Appendix Growth Regressions and Data Description

Abdul Abiad, Ashoka Mody, Susan Schadler, and Daniel Leigh
Published Date:
January 2007
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Panel Growth Regressions

The analysis of growth performance covered a broad range of countries over the period 1984–2004. While, in principle, the regressions attempted to cover the set of developed and developing economies listed in Table A1, gaps in the data reduced the number actually included in the regression by an extent that depended on the specification. In particular, because more data were available for recent years, the sample size increased over time. The dependent variable in the regressions are growth rates of real per capita GDP in PPP terms, calculated over nonoverlapping five-year periods (e.g., 2000–04, 1995–99, 1990–94), providing time-series as well as cross-sectional variation.

Table A1.Global Sample of Economies
AlgeriaGuinea-BissauPapua New Guinea
ArmeniaHong Kong SARPhilippines
BangladeshIran, I.R. ofRussia
BelarusIrelandSaudi Arabia
BoliviaItalySerbia and Montenegro
BotswanaJamaicaSierra Leone
Burkina FasoKazakhstanSlovenia
CameroonKenyaSouth Africa
ChileKuwaitSri Lanka
Congo, Dem. Rep. ofLibyaSwitzerland
Congo, Rep. ofLithuaniaSyrian Arab Rep.
Costa RicaMadagascarTaiwan Province of China
Czech RepublicMalaysiaThailand
Côte d’IvoireMaliTogo
DenmarkMexicoTrinidad and Tobago
Dominican Rep.MoldovaTunisia
El SalvadorMozambiqueUkraine
EstoniaMyanmarUnited Arab Emirates
EthiopiaNamibiaUnited Kingdom
FinlandNetherlandsUnited States
FranceNew ZealandUruguay
GabonNicaraguaRepública Bolivariana
Gambia, TheNigerde Venezuela

Variables used for explaining growth were influenced by recent findings on the robustness of growth determinants and the context of the CEECs. The robustness of growth influences was judged primarily by the results in SDM (2004). In addition to initial income per capita, other controls suggested by neoclassical growth models were population growth, the price of investment, and human capital accumulation (proxied by the average years of higher education). As discussed in the main text, additional controls in the benchmark regression include partner country growth, openness to trade, the size of government (proxied by tax revenues to GDP), and a measure of institutional quality. Further details on the construction of these variables can be found in the data description below.

One novel feature of the growth regressions is the inclusion of an interaction term between institutional quality and per capita income. Background studies for this paper found that institutional quality affects not only the steady-state level of income but also the speed at which countries converge to the steady state. This can be seen in Figure 5.1, which groups observations into quartiles of institutional quality and plots the best-fit lines through each quartile. Better institutional quality is associated not only with higher steady-state incomes (reflected in the best-fit lines shifting up) but also with a higher speed of convergence (reflected in the steeper slopes, so that countries with superior institutions move more quickly toward their steady-state income levels).

Following Barro and Sala-i-Martin (2004), we use the seemingly unrelated regression procedure to estimate the growth model. This procedure allows for country random effects that are correlated over time; that is, it estimates each five-year period as a cross section but controls for the possibility that the residuals in each cross-section regression are correlated, as they are likely to be in these growth regressions. Our results, however, are robust to using different econometric specifications, including simple random effects and cluster ordinary least squares where standard errors are adjusted for within-group correlation. As an additional robustness check, which also sheds substantive light on differences across country groups, the regressions were run on different subsamples—developing countries, emerging market countries, and advanced economies—to assess whether parameter estimates change systematically across the groups.

Regressions for different country groups (reported in Table A2) illustrate variations across the groups using a core set of explanatory variables. These variables behave directionally the same way across country groups, but the size and significance of coefficients are quite different. Thus, there is support both for commonality of growth drivers and for the dissimilarity of their potency. Of interest is the finding on variations in conditional convergence across country groups. In the group of non-emerging-market developing countries, even conditional convergence seems to be absent. In contrast, emerging and advanced economies are characterized by both absolute and conditional convergence. For this reason, when we use the global sample for analyzing growth, it is important to make allowance for variations in convergence rates. Other growth determinants similarly operate with differing force across country groups.

Table A2.Growth Regressions with Core Controls, Using Different Country Samples
GlobalDeveloping CountryEmerging Market CountryAdvanced and Emerging Market Country
Log of per capita GDP–0.43(2.40)–0.16(0.54)–1.71(6.10)–1.71(7.54)
Population growth–0.34(5.34)–0.28(3.13)–0.68(4.55)–0.68(6.02)
Relative price of investment–0.59(3.80)–0.30(1.58)–1.18(3.79)–1.09(3.88)
Dummy, 2000–046.81(4.47)3.73(1.65)17.33(6.65)19.36(8.87)
Dummy, 1995–996.12(4.05)2.93(1.31)16.24(6.22)18.87(8.64)
Dummy, 1990–945.23(3.38)1.10(0.48)18.05(6.89)18.90(8.60)
Dummy, 1985–895.81(3.82)1.85(0.81)17.49(6.79)19.22(8.88)
Dummy, 1980–845.28(3.42)1.62(0.69)17.58(6.79)18.44(8.48)
Dummy, 1975–796.55(4.22)3.09(1.32)19.20(7.49)19.54(8.97)
Dummy, 1970–747.47(4.90)4.14(1.80)19.02(7.56)20.15(9.40)
Dummy, 1965–697.41(4.92)3.68(1.62)19.19(7.89)20.32(9.62)
Number of observations740356218384
Source: IMF staff calculations.Note: Dependent variable is five-year growth in real GDP per capita (PPP). Estimation method is seemingly unrelated regression.
Source: IMF staff calculations.Note: Dependent variable is five-year growth in real GDP per capita (PPP). Estimation method is seemingly unrelated regression.

