## wpiea2025124-print-pdf ## Source details **Canonical URL:** [wpiea2025124-print-pdf](https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025124-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2025/english/wpiea2025124-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2025/english/wpiea2025124-print-pdf.pdf.json) --- ### Executive Summary: role, benefits, and data priorities - Role and purpose of data collection in personal insolvency regimes: - Measure effectiveness and efficiency of the personal insolvency regime. - Gather information to inform social policy design and economic analysis. - Robust collection supports informed decision-making, identification of trends, fairness in application of laws, research on consumer behavior, and international benchmarking. - Stakeholder impacts and benefits of personal insolvency laws: - Creditors: value maximization and preservation of inter-creditor equity through collective action; reduced duplicated enforcement; bundling administrative enforcement expenses; potential greater recovery via limited future-income collection under payment plans. - Debtors: relief via discharge (a “fresh start” or “second chance”); increased productivity, peace of mind, reduced anxiety, and family benefits. - Broader economy/society: limit systemic effects of unregulated distressed debt; encourage responsible lending; foster entrepreneurship; act as a safety valve during economic stressors (inflation, rising interest rates, soaring energy costs) and past crises (US mortgage crisis and European crisis). - Key considerations when designing data systems: - Define clear objectives aligned with regime goals. - Ensure data privacy and protection; privacy and stigma concerns are “much more significant” than in commercial insolvency. - Standardize data elements and formats; minimize respondent burden. - Integrate collection into insolvency processes and forms. - Prefer ex ante design of data frameworks during law design; use reforms to introduce or overhaul systems. - Leverage digitalization to limit human error and improve storage, sharing, search, and review. - Effective implementation practices: - Assign responsibility to a relevant authority (national statistics agency, insolvency regulator, or courts). - Design user-friendly forms, pilot-test, implement strong data security, train staff, and regularly review processes. - Recognize data supports but does not substitute for expert, context-specific analysis. - Recommended data categories and indicators: - System efficiency: number of applications; rejection rates; reasons for rejection; processing times; costs. - Procedural outcomes: discharge rates; reasons for denial of discharge; plan completion rates. - Debtor demographics: age, gender, education, occupation, geographic location. - Financial information: types of creditors; debt composition; asset values; income levels. - Institutional performance: measures for courts and insolvency practitioners. - Analytical value and policy implications: - Systematic data collection addresses policy concerns that discharge harms financial system; aids risk pricing, trend identification, allocation of institutional resources, and empirical impact assessments. - “The cost of not developing data collection systems is far higher than the investment required; continuous data feeds a virtuous feedback loop into policy and legislative reform.” - Conclusion: - Implement comprehensive data collection systems to assess objectives, guide reforms, and ensure insolvency regimes remain responsive, effective, and fair amid evolving economic challenges. ### Section IV: benefits, risks, measurement challenges, and data design - Benefits of personal insolvency laws (three categories): - Creditors: - Value maximization; preservation of inter-creditor equity; reduction of duplicated enforcement; bundling administrative expenses; independent neutral administrators; ongoing monitoring. - Recovery improved via limited access to future debtor income under payment plans. - Debtors: - Relief for “honest but unfortunate” debtors via discharge; economic and intangible benefits (productivity, peace of mind); societal benefits (prevent homelessness, reduce welfare reliance). - Economy and society: - Forces realistic valuation of impaired claims; enables timely supervisory action; saves creditor time; encourages responsible lending; predictable burden sharing; addresses social costs of prolonged distress; promotes entrepreneurship. - Risks associated with personal insolvency laws: - Moral hazard: incentives for “strategic default”; exploitation of design flaws, weak enforcement, delays, low institutional capacity, or lack of debtor-asset information. - Debtor fraud: concealment of assets or income; misrepresentation of financial situation. - Stigma: deters eligible good-faith debtors from initiating proceedings. - Empirical evidence: incidence of moral hazard and fraud is difficult to study; many countries adopted regimes without detrimental effects on credit culture; empirical studies on regime introduction are sparse. - Challenges in measuring benefits and risks: - Creditor impacts easier to measure (time, cost, recovery indicators); debtor and societal benefits are harder and may materialize over time. - Recommended measurements: - Creditors: time, cost, recovery indicators adjusted for payment-plan execution and administrative costs. - Debtors: number receiving discharge; demographics; surveys capturing intangible benefits. - Economy/society: proxies such as changes in unsecured personal loan interest rates pre/post reforms or cross-country comparisons. - Data limitations: difficulty collecting long-term societal benefit data and controlling for macro confounders; scarcity of empirical data. - Designing data collection systems: - Foundational principles: careful design is critical to avoid discontinuities; personal insolvency data needs differ from corporate insolvency data. - Two key functions: measure regime effectiveness/efficiency (primary effectiveness indicator: “number of discharged individuals”) and gather information to inform social policy. - Implementation guidance: align data with legal design elements; consult stakeholders; cross-reference with other databases; consider modern techniques (web scraping). ### Safeguards, privacy, and ethical collection - Privacy considerations: - Data collection must protect confidentiality and privacy; privacy concerns are “much more significant” than in commercial insolvency. - Use robust data protection, anonymization, aggregation, and informed consent; ensure strong security to prevent breaches and identity theft. - Trade-offs and risks: - Even anonymized data can reveal group trends and potentially reinforce stigma or creditor bias. - Responsible data collection is preferable because it enables targeted, equitable social policies addressing financial distress. - Implementation safeguards: - Anonymization and aggregation processes that balance data accuracy with sensitivity (e.g., gender, race or ethnicity). - Clear communication and transparency about objectives of data collection. ### Scope and variability of reporting practices across countries - No uniform best practice; countries reflect local priorities. - Observations: - Judicial systems may collect more internal data than is published. - Notable examples: - China: progress in insolvency data systems though “does not have a personal insolvency regime in place.” - India: strong corporate insolvency statistics; minimal information on insolvency of natural persons. - United States: special report on fraud and procedural abuse (Congress-mandated); AOC reports include annual filings and separations of business and nonbusiness proceedings. - Colombia: publishes detailed demographics of insolvent individuals. - Many countries publish only case counts as the sole public statistic. ### Illustrative data elements and report categories - Thematic reporting tables (Annex I): - Table 1: Access to the personal insolvency regime. - Table 2: Performance metrics (duration, cost, discharges, repayment plans, overall repayment rates). - Table 3: Institutional performance (judiciary and insolvency representatives). - Table 4: Information on debtors. - Table 5: Economic analysis of personal insolvency data. - Representative U.S. AOC dataset fields: - Filing and closing; name; joint filing indicator; district and county; number of creditors; dollar value of priority, secured and unsecured claims; total value of assets before exemptions; discharge/dismissal indicator; amounts paid to creditors by class; administrative costs. ### Policy implications and recommendations - Data reports are a public good for policymakers, researchers, financial institutions, and the public. - Recommended practices: - Publish richer data beyond case counts where feasible. - Share data with researchers for empirical study. - Balance comprehensiveness with privacy: anonymize, aggregate, obtain informed consent, ensure security. - Provide contextual information with aggregated statistics to reduce misinterpretation and mitigate stigmatization. - Rationale: better-managed data enables comprehensive policy responses and targeted social measures for those in financial distress. ### Data collected on procedures: monitoring metrics and operational indicators - Importance of monitoring: - Basic metric: number of applications for relief (aggregate and by pathway). - Applications often rejected; connect application data with overindebtedness analyses by central banks or specialized institutions. - Application admission and rejection tracking: - Track submitted and rejected applications; reasons for rejection (substantive and non-substantive); non-substantive barriers may warrant advisory services. - Jurisdictional examples and numeric references: - After 15 years of tightly restricted access, Denmark reversed a presumption of ineligibility in 2005 to presume eligibility. - Poland relaxed criteria between 2009 and 2015 to allow entry to debtors whose