## wpiea2019170-print-pdf

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---

### INTRODUCTION
- Background and motivation:
  - Traditional fiscal analysis focuses on government debt and deficits and often omits many asset components and public corporations.
  - Economic theory suggests higher government debt increases sovereign default risk and yields; containing gross debt helps build confidence in solvency and liquidity.
  - Literature has given limited attention to public assets and net worth as sources of macroeconomic strength.
- Objectives of the paper:
  - Introduce measures of public sector balance sheet strength (PSBS) for a large set of countries.
  - Investigate whether balance sheet strength affects sovereign yields and macroeconomic resilience.
  - Key questions:
    - What constitutes a strong public sector balance sheet?
    - Does PSBS strength have macroeconomic consequences beyond gross debt?
    - Are governments with stronger balance sheets better able to engage in countercyclical fiscal policy during recessions?
    - Do financial markets account for assets and balance sheet strength when pricing sovereign yields?
- Definition and coverage:
  - Public sector balance sheet: all resident institutional units controlled by government (general government, central bank, financial public corporations, and nonfinancial public corporations), consolidated for cross-holdings of assets and liabilities.
- Contributions:
  - Introduces a comprehensive set of PSBS strength measures and derives them for a large set of countries.
  - Shows PSBS strength is a determinant of macroeconomic resilience and influences sovereign yields.

### MEASURES OF PSBS STRENGTH
- Overview of indicators:
  - Size of balance sheet, net worth, net financial worth, risk-adjusted assets and liabilities, net liquidity assets, net foreign exchange assets, and degree of natural hedging.
- Exact definitions (as used in the paper):
  - Size of Balance Sheet: sum of the size of assets and liabilities (excluding net worth), in percent of GDP.
  - Net (Financial) Worth (Solvency):
    - net worth = total assets minus total liabilities, expressed in percent of GDP.
    - net financial worth = total financial assets less liabilities, expressed in percent of GDP.
    - A measure excluding pension-related liabilities is also introduced.
  - Risk-adjusted Assets and Liabilities: assets and liabilities corrected for their riskiness or underlying volatility based on estimated volatility of each asset/liability class relative to sum of volatilities.
  - Liquidity Mismatch (Net Liquid Assets): current assets less current liabilities (maturing within one year), expressed in percent of GDP.
  - Currency Mismatch (Net Foreign Exchange Assets): foreign exchange denominated assets less foreign exchange denominated liabilities, expressed in percent of GDP.
  - Natural Hedge: variance of valuation changes in net financial worth relative to variance of valuation changes in financial assets and liabilities; measures covariance between valuation changes in assets and liabilities.

### STYLIZED FACTS AND KEY STATISTICS
- Sample and scope:
  - Sample size: 69 countries (62 using general government data, 7 using central government data for comparability in Figure 1).
  - Excludes land and natural resource assets and pension liabilities in some comparable estimates.
- Levels and dispersion (selected statistics):
  - Assets (excluding natural resource assets and pension liabilities): average 102 percent of GDP; range from 398 percent of GDP (Norway) to 21 percent of GDP (India).
  - Composition of assets: roughly evenly split between financial and nonfinancial assets.
  - Average liabilities: 71 percent of GDP.
  - Static net worth: varies from –111 percent of GDP (Greece) to 348 percent of GDP (Norway); average positive net worth of 31 percent of GDP.
  - Net financial worth: averages –23 percent of GDP (with Greece and Norway as extremes).
  - Gross debt vs. net financial worth evolution (Europe, 2000–2015): gross debt misses average(median) of 14.3 (9.1) percent of GDP in the absolute change in public wealth over the 2000-2015 period.
- Balance sheet risks and mismatches:
  - Financial assets are more volatile than liabilities for almost all countries in the sample.
  - Volatility driven by equities and other investment (often held in social security funds); many liabilities are government debt securities measured at face value.
  - Example: Norway features a high average risk weight on its assets and a relatively large difference between total assets and risk-adjusted assets, while the risk adjustment for liabilities is small.
  - The combination of high exposure to volatile assets and relatively stable liabilities can result in rapid changes in solvency and liquidity.
- Liquidity and foreign exchange exposure:
  - Central bank foreign exchange reserves are excluded from this analysis.
  - General government liquid assets average 16 percent of GDP across the sample.
  - Liquid assets range from Moldova (5 percent of GDP) to Japan (62 percent of GDP).
  - Short-term liabilities average 14 percent of GDP.
  - Countries’ net liquid positions vary from –27 percent of GDP to 23 percent of GDP; The Gambia, Italy and Barbados exhibit the largest mismatches.
  - Net foreign exchange mismatches noted for Barbados, The Gambia, Kenya, Tanzania, and Uganda.
- Measurement notes:
  - Debt securities measured at face value; using market prices increases their volatility by 0.6 percent of GDP on average.
  - Intertemporal public balance sheets (NPV of future fiscal balances) are not the focus.
  - Nuanced liquidity definitions (ability to sell assets without adverse price impact) are precluded by current data limitations.

