Back Matter

Back Matter

Author(s):
International Monetary Fund
Published Date:
April 2000
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    Annex I Efficiency Improvements. Reallocation, and Investment as Proximate Sources of Growth in the Transition: A Conceptual Framework

    The mechanism or proximate sources of growth in the recovery phase of transition is illustrated in Figure 6.

    Figure 6.Growth in the Recovery Phase of Transition

    • CP* represents the point of potential production on the production possibilities functions (PPF) under central planning if full capacity is utilized, factors are not wasted (as at XI1, at which more K and L are being used to produce 1 unit of good B than at the efficiency frontier), and shadow prices of K and L are reflected in the central plan’s factor allocation. In this case, the only error under CP* is to ignore world prices.

    • CPI is the point where factor and technical inefficiency of central planning also exist, but capacity is fully utilized; historically this is roughly equivalent to pre-1990 production.

    • CPU reflects a decline in production levels (as in post-1990), and lower utilization of existing capacity.

    • XI1 is inside the efficiency frontier of the unit isoquant for good B; K and L are used wastefully relative to the theoretical best practice point for central planning, XI1*.

    • M1 is a point on the PPF“inherited” from resource accumulation during the period of central planning. No aggregate net investment is necessary in moving to M1, but there could be gross new investment in the expanding sector, so that allocation among goods can change, reflecting adjustment of production (allocation) to world prices.

    • M2 is an efficient goods allocation point with new net investment.

    Using Figure 6, five types of changes (structural shifts) can be defined that provide growth in the sense of more output for a given level of factor availability, and more factor inputs.

    • Recovery from capacity underutilization (from CPU to CPI).

    • Technical efficiency or X-efficiency gains = movement to efficiency frontier by eliminating wasteful usage of production factors (from CPI to CP*, and from XI1 to XI1*).

    • Efficiency gains from achieving optimal factor proportions (from XI1* to BCP*, and also from CPI to CP*).

    • Resource reallocation gains (from CP* to M1, that is, production of more B and less A, hence, also BCP* to BM1).

    • Net factor-expansion gains (from M1 to M2, and from BM1 to BM2, plus an analogous shift to a higher level isoquant for good A).

    Annex II Key Impediments and Contributing Factors to Growth: A Survey of IMF Country Economists’Views

    This annex summarizes the qualitative assessments of IMF country economists as regards the main impediments and contributing factors to growth in the transition economies they cover.

    Shocks related to the collapse of the CMEA and the U.S.S.R. were the main cause of the initial output decline for virtually all countries. These shocks affected mainly the terms of trade, owing to the movement to trade based on international prices, and disruptions (or the loss) of export markets. For a variety of country-specific reasons, exporters of energy (for example, Russia, Azerbaijan, or Turkmenistan), which might have gained significantly from the movement to international prices, have not been able to do so and thus have not started the transition from a particularly advantageous initial position. For one country (Hungary), an institutional feature of the legal framework (that is, a radical bankruptcy code) exacerbated somewhat the effect of external shocks.

    For a significant number of countries, problems related to internal and external security—including wars, civil conflicts, trade blockades, and problems related to refugees—created for some time an almost unsurmountable obstacle to the resumption of growth. Of the countries in this group, the only ones included among the “consistent growth” countries (see Box 2) are those for which an important growth factor was output recovery from a very low initial level (Armenia and Georgia).

    Many, but not all, countries pursued liberal trade policies as part of their transition, and these were universally thought of as an important positive growth factor. The main channels through which these policies affected output included allowing foreign demand to spur recovery, creating incentives for inward foreign direct investment (FDI), exposing domestic producers to competition, and helping realign domestic prices with international prices. In terms of the grouping set out in Box 2, some countries that are not in the “consistent growth” group limited the effectiveness of liberal trade policies by continuing to rely on state agreements and barter (Moldova), or inefficiently allocating spare capacity to exports (Russia). For two of these countries (Ukraine and Turkmenistan), the country teams noted that, if there were any positive prospects for growth in those countries at all, they were in particular associated with liberal trade policies.

    With the exception of the Slovak Republic and Croatia, the ability to attract substantial broadly based FDI was a common distinguishing feature among non-CIS “consistent growth” countries. Other countries that have also attracted significant FDI inflows have been able to attract such investments only for raw materials or energy (Azerbaijan, Kazakhstan, Kyrgyz Republic, and to some extent Turkmenistan). In some instances, such as Turkmenistan, foreign investment may have had little impact on output because it involved substantial new infrastructure with excess capacity. A note of caution might be in order, however, before one draws too close a connection between an FDI-friendly policy framework and having achieved sustained growth: all non-CIS “consistent growth” countries have enjoyed favorable geography (close proximity to developed market economies). Thus, geography, independent of policies, might have contributed to both FDI and growth.

    Macroeconomic stability also emerges as an important growth factor. On the one hand, it was classified in this way with some frequency for many “consistent growth” countries. On the other hand, macroeconomic instability, macroeconomic imbalances, and weak public finances were often listed as obstacles to growth. For countries with consistent growth, the primary reason given for why growth prospects were, at times, adversely affected (for example, the Czech Republic) was the need to reestablish macroeconomic equilibrium. For these countries, foreign and domestic demand were both important as an impetus for growth depending on the stance of macro policies. Reliance on foreign demand, however, seemed to be more fundamentally related to strong growth performance.

    An interesting picture emerges in regard to investment. A variety of countries, ranging from those with consistent growth (for example. Poland) to those with little or no growth (for example, Ukraine), appear to have managed to maintain a high level of investment throughout most of their transition process. However, only the consistent growth countries report investment that was broadly based and not subject to qualifications such as statistical problems (Moldova, Ukraine), or infrastructure with large initial excess capacity.

    Privatization, in itself and as an indicator of broad structural adjustment, was an important growth factor for almost all countries with consistent growth. An outlier was the Slovak Republic, which managed to effect enterprise restructuring—including significant labor shedding—without privatization. For countries that have been slower in introducing structural reforms (for example, Albania, Azerbaijan, Georgia), sectors that were privatized at an early stage report the most significant output gains. At the same time, nonexistent, slow, or inappropriate privatization efforts have been listed as an important obstacle to growth for many countries, including all countries (Albania, Bulgaria, and Romania) where there has been a reversal of growth (see Box 2). The most common characterization of inappropriate privatization is one in which insiders (management and labor) have gained control over the assets.

    Advantages of the initial economic or geographic position (for example, a large market and a developed private sector for Poland, pretransition external orientation for Slovenia, or entrepot trade for Estonia and Latvia) may have helped many countries that have entered into consistent growth. On the other hand, initial presumed geographic advantages have not helped some of the other countries (for example, Albania). Thus these advantages, while potentially quite important, would not appear to be either a sufficient or a necessary condition for sustained growth.