insolvency was not the direct result of their own gross negligence. - Shenzhen, China: 2021 law originally charged judges with reviewing all applications; beginning in June 2022 a bankruptcy administration department began first-round review, which boosted accepted applications by nearly a factor of three. - Cost-barrier reforms: Germany 2001; Poland 2015; Slovakia and Austria 2017; Russia July 2020 (adopted and later expanded simplified discharge access). - Kazakhstan: new law requires no payment of any kind to creditors for 12 full months and makes out-of-court bankruptcy available only to debtors with no property of any kind (could throttle access). - Costs and access barriers to track: - Fees to lawyers/advisors and timing; judicial/administration fees; applications rejected due to insufficient asset value to cover administration. - Allocation of distributable value and administrator remuneration: - Track preferential claims, administrative fees, and the level of remuneration for administrators. - Caveat: reducing remuneration may harm recruitment or shift costs to debtors. - Family support budgets and operational costs: - Track reasonable living expenses for debtors and dependents; insufficient budgets have led to high failure rates and unequal regional allocations. - Reforms in Denmark, Germany, France, Netherlands, and Taiwan Province of China set uniform objective guidelines, often increasing allowances. - Institutional efficiency and timing metrics: - Track time to decide acceptance/rejection; time for each distinct stage; numbers of qualified judges and administrators; cases per judge/administrator. - Example: United States practice—debtor’s filing automatically considered an order for relief per 11 U.S.C. § 301(b). - Stage durations and process simplification: - For liquidation-and-discharge: monitor investigation, liquidation, distribution durations; restrict creditor-claim verification in no-value cases; revisit processes if investigation/liquidation periods exceed expectations. - Example: Dutch 2008 reform scrapped hearings to verify creditors’ claims except where distribution expected. - Output measures: - Track numbers and duration of earned-discharge proceedings; reasons for denial of discharge; payment-plan acceptance/rejection and termination reasons; plan completion rates. - Policy interpretation: high admission with low discharge rates indicates failing relief delivery; track and support plan viability or divert to more viable relief. - Jurisdictional examples: - Taiwan Province of China and Shenzhen: majority involve debtor-proposed plans many likely to fail. - Taiwan Province of China: courts denied some 90% of discharge applications under original 2008 act, prompting relaxation in 2012. - Singapore 2016 reform provided indicators showing more discharges expected without erosion of payment morality. - Creditor recovery and pathway conversions: - Track promised vs distributed value; original debt totals to compute dividend percentages. - Low distributions may indicate undue complexity, excessive administrative expense, or pathways producing insufficient returns. - Track conversions between pathways (e.g., plan to liquidation) and reasons to detect systematic inefficiencies. ### Data collected on users: applicant types, demographics, financials, and enforcement - Applicant type and initiating party: - Track who files (debtor, creditor, government agency). - Modern procedures are overwhelmingly debtor-initiated; divergence may indicate information gaps. - Track demographics by initiating party; monitor repeat filings (see notation: repeat filing indicator, footnote 55). - Repeat filings and “revolving door” effect: - Repeat filers may indicate deficient relief or persistent vulnerability. - Historical reforms motivated by repeat-filing data: French reforms 2004 and 2010; Swedish Enforcement Agency information campaign in 2013. - Debtor categories and classification difficulties: - Aim to categorize by debtor type (business, small business, individuals); distinguish sole proprietors where possible. - Caution: separating consumer vs entrepreneurial debt often impractical (UNCITRAL note referenced). - Demographic and spatial patterns to collect (anonymized and aggregated): - Age; gender; marital and family status/number of children; race/ethnicity/religion; occupation; highest education; place of residence. - Use: detect regional concentrations, disparities, and lifecycle patterns (e.g., student loan burdens on older debtors noted for the United States). - Cautions about self-reported reasons for insolvency: - Permit multiple responses; debtors may lack perspective or understate chronic factors; causes often external shocks (unemployment, health, divorce, childbirth). - Financial data to collect: - Types of creditors and claims (payday lenders, taxing authorities; secured, preferential, unsecured; principal, interest, penalties). - Aggregate debt per debtor; assets present and anticipated; income available for distribution; actual value collected from asset sales and future income. - If majority report “no assets,” inform system structure and potential simplification. - Fraud, moral hazard, and enforcement data: - Track fraud investigations, prominent fraud categories (undervaluation, concealment, fictitious debts), and convictions (noted as vanishingly few). ### Implementing the data collection system: authority, cooperation, and operations - Choice of supervision authority: - No uniform practice; possible institutions: national statistics agency; insolvency regulator (Australia, Chile, England, New Zealand); courts (Colombia, Japan, Portugal, US); commercial registry (Spain); Ministry of Economy and Finance (Greece); central bank (France). - Key selection factors: objective/purpose, data integrity/impartiality, legal requirements, accessibility/transparency, technical expertise, privacy/security compliance. - Priorities: accuracy, objectivity, transparency, alignment with goals. - Inter-agency cooperation and impediments: - Cooperation is common and often necessary; reliable transmission mechanisms matter. - Impediments: privacy/confidentiality laws, conflicting data-protection obligations, technical incompatibilities, resource constraints, and institutional control concerns. - Remedies: legislative reform for clear sharing frameworks; anonymization/aggregation; technical standardization; formal inter-agency agreements and funding mechanisms. - Design and implementation steps: - Define objectives; design templates anticipating data sources; pilot/test forms; ensure technical safeguards (encryption, validation); train staff; regularly update while preserving continuity; document methodology publicly (examples: Spain; England and Wales). - Data forms and collection considerations: - Embed collection in formal insolvency steps (applications, representative reports, case management systems). - Minimize collected data to necessary fields; standardize elements (drop-downs, checkboxes); validate responses; ensure compliance with privacy laws and informed consent. - Testing, security, and operational measures: - Pilots identify gaps and test security; encrypt sensitive data; online platforms (example: Greece) support validation and real-time storage. - Staff training for quality control and verification essential. - Data processing, analysis, and publication: - Clean and validate data; segment into relevant categories; use regression analysis, visualization, hypothesis testing. - Linkages with court operations enable diagnostics, error detection, simulation, and prediction. - Publication: regular reporting (common periodicities: quarterly, semi-annual, annual); timely updates; ad hoc/special reports for reforms; public accessibility to create a “public good”. - Policy recommendations (concluding themes): - Assess existing systems and design comprehensive mechanisms aligned with regime and social/economic objectives. - Allocate resources and invest in data infrastructure; leverage existing systems. - Collaborate with stakeholders and experts to ensure unbiased, actionable data. - “The cost of not developing these systems is far higher than the cost of creating them.”68 ### Annex I and Annex II: tables and country examples (summary) - Annex I tables summarize what jurisdictions report across themes: - Table 1: Access (debtor vs creditor applications; reporting frequencies: M, Q, A). - Table 2: Performance (cost, time, discharges, repayment plans, recovery rates). - Table 3: Institutional capacity (cases start/end, judges, insolvency representatives). - Table 4: Debtor information (age, gender, income, education, residence, repeat filing). - Table 5: Economic analysis (debtor assets, income, expenses, debts, creditor classes). - Table 6: Entities publishing data (mix of judiciary, statistical, regulatory, registries). - Selected country practices (Annex II highlights): - Australia (AFSA): monthly, quarterly, annual statistics; example: “in the report published in January 2023, it was apparent that 9 insolvency practitioners administer 80 percent of all active personal insolvencies.” - Canada: quarterly and annual reports by territory and proceeding class. - Chile: monthly/quarterly cases by type, geography, debtor gender. - Colombia (SICAAC): annual filings, duration and cost, outcomes, demographics. - European Union: Eurostat trends; Directive art. 29 requires annual data on discharge procedures with discretionary collection of some categories. - France (Banque de France): debt types, reasons, debtor income, number of discharged individuals and total discharged debt. - Germany (Destatis): cases, cases closed for lack of assets, concluded repayment plans, occasional economic data. - Greece: advanced IT infrastructure linked to credit bureau; real-time accessibility noted. - Japan: judiciary reports minimal court-focused personal insolvency statistics. - Netherlands: monthly series separating entrepreneurs and non-entrepreneurs; annual Monitor Wsnp evaluates restructuring. - New Zealand: monthly/quarterly/annual reports with infographics (e.g., village-of-a-hundred-people depiction). - Russia: bi-annual and annual court reports; Fedresurs collects quarterly information. - Spain: insolvency statistics moved to Commercial Registry in 2020; distinctions restored since 2023Q4. - UK (England and Wales): quarterly and annual reports with metrics per 10,000 people. - USA: Administrative Office of the U.S. Courts publishes detailed personal bankruptcy data; private providers offer granular analytics. - Reporting frequency labels preserved where cited: M= Monthly; Q=Quarterly; A=Annual. ### Annex III: sample simplified proposal for a data collection system (Central Asian example, 2022) - Overview: - Data can be collected by the central bank (economic variables) and by courts/administration (personal insolvency data). - Monitor changes in over-indebted households and control for economic context (interest rates, GDP, unemployment). - 1. Economic variables to monitor (central bank): - Access to credit: Volume of loans to consumers; Volume of mortgage loans; Volume of loans to individual entrepreneurs. - Interest rates: Interest rate for consumer loans; Interest rates for mortgage loans; Interest rates for loans to individual entrepreneurs. - Non-performing loans: NPLs for consumer loans; NPLs for mortgage loans; NPLs for loans to individual entrepreneurs. - 2. Data from the personal insolvency system (courts/administration): - a) Relative use: - Total number of cases; number per month/year; number per type of procedure (out of court bankruptcy, judicial bankruptcy, restoration of solvency procedure). - Number of cases per 100.000 inhabitants (per procedure and total). - Number of applications by debtors, creditors (by banks/other credit institutions), and government agencies (esp. tax authorities). - Number of dismissed petitions (by procedure, per month/year). - b) Efficiency metrics (per procedure: Out of court bankruptcy; Judicial bankruptcy; Restoration of solvency procedure): - Number of discharged individuals. - Time (total, and time per phase). - Costs of the procedure (average, median). - Amount of claims involved (total, average, median). - Amount recovered by creditors (total, average, median). - c) Termination grounds (annual): 1. restoration of solvency (spontaneously). 2. settlement agreement. 3. all unpaid claims deemed unfounded. 4. all creditors abandoned claims or demand for declaration of bankruptcy. 5. all claims paid. 6. absence of resources to cover administrative costs. 7. failure to nominate a trustee candidate. 8. other. - d) Discharge policy: - Number of discharged individuals (total and by procedure); average time to discharge; number of repeat applicants; number excluded from discharge; reasons for denial (criminal/admin conviction, failure to cooperate, unlawful behavior). - e) Debtors’ data: - Gender, profession, age, region (total and by procedure and year); reason for insolvency. *Source: Executive Summary; Section IV concludes; Sections 2–4; Annexes I–III, IMF Working Paper "Personal Insolvency and Data Collection Systems" (wpiea2025124-print-pdf).* ### Executive Summary ...................................................................................................... ### Executive Summary ### Role and purpose of data collection in personal insolvency regimes - Data collection is crucial for effective policy making, legislative reforms, and economic analysis of personal insolvency regimes. - Two primary functions of data collection: - Measuring the effectiveness and efficiency of the personal insolvency regime. - Gathering information for analysis and development of social policies. - Robust data collection enables: - Informed decision-making and evidence-based policy reforms. - Identification of trends and patterns in personal insolvency. - Ensuring fairness and equity in the application of insolvency laws. - Research and analysis of consumer behavior and economic trends. - International comparisons and benchmarking. ### Benefits of personal insolvency laws (stakeholder impacts) - Creditors: - Maximize value and preserve inter-creditor equity through collective action processes. - Debtors: - Relief and opportunity for a "fresh start" or "second chance" through debt discharge. - Broader economy and society: - Limit negative systemic effects of unregulated distressed debt. - Encourage responsible lending practices. - Foster entrepreneurship. - Personal insolvency frameworks contribute to financial stability amid economic stressors (inflation, rising interest rates, soaring energy costs) and past crises (US mortgage crisis and European crisis). ### Key considerations when designing a data collection system - Define clear objectives aligned with the goals of the personal insolvency regime. - Ensure data privacy and protection of sensitive personal information; stigma and privacy concerns are more significant than in commercial insolvency. - Standardize data elements and formats for consistency and ease of analysis. - Minimize burden on respondents while collecting necessary information. - Integrate data collection into existing insolvency processes and forms. - Prefer ex ante consideration of data frameworks during law design; use reforms as opportunities to introduce or overhaul data systems. - Leverage digitalization and technological tools to limit human errors and to store, share, search, and review data. ### Effective implementation practices - Assign responsibility to a relevant authority (examples include a national statistics agency, insolvency regulator, or the courts). - Design user-friendly data collection forms with clear instructions. - Conduct pilot tests to identify and address potential challenges. - Implement robust data security measures. - Provide training for staff involved in data collection and analysis. - Regularly review and update the data collection process. - Recognize that data supports analytical work but does not substitute for expert and independent analysis of context-specific circumstances. ### Recommended data categories and indicators to collect - System efficiency metrics: - Number of applications. - Rejection rates. - Reasons for rejection. - Processing times. - Costs. - Procedural outcomes: - Discharge rates. - Reasons for denial of discharge. - Plan completion rates. - Debtor demographics: - Age, gender, education, occupation, geographic location. - Financial information: - Types of creditors. - Debt composition. - Asset values. - Income levels. - Institutional performance metrics: - Measures for courts and insolvency practitioners to assess administrative efficiency. ### Analytical value and policy implications - Systematic collection and analysis of data: - Helps assuage policy maker concerns that personal insolvency laws harm the financial system by enabling discharge without full repayment. - Allows better risk pricing, identification of trends, and efficient allocation of institutional resources. - Enables impact assessments and legislative reforms based on empirical evidence rather than anecdote. - The cost of not developing data collection systems is higher than the investment required; continuous data feeds a virtuous feedback loop into policy and legislative reform. ### Conclusion - Implementing comprehensive data collection systems is essential to assess whether personal insolvency laws meet their objectives and to provide insights into broader economic and social trends. - Countries should prioritize assessing and improving personal insolvency data collection mechanisms so insolvency regimes remain responsive, effective, and fair amid evolving economic challenges. *Source: Executive Summary, IMF Working Paper "Personal Insolvency and Data Collection Systems" (Executive Summary section).* ### Section IV concludes. ### Section IV concludes ### Benefits of personal insolvency laws - Personal insolvency differs from corporate insolvency due to the human element: natural persons cannot be liquidated and must be offered incentives to continue productive activities; privacy and stigma are more acute concerns. - Personal insolvency procedures deliver three broad categories of benefits: - (i) Benefits for creditors: - Value maximization and preserving inter-creditor equity through collective action processes. - Reduction of duplicated enforcement efforts and avoidance of fire sales of the debtor’s assets. - Bundling administrative enforcement expenses into one proceeding, use of an independent neutral administrator, and ongoing monitoring of the debtor’s situation. - Creditors often gain greater recovery through limited access to future debtor income (notably wages and other earnings) under payment plans than through isolated enforcement of low-value claims. - The incentive of a “fresh start” or “second chance” motivates debtor productivity. - (ii) Benefits for debtors: - Procedures can provide relief for “honest but unfortunate” debtors via discharge of residual debts, enabling recovery from over-indebtedness. - Economic and intangible benefits of discharge: increased productivity, peace of mind, reduced suffering and anxiety, freedom from pursuit by creditors, and benefits to the debtor’s family. - Societal benefits include preventing homelessness and reducing reliance on state welfare or unemployment claims as discharged individuals return to productive activity and contribute to fiscal revenue. - (iii) Benefits to the economy and society: - Personal insolvency regimes influence risk-taking by borrowers and financial institutions; they limit systemic effects of unregulated distressed debt. - Key economy/society effects identified: - Forces realistic valuation of impaired claims by banks and regulated financial institutions, revealing hidden weaknesses and enabling timely supervisory actions. - Saves creditor time and offers swift relief where insolvent individuals have little or no assets. - Encourages more responsible lending by concentrating risks with lenders rather than externalizing costs. - Enables predictable