Martin Ravallion, Edmond D. Villani Chair of Economics at Georgetown University.

1. MARTIN RAVALLION, EDMOND D. VILLANI CHAIR OF ECONOMICS, GEORGETOWN UNIVERSITY
GROWTH,
URBANIZATION AND
POVERTY IN INDIA
WRI Cities Research Seminar Series — February 11, 2016
Martin Ravallion
Georgetown University

2. Growth, Urbanization and Poverty
Reduction in India
Martin Ravallion
Dept. Econ. Georgetown University
Presentation at the World Resources Institute, Feb. 11, 2016
Based on:
(i) Gaurav Datt, Martin Ravallion, Rinku Murgai, “Growth, Urbanization
and Poverty Reduction in India”, 2016.
(ii) Martin Ravallion, The Economics of Poverty: History, Measurement,
and Policy, Oxford University Press, 2016.

3. Outline
• The urbanization of poverty globally
• Debates in India
• Data issues
• Overview of descriptive statistics
• Decomposition methods and results
• Some comparisons across states
• Conclusions on India and broader comments on
the urbanization of poverty
3

4. The urbanization of poverty globally
4

5. Debates on urbanization and poverty
• Development economics has long seen population
urbanization as a driving force for poverty reduction, even
though inequality might rise as the population urbanizes.
• Lewis and Kuznets models. Latter introduced inequality within
sectors but in a stylized way: population urbanization without
changing distribution withineither urban or rural areas =>
• Non-neutral distributional shifts: theoretically ambiguous
implications for inequality within sectors.
• Longstanding debates over relative importance of population
urbanization vs within-sector development(rural and urban).
• In policy circles: Rising concerns about urban poverty.
Restrictions on migration into cities; under-serviced rural in-
migrants.
5

6. The Kuznets Hypothesis: Inequality
6
Inequality
0 1
Between-group
Within group
Urban population share
Total inequality

7. The Kuznets Hypothesis: Poverty
7
Poverty
0 1
Urban population share

8. Urbanization has tended to come with
lower poverty incidence
8
Across countries, we find
that the overall (urban plus
rural) poverty rate tends to
be lower when the share of
the population living in
urban areas is higher.
This is mostly due to the association between urbanization
and economic growth. Ambiguous results on inequality.
0
20
40
60
80
100
0 20 40 60 80 100
Urban share of the population (%)
Nationalheadcountindexofpoverty(%below$2aday)

9. The urbanization of poverty must be put in a
national context
• Falling national poverty rates with population urbanization
may come much with less progress against urban poverty.
Indeed, we may see rising urban poverty measures.
• First-order gains to rural migrants to the cities.
• Plus important second-round impacts of urbanization on
the living standards of those who remain in rural areas:
– higher remittances from urban areas
– the fact that there are fewer people competing for the
available employment in rural areas.
• Population urbanization could well do more to reduce
rural poverty than urban poverty.
9

10. An example of the urbanization of poverty
10
Poverty
0 1
Urban population share
Rural
Urban
National
• Rural poor move to urban areas.
• Some migrants escape poverty; the rest remain poor.

11. Background on India
11

12. Early optimism but disappointing progress
• Post-independence planners hoped that India's urban-
based industrialization process would bring longer-term
gains to poor people, including through rural labor
absorption.
• That hope was largely shattered by the slow pace of
poverty reduction in the period from Independence until
the 1980s.
• Why? Some observers pointed to the slow pace of labor
absorption from agriculture associated with the more
inward-looking and capital-intensive development path of
this period.
12

13. Relatively slow pace of urbanization in India
• The urban population share has been rising steadily over time
in India, from 17% in 1950 to 31% today.
• India’s pace of population urbanization (proportionate
increase in the urban population share) has been less than
either South Asia as a whole, or lower middle-income
countries as a whole.
• And markedly slower than for China. The urban population
shares of China and India were about the same around 1990,
but the share now exceeds 50% in China.
13

