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What made Indian Cities and Towns Grow in the 2000s? Stylized Facts - - PowerPoint PPT Presentation

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What made Indian Cities and Towns Grow in the 2000s? Stylized Facts and Determinants Rana Hasan*, Yi Jiang*, and Debolina Kundu** India Policy Forum July 11-12, 2017 *Asian Development Bank and **NIUA The views expressed in this presentation

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What made Indian Cities and Towns Grow in the 2000’s? Stylized Facts and Determinants

Rana Hasan*, Yi Jiang*, and Debolina Kundu** India Policy Forum July 11-12, 2017

*Asian Development Bank and **NIUA The views expressed in this presentation are those of the authors and do not necessarily reflect the views and policies of the ADB or its Board of Governors or the governments they represent.

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Outline

  • 1. Background and motivation
  • 2. Data
  • 3. Results:

– City size – City growth

  • 4. Discussion
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  • 1. Background and motivation
  • Cities are widely believed to be “engines of

growth” … and (better) jobs

  • But, are there certain characteristics that cities

should possess to enable them to play this role?

  • Some skepticism about urbanizations

underway in the developing world (e.g., the “self-organized” cities of Hendersen, 2014; “consumption cities” of Gollin et al, 2016)

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The Indian context

  • Though slower than others, India

is urbanizing. Will this be good for growth? Will it generate more productive and better paying jobs?

  • Considerable concern that India

may be losing an important

  • pportunity (Ahluwalia et al,

2014; Kundu and Samanta, 2011; and GoI 2011)

– Urban infrastructure – Economic and spatial planning – Governance frameworks

10 15 20 25 30 35 40 45 50 55 60 Urban population (% of total)

Urban share, 1960-2015

Source: World Development Indicators Database.

PRC INO THA IND VIE

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Empirical literature

  • The empirical literature on urban issues in India is small but

growing.

  • Much of the work so far has been at the district level

– Chauvin et al (2016) on the importance of urban agglomeration

  • economies. They find 7-8% elasticity of nominal wages to

district urban density and a larger elasticity w.r.t. population – Lall et al. (2004) find little benefit of urban density at district level on firm output in India – Ghani et al. (2014) on whether the structure of economic activity influences employment growth. They find more diverse industrial structures associated with higher employment growth—but, this is driven by rural India

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Empirical literature (2)

Recently, more studies at the city level are emerging

  • Transport infrastructure and city/urban

growth (e.g., Alder, Roberts, and Tewari, 2017)

  • Size of urban agglomeration economies

(Hasan, Jiang and Rafols, 2017)

  • Urban form and shape of cities and economic

growth (Harari, 2016 and Tewari et al, 2017)

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SLIDE 7
  • Some takeaways:

– Unit of analysis matters. Example: Agglomeration economies seem to be smaller when switching from district to cities as the unit of analysis – Use of satellite image based data is growing

  • Better connected and more compact cities experience faster

economic growth (as proxied by nightlights data)

  • In this paper:

– Take a step back and use a traditional but rarely used combination of data: Population and economic censuses – Examine urbanization over 2001 and 2011 to see how city-level economic structure, infrastructure/connectivity, and human capital are related to city size and growth

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Main results

  • Notwithstanding the rapid growth of small towns between 2001

and 2011, urbanization has been driven by larger cities and higher income states

  • Larger cities score higher on measures of human capital,

infrastructure access, more diverse economic structures, larger share of employment in formal firms, etc.

  • However, not all of these factors seem to matter for city growth.

Measures of city level human capital and infrastructure are surprisingly poor correlates of urban growth

  • Connectivity to other locations and an economic environment

conducive to manufacturing, and, especially, new firm formation seem to be better predictors of urban growth

  • Results are consistent with the idea that Indian cities are

“centers of production”; urban policy should help cities work as labor markets.