The two benchmark regressions are presented in Table A3. So as not to introduce a proliferation of results, we worked toward two “benchmark” regressions: a suitably modified “global” regression, which deals with variations in convergence rates across countries, and an advanced economy–emerging market regression, which drops developing countries to ensure that the results are not being driven solely by low-income countries. As we report below, both regressions give qualitatively similar results in assessing the performance of the CEECs relative to their peers. However, the results from the global sample do a somewhat better job of matching actual and predicted growth rates. Following the discussion in the main text, explanatory variables are placed into two groups, those that are beyond the short-term control of policymakers and those that are potentially influenced by policy. The coefficient estimates are all correctly signed, are of plausible magnitudes, and are all significant with the exception of the relative price of investment in the global regression and the schooling variable in the advanced economy–emerging market subsample. The presence of the interaction term between institutional quality and initial income in the global regression implies that one cannot interpret the coefficient on (uninteracted) initial income as an indicator of conditional convergence; the convergence parameter in this regression is given by β0 + β1 · InstitutionalQuality, where β1 is the coefficient on the interaction term. The negative sign on β1 implies that as institutional quality improves, convergence speeds increase, supporting the scatterplot in Figure 5.1.

Table A3.Growth Regression Estimates
Global SampleAdvanced and Emerging Market Country Sample
Log of per capita GDP1.36(1.89)–2.27(6.34)
Population growth–1.46(8.94)–1.27(7.42)
Partner country growth0.62(3.39)0.61(3.24)
Relative price of investment goods–0.22(0.84)–0.75(2.41)
Openness ratio0.01(3.01)0.01(3.85)
Government taxation ratio–0.05(2.47)–0.02(1.20)
Institutional quality0.41(4.07)0.03(1.88)
Institutional quality * log of per capita GDP–0.04(3.86)
Dummy, 2000–04–10.66(1.69)20.93(6.74)
Dummy, 1995–99–11.32(1.80)20.31(6.50)
Dummy, 1990–94–10.33(1.65)20.96(6.72)
Dummy, 1985–89–11.12(1.78)20.51(6.50)
Number of observations96, 84, 52, 5658, 51, 41, 41
R-squared0.47, 0.02, 0.3, 0.370.58, –0.17, 0.36, 0.36
Source: IMF staff calculations.Note:The dependent variables are the growth rates of per capita GDP for the periods 2000–04, 1995–99, 1990–94, and 1985–89. Estimation method is seemingly unrelated regression.
Source: IMF staff calculations.Note:The dependent variables are the growth rates of per capita GDP for the periods 2000–04, 1995–99, 1990–94, and 1985–89. Estimation method is seemingly unrelated regression.

The same regressions can be run using TFP growth and capital per capita growth as the dependent variables, enabling analysis of the channels through which these variables affect growth. These growth accounting regressions, whose results are described in the main text, can be found in Table A4.

Table A4.Growth Accounting Regressions
Global SampleAdvanced and Emerging Market Country Sample
GDP per capitaCapital per capitaTFPGDP per capitaCapital per capitaTFP
Log of per capita GDP1.36(1.89)1.08(1.28)1.55(2.14)–2.27(6.34)–2.39(6.92)–1.43(4.55)
Population growth–1.46(8.94)–0.87(4.89)–1.09(7.87)–1.27(7.42)–0.88(4.87)–0.99(6.94)
Partner country growth0.62(3.39)0.53(2.72)0.62(3.78)0.61(3.24)0.64(3.31)0.59(3.44)
Relative price of investment–0.22(0.84)–0.26(1.02)0.11(0.58)0.20(1.40)–0.94(2.84)–0.28(1.08)
Openness ratio0.009(3.01)0.009(2.99)0.009(4.08)0.010(3.85)0.009(3.44)0.009(3.92)
Government taxation ratio–0.05(2.47)–0.08(3.92)–0.02(0.99)–0.02(1.20)–0.05(2.45)–0.008(0.46)
Institutional quality0.41(4.07)0.39(3.26)0.32(3.19)0.03(1.88)0.08(5.18)–0.01(0.75)
Institutions * log of per capita GDP–0.04(3.86)–0.03(2.60)–0.04(3.43)
Dummy, 2000–04–10.66(1.69)–11.21(1.45)–12.68(1.92)20.93(6.74)19.69(6.44)13.92(5.21)
Dummy, 1995–99–11.32(1.80)–11.28(1.46)–13.03(1.97)20.31(6.50)19.56(6.38)13.35(4.97)
Dummy, 1990–94–10.33(1.65)–10.54(1.38)–12.51(1.91)20.96(6.72)20.21(6.59)13.76(5.13)
Dummy, 1985–89–11.12(1.78)–11.54(1.51)–12.98(1.98)20.51(6.50)18.82(6.09)13.63(5.01)
Number of observations288255255191187187
Source: IMF staff calculations.
Source: IMF staff calculations.

Benchmark Models: Growth Predictions

Three general points emerge when comparing the benchmark model predictions and actual growth outcomes during the period 2000–04.