    IMF country economists have identified a long and varied list of obstacles to growth in their respective countries, ranging from a heavy external debt burden (Bulgaria, the Kyrgyz Republic), underdeveloped financial systems (Macedonia), to a poor statistical database (Kazakhstan). A notable feature of these obstacles, however, is that most of them have been present in one form or another in all transition economies. Thus, it appears that eliminating such obstacles to growth is not the key requirement of good growth performance. What appears to be both necessary and sufficient is that a core of strong, mutually supportive, pro-growth policies be in place for an adequate period of time (at least two to three years). The minimum set of such policies includes liberal trade policies, macroeconomic stability, ability to attract FDI, and enterprise privatization and restructuring. In terms of the groupings of Box 2. the distinguishing feature of most of the countries in the “consistent growth” category was their ability to put in place such a core of pro-growth policies relatively early in the transition process. The absence of various elements of the pro-growth policy core, or their late implementation, characterized countries grouped in the other categories (reversal of growth, and little or no growth).

    To summarize in terms of individual factors (Tables 11 and 12), the most frequently cited factors conducive to growth were macroeconomic stability, a liberal trade regime together with FDI, and broadly defined structural reforms. The structural reforms most frequently cited included privatization, resources freed from structural shifts, and reforms of the banking and tax systems. A fairly significant number of respondents also listed growth-conducive factors that were not policy-related; namely, an extremely low initial output base, advantageous initial conditions, and a favorable geographical position. As noted earlier, there was considerably more heterogeneity in responses about growth-impeding factors. However, some generalizations are still possible; in particular, the absence of growth-conducive factors is listed in many cases as an impediment. Thus, the most frequently listed growth-impeding factors include slow structural reforms (especially privatization and enterprise reforms), and macroeconomic instability. Second-generation issues such as governance and bankruptcy are also listed with some frequency. Last, noneconomic factors (political instability, transportation bottlenecks, and regional instability) are also mentioned in a fair number of cases.

    Table 11.Qualitative Assessment of Growth Process: Factors Conducive to Growth
    FactorsCountries1
    Macroeconomic stabilityALB, ARM, AZE, CZE, EST, GEO, HRV, HUN, KAZ, KGZ, LVA, LTU, MKD, MDA, POL, RUS, SVK, SVN(18)
    Liberal trade regimeALB, ARM, AZE, CZE, EST, KGZ, LVA, LTU, MKD, MDA, RUS, SVK, TJK(13)
    Geographical positionALB, BLR, EST, HRV, HUN, LVA, LTU, POL, ROM, SVN(10)
    PrivatizationARM, EST, GEO, HUN, KAZ, LVA, MDA, RUS, UKR(9)
    Recovery from extremely low initial output baseALB, ARM, AZE, GEO, KGZ, TJK, UKR(7)
    Foreign direct investmentAZE, EST, HUN, KAZ, POL, SVN, TJK(7)
    Domestic demandCZE, EST, HRV, MDA, ROM, SVK, SVN(7)
    Banking sector and tax reformsARM, KAZ, KGZ, LVA, MKD, MDA(6)
    Development of mineral resourcesAZE, KAZ, KGZ, ROM, TJK, UZB(6)
    Maintenance of high level of investmentBLR, CZE, POL SVK(4)
    Resources freed from sectoral shiftsEST, LTU, RUS, UKR(4)

    Country codes are as follows: ALB, Albania; ARM, Armenia; AZE, Azerbaijan; BLR, Belarus; BGR, Bulgaria; CZE, Czech Republic; EST, Estonia; GEO, Georgia; HRV, Croatia; HUN, Hungary; KAZ, Kazakhstan; KGZ, Kyrgyz Republic; LVA, Latvia; LTU, Lithuania; MKD, former Yugoslav Republic of Macedonia; MDA, Moldova; POL, Poland; ROM, Romania; RUS, Russia; SVK, Slovak Republic; SVN, Slovenia; TJK, Tajikistan; TKM, Turkmenistan; UKR, Ukraine; UZB, Uzbekistan.

    Table 12.Qualitative Assessment of Growth Process: Factors Inhibiting Growth
    FactorsCountries1
    Slow structural reforms in generalALB, A2E, BLR, BGR, HUN, MDA, ROM, SVN, TJK, TKM, UKR, UZB(12)
    Governance problems (for example, corruption)ALB, AZE, BLR, BGR, HRV, KAZ, KGZ, LVA, RUS, UKR(10)
    Political/regional instabilityARM, BGR, GEO, LVA, MKD, MDA, ROM, RUS, TJK(9)
    Slow enterprise reformsAZE, BLR, CZE, HRV, MKD, MDA, POL, SVN, TKM(9)
    Lack of foreign direct investmentARM, BLR, HRV, LTU, MKD, MDA, SVK, UKR, UZB(9)
    Weak bankruptcy lawsAZE, BLR, KAZ, LTU, MKD, MDA, ROM, SVK, UKR(9)
    Macro economic instabilityBGR, GEO, HUN, ROM, RUS, TJK, TKM, UKR(8)
    Underdeveloped financial sectorALB, ARM, AZE, LTU, POL, TJK, TKM(7)
    Poor regulatory climateALB, BLR, KAZ, UZB, RUS, UKR(6)
    Insufficient volume of investmentKGZ, LVA, RUS, SVN, UKR(5)
    Heavy external debt burdenARM, BGR, HUN, KGZ, UKR(5)
    Exchange restrictionsBLR, ROM, SVN, UZB(4)
    Macroeconomic imbalancesBLR, CZE, HUN, UKR(4)
    Slow privatizationBLR, CZE, HRV, LTU(4)

    Country codes are as follows: ALB, Albania; ARM, Armenia; AZE, Azerbaijan; BLR, Belarus; BGR, Bulgaria; CZE, Czech Republic; EST, Estonia; GEO, Georgia; HRV, Croatia; HUN, Hungary; KAZ, Kazakhstan; KGZ, Kyrgyz Republic; LVA, Latvia; LTU, Lithuania; MKD, former Yugoslav Republic of Macedonia; MDA, Moldova; POL, Poland; ROM, Romania; RUS, Russia; SVK, Slovak Republic; SVN, Slovenia; TJK, Tajikistan; TKM, Turkmenistan; UKR, Ukraine; UZB, Uzbekistan.

    Annex III Regression Analysis of Key Factors Underlying Growth

    Harberger (1998) makes a pithy observation about growth and empirical analysis of the growth record. At heart, growth is the sum total of “a thousand and one” individual initiatives by entrepreneurs and managers to make improvements in products and production processes; regression analysis does not “explain” growth but can, at best, illustrate its nature by organizing the underlying stylized facts. A background paper for this study (Havrylyshyn, Izvorski, and van Rooden, 1998) attempts such an exercise, similar to several recent studies on transition, by marshaling the stylized facts of growth in 25 countries for eight years (1990–97), together with values for variables reflecting some of the key factors hypothesized to affect growth. The basic relationship is formulated as follows:

    Growth = f(inflation, general structural reform progress, elements of reform, size of government, initial economic conditions, compliance with IMF-supported programs)

    With 200 observations for most but not all these variables, apart from estimating the above relationship for the entire period, the data set can also be broken down into subperiods. Although somewhat arbitrary, it is useful to consider the period of “decline” (1990–93), during which at most a handful of countries had seen the beginnings of recovery; and the period of “recovery” (1994–97), during which nearly a dozen countries experienced three or more years of growth and most others either began to grow or at least saw the decline approaching bottom.