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  • 2. Data
  • Population census: Town directory and primary census abstract, 2001 and

2011 – Urban centers classified as statutory towns and census towns. Urban agglomerations (and outgrowths) have been identified from the Primary Census Abstract. – Terminology: Urban centers with population 100,000 or above termed as cities; below that are towns. – City/town-level information on population, infrastructure (roads, electricity), literacy, educational and other social infrastructure, etc.

  • Economic Census 1998

– Covers establishments in all economic activities except crop production and plantation; location ID variables available at state, district, and city/town levels – Provides information on industry of activity (4-digit NIC 1987), total and hired employees, years of operation (a proxy for age), registration status, ownership type – Use data for 22 manufacturing and 22 service sectors to create various measures, including share of employment in manufacturing, in firms with 10+ workers, share of young firms, and measures of diversity and specialization of employment structure.

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Data (2)

  • Road connectivity and market access

– We compute the straight-line distance from a city’s center to the nearest state highway and to the nearest national highway or expressway by 2001 as two measures of a city’s road connectivity. – For market access, we consider each city’s access to the largest 74 cities with population above 500,000 in 2001 and calculate: where dci is the distance form city c to one of the 74 large cities, city i travelled through available road network. dci is set to 1 if c=i.

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Matching of town directories across 2001 and 2011

  • Limit attention to urban centers with population of 10,000+

in 2001

  • Use town IDs to merge the town directories, checking town

names, district names and state names to verify and match.

  • Information on composition of urban agglomerations (UA)
  • btained from primary census abstract; UAs are treated as

as an integrated city/town

  • Trim sample to towns/cities with population growth greater

than -50% and less than 500%; and area growth greater than -10% and less than 500%.

  • Final dataset: 2,427 cities/towns belonging to 502 districts

across 21 states and 4 union territories

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SLIDE 12

City size and growth

Population Size OFFICIAL NUMBERS OUR SAMPLE 2001 2011 Pop. Growth (%) Cont. to Growth 2001 2011 Pop. Growth (%) Cont. to Growth Below 100k 79,181,305 98,025,832 23.8 22.6 63,975,854 74,617,788 16.6 18.6 100k and above 200,098,105 264,745,519 32.3 77.4 182,180,228 228,831,647 25.6 81.4 Total 279,279,410 362,771,351 29.9 100.0 246,156,082 303,449,435 23.3 100.0

Population Size OFFICIAL NUMBERS OUR SAMPLE 2001 2011 Pop. Growth (%)

  • Cont. to

Growth 2001 2011 Pop. Growth (%)

  • Cont. to

Growth

Class I: 100k and above 200,098,105 264,745,519 32.3 77.4 182,180,228 228,831,647 25.6 81.4 Class II: 50K to 99,999 27,192,982 32,179,677 18.3 6.0 23,592,327 27,410,437 16.2 6.7 Class III and IV: 10K to 49,999 51,988,323 65,846,155 26.7 16.6 40,383,527 47,207,351 16.9 11.9

Total 279,279,410 362,771,351 29.9 100.0 246,156,082 303,449,435 23.3 100.0

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City-size distributions: 2001 vs 2011

.2 .4 .6 8 10 12 14 16 Log Population

2001 Low Income 2001 High Income 2011 Low Income 2011 High Income

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Initial population versus population growth

Ludhiana Delhi Jaipur Agra Lucknow Kanpur Varanasi Patna Kolkata Indore Bhopal Ahmadabad Vadodara Surat Greater Mumbai Nagpur Hyderabad Visakhapatnam Bangalore Kochi Chennai Coimbatore Madurai

  • 100

100 200 300 400 10 12 14 16 18

Log Population, 2001

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SLIDE 15

Matching of Town Directory 2001 and EC-4 (1998) by urban centers

  • EC-4 follows spatial boundaries as of August 1997
  • Used secondary sources such as the administrative atlas of

India, pin code database, digital maps and other online resources to track changes in boundaries and match locations with population census 2001

  • Nature of city/town level activity captured by various

measures:

– Share of manufacturing employment (22 industries) in “total” employment (22 mfg + 22 services industries) – Share of employment/firms in 10+ worker firms (to capture formality); also 100+ worker – Share of employment/firms in young firms (<=5 years of

  • perations)

– Indexes of diversity and specialization

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Summary Statistics

Variables Full Sample 10K to <50K 50K to <100K 100K to <500K 500K to <1 mil > 1 mil Number of Cities 2,427 1,735 341 283 36 32

Population, 2001 101,424 23,276 69,186 187,925 714,945 3,226,858 Population Growth 2001-2011: log(2011/2001) 0.153 0.143 0.138 0.210 0.261 0.224 Literacy Rate 0.744 0.737 0.767 0.777 0.782 0.792 Electricity Connections per 10000 population, 2001 1,705 1,673 1,786 1,791 1,893 1,610 Pucca Road Density (Kms of Road per sq. km of Area), 2001 4.005 3.552 4.163 6.119 5.879 6.100 Diversity Index, Manufacturing only 0.799 0.759 0.838 0.906 1.074 1.258 Specialization Index, Manufacturing only 7.067 6.906 7.813 7.325 5.950 6.813 Manufacturing employment share to manuf & srvcs employment 0.266 0.264 0.266 0.268 0.294 0.339 Share of young firms 0.396 0.390 0.417 0.396 0.450 0.440 Share of employment coming from 10+ firms in manufacturing 0.223 0.192 0.273 0.297 0.432 0.478 Share of employment coming from 100+ firms in manufacturing 0.076 0.056 0.110 0.128 0.168 0.270 Distance to state highway, 2001 6.0 6.6 4.4 4.2 6.3 4.6 Distance to expressway or national highway, 2001 12.5 13.8 11.8 8.2 2.7 1.9 Market access from G-roads data, 2001 194,820 194,865 190,096 199,423 206,504 188,775

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SLIDE 17
  • 3a. City Size and Human Capital

Variables Full Sample Low Income states/UTs High Income States/UTs (1) (2) (3) (4) (5) (6) Log (2001 population) Literacy rate 1.541*** 1.738*** 1.063** (0.284) (0.407) (0.394) District: Years of schooling 0.025** 0.020* 0.035 (0.010) (0.010) (0.022) PCA: College density 0.078** 0.161*** 0.037 (0.028) (0.047) (0.027) Constant 8.455*** 9.921*** 8.786*** 10.182*** 8.880*** 9.843*** (0.252) (0.076) (0.304) (0.057) (0.350) (0.175) Observations 2,113 2,120 1,176 1,171 937 949 R-squared 0.110 0.083 0.093 0.041 0.085 0.094 State Dummies YES YES YES YES YES YES

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SLIDE 18

City Size and Infrastructure

Variables Full Sample Low Income High Income Towns Cities (1) (2) (3) (4) (5) Log (2001 population) Electricity density

  • 0.000

0.000

  • 0.000
  • 0.000
  • 0.000

(0.000) (0.000) (0.000) (0.000) (0.000) Pucca road density 0.005 0.025** 0.002 0.001

  • 0.003

(0.005) (0.011) (0.002) (0.001) (0.005) Distance to state highway

  • 0.014***
  • 0.014***
  • 0.014**
  • 0.008***

0.009* (0.003) (0.004) (0.006) (0.002) (0.005) Distance to expressway or Nat'l Highway

  • 0.014***
  • 0.014***
  • 0.014***
  • 0.003***
  • 0.013***

(0.001) (0.002) (0.002) (0.001) (0.003) Market access

  • 0.000

0.000

  • 0.000
  • 0.000
  • 0.000

(0.000) (0.000) (0.000) (0.000) (0.000) Constant 10.205*** 10.301*** 10.393*** 9.960*** 12.015*** (0.185) (0.122) (0.118) (0.103) (0.238) Observations 2,404 1,310 1,094 2,055 349 R-squared 0.123 0.103 0.126 0.123 0.135 State Dummies YES YES YES YES YES