  • Both models predict well. The relative rankings of the country groups are well matched: high-growth countries or regions have high predicted growths and vice versa. This can be seen in Figure A1, which reports the results for regional country groups and for the top five emerging market performers excluding the Baltics.
  • Predictions are particularly close in absolute, or cardinal, terms in the mid-ranges of growth rates and diverge at the two extreme ends. At the high end, the Baltics and the top five performers are predicted to have lower growth rates than they actually achieved. In contrast, Latin America, which achieved particular low average per capita growth during this period should, the models say, have achieved higher growth. Thus, it appears as if extreme growth rates are the outcomes of special circumstances not easily captured by such growth models. Countries with very rapid growth already have the potential to grow fast, as implied by their high predicted growth rates, but, in addition, are positioned to benefit from positive surprises. In contrast, countries with lower growth potential are the ones most hurt by negative growth surprises.
  • Finally, while both models do well, the “global” model outperforms slightly with somewhat better predictions. While it is difficult to be precise in assessing the source of this difference, there is probably one substantive reason and another technical reason. Substantively, the global model allows for differing speeds of convergence. While the speeds of conditional convergence in emerging market countries and advanced economies are close, it appears that advanced countries, with their better institutions, may converge slightly faster. We are not able to pick up that nuance in the smaller sample. This leads to the second technical reason for the difference. In the smaller sample, the variation in explanatory variables is smaller, making it harder to achieve estimates with great precision.

Figure A1.Emerging Market Countries: Actual and Predicted Per Capita GDP Growth, 2000–04

(In percent, PPP, annual average)

Source: IMF staff estimates.

The models are almost spot-on in predicting the average growth rates in the CE-5, but underpredict growth in the Baltics (Figure A2). For the CE-5, both the actual and predicted growth rates are around 3½ percent a year. Once again, the predicted ranks for country growth rates line up with the actual performance and in no case is the difference between actual and predicted growth rates more than ½ percentage point. With respect to the Baltics, which achieved an annual average growth rate of 7½ percent over this period, the global model predicts a 6½ percent growth rate and the advanced economy–emerging market model predicts a little over 5½ percent. Once again, looking at the individual countries, growth rates are underpredicted, but less so by the global model, which comes close to matching Lithuania’s actual achievement.

Figure A2.CEECs: Actual and Predicted Per Capita GDP Growth, 2000–04

(In percent, PPP, annual average)

Source: IMF staff estimates.

Decomposition of Growth Differences

The growth regressions can provide useful decompositions of the importance of various factors in explaining differences in growth rates across regions or countries (Tables A5 and A6). The decomposition of growth predictions when no interaction terms are present is straightforward: it is given by (suppressing subscripts):

ŷŷR = β′(X–XR),

where the superscript R denotes the reference country.

Table A5.CE-5: Decomposition of Growth Differences
CE-5 Performance (2000–04) Relative to
EastAsiaLatinAmericaBaltic countriesTop five emerging market countriesOECD countries
Difference in GDP per capita growth to be explained–0.12.7–4.0–2.91.4
Global regressionAdvanced and emerging market country regressionGlobal regressionAdvanced and emerging market country regressionGlobal regressionAdvanced and emerging market country regressionGlobal regressionAdvanced and emerging market country regressionGlobal regressionAdvanced and emerging market country regression
Difference explained by “exogenous” variables
Log of per capita GDP–0.45–0.52–1.19–1.49–0.79–0.93–1.14–1.441.061.14
Population growth1.861.622.281.98–1.00–0.870.240.210.900.78
Partner country growth–0.87–0.86–0.18–0.18–0.82–0.80–0.63–0.61–0.21–0.20
Difference explained by “policy-influenced” variables
Relative price of investment–0.01––0.05–0.16
Institutional quality0.–0.11
Tax revenue/GDP–0.57–0.25–0.44–0.20–0.18–0.08–0.53–
Total explained difference–0.54–0.361.351.38–2.96–2.25–1.77–1.171.451.47
Source: IMF staff calculations.
Source: IMF staff calculations.
Table A6.Baltic Countries: Decomposition of Growth Differences
Baltic Countries’ Performance (2000–04) Relative to
EastAsiaLatinAmericaCE-5Top five emerging market countriesOECD countries
Difference in GDP per capita growth to be explained3.
Global regressionAdvanced and emerging market country regressionGlobal regressionAdvanced and emerging market country regressionGlobal regressionAdvanced and emerging market country regressionGlobal regressionAdvanced and emerging market country regressionGlobal regressionAdvanced and emerging market country regression
Difference explained by “exogenous” variables
Log of per capita GDP0.330.40–0.43–0.570.790.93–0.39–0.511.842.07
Population growth2.862.483.272.841.000.871.241.071.901.65
Partner country growth–0.05–0.050.640.630.820.800.190.190.610.60
Difference explained by “policy-influenced” variables
Relative price of investment–0.15–0.540.020.06–0.15–0.52–0.05–0.18–0.19–0.68
Institutional quality–0.01––0.03––0.23
Tax revenue/GDP–0.39–0.17–0.27––0.36–
Total explained difference2.421.894.323.622.962.
Source: IMF staff calculations.
Source: IMF staff calculations.

In the benchmark model, the effects of the interaction between initial income per capita and institutional quality needs to be reallocated into the part that is due to differences in initial income and the part that is due to differences in institutional quality. This is done as follows. Predicted growth for a country/region and the reference country/region are given by

Subtracting the second equation from the first gives

To reallocate the second term to differences in initial income and institutional quality, add and subtract β2It1yt1R and rearrange this with the last term above to get:

Finally, combine terms to get

Note that one can also perform the decomposition by adding and subtracting β2It1Ryt1, which would give a similar formula:

This decomposition allocates the cross term, β2(yt1yt1R)(It1It1R), to differences due to initial income, while the first decomposition allocates it to differences due to institutional quality. Since the cross-term is due to both types of differences, this choice is arbitrary.

