Chapter

III. Policy Priorities

Author(s):
International Monetary Fund. European Dept.
Published Date:
May 2016
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In the baseline, supportive monetary policy and medium-term fiscal consolidation remain valid for many economies in the region. In the event of a negative growth shock, monetary policy should be the first line of defense, while automatic fiscal stabilizers should be allowed to play freely, provided there is enough fiscal policy room to do so. In case of a major shock and depending on the nature of the shock, fiscal policy should ease within the medium-term adjustment plans that dispel concerns about sustainability. Against the backdrop of mediocre global growth prospects, structural reforms are critical to lift potential growth and re-accelerate convergence.

In the absence of negative shocks, supportive monetary policy and medium-term fiscal consolidation is an appropriate policy mix for many CESEE economies:

  • Monetary policy should stay accommodative in low-inflation countries with further rate cuts if inflation expectations continue to decline or interest rate differentials with the euro area widen. There are, however, many countries in the region, where conventional monetary policy space is limited: because they lack monetary policy autonomy or because inflation is above the target or the interest rate at zero bound. For Turkey, where inflation remains high, further tightening would be needed to address excess demand pressures and build up international reserves that are now below the IMF’s metric of reserve adequacy. In Russia, a resumption of monetary easing to support the weak economy is only feasible once inflation expectations fall. The pace of such future easing would need to be mindful of the uncertain external outlook and the need to build the credibility of the new inflation-targeting regime. About half of the countries in the region that have some sort of flexible exchange rate regime can also use this tool to counter adverse external shocks. However, high foreign currency indebtedness, euroization and financial openness could limit the benefits of currency depreciations.

  • Fiscal policy should continue to anchor medium-term debt sustainability and build policy buffers in most countries. Some have made progress with a noticeable decline in the structural fiscal deficit. However, with the debt-stabilizing primary gap still in the negative for several countries and public debt still high, more needs to be done to rebuild fiscal buffers in the medium term (Figure 3.1). For Russia, the roughly neutral fiscal stance remains appropriate, given cyclical weakness, but more consolidation is needed over the medium-run. In Turkey, a tightening of fiscal policy in the medium term would increase domestic savings and thereby soften excess demand pressures, while building more policy space.

  • Medium-term fiscal consolidation should rely, as much as possible, on more growth-friendly expenditure and revenue measures, as discussed in the Fall 2015 REI. On the expenditure side, it is important to reduce unproductive transfers and further reform entitlement programs, including public pension systems, while protecting productive spending on public investment. Restructuring of public employment may also be called for, especially where employment levels or public sector wages are higher than in the private sector. On the revenue side, policymakers should consider the introduction or strengthening of carbon and property taxes, and in some cases, the improvement of tax compliance and administration.

Figure 3.1.Estimated Remaining Adjustment Needs

(Percent of GDP)

Sources: World Economic Outlook, and IMF staff calculations and projections.

Note: The remaining adjustment needs reflect values for primary balance and structural balance as of end-2015 (negative values represent no adjustment need based on that particular measure). For Ukraine data refers to 2016. -1 percent of GDP is European Commission’s Medium Term Objective (MTO) for many but not all CESEE countries and actual adjustment needs based on country-specific MTO may be different. Debt-stabilizing primary balance is the ratio of primary balance to GDP that stabilizes the debt to GDP ratio at its projected 2021 value.

If growth and inflation surprise on the downside, monetary policy should be the first line of defense. Also, automatic fiscal stabilizers should generally be allowed to operate freely. In case of a very adverse external demand shock, fiscal stimulus may need to be deployed by countries that still have access to international capital markets on affordable terms. For those with this option, it is recommended that they rely on measures that are easy to pull back if economic conditions improve (for example, a temporary investment tax credit) or that enhance economy’s long-term growth potential (for example, targeting infrastructure). More generally, for such stimulus to be effective and not raise questions about sustainability, it should be overlaid on medium-term adjustment plans that noticeably reduce public debt. Deploying the latter, together with further repair of balance sheets – as discussed in the Spring 2015 REI, is the main macroeconomic policy challenge for many CESEE economies.

Against the backdrop of mediocre global growth prospects, structural reforms are critical to lift potential growth and re-accelerate convergence. As discussed in Chapter II, efforts should focus on active labor market policies and productivity-enhancing reforms. The analysis in this report suggests that the currently significant productivity gaps with advanced Europe could be reduced by upgrading institutions (protection of property rights, legal systems, healthcare), increasing the affordability of financial services (for small and productive firms), and improving government efficiency. While the structural reform recommendations in IMF country reports are generally more comprehensive9 and more tailored to country-specific circumstances, many of the cross-country themes are similar to the ones highlighted in this report (Figure 3.2): improving government efficiency and reducing regulatory burden on firms in most CESEE countries; strengthening governance and institutions in SEE and the CIS; and increasing labor force participation in CEE countries, while improving labor market flexibility in SEE countries. As discussed in the WEO, in cases where the necessary reforms could have negative short-term impact on growth in the context of significant economic slack, these negative effects would need to be mitigated through careful phasing or demand support, if possible10.

Figure 3.2.IMF Country Teams' Recommendations on Structural Reform Priorities

Source: Latest IMF Country Reports.

Note: CEE = Central and Eastern Europe; CESEE = Central, Eastern, and Southeastern Europe; CIS = Commonwealth of Independent States; SEE = Southeastern Europe; SEE-XEU = Southeastern European countries outside the EU.

In countries with greater structural challenges more far-reaching reforms may be needed to speed up convergence. As discussed in IMF country reports, reforms in SEE non-EU and CIS economies should aim to strengthen governance, to lower administrative and trade barriers, increase competition in domestic markets, and improve the transparency and efficiency of public investment procedures. In Belarus, deep and carefully sequenced structural reforms to re-orient the economy toward more private-sector-led growth remain critical. For Moldova, priority should be given to strengthening institutional quality. For Ukraine, critical reforms include anti-corruption and judicial measures, tax administration reforms, and reforms of state-owned enterprises to improve corporate governance and reduce fiscal risks.