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City Size and Economic Activity

(1) (2) (3) (4) (5) Log (2001 population) Unit of observation Full Sample High Income Low Income Towns Cities Regressors Manufacturing only Share of Manufacturing Emp. 0.417*** 0.655*** 0.152 0.028 0.994*** (0.141) (0.215) (0.197) (0.120) (0.322) Diversity Index (mfg only) 2.570*** 2.393*** 2.715*** 1.170*** 1.420*** (0.148) (0.158) (0.230) (0.110) (0.169) Specialization Index (mfg only) 0.013*** 0.013*** 0.012*** 0.008*** 0.002 (0.002) (0.003) (0.003) (0.001) (0.004) Share of employment in 10+ worker firms (mfg only) 1.037*** 0.932*** 1.138*** 0.430*** 0.891*** (0.110) (0.136) (0.170) (0.073) (0.166) Share of Young Firms (mfg only) 0.195 0.355 0.036 0.055 0.759* (0.166) (0.285) (0.145) (0.099) (0.433) Constant 7.454*** 7.544*** 7.995*** 8.670*** 10.123*** (0.139) (0.240) (0.170) (0.111) (0.199) Observations 2,425 1,095 1,330 2,074 351 R-squared 0.345 0.307 0.359 0.210 0.351 State Dummies YES YES YES YES YES

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SLIDE 20
  • 3b. City growth
  • City populations grow on account of natural

increase (birth rate – death rate), migration, and expansion of city/town area

  • We adopt a simple framework to analyze the

determinants of city growth: ln popct – ln popct-1 = a*ln popct-1 + b*Xct-1 + ect

  • This “difference-on-levels” formulation is

appropriate in contexts where mobility of population across cities is imperfect

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Demography and migration

Variable (1) (2) (3) (4) Population Growth

Log 2001 population 0.026*** 0.026*** 0.025*** 0.027*** (0.005) (0.005) (0.005) (0.005) D: age 0 to 5 (ratio to pop), urban 1.844*** 1.943*** (0.486) (0.469) D: Internal migration (ratio to pop), urban 0.062** (0.025) D: Intra district migration (ratio to pop), urban

  • 0.004

(0.021) D: Inter district migration (ratio to pop), urban

  • 0.007

(0.049) D: Inter state migration (ratio to pop), urban 0.188** (0.068) Constant

  • 0.129**
  • 0.295***
  • 0.134**
  • 0.337***

(0.051) (0.071) (0.050) (0.066) Observations 2,427 2,120 2,111 2,111 R-squared 0.121 0.145 0.131 0.144 State Dummies YES YES YES YES

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SLIDE 22

City growth and human capital

Variables Full Sample Low Income High Income (1) (2) (3) (4) (5) (6) (7) (8) (9) Population Growth: Log(2011 population / 2001 population)

Log 2001 population 0.019*** 0.025*** 0.026*** 0.027*** 0.024*** 0.024*** 0.010 0.027** 0.027** (0.006) (0.005) (0.005) (0.005) (0.003) (0.003) (0.010) (0.011) (0.010) Literacy rate

  • 0.181***
  • 0.182***
  • 0.201***

(0.033) (0.045) (0.041) D: Years of schooling

  • 0.001
  • 0.001

0.001 (0.002) (0.003) (0.004) PCA: College Density 0.001

  • 0.002

0.003 (0.003) (0.005) (0.004) Constant 0.093

  • 0.111**
  • 0.129**

0.010

  • 0.071*
  • 0.050

0.204

  • 0.141
  • 0.144

(0.075) (0.051) (0.052) (0.069) (0.037) (0.035) (0.118) (0.098) (0.105) Observations 2,113 2,120 2,427 1,176 1,171 1,330 937 949 1,097 R-squared 0.144 0.128 0.121 0.095 0.091 0.090 0.191 0.157 0.144 State Dummies YES YES YES YES YES YES YES YES YES

