indicators for istanbul +5 -...

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Stage III: Toolkits for Estimation of Indicators

6. UNCHS Toolkit for Istanbul +5 Indicators∗

Indicators for Istanbul +5

Indicator 3: House price and rent-to-income ratios

city level 1993 1998 Median house price $0 $0 A Median rent $0 $0 B Median annual household income $0 $0 C Median household income of renters $0 $0 D

House price / household income #DIV/0! #DIV/0! A/C House rent / household income #DIV/0! #DIV/0! B/D What changes do these results suggest in terms of housing affordability ?

Indicator 13: Consumption of water city level

daily household consumption / person:

1993

1998

in all settlements - - liters/pers./day

in informal settlements - - liters/pers./day

What measures have been taken for reducing water consumption or increasing the water supply ? Is the demand-supply for water managed in an effective manner ? ∗ Abstract from UN HABITAT Istanbul +5 Indicators Toolkit

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indicator 19: transport modes

city level

% of work trips undertaken by: 1993 1998

If details are not available, please provide the sub-total for each main category.

1. Private motorised: Private cars 0.00% 0.00% % of all work trips Motorcycle 0.00% 0.00% % of all work trips sub-total 0.00% 0.00% % of all work trips

2. Train and tram: Train 0.00% 0.00% % of all work trips Tram 0.00% 0.00% % of all work trips sub-total 0.00% 0.00% % of all work trips

3. Bus and minibus: Bus 0.00% 0.00% % of all work trips Minibus 0.00% 0.00% % of all work trips sub-total 0.00% 0.00% % of all work trips

4. Non-motorised: bicycle 0.00% 0.00% % of all work trips walking 0.00% 0.00% % of all work trips others 0.00% 0.00% % of all work trips

sub-total 0.00% 0.00% % of all work trips

TOTAL 0.00% 0.00% should be equal to 100%

What do these changes suggest in terms of quality of life for the population ? What policies have been undertaken in order to improve the transportation system ?

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indicator 21: city product city level

1993 1998

City product $0 $0 per person Gross National Product $0 $0 per person Which method was used for the calculation of the city product (see definitions) ?

method A yes / no

method B yes / no What do this trend suggest in terms of urban economic development ? Are cities economies growing more that national economies ? (compare trends of city product with trends in gross national product).

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7. SDS Toolkit for Estimation of City-specific Indicators∗ Indicator: Household Income Distribution Definition: Annual household income (in Rs.) by quintile income range and median

household income at city level Results: Household income distribution

year

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

8996-10639

8781-12883

8570-15600

8359-18317

8154-21507

7954-25253

7758-29651

7567-34816

7381-40879

7200-47999

7023-56359

10640-22539

12884-26003

15601-30000

18318-33996

21508-38526

25254-43658

29652-49474

34817-56066

40880-63535

48000-71999

56360-81591

22540-29273

26004-34963

30001-41760

33997-48556

38527-56459

43659-65649

49476-76333

56067-88757

63536-103202

72000-119999

81592-139529

29274-30338

34964-38256

41761-48240

4855758223

56461-70274

65650-84819

76334-102373

88758-123561

103203-149134

120000-179999

139530-217252

interval

30339-61169

38257-81186

48241-107754

58225-134322

70275-167441

84820-208725

102374-260189

123562-324342

149135-404312

180000-504000

217253-628267

Average

9248 16541 25865 29694 45632

10572 19420 30464 36555 59659

12085 22800 35880 45000 77997

13598 26180 41296 53445 96335

15301 30061 47530 63476

118,985

17217 34517 54706 75388

146960

19373 39633 62964 89537

181512

21799 45508 72469

106340 224188

24529 52254 83409

126297 276898

27600 60000 96000

150000 342000

31056 68894

110492 178151 422409

Median

23,662

27,497

31,961

36,424

41,516

47,324

53,952

61,515

70,147

80,000

91,248

Data: Year Quintile Range

(Rs. per Annum) Average Income (Rs. per Annum)

1993 8,570-15,600 15,601-30,000 30,001-41,760 41,761-48,240 48,241-107,754

12,085 22,800 35,880 45,000 77,997

2000 7,200-47,999 48,000-71,999 72,000-119,999 120,000-179,999 180000-504000

27,600 60,000 96,000 150,000 342,000

Source: SDS Primary Survey

∗ Abstract from Society for Developing Studies (2000), Bangalore City Indicators Programme: Working Sheets

