Abstract
Selected Issues
Inequality in China – Trends, Drivers and Policy Remedies1
China has grown rapidly and is on the brink of eradicating poverty. However, income inequality increased sharply from the early 1980s. While less equality is to be expected in the transition from central planning to a market-based economy China is now among the most unequal countries in the world, despite a recent modest improvement.
Inequality has been driven by structural factors (especially demographics, the urban/rural divide and education/skills), with little offset from fiscal policies. These structural factors are likely to drive inequality higher.
This calls for more proactive use of fiscal policies to reduce inequality. On the revenue side: (1) increasing the progressivity of social security contributions and of personal and property taxes. On the spending side: (2) boosting social spending and promoting equal access across provinces and regardless of residency.
A. Introduction
1. Over the past two decades, China has seen a sharp reduction of poverty, but also a substantial increase of inequality. China’s rapid economic growth since 1990 had lifted an estimated 731 million people out of poverty by 2013 (based on povcalnet data). But at the same time, the benefits of growth have accrued disproportionally to higher-income groups, resulting in a large increase in income inequality (which appears to have peaked around 2008). This is of concern as there is growing evidence that elevated levels of inequality are harmful for the pace and sustainability of growth.
B. What Is the Current State of Inequality? How Has it Evolved Over Time?
2. China moved from being a moderately unequal country in 1990 to being one of the most unequal countries. Income inequality in China today, as measured by the Gini coefficient2, is among the highest in the world. The Standardized World Income Inequality Database (SWIID) estimates the Net Gini3 coefficient for China at 50 points as of 2013, which is above various regional averages and the highest in Asia. Furthermore, the Gini coefficient has rapidly increased over the last two decades, by a total of about 10 to 15 Gini points since 1990. National data sources suggest that the increase in income inequality dates as far back as the beginning of the 1980s, with recent observations pointing toward a decline since 2008. The recent decline in inequality is welcome and policy efforts should be intensified to continue combating inequality.
Regional Comparison of Income Inequality Levels
(Net Gini Index; in Gini points; year of 2015 (or latest available); average across the region)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: SWIID Version 5.1; IMF, and IMF staff calculations.Note: ASEAN = Association of Southeast Asian nations; LIC = low-income county; NIE = newly industrialized economy; OECD = Organization for Economic Cooperation and DevelopmentRegional Comparison of Income Inequality Levels
(Net Gini Index; in Gini points; year of 2015 (or latest available); average across the region)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: SWIID Version 5.1; IMF, and IMF staff calculations.Note: ASEAN = Association of Southeast Asian nations; LIC = low-income county; NIE = newly industrialized economy; OECD = Organization for Economic Cooperation and DevelopmentRegional Comparison of Income Inequality Levels
(Net Gini Index; in Gini points; year of 2015 (or latest available); average across the region)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: SWIID Version 5.1; IMF, and IMF staff calculations.Note: ASEAN = Association of Southeast Asian nations; LIC = low-income county; NIE = newly industrialized economy; OECD = Organization for Economic Cooperation and Development
China’s Gini Coefficient, 1981–2016
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: Zhuang and Shi (2016).China’s Gini Coefficient, 1981–2016
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: Zhuang and Shi (2016).China’s Gini Coefficient, 1981–2016
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: Zhuang and Shi (2016).3. Despite the large increase in income inequality, much of China’s population experienced rising real incomes. While the largest gains accrued to the upper shares of the income distribution, even for the bottom 10 percent incomes rose by as much as 63 percent between 1980 and 2015. This has implied that China reduced the share of people living in poverty immensely. If measured by the headcount ratio4 the population in poverty decreased from 88 percent in 1981 to 2 percent in 2013.
Pre-Tax National Income by Decile
(Average, in constant 2015 PPP Chinese Yuan)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: Piketty et al. (2016).Pre-Tax National Income by Decile
(Average, in constant 2015 PPP Chinese Yuan)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: Piketty et al. (2016).Pre-Tax National Income by Decile
(Average, in constant 2015 PPP Chinese Yuan)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: Piketty et al. (2016).4. Despite significant progress, China also faces considerable inequality of opportunities. Inequality of opportunities are of even greater concern than income inequality as it sows the seeds for wider income inequality in the future and delinks economic outcomes from an individual’s efforts. While China managed to increase drastically secondary and tertiary enrollment ratios since the 1980s, data shows that in 2010 tertiary education enrollment was more unequally distributed than in other emerging and advanced economies, on various dimensions (e.g., regions and wealth). In addition, access to financial services lags that of major advanced economies, in particular with regards to borrowing and payment services. China did achieve high levels of health and pension coverage, but benefit levels remain low and there is room, in particular, to increase unemployment insurance coverage and the safety net for the elderly.
