## wp1782

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---

### I. Introduction
- Structural reforms are central to increasing potential growth and reducing public debt, especially where interest rates hit the zero lower bound and fiscal space is limited.
- In emerging markets, reforms are expected to promote faster economic convergence and to overcome the middle-income trap.
- At the 2014 G-20 meeting, structural reforms were emphasized as a means of promoting recovery; governments agreed to adopt national growth strategies.
- Empirical evidence cited includes Acemoglu, Simon, and Robinson 2004; Barkbu et al. 2012; Duval, Furceri, et al. 2016; Besley and Burgess 2002; Fabiano and Viviano 2011; Banerjee, Duflo, and Qian 2012; with cautionary references Krugman 2014; Babecky and Havranek 2013.
- Benchmarking motivation and choice:
  - Countries with higher per capita income tend to score better on structural indicators; reform priorities evolve with development.
  - Two benchmarks used: a generic country 40 percent richer and a hypothetical country with EU average per capita income.
  - Historical/regional context: ECA per capita GDP increased about 60 percent on average during 2000–2007; projected growth about 20 percent between 2015–21 for the region on average; Georgia projected to grow by some 40 percent during 2015-2021.
- Scope and headline finding preview:
  - Cross-sectional analysis of structural indicators for 25 Emerging ECA countries using WEF 2015-16 GCR data (more than 126 indicators, 12 pillars).
  - Relative to a country 40 percent richer, about one-third to more than half of countries have large reform needs in institutions, financial market development, infrastructure, goods and labor market efficiency, and innovation-related areas.
  - With the EU-average-income benchmark (except Slovenia), reform needs expand considerably.
- Caveats:
  - Data quality and consistency issues; GCR is survey-based while Doing Business is rule-based.
  - Analysis abstracts from explicitly linking structural reforms to growth (short time series, heterogeneity).
  - Approach compares structural indicators to higher-income generic country rather than estimating causal growth impact.

### II. Structural Reform Gaps: Estimation
- Data and measurement:
  - GCR 2015-16: more than 126 indicators for up to 148 countries, grouped in 12 pillars: 1–institutions, 2–infrastructure, 3–macroeconomic environment, 4–health and primary education, 5–higher education and training, 6–goods market efficiency, 7–labor market efficiency, 8–financial market development, 9–technological readiness, 10–market size (excluded), 11–business sophistication, 12–innovation.
  - Pillars scored 1 (worst) to 7 (best).
- Empirical strategy:
  - Regression for each indicator k: ln(structural indicator_k) regressed on ln GDP per capita (2015, 2005 prices) and other controls in robustness specs.
  - Gap for country i relative to a generic country 40 percent richer: residual evaluated at ln(gdp_pc)*(1.40) and standardized by inverse of its standard deviation to produce Z-scores.
  - Interpretation: gap = –1.65 implies country i performs worse than 95 percent of sample relative to a generic country 40 percent richer.
- Initial-specification regression results (ln gdp_pc coefficients and constants; Observations: 132 for each regression):
  - ln gdp_pc coefficients by pillar (*** p<0.01 unless noted):
    - Pillar 1: 0.07***
    - Pillar 2: 0.15***
    - Pillar 3: 0.05***
    - Pillar 4: 0.07***
    - Pillar 5: 0.12***
    - Pillar 6: 0.05***
    - Pillar 7: 0.03***
    - Pillar 8: 0.06***
    - Pillar 9: 0.15***
    - Pillar 11: 0.07***
    - Pillar 12: 0.09***
  - Constant terms by pillar:
    - Pillar 1: 1.47***
    - Pillar 2: 1.31***
    - Pillar 3: 1.64***
    - Pillar 4: 1.73***
    - Pillar 5: 1.42***
    - Pillar 6: 1.59***
    - Pillar 7: 1.60***
    - Pillar 8: 1.48***
    - Pillar 9: 1.32***
    - Pillar 11: 1.48***
    - Pillar 12: 1.33***
  - R-squared values by pillar:
    - Pillar 1: 0.45
    - Pillar 2: 0.80
    - Pillar 3: 0.21
    - Pillar 4: 0.56
    - Pillar 5: 0.77
    - Pillar 6: 0.48
    - Pillar 7: 0.15
    - Pillar 8: 0.38
    - Pillar 9: 0.86
    - Pillar 11: 0.63
    - Pillar 12: 0.54
  - Fit is closest in infrastructure and technological readiness; weakest in labor markets.
- Robustness and additional specifications:
  - Eight specifications in the Appendix include: ECA and resource-rich dummies, region and country dummies, ln GDP per capita squared.
  - Resource-rich (RR_dummy) countries trail others with same income in almost all reform areas.
  - Emerging ECA countries on average perform relatively better in infrastructure, macroeconomic environment, education and health care, and technological readiness; they lag in institutions, business sophistication, and innovation.
  - Overall qualitative similarity across specifications.

### III. Benchmarking: Results
- Benchmarks:
  - Generic country with per capita income 40 percent higher.
  - Hypothetical EU-average per capita income country.
- Presentation and interpretation:
  - Figure 3 (described): blue bars = range of gaps across eight specifications (relative to 40 percent-higher-income case); green diamonds = initial-specification gaps relative to 40 percent-higher-income country; red diamonds = average weighted gap across all specifications (weights are absolute values of the Akaike information criterion); blue circles = gaps relative to EU-average-income country.
  - Initial-specification gaps generally close to average gaps; gaps relative to EU average typically larger.
- Heat-map classification (gap thresholds):
  - Very large: gap < –1.65
  - Large: gap between –0.5 and –1.65
  - Medium: gap between –0.5 and 0.5
  - Low: gap > 0.5
  - Interpretation examples:
    - Gap = –0.5 implies country performs worse than about 70 percent of the sample (assuming normality).
    - Gap = –1.65 implies country below the 5th percentile.
    - Gap = 0 implies performance at about the median conditional on income.
- Summary empirical patterns:
  - Relative to a 40 percent higher income, about one-third to more than half of countries have large reform needs in: institutions, financial market development, infrastructure, goods and labor market efficiency, and innovation-related areas.
  - With EU-average-income benchmark (except Slovenia), reform needs expand considerably.
- Averaging method:
  - Average gap = weighted average of gaps from eight specifications; weight of specification i is w_i = |AIC_i| / sum |AIC|.