### SOVEREIGN BORROWING COST: EMPIRICAL SPECIFICATION AND MAIN FINDINGS
- Empirical model:
  - Fixed effects panel: 푦ᵢₜ = 휷풙ᵢₜ + 휸풛ᵢₜ + 푐ᵢ + 휆ₜ + 휖ᵢₜ; 푦ᵢₜ is long-term government bond yield, 풙ᵢₜ a lagged balance sheet variable.
  - Yields: mostly 10-year, from Thomson Reuters Datastream Economics database.
  - Balance sheet indicators (lagged, percent of GDP): general government gross debt, total assets, financial assets, net worth (NW), net financial worth (NFW).
  - Controls: growth rate of real per capita GDP, US 10-year bond yield, average inflation rate in country i, short-term interest rate, and general government primary balance; country and time fixed effects.
  - Sample period: 2001–2016; estimations for full sample, advanced economies, and emerging markets separately.
  - Notes: assets exclude land and natural resources; liabilities exclude pension liabilities.
- Main empirical results:
  - Financial markets account for government assets and net (financial) worth when pricing sovereign bonds; balance sheet indicators beyond gross debt matter.
  - Total or financial assets are highly significant variables, both alone and together with gross debt.
  - Net (financial) worth is a highly significant stand-alone explanatory variable.
  - Results clearest for the full sample and advanced economies; significance generally lower for emerging markets.
  - Robustness: results robust to different sample periods (excluding crisis years) and random effects; endogeneity/reverse causality concerns not fully resolved.
- Magnitude and interpretation (quoted from text):
  - "The magnitude of the impact of net (financial) worth on yields is comparable to the impact of gross debt."
  - Whole sample:
    - a one percent of GDP increase in government net (financial) worth lowers yields by some 1.5 (0.6) bps, compared to a 0.7 bps increase in yield when gross debt increases by the same amount.
    - Note: the impact of net financial worth not statistically significant in the full sample.
  - Advanced economies:
    - a one percent of GDP increase in either net financial worth or net worth can lower yields by some 1 bp.
  - Quantified impacts (based on Figure 3 and Table 1 coefficients):
    - A 10 percentage point of GDP increase in gross debt would increase yields by 6.9 (8.3) bps in the full sample (advanced economies).
    - An increase in both gross debt and total assets of 10 percentage points of GDP would increase yields by 15.7 (8.1) bps and decrease by 26.7 (9.1) bps respectively.
    - A 10 percent of GDP increase in net worth lowers yields by 15.4 (9.5) bps in the full sample (advanced economies).
    - Quantification caveat: "The quantification should be treated by caution as they are only suggestive and depend on the set of control variables, sample of countries, etc."
- Selected regression coefficients (Table 1) — preserved values and significance:
  - Full Sample (selected coefficients):
    - Lagged NW: -0.0154** [0.006]
    - Lagged NFW: -0.0056 [0.006]
    - Lagged Gross Debt: 0.0157** 0.0141** 0.0069* [0.007] [0.007] [0.004]
    - Lagged Total Asset: -0.0267*** -0.0250*** [0.007] [0.007]
    - Lagged Financial Assets: -0.0290** -0.0269** [0.012] [0.011]
    - Observations: 343, 343, 378, 378, 601, 377, 377
    - R Squared: 0.511, 0.502, 0.487, 0.472, 0.855, 0.495, 0.485
    - Number of countries: 31, 31, 33, 33, 33, 33, 33
  - Advanced Economies (selected coefficients):
    - Lagged NW: -0.0095*** [0.003]
    - Lagged NFW: -0.0095*** [0.003]
    - Lagged Gross Debt: 0.0081** 0.0092** 0.0083*** [0.004] [0.004] [0.003]
    - Lagged Total Asset: -0.0091** -0.0078** [0.004] [0.004]
    - Lagged Financial Assets: -0.0257*** -0.0229*** [0.006] [0.006]
    - Observations: 277, 277, 296, 296, 514, 295, 295
    - R Squared: 0.845, 0.853, 0.846, 0.843, 0.935, 0.843, 0.849
    - Number of countries: 24, 24, 25, 25, 24, 25, 25
  - Note: *, **, and *** represent statistical significance at 10, 5, and 1 percent, respectively.

