## 1. Total Net Private Lending to Public Sector in Developing Countries

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---

### Introduction
- Focus: ability of developing country governments to borrow from international credit markets over the international credit market cycle.
- Data: individual sovereign bond issuances and syndicated bank loans for the period 1982–2000.
- Data coverage: 2,053 individual bond issuances and 5,065 commercial bank syndicated loans to 144 developing countries.
- Main empirical question: explain binary market access (access vs. exclusion) rather than volumes or spreads.
- Sample partition (descriptive): 60 countries (no access), 74 countries (occasional access, less than 14 years out of 20), 10 countries (consistent access, at least 14 years).

### Theoretical predictions (repudiation and debt-service capacity frameworks)
- Debt-ceiling function (conceptual): L_max(i,t) = f(Size, Volatility, Productivity, Political stability, Links to world, Global conditions).
- Predicted influences:
  - Size (GDP): positive effect on debt ceiling.
  - Income variability: repudiation literature predicts positive effect; vulnerability argument for poor countries suggests negative effect.
  - Political instability: negative effect on borrowing capacity.
  - Links to world (trade, FDI): larger links increase punishment potential and thus borrowing capacity.
  - Global factors (world interest rate): affect cost of servicing and temptation to default.
- Policy and liquidity indicators expected to matter: quality of policies (CPIA), reserves/months of imports, exports/debt service, IMF programs (SBA, EFF, PRGF differentiated).

### Empirical strategy and identification
- Definition of market access: issuance of public or publicly guaranteed bond or public/publicly guaranteed syndicated loan that increases indebtedness; exclude cases where total stock of commercial public debt falls.
- Aggregation: annual measure of access to reduce voluntary short-term abstention concerns.
- Steps to distinguish rationing from voluntary abstention:
  1. Exclude industrial countries.
  2. Exclude IMF-classified “creditor countries”: Kuwait, Libya, Oman, Qatar, Saudi Arabia, UAE.
  3. Include formerly communist/socialist countries only after market reforms/market entry dates (Table A1).
  4. Exclude years where private sector (but not sovereign) borrowed internationally.
  5. Aggregate by year to reduce timing-choice issues.
  6. Examine substitution between private and official flows; find no evidence of substitution (complementarity instead).
  7. Group countries by long-run borrowing frequency (no/occasional/consistent) to mitigate identification problems.
  8. Control for time-varying aggregate factors (world interest rates, time dummies) in panel estimations.
- Robustness checks: alternative access definitions using World Bank GDF aggregate net flows; two-year aggregation; fixed-effects, random-effects, pooled logit models; inclusion of lagged explanatory variables to reduce endogeneity.

### Variables used
- Size: GDP (bill. US$).
- Liquidity: Exports/Debt service; Reserves/months of imports.
- Vulnerability/volatility: GDP per capita (US$), Agriculture/GDP (percent), Std. dev. GDP growth (10yrs), Std. dev. terms of trade (20 yrs).
- Productivity/solvency/policies: GDP growth (percent), Mining/GDP (percent), Fuel exports/total exports, Export growth, Inflation, Real exchange rate deviation from 5-year average (percent), Fiscal deficit/GDP (percent), Arrears/total debt, World Bank CPIA index, “State Failures” dummy.
- Links: FDI/GDP, Openness (Exp+Imp/GDP).
- Political risk: ICRG Political Risk Index.
- Defaults: S&P-based defaults from Beers and Bhatia (1999).
- IMF programs: SBA, EFF, PRGF (different expected effects).
- Market perceptions: Institutional Investor country ratings (0–100 scale).

