Abstract
Selected Issues
Household Indebtedness in China1
High household indebtedness could constrain consumption and real estate investment, and increase financial stability risks. In line with cross-country studies, we find that continued increase in household indebtedness could boost consumption in the short term, while reducing it in the medium-to-long term, and if left unaddressed, could undermine rebalancing and worsen external imbalances. Containing these risks would call for a strengthening of systemic risk assessment and macroprudential policies for the household sector. Other policies include improving the credit information system and establishing a well-functioning personal insolvency framework.
A. Context
1. Household debt has been rising rapidly in China since the global financial crisis… As of June 2018, total debt of Chinese households stood at over 50.3 percent of GDP, above the emerging market average, and 32 percentage points of GDP higher than in 2008. Part of that buildup can be attributed to rapid financial market development in China and improving financial inclusion, which, combined with rising incomes, increased availability of credit to households. However, the speed with which households accumulated leverage was the highest among all BIS-reporting countries since the global financial crisis, raising concerns whether further debt increases could lead to significant adverse effects on growth and financial stability (IMF, 2017a).
Large increase in China’s household debt-to-GDP ratio
(In percent)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: BIS and IMF staff calculations.Large increase in China’s household debt-to-GDP ratio
(In percent)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: BIS and IMF staff calculations.Large increase in China’s household debt-to-GDP ratio
(In percent)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: BIS and IMF staff calculations.
Household debt-to-GDP ratio
(Change since 2009 Q1, in percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: BIS and IMF staff calculations.Household debt-to-GDP ratio
(Change since 2009 Q1, in percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: BIS and IMF staff calculations.Household debt-to-GDP ratio
(Change since 2009 Q1, in percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: BIS and IMF staff calculations.2. … mostly due to increased mortgage lending. At the end of 2018, housing-related debt (mortgage loans and Housing Provident Fund lending) accounted for just below two-thirds of all household debt, a 7-percentage point (ppt) increase since 2013, while the remainder was almost evenly split between consumption loans (including credit card debt) and loans extended to households for commercial purposes (mostly SMEs). Most mortgages are extended on fixed-term rates, with maturity of about 10–20 years, and a minimum down payment of 20 (30) percent for the first (second) home that can be adjusted upwards by regional authorities. Mortgage rates are usually set at a discount/premium of 0–15 percent below/above the central banks’ benchmark lending rate, with the adjustment depending on market conditions (Ding et al., 2017).
Composition of Household Debt, Q4 2018
(Share in total, percent)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CEIC; HPF data for 2017; and IMF staff calculations.Composition of Household Debt, Q4 2018
(Share in total, percent)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CEIC; HPF data for 2017; and IMF staff calculations.Composition of Household Debt, Q4 2018
(Share in total, percent)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CEIC; HPF data for 2017; and IMF staff calculations.
Growth Rates of Consumer Loans by Type
(Percent, y/y)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: Haver Analytics; CEIC; and Fund staff calculations.Growth Rates of Consumer Loans by Type
(Percent, y/y)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: Haver Analytics; CEIC; and Fund staff calculations.Growth Rates of Consumer Loans by Type
(Percent, y/y)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: Haver Analytics; CEIC; and Fund staff calculations.3. Household indebteness varies significantly by province… Households in the richest, coastal provinces are the most indebted. In some provinces, debt has already risen above 60 percent of GDP—a threshold above which futher increases in debt can adversely impact consumption growth according to some studies (Lombardi et al., 2017). In addition, debt-to-income (DTI) ratios increased significantly in some of the poorer provinces.
Change in Household Indebtedness v.s. GDP Per Capita
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: WIND; CEIC; and IMF staff calculations.Change in Household Indebtedness v.s. GDP Per Capita
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: WIND; CEIC; and IMF staff calculations.Change in Household Indebtedness v.s. GDP Per Capita
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: WIND; CEIC; and IMF staff calculations.
China: Household Debt-to-GDP
(In percent; end-2018)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
China: Household Debt-to-GDP
(In percent; end-2018)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
China: Household Debt-to-GDP
(In percent; end-2018)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
4. …as well as income levels. Household survey data indicate that housing-related assets and debt are the main components of household balance sheets (Figure 1).2 On average, housing assets constitute around 60 percent of all household assets, though this ratio has declined over time for richer households as new investment opportunities became available. At the same time, as demand for home financing grew, mortgage debt became the main household liability.3 The riskiness of household debt increased as well: between 2010 and 2016, the share of debt held by highly indebted households—with a DTI ratio above 4—increased from around a quarter to almost half. Most of that debt, however, is held by richer households. Nevertheless, the increase in DTI ratio among the lower income households was quite substantial as well, reaching almost 600 percent. However, mortgage participation rate (share of households that have mortgages in the survey data), which increased from 8 to 18 percent between in 2010 and 2016, is still relatively low and serves as a mitigating factor.
