REFERENCES
#1 BACKGROUND & RESEARCH QUESTION
#2 POPULATION HETEROGENEITY IN INDIA
#6 CONCLUSIONS
#5 RESULTS (cntd.)
#3 MULTI-DIMENSIONAL MULTI-STATE POPULATION MODEL
#5 RESULTS
# 4 MI GR ATI ON IN IN DIA
DL - NCT OF DELHI; UP - UTTAR PRADESH; BR - BIHAR; JH - JHARKHAND; WB - WEST BENGAL; NE - NE STA
TES; OR
- ODISHA; C T - CH
HATTISGA RH; A
P - AND HRA PRA
DESH; T N - TAM
IL NADU; K
L - K LA; K ERA A - K ATA ARN KA; M H - M RASH AHA
TRA; G J - GU T; MP JARA
ADH - M RAD YA P
; RJ ESH AJA - R
AN; STH HA HR -
NA; RYA PU PB -
; JK NJAB - JA U & MM
SHM KA IR; H HIM U - HA AC RA L P SH DE
&
TA UT KHRA
D; AN
NORTHERN NORTHEAST
CENTRAL EASTERN
SOUTHERN WESTERN
# Dadra & Diu | Daman & Diu | Goa | Gujarat | Maharashtra
# Bihar | Chhattisgarh | Madhya Pradesh | Rajasthan | Uttar Pradesh
# Chandigarh | NCT Delhi | Haryana |Himanchal
Pradesh | Jammu & Kashmir | Punjab | Uttarakhand # Assam | Manipur | Meghalaya | Mizoram | Nagaland | Sikkim | Tripura
# Jharkhand | Odisha | West Bengal
# Andaman & Nicobar Is. | Andhra Pradesh | Karnataka | Kerala | Lakshadweep | Puducherry | Tamil Nadu
REGIONAL & STATE DIVISION OF INDIA
(States and Union Territories):
0 0 100000 200000 300000 0 100000
200000 300000
400000 500000
600000 700000
0 100000
200000 300000
0 100000
200000 300000
0 100000
0 0
0 100000
0 100000
0 100000 0
0 100000 0 100000 0 100000 0 0 100000 200000 1000000 200000 100000 0 100000 0
KL
rural
0
urban KL
0
KA rural
0
100000 200000
KA urban
0 100000 200000
MHrural
0 100000 200000 300000 400000 500000
MHurban
100000 0 200000 300000 400000 500000 600000 700000 800000
GJrural 1000000
200000 300000
urbanGJ
0 100000 200000 300000
ruralMP
0 100000
200000
urbanMP
0 100000
200000
ruralRJ
0 100000
200000
urbanRJ
0 100000
ruralHR
0 100000
urbanHR
0 100000
PB
rural
0
PB
urban
0 0 0 0 0
urban TN rural TN urbanAP
ruralAP
CT
urban ruralCT
urbanOR
ruralOR
urbanNE
ruralNE
urbanWB
ruralWB
urbanJH
ruralJH
urbanBR BRrural
UPurban
UPrural
DL urban
DL rural
HU
urban
HU
rural
JK rural
JK urban
20-24y 25-29y 30-34y
15-19y 10-14y
t t+5 t+10
9%
10%
11%
0%
5%
10%
Fig.2) Scheme of Cohort Component Method with proportion in certain education group
AGE
RURAL URBAN
TOTAL FERTILITY RATE
(Source: SRS | authors illustration)
EDUCATION
Fig.3) Population of India by Residence, 2010-2100
0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2
0.0 0.2 0.4 0.6 0.8 1.0
1.2 India
NorthNortheastEastCentralWestSouth
2010 Kerala 2050
RURAL
URBAN
Fig.6) Female to Male Ratio of population aged 25y plus with Upper Secondary and higher by region, 2010 & 2050
Data:
India 2011 Population and Household Census. (http://www.censusindia.gov.in/2011-common/census_2011.html)
India 2001 Population and Household Census. (http://www.censusindia.gov.in/2011-common/census_data_2001.html) India Sample Registration System (SRS). (http://www.censusindia.gov.in/2011-common/Sample_Registration_System.html) India Demographic and Health Survey 2014-15 (DHS). (http://www.dhsprogram.com)
India Demographic and Health Survey 2005-06 (DHS). (http://www.dhsprogram.com) Literature:
Lewin (2014) The Meaning and the Implications of Heterogeneity for Social Science Research.