    Because data for 1998 were still incomplete at the time of writing, the sample period comprises the years 1990–97. However, when the sample period is extended to include estimates for 1998. the results remain intact.

    By far the most dominant statistical association is found for two policy variables (Table 13; equation A1): the degree of price stabilization and general progress in structural reforms (see Annex IV for a more detailed description of the index used to represent this). The “explanatory power” is quite high for this type of data set and ranges from about 0.50 for the overall period to around 0.80 for the annual growth observed in the “recovery” period 1994–97. It is notable that the results are stronger and have higher statistical significance (as standard econometric statistical tests of probability indicate) for the recovery period with 100 observations, than in regressions using the full database of 200 observations in the period 1990–97. This is to be expected, of course, because the factors that explain decline are not exactly the same as those that explain recovery, and in particular one factor—the price liberalization subcomponent of the reform index—has an initial partially negative effect on output (which could be due to the Schumpeterian destruction of the “bad”). But as reform and time progress, the price liberalization effect is fully positive (Schumpeterian creation of the new “good”); or put differently, early price reform begins to matter once additional reforms in other areas also take place.

    When separate equations are estimated for the countries of CEE and the Baltics on one hand, and for the CIS countries on the other, it is notable that a given “amount” of reforms, as measured by the index, has a stronger impact on growth in the first group of countries. This could reflect that, from the onset of the sample period, countries of the CEE and the Baltics were already at a more advanced stage of the transition process than the CIS countries.

    Table 13.Growth Determinants: Panel Estimates
    (Dependent Variable GR; 1990–97)
    Independent Variables
    Equation Number and SampleLNPRlEXP1INCONDR2 Adj.
    Al−2.5711.210.54
    ALL(−17.70)(10.86)
    A2−3.2714.690.58
    CEE + BAL(−12.56)(10.10)
    A3−2.278.870.46
    CIS(−10.91)(3.68)
    A4−2.6213.01−2.060.55
    ALL(−17.92)(10.85)(−3.02)
    A5−3.2615.68−1.180.58
    CEE + BAL(−12.23)(9.79)(−1.19)
    A6−2.289.14−3.050.50
    cis(−11.68)(3.99)(−2.99)
    A7−2.5519.93−0.10−2.530.66
    ALL(−14.30)(8.67)(−2.43)(−3.63)
    A8−3.0729.44−0.20−4.000.62
    CEE+BAL(−10.31)(5.44)(−2.45)(−4.15)
    A9−1.8516.52−0.17−1.470.61
    cis(−4.44)(5.17)(−2.17)(−0.93)
    (Dependent Variable GR; 1990–97)
    (Dependent Variable GR; 1990–97)
    Independent Variables
    Equation Number and SampleLNPRlINCONDR2 Adj.
    Al’−2.5211.140.55
    ALL(−18.52)(12.13)
    A2−3.1314.140.58
    CEE + BAL(−13.22)(11.19)
    A3’−2.168.110.48
    cis(11.63)(3.98)
    A4’−2.5812.81−1.980.55
    ALL(−18.77)(12.08)(−3.23)
    A5’−3.1315.47−1.500.59
    CEE + BAL(−12.95)(10.88)(−1.67)
    A6’−2.228.15−3.090.53
    cis(−12.51)(4.23)(−3.29)
    (Dependent Variable GR; 1990–97)
    Independent Variables
    Equation NumberLNPRl2LIPLENLEXR2 Adj.
    Bl−2.082.510.48
    (−12.00)(1.53)
    B2−1.9113.75−12.400.53
    (−11.01)(2.99)(−2.58)
    B3−2.001.380.51
    (−11.67)(0.82)
    B4−2.08−0.814.420.48
    (−12.00)(−0.27)(1.15)
    B5−2.093.520.49
    (−12.66)(1.76)
    B6−2.02−3.034.080.50
    (−11.36)(−0.57)(1.09)
    B7−2.082.280.48
    (12.70)(1.67)
    (Dependent Variable GR; 1994–97)
    Independent Variables
    Equation NumberLNPRI2LIPLENLEXLEGPROR2 Adj.
    C1−2.4414.110.81
    (−16.05)(21.12)
    C2−2.843.0112.470.77
    (−11.88)(1.10)(3.45)
    C3−2.3016.000.76
    (−14.04)(17.15)
    C4−2.6413.08−0.430.74
    (−12.21)3.73−0.12
    C5−2.3013.910.80
    (−16.01)(20.41)
    C6−2.395.756.090.75
    (−13.47)(1.38)(1.93)
    C7−2.3410.260.74
    (−13.00)(16.73)
    C8−2.0515.37−2.250.87
    (−12.43)(10.08)(−1.97)
    C9−1.429.780.74
    (−6.77)(12.91)
    C10−2.500.110.65
    (−10.94)(12.61)
    Sources: National authorities; De Melo, Denizer, and Gelb (1996);De Melo and others (1997); EBRD Transition Reports; and IMF staff estimates.Note: Variables are as follows (t-statistics in parentheses):GR Real GDP growthLNP Natural logarithm of inflation (CPI, year-on-year)RI Structural reform indexLIP Structural reform subindex for liberalization of internal pricesLEN Structural reform subindex for private entry in marketsLEX Structural reform subindex for liberalization of trade and exchange systemsLEG Structural reform index for legal reformEXP Government expenditure as a percent of GDPINCOND Initital conditions (structural indicators)PRO IMF-supported program implementation indicator (percent of performance criteria met)CEE Central and Eastern European countriesBAL Baltic countriesCIS CIS countriesR2 Adj. Adjusted coefficient of determination.

    Equations including government expenditure (EXP) are estimated using data for the period 1992–97.

    Reform index (RI) excludes subcomponent when a particular subcomponent is included separately in the specification.

    Of the reform subcomponents, price liberalization stands out, as noted, and so too does its statistical explanatory power when included together with the general reform index, which then excludes the price liberalization subcomponent (equations B2 and C2). The other components (trade and exchange regime, privatization and ease of new entry, legal reform) are each close substitutes for the general index but do not have strong additional separate significance in the statistical analysis, except for trade and exchange liberalization in the recovery period, which has a positive effect (equation C6). The generally very close association of the various subcomponents of reform is similar to the results for other economies shown in Aziz and Wescott (1997): it is a combination of policies that is more critical for growth than any single type of policy.