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Methodology: The interval ranges for the year 1993 are taken as base year and the data for the year 2000 have been collected through a primary survey. The annual growth has been estimated to project the data for the period 1991-2001.The rate of growth has been projected by using the following formula: R=[(p1/p2) ^ (1/n) -1] * 100 …(I) Or, p1=p2 (1+R/100) ^ (n) …(II) Where, p1 = income to be projected p2 = base year income R = rate of growth n = number of years

The median household income has been estimated by the following formula, Median Household Income=Lp+(50-Cn-1)*(Up-Lp)/Pn …(III) Where, Lp = lower limit of the median category Up = upper limit of the median category Cn-1 = cummulative percentage before median category Pn = percentage of observations in median category

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Indicator: House Price to Income Ratio Definition: Ratio of free market price of a median dwelling unit and the median annual

household income. Results: 1993 1994 1995 1996 1997 1998 1999 2000 12.52 13.43 12.59 13.40 13.40 13.77 14.05 14.34 Data: Distribution of Housing Stock: Housing Stock Categories

1993 1994 1995 1996 1997 1998

SFS-KHB HIG-BDA MIG-BDA HIG-KHB EWS-KHB LIG-BDA EWS-BDA MIG-KHB LIG-KHB Resettlement Private Squatter CITB Revenue Old-housing Allotted

523 628

1,009 658

2,264 2,266 2,562 2,604 3,390 5,201

60,000 66,000

129,312 59,682

290,803 236,925

523 628

1,009 855

2,270 2,266 2,562 2,732 3,426 5,383

63,400 70,517

129,312 63,367

290,803 268,215

523 628

1,009 1,052 2,275 2,266 2,562 2,860 3,462 5,539

68,350 75,343

129,312 88,539

290,803 278,271

523 628

1,009 1,095 2,276 2,266 2,562 3,186 3,595 5,915

72,500 80,499

129,312 114,446 290,803 289,911

523 628

1,086 1,165 2,278 2,311 2,562 3,382 3,708 5,943

74,450 86,008

129,312 134,047 290,803 312,102

524 628

1,086 1,286 2,280 2,311 2,562 3,494 4,995 5,943

75,800 91,894

129,312 167,342 290,803 322,650

Year

House price (Rs.)

1993 1994 1995 1996 1997 1998 1999 2000

SFS-KHB HIG-BDA MIG-BDA HIG-KHB EWS-KHB LIG-BDA EWS-BDA MIG-KHB LIG-KHB Resettlement Private Squatter CITB Revenue Old-housing Allotted

500,000 500,000 350,000 392,663 131,037 125,796 100,000 250,000 200,000 60,000 400,000 5,500 2,500,000 240,000 5,000,000 365,120

534,724 519,096 461,044 421,979 150,000 144,000 111,307 292,701 209,309 79,267 435,469 20,000 2,786,173 489,166 5,785,469 436,767

571,860 538,921 607,318 453,483 190,000 164,211 123,892 342,696 219,051 104,720 474,082 24,101 3,105,103 486,750 6,694,330 522,475

611,575 559,504 800,000 487,339 230,000 187,259 137,900 401,230 229,246 138,346 516,120 29,044 3,460,541 634,166 7,745,967 625,000

654,049 580,872 800,000 523,723 236,482 213,541 153,492 469,762 239,916 182,771 561,885 35,000 3,856,666 600,000 8,962,809 727,525

699,471 603,057 800,000 562,824 243,147 243,513 170,847 550,000 251,083 241,460 611,708 35,000 4,298,135 800,000 10,370,811 846,869

748,049 626,089 800,000 604,843 250,000 277,691 190,165 437,500 262,770 318,995 665,949 101,562 4,790,138 550,000 12,000,000 985,790

800,000 650,000 800,000 650,000 250,000 316,666 211,666 325,000 275,000 421,428 725,000 168,125 5,338,461 631,555 12,000,000 1,147,500

