## The HIPC Initiative and China’s Emergence as a Lender: post hoc or propter hoc? Working Paper No. WP/2025/033

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---

### Net versus gross credit flows — key findings
- Gross lending to HIPCs declined dramatically after HIPC debt relief.
- Average reduction in gross lending to HIPCs (vis-à-vis their synthetic controls) of about 1 percent of GDP per year.
- The decline in gross flows is common to most HIPCs:
  - Somewhat larger for the median HIPC country.
  - Almost twice as large for the HIPC at the 25th percentile of the distribution.
- Decline in gross flows likely reflects reduced defensive lending following debt forgiveness.
- Net flows are essential to assess whether debt relief provided additional resources or simply ended defensive lending cycles.
- Virtually all HIPCs received positive net flow before debt relief (Easterly, 2002; Bird and Milne, 2003), so debt relief alone does not imply an increase in resources.

### Net flows overall
- Net flows as a share of recipient countries’ GDP declined during the 2000s in raw terms.
- When comparing HIPCs to their synthetic controls:
  - Post-decision point (DP) net flows to HIPCs increased both on average and for the median country.
  - There is substantial cross-country variation; the interquartile range extends into negative territory.
- Cumulative flows trend: positive for the first ten years, with a partial reversal afterwards stronger for the mean than the median country.

### Net flows by creditor type — contributors to post‑DP changes
- Multilateral donors:
  - Followed their pledge to provide additional help; net flows from multilaterals increased substantially after the HIPC initiative.
- Paris Club creditors:
  - On average, kept net flow roughly constant post-DP.
- China:
  - Reduced its support for HIPCs post-debt relief compared to non-HIPCs.
  - China did increase support in absolute terms for many countries, but debt relief negatively impacted its lending volumes.
- Other non-Paris club bilateral donors (mostly Gulf states not part of the Paris Club):
  - Also reduced net flows to HIPCs post-DP.
- Aggregate effect of China plus other bilateral emerging creditors:
  - Sharp and persistent net flow reduction to HIPCs.
  - Close to 10 percent of GDP for the median country 20 years after decision point.
  - Close to 15 percent of GDP on average 20 years after decision point.
- Private creditors and bondholders:
  - Did not take advantage of the new borrowing space created by HIPC initiative.
  - Possible explanation: non-concessional lending clause in HIPC that aimed to handle the “free rider problem [which] involves both a collective action issue vis-à-vis creditors, and moral hazard risks vis-à-vis borrowers” (World Bank, 2016).

### Methodological considerations and robustness
- Synthetic control (SC) method assumptions and plausibility:
  - No anticipation: variables used to construct synthetic controls should not anticipate debt relief; violation considered unlikely given HIPC eligibility criteria.
  - No interference: treatment should not affect donor-pool countries used to estimate synthetic controls (no spillovers); HIPC-eligible countries are among poorest and not systemic; major African spillover sources (South Africa, Nigeria) were not HIPC-eligible.
  - No clear-cut HIPC effects on growth and investment in recipient countries argues against cross-border spillovers.
- Placebo and robustness tests:
  - In-space placebo: treatment assigned to 16 DSSI-eligible non-HIPC countries; median and average gaps very close to zero for the 20-year period post-treatment.
  - Some variation across countries in net flows from China; countries split between positive and negative gaps; no evident trends.
  - Additional robustness checks:
    - i) Simple SC: match HIPCs and donor pool only on lagged values of net flows (no macro controls).
    - ii) SC variant dropping small island states from donor pool.
  - Results from these checks align well with main findings (reported in Appendix Figures A2 and A3).

