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The role of large countries (China and India in particular)

The role of large countries (China and India in particular). Milanovic, “Global inequality and its implications” Lecture 10. 1. Large countries: an overview. See also Table 4. 2. Concept 1 and Concept 2 inequalities in large countries. Three concepts of inequality.

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The role of large countries (China and India in particular)

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  1. The role of large countries (China and India in particular) Milanovic, “Global inequality and its implications” Lecture 10

  2. 1. Large countries: an overview

  3. See also Table 4

  4. 2. Concept 1 and Concept 2 inequalities in large countries

  5. Three concepts of inequality • Concept 1: unweighted inequality of regions (or countries) useful for study of convergence (is growth faster in poorer regions?) • Concept 2: population weighted inequality of regions (countries); "feeling" of inequality, particularly if there are regional cleavages. Also proxy to... • Concept 3: inequality between individuals in a country (or world)

  6. Example: population weighted divergence • 2 rich and small regions, A and B • 2 poor and populous regions, C and D • A and C grow fast, B and D slowly, then • no change (or small change) in Concept 1 inequality, no income convergence. • no ρ between population size and growth • But Concept 2 inequality goes up, population weighted divergence (since C and D become dissimilar)

  7. Why it matters? • Concept 1. An economic question. Will there be convergence if L,K, goods move relatively freely (compared to impediments that exist between countries) • Concept 2. A social question. What is the "feeling" of inequality/exclusion (particularly if there are ethnic/religious cleavages). Threat to national cohesion.

  8. The data we use • Regional GDPs per capita • Concept 1 & 2 inequality calculated across nominal and real GDP per capita; overestimate of inequality (some regional redistribution; price levels higher in richer regions) • Also in PPPs

  9. Concept 1 Gini (unweighted inter-regional inequality) (across nominal GDPs per capita) Highest regional inequality in China; lowest in the US (despite having 50 units) China: regional convergence in the '80s India & Indon. regional divergence throughout US: regional convergence since early 80's

  10. China: Concept 1 Gini inequality in nominal and real terms No real convergence: no systematic difference in real growth rates btw. the provinces Between 1978 and 1990 prices rose faster in poorer regions

  11. India: Real and nominal divergence Nominal and real inequality rise step in step up to about 1991 Since then nominal divergence stops while real continues Pricecatch-up of poorer provinces (better integrated domestic market?)

  12. China (1980-2000) North to South Shandong Jiangsu Zhejiang Fujian Guangdong Red: fast growth (1σ above the mean) Yellow: average Light yellow: slow (1σ below the mean)

  13. India (1980-1999) Maharashtra (Bombay) Karnataka (Bangalore) Tamil Nadu (Madras)

  14. United States New HampshireMassachusetts Connecticut

  15. Brazil West to East Amazonas Para Mato Grosso

  16. Indonesia West to East West Nusa Tenggara Jakarta/ Bali Lampung Irian Jaya Does not include oil and gas sectors.

  17. Chinese provincial growth 1978-90 and 1990-00 In 1990-2000, poorer regions growing slower than the average Beijing, Shanghai and Tienjin not shown

  18. China's rural and urban mean provincial incomes in 2000 Source: from Kanbur and Zhang; 26 provincial means for rural and 26 for urban.

  19. Concept 2 Gini (population-weighted inter-regional inequality) 1990's: Increasing Concept 2 inequality in the three Asian countries Highest inequality in Brazil. If all people in each state had the same income, Gini would be still more than 30. In the United States less than 10!

  20. What drives Concept 2 inequality? • Different population growth rates by region • Correlation between growth rates and population size (do more populous states grow faster implications for the productivity view of growth; poverty reduction)

  21. Impact of differential population and GDP per capita growth on Concept 2 Gini

  22. Results (for Concept 2 inequality) • Differential population growth not important • Growth disequalizing in India throughout • China: differential growth rates equalizing in 1980-90, then disequalizing in 1990-2000

  23. Importance of population-weighted divergence India: β and 95% confidence interval

  24. Economic and "political economy" convergence

  25. Conclusions • Asia: increasing regional inequality in the 1990's (India and China; not Indonesia) • Concept 2 increases important for national cohesion (India and China) • Growth disequalizing; higher income level equalizing; no evidence that nation-wide openness positively related to Concept 2 inequality • Populous states’ outcomes diverge in both India and China

  26. Complexity of the process • In both China and India, a process directly opposite to what we observe at global level • In China & India: Concept 1 inequality going down, Concept 2 inequality up • World: Concept 1 inequality up, Concept 2 inequality down (and the latter solely due to high average growth of China & India)

  27. 3. China and India Concept 3 inequalities

  28. China: Inequality according to HS data • Increase in Concept 3 between 1980 and 2000 about 14 Gini points (according to Ravallion and Chen) • Explained by rising differences between mean provincial incomes (~8 Gini points), • rising differences urban and rural areas (~2 Gini points) • rising differences within urban and rural areas (another 3 Gini points)

  29. Illustration of Concepts 2 and 3: China, inequality according to HS data

  30. Decomposing total inequality in China Based on Ravallion & Chen (2004), Kanbur & Zhang (2002), Milanovic (2004)

  31. China and India compared (Gini points) From IndiaChina.xls file; China: based on HBS data; India based on state GDIs, italics: estimates

  32. 4. Role in global income distribution

  33. Shares of US, China and India in world GDI (in $PPP terms)

  34. Recall Concepts 2 calculation: • In Gini terms: • where Gi=individual country Gini, π=income share, yi = country income, pi = population share, μ=overall mean income, n = number of countries • For each pair of countries depends on the mean-normalized gap between their per capita incomes and population shares

  35. As China’s GDI pc (in $PPP terms) is some 10 times less than the US’s, if China grows at 10% per annum, US needs to grow only 1% to keep the numerator the same. • Then, only if world mean income grows, will the China-US contribution to international ineqaulity go down. • Almost all of China’s contribution to reduced Concept 2 inequality comes from its catching up of other countrieds (not the United States); and (as we shall see below) only 2/3 of it is due to growth.

  36. Mean-normalized income distances between China, India and the US

  37. Contributions (in Gini points) of differences in mean incomes between Ch, In, US to Concept 2 inequality

  38. About 20% of Concept 2 inequality explained by the “triangle” • US-China mean-normalized GDI per capita gap decreased from 4.5 to 4 (btw. 1965 and 2000) • Gini contribution of US-China decreased 6.3 to 4.2 points (over the same period) • Between 1978 (reforms in China) and 2000, more than 1/3 of the China decrease to Concept 2 inequality due to the population effect (↓ share of world population; from 24% to 22%) • Difference between China and India adds to global inequality

  39. China component in Concept 2 inequality Source: Jiang Zhiyong (2005)

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