Annex I. CESEE: Growth of Real GDP, Domestic Demand, Exports, and Private Consumption
(Percent)
Real GDP GrowthReal Domestic Demand GrowthReal Export Growth (goods and services)Real Private Consumption Growth
2014201520162017201420152016201720142015201620172014201520162017
Baltics12.81.82.83.22.63.83.93.82.80.63.03.63.44.44.23.8
Estonia2.91.12.22.84.1−0.72.63.51.7−1.23.13.53.34.83.53.5
Latvia2.42.73.23.60.93.13.93.83.11.02.33.62.33.34.03.9
Lithuania3.01.62.73.12.96.34.64.03.01.23.43.64.14.94.74.0
Central and Eastern Europe13.13.63.13.14.13.53.13.56.76.96.46.12.12.83.33.4
Czech Republic2.04.22.52.42.34.72.92.88.97.06.25.01.52.83.22.6
Hungary3.72.92.32.54.21.90.42.67.68.46.56.31.52.62.62.2
Poland3.33.63.63.65.03.34.03.96.46.56.76.62.53.03.74.1
Slovak Republic2.53.63.33.43.14.93.23.73.67.05.45.62.32.43.02.9
Slovenia3.02.91.92.01.62.11.62.85.85.23.83.30.71.72.22.2
Southeastern Europe-EU12.23.33.53.12.33.84.63.76.56.05.95.92.94.35.44.1
Bulgaria1.53.02.32.32.61.02.12.1−0.17.64.14.22.70.82.52.5
Croatia−0.41.61.92.1−1.71.21.92.47.39.28.58.3−0.71.21.72.0
Romania3.03.74.23.63.15.36.04.58.64.75.96.03.86.17.15.1
Southeastern Europe-non-EU10.32.12.73.01.41.43.32.66.25.75.86.40.80.42.22.0
Albania2.02.63.43.83.51.45.93.11.83.8−0.14.42.7−0.63.41.5
Bosnia and Herzegovina1.12.83.03.23.21.93.52.94.62.16.85.02.32.42.32.5
Kosovo1.23.33.44.31.83.32.45.216.76.36.97.34.82.52.83.6
Macedonia, FYR3.53.73.63.64.32.83.84.118.24.67.28.12.13.22.82.8
Montenegro1.84.14.72.52.74.910.32.7−1.28.3−0.23.55.0−3.814.92.6
Serbia−1.80.71.82.3−1.10.41.51.85.77.87.77.6−1.3−0.60.21.7
European CIS countries10.2−4.3−1.60.90.1−10.2−2.70.5−0.71.1−0.12.21.0−10.3−2.20.7
Belarus1.6−3.9−2.70.4−0.7−7.6−3.00.07.0−8.4−5.5−0.34.4−4.8−2.10.8
Moldova4.8−1.10.52.53.0−8.5−2.42.21.06.12.64.33.2−2.3−0.93.1
Russia0.7−3.7−1.80.81.2−10.1−3.20.30.33.10.52.11.6−9.7−2.60.4
Ukraine−6.6−9.91.52.5−11.4−12.72.53.2−14.2−16.9−4.13.6−8.1−20.12.83.0
Turkey2.93.83.83.41.13.64.73.76.8−0.10.12.01.44.45.43.9
CESEE1,21.4−0.40.92.11.3−3.50.62.02.92.52.03.41.5−3.21.12.2
Emerging Europe1,31.3−0.80.72.01.2−4.20.31.92.62.31.73.31.4−3.80.92.1
New EU member states1,42.83.43.23.13.63.63.53.56.46.36.05.92.43.33.93.6
Memorandum
Euro Area10.91.61.51.60.91.81.71.74.15.03.44.10.81.71.61.6
European Union11.42.01.81.91.72.22.02.03.75.03.74.41.32.12.12.0
Source: IMF, World Economic Outlook database, Spring 2016 published version.

Weighted averages using 2014 GDP valued at purchasing power parity.

Includes Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kosovo, Latvia, Lithuania, Macedonia FYR, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Turkey, and Ukraine.

CESEE excluding Czech Republic, Estonia, Latvia, Lithuania, Slovak Republic, and Slovenia.

Includes Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia.

Source: IMF, World Economic Outlook database, Spring 2016 published version.

Weighted averages using 2014 GDP valued at purchasing power parity.

Includes Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kosovo, Latvia, Lithuania, Macedonia FYR, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Turkey, and Ukraine.

CESEE excluding Czech Republic, Estonia, Latvia, Lithuania, Slovak Republic, and Slovenia.

Includes Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia.

Annex II. CESEE: Consumer Price Index Inflation, Current Account Balance, and External Debt
(Percent)
CPI Inflation (Period average)CPI Inflation (End of period)Current Account Balance to GDPTotal External Debt to GDP
2014201520162017201420152016201720142015201620172014201520162017
Baltics10.4−0.30.92.00.00.01.62.31.4−1.1−1.8−2.092.892.390.485.7
Estonia0.50.12.02.90.1−0.22.12.91.01.91.20.594.693.586.779.9
Latvia0.70.20.51.50.30.41.62.1−2.0−1.6−2.0−2.2142.2143.5144.5139.7
Lithuania0.2−0.70.61.9−0.2−0.31.42.33.6−2.3−3.0−2.962.561.359.856.3
Central and Eastern Europe10.0−0.50.11.6−0.7−0.20.82.0−0.50.80.1−0.274.678.878.575.0
Czech Republic0.40.31.02.20.10.11.52.60.20.90.60.666.762.563.261.5
Hungary−0.2−0.10.52.4−0.90.91.22.62.35.15.45.2106.4100.496.487.8
Poland0.0−0.9−0.21.3−1.0−0.50.51.7−2.0−0.5−1.8−2.165.174.674.871.7
Slovak Republic−0.1−0.30.21.4−0.1−0.40.71.80.1−1.1−1.0−1.083.390.992.790.9
Slovenia0.2−0.50.11.00.1−0.40.70.87.07.37.67.1115.3112.5108.6105.0
Southeastern Europe-EU10.3−0.7−0.22.50.0−0.81.32.70.00.4−0.4−1.171.469.770.767.3
Bulgaria−1.6−1.10.21.2−2.0−0.91.11.31.22.11.70.885.687.282.375.8
Croatia−0.2−0.50.41.3−0.5−0.20.81.50.74.42.72.1108.4106.7105.5102.0
Romania1.1−0.6−0.43.10.8−0.91.53.4−0.5−1.1−1.7−2.558.655.859.256.8
Southeastern Europe-non-EU11.00.81.12.30.80.81.82.6−7.2−6.3−6.3−6.460.866.167.566.2
Albania1.61.91.92.50.72.02.22.7−12.9−11.4−12.7−12.634.240.643.043.1
Bosnia and Herzegovina−0.9−1.0−0.71.1−0.5−1.2−0.31.5−7.8−6.8−5.8−5.544.846.948.047.1
Kosovo0.4−0.50.21.5−0.4−0.11.21.7−7.9−8.0−8.3−8.9
Macedonia, FYR−0.1−0.20.51.5−0.5−0.31.41.6−0.8−1.4−1.7−2.665.266.870.968.7
Montenegro−0.71.60.91.3−0.31.41.41.4−15.2−13.2−16.5−17.0154.8152.2154.9161.5
Serbia2.11.41.73.11.81.62.63.3−6.0−4.8−4.4−4.376.285.385.883.0
European CIS countries18.618.19.17.112.715.58.66.32.04.23.34.135.947.653.848.5
Belarus18.113.513.612.116.212.014.511.3−6.8−1.9−3.5−3.154.672.285.278.8
Moldova5.19.69.87.44.713.58.16.4−3.7−6.6−4.0−4.482.7101.5109.4107.3
Russia7.815.58.46.511.412.97.95.92.95.04.25.129.438.143.138.4
Ukraine12.148.715.111.024.943.313.08.5−4.0−0.3−2.6−2.395.4136.4152.3140.3
Turkey8.97.79.88.88.28.810.96.5−5.5−4.4−3.6−4.150.456.057.256.9
CESEE1,26.010.16.25.77.79.36.65.1−0.31.30.80.951.158.761.958.1
Emerging Europe1,36.511.06.86.08.410.17.15.3−0.41.30.81.048.456.760.356.4
New EU member states1,40.1−0.50.11.9−0.5−0.31.02.2−0.30.6−0.1−0.575.077.577.473.8
Source: IMF, World Economic Outlook database, Spring 2016 published version.

Weighted averages using 2015 GDP valued at purchasing power parity.

Includes Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kosovo, Latvia, Lithuania, Macedonia FYR, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Turkey, and Ukraine.

CESEE excluding Czech Republic, Estonia, Latvia, Lithuania, Slovak Republic, and Slovenia.