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SLIDE 23

City growth and infrastructure

Variables Full Sample Low Income High Income Towns Cities (1) (2) (3) (4) (5) Population Growth: Log(2011 population / 2001 population)

Log 2001 population 0.025*** 0.023*** 0.028** 0.015** 0.013* (0.005) (0.003) (0.010) (0.007) (0.007) Electricity density

  • 0.000*
  • 0.000
  • 0.000
  • 0.000**

0.000 (0.000) (0.000) (0.000) (0.000) (0.000) Pucca road density

  • 0.000
  • 0.001
  • 0.000
  • 0.000
  • 0.000

(0.000) (0.001) (0.000) (0.000) (0.000) Distance to state highway 0.000 0.000

  • 0.000
  • 0.000

0.001 (0.000) (0.001) (0.001) (0.000) (0.001) Distance to expressway or nat'l highway

  • 0.000**
  • 0.000*
  • 0.000
  • 0.001***

0.001 (0.000) (0.000) (0.000) (0.000) (0.001) Market access 0.000*** 0.000** 0.000*** 0.000*** 0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) Constant

  • 0.165***
  • 0.189***
  • 0.218**
  • 0.043
  • 0.128

(0.051) (0.045) (0.101) (0.068) (0.095) Observations 2,404 1,310 1,094 2,055 349 R-squared 0.170 0.124 0.207 0.161 0.382 State Dummies YES YES YES YES YES

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SLIDE 24

City growth and economic structure

Variables Full Sample Towns Cities (1) (2) (3) (4) (5) Log 2001 population 0.020*** 0.023*** 0.019*** 0.006 0.016 (0.006) (0.006) (0.006) (0.006) (0.010) Mfg employment share 0.093*** 0.066* 0.069* 0.072* 0.105 (0.032) (0.037) (0.035) (0.036) (0.080) Diversity Index 0.034 0.008 0.034 0.073*** 0.023 (0.023) (0.013) (0.029) (0.021) (0.036) Share of young firms 0.076*** 0.074*** 0.056** 0.034* 0.332** (0.019) (0.025) (0.021) (0.019) (0.139) Dummy: Large firms, Mfg 0.011 0.011 0.041

  • 0.040***

(0.017) (0.018) (0.025) (0.013) Dummy: Large firms, Ser

  • 0.003

(0.013) D: Years of Schooling

  • 0.003
  • 0.003
  • 0.002
  • 0.006

(0.002) (0.002) (0.003) (0.005) Market access 0.000*** 0.000*** 0.000** 0.000** (0.000) (0.000) (0.000) (0.000) Distance to express way/national highway

  • 0.000**
  • 0.000**
  • 0.001***

0.001 (0.000) (0.000) (0.000) (0.001) PCA: Infrastructure

  • 0.007
  • 0.007
  • 0.007
  • 0.001

(0.005) (0.005) (0.005) (0.014) Constant

  • 0.139***
  • 0.174***
  • 0.146***
  • 0.037
  • 0.168

(0.045) (0.048) (0.047) (0.061) (0.114) Observations 2,425 2,097 2,097 1,779 318 R-squared 0.135 0.189 0.189 0.188 0.432 State Dummies YES YES YES YES YES

Note: Numbers in column 2 correspond to "Manufacturing and Services" instead of “Manufacturing only” for diversity and share young firms.

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SLIDE 25

Encouraging manufacturing firm entry

Towns Cities Share of Young Manufacturing Firms (average per city) 0.3691 0.3889 Total number of Firms (average per city) 331 4,172 Total number of Young Manufacturing Firms (average per city) 122 1,622 2001 population (average per city) 35,943 651,942 Original predicted population growth (average per city) 0.1414 0.2153 Original predicted 2011 population (average per city) 41,404 808,540 Share of Young Manufacturing Firms (average) + 10 % points 0.4691

  • Resulting number of additional young manufacturing firms (average per city)

62

  • Resulting sum of additional Young Manufacturing Firms (over all towns/cities)