### Results — Cross-sectional findings
- Sample partition counts:
  - No access: 60 countries (more than 40 percent of 144).
  - Occasional access: 74 countries (less than 14 years of access out of 20).
  - Consistent access: 10 countries (at least 14 years).
- Selected cross-group means and medians (from Table 2):
  - GDP (bill. US$) mean: No Access = 7.5 (median 2.1); Occasional = 38.0 (median 9.7); Consistent = 136.0 (median 89.3).
  - Total Debt 1981/GDP (percent) mean: No Access = 49 (median 34); Occasional = 44 (median 39); Consistent = 22 (median 26).
  - Exports/Debt service mean: No Access = 65.8 (median 17.6); Occasional = 101.8 (median 7.6); Consistent = 7.5 (median 7.9).
  - Reserves/months of imports mean: No Access = 3.0 (median 2.3); Occasional = 3.2 (median 2.8); Consistent = 4.2 (median 4.1).
  - GDP per capita (US$) mean: No Access = 1,686 (median 685); Occasional = 1,954 (median 1,467); Consistent = 3,214 (median 2,209).
  - Agriculture/GDP (percent) mean: No Access = 28 (median 28); Occasional = 20 (median 18); Consistent = 14.5 (median 15.5).
  - Std. dev. GDP growth (10 yrs) mean: No Access = 6.1 (median 4.8); Occasional = 5.6 (median 4.8); Consistent = 3.5 (median 3.5).
  - CPIA mean: No Access = 2.69 (median 2.85); Occasional = 3.05 (median 3.05); Consistent = 3.92 (median 3.77).
  - Institutional Investor Rating mean: No Access = 16.0 (median 15.2); Occasional = 29.6 (median 28.1); Consistent = 50.1 (median 45.9).
  - IMF program frequency (percent of years) SBA mean: No Access = 0.07; Occasional = 0.14; Consistent = 0.05. PRGF mean: No Access = 0.04; Occasional = 0.03; Consistent = 0.00.
- Formal tests and Tobit regressions identify significant predictors of access frequency:
  - Positive correlates: GDP, CPIA, Institutional Investor rating, SBA program presence (in some specifications).
  - Negative correlates: Share of agriculture in GDP, PRGF programs, arrears (in simpler specs), Sub-Saharan Africa dummy (in some models).
- Surprising cross-sectional facts:
  - FDI/GDP and trade openness do not increase probability of market access; consistent-access countries are often less open than no-access countries (not consistently significant).
  - No strong evidence that resource-abundant countries (mining/fuel shares) tap markets more easily.

### Results — Panel regressions and robustness
- Estimation approaches: pooled, random-effects, fixed-effects logit models with time dummies; random-effects favored (Hausman tests do not reject).
- Lagging explanatory variables (one year) to address endogeneity except for truly exogenous dummies.
- Key panel results (selected coefficients and findings):
  - Sub-Saharan Africa dummy: consistently negative and significant (example coefficient range in random-effects logit Table 4: -1.298 to -2.536 across specifications; z-statistics significant).
  - Vulnerability measures:
    - Agriculture/GDP: negative and significant in many specifications (example: coefficient -0.082 with z = 6.26*** in Table 4 (1)).
    - GDP per capita: positive and significant across specifications (example: coefficient 0.001 with z = 5.03*** in Table 4 (3)).
    - Std. dev. GDP growth (10y): negative and significant in several specs (example: -0.084, z = 2.18**).
  - Policy quality (CPIA index): positive and significant (example: coefficient 0.996 with z = 4.29*** in Table 4 (5)).
  - Market perceptions (Institutional Investor residual): positive, significant (example: 0.110, z = 6.31*** in Table 4 (8); treated as residual after regressing on fundamentals).
  - Defaults: negative but quantitatively modest effect; in simple tests probability of access after default ~3 percent lower; in logit specs default coefficients range (example: -1.483 z = 2.47** to -2.038 z = 2.85*** in various columns).
  - IMF programs: SBA and EFF do not show robust catalytic effects once CPIA/perceptions controlled for; PRGF negatively associated with access.
  - Liquidity variables (reserves/months of imports, exports/debt service): generally insignificant.
  - Links (FDI/GDP, openness): do not increase market access; openness sometimes enters with negative sign.
- Model fit and prediction:
  - Using overall in-sample access probability 0.22 as cut-off, Type I errors (missed accesses) vary; adding variables improves correct prediction (example: specification (9) correctly predicts access in 82 percent of cases).
- State dependence:
  - Including lagged dependent variable increases fit and shows persistence, but main variable effects remain broadly unchanged.

