metadata: authors: 0: author: International Economics Department
count: Russian Federation,Lithuania,Latvia,Moldova,Kyrgyz Republic,Kazakhstan,Tajikistan,Turkmenistan,Ukraine,Uzbekistan,Estonia,Georgia,Belarus,Armenia,Azerbaijan
docna: 0: docna: Measuring the incomes of economies of the
            former Soviet Union
docty: Policy Research Working Paper
subtopic: Macro-Fiscal Policy,Taxation & Subsidies,Economic Adjustment and Lending,Public Sector Economics,International Trade and Trade Rules,Inflation,Macroeconomic Management,Energy Demand,Energy and Environment,Energy and Mining,Public Finance Decentralization and
            Poverty Reduction
teratopic: Finance and Financial Sector Development
abstracts: cdata!: There is as yet no fully satisfactory
            way to compare income per capita of the former Soviet Union
            with that of other economies. Even more problematic is
            compiling estimates for the separate economies that have
            emerged with the breakup of the Soviet Union. The main
            problem is the isolated non-market economy of the country,
            compounded by the chaotic state of information services. The
            results presented here, while subject to considerable
            uncertainty, are considered reliable enough for their
            primary purpose: to assign the new states of the Soviet
            Union to income categories for Bank analytical and
            operational purposes. The main difficulty was choosing a
            ruble-dollar conversion factor that accords reasonably well
            with the Bank's Atlas method. Official rates cannot be
            used because they are as artifical and misleading as any
            other planned price, meaning that they diverge by a large
            margin from the rate effectively applied to international
            transactions. This study investigated three alternative
            conversion methods, yielding GNP per capita estimates for
            the former Soviet Union for 1990 ranging from $2,440 to
            $3,720. The method judged most reliable (referred to as the
            synthetic Atlas-type conversion factor) gave an estimate of
            $2,870. The figure is somewhat at odds with Atlas estimates
            for the former Soviet Union and other members of the Council
            for Mutual Economic Assistance (CMEA), which may reflect the
            limited applicability of the Atlas methods for historically
            planned economies. Income per capita is calculated for each
            of the states of the former Soviet Union and for the other
            European members of CMEA. The method developed here relies
            on a purchasing power parity bridge from planned to market
            economies. Unlike conventional use of this measure, the
            study uses the relationship between purchasing power parity
            and exchange rates for comparator market economies to
            suggest an Atlas-type conversion factor. The estimations for
            the state of the former Soviet Union have a suggested margin
            of error of plus or minus 10 percent. Incomplete reports for
            1991-92 show large declines in real GDP in all countries of
            the former Soviet Union - as much as 25 percent in some
            cases. It is unlikely that mechanically extending results to
            1992 will yield meaningful results, so this study is just a beginning.
geo_reg_key: 517208,517198,545134,82689,517190,517191
repnme: repnme: Measuring the incomes of economies of the
            former Soviet Union
display_title: Measuring the incomes of economies
            of the former Soviet Union
listing_relative_url: /research/1992/12/699492/measuring-incomes-economies-former-soviet-union
topicv3: Energy and Extractives m1326595 507,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Trade m1326924 698,Taxation m1327615 1121,National Accounting m1504543 3121
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Finance and Financial Sector Development
origu: International Economics Department
no_of_pages: 77
model_extractions: <!-- Start of picture text --> Policy Recearch<br>Soclo-Economic Data<br>WPS 1057<br><!-- End of picture text --> This paper- a product of the Socio-Economic Data Division, Intemeational Economics Department is part of a larger effort in the department to facilitate integration of the historically planned economies, including the former Soviet republics, into the global economy. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Estela Zamora, room S7-136, extension 33706 (December 1992, 70 pages).

There is as yet no fully satisfactory way to compare income per capita of the former Soviet Union with that of other economies. Even more problematic is compiling estimates for the separate economies that have emerged with the breakup of the Soviet Union. The main problem is the isolated non-market economy of the country, compounded by the chaotic state of information services.

The results presented here, while subject to considerable uncertainty, are considered reliable enough for their primary purpose: to assign the new states of the Soviet Union to income categories for Bank analydcal and operadonal purposes.

The rrain difficulty was choosing a rubledollar conversion factor that accords reasonably well with the Bank's _Atlas_ method. Official rates cannot be used because they are as artificial and misleading as any other planned price, meaning that they diverge by a large margin from the rate effectively applied to intemational transactions. This study investigated three altemative conversion methods, yielding GNP per capita estimates for the former Soviet Union for 1990 ranging from $2,440 to $3,720.

gave an estimate of $2,870. That figure is somewhat at odds with _Atlas_ estimates for the former Soviet Union and other members of the Council for Mutual Economic Assistance (CMEA), which may reflect the limited applicability of the _Atlas_ methods for historically planned economies. Income per capita is calculated for each of the states of the former Soviet Union and for the other European members of CMEA.

The method developed here relies on a purchasing power parity bridge from planned to market economies. Unlike convendonal use of this measure, the study uses the relationship between purchasing power parity and exchange rates for comparator market economies to suggest an Atlas-type conversion factor. The estimations for the states of the former Soviet Union have a suggested margin of error of plus or minus 10 percent.

Incomplete reports fcr 1991-92 show large declines in real GDP in all countries of the former Soviet Union - as much as 25 percent in some cases. It is unlikely that mechanically extending results to 1992 will yield meaningful results, so this study is just a beginning.

The method judged most reliable (referred to as the synthetic Atlas-type conversion factor) The Policy ResearchWorking PaperSeries disseminates the fmdings of work under way in the Bank. Anobjectiveof the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily reprcsent official Bank policy.

Produced by the Policy Research Disseminadon Center
Measuring the Incomes of Economies of the former Soviet Union Socio-Economic Data Division (IECSE) International Economics Department Development Economics, World Bank lI _
# Table of Co4Aents

||Page|
|---|---|
|Executi<br>S|ve<br>ummary<br>...........<br>.<br>....................|
|Intro|duction<br>.......................<br>.<br>.<br>.<br>..<br>.<br>.<br>.|
|I.|Problems<br>with Exchange Rate Conversion.<br>.<br>. _5_<br>|
||a. Background ..............<br>.5<br>b. Attempt to Estimate Multiple Exchange Rates .....<br>.. .. .....<br>6<br>c. The Ruble as Regional Currency .....................<br>9|
|H.|SyntheticAtlas-Type<br>Conversion Factor .........<br>.........<br>10|
||a. Role of Purchase Power Parities (PPPs) .........<br>......<br>10|
||b. Attempts Using Regressions .......................<br>11|
||c. Preferred Synthetic Atlas-Type Conversion Factor (SACF) ....<br>12|
||d. More Narrowly Focused SACF .....................<br>13|
|HI.|Implications for Other CMEA Economies .................<br>14|
|IVt|.National Accounting Issues .........<br>.................<br>15<br>|
||a. Basic Data Issues ..............<br>................<br>16|
||b. Estimates of Outside Experts ......................<br>17|
||c.Treatment of Cross-Border Transactions ................<br>18|
|V.|Relationship to OtherStudies ........................<br>19|
|VI.|Directions for Near-Term Work .......................<br>22|
|Table 1:|Basic Indicators ..................................<br>3|
|Table 2:|Composition of FSU's Extra-Union Trade in 1990.<br>8|
|Table3:<br>Table 4:|GDPEstimates of Different Compilers, 1987 and 1990.18<br>Alternative Relativities for FSU in International Comparisons<br>20|
|Annex 1:|Estimation of FSU Economies' GNPs inRuble<br>Terms.28|
|Annex 2:|Conceptual Approach to Converting GNP into US$ for<br>FSUEconomies<br>.<br>.33|
|Annex 3:|Conversion Factors Adjusted by Foreign Trade Price<br>Differentials (FTDs) .<br>......<br>38|
|Annex 4:|<br>Synthetic Atlas-Type Conversion Factor (SACF).<br>40|
|Annex 5:|ICP 1990 Preliminary Results for Austria and the FSU..<br>45|
|Annex 6:|Quality<br>and Diversity<br>Adjustments<br>.....................<br>54|
|Annex 7:|<br> <br> <br>Overlap between ICP 1990 and CIA 1976 Item Prices<br>.68|
|References|.....<br>.<br>......................<br>.........<br>70|
# Executive Summary

At present,' there is no fully satisfactory way to compare<sup>per capita income of the</sup> former Soviet Umon (FSU) with that of most other economies.<sup>Compiling estimates for the</sup> separate economies that have emerged from the FSU, which<sup>is the goal of this study, is even</sup> more problematic. The root cause is that the FSU had a non-market<sup>and exceptionally isolated</sup> economy but matters are further complicated by the chaotic<sup>state of FSU information and</sup> uncertainties about the impact of their common past on the<sup>present economies of the region.</sup> The results given here are inevitably subjec **t** o more<sup>than the usual range of uncertainty but are</sup> judged reliable enough for assigning FSU economies to income<sup>categories of analytical and</sup> operational interest to the Bank.

The main difficulty is in choosing a ruble-dollar conversion<sup>factor that makes sense and</sup> accords reasonably well with the Bank's _Atlas_ method. The<sup>seemingly simple case for using</sup> official exchange rates proves untenable because, like other<sup>planned prices, such rates prove to</sup> be artificial and misleading.

Once an official conversion factor is judged to diverge by<sup>an exceptionally large margin</sup> from the rate effectively applied to international transactions,<sup>Bank procedure (as noted in</sup><sup>_World_</sup> _Development Indicators,_ etc.) is to devise an alternative.<sup>Three alternative conversion methods</sup> were investigated; they suggest a 1990 per capita income<sup>range of $2440-3720 for the FSU. The</sup> method that seems most viable yields $2870, and is referred<sup>to as the 'synthetic Atlas-type</sup> conversion factor" (SACF). This figure is rather at odds with<sup>_Atlas_estimates reported for other</sup> countries of the Council for Mutual Economic Cooperation (CMEA).<sup>For some countries it gives</sup> very low GNP per capita level in percentage to FSU as compared<sup>to that of the other sources</sup> shown in Table 4, e.g., Poland (59% or $1690) for 1990--<sup>and probably even more so for</sup> Bulgaria (69% or $1840) for 1991. Since this may reflect<sup>the limited applicability of theAtlas</sup> method for historically planned economies (HPEs), this report<sup>shows SACF computations for</sup> other CMEA countries. Table 1 (pages 4-5) reports 1990 estimates<sup>of $GNP per capita for each</sup> FSU economy (together with a figure for the entire FSU, for ease<sup>of reference), plus SACF-type</sup> estimates for the other European members of the CMEA, along<sup>with 1990 estimates actually</sup> reported by the Bank based on the _Atlas_ method.

The SACF relies on a purchasing power parity (PPP) "bridge'<sup>to transit from planned</sup> to market economies. Unlike conventional PPPs, however, the<sup>SACF uses the relationship</sup> between PPPs and exchange rates for comparator market economies<sup>to suggest an</sup><sup>_Atlas-type_</sup> conversion factor. To minimize distortions known to arise when<sup>comparing economies at</sup> disparate levels of income, only relationships for market economies<sup>in the same broad income</sup> range (upper middle income) were used for the SACF<sup>bridge.</sup> 'This report reflects information available to the Bank by June<sup>1992, at which time decisions had to be taken on</sup> the subject, for Bank operational purposes. This version is edited<sup>mainly to make the material more undertadable to</sup> a wider audience.
New information is presented on the estimation of GNPs for individual FSU economies. It is suggested that a margin of error of +/-10% arises in apportioning income, in ruble terms, among the 15 FSU economies.

Incomplete reports for 1991-92 show that real GNP of all FSU economies declined, some by as much as 25 per cent per annum. This should be kept in mind when considering the ranking of FSU economies in Table 1, which is based on 1990.2 Moreover, national compilers will need a workshop to digest the information collected and analyzed for this study. Even in the Bank, the task is far from complete (as is apparent from the Bank's new _Stadsdcal Handbook: States of the foimer USSR)._ And the collapse of the Union in 1991 shook reporting procedures as profoundly as the rest of the administrative apparatus.

Finally, these results are compared to other studies, in Table 4 (page 20). The "relativities" given here appear to be well within the bounds of other studies. It is unlikely that mechanical extensions of this study, to 1992, will produce meaningful results. This study is therefore just a start.

2Estimas for 1991 were doveloped after this paper was prepared, by applying GDP growth rates to the 1990 figures prsenMed here for FSU economies, and then scaling all up by the U.S. inflation rate (as measured by its GNP deflator).

ii
# **Introduction**

IMore time will be needed for more definitive results<sup>as well as to sort out unresolved</sup> factual issues. The experience of the more market-oriented<sup>economies of the post-FSU period</sup> should serve as a reality check on historical estimates.<sup>But tentative judgements must be made</sup> now, in deciding the terms under which FSU economies<sup>may borrow from the Bank. Since the</sup> results are inevitably subjec **t** o a large margin of<sup>error, the goal of this report is to allocate FSU</sup> economies to income brackets or categories used<sup>for the Bank's operational guidelines.</sup> The most difficult aspect of assigning FSU economies<sup>to Bank lending categories is</sup> finding a ruble-dollar conversion factor that is broadly in line<sup>with usual Bank practices, or the</sup> so-called _Atlas_ method. Well-known studies can be<sup>quoted that place per capita income of the</sup> FSU over $9000 or under $2000 per capita in 1989-90,<sup>primarily because of differing</sup> approaches to the ruble-dollar conversion factor.<sup>This arises mainly from oft-discussed</sup> 3 issues about how best to convert income estimates from national<sup>currencies to a common numeraire.</sup> A strong ruble and high $GNP per capita for the FSU<sup>(over $9000) emerges from official US</sup> assessments of the FSU (see US ACDA)<sup>4which are based strictly on a bilateral study of</sup> purchasing power parities (PPPs); a weak ruble and a low<sup>$GNP per capita ($1780 in 1989) was</sup> posited by the Houston Summit report of the Bank<sup>and other international agencies, based on</sup> a "back-cast" of the so-called commercial exchange<sup>rate, intrduced in late 1990 (see IMF,</sup> World Bank, OECD and EBRD 1991).5 The bulk of the<sup>report, Section I-r, is devoted to</sup> taidng a position on this issue.