Includes Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia.

Source: IMF, World Economic Outlook database, Spring 2016 published version.

Weighted averages using 2015 GDP valued at purchasing power parity.

Includes Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kosovo, Latvia, Lithuania, Macedonia FYR, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Turkey, and Ukraine.

CESEE excluding Czech Republic, Estonia, Latvia, Lithuania, Slovak Republic, and Slovenia.

Includes Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia.

Annex III. CESEE: Evolution of Public Debt and General Government Balance
(Percent of GDP)
General Government BalancePublic Debt
20142015201620172014201520162017
Baltics2−0.6−0.7−0.8−1.034.233.132.832.3
Estonia0.80.50.50.010.410.19.79.2
Latvia3−1.7−1.5−1.3−1.638.534.834.834.7
Lithuania−0.7−0.7−1.2−1.042.542.542.141.4
Central and Eastern Europe2−3.0−2.6−2.5−2.654.054.154.354.7
Czech Republic−1.9−1.9−1.6−1.542.740.941.341.0
Hungary−2.5−2.2−2.1−2.276.275.574.874.5
Poland−3.3−2.9−2.8−3.150.451.352.052.8
Slovak Republic−2.8−2.7−2.2−2.053.352.652.151.9
Slovenia3−7.1−3.5−3.9−3.080.883.380.781.8
Southeastern Europe-EU2−2.8−2.1−2.7−2.543.843.644.645.1
Bulgaria3−3.6−2.9−2.0−1.426.426.930.230.6
Croatia3−5.6−4.0−3.3−2.885.187.789.089.0
Romania−1.9−1.5−2.8−2.840.539.439.740.2
Southeastern Europe-non-EU2−5.0−3.4−3.2−2.761.364.565.164.6
Albania3−5.4−4.0−2.4−2.571.771.970.467.8
Bosnia and Herzegovina−3.3−1.6−1.6−1.244.045.545.544.5
Kosovo3,4−2.5−1.8−2.0−2.316.719.222.623.5
Macedonia, FYR−4.2−3.7−3.5−3.338.338.638.339.7
Montenegro3−2.6−7.4−9.1−8.959.966.470.577.0
Serbia3−6.6−3.7−3.6−2.772.077.478.977.9
European CIS countries2−1.3−3.2−4.4−3.221.724.526.527.3
Belarus3,51.3−0.9−4.7−6.740.459.969.568.9
Moldova3−1.7−2.3−3.2−3.031.442.044.045.0
Russia3−1.1−3.5−4.4−3.016.317.718.419.4
Ukraine3−4.5−1.2−3.7−3.070.380.292.892.3
Turkey3−1.7−1.5−2.3−1.633.532.630.729.2
CESEE2,6−1.9−2.6−3.4−2.733.534.735.535.7
Emerging Europe2,7−1.9−2.7−3.5−2.732.333.834.634.9
New EU member states2,8−2.8−2.4−2.4−2.450.350.250.650.9
Source: IMF, World Economic Outlook database, Spring 2016 published version.

As in the WEO, general government balances reflect IMF staff’s projections of a plausible baseline, and as such contain a mixture of unchanged policies and efforts under programs, convergence plans, and medium-term budget frameworks. General government overall balance where available; general government net lending/borrowing elsewhere. Public debt is general government gross debt.

Weighted averages using 2015 GDP valued at purchasing power parity.

Reported on a cash basis.

Regarding the overall balance, this includes fiscal room for donor-financed capital projects (for 2016-2018 period), which might not be fully utilized by year-end. Public debt includes former Yougoslav debt, not yet recognized by Kosovo.

General government balance: the measure reflects augmented balance, which adds to the balance of general government outlays for banks recapitalizations and is related to called guarantees of publicly-guaranteed debt.

Includes Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kosovo, Latvia, Lithuania, Macedonia FYR, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Turkey, and

CESEE excluding Czech Republic, Estonia, Latvia, Lithuania, Slovak Republic, and Slovenia.

Includes Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and

Source: IMF, World Economic Outlook database, Spring 2016 published version.

As in the WEO, general government balances reflect IMF staff’s projections of a plausible baseline, and as such contain a mixture of unchanged policies and efforts under programs, convergence plans, and medium-term budget frameworks. General government overall balance where available; general government net lending/borrowing elsewhere. Public debt is general government gross debt.

Weighted averages using 2015 GDP valued at purchasing power parity.

Reported on a cash basis.

Regarding the overall balance, this includes fiscal room for donor-financed capital projects (for 2016-2018 period), which might not be fully utilized by year-end. Public debt includes former Yougoslav debt, not yet recognized by Kosovo.

General government balance: the measure reflects augmented balance, which adds to the balance of general government outlays for banks recapitalizations and is related to called guarantees of publicly-guaranteed debt.

Includes Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kosovo, Latvia, Lithuania, Macedonia FYR, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Turkey, and

CESEE excluding Czech Republic, Estonia, Latvia, Lithuania, Slovak Republic, and Slovenia.

Includes Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and

Annex IV. The Determinants of Saving Rates in CESEE EU Countries11
Table 1.Comparison of Household Saving Rate Determinants in CESEE and Advanced Europe
Macroeconomic Determinants:Contribution to Savings 1/Correlations 2/AEHigh Saving CESEEMedium Saving CESEELow Saving CESEE
Household Saving Rates (%)10.89.63.1−8.4
Macroeconomic Determinants:
Real GDP per capita (Euros)(+/-)0.6032,53611,9389,7385,296
Debt to GDP (%)(−)0.25170.677.188.778.7
Household debt to GDI (%)(−)0.26122.142.455.028.6
Labor share of value added (%)(+)0.6359.960.155.550.0
Remittances to GDP (%)(−)−0.600.71.63.23.5
Fiscal Policy:
Government saving rates (%)(−)0.020.72.79.612.7
Government expenditures (%)(−)0.6347.944.837.337.4
Tax rate (single w/o children) (%)(−)0.1718.621.420.619.8
Social Security benefits share (%)(−)0.4015.112.99.410.7
Demographics:
Labor force participation (%)(+)0.3271.767.072.064.7
Age dependency ratio(−)0.2449.943.848.446.5
Migration share of population (%)(−)0.562.10.5−3.0−2.0
Population growth (%)(+)0.560.60.0−1.0−0.8
Other:
Bank deposits to GDP (%)(+)0.35112.251.936.137.3
Sources: Eurostat; World Bank; and IMF staff calculations.Note: CESEE = Central, Eastern, and Southeastern Europe. High-saving CESEE countries (>5 percent) are Croatia, Czech Republic, Hungary, Poland, Slovak Republic, and Slovenia. Medium-saving CESEE countries (>0 percent) are Estonia Latvia, and Lithuania. Low-saving CESEE countries (<0 percent) are Bulgaria and Romania.

Expected sign based on literature review.

Correlations between saving rates and their potential determinants in the sample of CESEE and AE EU countries.

Sources: Eurostat; World Bank; and IMF staff calculations.Note: CESEE = Central, Eastern, and Southeastern Europe. High-saving CESEE countries (>5 percent) are Croatia, Czech Republic, Hungary, Poland, Slovak Republic, and Slovenia. Medium-saving CESEE countries (>0 percent) are Estonia Latvia, and Lithuania. Low-saving CESEE countries (<0 percent) are Bulgaria and Romania.

Expected sign based on literature review.

