## _wp13272 (2013) — Further information on the EBA project, including datasets and the application of the EBA method to recent ## Source details **Canonical URL:** [_wp13272 (2013) — Further information on the EBA project, including datasets and the application of the EBA method to recent](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13272.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13272.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13272.pdf.json) --- ### Methodology and two-stage structure - EBA comprises three methods and emphasizes a sharper distinction between “positive (descriptive)” analysis and “normative” evaluations of current accounts (CA) and real exchange rates (REER). - Two-stage regression-based structure: - Stage 1 — Positive analysis: estimation of panel regressions to understand CA and REER developments. - Stage 2 — Normative-oriented estimates: uses regression results plus benchmark policy settings to estimate contributions of “policy gaps” to CA and REER and to EBA “Total Gaps.” - Key methodological enhancements (Spring 2013): - Policies: accounts for financial policies (or proxies) and monetary policy. - FX intervention: modeled in both REER and CA regressions. - Fundamentals: includes productivity/level of development interacted with capital account openness. - Institutional/political risk: included in CA regression. - Exhaustible resources: extended to all net exporters of oil and natural gas. ### Analytical framework and reduced-form relations - Core relations: - IS relation (CA as saving-investment gap) and balance-of-payments relation with variables Y (domestic output gap), REER, NFA, r, ΔR, CF. - Reduced-form representation (when monetary policy targets output): - CA = f(Xs, X I, CA X, CF X, Z, ΔR) (equation (3)) - REER = f(Xs, X I, CA X, CF X, Z, ΔR) (equation (4)) - Z may be output gap or short-term interest rate (or both). - Key implications: - Many factors affect both CA and REER; REER acts as an expenditure-switching channel but is not an exogenous driver of CA. - Empirical pattern: a factor that lowers CA by 1 percent of GDP often is found to raise REER by about 3–5 percent (directional proportionality noted). - Multilateral consistency achieved by measuring country variables relative to a GDP-weighted “world” average. ### Estimation, sample, and data - Estimation method: pooled GLS with a panel-wide AR(1) correction (addresses strong autocorrelation in CA data). - Sample: 49 economies covering about 90 percent of global GDP (mainly advanced and emerging market economies). - Data frequency and period: annual data for 1986-2010; Spring 2013 EBA analyzes 2012 outcomes. - Country exclusions/considerations: - Excludes countries with very low per capita income, very small area, or where oil exports are highly dominant (e.g., Saudi Arabia, Venezuela) from the panel CA regression. ### Current account regression — main positive-analysis findings - General: - Regressors measured as country deviations from contemporaneous GDP-weighted world averages. - Regressors grouped: traditional fundamentals (D.1), financial factors (D.2), cyclical/temporary factors (D.3), policy-related regressors (D.4). - Most coefficients have expected signs and nearly all are statistically significant. - Selected coefficient magnitudes and interpretations (preserve exact figures): - Productivity/level of development interacted with capital account openness: - Increase in relative productivity by 10 percent → improvement in CA by about 0.6 percent of GDP in countries with open capital accounts (virtually no effect with capital controls). - Expected GDP growth rate 5 years ahead: - Increase in relative forecast growth by 1 percentage point → reduction in CA of almost 0.5 percentage point of GDP. - Lagged NFA/GDP: - Estimated coefficient ≈ +0.015. - Interaction: different slope when NFA < -60 percent of GDP. - Exhaustible resources (oil and natural gas, temporariness-adjusted): - A 1 percent of GDP in ‘temporariness adjusted’ exhaustible resources increases CA by about 0.6 percent of GDP. - Aging and demographics: - Aging speed: increase by 1 percentage point → stronger CA by 0.16 percent of GDP. - Population growth: increase by 1 percentage point → weaker CA by 0.6 percent of GDP. - Dependency ratio: increase by 1 percentage point → weaker CA by 0.03 percent of GDP. - Financial center effect: - Financial centers have CA about 3½ percent of GDP higher on average. - Institutional/political risk (ICRG-based index): - Reduction in the risk indicator by one standard deviation → weaker CA by about 1½ percentage points of GDP. - Reserve currency status (own currency share in world reserves): - For every 10 percent of global reserve held in its own currency → CA deficit lower by 0.45 percentage points. - Global risk aversion (VIX/VXO), interactions: - Increase in VIX by 10 percentage points → CA improvement ≈ 0.7 percent of GDP in non-reserve-currency countries with open capital accounts. - With 10 percent own-currency reserve share, a 10 percentage point VIX increase → CA worsens by 0.14 percent of GDP. - Private credit/GDP (demeaned): - Increase by 10 percentage points → CA weaker by 0.3 percentage points. - Relative output gap: - Increase by 1 percentage point → CA declines by about 0.4 percent of GDP. - Commodity terms-of-trade gap (cyclical), interacted with trade openness: - Increase in terms of trade relative to trend by 10 percentage points → CA improvement about 0.75 percent of GDP for trade openness = 30 percent of GDP. - Fiscal policy (cyclically-adjusted fiscal balance, instrumented): - Increase in relative fiscal balance by 1 percentage point of GDP → CA improvement ≈ 0.324 (about one-third) percentage point of GDP. - Public health expenditure (share of GDP): - Increase by 1 percentage point → CA lower by about 0.551 (about ½) percent of GDP. - FX intervention (change in reserves/GDP, instrumented, interacted with capital controls): - Coefficient ≈ +0.35. - Increase in reserve accumulation of 2 percentage points of GDP → CA higher by one third of a percentage point for capital control index value 0.5. ### Fit, residuals, and limitations - Fit statistics: - RMSE ≈ 3.2 percent of GDP. - Median absolute residual ≈ 2 percent of GDP. - Limitations: - Large dispersion in observed CAs (sample extremes: deficits ≤ -15 percent of GDP to surpluses ≥ 18 percent of GDP). - Regression struggles to fully explain pre-crisis divergences (e.g., 2007). - Addition of private credit improves fit pre-crisis for some countries by up to 2 percentage points of GDP but does not fully capture boom-bust swings. - Residuals may reflect uncaptured distortions, omitted fundamentals, or measurement error. ### Channels: saving versus investment - Separate saving and investment regressions (same specification) indicate: - Majority of significant CA determinants operate mainly through the saving channel. - Investment-dominant determinants: output gap, expected GDP growth, and VIX (in non-reserve-currency countries). - Investment channel also significant, but smaller than saving, for demographics, public health spending, and fiscal balance. - Institutional risk effect on investment is borderline significant and sizable. ### Other hypotheses and robustness checks - Monetary policy: - Not significant in CA regression (likely opposing channels); not significant even when output gap regressor dropped. - Financial proxies: - Alternative private credit indicators or housing-price measures were not superior to demeaned private credit/GDP. - Stock market volatility and rolling GDP-per-capita volatility were significant in some tests but had coverage and robustness issues; not included in final specification. - Structural indicators: - Labor market flexibility had a robust negative coefficient