A smaller margin of error is though **t** o surround the underlying<sup>ruble estimates of 1990</sup> GNP for each FSU economy (see page 18). However, subtle<sup>points of methodology become</sup> important at this level since the GNP per capita of the<sup>richest FSU economy is about four times</sup> that of the poorest. For practical reasons, this study<sup>assumes that the same conversion factor</sup> can be applied to each FSU economy. And several<sup>themes are developed in Section IV to</sup> suggest why a less sanguine view may be appropriate<sup>for more recent estimates and projections.</sup> Section V considers the relative position of the FSU and<sup>other economies, as presented here and</sup>

> 3See, for example, a Bank working paper, **_Estimadng_** _Per Capita_ **_Income,_** available on request.

4Data accord with CIA methodology. CIA continues to report<sup>growth rates but no longer publishes dollar e_tmates</sup> for the FSU, given concerns about the resuls<sup>in the present context.</sup>

> 5The Summary and Recommendations to **_the Economy_** _of_ **_de USSR,_** _by_ the _M,_ World Bank,<sup>OECD, and BRD,</sup> report $1780 for 1989 (page 51). Further detail was<sup>provided in the IMP, World Bank, OECD and EBRD 1991 study;</sup> hereafter referred to as JSSE.
in other international comparisons. It concludes that the relativities reported in Table 1 are within the bounds of other studies, considering how others' sources and methods are known to differ from those normally used for _Atlas_ purposes. Table 4 suggests that the results are. distinct from, but not out of line with, other studies that have been made with less, and less current, information.

However, this study is viewed as the beginning rather than the end of the task. It is universally agreed that more work is needed on essentially all fronts if reliable national accounts are to be compiled by all economies of the FSU region. The Bank is working closely with other international agencies as well as new member governments to provide support where needed and within Bank competencies. Section VI suggests some tasks that seem particularly important for clarifying the issue of $GNPs per capita for the FSU economies, and for which significant progress seems feasible within the next year or so.

Table 1 shows the proposed point estimates for $GNP per capita in 1990, for each PSU economy derived from the estimate of $2870 for the FSU as a whole; and comparabl **e** stimates for the other CMEA economies. The dramatic changes taking place in the FSU region make any historical exercise an uncertain guide to where the economies stand today. Apart from prospects of real GNP declines in the 20-50 per cent range in the FSU region, during 1991-92, fundamental changes are taking place in some FSU economies in the structure of domestic prices and the price of foreign currency (exchange rate).

2
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* =_Q2;Ee_--'
# I. Problems with Exchange Rate Conversion

This section reviews the Idnd of exchange rate conversion practices<sup>that normally suffice</sup> for Bank purposes. Conversion by the official ruble-dollar<sup>rate is judged to "egregiously'</sup> overstate FSU's per capita income; and while precedents exist for<sup>using staff estimates to deal</sup> with such problems, the ad hoc approach that has been used elsewhere<sup>seems inad3quate in the</sup> present context. A more refined procedure, referred to as the Synthetic<sup>Atlas-type Conversion</sup> Factor (SACF) is recommended in Section II. The SACF shows<sup>promise beyond the FSU and</sup> conforms with the announced intention of the Bank staff<sup>to explore uses of purchasing power</sup> pareties (PPPs). Its application to other CMEA economies is detailed<sup>in Section HI.</sup>

# a. Background

There are still significant gaps in our understanding of<sup>the accounting and administrative</sup> underpinnings to the formal structure of FSU exchange rates. In CMEA economies,<sup>the official</sup> rate is said to have been a simple anachronism, not unlike the gold parities<sup>of the old par value</sup> system of the West; and a clear overstatement of the value of CMEA<sup>currencies. By</sup> conventional exchange rate methods, then, it is usually considered to<sup>be the upper limit of</sup> plausible estimates. For the FSU, however, it seems to have had<sup>meaning since it was adjusted</sup> annually against a basket of Western currencies; it appreciated against<sup>the dollar in 1986 and</sup> then remained largely unchanged through 1991.

The commercial rate was as artificial as the so-called official rate<sup>that was the only</sup> acknowledged rate until late 1990. Where the official rate of<sup>0.59 rubles per dollar is almost</sup> certainly an egregious over-valuation (yielding per capita income<sup>of $6180), a rate of almost</sup> exactly three times that (the commercial rate was pegged at 1.76 rubles<sup>per dollar in November</sup> 1990 and moved thereafter in tandem with the official rate) may<sup>be just as egregiously under-</sup> valued (yielding a per capita income of only $2070).

The FSU's commercial rate only came into existence in November<sup>1990; it was said to</sup> have been set at a level estimated to ensure that local currency proceeds<sup>would be at least as</sup> high as domestic wholesale prices, for 90 percent of exports (see IMO,<sup>World Bank, OECD and</sup> EBRD 1991, p. 426). This could be viewed as a depreciation, relative<sup>to the erstwhile official</sup> rate, of 300 per cent. Presumably, the rate was more than high<sup>enough, perhaps much more</sup> than high enough, to achieve that result for the bulk of exports.<sup>6That can be easily read to</sup> mean that it undervalued the ruble. The alternative is to argue that<sup>the commercial rate was</sup> more a formal acknowledgment of schemes previously in place to achieve the same result. in terms of foreign trade price differentials (FTDs), which require some explanation<sup>(given below).</sup> 6Analysis of the most detailed file available on PTDs, providing separate information by partner country and S-digit CMEA trade classification, suggests that a rate about twice the<sup>official rate would have satisfied the 90 percent criterion</sup> specified.

_5_
More generally, application of any exchange rate such as customarily used in deriving _Atlas-type_ GNP per capita measures encounters formidable difficulties because of the notable distortions in the PSU price structure, and the resultant extreme disparities from world relative prices.

# b. Attemp **t** o Estimate Multiple Exchange Rates

As a rule, problems arise for the _Atlas_ method when official market interventions drive a sizable wedge between the price of foreign currency (the conversion factor) and domestic prices. Since governments are wont to intervene in foreign currency markets, procedures have been developed to deal with the usual source of 'egregiously' distorted conversion factors, where an over-valued currency is protected by exchange and payments restrictions (see Hte 1990); and the first line of defense for the _Atlas_ method is to construct a trade-weighted average of the multiple exchange rates generated, implicitly if not explicitly, by the restrictive practices. These procedures presume that domestic prices are market-determined, which clearly isn't the case in planned economies like the FSU of 1990. Nonetheless, an attempt was made to follow this approach.

Experimentation with what this study calls foreign trade price differentials (FTs), which in principle should link the commercial to the official rate, suggests a GNP per capita figure of about $3800 for the FSU. These meastires are probably closer to the mark than the official exchange rate, but still too problematic to rely on for _Atlas_ purposes.

An HPE's commercial exchange rate is usually said to differ from its official rate because of the operations of foreign trade organizations (FTOs) and related institutional arrangements that intervened between nonresidents and domestic producers or consumers. Briefly, FTOs were government monopolies for purchasing imports from, and selling exports to, nonresidents. They transacted with domestic economic agents in rubles at prces in line with those set by the plan for domestic agents regardless of the ruble proceeds generated by exports or the ruble outlays required to obtain imports. Such interventions between foreign prices and domestic prices of traded goods are referred to here, generically, as foreign trade price differentials (FTMs).

FSU national accounts included foreign trade at internal prices, meaning after adjustment by FTDs.<sup>7</sup> On that basis, some analysts derive an imputed dollar commercial rate for earlier years,<sup>8</sup> which may remain at about three times the official rate or vary in line with the gap between US and FSU inflation rates. Given past use of internal prices for traded goods, in national accounts, it is arguable that there was no depreciation, just an acknowledgement of the average price for foreign goods, after FTDs, that was implicit in national accounts all along.

> 7The ONP data used for Table 1 include extra-Union exports and imports at foreign prices, i.e., foreign currency prices converted at the official devisa rate.

> 8See, for example, CSO of Estonia, 1991. The same back.casting logic was used in JSSE.

6
Available information on FIDs must be used advisedly in investigating this hypothesis because it only began to appear in official FSU reports in the late 1980s, when foreign trade data were reported at both internal and foreign trade prices. Moreover, there is no Western analog to FTDs, which arise as ce'ntral planners try to fit world prices to those they establish in their plans. In discussions of fiscal policy they have come to be regarded as net indirect taxes; for monetary policy they are often noted as multiple currency practices. The distinction between monetary and fiscal instruments has no meaning for central planners but it does matter in deciding how to value national accounting aggregates in domestic currencies and which conversion procedures are consistent with which hypotheses about FTJ)s.

Table 2 reports an IECSE collation on FTDs. It summarizes foreign trade data of FSU for 1990 by individual partner country and several thousand commodity groups (to the 5-digit level of the CMEA trade classification system); with valuation both according to the foreign and intemal price (as differentiated in the notes to the table). It shows the tumaround on the export side, from tax to subsidy, when minerals are excluded. For nonfuel trade, the picture that emerges is what one would expect with an over-valued currency: the effective rate for imports implies a heavy tax while the effective export rate implies a hefty subsidy, relative to the official rate.

However, FsDs don't explain the gap between the official and back-casted commercial ate for the FSU, as they should given the presumed mechanism for fixing the commercial rate, as discussed above. While it is not yet possible to fully reconcile all data, enough is known to show that the conversion factor appropriate for Bank purposes lies below the official but above the commercial (once it existed) exchange rates.

Analysis of FTDs is complicated in the case of the FSU by the predominance of petroleum on the export side-- and the de facto inclusion of what most economies would record separately as direct taxes or royalties paid to government by oil companies. As a rough compensation, an export-side adjustment of the official rate by F1Vs could be envisaged exclusive of oil; which would imply an overvaluation of only 21 percent, which would produce a GNP per capita figure of about $5100 for 1990. However, that too seems colored by the asyet unexplained taxation of machinery exports, apart from those to high income OECD countries; depending on which further disaggregations of exports one considers, discounts 30, 60, or even higher percentages might seem reasonable. The picture is somewhat clearer when the import side is taken into account. There, the overvaluation appears to average around 60 percent (again ignoring fuels), which suggests a GNP per capita of $3840.

Even this is not unambiguous, however. For example, some discount should be applied to the import-side FTD adjustment for items otherwise subjec **t** o domestic turnover taxes. Once an FMD is applied, there is no separate levying of tumover tax. In this case, the FMD adjustment is around 40 percent, that results a GNP per capita of $4350.