### RECOVERY AND FISCAL POLICY: METHODOLOGY AND EMPIRICAL FINDINGS
- Purpose:
  - Investigate whether countries with healthier PSBS have more room for countercyclical fiscal policy after recessions and whether they experience shallower recessions and faster returns to growth.
- Methodology:
  - Local projection method (LPM) per Jordà (2005) and Jordà, Schularick, and Taylor (2016).
  - Sample: 17 advanced economies; data 1970–2015.
  - Net financial worth data from World Inequality Database (WID); real per capita GDP from WEO and Penn World Table; government spending from Mauro and others (2015); public and private debt from IMF (2016) and Bernardini and Forni (2017).
  - Baseline regression uses cumulative growth rates (log differences) in real GDP or real government spending per capita h years after the business cycle peak.
  - Strong (weak) balance sheets: net financial worth above (below) the sample median.
  - Controls include average annual change in five years before peak of private debt and level of public debt at peak, interactions, lags of real per capita GDP growth, government expenditures, public debt and private debt.
  - Standard errors: Driscoll and Kraay (1998).
- Main results:
  - Countries with strong public sector balance sheets face shorter and shallower recessions and increase real per-capita expenditure after a recession.
  - Expenditure results: statistically significant difference between strong and weak balance sheet countries with p-values below 5 percent starting from the second year.
  - GDP regression: p-values for difference between strong and weak balance sheet coefficients are below 5 percent in years 4 and 5.
  - Sample limitations: limited number of observations (53 observations only) likely reduces significance for some horizons.
  - Robustness: results robust to different control variables, to using net worth instead of net financial worth, and to excluding all control variables.
- Policy implication:
  - Economies with weak PSBS find it harder to return to growth after downturns.
  - Countries with strong balance sheets have greater fiscal space to increase public expenditure during a recession and hence return to growth more quickly; effect is above and beyond channels of private and public debt.
- Selected numerical results from Local Projections (Table 2) — preserved coefficients and standard errors:
  - Real GDP Per Capita (cumulative change from peak), coefficients (Year 1 to Year 5):
    - -1.60*** (0.32)
    - -0.77* (0.62)
    - 1.23* (0.60)
    - 4.29*** (0.73)
    - 9.30*** (0.95)
  - Real Government Expenditure Per Capita (cumulative change from peak), coefficients (Year 1 to Year 5):
    - 3.90*** (1.05)
    - 8.77*** (1.24)
    - 14.69*** (3.04)
    - 24.39*** (4.02)
    - 33.46*** (3.80)
  - Additional selected rows (coefficients with standard errors in parentheses):
    - -2.78*** -2.84** -0.70 -0.06 2.67* 1.31 0.30 1.92 -11.31** -2.81* (0.96)(1.13)(1.28)(1.31)(1.56)(2.21)(1.91)(2.24)(4.41)(1.99)
    - 0.10 0.36** 0.50* 0.53* 0.82** 0.19 -0.79* -1.34* 0.16 1.16 (0.16)(0.16)(0.38)(0.39)(0.33)(0.32)(0.61)(0.72)(1.28)(1.46)
    - 0.32* -0.24 -0.85** -0.90** -1.41* -1.24** -0.72* -2.04** -5.28*** -5.33*** (0.25)(0.24)(0.31)(0.41)(0.67)(0.46)(0.65)(0.72)(1.43)(1.60)
    - 0.06* 0.01 -0.01 0.04 -0.10 -0.12 -0.46*** -0.16 -1.01** -1.60*** (0.04)(0.06)(0.09)(0.12)(0.11)(0.15)(0.13)(0.25)(0.42)(0.14)
    - 0.03* -0.01 -0.07** -0.09** -0.13*** -0.11** -0.18** -0.36*** -0.34*** -0.34** (0.02)(0.02)(0.03)(0.03)(0.04)(0.04)(0.06)(0.10)(0.08)(0.13)
  - R2 (Real Government Expenditure Per Capita): 0.80 0.84 0.85 0.85 0.91
  - R2 (Real GDP Per Capita): 0.34 0.12 0.26 0.03 0.01
  - Peaks: Real Government Expenditure Per Capita: 53 53 52 52 42
  - Peaks: Real GDP Per Capita: 53 52 52 42

### COUNTRY CASE: KAZAKHSTAN (SELECTED FACTS)
- Context: 2014 oil price shock combined with external demand shock from Russia and China.
- Fiscal balance evolution:
  - +5 percent of GDP in 2013
  - -6 percent of GDP in 2015
- National Fund of the Republic of Kazakhstan (NFRK):
  - NFRK worth some 46 percent of GDP in 2016
  - NFRK asset composition: 80 percent foreign currency holdings and 20 percent equities
- Policy response and use of buffers:
  - Government undertook a 10 percent of GDP fiscal stimulus between 2014-17
  - Provided 4 percent of GDP in financial sector support in 2017, largely financed by NFRK resources

### CONCLUSIONS AND POLICY IMPLICATIONS
- The paper’s measures cover:
  - Size and liquidity of assets and liabilities
  - Volatility and mismatches
  - Correlations among balance sheet items
- Empirical findings:
  - Balance sheet strength matters for sovereign yields and economic resilience.
  - Financial markets consider governments’ asset positions in addition to debt levels when determining borrowing costs.
  - Countries with stronger balance sheets experience shallower and shorter recessions, partly due to ability to boost demand via higher public expenditures.
- Policy implications:
  - Fiscal policy debate should incorporate the entire public sector balance sheet, including assets, not only gross public debt.
  - Importance of building resilience and buffers in the PSBS to counter downturns.
- Suggestions for future research:
  - Extend IMF (2019) dataset to include more countries (especially emerging markets) and longer time periods.
  - Study intertemporal net worth including future revenue and expenditure flows to account for prospective ageing-related liabilities.
  - Explore channels through which PSBS strength cushions recessions.

### ANNEX — METHODOLOGY AND SUPPLEMENTARY TABLES
- Risk-adjusted assets and liabilities — construction steps:
  - Compute valuation changes for each asset and liability item by deducting transactions from total changes in value.
  - Define each item’s risk weight (RW) as the item’s relative volatility:
    - RW_i = σ_i^2 / ∑_i σ_i^2
  - Risk weights calculated on a European-country sample (detailed list provided in the source).
  - Compute riskiness measures ∑ RW_i A_i and ∑ RW_i L_i and derive:
    - RAA = ∑ A_i − ∑ RW_i A_i
    - RAL = ∑ L_i − ∑ RW_i L_i
  - Cross-country comparability: total assets exclude land and natural resources; total liabilities exclude pension liabilities.
- Annex Table: Risk Weights of Assets and Liabilities by Instrument (preserved values):
  - Financial assets by instrument (Weight):
    - Monetary gold and SDRs 0.000
    - Currency and deposits 0.000
    - Debt securities 0.049
    - Loans 0.064
    - Equity and investment fund shares 0.564
    - Insurance, pension, and standardized guarantee schemes 0.000
    - Financial derivatives and employee stock options 0.049
    - Other accounts receivable 0.049
  - Liabilities by instrument (Weight):
    - SDRs 0.000
    - Currency and deposits 0.000
    - Debt securities 0.000
    - Loans 0.122
    - Equity and investment fund shares 0.000
    - Insurance, pension, and standardized guarantee schemes 0.000
    - Financial derivatives and employee stock options 0.014
    - Other accounts payable 0.090
  - Sum of weights 1.000
  - Note: SDRs = Special Drawing Rights.
- Natural Hedge (Net Financial Worth Volatility Decomposition):
  - OEEF_NFW = OEEF_FA − OEEF_L (all expressed in percent of GDP)
  - σ_NFW^2 = σ_FA^2 + σ_L^2 − 2 Cov_FA L
  - Normalized relative volatility measure:
    - σ_n = σ_NFW^2 / (σ_FA σ_L)
  - Rewritten form:
    - σ_n = σ_FA/σ_L + σ_L/σ_FA − 2 Cor_FA L, i.e., σ_n = x + 1/x − 2 Cor_FA L where x = σ_FA/σ_L
  - Interpretation:
    - x and 1/x capture contribution of size mismatch between financial assets and liabilities to variation in net financial worth.
    - Cor_FA L captures how valuation changes in financial assets and liabilities move together.
  - Figure 2 (main text) displays: σ_n (relative volatility of net financial worth), x + 1/x (relative volatility of financial assets and liabilities), and 2 Cor_FA L (relative volatility increase or decrease).
- Appendix Tables (selected notes and preserved coefficients):
  - Appendix Table 1 (Fixed effects; Dependent variable: Long term government bond yields) — Full Sample and Advanced Economies selected coefficients (preserved values reported in source).
  - Appendix Table 2 (Random effects; lagged indicators) — selected lagged coefficients for Full Sample, Advanced Economies, and Emerging Markets (preserved values reported in source).