14. Pre-1991
• Economic growth in India tended to come with lower poverty
measures.
• The elasticity of the incidence of poverty with respect to
mean household consumption was -1.3 over 1958-1991 (Datt
and Ravallion).
• Given the modest rate of growth over this period, success at
avoiding rising inequalityprior to the 1990s was key to this
finding.
• Higher absolute elasticities for measures of the depth and
severity of poverty, indicating that those well below the
poverty line have benefited from economic growth, as well as
those near the poverty line.
14

15. Major policy regime change in India:
Dismantling of the “license raj”
15
• While the reform process can be dated back to the early
1980s, “large-scale” reform started in the aftermath of the
macroeconomic crisis of 1991
– Trade liberalization
• Reduction in tariff and non-tariff barriers on imports
• Flexible exchange rate and convertibility of the rupee on the
current account
– Easing of restrictions on domestic and foreign private sector
– Dilution of state control of banking and insurance
– Dismantling of public sector monopolies
• Evidence of rising inequality in the wake of these reforms.

16. New growth path since early 1990s
• The trend rate of growth in India’s Net Domestic Product
(NDP) per capita in the period 1958-1991 was under 2% per
annum,
• But it was more than double this rate in the period since
1992.
• There was much hope in India that the higher growth rates
attained in the wake of the economic reforms that started in
earnest in the early 1990s would bring a faster pace of
poverty reduction.
• However, the signs of rising inequality in the post-reform
period, raising doubts about how much the poor have shared
in the gains from higher growth rates.
16

17. Tightening of India’s casual labor markets
• Tightening of rural casual labor markets.
– Rising real wage rates.
– Narrowing of the urban-rural wage gap (Hnatkovska and Lahiri,
2013).
• Why?
1. Schoolinghas expanded, reducing the supply of
unskilled labor, especially in rural areas.
2. There has also been a decline in female labor-force
participation rates.
3. Construction boom =>
17

18. Construction boom
• Construction boom across India, especially in (rural and
urban) infrastructure, which had been neglected for a long
period.
• Rising labor demand from construction => higher wages of
unskilled labor relative to skilled labor within rural areas, as
well as rising rural relative to urban wages (for male workers).
• Unclear how permanent this change will prove to be.
– It may be conjectured that (like China) India has reached its Lewis
Turning Point.
– However, other factors leading to higher wages even while there is still
rural underemployment.
– And reversals might be expected.
18

19. This paper
19

20. New data series on poverty in India
• For the purpose of this paper we have compiled a new data
series on poverty and related data spanning 60 years,
extending the period of analysis in past research.
• With the benefit of nearly two decades of post-1991 data, we
believe there is now sufficient data for the post-1991 period
to revisit the earlier findings on the poverty implications of
the rate and pattern of growth in post-reform India.
• Attributionto reforms per se is problematic, but a further
scrutiny of the emergent properties of the changing growth
process with respect to poverty reduction is clearly important.
20

21. Decompositions of the changes in poverty
measures
• Decompositions identifying contribution of urban versus rural
economic growth, as well as population urbanization.
• Decomposition of poverty reduction by sector of NDP.
• New decomposition method that allows us to identify the
difference between population urbanization effects with
constant within-sector distribution (as in the Kuznets process)
versus changing within-sector distributions.
21

22. Data issues
22

23. 23
Cross-country comparisons can be deceptive
• Past research has relied on cross-country comparisons
– Single cross-sections (such as in the many tests of the Kuznets
hypothesis)
– Sometimes using panel data, though the typically short time-
series has meant that the cross-country variability is dominant.
• However, development over time within countries need
not accord with the cross-country patterns.
• Desirable to have a reasonably long time series of surveys;
a short series can be deceptive for inferring a trend.