### IV. Appendix 1: Summary Statistics (Table A1.1 and A1.2 highlights)
- Structural Reform Indicators: Summary Statistics (sample size: 132 countries with all data):
  - Pillar 1: Institutions — count 132; mean 4.07; sd 0.86; p10 3.19; p90 5.46
  - Pillar 2: Infrastructure — count 132; mean 4.04; sd 1.22; p10 2.43; p90 5.71
  - Pillar 3: Macroeconomic environment — count 132; mean 4.79; sd 0.96; p10 3.60; p90 6.15
  - Pillar 4: Health and primary education — count 132; mean 5.52; sd 0.89; p10 4.28; p90 6.44
  - Pillar 5: Higher education and training — count 132; mean 4.26; sd 1.01; p10 2.78; p90 5.59
  - Pillar 6: Goods market efficiency — count 132; mean 4.38; sd 0.53; p10 3.78; p90 5.13
  - Pillar 7: Labor market efficiency — count 132; mean 4.24; sd 0.56; p10 3.61; p90 5.00
  - Pillar 8: Financial market development — count 132; mean 3.95; sd 0.70; p10 3.11; p90 4.99
  - Pillar 9: Technological readiness — count 132; mean 4.09; sd 1.21; p10 2.63; p90 5.91
  - Pillar 10: Market size — count 132; mean 3.91; sd 1.16; p10 2.57; p90 5.45
  - Pillar 11: Business sophistication — count 132; mean 4.07; sd 0.71; p10 3.31; p90 5.28
  - Pillar 12: Innovation — count 132; mean 3.54; sd 0.85; p10 2.67; p90 4.98
  - GDP per capita, thousands 2010 USD — count 132; mean 15.81; sd 20.41; p10 0.83; p90 45.41
  - =1 if from Emerging ECA — mean 0.20; sd 0.40; p10 0.00; p90 1.00
  - =1 if from SSA — mean 0.23; sd 0.42; p10 0.00; p90 1.00
  - =1 if OECD member and HIC as of 2010 — mean 0.23; sd 0.42; p10 0.00; p90 1.00
  - =1 if resource-rich (IMF 2012) — mean 0.30; sd 0.46; p10 0.00; p90 1.00
- Global Competitiveness Index: selected country pillar scores (1-7 best) — examples from Table A1.2 (GCR 2015-16):
  - Albania: 3.68 3.55 3.96 5.97 4.74 4.34 3.97 3.24 3.40 3.65 2.76
  - Estonia: 5.03 4.87 6.15 6.34 5.50 4.93 5.00 4.63 5.32 4.26 4.03
  - Georgia: 4.38 4.19 4.95 5.79 4.00 4.48 4.56 3.87 3.81 3.48 2.71
  - Kyrgyz Republic: 3.29 2.84 4.62 5.30 4.09 4.23 4.06 3.44 3.27 3.41 2.67
  - Slovenia: 3.93 4.79 4.45 6.44 5.41 4.50 4.00 2.85 5.14 4.15 3.83
  - Turkey: 3.84 4.43 4.75 5.69 4.58 4.53 3.46 3.93 4.08 4.07 3.35
  - Ukraine: 3.07 4.07 3.12 6.06 5.03 4.02 4.33 3.18 3.45 3.70 3.41

### V. Appendix 2: Regression Results — Selected Robust-Specification Findings
- Pillar 1, Institutions:
  - Observations: 132 (column (1)); lngdp_pc: 0.07*** (column (1)); emerg_ECA: -0.08*** (column (2)); RR_dummy: -0.04 (column (2)); R-squared: 0.45 (column (1)) up to 0.66 (column (8)); AIC range: -179.4 to -218.
- Pillar 2, Infrastructure:
  - lngdp_pc: 0.15*** (column (1)); RR_dummy: -0.07*** (columns (2)–(3)); R-squared: 0.80 (column (1)) up to 0.84 (column (4)); AIC range: -199.7 to -161.6.
- Pillar 3, Macroeconomic Environment:
  - lngdp_pc: 0.05*** (column (1)); RR_dummy: 0.07** (column (3)); R-squared: 0.21 (column (1)) up to 0.35 (column (8)); AIC range: -121.1 to -124.8.
- Pillar 4, Health and Primary Education:
  - lngdp_pc: 0.07*** (columns (1)–(3)); RR_dummy: -0.07*** (columns (2)–(4)); R-squared: 0.56 (column (1)) up to 0.75 (column (4)); AIC range: -232.4 to -273.8.
- Pillar 5, Higher Education and Training:
  - lngdp_pc: 0.12*** (column (1)); emerg_ECA: 0.08*** (column (2)); R-squared: 0.77 (column (1)) up to 0.85 (column (4)); AIC range: -239.8 to -235.2.
- Pillar 6, Goods Market Efficiency:
  - lngdp_pc: 0.05*** (column (1)); R-squared: 0.48 (column (1)) up to 0.61 (column (8)); AIC range: -318.3 to -332.5.
- Pillar 7, Labor Market Efficiency:
  - lngdp_pc: 0.03*** (column (1)); R-squared: 0.15 (column (1)) up to 0.48 (column (8)); AIC range: -236 to -276.7.
- Pillar 8, Financial Market Development:
  - lngdp_pc: 0.06*** (column (1)); RR_dummy: -0.03* to -0.05** across specs; R-squared: 0.38 (column (1)) up to 0.51 (column (4)); AIC range: -202.6 to -208.6.
- Pillar 9, Technological Readiness:
  - lngdp_pc: 0.15*** (column (1)); R-squared: 0.86 (column (1)) up to 0.91 (column (4)); AIC range: -264.1 to -248.7.
- Pillar 11, Business Sophistication:
  - lngdp_pc: 0.07*** (column (1)); emerg_ECA: -0.08*** (column (2)); R-squared: 0.63 (column (1)) up to 0.73 (column (4)); AIC range: -277.6 to -303.9.
- Pillar 12, Innovation:
  - lngdp_pc: 0.09*** (column (1)); oecd_hic: 0.15*** (column (2)); RR_dummy: -0.06*** (column (2)); R-squared: 0.54 (column (1)) up to 0.75 (column (8)); AIC range: -185.9 to -244.
- Note: Robust standard errors in parentheses; statistical significance: *** p<0.01, ** p<0.05, * p<0.1.

### VI. Appendix 3–5: Alternative Specifications, Standard Deviations, and Distributional Notes
- Alternative heat maps:
  - Table A3.1: Differences relative to generic country with 40% higher income using potential output instead of actual output.
  - Table A3.2: Differences relative to generic country with higher income using projected 10-year GDP per capita growth.
  - Projected GDP per capita growth for country X implied from regression of 2005–2015 growth on 2004 GDP per capita for Emerging ECA.
  - Under country-specific growth-target alternative, projections as high as 64 percent for Tajikistan and 63 percent for Kyrgyz Republic, and as low as 19 percent for Czech Republic and 17 percent for Slovenia.
  - Under this alternative, reform needs rise for poorer countries and fall for richer countries; within-country ranking of reform gaps does not change.
- Distributional observations:
  - 135 countries analyzed in some sections; for 2015 reform gaps across 135 countries approximate a normal distribution reasonably well.
  - 5.6 percent of countries have very large gaps (smaller than –1.65).
  - 28 percent of countries have gaps smaller than –0.5.
- Standard deviations of gaps across specifications (selected country examples from Appendix 4):
  - Albania: Institutions 0.29; Infrastructure 0.16; Technological readiness 0.36; Business sophistication 0.44; Innovation 0.34.
  - Kyrgyz Republic: Institutions 0.41; Infrastructure 0.59; Technological readiness 0.84; Higher education and training 0.74.
  - Tajikistan: Infrastructure 0.66; Technological readiness 0.93; Higher education and training 0.82.
  - Estonia: Institutions 0.42; Technological readiness 0.43; Innovation 0.33.
  - Average deviation from the mean in emerging ECA: 0.28.
- Stochastic Frontier Analysis (SFA) vs OLS:
  - SFA parametric version (half-normal inefficiency) used with same specification.
  - Fit between OLS-based gaps and SFA-based gaps: R2 = 0.93 for regression of one on another.
  - Country ranking generally preserved between methods.
- Comparison with Doing Business (DB) indicators:
  - Aggregate GCR and DB score gaps positively correlated; R2 of correlations between GCR and DB gaps ranges from 0.3 to 0.5.
  - Selected R2 values reported: 0.25, 0.24, 0.28, 0.39, 0.27, 0.57.
  - Countries appearing more favorable in GCR than DB in 2015: Tajikistan, Azerbaijan, Kazakhstan, Ukraine, Russian Federation, Turkey.
  - Countries assessed less favorably in GCR vs DB: Georgia, Estonia, Macedonia, Armenia.