*Source: wpiea2019170-print-pdf*

### INTRODUCTION _______________________________________________________________________________ 4

### INTRODUCTION

### Background and motivation
- Traditional fiscal analysis focuses on government debt and deficits and often omits many asset components and public corporations.
- Economic theory suggests higher government debt increases sovereign default risk and yields; containing gross debt helps build confidence in solvency and liquidity.
- The literature has given limited attention to public assets and net worth as sources of macroeconomic strength, though some studies analyze net debt, natural resources, nonfinancial assets, and financial asset returns.

### Objectives of the paper
- Introduce measures of public sector balance sheet strength (PSBS) for a large set of countries.
- Investigate whether balance sheet strength affects sovereign yields and macroeconomic resilience.
- Address these questions:
  - What constitutes a strong public sector balance sheet?
  - Does PSBS strength have macroeconomic consequences beyond gross debt?
  - Are governments with stronger balance sheets better able to engage in countercyclical fiscal policy during recessions?
  - Do financial markets account for assets and balance sheet strength when pricing sovereign yields?

### Definition and coverage of the public sector balance sheet
- The public sector balance sheet encompasses all resident institutional units controlled by government: general government, central bank, financial public corporations, and nonfinancial public corporations, consolidated for cross-holdings of assets and liabilities (Alves and others 2019).

### Contributions of the paper
- Introduces a comprehensive set of PSBS strength measures and derives them for a large set of countries.
- Uses these measures to assess whether financial markets consider governments’ asset positions.
- Establishes that PSBS strength is a determinant of macroeconomic resilience: stronger balance sheets provide more freedom for countercyclical policy and lead to shallower and shorter recessions.

### Structure of the paper
- Section II: Introduces measures of PSBS strength and stylized facts.
- Section III: Studies impact of balance sheet strength on government bond yields.
- Section IV: Investigates whether stronger balance sheets lead to shallower and shorter recessions.
- Section V: Concludes.

---

### MEASURES OF PSBS STRENGTH

### Overview of indicators
- Indicators derived from both asset and liability sides: size of balance sheet, net worth, net financial worth, risk-adjusted assets and liabilities, net liquidity assets, net foreign exchange assets, and degree of natural hedging.
- PSBS data compiled by IMF (2019) underpin these measures.

### Definitions (as used in the paper)
- Size of Balance Sheet: sum of the size of assets and liabilities (excluding net worth), in percent of GDP.
- Net (Financial) Worth (Solvency): net worth = total assets minus total liabilities, expressed in percent of GDP. Net financial worth = total financial assets less liabilities, expressed in percent of GDP. A measure excluding pension-related liabilities is also introduced.
- Risk-adjusted Assets and Liabilities: assets and liabilities corrected for their riskiness or underlying volatility based on estimated volatility of each asset/liability class relative to sum of volatilities (technical details in Annex).
- Liquidity Mismatch (Net Liquid Assets): current assets less current liabilities (maturing within one year), expressed in percent of GDP.
- Currency Mismatch (Net Foreign Exchange Assets): foreign exchange denominated assets less foreign exchange denominated liabilities, expressed in percent of GDP.
- Natural Hedge: variance of valuation changes in net financial worth relative to variance of valuation changes in financial assets and liabilities; measures covariance between valuation changes in assets and liabilities (technical details in Annex).

---

### STYLIZED FACTS AND KEY STATISTICS

### Cross-country variation and sample
- Sample size for measures and stylized comparisons: 69 countries (with 62 countries using general government data, and 7 using central government data for comparability in Figure 1).
- Excludes land and natural resource assets and pension liabilities in some comparable estimates noted in the text.

### Level and dispersion of balance sheet components (selected statistics)
- Assets (excluding natural resource assets and pension liabilities): average 102 percent of GDP; range from 398 percent of GDP (Norway) to 21 percent of GDP (India).
- Composition of assets: roughly evenly split between financial and nonfinancial assets.
- Average liabilities: 71 percent of GDP.
- Static net worth: varies from –111 percent of GDP (Greece) to 348 percent of GDP (Norway); average positive net worth of 31 percent of GDP.
- Net financial worth: averages –23 percent of GDP (with Greece and Norway as extremes).
- Gross debt vs. net financial worth evolution (Europe, 2000–2015): gross debt misses average(median) of 14.3 (9.1) percent of GDP in the absolute change in public wealth over the 2000-2015 period.