24. 24
Data issues for India
• Amongst developing countries, India has the longest series
of national surveys suitable for tracking living conditions.
– Highly comparable surveys up to 1999/2000
– Changes in survey design in 1999/2000 created a serious
comparability problem
– New surveys reasonably comparable to 1993/94 and prior surveys
– Uniform vs. mixed (post-early) recall periods
– Survey compliance problems, esp., urban areas
– Worrying discrepancies between NSS and NAS.
• Two poverty lines:
– Lower line anchored to historical lines of the Planning Commission
– Upper line anchored to the international line of $1.25 a day at
2005 PPP (about $1.90 a day at 2011 PPP)
• Three poverty measures:
– Headcount index, poverty gap index and squared poverty gap
index (Foster, Greer, Thorbecke, 1984)

25. Overview of time series
25

26. Growth and poverty in India over 60 years
0
20
40
60
80
100
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
1950 1960 1970 1980 1990 2000 2010 2020
Net domestic product per capita
(log, right axis)
Headcount index
(%, left axis)
Urban population share
(%, left axis)
Total
Primary
Second-
ary
Tertiary
Lower
line
Upper
line
26

27. Anti-Kuznets 1: falling then rising inequality
within sectors
25.0
27.5
30.0
32.5
35.0
37.5
40.0
42.5
45.0
1950 1960 1970 1980 1990 2000 2010 2020
Giniindex(%)
Urban
Rural
27

28. Anti-Kuznets 2: U, not inverted U!
Rising between-sector inequality
since 1970
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1950 1960 1970 1980 1990 2000 2010
Ratioofurbanmeantoruralmean
(bothinconstantruralprices)
Signs of levelling off
28

29. Convergence of rural and urban poverty
measures
0
10
20
30
40
50
60
70
1950 1960 1970 1980 1990 2000 2010 2020
Rural
Urban
National
Headcountindex(%,lowerline)
29

30. Convergence of rural and urban poverty
measures
-4
0
4
8
12
16
20
1950 1960 1970 1980 1990 2000 2010 2020
Upper line Lower line
Ruralminusurbanheadcountindex(%)
Upper line
Lower line
30

31. Urbanization of poverty in India
0
4
8
12
16
20
24
28
32
36
1950 1960 1970 1980 1990 2000 2010 2020
Lower line
Upper line
Urbanshareofthepoor(%)
31

32. Growth elasticities of poverty
reduction
32

33. Post-reform growth; accelerated poverty
reduction, but rising inequality
• Significant spurt in economic growth, driven by growth in the
tertiary and (to a lesser extent) secondary sectors.
• The pace of poverty reduction also accelerated post-1991,
with a 3-4 fold increase in the proportionate rate of decline in
the post-91 period.
• The acceleration in rural poverty decline was even higher than
that for urban poverty.
• This happened alongside a significant increase in inequality
both within and between urban and rural areas.
33

34. Higher growth + higher growth elasticities
post-1991
• Despite the increase in inequality, we find greater post-91
responsiveness of poverty to growth in the aggregate.
• This holds regardless of whether growth is measured
based on national accounts or survey-based consumption.
• Also robust to an allowance for correlated measurement
errors using IVs drawn from other data.
34

35. Elasticities
(Regression)
Mean consumption per capita (NSS)
OLS IV
Elasticity t-stat Elasticity t-stat
Headcount: higher line
Whole period -1.45 -10.8 -1.32 -19.3
Pre-1991 -1.13 -18.2 -1.11 -31.4
Post-1991 -1.99 -34.2 -1.98 -37.8
H0 : pre-91= post-91
prob > F(1,35) or F(1,34) 0.00 0.00
Poverty gap: higher line
Whole period -2.34 -17.8 -2.26 -26.0
Pre-1991 -1.99 -15.1 -1.96 -23.0
Post-1991 -2.79 -30.3 -2.71 -40.0
H0 : pre-91 = post-91
prob > F(1,35) or F(1,34) 0.00 0.00
Squared poverty gap: higher line
Whole period -3.00 -24.0 -2.98 -31.5
Pre-1991 -2.65 -13.2 -2.57 -18.0
Post-1991 -3.30 -26.1 -3.18 -35.7
H0 : pre-91 = post-91
prob > F(1,35) or F(1,34) 0.01 0.00
35