### Discussion — interpretation and implications
- Main takeaway:
  - Net flows’ response to HIPC debt relief is highly heterogeneous.
  - Total net flows show some moderate pickup (on average and for the median country) relative to similar non-HIPCs.
  - Main driver: substantial post-DP increase in net flows from multilateral lenders.
  - New flows from China and other non-Paris Club creditors to HIPCs declined compared to non-HIPCs.
  - Result: IFIs’ exposure to HIPCs increased relative to China’s and private borrowers’.
- Tested geopolitical explanation:
  - Used ideal point distance (Bailey et al., 2017) in the five years before DP to split HIPCs by geopolitical proximity to China and the US.
  - Evidence: neither Chinese nor multilateral net flows to HIPCs after debt relief are associated with geopolitical proximity — data reject the simple geopolitical narrative.
- Alternative explanations and mechanisms:
  - Indebtedness may not be a significant pull factor for bilateral developmental assistance because net flows are usually positive and bilateral donors can use other levers to maintain influence.
  - Debt levels may be irrelevant in LICs with poor governance (Cordella et al., 2010).
- Multilaterals’ behavior and institutional incentives:
  - Influenced by Debt Sustainability Analyses (DSAs) initiated in 2005 under the joint Debt Sustainability Framework for LICs.
  - DSAs affect lending decisions and the mix of grants vs. loans; more indebted countries receive more grants and thus lower net flows for a given level of assistance (Cordella and Ulku, 2007).
  - Incentives within multilateral institutions favor higher lending volumes (Ravallion, 2016).
  - Multilaterals appear to have increased net flows when HIPC/MDRI created room, fulfilling the IDA (2006) additionality pledge.

### Conclusions — concise summary
- Contrary to common wisdom, no evidence that China “free-rode” by increasing lending to HIPCs because of HIPC/MDRI; the analysis finds that debt relief decreased China’s and other emerging lenders’ net flows to HIPCs.
- Net flows, not debt stocks, should matter most in assessing aid and geopolitical dynamics; debt stocks appear less relevant for geopolitical competition.
- The paper’s main contribution is methodological and historical: a rigorous SC-based framework to assess how HIPC/MDRI affected the composition of credit flows, leaving interpretation of lessons for current/future restructuring to the reader.

*Source: wpiea2025033-print-pdf - 4.1    Net versus Gross Credit Flows*

### 4.1    Net versus Gross Credit Flows

### 4.1    Net versus Gross Credit Flows

### Key findings on gross versus net flows
- Gross lending to HIPCs declined dramatically after HIPC debt relief.
- Average reduction in gross lending to HIPCs (vis-à-vis their synthetic controls) of about 1 percent of GDP per year.
- The decline in gross flows is common to most HIPCs:
  - Somewhat larger for the median HIPC country.
  - Almost twice as large for the HIPC at the 25th percentile of the distribution.
- Decline in gross flows likely reflects reduced defensive lending following debt forgiveness.
- Net flows are essential to assess whether debt relief provided additional resources or simply ended defensive lending cycles.
- Virtually all HIPCs received positive net flow before debt relief (Easterly, 2002; Bird and Milne, 2003), so debt relief alone does not imply an increase in resources.

### Net flows overall
- Net flows as a share of recipient countries’ GDP declined during the 2000s in raw terms.
- When comparing HIPCs to their synthetic controls:
  - Post-decision point (DP) net flows to HIPCs increased both on average and for the median country.
  - There is substantial cross-country variation; the interquartile range extends into negative territory.
- Cumulative flows trend: positive for the first ten years, with a partial reversal afterwards stronger for the mean than the median country.

---

### 4.2    Net Flows by Creditor Type

### Which creditors contributed to post‑DP net flow changes?
- Multilateral donors:
  - Followed their pledge to provide additional help; net flows from multilaterals increased substantially after the HIPC initiative.
- Paris Club creditors:
  - On average, kept net flow roughly constant post-DP.
- China:
  - Reduced its support for HIPCs post-debt relief compared to non-HIPCs.
  - China did increase support in absolute terms for many countries, but debt relief negatively impacted its lending volumes.
- Other non-Paris club bilateral donors (mostly Gulf states not part of the Paris Club):
  - Also reduced net flows to HIPCs post-DP.
- Aggregate effect of China plus other bilateral emerging creditors:
  - Sharp and persistent net flow reduction to HIPCs.
  - Close to 10 percent of GDP for the median country 20 years after decision point.
  - Close to 15 percent of GDP on average 20 years after decision point.
- Private creditors and bondholders:
  - Did not take advantage of the new borrowing space created by HIPC initiative.
  - Possible explanation: non-concessional lending clause in HIPC that aimed to handle the “free rider problem [which] involves both a collective action issue vis-à-vis creditors, and moral hazard risks vis-à-vis borrowers” (World Bank, 2016).

