Presentation on theme: "Partially Awakened Giants: Uneven Growth in China and India Martin Ravallion World Bank This presentation is based on: Shubham Chaudhuri and Martin Ravallion,“Partially."— Presentation transcript:
Partially Awakened Giants: Uneven Growth in China and India Martin Ravallion World Bank This presentation is based on: Shubham Chaudhuri and Martin Ravallion,“Partially Awakened Giants: Uneven Growth in China and India” in Dancing with Giants: China, India, and the Global Economy (edited by L. Alan Winters and Shahid Yusuf), World Bank, 2007. Martin Ravallion and Shaohua Chen, “China’s (Uneven) Progress Against Poverty,” Journal of Development Economics, Vol. 82(1), Jan. 2007, pp.1-42. Gaurav Datt and Martin Ravallion, “Has India’s Post-Reform Economic Growth Left the Poor Behind,”, Journal of Economic Perspectives Vol. 16(3), Summer 2002, pp. 89-108. Seminar at Chinese Academy of Social Sciences, Beijing, October 2007
China and India: Growth with poverty reduction, but rising inequality Economic growth in China and India since the 1980s has been accompanied by a falling incidence of absolute poverty. => However, concerns are being expressed about the distributional impacts of the growth processes in both countries.
Growth + poverty reduction in both countries since early 1980s
Signs of rising “income” inequality, although the trend is only clear for China New trend? Too early to say Long-term trend, though not monotonic
Incidence of growth in the 1990s Growth incidence curves for China and India
Aside: Growth incidence curve Further reading: On the growth incidence curve see Martin Ravallion and Shaohua Chen, “Measuring Pro-Poor Growth”, Economics Letters, 2003. where y t (p) is the quantile function: y t =F t -1 (p) Ordinary growth factor Distribution correction Growth factor at percentile p
Data issues: China Separate urban and rural surveys; comparability problems Comparability problems over time, esp., changes in valuation methods in rural household surveys in 1990 (Chen-Ravallion corrections). Problems with price deflators (esp., spatial) “Floating population”: Sample frame (pre-2002) based on registrations not street addresses. –Bias due to this is very small –For example, if 5.0% of urban population is deemed poor, this only falls to 4.6% if one excludes those with rural registration.
Data issues: India Highly comparable surveys up to 1999/2000 Changes in survey design in 1999/2000 have created comparability problems. Various corrections (Deaton-Tarozzi; Sundaram- Tendulkar) New survey (2004/05) is comparable with 1993/94.
Measurement: What weight on between- group inequalities? We focus on aggregate inequality and its sources. However, specific between-group inequalities matter more to perceptions of social justice than is evident in standard decompositions Urban-rural and geographic inequalities appear to be examples. –China: Salience of regions (coastal-inland) and urban-rural disparities –India: “Shining India”? Not if large segments of the rural population are left behind.
How uneven is the growth process? What does this mean for poverty and inequality?
Growth has been uneven across regions in both countries India: Amongst the 16 major states, Bihar (including Jharkand) had the lowest growth rate, 2.2%, while Karnataka had the highest, 7.2%. China: provincial GDP growth rates varied widely, ranged from a low of 5.9% in Qinghai to a high of 13.3% in Zhejiang.
Growth divergence? “Yes” in India, but qualified “no” for China (though divergence between coastal areas and inland)
Corresponding unevenness in progress against poverty China: the coastal areas fared better than inland areas. –The trend rate of decline in the poverty rate between 1981 and 2001 was 8% per year for inland provinces, –versus 17% for the coastal provinces. India: good performances in poverty reduction in most of the western and southern states—peninsular India (with the exception of AP) Poor performances in the BIMARU states (Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh) + the eastern region.
Higher growth was not found where it would have the most impact on poverty China India
Growth has been sectorally uneven Growth rates in the primary sector (agriculture) have: lagged behind other sectors and declined over the last quarter century
+ uneven between urban and rural areas China: trend increase in ratio of urban to rural mean over 1981-2002 –This is greatly reduced allowing for higher urban inflation rate –But rising trend is still evident since mid-1990s. India: trend increase in ratio of urban to rural mean consumption since 1980s
Mean income: Growth rate: Test equation: Null hypothesis: Do sectoral imbalances matter to the rate of poverty reduction? Regression decomposition test
Poverty reduction and the urban-rural composition of growth Sectoral imbalances matter to the rate of poverty reduction
Poverty reduction and the sectoral composition of growth Similarly for GDP sources by sector
Uneven growth has contributed to rising inequality Differing initial conditions –Lower inequality of agricultural land holding in China –Also lower inequalities in human capital in China –Larger urban-rural inequality in China China: Primary sector growth has been inequality decreasing; secondary and tertiary have had no effect. A (moving average) growth rate of 7.0% p.a. would be needed to avoid rising inequality whereas the mean primary- sector growth rate was under 5% between 1981 and 2001.