Correlations between saving rates and their potential determinants in the sample of CESEE and AE EU countries.

Table 2.Comparison of Corporate Saving Rate Determinants in CESEE and Advanced Europe
Macroeconomic Determinants:Contribution to SavingsCorrelationsAEHigh Saving CESEEMedium Saving CESEE
Corporate Saving Rates (%)24.134.424.8
Macroeconomic Determinants:
Real GDP per capita (Euros)(+/-)−0.2932,53611,9389,738
Debt to GDP (%)(−)−0.34170.677.188.7
Corporate debt to GDI (%)(−)−0.42452.1242.5335.4
FDI stock share of GDP (%)(−)−0.0270.249.245.3
Fiscal Policy:
Government saving rates (%)(−)0.020.72.79.6
Government expenditures (%)(−)−0.2747.944.837.3
Corporate taxes (%)(−)−0.32“-”7.66.1
Profitability
Gross operating income (%)(+)0.2622.929.829.0
Sources: IMF, Eurostat, World Bank. IMF staff calculations.Note: High-saving CESEE countries (>30 percent) are Croatia, Czech Republic, Estonia, Hungary, Lithuania, and Slovenia. Medium-saving CESEE countries (>20 percent) are Bulgaria, Latvia, Poland, Romania and the Slovak Republic. FDI = foreign direct investment.

Expected sign based on literature review.

Correlations between saving rates and their potential determinants in the sample of CESEE and advanced European Union countries.

Sources: IMF, Eurostat, World Bank. IMF staff calculations.Note: High-saving CESEE countries (>30 percent) are Croatia, Czech Republic, Estonia, Hungary, Lithuania, and Slovenia. Medium-saving CESEE countries (>20 percent) are Bulgaria, Latvia, Poland, Romania and the Slovak Republic. FDI = foreign direct investment.

Expected sign based on literature review.

Correlations between saving rates and their potential determinants in the sample of CESEE and advanced European Union countries.

Annex V. Derivation of the Golden Rule Benchmark12

In the neoclassical (Ramsey-Cass-Koopmans) growth model, an economy converges to its steady-state equilibrium in which consumption is maximized, the saving/investment rate is constant at its “golden-rule” value, and income, consumption, and capital all grow at a fixed rate equal to the sum of the exogenous growth rates of the labor force and labor-augmenting productivity.

In the Ramsey-Cass-Koopmans model, the “golden-rule” of capital accumulation is given by:

where α is the capital share of output; p is the social rate of time preference; δ is the depreciation rate; n is the growth of the labor force; and g is the rate of technical progress.

The model is calibrated for European Union (EU) countries using national accounts data, labor market surveys, and the Penn World Tables Version 8.1 (PWT). In the case of EU members, for which standardized data exist from Eurostat, the estimates of the growth of the labor force and the capital share of output are derived from labor market surveys and national accounts data, respectively. Similar to the adjustments made to the raw data in the PWT, the estimates of the labor share of output are augmented by 63 percent of self-employment income. For non-EU members, data for these variables are from the PWT. In addition, estimates of capital stocks, depreciation rates, and total factor productivity are taken from the PWT.

Under a typical calibration and with a starting value of the capital-to-labor ratio below the steady state, the model implies that the investment rate would fall monotonically toward the “golden-rule” as the economy converges to its steady state. As such, the closed-economy, golden-rule saving/investment can be interpreted as a lower bound for the investment rate along its path of convergence to euro area income levels. The social rate of time preference is constant and set equal to 5 percent for all CESEE countries. This value corresponds to the social rate of time preference in the euro area, derived from the golden rule under the assumption that the euro area has been close to its steady-state path of development on average over 2002–14.

Annex VI. Derivation of the Historical Benchmark13

The purpose of the benchmark is to provide a proxy for a sustainable path of the investment rate during the transition to a steady state. Although neoclassical growth theory does not offer a closed-form solution for such transition dynamics, the “catch-up” is essentially driven by differences in real interest rates that affect intertemporal choices of consumption and savings (the Euler equation; see Barro and Sala-i-Martin 2003). When relative capital scarcity makes capital more productive, bearing a higher real interest rate, it stimulates saving and investment rates and leads to faster pace of capital accumulation. With a rising K/L ratio, the real return to capital declines and saving and investment rates gradually fall to their steady-state constant level. The further the economy is from its steady-state K/Y ratio, the faster it will accumulate capital. Therefore, the transition path for the investment rate I/Y may be approximated by a function of the real return to capital (given by the marginal product of capital, using Cobb-Douglas production function, where A is labor-augmenting productivity, K is capital, L is labor, and α is the capital share):

and where in the steady-state c equals ln(α) and β equals (1 − α).

An economy will gradually slow capital accumulation as it approaches its steady state. In the steady state, Δln(ItYt)=0. Denoting Δln(At)= g and Δln(Lt)= n, the expression results in: Δln(Kt)=β1αg+n. Using the capital accumulation equation and substituting for Δln(Kt)=ItKt1δ and β = (1 − α), we obtain the steady-state golden rule investment rate in the Solow-Swan growth model with labor-augmenting technological progress:

This suggests that our approximation of the transition path is a plausible transition dynamic, since it converges into the balanced growth path.

In order to evaluate the parameters c, α, and β, we use the historical experiences of countries in Western Europe with their capital accumulation path over 1951–2011. Fitting the above specified transition path for the investment rate on a panel for Germany, France, Italy, and Spain over 1951–2011 (R2=0.87, asterisks denote statistical significance with *** at 1 percent and ** at 5 percent), yields:

Using these parameters and a CESEE country-specific K/L ratio and labor-augmenting productivity, we can compute sustainable “historical benchmark” investment rate which mimics earlier transition dynamics of advanced economies.

Annex VII. The Effect of Structural Factors on Total Factor Productivity14

Methodology

Stochastic frontier models are used to analyze the efficiency of economic agents, regions, or countries. The intuition behind the models is that frontier technology may not be exceeded by any of the economic agents and the distance from the frontier reflects the inefficiency of individual agents. The frontier represents the maximum amount of output that can be obtained from a given level of inputs. Stochastic frontier models are characterized by composite error that is composed of idiosyncratic disturbance (to capture measurement errors and other noise) and one-sided disturbance, which represents inefficiency. In this annex we use a stochastic frontier panel-data model proposed by Battese and Coelli (1995) to estimate the contributions of technological progress and country-specific technical efficiency to total factor productivity (TFP) growth.15 Stochastic frontier models could be described by the following equations:

where yi,t-is the output of country i at time t; xi,t is a vector of production function inputs (in our case, capital, K, and human capital augmented labor, LHC, and the time trend representing technological change; εi,t is the composed error term; vi,t is assumed to be iid random error, independently distributed from the ui,t; ui,t is non-negative random variables associated with the technical inefficiency of production, which are assumed to be independently distributed, such that they are obtained by truncation (at zero) of the normal distribution with the mean; zi,tδ, and variance, σu2,zi,t are a vector of explanatory variables associated with the technical efficiency of production of country i, at time t; δ is an (m x 1) vector of unknown coefficients; and wi,t is defined by the truncation of the normal distribution with zero mean and variance, σu2, such that the point of truncation is zi,tδ i.e., wi,tzi,tδ. These assumptions ensure non-negativity of ui,t. Parameters of the stochastic frontier and the model for the technical inefficiency effects are simultaneously estimated with a maximum likelihood method.