C. What Are the Main Drivers That Explain Trends in Inequality?
5. The main drivers of the increase in inequality from 1980 to 2008 and the recent modest decline are analyzed using the Theil index5, which allows total inequality to be divided into inequality within certain groups (e.g., urban) and inequality between groups (e.g. the rural-urban income gap).
6. Differences in education and the skill premium are significant drivers of the increase and the subsequent modest decline in income inequality. China started its transition period with impressively high primary and middle school enrollment rates, while lagging in tertiary enrollment (Heckman and Yi, 2012). With rapid technological transformation and fast capital accumulation, the demand for high-skilled labor grew quickly and with it returns to education and wage inequality (Dollar, 2007; Zhang et al., 2005; Liu, 2009). More recent empirical evidence suggests an easing or even decrease in the skill premium. This could be driven by an increase in graduates and recent hikes in minimum wages.
Income Inequality Decomposition 1995, 2007, and 2013
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: CHIPS Household Surveys, authors’ calculations.Income Inequality Decomposition 1995, 2007, and 2013
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: CHIPS Household Surveys, authors’ calculations.Income Inequality Decomposition 1995, 2007, and 2013
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: CHIPS Household Surveys, authors’ calculations.7. The rural-urban gap explains a large share of inequality and its trends, but the contribution of regional disparities has been declining. Differences between rural and urban areas have been found to be a key driver of rising income inequality in China (Li et al., 2014; Lin et al., 2010). Low educational attainment and low returns to education in rural areas, and the hukou system constraining rural-urban migration, are the main explanations (Liu, 2005; Dollar, 2007). Factors driving the recent decline include rapid urbanization, causing a decline in rural surplus labor, (Zhuang and Li, 2016) and government policies, such as the dibao system, New Rural Cooperative Medicare and other poverty alleviation programs. Differences in income based on the sector of employment have declined sharply, contributing to the recent decline in inequality.6
D. Looking Ahead: What Will Be the Impact of Structural Trends and Policies on Inequality in The Future?
8. A cross-country panel regression is used to compare the historic trend in inequality in China to other countries, to quantify the impact of policies and structural factors, and to predict levels of inequality in the future based on projections of structural trends and active policy adjustments.
9. The regression captures well the past rapid increase in China’s inequality and indicates that it can be attributed mainly to structural factors. In general, the regression’s fitted values track China’s actual net Gini index well for the period between 1990 and 2002. Before 1990, actual inequality was lower than implied by the regression, while after 2002 inequality has been above the fitted values. Structural factors explain most of the rise in inequality until 2010, driven by urbanization and demographic changes. The role of policies in containing the rise in inequality in this period was modest.
Contributions (with constant policies)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.Contributions (with constant policies)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.Contributions (with constant policies)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.
Contributions of structural trends
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.Contributions of structural trends
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.Contributions of structural trends
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.10. Inequality is predicted to rise further due to structural factors, but more proactive policies can meaningfully reduce inequality. Using projections of the structural variables and keeping all other variables (controls and policies) constant, inequality is predicted to rise further due to demographic changes7. For illustrative purposes, we assume a gradual adoption of fiscal policies that takes China from current levels to reach the levels of the most proactive countries of the G7 by 2050. Under this proactive policies scenario, inequality flattens out after 2010 rather than increasing as was the case under unchanged policies. Tax changes and increased redistribution have a potentially large dividend.8
E. What Role Can Fiscal Policy Play in Reducing Inequality?
Given the possibly large role of policies, several reforms could be envisaged to make fiscal policy more inclusive, both on the tax and expenditure side.
Norms (unchanged vs. active policies)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.Norms (unchanged vs. active policies)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.Norms (unchanged vs. active policies)
Citation: IMF Staff Country Reports 2017, 248; 10.5089/9781484314722.002.A009
Sources: authors’ calculations.Tax reforms to boost inclusiveness
11. Increasing the reliance on personal income tax could allow China to improve redistribution through the tax system as it more easily accommodates a progressive structure. In addition, lowering the current high basic personal allowance, transforming it into a tax credit, and redesigning the tax brackets would ensure that middle and high income households with higher ability to pay contribute more to financing the national budget and the provision of public goods. Also, imputed minimum earnings for social security contributions have been found to be regressive and should be removed, as this would not only contribute to more equitable direct taxes, but would also improve incentives for workers to join the formal sector.
12. Property and wealth taxes remain limited in China. Such taxes are broadly viewed as progressive and are also considered to be a very efficient source of tax revenues, as they tend to be the least distortive to growth (Norregaard 2013). Consideration should therefore be given to adopting a recurrent market-value based property tax, which would have the added benefit of supporting ongoing urbanization and intergovernmental fiscal reforms.
Expenditure side reforms to boost inclusiveness
13. While important gains have been made in recent years, China still lags other emerging economies and OECD countries in public spending on education, health and social assistance. Beyond the negative impact on current levels of inequality, the rapidly aging population will further strain public health services budgets and pension funds.