in broad sample (aggregate index coefficient = -0.30; one standard deviation 0.014 → CA weaker by 0.4 percent of GDP) but ambiguous channels and EMU-subsample reversal led to exclusion from final benchmark regressions. - Policy-gap decomposition example: - Cyclically-adjusted fiscal balance coefficient γ_fiscal = 0.32. - Illustration: actual CA = -2% of GDP, residual = 0, cyclically-adjusted fiscal balance = -6% of GDP, desirable = 0 → fiscal gap = -6% → contribution to Total CA Gap ≈ (-6%)*0.32 ≈ -2% of GDP. ### Annex III — Exhaustible resources: implementation and findings - Variable construction: - Oil and natural gas trade balance = 5-year moving average of net exports (as % of GDP) × temporariness index (current extraction / proven reserves normalized to Norway’s oil ratio in 2010). - Resource exporter threshold lowered from 10 percent of GDP to 0 percent of GDP; natural gas included. - Estimated effect: - 1 percent of GDP in temporariness-adjusted exhaustible resources → CA ↑ about 0.6 percent of GDP. - Applies to 13 economies in sample; excluding Russia and Norway leaves coefficient broadly unchanged. - For most countries estimated effects generally in range 0 to 1.5 percent of GDP. ### Annex IV — Financial factors: specification and interpretation - Regressor: demeaned private credit-to-GDP ratio (captures financial excesses and financial depth). - Interpretation and coefficient: - Increase of credit by 10 percentage points of GDP → CA/GDP lower by 0.3 percent of GDP. - Limitations: cannot fully separate financial excess from financial development; endogeneity concerns; no clear P* for optimal credit/GDP. - Robustness: - Alternative detrending and non-linear specifications generally not superior. - Credit growth coefficient alone = -0.043 (significant when alone), but insignificant when demeaned credit included. - Housing price measures and many other financial indicators not robust. ### Annex V — Structural factors: institutional index and labor market - Institutional/political environment index: - Average of 5 ICRG sub-indices; one standard deviation = 0.13. - Coefficient: 0.13 * -0.11 = -1.4 percent of GDP (increase by one standard deviation → CA weaker by 1.4 percent of GDP). - Safer environment associated with more investment and less saving. - Labor market regulation: - Aggregated flexibility index coefficient = -0.30; one standard deviation (0.014) → CA weaker by 0.4 percent of GDP. - Results did not hold for EMU subset (positive and significant for EMU). - Ambiguous channels and lack of robust mechanism → variable excluded from final specification. - Product market regulation and other governance indicators: - Product market index limited in coverage and insignificant where available. - Other governance indicators were significant preliminarily but dropped in favor of ICRG-based political/institutional risk variable for better coverage. ### Annex VI — Suggested policy benchmark for public health spending - Benchmark methodology: - Cross-sectional regression on 2005-2010 averages for 49 EBA countries; explanatory variables: income, demography, inequality. - Fitted values used as suggested benchmarks (reference points, not prescriptive). - Primary cross-sectional regression (2005–2010 averages, 49 countries): - Log(PPPGDPpc): 0.018 [6.54]*** - Dependency Ratio: 0.094 [3.23]*** - Gini Coefficient: 0.053 [2.77]*** - Constant: -0.145 [7.45]*** - Observations: 49 - R-squared: 0.830 - Robust t-statistics in brackets; significance: *** at 1% - Interpretation: - Higher relative income, faster aging, and higher income inequality imply higher suggested public health spending as share of GDP. - Benchmarks are suggested reference points to inform fiscal policy discussions and should be used alongside country-specific judgments. *Source: IMF Working Paper _wp13272 (2013).* ### 2013. Further information on the EBA project, including datasets and the application of the EBA method to recent ### _wp13272 - 2013. Further information on the EBA project, including datasets and the application of the EBA method to recent ### Introduction - The External Balance Assessment (EBA) methodology is developed by the IMF’s Research Department as a successor to the former CGER exercise and builds on CGER. - EBA comprises three methods, each based on its corresponding CGER predecessor.3 - EBA emphasizes a sharper distinction between positive (descriptive) analysis and normative evaluations of current accounts (CA) and real exchange rates (REER). - EBA expands the set of factors considered relative to CGER to include policies, cyclical conditions, and global capital market conditions. ### Two-stage structure of the regression-based methods - Stage 1 — Positive (descriptive) analysis: - Focused on understanding current account and real exchange rate developments via estimation of panel regressions. - Stage 2 — Normative-oriented estimates: - Uses information from regression results to estimate contributions of “policy gaps” to current accounts and real exchange rates. ### Key methodological enhancements implemented in Spring 2013 - Panel regression-based methods were enhanced relative to the first version: - Policies: analysis now accounts for effects of financial policies (or proxies for policies intended to avoid or contain financial excesses) and monetary policy. - FX intervention: modeled in both the real exchange rate regression and the current account regression to enhance consistency between approaches. - Fundamentals: both regressions include terms for productivity/level of economic development interacted with capital account openness. - Institutional/political risk: the CA regression now accounts for risks related to the institutional/political environment. - Exhaustible resources: the role of exhaustible resources is extended to all net exporters of oil and natural gas. - A number of other modifications and alternative specifications/hypotheses are discussed in the paper. ### Paper organization (sections and topics) - Section II: Basic conceptual framework for empirical analysis of current accounts and real exchange rates. - Section III: Positive analysis — current account panel regressions; discusses specification variable by variable, changes relative to first version, alternative specifications and hypotheses; includes subsections: - A. Current account regression specification - B. Estimation - C. Country sample and sample period - D. Current account regression model with D.1–D.4 covering traditional fundamentals, financial factors, cyclical/temporary factors, and policy-related regressors - E. Effects on current account via saving or investment? - F. Fit of the CA regression - G. Other hypotheses explored in the CA regressions - Section IV: Positive analysis — REER panel regression; discusses REER measure, estimation method and sample, explanatory variables and regression results with C.1–C.2 on non-policy fundamentals/financial factors and policy-related regressors, plus fit and other hypotheses. - Section V: Toward normative evaluation — estimation of policy gaps and total gaps; includes: - A. Policy gaps - B. Specifying benchmarks for policy variables - C. Confirming multilateral consistency - Section VI: EBA External Sustainability (ES) approach - Section VII: Interpreting EBA results: relevance, reliability, and pending issues - Annexes cover variable glossary, country samples, role of exhaustible resources, financial factors, structural factors, and suggested policy benchmark for public health spending. - Tables enumerated (Tables 1–10) cover CA and REER regression results, alternative financial indicators, and structural indicators. ### Notable explicit points and terminology preserved - The document explicitly distinguishes “positive (descriptive)” and “normative” evaluations. - The