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Percentage distribution of housing stock Year 1993 1994 1995 1996 1997 1998 1999 2000Category %age %age %age %age %age %age %age %ageSFS (KHB)* 0.06 0.06 0.06 0.05 0.05 0.05 0.05 0.05HIG (BDA)* 0.07 0.07 0.07 0.06 0.06 0.06 0.06 0.06MIG (BDA)* 0.12 0.11 0.11 0.10 0.10 0.10 0.10 0.10HIG (KHB)* 0.08 0.09 0.11 0.11 0.11 0.12 0.12 0.12EWS (KHB)* 0.26 0.25 0.24 0.23 0.22 0.21 0.21 0.21LIG (BDA)* 0.26 0.25 0.24 0.23 0.22 0.21 0.21 0.21EWS (BDA)* 0.30 0.28 0.27 0.26 0.24 0.23 0.23 0.23MIG (KHB)* 0.30 0.30 0.30 0.32 0.32 0.32 0.32 0.32LIG (KHB)* 0.39 0.38 0.36 0.36 0.37 0.45 0.45 0.45Resettlement 0.60 0.59 0.58 0.59 0.57 0.54 0.54 0.54Private Builders 6.90 7.00 7.20 7.20 7.10 6.90 6.90 6.90Squatter 7.64 7.77 7.91 8.05 8.19 8.33 8.33 8.33CITB 14.97 14.25 13.57 12.92 12.31 11.72 11.70 11.70Revenue@ 6.91 6.98 9.29 11.40 12.80 15.20 15.20 15.20Old hsg. 33.66 32.05 30.52 29.07 27.68 26.37 26.37 26.37Allotted 27.43 29.56 29.21 28.98 29.71 29.25 29.30 29.30Total Housing Stock

905,027 948,468 993,994 1,041,706 1,091,708 1,144,110 1,199,027 1,256,581

Marketable Housing Stock

863,827 907,268 952,794 1,000,506 1,050,508 1,102,910 1,157,827 1,215,381

Note: * - Constructed housing units alloted by Bangalore Development Authority (BDA)

and Karnataka Housing Board (KHB) to households in the Economically Weaker Section (EWS), Low Income Group (LIG), Middle Income Group (MIG) and High Income Group (HIG)

@ - Housing units constructed on revenue sites Allotted - Housing units constructed by households on plots allotted by BDA, KSCB, KHB and Cooperative Societies

Identification of Median Housing Category Category Dist. Price-1993 C.P. Category Dist. Price-1994 C.P. Squatter 7.64 5,500 7.64 Squatter 7.77 20,000 7.77 Resettlement 0.6 60,000 8.24 Resettlement 0.59 79,267 8.36 EWS-BDA 0.3 100,000 8.54 EWS-BDA 0.28 111,307 8.64 LIG-BDA 0.26 125,796 8.8 LIG-BDA 0.25 144,000 8.89 EWS-KHB 0.26 131,037 9.06 EWS-KHB 0.25 150,000 9.14 LIG-KHB 0.39 200,000 9.45 LIG-KHB 0.38 209,309 9.52 Revenue 6.91 240,000 16.36 MIG-KHB 0.3 292,701 9.82 MIG-KHB 0.3 250,000 16.66 HIG-KHB 0.09 421,979 9.91 MIG-BDA 0.12 350,000 16.78 Private 7 435,469 16.91 Allotted 27.43 365,120 44.21 Allotted 29.56 436,767 46.47 HIG-KHB 0.08 392,663 44.29 MIG-BDA 0.11 461,044 46.58 Private 6.9 400,000 51.19 Revenue 6.98 489,166 53.56 SFS-KHB 0.06 500,000 51.25 HIG-BDA 0.07 519,096 53.63 HIG-BDA 0.07 500,000 51.32 SFS-KHB 0.06 534,724 53.69 CITB 14.97 2,500,000 66.29 CITB 14.25 2,786,173 67.94 Old-housing 33.66 5,000,000 99.95 Old-housing 32.05 5,785,469 99.99

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Category Dist. Price-1995 C.P. Category Dist-1996 Price C.P. Squatter 7.91 24,101 7.91 Squatter 8.05 29,044 8.05 Resettlement 0.58 104,720 8.49 EWS(BDA) 0.26 137,900 8.31 EWS-BDA 0.27 123,892 8.76 Resettle 0.59 138,346 8.9 LIG-BDA 0.24 164,211 9 LIG(BDA) 0.23 187,259 9.13 EWS-KHB 0.24 190,000 9.24 LIG(KHB) 0.36 229,246 9.49 LIG-KHB 0.36 219,051 9.6 EWS(KHB) 0.23 230,000 9.72 MIG-KHB 0.3 342,696 9.9 MIG(KHB) 0.32 401,230 10.04HIG-KHB 0.11 453,483 10.01 HIG(KHB) 0.11 487,339 10.15Private 7.2 474,082 17.21 Private 7.2 516,120 17.35Revenue 9.29 486,750 26.5 HIG(BDA) 0.06 559,504 17.41Allotted 29.21 522,475 55.71 SFS(KHB) 0.05 611,575 17.46