    Two traditional factors that usually play a large role in statistical studies of long-term growth in other economies—the investment ratio, and openness (or a proxy for export growth)—were not found to show a significant statistical association with growth in this study. For investment, the reason has been noted in Section II and illustrated in Section III: the nature of transition is such that efficiency improvements are a particularly important potential source of early growth, and large new investments may not be necessary. They may, of course, be necessary to some degree for the growing portions of the economy, but this may be accompanied by shrinking areas experiencing disinvestment. Openness is difficult to measure, and export growth is as much an effect or reflection of early efficiency-based growth as it is a cause; for these reasons, its explanatory power may not be large. Foreign direct investment (FDI) is generally unlikely to contribute to the early recovery, for it will not be forthcoming until after the conditions that generate growth (stabilization and reforms) are well in place.

    Initial conditions do matter, including overindustrialization, and show a statistically significant negative effect on growth (equations A4 and A7). But the magnitude of this effect is very small compared with that of the main policy variables; it requires relatively little additional improvements in macro-economic and structural policies to compensate for more adverse initial conditions. For example, an industry share of 10 percentage points higher in 1990 (say 40 compared with 30) would have lowered the growth rate by 0.8 percentage point, but this could have been fully offset by a small increase of the reform index (scaled 0.0 to 1.0) from, say, 0.2 to 0.24 or 0.3 to 0.34. From this, we may conclude that policies are the most important factor explaining differences in growth performance among countries.

    Equations A6 and A6’ suggest that initial conditions have had a more pronounced adverse impact on growth in the CIS countries relative to the countries in CEE and the Baltics. However, this again may reflect the more advanced stage of the transition process in the latter group, since the impact of initial conditions can be expected to diminish over time. Moreover, when the equation controls for the size of the government, the impact of initial conditions is actually less strong in CIS countries (equation A9).

    Finally, turning to the association with IMF-supported programs, it is found that an index of effective program implementation (PRO, based on compliance information in the MONA database and described in a paper by Mercer-Blackman and Uni-govskaya (1998) is positively correlated with growth in the regressions (equation C10)). In effect, the program implementation variable is closely correlated with the reform index, and either of the two has a very similar effect in the regression equations; when both variables are included in the equation for 1994–97, PRO displaces the structural reform indicator. This “stylized fact” might be interpreted as a reflection of an underlying (and unmeasurable) factor: commitment of the authorities. Where authorities are committed to reform, they know what needs to be done or know how to design a policy program. They ensure that it is implemented, and therefore one observes early stabilization and early and strong progress on reforms, effective implementation of IMF-supported programs (recall that all but Slovenia of the sustained-growth transition economies have had—and generally still have—IMF-supported programs), and finally, as a result, good growth performance. The variable that is “missing” in the econometrics may be the most important variable in practice.

    Annex IV The Structural Reform Index

    Set forth below is a description of the transition index utilized by the EBRD. This is followed by a listing of the 25 transition economies and each country’s performance in the structural reform index (Table 14) as a whole RI and three subcomponents of structural reform (price liberalization, LIP; private entry in markets and enterprise reform, LEN; and liberalization of trade and exchange regimes, LEX for each year during 1990–97. In addition, an index for legal reform LEG is presented for 1995–97.