Household Assets, Liabilities, and Debt Burden
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CFPS surveys 2010–16; and IMF staff calculations.1/ Share of households that have mortgages in total number of households in the survey data.Household Assets, Liabilities, and Debt Burden
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CFPS surveys 2010–16; and IMF staff calculations.1/ Share of households that have mortgages in total number of households in the survey data.Household Assets, Liabilities, and Debt Burden
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CFPS surveys 2010–16; and IMF staff calculations.1/ Share of households that have mortgages in total number of households in the survey data.B. Macro-Financial Risks from High Household Debt
5. Higher household debt could increase financial stability risks. With high indebtedness, households are more vulnerable to adverse shocks, which could force households to deleverage abruptly with a significant macro-financial impact. Given that more than half of household debt in China is mortgages, deleveraging could constrain housing demand growth, putting pressure on house price growth and the financial soundness of property developers. Indeed, house price growth seems to be negatively correlated with lagged household indebtedness. Severe house price corrections could further reduce banks’ financial soundness through lower collateral valuation (real estate collateral for loans), and may also increase funding pressure for property developers, further increasing risks for lending banks. Higher household debt may thus increase the probability of banking distress, and worsen any subsequent recessions— research has shown that recessions preceded by housing busts are longer and more severe (IMF, 2012). A mitigating factor for China is that most of the mortgages are first-home mortgages which are less prone to defaults. In fact, more than 93 percent of mortgages outstanding were first-home mortgages as of end-March 2019.
2018 House Price Growth v.s. 2017 Household Debt/GDP
(Provincial data)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CEIC; and Fund staff calculations.2018 House Price Growth v.s. 2017 Household Debt/GDP
(Provincial data)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CEIC; and Fund staff calculations.2018 House Price Growth v.s. 2017 Household Debt/GDP
(Provincial data)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: CEIC; and Fund staff calculations.6. Higher household borrowing could also increase macroeconomic risks. Cross-country studies indicate that higher household debt may lead to lower GDP growth (Mian et al., 2017; Jorda et al., 2016). Higher debt repayments could constrain future consumption growth. Real estate investment may also be affected as housing demand slows, property developers face funding pressures, and banks are more reluctant to lend due to higher household credit risk. Lower fiscal revenues and public support of home ownership also create risks for the fiscal sector. These in turn could contribute to excess savings and growing external imbalances.
Macro-Financial Risks from Elevated Household Indebtedness
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff.Macro-Financial Risks from Elevated Household Indebtedness
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff.Macro-Financial Risks from Elevated Household Indebtedness
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff.7. In particular, higher household debt could increase consumption and GDP growth in the short term but reduce them in the longer term. IMF (2017a) found that there is a trade-off between the short-term benefits of rising household debt and medium-term costs to growth. Using cross-country panel data, Lombardi et al. (2017) found that household debt increases consumption and GDP growth within one year but reduces them in the long run, with the negative long-run effects intensified when household debt/GDP ratio exceeds certain thresholds (60 percent for effects on consumption growth). Tian et al. (2018), using provincial data for China, found that high household debt in certain provinces has been associated with lower consumption growth. Higher household debt could also affect the income elasticity of consumption, particularly when facing a negative income shock (Nakajima, 2018; Baker, 2014). Given the importance of sustainable consumption growth in China’s rebalancing process, this paper focuses on the impact of higher household indebtedness on consumption growth in China.
C. Impact of High Household Indebtedness on Consumption
8. We use panel regressions with both micro-level household survey data and macro-level provincial data to estimate the impact of higher household indebtedness on consumption growth (Annex I). The CFPS survey data allow us to compute two widely used measures of household indebtedness—debt/disposable income (DTI) ratio and debt/asset ratio. The database also has detailed information about household expenditure, disposable income, and demographic characteristics. The quarterly provincial data span from 2015Q1 to 2018Q4 and cover 24 provinces. Debt/GDP ratio and DTI ratio are used as household indebtedness measures in the province-level regressions.4
9. Regression results suggest that an increase in household indebtedness is associated with higher contemporaneous consumption growth but lower consumption growth two years later (Annex II). Households with higher DTI or debt/asset ratios experienced higher consumption growth in the same year but lower consumption growth two years later. In particular, a 100-percent increase in DTI ratio is associated with a 3.5-ppt increase in contemporaneous consumption growth but a 4.3-ppt decline in consumption growth two years later.5 Similar results are observed with the debt/asset ratio. Provincial regression results also find the lagged negative effects of household indebtedness on consumption growth, although the contemporaneous effects are not statistically significant.
Impact of Household Indebtedness on Consumption Growth
(In percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff estimates.Impact of Household Indebtedness on Consumption Growth
(In percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff estimates.Impact of Household Indebtedness on Consumption Growth
(In percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff estimates.
Income Elasticity of Consumption
(In percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff estimates.Note: The chart shows the impact of a 1-ppt increase in real income growth on real consumption growth based on estimates from household survey regressions and provincial regressions. Solid bars mean that the impact is statistically significant at the 95-percent confidence level.Income Elasticity of Consumption
(In percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff estimates.Note: The chart shows the impact of a 1-ppt increase in real income growth on real consumption growth based on estimates from household survey regressions and provincial regressions. Solid bars mean that the impact is statistically significant at the 95-percent confidence level.Income Elasticity of Consumption
(In percentage points)
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF staff estimates.Note: The chart shows the impact of a 1-ppt increase in real income growth on real consumption growth based on estimates from household survey regressions and provincial regressions. Solid bars mean that the impact is statistically significant at the 95-percent confidence level.10. When household indebtedness exceeds a certain threshold, higher income growth does not lead to higher consumption growth (Annex II). Specifically, threshold panel regressions suggest that, when lagged DTI ratio is below a threshold of 6.3, a 1-ppt increase in real income growth could boost consumption growth by 0.1 ppt. However, the estimate is not statistically significant if DTI ratio is above that threshold (about 10 percent of households in the sample).