### Balance sheet risks and mismatches
- Risk-adjusted assets and liabilities:
  - Financial assets are more volatile than liabilities for almost all countries in the sample.
  - Volatility driven by components such as equities and other investment (often held in social security funds), while many liabilities are government debt securities measured at face value.
  - Example: Norway features a high average risk weight on its assets and a relatively large difference between total assets and risk-adjusted assets, while the risk adjustment for liabilities is small.
  - The combination of high exposure to volatile assets and relatively stable liabilities can result in rapid changes in solvency and liquidity.

### Measurement and data notes
- Debt securities are measured at face value because they are almost always repaid at maturity; using market prices increases their volatility by 0.6 percent of GDP on average.
- Intertemporal public balance sheets (net present value of future fiscal balances) are not the focus of this paper.
- A more nuanced liquidity definition would account for the ability to sell assets without adverse price impact; current data limitations preclude that.

---

*Source: https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019170-print-pdf.pdf*

### 3. Foreign Exchange Assets and Liabilities 4. Risk-Adjusted Assets and Liabilities

### 3. Foreign Exchange Assets and Liabilities 4. Risk-Adjusted Assets and Liabilities

### Foreign exchange exposure and liquidity
- Data exclusions and scope:
  - Central bank foreign exchange reserves are excluded from this analysis.
  - In all panels, the data exclude land and natural resources assets and pension liabilities.
- Foreign exchange exposure:
  - Many countries borrow in foreign currency and thus have significant foreign exchange liabilities; some have significant foreign exchange assets that need to be taken into account when assessing exchange rate risk.
  - Net foreign exchange exposure can reveal significant mismatches: Barbados, The Gambia, Kenya, Tanzania, and Uganda all have significant foreign exchange debt with little compensating foreign exchange assets.
  - Data on foreign exchange assets are scarce, which limits the analysis.
- Liquidity statistics:
  - General government liquid assets average 16 percent of GDP across the sample.
  - Liquid assets range from Moldova (5 percent of GDP) to Japan (62 percent of GDP).
  - Short-term liabilities average 14 percent of GDP.
  - Countries’ net liquid positions vary from –27 percent of GDP to 23 percent of GDP, with The Gambia, Italy and Barbados exhibiting the largest mismatches.
- Natural hedge:
  - Many countries show significant co-movement between valuation changes of assets and liabilities, which often dampens valuation changes of net financial worth (natural hedge).
  - In some countries valuation changes in assets and liabilities reinforce each other, amplifying the impact on net financial worth.

### Sovereign borrowing cost: empirical specification and main findings
- Empirical model:
  - Fixed effects panel specification: 푦ᵢₜ = 휷풙ᵢₜ + 휸풛ᵢₜ + 푐ᵢ + 휆ₜ + 휖ᵢₜ, where 푦ᵢₜ is the long-term government bond yield and 풙ᵢₜ a balance sheet variable (lagged).
  - Long-term bond yields are from Thomson Reuters Datastream Economics database.
  - Balance sheet indicators: general government gross debt, total assets, financial assets, net worth, and net financial worth — all lagged and expressed as percent of GDP.
  - Controls (풛ᵢₜ): growth rate of real per capita GDP, US 10-year bond yield, average inflation rate in country i, short-term interest rate, and general government primary balance.
  - Country and time fixed effects included; sample period 2001–2016; estimations for full sample, advanced economies, and emerging markets (EMs) separately.
  - Notes: assets exclude land and natural resources; liabilities exclude pension liabilities; yields are mostly 10-year with exceptions for specific countries.
- Main empirical results:
  - Financial markets account for government assets and net (financial) worth when pricing sovereign bonds; balance sheet indicators beyond gross debt matter.
  - Total or financial assets are highly significant variables, both alone and together with gross debt.
  - Net (financial) worth is a highly significant stand-alone explanatory variable.
  - Results are clearest for the full sample and advanced economies; significance is generally lower for emerging markets (smaller sample).
  - Robustness: results robust to different sample periods (excluding crisis years) and random effects; identification does not fully resolve endogeneity/reverse causality concerns.
- Magnitude and interpretation:
  - "The magnitude of the impact of net (financial) worth on yields is comparable to the impact of gross debt."
  - In the whole sample:
    - a one percent of GDP increase in government net (financial) worth lowers yields by some 1.5 (0.6) bps, compared to a 0.7 bps increase in yield when gross debt increases by the same amount.
    - Note: the impact of net financial worth not statistically significant in the full sample.
  - In advanced economies:
    - a one percent of GDP increase in either net financial worth or net worth can lower yields by some 1 bp.
  - These results align with Hadzi-Vaskov and Ricci (2016) and Gruber and Kamin (2012): markets account for assets and net worth; effects of fiscal variables on yields/spreads larger for EMs than AEs.
- Quantified impacts (based on Figure 3 and coefficients in Table 1):
  - A 10 percentage point of GDP increase in gross debt would increase yields by 6.9 (8.3) bps in the full sample (advanced economies).
  - An increase in both gross debt and total assets of 10 percentage points of GDP would increase yields by 15.7 (8.1) bps and decrease by 26.7 (9.1) bps respectively, implying that total assets also affect yields.
  - A 10 percent of GDP increase in net worth lowers yields by 15.4 (9.5) bps in the full sample (advanced economies).
  - Quantification caveat: "The quantification should be treated by caution as they are only suggestive and depend on the set of control variables, sample of countries, etc."