A final alternative would be to “split the difference” and attribute equal parts of the cross term to initial income differences and institutional quality differences:

This also has the advantage of being symmetric, so that a decomposition of y^t1y^t2 (that is, where country/region 2 is used as the reference) will provide the same breakdown as that for y^t2y^t1 (where country/region 1 is used as the reference). This is the decomposition formula used here.

Modeling Financial Integration and Growth in the EU

This section provides a brief description of the model of financial integration and growth in the EU, which was used in Section VI of the main text. Within this group of countries there is a clear link between growth and current account deficits, with higher growth being associated with larger current account deficits, both over short one-year horizons and over longer periods (Figure A3). The best-fit line suggests that an increase in the current account deficit of 2 percentage points of GDP is associated with a 1–1.4 percentage point increase in GDP growth. Deviations from the best-fit line are also informative, as they show that some countries are growing rapidly at present without incurring significant external liabilities, while others could be expected to grow faster given the level of the current account (or conversely, that they should have a lower current account deficit given their present growth rates).

Figure A3.Current Account Balances and GDP Growth in the European Union, 2000–04

Sources: IMF, World Economic Outlook; and Penn World Tables.

Theoretical models suggest, however, that this relationship is bidirectional and complex. Current accounts are affected by the level of per capita income, with lower levels of per capita income associated with greater external borrowing. In addition, Blanchard and Giavazzi (2002) note that the growth rate of income can also affect the current account, as it is an indicator of future growth prospects, and also captures cyclical effects of output movements on the current account. But current account deficits also affect growth, in two ways. Most obviously, external borrowing removes constraints on investment and consumption. An additional effect is suggested by open-economy versions of the neoclassical growth model, as elaborated, for example, by Barro and Sala-i-Martin (2004). In an open economy, if factors are fully mobile and the technology across countries does not differ, factor returns should equalize almost instantaneously, achieving income convergence. If, however, some forms of capital (e.g., human capital) provide unacceptable security for loans, then the extent of foreign debt will be limited by quantity of physical capital that can serve as collateral. In such a model, Barro and Sala-i-Martin write, “the opportunity to borrow on the world credit market … will turn out to affect the speed of convergence” (p. 105). Empirically, this suggests that the coefficient on per capita income in standard growth regressions may itself be influenced by the current account, as explained below.

The empirical specification of the above model consists of two simultaneous equations for the current account and for growth. In equation (1), growth in per capita income in country i in year t, Δyit, depends on lagged income relative to the steady-state income level, (yit1yt1*). The steady-state income level, yt*, is allowed to change over time, but is assumed to be the same for all the countries in the sample. If poor countries grow faster as they converge to income levels of their richer neighbors, then the coefficient on lagged relative income should be negative. Here, this “speed of convergence” coefficient consists of two parts: a part that is influenced by the current account, α2cait–1, and an independent part, α1t. If current account deficits (cait<0) accelerate income convergence, then the coefficient α2 should be positive. The specification also allows for the possibility that the current account influences actual growth directly, and this effect is captured by the terms α3cait–1. In addition, growth is allowed to be influenced by standard neoclassical growth controls, that is, schooling and population growth, that are denoted by matrix Z1,it. The growth equation is thus

where x is the steady-state growth rate, often associated with the rate of technological progress in the literature. Finally, actual growth is allowed to be influenced by cyclical factors that may change from year to year. For this reason, equation (1) is augmented by a year dummy, Dt, which equals one in year t and zero otherwise. The equation can be rewritten as

where the term α0t=xα1tyt1*+α5Dt, and (v1i1it) represents a mean-zero composite error term.

Equation (3) describes the dynamics of the current account. The current-account-to-GDP ratio in country i in year t, cait, depends on the current level of income, yit, on current growth, Δyit, and on the dependency ratio, denoted by Z2. Other things equal, a country with a relatively high dependency ratio is expected to save less.

The specification is largely standard, except that the effect of income per capita on the current account is allowed to vary over time, following Blanchard and Giavazzi (2002). If the process of increasing financial integration in Europe enabled poor countries to borrow more and rich countries to lend more, then one would expect the coefficient on relative income, β1t to increase over time. As in standard specifications, current growth also enters the equation, both as a predictor of future income and in order to capture cyclical effects of output movements on the current account. The effect of growth on the current account is also allowed to vary over time. Finally, as in the growth equation, the equation has a common time effect, captured by the year dummy, Dt. The equation can thus be rewritten as:

where the term β0t=β1tyt*+β4Dt, and (v2i2it) represents a mean-zero composite error term.

The estimation method used is three-stage least squares, a standard technique for the estimation of simultaneous equations in the panel data context. This method, first proposed by Zellner and Theil (1962), permits the estimation of a system of equations, where some of the explanatory variables are endogenous. Here, both the current account and growth are explanatory variables and are endogenous. The three-stage least-squares procedure uses an instrumental variable approach to produce consistent estimates and generalized least squares to account for the correlation structure in the disturbances across the equations. For further discussion of the three-stage least-squares approach to estimation, see, for instance, Greene (2003, pp. 405–407). Table A7 presents the estimation results based on EU-25 data from 1975 to 2004.