110,934 110,934 Share of young manufacturing firm + equivalent % point increase

  • 0.4725

Resulting predicted population growth (average per city) 0.1449 0.2445 Resulting predicted 2011 population (average per city) 41,547 832,529 Resulting additional population in 2011 (average per city) 143 23,989 Resulting total additional population in 2011 254,542 7,628,615 Share of Young Manufacturing Firms Coefficients 0.034* 0.332** Number of Towns/Cities 1,779 318

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SLIDE 26
  • 4. The link to policy
  • Larger cities are driving urban growth and are characterized by

features commonly associated with greater “dynamism”

  • Using the Gollin et al metric, continued urbanization along current

lines will be good for economic growth and incomes

  • But, a “big city” bias may lead to missed opportunities. Assuming

that the two elements of urban success—growing urban population and higher incomes—go together, interventions that (a) expand employment in mfg/tradeables and industrial diversity and (b) improve connectivity seem particularly effective in spurring urban growth in towns

  • At the same time, the benefits from firm entry seems to be much

larger in bigger cities.

  • Not surprising since entrepreneur characteristics are unlikely to be

the same. Plus, the benefits of agglomeration for entrepreneurship may be kicking in (Ghani et al 2011).

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SLIDE 27

Next steps in the research agenda

  • Explore how structure of economic activity is

evolving at the town and city level using EC-4 to EC-6

– Access to “years of operation” in EC-5 and EC-6?

  • Linking structure to better/more measures of

urban success

  • Linking structure to policy amenable factors

– Land management: firms and households – City form and within-in city transport – Ease of business and urban governance

  • Open to combination of qualitative and

quantitative analysis

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SLIDE 28

Appendix

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SLIDE 29

Manufacturing by 2-digit industry classification

NIC’98 2-digit Description 15 Manufacture of food products and beverages 16 Manufacture of tobacco products 17 Manufacture of textiles 18 Manufacture of wearing apparel; dressing and dyeing of fur 19 Tanning and dressing of leather harness and footwear; manufacture of luggage, handbags, saddlery, 20 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials 21 Manufacture of paper and paper products 22 Publishing, printing and reproduction of recorded media 23 Manufacture of coke, refined petroleum products and nuclear fuel 24 Manufacture of chemicals and chemical products 25 Manufacture of rubber and plastics products 26 Manufacture of other non-metallic mineral products 27 Manufacture of basic metals 28 Manufacture of fabricated metal products, except machinery and equipment 29 Manufacture of machinery and equipment n.e.c. 30 Manufacture of office, accounting and computing machinery 31 Manufacture of electrical machinery and apparatus n.e.c. 32 Manufacture of radio, television and communication equipment and apparatus 33 Manufacture of medical, precision and optical instruments, watches and clocks 34 Manufacture of motor vehicles, trailers and semi-trailers 35 Manufacture of other transport equipment 36 Manufacture of furniture; manufacturing n.e.c.

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Services by 2-digit industry classification

NIC’98 2-digit Description 50 Sale, maintenance and repair of motor vehicles and motorcycles; retail sale of automotive fuel 51 Wholesale trade and commission trade, except of motor vehicles and motorcycles 52 Retail trade, except of motor vehicles and motorcycles; repair of personal and household goods 55 Hotels and restaurants 60 Land transport; transport via pipelines 61 Water transport 62 Air transport 63 Supporting and auxiliary transport activities; activities of travel agencies 64 Post and telecommunications 65 Financial intermediation, except insurance and pension funding 66 Insurance and pension funding, except compulsory social security 67 Activities auxiliary to financial intermediation 70 Real estate activities 71 Renting of machinery and equipment without operator and of personal and household goods 72 Computer and related activities 73 Research and Development 74 Other business activities 80 Education 85 Health and social work 90 Sewage and refuse disposal, sanitation and similar activities 91 Activities of membership organizations n.e.c. 92 Recreational, cultural and sporting activities