### VII. Main Reform Needs and Policy Implications (Conclusions)
- Average relationship and interpretation:
  - Countries with higher per capita income tend to score better on structural indicators.
  - Empirical evidence suggests structural reforms are conducive to increasing potential growth in the medium term, though reverse causality or mutual reinforcement cannot be ruled out.
- Priority reform areas for Emerging ECA:
  - institutions
  - financial market development
  - infrastructure
  - goods and labor market efficiency
  - innovation-related areas
- Use of approach:
  - The benchmarking approach helps detect granular reform elements within broad pillars and can guide policy prioritization subject to country knowledge, further analysis, and judgment.
- Methodological caveats emphasized for policy use:
  - Measurement errors, omitted variable bias, perception vs fact indicator differences.
  - Closing larger gaps in one area may not always yield larger growth payoffs than closing smaller gaps elsewhere; country-specific analysis required.

*Source: wp1782.*

### References .............................................................................................................

### wp1782 - References .............................................................................................................

### I. Introduction
- Structural reforms are presented as central to increasing potential growth (which has declined since the global financial crisis) and to reducing public debt, especially where interest rates hit the zero lower bound and fiscal space is limited.
- In emerging markets, reforms are expected to promote faster economic convergence and to overcome the middle-income trap.
- At the 2014 G-20 meeting, structural reforms were emphasized as a means of promoting recovery; governments agreed to adopt national growth strategies.
- Empirical evidence cited:
  - Cross-country and microeconomic evidence suggests structural reforms are beneficial for growth (references include Acemoglu, Simon, and Robinson 2004; Barkbu et al. 2012; Duval, Furceri, et al. 2016; Besley and Burgess 2002; Fabiano and Viviano 2011; Banerjee, Duflo, and Qian 2012).
  - Some studies advise caution (Krugman 2014; Babecky and Havranek 2013).
- Motivation for benchmarking by development level:
  - Countries with higher per capita income tend to score better on structural indicators; reform priorities evolve with development.
  - The paper asks how a country compares to a generic country with higher income (example benchmark: a country 40 percent wealthier).
- Regional context and benchmark choice:
  - Prior to the global financial crisis, between 2000–2007 per capita GDP in the ECA region increased by about 60 percent on average.
  - Projected growth is about 20 percent between 2015–21 for the region on average; the best performing country, Georgia, is projected to grow by some 40 percent during 2015-2021.
  - The paper uses a 40 percent increase in income as one benchmark and the EU average income as a second, more ambitious benchmark.
- Scope and findings preview:
  - Cross-sectional analysis of structural indicators for 25 countries in emerging Europe, the Caucasus, and Central Asia (ECA) using WEF 2015-16 GCR data.
  - When benchmarking to a country 40 percent richer, about one-third to more than half of countries have large reform needs in: institutions, financial market development, infrastructure, goods and labor market efficiency, and areas related to innovation.
  - With the EU-average-income benchmark (except Slovenia), the list of reform needs expands considerably.
- Caveats highlighted:
  - Data quality and consistency issues; GCR is survey-based while Doing Business is rule-based—results may differ by data source.
  - The analysis abstracts from explicitly linking structural reforms to growth due to short time series and post-crisis heterogeneity in macro policies.
  - The approach focuses on comparing structural indicators to a higher-income generic country rather than causal impact on growth.

### II. Structural Reform Gaps: Estimation
- Data and measurement:
  - GCR 2015-16 reports more than 126 indicators for up to 148 countries, grouped in 12 pillars: 1–institutions, 2–infrastructure, 3–macroeconomic environment, 4–health and primary education, 5–higher education and training, 6–goods market efficiency, 7–labor market efficiency, 8–financial market development, 9–technological readiness, 10–market size, 11–business sophistication, 12–innovation.
  - Each pillar is scored from 1 (worst) to 7 (best). Pillar 10 (market size) is excluded from the analysis.
- Empirical strategy:
  - Regression specification for each indicator k: logged structural indicator as dependent variable; controls include log GDP per capita (2015, in 2005 prices) and other structural characteristics as applicable.
  - Initial specification includes only a constant and per capita income; all structural reform indicators are logged.
  - The gap k for country i relative to a generic country 40 percent richer is defined as the residual evaluated at a 40 percent higher income and weighted by the inverse of its standard deviation to produce Z-scores (standardized gaps).
  - Interpretation of Z-scores: e.g., a gap of –1.65 implies country i performs worse than 95 percent of the sample relative to a generic country 40 percent richer.
- Regression results (initial specification) — Table 1 highlights:
  - For 11 out of 12 GCR pillars: ln(gdp_pc) coefficients are positive and statistically significant in all areas.
  - Exact coefficients (ln gdp_pc) by pillar:
    - Pillar 1: 0.07***
    - Pillar 2: 0.15***
    - Pillar 3: 0.05***
    - Pillar 4: 0.07***
    - Pillar 5: 0.12***
    - Pillar 6: 0.05***
    - Pillar 7: 0.03***
    - Pillar 8: 0.06***
    - Pillar 9: 0.15***
    - Pillar 11: 0.07***
    - Pillar 12: 0.09***
  - Constant terms by pillar:
    - Pillar 1: 1.47***
    - Pillar 2: 1.31***
    - Pillar 3: 1.64***
    - Pillar 4: 1.73***
    - Pillar 5: 1.42***
    - Pillar 6: 1.59***
    - Pillar 7: 1.60***
    - Pillar 8: 1.48***
    - Pillar 9: 1.32***
    - Pillar 11: 1.48***
    - Pillar 12: 1.33***
  - Observations: 132 for each regression.
  - R-squared values by pillar:
    - Pillar 1: 0.45
    - Pillar 2: 0.80
    - Pillar 3: 0.21
    - Pillar 4: 0.56
    - Pillar 5: 0.77
    - Pillar 6: 0.48
    - Pillar 7: 0.15
    - Pillar 8: 0.38
    - Pillar 9: 0.86
    - Pillar 11: 0.63
    - Pillar 12: 0.54
  - Fit is closest in infrastructure and technological readiness; weakest in labor markets.
- Robustness and additional specifications:
  - Appendix presents eight specifications including dummies for ECA and resource-rich countries, region and country-specific dummies, and ln GDP per capita squared to allow for nonlinearity.
  - Resource-rich countries trail others with the same income in almost all reform areas, suggesting resource rents may crowd out reforms.
  - Emerging ECA countries on average perform relatively better in infrastructure quality, macroeconomic environment, education and health care, and technological readiness; they lag in institutions, business sophistication, and innovation.
  - Overall qualitative similarity across specifications.