### Selected regression coefficients (Table 1) — preserved values and significance
- Full Sample:
  - Lagged NW: -0.0154** [0.006]
  - Lagged NFW: -0.0056 [0.006]
  - Lagged Gross Debt: 0.0157** 0.0141** 0.0069* [0.007] [0.007] [0.004]
  - Lagged Total Asset: -0.0267*** -0.0250*** [0.007] [0.007]
  - Lagged Financial Assets: -0.0290** -0.0269** [0.012] [0.011]
  - Observations: 343, 343, 378, 378, 601, 377, 377 (across specifications)
  - R Squared: 0.511, 0.502, 0.487, 0.472, 0.855, 0.495, 0.485
  - Number of countries: 31, 31, 33, 33, 33, 33, 33
- Advanced Economies:
  - Lagged NW: -0.0095*** [0.003]
  - Lagged NFW: -0.0095*** [0.003]
  - Lagged Gross Debt: 0.0081** 0.0092** 0.0083*** [0.004] [0.004] [0.003]
  - Lagged Total Asset: -0.0091** -0.0078** [0.004] [0.004]
  - Lagged Financial Assets: -0.0257*** -0.0229*** [0.006] [0.006]
  - Observations: 277, 277, 296, 296, 514, 295, 295
  - R Squared: 0.845, 0.853, 0.846, 0.843, 0.935, 0.843, 0.849
  - Number of countries: 24, 24, 25, 25, 24, 25, 25
- Note from table:
  - Total Assets exclude land and natural resources; liabilities exclude pension liabilities.
  - Control variables include Real GDP per capita growth, US 10-year bond yield, average inflation rate, short-term interest rate, general government primary balance, country- and time-fixed effects.
  - Sample period: 2001-16.
  - NFW stands for net financial worth, NW denotes net worth.
  - *, **, and *** represent statistical significance at 10, 5, and 1 percent, respectively.

### Recovery and fiscal policy: methodology and empirical findings
- Purpose:
  - Investigate whether countries with healthier public sector balance sheets have more room for countercyclical fiscal policy after recessions and whether they experience shallower recessions and faster returns to growth.
- Methodology:
  - Local projection method (LPM) per Jordà (2005) and Jordà, Schularick, and Taylor (2016).
  - Sample: 17 advanced economies with long time series; data 1970–2015.
  - Net financial worth data from World Inequality Database (WID); real per capita GDP from WEO and Penn World Table; government spending from Mauro and others (2015); public and private debt from IMF (2016) and Bernardini and Forni (2017).
  - Baseline regression uses cumulative growth rates (log differences) in real GDP or real government spending per capita h years after the business cycle peak.
  - Strong (weak) balance sheets: net financial worth above (below) the sample median.
  - Controls include average annual change in five years before peak of private debt (푥ᵢ,ₚᴾʳ) and level of public debt as percent of GDP at peak (푥ᵢ,ₚᴾᵘ), interactions, lags of real per capita GDP growth, government expenditures, public debt and private debt.
  - Standard errors: Driscoll and Kraay (1998) to correct for heteroskedasticity, cross-sectional dependence and serial correlation.
- Main results:
  - Countries with strong public sector balance sheets face shorter and shallower recessions and increase real per-capita expenditure after a recession.
  - Expenditure results: statistically significant difference between strong and weak balance sheet countries with p-values below 5 percent starting from the second year.
  - GDP regression: p-values for the difference between strong and weak balance sheet coefficients are below 5 percent in years 4 and 5.
  - Sample limitations: limited number of observations (53 observations only) likely reduces significance for some horizons.
  - Robustness: results robust to different control variables, to using net worth instead of net financial worth, and to excluding all control variables.
- Policy implication:
  - When hit by a downturn, economies with weak public sector balance sheets find it hard to return to growth.
  - Countries with strong balance sheets have greater fiscal space to increase public expenditure during a recession and hence return to growth more quickly; this effect is above and beyond the channels of build-up of private and public debt.
  - Case example: Kazakhstan illustrates how assets and natural hedge between assets and liabilities allowed authorities to boost the economy with fiscal stimulus after the 2014 oil price shock.

### Selected numerical results from Local Projections (Table 2) — preserved layout and values
- Real GDP Per Capita (cumulative change from peak): coefficients (Year 1 to Year 5):
  - -1.60*** (0.32)
  - -0.77* (0.62)
  - 1.23* (0.60)
  - 4.29*** (0.73)
  - 9.30*** (0.95)
- Real Government Expenditure Per Capita (cumulative change from peak): coefficients (Year 1 to Year 5):
  - 3.90*** (1.05)
  - 8.77*** (1.24)
  - 14.69*** (3.04)
  - 24.39*** (4.02)
  - 33.46*** (3.80)
- Additional selected rows from Table 2 (preserved values and standard errors in parentheses):
  - -2.78*** -2.84** -0.70 -0.06 2.67* 1.31 0.30 1.92 -11.31** -2.81* (0.96)(1.13)(1.28)(1.31)(1.56)(2.21)(1.91)(2.24)(4.41)(1.99)
  - 0.10 0.36** 0.50* 0.53* 0.82** 0.19 -0.79* -1.34* 0.16 1.16 (0.16)(0.16)(0.38)(0.39)(0.33)(0.32)(0.61)(0.72)(1.28)(1.46)
  - 0.32* -0.24 -0.85** -0.90** -1.41* -1.24** -0.72* -2.04** -5.28*** -5.33*** (0.25)(0.24)(0.31)(0.41)(0.67)(0.46)(0.65)(0.72)(1.43)(1.60)
  - 0.06* 0.01 -0.01 0.04 -0.10 -0.12 -0.46*** -0.16 -1.01** -1.60*** (0.04)(0.06)(0.09)(0.12)(0.11)(0.15)(0.13)(0.25)(0.42)(0.14)
  - 0.03* -0.01 -0.07** -0.09** -0.13*** -0.11** -0.18** -0.36*** -0.34*** -0.34** (0.02)(0.02)(0.03)(0.03)(0.04)(0.04)(0.06)(0.10)(0.08)(0.13)
  - R2 (Real Government Expenditure Per Capita): 0.80 0.84 0.85 0.85 0.91
  - R2 (Real GDP Per Capita): 0.34 0.12 0.26 0.03 0.01
  - Peaks: 53 53 52 52 42 (Real Government Expenditure Per Capita)
  - Peaks: 53 52 52 42 (Real GDP Per Capita)