7
Table 2. carposition of FSU 1s Extra-Uhifn Tracde in 1990

|||Exportsf|f|<br>|Inports<br>|aFTDs<br>||Net Exports||
|---|---|---|---|---|---|---|---|---|---|
||UFo-refgn <br>Prices<br>(billions of|_<br> Binternat_<br>Prfces<br>rubles)M|<br>Ff5Ds<br>+* tax<br>Xfrgn pr)|_______<br>Uoroign<br>Prices<br>(billions o|<br>uinternat--<br>Prices<br> <br>f rmbles) (X|<br>- 5<br>_FI_<br>+atax<br>frgn pr)|M_Foreigs-n_<br>Prices<br>(billions|<br> alntemnat<br>Prices<br>of rubles) t|<br>P1M<br>+atax<br>% frgr pr)|
|.,...................................................<br>1-0 TOTAL<br>/t|.............................<br>60.40|....<br>45.63|24|.........................<br>68.80|.............<br>114.10|66|...........................<br>-8.40|......<br>-68.47|46|
|Excluded Trade|-15.86|-15.34|3|-17.03|*36.53|115|<br>1.17|21.19|61|
|DOT TOTAL<br>CI+11)|44.54|30.29|32|51.77|77.57|50|-7.23|-47.28|42|
|<br>..................|.....|.....|..|.....|.....|..|.....|.....|.|
|Hi-Incan OECD|22.39|15.10|33|26.24|43.79|67|-3.85|-28.69|51|
|HPEs|22.15|15.19|31|25.53|33.78|32|-3.38|-18.59|32|
|<br>Other LDCs|<br>3.14|<br>2.52|<br>30|<br>4.59|<br>15.21|<br>231|<br> <br>-1.45|<br>-12.69|145|
|1. FUELS, MINERALS<br>/2|33.59|15.21|55|4.59|3.66|-20|29.01|11.55|46|
|<br>., .......|.....<br>..|...|..<br>.|....<br>....||.....|..|...<br>|....|
|fi-inccseOECD|17.16|7.85|54|2.03|1.76|-14|15.13|6.09|47|
|<br>HPEs|<br>14.57|<br>6.63|55|2.18|1.61|-26|12.40|5.02|44|
|Other LDCs|1.86|0.74|60|0.38|0.30|-22|1.48|0.44|46|
|11.OTHER<br>TRADE<br>(A-> 0)|12.80|15.82|-24|47.56|74.20|56|-34.76|-58.39|39|
|<br>........................|-----|.....|..|.....|.......||...|....||
|Hi-Ircone OECD|5.23|7.25|-39|24.21|42.03|74|-18.98|-34.78|54|
|HPEs|7.58|8.56|-13|23.35|32.17|38|-15.78|-23.61|25|
|Other LDCs|1.29|1.78|**-39**|4.22|14.92|254|<br>-2.93|-13.13|185|
|A. Intermediate Goods /3|6.17|8.28|-34|6.92|10.90|**58**|-0.75|-2.63|14|
|.....................|.....<br>|.<br>**....**.|||||.. ...|.<br>-----... ...**.**|.**..** **..**|
|Hi-lrane CECD|3.13|4.10|-31|4.59|6.68|45|-1.46|-2.58|14|
|HPEs|2.46|3.43|**-39**|1.36|1.78|31|1.18|1.64|-14|
|Other LDCs|0.58|0.75|.29|0.96|2.44|153|<br>-0.38|-1.69|85|
|B. Machinery, etc. /4|5.76|5.13|11|23.70|26.43|12|-17.94|-21.30|11|
|<br>..................|-----....|.....||.. ....|.<br>.....||.. ....|.. .. ..... .. .||
|Hi-Incan OECD|1.06|1.28|-20|11.37|13.86|22|-10.31|-12.58|18|
|HPEs|415|330|20|1169|1157|-1|-754|-827|5|
|<br>Other LDCs|.<br>0.55|.<br>0.55|<br>-1|.<br>0.64|.<br>1.00|<br>57|.<br>-0.09|.<br>-0.45|30|
|C. Foodstuffs /5|0.75|1.82|-144|11.42|20.99|84|-10.68|-19.16|70|
|.............|.....|.....|..|.....|.....||----<br>....|-...-.||
|Hi-Ircam OECD|0.38|0.82|-116|4.58|7.96|74|-4.21|-7.14|59|
|<br>HPEs|<br>0.27|<br>0.69|<br>-159|<br>5.93|<br>8.53|<br>44|<br>-5.66|<br>-7.84|35|
|<br>Other LOCs|<br>0.11|<br>0.32|<br>-201|<br>0.91|<br>4.50|393|<br>-0.81|<br>-4.18|331|
|0. Ccnuner Goods /6|1.42|2.37|-67|9.73|30.80|216|-8.32|-28.43|180|
|.................|.....<br>|.<br>.. .....||.. ...|.<br>.<br>.....||----|.....<br>....**.**|**.**..|
|Hi-lrcome CECD|0.67|1.06|*59|3.67|13.54|269|-3.00|-12.48|219|
|HPEs|0.70|1.15|64|4.37|10.28|136|-3.66|-9.13|108|
|Other LDCs|0.05|0.16|-223|1.70|6.98|310|-1.65|-6.81|295|
|MNe Item:||||||||||
|Excluded imterial services|<br>0.67|0.63|6|0.96|0.93|**-3**|-0.29|-0.30|1|
|Hi-lncomeOECD<br>|0.50<br>|0.53<br>|-6<br>|0.43<br>|0.41<br>|-5<br>|0.07<br>|0.12<br>|-5<br>|
|HKPs|017|010|41|053|052|-2|-036|-042|9|
|<br>Other LOCs|.<br>0.10|.<br>0.06|<br>40|.<br>0.03|.<br>0.03|<br>0|.<br>0.07|.<br>0.03|31|

Scurce: IECSE repackaging of direction of trade (DOT) file availebi by five-digit COA iten ard partner cosntry, via CIR.

/1 As reported via Intelligent Decisions System (IDS).

/2 OfA category 2.

/3 CWA categories 3 - 6.

/4 CMfA category 1.

/5 OEA categories 7 ard 8.

/6 CMEA category 9.

Notes: Foreign trade differentials (FTDs) reflect the difference between trade at foreign ard internal prices. On the export side, positive amoLnts arise Wmen foreign trade organizations (FTOs) receive mre rubles for foreign exchune earned than they wust turn over to dwrestic praioers of the exports, based on plumed intemal pricess; ,iLe negative FTDs ifrdcate that FTOs receive fewer rubles for foreign exchang **e** arned than they mnst transfer to desestic praders. On the lfport side, positive FTDs irdicate that FTOs take in nmre nrbles fran domestic users then they mist turn in to settle (e.g., with V-Bar*) for foreign exchbW obtained; negative ammts (mfinLy for fuels end mnierals) men FTOs absorb part of the higher cost of inports.

FTs colurn for net exports expresses the fiscal revomis generated as a percent of total trade (exports ptus nports) at foreign prices. Excluded trade (the differae beten totals frm DOT ad 1-0 tabLes) is assuned to relate to military and other security transactions but nfay alts cafprise barter truanctions, exclude trars-shfpmnts, etc.

As a sorce of fiscal revems, trade in fuels is about as aefficient" as the avwr for nonfuel. Within nonfuel trade, the favorabLe treatment of industry is apprent fram the limited effort to generate reven fran trade in intenaediate goods or machinery, with foodstuffs and particularly cansuner **goods** providirs nmst of the **net** revenue. FM **'wedges"** between trade **blocs** are eparent within ncnfuel trade, particularly **ken** the treatent of hi-incn CECD econonies is contrasted with that of other historically plamed economies (HPEs). This is dramatic for machinary.

**8**
# c. The Ruble as a Regional Currency

An alternative explanation is that the commercial rate applies only to dollar-zone transactions; and that the official rate is relevant for transactions within the CMEA. Given the dominant role of the FSU in the CMEA, and use of the (misnamed) transferable ruble as numeraire for CMEA transactions, only ad hoc corrections could be considered for Hungary, Poland, etc., since the ruble-dollar rate had to be based mainly on perceptions of FSU's trade partners, transacting under mostly nonmarket arrangements.

No attempt was made here to account for the broken (transferable) ruble-dollar crossrates that became increasingly clear as more CMEA members joined the Bank, on the grounds that the major unknown was the dollar-ruble rate of the FSU. The justification for inaction cannot carry much weight now. Once a uniform ruble-dollar conversion factor is estimated for the FSU, figures for other CMEA members will have to be recalibrated.

The issue was given some recognition in the Bank-sponsored publication, _Historcally Planned Economies: A Guide to the Data_ (see Marer et al. 1992). Building on work by outside exp-rts, the publication develops a uniform ruble-dollar cross-rate by averaging those of aU CMEA members. This does not correct for the systemic undervaluation of the ruble that many experts see in such cross-rates but it makes the point that essentiaUy the same ruble-dollar rate should prevail in all these "markets." Fixing such broken cross-rates for other CMEA countries was beyond the scope of this study, although recognition of the problem should add weight to the case for using the SACF for these economies as well as the FSU. Also beyond the scope of this study but potentially important for 1992 projections, there is a strong analogy in terms of trade flows and valuations, between the collapse of the CMEA and the current uncertainties about what had been interrepublic trade, among FSU economies, and the potential role of the ruble within the region.

A somewhat different approach might make sense for the FSU through 1990, given ambiguities about the role of the official and commercial rates. For example, it may be that the official rate has some meaning in trading among CMEA members, given similar planning systems; while the commercial rate makes more sense for transactions with others. This would imply an average of the official rate (weighted by CMEA trade) and the commercial rate (weighted by the rest of trade), which would produce a conversion factor of 1.13 rubles per dollar and a GNP per capita of $3220.

9
# I. Synthetic Atlas-type Conversion Factor (SACF)

It is Bank practice to seek an alternative to official rates when such rates differ egregiously from effective transactions rates. Until now, altemate conversion factors have only been used by the Bank to deal with temporarily over-valued currencies defended by increasing trade and exchange restrictions. The normal method for deriving such alternates depends on there being some earlier period when the exchange rate was accepted as reasonable, and exchange and trade restriction were lighter. Under these conditions, a fairly objective alternate can be computed by moving the reasonable historical rate forward based on the difference in inflation rates between the country and the United States. This approach cannot work for the FSU since there is no earlier period of rational exchange rates and lighter restrictions.

Having found exchange rates wanting even in this historical sense, there seems no alternative to some use of PPPs in deducing _Atlas-type_ estimates for the FSU economies. This requires some correction for the difference in "scale' between PPPs and Atlas-type conversion factors. The simplest way is to reverse the regression equation used to infer PPP-based from Atlas-type estimates in the 1992 edition of the Bank's _World Development Indicators_ (rable 30), by adding a PPP for the FSU based on its preliminary 1990 submissions to the global International Comparisons Programme (ICP).

However, this method did not give us plausible results (see Ahmad 1992) that led this study to an approach that links PPP data from the CMEA and the global International Comparison Programme (ICP) to obtain a ruble/dollar cross-rate in PPP terms. This is then adjusted from a PPP to an Atlas-type measure by the relation of corresponding measures for available comparator countries.

# a. Role of Purchasing Power Parities (PPPs)

There is a rich literature on "short-cut" methods to deduce PPP-based estimates of per capita income from Atls-type estimates. This section considers how these led to the synthetic _Atlas-type_ conversion factor (SACF) proposed for the FSU, and perhaps other CMEA economies. The common characteristic is that all use the known relationship between the two types of conversion factors _(Atlas_ and PPPs) for some country or countries, plus one of the two for an additional country, to deduce the other for the additional country.

Perhaps not coincidentally, the FSU and other historically planned economies had a tradition of PPP comparisons, within the CMEA. Thus, the practical constraints usually found in relating PPPs to Atlas-type conversion factors are reversed. National compilers and decisionmakers in CMEA countries are used to PPP-based comparisons but not those based on exchange rates.

10
Three sources of PPPs for the FSU were available: as-yet unpublished results of ICP's 1990 exercise linking the FSU to Austria;<sup>9</sup> the 1988 multilateral exercise of the CMEA (see CMEA 1990)10; or a 1976 bilateral comparison performed by the U.S. Government, updated by US price trends (see US JEC 1981). A fourth possibility, a German-FSU comparison<sup>with</sup> a 1988 base, is known to exist but has proven difficult to obtain. In choosing among the available sources, there were strong a _priori_ grounds for relying on ICP; however, the 1990 results of the FSU-Austria comparison proved too partial and tenuous. Hence, pending more complete ICP results for 1990, attention had to focus on the CMEA study, linked with the 1985 ICP exercise by common reporters. Poland was a full reporter in both PPP exercises and serves as the main linkage country. Partial reports for Hungary and Yugoslavia in the CMEA report serve to corroborate the results.

Dollar GNP estimates higher than those obtained by using even the official exchange rate, notably those compiled by US Government, arise from PPP comparisons with advanced economies. This study takes the position that these calculations come in so high mainly because of underlying differences in the treatment of quality and diversity of goods and services. This is usually discussed as a problem of deteriorating quality in HPEs but the case is made here that the problem is as much one of imputing to HPEs the kind of improving quality and diversity that is taken for granted in dynamic countries, but not fully washed out of OECD price measures."1

# b. Attempts Using Regressions

For such reasons, PPP-:tased estimates cannot be slipped directly into a set of _Atlas-type_ estimates. The reasons for systemic differences in these scales, which seem to depend heavily on level of development, are discussed extensively in the literature. What is relevant here is that some form of regression is usually run on _Atlas_ estimates to express them on an ICP scale, or 91tem prices (plus separate notation of so-called quality adjustments) from 1985 comparisons with Hungary, Poland, and Yugoslavia as well as from the 1990 exercise with the FSU are in hand. The remaining details from the 1990 exercise (which also covers Czechoslovaki **a** nd perhaps Romania) should be available by end-1992, as soon **as** Austria has evaluated and processed the incoming data. I lTwo additional bilateral exercises were performed by the FSU, one with Hungary based on 1985 and the other with West Germany **based** on 1988. Summary results of the Hungary-FSU exercise were reviewed.

**'IFor** example, the Volga passenger car that figured in the CIA's 1976 bilateral **US-FSU** comparison<sup>is basically</sup> the same as the passenger car used in ICP's Austria-FSU comparison for 1990. The ruble price for individual<sup>FSU cars</sup> is virtually the same in each comparison, which conforms with the fixity of **planned** prices. While the Austrian comparison of 1990 actually **uses** the same vehicle, the CIA comparison had to match the Volga with some US car. But any US comparator car of 1990 is fundamentally different from any comparator car of<sup>1976, in ways that are</sup> ambiguously **treated** in price indicators of the US and other dynamic economies. Classic proofs **concern** the<sup>introduction</sup> of catalytic convertors, which can be regarded as a quality improvement or a cost increase **for**<sup>the same</sup><sup>**tranwportation**</sup> *service and the change in consumer preference in favor of lighter vehicles as energy costs rise. **These** considerations, and derivative issues like increased investment costs when retooling is the norm, were not relevant in the FSU of 1976-90 and so can distort US but not FSU data, per se.

11
vice-versa. The 1992 edition of _World Development Indicators_ (Table 30) gives a very simple view of how this is done, using _Atlas_ estimates to complete an array of ICP-based figures.