    Table 14.Structural Reform Indices
    199019911992199319941995199619971998
    Overall reform index
    Albania0.000.240.660.700.630.610.640.630.63
    Armenia0.040.130.390.420.460.540.610.610.65
    Azerbaijan0.040.040.250.310.330.400.440.510.56
    Belarus0.040.100.200.330.420.500.440.370.34
    Bulgaria0.190.620.860.660.630.610.570.670.67
    Croatia0.620.620.720.790.790.710.750.740.74
    Czech Republic0.160.790.860.900.880.820.820.820.82
    Estonia0.200.320.640.310.830.770.780.820.82
    Georgia0.040.220.320.350.330.500.610.660.66
    Hungary0.570.740.780.820.830.820.820.870.88
    Kazakhstan0.040.140.350.350.420.500.640.660.68
    Kyrgyz Republic0.040.040.330.600.710.710.670.700.70
    Latvia0.130.290.510.670.710.670.740.740.73
    Lithuania0.130.330.550.780.790.710.740.740.74
    Macedonia, F.Y.R.0.620.650.680.780.710.640.670.670.67
    Moldova0.040.100.380.510.540.640.640.640.66
    Poland0.680.720.820.820.830.790.790.810.82
    Romania0.220.360.450.580.670.650.640.660.65
    Russia0.040.100.490.590.670.640.710.720.61
    Slovak Republic0.160.790.860.830.830.790.790.770.78
    Slovenia0.620.710.780.820.790.790.790.790.79
    Tajikistan0.040.110.200.260.420.400.400.390.46
    Turkmenistan0.040.040.130.160.290.270.270.360.35
    Ukraine0.040.100.230.130.330.540.570.590.58
    Uzbekistan0.040.040.260.300.500.570.570.540.51
    Price liberalization
    Albania0.000.170.750.750.750.710.710.71
    Armenia0.000.170.420.500.750.710.710.71
    Azerbaijan0.000.000.420.580.750.710.710.71
    Belarus0.000.080.170.250.500.710.710,71
    Bulgaria0.000.750.750.750.750.710.470.71
    Croatia0.5B0.580.670.750.750.710.710.71
    Czech Republic0.000.750.750.750.750.710.710.71
    Estonia0.250.420.750.750.750.710.710.71
    Georgia0.000.250.420.500.500.710.710.71
    Hungary0.670.750.750.750.750.710.710.76
    Kazakhstan0.000.080.420.420.500.710.710.71
    Kyrgyz Republic0.000.000.250.500.750.710.710.71
    Latvia0.250.420.670.750.750.710.710.71
    Lithuania0.250.420.670.750.750.710.710.71
    Macedonia, F.Y.R.0.580.580.670.750.750.710.710.71
    Moldova0.000.080.420.580.750.710.710.71
    Poland0.580.580.750.750.750.710.710.71
    Romania0.420.420.420.580.750.710.710.71
    Russia0.000.080.500.580.750.710.710.71
    Slovak Republic0.000.750.750.750.750.710.710.71
    Slovenia0.580.750.750.750.750.710.710.71
    Tajikistan0.000.080.330.420.750.710.710.65
    Turkmenistan0.000.000.170.250.500.470.470.47
    Ukraine0.000.080.250.170.500.710.710.71
    Uzbekistan0.000.000.330.330.750.710.710.65
    Private sector entry
    Albania0.000.300.300.400.500.520.560.55
    Armenia0.100.100.300.300.380.470.520.53
    Azerbaijan0.100.100.100.100.250.330.380.47
    Belarus0.100.100.200.300.440.470.380.33
    Bulgaria0.100.200.300.300.500.520.520.61
    Croatia0.500.500.600.700.750.670.720.71
    Czech Republic0.400.700.800.900.880.810.810.81
    Estonia0.200.200.400.600.810.750.760.82
    Georgia0.100.100.200.200.310.470.560.59
    Hungary0.300.500.600.700.810.810.810.87
    Kazakhstan0.100.200.200.200.380.420.560.59
    Kyrgyz Republic0.100.100.300.600.690.660.610.65
    Latvia0.100.200.300.400.630.610.710.71
    Lithuania0.100.300.400.600.750.660.710.71
    Macedonia. F.Y.R.0.500.500.500.600.630.560.610.61
    Moldova0.100.100.200.300.500.560.560.56
    Poland0.500.600.700.700.810.760.760.79
    Romania0.100.300.300.400.560.560.610.59
    Russia0.100.100.400.500.630.610.660.68
    Slovak Republic0.400.700.800.800.810.760.760.75
    Slovenia0.500.500.600.700.750.760.760.76
    Tajikistan0.100.200.200.200.380.330.330.33
    Turkmenistan0.100.100.100.100.250.240.240.36
    Ukraine0.100.100.200.100.310.470.520.54
    Uzbekistan0.100.100.200.300.440.560.560.54
    Trade and exchange regime liberalization
    Albania0.000.200.900.901.000.940.940.94
    Armenia0.000.100.400.400.500.710,940.94
    Azerbaijan0.000.000.200.200.250.470.470.53
    Belarus0.000.100.200.400.250.470,470.24
    Bulgaria0.500.900.900.901.000.940.940.94
    Croatia0.700.700.800.801.000.940.940.94
    Czech Republic0.000.800.900.901.001.001.001.00
    Estonia0.100.300.701.001.000,940.940.94
    Georgia0.000.300.300.300.250.470.710.94
    Hungary0.700.900.900.901.001.001.001.00
    Trade and exchange regime liberalization
    Kazakhstan0.000.100.400.400.500.710.940.94
    Kyrgyz Republic0.000.000.400.600.750.940.940.94
    Latvia0.000.200.500.801.000.940.940.94
    Lithuania0.000.200.5O0.901.000.940.940.94
    Macedonia, F.Y.R.0.700.800.800.901.000.940.940.94
    Moldova0.000,100.500.600.500.940.940.94
    Poland0.900.900.900.901.001.001.001.00
    Romania0.100.300.600.701.001.000.710.94
    Russia0.000.100.500.600.750.710.940.94
    Slovak Republic0.000.800.900.801.001.001.000.94
    Slovenia0.700.800.900.901.001.001.001.00
    Tajikistan0.000.000.000.100.250,470.470.47
    Turkmenistan0.000.000.100.100.250.240.240.24
    Ukraine0.000.100.200.100.250.710.710.71
    Uzbekistan0.000.000.200.200.500.470.470.41
    Legal reform
    Albania0.000.000.000.000.000.470.710.47
    Armenia0.000.000.000.000.000.470.710.71
    Azerbaijan0.000.000.000.000.000.240.470.24
    Belarus0.000.000.000.000.000.470.240.47
    Bulgaria0.000.000.000.000.000.710.940.71
    Croatia0.000.000.000.000.000.710.940.94
    Czech Republic0.000.000.000.000.000.940.940.94
    Estonia0.000.000.000.000.000.710.940.94
    Georgia0.000.000.000.000.000.470.470.47
    Hungary0.000.000.000.000.000.940.940.94
    Kazakhstan0.000.000.000.000.000.470.470.47
    Kyrgyz Republic0.000.000.000.000.000.470.470.47
    Latvia0.000.000.000.000.000.470.940.71
    Lithuania0.000.000.000.000.000.470.470.71
    Macedonia. F.Y.R.0.000.000.000.000.000.470.710.47
    Moldova0.000.000.000.000.000.470.710.47
    Poland0.000.000.000.000.000.940.940.94
    Romania0.000.000.000.000.000.470.710.71
    Russia0.000.000.000.000.000.470.710.71
    Slovak Republic0.000.000.000.000.000.710.710.71
    Slovenia0.000.000.000.000.000.710.710.71
    Tajikistan0.000.000.000.000.000.240.470.24
    Turkmenistan0.000.000.000.000.000.240.240.24
    Ukraine0.000.000.000.000.000.470.710.47
    Uzbekistan0.000.000.000.000.000.470.710.47
    Sources: De Melo, Denizer, and Gelb (1996); and EBRD Transition Reports.

    The structural reform indices for 1990–93 owe much to De Melo, Denizer. and Gelb (1996). They have been updated for 1994–97 using the transition indicators of the EBRD Transition Reports. The price liberalization index LIP is based on the price liberalization indicator of the EBRD (see EBRD, 1997), while the trade and exchange regimes index LEX is based on the EBRD’s trade and foreign exchange system indicator. Similarly, the private entry in markets and enterprise reform index LEN is based on the small-scale and large-scale privatization indicators, the governance and restructuring, and the banking reform and interest rate liberalization and securities markets indicators. The legal reform index is based on the EBRD’s overall legal transition indicator. To link the two series, the EBRD transition indicators have been rescaled in such a way that the values range between 0 and 1, with an EBRD score of 4* being reset to a value of 1. In case an index comprises more than one EBRD indicator (such as the overall index RI), the unweighted average of the individual rescaled indicators is used. For the one year (1994) for which observations of both the indices of De Melo, Denizer, and Gelb and the rescaled EBRD indicators were available, the two indices showed a very close match.

    Annex V Detailed Data on Growth and IMF Programs

    Table 15 provides detailed data for individual countries on growth, including an index of output and annual growth in so-called transition time, that is, the number of years since the start of transition. The main text explains this further and gives the starting point years for each country.