term “policy gaps” is used for contributions of policies to CA and REER deviations. - The enhancement list includes: financial policies (or proxies), monetary policy, FX intervention, productivity/level of economic development interacted with capital account openness, institutional/political risk, and exhaustible resources for net exporters of oil and natural gas. *Source: IMF Working Paper _wp13272 (2013)* ### Section V explains the second stage: the shift from positive analysis to normative evaluation, ### _wp13272 - Section V explains the second stage: the shift from positive analysis to normative evaluation ### Sections covered and purpose - Section V: describes the shift from positive analysis to normative evaluation, combining regression results with benchmark policy settings to estimate contributions of “policy gaps” to current accounts and real exchange rates, and to EBA “Total Gaps.” - Section VI: describes the EBA External Sustainability approach to assessing current accounts. - Section VII: discusses key issues using EBA results for assessments, including relevance and reliability of each of the three EBA methods, strengths of the EBA exercise, and limitations requiring further work. ### Framework for CA and REER analysis (analytical backbone) - Two core relations: - IS relation (current account as saving-investment gap): - (, ,, ) (,, )(,, ,) wo sICA S NFA Y r XI Y r XCA Y REER YX (equation (1)) - Balance-of-payments relation: - (,, ,)(,,) wowo CACF CA Y REER YXCF r rREER XR (equation (2)) - Definitions provided: - Y = the domestic output gap - REER = the real effective exchange rate - NFA = net foreign assets (measured at the beginning of the period) - r = interest rate - ΔR = change in foreign exchange reserves - CF = balance on the financial account - Exogeneity note: ΔR is taken as exogenous (policy determined) and is not written as a function of any other variable. - Regressor groups (Xs): - sX = consumption/saving shifters (income per capita, demographics, expected income, social insurance, budget balance, financial policies, institutional environment, net exports of exhaustible resources) - IX = investment shifters (income per capita, expected income/output, governance, financial policies) - CAX = export/import shifters (world commodity price-based terms of trade, functions of commodity shares) - CFX = capital account shifters (global risk aversion, reserve currency “exorbitant privilege,” financial home bias, capital controls) ### Reduced-form representation and endogeneity - If monetary policy sets an interest rate to target output, reduced forms for CA and REER take the form: - CA = (, , , ,, , ) wo ISC A CF CA CA X X XXZ ZR (equation (3)) - REER = (, , , ,, , ) wo ISC A CF REERREER X X XXZ ZR (equation (4)) - Z could be output gap or short-term interest rate (or both) - Implications: - Many factors affect both CA and REER; REER plays an expenditure-switching role but is not an exogenous driver of CA. - Empirical pattern: a factor that lowers CA by 1 percent of GDP often is found to raise (appreciate) the REER by about 3-5 percent (directional proportionality noted). - Some variables (e.g., interest rates) may affect REER clearly but have ambiguous net effects on CA due to opposing channels. - Permanent gains in terms of trade or tradable productivity can appreciate REER without clear CA implication. - Multilateral consistency: - Country variables are measured relative to a weighted average of other countries’ values to approximate general equilibrium and enhance multilateral consistency. - Caveat: - The CA and REER regressions are not true reduced-form specifications; issues of dynamics and endogeneity arise and are addressed in subsequent sections. ### EBA current account panel regression — positive analysis - Regression target: equation (3) (reduced-form CA regression). - Variables largely measured as a country’s deviation in a given year from the relevant “world” counterpart (the world counterpart is a GDP-weighted average in all regressions where applicable). - Rationale for relative measurement: - Captures influence of country size on CA responsiveness (small economies can move CA with little global pushback; large economies face more “pushback”). - Weighting scheme adjusts for that via relative measures rather than separate coefficients by size. ### Estimation approach - Estimation method: pooled GLS with a panel-wide AR(1) correction to address strong autocorrelation in current account data. - Rationale: - Including lagged CA in pooled data would act like a quasi-fixed effect and could capture sustained distortions, complicating normative interpretation. - Fixed effects specification is avoided to prevent country dummies from mechanically determining CA gaps. ### Country sample and sample period - Country coverage: a set of 49 economies (mainly advanced and emerging market economies), encompassing about 90 percent of global GDP. - Sample considerations: - Focus on countries with sizeable access to global capital markets and sufficient data quality. - Mostly exclude countries with very low per capita income or small geographical area. - Exclude countries where oil exports are highly dominant (e.g., Saudi Arabia, Venezuela) because such cases require special consideration beyond the EBA panel regression. - Data frequency and period: - Regression run on annual data, for the period 1986-2010. - Purpose of annual data: to uncover cyclical sources of current account behavior and allow cyclical adjustment of the current account; EBA exercise in Spring 2013 analyzes 2012 current account outcomes. ### Current account regression model and key empirical findings - Regressors divided into four groups; most coefficients have expected signs and nearly all are statistically significant. - D.1 Traditional fundamentals: non-policy variables — notable findings: - Significant regressors: lagged NFA, relative per-worker income, GDP growth rate, net oil and gas trade balance, aging speed, financial center dummy. - Productivity/level of development (interacted with capital account openness): - Productivity measured as output (PPP) per working-age population relative to three frontier economies, demeaned before interaction with capital account openness. - Finding: an increase in relative productivity by 10 percent is associated with an improvement in the current account by about 0.6 percent in countries with open capital accounts (virtually no effect in countries with capital controls). - Expected GDP growth rate 5 years ahead: - Measured using WEO projections 5 years from now (forward-looking). - Finding: an increase in relative forecast growth rate by 1 percentage point is associated with a reduction in the current account of almost half a percentage point of GDP. - Relationship with NFA position: - Lagged NFA/GDP included; countries with more positive NFA tend to have somewhat higher CA balances. - Estimated coefficient on NFA/GDP ≈ +0.015. - Nonlinearity: the positive association flattens or disappears when NFA/GDP is far into the negative range; interaction term allows a different slope when NFA is below negative 60 percent of GDP. - Exhaustible resources (oil and natural gas): - Model captures tendency of resource-rich exporters to run CA surpluses and to save a portion of income recognizing exhaustibility. - “Temporariness” constructed as production-to-reserves ratio relative to Norway’s oil ratio in 2010; oil and gas trade balances are adjusted by this temporariness measure. - Note: net exporters’ CA expected to be positively related to both size of exports and temporariness; construction details provided in Annex III. *Source: _wp13272 - Section V explains the second stage: the shift from positive analysis to normative evaluation (https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13272.pdf)* ### 0.6 percentage points of GDP. ### _wp13272 - 0.6 percentage points of GDP. ### Demographic factors - An “aging speed” regressor (not used in CGER) is statistically significant; faster projected aging is associated with a stronger current account. - Quantitative associations: - An increase in relative aging speed by 1 percentage point is associated with a stronger current account by 0.16 percent of GDP. - An increase in relative population growth by 1 percentage point is associated with a weaker current account by 0.6 percent of GDP. - An increase in relative dependency ratio by 1 percentage point is associated with a weaker current account by 0.03 percent of GDP. - With the aging speed variable included, population growth and old age dependency ratio enter with expected negative signs but are not statistically significant (population growth just misses significance at the 10 percent level). Both are retained as controls. ### Resource-exporter and financial-center effects - Oil and gas exporters (where oil and gas balance exceeds zero) show a relationship between resource balances and CA; effect example: - 0.6 would be the effect when resources are expected to be depleted in about 9 years (Norway’s oil estimated years-until-exhaustion in 2010). - Financial center dummy (small economies with financial center characteristics: Netherlands, Switzerland, and Belgium in part of sample): - On average, financial centers have a CA balance about 3½ percent of GDP higher than others’. ### Institutional/political risk - Indicator from ICRG designed to measure less risk or safer environment; effect is significant and robust. - A reduction in the risk indicator by one standard deviation is associated with a weaker current account by about 1½ percentage points of GDP. - EBA treats these risks as given characteristics (not policy distortions) in the normative stage. ### Financial factors - Reserve currency status (share of country’s own currency in total world reserves): - Coefficient negative and statistically significant. - For every 10 percent of global reserve held in its own currency, a country experiences a current account deficit which is lower by 0.45 percentage points. - Global risk aversion (VIX/VXO), interacted with reserve share and capital account openness: - An increase in the VIX by 10 percentage points is associated with an improvement in the current account by about 0.7 percent of GDP in non-reserve currency countries with open capital accounts. - In countries with 10 percent of global reserve held in own currency, a 10 percentage point increase in the VIX is associated with worsening of the current account by 0.14 percent of GDP. - Private credit/GDP (relative to own historical average) as proxy for financial excesses: - Inclusion motivated by strong statistical association with CA and to capture part of pre-crisis CA deterioration. - An increase in relative private credit to GDP by 10 percentage points is associated with a weaker current account by 0.3 percentage points. ### Cyclical / temporary factors - Relative output gap: - An increase in the relative output gap by 1 percentage point is associated, other things constant, with a decline of the current account by about 0.4 percent of GDP. - Regresser is statistically significant and explains short-term CA movements. - Commodity terms of trade (cyclical element), interacted with trade openness: - An increase in the terms of trade relative to trend by ten percentage points is associated with an improvement of the current account of about ¾ percent of GDP in a country with trade openness of 30 percent of GDP. - Note: some demand shocks driving the output gap could arise from movements in other included variables (e.g., cyclically-adjusted fiscal balance), and estimated coefficients measure effects for a given output gap. ### Policy-related regressors - Fiscal policy (cyclically-adjusted fiscal balance, instrumented): - Coefficient positive and statistically significant. - An increase in the relative fiscal balance by 1 percentage point of GDP is associated with an improvement of the current account by about one-third of a percentage point of GDP. - Public health expenditure (share of GDP): - Coefficient negative and statistically significant. - An increase in the relative health expenditure by 1 percentage point of GDP is associated with a lower level of the current account by about ½ percent of GDP. - FX intervention (change in international reserves as share of GDP, instrumented, interacted with capital controls): - Coefficient about +0.35. - An increase in reserve accumulation of 2 percentage points of GDP is associated with a current account that is higher by one third of a percentage point of GDP for a country with a capital control index value of 0.5. - Endogeneity concerns acknowledged; instruments used include: ratio of M2 to GDP, the U.S. short term real interest rate, and the global rate of reserve accumulation, each interacted with capital controls. - Capital controls: - Degree of capital controls enters via interaction terms with reserves and level of development; standalone capital controls regressor was statistically insignificant once interactions included, so standalone term excluded. - Private credit/GDP discussed above as indirect indicator of policies to contain financial excesses: - Explains up to 2½ percentage points of GDP deterioration in some countries’ CA prior to the global crisis. ### Channels: saving versus investment - Separate saving and investment regressions run with same specification as CA benchmark. - Majority of significant CA determinants operate mainly through the saving channel. - Investment channel dominant for: - Output gap (investment highly cyclical). - Expected GDP growth (mainly associated with investment). - VIX for non-reserve currency countries (foreign flows may finance mainly investment). - Investment channel also significant, but smaller than saving channel, for: - Demographics (aging and slow population growth: invest more but save even more). - Public expenditure on health (associated with less investment and even less saving). - Fiscal balance (associated with more investment, and even more saving). - Institutional risk effect on investment is borderline significant and sizable. ### Fit of the CA regression - Root mean squared error (RMSE): about 3.2 percent of GDP. - Median absolute value of the residual (typical error in a recent year): about 2 percent of GDP. - Regression struggles with large dispersion in observed CAs (examples in sample range from deficits of 15 percent of GDP or more to surpluses of 18 percent of GDP or more). - Regression fit varies by period; divergence before the global crisis (e.g., 2007) is difficult to fully explain. - Addition of private credit regressor improves fit during pre-crisis period for economies with widening CA deficits (up to 2 percentage points of GDP improvement for some countries). - Still difficult to fully explain wide swings in CAs for countries with severe credit and asset market boom-bust episodes. - Interpretation of regression residuals (uncaptured distortions vs uncaptured fundamentals or error) remains to be discussed further. *Source: _wp13272 - 0.6 percentage points of GDP.