HIG-BDA 0.07 538,921 55.78 Allotted 28.98 625,000 46.44SFS-KHB 0.06 571,860 55.84 Revenue 11.4 634,166 57.84MIG-BDA 0.11 607,318 55.95 MIG(BDA) 0.1 800,000 57.94CITB 13.57 3,105,103 69.52 CITB(HOU) 12.92 3,460,541 70.86Old-housing 30.52 6,694,330 100.04 Old hsg. 29.07 7,745,967 99.93 Category Dist.-1997 Price C.P. Category Dist Price-1998 C.P. Squatter 8.19 35,000 8.19 Squatter 8.33 35,000 8.33 EWS(BDA) 0.24 153,492 8.43 EWS(BDA) 0.23 170,847 8.56 RESETTLE 0.57 182,771 9 Resettle 0.54 241,460 9.10 LIG(BDA) 0.22 213,541 9.22 EWS(KHB) 0.21 243,147 9.31 EWS(KHB) 0.22 236,482 9.44 LIG(BDA) 0.21 243,513 9.52 LIG(KHB) 0.37 239,916 9.81 LIG(KHB) 0.45 251,083 9.97 MIG(KHB) 0.32 469,762 10.13 MIG(KHB) 0.32 550,000 10.29 HIG(KHB) 0.11 523,723 10.24 HIG(KHB) 0.12 562,824 10.41 Private 7.1 561,885 17.34 HIG(BDA) 0.06 603,057 10.47 HIG(BDA) 0.06 580,872 17.4 Private 6.90 611,708 17.37 Revenue 12.8 600,000 30.2 SFS(KHB) 0.05 699,471 17.42 SFS(KHB) 0.05 654,049 30.25 MIG(BDA) 0.10 800,000 17.52 Allotted 29.71 727,525 59.96 Revenue 15.20 800,000 32.72

MIG(BDA) 0.1 800,000 60.06 Allotted 29.25 846,869 61.97

CITB 12.31 3,856,666 72.37 CITB 11.72 4,298,135 73.69 Old hsg. 27.68 8,962,809 100.05 Old hsg. 26.37 10,370,811 100.06

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Cat*-1999 Dist Price C.P. Cat-2000 Dist Price C.P. Squatter 8.33 101,562 8.33 Squatter 8.33 168,125 8.33 EWS(BDA) 0.23 190,165 8.56 EWS(BDA) 0.23 211,666 8.56 EWS(KHB) 0.21 250,000 8.77 EWS(KHB) 0.21 250,000 8.77 LIG(KHB) 0.45 262,770 9.22 LIG(KHB) 0.45 275,000 9.22 LIG(BDA) 0.21 277,691 9.43 LIG(BDA) 0.21 316,666 9.43 RESETTLE 0.54 318,995 9.97 RESETTLE 0.32 325,000 9.75 MIG(KHB) 0.32 437,500 10.29 MIG(KHB) 0.54 421,428 10.29 Revenue 15.2 550,000 25.49 Revenue 15.2 631,555 25.49 HIG(KHB) 0.12 604,843 25.61 HIG(KHB) 0.06 650,000 25.55 HIG(BDA) 0.06 626,089 25.67 HIG(BDA) 0.12 650,000 25.67 Private 6.9 665,949 32.57 Private 6.9 725,000 32.57 SFS(KHB) 0.05 748,049 32.62 SFS(KHB) 0.05 800,000 32.62 MIG(BDA) 0.1 800,000 32.72 MIG(BDA) 0.1 800,000 32.72

Allotted 29.25 985,790 61.97 Allotted 29.3 1,147,500 62.02 CITB 11.7 4,790,138 73.67 CITB 11.7 5,338,461 73.72 Old hsg. 26.37 12,000,000 100.04 Old hsg. 26.37 12,000,000 100.09 Note: * - The housing stock distribution for the years 1999 and 2000 is assumed to be as in

1998 Dist – Housing stock distribution in per cent C. P. – Cummulative Percentage Household Income Distribution (Rs. per annum)

Year

Interval 1991 L.L U.L.

1992 L.L U.L.

1993 L.L U.L.

1994 L.L U.L.

1995 L.L U.L.

1996 L.L U.L.

8996-10639 8781-12883 8570-15600 8359-18317 8154-21507 7954-25253 10640-22539 12884-26003 15601-30000 18318-33996 21508-38526 25254-43658 22540-29273 26004-34963 30001-41760 33997-48556 38527-56459 43659-65649 29274-30338 34964-38256 41761-48240 4855758223 56461-70274 65650-84819

Quintile I Quintile II Quintile III Quintile IV Quintile V 30339-61169 38257-81186 48241-107754 58225-134322 70275-167441 84820-208725 Average Income Quintile I Quintile II Quintile III Quintile IV Quintile V

9248 16541 25865 29694 45632

10572 19420 30464 36555 59659

12085 22800 35880 45000 77997

13598 26180 41296 53445 96335

15301 30061 47530 63476 118985

17217 34517 54706 75388 146960

Median income

23662 27497 31961 36424 41516 47324

Interval 1997 L.L. U.L.