11. To better illustrate the impact of household indebtedness on consumption growth, we simulate the path of consumption growth under two scenarios with different dynamics of household indebtedness.6 In line with staff’s baseline projections for real GDP growth, we assume that real income growth slows gradually to 5.6 percent by 2023 in the two scenarios. In scenario 1, the DTI ratio is assumed to increase at the average pace of last five years while remaining at the end-2018 level in scenario 2. Simulations suggest that rising household debt could reduce annual consumption growth from nearly 7 percent in 2017 to less than 5 percent by 2030. While consumption growth in scenario 1 is higher in the first few years due to the higher borrowing, it turns lower than scenario 2 over the medium-to-long term. If the household debt growth is between scenario 1 and scenario 2, then the projected consumption growth would also lie between the two scenarios. Moreover, if household debt can be maintained at the current level, consumption growth could stabilize at a rate higher than the baseline income or GDP growth rate, facilitating further internal rebalancing towards consumption.
Illustrative Scenario Simulations
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: National Bureau of Statistics; WIND; CEIC; IMF’s World Economic Outlook database; and IMF staff estimates.Illustrative Scenario Simulations
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: National Bureau of Statistics; WIND; CEIC; IMF’s World Economic Outlook database; and IMF staff estimates.Illustrative Scenario Simulations
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Sources: National Bureau of Statistics; WIND; CEIC; IMF’s World Economic Outlook database; and IMF staff estimates.D. International Experience: Measures to Contain Household Vulnerabilities
12. International experience suggests that improved data quality and strong policy and institutional frameworks can help counteract the negative effects of higher household debt. Accurate and comprehensive indebtedness measures could help better monitor household vulnerabilities. Cross-country studies have also shown that both demand-side macroprudential measures—such as limits on the debt-service-to-income (DSTI) and loan-to-value (LTV) ratios—and supply-side measures targeted at loans—such as limits on bank credit growth and loan loss provisions—tend to be highly effective in mitigating the negative effects of household debt on consumption and growth. Better financial sector regulations and institutions play an important role: countries with stricter banking sector supervision and more capitalized banking systems are more able to withstand higher levels of household debt. The existence of credit registries can also reduce the risk of a crisis as it is associated with greater overall financial sector transparency, and can help detect the problems early (IMF, 2017a).
13. Macroprudential measures currently in place in China appear broadly comparable to other countries. The maximum LTV ratio for first-home buyers in China stands at 70 percent for cities with home purchase restrictions (restrictions on who can buy a house in the city and how many houses people can buy) and 80 percent for cities without purchase restrictions. However, provinces can independently reduce that ratio—in some it is as low as 60 percent, which is comparable to other jurisdictions with high household debt (e.g., Norway). The current regulation in China requires a borrower’s monthly DSTI ratio to be less than 55 percent (with a cap on mortgage-service-to-income ratio of 50 percent), which is relatively high compared to the international norm of 30–50 percent. However, the absence of comprehensive credit registry hinders lenders’ ability to properly evaluate household’s total liabilities. China has not put in place supplemental capital requirements of different risk weights on household lending.
Macroprudential Measures: International Comparison 1/
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF’s Annual Macroprudential Policy Survey.1/ There are no LTV limits for Australia, the U.S., the U.K., and Thailand. For China, the average LTV limit on first-home buyers is shown. For Hong Kong SAR, the highest LTV ratio of 60 percent is shown.2/ Fiscal measures to cintain systemic risk , including non-resident tax, deed tax, and others, are in place.3/ Capital requirements on household sector loans in place.4/ Norway has a total DTI ratio requirement in place: a maximum of 5.Macroprudential Measures: International Comparison 1/
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF’s Annual Macroprudential Policy Survey.1/ There are no LTV limits for Australia, the U.S., the U.K., and Thailand. For China, the average LTV limit on first-home buyers is shown. For Hong Kong SAR, the highest LTV ratio of 60 percent is shown.2/ Fiscal measures to cintain systemic risk , including non-resident tax, deed tax, and others, are in place.3/ Capital requirements on household sector loans in place.4/ Norway has a total DTI ratio requirement in place: a maximum of 5.Macroprudential Measures: International Comparison 1/
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Source: IMF’s Annual Macroprudential Policy Survey.1/ There are no LTV limits for Australia, the U.S., the U.K., and Thailand. For China, the average LTV limit on first-home buyers is shown. For Hong Kong SAR, the highest LTV ratio of 60 percent is shown.2/ Fiscal measures to cintain systemic risk , including non-resident tax, deed tax, and others, are in place.3/ Capital requirements on household sector loans in place.4/ Norway has a total DTI ratio requirement in place: a maximum of 5.E. Policy Implications
14. Systemic risk assessment of the household sector should be strengthened and extended beyond mortgages. Given that non-housing loans account for about half of household debt for lower-income households (Figure 1), the authorities should step up efforts to collect and process data beyond the aggregate credit and housing market indicators. This could include having in place a wide range of household vulnerability indicators, such as leverage (e.g., consistent and comprehensive DSTI and DTI ratios), liquidity (average maturity of household loans or assets by type of loan or asset), composition of assets and liabilities, and interconnectedness with other sectors (such as banks and non-bank financial sector). The systemic risk monitoring framework should also take into account the distributional aspects of household debt by, for example, including the share of “debt at risk” and the share of “risky” borrowers (for example, the Bank of England monitors the share of households with high DSTI or loan-to-income ratios; see BoE, 2018). Interagency information and data sharing should also be strengthened, as recommended by the 2017 FSAP (IMF, 2017b).