*Italicized attribution: Source: wpiea2019170-print-pdf - 3. Foreign Exchange Assets and Liabilities 4. Risk-Adjusted Assets and Liabilities*

### 1. Real Government Expenditure per Capita 2. Real GDP per Capita

### wpiea2019170-print-pdf - 1. Real Government Expenditure per Capita 2. Real GDP per Capita

### Recessions, Fiscal Policy, and Recovery (Local Projections evidence)
- Estimations use the Local Projections Model; first five columns report coefficients for real GDP per capita (cumulative changes from the peak before recessions) and the second five for real government expenditure per capita (cumulative changes from the peak).
- Sample restricted to recession episodes; dotted lines in figures represent the 90 percent confidence bands.
- Robust standard errors reported; significance notation in the table: *, **, *** denote p-values less than 0.32 (1 standard deviation), 0.05 (2 standard deviations), and 0.01 (3 standard deviations) percent respectively.
- Key visual ranges shown in the figures (percentage change by year) span roughly:
  - For GDP-related figure: y-axis points illustrating values from -20 to 40 (percentage change) across Years 0–5.
  - For government expenditure: y-axis points illustrating values from -6 to 12 (percentage change) across Years 0–5.

### Country Case: Kazakhstan — Evolution of Public Sector Balance Sheet
- Context: 2014 oil price shock combined with external demand shock from Russia and China.
- Fiscal balance evolution:
  - +5 percent of GDP in 2013
  - -6 percent of GDP in 2015
- National Fund of the Republic of Kazakhstan (NFRK) details:
  - NFRK worth some 46 percent of GDP in 2016
  - NFRK asset composition: 80 percent foreign currency holdings and 20 percent equities
- Policy response and use of buffers:
  - Government undertook a 10 percent of GDP fiscal stimulus between 2014-17
  - Provided 4 percent of GDP in financial sector support in 2017, largely financed by NFRK resources
- Net worth evolution visualization described in percent of 2016 GDP showing components: 2013 Net worth, Deficit, Oil price, Depletion, Currency, 2016 Net worth; positive and negative changes to net worth are highlighted.

### Conclusions: Balance Sheet Strength and Macroeconomic Relevance
- The paper introduces measures of public sector balance sheet strength covering:
  - Size and liquidity of assets and liabilities
  - Volatility and mismatches
  - Correlations among balance sheet items
- Empirical findings:
  - Balance sheet strength matters for sovereign yields and economic resilience.
  - Financial markets consider governments’ asset positions in addition to debt levels when determining borrowing costs.
  - Countries with stronger balance sheets experience shallower and shorter recessions versus those with weaker balance sheets, due in part to the ability to boost demand via higher public expenditures.
- Policy implications:
  - Fiscal policy debate should incorporate the entire public sector balance sheet, including assets, not only gross public debt.
  - Importance of building resilience and buffers in the PSBS to counter downturns.
- Suggestions for future research:
  - Extend IMF (2019) dataset to include more countries (especially emerging markets) and longer time periods.
  - Study intertemporal net worth including future revenue and expenditure flows to account for prospective ageing-related liabilities.
  - Explore channels through which PSBS strength cushions recessions.

*Italicized source attribution: Content derived from wpiea2019170-print-pdf (IMF working paper material).*

### Appendix results: Government Balance Sheet and Sovereign Bond Yields (Concurrent and Lagged Estimates)
- Appendix Table 1 (Fixed effects; Dependent variable: Long term government bond yields) — selected coefficients (Full Sample and Advanced Economies):
  - Full Sample:
    - NW: -0.0183*** [0.006]
    - NFW: -0.0126** [0.005]
    - Gross Debt: 0.0262*** [0.007]; 0.0230*** [0.007]; 0.0093** [0.004] (reported across columns)
    - Total Asset: -0.0260*** [0.007]; -0.0214*** [0.007]
    - Financial Assets: -0.0267** [0.011]; -0.0235** [0.011]
    - Observations: 359, 358, 395, 394, 600, 394, 393 (across columns)
    - R Squared values: 0.543, 0.536, 0.519, 0.505, 0.856, 0.514, 0.51
    - Number of countries: 31, 31, 33, 33, 33, 33, 33
  - Advanced Economies:
    - NW: -0.0094*** [0.003]
    - NFW: -0.0105*** [0.003]
    - Gross Debt: 0.0162*** [0.004]; 0.0158*** [0.004]; 0.0101*** [0.003]
    - Total Asset: -0.0054 [0.004]; -0.0026 [0.004]
    - Financial Assets: -0.0125** [0.006]; -0.0113* [0.006]
    - Observations: 291, 290, 311, 310, 513, 310, 309
    - R Squared values: 0.86, 0.861, 0.854, 0.852, 0.936, 0.849, 0.85
    - Number of countries: 24, 24, 25, 25, 24, 25, 25
- Note: Total Assets exclude land and natural resources; Liabilities exclude pension liabilities. NW excludes the above-mentioned items; NFW excludes pension liabilities. Control variables include lagged Real GDP per capita growth, US 10-year bond yield, average inflation rate, short-term interest rate, general government primary balance, country- and time-fixed effects. Sample period is 2001-16. *, **, *** represent statistical significance at 10, 5, and 1 percent respectively.

