Presentation by Kunal Kumar, Joint Secretary and Mission Director, Smart Cities Mission, GoI on "Regional Insights" at #TheIndiaDialog on February 23 at Stanford University. The #TheIndiaDialog was organised by Institute for Competitiveness and US Asia Technology Management Center at Stanford University.

1. Regional
Insights
KUNAL KUMAR
JS & MD, SMART CITIES MISSION, INDIA

3. Defining “Regions”
Traditionally three different approaches :
1. Homogeneity : A combination of physical, economic, social or other characteristics, such
that locations depicting similarities along such specified lines come together to form a
region.
2. Nodality/Polarization: usually around some central urban place and the summation of all
settlements around this place forms the region.
3. Programming or policy-oriented: Based on administrative coherence or identity between
the area being studied and available political institutions for taking policy decisions
R.E.D applies the homogeneity criteria to take a deeper look at the Indian regional economy.
As per the classification adopted by the National Sample Survey Organization (NSSO),
India has been divided into 78 – 88 NSS regions.

4. Approach & Variables
Absence of output data at regional level
(1) R𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑜𝑟 𝑅𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 𝐴𝑑𝑑𝑒𝑑 𝑅 𝑉𝐴
= 𝑆𝑡𝑎𝑡𝑒 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 ∗
𝑅𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡
𝑆𝑡𝑎𝑡𝑒 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡
∗
𝑅𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑊𝑎𝑔𝑒
𝑆𝑡𝑎𝑡𝑒 𝑊𝑎𝑔𝑒
= 𝑁𝑆𝐷𝑃 ∗
𝑅𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡
𝑆𝑡𝑎𝑡𝑒 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡
∗
𝑅𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑊𝑃𝑅
𝑆𝑡𝑎𝑡𝑒 𝑊𝑃𝑅
RED (2022), Mitra & Mehta 2011, and UN Habitat Guidelines
*Data on State Domestic Product from the Handbook of Statistics on the Indian Economy, Reserve Bank of India.
Where wage data is not directly available NSS doesn’t capture self employment data, WPR is hence used as a proxy.
What is a good way to account for skills intensity in the chosen sub-state geographical unit?
4

5. (2) Diversification
• To capture sectoral diversity, RED constructs a Diversity Index for each region for the two years separately. They calculate
the percentage employment shares by industry using 2 digit level data across 14 industry sectors .The Diversity Index (DV)
is given by:
D𝑉
! = 1 − 𝐻!
where 𝐻! = ∑"
#!"
#"
$
and 0 ≤ 𝐷𝑉
! ≤ 1
Such that 𝐻! is the sum of squares of employment shares of all industries j in region r (Hirschman Herfindahl Index) .
- DV ranges from 0 (minimum diversity) to 1 (maximum diversity)
- National Industrial Classification (NIC) at 2 digit level has 14 sectors – 4 broad ones – AGRI, MFG, CON, SVS
(3) Degree of Urbanization
• Productivity is expected to be higher in larger economic units, particularly cities. RED analyses the share of urbanization as
well as existence of Mn plus cities in the region to reflect this.
𝑈𝑅𝐵!=
%&!'()' *+,+)- +) .!/0) 1!&0' +) 23& 4&-+()
5(20* %(67*02+() (8 23& 4&-+()
∗ 100
5

6. NSS Region
as a Unit of
Analysis
Performance & Outlook
Ø Output : Regional Value Added (RVA)
Ø Sectoral Shares : Employment / Productivity
Ø Determinants – Urbanization, Diversity?

7. Tracing Relative performance across Regions
Ranking of states in contribution
to VA is relatively unchanged over
this time period.
The magnitude of the increase
differs widely across states in
absolute terms
At a regional level, we find
significant variation in value added
in absolute terms
The rankings change substantially
at the regional level
RVA Rank
2011-12
RVA Rank
2004-05 No. Region State
RVA in Rs Cr.
2004-05
RVA in Rs Cr.
2011-12 Change State Rank
1 2 272 Inland Western Maharashtra 84,673 152,128 67,455 1,1
2 New 241 South Eastern Gujarat 36,642 146,435 109,793 5-6
3 10 271 Coastal Maharashtra 61,932 144,858 82,926 1-1
4 5 71 Delhi Delhi 74,411 142,297 67,886 8-11
5 11 294 Inland Northern Karnataka 59,218 132,606 73,388 7-7
Average of top 5 Regions 63,375 143,665 80,290

