Transcript China

Seminar at Chinese Academy of Social Sciences, Beijing, October 2007
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.
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
70
1200
1000
India
poverty rate
China
per-capita GDP
800
600
50
40
China poverty
rate
30
400
200
60
20
India
per-capita GDP
0
10
0
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003
% of population poor (below $-aday)
Per-capita GDP (constant 2000 USD)
1400
Signs of rising “income” inequality,
although the trend is only clear for China
Gini coefficient of inequality
45.0
Long-term trend,
though not
monotonic
40.0
China
(income)
35.0
New trend?
Too early to say
30.0
India
(consumption)
25.0
1978
1983
1988
1993
1998
2003
Incidence of growth in the 1990s
Growth incidence curves for China and India
Annual growth in income/expenditure per person (%)
10
9
8
China (income) 1993-2004
7
6
5
4
3
2
Median
India (expenditure)1993/1994-2004/2005
1
0
0
10
20
30
40
50
60
70
80
The poorest p% of population ranked by per capita income/expenditure
90
Aside: Growth incidence curve
yt ( p )  yt 1 ( p )
gt ( p) 
yt 1 ( p )
where yt(p) is the quantile function: yt=Ft-1(p)
Lt( p )
gt ( p )  1 
( g t  1)
Lt1 ( p )
Growth factor
at percentile p
Distribution
correction
Ordinary
growth factor
Further reading: On the growth incidence curve see Martin Ravallion and Shaohua Chen,
“Measuring Pro-Poor Growth”, Economics Letters, 2003.
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; SundaramTendulkar)
• New survey (2004/05) is comparable with 1993/94.
Measurement: What weight on betweengroup 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?
Annual growth rate (%) of percapita state GDP between
1978/1980 and 2004
“Yes” in India, but qualified “no” for China
(though divergence between coastal areas and inland)
14.0
Indian states
12.0
Chinese provinces
10.0
8.0
6.0
4.0
2.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
Per-capita GDP of province(state) in 1978(1980)
relative to poorest province(state)
8.0
9.0
10.0
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
India
14
7
6
5
4
3
H e n an
2
1
0
-.8 -.7 -.6 -.5 -.4 -.3 -.2 -.1
.0
.1
S h a re w e ig h te d to ta l e la s tic ity o f th e h e a d c o u n t in d e x to g ro w th
Growth rate in non-farm output per capita
1993/94-1999/00 (%/year)
Trend rate of growth in mean rural income (%/year)
China
12
10
8
6
4
2
-0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00
Impact on national poverty of non-farm output growth by state
(Share–weighted elasticity for 1993/94)
Average annual growth rate (%)
Growth has been sectorally uneven
1980-1985
16.0
1985-1990
1990-1995
1995-2000
2000-2005
14.0
12.0
China
10.0
India
8.0
6.0
4.0
2.0
0.0
Agriculture
Industry
Services
Agriculture
Industry
Services
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
Do sectoral imbalances matter to the
rate of poverty reduction?
Regression decomposition test
• Mean income: t  ntr tr  ntu tu
• Growth rate:
 ln t  str  ln tr  stu  ln tu  [ str  stu ( ntr / ntu )] ln ntr
sti  nti  ti /  t
• Test equation:
 ln Pt  0   r str  ln tr   u stu  ln tu

n
( str
• Null hypothesis:

r
u nt
st . u
nt
)  ln ntr   t
H0: i   for i=r,u,n
Sectoral imbalances matter to the rate of
poverty reduction
Poverty reduction and the urban-rural composition of growth
r
n
 ln Pt   0   r str  ln  tr   u stu  ln  tu   n ( str  stu . tu ) ln ntr   t
nt
Growth rate of mean rural income (shareweighted)
Growth rate of mean urban income
(share-weighted)
Population shift effect
R2
China
India
-2.56
(-8.43)
-1.46
(12.64)
0.09
(0.20)
-0.55
(-1.37)
0.74
(0.16)
0.82
-4.46
(-1.31)
0.90
Similarly for GDP sources by sector
Poverty reduction and the sectoral composition of growth
n
 ln Pt   0    i sit  ln Yit   t
i 1
China
n.a.
n.a.
Growth rate of GDP per
capita
Primary (share-weighted)
-2.60
(-2.16)
n.a.
Secondary (shareweighted)
Tertiary (share-weighted)
n.a.
Secondary+tertiary
n.a.
-8.07
(-3.97)
-1.75
(-1.21)
-3.08
(-1.24)
n.a.
R2
0.21
0.43
n.a.
-7.85
(-4.09)
n.a.
-0.99
(-3.38)
n.a.
n.a.
n.a.
n.a.
-2.25
(-2.20)
0.42
n.a.
India
n.a.
-1.16
(-2.96)
3.41
(1.84)
-3.42
(-2.74)
n.a.
0.75
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.
 ln Gt  0.0522 0.746( ln Y1t   ln Y1t 1 ) / 2  ˆtG
( 4.563)
( 3.723)
• A (moving average) growth rate of 7.0% p.a. would be
needed to avoid rising inequality whereas the mean primarysector 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
Annualized log difference (%/year)
Inequality
1.
2.
3.
4.
1981-85
1986-94
1995-98
1999-2001
Falling
Rising
Falling
Rising
Gini
index
-1.12
2.81
-0.81
2.71
Mean
household
income
8.87
3.10
5.35
4.47
GDP per
capita
8.80
7.99
7.75
6.61
Provinces with higher growth did not have
steeper rises in inequality
log X it   iX   iX t  itX
Trend in rural Gini index (% per year)
3.2
2.8
2.4
2.0
1.6
1.2
0.8
r = -0.18
0.4
0.0
-0.4
0
1
2
3
4
5
6
7
Trend growth rate in mean rural income (% per year)
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:
 iH /  iY  ( 5.935 0.0136 y80R i )(1  G83R i )  1.365  iG  ˆt
( 4.487 )
( 2.560 )
( 2.392 )
R2=0.386; n=29
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
R
 iH   67.877  0.141Y80i  0.463 G83
i  6.797 URi
( 6.239)
( 8.090)
( 3.313)
( 3.201)
 9.291 COASTi  25.012 GDONGi  ˆt
( 5.292)
( 15.160)
R2=0.827
Initial ratio
of urban mean
to rural mean
R
 iY  14.143 0.007 Y80i  0.149 G83
i  1.632 URi
( 3.759 )
( 1.294 )
( 2.526 )
 0.507 COASTi  1.290 GDONGi  ˆt
( 0.913)
R2=0.423
(1.875 )
( 2.682 )
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.
Elasticity of
• Inequality is continuing to rise
poverty rate to
Gini index
and poverty is becoming much
1981
0.0
more responsive to rising
2001
3.7
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