Why should we care about uneven growth? What should be done about it?
Good and bad inequalities Claim: post-reform development paths of both India and China have been influenced by and have generated both good and bad inequalities. “Good” or “bad” in terms of what they mean for living standards of the poor
Good inequalities … reflect and reinforce market-based incentives that foster innovation, entrepreneurship and growth Examples for China –Household Responsibility System: initially inequality reducing, but then inequality increasing forces created –Wage de-compression: higher returns to schooling (from low base) Examples for India –Greater responsiveness of private investment flows to differences in the investment climate –Exploiting agglomeration economies in industrial location
Bad inequalities … prevent certain segments of the population from escaping poverty. –Geographic poverty traps, patterns of social exclusion, inadequate levels of human capital, lack of access to credit and insurance, corruption and uneven influence …are rooted in market failures, coordination failures and governance failures Credit market failures often lie at the root of the problem –it is poor people who tend to be most constrained in financing lumpy investments in human and physical capital.
Example 1: Geographic poverty traps Living in a well-endowed area entails that a poor household can eventually escape poverty, while an otherwise identical household living in a poor area sees stagnation or decline. In both countries, initially poorer provinces saw lower subsequent growth. China: Evidence of geographic externalities stemming from both publicly-controlled endowments (such as the density of rural roads) and largely private ones (such as the extent of agricultural development locally). * * Jalan, Jyotsna and Martin Ravallion, “Geographic Poverty Traps? A Micro Model of Consumption Growth in Rural China?” Journal of Applied Econometrics, 2002, Vol. 17, pp. 329–46.
Example 2: Inequalities in human capital …are a key factor impeding pro-poor growth in both countries. China: Widespread basic schooling at the outset of the reform period But rising inequalities over time threaten current and future prospects for both growth and poverty reduction. India: Long-standing inequalities in schooling (higher than in China) that have retarded the pace of poverty reduction at given growth rates, esp., from non-farm economic growth.
Good inequalities can turn into bad ones Those who benefit initially from the new opportunities can sometimes act to preserve newly realized rents –by restricting access to these opportunities –or by altering the rules of the game. China: Example of TVEs. Bad inequalities can drive out good ones Two costs of bad inequalities: –Directly reduce growth potential –Undermine support for reform Signs that this is happening in both countries
Should policy-makers be worried? Possibly it is inevitable to some degree. Arthur Lewis: “Development must be inegalitarian because it does not start in every part of the economy at the same time.” However, policy makers aiming for inclusive economic growth should be concerned about the “bad inequalities.” Does China’s experience support the view that rising inequality is a necessary by-product of the growth needed to reduce poverty?
China: Surprisingly little sign of an aggregate growth-equity trade off The strong positive correlation over time between China’s GDP per capita and inequality is driven by common time trends. Near zero correlation between changes in (log) Gini and growth rate. The periods of more rapid growth did not bring more rapid increases in inequality. Indeed,…
The periods of falling inequality had highest growth in mean household income
Provinces with higher growth did not have steeper rises in inequality r = -0.18
Double handicap in unequal provinces More unequal provinces faced two handicaps in rural poverty reduction in China: 1.High inequality provinces had a lower growth elasticity of poverty reduction: At zero trend in inequality, (mean) growth elasticity is zero at maximum inequality and -6 at minimum inequality 2.High inequality provinces had lower growth: Signs of “inefficient inequality” both within rural areas, and between urban and rural areas =>
Initially poorer and less unequal provinces had higher rates of poverty reduction Large effects: going from the province with lowest initial inequality to the highest inequality cuts 7% points off the annual rate of poverty reduction. Initial distribution matters independently of growth: both inequality measures remain significant (though with smaller coefficients) when one adds the trend growth rate to the regression for trend poverty reduction
Regressions for provincial trends Initial conditions (mean and distribution) + location Initial ratio of urban mean to rural mean Initial Gini index
Inequality is now an issue for China High inequality in many provinces will inhibit future prospects for both growth and poverty reduction. Aggregate growth is increasingly coming from sources that bring limited gains to the poorest. Inequality is continuing to rise and poverty is becoming much more responsive to rising inequality. Perceptions of what “poverty” means are also changing, which can hardly be surprising in an economy that can quadruple its mean income in 20 years.
The challenge for policy looking forward… … preserving the good inequalities and reducing the bad ones Avoiding false trade-offs: periods of more rapid growth have not necessarily meant rising inequality; indeed, no such correlation for China Helping the rural poor connect to markets Recent initiatives in both China and India are steps in the right direction But governance problems loom large