* ### Section V. ### Section V. ### G. Other hypotheses explored in the CA regressions - Monetary policy: - The role of monetary policy was not significant in the CA regression (unlike in the REER regression), probably owing to opposing effects (exchange rate appreciation/expenditure switching vs. lower domestic demand). - The lack of significance is not due to the presence of the output gap regressor; the interest rate was not significant even when the output gap regressor was dropped. - Financial policies and indicators: - Alternative proxies of financial excesses (alternative private credit indicators or housing-price based indicators) were not superior to the private credit term—either not significant or not economically relevant. - Measures of financial risk (stock market volatility, macroeconomic volatility) or of risk pooling (insurance market extent) were not robust. - Indicators of financial structure (e.g., bank concentration) were not found to be relevant. - Structural indicators (labor and product market regulation): - Only labor market flexibility was robustly significant: associated with a lower current account (apart from EMU countries). - Channel of influence for labor market flexibility unclear; thus omitted from benchmark regressions to avoid introducing a policy gap contribution to CA and REER misalignment. - No visible higher investment channel in saving-investment regressions; unemployment inclusion did not absorb the effect. Interaction between employment protection and unemployment insurance not supported. - Other variables tried and found insignificant or inferior: - Other public expenditure/social insurance variables (education spending often not significant and often switched sign; pensions hard to capture with single total-expenditure numbers). - Djankov et al. (2005) de jure social protection index: statistically significant with right sign but data limitations (outdated, incomplete coverage). - Composition of net foreign liabilities (e.g., share of FDI in gross liabilities) tried without success. - Composition of government spending, alternative proxies of global risk aversion (US corporate spread, US treasury bond real interest rate), and a country’s historical terms of trade volatility were unsuccessful or yielded signs inconsistent with theoretical priors. - Example illustrating policy-gap decomposition: - Cyclically-adjusted fiscal balance combined coefficient in CA regression: γ_fiscal = 0.32. - Example: country with actual CA deficit of 2% of GDP, regression residual 0, cyclically-adjusted fiscal balance = -6% of GDP, desirable medium-term fiscal balance = 0 → fiscal gap = -6% → contribution to Total CA Gap = (-6%)*0.32 ≈ -2% of GDP (i.e., entire CA deficit attributed to fiscal deviation in this illustration). - International relativities: - Policy gaps measured relative to foreign (world) counterparts; if every country had same-sized fiscal gap, fiscal gap contributions to each country’s CA would be zero. - In the present environment, sizable negative fiscal gaps in many countries imply that a country with zero “own” fiscal gap may see its CA influenced upward by about 1 percent of GDP due to others’ gaps. *Italic line: Source: _wp13272 - Section V.* ### References ### _wp13272 - References ### References - Adam, K., Kuang, P., and A. Marcet, 2011, “House Price Booms and the Current Account,” NBER Working Paper No. 17224. - Aizenman, J. and Y. Jinjarak, 2009, “Current Account Patterns and National Real Estate Markets,” Journal of Urban Economics 66: 2, pp. 75–89. - Aleksynska, M. and M. Schindler, 2011, “Labor Market Regulations in Low-, Middle- and High-Income Countries: A New Panel Database,” IMF Working Paper No. 11/154 (Washington: International Monetary Fund). - Araujo, J., G. B. Li, M. Poplawski-Ribeiro, L.F. Zanna, 2013, “Current Account Norms in Natural Resource Rich and Capital Scarce Economies,” IMF Working Paper No. 13/80 (Washington: International Monetary Fund). - Arcand, J., E. Berkes, and U. Panizza, 2012, “Too Much Finance?” IMF Working Paper No. 12/161 (Washington: International Monetary Fund). - Arora, Vivek, Karl Habermeier, Jonathan D. 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Zhao, 2012. “Financial Structure, Corporate Savings and Global Imbalances,” Manuscript, China Center for Economic Research, Peking University. ### Annex I. Glossary of Variables in the Current Account and REER Regressions - Note: Most variables in the CA and REER regressions are defined and measured relative, respectively, to the contemporaneous GDP-weighted “world” (sample) average level or to the trade–weighted average of other economies’ levels. The treatment used is clearly indicated in each of the regression results tables (Tables 1-10). - NFA/Y (net foreign assets to GDP ratio). This enters directly, as in levels as well as interacted with a dummy that takes on the value of one if the NFA is below negative 60 percent of GDP. The Net Foreign Asset data employed in this paper is an updated and extended version of the Lane and Milesi-Ferretti (2007) dataset. - Financial center dummy. Dummy variable that equals 1 for The Netherlands and for Switzerland throughout the estimation period, and for Belgium also, but only through 2004. - Output per worker, relative to top 3 economies. Ratio of PPP GDP to working age population relative to average of Germany, Japan, and U.S., demeaned. The variable is also interacted with capital account openness. - Oil and gas trade balance, adjusted for ‘temporariness.’ Exports of oil and natural gas minus imports of the same, as percentage of GDP. This enters only when the balance is positive. The balance is multiplied by a measure of temporariness, which is: the ratio of current extraction to proven reserves from the BP Statistical Review to (i.e. the inverse of ‘years-till-exhaustion’) divided by the same ratio for Norway’s oil in 2010. Higher values of the temporariness term indicate that the resource is expected to be exhausted sooner. - Population growth. - Old age dependency ratio. Ratio of population aged over 65 divided by population between 30 and 64 years old. - Aging speed. Projected change in the dependency ratio (above), ratio 20 years out, relative to current level. - 5-year growth forecast. WEO projections of the rate of real GDP growth 5 years ahead. This is expected to measure underlying growth potential (at a time when the output gap is likely to be closed). - Public health spending/GDP. A proxy for one type of social protection policy, which tends to reduce private agents’ need for precautionary saving. - VIX/VXO, interpreted as a measure of global risk aversion. The VXO is an index of implied U.S. stock market volatility (very similar to the VIX, but available for a longer period). Annual average varies between 0.12 and 0.35 during the sample period. The VXO is interacted with capital account openness. Such interacted term is entered alone as well as interacted also with the respective country’s share of its own currency in reported reserves held by central banks worldwide (see below). - Own currency share in world reserves. Share of the country’s own currency in total stock of world reserves, as a proxy for the “exorbitant privilege.” Varies somewhat over time. For example, it was 73 percent for the US in 1985, down to 61 percent in 2010. For a country such as Greece, it moves from zero in 1998 to 19 percent in 2001 (when it joined the euro). For Germany, the change between 1998 and 2001 is less dramatic (from 14 percent to 19 percent). This variable enters both alone and interacted with the VIX. - Output gap. For most countries and years, this reflects estimates from IMF country teams. For those countries and/or years for which such country team estimates are not available, HP filtered estimates of the output gap (based on data over 1980-2018, and using WEO projections for 2012-2018, are used). This variable is also measured relative to the weighted world GDP averaged output gap. - Commodity terms of trade gap, interacted with trade openness (in the CA regression). This regressor aims to capture the role of cyclical developments in commodity prices in influencing a country’s overall terms of trade, by taking into account for each country the detailed structure of its own trade