### III. Benchmarking: Results
- Two benchmarks used:
  - A generic country with per capita income higher by 40 percent.
  - A hypothetical country with EU average per capita income.
- Presentation of results:
  - Figure 3 displays gaps for 25 ECA countries:
    - Blue bars: range of gaps relative to the 40 percent-higher-income case calculated from eight specifications.
    - Green diamonds: gaps based on the initial specification relative to a 40 percent-higher-income country.
    - Red diamonds: average weighted gap across all specifications (weights are absolute values of the Akaike information criterion).
    - Blue circles: gaps relative to the hypothetical EU-average-income country.
  - The initial-specification gaps (green diamonds) are generally close to the average gaps (red diamonds); gaps relative to the EU average are typically larger.
- Heat-map classification (Tables 2–3):
  - Gaps transformed into reform heat maps for each country relative to the two benchmarks.
  - Gap classifications:
    - Very large: gap < –1.65
    - Large: gap between –0.5 and –1.65
    - Medium: gap between –0.5 and 0.5
    - Low: gap > 0.5
  - Interpretation examples:
    - A gap of –0.5 implies the country performs worse than about 70 percent of the sample (assuming normality).
    - A gap of –1.65 implies the country is below the 5th percentile.
    - A gap of zero implies performance at about the median conditional on income.
- Summary of empirical patterns:
  - When aspiring to a 40 percent higher income, about one-third to more than half of countries show large reform needs in institutions, financial market development, infrastructure, goods and labor market efficiency, and innovation-related areas.
  - With the EU-average-income benchmark (except Slovenia), reform needs expand considerably.
- Methodological notes on averaging:
  - The average gap is a weighted average of gaps from all eight specifications, where the weight of a gap from specification i is w_i = |AIC_i| / sum |AIC|.

*Source: wp1782 - References .............................................................................................................*

### Appendix table A1.2 shows the (absolute) scores of the global competitiveness index (1-7(best)) for each pillar.

### Appendix table A1.2 — Global Competitiveness Index (1-7(best)) scores by pillar

### Overview
- Appendix table A1.2 shows the (absolute) scores of the global competitiveness index (1-7(best)) for each pillar.
- Pillar numbering and labels as used in the source:
  - 1–institutions, 2–infrastructure, 3–macroeconomic environment, 4–health and primary education, 5–higher education and training, 6–goods market efficiency, 7–labor market efficiency, 8–financial market development, 9–technological readiness, 11–business sophistication, and 12–innovation.

### Distribution of reform gaps (aggregate observations)
- Sample coverage and distributional observations:
  - 135 countries analyzed.
  - For 2015 the reform gaps in 135 countries approximate a normal distribution reasonably well.
  - 5.6 percent of countries have very large gaps (smaller than –1.65).
  - 28 percent of countries have gaps smaller than –0.5.
- Note on axis interpretation:
  - The vertical axis in figures depicts the standard deviation from the trend of the generic benchmark. A negative number implies that the country lags.

### Main reform needs identified (Emerging ECA regional summary)
- Assuming countries seek to raise per capita income by 40 percent over the next 7–10 years, the largest reform needs are:
  - institutions
  - financial market development
  - infrastructure
  - product and labor market efficiency
  - areas related to innovation
- Heterogeneity:
  - Despite common challenges, ECA countries are quite heterogeneous; some low-income countries have only a small number of areas in “red”.
  - The methodology accounts explicitly for the level of income (lower per capita income countries are not expected to score as well as higher income countries).
- More granular findings:
  - In 10 out of 11 main GCR pillars, there are three or more large reform gaps at a more granular (subpillar) level in emerging ECA countries.
  - Areas with longer lists of granular reform needs include: institutions, business sophistication, goods market efficiency, labor market efficiency, financial market development.
  - At least three key granular areas identified in: infrastructure, technological readiness and R&D innovation.
  - Example granular labor market needs: wage flexibility and the ability to attract and retain talent.

### Robustness checks and alternative specifications
- Robustness across specifications:
  - A gap is interpreted as robust if its maximum or minimum distance from all eight specifications is no more than one threshold apart from the main specification gap.
  - Almost all main specification gaps are robust.
  - Average deviation from the mean in emerging ECA is 0.28.
- Time period and income-group re-estimations:
  - Re-estimating for 2006–14 yields consistent findings: income per capita is positively correlated with structural indicators for each pillar.
  - Being a resource-rich country associated with weaker performance across most pillars except macroeconomic environment.
  - For the ECA region, infrastructure and technological readiness results differ over the whole time period (ECA dummy no longer positive and significant).
  - When re-estimated separately by World Bank income groups, income per capita remains positively correlated with structural indicators in most cases; statistically significant relationship between income per capita and the labor market pillar found only for high-income countries and the full sample.
- Alternative benchmark and potential GDP:
  - Main specification benchmark: a 40 percent increase in income over the next 7–10 years for all countries.
  - Alternative: potential GDP per capita (Hodrick-Prescott filter with three-year ahead forecast) — in 2015 heat maps are almost identical to main specification.
  - Alternative: country-specific GDP growth targets implied by a simple growth regression for Emerging ECA — this implies projections as high as 64 percent for Tajikistan and 63 percent for Kyrgyz Republic, and as low as 19 percent for Czech Republic and 17 percent for Slovenia. Under this alternative, reform needs rise for poorer countries and fall for richer countries, but within-country ranking of reform gaps does not change.

### Comparison with Doing Business (DB) indicators and other methodologies
- Indicator types:
  - GCR indicators: perception-based (survey of business executives) — capture perceived quality and implementation of institutions/policies but have sampling, interpretation, and scaling caveats.
  - DB indicators: based on primary (fact) data — reflect existence of laws/regulations “on the books” and are actionable but may not reflect effectiveness or conflicts among laws.
- GCR vs DB comparisons:
  - Income per capita remains positively correlated with DB indicators.
  - Aggregate GCR and DB score gaps are positively correlated; correlation for sub-indicators (e.g., DB getting credit vs GCR financial markets development; DB paying taxes vs GCR total tax rate and incentives of taxation to invest) also positive.
  - R2 of correlations between GCR and DB gaps ranges from 0.3 to 0.5.
  - In 2015 emerging ECA countries that look more favorable in GCR than DB in these areas: Tajikistan, Azerbaijan, Kazakhstan, Ukraine, the Russian Federation, and Turkey.
  - Assessed less favorably in GCR vs DB: Georgia, Estonia, Macedonia, and Armenia.
- Table 5 reported regression fit statistics (selected R2 values from the table):
  - R2 values listed: 0.25, 0.24, 0.28, 0.39, 0.27, 0.57.