15. The macroprudential policy toolkit should be strengthened. International experience suggests that demand-side macroprudential measures and supply-side measures targeted at loans are typically effective in mitigating the negative effects of household debt on consumption and growth. However, household debt in China still grew rapidly in the last three years (by around 20–30 percent per year) despite the existing LTV and DSTI limits.7 The DSTI caps should be adjusted to the international norm of 30–50 percent and extended to other types of household loans including those from non-bank financial institutions. Stress testing of household DSTI ratio to interest rate and income shocks can also be used to gauge the potential risks in adverse scenarios. Sectoral capital requirements on banks’ exposures to the real estate sector—a supply-side measure—may also help, but should be treated with caution, as they may result in leakages where loans are provided by non-banks and are often less effective in constraining credit growth than demand-side tools (Ding, et al. 2017).
16. Combining different tools and increasing the scope of macroprudential policy may enhance policy effectiveness and reduce the leakages from any single measure. More active use of DSTI limits backed by comprehensive analyses could enhance the effectiveness of LTV limits by restricting the use of short-term consumption loans for housing down payment. In this context, all leveraged providers of credit should be included in the purview of macroprudential policy. Otherwise, there is a risk that credit provision will migrate from banks to less-constrained nonbanks (Jacome and Nier, 2011). Given the importance of mortgages in household debt and the specific land ownership structure in China, land policies should also be used to increase effective housing supply and promote a transparent and efficient secondary market for land transactions (Box 1).
17. China should also consider developing a legal framework for personal insolvency, while also ensuring effective debt enforcement of commercial claims. The Enterprise Bankruptcy Law that came into effect in 2007 only applies to companies and not to individual debtors.8 Chinese law includes provisions that allow certain protections for the debtor against enforcement by creditors.9 However, these mechanisms are not well suited for situations where the debtor may have multiple creditors. Without a well-functioning personal insolvency regime, debtors cannot get a “fresh start” from over-indebtedness and may have to cut consumption more substantially to repay debt. As access to credit continues to increase in China, consideration should be given to developing a personal insolvency framework that would help address inevitable situations of over-indebtedness while mitigating moral hazard (Box 2). Effective debt enforcement, including mortgage enforcement, helps facilitate access to credit and allows banks to recover assets in the event of default.
18. A comprehensive credit information system should be developed as a prerequisite for strong policy frameworks. The quality and scope of the credit registry should be strengthened in line with the 2017 FSAP recommendation—by capturing all individual debt obligations including those from nonbanks (such as P2P lenders) and other service providers (e.g., utilities and telecommunications). A comprehensive credit information system would help lenders better assess the credit risk of borrowers and allow policy makers to better monitor and assess financial stability risks of the system. It is also important for bankruptcy prevention and help ensure the effective functioning of the personal insolvency regime (Box 2).
19. Financial sector supervision and consumer protection should be strengthened. Micro-prudential supervision with stronger supervisory powers or more stringent capital regulation frameworks would allow China to better contain any negative effects of rapidly rising household debt on macroeconomic and financial stability. Measures to strengthen consumer financial protection—including, for example, expanding financial education, increasing the transparency of financial contracts, and regulating certain financial innovation products—would help unsophisticated consumers make wiser finance decisions and enhance overall financial stability (IMF, 2017a).
Land Policies and House Prices in China
House prices in China are mainly affected by four types of policies—monetary (mainly interest rates and credit volume), macroprudential (mainly down payment requirements), tax, and land policies. Land policies are China-specific and are not seen as a major policy tool to contain rapidly rising house prices in international peers. This box aims to explain the main land policies in China and how they tend to affect house prices.
In China, land policies affect house prices mainly through the volume and composition of land supply.
In China, land is owed not privately but by the state (urban land) or by rural collectives (rural land). Land-use rights are privately owned, but those for residential use are only valid for 70 years. Higher land supply, without significant land hoarding by developers, should increase housing supply and alleviate the upward pressure on house prices. The composition of land supply, e.g., for residential, or commercial, or office use, could also affect residential housing supply and house prices.
Different types of land have different pricing schemes, leading to potential arbitrage opportunities. Residential land is further divided into four types of residential housing, i.e., small- and medium-sized apartments, villa and deluxe apartments, ‘economic’ houses (subsidized houses), and others. Current land policies aim to increase land supply for small- and medium-sized apartments while restricting the supply for villas and deluxe apartments, which should benefit general population. At the same time, increases in land supply for economic houses may also limit land supply for other houses, leading to higher market-determined house prices.
Administrative measures such as restrictions on floor area ratios (defined as the ratio of a building’s total floor area to the size of the land upon which it is built) could also be used to guide land supply towards small- and medium-sized apartments. For example, the floor area ratios in Shenzhen were recently raised in the future planning of the Great Bay Area to facilitate higher-floor buildings.