Kumbakhar and Lovell (2000) demonstrate that a change in the TFP, which is defined as output growth not explained by input growth, can be expressed as:

where, ΔTP is technological change, which is represented by the coefficient of the time trend in equation (1) of the production frontier; ΔTP is the change in technical efficiency; lhc and k are output elasticities with respect to human-capital-augmented labor and capital, respectively; and = lhc + k represents the return to scale. In the case of constant return to scale, = 1 factor accumulation does not have any impact on TFP growth.

We use stochastic frontier analysis to estimate the production frontier and technical inefficiency, and to identify structural, regulatory, and institutional factors that are associated with technical inefficiency. The analysis applies the stochastic frontier method to 30 advanced and emerging European economies and the United States for the period 1995–2014. 16 The model is estimated using purchasing power parity-adjusted annual data from the Penn World Tables (PWT) and structural variables from the World Bank’s Global Competitiveness Report and from the Economic Freedom of the World Survey. 17 The production function approach is used to remove cyclical components from output and labor series (for a detailed description, see Podpiera, Raei, and Stepanyan, forthcoming). Structural variables cover the following broad areas: (1) product and labor markets, (2) institutional quality, (3) quality of infrastructure, (4) innovation and R&D, and (5) quality of labor and capital.

Regression Results

According to our estimation, technology progressed at 0.5 percent per year, on average, during 1995–2014 (Annex Table VII.1). However, before the global financial crisis the average growth rate of technological progress was higher, at about 1 percent, while after the crisis technological progress stalled. Estimated coefficients for physical capital and human-capital-augmented labor in the production function are very close to the calibrated labor and capital shares used in the literature. These results are robust to the different model specifications and different samples.

Annex Table VII.1.Estimates of Production Function and Efficiency Components
Whole SampleBefore 2008After 2008Whole SampleWhole SampleWhole Sample
VariablesParametersParametersParametersParametersParametersParameters
(1)(2)(3)(4)(4)(4)
Production Function
Capital0.34***0.33***0.43***0.33***0.33***0.33***
(0.02)(0.03)(0.030)(0.021)(0.020)(0.020)
Human capital augmented labor0.69***0.70***0.60***0.70***0.70***0.70***
(0.02)(0.03)(0.031)(0.021)(0.020)(0.020)
Time0.005**0.009***−0.00050.005***0.006***0.004***
(0.001)(0.002)(0.004)(0.001)(0.001)(0.001)
Intercept6.18***6.2***5.2***6.26***6.27***6.31***
(0.23)(0.31)(0.35)(0.23)(0.23)(0.22)
Inefficiency component Corruption−0.13***

(0.026)
−0.12***

(0.044)
−0.13***

(0.030)
Restrictiveness of FDI regulation−0.04***−0.05***−0.03***−0.052***−0.05***−0.045***
(0.008)(0.010)(0.001)(0.008)(0.008)(0.008)
Human capital squared0.44***0.53***0.21***0.43***0.41***0.44***
(0.047)(0.061)(0.062)(0.046)(0.045)(0.044)
Business regulation−0.06***−0.07***−0.05**−0.07***−0.06***−0.04***
(0.013)(0.018)(0.021)(0.013)(0.013)(0.013)
Employment in services−0.012***−0.014***−0.005***−0.013***−0.014***−0.012***
(0.001)(0.001)(0.002)(0.001)(0.001)(0.001)
Life expectancy−0.036***−0.04***−0.019***−0.044***−0.05***−0.038***
(0.004)(0.007)(0.006)(0.004)(0.004)(0.004)
Judicial independence−0.03**

(0.004)
Impartial courts−0.05***

(0.009)
Property rights−0.06***

(0.010)
Intercept4.07***4.43***2.4***5.0***5.35***4.42***
(0.364)(0.560)(0.507)(0.261)(0.266)(0.279)
Standard errors are in parentheses. *** (**) (*) Denotes significance at 1% (5%) (10%) level.Source: IMF staff estimates.Note: FDI=Foreign direct investment.
Standard errors are in parentheses. *** (**) (*) Denotes significance at 1% (5%) (10%) level.Source: IMF staff estimates.Note: FDI=Foreign direct investment.

Differences in structural factors explain the variation in inefficiency across countries in our sample. The high levels of corruption and restricted business regulations, including for foreign direct investment (FDI), give rise to technical inefficiencies. The higher share of employment in the services sector and the longevity of the population are conducive to technical efficiency. The square of human capital has a positive sign on inefficiency, reflecting the diminishing return on human capital.18

We zoomed in to identify specific factors that influence technical inefficiency and, according to the literature, are behind corruption. We used a variety of indicators representing the legal and judicial system in lieu of corruption indicators in our analysis.19 Data limitations prevent us from including all variables simultaneously, and therefore, we used one at a time. The results suggest that judicial independence, impartiality of the courts, and property rights play an important role in improving technical efficiency.

Implications for Structural Reform Priorities

Structural reform priorities vary across countries depending on potential relative efficiency gains (Annex Figure VII.1):

Legal system and protection of property rights: Among CESEE countries, Bulgaria, Croatia, the Slovak Republic, and Slovenia have relatively large room to increase efficiency by improving the legal system (independence of the judicial system and impartiality of courts) and protection of property rights. Turkey also has significant room to improve the independence of the judicial system. Albania, Hungary, Serbia, and the CIS countries could also benefit from improving protection of property rights. In general, the Baltic countries have institutional and structural characteristics very close to the EU-15 average. Thus, the room to gain efficiency by improving these characteristics is limited.

Annex Figure VII.1.Potential Efficiency Gains from Improving Selected Structural Characteristics of CESEE Economies to the Average EU-15 Level

(Percent)

Sources: World Bank; Global Competitiveness Report; Economic Freedom of World; and IMF staff calculations.

Note: CEE = Central and Eastern Europe; CESEE = Central, Eastern, and Southeastern Europe; CIS = Commonwealth of Independent States; SEE = Southeastern Europe; SEE-XEU = Southeastern European countries outside the EU.

Business regulation: Croatia, the Czech Republic, and the Slovak Republic could gain the most among CESEE countries from easing general business regulation and restrictions for FDI.

Structural transformation: In Albania, Romania, Turkey, and, to a lesser extent, Poland, there is scope to raise productivity by shifting labor from relatively lower productivity sectors (agriculture) to higher-productivity (services) sector.

Life expectancy: the Baltic and the CIS countries have the greatest room to improve the life expectancy of the population.