Reversing the process, estimates from ICP's 1990 Austria-FSU comparison can suggest a conversion factor to estimate $GNP per capita on the _Atlas_ scale. It will be some months before complete results are available from the 1990 ICP exercise, for Czechoslovakia, Hungary, Poland, Yugoslavia, and possibly Romania (Bulgaria is joining only for 1993); as well as FSU. In the meantime, the available 1990 data from the Austria-FSU comparison (excluding comparison-resistant items and construction) and full details from the 1985 ICP exercise (which linked Hungary, Poland, and Yugoslavia to Austria) have been blended. These suggest a PPP of about 0.56 rubles per dollar; or a GNP per capita of about 6510 on an ICP scale (referred to international dollars, or 1$). The subsequent regression work yields an Atlas-type measure of $2440 per capita for the FSU in 1990, but the regression is of doubtful validity. This approach is not recommended here for two reasons. Only preliminary results from the 1990 ICP are currently available for the FSU, and none for other HPE participants. Also, such an operationally significant use would not seem appropriate until a sensitivity analysis of the regression technique used for WD192 has been performed, using variants discussed in a working paper prepared on the subject (see Ahmad 1992). This is particularly necessary since the outcome may depend heavily on acceptance of any particular market exchange rate between the dollar and the currency of a "linkage" country, to convert the FSU's GNP into dollars. In effect, blending an exchange rate between two well developed economies (Austria and US) with PPPs linking one of them (Austria) to a planned economy (PSU) exacerbates some arcane but important methodological issues imbedded in PPP arithmetic.'2

# c. Preferred Synthetic Atlas-type Conversion Factor (SACF)

These concerns can be mitigated by using PPP linkages first from the FSU to other CMEA economies and then from those to market economies at roughly the same level of development. There are other CMEA economies, e.g., Poland, who have participated in ICP as well as CMEA exercises;'<sup>3</sup> such double-participation provides a bridge from planned to market economies at roughly the same level of development, within the same (PPP) methodology. Using a PPP bridge to transit from planned to market economies mitigates concerns about fundamental differences between the two conversion scales, with regard to how exchange rates and domestic prices inter-relate. It is the relationship between PPPs and 1<sup>2</sup> For a review of such issues **see** Hill, 1981.

t3 Hungary and Yugoslavia have also participated in **ICP** as **well** as CMEA exercises. However, Yugoslavia was never a full participant (since its trade arrangements with CMUA economies were similar to but outside formal CMEA mechanisms); and Hungary's last participation in the CMBA exercise was in 1983. In place of a 1988 CMEA exercise, Hungary and the FSU conducted a 'dry-run' ICP exercise based on 1985; this put the FSU's GNP per capita at 110134% of that for Hungary, depending on whether Hungarian or FSU expenditure patterns and price structures were taken as the reference.

12
exchange rates for comparator market economies (in the upper middle income group) that is used to suggest the kind of Atlas-type conversions factor the FSU or any other CMEA economy could be expected to have, given its PPP.

Having used the Polish zloty as the bridge from the ruble to the PPP-based "International dollar, " one needs a link from there to the _Atlas_ dollar. The approach taken here is to build the link via other upper middle income economies for which PPPs are available, through ICP.

The basic procedure involves (i) linking the PPP relationship between the currency of the country in question and that of a comparator country, with the PPP-exchange rate relationship of comparator countries for a benchmark year, and then (ii) extending the linked factor, SACF, to more recent years based on the relative inflation of the relevant country against the U.S. inflation, between the benchmark and the target year.

The SACF recommended here relies on the five other middle income countries'<sup>4</sup> who participated in the 1985 ICP exercise (Greece, Portugal, Hungary, 'iugoslavia, and Korea). Using orthodox PPP logic, separate relativities to the FSU were constructed for each of these and a geometric mean, $2870, was then computed. It uses chain-linking procedures common to PPP exercises." For the GNP per capita figures in Table 1, the ruble-dollar conversion rate is the geometric mean of rates derived through this preferred SACF approach.'<sup>6</sup> This approach has interesting possibilities for harmonizing estimates for some other economies (e.g., Mongolia). With the 1990 FSU per capita income at $2870, the implied Atlas-type exchange rate is 1.27 rubles per dollar. That compares with an official rate of 0.59 per dollar and a commercial rate of 1.76 per dollar (for November 1990).

# d. More Narrowly Focused SACF

A more regionally focused variant was also considered, looldng only at Greece and Portugal to avoid possible bias from including other CMEA economies. The 1985 ICP placed "The so-called Gershenkron effect means that countries look richer when perceived through PPP comparisons with rich than poor countries. For this and similar methodological reasons, the scale for PPP and _Atlas-type_ estimates is different, and rather like in reporting temperatures it is necessary to distinguish the scale in which numbers are expressed. Thus, the term, international **dollars** (1$) is often used to identify PPP results expressud relative to the US. By referring only to economies at about the same level of development as the FSU, **those** classified as upper middle income for Bank purposes, the Gershenkron effect should be minimal.

l<sup>5</sup> Given the extensive detail required, PPP exercises have tended **to be** conducted for selected countries at different times, and often with somewhat different methodologies. Connecting these, to produce a chain-linked set of PPPs **for** the maximum set of countries, requires some use of basic national accounting series (e.g., **GDP** growth rates) that are not **strictly** comparable. It is these procedures which are emulated here.

> t See Annex SD/IID **for** a detailed explanation of the SACF approach.

13
them at about the _samce_ level, at 37 and 34 percent of the US, respectively. This compared with figures of 24-30 percent for the economies that are also covered by the CMEA exercise (Hungary, Poland, and Yugoslavia). If Portugal is taken as the linkage country into the _Atlas_ scale, and all (plus the FSU) are moved to 1990 by normal benchmark procedures, Hungary and Poland end up about where the Atlas estimates now put them; and the FSU should be about $2480. If Greece is the linkage country, FSU comes in around $3570; all others becoming proportionately higher. The widely disparate measures underscore what might have been expected: such calculations are subject to quite a range of error.

The geometric mean of the two, $2970, does not differ appreciably from the mean obtained form the preferred SACF, which covers all of upper middle income economies that participated in the 1985 ICP exercise. Including Korea (but still omitting Hungary and Yugoslavia) would yield a somewhat higher figure.

# m. Implications for Other CMEA Economies

The problem of estimating a ruble-dollar conversion factor is not new. It was noted as other CMEA members joined the Bank, partly because trade with the FSU loomed so large in CMEA trade (and the so-called transferable ruble was the ( .-- MA's unit of account) but also because of common traits in the exchange rate and domestic price regimes of CMEA members. And while the collapse of CMEA, in 1991, is formally beyond the view of this study, it should be recognized that this too is a shared experience that is likely to affect Atlas-type conversion factors. The question arises, then, whether SACFs would not be more appropriate for at least some, and possibly all, other CMEA economies.

Time constraints have not permitted the kind of detailed review of FDs, etc., that was conducted for the FSU, for each other CMEA member. At the same time, it was possible to compute the $GNPs per capita that would result from application of SACFs to other CMEA members, obtaining revised estimates of GNP per capita that are consistent with the new estimate for the FSU. The revisions, presented below (and in Table 1), are invariably upwards, suggesting that all CMEA currencies were undervalued. Relative to the Bank's currently published estimates of 1990 GNP per capita, the implied upward adjustment ranges from a few percentage points for most to 16 percentage points for Poland.

The comparator countries used for calculating SACFs for Poland and Hungary are all the upper-middle income countries that were included in the 1985 benchmark ICP exerciseHungary, Yugoslavia, Portugal, Korea and Greece. For East European countries excluded from the 1985 ICP but included in the 1988 CMEA price comparison, i.e., Bulgaria and Czechoslovalda, the relationships with the above comparator countries were indirectly established through Poland, which was included in both the ICP and CMEA comparisons. For Romania, which was included only in 1975 ICP, the relationships with the comparator countries were indirectly determined through Hungary, which was included in both 1975 and 1985 ICP.

14
||GNP Per Capita, Dollars, 1990|
|---|---|
||Previous Atlas Measures<br>SACF Estimate|
|Romania|1,640<br>1,750|
|Poland|1,690<br>1,960|
|Bulgaria|2,250<br>2,400|
|Hungary|2,780<br>2,930|
|Czechoslovalda|3,140<br>3,170|
|FSU|NA<br>2,870|

It seems likely that the necessity for a shift to SACF will become more apparent when 1991 estimates are being prepared. Tentative figures suggest that "egregious" undervaluations will become more widespread (certainly affecting Bulgaria in 1991); that declines in nominal per capita income will far exceed what can be explained by real output ?~clines, if standard _Atlas_ methodology is maintained. However, a separate study will have tk be prepared on this.

# IV. National Accounting Issues

The break-up of the FSU presents a rare set of problems, in estimating the relative per capita incomes of constituent states, even in ruble terms. To deal with these, the Bank commissioned two independent compilations of national accounts estimates in rubles'<sup>7</sup> to check the intemational comparability of estimates provided by national authorities to Bank missions. While further work with national compilers is essential, particularly for assessments beyond 1990, the margin of error for ruble figures appear minor compared to uncertainties regarding the ruble-dollar conversion factor--in that year.

Even with the two commissioned studies, considerable work was required to assemble national accounts for each FSU economy. Positions had to be taken on traditional concerns about FSU national accounts but, insofar as possible, these are documented elsewhere (see Steinberg 1992) or relegated to Annexes to this report, in order to focus on operationally relevant concerns about transition. As the Union-wide central planning process decayed, more than the relevance of the information generated by the p:ocess declined. There is growing 171a addition to Steinberg 1992, the Bank commissioned a study by the US Census Bureau's Center for International Research, which provides 1987-90 esfimates using income-ouday approaches. Complete results are due by October 1992.

_15_
evidence that the reliability of such information also eroded, so that trends in indicators, from 1987 to the present, may represent genuine economic changes or effects of incomplete reports with new biases. Hence, the starting point for this study was 1987 and trends since then were analyzed with an eye on likely statistical distortions.

# a. Basic Data Issues

It is generally recognized that available sources and methods do not, for the FSU, conform with the UN System of National Accounts (SNA). The problem is more complicated however, since the new nations themselves have had little time to compile and analyze the available data, particularly information regarding so-called Union-wide enterprises and activities, which encompasses the bulk of foreign trade and defense-related activities. This may explain why data they submit to the Bank and other international agencies differ from data available to IECSE from the old FSU central records, sometimes by analytically significant amounts.

As republics distanced themselves from the Union, as penalties for noncompliance lost force, economic agents seem to have altered their reporting behavior in ways that color seemingly objective indicators for 1990-91; the picture is even cloudier when preliminary reports and projections for 1992 are considered. Nor is certain that such problems will ever be resolved for 1992--and 1993 will be problematic unless supporting actions occur soon.

The nations emerging from the FSU did not, as republics within the Union, have full access to the information used by Union-level planners; even today, it is not clear that the authorities for new nations have received and had time to digest the relevant information. To an unusual extent, staffs of international agencies have had to help national authorities assemble and analyze basic information. This study benefited from essentially all such international and national efforts, and relied heavily on data collected by Bank missions to the 15 economies.

National accounts brought back by missions tend to be based on the Material Product System (MPS) traditionally used by CMEA economies. Adjustment to international standards of the SNA have tended to be limited to 'bridge tables" showing major adjustment items from an expenditure approach, to move from the Net Material Product (NMP) of the MPS to the Gross Domestic Product (GDP) measure of the SNA. Major adjustments are addition of depreciation (since NMP is a "net" and GDP is a "gross' concept) and the bulk of services (which MPS ignores because they are not "material"). This explains the paucity of details, for most FSU economies, under GDP measures reported in the Bank's new _Statisdcal Handbook: States of the former USSR._ MPS concepts may have been the same throughout CMEA but there were noticeable differences in practice.<sup>18</sup> Such expenditure-side bridge tables are not well designed to identify

> t8 See, for example, the country practice notes in Marer et al.

16
differing national practices and do not permit the kind of cross-checks normally built into national accounting, by reconciling estimates from the sources (production) and income with uses (expenditure) approaches. This issue is particularly important for the PSU, given uncertainties about measurement of defense and security activities, informal markets, fiscal interventions (such as the FTDs), etc.

# b. Estimates of Outside Experts

As useful as the bridge tables are as a sign of the move towards SNA, they provide only qualified indications of SNA measures for the FSU. This was tacitly recognized by FSU compilers when they entered into detailed discussions with the US Bureau of Census' Center for International Research (CIR) about CIR's estimates of GNP for the FSU as a whole, which are built up from detailed income and outlay approaches rather than adjusting NMP with summary bridge items. While experts from the two governments were iterating towards comparable estimates, there were still noticeable differences for the FSU as a whole and clear signs that the bridge tables for individual FSU economies, which was not then an issue, would not necessarily show the same GNP/NMP relativities as prevailed for the whole.

Possible differences in choosing basic sources and methods were gauged by following two independent estimation procedures for compilation of GNP estimates in nominal rubles. One extends CIR's work to the 15 FSU economies, for 1987-90. The other provides a detailed, input-output, analysis of each FSU economy for 1987. The latter, prepared by Dmitri Steinberg of Intelligent Decisions Systems (IDS), includes adjustments from CMEA-style MPS accounting to more conventional SNA national accounts and extensive documentation (available upon request from IECSE). The attraction of this study is that it harmonizes sources and uses approaches, as well as the income approach, to measuring GNP.

This was extended forward to 1990 using national accounting time series collected during missions. For eight FSU economies (Estonia, Latvia, Lithuania, Turkmenistan, Tajildstan, Uzbekistan, Kyrgyzstan, and Ukraine), IECSE staff participated in Bank missions to promote harmonization of the benchmark and mission compilations. This combination of sources was taken as the reference point for the estimates used in this paper.

The second study supported CIR's effort to extend its FSU estimation procedures to the 15 individual FSU economies. CIR relies heavily on financial statements like reports on the cash income and outlays of the population. Its results (shown in Table 3) differ somewhat from IDS results in the common year, 1987; differences tend to increase as one moves towards 1990 (IECSE = 100). The correlation coefficients between the four set of data are all virtually one.

17
Table 3. GDP Estimates of Different Compilers, 1987 and 1990 (IECSE **=** 100)

||C|IR|**OFF**|**ICIAL**|<br>**Pla**|**nEcon**|
|---|---|---|---|---|---|---|
|**Country**|**1987**|**1990**|**1987**|**1990**|**1987**|**1990**|
|FSU|96|101|..|..|93|95|
|Armenia|98|99|98|97|99|96|
|Azerbaijan|101|112|100|99|103|98|
|Belarus|96|103|..|99|105|100|
|Estonia|99|82|95|95|91|89|
|Georgia|100|109|104|101|99|101|
|Kazakhstan|96|82|..|82|91|83|
|Kyrgyzstan|97|98|94|95|94|93|
|Latvia|104|96|122|100|96|98|
|Lithuania|97|93|96|90|100|93|
|Moldova|101|97|95|97|104|98|
|Russia|99|102|..|97|95|95|
|Tajikistan|100|107|97|94|95|99|
|Turkmenistan|98|96|..|93|95|92|
|Ukraine|103|102|99|100|99|98|
|Uzbekistan|96|95|..|..|93|92|

## Sources: CR: US Census' Center for International Research.

Official: Reports to World Bank as reprinted in the Statistical Handbook of the Former USSR. PlanEcon: Planlcon Report, Vol. VIII, March 27, 1992.

