A Comparison by two different income concepts: It is useful to compare the estimates of assessment values for different income concepts. In order to examine the difference in the individual country assessment values due to different concepts of income, we compare two parallel sets of calculations by Rule 4 for each country, from the same array of countries. In one, as may be recalled, market exchange rates are used as conversion factors, and in the other purchasing-power-parity (PPP) conversion rates are applied to individual country values of GDP. Final results are expressed in US dollars.

Table 6 provides estimates for individual country values of assessments. The first and fourth columns of Table 6 are based on market exchange rates and GNP values. That is,

(TX04) Ti/Ni = 137 * 10-8 (Yi/Ni)1.3714 .

The second and fifth columns of Table 6 are based on PPP conversion rates and GDP values. That is,

(FN04) Ti/Ni = 76.238 * 10-8 (Yi/Ni)1.4312

In general, the poorer countries have relatively lower values of income per capita and also of assessment per capita if market exchange rates are used than if PPP conversions are used. At the upper end of the income and assessment scales PPP valuations are relatively lower. Some major changes are to be noted for large developing countries such as India, Pakistan, Bangladesh, China, Indonesia, Brazil, Russia, Mexico, Korea.

Changes in Estimated Assessments

($ million)

MER PPP

India 3.08 28.94

Pakistan .74 4.36

Bangladesh .25 4.77

China 8.02 51.94

Indonesia 2.26 12.23

Brazil 12.18 25.18

Russia 8.45 29.60

Mexico 9.00 25.00

Korea 12.79 20.01

Correspondingly, large reductions in estimated assessments can be seen, from Table 6, for France, Germany, and Japan as we switch from MER to PPP valuations, by the very nature of our approach.

Since this is a progressive system, it is evident that the degree of inequality in assessment values across countries will be larger than the degree of inequality of income values, both in per capita terms. The differences are shown in the two Lorenz diagrams of Fig.10. The differences are so great that they can be seen immediately in the two diagrams.