36. Regression-based decompositions
36

37. 37
Urban-rural regression decomposition
• Mean income:
• Growth rate:
• Test equation:
• Null hypothesis:
u
t
u
t
r
t
r
tt nn µµµ +=
r
t
u
t
r
t
u
t
r
t
u
t
u
t
r
t
r
tt nnnssss ln)]/([lnlnln Δ−+Δ+Δ=Δ µµµ
t
i
t
i
t
i
t ns µµ /=
t
r
tu
t
r
tu
t
r
t
nu
t
u
t
ur
t
r
t
r
t n
n
n
ssssP εηµηµηη +Δ−+Δ+Δ+=Δ ln).(lnlnln 0
H0: ηη =i
for i=r,u,n Population urbanization:
Kuznets + within-sector
distributional shifts

38. Encouraging signs that India’s urban economic
growth is benefiting the rural poor
• Regime 1 (Pre-1991): Urban economic growth helped reduce
urban poverty but brought little or no overall benefit to the
rural poor. The main driving force for overall poverty
reduction was rural economic growth.
• Regime 2 (Post-1991): As before, urban growth reduced
urban poverty, and rural growth reduced rural poverty.
• But much stronger evidence of a positive feedback effect from
urban growth to rural poverty than in the pre-1991 data.
38

39. 39
Urban-rural decomposition
before and after 1991
(Lower line; headcount index) Pre-1991 Post-1991
Growth rate of mean rural income
(share-weighted)
-1.28 -1.96
(-11.1) (-3.59)
Growth rate of mean urban income
(share-weighted)
0.20 -6.40
(0.30) (-4.26)
Population shift effect (log points) -0.23 0.26
(-2.76) (1.30)
R2
0.87
Poverty reduction and the urban-rural composition of growth
t
r
tu
t
r
tu
t
r
t
nu
t
u
t
ur
t
r
t
r
t n
n
n
ssssP εηµηµηη +Δ−+Δ+Δ+=Δ ln).(lnlnln 0

40. Effects across sectors:
Headcount index
Change in log poverty measure
National Urban Rural
Coeff. t -stat Coeff. t -stat Coeff. t -stat
HEADCOUNT: Higher line
Pre-91 Urban growth -0.023 -0.04 -0.624 -7.54 0.694 1.39
Pre-91 Rural growth -0.940 -12.2 -0.014 -0.65 -0.921 -15.4
Pre-91 Pop. urbanization -0.146 -3.47 -0.015 -1.84 -0.121 -3.32
Post-91 Urban growth -3.590 -4.37 -1.244 -9.01 -2.423 -3.24
Post-91 Rural growth -2.076 -7.41 -0.165 -1.68 -1.918 -7.37
Post-91 Pop. urbanization 0.195 2.47 -0.039 1.04 0.173 2.66
R-squared 0.905 0.850 0.900
Pre91=Post91 prob.> F(2,33) 0.000 0.006 0.000
Pre91=Post91 prob.> F(3,33) 0.000 0.005 0.000
40

41. Effects across sectors:
SPG index
Change in log poverty measure
National Urban Rural
Coeff. t -stat Coeff. t -stat Coeff. t -stat
SQUARED POVERTY GAP: Higher line
Pre-91 Urban growth -1.212 -1.10 -0.394 -1.30 -0.387 -0.41
Pre-91 Rural growth -1.990 -7.68 -0.130 -2.71 -1.876 -7.64
Pre-91 Pop. Urbanization -0.259 -1.59 -0.051 -2.16 -0.221 -1.48
Post-91 Urban growth -8.482 -3.44 -3.609 -9.05 -5.311 -2.79
Post-91 Rural growth -1.772 -2.03 -0.356 -1.84 -1.388 -2.00
Post-91 Pop. Urbanization 0.269 1.04 0.252 3.46 0.080 0.40
R-squared 0.840 0.819 0.810
Pre91=Post91 prob.> F(2,33) 0.020 0.000 0.063
Pre91=Post91 prob.> F(3,33) 0.012 0.000 0.071
41