- Appendix Table 2 (Random effects; lagged indicators; Dependent variable: Long term government bond yields) — selected lagged coefficients:
  - Full Sample (selected entries):
    - Lagged NW: -0.0066*** [0.003]
    - Lagged NFW: -0.006** [0.003]
    - Lagged Gross Debt: 0.0141*** [0.003]; 0.013*** 0.0105*** [0.003] [0.003]
    - Lagged Total Asset: -0.0087*** -0.009*** [0.003] [0.003]
    - Lagged Financial Assets: -0.007*** -0.010*** [0.003] [0.003]
    - Observations across columns: 409, 415, 445, 447, 685, 448, 454
    - Number of countries: 31, 31, 33, 33, 33, 33, 33
  - Advanced Economies:
    - Lagged NW: -0.005*** [0.001]
    - Lagged NFW: -0.006*** [0.001]
    - Lagged Gross Debt: 0.015*** 0.014*** 0.012*** [0.002] [0.002] [0.002]
    - Lagged Total Asset: -0.003** -0.003** [0.001] [0.001]
    - Lagged Financial Assets: -0.004*** -0.007*** [0.002] [0.002]
    - Observations: 328, 334, 348, 350, 579, 351, 357
    - Number of countries: 24, 24, 25, 24, 25, 24, 25
  - Emerging Markets:
    - Lagged NW: -0.025*** [0.009]
    - Lagged NFW: -0.013 [0.010]
    - Lagged Gross Debt: 0.041** 0.008 0.006 [0.019] [0.022] [0.015]
    - Lagged Total Asset: -0.031*** -0.024*** [0.010] [0.009]
    - Lagged Financial Assets: -0.046* -0.041** [0.026] [0.019]
    - Observations: 818, 197, 971, 069, 797 (as reported)
    - Number of countries: 7, 7, 8, 8, 9, 8 (as reported)
- Note: Random effects estimations include control variables similar to Appendix Table 1; sample period 2001-16.

### Annex: Methodology — Risk-Adjusted Assets and Liabilities
- Construction steps:
  - Compute valuation changes for each asset and liability item by deducting transactions from total changes in value.
  - Define each item’s risk weight (RW) as the item’s relative volatility:
    - RW_i = σ_i^2 / ∑_i σ_i^2
  - Risk weights are calculated on a European-country sample with detailed transactions and valuation change data (Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Norway, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom).
  - Compute riskiness measures ∑ RW_i A_i and ∑ RW_i L_i and derive:
    - RAA = ∑ A_i − ∑ RW_i A_i
    - RAL = ∑ L_i − ∑ RW_i L_i
- Cross-country comparability: total assets exclude land and natural resources; total liabilities exclude pension liabilities.

### Annex Table: Risk Weights of Assets and Liabilities by Instrument
- Financial assets by instrument (Weight):
  - Monetary gold and SDRs 0.000
  - Currency and deposits 0.000
  - Debt securities 0.049
  - Loans 0.064
  - Equity and investment fund shares 0.564
  - Insurance, pension, and standardized guarantee schemes 0.000
  - Financial derivatives and employee stock options 0.049
  - Other accounts receivable 0.049
- Liabilities by instrument (Weight):
  - SDRs 0.000
  - Currency and deposits 0.000
  - Debt securities 0.000
  - Loans 0.122
  - Equity and investment fund shares 0.000
  - Insurance, pension, and standardized guarantee schemes 0.000
  - Financial derivatives and employee stock options 0.014
  - Other accounts payable 0.090
- Sum of weights 1.000
- Note: Risk weight equals the standard deviation of valuation changes in that instrument relative to the sum of standard deviations of all asset and liability components. SDRs = Special Drawing Rights.

### Annex: Natural Hedge (Net Financial Worth Volatility Decomposition)
- Valuation changes in net financial worth from other economic flows:
  - OEEF_NFW = OEEF_FA − OEEF_L (all expressed in percent of GDP)
- Volatility decomposition:
  - σ_NFW^2 = σ_FA^2 + σ_L^2 − 2 Cov_FA L
- Normalized relative volatility measure:
  - σ_n = σ_NFW^2 / (σ_FA σ_L)
- Rewritten form:
  - σ_n = σ_FA/σ_L + σ_L/σ_FA − 2 Cor_FA L, i.e., σ_n = x + 1/x − 2 Cor_FA L where x = σ_FA/σ_L and Cor_FA L is the correlation between financial assets and liabilities
- Interpretation:
  - x and 1/x capture the contribution of size mismatch between financial assets and liabilities to variation in net financial worth.
  - Cor_FA L captures how valuation changes in financial assets and liabilities move together.
- Figure 2 in the main text displays: σ_n (relative volatility of net financial worth), x + 1/x (relative volatility of financial assets and liabilities), and 2 Cor_FA L (relative volatility increase or decrease).

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_Source: https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019170-print-pdf.pdf_