(Figure 10, Two Lorenz Curves

TABLE 6

ESTIMATES FROM LINEAR LOGARITHMIC FUNCTION, GNP and GDP per capita 1993

MER and PPP Valuation

Ti/Ni Ni Ti

MER PPP million $ million

GNP GDP persons MER/GNP PPP/GDP

ZAIRE .0039 NA .16

ETHIOPIA .0008 .0054 51.9 .04 .28

CHAD .0021 .0056 6.0 .01 .03

NIGER .0030 .0067 8.6 .03 .06

MALAWI .0020 .0071 9.7 .02 .07

TOGO .0041 .0073 3.9 .02 .03

GUINEA .0069 .0078 6.3 .04 .05

CENTRAL AFR.R. .0051 .0079 3.2 .02 .025

BURKINA FASO .0034 .0083 9.8 .03 .08

BURUNDI .0017 .0084 6.0 .01 .05

MALI .0030 .0086 10.1 .03 .09

TANZANIA .0007 .0087 28.0 .02 .24

UGANDA .0017 .0090 19.9 .03 .16

COMOROS .0080 .0091 0.6 .005 .004

MADAGASCAR .0022 .0092 13.9 .03 .13

HAITI .0111 NA .08

KAMPUCHEA* .0118 NA .11

MYANMAR .0118 NA .53

SUDAN .0120 NA .32

RWANDA .0021 .0121 7.6 .02 .09

GUINEA-BISS .0025 .0125 1.0 .0025 .01

ANGOLA .0125 NA .13

ZAMBIA .0047 .0125 8.9 .04 .11

SIERRA LEONE .0010 .0134 4.3 .006 .06

SOMALIA .0135 NA .12

AFGHANISTAN* .0136 NA .24

IRAQ* .0138 NA .27

GAMBIA .0042 .0142 1.0 .004 .01

VIETNAM .0016 .0142 71.3 .11 1.01

DJIBOUTI .0152 NA .008

CAMEROON .0136 .0155 12.5 .17 .19

LESOTHO .0099 .0166 1.9 .02 .03

LIBERIA .0172 NA .05

MAURITANIA .0069 .0179 2.2 .015 .04

NIGERIA .0034 .0181 105.3 .36 1.91

MOZAMBIQUE .0007 .0197 15.1 .01 .30

BHUTAN .0206 NA .01

KENYA .0030 .0206 26.4 .08 .52

IVORY COAST .0095 .0217 13.3 .13 .29

GHANA .0056 .0218 16.4 .09 .36

BENIN .0056 .0226 5.1 .03 .11

CAPE VERDE IS. .0159 .0234 0.4 .006 .009

SENEGAL .0120 .0243 7.9 .09 .19

NICARAGUA .0041 .0244 4.1 .02 .10

Ti/Ni Ni Ti

MER PPP million $ million

GNP GDP persons MER/GNP PPP/GDP

NEPAL .0018 .0248 20.8 .04 .52

TAJIKISTAN* .0063 .0258 5.8 .04 .15

ZIMBABWE .0073 .0278 10.7 .08 .30

INDIA .0034 .0322 901.5 3.08 28.94

ARMENIA .0101 .0334 3.5 .04 .12

GUYANA .0042 .0342 0.8 .003 .03

PAKISTAN .0056 .0355 132.9 .74 4.36

LAOS .0031 .0377 4.6 .01 .17

GEORGIA* .0084 .0379 5.4 .05 .21

HONDURAS .0088 .0390 5.3 .05 .21

MONGOLIA .0049 .0392 2.3 .011 .09

VANUATU .0237 .0413 0.2 .005 .007

BANGLADESH .0022 .0414 115.2 .25 4.77

AZERBAIJAN .0116 .0440 7.4 .09 .32

CHINA .0067 .0441 6196.4 8.02 51.94

ROMANIA .0213 .0470 23.0 .49 1.07

PHILIPPINES .0143 .0483 64.8 .92 3.13

EGYPT .0101 .0489 60.3 .61 2.76

KYRGYZSTAN* .0143 .0491 4.0 .07 .23

PAPUA N.GUINEA .0211 .0513 4.1 .09 .21

BOLIVIA .0122 .0513 7.1 .087 .36

WESTERN SAMOA .0166 .0516 0.2 .003 .009

EL SALVADOR .0261 .0531 5.5 .14 .29

CONGO .0166 .0573 2.4 .04 .14

MOLDAVIA* .0193 .0585 4.4 .08 .26

LITHUANIA .0261 .0614 3.7 .10 .23

PARAGUAY .0314 .0629 4.7 .15 .30

SWAZILAND .0226 .0640 0.8 .018 .06

TONGA .0650 NA .006

INDONESIA .0118 .0653 191.7 2.26 12.23

PERU .0308 .0659 22.9 .71 1.51

UZBEKISTAN* .0171 .0669 21.9 .37 1.46

YEMEN .0675 NA .89

MOROCCO .0188 .0679 25.9 .49 1.76

SOLOMON IS. .0118 .0708 0.4 .005 .025

SRI LANKA .0088 .0719 17.9 .16 1.29

DOMINICAN REP. .0237 .0723 7.5 .18 .54

GUATEMALA .0203 .0728 10.0 .20 .73

ALGERIA .0393 .0749 26.7 1.05 2.00

SURINAME .0224 .0770 0.4 .009 .03

KOREA, NORTH* .0773 NA 1.78

JAMAICA .0294 .0795 2.4 .07 .19

NAMIBIA .0405 .0882 1.5 .06 .13

ECUADOR .0229 .0946 11.0 .25 1.04

BOTSWANA .0728 .0994 1.4 .10 .14

KAZAKHSTAN .0328 .1032 17.0 .56 1.75

TUNISIA .0375 .1051 8.6 .32 .91

JORDAN .0226 .1073 4.9 .11 .44

Ti/Ni Ni Ti

MER PPP million $ million

GNP GDP persons MER/GNP PPP/GDP