8. Regions of Maharashtra (NSS)
Gondia
Bhandara
Nagpur
Wardha
Gadchiroli
Yavatmal
Amarawati
Akola
Buldhana
Washim
Hingoli
Nanded
Jalna
Parbhani
Latur
Beed
Osmanabad
Solapur
Sangli
Jalgaon
Dhule
Nandurbar
Nashik
Aurangabad
Ahmednagar
Pune
Satara
Kolhapur
Sindhudurg
Ratnagiri
Raigad
Thane
Mumbai
Chandrapur
271
272
273
275
2
7
4
276
Maharashtra
No. Region 2011-12 2004-05 2011-12 2004-05
271 Coastal 3 10 144858 61932
272 Inland Western 1 2 152128 84673
273 Inland Northern 18 24 76496 38690
274 Inland Central 8 9 119063 65923
275 Inland Eastern 13 12 103170 57019
276 Eastern 45 39 43455 24024
RVA Ranks RVA in Rs. Cr.

9. Agglomeration by RVA
9
2004-05 Top 10 by RVA 2011-12 Top 10 by RVA
Region RVA DV URB Region RVA DV URB
93 88157 0.58 11% 272 152128 0.72 37%
272 84673 0.64 33% 241 146435 0.67 40%
193 83017 0.83 42% 271 144858 0.87 79%
281 78069 0.64 27% 71 142297 0.85 92%
71 74411 0.84 92% 294 132606 0.61 29%
91 72248 0.72 27% 334 120948 0.79 45%
282 70249 0.63 26% 274 119063 0.50 28%
322 67056 0.86 26% 293 116828 0.79 51%
274 65923 0.46 23% 93 116284 0.69 13%
271 61932 0.85 72% 331 107189 0.84 53%
Average 74574 0.71 38% Average 129864 0.73 47%
National Average 29746 0.63 26% National Average 50530 0.70 29%
2004-05 Bottom 10 by RVA 2011-12 Bottom 10 by RVA
Region RVA DV URB Region RVA DV URB
11 3871 0.82 28% 12 7493 0.74 10%
131 2319 0.61 31% 221 7474 0.30 12%
121 2244 0.41 12% 131 5153 0.66 35%
142 1851 0.27 3% 111 3838 0.60 18%
141 1775 0.80 36% 121 3639 0.52 19%
151 1686 0.48 40% 151 3370 0.64 49%
12 1389 0.63 13% 141 2958 0.82 40%
111 1301 0.67 12% 351 2256 0.87 36%
183 1279 0.58 10% 142 2212 0.64 2%
351 1139 0.88 36% 14 556 0.85 13%
Average 1885 0.62 22% Average 3895 0.66 23%
National Average 29746 0.63 26% National Average 50530 0.70 29%
Gradual but definite
shift towards higher
levels of DV between
2004-05 and 2011-12.
Top 10 regions have
significantly higher
shares of urbanisation.
The bottom 10 RVA regions have a very low share of urbanization, around 22-23%.

10. Output (RVA) -- Mapped against Urban Shares
Source: RED 2022
§ Constructed output measures for
India at the regional level for the
first time.
§ Higher levels of urbanization are
highly correlated with higher
output levels.
§ However, this experience is not
associated with the presence or
absence of a million-plus city.

11. 4 Core questions attempted
• Are patterns of development more dynamic at the regional level compared to State level?
• Yes, across NSS regions
• What is the extent of sectoral diversity at regional levels? What is this diversity a function of?
• It depends! Not much in this research, skills possible suspect! Needs further work
• How can we measure differences in output at the regional level?
• Method used in RED is a mix of RBI, Mitra and Mehta 2011, UN Habitat guidelines; open for debate
• How are output levels influenced by the level of sectoral diversity, urbanization and other
factors?
• Not straightforward! Data a major issue!

12. Key Takeaways for Business and Policy
• Diversification clearly emerges as an important driver of the regional economy.
Business leaders need to find ways to identify sectors of strength.
• Focus on skilling and improving quality of workforce is essential if such diversified
economic regions are expected to grow and multiply
• The choice of locations for fresh investments, new offices, plants and markets should
take into account a wider set of emerging options
• Policy interventions for small cities and census towns to identify and develop sectors
of increasing economic complexity
• There seems to be merit in using the NSS region framework to define policy or
devolve an implementation protocol at the regional level
• It is desirable to develop data systems so that nuances at the micro level can be
captured and leveraged
Business
Government