The use of two independent estimation procedure adopted for this study (together with estimates reported by the private concern, PlanEcon, for ease of reference) is regarded as an adequate indication of the range of uncertainty surrounding the nominal ruble accounts. There is less certainty, however, about price indicators. Since constant price national accounts are usually computed by deflating nominal values, distortions in price indices can affect estimates of growth rates. This seems to have become an increasing problem after 1989 and will add a major element of uncertainty to estimates of $GNP per capita by 1992.

.

# c. Treatment of Cross-Border Transactions

What had been a nation is now 15 distinct economies bound together, at least in the nearterm, by complex webs of interdependence spun by decades of central planning. After the comparatively simple task of apportioning extra-Union trade among the 15, trade among what were subnational units must be reclassified as cross-border transactions. But the value assigned to such transactions depends heavily on how one interprets, for each of the 15, past FSU 18
practices like segmenting markets and differentiating prices to insulate domestic economic a,ents from the pressures of foreign markets. This is not a trivial matter, since merchandise trade among FSU economies was about 2.5 times as large as their combined trade with the rest of the world (not unlike the importance of trade among economies of the European Community, relative to trade with others). A decision to value intra-PSU trade at prices prevailing for extraregion trade would shift positions of several FSU economies, dramatically.

The issue has no practical meaning for GNP of the FSU as a whole, in rubles or dollars, but may affect how the GNP is divided into GNPs for each economy, depending on how the GNPs are compiled in ruble terms and how they are converted to dollars. The answer hinges on whether one assumes the successor to inter-republic trade will be rather like its predecessor, like FSU trade with the rest of the world, or some hybrid shaped by the emergence of a regional currency and/or payments zone. IDS was asked to prepare its study of ruble-based accounts on the assumption that inter-republic should be valued at foreign trade rather than internal prices. However, the implied redistribution of income among FSU economies was not actually pursued for Bank purposes, in large part because of its uncertain effects on choice of conversion factor.

For practical reason. this study assumes that the same conversion factran be applied to each PSU economy. Union-wide tax/subsidy mechanisms certainly had a different impact on each, and some (e.g., FTDs) can be interpreted as being equivalent to distinct multiple exchange rate practices in each PSU economy. The demise of such Union-wide mechanisms ends a real resource transfer mechanism, which will reduce GNP for some and raise it for other FSU economies, by several percentage points. This study regard such issues as a concern in projecting trends through 1992 but not of compiling 1990 estimates. But it is worth noting that decisions about conversion factors cannot be made independently of initial decisions by national accountants, about how they will value transactiors. There is no fully satisfactory way to estimate an Atlas-type conversion factor where market forces are thwarted; and a clearer picture of regional economic tendencies will be needed before much can be said about the 'rank" of these economies in today's world.

# V. Relationship to Other Studies

As a final step, the relative position of FSU economies recommended here was compared with "relativities" suggested by other studies. Those aiming to compare economies in terms of GNP or GDP per capita (which differ little for the FSU) are given in Table 4, with estimates for aU other economies expressed relative to that for the FSU (FSU= 100). Major differences seem about what would be expected given differences in methods (e.g., between PPPs and exchange rate conversions) or time. Widtin the limits of available documentation, the estimates proposed here do not appear as outliers; it is not unreasonable to sugges **t** hat they may represent the consensus. Correlation coefficients have been estimated between the data sets. The level of correlation for SACF and Atlas was high except when compared to UNSO (0.66 and 0.70) and to USG-2/2 (0.88 and 0.89). The rest of the correlation coefficients is higher than 0.94.

19
Table 4. Atternute Retativities for fawmer Sovit Union In intemetimret C.petam _of_ UNP/6 _Per_ Cepita (FSUlIOG)

|Year<br>*<br>Source<br>market Econem|1967<br>M*<br>RI<br>ie|1968<br> USG-2I|1989<br> WSOI|199<br>USG-<br>|1990<br>2<br>WDIPP<br>D|1990<br>P<br>.Bae|1199<br>* WHO P|0990<br>lueccr|1990<br>U CIR E|1990<br>ccMInh|190M<br>taletin Itl|99 19<br>farnw|90<br>SACF|199<br>IM<br>AtlasI|1991<br>SACF|1991<br>Atlas|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|United States<br>Gonuny<br>Austria|294<br>247<br>206|225<br>225<br>160|419<br>356<br>297|239<br>176|344<br>262<br>0<br>...|<br>..<br> <br>..|..<br>..<br>.8..|..<br>..<br>.|..<br>..<br>.|550<br>480<br>48<br>...<br>4|346<br>...|370<br>282<br>255|<br>759<br> <br>77<br> <br>664|<br>759<br>77<br> <br>664|<br>836<br>87<br> <br>755|836<br>876<br> <br>755|
|Greece<br>|...<br>|59<br>|<br>104|<br>|lie11<br>|<br>...<br>|...|||||127<br>|209<br>|209<br>|<br>241<br>|<br>241<br>|
|Korea<br>Ptt|79|45<br>44|...<br> <br>75|61<br>|116<br>128|<br>......<br>|||...|124||121<br>138|188<br> <br>171|188<br> <br>171|235<br> <br>208|235<br> <br>208|
|oruga<br>Turkey|...<br>...|<br>14|<br> <br> <br>31|<br>.<br>|<br>SI<br>81.|<br>..<br>|...<br>..|||<br>41|...|<br>80|<br> <br>57|<br> <br>57|<br> <br>67|<br><br> <br>67|
|Other HPEs|||||||||||||||||
|GDR<br>Czechoslaov<br>|132<br>kia<br>129<br>|127<br> <br>101<br> <br>|<br>227<br> <br>74<br>|<br> <br>_89_<br>|..<br> <br>IS<br> <br>|188<br>|0<br>|||.|.<br>..|37<br>|<br>10<br> <br>|<br>11<br>|<br>06<br>|<br>9|
|Yugoslav<br>|a 82<br>|<br>29<br>|63<br> <br>|58<br> <br>|<br>82<br> <br>|129<br>|<br>|||.<br>|.<br>|91<br>|<br>107<br>|102<br> <br>|<br>..<br> <br>||
|Hurory|76|77|<br>55|<br>67|<br>100|15||||70<br>|<br>..|109|102|<br>97|<br>10|100|
|<br>Bulgaria<br>|79<br>|68<br>|74<br> <br>|<br>61<br> <br>|<br>127<br>|12<br>|9<br>|||**56**<br>|..|96<br>|<br>84<br>|<br>81<br> <br>|<br>76<br>|<br>68<br>1/<br> <br>|
|Rurwia<br>Potard|s0<br>68|58<br>54|<br>47<br> <br>46|<br>35<br> <br>48|109<br> <br>73|57<br> <br>86|<br>|||42<br>43.<br>43|.<br>|65<br>79|61<br>68|<br>**56**<br> <br>59|**58**<br> <br>71|<br>s0<br> <br>68|
|FormrSoWet<br>U|<br>100|<br>100|100|<br>100|100|100|100|100|100|100|<br>100|100|100|100|100|100|
|Estonia<br>|..|..|...||.||119<br>|137<br>|121<br>|126<br>|<br>142<br>|139<br>|145<br>|145<br>|142<br>|142<br>|
|Latvia|...||||...|..|124.|129|120|112|125|125|125|125|126|126|
|Ri|||||||119|119|121|117|**15**|118|<br>120|<br>120|<br>119|119|
|ussa|||..||||<br>|<br>|<br>|<br>|**¶**<br>|<br>|<br> <br>|<br> <br>|<br> <br> <br>|<br>|
|BelmruB|||||||125|114|112|118|105|115|108|108|<br>115|115|
|<br>Lithuania||...|<br>|.....|...|..<br>...|<br>99|<br> <br>105|<br>100|<br>124|<br> <br>125|<br>98|<br>108|<br>108|<br> <br>100|<br>100|
|Khk|||||||70|79|<br>70||97|69|91|91|91|<br>91|
|azastan<br>|...||...|<br>...|||<br>|<br> <br>|<br> <br> <br>|<br>...<br> <br>|<br>|<br>|<br> <br>|<br> <br>|<br> <br>|<br><br> <br>|
|Ukrafre<br>||..|...|..<br>...|||94<br>|<br>90<br> <br>|<br>91<br> <br>|<br>93<br>|86<br>|94<br>|<br>87<br> <br>|<br>87<br> <br>|<br>87<br> <br>|<br>87<br> <br>|
|Notd6<br>Aif|..|...|<br>...|<br>...|||92<br>76|<br>86<br>81|<br>**82**<br>78|84|64<br>126|77<br>S1|<br>83<br>83|<br>83<br>83|<br>80<br>80|<br>80<br> <br>80|
|mvna|||.|||.....||.||...||<br>|<br> <br>|<br> <br>|<br>|<br>|
|Georgia<br>|..<br>|.|.||||79<br>|79<br>|80<br>|...|112<br>|74<br>|<br>74<br>|<br>74<br>|<br>61<br>|<br>61<br> <br>|
|Turkmeonista|n..|..|.||||60|**58**|60|...|75|56|59|59|63|<br>63|
|Azerbfjiani|||||||61|59|63||52|62|57|<br>57|62|<br>62|
||.|.|.||||<br> <br>|<br>|<br>|...|<br>|<br>|<br>|<br> <br>|<br>|<br><br>|
|Kyrgyzstan<br>||.|..||||ST<br>5<br>|54<br>|52<br>|...|59<br>|56<br>|55<br>|55<br>|57<br>|57<br> <br>|
|Uzbekfstan|...|..|.||||6|4|4|..|**60**|45|47|47|50|<br>s0|
|Tajfkistan||...|I<br>...|...|...||45|41|41|...|**53**|**36**|**39**|**39**|39|<br>39|
|Sources:|||||||||||||||||
|UNSO<br>Unilt<br>W4OAUNICEF<br>The L<br>USG-2<br>UMft|edNatfa<br> <br>oaolrw C<br>edStates|w Statis<br>risis In<br>Oovern|tical Of<br>Health a<br>annt,<br>Aim|fice, Di<br>rd the<br>s Contr|stributi<br>Need_for_<br>ol and Di|on of<br> Inten<br>wwaa|WorldM<br>autioa<br>nout<br>Ap|P1970<br> Suppt,<br>nocyAC|.89;/c<br> <br>Tab<br>AWA)<br>Wo|ornvers<br>le 1.<br>.<br>rldMilita|fon byPri<br>ry REpe|ce<br>Adju<br>nditur<br>|sted<br>Ra<br>es<br>**anid An**|<br>tes<br>of E<br>**m**Tran|xcha<br>sfer|rige<br>(PARE)<br>s|
|HDR<br>IMite|d Natio|m. Dewlo|pmwIt<br>**Pr**|**ogram**I|Sini<br>Oe|valop<br>f|m<br>l Rep|ort (attri|bution|to Pew|nWhrldTa|bLes).|||||
|<br> <br>koI-PPPWorl|<br>d<br>BankWo|<br>rtd<br>Dew|<br> <br>tap<br>unt|**,** <br> Report 1|<br> <br>992, Ta|<br> <br>ble 30.|<br> <br>baseda|<br> n"con|<br>sistentf|<br>ted" ICP|<br>Phasell|-Van|d regre|ssionfi|t<br>for o|thers.|
|Economist<br>Edit|ion<br>of_Ju_|_ly_11-1|7<br>1992<br>|<br>age<br>26|(dhar|).|||||||||||
|<br> <br>bootitn Bori<br>lIIare Vos|<br> <br>s<br>Bolot<br>ros<br>Ekon|<br>fn,<br>"Rin<br> <br>mifki<br>*N|,<br> <br> <br>g<br>out<br>the<br>o<br>o 4-6|<br> <br> <br> <br>ld,<br>br<br>992; m|<br>feg<br>in th<br>smo|<br>e<br>nhw|,"<br>in Bu|sinhs<br>f|s<br>nex-U|SSR,<br>J~a|may<br>1992||||||
|y<br>D.Ban* Deut|y<br> <br>sche<br>Ba;<br>|<br> <br>*<br>repor<br>|. ,<br>t<br>on "Rdi|<br>itdln<br>E|<br>g<br>aster<br>E|n<br>umWo|, 1991<br>M|<br>tarh|||||||||
|SACF<br>Synth<br> "|etic<br>Att<br> <br>|<br>as-tv<br>C<br> <br> <br>|pa<br>owrsi<br> <br> <br>|on<br>Fact<br> <br> <br>|or<br>dawlo<br> <br>|pd<br>by<br> <br> <br>|'<br>the<br>Wo<br> <br>|rld<br>Bank<br>'<br>|'sSoef<br> <br>|o-Ecn<br> <br> <br>|anlc<br>Data<br>Di<br> <br>|visio<br> <br>|n<br>|<br> <br>|<br> <br>|<br>|
|_I/An_except|foatO<br>con|<br>versi<br>f|on<br>actor<br>wa|<br>s<br>used|.<br>Other|wise<br>Bu|,<br>lgar|as<br>_WP pa_|_r_<br>capi|ta<br>woul<br>|<br>ew ftL|tn<br>to 4|X of t|e<br>FSU<br>f|<br>igure<br>i|<br>n 1991.|

20
Until recently, studies by the U.S. Government, here referred to as USG-2, were about the only recognized source of estimates concerning the relative economic size of the FSU. While estimates for the FSU (and US-FSU relativities) were unaffected, there was a major change in USG reporting about other CMEA economies, in 1991. USG sources report _Atlas-_ type estimates for most economies but have relied on PPP-type estimates for CMEA economies. In 1991, USG shifted from PPP estimates it specially commissioned to those in line with the 1985 ICP exercise, and it is the latter that are reported in Table 4 as USG-2, for CMEA economies other than the FSU.'<sup>9</sup> A particularly sophisticated PPP, the so-called adjusted factor cost method, has been used throughout for the PSU, which in any event did not participate in ICP before the 1990 exercise.