burden sharing and fair distribution of costs among stakeholders. - Addresses social costs of prolonged debt distress (reduced dependence on state resources; restored tax and social-security contributions; increased consumption). - Promotes entrepreneurship by lowering barriers to risk-taking. - Functions as a safety valve to release pressure from excessive leverage and pent-up productivity. ### Risks associated with personal insolvency laws - Three critical risks that can hamper adoption or functioning: - Moral hazard: - Creation of incentives for debtors to act irresponsibly or seek insolvency relief when not insolvent (“strategic default”). - Exploitation of design flaws, weak enforcement, delays, low institutional capacity, high application volumes, or lack of information on debtor assets. - Debtor fraud: - Debtors may lie about financial situations or conceal assets or income to evade obligations. - Stigma: - Acts as a deterrent to initiating formal insolvency proceedings; eligible good-faith debtors may avoid or delay seeking relief even in jurisdictions with established frameworks. - Empirical evidence: - Moral hazard and fraud may be exaggerated as objections to debtor-friendly reforms; incidence is difficult to study because debtors conceal actions and detected cases are only a subset. - Many countries have adopted personal insolvency laws without detrimental effects on credit culture, but empirical studies on the introduction of personal insolvency regimes are sparse. - A correlation appears between development of consumer credit markets and active personal insolvency practice, suggesting reforms do not necessarily damage access to credit; more research is needed (see Annex III suggestion on economic indicators). ### Challenges in measuring benefits and risks - Measuring impacts is difficult, especially for debtor and societal benefits which often materialize over time; creditor-related measurements are more straightforward. - Relevant indicators and measurement approaches: - For creditors: time, cost, and recovery indicators (as in corporate insolvency) adjusted for execution of payment plans and administrative costs of payment-plan management. - For debtors: number of debtors receiving a “second chance” or discharge is straightforward; debtor demographics and surveys of discharged debtors can capture intangible benefits (productivity, peace of mind, reduced anxiety). - For economy and society: long-term effects are hard to isolate; possible measurable proxies include changes in interest rates for unsecured personal loans before and after reforms or cross-country comparisons of interest rates under different insolvency regimes. - Data limitations: - Difficulty in collecting long-term societal benefit data and controlling for macroeconomic confounders. - Scarcity of empirical data constrains robust research into reform impacts. ### Designing a data collection system for personal insolvency - Foundational principles: - Careful, conscientious design is critical; errors are costly due to discontinuities and incompleteness in statistical series. - Data collection systems should be integral to the personal insolvency regime and cannot simply be grafted from corporate/commercial insolvency systems because personal insolvency data needs differ. - Personal insolvency reforms are an opportunity to establish or overhaul data collection aligned with substantive and procedural law design. - Two key functions of data collection: - Measuring the effectiveness and efficiency of the personal insolvency regime: - Effectiveness: establish desired objectives; primary effectiveness indicator is the “number of discharged individuals.” - Efficiency: measure time, cost, and resources required to achieve regime goals. - Gathering information to inform social policy: - Systematic data on debtors (and creditors) illuminates root causes of financial distress and informs targeted policy responses. - Enables monitoring of inequalities in impact across demographic groups and regions; supports evidence-based social policies and efficient resource allocation. - Additional advantages of data collection: - Identifying trends and patterns to anticipate challenges (e.g., rising student loan-related insolvencies). - Ensuring fairness and equity by monitoring demographic disparities and preventing fraud through financial-data tracking. - Facilitating research on consumer behavior, financial literacy, sociology of insolvency, and broader social and economic trends. - Enabling international comparisons and benchmarking against practices elsewhere. - Implementation guidance: - Align data points with legal design elements (types of procedures, information included in applications or reports). - Consult stakeholders, relevant authorities, and experts during system design. - Cross-reference insolvency data with other databases (e.g., income, census, automobile ownership) and consider modern techniques (web scraping) to enrich analysis. *Source: wpiea2025124-print-pdf - Section IV concludes* ### 2022. This work required extensive analysis of source data and would have benefited greatly from public reports. ### Personal Insolvency and Data Collection Systems ### Safeguards, privacy, and ethical data collection - Data collection in personal insolvencies requires careful, ethical design to protect confidentiality and privacy; privacy concerns are “much more significant” than in commercial or corporate insolvencies. - Necessary safeguards and methods: - Robust data protection measures. - Anonymization and aggregation processes to reconcile data accuracy with sensitivity (e.g., gender, race or ethnicity of insolvent debtors). - Informed consent of affected persons, with clear communication and transparency about objectives of data collection. - Strong data security measures to prevent breaches and identity theft. - Risks and trade-offs: - Even anonymized and aggregated data can create or reinforce creditor or social bias through revealed group trends, leading to stigmatization. - Despite risks, responsible data collection is preferable because it can enable more effective and equitable social policies addressing needs of individuals facing financial distress. - Footnotes in the source: 29, 30, 31. ### Scope and variability of data reporting practices across countries - There is no single best practice or uniform guideline for contents of personal insolvency data reports; countries implement diverse reporting approaches that reflect local priorities and circumstances. - Observations on data availability and reporting: - Some authorities collect substantially more internal data (e.g., judiciary-managed systems) than is published. - Examples of national emphases: - China: notable progress in insolvency data collection systems, though it “does not have a personal insolvency regime in place.” - India: front-runner in statistical corporate insolvency reports; information on insolvency of natural persons is minimal. (Footnote 32 explains legal status.) - United States: produces a special report on fraud and procedural abuse in personal bankruptcy (Congress-mandated); Administrative Office of the U.S. Courts (AOC) reports include annual data on filings and separations of business and nonbusiness proceedings. - Colombia: publishes detailed information on demographics of insolvent individuals. - Many countries with personal insolvency regimes publish only the number of cases as the sole public statistic. ### Illustrative data elements and report categories - The source groups reporting practices into thematic tables (Annex I) and examples (Annex II). The thematic tables categorize reported aspects as: - Table 1: Access to the personal insolvency regime. - Table 2: Performance metrics of the personal insolvency regime (duration, cost, discharges, repayment plans, overall repayment rates). - Table 3: Institutional performance of the judiciary and insolvency representatives in personal insolvency cases. - Table 4: Information on debtors. - Table 5: Economic analysis of data in personal insolvency proceedings. - Example of detailed U.S. AOC dataset fields (representative fraction of personal insolvency cases): - Filing and closing. - Name of the person(s) filing. - Whether the filing was joint. - District and county of residence. - Number of creditors. - Dollar value of priority, secured and unsecured claims. - Total value of assets of the person filing before the bankruptcy exemption. - Whether a discharge or dismissal was issued. - How much (if anything) was paid to priority, secured and unsecured creditors. - Amount of various types of administrative costs incurred. (Footnote 33.) ### Policy implications and recommendations - Data reports on personal insolvency are a public good and valuable for policymakers, researchers, financial institutions, and the public. - Recommended practices and priorities: - Encourage authorities responsible for data collection to publish richer data beyond simple case counts where feasible. - Share data with researchers for empirical study to inform policy design and evaluation. - Balance comprehensiveness with privacy protections: collect sufficient information for assessment and reform design while implementing anonymization, aggregation, informed consent, and strong security. - Provide contextual information with aggregated statistics to reduce misinterpretation and mitigate stigmatization risks. - Rationale: improved and responsibly managed data collection enables more comprehensive policy responses and better-targeted social measures for individuals in financial distress. *Source: IMF Working Paper (excerpt). wpiea2025124-print-pdf - 2022.