---

### 4.3    Methodological Considerations and Robustness Analysis

### Synthetic control (SC) method assumptions and plausibility
- No anticipation:
  - Variables used to construct synthetic controls should not anticipate debt relief.
  - Violation would require that flows from a particular creditor predict debt relief more than others; considered unlikely given HIPC eligibility criteria.
- No interference:
  - Treatment should not affect donor-pool countries used to estimate synthetic controls (no spillovers).
  - HIPC-eligible countries are among poorest and not systemic; major African spillover sources (South Africa, Nigeria) were not HIPC-eligible.
  - No clear-cut HIPC effects on growth and investment in recipient countries argues against cross-border spillovers.

### Placebo and robustness tests
- In-space placebo:
  - Treatment assigned to 16 DSSI-eligible non-HIPC countries.
  - Median and average gaps very close to zero for the 20-year period post-treatment.
  - Some variation across countries in net flows from China; countries split between positive and negative gaps; no evident trends.
- Additional robustness checks:
  - i) Simple SC: match HIPCs and donor pool only on lagged values of net flows (no macro controls).
  - ii) SC variant dropping small island states from donor pool.
  - Results from these checks align well with main findings (reported in Appendix Figures A2 and A3).

---

### 5    Discussion

### Main takeaway
- Net flows’ response to HIPC debt relief is highly heterogeneous.
- Total net flows show some moderate pickup (on average and for the median country) relative to similar non-HIPCs.
- Main driver: substantial post-DP increase in net flows from multilateral lenders.
- New flows from China and other non-Paris Club creditors to HIPCs declined compared to non-HIPCs.
- Result: IFIs’ exposure to HIPCs increased relative to China’s and private borrowers’.

### Interpretations and educated guesses
- Geopolitical explanation tested:
  - Used ideal point distance (Bailey et al., 2017) in the five years before DP to split HIPCs by geopolitical proximity to China and the US.
  - Defined geopolitically close/distant groups and compared net flow gaps.
  - Evidence: neither Chinese nor multilateral net flows to HIPCs after debt relief are associated with geopolitical proximity — data reject the simple geopolitical narrative.
- Alternative explanation:
  - Indebtedness (or lack thereof) may not be a significant pull factor for bilateral developmental assistance because:
    - Net flows are usually positive; recipients typically receive more than they repay annually.
    - Bilateral donors can use other levers (e.g., military assistance) to enforce repayment or maintain influence.
    - Evidence that debt levels are irrelevant in LICs with poor governance (Cordella et al., 2010).
- Multilaterals’ behavior:
  - Influenced by Debt Sustainability Analyses (DSAs) initiated in 2005 under the joint Debt Sustainability Framework for LICs.
  - DSAs affect lending decisions and the mix of grants vs. loans; more indebted countries receive more grants and thus lower net flows for a given level of assistance (Cordella and Ulku, 2007).
  - Incentives within multilateral institutions favor higher lending volumes (Ravallion, 2016).
  - Multilaterals appear to have increased net flows when HIPC/MDRI created room, fulfilling the IDA (2006) additionality pledge.

---

### 6    Conclusions

- Contrary to common wisdom, no evidence that China “free-rode” by increasing lending to HIPCs because of HIPC/MDRI; the analysis finds that debt relief decreased China’s and other emerging lenders’ net flows to HIPCs.
- Net flows, not debt stocks, should matter most in assessing aid and geopolitical dynamics; debt stocks appear less relevant for geopolitical competition.
- The paper’s main contribution is methodological and historical: a rigorous SC-based framework to assess how HIPC/MDRI affected the composition of credit flows, leaving interpretation of lessons for current/future restructuring to the reader.