### Default and resumption evidence
- Median years until resumption after default:
  - 1980s median = 4 years.
  - 1990s median = 0 years.
- Mean private gross capital flows (percent of GDP):
  - 2 years prior to default in 1980s mean = 4.7; 2 years after default mean = 6.4.
  - For 1990s: prior mean = 0.3; after mean = 8.1.
- Number of defaults considered:
  - 1980s included 49; 1990s included 13.

### Robustness checks and additional findings
- Substitution between private and official flows: evidence indicates complementarity rather than substitution; regression of private flows on official flows (fixed country & time effects) yields positive and significant coefficient (t-statistic = 3.07).
- Aggregating over two years yields very similar results.
- Fixed-effects logit supports vulnerability and policy quality importance; CPIA and Institutional Investor rating significant; public debt with private creditors negative in fixed-effects (example: -0.056 z = 2.85*** in column (1) Table A6).
- Inflation gains significance in fixed-effects models.

### Conclusions — six robust regularities
- (1) The perceived quality of policies and institutions matters substantially (CPIA index significant).
- (2) Countries more vulnerable to shocks are less likely to tap international credit markets (GDP per capita, Agriculture/GDP, GDP volatility significant).
- (3) Standard liquidity indicators (reserve coverage) do not help predict market access; most standard macro policy indicators do not, except inflation.
- (4) Links to the rest of the world (FDI, trade openness) do not increase market access contrary to some theory predictions.
- (5) After controlling for perceived policy quality, no clear catalytic effect of IMF SBA or EFF programs; PRGF programs negatively affect probability of market access (consistent with program constraints on private borrowing).
- (6) Defaults do not strongly reduce probability of market access in subsequent years; in the 1990s, on average, countries that defaulted did not experience interruptions in market access.

### Appendix (data sources and variable construction — selected points)
- GDP and macro variables: World Bank WDI, supplemented with IMF WEO and IMF country desks where possible.
- Terms-of-trade volatility: Cashin and Pattillo (2000) dataset.
- CPIA: World Bank Country Policy and Institutional Assessment index (yearly).
- State failures: State Failure dataset (dummy for civil wars/major regime transitions/major ethnic conflicts).
- Defaults: Standard & Poor’s classification reported in Beers and Bhatia (1999).
- IMF program dummies: SBA, EFF, PRGF; PRGF programs are concessional, three-year arrangements and impose strict limits on private sector sovereign borrowing.
- Transition economies: inclusion date per Table A1.

*Source: _wp04221 - 1. Total Net Private Lending to Public Sector in Developing Countries (PDF content unit).*

### 1. Total Net Private Lending to Public Sector in Developing Countries ................................3

### 1. Total Net Private Lending to Public Sector in Developing Countries ................................3

### Main sections
- 1. Total Net Private Lending to Public Sector in Developing Countries ................................3
- 2. Access Frequency and Economic Size (GDP in U.S. dollars) ..........................................15
- 3. Access Frequency and Income Volatility ..........................................................................15
- 4. Access Frequency and Foreign Direct Investment as a Fraction of GDP .........................15

### Tables (listed in source)
- 1. Variables Affecting Creditworthiness .................................................................................9
- 2. Means and Medians of Country Characteristics, by Access Frequency Groups ...............14
- 3. Determinants of Access Frequency (Tobit).......................................................................17
- 4. Determinants of Access: Random-Effect Logit Model with Time Effects .......................20
- 5. Default and Resumption of Access ...................................................................................21
- 6. Relation between Official Flows and Probability of Observing No Access .....................21