Illustration of Land Policy Transmission in China
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Illustration of Land Policy Transmission in China
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Illustration of Land Policy Transmission in China
Citation: IMF Staff Country Reports 2019, 274; 10.5089/9781513511252.002.A002
Land policies should aim to reduce land hoarding and enhance the market mechanism in the secondary land transaction market. Land hoarding by property developers and significant supply-side distortions—such as local governments’ control over land supply and high reliance on land sales to finance spending—render China’s property market susceptible to both price misalignment and overbuilding.1 In this context, policies to contain land hoarding including penalties for developers could be enforced more strictly, while alternative sources for local government financing such as property taxes should be adopted. The recent relaxation of Hukou requirements in smaller cities and allowing some collectively-owned rural land to be transacted in the land market (without having to be purchased by the state first) are a step in the right direction. There may also still be room for raising floor area ratios in Tier 1 cities (less than 2 in 2010), compared to New York, Singapore, and Seoul which were mostly above 3 in the same period according to CICC estimates.2 These policies may help promote market mechanisms in land price formulation and increase housing supply.
1/ Land hoarding refers to the practice of acquiring land and deliberately holding it and waiting for prices to increase in the future before selling it for a higher return.2/ Floor area ratio is the ratio of a building’s total floor area to the size of the land upon which it is built.Is It Time for China to Develop a Personal Insolvency Framework?
An effective personal insolvency regime is an important part of the toolkit to address excessive personal debt. While ex-ante prevention of excessive debt accumulation by households should be the first line of defense, personal insolvency is a tool that helps address the consequences of over-indebtedness by establishing rules and procedures for fair burden sharing between debtors and creditors. Unlike individual enforcement/collection proceedings against a debtor, personal insolvency is a collective proceeding with participation of multiple creditors. In essence, it allows honest debtors to get a “fresh start”, thus allowing the debtor to return to economic activity. In some countries, personal insolvency regime covers also individual entrepreneurs and unincorporated micro and small enterprises, thus seeking to reduce the stigma of business failure and promoting entrepreneurship (Bergthaler, et al. 2015).
Over the past decade many countries have reformed their insolvency frameworks. Whereas the Asian crisis in 1997 spurred corporate insolvency reforms in the region, the global financial crisis catalyzed a reassessment of insolvency frameworks in many parts of the world, especially in European countries. The reforms, aimed at strengthening legal and institutional frameworks for corporate debt restructuring and establishing new regimes for personal insolvency, were often part of broader strategies for dealing with high non-performing loans (NPLs) that were weakening countries’ financial sectors (Aiyar, et al. 2015). Several Asian countries have personal insolvency frameworks, and some of them are considering the introduction of relevant reforms.1
The adoption of a personal insolvency law would be an important step. Unlike in the area of corporate insolvency, there are no international standards for personal insolvency, but cross-country experience and careful attention to the circumstances of China could guide the design of such a framework (Insolvency and Creditor/Debtor Regimes Task Force, 2014). As a general matter, the law—which could apply to individual debtors, including individual entrepreneurs and unincorporated enterprises—should establish procedures that allow insolvent debtors with sufficient repayment capacity the opportunity to restructure their debt (including secured debt) over a period of time by making partial payments to creditors, while providing a swift discharge from debt (a “fresh start”) after liquidation of the debtors’ assets for honest debtors with no repayment capacity. Given the novelty of personal insolvency in China, the law should be developed in broad consultation with all interested stakeholders. In addition to developing a personal insolvency law, consideration should be given to developing out-of-court mechanisms to facilitate resolution of personal debt distress. Cross-country experience demonstrates that such mechanisms, e.g., debt counseling, Financial Ombudsmen, mediation, arbitration, can be a useful supplement to formal personal insolvency procedures.
Important conditions will have to be in place to ensure effective implementation of the new framework. Legislative reforms should go hand-in-hand with putting in place and/or strengthening institutional arrangements that would help ensure the effective functioning of the personal insolvency regime. Those include but are not limited to: (i) an effective system of registration and enforcement of secured transactions, including mortgages; (ii) a well-functioning system of disclosure and verification of information about the debtor’s financial situation, (iii) establishment of a community of qualified and regulated insolvency practitioners who can help insolvent debtors to navigate the insolvency process, including the preparation of a repayment plan to restructure debt, and (iv) increasing judicial knowledge of and competency in handling insolvency matters, including by continuing with the development of specialized expertise in courts. Consideration can also be given to establishing an agency (or assigning this function to one of the existing agencies) to provide advice to debtors facing financial difficulties. More would have to be done to explain the concept of personal insolvency to the Chinese society to allow it to gain acceptance as one of the standard tools of a market economy.
1/ Japan and Korea have experience with personal insolvency laws which are widely used. There are also personal insolvency regimes in the Philippines, Thailand, Singapore, and Malaysia. The comprehensive reform of personal bankruptcy in India introduced in 2016 is pending implementation.References
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Baker, S., 2013, “Debt and the Consumption Response to Household Income Shocks,” Stanford University.
Bergthaler, W., Kang, K., Liu, Y., and D. Monaghan, 2015b, “Tackling Small and Medium Sized Enterprise Problem Loans in Europe,” IMF Staff Discussion Note 15/04, International Monetary Fund, Washington, DC.