* ### 2. Data collected on procedures ### 2. Data collected on procedures ### Importance of monitoring and primary metrics - Monitoring the output and operation of a personal insolvency system is vital, especially after introduction of a new regime or new provisions. - The most basic data point: number of applications for relief, both in the aggregate and for each separate pathway (e.g., liquidation-and-discharge versus reorganization payment plan). - Applications do not always result in opened cases; historically the great bulk of such applications have been rejected in some jurisdictions, imposing significant expenditure of public resources at the application-and-admission stage. - To assess whether a system is attracting the proper level of debtor applications, connect application data with analyses of overindebtedness by central banks or specialized institutions. ### Application admission and rejection: detailed tracking - Track: - Total numbers of submitted and rejected applications. - Reasons for rejection, especially where a limited number of bases account for significant percentages of rejection. - Non-substantive reasons (e.g., failure to complete forms or submit proper paperwork) that hinder access and may warrant advisory services to assist debtors. - Substantive restrictions on access and whether criteria are being interpreted or applied in ways undesirable to lawmakers. - Jurisdictional examples and numeric references from the source: - After 15 years of tightly restricted access, Denmark reversed a presumption of ineligibility in 2005 to presume eligibility. - Poland relaxed criteria between 2009 and 2015 to allow entry to debtors whose insolvency was not the direct result of their own gross negligence. - Shenzhen, China: 2021 law originally charged judges with reviewing all applications; beginning in June 2022 a bankruptcy administration department began first-round review, which boosted accepted applications by nearly a factor of three. - Cost-barrier examples: easing of cost barriers in Germany in 2001 immediately resulted in a spike in admitted applications; similar reforms in Poland in 2015 and in Slovakia and Austria in 2017. Russia in July 2020 adopted and later expanded simplified discharge access to increase access for low-income debtors. - Kazakhstan example: new law imposes requirement of no payment of any kind to creditors for 12 full months and makes out-of-court bankruptcy available only to debtors with no property of any kind — provisions that could throttle access unless carefully tracked. ### Costs and access barriers - Track cost-related data: - Fees paid to lawyers or other advisors (and timing: before or as part of asset-collection). - Judicial and official access and administration fees (e.g., fees paid to an administrator/trustee). - Applications rejected or cases dismissed due to insufficiency of available or anticipated asset value to cover case administration expenses. - These cost barriers have frequently been among the greatest obstacles to relief delivery and have prompted policy responses when revealed by data. ### Allocation of distributable value and administrator remuneration - Where substantial distributable value exists, track allocation to avoid inefficiency and unfairness. - Data points to monitor: - Amounts allocated to preferential claims (e.g., government tax claims). - Administrative fees consuming available value. - Consideration of priority of government or other preferential claims and the proper level of remuneration for case administrators. - Caveat: reducing administrators’ remuneration may harm recruitment or lead administrators to impose costs on debtors through other avenues. ### Family support budgets and operational costs - Track the family support budget (reasonable living expenses for debtors and dependents) where income collection periods or repayment plans apply. - Insufficient reserve budgets have led to high failure rates and unequal allocations across regions; many systems revised allowances after data revealed inadequacies. - Jurisdictional examples: reforms in Denmark, Germany, France, the Netherlands, and Taiwan Province of China set uniform objective guidelines for minimum debtor budgets, often substantially increasing allowances over prior practice. ### Institutional efficiency and timing metrics - Key metrics: - Time taken to decide on acceptance or rejection of applications (delay between application and decision). - Time for each distinct stage of each type of proceeding. - Numbers of qualified judges and administrators/trustees willing to take on personal insolvency matters, both nationally and by district. - Observations: - Systems with simple entry criteria sometimes effect instantaneous admission (e.g., United States: debtor’s filing of a bankruptcy petition is automatically considered a Bankruptcy Court “order for relief.” 11 U.S.C. § 301(b)) but may rely on eventual dismissal for irregularities (which should be tracked). - If application consideration is numerous, nuanced, or complex, the application stage can be the most burdensome and time-consuming. - Tracking timing can reveal institutional shortcomings (e.g., that administrative agencies, not courts, should review applications, or that institutions are understaffed and need resources). - Track admission timing separately for different procedures (e.g., liquidation-and-discharge versus restructuring) to reveal disparities and inform resource allocation decisions. ### Stage durations and process simplification - For liquidation-and-discharge cases: - Monitor duration of each stage (investigation, liquidation, distribution). - If outcome is little or no value for creditors, extensive creditor-claim verification may be inefficient and could be restricted to cases with expected distributions. - If administrators’ investigation/liquidation/distribution periods exceed expectations, revisit the process for efficiency and harm reduction for creditors and debtors. - If law requires discharge application after liquidation/income collection, keep consideration of such applications within tight limits and monitor timing closely. - Example: Dutch 2008 reform scrapped hearings to verify creditors’ claims except where a distribution to creditors is expected; U.S. practice often discourages premature creditor filings unless distributable value appears. ### Output measures: discharge rates and plan success - Output data (debtors who ultimately receive discharge) are essential and often more variable and less vigilantly tracked than input data. - Track: - Numbers and duration of earned-discharge proceedings (especially where multiple timetables exist). - Reasons for denial of discharge. - Numbers of payment plans accepted and rejected by creditors and courts, with reasons for rejection and terms (including duration). - Plans terminated before successful completion and reasons for termination (failure to make payments likely most prominent). - Policy implications: - High admission rates with low ultimate discharge rates indicate failing relief delivery despite good access metrics. - Systems admitting many debtor-proposed payment plans that are prone to failure are not successful and should be modified to track and support plan viability or divert debtors to more viable relief (e.g., standardized disposable-income-collection periods). - Jurisdictional examples: - Taiwan Province of China and Shenzhen: overwhelming majority of cases involve debtor-proposed payment plans many of which appear likely to fail given mismatch between promises and resources. - Taiwan Province of China: courts denied some 90% of applications for discharge under the original 2008 consumer debt clearance act, prompting legislative relaxation in 2012. - Singapore 2016 reform provided indicators showing more discharges expected without a race to relief or erosion of payment morality in subsequent filing figures. ### Creditor recovery and conversions between pathways - Track creditor-focused metrics: - Amount of value promised to and ultimately distributed to creditors. - Aggregate amount of original debts to reveal percentage payment (dividend) delivered to creditors. - Interpretations of depressed distributions: - (1) Procedures may be unduly complex and should be simplified. - (2) Too much value may be diverted to administrative expense, prompting systemic simplification. - (3) A procedural pathway (e.g., formal investigation and liquidation) may produce insufficient returns to justify public resource expenditure. - For payment plans: - If successfully completed plans deliver only nominal returns, restrict access to that pathway unless more substantial dividends are expected. - Track conversions between pathways (e.g., payment plan to liquidation-and-discharge or vice versa) and reasons for conversion to detect unjustified preferences for one pathway that is frequently converted to another. *Source: IMF Working Paper — “2. Data collected on procedures”* ### 3. Data collected on users ### 3. Data collected on users ### Overview - Collecting data on users of the personal insolvency system complements operational data and can reveal broader societal or economic issues that warrant policy attention beyond case resolution. - User data should inform whether the system is being used as intended, identify vulnerable groups, and assess the effectiveness of relief mechanisms. ### Applicant type and initiating party - Track which party files for relief (debtor-initiated vs. creditor-initiated vs. government agencies such as tax or social benefit agencies). - Findings and implications to track: - Modern personal insolvency procedures are overwhelmingly invoked by debtors petitioning for their own relief; lack of such a pattern may indicate insufficient public information about the system. - A significant portion of applications filed by government agencies may prompt lawmakers to consider whether that use is appropriate or constructive. - Track demographic data separated by the party initiating the procedure to reveal patterns (e.g., if tax authorities are primary applicants for insolvency of small business entrepreneurs, this might indicate a need to better support such debtors). - Once established, the degree of repeat filing is an indicator of system effectiveness and should be specifically tracked.55 ### Repeat filings and “revolving door” effect - A significant presence of repeat