In down-sizing CMEA economies (other than the FSU), USG sources noted that these economies operate in a branch of ICP (called Group UI) that uses "quality adjustments." Little was known about these adjustmentse at the time and USG conjectured that this explains the down-sizing. In late 1991, however, ICP experts in Group II prepared a report on quality adjustments in Poland's 1985 ICP work; and since then, &.e Bank has been provided with itemlevel detail on prices and quality adjustments for Hungary and Yugoslavia as well as Poland. The details show that PPP-type GNPs per capita would rise perhaps _5_ percent, or negligibly as percentage points of the US figure, if such quality adjustments had not been made.<sup>2</sup> " Basicaly, the present study implies that a similar down-sizing would be appropriate in USG estimates for the FSU. In effect, the FSU is the last PPP-based figure in the USG column of Table 2, which is otherwise essentaly _Atlas-type_ estimates. This mixing of scales is the main explanation for the differing relativities between the FSU and other historically planned economies, between USG-2 and SACF-based figures compiled for this study.

The UNDP's _Human Development Report_ (HDR in Table 4) gives GDP per capita estimates that are based broadly on USG sources and methods, for CMEA economies. However, unlike USG, the underlying source (Penn World Tables) reports PPP-based estimates for aUl economies. These estimates can be compared with SACF only for economies at broadly similar levels of development; for higher income economies, note that HDR results are broadly in line with the Bank's published recalibration of its Atlas-type estimates to a PPP scale (WDIPPP) in Table 4.

1<sup>9</sup> Since Yugoslavia was not a CMBA member, Adas-type estimates were used throughout in USO reports.

20They are discounts applied to observed prices where ICP experts judge that there are intrinsic differences between items actually available in two economies, for international comparison. The practice is in fact widespread in ICP exercises in developing economies; the 02 exercise differs mainly in its systematic approach to such adjustments.

21There is a deeper problem of radicaUly different sample frames for comparison items, between the Group 11 and OECD branches of ICP; and work initiated for this study stongly suggests that this could be a significant source of *quality adjustment." However, this reflects inherent differences in the economies and goes to the core of the so-called Gershekron effect and why PPP and Atlas-type conversion factors differ.

21
WDI-PPP recasts figures from the 1992 edition of the Bank's _World Development Indicators (WDI),_ to FSU= 100. The technique used is described in Ahmad (1992).22 The WDI did not report a figure for the FSU; for the present purpose IECSE used a preliminary PPP from the 1990 Austria-FSU work taking place under ICP auspices (somewhat under $7000, moving from schilling to dollars at prevailing exchange rates). The figures for other historically planned economies are as reported in WDI and have not been adjusted to reflect proposed revision by the SACF method.

The United Nations Statistical Division (UNSTAT) is publishing its estimates of world GDP, with country-level figures converted with its Price Adjusted Rates of Exchange (PARE). PARE is a variant on Atlas-type conversion; the main difference being that PARE relies on a longer-term averaging of apparent changes in real exchange rates. Country-level figures computed in this way are used in decisions of the UN Committee on Contributions. For the FSU, PARE seems to accept the official rate, which this study considers egregiously overvalued.

Two studies separately by Russian economists, Bolotind and llliarnov,4 also show relative GNP per capita levels for FSU republics although little is known about their sources and methods. Bolotin describes his work as a PPP study and his FSU-US relative in GNP per capita level parallels that inferred from WDI-PPP, as noted above. llliarnov's is likely to be a PPP study as well and differences between Bolotin and Illiarnov, for individual PSU economies, p:obably reflect differing ruble-based per capita GNP estimates rather than conversion issues. It is almost certain that each uses a single PPP estimate for the FSU as a whole, since there is no evidence of that the detailed price comparisons required for PPPs have been made at the republic-level. The main difference, relative to SACF figures recommended here, is that Bolotin ranks Arnenia and Georgia much higher (and Lithuania and Turkmenistan somewhat higher) than we do.

A recent survey of Eastern Europe, by Deutsche Bank, seems to have used commercial exchange rates. This would explain not only why the FSU slides so far down the relativity scale but also why Bulgaria in particular looks so high in 1990, before the sharp depreciation of 1991.

# VI. **Directions** for Near-Term Work

All data used in this study are from official FSU sources, although some of it was obtained indirectly, as a by-product of commissioning independent evaluations of national accounts. It is not certain that national officials in all 15 economies emerging from the FSU have, and have digested, all the information obtained for this study. For that reason, a special

## 22Availab1e on request from IECSE.

**NSee** Bolotin, 1992.

**24Se** Mliamnov, 1992.

22
effort is being made to assemble as much as possible into Supporting Documents (SDs) for this study, which would be provided to national compilers as soon as possible.

The issues discussed are complex; and few of the FSU economies have national compilers with much experience with the type of macroeconomic analysis t is study is designed to support. Hence, a workshop for relevant authorities from thc 15 FSU economies, to discuss these matters in greater detail, should be a high priority for the Bank. Quick estimates for 1991 were devised to initiate the Bank's FY93 operational guideline exercise but these estimates are subject to particularly wide margins of error. And the continued, probably sharper, decline experienced by most of these economies in 1992 means that an assessment of Bank lending terms based solely on historical standings could be misleading. A Bank effort to help national compilers with "transitional' technical assistance is urgently needed.

The two major studies of ruble-based national accounts (IDS and CIR), are available separately from IECSE. Preliminary indications are that although IDS relies more on industry reporting and CIR on financial accounts, the two reach much the same results. This is not only reassuring for the historical period; it suggests a form of cross-checking that should prove useful during the transitional period ahead.

23
# ANNEXES

2 lv~~~~~~~~~~~~~~~~~~~
# Annexes

# General Explanation

Annex 1 describes how IECSE combined the IDS 1987 benchmark results with information obtained by Bank and Fund missions, in order to produce tentative time series through 1990 and sometimes 1991, for use in this study pending CIR's final report. One advantage of IDS' 1-0 approach is that it permitted the unwrapping of the residual category commonly found on the expenditure side of material product system (MPS) accounts, covering both government and the resource balance. This revealed an important differences in some cases, notably the treatment of FTDs among FSU economies.

Annex 2 places the _Atlas_ method in a conceptual framework, identifies assumptions that don't seem applicable in the present case(s), and explains conceptual refinements that are should mitigate the problems. It also provides a rigorous explanation of the conceptual framework lying behind the traditional _Atlas_ method and then attempts to identify where its applicability seems doubtful, in the case of economies with pervasive price controls.

The fact that FTDs affected individual FSU republics differently suggests that the economies emerging from each republic begins with their own "tailored" set of multiple exchange rates. Beneath what may seem like accounting issues, there were genuine transfers of resources, among the republics that formed the FSU; dissolution of the Union severed the FTD transfer mechanism. The significance of this depends on the extent to which each economy depended on inter-republic trade, as well as the extent and speed of transition to market mechanisms in each. Basic information on this is available in the study by Michalopoulos and Tarr (1992). Annex 3 recasts the underlying data to suggest how ruble-dollar conversion factors would look if each FSU economy's trade, with each other (inter-republic) and the rest of the world (extra-Union) were adjusted to just compensate for removal of FTDs. This is suggested by the variety of import and export rates computed separately for each FSU economy. The ruble-dollar rate implied by the SACF is shown across-the-board, as the rate for nontraded goods. New tools will have to be developed before the incidence of terninating Union-wide FTDs can be gauged.I Depending on the path taken in the coming months, it should be possible to focus on one of the "impact" statements implied by this study or to prepare somewhat more realistic "incidence" estimates using a regional input-output framework being developed as an outgrowth of this study.

Annex 4 details the SACF procedures underlying the estimates ultimately recommended in this study, which links HPEs to the rest of the world through a combination of dollar exchange rates for other upper middle income economies benchmarked in 1985, the 1985 ICP lAn integrated framework of input-outpu **t** ables, including bilateral trade flows among the 15 FSU economies, is in preparation. The proposal document is available on request.

26
results for these two linkage economies; the 1988 PPP exercise of the CMEA, which included the PSU; plus relative growth and inflation measures to move PPP results forward to 1990.

Annex S describes the preliminary details available from the 1990 ICP exercise, for the FSU and Austria. It explains how the matched item prices were averaged below the level for which expenditure weights are available; and the effects of using 1985 weights (and sometimes to rely on Austrian weights, where detailed FSU weights were not yet available).

Annex 6 provides basic data on the "two Austrias" that emerge for 1985 when the items priced for its OECD comparison are compared with those priced for its G2 comparisons with Eastern Europe and the FSU. Even after discounting for the far greater diversity of items priced for OECD purposes (by discarding information on VCRs, microwave ovens, etc., that were not even considered for the Group II comparison), there is a clear difference in the sample frame which systematically steers the OECD comparison towards higher, and the Group II comparison towards lower, quality goods.

Annex 7 explores the extent of overlap and deviation, at the item level, between the preliminary ICP results for 1990 and the CIA's 1976 exercise. While further documentation from Austria will be required to complete the matching process, the comparison lends credence to the hypothesis that goods in the FSU have changed far less than their US comparators; and that imputation of US price trends to constant ruble value series inserts a spurious upgrading of quality/diversity into FSU series. And while that upgrading is modest from year to year, its cumulativ **e** ffect could well explain much of the difference between 1990 estimates from CIA and ICP PPP exercises.

27
Annex 1

# Estimation of FSU Economies' GNPs in Ruble Terms

# A. GNP Series up to 1990

1. The ruble GNP for non-Baltic economies emerging from the FSU was esdmated in three steps. First "official" time series were obtained from Bank/Fund documents. Second, a set of 1987 benchmark estimates was extrapolated, based on the trends of the "official' series. Finally, the extrapolated series were adjusted to express the extra-Union trades in foreign prices, to be consistent with the concept of the System of National Accounts.

# "Official" Time Series

2. For the period of 1987 - 1991, GNP data were taken from the Bank/Fund's documents. One exception is Russia, for which the missing 1987 and 1988 data were extrapolated backward from the 1989 numbers. Constant prices for some FSU economies were taken from the same source if they were available. If not, full series of NMP in constant prices were determined through extra- or interpolation, first; then the difference between GNP and NMP in the base year of 1987 was extrapolated via the capital stock series in constant prices for each FSU economy; finally these extrapolated GNP-NMP differences were added to the constant price NMPs as described above. GNP deflators were calculated from the GNP data in current and constant prices.

3. For the period of 1980-87 a different method was used. Some republics had current price data back to 1985. For others, 1980 data were available. For the missing data the following method was used. First, constant price series were prepared via backward extrapolation or interpolation, using the NMP trend at constant prices. Second, time series of GNP deflators were estimated, based on NMP deflator's trend. Current price data were then obtained by multiplying GNP series in constant prices by the corresponding deflator series.

4. For the Baltic republics, complete time series were available in current and constant prices for 1980-90; thus no estimation was necessary. All "official" GNP series as mentioned above are shown in Tables 4-6.

# Benchmark Data and Extrapolation

_5._ For 1987, the Intelligent Decision Systems (IDS) prepared a set of GNP data for each republic (Table 7.). IDS also supplied extra-Union trade data at both domestic and foreign prices for 1987-90 by republics. These GNP data, however, include both inter-Republic and extraUnion trade values in foreign prices. To make these data conceptually consistent with the 28
"official' data, an adjustment was made to express the trade values in domestic prices, based on the IDS foreign trade data. Still, the adjusted GNP figures from IDS differ from the 'official" data. The reasons for that lie in the different estimations of military expenditures, private sector activities and also in the estimation of foreign trade in non-material services (for data comparisons, see Summary Table).' 6. The 1987 benchmark data from IDS were first extrapolated to 1988-90 via trend of the "official series. The extrapolation was done for all Republics. Then the extrapolated data were adjusted for re-evaluation of extra-Union trade from domestic to foreign prices.

7. For 1980-86, the foreign trade adjustments for 1987 were extrapolated backward via the trend of the foreign trade of the FSU.

8. The adjusted GNPs in constant prices were obtained by applying the deflators of the "official" time series to the current price data as obtained in paras 6-7 above. The results are shown in Tables 1-3.

**1** For the Baltic republics, the 1987 benchmark GNP figures were adjusted only for inter-Republic trade, to make them consistent with the data from the national authorities.