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Annex I. Design of Panel Regressions
Effects of Household Debt on Consumption Growth
1. Two panel regression models are estimated to explore the trade-off between positive short-term and negative longer-term effects. The first model focuses on lagged effects of household indebtedness on consumption growth:
where Δci,t and Δyi,t are household (real) expenditure growth and (real) disposable income growth, HDi,t-1 is a measure of household indebtedness with a two-year lag (given that the CFPS survey was conducted every two years), Δyi,t * HDi,t-1 is an interaction term between income growth and lagged household indebtedness, Xi,t is a vector of household characteristics, and αt is the household fixed effect1 For a household with income growth Ay, the impact of lagged household indebtedness on consumption growth is β2 + β3Δyi,ty. For a household with a lagged household indebtedness measure of HD, the income elasticity is β1 + β3HD. Hence, β3 measures the impact of household indebtedness on income elasticity. To estimate the short-term effects, we estimate a similar panel regression model but with household indebtedness in the same year:
Threshold Effects of Household Debt on Income Elasticity
2. When household debt exceeds a certain threshold, higher income growth may not lead to higher consumption growth as households suffer from a higher debt repayment burden. This is because households may choose to cut consumption growth to a minimum level and use the rest o their income to pay back the higher debt. This may be particularly true in countries without a well-established household bankruptcy regime such as China, as those households could not choose to default and have a “fresh start”.
3. We estimate the following threshold panel regression model to examine the threshold effects of household debt on income elasticity:
where:
When household indebtedness is lower (or higher) than the threshold HD*, we would expect a positive (or zero) income elasticity, i.e., β1 > 0 (or β2, = 0).
4. We also estimate similar panel regression models with provincial data at the macro level. In particular, we estimate a province-level panel regression model similar to equation (1) but with different control variables, mainly province-level macroeconomic variables including change in lending rate, house price growth, public consumption growth, fixed-asset investment growth, and (national) stock price growth (GFSR, 2017).2 We also use dynamic panel models to control for the effects of lagged dependent variable due to consumption inertia. Finally, we estimate a similar threshold panel regression model to examine the threshold effect of household debt on income elasticity at the macro level. The models are estimated with a quarterly database spanning from 2015Q1 to 218Q4 and covering 24 provinces for which all the variables are available, and with one-year lagged household indebtedness measures (i.e., debt/GDP ratio and DTI ratio).
Annex II. Panel Regression Results
Household-Level Panel Regression Results
Household-Level Panel Regression Results
| Dependent variable | Real expenditure growth Δci,t | |||
|---|---|---|---|---|
| Lagged or contemporaneous HD | Lagged HD | Contemporaneous HD | ||
| Household indebtedness measure (HD) | Debt/disposa ble income | Debt/tot al asset (%) | Debt/disposa ble income | Debt/tot al asset (%) |
| Real income growth Δyi,t | 0.13*** (0.02) | 0.12*** (0.02) | 0.11*** (0.02) | 0.08*** (0.02) |
| HD | -4.39*** (1.09) | -0.51*** (0.12) | 3.54*** (0.68) | 0.33*** (0.12) |
| Δyi,t * HD | -0.0002 (0.01) | 0.002*** (0.001) | 0.01** (0.003) | -0.001 (0.001) |
| Change in family Size | 4.99*** (1.71) | 4.91*** (1.78) | 5.02*** (1.70) | 6.69*** (1.88) |
| Change in household head marital status | -9.90*** (3.74) | -9.11** (4.31) | -6.61** (3.22) | -5.19 (3.60) |
| Change in household head education level | 3.38** (1.62) | 4.06** (1.81) | 2.86** (1.34) | 4.36*** (1.47) |
| Constant | 26.64*** (2.76) | 31.74*** (3.25) | 35.90*** (2.88) | 41.70*** (3.46) |
| Household fixed effects | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes |
| # obs. | 9,059 | 8,559 | 8,990 | 8,434 |
| # households | 6,365 | 6,112 | 6,199 | 5,905 |
| Overall-R2 | 0.04 | 0.03 | 0.03 | 0.02 |
Household-Level Panel Regression Results
| Dependent variable | Real expenditure growth Δci,t | |||
|---|---|---|---|---|
| Lagged or contemporaneous HD | Lagged HD | Contemporaneous HD | ||
| Household indebtedness measure (HD) | Debt/disposa ble income | Debt/tot al asset (%) | Debt/disposa ble income | Debt/tot al asset (%) |