*Source: wpiea2025033-print-pdf - 4.1    Net versus Gross Credit Flows*

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### Figures (captions and notes)
- Figure 1: The big picture: Rising debt and change in creditor composition
  - (a) External public debt over GDP(b) Increasing debt service(c) Rising role of emerging creditors
  - Notes: Panel A plots the median and interquartile range of the public and publicly guaranteed external debt over GDP ratio. Panel B plots the amount of public and publicly guaranteed external debt (in constant 2017 USD) by creditor type. Panel C plots the distribution of the average of the ratio of debt service on public and publicly guaranteed external debt over revenues (winsorized at the 1st and 99th percentiles) over three periods of time (1995−2000; 2010−2015; 2020−2023) and across the 68 DSSI-eligible countries. Panels A and C are based on a slightly unbalanced sample of 68 DSSI-eligible countries (only three countries—Afghanistan, Kosovo, and Timor-Leste—are not observed for the entire time series of 23 years). The sample for panel B is balanced starting in 2013 and slightly unbalanced earlier.
  - Sources: International Debt Statistics (The World Bank); World Development Indicators (The World Bank); World Economic Outlook database (International Monetary Fund); and authors’ calculations.
- Figure 2: Chinese and multilateral net flows around debt relief initiatives
  - (a) Net flows(b) Net flows from China(c) Net flows from multilaterals
  - Notes: Each panel plots the net flows by a specific creditor (China and multilaterals) for the average country, for the whole sample of DSSI-eligible countries (panel A), and separately for HIPC and non-HIPCs (panels B and C). The sample includes all 68 DSSI-eligible countries. Net flows are 3-year moving averages scaled by the recipient country’s GDP.
  - Sources: International Debt Statistics (The World Bank); World Development Indicators (The World Bank); and authors’ calculations.
- Figure 3: Gross vs net flows
  - (a) Gross flows(b) Net flows
  - Notes: Panel A(B) plots the difference between the actual gross (net) flows and those of the respective synthetic control (gap). The synthetic controls are built matching treated countries with those in the donor pool on past net flow and key macro characteristics; see Section 3. The solid line is the median value of the gaps in the sample of HIPCs; the dashed line is the average. The shaded area indicates the interquartile range. The vertical line indicates the country-specific HIPC decision point. Net and gross flows are 3-year moving averages scaled by the recipient country’s GDP.
  - Sources: International Debt Statistics (The World Bank); World Development Indicators (The World Bank); and authors’ calculations.
- Figure 4: Net flows post HIPC initiative, by creditor type
  - (a) Multilaterals(b) Paris Club (PC)
  - (c) China(d) Other bilaterals (non PC)
  - (e) China and other bilaterals (non PC)(f) Private creditors and bondholders
  - Notes: Each panel plot the difference between the actual net flows by a specific creditor and those of the respective synthetic control (gap). The synthetic controls are built matching treated countries with those in the donor pool on past net flow and key macro characteristics, see Section 3. The solid line is the median value of the gaps in the sample of HIPCs; the dashed line is the average. The shaded area indicates the interquartile range. The vertical line indicates the country-specific HIPC decision point. Net flows are 3-year moving averages scaled by the recipient country’s GDP.
  - Sources: International Debt Statistics (The World Bank); World Development Indicators (The World Bank); and authors’ calculations.
- Figure 5: Geopolitical Lending
  - (a) Multilaterals(b) China
  - Notes: Each panel plots the difference between the actual net flows by a specific creditor (multilaterals in panel A and China in panel B) and those of the respective synthetic control (gap), separately for geopolitically close and distant countries. Countries geopolitically close (distant) to China and and to multilaterals are those above (below) the median of the Ideal Point Distance from China and the U.S. (computed as average over the five years before the decision point), respectively. The synthetic controls are built matching treated countries with those in the donor pool on past net flow and key macro characteristics, see Section 3. The solid line is the median value of the gaps in the sample of HIPCs; the dashed line is the average. The shaded areas indicate the interquartile range. The vertical line indicates the country-specific HIPC decision point. Net flows are 3-year moving averages scaled by the recipient country’s GDP.
  - Sources: International Debt Statistics (The World Bank); World Development Indicators (The World Bank); Baileyet al. (2017); and authors’ calculations.