    Table 15.GDP Growth in Transition Economies (Transition Time)
    First Year of Transition T(1)Index (T(0)=100)Percent Change from Previous PeriodCumulative Growth Since T(0)1Number of Years of Decline Before Initial RecoveryCumulative Decline Since T(0) Before Initial RecoveryAverage Growth Since Initial Recovery
    T(0)T(1)T(2)T(3)T(4)T(5)T(6)T(7)T(8)T(9)T(1)T(2)T(3)T(4)T(5)T(6)T(7)T(8)T(9)
    Consistent growth
    Central Europe1990–92100.094.085.685.887.691.296.2100.1−6.0−8.90.32.04.25.54.05.02.714.44.3
    Croatia1992100.088.381.286.091.997.4103.7106.5−11.7−8.05.96.86.06.517652−18.85.6
    Czech Republic1990100.098.884.7B1.982.484.690.093.593.791.6−1.2−14.3−3.30.62.76.43.80.3−2.3−8.43−16.11.9
    Hungary1990100.096.585.082.481.984.335.686.890.895.4−3.5−11.9−3.1−0.62.91.51.34.65.1−4.64−18.13.1
    Poland1990100.088.482.284.487.692.198.6104.5111.6117.011.6−7.02.63.85.27.06.06.84.817.02−17.85.2
    Slovak Republic1990100.097.563.377.975.078.784.189.795.599.7−2.5−14.6−6.5−3.74.96.96.66.54.4−0.34−25.05.9
    Slovenia1992100.094.597.2102.4106.6110.3115.4119.9−5.52.85.34.13.54.63.919.91−5.54.0
    Baltics1992100.074.064.161.963.666.271.774.8−26.0−13.3−3.42.84.08.34.3−25.22.7−38.14.7
    Estonia1992100.078.472.070.773.776.784.888.2−21.6−8.2−1.84.34.010.64.0−11.83−29.35.7
    Latvia1992100.064.854.455.555.757.561.363.6−35.2−16.12.10.33.36.53.8−36.42−45.63.2
    Lithuania1992100.078.766.059.561.64.369.072.6−21.3−16.2−9.83.34.77.35.1−27.43−40.55.1
    CIS1992100.074.464.257.155.057.361.464.7−25.6−13.8−10.9−3.84.37.05.4−35.33.5−45.05.7
    Armenia1992100.047.740.642.845.848.449.953.5−52.3−14.85.46.95.83.17.2−46.52−59.45.7
    Azerbaijan1992100.077.959.949.143.744.246.851.5−22.1−23.1−18.1−11.01.35.810.0−48.54−56.35.6
    Belarus1992100.090.483.573.065.467.375.081.2−9.6−7.6−12.6−10.42.911.48.3−18.84−34.67.5
    Georgia1992100.055.241.236.537.441.345.847.2−44.8−25.4−11.42.410.511.02.9−52.83−63.56.6
    Kyrgyz Republic1992100.086.172.858.155.058,964.766.0−13.9−15.5−20.1−5.47.19.92.0−34.04−45.06.3
    Uzbekistan1992100.089.086.983.382.583.885.888.7−11.0−2.3−4.2−0.91.62.43.3−11.34−17.52.4
    Southeast Europe1992100.092.083.682.181.181.883.085.4−8.0−9.1−1.8−1.20.81.52.9−14.64.0−18.91.7
    Macedonia. F.Y.R.1992100.092.083.682.131.181.883.085.4−8.0−9.1−1.8−5.20.81.52.9−14.64−18.91.7
    Growth reversals
    Southeast Europe1990–91100.085.376.474.276.580.385.381.580.2−14.2−10.9−2.93.14.96.3−1.6−1.5−21.03.0−25.81.4
    Albania1991100.072.066.873.280.187.295.288.595.6−28.0−7.29.69.48.99.1−7.08.0−4.42−33.26.1
    Bulgaria1990100.090.980.374.473.374.576.267.963.165.3−9.1−11.7−7.3−1.51.72.2−10.9−7.0−3.5−34.74−26.7−2.3
    Romania1990100.094.482.275.076.179.134.738.081.975.9−5.6−12.9−8.81.53.97.13.9−6.9−7.3−24.13−25.00.2
    Little or no growth
    CIS1992100.083.275.561.056.353.451.250.4−16.8−9.3−19.2−7.8−5.I−4.1−1.6−49.65.3−49.60.5
    Kazakhstan1992100.094.786.075.269.069.370,769.0−5.3−9.2−12.6−8.20.52.0−2.5−31.04−31.00.0
    Moldova1992100.070.369.547.847.143.444.040.2−29.7−1.2−31.2−1.4−7.81.3−8.6−59.85−56.6−3.8
    Russia1992100.085.578.168.265.463.163.760.8−14.5−8.7−12.6−4.2−3.40.9−1.6−39.25−36.9−1.9
    Tajikistan1992100.071.063.251.244.842.943.645.9−29.0−11.0−18.9−12.5−4.41.75.3−54.15−57.13.5
    Turkmenistan1992100.094.785.068.963.258.443.245.2−5.3−10.2−19.0−8.2−7.7−25.94.5−54.86−56.84.5
    Ukraine1992100.083.071.254.943.243.442.141.4−17.0−14.222.9−12.2−10.0−3.0−1.7−58.67
    Sources: National authorities; and IMF staff estimates.

    Where the length of transition time varies across countries, the regional average has been calculated as the average of each country’s cumulative growth. In all other cases calculations were based on index averages.

    Tables 16 and 17 give information on Fund programs in each of the transition countries, including, respectively, the dates and amounts of each program, and the content of structural benchmarks in programs.