### Stochastic Frontier Analysis (SFA) vs OLS
- SFA approach:
  - Estimates productivity frontier (most productive economy given income) and inefficiency term (non-negative distance to frontier).
  - Parametric version employed with inefficiency term half-normally distributed (Aigner et. al. 1977).
  - Same regression specification as OLS (dependent variable: log GDP per capita; dependent variable for structural indicator: aggregate GCR score).
- Comparison results:
  - Fit between OLS-based gaps and SFA-based gaps is close: R2 of regression of one on another is 0.93.
  - Country ranking is generally preserved between methods.
  - Overall expectation: both methods yield similar country rankings and relative gap magnitudes.

### Key methodological caveats and interpretation guidance
- The analysis offers a bird’s-eye view of structural reform needs but does not directly quantify the growth dividend from closing specific gaps.
- Important caveats:
  - Measurement errors in underlying data may distort estimated gaps.
  - Estimated gaps may reflect omitted variables and unobserved factors that bias results.
  - Perception-based and fact-based indicators each have advantages and shortcomings; detailed country analysis should compare multiple indicator types.
  - Closing larger gaps in one area may not always yield a larger growth payoff than closing smaller gaps elsewhere; judgment and further country-specific analysis are required.

### Conclusions and policy implications
- Average relationship:
  - Countries with higher per capita income tend to score better on structural indicators.
  - Empirical evidence suggests structural reforms are conducive to increasing potential growth in the medium term, though reverse causality or mutual reinforcement cannot be ruled out.
- Priority reform areas for the region (Emerging ECA) going forward:
  - institutions
  - financial market development
  - infrastructure
  - goods and labor market efficiency
  - innovation-related areas
- Use of the approach:
  - The approach helps detect granular reform elements within broad pillars and can guide policy prioritization subject to country knowledge, further analysis, and judgment.

*Source: wp1782 - Appendix table A1.2 shows the (absolute) scores of the global competitiveness index (1-7(best)) for each pillar.*

### Appendix 1: Summary Statistics

### Appendix 1: Summary Statistics

### Structural Reform Indicators: Summary Statistics (Table A1.1)
- Sample size: 132 countries for which all data is available.
- Pillar 1: Institutions — count 132; mean 4.07; sd 0.86; p10 3.19; p90 5.46
- Pillar 2: Infrastructure — count 132; mean 4.04; sd 1.22; p10 2.43; p90 5.71
- Pillar 3: Macroeconomic environment — count 132; mean 4.79; sd 0.96; p10 3.60; p90 6.15
- Pillar 4: Health and primary education — count 132; mean 5.52; sd 0.89; p10 4.28; p90 6.44
- Pillar 5: Higher education and training — count 132; mean 4.26; sd 1.01; p10 2.78; p90 5.59
- Pillar 6: Goods market efficiency — count 132; mean 4.38; sd 0.53; p10 3.78; p90 5.13
- Pillar 7: Labor market efficiency — count 132; mean 4.24; sd 0.56; p10 3.61; p90 5.00
- Pillar 8: Financial market development — count 132; mean 3.95; sd 0.70; p10 3.11; p90 4.99
- Pillar 9: Technological readiness — count 132; mean 4.09; sd 1.21; p10 2.63; p90 5.91
- Pillar 10: Market size — count 132; mean 3.91; sd 1.16; p10 2.57; p90 5.45
- Pillar 11: Business sophistication — count 132; mean 4.07; sd 0.71; p10 3.31; p90 5.28
- Pillar 12: Innovation — count 132; mean 3.54; sd 0.85; p10 2.67; p90 4.98
- GDP per capita, thousands 2010 USD — count 132; mean 15.81; sd 20.41; p10 0.83; p90 45.41
- =1 if from Emerging ECA — mean 0.20; sd 0.40; p10 0.00; p90 1.00
- =1 if from SSA — mean 0.23; sd 0.42; p10 0.00; p90 1.00
- =1 if OECD member and HIC as of 2010 — mean 0.23; sd 0.42; p10 0.00; p90 1.00
- =1 if resource-rich (IMF 2012) — mean 0.30; sd 0.46; p10 0.00; p90 1.00
- Note: 1/ 132 countries for which all data is available.

### Global Competitiveness Index: Score (1-7 (best)) (Table A1.2)
- Country scores by pillar (Institutions; Infrastructure; Macroeconomic environment; Health and primary education; Higher education and training; Goods market efficiency; Labor market efficiency; Financial market development; Technological readiness; Business sophistication; Innovation):
  - Albania 3.68 3.55 3.96 5.97 4.74 4.34 3.97 3.24 3.40 3.65 2.76
  - Armenia 3.78 3.72 4.71 5.35 4.26 4.46 4.30 3.53 3.67 3.65 3.02
  - Azerbaijan 3.94 4.15 6.35 5.22 3.90 4.31 4.57 3.33 4.26 3.86 3.33
  - Bosnia and Herzegovina 3.18 3.08 4.32 6.03 3.77 3.69 3.36 3.34 3.60 3.31 2.79
  - Bulgaria 3.39 3.99 4.94 5.97 4.48 4.35 4.23 3.98 4.87 3.64 3.11
  - Croatia 3.63 4.58 4.19 5.85 4.62 4.05 3.83 3.59 4.65 3.74 3.13
  - Czech Republic 4.09 4.69 5.97 6.31 5.10 4.63 4.44 4.62 5.43 4.49 3.79
  - Estonia 5.03 4.87 6.15 6.34 5.50 4.93 5.00 4.63 5.32 4.26 4.03
  - Georgia 4.38 4.19 4.95 5.79 4.00 4.48 4.56 3.87 3.81 3.48 2.71
  - Hungary 3.52 4.51 4.94 5.71 4.56 4.29 4.15 3.93 4.60 3.70 3.44
  - Kazakhstan 4.16 4.25 5.72 5.37 4.53 4.48 4.90 3.56 4.19 3.79 3.27
  - Kyrgyz Republic 3.29 2.84 4.62 5.30 4.09 4.23 4.06 3.44 3.27 3.41 2.67
  - Latvia 4.18 4.47 5.56 6.18 5.05 4.64 4.72 4.39 5.29 4.06 3.33
  - Lithuania 4.12 4.68 5.56 6.19 5.35 4.64 4.35 3.99 5.63 4.32 3.73
  - Macedonia 4.14 3.77 5.09 5.61 4.79 4.65 4.07 4.09 4.15 3.87 3.38
  - Moldova 3.20 3.69 4.86 5.39 4.09 4.06 4.07 3.28 4.39 3.29 2.56
  - Montenegro 3.89 3.98 4.62 6.21 4.58 4.30 4.18 4.26 4.33 3.62 3.28
  - Poland 4.07 4.30 5.11 6.15 5.05 4.51 4.11 4.26 4.78 4.09 3.32
  - Romania 3.66 3.61 5.44 5.49 4.55 4.28 4.13 4.05 4.63 3.71 3.24
  - Russian Federation 3.46 4.81 5.29 5.94 4.96 4.16 4.40 3.53 4.22 3.79 3.29
  - Serbia 3.24 3.87 3.60 5.87 4.27 3.74 3.72 3.23 4.47 3.14 2.90
  - Slovak Republic 3.43 4.28 5.21 6.01 4.62 4.43 3.90 4.41 4.64 4.07 3.29
  - Slovenia 3.93 4.79 4.45 6.44 5.41 4.50 4.00 2.85 5.14 4.15 3.83
  - Tajikistan 4.10 2.93 4.64 5.61 4.12 4.12 4.42 3.38 2.81 3.80 3.32
  - Turkey 3.84 4.43 4.75 5.69 4.58 4.53 3.46 3.93 4.08 4.07 3.35
  - Ukraine 3.07 4.07 3.12 6.06 5.03 4.02 4.33 3.18 3.45 3.70 3.41
- Source lines in the Tables:
  - Table A1.1 Source: GCR 2015, authors’ calculations.
  - Table A1.2 Source : GCR 2015-16.