| Real income growth Δyi,t | 0.13*** (0.02) | 0.12*** (0.02) | 0.11*** (0.02) | 0.08*** (0.02) |
| HD | -4.39*** (1.09) | -0.51*** (0.12) | 3.54*** (0.68) | 0.33*** (0.12) |
| Δyi,t * HD | -0.0002 (0.01) | 0.002*** (0.001) | 0.01** (0.003) | -0.001 (0.001) |
| Change in family Size | 4.99*** (1.71) | 4.91*** (1.78) | 5.02*** (1.70) | 6.69*** (1.88) |
| Change in household head marital status | -9.90*** (3.74) | -9.11** (4.31) | -6.61** (3.22) | -5.19 (3.60) |
| Change in household head education level | 3.38** (1.62) | 4.06** (1.81) | 2.86** (1.34) | 4.36*** (1.47) |
| Constant | 26.64*** (2.76) | 31.74*** (3.25) | 35.90*** (2.88) | 41.70*** (3.46) |
| Household fixed effects | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes |
| # obs. | 9,059 | 8,559 | 8,990 | 8,434 |
| # households | 6,365 | 6,112 | 6,199 | 5,905 |
| Overall-R2 | 0.04 | 0.03 | 0.03 | 0.02 |
Household-Level Threshold Panel Regression Results
Household-Level Threshold Panel Regression Results
| Dependent variable | Real expenditure growth Δci,t | |
|---|---|---|
| Household indebtedness measure (HD) | Debt/disposable income | Debt/asset (%) |
| Real income growth Δyi,t if HDi,t-1 < HD* | 0.14*** (0.03) | 0.62*** (0.09) |
| Real income growthΔyi,t if HDi,t-1 ≥ HD* | -0.04 (0.07) | 0.03 (0.02) |
| Change in family Size | 1.98 (2.38) | 3.15 (2.76) |
| Change in household head marital status | -10.50* (6.03) | -12.08 (8.33) |
| Change in household head education level | 1.10 (2.47) | 0.89 (2.76) |
| Constant | 28.03*** (3.44) | 33.77*** (3.65) |
| Household fixed effects | Yes | Yes |
| Year fixed effects | Yes | Yes |
| # obs. | 1,689 | 1,455 |
| # households | 563 | 485 |
| Overall-R2 | 0.03 | 0.05 |
| Estimated threshold HD* | 6.3 | 2.4% |
Household-Level Threshold Panel Regression Results
| Dependent variable | Real expenditure growth Δci,t | |
|---|---|---|
| Household indebtedness measure (HD) | Debt/disposable income | Debt/asset (%) |
| Real income growth Δyi,t if HDi,t-1 < HD* | 0.14*** (0.03) | 0.62*** (0.09) |
| Real income growthΔyi,t if HDi,t-1 ≥ HD* | -0.04 (0.07) | 0.03 (0.02) |
| Change in family Size | 1.98 (2.38) | 3.15 (2.76) |
| Change in household head marital status | -10.50* (6.03) | -12.08 (8.33) |
| Change in household head education level | 1.10 (2.47) | 0.89 (2.76) |
| Constant | 28.03*** (3.44) | 33.77*** (3.65) |
| Household fixed effects | Yes | Yes |
| Year fixed effects | Yes | Yes |
| # obs. | 1,689 | 1,455 |
| # households | 563 | 485 |
| Overall-R2 | 0.03 | 0.05 |
| Estimated threshold HD* | 6.3 | 2.4% |
Province-Level Panel Regression Results
Province-Level Panel Regression Results
| Dependent variable | Real retail sales growth (qoq) Δci,t | |||
|---|---|---|---|---|
| Household indebtedness measure (HD) | Debt/GDP (%) | Debt/disposable income (%) | ||
| Estimation method | Fixed-effects with Driscoll-Kraay s.e. | Dynamic panel GMM | Fixed-effects with Driscoll-Kraay s.e. | Dynamics panel GMM |
| Δci,t-1 | — | -0.25** (0.10) | — | -0.26** (0.09) |
| Real income growth Δyi,t | 0.20*** (0.06) | 0.23** (0.10) | 0.13* (0.07) | 0.20* (0.12) |
| HDi,t-4 | -0.10** (0.04) | -0.12** (0.05) | -0.05** (0.02) | -0.08*** (0.02) |
| Δyi,t * HDi,t-4 | -0.002 (0.001) | -0.003 (0.002) | -0.0001 (0.001) | -0.001 (0.001) |
| Real house price growth (lagged) | 0.06** (0.02) | -0.04 (0.03) | 0.06** (0.02) | -0.03 (0.03) |
| Real stock price growth (lagged) | -0.02 (0.02) | 0.004 (0.04) | -0.02 (0.02) | -0.01 (0.05) |
| Lending rate (lagged) | -1.34 (1.02) | 6.42 (3.99) | -1.41 (1.11) | 5.90 (3.92) |
| Real public consumption growth (lagged) | 0.03 (0.14) | -0.22 (0.19) | 0.05 (0.15) | -0.19 (0.19) |
| Real fixed asset investment growth (lagged) | 0.02*** (0.004) | 0.02 (0.02) | 0.02*** (0.005) | 0.02 (0.02) |
| # obs. | 288 | 240 | 288 | 240 |
| # provinces | 24 | 24 | 24 | 24 |
Province-Level Panel Regression Results
| Dependent variable | Real retail sales growth (qoq) Δci,t | |||
|---|---|---|---|---|
| Household indebtedness measure (HD) | Debt/GDP (%) | Debt/disposable income (%) | ||
| Estimation method | Fixed-effects with Driscoll-Kraay s.e. | Dynamic panel GMM | Fixed-effects with Driscoll-Kraay s.e. | Dynamics panel GMM |
| Δci,t-1 | — | -0.25** (0.10) | — | -0.26** (0.09) |
| Real income growth Δyi,t | 0.20*** (0.06) | 0.23** (0.10) | 0.13* (0.07) | 0.20* (0.12) |
| HDi,t-4 | -0.10** (0.04) | -0.12** (0.05) | -0.05** (0.02) | -0.08*** (0.02) |
| Δyi,t * HDi,t-4 | -0.002 (0.001) | -0.003 (0.002) | -0.0001 (0.001) | -0.001 (0.001) |
| Real house price growth (lagged) | 0.06** (0.02) | -0.04 (0.03) | 0.06** (0.02) | -0.03 (0.03) |