Annex VIII. Is There a Role for Structural Policies in Improving Allocative Efficiency?20

Methodology

This annex analyzes the role of structural policies in improving the efficiency of resource allocation. The evidence from Organization for Economic Cooperation and Development (OECD) countries, as shown in Andrews and Cingano (2014), suggests that policy-induced frictions in labor, product, and credit markets have an economically and statistically significant negative relationship with aggregate productivity, as they can hinder efficient resource allocation from less to more productive firms. This annex applies a similar methodology to 14 CESEE countries using firm-level data from ORBIS for the period 2010–13, and examines whether certain reforms could help close productivity gaps by facilitating more efficient resource allocation.21

In order to test the role of the quality of institutions and regulations in resource allocation, we estimate the following fixed-effect model with time dummies:

where AEi,c,t denotes allocative efficiency, measured by the covariance between firms’ labor productivity and their labor share within industry i of country c, and year t;22, 23Rc,tm denotes the country-level m-th indicator of regulation and institutional quality; μixc denotes the fixed effects for industry and country groups; and μt denotes time dummies. For structural indicators, we use the World Economic Forum’s Global Competitiveness Index, particularly in the areas of government efficiency, flexibility in wage determination, and affordability of financial services.24

Results suggest that the quality of institutions matters for allocative efficiency, and, in CESEE, the improvement in government efficiency and affordability of financial services could yield significant potential productivity gains through better resource allocation. The regression results suggest that, for instance, an increase in the affordability of financial services indicator by one notch is associated with a rise in allocative efficiency by 13 percentage points. The productivity gains from reforms in these areas (government efficiency and affordability of financial services) combined to bring them up to the higher level observed in the benchmark case of Sweden can be sizable—between 10 and 20 percent depending on a country’s gap from the benchmark (Annex Table VIII.1)

Annex Table VIII.1.Allocative Efficiency and Structural Indicators
Dependent VariableAllocative Efficiency
(1)(2)(3)(4)
Government
efficiency0.082***0.041*
(0.020)(0.021)
Flexible wages0.048*0.063**
(0.025)(0.025)
Affordable finances0.142***0.137***
(0.027)(0.029)
Constant0.0100.028−0.272**0.708***
(0.062)(0.132)(0.106)(0.176)
Observations3,7312,7952,7952,795
Number of panels953946946946
Sources: ORBIS; and IMF staff calculations.Note: Allocative efficiency is measured as the covariance between a firm’s labor share within industry and its log productivity. Robust standard errors are in parentheses. The coefficients on fixed effects and year dummies are omitted. *** coefficient significant at 1 percent; ** significant at 5 percent; * significant at 10 percent.
Sources: ORBIS; and IMF staff calculations.Note: Allocative efficiency is measured as the covariance between a firm’s labor share within industry and its log productivity. Robust standard errors are in parentheses. The coefficients on fixed effects and year dummies are omitted. *** coefficient significant at 1 percent; ** significant at 5 percent; * significant at 10 percent.

Data Description

The allocative efficiency analysis uses firm-level data from the ORBIS database, covering over 1.5 million firms each year for the period between 2010 and 2013 (1.8 million firms for 2013) for 14 countries. The sample excludes the self-employed (firms with one employee) and the outlier firms at the top and bottom 1 percentile in terms of their productivity. Allocative efficiency is calculated using each firm’s labor productivity and labor share within industry (using a narrow classification according to NACE Rev. 2, first two-digit level). Those industries with less than 20 firms available are excluded from the sample. Annex Table VIII.2 shows the data coverage (comparing the total number of employees hired by sample firms to the aggregate-level employment data, excluding the finance and insurance sector) and the number of firms and industries for each country.

Annex Table VIII.2.Number of Observations and Data Coverage by Country, 2013
Subsample with 20 or More Employees (percent of all firms with more than one employee)
CountryCoverage (percent)Number of Firms (thousands)Number of IndustriesNumber of Employees (thousands)Number of firmsNumber of employeesTurnoverTotal assets
Bulgaria60.0175.1762012.38.863.372.673.1
Czech
Republic63.0204.6773143.19.971.879.488.0
Estonia47.222.874279.19.453.870.572.0
Croatia45.141.775692.511.566.677.180.9
Hungary44.5113.5771758.010.969.679.682.9
Lithuania40.714.973517.833.781.182.788.2
Latvia60.249.675524.98.654.470.770.0
Poland4.814.276742.040.791.691.289.8
Romania40.7218.9773453.511.370.680.078.0
Serbia43.629.476748.215.974.979.384.4
Russia49.7749.87732274.946.591.391.492.0
Slovenia42.426.974381.79.663.773.778.2
Slovak
Republic48.555.1761042.212.271.381.082.5
Ukraine31.1182.1776007.020.484.486.083.7
Sources: Eurostat; and ORBIS.Note: Coverage is the share of total employment by sample firms to aggregate-level nonfinancial sector employment. The sample includes firms with more than one employee.
Sources: Eurostat; and ORBIS.Note: Coverage is the share of total employment by sample firms to aggregate-level nonfinancial sector employment. The sample includes firms with more than one employee.

The data contain a large number of micro firms (with less than 20 employees) that accounts for only a small fraction in terms of employment and turnover—we also examined allocative efficiency based on a subsample excluding such micro firms (Annex Figure VIII.1). The table shows the size of the subsample as a share of the full sample in terms of number of observations, employment, turnover, and total assets.

Annex Figure VIII.1.Average Firm Productivity by Firm Size

(Log of turnover-to-employment ratio, weighted by employment)

Sources: ORBIS; and IMF staff calculations.

CEE = Central and Eastern Europe; CIS = Commonwealth of Independent States; SEE = Southeastern Europe; SEE-XEU = Southeastern European countries outside the EU.

Annex IX. Decomposing TFP Growth into Common and Idiosyncratic Components25

Productivity growth has slowed across countries regardless of their level of development (Eichengreen, Park, and Shin 2015). A widespread slowdown in total factor productivity (TFP) growth raises a natural question: Are common factors behind this slowdown? This annex describes the framework that we used to decompose TFP growth into common/external and country-specific/idiosyncratic factors. For this purpose we run the following regression for each CESEE country separately:

where ΔTFPt is TFP growth at time t; ΔTFP_PARt is weighted average TFP growth of trading partners at time t (weighted by exports); ΔTFP_COMt is average TFP growth across countries in the sample at each point in time, which represents other common factors for TFP growth; N is the number of countries in our sample; εt is the country-specific component of TFP growth; and as are parameters that need to be estimated. Vectors of a1 and a2 across all countries in our sample represent common factor loading vectors. To control for country fixed and time effects, all data are de-meaned and de-trended in advance. TFP growth data for CESEE countries are from Podpiera, Raei, and Stepanyan (forthcoming). TFP growth for trading partners is calculated using the production function approach described in Podpiera, Raei, and Stepanyan (forthcoming).