Table A7.Growth and Current Account Deficit Regressions
Growth EquationCurrent Account Deficit Equation
Log of per capita GDP1–4.76–10.52
Population growth–0.06
Current account deficit3.68
Log of per capita GDP * CA deficit–0.39
Old-age dependency ratio0.08
Growth of per capita GDP10.12
Number of observations503503
Source: IMF staff calculations.Note: For ease of exposition, the table presents results in terms of the current account deficit rather than the current account balance. Absolute value of z-statistics in brackets. *, **, and *** indicate significance at 10 percent, 5 percent, and 1 percent levels, respectively.

The coefficients on income and on growth are time-varying. For these variables, the table shows the parameter estimates for 2004.

Source: IMF staff calculations.Note: For ease of exposition, the table presents results in terms of the current account deficit rather than the current account balance. Absolute value of z-statistics in brackets. *, **, and *** indicate significance at 10 percent, 5 percent, and 1 percent levels, respectively.

The coefficients on income and on growth are time-varying. For these variables, the table shows the parameter estimates for 2004.

Once the parameters are estimated, the model can be used to generate predicted values of the current account and growth, along with 95 confidence intervals. These benchmark values can be compared to actual outcomes to assess the performance of growth and the current account. The (in-sample) predicted values are obtained using the equations:

where the “^” superscripts denote estimates. For each period t, the matrix of prediction standard errors is denoted by st. The standard errors are computed using the following formula:

where xt is the matrix of right-hand-side variables up to and including period t, and V is the estimated variance covariance matrix of the parameter estimates. Standard error bands around the predicted values can then be computed using a band of ±1.96 times the prediction standard errors. Estimates of the key parameters as well as the predicted current accounts and growth rates can be found in Table A7, the table in Box 6.1, and Figures 6.3 and 6.4. Further estimation details, as well as robustness checks, can be found in Abiad and Leigh (2005).

Data Description

The set of countries covered by the study was determined by the availability of key variables; small countries (with population less than one million) were also excluded. The global sample of 125 countries that are in the data set are listed in Table A1. Within this global sample, we define a group of emerging market countries as those covered by the Morgan Stanley Capital International Emerging Markets index; in addition, all of the EU’s new member states and accession candidates are treated as emerging market countries. The countries included in the emerging market country sample are listed in Table A8. Finally, we identify a subsample of 21 advanced economies, defined as the set of OECD countries that are not in the emerging market country subsample. Data were collected in the early part of 2005 and so may not reflect more recent revisions.

Table A8.Emerging Market Economies
ChinaLebanonSouth Africa
ColombiaLithuaniaSri Lanka
CroatiaMalaysiaTaiwan Province
Czech Rep.Mexicoof China
Hong Kong SARPeruRepública
HungaryPhilippinesBolivariana de

Income levels and growth rates are chain-weighted real GDP per capita in PPP terms (rgdpch) from Penn World Tables (PWT) Version 6.1 ( As these data end in 2000 and are sparse for the Baltic countries, we supplement and extend this using growth rates from the World Bank’s World Development Indicators (WDI) or the IMF’s World Economic Outlook (WEO). To analyze the impact of physical capital accumulation we use the relative price of investment, which was found by SDM(2004) to be more robust than investment share as a growth determinant, and which is also less subject to endogeneity. Relative price of investment is calculated as the ratio of the investment price deflator to the GDP deflator, both of which are also taken from PWT. Data for the growth accounting regressions were taken from Bosworth and Collins (2003).

Data on schooling are taken from the Barro-Lee educational attainment data set (, and are defined as the average years of secondary and higher education in the population. For countries not covered by the Barro-Lee data set, we regress their data on secondary and tertiary enrollment rates from the WDI and use predicted values from that regression. Population growth is from the WDI, supplemented when missing with PWT data.

Openness is the sum of exports and imports divided by GDP, defined as the variable openc in PWT. This was supplemented by WEO data when missing. Tax revenue to GDP is taken from several sources, including the OECD database, the IMF’s Government Finance Statistics, and the WDI. Partner country growth is from the Global Economic Environment of the WEO, and is calculated as the average of growth in partner countries, weighted by their shares in total exports. The dependency ratio is taken from the WDI, and is defined as the share of the population that is either younger than 15 years or older than 64 years divided by the share of the population that is between the ages of 15 and 64.

Finally, our measure of institutional quality is taken from the International Country Risk Guide, compiled by the private consultancy firm Political Risk Services. This data set covers 143 countries, from 1984 to the present. First used by Keefer and Knack (1997), it has become a standard measure of institutional quality in the literature, as it has the advantage of both cross-sectional breadth and long-time coverage. The composite index is an aggregation of various subcomponents that measure factors such as government stability, democratic accountability, law and order, quality of bureaucracy, and corruption in government. To ensure comparability among countries and over time, points are assigned based on preset questions for each risk subcomponent.