### Appendix and appendix tables
- Appendix.......................................................................................................................................24
- Apendix Tables 
  - A1. Date of Inclusion of Formerly Centrally Planned Economies ..........................................26
  - A2. Market Access, By Country ..............................................................................................27
  - A3. Tests of Equality of Means and Medians Across Groups.................................................30
  - A4. Determinants of Access Frequency (Tobit) ......................................................................31
  - A5. Determinants of Access: Random-Effects Logit Model with Time Dummies.................33
  - A6. Determinants of Access: Fixed-Effect Logit Model with Time Dummies.......................35
  - A7.  Default and Resumption of Access ..................................................................................36

*Source: _wp04221 - 1. Total Net Private Lending to Public Sector in Developing Countries ................................3*

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

### _wp04221 - References..............................................................................................................

### Introduction
- Focus: ability of developing country governments to borrow from international credit markets over the international credit market cycle.
- Data: individual sovereign bond issuances and syndicated bank loans for the period 1982–2000.
- Data coverage: 2,053 individual bond issuances and 5,065 commercial bank syndicated loans to 144 developing countries.
- Key empirical question: explain binary market access (access vs. exclusion) rather than volumes or spreads.
- Main descriptive finding (sample partition): 60 countries (no access), 74 countries (occasional access, less than 14 years out of 20), 10 countries (consistent access, at least 14 years).

### Theoretical predictions (repudiation and debt-service capacity frameworks)
- Debt-ceiling function (conceptual): L_max(i,t) = f(Size, Volatility, Productivity, Political stability, Links to world, Global conditions).
- Predicted influences:
  - Size (GDP): positive effect on debt ceiling.
  - Income variability: repudiation literature predicts positive effect; vulnerability argument for poor countries suggests negative effect.
  - Political instability: negative effect on borrowing capacity.
  - Links to world (trade, FDI): larger links increase punishment potential and thus borrowing capacity.
  - Global factors (world interest rate): affect cost of servicing and temptation to default.
- Policy and liquidity indicators expected to matter: quality of policies (CPIA), reserves/months of imports, exports/debt service, IMF programs (SBA, EFF, PRGF differentiated).

### Empirical strategy and identification
- Definition of market access: issuance of public or publicly guaranteed bond or public/publicly guaranteed syndicated loan that increases indebtedness; exclude cases where total stock of commercial public debt falls.
- Aggregation: annual measure of access to reduce voluntary short-term abstention concerns.
- Steps to distinguish rationing from voluntary abstention:
  1. Exclude industrial countries.
  2. Exclude IMF-classified “creditor countries”: Kuwait, Libya, Oman, Qatar, Saudi Arabia, UAE.
  3. Include formerly communist/socialist countries only after market reforms/market entry dates (Table A1).
  4. Exclude years where private sector (but not sovereign) borrowed internationally.
  5. Aggregate by year to reduce timing-choice issues.
  6. Examine substitution between private and official flows; find no evidence of substitution (complementarity instead).
  7. Group countries by long-run borrowing frequency (no/occasional/consistent) to mitigate identification problems.
  8. Control for time-varying aggregate factors (world interest rates, time dummies) in panel estimations.
- Robustness checks: alternative access definitions using World Bank GDF aggregate net flows; two-year aggregation; fixed-effects, random-effects, pooled logit models; inclusion of lagged explanatory variables to reduce endogeneity.

### Variables used (as described)
- Size: GDP (bill. US$).
- Liquidity: Exports/Debt service; Reserves/months of imports.
- Vulnerability/volatility: GDP per capita (US$), Agriculture/GDP (percent), Std. dev. GDP growth (10yrs), Std. dev. terms of trade (20 yrs).
- Productivity/solvency/policies: GDP growth (percent), Mining/GDP (percent), Fuel exports/total exports, Export growth, Inflation, Real exchange rate deviation from 5-year average (percent), Fiscal deficit/GDP (percent), Arrears/total debt, World Bank CPIA index, “State Failures” dummy.
- Links: FDI/GDP, Openness (Exp+Imp/GDP).
- Political risk: ICRG Political Risk Index.
- Defaults: S&P-based defaults from Beers and Bhatia (1999).
- IMF programs: SBA, EFF, PRGF (different expected effects).
- Market perceptions: Institutional Investor country ratings (0–100 scale).