*Source: GCR 2015, authors’ calculations; Source : GCR 2015-16.*

### Appendix 2: Regression Results: Different Specifications*

### Appendix 2: Regression Results: Different Specifications

### Pillar 1, Institutions
- Observations: 132 (column (1)); Observations: 131 (column (4)/(8) where indicated)
- lngdp_pc: 0.07*** (column (1)); 0.08*** (column (4))
- emerg_ECA: -0.08*** (column (2)); -0.07*** (column (3)); -0.10** (column (4))
- oecd_hic: 0.04 (column (3)); -0.02 (column (4)); -0.08** (columns (6) and (7))
- RR_dummy: -0.04 (column (2)); -0.03 (column (3)); -0.04* (columns (6) and (7))
- gdpcap_thous: 0.01*** (columns (5)–(8))
- gdp_sq: -0.00*** (columns (5)–(8))
- R-squared: 0.45 (column (1)); up to 0.66 (column (8))
- AIC range shown: -179.4 to -218

### Pillar 2, Infrastructure
- Observations: 132 (column (1)); Observations: 131 (column (4)/(8) where indicated)
- lngdp_pc: 0.15*** (column (1)); 0.14*** (column (4))
- emerg_ECA: 0.01 (columns (2) and (3)); -0.03 (column (4)); 0.08*** (columns (5) and (6))
- oecd_hic: -0.01 (column (3)); -0.03 (column (4)); -0.09** (column (6)); -0.07* (column (7))
- RR_dummy: -0.07*** (column (2)); -0.07*** (column (3)); -0.06** (column (4)); -0.05* (column (6)); -0.07** (column (7))
- region4: -0.10** (column (3)); -0.11** (column (7))
- gdpcap_thous: 0.02*** (columns (5)–(8))
- gdp_sq: -0.00*** (columns (5)–(8))
- R-squared: 0.80 (column (1)); up to 0.84 (column (4))
- AIC range shown: -199.7 to -161.6

### Pillar 3, Macroeconomic Environment
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.05*** (column (1)); 0.07*** (column (4))
- emerg_ECA: 0.04 (columns (2) and (3)); 0.09 (column (4)); 0.08*** (column (6)); 0.19*** (column (8))
- oecd_hic: -0.03 (column (3)); -0.01 (column (4)); -0.11** (column (6))
- RR_dummy: 0.07** (column (3)); 0.06** (column (4)); 0.07** (columns (6)–(8))
- region6: 0.17* (column (2)); 0.24** (column (4))
- gdpcap_thous: 0.00*** to 0.01*** (columns (5)–(8))
- gdp_sq: significance varies; -0.00*** (column (7)); -0.00** (column (8))
- R-squared: 0.21 (column (1)); up to 0.35 (column (8))
- AIC range shown: -121.1 to -124.8

### Pillar 4, Health and Primary Education
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.07*** (columns (1)–(3)); 0.05*** (column (4))
- emerg_ECA: 0.05*** (columns (2) and (3)); -0.00 (column (4)); 0.08*** and 0.09*** (columns (6) and (7))
- oecd_hic: -0.02 (columns (3)–(4)); -0.06** (column (6)); -0.03* (column (7))
- RR_dummy: -0.07*** (columns (2)–(4)); -0.06*** to -0.07*** (columns (6)–(8))
- region4: -0.15*** (column (3) and column (7))
- gdpcap_thous: 0.01*** (columns (5)–(8))
- gdp_sq: -0.00*** (columns (5)–(8))
- R-squared: 0.56 (column (1)); up to 0.75 (column (4))
- AIC range shown: -232.4 to -273.8

### Pillar 5, Higher Education and Training
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.12*** (columns (1)–(2)); 0.11*** (column (3)); 0.09*** (column (4))
- emerg_ECA: 0.08*** (columns (2)–(3)); 0.03 (column (4)); 0.14*** (columns (5)–(6))
- oecd_hic: 0.01 to -0.00 (columns (3)–(4)); -0.04 to -0.03 (columns (6)–(7))
- RR_dummy: -0.04** (column (2)); -0.04* (column (3)); -0.03 (column (4))
- region4: -0.16*** (column (3)); -0.17*** (column (4))
- gdpcap_thous: 0.02*** (columns (5)–(8))
- gdp_sq: -0.00*** (columns (5)–(8))
- R-squared: 0.77 (column (1)); up to 0.85 (column (4))
- AIC range shown: -239.8 to -235.2

### Pillar 6, Goods Market Efficiency
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.05*** (column (1)); 0.05*** (column (4))
- emerg_ECA: -0.01 to -0.03 (columns (2)–(4)); 0.01 to 0.02 (columns (5)–(7))
- oecd_hic: 0.01 (column (3)); -0.02 (column (4)); -0.05** (columns (6) and (7))
- RR_dummy: -0.03* (column (2)); -0.03 (column (3)); -0.03* (columns (6) and (7))
- region1: -0.04* (column (1))
- region2: -0.03** (column (2))
- region3: -0.05** (column (2))
- region5: -0.11*** (column (2))
- region6: 0.05** (column (7))
- gdpcap_thous: 0.01*** (columns (5)–(8))
- gdp_sq: -0.00*** (columns (5)–(8))
- R-squared: 0.48 (column (1)); up to 0.61 (column (8))
- AIC range shown: -318.3 to -332.5