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1The CEECs comprise the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic (henceforth, Slovakia), and Slovenia.
2The East Asian economies considered are China, Hong Kong SAR, Indonesia, Korea, Malaysia, the Philippines, Singapore, Taiwan Province of China, and Thailand.
3Because of the commonality of regional issues, the focus is on the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia.
4Emerging market countries are a group of 37 middle- and low-income countries that have significant interactions with world capital markets. They comprise countries in the Morgan Stanley Capital International Emerging Markets Index, the CEECs, Bulgaria, and Romania. For the analytical usefulness of the emerging market categorization, see Rogoff and others (2004).
5Fischer, Sahay, and Végh (1996); Havrylyshyn and van Rooden (2000); Campos and Coricelli (2002); and EBRD (2004) establish the effect of stabilization policies and institutional development on the speed of recovery from initial output losses.
6This conclusion stands even when account is taken of hours worked; average weekly hours worked in the CEECs have been stable or have declined slightly in the past decade.
7Sachs and Warner (1995) discuss these and other benefits of trade openness.
8Assessments of public institutions are based on indices such as those in the International Country Risk Guide (ICRG). This publication for investors includes five indices of perceptions of government stability, democratic accountability, law and order, quality of bureaucracy, and corruption in government. Keefer and Knack (1997) initiated this inquiry. Burnside and Dollar (2000); Gallup, Sachs, and Mellinger (1999); and Crafts and Kaiser (2004) find similarly robust effects of ICRG variables.
10Fischer, Sahay, and Vegh (1996), for example, find that the size of fiscal deficits does not directly affect either inflation or growth once the influence of structural reforms has been taken into account.
11Results excluding inflation are shown in the box and used in the rest of the analysis, because omitting the variable had virtually no effect on other estimates and inflation in the CEECs is well below the threshold.
12On the assumption that TFP in these countries is 70 percent of German TFP, they estimate that marginal product of capital in the CEECs excluding Slovenia is between 1.7 to 10.6 times the marginal product of capital in Germany. Adjusting their estimates for the lower TFP ratios in Table 4.2 still leaves most of the CEECs with marginal products of capital 1.2 to 6.3 times that in Germany.
13See Blanchard and Giavazzi (2002) for similar results for the 15 member countries of the EU (henceforth, EU-15) prior to 2004.
14See, for example, Harberger (2003) and Edwards (2003).
15Consistent with this risk interpretation, Adam and Bevan (2005) find that deficits hurt growth only if they exceed 1.5 percent of GDP. Moreover, larger deficits are more harmful to growth the higher the public debt.

Concluding Remarks by the Acting Chair

Growth in the Central and Eastern European Countries of the European Union

Executive Board Seminar, February 27, 2006

Today’s Board seminar has been a welcome opportunity for Directors to discuss the challenges facing the Central and Eastern European countries (CEECs) of the European Union (EU) as they raise living standards to Western European levels. Directors welcomed the staff’s comprehensive analysis of the CEECs’ recent growth performance, the policies required to support rapid catch-up, and the vulnerabilities that will need to be monitored as convergence proceeds. They also made a number of useful suggestions on how the analysis of these issues can be deepened going forward, including with some case studies, and looked forward to similar regional surveillance reviews in the future.

Directors recognized the difficulty in disentangling the unique forces that shaped the CEECs’ growth over the past 15 years, including the steep post-transition drop in output, the macroeconomic and institutional reforms related to EU accession, and the benign global conditions in the more recent period. While the CEECs’ per capita output growth in the past five years has put them in the upper half of the emerging market comparator group—with the Baltics among the top five performers—Directors cautioned that the continuation of these rapid growth rates cannot be taken for granted.

Directors noted important differences in the pattern of growth in the CEECs vis-à-vis other emerging markets, particularly the lack of employment growth, and the heavy contribution of total factor productivity (TFP) gains. They acknowledged that the convergence experience of other EU members, such as Greece, Ireland, Portugal, and Spain, demonstrates the viability of sustained periods of high productivity growth. Nevertheless, they pointed out that the CEECs’ recent TFP growth may have been heavily influenced by the elimination of the inefficiencies of central planning—implying the possibility of some trailing off in the absence of strong efforts to improve the business environment.

Directors emphasized that prospects for the CEECs will depend on how well they do in establishing macroeconomic and structural conditions conducive to sustained growth, which is expected to be based on greater labor use and higher investment rates. They welcomed staff’s use of empirical growth models to shed light on the key environmental and policy characteristics that will shape the CEECs’ growth prospects. Directors noted that certain environmental features—including initial income gaps, population growth and aging, and historical trade relationships—as well as conditions more subject to policy influence play important roles in supporting growth. Among the latter, our discussion highlighted, in particular, the quality of legal and economic institutions, size of government, real cost of investment, educational attainment and labor market performance, openness to trade, and inflation. While Directors were encouraged that the CEECs do reasonably well in meeting these conditions, they also noted that differences tend to favor growth in the Baltics over the CE-5, reinforcing other indications that a two-speed catch-up—rapid for the Baltics, more moderate for the CE-5—may be emerging.

Directors agreed that the process of European integration will play a critical role in supporting a rapid catch-up in the CEECs. Substantial transfers from the European Union to the new member states are one obvious benefit, but potentially more important will be the benefits from closer institutional, trade, and financial integration with Western Europe. In this regard, Directors were encouraged by indications that thus far foreign savings have contributed significantly and appropriately to growth in most CEECs, and that the even large current account deficits of some countries have been in line with their growth rates.