13. How can regular economic data help?
Public Interest
Economic Development
Infrastructure Development Finance
Demand forecasting
Tailored Planning
Targeted infra
Revenue planning
Financial allocation
Bond market deepening
Private sector:
Tailored
investments &
diversification
Policy action
Tailored policies
Data points for academic
and civil society action
Example: Think Perth
Example: Scottish city
municipal bonds
Example: Cape Town 2040

14. Current level of complexity
Ease
to
jump
to
new
products
Low High
Low
High
Bridge over troubled waters
Strategic bets
Little space to improve quality and
few nearby capabilities
Stairway to heaven
Parsimonious industrial
policy
Help jump short distances to other
products
Let it be
It ain’t broke
Ample space to move
in all directions
Hey Jude: make it better
Competitiveness policy
Improve the conditions of the
sectors that already exist
The strategic setting based on complexity framework

15. What prevents economic monitoring of India’s regions and cities?
Absence of institutional
mechanism
• Central Statistics Office (CSO) estimates national
GDP, provides some sector estimates of state GDP
• State statistical departments estimate state GDP as
per CSO direction, some states go to district level
• CSO estimates rural-urban GDP for base years of
National Accounts Statistics. Last available for
2011-12.
Data gaps
• Periodic industrial surveys available. However,
do not have adequate sample size for region/
city level estimation of GDP
• No surveys of the organised services sector
• Censuses are dated – last population Census
data from 2011, last Economic Census in 2013
Absence of institutional
mechanism
Data gaps

16. Under consideration of the
Ministry of Housing and Urban
Affairs

17. Three-step approach to CEP estimation
Calculate rural and urban per
worker Gross State Value Added
(GSVA)
Step 1: State
Calculate urban Gross District
Value Added (GDVA) per worker
Step 2: District
Multiply per worker urban
GDVA with the number of
workers in the city
Step 3: City
GSVA by
PPC
GSVA by
PUC
Rural GSVA
Urban GSVA
Rural GSVA
Urban GSVA
Rural GDVA
Urban GDVA
Rural GDVA
Urban GDVA
Rural GDVA per
worker
Urban GDVA per
worker
Rural GDVA per
worker
Urban GDVA per
worker
CEP in the PPC
sector
CEP in the PPC
sector
Total CEP

18. CEP estimate (value in lakh crore)
*Population estimated using MoHFW projections
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
2.20
2.40
2.60
2.80
3.00
3.20
3.40
3.60
3.80
4.00
4.20
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19. Possible solutions
A. Use of
geospatial
data
Does tech, AI
etc. have a
solution,
even if
imperfect?
Premise in
Nigeria

20. Validation of CEP results
*Population estimated using MoHFW projections
City to district
population
CEP to District GVA City to district
nightlight
City to district built-
up area
89%
39%
63%
40%
73%
Bengaluru 82%
31%
59%
57%
89%
42%
75%
56%
68% 86%
84%
31%
47%
27%
61%
52% 54% 50% 47%
61% 66% 78% 58%
Pune
Jaipur
Lucknow
Nashik
Ludhiana
Indore

21. Possible solutions
• GST introduced in India in 2017
• GST returns collect a wealth of information
from taxpayers.
• Includes invoice level data on goods sold to
registered persons, and e-way bill data in case
of transportation of goods between buyer and
seller.
• Anonymized data would indicate the city
where the sale or purchase happens.
• More regular surveys of all sectors including
services and manufacturing
• Increased sample size at the city level for
estimation
B. Use of GST data C. Micro-level surveys

22. Liveability Economic-ability Sustainability
(>4800)
27
100 100 AMRUT 2.0 CITIES
SBM 2, AMRUT 2.0
27 Cities metro rail is either operational or under
construction
100 Smart Cities leverage digital technology to improve Ease of
Living & optimize resources utilization act as lighthouses for other
cities.
All Cities implementing DAY-NULM, PMAY, AMRUT 2.0 & SBM 2.0.
Sanitation, to address Poverty Alleviation & Affordable Housing
3-Tier National Programs for Urban Sector

23. Finally!
§ Deepen economic data generation and analysis at regional/ city levels
§ Policy environment wherein there is harmonious focus on economic growth at all levels
§ Understand local cause-effect relationships between economic growth and urbanization/ diversity etc.
§ Focused investments through the lens of Economic Complexity to get max bang for the buck
§ Transform local governments into economic development enterprises (triple-engine govt!)
§ Increased focus on smaller towns and cities
§ Prioritize understanding of tradeoffs between balanced regional development and economic imperatives
§ Possibly an Integrated Ministry/ empowered unit for Economic Growth, at all 3 levels - centre, state, city levels

24. Thank You