pattern in commodities and the importance of such trade in relation to its total trade. The regressor is constructed in several stages. The commodity index is the ratio of a geometric weighted average price of 43 commodity export categories to a geometric weighted average price of 43 commodity imports, each relative to advanced economies manufactured goods prices. Weights are given by their share in the countries’ export to imports. To produce a cyclical gap measure, the time series is first extended into the medium term (using commodity prices projected as part of the IMF’s latest WEO round) and then filtered by the HP procedure for each country, so has a zero country-specific mean. Finally, the resulting gap series is interacted with a measure of the country’s trade openness, the ratio of exports plus imports of goods and services in GDP. - Footnote example: To illustrate, consider a country that exports no commodities. Then the numerator will be the product of each of the 43 commodity relative price indices to the power of zero which will equal one. Conversely, if a country has a balanced trade in one commodity (say a given foodstuff variety), with exports and imports of that commodity being 20 percent of its total average trade (=(exports+imports)/2). Then country’s TOT will not be affected for global relative price of that commodity as the index will deliver (Pfood/Pman)0.2/(Pfood/Pman)0.2=1, irrespective of the value of Pfood/Pman. Finally, take a country that the same food commodity accounts for 20 percent of its exports and 20 percent of its imports but overall imports are twice as large exports. Then that TOT index will be (Pfood/Pman)0.1/(Pfood/Pman)0.2=(Pfood/Pman)-0.1. Taking logs, it can be seen that the country will experience a TOT deterioration of 1 percent when the price of that commodity rises by 10 percent. - Commodity terms of trade (in the REER regression). For continuity with the CGER exercise, the commodity terms of trade employed in the REER regression is the ratio of a geometric weighted average price of the main commodity exports to a geometric weighted average price of the main commodity imports (same formula as in the previous bullet). The index is constructed from the prices of six commodity categories (food, fuels, agricultural raw materials, metals, gold, and beverages), measured against the advanced economies manufacturing goods prices from WEO. These relative commodity prices of six categories are weighted by the time average of export and import shares of each commodity category in total trade (exports and imports of goods and services). The terms of trade gap employed in the CA regression was found to be insignificant in the REER regression. - Cyclically-adjusted fiscal balance, instrumented. For most countries and years, the cyclically-adjusted fiscal balance is based on country team estimates of cyclical adjustment. Otherwise, it is computed as the residual of a regression of the fiscal balance on the output gap. Because of the potential endogeneity of the fiscal balance, the variable is instrumented with the lagged cyclically-adjusted global fiscal balance, a time trend, lagged world GDP growth, lagged domestic and world output gaps, US corporate credit spreads (worked marginally better than the VIX), FX regime, the polity index, and the average cross-sectional fiscal balance (the first stage regression also controls for the independent CA regressors). - Capital controls index. Quinn index on overall capital controls on the private sector. It is scaled to vary from 0 (no controls) to 1 (full control). Within the sample, the mean across countries for 2011 is 0.17, while the maximum value in 2011 is 0.625. Note that this variable is used in interaction terms with other variables, but not as a standalone regressor. - Changes in reserves, instrumented. Change in central bank foreign exchange reserves during the year scaled by nominal GDP, both in U.S. dollars. As explained more in detail in the text, it was instrumented via M2/GDP, U.S. interest rates, and global reserve accumulation, with country specific slopes, in order to account for various reserve accumulation motives (the first stage regression also controls for the independent regressors of the respective CA or REER regression). - Real interest rate. This variable is the difference between the nominal short-term interest rate and the annual inflation rate. The short-term interest rate is more widely and more consistently available than the policy rate, and it is anyhow close to the first step of the monetary transmission mechanism. As described in the text, it is interacted with capital account openness. - Private credit to GDP. This variable was demeaned to eliminate cross-country differences in the level of financial development and capture more closely financial excesses. It measures credit provided to the non-financial private sector by domestic non-bank financial and banking institutions. - Safer institutional/political environment. This variable is the average of 5 indicators from the International Country Risk Guide dataset: socioeconomic conditions; investment profile; corruption; religious tensions; and democratic accountability. The indicators are drawn from surveys of risk perceptions related to each of these 5 characteristics; higher values signify less risk. (See Annex V for more details.) - Trade openness. Average ratio of exports and imports to GDP. - Financial home bias. It is proxied by the share of domestic debt owned by residents, from the BIS database. *Compiled from _wp13272 - References and Annex I.* ### Annex II. Countries in the EBA Regression Samples ### Annex II. Countries in the EBA Regression Samples ### Countries included - Argentina* - Australia - Austria - Belgium - Brazil - Canada - Chile - China - Colombia - Costa Rica* - Czech Republic - Denmark - Egypt* - Finland - France - Germany - Greece - Guatemala* - Hungary - India - Indonesia - Ireland - Israel* - Italy - Japan - Korea - Malaysia - Mexico - Morocco* - Netherlands - New Zealand - Norway - Pakistan - Peru - Philippines - Poland - Portugal - Russia - South Africa - Spain - Sri Lanka* - Sweden - Switzerland - Thailand - Tunisia* - Turkey - United Kingdom - United States - Uruguay* Notes: - Asterisks (*) denote countries included in current account regression but not included in REER regression for data availability reasons. ### Annex III — Role of Exhaustible Resources: key implementation and findings - Rationale: - Income from exhaustible resources should generally affect the current account (CA) for exporters of such resources, but not necessarily for importers. - Resource exporters expect temporariness of resources and should save a fraction of related income; the fraction should be an increasing function of temporariness. - A resource-income variable should apply only to exporters and capture (i) temporariness of the resource and (ii) extraction size, relative to GDP. - Temporary commodity price movements are controlled for by a separate regressor: the detrended commodity terms of trade. - Changes from 2012 EBA to 2013 EBA: - Lowered the minimum threshold defining a resource exporting country from net resource exports of "10 percent of GDP" to "0 percent of GDP". - Widened resource definition to include natural gas as well as oil. - Variable construction: - The oil and natural gas trade balance is defined as the 5-year moving average of the net exports for oil and natural gas, relative to GDP, multiplied by a temporariness index for oil and gas. - Temporariness values (temp_k,i,t) are computed as the ratio of current extraction to proven reserves (the inverse of ‘years-till-exhaustion’), from the BP Statistical Review. - The temporariness term is normalized relative to the measure for oil in