14. In addition to the low level of social spending, another important dimension is the unequal provision of public services. This is particularly the case for the hukou—or household registration—system. Liberalizing the residency system, as some provinces have started doing, will allow more migrants to contribute to and benefit from the social safety net. This would reduce disparities and strengthen the redistributive effect of fiscal policy.
15. Provincial and regional inequalities in public service provision and access have also been growing in recent years, with richer provinces outpacing poorer areas. The recently announced reform plans by the State Council to address intergovernmental relations will reduce regional disparities by increasing transfers to poorer regions. This will require an increase in the pool of funds used to finance equalization grants and more reliance on a rules-based system, as opposed to the ad hoc process currently used in the annual budget preparation (Liu, Martinez-Vazquez and Qiao, 2014). Reforming the overly complex system of conditional transfers, with a stronger focus on outcomes as opposed to inputs, should also support improvement in public service delivery. A recentralization of social insurance would also improve equality, risk sharing and labor mobility.
References
Barro, R. and Lee, J., 2013, “A New Data Set of Educational Attainment in the World, 1950–2010.” Journal of Development Economics, vol 104, pp. 184–198.
Dollar, D., 2007, “Poverty, inequality and social disparities during China’s economic reform,” Policy Research Working Paper 4253, World Bank, Washington.
Heckman, J., and Yi, J., 2012 “Human Capital, Economic Growth, and Inequality in China,” NBER Working Paper 18100.
Jain-Chandra, S., Khor, N., Mano R., Schauer, J., Wingender, P. and Zhuang, J., 2017, “Inequality in China – Trends, Drivers and Policy Remedies”, IMF WP forthcoming
Li, S., Wan, G., and Zhuang, J., 2014, “Income inequality and redistributive policy in the People’s Republic of China,” In: Kanbur, R., Rhee, C. and Zhuang, J. (eds), Inequality in Asia and the Pacific: Trends, Drivers and Policy Implications, Routledge: London.
Lin, T., J. Zhuang, D. Yarcia, and F. Lin, 2010, “Decomposing Income Inequality: People’s Republic of China, 1990–2005”, In J. Zhuang, ed. Poverty, Inequality, and Inclusive Growth in Asia: Measurement, Policy Issues, and Country Studies. Manila: ADB and London: Anthem Press.
Liu, L., 2009, “Skill Premium and Wage Differences: The Case of China”, Conference paper for the Second International Symposium on Knowledge Acquisition and Modeling.
Liu, Y., Martinez-Vazquez, J., and Qiao, B. (2014). “Falling Short: Intergovernmental Transfers in China,” Public Finance and Management, 14 (4), 374–398.
Liu, Z., 2005, “Institution and inequality: the hukou system in China,” Journal of Comparative Economics, 33, pp.133–157.
Norregaard, J., (2013), “Taxing Immovable Property: Revenue Potential and Implementation Challenges,” IMF Working Paper No. 13/129 (Washington: International Monetary Fund).
Zhang, X., and Kanbur, R., 2005, “Spatial inequality in education and health care in China,” China Economic Review, 16, pp. 189–204.
Zhuang, J., P. Vandenberg, and Y. Huang. 2012. Growing beyond the Low-Cost Advantage: How the People’s Republic of China Can Avoid the Middle-Income Trap. Manila: ADB.
Zhuang, J., and Li, S. (2016), “Understanding Recent Trends in Income Inequality in the People’s Republic of China”, ADB Economics Working Paper Series.
Appendix I. Cross-Country Regression
The cross-country regression takes the following form:
where S is a vector containing the structural variables, P includes policy variables, X are the controls and μ the country-fixed effects. The sample includes Argentina, Australia, Brazil, Bulgaria, Canada, China, Denmark, Hungary, India, Italy, Japan, Mexico, Netherlands, New Zealand, Norway, Panama, Philippines, Poland, Portugal, Korea, Singapore, Spain, Sweden, Switzerland, Thailand, the United Kingdom, the United States of America, and Venezuela.
These variables are based on Higgins (1998) and allow to introduce the complete age distribution in a non-linear way.
This variables is from the SWIID dataset and represents the difference between the Market and Net Gini.