29
||(atcurre<br>1980|nt prices<br>1981|, biltions<br>1982|of rou<br>.1983|bles)<br> 1984|1985|1986|1987|1988|1989|1990|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Former<br>USS<br>Arawds<br>Azerbaijan<br>Felarus<br>Estonia<br>Georgia<br>Kazakhstan<br>Kyrgyustan<br>Latvia<br>Lithuania<br>Moldove<br>Russia<br>tajikistan<br>Turkmenista<br>Ukrafne<br>Uzbekista|R<br>678.6<br>7.0<br>12.4<br>25.0<br>4.7<br>10.9<br> <br>28.8<br>5.2<br>7.7<br>8.0<br>8.3<br>414.6<br>5.5<br>n<br>4.8<br>110.8<br>n<br>24.1|<br>709.9<br>7.6<br>13.3<br>27.2<br> <br>4.9<br>11.9<br>29.4<br>5.6<br>8.3<br>9.0<br>8.7<br>429.4<br> <br>5.7<br>5.0<br>117.5<br>25.8|755.2<br>7.9<br>14.2<br>26.1<br> <br>5.0<br>12.5<br>30.6<br>5.8<br>8.7<br>9.7<br>9.9<br>461.6<br> <br>5.9<br>5.5<br>123.2<br>27.9|786.4<br>8.0<br>14.8<br>28.3<br>5.4<br>12.5<br>32.7<br>6.7<br>8.8<br>10.0<br>10.6<br>477.0<br>6.1<br>5.8<br>129.5<br>29.5|815.3<br>8.5<br>15.3<br>29.8<br> <br>5.5<br>13.4<br>33.5<br>7.0<br>9.2<br>10.4<br>10.6<br>495.9<br> <br>6.1<br> <br>5.8<br>135.0<br>28.8|830.0<br>8.6<br>14.7<br>31.0<br>5.2<br>13.8<br>32.9<br>6.1<br>8.9<br>10.3<br>9.1<br> 516.7<br> <br>6.1<br> <br>5.5<br>133.0<br>27.7|851.0<br>8.7<br>14.3<br>32.3<br>5.4<br>14.2<br>34.7<br>6.2<br>9.2<br>11.3<br>9.7<br>529.7<br>6.0<br> <br>5.8<br>135.8<br>27.3|855.3<br>8.5<br>14.7<br>32.9<br> <br>5.7<br>13.7<br>37.8<br>6.7<br>9.3<br>11.4<br>9.9<br>525.8<br>6.2<br> <br>6.5<br>137.3<br>28.8|907.2<br>8.3<br>14.7<br>34.8<br>6.0<br>14.1<br>42.2<br>7.4<br>9.9<br>12.4<br>10.3<br>559.1<br>7.0<br>6.9<br>143.1<br>31.1|973.7<br>9.8<br>15.6<br>38.0<br>6.7<br>14.0<br>45.6<br> <br>8.1<br>10.9<br>13.4<br>11.7<br>598.6<br>7.0<br>7.1<br>154.7<br>32.6|1,053.8<br>10.0<br> <br>14.9<br>40.6<br>8.4<br>14.7<br>55.2<br>8.8<br>12.2<br>14.7<br>13.2<br>646.2<br>7.6<br>7.9<br>164.9<br>35.1|

|Table 2|GN P (Fi<br>(atcons|nal)<br>tant prices|, biltion|s of 198|7 roubtles|)||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|
|Formaer<br>USSR<br>Armenia<br>Azerbaija<br>3etarus<br>Estonia<br>Georgia<br>Kazakhstan<br>Kyrgyzs:an<br>Latvtia<br>Lithuanie<br>Moldova<br>Russia<br>Tajikistan<br>Turkmnistan<br>Ukraine<br>Uzbekista|<br>751.<br>6.4<br>n<br>11.7<br>25.0<br>4.5<br>11.0<br> <br>33.0<br>5.3<br>7.3<br> <br>8.5<br>8.4<br>480.7<br>5.3<br> <br>5.3<br>115.5<br>n<br>23.8|7 742.5<br> <br>7.1<br> <br>12.7<br>27.0<br> <br>4.6<br> <br>11.5<br>33.4<br>5.7<br>7.6<br> <br>9.7<br>8.4<br>461.0<br>5.3<br>5.4<br>118.5<br>24.7|752.6<br> <br>7.1<br> <br>12.6<br>27.0<br> <br>4.9<br> <br>11.5<br>33.2<br>5.5<br>7.8<br> <br>10.0<br>9.6<br>470.8<br>5.3<br>5.3<br>117.5<br>24.6|783.6<br> <br>7.5<br> <br>12.9<br>28.6<br> <br>5.0<br> <br>12.0<br>34.7<br>5.9<br>8.3<br> <br>10.3<br>10.0<br>488.3<br>5.5<br>5.6<br>123.4<br>25.6|808.9<br> <br>8.0<br> <br>14.0<br>30.0<br>5.2<br> <br>12.7<br>35.0<br>6.2<br>8.7<br> <br>11.1<br>10.3<br>503.1<br>5.6<br>5.4<br>128.0<br>25.5|842.1<br> <br>8.4<br> <br>14.4<br>31.3<br>5.3<br> <br>13.9<br>35.9<br>6.2<br>8.7<br> <br>10.3<br>9.5<br>529.3<br>5.8<br>5.7<br>130.5<br>26.9|860.1<br> <br>8.7<br> <br>14.7<br>32.8<br>5.4<br> <br>14.7<br>34.1<br>6.3<br>9.1<br> <br>11.4<br> <br>10.2<br>539.4<br>6.2<br> <br>6.0<br>134.0<br> <br>27.0|855.3<br> <br>8.5<br> <br>14.7<br>32.9<br>5.7<br> <br>13.7<br>37.8<br>6.7<br>9.3<br> <br>11.4<br> <br>9.9<br>525.8<br>6.2<br>6.5<br>137.3<br> <br>28.8|895.0<br> <br>8.3<br> <br>14.9<br>34.5<br>5.9<br> <br>14.8<br>39.8<br>7.4<br>9.8<br> <br>12.5<br> <br>10.5<br>550.3<br>6.8<br>7.1<br>141.4<br> <br>31.0|920.5<br> <br>9.0<br> <br>14.4<br> <br>37.1<br> <br>6.1<br> <br>14.7<br>39.8<br>7.7<br>10.3<br> <br>12.6<br> <br>11.3<br>565.3<br>6.5<br>6.8<br>147.0<br> <br>31.9|909.5<br> <br>8.6<br> <br>13.5<br> <br>36.9<br>5.6<br> <br>13.6<br>39.4<br>8.1<br>10.0<br> <br>11.8<br> <br>11.3<br>556.3<br>6.6<br>6.9<br>147.9<br> <br>33.3|

|TabLe<br>3|C N P (F<br>(price de|inaL)<br>flator, 1|987a100|)||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|
|Former<br>USSR<br>Armenia.<br>Azerbaijan<br>Oelarus<br>Estonia<br>Georgia<br>Kazakhstan<br>Kyrgyzstan<br>Latvia<br>Lithuania<br>Moldova<br>Russia<br>Tajikistan<br>Turkmenista<br>Ukraine<br>Uzbekista|<br>90.3<br>109.0<br>106.0<br>100.0<br>104.?<br>99.1<br> <br>87.2<br>98.4<br>105.9<br>94.3<br>99.0<br>86.2<br>103.5<br>n<br>91.7<br>96.0<br>n 101.4|<br>95.6<br> <br>106.3<br> <br>105.5<br> <br>100.5<br> <br>106.4<br>103.2<br>88.0<br>W.7<br> <br>108.8<br>92.9<br>103.2<br>93.2<br>107.2<br>93.2<br> <br>99.2<br>104.6|100.3<br>110.7<br>113.5<br>96.8<br>102.4<br>109.0<br>92.2<br>105.6<br>111.3<br>97.0<br>103.5<br>98.1<br>111.3<br>104.0<br>104.8<br>113.3|100.4<br>106.6<br>115.1<br>98.9<br>106.9<br>104.8<br>94.5<br>113.5<br>106.5<br>97.4<br>105.7<br>97.7<br>109.4<br>104.7<br>105.0<br>115.0|100.8<br>105.7<br>109.6<br>99.2<br>106.3<br>105.6<br>95.9<br>112.7<br>104.8<br>93.5<br>102.9<br>98.6<br>109.1<br>107.7<br>105.4<br>113.0|98.6<br>102.5<br>101.8<br>99.2<br>97.7<br>99.3<br>91.7<br>98.6<br>102.4<br>100.6<br>95.6<br>97.6<br>104.2<br>96.5<br>101.9<br>103.0|98.9<br>".8<br>97.6<br>98.4<br>100.2<br>96.5<br>101.6<br>98.5<br>100.9<br>98.6<br>94.5<br>98.2<br>97.5<br> <br>97.0<br>101.3<br>101.2|100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0|101.4<br>100.0<br>98.8<br>101.0<br>102.7<br>95.1<br>106.1<br>99.8<br>101.2<br>99.1<br>98.2<br>101.6<br>102.5<br>97.7<br>101.2<br>100.4|105.8<br>108.9<br>107.9<br>102.7<br>110.3<br>95.4<br>114.6<br>105.0<br>105.9<br>105.6<br>103.8<br>105.9<br>107.2<br>104.8<br>105.2<br>102.3|115.9<br>116.6<br>110.9<br>110.1<br>150.3<br>108.5<br>140.4<br>109.1<br>122.6<br>125.2<br>117.5<br>116.2<br>114.8<br>114.3<br>111.5<br>105.2|

30
|Table 4|0 N P ("O<br>(at curren|fficlat")<br>t prices,|bltlions|ofroubl|es)|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|
|Former USSR<br>Armenia<br>AzerbalJan<br>getarus<br>Estonia<br>Georgia<br>Kazakhstan<br>Kyrgyzstan<br>Latvia<br>Lithuania<br>Moldova-<br>Russia<br>Tajikistan<br>Turkmenistan<br>Ukraine<br>Uzbekistan|<br>613.2<br>6.5<br>11.8<br>23.0<br>4.5<br>10.2<br>27.6<br>5.0<br>7.9<br>7.7<br>7.8<br>363.6<br>5.3<br> <br>4.7<br>103.8<br>23.9|644.5<br>7.1<br>12.7<br>25.2<br>4.7<br>11.1<br>28.2<br>5.4<br>8.4<br>8.6<br>8.1<br>378.5<br>5.5<br>4.9<br>110.5<br>25.6|689.8<br>7.4<br>13.6<br>24.2<br>4.8<br>11.8<br>29.4<br>5.6<br>8.9<br>9.3<br>9.3<br>410.7<br>5.7<br>5.4<br>116.2<br>27.6|722.5<br>7.5<br>14.2<br>26.4<br>5.1<br>11.8<br>31.6<br>6.5<br>8.9<br>9.6<br>10.0<br>427.2<br>5.9<br>5.7<br>122.7<br>29.3|753.0<br>8.0<br>14.7<br>27.9<br>_5.2_<br>12.7<br>32.4<br>6.8<br>9.3<br>9.9<br>10.1<br>447.4<br>6.0<br>5.7<br>128.3<br>28.6|769.1<br>8.1<br>14.1<br>29.2<br>5.0<br>13.1<br>31.8<br>5.9<br>9.1<br>9.9<br>8.6<br>469.2<br>5.9<br>5.4<br>126.5<br>27.4|791.5<br>8.2<br>13.7<br>30.5<br>_5.2_<br>13.5<br>33.6<br>6.0<br>9.3<br>10.8<br>9.1<br>483.4<br>5.9<br>.5.7<br>d29.4<br>27.1|817.7<br>8.3<br>14.7<br>32.5<br>5.4<br>14.2<br>35.0<br>6.3<br>9.5<br>11.0<br>9.4<br>495.7<br>6.0<br>6.1<br>136.3<br>27.3|870.9<br>8.1<br>14.6<br>34.4<br>5.8<br>14.5<br>39.0<br>6.9<br>9.9<br>11.8<br>9.8<br>531.2<br>6.7<br>6.5<br>142.2<br>29.4|939.1<br>9.5<br>15.5<br>37.7<br>6.4<br>14.3<br>42.0<br>7.6<br>10.9<br>12.7<br>11.2<br>573.2<br>6.6<br>.6.7<br>154.1<br>30.7|1,018.5<br>9.7<br>14.7<br>40.1<br>8.0<br>14.9<br>51.0<br>8.3<br>12.2<br>13.3<br>12.8<br>622.0<br>7.1<br>7.3<br>164.8<br>32.4|

|Table 5|GN P ("<br>(at consta|Official")<br>nt prices,|<br>billions|of 198|7 roubLes)|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|
|Former USSR<br>Armenia<br>Azerbaijan<br>Selarus<br>Estonia<br>Georgia<br>Iazakhstan<br>Kyrgsystan<br>Latvia<br>Lithuania<br>Noldova<br>Russia<br>Talikistan<br>Turkmenistan<br>Ukraine|<br>678.0<br>6.0<br>11.1<br>23.0<br>4.3<br>10.1<br>31.7<br>5.0<br>7.4<br>8.1<br>7.5<br>421.7<br>5.1<br> <br>5.1<br>108.2|673.4<br>6.6<br>12.0<br>25.1<br>4.4<br>10.6<br>32.2<br>5.4<br>7.8<br>9.3<br>7.5<br>406.3<br>5.1<br>5.2<br>111.4|686.7<br>6.7<br>12.0<br>25.0<br>4.6<br>10.6<br>32.0<br>5.3<br>7.9<br>9.6<br>8.7.<br>418.8<br>5.1<br> <br>5.2<br>110.8|719.1<br>7.0<br>12.3<br>26.7<br>4.8<br>11.1<br>33.5<br>5.7<br>8.4<br>9.9<br>9.1<br>437.4<br>5.4<br>5.4<br>116.9|746.3<br>7.6<br>13.4<br>28.1<br>4.9<br>11.8<br>33.9<br>6.0<br>8.9<br>10.6<br>9.5<br>453.8<br>5.5<br>5.3<br>121.7|779.7<br>7.9<br>13.6<br>29.5<br>5.1<br>13.0<br>34.8<br>5.9<br>8.8<br>9.8<br>8.6<br>480.7<br>5.7<br>5.5<br>124.1|799.4<br>8.2<br>13.9<br>31.0<br>5.1<br>13.7<br>33.2<br>6.1<br>9.3<br>10.9<br>9.3<br>492.3<br>6.0<br>5.9<br>127.7|817.1<br>8.3<br>14.7<br>32.5<br>5.4<br>14.0<br>35.1<br>6.2<br>9.5<br>11.0<br>9.1<br>495.7<br>6.0<br>6.1<br>136.3|855.0<br>8.1<br>14.8<br>34.0<br>5.6<br>15.1<br>37.0<br>6.9<br>9.9<br>12.0<br> <br>9.7<br>518.8<br>6.6<br>6.7<br>140.3|879.4<br>8.8<br>14.4<br>36.6<br>5.8<br>14.9<br>37.0<br>7.2<br>10.5<br>12.1<br>10.4<br>532.9<br>6.4<br>6.4<br>145.9|868.9<br>8.4<br>13.4<br>36.4<br>5.3<br>13.8<br>36.6<br>7.5<br>10.1<br>11.3<br>10.3<br>524.4<br>6.4<br>6.5<br>146.7|
|Uzbekistan|23.5|24.5|24.4|25.4|25.3|26.6|26.8|27.3|29.4|30.2|31.6|