### Pillar 7, Labor Market Efficiency
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.03*** (column (1)); 0.02* (column (3)); 0.03*** (column (4))
- emerg_ECA: -0.01 to -0.04 (columns (2)–(4)); 0.02 to 0.03 (columns (5)–(7))
- oecd_hic: 0.06* (column (2)); -0.01 (column (3)); -0.06* (column (7))
- RR_dummy: -0.01 to 0.00 (columns (2)–(4)); effectively no consistent significant effect across specifications
- Regional effects (columns (1)/(4)): region1 -0.19***; region2 -0.11***; region3 -0.21***; region4 -0.11***; region5 -0.23***; region6 -0.09*** (in column (1))
- gdpcap_thous: 0.00*** to 0.01*** (columns (5)–(8))
- gdp_sq: -0.00* (column (8))
- R-squared: 0.15 (column (1)); up to 0.48 (column (8))
- AIC range shown: -236 to -276.7

### Pillar 8, Financial Market Development
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.06*** (columns (1)–(3)); 0.08*** (column (4))
- emerg_ECA: -0.05*** (column (2)); -0.06*** (column (3)); 0.03 (column (4)); 0.11** (column (8))
- oecd_hic: -0.04 to -0.02 (columns (3)–(4)); -0.10** (column (6))
- RR_dummy: -0.03* (column (1)); -0.04** (column (2)); -0.04* (column (3)); -0.05** (columns (6) and (7))
- region2: -0.20*** (column (2)); -0.18*** (column (4))
- region3: -0.15*** (column (2)); -0.09* (column (4))
- gdpcap_thous: 0.01*** (columns (5)–(8))
- gdp_sq: -0.00*** to -0.00** (columns (5)–(8))
- R-squared: 0.38 (column (1)); up to 0.51 (column (4))
- AIC range shown: -202.6 to -208.6

### Pillar 9, Technological Readiness
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.15*** (column (1)); 0.13*** (column (4))
- emerg_ECA: 0.05*** (column (2)); 0.06*** (column (3)); 0.01 (column (4)); 0.13*** (columns (5)–(7))
- oecd_hic: 0.06*** (column (2)); 0.03 (column (3)); non-significant or small negative in later columns
- RR_dummy: -0.07*** (column (2)); -0.05*** (columns (3)–(4)); -0.05** to -0.06*** (columns (6)–(8))
- region1: -0.11*** (column (1))
- region2: 0.01 (column (2)); 0.06*** (column (8))
- region6: 0.03 (column (1)); 0.10*** (column (8))
- gdpcap_thous: 0.02*** (columns (5)–(8))
- gdp_sq: -0.00*** (columns (5)–(8))
- R-squared: 0.86 (column (1)); up to 0.91 (column (4))
- AIC range shown: -264.1 to -248.7

### Pillar 11, Business Sophistication
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.07*** (columns (1)–(2)); 0.06*** (columns (3)–(4))
- emerg_ECA: -0.08*** (column (2)); -0.07*** (column (3)); -0.10*** (column (4)); -0.03*** (columns (6)–(8))
- oecd_hic: 0.05** (column (2)); 0.02 (column (3)); later columns show small or non-significant effects
- RR_dummy: -0.05*** (column (2)); -0.04** (column (3)); -0.03* (column (4)); -0.04** (columns (6)–(7))
- region5: -0.10** (column (2))
- gdpcap_thous: 0.01*** (columns (5)–(8))
- gdp_sq: -0.00*** (columns (5)–(8))
- R-squared: 0.63 (column (1)); up to 0.73 (column (4))
- AIC range shown: -277.6 to -303.9

### Pillar 12, Innovation
- Observations: 132 (columns (1)–(3)); 131 (columns (4)/(8) where indicated)
- lngdp_pc: 0.09*** (column (1)); 0.06*** (columns (3)–(4))
- emerg_ECA: reported as non-present in some columns; -0.09*** (column (3)); -0.07*** (column (4)); -0.11** (column (6)); -0.03** (columns (6)–(8))
- oecd_hic: 0.15*** (column (2)); 0.10*** (column (3)); 0.06* (column (6))
- RR_dummy: -0.06*** (column (2)); -0.03 (columns (3)–(4)); -0.05** to -0.04** (columns (6)–(7)); -0.03* (column (8))
- region5: -0.16** (column (3))
- gdpcap_thous: 0.01*** (columns (5)–(8))
- gdp_sq: -0.00*** (columns (5)–(8))
- R-squared: 0.54 (column (1)); up to 0.75 (column (8))
- AIC range shown: -185.9 to -244

*Robust standard errors in parentheses; statistical significance: *** p<0.01, ** p<0.05, * p<0.1.*

*Source: Appendix 2 tables of regression results (wp1782).*

### Appendix 3.

### Appendix 3. Differences in Structural Reform Indicators in Emerging ECA: Alternative Specifications

### Heat maps (Table A3.1 and Table A3.2)
- Table A3.1: Heat Map: Differences in Structural Reform Indicators in Emerging ECA Relative to a Generic Country with 40% Higher Income – Potential Output instead of Actual Output.
- Table A3.2: Heat Map: Differences in Structural Reform Indicators in Emerging ECA Relative to a Generic Country with Higher Income – Projected 10-year GDP Per Capita Growth.

### Methodological note on comparisons
- Country X is compared with a generic country whose income per capita is the same as that of country X, projected for 2024.
- The projected GDP per capita growth in country X is implied from a regression of GDP per capita growth in 2005-2015 on GDP per capita in 2004, estimated on a sample of Emerging ECA countries.