Relative to weighted G7 Average
| Net Gini Coefficient | ||
|---|---|---|
| Structural Variable | Share of Employment in Services | −0.356* (−2.42) |
| Share of Employment in Services Squared | 0.00228+ (1.76) | |
| Share of Employment in Industry | −1.842* (−1111) | |
| Share of Employment in Industry Squared | 0.0283* (9.37) | |
| Age Distribution D11 | 71.59* (7.80) | |
| Age Distribution D31 | −9.693* (−8.12) | |
| Age Distribution D31 | 0.389* (8.43) | |
| Share of Population living in Urban Areas | 1.756* (15.46) | |
| Share of Population living in Urban Areas squared | −0.0134* (−15.98) | |
| Share of Population without Education | 0.0469 (1.04) | |
| Share of Population with some Primary Education | −0.0238 (−0.75) | |
| Share of Population with some Secondary Education | −0.00337 (−0.12) | |
| Policy Variables | Public Social Protection Expenditure as Share of GDP | −0.0305 (−0.86) |
| Public Health Expenditure as Share of GDP | −0.670* (−3.22) | |
| Public Health Expenditure as Share of GDP Squared | 0.0808* (3.72) | |
| Absolute Redistribution2 | −0.175* (−4.65) | |
| Property Tax Revenue as a Share of GDP | −0.617* (−3.18) | |
| Individual Income Tax Revenue as Share of GDP | −0.0426 (−0.58) | |
| Top Personal Income Tax Rate | −0.0553* (−4.76) | |
| Control Variables | Relative GDP per Capita3 | −9.715* (−3.30) |
| Relative GDP per Capita squared | 5.278* (3.52) | |
| Trade Openness | −0.0171* (−3.50) | |
| Number of Observations | 646 | |
| Adjusted R-squared | 0.962 | |
| Country Fixed Effects | Yes |
These variables are based on Higgins (1998) and allow to introduce the complete age distribution in a non-linear way.
This variables is from the SWIID dataset and represents the difference between the Market and Net Gini.
Relative to weighted G7 Average
| Net Gini Coefficient | ||
|---|---|---|
| Structural Variable | Share of Employment in Services | −0.356* (−2.42) |
| Share of Employment in Services Squared | 0.00228+ (1.76) | |
| Share of Employment in Industry | −1.842* (−1111) | |
| Share of Employment in Industry Squared | 0.0283* (9.37) | |
| Age Distribution D11 | 71.59* (7.80) | |
| Age Distribution D31 | −9.693* (−8.12) | |
| Age Distribution D31 | 0.389* (8.43) | |
| Share of Population living in Urban Areas | 1.756* (15.46) | |
| Share of Population living in Urban Areas squared | −0.0134* (−15.98) | |
| Share of Population without Education | 0.0469 (1.04) | |
| Share of Population with some Primary Education | −0.0238 (−0.75) | |
| Share of Population with some Secondary Education | −0.00337 (−0.12) | |
| Policy Variables | Public Social Protection Expenditure as Share of GDP | −0.0305 (−0.86) |
| Public Health Expenditure as Share of GDP | −0.670* (−3.22) | |
| Public Health Expenditure as Share of GDP Squared | 0.0808* (3.72) | |
| Absolute Redistribution2 | −0.175* (−4.65) | |
| Property Tax Revenue as a Share of GDP | −0.617* (−3.18) | |
| Individual Income Tax Revenue as Share of GDP | −0.0426 (−0.58) | |
| Top Personal Income Tax Rate | −0.0553* (−4.76) | |
| Control Variables | Relative GDP per Capita3 | −9.715* (−3.30) |
| Relative GDP per Capita squared | 5.278* (3.52) | |
| Trade Openness | −0.0171* (−3.50) | |
| Number of Observations | 646 | |
| Adjusted R-squared | 0.962 | |
| Country Fixed Effects | Yes |
These variables are based on Higgins (1998) and allow to introduce the complete age distribution in a non-linear way.
This variables is from the SWIID dataset and represents the difference between the Market and Net Gini.
Relative to weighted G7 Average
Prepared by Sonali Jain-Chandra, Rui Mano, Johanna Schauer, Philippe Wingender (IMF), Juzhong Zhuang and Niny Khor (ADB), and is based on a forthcoming IMF Working Paper.
The Gini coefficient is an inequality measure ranging from 0 to 100, where 0 signifies that everyone has the same income and 100 implies that the richest person has all the income.
The Net Gini coefficient is calculated based on post-tax and -transfer income.
Headcount ratio refers to the percentage of the population living in households with consumption per person below the chosen poverty line (here $1.90 a day at 2011 PPP).
The Theil index, like the Gini coefficient, can be applied to measure inequality. Like the Gini it is 0 if everyone receives equal income and higher values imply higher inequality. Unlike the Gini coefficient it has the desirable property of decomposition.
The sectors included in the analysis are: without work, agriculture, secondary, and services.
Demographics and urbanization from UN, education attainment from Barro and Lee (2013) and sectoral change derived from IMFs own projections.
Spending on social protection and health together decrease the Gini by 3 points compared to that observed in 2010, this even after controlling for redistribution. In addition, this cross-country analysis equates policies with levels of spending and does not capture their degree of inclusiveness, which would likely increase the role of inequality-reducing policies.