### Appendix: Additional Tables and Figures
- Table A1: HIPC and MDRI debt relief: timing and value
  - Notes: The table reports the list of 36 HIPCs that qualified for HIPC and MDRI debt relief, together with the date of completion and decision points and the amount of debt relief granted, as of September 2019. Countries initalicsare not included in our sample because of data availability or the poor fit of our synthetic controls (Nicaragua). DP: decision point; CP: completion point. Figures are in million USD.
  - Source: World Bank (2019).
  - HIPCsDateHIPC reliefMDRI reliefTotal relief
  - DPCPPV at DPNominalNominalNominal
  - AfghanistanJul-07Jan-105821,280371,317
  - BeninJul-00Mar-032624601,0851,545
  - BoliviaFeb-00Jun-011,3302,0602,7714,831
  - Burkina FasoJul-00Apr-025539301,1502,080
  - BurundiAug-05Jan-098331,366651,431
  - CameroonOct-00Apr-061,2674,9171,2066,123
  - Central African RepublicSep-07Jun-095788042531,057
  - ChadMay-01Apr-151702607641,024
  - ComorosJun-10Dec-1214613672208
  - Congo, Dem. Rep. of theJul-03Jul-107,25215,22297316,195
  - Congo, Rep. ofMar-06Jan-101,5751,7381871,925
  - Cote d’IvoireMar-09Jun-121,5763,4151,7455,160
  - EthiopiaNov-01Apr-041,9353,2753,1366,411
  - Gambia, TheDec-00Dec-0767112364476
  - GhanaFeb-02Jul-042,1873,5003,7357,235
  - GuineaDec-00Sep-126398009161,716
  - Guinea-BissauDec-00Dec-10489790114904
  - GuyanaNov-00Dec-036101,3547012,054
  - HaitiNov-06Jun-091402139471,160
  - HondurasJun-00Apr-055561,0002,6773,677
  - LiberiaMar-08Jun-102,7394,6002584,858
  - MadagascarDec-00Oct-048361,9002,3054,205
  - MalawiDec-00Aug-069391,6281,4913,119
  - MaliSep-00Mar-035398951,8942,789
  - MauritaniaFeb-00Jun-026221,1008401,940
  - MozambiqueApr-00Sep-012,1434,3001,9646,264
  - NicaraguaDec-00Jan-043,3084,5001,8716,371
  - NigerDec-00Apr-046441,1901,0042,194
  - RwandaDec-00Apr-056511,3164651,781
  - São Tomé and PríncipeDec-00Mar-0711726358321
  - SenegalJun-00Apr-044888502,3743,224
  - Sierra LeoneMar-02Dec-066759946331,627
  - TanzaniaApr-00Nov-012,0263,0003,6836,683
  - TogoNov-08Dec-102823606831,043
  - UgandaFeb-00May-001,0271,9503,3665,316
  - ZambiaDec-00Apr-052,4993,9002,6456,545
  - Total76,37848,432124,809
- Table A2: Synthetic controls
  - Notes: The table lists the composition of the synthetic control for each HIPC country. The last two columns report the root mean square prediction error (RMSPE) of the outcome variable in the pre-treatment period (before the country-specific decision point) and the Fit Index, i.e., the ratio between the normalized RMSPE and the variance of the outcome variable in the pre-treatment period. The synthetic control estimates refer to those plotted in Figures 3 and 4.
  - CountryComposition of the Synthetic ControlRMSPEFit Index
  - BurundiAngola (26%), Buthan (25.5%), Kenya (9.6%), Papua New Guinea (19.3%), Vanuatu (19.6%)32.47430.0045
  - BeninAngola (10.1%), Bangladesh (8.1%), Kenya (4.7%), Lao PDR (12.3%), Lesotho (9.7%), Nepal (34.2%), Tonga (15.4%), Samoa (5.5%)18.95540.0048
  - Burkina FasoAngola (15.6%), Cabo Verde (21.8%), Lesotho (8.3%), Nepal (54.3%)69.20330.0041
  - BoliviaAngola (9.4%), Cabo Verde (2.8%), Kenya (0.1%), Lao PDR (1.9%), Lesotho (35.2%), Solomon Islands (18.5%), Tonga (32.1%)33.10990.0023
  - Central African Rep.Angola (3.0%), Kenya (53.8%), Nepal (21.6%), Papua New Guinea (19.8%), Vanuatu (1.7%)51.03620.0025