42. In summary
• Structural break around 1991 in the relationship between
poverty and the composition of growth.
• Both urban-rural and sectoral (output) decompositions are
suggestive of stronger inter-sectoral linkages, whereby growth
in one sector transmits its gains elsewhere.
• Post-91, urban growth has emerged as the primary driver of
poverty reduction
– Directly, as urban poverty has become significantly more responsive to
urban growth,
– Indirectly: urban growth has become significantly more rural poverty
reducing since 1991.
42

43. Unified decomposition
ttt
u
t
r
tt IKNGGPE ++++=Δ )ln(
•
r
G and
u
G : rural and urban growth in consumption.
• N: effect of the population shift controlling for growth
in mean consumption within each of the urban and
rural sectors. Thus this term also reflects any within-
sector distributional effects.
• K: the Kuznets effect of population shift holding
within-sector poverty levels constant.
• I : the interaction effects between sectoral poverty
change and population shift.
43

44. Unified decomposition results
Components of predicted change in poverty:
GR
GU
N K I
Total
predicted
change in
poverty
Rural
growth
Urban
growth
Population
shift with
intra-sector
distributional
change
Population shift
holding intra-
sectoral
distribution
constant (Kuznets
effect)
Interaction
between
sectoral poverty
change and
population shift
Headcount: higher line
Pre-91
Annual %age change -1.16 -0.53 0.01 -0.60 -0.04 0.00
Share of pred. annual change 100.0 45.7 -1.0 52.0 3.2 0.1
Post-91
Annual %age change -4.67 -3.26 -2.93 1.55 -0.04 0.01
Share of pred. annual change 100.0 69.8 62.7 -33.1 0.9 -0.2
Poverty gap: higher line
Pre-91
Annual %age change -1.86 -0.86 -0.12 -0.89 0.00 0.00
Share of pred. annual change 100.0 46.3 6.3 47.7 -0.2 -0.2
Post-91
Annual %age change -6.49 -2.71 -5.32 1.52 0.01 0.01
Share of pred. annual change 100.0 41.7 82.0 -23.4 -0.2 -0.2
44

45. 45
Sectoral NDP decompositions
(Lower line; headcount) Pre-1991 Post-1991
Primary (share-weighted) -1.14 -1.97
(-4.59) (-0.22)
Secondary (share-weighted) 4.50 -1.64
(2.71) (-0.32)
Tertiary (share-weighted) -3.58 -1.51
(-3.20) (-1.07
Primary+Secondary
+tertiary
-1.55
(-2.79)
R2
0.66
t
n
i
ititit YsP εππ +Δ+=Δ ∑
=1
0 lnln
Poverty reduction and the sectoral composition of growth

46. Change in log Headcount
(Lower Line)
Change in log Headcount
(Higher Line)
Unrestricted
model Restricted model
Unrestricted
model
Restricted
model
Variable or statistic Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio
Pre-91 variables:
Primary sector growth -1.138 -4.59 -1.143 -4.58 -0.823 -3.41 -0.817 -3.43
Secondary sector growth 4.496 2.71 4.510 2.75 2.941 2.64 2.921 2.66
Tertiary sector growth -3.582 -3.20 -3.581 -3.28 -2.395 -3.30 -2.396 -3.42
Post-91 variables:
Primary sector growth -1.965 -0.22 -2.053 -0.33
Secondary sector growth -1.637 -0.32 -0.548 -0.15
Tertiary sector growth -1.510 -1.07 -1.360 -1.33
NDP growth -1.551 -2.79 -1.199 -2.95
Variables common to both
periods:
Change in log ratio of CPI to NDP
deflator 1.224 4.44 1.235 4.22 0.847 4.08 0.831 3.76
Change in binary var. for an MRP
estimate -0.320 -4.73 -0.321 -5.45 -0.219 -4.91 -0.224 -5.56
Number of observations 40 40 40 40
R-squared 0.656 0.655 0.660 0.658
Wald test of restrictions: prob >
F(2, 32)
Pre-91: π1 = π2 = π3 0.002 0.008
Post-91: π1 = π2 = π3 0.999 0.977
Note: The Table gives least squares estimates with heteroscedasticity and autocorrelation-consistent standard errors.
46