Annex X. Description of Variables
Variable NameDescriptionSource
Human capitalIndex of human capital per person, based on years of schooling (Barro/Lee, 2012) and returns to education (Psacharopoulos, 1994).Penn World Tables; Psacharopoulos and Patrinos (2004)
Physical capitalCapital stock at PPP international dollars.Penn World Tables
EmploymentNumber of persons engaged (in millions).World Economic Outlook
GDPGross Domestic Product at PPP international dollars.Penn World Tables
Average hours workedAverage number of usual weekly hours of work in main job.Eurostat
Capacity utilizationCurrent level of capacity utilization (percent).Eurostat
Domestic credit to private sector (percent of GDP)Domestic credit to private sector refers to financial resources provided to the private sector by financial corporations. For some countries these claims include credit to public enterprises.IFS, World Bank (WB) and OECD
Financial market developmentThis index covers availability of financial services, affordability of financial services, financing through local equity market, ease of access to loans, venture capital availability, soundness of banks, regulation of securities exchanges, legal rights index.World Economic Forum (WEF); World Bank (WB) Doing Business
Availability of financial servicesBased on a survey that asks the following question: In your country, to what extent does the financial sector provide a wide range of financial products and services to businesses?World Economic Forum (WEF)
Affordability of financial servicesBased on a survey that asks the following question: In your country, to what extent are financial services affordable for businesses?World Economic Forum (WEF)
Mobile cellular subscriptions (per 100 people)Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology.World Telecommunication/ ICT Development Report
Internet users (per 100 people)Internet users are individuals who have used the internet (from any location) in the last 12 months. Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV.World Telecommunication/ ICT Development Report and World Bank (WB)
ICT goods imports (percent in total goods imports)Information and communication technology goods imports include telecommunications, audio and video, computer and related equipment; electronic components; and other information and communication technology goods.United Nations Conference on Trade and Development’s UNCTADstat database
Technological adoptionThis index covers availability of latest technologies, firm-level technology absorption, FDI and technology transfer.World Economic Forum (WEF)
ICT useThis index covers internet users, fixed-broadband internet subscriptions, internet bandwidth, mobile-broadband subscriptions.International Telecommunication Union
Technological readinessThis is an aggregate index of technology adoption and ICT use.World Economic Forum (WEF)
Spending on R&D (percent in GDP)Expenditures for research and development are current and capital expenditures on creative work undertaken systematically to increase knowledge, including knowledge of humanity, culture, and society, and the use of knowledge for new applications. R&D covers basic research, applied research, and experimental development.United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics
Researchers in R&D (per million people)Researchers in R&D are professionals engaged in the conception or creation of new knowledge, products, processes, methods, or systems and in the management of the projects concerned.UNESCO Institute for Statistics.
New business densityNew businesses registered are the number of new limited liability corporations registered in the calendar year.World Bank’s (WB) Entrepreneurship Survey and database
InnovationThis index covers capacity for innovation, quality of scientific research institutions, company spending on R&D, university-industry collaboration in R&D, government procurement of advanced technology products, availability of scientists and engineers, PCT patent applications.World Economic Forum (WEF); OECD, Patent Database
InfrastructureThis index is covers quality of overall infrastructure, quality of roads, quality of railroad infrastructure, quality of port infrastructure, quality of air transport infrastructure, available airline seat kilometers, quality of electricity supply, mobile-cellular telephone subscriptions, fixed-telephone lines.World Economic Forum (WEF); International Air Transport Association; International Telecommunication Union
Stock of public capitalPublic capital stock is constructed following the perpetual inventory method.Fiscal Monitor
Road densityRoad density is the ratio of the length of the country’s total road network to the country’s land area.World Bank (WB) World Development Indicators
Strength of legal rights indexStrength of legal rights index measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders. The index ranges from 0 to 12, with higher scores indicating that these laws are better designed to expand access to credit.World Bank (WB) Doing Business
InstitutionsThe index covers physical and intellectual property rights, diversion of public funds, public trust in politicians, irregular payments and bribes, judicial independence, favoritism in decisions of government officials, wastefulness of government spending, efficiency of legal framework in settling disputes, burden of government regulation, efficiency of legal framework in challenging regulations, transparency of government policymaking, business costs of terrorism, crime, and violence, organized crime, reliability of police services, ethical behavior of firms, strength of auditing and reporting standards, efficacy of corporate boards, protection of minority shareholders’ interests, strength of investor protection.World Economic Forum (WEF)
Control of CorruptionControl of corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.The Worldwide Governance Indicators
Government efficiencyThis index covers diversion of public funds, favoritism in decisions of government officials, wastefulness of government spending, burden of government regulation.World Economic Forum (WEF)
Judicial independenceThis index is based on the following question: Is the judiciary in your country independent from political influences of members of government, citizens, or firms?Fraser Institute Index of Economic Freedom (IEF); World Economic Forum (WEF)
Impartial courtsThis index is based on the following question: the legal framework in your country for private businesses to settle disputes and challenge the legality of government actions and/or regulations is inefficient and subject to manipulation (= 1) or is efficient and follows a clear, neutral process (= 7).Fraser Institute Index of Economic Freedom (IEF); World Economic Forum (WEF)
Protection of property rightsThis index is based on the following question: Property rights, including over financial assets, are poorly defined and not protected by law (= 1) or are clearly defined and well protected by law (= 7).Fraser Institute Index of Economic Freedom (IEF); World Economic Forum (WEF)
Integrity of the legal systemThis component is based on the International Country Risk Guide Political Risk Component I for Law and Order.Fraser Institute Index of Economic Freedom (IEF); PRS Group, International Country Risk Guide.
Legal enforcement of contractsThis component is based on the World Bank’s Doing Business estimates for the time and money required to collect a debt.Fraser Institute Index of Economic Freedom (IEF); World Banks (WB) Doing Business.
Regulatory restrictions on the sale of real propertyThis sub-component is based on the WB’s Doing Business data on the time measured in days and monetary costs required to transfer ownership of property that includes land and a warehouse.Fraser Institute Index of Economic Freedom (IEF); World Bank (WB) Doing Business.
Reliability of policeThis index is based on the following question: to what extent can police services be relied upon to enforce law and order in your country?Fraser Institute Index of Economic Freedom (IEF); World Economic Forum (WEF)
Business costs of crimeThis index is based on the following question: to what extent does the incidence of crime and violence impose costs on businesses in your country?Fraser Institute Index of Economic Freedom (IEF); World Economic Forum (WEF)
Pupil-teacher ratio, secondaryThis is the number of pupils enrolled in secondary school divided by the number of secondary school teachers.UNESCO Institute for Statistics.
Life expectancy at birth, total (years)Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.World Bank (WB), World Development Indicators
Labor market regulationsThis is based on the following sub-components: hiring market regulations, hiring and firing regulations, hours regulations, mandated cost of worker dismissal, conscription.World Bank (WB) Doing Business and World Economic Forum (WEF)
Quality of primary educationBased on a survey that asks the following question: in your country, how do you assess the quality of primary schools.World Economic Forum (WEF)
Quality of the education systemBased on a survey that asks the following question: in your country, how well does the education system meet the needs of a competitive economy?World Economic Forum (WEF)
Extent of staff trainingBased on a survey that asks the following question: in your country, to what extent do companies invest in training and employee development.World Economic Forum (WEF)
Labor market efficiencyThis index covers cooperation in labor-employer relations, flexibility of wage determination, hiring and firing practices, redundancy costs, effect of taxation on incentives to work, pay and productivity, reliance on professional management, country capacity to retain and attract talent, female participation in the labor force.World Economic Forum (WEF); ILO; and World Bank (WB), Doing Business.
Hiring and firing practicesBased on a survey that asks the following question: in your country, to what extent do regulations allow flexible hiring and firing of workers?World Economic Forum (WEF)
Flexibility of wage determinationBased on a survey that asks the following question: in your country, how are wages generally set?World Economic Forum (WEF)
Start-up procedures to register a businessStart-up procedures are those required to start a business, including interactions to obtain necessary permits and licenses and to complete all inscriptions, verifications, and notifications to start operations.World Bank (WB) Doing Business
Foreign ownership/inves tment restrictionsThis is based on the following two questions: how prevalent is foreign ownership of companies in your country? and how restrictive are regulations in your country relating to international capital flows?World Economic Forum (WEF)
Business regulationsThis is based on the following sub-components: administrative requirements, bureaucracy costs, starting a business, extra payments/bribes/favoritism, licensing restrictions, cost of tax compliance.World Bank (WB) Doing Business and World Economic Forum (WEF)
Goods market efficiencyThis index covers intensity of local competition, extent of market dominance, effectiveness of anti-monopoly policy, effect of taxation on incentives to invest, total tax rate, number of procedures required to start a business, time required to start a business, agricultural policy costs, prevalence of non-tariff barriers, trade tariffs, prevalence of foreign ownership, business impact of rules on FDI, burden of customs procedures, imports as a percentage of GDP, degree of customer orientation, buyer sophistication.World Economic Forum; World Trade Organization; International Trade Centre; World Bank (WB) Doing Business
Tax WedgeTax wedge is defined as the ratio between the amount of taxes paid by an average single worker (a single person at 100% of average earnings) without children and the corresponding total labor cost for the employer.OECD
Foreign direct investment, net inflows (percent of GDP)Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor.IMF’s IFS and Balance of Payments databases, World Bank’s (WB) International Debt Statistics, and OECD
Employment in servicesThis indicator covers the employment in the services sector, which consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services.International Labor Organization
Employment in agricultureThis indicator covers the employment in the agriculture sector, which consists of activities in agriculture, hunting, forestry, and fishing.International Labor Organization
Business sophisticationThis index covers local supplier quantity, local supplier quality, state of cluster development, nature of competitive advantage, value chain breadth, control of international distribution, production process sophistication, extent of marketing, willingness to delegate authority.World Economic Forum (WEF)
Value chain breadthThis indicator is based on a survey that askes the following question: In your country, how broad is companies’ presence in the value chain?World Economic Forum (WEF)