filers may indicate: - Deficiency in the nature of relief offered, prompting consideration of more expansive and effective initial relief. - Alternatively, for long-term low-income debtors, repeat filing may reflect persistent vulnerability rather than system failure.56 - Historical reforms motivated by repeat-filing data: - French consumer overindebtedness reforms (2004 and 2010) empowered administrators to route hopelessly insolvent debtors to immediate liquidation and discharge and later to skip liquidation when unlikely to produce value.52 - Swedish Enforcement Agency in 2013 launched an information campaign when liberalized procedures had slower-than-expected uptake, leading to a sustained increase in applications.54 ### Debtor categories and difficulties of classification - Where possible, categorize applications/cases by debtor type: - Business entities, potentially a separate category for “small” business entities (however defined), and individuals. - Distinguish cases involving self-identified sole proprietors (or partners) from other individual cases. - Caution: - Distinguishing “pure” consumer debtors from individual entrepreneurs is fraught; individuals frequently substitute personal and business debt and UNCITRAL notes it may not always be possible to separate debts into clear categories.57 ### Demographic data and spatial patterns - Recommended demographic variables to collect (anonymized and aggregated): - Age, gender, marital and family status, race/ethnicity/religion, occupation, highest education level attained, place of residence. - Use of demographic data: - Concentration of cases in a region may indicate localized economic challenges or disparate treatment by regional courts/administrators, suggesting need for regulation to ensure greater equality of treatment.58 - Disproportionate representation of women, especially those with minor children, may indicate need for targeted social support. - Lifecycle patterns: while debtors often face challenges in mid-life, unusual concentrations at age distribution margins (e.g., older debtors burdened by student loans) may indicate specific problems (student loan burden on those over 50 in the United States noted as a growing concern).59 ### Cautions about self-reported reasons for insolvency - Treat reasons for insolvency with caution: - Insolvency is a complex phenomenon; questionnaires should permit multiple responses. - Debtors often lack the perspective or information to identify true causal factors; sophisticated marketing and lending practices can increase vulnerability without debtors’ awareness. - Debtors may over-emphasize recent salient events and understate chronic factors; presenting single-cause conclusions can be misleading. - Empirical tendency: - Causes of personal insolvency tend to be external interruptions or shocks (e.g., unemployment, health issues, divorce, childbirth) rather than exclusively debtor behavior. ### Financial data on debts, creditors, assets, and outcomes - Track creditor and claim composition: - Types of creditors (e.g., payday lenders, taxing authorities), types of claims (credit card bank, personal loan, utility arrearage). - Break down claims by secured, preferential, and unsecured status and by principal, interest, and penalty amounts to profile creditor exposure in personal insolvency cases.60 - Track debtor-level financials: - Aggregate debt burden per debtor to distinguish large-volume business debt from lower-volume consumer debt; prevalence of low-debt cases may counsel simplification or development of low-formality procedures. - Debtors’ assets, present and anticipated, and income available for distribution to creditors, even for cases not proceeding to relief, to determine whether the system treats genuine financial distress rather than offering an “easy way out.” - For cases that proceed, separately report actual value collected from asset sales and future income collection to assess: - Nature and degree of debtor distress. - Whether creditors receive meaningful dividends. - Whether administrative expense of procedures is justified by value collected and distributed. - If the majority of debtors report “no assets” available for distribution (as in many systems), this informs optimal system structure and assessment. ### Fraud, moral hazard, and enforcement data - Track instances of fraud investigations and pursued cases, including conclusions: - Report nature of fraud by prominent categories (e.g., undervaluation of assets, concealment of assets or income, inclusion of fictitious debts to related persons). - Report convictions (noted as vanishingly few) to inform public debate and assuage concerns about debtor fraud or system abuse. *Source: IMF WORKING PAPERS — Personal Insolvency and Data Collection Systems, Section 3. Data collected on users.* ### 4. Implementing the data collection system ### 4. Implementing the data collection system ### Choice of authority to supervise data collection - No uniform practice exists for assigning responsibility for personal insolvency data collection; assignments vary by country and purpose. - Examples of institutions assigned responsibility include: national statistics agency; insolvency regulator (Australia, Chile, England, New Zealand); courts (Colombia, Japan, Portugal, US); commercial registry (Spain); Ministry of Economy and Finance (Greece); central bank (France) (see Table 6). - Key factors determining the choice of authority: - Objective and purpose: entities best suited depend on whether the goal is to inform policy/regulation—insolvency regulators are often natural candidates.62 - Data integrity and impartiality: independent bodies like statistics offices are often preferable. - Legal requirements: statutory assignments may already exist. - Accessibility and transparency: consider mechanisms for public access. - Technical expertise: necessary for data collection, analysis, and reporting. - Privacy and security: authority must have strong privacy and security measures and comply with data protection rules and guidance by data protection authorities. - Ultimate priorities for selecting the publishing authority: accuracy, objectivity, transparency, and alignment with data collection goals. - Footnote context: different authorities may emphasize different data aspects (judicial authorities—stock and flow of cases; insolvency regulators—insolvency professionals; Ministries/registries/central bank—economic aspects).61 ### Inter-agency cooperation and impediments - Cooperation among authorities is common and often necessary because data origins can be judicial or administrative. - Reliable mechanisms for data transmission are more important than the original data source in determining responsibility. - Cross-stakeholder collaboration is important: court officials, practitioners, law experts (for application templates and metadata); statistical expertise for correct data treatment. - Main impediments to effective information sharing: - Legal and regulatory barriers: privacy legislation, confidentiality requirements, statutory restrictions. - Conflicting obligations under data protection laws, creating uncertainty about permissible uses/disclosures. - Technical incompatibilities: incompatible data formats, collection methodologies, security protocols. - Institutional factors: resource constraints, competing priorities, control concerns. - Remedies where impediments exist: - Legislative reform to establish clear legal frameworks for data sharing, including tailored exemptions/permissions within privacy laws. - Technical solutions: data anonymization and aggregation to preserve statistical value while mitigating privacy concerns. - Technical standardization: common data formats and secure transmission protocols. - Institutional reforms: formal inter-agency agreements, joint governance structures, and funding mechanisms to distribute costs. ### Design and implementation steps - Start with a clear definition of data collection objectives (section 3.1). - Design data templates that foresee data sources for report elaboration (sections 3.2 and 3.3). - Careful design facilitates swift adaptation after legal reforms. - Implementation sequence: - Design data collection forms and mechanisms. - Pilot/test forms and systems. - Ensure technical safeguards (encryption, validation, real-time storage where applicable). - Train staff responsible for collection and treatment to ensure quality control and data accuracy. - Regularly review and update collection forms to reflect legal or technological changes while preserving continuity of core data. - Document methodology and disclose it publicly to increase legitimacy and acceptance (examples: Spain; England and Wales). ### Data forms and collection considerations - Embed data collection in formal steps of insolvency proceedings where feasible (e.g., application forms, reports of insolvency representatives, court case management systems). - Minimize collected data to what is necessary to achieve objectives; avoid extraneous information to reduce burden and privacy risk. - Standardize data elements and formats using technical solutions (drop-down menus, checkboxes, predefined responses). - Use clear and simple language in forms intended for non-expert respondents; use inter-institutional forms for communication between institutions. - Adopt best practices: prioritize essential fields early, include validation checks, integrate guidance to improve responses. - Ensure compliance with privacy and data protection laws: obtain informed consent where required and anonymize data during collection. ### Testing, security, and operational measures - Pilots help identify feasibility, data gaps, and evaluate data security measures. - Protect sensitive data from unauthorized access and breaches; encrypt sensitive data. - Online platforms (example: Greece) enable advanced features such as data validation and real-time storage, reducing errors. - Staff training is critical for quality control, verification, and accurate data treatment. ### Data