| Real stock price growth (lagged) | -0.02 (0.02) | 0.004 (0.04) | -0.02 (0.02) | -0.01 (0.05) |
| Lending rate (lagged) | -1.34 (1.02) | 6.42 (3.99) | -1.41 (1.11) | 5.90 (3.92) |
| Real public consumption growth (lagged) | 0.03 (0.14) | -0.22 (0.19) | 0.05 (0.15) | -0.19 (0.19) |
| Real fixed asset investment growth (lagged) | 0.02*** (0.004) | 0.02 (0.02) | 0.02*** (0.005) | 0.02 (0.02) |
| # obs. | 288 | 240 | 288 | 240 |
| # provinces | 24 | 24 | 24 | 24 |
Province-Level Threshold Panel Regression Results
Province-Level Threshold Panel Regression Results
| Dependent variable | Real retail sales growth (qoq) Δci,t | |
|---|---|---|
| Household indebtedness measure (HD) | Debt/GDP | Debt/disposable income (%) |
| Estimation method | Fixed-effects threshold | Fixed-effects threshold |
| Real income growth Δyi,t if HDi,t-4 < HD* | 0.17*** (0.05) | 0.13** (0.06) |
| Real income growth Δyi,t if HDi,t-4 ≥ HD* | -0.02 (0.07) | 0.03 (0.09) |
| Lending rate (lagged) | -2.29 (2.04) | -1.60 (2.27) |
| Real house price growth (lagged) | 0.05 (0.04) | 0.06 (0.04) |
| Real stock price growth (lagged) | -0.02 (0.04) | -0.01 (0.04) |
| Real public consumption growth (lagged) | -0.01 (0.21) | 0.02 (0.22) |
| Real fixed asset investment growth (lagged) | 0.02 (0.01) | 0.02 (0.01) |
| Constant | 12.73*** (1.23) | 12.79*** (1.30) |
| # obs. | 288 | 288 |
| # groups | 24 | 24 |
| Estimated thresholdHD* | 49% | 131% |
Province-Level Threshold Panel Regression Results
| Dependent variable | Real retail sales growth (qoq) Δci,t | |
|---|---|---|
| Household indebtedness measure (HD) | Debt/GDP | Debt/disposable income (%) |
| Estimation method | Fixed-effects threshold | Fixed-effects threshold |
| Real income growth Δyi,t if HDi,t-4 < HD* | 0.17*** (0.05) | 0.13** (0.06) |
| Real income growth Δyi,t if HDi,t-4 ≥ HD* | -0.02 (0.07) | 0.03 (0.09) |
| Lending rate (lagged) | -2.29 (2.04) | -1.60 (2.27) |
| Real house price growth (lagged) | 0.05 (0.04) | 0.06 (0.04) |
| Real stock price growth (lagged) | -0.02 (0.04) | -0.01 (0.04) |
| Real public consumption growth (lagged) | -0.01 (0.21) | 0.02 (0.22) |
| Real fixed asset investment growth (lagged) | 0.02 (0.01) | 0.02 (0.01) |
| Constant | 12.73*** (1.23) | 12.79*** (1.30) |
| # obs. | 288 | 288 |
| # groups | 24 | 24 |
| Estimated thresholdHD* | 49% | 131% |
Prepared by Fei Han, Emilia M. Jurzyk, Wei Guo, Yun He, and Nadia Rendak.
The China Family Panel Studies (CFPS) provide a household survey database with detailed debt and asset information of surveyed households every two years during 2010–16.
The share of mortgages in total household debt could be even higher than shown in Figure 1, as some households took short-term loans for mortgage down payments.
Independent variables include (province-level) house price growth, change in lending rate, public consumption growth, fixed-asset investment growth, and (national) stock price growth. Some other macroeconomic variables (such as unemployment rate) are also considered in the regressions but do not alter the main results.
These numbers are normalized to the impact on national consumption growth in the text figure using standard deviations of consumption growth and indebtedness measures in the CFPS survey and national accounts.
Provincial regression estimates with DTI ratio are used in the simulations. For contemporaneous effects, we use the normalized impact estimates from survey regressions with DTI ratio. Simulation results using the estimates with debt/GDP ratio are very similar.
For example, some home buyers use financial innovation (e.g., P2P lending) to borrow money for mortgage down payments, circumventing LTV and DTSI limits, although the authorities are trying to crack down on such practices (IMF, 2017a).
The introduction of a personal insolvency law was considered in the past but no law has yet been enacted.
For example, courts can allow debtors to pay their obligation in installments (Article 108 of the General Principles of the Civil Law of the People’s Republic of China). There are some other protections for debtors built into the law, e.g., allowing debtors to keep certain minimum assets to take care of themselves and their families. Also, some local courts are reportedly developing tools that would allow debt write-off for debtors with virtually no assets.
Household characteristics include changes in household size and the household head’s marital and education level. Adding more household characteristics (e.g., household location) does not alter the main results.
Some other macroeconomic variables (such as unemployment rate) are also considered in the regression but do not alter the main results.