Norway in 2010 to make coefficients comparable with the first EBA method. - Higher values of the normalized temporariness term indicate the resource is more temporary (expected to be exhausted sooner). - Estimated effect: - A 1 percent of GDP in ‘temporariness adjusted’ exhaustible resources increases CA by about 0.6 percent of GDP. - This coefficient is similar to the pilot EBA’s CA regression but now applies to 13 economies rather than Russia and Norway only. - Excluding Russia and Norway leaves the coefficient broadly unchanged (see regression 7 in Table 10 in source). - For most countries (with few exceptions such as Norway, Colombia, Russia), estimated effects on the CA in recent years are generally in the range of 0 to 1.5 percent of GDP because revenues from exhaustible resources are small for the majority of countries. ### Annex IV — Financial Factors for EBA Methodology: purpose and specification - Objectives of including a financial variable (private credit): - Capture financial excesses (and policies that drive or allow those excesses), given links between credit booms and external imbalances. - Capture effects of financial depth or structural financial characteristics on saving and investment. - Choice of regressor: - The demeaned private credit-to-GDP ratio is used to measure both “financial excesses” and financial depth. - Rationale: large deviation from country mean indicates substantial credit rise; credit/GDP is widely available and commonly used. - Limitations and challenges: - Cannot clearly parse the two interpretations (financial excess vs. financial development). - Endogeneity concerns: shocks driving foreign borrowing may also increase domestic credit growth. - Cross-border bank loans may show up in domestic credit growth. - Credit/GDP as an indirect proxy does not identify specific policy weaknesses. - Theory does not provide a clear basis for an optimal P* level of credit/GDP. - Empirical estimates and interpretation: - An increase of credit by 10 percentage points of GDP is associated with a CA/GDP that is lower by 0.3 percent of GDP. - For some pre-crisis countries, the regressor contributed notably to CA deterioration, but did not explain the full deterioration. - Alternative specifications and robustness checks (summary of results): - Other detrending techniques (linear, cubic, Hodrick-Prescott): coefficients are insignificant; demeaned credit level chosen for parsimony. - Non-linearities/threshold effects to capture booms: no robust evidence; fit did not improve. - Credit growth measure: significant when included alone with coefficient of -0.043, but insignificant when including demeaned measure. - Interactions: - Interacting credit with output gap produced inconsistent results across CA and REER regressions. - Interacting credit with capital controls yielded no robust results. - Other financial measures examined (results summary): - Financial excess measures: stock market growth, bond market growth, housing prices, corporate leverage — no robust results; demeaned credit/GDP dominated. - Housing price measures: average real housing appreciation coefficient = -0.003 (insignificant and tiny); nominal housing price growth also insignificant with point estimate near zero. - Financial depth measures: stock market capitalization/GDP, bond market capitalization/GDP, stock market turnover, current liabilities/total liabilities, liabilities/assets, debt/equity — generally no robust results. Specific findings: - Coefficient on private debt markets = 0.023 (positive and significant) but data coverage is little over one-third of baseline sample. - Coefficient on stock market capitalization insignificant (sign positive as expected). - Entering country mean of private credit as separate variable: not significant. - Financial structure measures (bank vs. market financing): results not robust; coefficient marginally significant with coefficient of zero (Table 9). - Drivers/motivators of precautionary saving: - Stock market volatility coefficient = 0.029 (significant and economically important). - Rolling GDP-per-capita volatility coefficient = 0.402 (significant and economically important). - Interpretation caveats: stock market volatility varies in coverage; rolling output volatility may reflect crisis episodes and is sensitive to lagging (lagging by one period eliminates significance), so not included in the regression. *Italic source attribution line.* ### Annex V. Role of Structural Factors ### Annex V. Role of Structural Factors ### Challenges and conceptual ambiguities - Data availability constraints: - Many structural variables are available for short time series. - Available data tend to be limited for earlier sample years, or to end in 2005, pending an update. - Some variables are only available for a subset of the EBA countries (e.g., product market regulation data tend to be limited to OECD countries only). - Ambiguity about expected implications of structural policies: - Theoretically identified channels may affect both investment and saving rates in the same direction, producing no clear-cut prediction for the current account (CA). - Some channels may be temporary (e.g., a reform raises incentives for investment until a new, higher capital stock is reached) or relevant primarily during transitional adjustment (e.g., increased price flexibility speeds shock adjustment). - Structural factors could be important without producing clear patterns in current account panel data. ### Institutional and political environment index: construction and estimated effect - Index construction: - Summary index is a simple average of five sub-indices: (i) Socioeconomic Conditions, (ii) Investment Profile, (iii) Corruption, (iv) Religious Tensions, (v) Democratic Accountability. - Each component is in 0 – 1 range; aggregate index is the simple average. - A safer (less risky) political/institutional environment is assigned higher ratings. - Data source: ICRG (draws on surveys of experts). - Estimated effect on the CA: - Estimated coefficient implies that an increase by one standard deviation (0.13) in the summary index is associated with a CA change of 0.13 * -0.11 = -1.4 % of GDP. - Separate S (saving/GDP) and I (investment/GDP) regressions indicate that a safer institutional and political environment is associated both with more investment and less saving. ### Labor market regulation: investigation, findings, and caveats - Rationale and channels considered: - Labor market regulation could affect CA through multiple, potentially offsetting channels: encouraging or discouraging investment; affecting unemployment risk and precautionary saving; altering credit constraints via unemployment outcomes; transitional effects on CA adjustment. - No clear theoretical prediction exists in the literature about the net impact on the CA. - Variables investigated: - Detailed and summary labor market indexes with EBA sample coverage, including measures of (i) minimum wage, (ii) unemployment insurance, and (iii) employment protection. - Main empirical findings (EBA sample as a whole): - The aggregated index of labor market flexibility has coefficient -0.30. - An increase by one standard deviation (0.014) in the aggregated flexibility index is associated with 0.014 * -0.30 = -0.4 % of GDP weaker CA. - Labor market indexes from two different sources (OECD/Aleksynska and Schindler, 2011, and EFW) showed similar results within the sample for which both were available. - Construction details of the EFW summary index (as used in the analysis): - Six components: (i) hiring regulations and minimum wage (de jure), (ii) hiring and firing regulations (survey), (iii) centralized collective bargaining (survey), (iv) hours regulations (de jure), (v) mandated cost of worker dismissal (de jure), (vi) conscription (de jure). - Each component is in 0 – 0.1 range: more flexible markets are assigned higher ratings. - Summary index is the simple average of sub-indices. - In a CA regression entering the sub-indices separately, all six components had negative coefficient point estimates, and four were significant at 10 percent. - Data source: Economic Freedom of the World (drawing on WB Doing Business surveys and Global Competitiveness Reports). - Note: validity and interpretation of these data has been controversial and questioned in the past. - Reasons for caution and limits of the labor market results: - Results did not hold within the 11 EMU country subset: for EMU the coefficient was positive and significant. - No empirically identifiable coherent channel was found through which more labor market regulation would boost the CA. - For the aggregate labor market index, separate Saving/GDP and Investment/GDP regressions pointed to more regulations being associated with a higher saving rate, but no significant change in the investment rate. - No statistically significant link was found between labor market regulations and the unemployment rate. - The effect of the labor market index on the CA is not affected by inclusion of the unemployment rate in the regression. - No evidence was found that the labor market regulations variable works through an interaction between employment/unemployment policies. - Given these ambiguities and lack of robust channel identification, the labor market variable was not included in the final EBA regression specification. ### Product market regulation and other structural indicators - Product market regulation: - Could not be examined satisfactorily because the available index had very limited sample coverage (generally including only OECD countries). - The coefficient estimated within the available sample was not significant. - Other governance and institutional quality indicators: - Several indicators of governance and institutional quality were significant in preliminary investigations, with higher quality associated with a lower CA. - These indicators were dropped in favor of including the political/institutional uncertainty and risk variable because that variable has better sample coverage. *Source: Annex V. Role of Structural Factors, _wp13272 - Annex V. Role of Structural Factors* ### Annex VI. Suggested Policy Benchmark for Public Health Spending ### Annex VI. Suggested Policy Benchmark for Public Health Spending ### Methodology and purpose - Potential policy benchmarks for public health spending (share of GDP) are generated from a cross-sectional regression on 2005-2010 averages for 49 EBA countries. - Explanatory variables used: income, demography, and inequality. - Fitted values from this regression serve as suggested benchmarks; they need not be used to identify P* (desirable) levels in all cases. - Coefficients are relatively stable when different time periods are used in both averaged and pooled data format; the 2005-2010 average is retained for consistency with the 2012 pilot EBA exercise. ### Key explanatory variables and interpretation - Relative income: - Measured as the log difference between a country’s PPP-based GDP per capita and the world average (PPP GDP data are from WEO and extended backward using Penn World Table when possible). - Higher relative income is associated with higher public health spending as a share of GDP. - Demography: - Old age dependency ratio (as described in Annex I), based on U.N. data, is used to capture the relationship between population aging and public health spending. - Faster aging (projected change in old age dependency ratio) is positively associated with higher public health spending. - Income inequality: - Measured by the Gini coefficient (gross income concept) from Solt (2011). - Higher income inequality is positively related to higher public health spending per GDP. - Note: this variable was not included in the pilot 2012 EBA calculations. ### Primary cross-sectional regression (2005-2010 averages, 49 countries) - Log(PPPGDPpc): 0.018 [6.54]*** - Dependency Ratio: 0.094 [3.23]*** - Gini Coefficient: 0.053 [2.77]*** - Constant: -0.145 [7.45]*** - Observations: 49 - R-squared: 0.830 - Robust t-statistics in brackets - Significance notation: * significant at 10%; ** significant at 5%; *** significant at 1% ### Robustness and supplementary empirical findings (selected coefficients from EBA CA and REER regressions) - CA regression highlights (representative coefficients and significance from multiple specifications): - L. NFA/Y: 0.016** (p-values reported in tables) - Financial Center Dummy: 0.033*** - L.Relative output per worker*K openness: 0.065*** - Oil and Natural Gas Trade Balance * resource temporariness ＃: 0.615*** - Aging Speed (proj. change in old age dependency ratio) ＃: 0.156*** - GDP growth, forecast in 5 years ＃: -0.471*** - L.Public Health Spending/GDP ＃: -0.551*** - L.demeaned VIX*K openness: 0.068*** - L.demeaned VIX*K openness*share in world reserves: -0.136* - Own currency’s share in world reserves: -0.045*** - Output Gap ＃: -0.400*** - Commodity ToTgap*Trade Openness: 0.230*** - Safer Institutional/Political Environment (index) ＃: -0.109*** - Demeaned Private Credit/GDP ＃: -0.026*** - Cyclically adjusted Fiscal Balance, instrumented ＃: 0.324*** - (∆Reserves)/GDP* K controls, instrumented ＃: 0.346** - REER regression highlights (selected coefficients and significance): - (∆Reserves)/GDP* K controls, instrumented ＃: -1.43*** - L.Public Health Spending/GDP ＃: 1.78** - real interest rate differential interacted with K openness ＃: 0.71*** - Demeaned Private Credit/GDP ＃: 0.13*** - L.Output per worker, relative to top 3 economies: 0.81*** - L.Relative output per worker*K openness: -0.58*** - L.demeaned VIX*K openness: -0.24*** - L.demeaned VIX*K openness*share in world reserves: 0.84** - L.Financial home bias (share of domestic debt owned by residents) ＃: 0.34*** - L.Trade openness (avg exp+imp to GDP) ＃: -0.36*** - GDP growth, forecast in 5 years ＃: 2.32*** - Share of administered prices: -1.86*** - Constant: 4.30*** - Observations: 769; Number of countries: 40; RMSE: 0.081 - Robustness checks across Tables 2–10 explore reserves and capital controls, monetary policy, savings and investment breakdowns, alternative financial indicators, structural indicators, and sample exclusions; key signs and many magnitudes remain consistent across specifications. ### Practical implications for benchmarks and policy use - Suggested benchmarks for public health spending (share of GDP) can be computed as fitted values from the regression using country-specific values of: - Relative PPP GDP per capita (log difference vs world) - Old age dependency ratio (and projected aging speed) - Gini coefficient (Solt (2011)), where available - Other country controls used in extended EBA CA/REER specifications when appropriate - Interpretation guidance from the model: - Higher relative income, greater population aging, and higher income inequality imply higher suggested public health spending as a share of GDP. - Macro-financial and external variables (e.g., reserves dynamics, openness interactions, output gap, fiscal balance) appear in related CA and REER specifications and affect broader policy assessments tied to public spending and macro stability. - Use of the benchmark: - Benchmarks are suggested reference points to inform fiscal policy discussions on public health spending; they are not prescriptive ceilings or floors and should be considered alongside country-specific circumstances and policy judgments. *Source: Annex VI. Suggested Policy Benchmark for Public Health Spending (EBA regressions and tables, 2005–2010 averages and pooled specifications)* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13272.pdf_