GRENADA .0585 .1118 0.1 .006 .01

IRAN .1120 NA 7.19

SOUTH AFRICA .0797 .1122 39.7 3.16 4.45

TURKMENISTAN .1123 NA .44

LEBANON* .1139 NA .44

LATVIA .0464 .1210 2.6 .12 .32

COLOMBIA .03 .1217 34.0 1.02 4.34

CUBA* .1241 NA 1.35

PANAMA .07 .1256 2.5 .18 .32

REUNION .1340 NA .08

ST.VINCENT&GRE .0499 .1347 0.1 .005 .01

ESTONIA .0833 .1378 1.6 .13 .21

UKRAINE .0529 .1415 51.6 2.73 .73

COSTA RICA .05 .1431 3.3 .16 .47

ST.LUCIA .0947 .1480 0.1 .009 .02

DOMINICA .0703 .1489 0.1 .007 .01

GABON .1602 .1517 1.2 .19 .15

BELARUS .0757 .1554 10.2 .77 1.58

POLAND .0545 .1603 38.3 2.09 6.14

BRAZIL .0778 .1609 156.5 12.18 25.18

TURKEY .0793 .1640 59.6 4.73 9.77

OTHERS .1690 NA .67

FIJI .05 .1705 0.8 .04 .13

SYRIA .1709 NA 2.34

CZECHOSLOVAKIA .0699 .1718 10.3 .72 2.68

.0445 5.3 .24

THAILAND .08 .1744 57.6 2.88 10.13

HUNGARY .0935 .1867 10.2 .95 1.91

BELIZE .0609 .1955 .2 .012 .04

YUGOSLAVIA .1972 NA 2.42

RUSSIA .0572 .1991 147.8 8.45 29.60

ALBANIA* .0041 .2123 3.4 .01 .72

BULGARIA .0213 .2123 8.9 .19 1.88

SEYCHELLES .2214 .2171 .1 .022 .02

ST.KITTS&NEVIS .14 .2255 .1 .01 .009

URUGUAY .11 .2312 3.1 .34 .73

CHILE .09 .2340 13.8 1.24 3.23

ANTIGUA&BARBUDA .23 .2432 0.1 .02 .02

ARGENTINA .27 .2572 33.8 9.13 8.69

MEXICO .10 .2777 90.0 9.00 25.00

MALAYSIA .09 .2876 19.2 1.73 5.48

LIBYA .2937 NA 1.48

VENEZUELA .07 .3151 20.9 1.46 6.59

OMAN .16 .3198 2.0 .32 .64

KUWAIT* 1.04 .3321 1.8 1.87 .59

MAURITIUS .08 .3376 1.1 .09 .37

BARBADOS .22 .3553 0.3 .06 .09

GREECE .28 .3595 10.4 2.88 3.73

Ti/Ni Ni Ti

MER PPP million $ million

GNP GDP persons MER/GNP PPP/GDP

TRINIDAD&TOBAGO .11 .4011 1.3 .14 .51

PORTUGAL .37 .4123 9.8 3.63 4.06

MALTA .31 .4201 0.4 .12 .15

SAUDI ARABIA .4231 NA 7.24

KOREA,REP. .29 .4535 44.1 12.79 20.01

CYPRUS .44 .5372 0.7 .31 .39

PUERTO RICO .5417 NA 1.96

BAHRAIN .31 .5682 0.5 .16 .30

TAIWAN .5785 NA 12.12

SPAIN .64 .5964 39.5 25.21 23.55

IRELAND .60 .6015 3.5 2.10 2.12

ISRAEL .66 .6156 5.3 3.50 3.21

BAHAMAS .50 .7668 0.3 .15 .21

FINLAND 1.03 .7754 5.1 5.25 3.92

QATAR .73 .7938 0.5 .36 .42

NEW ZEALAND .58 .8349 3.5 2.01 2.91

ICELAND 1.47 .8510 0.3 0.44 .22

UK .94 .8737 57.9 54.58 50.61

ITALY 1.07 .8889 57.1 61.24 50.78

AUSTRIA 1.35 .9066 7.9 10.69 7.13

UNITED ARAB E. 1.19 .9252 1.8 2.14 1.67

NETHERLANDS 1.16 .9287 15.3 17.68 14.19

NORWAY 1.55 .9527 4.3 6.67 4.09

BELGIUM 1.21 .9625 10.0 12.08 9.67

SWEDEN 1.45 .9727 8.7 12.63 8.45

FRANCE 1.27 .9838 57.5 73.23 56.54

GERMANY* 1.36 .9857 80.9 109.82 79.54

SINGAPORE 1.07 1.0261 2.8 3.00 2.86

DENMARK 1.61 1.0599 5.2 8.39 5.47

AUSTRALIA .90 1.0720 17.6 15.89 18.86

JAPAN 2.02 1.1281 124.5 251.58 140.41

CANADA 1.08 1.1768 28.8 31.10 33.87

SWITZERLAND 2.41 1.2178 7.1 17.08 8.59

LUXEMBOURG 2.55 1.2242 0.4 1.02 .48

HONG KONG* .94 1.3272 5.8 5.45 7.70

BRUNEI 1.4192 NA .39

USA 1.45 1.4544 257.9 374.35 375.00

VI. CONCLUSIONS

Where does this leave the analysis of assessment rules based on ability to pay, progressivity, and horizontal equity? We can make many allowances for special considerations such as debt burdens, income inequality, or other aspects of poverty. If we are given definite objectives, parameter values can be found by varying exemption levels, setting minimum or maximum levels, setting median income levels of assessment, and so on. With a definite directive, the computing problem is not formidable for reaching a preassigned total.