### Results — Cross-sectional findings
- Sample partition counts:
  - No access: 60 countries (more than 40 percent of 144).
  - Occasional access: 74 countries (less than 14 years of access out of 20).
  - Consistent access: 10 countries (at least 14 years).
- Cross-group mean/median comparisons (selected highlights from Table 2):
  - GDP (bill. US$) mean: No Access = 7.5 (median 2.1); Occasional = 38.0 (median 9.7); Consistent = 136.0 (median 89.3).
  - Total Debt 1981/GDP (percent) mean: No Access = 49 (median 34); Occasional = 44 (median 39); Consistent = 22 (median 26).
  - Exports/Debt service mean: No Access = 65.8 (median 17.6); Occasional = 101.8 (median 7.6); Consistent = 7.5 (median 7.9).
  - Reserves/months of imports mean: No Access = 3.0 (median 2.3); Occasional = 3.2 (median 2.8); Consistent = 4.2 (median 4.1).
  - GDP per capita (US$) mean: No Access = 1,686 (median 685); Occasional = 1,954 (median 1,467); Consistent = 3,214 (median 2,209).
  - Agriculture/GDP (percent) mean: No Access = 28 (median 28); Occasional = 20 (median 18); Consistent = 14.5 (median 15.5).
  - Std. dev. GDP growth (10 yrs) mean: No Access = 6.1 (median 4.8); Occasional = 5.6 (median 4.8); Consistent = 3.5 (median 3.5).
  - CPIA mean: No Access = 2.69 (median 2.85); Occasional = 3.05 (median 3.05); Consistent = 3.92 (median 3.77).
  - Institutional Investor Rating mean: No Access = 16.0 (median 15.2); Occasional = 29.6 (median 28.1); Consistent = 50.1 (median 45.9).
  - IMF program frequency (percent of years): SBA mean: No Access = 0.07; Occasional = 0.14; Consistent = 0.05. PRGF mean: No Access = 0.04; Occasional = 0.03; Consistent = 0.00.
- Formal tests and Tobit regressions (Appendix, Table A3, A4) identify significant predictors of access frequency:
  - Positive correlates: GDP, CPIA (policy/institution quality), Institutional Investor rating, SBA program presence (in some specifications).
  - Negative correlates: Share of agriculture in GDP, PRGF programs, arrears (in simpler specs), Sub-Saharan Africa dummy (in some models).
- Surprising cross-sectional facts:
  - FDI/GDP and trade openness do not increase probability of market access; consistent-access countries are often less open than no-access countries (not consistently significant).
  - No strong evidence that resource-abundant countries (mining/fuel shares) tap markets more easily.