### Appendix 4 — Standard deviations of gaps across specifications: Key statistics by country and pillar
- Albania: Institutions 0.29, Infrastructure 0.16, Macroeconomic environment 0.30, Health and primary education 0.06, Higher education and training 0.22, Goods market efficiency 0.16, Product market efficiency 0.08, Financial sector development 0.24, Technological readiness 0.36, Business sophistication 0.44, Innovation 0.34.
- Armenia: Institutions 0.16, Infrastructure 0.24, Macroeconomic environment 0.08, Health and primary education 0.44, Higher education and training 0.44, Goods market efficiency 0.11, Product market efficiency 0.08, Financial sector development 0.11, Technological readiness 0.50, Business sophistication 0.22, Innovation 0.19.
- Azerbaijan: Institutions 0.31, Infrastructure 0.15, Macroeconomic environment 0.26, Health and primary education 0.19, Higher education and training 0.42, Goods market efficiency 0.18, Product market efficiency 0.13, Financial sector development 0.25, Technological readiness 0.19, Business sophistication 0.48, Innovation 0.44.
- Bosnia and Herzegovina: Institutions 0.13, Infrastructure 0.40, Macroeconomic environment 0.10, Health and primary education 0.33, Higher education and training 0.57, Goods market efficiency 0.17, Product market efficiency 0.16, Financial sector development 0.10, Technological readiness 0.57, Business sophistication 0.18, Innovation 0.16.
- Bulgaria: Institutions 0.16, Infrastructure 0.31, Macroeconomic environment 0.12, Health and primary education 0.35, Higher education and training 0.46, Goods market efficiency 0.17, Product market efficiency 0.10, Financial sector development 0.14, Technological readiness 0.34, Business sophistication 0.25, Innovation 0.25.
- Croatia: Institutions 0.18, Infrastructure 0.25, Macroeconomic environment 0.19, Health and primary education 0.37, Higher education and training 0.46, Goods market efficiency 0.19, Product market efficiency 0.19, Financial sector development 0.16, Technological readiness 0.44, Business sophistication 0.23, Innovation 0.26.
- Czech Republic: Institutions 0.25, Infrastructure 0.21, Macroeconomic environment 0.16, Health and primary education 0.29, Higher education and training 0.39, Goods market efficiency 0.21, Product market efficiency 0.19, Financial sector development 0.25, Technological readiness 0.41, Business sophistication 0.27, Innovation 0.26.
- Estonia: Institutions 0.42, Infrastructure 0.23, Macroeconomic environment 0.19, Health and primary education 0.29, Higher education and training 0.36, Goods market efficiency 0.29, Product market efficiency 0.32, Financial sector development 0.28, Technological readiness 0.43, Business sophistication 0.29, Innovation 0.33.
- Georgia: Institutions 0.22, Infrastructure 0.21, Macroeconomic environment 0.08, Health and primary education 0.37, Higher education and training 0.49, Goods market efficiency 0.12, Product market efficiency 0.12, Financial sector development 0.13, Technological readiness 0.48, Business sophistication 0.20, Innovation 0.14.
- Hungary: Institutions 0.31, Infrastructure 0.30, Macroeconomic environment 0.21, Health and primary education 0.33, Higher education and training 0.49, Goods market efficiency 0.28, Product market efficiency 0.25, Financial sector development 0.31, Technological readiness 0.61, Business sophistication 0.29, Innovation 0.37.
- Kazakhstan: Institutions 0.31, Infrastructure 0.19, Macroeconomic environment 0.30, Health and primary education 0.16, Higher education and training 0.31, Goods market efficiency 0.18, Product market efficiency 0.13, Financial sector development 0.24, Technological readiness 0.30, Business sophistication 0.46, Innovation 0.43.
- Kyrgyz Republic: Institutions 0.41, Infrastructure 0.59, Macroeconomic environment 0.32, Health and primary education 0.43, Higher education and training 0.74, Goods market efficiency 0.32, Product market efficiency 0.10, Financial sector development 0.46, Technological readiness 0.84, Business sophistication 0.56, Innovation 0.44.
- Latvia: Institutions 0.18, Infrastructure 0.27, Macroeconomic environment 0.15, Health and primary education 0.34, Higher education and training 0.39, Goods market efficiency 0.16, Product market efficiency 0.12, Financial sector development 0.13, Technological readiness 0.34, Business sophistication 0.27, Innovation 0.29.

- Lithuania: Institutions 0.18, Infrastructure 0.24, Macroeconomic environment 0.16, Health and primary education 0.34, Higher education and training 0.37, Goods market efficiency 0.16, Product market efficiency 0.14, Financial sector development 0.15, Technological readiness 0.33, Business sophistication 0.30, Innovation 0.36.
- FYR Macedonia: Institutions 0.21, Infrastructure 0.28, Macroeconomic environment 0.09, Health and primary education 0.40, Higher education and training 0.37, Goods market efficiency 0.14, Product market efficiency 0.08, Financial sector development 0.14, Technological readiness 0.43, Business sophistication 0.27, Innovation 0.29.
- Moldova: Institutions 0.19, Infrastructure 0.37, Macroeconomic environment 0.12, Health and primary education 0.47, Higher education and training 0.52, Goods market efficiency 0.17, Product market efficiency 0.05, Financial sector development 0.19, Technological readiness 0.60, Business sophistication 0.23, Innovation 0.20.
- Montenegro: Institutions 0.19, Infrastructure 0.31, Macroeconomic environment 0.12, Health and primary education 0.32, Higher education and training 0.44, Goods market efficiency 0.17, Product market efficiency 0.10, Financial sector development 0.14, Technological readiness 0.46, Business sophistication 0.24, Innovation 0.28.
- Poland: Institutions 0.18, Infrastructure 0.29, Macroeconomic environment 0.17, Health and primary education 0.34, Higher education and training 0.39, Goods market efficiency 0.17, Product market efficiency 0.16, Financial sector development 0.14, Technological readiness 0.42, Business sophistication 0.27, Innovation 0.29.
- Romania: Institutions 0.17, Infrastructure 0.41, Macroeconomic environment 0.13, Health and primary education 0.43, Higher education and training 0.46, Goods market efficiency 0.17, Product market efficiency 0.11, Financial sector development 0.14, Technological readiness 0.42, Business sophistication 0.25, Innovation 0.28.
- Russia: Institutions 0.25, Infrastructure 0.15, Macroeconomic environment 0.32, Health and primary education 0.11, Higher education and training 0.23, Goods market efficiency 0.17, Product market efficiency 0.10, Financial sector development 0.24, Technological readiness 0.29, Business sophistication 0.46, Innovation 0.43.
- Serbia: Institutions 0.14, Infrastructure 0.29, Macroeconomic environment 0.13, Health and primary education 0.36, Higher education and training 0.47, Goods market efficiency 0.18, Product market efficiency 0.11, Financial sector development 0.11, Technological readiness 0.38, Business sophistication 0.17, Innovation 0.19.
- Slovak Republic: Institutions 0.22, Infrastructure 0.27, Macroeconomic environment 0.18, Health and primary education 0.29, Higher education and training 0.46, Goods market efficiency 0.22, Product market efficiency 0.16, Financial sector development 0.27, Technological readiness 0.57, Business sophistication 0.26, Innovation 0.31.
- Slovenia: Institutions 0.20, Infrastructure 0.20, Macroeconomic environment 0.16, Health and primary education 0.30, Higher education and training 0.38, Goods market efficiency 0.15, Product market efficiency 0.12, Financial sector development 0.26, Technological readiness 0.46, Business sophistication 0.22, Innovation 0.24.
- Tajikistan: Institutions 0.35, Infrastructure 0.66, Macroeconomic environment 0.25, Health and primary education 0.58, Higher education and training 0.82, Goods market efficiency 0.35, Product market efficiency 0.11, Financial sector development 0.40, Technological readiness 0.93, Business sophistication 0.46, Innovation 0.31.
- Turkey: Institutions 0.17, Infrastructure 0.27, Macroeconomic environment 0.16, Health and primary education 0.40, Higher education and training 0.47, Goods market efficiency 0.17, Product market efficiency 0.22, Financial sector development 0.14, Technological readiness 0.58, Business sophistication 0.29, Innovation 0.29.
- Ukraine: Institutions 0.14, Infrastructure 0.26, Macroeconomic environment 0.13, Health and primary education 0.34, Higher education and training 0.41, Goods market efficiency 0.13, Product market efficiency 0.08, Financial sector development 0.12, Technological readiness 0.53, Business sophistication 0.21, Innovation 0.24.

### Appendix 5 — Histograms of gaps
- Figure A5.1: Histograms of Gaps: Main Pillars.
- Source: Own computations. Note: gaps are relative to a peer with the same income per capita.

*Source: wp1782 - Appendix 3.*

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_Source: https://www.imf.org/-/media/files/publications/wp/2017/wp1782.pdf_