    Table 16.Arrangements with the IMF, 1990–98
    CountryArrangement1Approval DateOfficial End DateCanceled2PrecautionaryTotal Access to IMF CreditActually PurchasedRemaining BalanceIMF Quota3Purchase (percent of quota)Cumulative Purchase (percent of quota)
    (In millions of SDRs)
    AlbaniaSBA26-Aug-9225-Aug-93July 14,199320.013.16.925.052.5
    ESAFl4-Jul-9313-jul-9642.431.111.335.388.0140.5
    ArmeniaSBA28-Jun-9527-Jun-96Feb. 14.199643.913.530.467.520.0
    ESAF14-Feb-9613-Feb-99101.350.650.667.575.095.0
    AzerbaijanSBA17-Nov-9516-Nov-9658.558.5117.050.0
    ESAF20-Dec-9619-Dec-9993.655.638.0117.047.5
    EFF20-Dec-9619-Dec-9958.525.233.4117.021.5119.0
    BelarusSBAl2-Sep-9511-Sep-96196.350.0146.3280.417.617.8
    BulgariaSBAIS-Mar-91l4-Mar-92279.0279.0310.090.0
    SBA17-Apr-9216-Apr-93155.0124.031.0310.040.0
    SBA11-Apr-9431-Mar-95139.5116.223.2464.925.0
    SBAl9-Jul-96IB-Mar-9BCanceled.400.080.0320.0464.917.2
    off track
    SBA11-Apr-9710-Jun-98371.9247.6124.3464.953.3225.5
    CroatiaSBA14-Occ-94l3-Apr-9665.413.152.3261.65.0
    EFF12-Mar-9711-Mar-00353.228.8324.4261.611.016.0
    Czechoslovakia (former)SBA07-Jan-9l03-Apr-92619.5619.5590.0105.0
    SBA03-Apr-9202-Apr-93Dec.31, 1992236.036.0200.0590.06.1lll.l
    Czech RepublicSBA17-Mar-9316-Mar-94177.070.0107.0589.611.911.9
    EstoniaSBA16-Sep-9215-Sep-9327.927.931.090.0
    SBA27-Oct-9327-Mar-9511.611.646.525.0
    SBA11-Apr-9510-Jul-9614.014.046.5
    SBA29-Jul-9628-Aug-97Yes14.014.046.5
    SBA17-Dec-9716-Mar-99Yes16.116.146.5115.0
    GeorgiaSBA26-Jun-9527-Jun-96Feb. 28, 199672.222.250.0111.020.0
    ESAF28-Feb-9627-Feb-99166.5111.055.5111.0100.0120.0
    HungarySBA14-Mar-9013-Mar-91Feb. 20, 1991159.2127.431.8530.724.0
    EFF20-Feb-9119-Feb-94Sep. IS, 19931,114.0557.2556.8530.7105.0
    SBA15-Sep-9314-Dec-94Disagreement340.056.7283.3754.87.5
    with IMF
    SBA15-Mar-9614-Feb-98Yes264.2264.2754.8136.5
    KazakhstanSBA26-Jan-9425-Jan-95Off track123.874.349.5247.530.0
    SBA05-Jun-9505-Jun-96185.6185.6247.575.0
    EFF17-Jul-9616-Jul-99Yes309.4309.4247.5105.0
    Kyrgyz RepublicSBA12-May-9311-Apr-94Off track27.111.615.564.518.0
    ESAF20-Jul-9426-Mar-9888.288.264.5136.7154.7
    LatviaSBA14-Sep-9213-Sep-9354.954.961.090.0
    S8A15-Dec-9314-Mar-9522.99.213.791.510.0
    SBA21-Apr-9520-May-96Yes27.527.591.5
    SBA24-May-9623-Aug-97Yes30.030.091.5
    SBA10-Oct-9709-Apr-99Yes33.033.091.5100.0
    LithuaniaSBA21-Oct-9220-Sep-9356.956.969.082.5
    SBA22-Oct-9321-Mar-95Oct. 24, 199425.95.220.7103.55.0
    EFF24-Oct-9423-Oct-97134.6113.920.7103.5110.0197.5
    Macedonia. FY. R.SBA05-May-95O5-Jun-9622.321349.645.0
    ESAF11-Apr-9710-Apr-0054.618.236.449.636.781.6
    MoldovaSBA17-Dec-9316-Mar-9551.851.890.057.5
    SBA22-Mar-9521-Mar-9658.532.426.190.036.0
    EFF20-May-9619-May-9935.037.597.590.041.7135.2
    PolandSBA05-Feb-9004-Mar-9l545.0357.5187.5680.052.6
    EFF18-Apr-9117-Apr-94Mar.8, 19931.224.076.51147.5680.011.3
    SBA08-Mar-9308-Apr-94476.0357.0119.0988.536.1
    SBA05-Aug-9404-Mar-96333.3283.350.0988.528.7128.6
    RomaniaSBA11-Apr-9110-Apr-92380.5318.162.4523.460.8
    SBA29-Mar-9223-Mar-93314.0261.752.3523.450.0
    SBA11-May-9424-Apr-97Off track320.594.3226.2754.112.5
    SBA22-Apr-9721-May-9B301.5120.6180.9754.116.0139.3
    Russian FederationSBA4OS-Aug-9204-Jan-93719.0719.02,876.025.0
    SBA11-Apr-9510-Apr-96Mar. 26,19964,313.14,313.14,313.1100.0
    EFF26-Mar-9625-Mar-996,901.03,836.33,064.74,313.188.9213.9
    Slovak RepublicSBA22-Jul-9421-Mar-96115.832.283.7257.412.512.5
    TajikistanSBA408-May-9607-Dec-9615.015.060.025.025.0
    UkraineSBA07-Apr-9506-Apr-96Off track997.3538.7458.7997.354.0
    SBA10-May-9609-Feb-97598.2598.2997.360.0
    SBA25-Aug-9724-Aug-98398.9161.3217.6997.318.2132.2
    UzbekistanSBA15-Dec-9517-Mar-97Off track124.765.559.2199.532.832.8
    Sources: IMF, MONA database and IFS, Transactions with the Fund

    EFF, Extended Fund Facility; ESAF, Enhanced Structural Adjustment Facility; SBA, Stand-By Arrangement. For a description of IMF facilities, see http:\www.imf.org.

    No information available on whether cancellation was result of program going off track (except for one country).

    At date of approval of arrangement. In November 1992 all quotas were raised.

    First credit tranche.

    Table 17.Indicators of IMF Program Design: Distribution of Structural Benchmarks According to Structural Benchmark Groups
    CountryArrangementStructural Benchmarks1Total
    1234567
    AlbaniaESAF4145441234
    In percent1231215121235100
    ArmeniaSBA3421282435
    ESAF275418
    Total34419132853
    In percent6883625415100
    AzerbaijanEFf33I10471341
    ESAF3311047331
    SBA232457326
    Total6942413211998
    In percent89424132119100
    BelarusSBA115523421
    In percent552424101419100
    BulgariaSBA31245419
    SBA21318
    Total332376428
    In percent1111711252114100
    CroatiaEFF121326149
    SBA44311
    Total121769160
    In percent2321210152100
    EstoniaSBA231623118
    In percent111763311176100
    GeorgiaESAF21554522
    SBA1382317
    Total1241377539
    In percent351033181813100
    HungarySBA112215
    In percent78013100
    KazakhstanEFF13854223
    SBA138113
    SBA11213311
    Total24391115347
    In percent4961923326100
    Kyrgyi Rep.ESAF171084535
    SBA4116
    Total1471085641
    In percent2101724201215100
    LatviaSBA1910
    SBA117
    Total111012
    In percent8883100
    LithuaniaEFF2161111
    In percent1895599100
    Macedonia, F.Y.R.SBA1871219
    ESAF533617
    Total113104836
    In percent33628122100
    MoldovaEFF61214116
    SBA4132313
    SBA2125
    Total1222S39134
    In percent3566159263100
    PolandSBA21442114
    SBA1113
    Total31543117
    In percent1862924186100
    RomaniaSBA11437319
    SBA444315
    Total118711634
    In percent3324213218100
    RussiaEFF2111876237
    SBA3126
    Total5111976443
    In percent12724416149100
    UkraineSBA2115312
    SBA122121211
    Total322236523
    In percent13999132672100
    UzbekistanSBA4322112
    In percent332517178100
    Source: IMF, MONA database.

    Numbers indicate the following structural benchmark groups: I, trade/exchange systems; 2, pricing and marketing; 3, public enterprise; 4, tax/expenditure reform; 5, financial sector; 6, systemic/ownership reform; and 7, other.

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    All of these results, however plausible, need to be interpreted with caution because of the measurement and methodological problems involved, not least because many of the variables used are likely to be highly correlated. The pitfalls are well reflected in a recent paper by Sala-i-Martin (1997).

    Bruno and Easterly (1998) suggest the higher range; others, such as Fischer (1993), Sarel (1996), IMF (1997), and Cottarelli and Doyle (1999). find the threshold to be much lower.

    It is a simplification to say that all the efficiency improvements (all but the last item in the list) can come about without new net investments; what is meant here is that often the investment required is small. Also, such efficiency improvements can take place at the sector or firm level even if aggregate net investment in the economy is zero, since new gross investment is directed not to replace depreciated stocks in “old” industries but to expand it in the “new”ones.

    Mongolia is not included in this study.

    The CMEA consisted of Bulgaria, Cuba, the former Czechoslovakia, the former German Democratic Republic, Hungary, Mongolia. Poland, Romania, the former Soviet Union, and Vietnam.