Directors observed, however, that alongside the scope for accelerating the convergence process are the risks that increased reliance on foreign savings will generate significant vulnerabilities in the CEECs. They noted that large current account deficits are a potential source of increased indebtedness. The use of foreign savings, therefore, needs to be watched closely, and the composition of current account deficits—including the extent to which they are caused by reinvested earnings on foreign direct investment—deserves careful assessment. The use of foreign savings has also stimulated rapid credit growth both for businesses and, especially for households that have had little access to credit, growing confidence in the future means sizable borrowing to smooth consumption. In this regard, Directors cautioned that, especially in the Baltics and Hungary, various combinations of high external debt ratios, rapid credit growth (with a sizable share in foreign currency), and, in the Baltics, low reserve coverage of short-term debt need to be monitored carefully. For the immediate future, Directors were reassured that a number of factors—high reserves in the CE-5, strong fiscal positions in the Baltics, satisfactory competitiveness, relatively high standards of transparency, and well-supervised and predominantly foreign-owned banks—help mitigate these vulnerabilities.

Against this backdrop, our discussion identified a number of policy priorities for CEEC governments. Among them, the need to establish cushions against shocks; to contribute to domestic savings appropriately through sizable fiscal surpluses when catch-ups are rapid; to avoid disincentives to private saving; to support strong financial supervision; to ensure strong corporate governance and efficient bankruptcy procedures; and to increase transparency across the spectrum of economic activities. Directors also encouraged authorities to enact policies that will enable early euro adoption—the growth-enhancing and vulnerability-reducing opportunity unique to the CEECs. They considered that the adoption of the euro by the new EU member states should be predicated on a sound macroeconomic basis. This was seen as important especially to allow these countries sufficient flexibility to respond to asymmetric economic shocks in the absence of an independent monetary policy.

Directors considered that assessing the vulnerabilities associated with rapid catch-up—especially those related to strong capital inflows—will be the key challenge for Fund surveillance in the CEECs in the foreseeable future. Fund surveillance, Directors stressed, should encourage policies that are supportive of convergence, while closely monitoring accompanying vulnerabilities and helping to keep them contained. In this regard, several Directors noted that surveillance should focus on core issues related to macroeconomic and financial stability and its institutional underpinnings, while broad institutional development should remain the domain of development banks. Further, it was noted that Fund advice should continue to be sensitive to country-specific factors, while being mindful of the risk of potential adverse regional spillovers.

Recent Occasional Papers of the International Monetary Fund

252. Growth in the Central and Eastern European Countries of the European Union, by Susan Schadler, Ashoka Mody, Abdul Abiad, and Daniel Leigh. 2006.

251. The Design and Implementation of Deposit Insurance Systems, by David S. Hoelscher, Michael Taylor, and Ulrich H. Klueh. 2006.

250. Designing Monetary and Fiscal Policy in Low-Income Countries, by Abebe Aemro Selassie, Benedict Clements, Shamsuddin Tareq, Jan Kees Martijn, and Gabriel Di Bella. 2006.

249. Official Foreign Exchange Intervention, by Shogo Ishi, Jorge Iván Canales-Kriljenko, Roberto Guimarães, and Cem Karacadag. 2006.

248. Labor Market Performance in Transition: The Experience of Central and Eastern European Countries, by Jerald Schiff, Philippe Egoumé-Bossogo, Miho Ihara, Tetsuya Konuki, and Kornélia Krajnyák. 2006.

247. Rebuilding Fiscal Institutions in Post-Conflict Countries, by Sanjeev Gupta, Shamsuddin Tareq, Benedict Clements, Alex Segura-Ubiergo, Rina Bhattacharya, and Todd Mattina. 2005.

246. Experience with Large Fiscal Adjustments, by George C. Tsibouris, Mark A. Horton, Mark J. Flanagan, and Wojciech S. Maliszewski. 2005.

245. Budget System Reform in Emerging Economies: The Challenges and the Reform Agenda, by Jack Diamond. 2005.

244. Monetary Policy Implementation at Different Stages of Market Development, by a staff team led by Bernard J. Laurens. 2005.

243. Central America: Global Integration and Regional Cooperation, edited by Markus Rodlauer and Alfred Schipke. 2005.

242. Turkey at the Crossroads: From Crisis Resolution to EU Accession, by a staff team led by Reza Moghadam. 2005.

241. The Design of IMF-Supported Programs, by Atish Ghosh, Charis Christofides, Jun Kim, Laura Papi, Uma Ramakrishnan, Alun Thomas, and Juan Zalduendo. 2005.

240. Debt-Related Vulnerabilities and Financial Crises: An Application of the Balance Sheet Approach to Emerging Market Countries, by Christoph Rosenberg, Ioannis Halikias, Brett House, Christian Keller, Jens Nystedt, Alexander Pitt, and Brad Setser. 2005.

239. GEM: A New International Macroeconomic Model, by Tamim Bayoumi, with assistance from Douglas Laxton, Hamid Faruqee, Benjamin Hunt, Philippe Karam, Jaewoo Lee, Alessandro Rebucci, and Ivan Tchakarov. 2004.

238. Stabilization and Reforms in Latin America: A Macroeconomic Perspective on the Experience Since the Early 1990s, by Anoop Singh, Agnès Belaisch, Charles Collyns, Paula De Masi, Reva Krieger, Guy Meredith, and Robert Rennhack. 2005.

237. Sovereign Debt Structure for Crisis Prevention, by Eduardo Borensztein, Marcos Chamon, Olivier Jeanne, Paolo Mauro, and Jeromin Zettelmeyer. 2004.

236. Lessons from the Crisis in Argentina, by Christina Daseking, Atish R. Ghosh, Alun Thomas, and Timothy Lane. 2004.

235. A New Look at Exchange Rate Volatility and Trade Flows, by Peter B. Clark, Natalia Tamirisa, and Shang-Jin Wei, with Azim Sadikov and Li Zeng. 2004.