|TabLe 6|aNP("O<br>(price de|fficiaL")<br>flator,<br>1|987a100|)||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|
|FormerUSSR<br>_Armeia_<br>AzarbaiJan<br>Betarus<br>Estonia<br>Georgia<br>Kazakhstan<br>Kyrgyzstan<br>Latvia<br>Lithuania<br>Noldova<br>Russia<br>Tajikistan<br>Turkuenistan<br>Ukrsine<br>Uzbekistan|<br>90.4<br>109.0<br>106.0<br>100.0<br>104.7<br>99.1<br>87.2<br>98.4<br>105.9<br>94.3<br>99.0<br>86.2<br>103.5<br>91.7<br>96.0<br>101.4|95.6<br>106.3<br>105.5<br>100.5<br>106.4<br>103.2<br>88.0<br>99.7<br>108.8<br>92.9<br>103.2<br>93.2<br>107.2<br>93.2<br>99.2<br>104.6|100.4<br>110.7<br>113.5<br>96.8<br>102.4<br>109.0<br>92.2<br>105.6<br>111.3<br>97.0<br>103.5<br>98.1<br>111.3<br>104.0<br>104.8<br>113.3|100.4<br>106.6<br>115.1<br>**98.9**<br>106.9<br>104.8<br>94.5<br>113.5<br>106.5<br>97.4<br>105.7<br>97.7<br>109.4<br>104.7<br>105.0<br>115.0|<br>100.8<br>105.7<br>109.6<br>**99.2**<br>106.3<br>105.6<br>95.9<br>112.7<br>104.8<br>93.5<br> <br>102.9<br>98.6<br>109.1<br>107.7<br>105.4<br>113.0|98.6<br>102.5<br>101.8<br>**99.2**<br>97.7<br>99.3<br>91.7<br>98.6<br>102.4<br>100.6<br>95.6<br>97.6<br>104.2<br>96.5<br>101.9<br>103.0|98.9<br>99.8<br>97.6<br>98.4<br>100.2<br>96.5<br>101.6<br>98.5<br>100.9<br>98.6<br>94.5<br>98.2<br>97.5<br>97.0<br>101.3<br>101.2|100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0<br>100.0|101.8<br>99.3<br>98.5<br>101.1<br>103.1<br>94.4<br>105.7<br>99.5<br>100.1<br>98.6<br>98.1<br>102.4<br>101.4<br>97.3<br>101.3<br>100.0|106.7<br>108.5<br>107.5<br>103.1<br>111.4<br>93.9<br>113.9<br>104.4<br>104.0<br>104.6<br>104.0<br>107.6<br>104.6<br>104.0<br>105.6<br>101.6|117.1<br>115.4<br>109.3<br>**110.3**<br>150.6<br>106.0<br>139.8<br>108.9<br>120.3<br>117.7<br>118.8<br>118.6<br>110.7<br>112.0<br>112.3<br>102.6|

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Table 7: Differences between Bank/Fund Fsthmates and IDS Data GNP

# (Billions of Rubles)

# Bank/Fund

<!-- Start of picture text --> IDS<br><!-- End of picture text -->

|||Estimates<br>1987||
|---|---|---|---|
|FSU|817.7||797.2|
|Armenia|8.3||8.1|
|Azerbaijan|14.7||14.1|
|<br>Belarus|32.5||31.2|
|Estonia|5.4||5.5|
|Georgia|14.2||13.1|
|<br>Kazakhstan|_35.0_||36.7|
|Kyrgyzstan|6.3||_6.5_|
|<br>Latvia|_9.5_||9.0|
|Lithuania|11.0||11.0|
|Moldova|9.4||9.4|
|Russia|495.7||480.6|
|Tajikistan|6.0||6.1|
|<br>Turkmenistan|6.1||6.3|
|Ukraine|136.3||131.1|
|Uzbeldstan|27.3||28.6|

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Annex 2

# Conceptual Approach to Converting GNP into US$ for FSU

# The Atlas Method

1. The Bank's Atlas method stipulates that when the domestic prices embodied in national GNP data are replaced by the corresponding international prices, the resulting GNP figures are comparable across countries ("law of one commodity-one price"). This procedure is equivalent to converting national currency GNP at the market exchange rate, provided that all the products included in GNP are tradables and the price system including the foreign exchange market works without government intervention. Even if the GNP includes non-tradables, the above conversion procedure would still produce internationally comparable GNP figure, when we further assume that domestic prices are undistorted and resources are efficiently allocated for production of different products (see below).

# QtPt(d) + QnPn(d) = GNP(d) ..... (1)

where Qt=composite quantity of tradables, Pt(d) = average domestic price of tradables, Qn=composite quantity of non-tradables, Pn(d)=average domestic price of nontradables, GNP(d)= GNP in national currency; Pn(d) = KPt(d) ..... (2) where K is a parameter, which may vary with the general income level of the economy; E = Pt(d)/Pt(w) ..... (3) where E= free market exchange rate, Pt(w) = international prices of tradables in U.S. dollars.

Substituting (2) into (1), QtPt(d) + QnKPt(d) = GNP(d) ...... (4).

# Converting GNP(d) in (4) at E in (3),

GNP(d)/E = [QtPt(d)]/[Pt(d)/Pt(w)] + [QnKPt(d)]/[Pt(d)/Pt(w)] = QtPt(w) + QnKPt(w) = GNP(w), where GNP(w)=GNP in international prices.

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# Conversion Method for Former Soviet Republics

2. Obviously, the above assumptions do not hold for many economies, including former Soviet Republics. Particularly, the official exchange rate does not link closely the average domestic prices to the corresponding international prices even for tradables in many market economies. Thus, Equation 3 in para 1 above may have to be modified as follows: E = Pt(d)/Pt(w) = (1 +A) Eo ....... (3'), where A is the average net indirect taxes on traded goods. In this case, the conversion of GNP at Eo would result in

[QtPt(d) + QnKPt(d)]/Eo from (4), pam 1 above = (1+A)[QtPt(w) + QnKPt(w)] = (1+A)GNP(w) .... _(5)_ 3. The Bank's Atlas method assumes that A in Equation (5), pam 2 above, is "small" for most economies and thus (1 +A)GNP(w) is still broadly comparable across countries. If A is considered to be "too large" for an economy, then an alternative conversion rate, which approximates E, is used for the economy.

4. Most of domestic prices of tradables in Soviet Republics before 1992 were. administratively determined independently of their foreign prices. Thus, the official exchange rate did not link foreign currency prices of tradables to their domestic prices. Further, foreign prices of prducts traded with for-e.r CMEA countries were negotiated between trading parties and included substantial implicit sL.,sidy and tax elements. They diverge significantly from the international prices. For products "traded" between Republics, their foreign prices need to be imputed. All these suggest that the official ruble-dollar exchange rate can not be used to derive Soviet Republic's GNP in dollars, which would be internationally comparable.

5. National accounts data of former Soviet Union (FSU) on external trade suggest that FSU's official exchange rate deviated, by exceptionally large margin, from the implicit exchange rate linking the domestic prices of traded goods and the corresponding international prices. Therefore, an alternative conversion rate needs to be determined. Further, because of the seriously distorted domestic price structures, particularly highly subsidized service prices, in FSU Republics, the ratio of the average price of non-traded goods to those of traded goods may be downward-biased considerably.

6. If some average relationships between domestic prices of traded goods and their international prices (proxy E in (3) above) and between resource costs for tradables and nontradables (proxy K in (2) above) are known, GNPs in international prices could still be estimated for these economies. The assumption that price elasticities of demands in these economies are very low simplifies the procedure. That is, when international prices are applied to tradables and service prices are adjusted for FSU Republics, there would be no need to impute quantity changes possibly responding to the hypothetical price changes.

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7. For each Republic, proxy E (E*) could be estimated, based on data from the International Comparison Program (ICP) or similar studies. Proxy K (K*) could be found from fiscal data on indirect taxes and subsidies for traded and nontraded products or GDP estimates by sectors at factor cost. More specifically, E* can be computed as the ratio of the average domestic prices of major tradables to the average international prices: E* = SUM[wiPt(d)i]/SUM[wiPt(w)i] -Pt(d)/Pt(w)... (6) where wi is weights defined either as [Vi/Pt(d)i]/SUM[Vi/Pt(d)i] with Vi =ruble value of valueadded for, or expenditures on, tradable group i.

K* could be established as the ratio of average "resource costs' for non-traded to traded goods: Let Pn(d)([Vn - tnl/Vn} = (K*) Pt(d){[Vt - tt]/Vt} ... (7), where Vn=ruble value of non-tradable production, tn=net indirect tax on nontradables, Vt=ruble value of tradable production, tt=net indirect tax on tradables. The assumption here is that the resource costs are much less distorted than the "established" prices in FSU Republics.

# To estimate GNP(w) for a FSU Republic,

First, the value added or gross output of the nontradable sectors should be multiplied by [(Vn - tn)/Vn]/[(Vt - tt)/Vt]; i.e., QnPn(d){[(Vn - tn)/Vn]/{[Vt - tt]/Vt}. Let this be QnPn*(d)....(8), which is equal to Qn(K*)Pt(d), from (7) above; Second, (8) is added to the value-added or gross output of the tradable sectors: QnPn*(d) + QtPt(d) = Qn(K*)Pt(d) + QtPt(d) ..... (9).

- Finally, (9) is converted at E* Pt(d)/Pt(w): [QnPn*(d) + QtPt(d)]/E* = Qn(K*)Pt(w) + QtPt(w) = GNP(w)*.

8. For practical reasons, it could be assumed that the tradables are products from agricultural, mining and manufacturing sectors and the non-tradables are those from construction and service sectors.

9. Given the general pattern that the prevailing exchange rate tends to understate significantly the relative purchasing power of the local currency for the economies with price controls, the method discussed in paras 6 and 7 above may be considered as a special case where an alternative GNP conversion rate is sought because of the "overly" appreciated or depreciated prevailing exchange rate.

35
10. Some considerations may be given to quality differences of traded goods, especially nonprimary goods, between intra-CMEA and convertible currency areas. The proxy k could be computed for the whole union and uniformly applied to all Republics.

11. More specifically, for the sample primary products, regardless of their destinations/origins, their intemational prices could be assumed to be equal to the prices in the "Western" market. For the sample manufactured goods, their international prices for trade with Western economies should be the actual transaction prices, while those for inter-Republic and intra-CMEA trade could be assumed to be equal to the quality-adjusted transaction prices in Westem market. These quality-adjusted prices could be estimated, based on the 1988 study by Oblath and Tar on Hungary-USSR trade.

12. Proxy Es could be computed for sample products taken from the final demand side in input-output tables or for sample products from the production side. In the latter case, proxy Es should be computed from the value-added estimates in domestic and international prices, where the imported raw materials as re-evaluated at international prices would be subtracted from the gross output values as re-evaluated at the international prices.

13. Some people may consider that the above method will yield $GNP figures close to those converted at the purchasing power parity (PPP), which would be much higher than those converted at the exchange rates. Generally, the "higher" PPP-converted GNPs would result mainly from using PPPs for non-traded goods and services, which are much "lower" than those for traded goods. Even then, according to 1985 ICP data, the PPPs for tradables are significantly "lower" than the official exchange rates for most market economies (Attachment 1). This suggests that using E* directly for FSU Republics would result in overstated $GNP figures for the Republics compared to market economies. To ensure internationally comparability of $ GNP figures, E* for FSU Republics may have to be adjusted such that the adjusted E* would deviate from the PPPs for tradables to the same extent as for the "average' market economy.

14. One option for adjusting E* is as follows: (a) First, PPPs for tradables are computed for benchmark countries of 1985 ICP; (b) Second, the ratios of the official exchange rates to the above PPPs are computed for the benchmark countries and simply averaged: (Eo/PPP); (c) E* for each FSU Republic is multiplied by (Eo/PPP) from (b) above: E**=(E*)(Eo/PPP).

# _15._ Alternatively, E** could be approximated as follows:

E**=average of [(E*)/PPPi](Eoi) =average of (PPP*i)(Eoi) for comparator economy i-1,2,3...n, where PPP*i =PPP between FSU republic and a comparator economy i and Eoi=prevailing exchange rate vis-a-vis the U.S.dollar in comparator i.

36
Here, PPP*i could be computed from the 1988 price comparison study among former CMEA countries and also 1990 ICP data.

16. E* could be also computed from actual foreign trade statistics, where traded items are evaluated at both domestic and "foreign" prices. The foreign prices here are the actual "invoice" prices converted from foreign currencies into rubles at the official exchange rates. The estimated E* should be close to the so-called commercial exchange rate.

17. In many developing countries, the ratio of the average market prices of non-tradables to tradables may not equal to the same ratio in resource cost terms, because of various government interventions to markets. Thus, K for FSU in equation (9) in para 7 above should be also adjusted to approximate the average K value of developing economies.

37