lamb, milk, and wool valued at international prices. As with croplands, this rental share of output values is applied to country-specific outputs of pastureland valued at world prices. The present value of this flow is then calculated using a 4 percent discount rate over a 25-year time horizon.

In order to reflect the sustainability of current grazing practices, the annual return in 2000 is projected to the year 2020 based on growth in production (land areas are assumed to stay constant). Between 2020 and 2024, the value of production was held constant. The growth rates are 0.89 percent and 2.95 percent in developed and developing countries respectively (Rosengrant 1995). The discounted present value of this flow was then calculated using a discount rate of 4 percent.

Protected Areas

Protected areas provide a number of benefits that range from existence values to recreational values. They can be a significant source of income from a thriving tourist industry. These values are revealed by a high willingness-to-pay for such benefits. The establishment and good maintenance of protected areas preserves an asset for the future and therefore protected areas form an important party of the natural capital estimates.

We have valued protected areas using a per hectare value that is the minimum between that for cropland and pastureland, that is, the cost of demarcating these areas as protected are the foregone benefits from converting them to pasture or agricultural land. The willingness-to-pay to preserve natural regions varies considerably and there is no comprehensive data set on this. Limiting the value of protected areas to the opportunity cost of preservatio n probably captures the minimum value, and not the complete value, of protected areas. Protected areas (the World Conservation Union [IUCN] categories I-VI) are valued at the lower of per hectare returns to pasture land and cropland. These are then capitalized over a 25-year time horizon, using a 4 percent discount rate.

Data on protected areas is taken from the World Database of Protected Areas (WDPA) which is compiled by United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC). Given the frequent revisions to the database, the data used is for 2003. In the cases of missing data on protected areas, this was assumed to be zero.



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The figures and text above are copyright the World Bank. Further information, along with the full report in PDF form, is available at www.worldbank.org/sustainabledevelopment

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