47. Change in log Poverty Gap
(Higher Line)
Change in log Squared Poverty Gap
(Higher Line)
Unrestricted
model
Restricted
model
Unrestricted
model
Restricted
model
Variable or statistic Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio
Pre-91 variables:
Primary sector growth -1.378 -5.47 -1.367 -5.56 -1.763 -5.13 -1.751 -5.30
Secondary sector growth 4.931 2.66 4.899 2.72 6.354 2.38 6.319 2.45
Tertiary sector growth -4.128 -3.50 -4.129 -3.64 -5.376 -3.30 -5.377 -3.43
Post-91 variables:
Primary sector growth -0.217 -0.02 1.747 0.15
Secondary sector growth -1.349 -0.25 -2.104 -0.30
Tertiary sector growth -1.856 -1.28 -2.138 -1.18
NDP growth -1.624 -2.86 -1.880 -2.71
Variables common to both periods:
Change in log ratio of CPI to NDP
deflator 1.427 4.46 1.403 4.73 1.864 4.38 1.837 4.94
Change in binary var. for an MRP
estimate -0.354 -4.68 -0.351 -5.59 -0.458 -4.32 -0.448 -5.19
Number of observations 40 40 40 40
R-squared 0.668 0.666 0.652 0.648
Wald test of restrictions: prob > F(2, 32)
Pre-91: π1 = π2 = π3 0.005 0.019
Post-91: π1 = π2 = π3 0.981 0.945
Note: The Table gives least squares estimates 47

48. Decomposition of change in national measures
Components of predicted change in poverty:
Total
change
in
poverty
Total
predicted
change in
poverty
Total
predicted
change in
poverty
without
population
growth
Primary
sector
NDP
growth
Secondary
sector
NDP
growth
Tertiary
sector
NDP
growth
NA
Deflator-
CPI drift
Headcount: higher line
Pre-91
Annual %age change -0.90 -0.78 -1.80 -0.78 3.41 -4.47 0.05
Share of predicted annual change 100.0 43.1 -189.6 249.0 -2.6
Post-91
Annual %age change -4.79 -5.87 -7.89 -0.73 -1.87 -4.99 -0.25
Share of predicted annual change 100.0 9.3 23.7 63.3 3.1
Squared Poverty Gap: higher line
Pre-91
Annual %age change -2.68 -1.89 -4.23 -1.66 7.37 -10.04 0.10
Share of predicted annual change 100.0 39.3 -174.1 237.2 -2.4
Post-91
Annual %age change -8.16 -9.35 -12.52 -1.14 -2.94 -7.82 -0.54
Share of predicted annual change 100.0 9.1 23.4 62.5 4.3
48

49. In summary
• Post-1991 data suggest sector-neutrality in the poverty
reducing effect of growth in net domestic product.
– Unlike the pre-91 period, when only primary and tertiary
sector growth contributed to poverty reduction, after 91 all
three sectors have had a significant impact.
• The tertiary sector has the highest (absolute) growth
elasticity of poverty reduction, about twice as high as
those for the primary and secondary sector.
• This reflects both the changing nature of the growth
process as well as the large structural transformation of
the Indian economy over the last two decades with the
secondary and tertiary sectors now accounting for much
larger shares of national output and employment.
49