### Results — Panel regressions and robustness
- Estimation approaches: pooled, random-effects, fixed-effects logit models with time dummies; random-effects favored (Hausman tests do not reject).
- Lagging explanatory variables (one year) to address endogeneity except for truly exogenous dummies.
- Key panel results (Table 4, Table A5):
  - Sub-Saharan Africa dummy: consistently negative and significant (example coefficient range in random-effects logit Table 4: -1.298 to -2.536 across specifications; z-statistics significant).
  - Vulnerability measures:
    - Agriculture/GDP: negative and significant in many specifications (e.g., coefficient -0.082 with z = 6.26*** in Table 4 (1)).
    - GDP per capita: positive and significant across specifications (e.g., coefficient 0.001 with z = 5.03*** in Table 4 (3)).
    - Std. dev. GDP growth (10y): negative and significant in several specs (e.g., -0.084, z = 2.18**).
  - Policy quality (CPIA index): positive and significant (e.g., coefficient 0.996 with z = 4.29*** in Table 4 (5) and other specs).
  - Market perceptions (Institutional Investor residual): positive, significant (e.g., 0.110, z = 6.31*** in Table 4 (8); treated as residual after regressing on fundamentals).
  - Defaults: negative but quantitatively modest effect; in simple tests probability of access after default ~3 percent lower; in logit specs default coefficients range (e.g., -1.483 z = 2.47** to -2.038 z = 2.85*** in various columns).
  - IMF programs: SBA and EFF do not show robust catalytic effects once CPIA/perceptions controlled for; PRGF negatively associated with access.
  - Liquidity variables (reserves/months of imports, exports/debt service): generally insignificant.
  - Links (FDI/GDP, openness): do not increase market access; openness sometimes enters with negative sign.
- Model fit and prediction:
  - Using overall in-sample access probability 0.22 as cut-off, Type I errors (missed accesses) vary; adding variables improves correct prediction (example: specification (9) correctly predicts access in 82 percent of cases).
  - Fixed-effects estimations produce similar main results; inflation gains significance in fixed-effects models.
- State dependence:
  - Including lagged dependent variable increases fit and shows significance of persistence, but main variable effects remain broadly unchanged.
- Default and resumption timing (Table 5 and A7):
  - Median years until resumption after default: 1980s median = 4 years; 1990s median = 0 years.
  - Mean private gross capital flows (percent of GDP): 2 years prior to default in 1980s mean = 4.7; 2 years after default mean = 6.4. For 1990s: prior mean = 0.3; after mean = 8.1.
  - Number of defaults considered: 1980s included 49; 1990s included 13 (Table A7 details country-by-country resumption intervals).

### Robustness checks and additional findings
- Substitution between private and official flows: evidence indicates complementarity rather than substitution; regression of private flows on official flows (fixed country & time effects) yields positive and significant coefficient (t-statistic = 3.07).
- Aggregating over two years yields very similar results.
- Fixed-effects logit (Table A6) supports vulnerability and policy quality importance; CPIA and Institutional Investor rating significant; public debt with private creditors negative in fixed-effects (e.g., -0.056 z = 2.85*** in column (1) Table A6).

### Conclusions (six robust regularities)
- (1) The perceived quality of policies and institutions matters substantially (CPIA index significant).
- (2) Countries more vulnerable to shocks are less likely to tap international credit markets (GDP per capita, Agriculture/GDP, GDP volatility significant).
- (3) Standard liquidity indicators (reserve coverage) do not help predict market access; most standard macro policy indicators do not, except inflation.
- (4) Links to the rest of the world (FDI, trade openness) do not increase market access contrary to some theory predictions.
- (5) After controlling for perceived policy quality, no clear catalytic effect of IMF SBA or EFF programs; PRGF programs negatively affect probability of market access (consistent with program constraints on private borrowing).
- (6) Defaults do not strongly reduce probability of market access in subsequent years; in the 1990s, on average, countries that defaulted did not experience interruptions in market access.

### Appendix (data sources and variable construction — selected points)
- GDP and macro variables: World Bank WDI, supplemented with IMF WEO and IMF country desks where possible.
- Terms-of-trade volatility: Cashin and Pattillo (2000) dataset.
- CPIA: World Bank Country Policy and Institutional Assessment index (yearly).
- State failures: State Failure dataset (dummy for civil wars/major regime transitions/major ethnic conflicts).
- Defaults: Standard & Poor’s classification reported in Beers and Bhatia (1999).
- IMF program dummies: SBA, EFF, PRGF; PRGF programs are concessional, three-year arrangements and impose strict limits on private sector sovereign borrowing.
- Transition economies: inclusion date per Table A1.

*Source: Authors’ calculations and tables as presented in the PDF content unit _wp04221 - References.*

### REFERENCES

### REFERENCES

### References list

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*Source: _wp04221 - REFERENCES*

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