processing, analysis, and publication - After collection, perform data cleaning and validation to address inconsistencies and errors. - Segment data into relevant categories for personal insolvency reports; apply statistical methods to identify trends, correlations, and patterns. - Analytical tools suggested: regression analysis, data visualization, hypothesis testing. - Close linkage with court operations allows extended analytics (diagnosis, error detection, simulation, prediction). - Publication: - Publication of statistical reports is essential for transparency and public awareness of personal insolvency regimes. - Reports should be as comprehensive as possible to support analysis of the regime and related economic and social circumstances. - Periodicity considerations: regular reporting (common periodicities include quarterly semi-annual, and annual reports); timely updates; provisions for ad hoc or special reports (e.g., impact assessments for reforms). - Reports should be broadly accessible to the public while allowing for internal reports on especially sensitive matters. - Dissemination through government websites or public databases creates a “public good”.63 ### Policy implications and recommendations (conclusion themes) - Personal insolvency regimes require proper data collection systems to assess whether laws achieve their objectives. - Comprehensive data collection offers insights into causes of insolvency, effects of over-indebtedness, and supports targeted support measures. - Data-driven and evidence-based policy design depends on comprehensive personal insolvency datasets. - Countries should: - Assess existing data collection systems and design mechanisms that collect comprehensive data. - Allocate sufficient resources and invest in data infrastructure, leveraging existing systems where possible. - Align collected data with the purposes of the personal insolvency regime and broader social/economic policy objectives. - Collaborate with stakeholders and experts to ensure unbiased, actionable data collection mechanisms. - Investment in data collection systems is portrayed as necessary and cost-effective: “The cost of not developing these systems is far higher than the cost of creating them.”68 *IMF Working Paper — Section 4: Implementing the data collection system* ### Annex I. Information on Personal Insolvency in ### Annex I. Information on Personal Insolvency in Selected Sources ### Access to personal insolvency regime (Table 1 — summary observations) - Many jurisdictions report availability of both debtor and creditor applications; explicit entries indicating debtor vs creditor applications are present for: Armenia, Greece, Kazakhstan, Netherlands, Poland, Russia, Singapore, UK (England and Wales), USA, and others as shown in the table. - Reporting frequency labels used: M= Monthly; Q=Quarterly; A=Annual. - Some jurisdictions publish multiple frequencies (M, Q, A); examples explicitly noted in the table include Australia (M, Q, A), Chile (M, Q), Netherlands (A), New Zealand (M, Q, A), Poland (A), Russia (Q, A), Singapore (A), Spain (Q, A), USA (M, Q, A). - Rejection information: several jurisdictions report rejections with or without reasons. Entries explicitly indicate: - Armenia: rejections recorded (no reasons). - Greece: rejections recorded (no reasons). - Kazakhstan: implicit, no reasons. - Poland: rejections w/ reasons. - Russia: rejections recorded, no reasons. - Trends and real-time accessibility: Greece notes information is accessible in real time, even day by day. Netherlands notes trends broken down by court and percentage. - Processing time for acceptance and breakdown by procedures are reported in a subset of jurisdictions (table entries show such fields populated in multiple countries). ### Performance of the personal insolvency regime (Table 2 — summary observations) - Performance indicators captured variably across jurisdictions; key indicators in the table include: - Cost, Time to complete (each phase), Number of discharges, Denied discharges, Number of fraud cases, Repayment plans, Success repayment plans, Claim recovery (compared to total debt). - Examples of country-specific reporting detail from the table: - Chile: reports cost, time to complete (global), number of accepted plans. - Colombia: reports global measures (average length), concluded cases (A), numbers of new businesses; includes info on outcomes and average by creditor class. - EU: reports concluded cases and total amount of discharged debt; only number of concluded plans for repayment plans. - France: reports concluded cases and number of discharges (absolute and as a percentage of all cases); includes causes; reports average recovery rate. - Greece: reports average length, number of discharges; notes “[discharge is the rule by law – so; court decisions denying discharge]”. - Kazakhstan: reports fees, problems with completion/discharge and restructuring plans; average duration (A); breakdown; concluded cases and types of termination; duration in years; termination rate success. - Russia and Singapore: report numbers of restructuring cases in progress, completed, failed (Russia: M; Singapore: A). - USA: reports time to completion by days mean and median, by proceeding; includes number of modifications. - Data frequency tags (A, M, Q) appear in table cells for specific indicators (preserve as shown in the table). ### Institutional framework and capacity (Table 3 — summary observations) - Institutional metrics tracked in the table include: Number of cases – start of period, Number of cases – end period, Number of judges, Number of insolvency representatives, Cases/judge, Cases/insolvency administrator. - Select entries: - Greece: all institutional fields (start, end, judges, insolvency representatives, cases/judge, cases/insolvency administrator) have entries. - Netherlands: number of cases start/end, number of judges, number of insolvency representatives are recorded. - USA: number of cases start/end, number of judges, number of insolvency representatives are recorded. - Several jurisdictions show missing or partial institutional data in the table. ### Information on debtors (Table 4 — summary observations) - Debtor-level demographic and socioeconomic variables included in reporting across jurisdictions: Age, Gender, Family/marital status/number of children, Race/Ethnicity, Income, Education, Residence, Activity (business/non-business), Repeat filing. - Examples of coverage from the table: - Chile: reports Age; Gender; Residence. - Colombia: reports Age; Gender; Income; Education. - France: reports Gender; Residence (owners and renters). - Greece: reports Gender; Income; Education; Residence; Activity. - Netherlands and New Zealand: report Age, Gender, Family status, and other debtor attributes; New Zealand reports a Median value in the table. - UK (England and Wales) and USA: report multiple debtor attributes including income and education in some instances. - Notable methodological note in source text: In the USA, prior to 1980, joint filings were counted as two cases; subsequently joint filings are counted as separate cases. ### Economic analysis of data in personal insolvency proceedings (Table 5 — summary observations) - Economic variables tracked across jurisdictions include: Debtor assets, Debtors’ income, Debtors’ expenses, Debtors’ debts, Creditors, Reasons for insolvency. - Jurisdictional examples from the table: - Australia: reports debt levels and creditors; indicates coverage of debt levels and other economic indicators. - France: reports average and median debtor income; types of debt (mortgage, consumer, tax, etc.); reasons for over-indebtedness. - Germany: reports total debt. - Greece: reports debtor assets, income, expenses, debts. - Netherlands: reports median values for assets, income, expenses. - USA: reports real estate and personal property totals; total, average and median for income, expenses, debts; classes of claims (totals); identifies classes of creditors. - Russia: reports numbers per debt brackets. - Several jurisdictions do not report all economic variables; reporting is uneven across countries. ### Entities publishing personal insolvency data (Table 6 — summary observations) - The table lists entities and short descriptions of their roles or mandates in publishing personal insolvency statistics. Notable entries: - Armenia: Ministry of Justice — Government, in cooperation with the judiciary. - Australia: AFSA — Securities regulator, IP regulator. - Canada: Office of the Superintendent of Bankruptcy of Canada — Insolvency regulator. - Chile: Superintendency of Insolvency — Insolvency regulator. - Colombia: SICAAC — Personal insolvency ADR system under the Ministry of Justice. - EU: Eurostat (European Commission) — Statistical Office; monitor compliance with EU law, set secondary rules for member states. - France: Banque de France — Central Bank; active in administrative procedures for resolving over-indebtedness. - Germany: Destatis — Statistics agency. - Greece: General Secretariat for Financial Sector and Private Debt Management — Official Agency, MoF. - Japan: Judicial Council — Judiciary. - Kazakhstan: Ministry of Finance’s Committee on Government Revenue — Responsible for personal insolvency legislation; data reporting prepared by a private entity. - Netherlands: Legal Aid Board; CBS — Legal Aid entity ascribed to Ministry of Security and Justice, and Statistics Agency. - New Zealand: Insolvency and Trustee Service — Insolvency regulator. - Portugal: Ministry of Justice — Government. - Russia: Judicial Department of the Supreme Court; Fedresurs (Unified Federal Register) — Judiciary; Commercial Registry. - Singapore: Insolvency Office — Department of Ministry of Law, Government. - South Africa: Statistics South Africa; NCR — Statistical agency; Personal credit regulator. - Spain: Commercial Registry — Receives information from the courts; has replaced the statistical agency for insolvency statistics. - UK (England and Wales): Insolvency Service — Insolvency