Model, Data, Charts & Illustrations:
The projections and the here shown charts were prepared by the authors in R. For the final printing the charts got edited in Adobe Illustrator CS5 The Circos plot with domestic net migration flows in India 2001 was conducted via a webinterface (http://mkweb.bcgsc.ca/tableviewer/visualize) Illustrations of urban structures, villages and industry (http://www.freepik.com/free-vector)
Poster designed by Markus Speringer 0.0
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
illiterate some primar
y primar
y
lower secondar y tenth grade
twelth gr ade
university
illiterate some primar
y primar
y
lower secondar y tenth grade
twelth gr ade
university
Fig.1) Differentials in Total Fertility Rate in India by state, residence and region, 2013
Samir K.C. (kc@iiasa.ac.at) (A/B/C)
Markus Speringer (speringe@iiasa.ac.at / markus.speringer@oeaw.ac.at) (A/B) Marcus Wurzer (wurzer@iiasa.ac.at / mwurzer@wu.ac.at) (A/B)
(A) International Institute for Applied Systems Analysis (IIASA)
AT-2361 Laxenburg, Schlossplatz 1, AUSTRIA
(B) Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/ÖAW, WU)
AT-1020 Vienna, Welthandelsplatz 2 / Level 2, AUSTRIA
(C) School of Sociology and Political Science, Shanghai University
CN-200444 Shanghai, 99 Shangda Road, BaoShan District, CHINA
CONTACT
RURAL URBAN
by S. K.C., M. SPERINGER & M. WURZER (IIASA-SCHEMA Project)
Preliminary results show that overall population size will be higher when spatial heterogeneity is considered.
For India, with a population more than 1.2 billion and very high level of demographic and socioeconomic heteroge-
neity, the quality of population projections (for the coun- try as well as for States/UTs) is enhanced when done by taking into account both spatial and socioeconomic (represented by educational attainment) heterogeneity.
Currently, work is underway to better represent the ur- banization process in the projection model and to define alternative narratives for the future.
TOTAL POPULATION(in billion)
1.25
1.00
0.75
0.50
0.25
0.00
2010 2020 2030 2040 2050 2060YEAR 2070 2080 2090 2100
RURAL
URBAN
- by states - by country
2010 2030 2040 2050 2060 2070 2080 2090 2100
100
75
50
25
0
YEAR
EDUCATION(in percent)
Fig.5) Education in India, 2010-2100
post-secondaryupper sec.lower sec.primaryinc. prim.no education
2020
TOTAL POPULATION(in billion)
- by states - by country
2010 2020 2030 2040 2050 2060YEAR 2070 2080 2090 2100
2.00
1.80
1.60
1.40
1.20
1.00
1.88 billion
1.82 billion
Fig.4) Population of India, 2010-2100
lion by 2100 (similar to UN and IIASA/WIC projec- tion).
• Explained by “population weight” and ignoring large portion of domestic migration flows between States
Maintaining of internal migration slows rate of urbanization
• Proportion urban population increased from 31 percent in 2010 to 34 percent in 2050 and 35 per- cent in 2100.
• Much lower than UN’s expectation
• Source of urbanization due to reclassification of rural to urban region is not yet implemented
• Preliminary result (under final internal review) shows significant increase in proportion urban.
Significant increase in the population’s human capital
• For e.g., the proportion among 25+ years old with upper secondary and post-secondary edu- cation would increase from 28.4 percent in 2010 to 53.6 percent by 2050 and 81.1 percent in 2100.
(see Fig. 5)
Towards Gender Balance in higher education (see Fig. 6)
• In 2010, women in urban areas more educated than those living in rural areas
• But women in both areas were lagging behind men, except in Kerala (KL).
• By 2050, all States/UTs will catch up fast converg- ing to gender balance.
• Also the urban and rural differences get narrower in almost all States, Regions.
• This convergence is an implicit assumptions of the projection that leads in the long run to a higher societal equality within India.
• Developed a multi-dimensional population PROJECTION MODEL that projects the population of India by five dimensions (see Fig. 1)
• Three personal characteristics: age, sex, and educational attainment
• Two spatial characteristics: 35 States/Union Territories (UT) by rural and urban place of residence
• In total 70 sets of subnational populations are projected in 5 yearly steps from 2010 up to 2100.
• Data from Census (2001 and 2011) and Sample Registration Survey (1970-2013)
• Defined a BASE-LINE SCENARIO to study the impact of spatial and socioeconomic dif- ferentials in demographic rates and education transitions on the population pro- jection outcome.
• ESTIMATES and PROJECTION for 70 spatial units
o FERTILITY (age & edu) o MORTALITY (age & sex)
o INTERNAL MIGRATION FLOWS (age & sex) (see Circos plot)
o EDUCATION PROGRESSION RATIO (age, sex & edu)
RIP
The work is embedded in an interdisciplinary case-study at the In- ternational Institute for Applied Systems Analysis (IIASA) that in- vestigates the impact of Socioeconomic Heterogeneity in Model Applica- tions (SCHEMA). Research question:
“How does the accounting of socioeconomic heterogeneity, measured by educational attainment, and spatial heterogeneity (by place of rural/urban residence and States) improve population projections
for India?”
• Demographic rates differ greatly by educa- tional attainment and place of residence in India.
• Educational attainment rates as well differ by place of residence.
Differential Fertility (see Fig. 1)
• A visible negative association between edu- cation and fertility with a slight positive slope for university degree.
• Visible for both, urban and rural areas, but on different levels.
• A large deviation within and between States, for e.g. in Central India with higher fertility levels.
Population growth and decline in the 21st Century
• When spatial heterogeneity consid- ered, population of India expected to peak in 2080 at 1.88 billion (see Fig. 4)
• In addition to births and population mo-
mentum, better future mortality situation is contributing to the population growth
Spatial Heterogeneity matters in India
• When States/UT NOT considered in the projection, the population will peak at lower level (1.82 bil- lion) earlier by 2075 before declining to 1.74 bil-