  - Cote d’IvoireAngola (14.6%), Fiji (23.0%), Nigeria (39.3%), Papua New Guinea (19.9%), Solomon Islands (2.7%), Tonga (0.1%), Vanuatu (0.2%), Samoa (0.1%)170.25470.0030
  - CameroonAngola (5.6%), Bangladesh (17.3%), Cabo Verde (8.0%), Nigeria (2.8%), Papua New Guinea (40.7%), Tonga (9.5%), Samoa (16.1%)16.42470.0038
  - Congo, Dem. Rep.Angola (0.4%), Lesotho (10.5%), Nigeria (42.0%), Nepal (3.5%), Papua New Guinea (39.0%), Solomon Islands (4.6%)76.17450.0031
  - Congo, Rep.Fiji (25.0%), Nigeria (65.7%), Solomon Islands (9.3%)72.07440.0122
  - ComorosAngola (3.7%), Bhutan (7.0%), Nigeria (30.0%), Solomon Islands (59.3%)18.08690.0015
  - EthiopiaAngola (4.2%), Bhutan (2.6%), Lesotho (7.5%), Nigeria (34.6%), Nepal (51.2%)59.15730.0040
  - GhanaAngola (2.4%), Bhutan (9.3%), Lao PDR (49.7%), Lesotho (19.7%), Nepal (14.3%), Samoa (4.3%)116.53060.0081
  - GuineaAngola (12.8%), Bhutan (19.2%), Lao PDR (1.7%), Lesotho (26.2%), Nigeria (4.9%), Nepal (25.0%), Samoa (10.2%)11.24320.0028
  - Gambia, TheBangladesh (11.2%), Fiji (23.3%), Kenya (11.9%), Lao PDR (11.8%), Pakistan (39.8%), Samoa (2.1%)58.84280.0048
  - Guinea-BissauLao PDR (78.5%), Samoa (21.5%)27.15660.0381
  - GuyanaLao PDR (28.2%), Samoa (71.8%)7.68200.0382
  - HondurasAngola (3.8%), Bhutan (13.9%), Cabo Verde (24.6%), Fiji (22.1%), Lesotho (0.6%), Nigeria (2.9%), Pakistan (1.0%), Samoa (31.1%)31.63680.0076
  - HaitiAngola (5%), Fiji (17.4%), Kenya (7.1%), Lesotho (31.1%), Nepal (33.1%), Tonga (4.9%), Vanuatu (1.4%)18.92440.0027
  - MadagascarAngola (12.3%), Bhutan (8.6%), Cabo Verde (20.8%), Lao PDR (15.8%), Nigeria (42.6%)120.48390.0058
  - MaliAngola (16.8%), Cabo Verde (14.4%), Lao PDR (14.9%), Lesotho (3.9%), Nigeria (0.9%), Nepal (49.0%)49.15930.0067
  - MauritaniaAngola (44.2%), Bhutan (4.7%), Cabo Verde (19.0%), Solomon Islands (6.0%), Tonga (26.1%)16.42840.0033
  - MalawiCabo Verde (24.2%), Lesotho (75.8%)142.09520.0182
  - NigerBangladesh (10.8%), Bhutan (9.2%), Cabo Verde (15.9%), Nigeria (12.5%), Nepal (32.1%), Solomon Islands (19.4%65.35430.0017
  - RwandaAngola (6.3%), Bhutan (12.1%), Cabo Verde (42.0%), Lao PDR (4.2%), Nepal (35.3%)235.32500.0051
  - SenegalAngola (5.1%), Fiji (16.5%), Lao PDR (1.5%), Lesotho (9.8%), Pakistan (39.4%), Papua New Guinea (9.0%), Solomon Islands (18.8%)106.84710.0024
  - Sierra LeoneBhutan (17.7%), Lao PDR (2.6%), Lesotho (79.7%)84.06690.0208
  - ChadLao PDR (22.9%), Lesotho (21.6%), Nepal (55.5%)265.33470.0097
  - TogoKenya (27.3%), Lesotho (14.6%), Nepal (57.1%), Papua New Guinea (1.1%)37.07850.0026
  - TanzaniaAngola (15.2%), Bhutan (15.1%), Lao PDR (1.5%), Lesotho (27.1%), Nigeria (31.0%), Papua New Guinea (0.3%), Vanuatu (5.3%), Samoa (4.3%)29.51600.0043
  - UgandaAngola (13.7%), Lesotho (37.2%), Nepal (35.0%), Samoa (14.1%)17.68290.0074
  - ZambiaAngola (41.7%), Cabo Verde (47.7%), Nigeria (0.6%), Solomon Islands (10.0%)55.88720.0122
- Figure A1: Net flows post HIPC initiative, by creditor type: In-space placebo
  - (a) Multilaterals(b) Paris Club
  - (c) China(d) Other bilaterals (non PC)
  - (e) China and other bilaterals (non PC)(f) Private creditors and bondholders