234. Adopting the Euro in Central Europe: Challenges of the Next Step in European Integration, by Susan M. Schadler, Paulo F. Drummond, Louis Kuijs, Zuzana Murgasova, and Rachel N. van Elkan. 2004.

233. Germany’s Three-Pillar Banking System: Cross-Country Perspectives in Europe, by Allan Brunner, Jörg Decressin, Daniel Hardy, and Beata Kudela. 2004.

232. China’s Growth and Integration into the World Economy: Prospects and Challenges, edited by Eswar Prasad. 2004.

231. Chile: Policies and Institutions Underpinning Stability and Growth, by Eliot Kalter, Steven Phillips, Marco A. Espinosa-Vega, Rodolfo Luzio, Mauricio Villafuerte, and Manmohan Singh. 2004.

230. Financial Stability in Dollarized Countries, by Anne-Marie Gulde, David Hoelscher, Alain Ize, David Marston, and Gianni De Nicoló. 2004.

229. Evolution and Performance of Exchange Rate Regimes, by Kenneth S. Rogoff, Aasim M. Husain, Ashoka Mody, Robin Brooks, and Nienke Oomes. 2004.

228. Capital Markets and Financial Intermediation in The Baltics, by Alfred Schipke, Christian Beddies, Susan M. George, and Niamh Sheridan. 2004.

227. U.S. Fiscal Policies and Priorities for Long-Run Sustainability, edited by Martin Mühleisen and Christopher Towe. 2004.

226. Hong Kong SAR: Meeting the Challenges of Integration with the Mainland, edited by Eswar Prasad, with contributions from Jorge Chan-Lau, Dora Iakova, William Lee, Hong Liang, Ida Liu, Papa N’Diaye, and Tao Wang. 2004.

225. Rules-Based Fiscal Policy in France, Germany, Italy, and Spain, by Teresa Dában, Enrica Detragiache, Gabriel di Bella, Gian Maria Milesi-Ferretti, and Steven Symansky. 2003.

224. Managing Systemic Banking Crises, by a staff team led by David S. Hoelscher and Marc Quintyn. 2003.

223. Monetary Union Among Member Countries of the Gulf Cooperation Council, by a staff team led by Ugo Fasano. 2003.

222. Informal Funds Transfer Systems: An Analysis of the Informal Hawala System, by Mohammed El Qorchi, Samuel Munzele Maimbo, and John F. Wilson. 2003.

221. Deflation: Determinants, Risks, and Policy Options, by Manmohan S. Kumar. 2003.

220. Effects of Financial Globalization on Developing Countries: Some Empirical Evidence, by Eswar S. Prasad, Kenneth Rogoff, Shang-Jin Wei, and Ayhan Kose. 2003.

219. Economic Policy in a Highly Dollarized Economy: The Case of Cambodia, by Mario de Zamaroczy and Sopanha Sa. 2003.

218. Fiscal Vulnerability and Financial Crises in Emerging Market Economies, by Richard Hemming, Michael Kell, and Axel Schimmelpfennig. 2003.

217. Managing Financial Crises: Recent Experience and Lessons for Latin America, edited by Charles Collyns and G. Russell Kincaid. 2003.

216. Is the PRGF Living Up to Expectations? An Assessment of Program Design, by Sanjeev Gupta, Mark Plant, Benedict Clements, Thomas Dorsey, Emanuele Baldacci, Gabriela Inchauste, Shamsuddin Tareq, and Nita Thacker. 2002.

215. Improving Large Taxpayers’ Compliance: A Review of Country Experience, by Katherine Baer. 2002.

214. Advanced Country Experiences with Capital Account Liberalization, by Age Bakker and Bryan Chapple. 2002.

213. The Baltic Countries: Medium-Term Fiscal Issues Related to EU and NATO Accession, by Johannes Mueller, Christian Beddies, Robert Burgess, Vitali Kramarenko, and Joannes Mongardini. 2002.

212. Financial Soundness Indicators: Analytical Aspects and Country Practices, by V. Sundararajan, Charles Enoch, Armida San José, Paul Hilbers, Russell Krueger, Marina Moretti, and Graham Slack. 2002.

211. Capital Account Liberalization and Financial Sector Stability, by a staff team led by Shogo Ishii and Karl Habermeier. 2002.

210. IMF-Supported Programs in Capital Account Crises, by Atish Ghosh, Timothy Lane, Marianne Schulze-Ghattas, , Javier Hamann, and Alex Mourmouras. 2002.

209. Methodology for Current Account and Exchange Rate Assessments, by Peter Isard, Hamid Faruqee, G. Russell Kincaid, and Martin Fetherston. 2001.

208. Yemen in the 1990s: From Unification to Economic Reform, by Klaus Enders, Sherwyn Williams, Nada Choueiri, Yuri Sobolev, and Jan Walliser. 2001.

207. Malaysia: From Crisis to Recovery, by Kanitta Meesook, Il Houng Lee, Olin Liu, Yougesh Khatri, Natalia Tamirisa, Michael Moore, and Mark H. Krysl. 2001.

206. The Dominican Republic: Stabilization, Structural Reform, and Economic Growth, by a staff team led by Philip Young comprising Alessandro Giustiniani, Werner C. Keller, and Randa E. Sab and others. 2001.

205. Stabilization and Savings Funds for Nonrenewable Resources, by Jeffrey Davis, Rolando Ossowski, James Daniel, and Steven Barnett. 2001.

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

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