50. 50
Comparisons across states:
The economic geography of
poverty reduction

51. 51
Trend rates of poverty reduction by state (1970-2000)
0.4
0.5
0.6
0.6
0.8
0.8
0.9
0.9
1.1
1.1
1.2
1.2
1.3
1.8
0.0 0.5 1.0 1.5 2.0
Assam
Bihar
Punjab & Haryana
Uttar Pradesh
Karnataka
Madhya Pradesh
Rajasthan
Maharashtra
Orissa
Andhra Pradesh
Gujarat
Tamil Nadu
West Bengal
Kerala
% points per year
India: Some lessons from sub-national
data

52. 52
Why has poverty fallen so much faster in
some states than others?
• Higher average farm yields, higher public spending on
development, higher non-farm output and lower inflation
were all poverty reducing in India
• Agricultural growth, development spending and inflation
had similar effects across states
• However, the response of poverty to non-farm output
growth in India varied significantly between states.
• The states with initially higher levels of human
development saw greater poverty impacts from non-farm
growth. Better infrastructure also helped.
εηπγβββ itiiitiit
GOV
iit
YLD
iit
NFP
iit ++tINF+GOVYLDNFP=P +++ lnlnlnln

53. 53
India: Elasticities of poverty to non-farm
economic growth
0.0
0.5
1.0
1.5
2.0
2.5
2 4 6 8 10 12 14
H PG SPG
Elasticities of poverty to
non-farm output
Kerala WB
Bihar
AP

54. 54
Initial conditions matter to the impact of
growth on poverty
• Low farm productivity, low rural living standards
relative to urban areas and poor basic education all
inhibited the prospects of the poor participating in
growth of India’s non-farm sector.
• Rural and human resource development appear to be
strongly synergistic with poverty reduction through an
expanding non-farm economy.

55. 55
Conclusions

56. Falling poverty with changing sectoral
pattern of growth in India
• Economic growth has not only come with a lower incidence of
absolute poverty but there has been an acceleration in the
pace of progress against poverty post-1991.
• The new pattern of growth has brought greater benefits to
India’s poor.
• While there has been rising inequality within the rural and
(especially) urban sectors, growth within sectors has delivered
sufficient gains to India’s poor to mitigate higher inequality.
• The Kuznets process has played little role. On considering the
sectoral pattern of NDP growth we find an indication that the
sectoral pattern of growth matters less to progress against
poverty than was the case in the pre-1991 period.
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57. New sectoral pattern of poverty reduction
• The contribution of primary sector growth has rapidly
dwindled from accounting for about two-fifths of the total
poverty decline pre-91 to less than 10 percent of the total
(and larger) poverty decline post-91.
• The tertiary sector alone has contributed over 60% of the
post-91 poverty reduction.
• The secondary sector growth has contributed about a quarter.
India’s construction boom since 2000 has clearly helped
assure a more pro-poor growth process from the secondary
sector, although the sustainability of this change is unclear.
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58. Urbanization of poverty
• Urban economies create new opportunities that poor people
in rural areas have often sought out to improve their lives.
• Distorted urban labor markets can readily create excessive
urbanization,
• … as can the lack of effective public efforts to promote
agriculture and rural development; indeed, many developing
countries have gone even further in (explicitly or otherwise)
taxing the rural economy to support the urban economy.
• However, the urbanization of poverty—whereby poverty rates
fall more slowly in urban areas than in rural areas—is to be
expected in a developing country that is successful in reducing
poverty overall.
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59. Between a rock and a hard place
• Poor people are often trapped as the victims of policies that
simultaneously repress agriculture while making life difficult
for rural migrants to the cities.
– Removing long-standing policy biases in both taxation and
public spending remains a high priority for pro-poor growth.
– No less misguided are restrictions on migration and urban
policies that under-supply services to poor urban residents,
including rural migrants.
• More pro-poor development policies will probably entail an
urbanization of poverty, but that should not be a cause for
alarm as long as poverty is falling overall.
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60. For more on this topic:
economicsandpoverty.com
Thank you for your attention!
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