  - Notes: Each panel plots the difference between the actual net flows by a specific creditor and those of the respective synthetic control (gap). The in-space placebo considers as treated the 16 DSSI-eligible countries that are not eligible for the HIPC Initiative: Angola, Bangladesh, Bhutan, Cabo Verde, Fiji, Kenya, Lao PDR, Lesotho, Nepal, Nigeria, Pakistan, Papua New Guinea, Samoa, Solomon Islands, Tonga, and Vanuatu. The synthetic controls are built matching these treated countries with those in the donor pool (i.e. the original donor pool excluding the country which is treated) on past net flow and key macro characteristics, see Section 3. The solid line is the median value of the gaps in the sample; the dashed line is the average. The shaded area indicates the interquartile range. The vertical line corresponds to year 2000, the decision point for most HIPCs. Net flows are 3-year moving averages scaled by the recipient country’s GDP.
  - Sources: International Debt Statistics (The World Bank); World Development Indicators (The World Bank); and authors’ calculations.
- Figure A2: Robustness: Net flows post HIPC initiative, by creditor type, dropping macroeconomic controls
  - (a) Multilaterals(b) Paris Club
  - (c) China(d) Other bilaterals (non PC)
  - (e) China and other bilaterals (non PC)(f) Private creditors and bondholders
  - Notes: Each panel plots the difference between the actual net flows by a specific creditor and those of the respective synthetic control (gap). The synthetic controls are built matching treated countries with those in the donor pool only on past net flow, see Section 3. The solid line is the median value of the gaps in the sample of HIPCs; the dashed line is the average. The shaded area indicates the interquartile range. The vertical line indicates the country-specific HIPC decision point. Net flows are 3-year moving averages scaled by the recipient country’s GDP.
  - Sources: International Debt Statistics (The World Bank); World Development Indicators (The World Bank); and authors’ calculations.
- Figure A3: Robustness: Net flows post HIPC initiative, by creditor type, dropping small island states from the donor pool
  - (a) Multilaterals(b) Paris Club
  - (c) China(d) Other bilaterals (non PC)
  - (e) China and other bilaterals (non PC)(f) Private creditors and bondholders
  - Notes: Each panel plots the difference between the actual net flows by a specific creditor and those of the respective synthetic control (gap). The synthetic controls are built matching treated countries with those in the donor pool on past net flow and key macro characteristics, see Section 3. The donor pool excludes small island states. The sample of treated countries is made by 29 HIPCs, as the SCs for Burundi and Guinea-Bissau are not available with this sample. The solid line is the median value of the gaps in the sample of HIPCs; the dashed line is the average. The shaded area indicates the interquartile range. The vertical line indicates the country-specific HIPC decision point. Net flows are 3-year moving averages scaled by the recipient country’s GDP.
  - Sources: International Debt Statistics (The World Bank); World Development Indicators (The World Bank); and authors’ calculations.

_The HIPC Initiative and China’s Emergence as a Lender: post hoc or propter hoc? Working Paper No. WP/2025/033_

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_Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025033-print-pdf.pdf_