Abbreviations

ALB

Albania

AQR

Asset Quality Review

AUT

Austria

BGR

Bulgaria

BiH

Bosnia and Herzegovina

BIS

Bank for International Settlements

BLR

Belarus

CEE

Central and Eastern Europe

CESEE

Central, Eastern, and Southeastern Europe

CHF

Swiss franc

CIS

Commonwealth of Independent States

CZE

Czech Republic

DEU

Germany

ECB

European Central Bank

EIB

European Investment Bank

EM

Emerging Market

EMBIG

Emerging Markets Bond Index Global

EPFR

Emerging Portfolio Fund Research

EST

Estonia

EU

European Union

FIN

Finland

FDI

Foreign direct investment

FRA

France

FX

Foreign exchange

GBR

United Kingdom

GDP

Gross domestic product

GRC

Greece

HICP

Harmonised Index of Consumer Prices

HRV

Croatia

HUN

Hungary

ICR

Interest coverage ratio

IMF

International Monetary Fund

ITA

Italy

LTU

Lithuania

LVA

Latvia

LUX

Luxembourg

MDA

Moldova

MKD

Former Yugoslav Republic of Macedonia

MNE

Montenegro

NPL

Nonperforming loan

OECD

Organisation for Economic Co-operation and Development

PMI

Purchasing Managers Index

POL

Poland

REI

Regional Economic Issues

ROU

Romania

RUS

Russia

SA

Seasonally adjusted

SEE

Southeastern Europe

SRB

Serbia

SVK

Slovak Republic

SVN

Slovenia

TFP

Total productivity factor

TUR

Turkey

UKR

Ukraine

UVK

Kosovo

WEO

World Economic Outlook

References

Other examples include mostly off-shore centers (Ireland, Iceland, Hong Kong, Singapore) and seem less relevant.

Given that potential output is not observable and that TFP is estimated as a residual after accounting for the contributions of other factors of production, the usual caveats apply.

The declines in historical benchmarks were smaller, as they depend on both TFP growth rates and K/L ratios (which are still very low in most CESEE countries), while the golden rule rates are influenced by TFP growth rates, but not capital gaps.

In advanced economies, potential growth fell from slightly less than 2 percent in the precrisis period (2006–07) to about 1 percent during 2013–14. See, IMF Spring 2015 WEO for more details.

R&D spending is statistically significant in some specifications, but the relationship is not robust. The lack of statistical significance of labor market flexibility could be due to the fact that labor markets in CESEE outside SEE are fairly flexible, especially when compared to advanced Europe. The absence from our sample (due to data limitations) of several Western Balkan countries, where infrastructure gaps are large, could be one of the reasons why, in addition to measurement issues, all of the variables proxying infrastructure gaps turned out to be insignificant. See Annexes VII and XI for more details on the methodology and a complete list of variables used in the analysis.

We also looked at the correlations of global FDI flows and oil prices with the common component of the TFP growth for CESEE countries. The correlation between FDI and the common component of TFP growth was positive, but not strong. The correlation between oil prices and the common component of TFP growth turned out to be insignificant in our sample of countries, in contrast with the findings of Eichengreen, Park, and Shin (2015).

See Atoyan et al (forthcoming) for more detailed discussion of policy recommendations.

A comprehensive approach may indeed be critical. For example, the recent paper by Aiyar et al (2015) discusses the need for a comprehensive approach towards addressing multiple interlinked institutional obstacles to NPL resolution in Europe.

Chapter 3 of the Spring 2016 WEO (IMF, 2016) finds that certain labor market reforms may have further adverse effects on growth if carried out during economic downturns.

This annex was prepared by Dilyana Dimova.

This annex was prepared by Plamen Iossifov.

This annex was prepared by Jiri Podpiera.

This annex was prepared by Ara Stepanyan.

The advantage of panel-data stochastic frontier models is that they allow for considering a more realistic characterization of inefficiencies, including estimating time-variant and country-specific inefficiency.

Countries included in the analysis are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Russia, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, Ukraine, and United States.

Data in the PWT are available until 2011. We used purchasing power parity (PPP) conversion factors from the World Bank’s World Development Indicators for the period 2011–14 to calculate the growth rate of output and investment in PPP-adjusted terms. We extended the PWT series to 2014 using these growth rates.

We considered structural indicators in the broad areas of institutions, legal framework, labor market, product market efficiency, financial market developments, innovation and R&D, quality of education, physical and human capital, and infrastructure. We found robust and significant results only for the variables presented in Annex Table 7.1. For the rest of the variables, the relationship was either not statistically significant or not robust.

Some of the factors in the product market that may give rise to corruption are already included in the model through business regulation.

This annex was prepared by Jiae Yoo.

The countries included are Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, and Ukraine. Within the sample years, the numbers in the sample remains relatively stable. Firms with one employee and in the top and bottom 1 percent of the productivity distribution from the sample are excluded, following the standard in literature.

The measure of allocative efficiency, proposed by Olley and Pakes (1996), is based on the decomposition of the industry-level aggregate productivity index (defined as average firm-level log productivity, weighted by the firm’s labor share) into the unweighted average and covariance term Pi=niQnPn=P¯i+ni(Qn+Q¯i)(PnP¯i), where P¯i is the unweighted average of firm log-productivity, and Q denotes a firm’s labor share within industry. The second term, the covariance between a firm’s size and productivity, measures allocative efficiency by capturing the extent to which firms with higher productivity have greater resources (zero in random allocation and increasing with better allocation). When productivity is measured in log, the term captures how much higher and by how much of a percentage the industry productivity index is higher than in the case of random resource allocation.

Industries are classified according to NACE Rev 2, first two-digit level.

We considered structural indicators in the broad areas of public institutions, labor markets and financial market efficiency, and product market efficiency. We did not a find robust, consistent relation between product market efficiency and allocative efficiency (for example, burdens on starting a business, domestic and foreign competition). Within the broad areas of public institutions, labor markets, and financial market efficiency, the indicators included in the results show a more robust and significant relation with allocative efficiency than other subindicators (for example, public security, undue influence, hiring and firing practices, and trustworthiness and confidence in financial markets).

This annex was prepared by Ara Stepanyan.

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