Although the funds must be collected in US dollars, from the point of view of economic analysis, it seems that burden sharing is better tuned to PPP pricing, on a moving-average basis, for indicating ability to pay. This remark is based on the inference that PPP valuations give a better indication of a country's overall economic strength. It follows that the fundamental ability of a country to draw upon its own reserves or to raise funds in the international capital market will be related to its economic strength more than to the short run valuations that are implied by buying and selling in the foreign exchange markets, where speculation, arbitrary price fixing, central bank or exchequer policy, and many transitory global developments have significant effects.

An argument against PPP estimates is that they are based on inadequate information in some cases and not directly available to many countries. The sample has been expanded to include indirect estimates of several countries. Some interim results could be used while the United Nations Statistical Office is making a comprehensive and detailed study of PPP valuations so that the quality of such information would become available for all countries, on a sound basis. Naturally, this requires a significant input effort, but it is extremely worthwhile. The information that would be produced by such an investigation could be used in many ways, for analysis of world economic conditions, much beyond the present focus on assessments of burden sharing.

The early studies by Kravis, Summers, Heston, and Davenport have indicated possible steps to be taken to introduce income distribution, in addition to income level, in the estimation of international assessments in burden sharing. Any such approach must rely heavily on income distribution data for all countries. This too, as an extension and enhancement of our proposed methods, calls for additional data gathering and information. As in the case of PPP valuations, such fresh data have many potential and valuable uses in international economics. The United Nations Statistical Office would be well advised to extend our knowledge in this direction as far as possible. We should be able, within a few years' time, to widen the scope of our proposed formulas for burden sharing, by considering intranational distribution as well as level of income in assessing individual countries.

Regardless of our views about price valuations across countries and the importance of using information on internal statistics of income distribution, we have demonstrated how one can use presently available data to generate, quickly, assessment values for different assumptions about ceiling values, floor values, degrees of progressivity - all the while preserving horizontal equity.

The other criteria of availability and transparency are also taken into account. We have used published and available data that are well understood among policy makers as well as economic statisticians. Using the materials that are readily at hand, we can tailor assessment programs to policy makers' tastes or needs, not in every dimension but in several. As the underlying economic data basis is enhanced and improved, our formula approach is ready and available to make relevant calculations.


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