    These official data may include breaks in series attributable to revisions of GDP, and there may be some reason to believe that the underestimates are less serious for growth rates than for absolute levels.

    The time lag in factor reallocation was particularly pronounced in the transition economies, since the labor market was underdeveloped, labor mobility was severely impeded by housing shortages, and capital markets were similarly underdeveloped.

    See IMF (1994) and also Tarr (1994), who shows that the BRO on average were subjected to greater terms of trade shocks than most CEE countries. Koen and De Masi (1997) also provide evidence on price distortions.

    Wolf (1997) attempts a statistical explanation of the political will to reform.

    The concept of “transition time” is developed in Berg and others (1999).

    These geographical categories, which are based on a consideration of growth performance, to some degree reflect the initial conditions noted earlier, with central Europe and the Baltics showing better growth performance than southeast Europe and most of the CIS.

    The period before the initial level of GDP is recovered is in most cases probably exaggerated, owing both to the probable underestimation by official statistics of the size of the unofficial economy during the transition and to the likelihood that initial GDP was in most cases overstated since it did not reflect the welfare losses from disequilibrium pricing and associated shortages and queues.

    In the case of Bulgaria, this can in part be attributed to a break in the GDP time series; in the case of Romania, the output decline had already started during the 1980s.

    As noted in footnote 12, however, initial output in most of these countries was overstated.

    Unfortunately, time series for output shares at constant prices—a preferable measure—were available for only a few countries for this study. It should be kept in mind that changes in output shares at current prices reflect changes to both real output and relative prices. Data available elsewhere on output shares in constant prices for countries in transition tend to confirm the general trend of an increasing services sector share, and a transition from heavy to lighter, more Western-oriented industry (see PlanEcon, 1998: Vienna Institute for Comparative Economic Studies, 1997; and World Bank, 1996b).

    Sevcral countries—notably Armenia, Albania, the Kyrgyz Republic, and Tajikistan—have shown an increase in recent years in the share of output generated in agriculture, although in some cases this may reflect as much a sharp decline in nonagricultural output as a recovery in agriculture.

    A World Bank project in the mid-1990s provided considerable firm level data for Poland. A more recent World Bank project has begun to put together survey results for several thousand firms in 12 of the transition economies. Some preliminary results (Pohl and others, 1997) that focus on performance rather than reallocation per se are discussed in Box 3.

    “Unemployment trends are not easy to analyze because data are even less systematic and of poorer quality than for employment or output. Aslund, Boone, and Johnson (1996) do attempt to review the experiences more broadly and arrive at two principal conclusions. First, early and successful reformers experience sharp unemployment, which then gradually declines as recovery occurs. Second, slow reformers appear to succeed in keeping unemployment low, but it is not clear how much of this is due to a slower decline in output and how much to an underestimation of hidden unemployment.

    Based on a sample of 15 countries for which productivity data were available since 1992, and a sample of 19 for which such calculations could be made beginning in 1993.

    For the 12 (out of 16) “recovering” countries for which sufficient employment data were available, overall employment in 9 countries continued to decline in the year in which the recovery of output occurred. (This tends to be a standard cyclical pattern in industrial countries as well.)

    Econometric analysis, reported later in this paper, did not, however, find a statistically significant effect of the degree of openness of the economy on growth.

    Adequate data do not exist to test the hypothesis that those countries entering into consistent growth have been more successful in shifting investment from traditional industries to services and more dynamic industrial branches.

    The econometric analysis summarized in Annex III does not find a systematic contribution of aggregate investment to the output recovery in transition economies. This in no way denies, however, the key importance of individual investment projects in spurring growth in certain sectors, or that the level of aggregate investment does become more critical in sustaining growth as the recovery proceeds.

    See also Annex II, which sets out IMF country economistsx2019; qualitative assessments of factors that have either impeded or promoted growth.

    The econometric analysis in Annex III does not purport to reproduce more completely specified models found in the literature such as Berg and others (1999) or De Melo and others (1997), but rather focuses on the key stylized facts, in the same spirit as Ghosh and Phillips (1998), who note that the complex interactions that doubtless exist are difficult to model because “growth has many possible determinants” and “theory provides little guidance on the appropriate specification.” The last phrase may be particularly relevant in transition countries.

    These papers also analyzed the factors behind disinflation success such as budget deficits and exchange rates.

    The magnitudes of these trade-offs are examined later in this section, with reference to Annex III.

    A complete discussion is found in a background paper by Havrylyshyn, izvorski, and van Rooden (1998),

    In an empirical study of growth in transition. Wolf(1997) attempts to correlate growth with investment ratios, finding that it is negative rather than positive, contrary to other recent growth studies. Annex III also reports similar results.

    It is probably due to this simultaneity that the econometric analysis of growth was unable to isolate foreign direct investment as an explanatory factor.

    There are several instances of countries that began recovery with inflation still around or above 50 percent. These include the three reversal cases plus Belarus and Uzbekistan, whose recent recovery is questioned by some analysts, and also Armenia and Croatia.

    Note the low private sector shares for Belarus and Uzebekislan, which is one reason to believe the consistent growth of these countries may well not be sustainable, unless privatization is accelerated.

    The underlying evidence is surveyed by Havrylyshyn and McGettigan (1999). The survey of IMF country economists also lends weight to this conclusion (Annex II).

    The continued use of interenterprise arrears may itself be a sign of incomplete imposition of a hard budget, though other factors such as underdeveloped financial systems also have contributed to the importance of such arrears and barter transactions. Detailed analysis of these phenomena is nearly impossible because of data problems; indeed, unlike the issue of privatization, even selective, country-specific studies are very limited.

    One factor that might also play a role but that has not generally been analyzed in the growth studies, perhaps because of the difficulty of measuring it, is the fragility of banking systems and the low degree of financial intermediation.

    Mn Belarus, within-year inflation exceeded 150 percent in 1998.

    Members provided IMF financial support under the STF facility included the BRO (except for Tajikistan and Turkmenistan) as well as Bulgaria, Croatia, F.Y.R. Macedonia, Romania, and the Slovak Republic.

    The 25 transition countries make up less than 10 percent of the IMF’s membership in terms of quotas.

    These include the Compensatory and Contingent Financing Facility and postconflict emergency assistance.

    For indicators of program design in the structural area, see Table 17 in Annex V.

    An obvious exception to this, of course, is with respect to the exchange rate regime.

    The MONA database is maintained by the IMF’s Policy Development and Review Department. A full discussion is provided in the background paper of Mercer-Blackman and Unigovskaya (1998).

    This bias results from the fact that the index can only be calculated for as long as new performance criteria were being set under programs. Thus, a country whose program had gone off track completely might still register an implementation index value of. say. 60 percent or 70 percent, compared with a country with an index value of. say, 80 percent or 90 percent that had remained essentially on track throughout the program period.

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    Note: For information on the title and availability of Occasional Papers not listed, please consult the IMF Publications Catalog or contact IMF Publication Services.

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