Regulator. - USA: Administrative Office of the U.S. Courts — Judiciary. - The table highlights a mix of judicial, statistical, regulatory, and registry entities responsible for data publication, with varying degrees of integration between court data and administrative or credit bureau data. ### Illustrative examples of data reporting practices (Annex II — selected country examples) - Australia (AFSA): - Publishes monthly, quarterly and annual statistics and regular analysis on personal insolvency. - Reports include number of commenced insolvency cases, trends, links to economic circumstances, levels of debt of filers, breakdown of business vs nonbusiness personal insolvencies, causes of insolvency in some reports, creditor information, and distribution of cases among insolvency professionals. - Example statistic cited in source: “in the report published in January 2023, it was apparent that 9 insolvency practitioners administer 80 percent of all active personal insolvencies.” - Canada: - Office of the Superintendent of Bankruptcy publishes quarterly and annual reports on consumer and business insolvencies with breakdowns by territory and class of proceedings (bankruptcies and proposals), and year-to-year comparisons including variation in percentage. - Chile: - Superintendency of Insolvency publishes monthly and quarterly reports with number of cases by proceeding type (liquidation or composition), geography, and debtor’s gender. - Colombia (SICAAC): - Publishes annual personal insolvency filings, distinguishes state vs private creditor cases, reports duration and cost (distinguishing free vs charged applications), outcomes (agreement achieved or not), and debtor demographics including gender, class, age and educational background (noting many debtors refuse to self-identify class). - European Union: - Eurostat publishes general trends and number of insolvency proceedings; Directive art. 29 of the EU restructuring and insolvency Directive requires annual data on number of debt discharge procedures applied for, opened, pending, or closed; average length of procedures; and information on outcomes. Collection of some categories (average cost, recovery rates, number of entrepreneurs launching new businesses after discharge) is discretionary for member states. EC is working on secondary legislation to specify scope and methodology. - France (Banque de France): - Administrative procedures and statistical reports analyze contributing factors (divorce, illness, unemployment), debtor age, owner vs renter status, income-generating capacity, average and median debt, nature of debt (mortgage, consumer, revolving loans, utilities, taxes), types of procedures and outcomes (number of discharged individuals and total discharged debt). - Germany (Destatis): - Produces series on number of insolvency cases, cases closed for lack of assets, number of concluded repayment plans; occasionally publishes economic data such as global value of claims, recovery rates, losses to creditors, and outcomes including number of discharges granted and refusals. - Greece: - Platform for negotiation of restructuring agreements collects large volume of data via advanced IT infrastructure connected to credit bureau and administration databases; published reports focus on system usage and performance; authorities are considering increasing compiled and published information and coordinating with judicial procedures. - Japan: - Judiciary’s annual report on judicial statistics includes number of personal insolvencies (minimal, court-activity focused); private actors (credit bureaus) supplement with data on nature of debt problems. - Netherlands: - Statistics Netherlands produces monthly series separating entrepreneurs and non-entrepreneurs; annual Monitor Wsnp (commissioned by Ministry of Justice) analyzes restructuring effectiveness, number and success rate of agreements, modalities, and effectiveness of debtor support programs; data collection integrated with legislative reforms and evaluation. - New Zealand: - Insolvency and Trustee Service provides monthly, quarterly and annual reports with filings for bankruptcy, DRO, no-asset procedures, distinguishes debtor vs creditor applications, analyzes demographics and publishes user-friendly infographics (e.g., village-of-a-hundred-people depiction). - Portugal: - Ministry of Justice publishes statistical reports including number of personal insolvency cases; main focus remains business/corporate insolvency. - Russia: - Supreme Court statistical service publishes bi-annual and annual reports detailing number of cases, admitted cases, rejections and reasons, stages of proceedings, outcomes (with/without discharge), distinction between individual entrepreneurs and other individuals, fraud cases, appeals and ancillary litigation; Commercial Registry (Fedresurs) collects quarterly information. - South Africa: - Statistics South Africa produces reports on number and trends of personal insolvencies; National Credit Regulator and National Treasury collect over-indebtedness and debt counseling information but have not regularly published statistics. - Spain: - Since 2020, insolvency statistics moved from national statistics agency to Commercial Registry; reports include number of filings and geographic distribution; distinction between individual entrepreneurs and consumers was unclear in 2022Q2–2023Q3 but restored since 2023Q4. - United Kingdom (England and Wales): - Insolvency Service releases quarterly and annual reports (with supplements for Scotland and Northern Ireland) including applications, breakdown by proceedings, causes of insolvency, regional variations, trends; reports use metric of number of insolvent individuals per 10,000 people and often include infographics. - United States: - Administrative Office of the U.S. Courts publishes annual reports on bankruptcy filings and outcomes; post-BAPCPA reports include detailed information on personal bankruptcy proceedings, including misconduct; private providers (e.g., Epiq analytics) offer interactive, granular analysis including duration by court and case-level modifications. *IMF Working Paper — Annex I. Information on Personal Insolvency in Selected Sources* ### Annex III. Sample of simplified proposal for data ### Annex III. Sample of simplified proposal for data collection system ### Overview - This sample corresponds to a proposal formulated to a Central Asian country in 2022, for the set-up of a data collection system in connection with a brand-new personal insolvency regime. - These data can be collected by the central bank (for economic variables) and by the courts and the administration in charge of operating the system (for personal insolvency system data). - Additional monitoring recommendations: - If there is any quantification or survey on over-indebted households, monitor any changes. - Control for economic context: evolution of general interest rates, GDP, and unemployment. ### 1. Economic variables to monitor (before and after the entry into force of the law) - These data can be collected by the central bank. a) Access to credit - Volume of loans to consumers - Volume of mortgage loans - Volume of loans to individual entrepreneurs b) Interest rates - Interest rate for consumer loans - Interest rates for mortgage loans - Interest rates for loans to individual entrepreneurs c) Non-performing loans - NPLs for consumer loans - NPLs for mortgage loans - NPLs for loans to individual entrepreneurs ### 2. Data from the personal insolvency system - The collection of data should be done by the courts and the administration in charge of operating the system. a) Relative use - Total number of cases, and number of cases per month/year. Number of cases per type of procedure (out of court bankruptcy, judicial bankruptcy, restoration of solvency procedure) (per month and per year). - Number of cases per 100.000 inhabitants (per procedure, and total) - Number of applications per type of procedure (out of court bankruptcy, judicial bankruptcy, restoration of solvency procedure) (per month and per year). - Number of applications by debtors, by creditors (of which by banks and other credit institutions) and by government agencies (esp. tax authorities) - Number of dismissed petitions (per type of procedure, and per month/year) b) Efficiency of the system - Table of metrics to collect for each procedure (Out of court bankruptcy; Judicial bankruptcy; Restoration of solvency procedure): - Number of discharged individuals - Time (total, and time spent in each phase of the process) - Costs of the procedure (average, median) - Amount of claims involved in procedures (total, average, median) - Amount recovered by creditors in each process (total, average, median) c) Termination - Grounds for termination of procedures (per procedure, on annual basis) 1. restoration of solvency (spontaneously—this is not an explicit goal of the Russian law) 2. settlement agreement 3. all unpaid claims deemed unfounded 4. all creditors abandoned claims or their demand for declaration of bankruptcy 5. all claims paid 6. absence of resources to cover administrative costs 7. failure to nominate a trustee candidate 8. other d) Discharge policy - Number of discharged individuals (total, and for each procedure) (total, and annual data) - Average time from start of the procedure until discharge, for each procedure - Number of persons starting an insolvency procedure who have also completed an insolvency procedure in the past (i.e., repeat applicants). - Number of debtors excluded from discharge - Reasons for denial of discharge: - criminal/admin conviction for bankruptcy crime - failure to cooperate with trustee/court - unlawful behavior by debtor in connection with claims e) Debtors’ data - Gender, profession, age, region (total, and for each procedure) (total, and annual data) - Reason for insolvency *Annex III. Sample of simplified proposal for data collection system (from the cited content).* --- _Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025124-print-pdf.pdf_