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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

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

Milanovic, “Global inequality and its implications”

Lecture 10

three concepts of inequality
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)
example population weighted divergence
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)
why it matters
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.
the data we use
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
concept 1 gini unweighted inter regional inequality across nominal gdps per capita
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

china concept 1 gini inequality in nominal and real terms
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

india real and nominal divergence
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?)

china 1980 2000
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)

india 1980 1999
India (1980-1999)

Maharashtra (Bombay)

Karnataka (Bangalore)

Tamil Nadu (Madras)

united states
United States

New HampshireMassachusetts Connecticut

brazil
Brazil

West to East

Amazonas

Para

Mato Grosso

indonesia
Indonesia

West to East

West Nusa Tenggara

Jakarta/ Bali

Lampung

Irian Jaya

Does not include oil and gas sectors.

chinese provincial growth 1978 90 and 1990 00
Chinese provincial growth 1978-90 and 1990-00

In 1990-2000, poorer regions growing slower than the average

Beijing, Shanghai and Tienjin not shown

china s rural and urban mean provincial incomes in 2000
China\'s rural and urban mean provincial incomes in 2000

Source: from Kanbur and Zhang; 26 provincial means for rural and 26 for urban.

concept 2 gini population weighted inter regional inequality
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!

what drives concept 2 inequality
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)
results for concept 2 inequality
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
importance of population weighted divergence
Importance of population-weighted divergence

India: β and 95% confidence interval

conclusions
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
complexity of the process
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)
china inequality according to hs data
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)
decomposing total inequality in china
Decomposing total inequality in China

Based on Ravallion & Chen (2004), Kanbur & Zhang (2002), Milanovic (2004)

china and india compared gini points
China and India compared (Gini points)

From IndiaChina.xls file; China: based on HBS data; India based on state GDIs, italics: estimates

slide36

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
slide37
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.
slide39
Contributions (in Gini points) of differences in mean incomes between Ch, In, US to Concept 2 inequality
slide40
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
china component in concept 2 inequality
China component in Concept 2 inequality

Source: Jiang Zhiyong (2005)

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