1998 L.L U.L.

1999 L.L U.L.

2000 L.L U.L.

2001 L.L U.L.

Quintile I 7758-29651 7567-34816 7381-40879 7200-47999 7023-56359 Quintile II 29652-49474 34817-56066 40880-63535 48000-71999 56360-81591 Quintile III 49476-76333 56067-88757 63536-103202 72000-119999 81592-139529 Quintile IV 76334-102373 88758-123561 103203-149134 120000-179999 139530-217252 Quintile V 102374-260189 123562-324342 149135-404312 180000-504000 217253-628267 Average Income Quintile I Quintile II Quintile III Quintile IV Quintile V

19373 39633 62964 89537 181512

21799 45508 72469 106340 224188

24529 52254 83409 126297 276898

27600 60000 96000 150000 342000

31056 68894 110492 178151 422409

Median income 53952

61515 70147 80000 91248

Year Median House Median House Median Household House Price to

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Price Income Income Ratio 1993 Private 400,000 31,961 12.52 1994 Revenue 489,166 36,424 13.43 1995 Allotted 522,475 41,516 12.59 1996 Revenue 634,166 47,324 13.40 1997 Allotted 727,525 53,952 13.48 1998 Allotted 846,869 61,515 13.77 1999 Allotted 985,790 70,147 14.05 2000 Allotted 1,147,500 80,000 14.34 The median household income is worked out by using the following formula:

Median household income=Lp+(50-Cn-1)*(Up-Lp)/Pn …(I)

Where, Lp=lower limit of the median category Up=upper limit of the median category Cn-1=cummulative percentage before median category Pn=percentage of observations in median category The income ranges are arranged in five quintiles and each quintile comprises 20% of the observations. The most representative income range is the one in which the cummulative percentage is 50% and this range is the median household income category. Housing Stock Distribution – Assumptions:

1. There are, on an average, 2 dwelling units on each site allotted by CITB. 2. Allotted Housing category includes dwelling units on sites allotted by BDA, KHB,

KSCB, Cooperative Societies and these have been estimated on the assumption that there are, on an average, 3 DUs on each of these sites.

3. Old Housing category includes dwelling units constructed prior to the setting up of BDA and these have been taken as the housing stock in the city in 1976, excluding housing on sites allotted by CITB.

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Indicator: City Product Definition: Annual gross city product. Results:

Year Per Capita City Product (in Rs.)

City Product (in Rs. Lakh)

1991 11,153 460,645.241992 11,829 505,775.971993 13,341 590,492.551994 15,347 703,204.161995 17,317 821,368.211996 19,186 942,084.651997 21,516 1,093,683.201998 23,724 1,248,349.091999 26,971 1,469,174.002000 30,264 1,706,596.002001 33,951 1,981,855.30

Source: SDS Estimates Methodology: The steps to estimate the city product of Bangalore U.A. are as follows: 1. Estimation of Work Force Distribution for Karnataka and Bangalore 2. Harmonisation of data on workforce distribution and State Domestic Product 3. Estimation of Productivity per Worker at State-level 4. Estimation of City Product. Data: Step 1 Work-Force Distribution, Karnataka, 1991-2001 1991 1992* 1993* 1994* 1995* 1996* 1997* 1998* 1999* 2000* 2001*

I 5,915,633 5,989,870 6,065,038 6,141,150 6,218,217 6,296,252 6,375,265 6,455,270 6,536,279 6,618,305 6,701,360II 4,999,959 5,159,077 5,323,259 5,492,665 5,667,463 5,847,824 6,033,924 6,225,947 6,424,080 6,628,519 6,839,464

III 616,733 627,356 638,162 649,153 660,335 671,709 683,278 695,047 707,019 719,197 731,585IV 116,369 122,068 128,046 134,317 140,895 147,795 155,033 162,625 170,589 178,944 187,707Va 322,151 304,838 288,455 272,953 258,284 244,404 231,269 218,840 207,079 195,950 185,420Vb 1,528,975 1,580,319 1,633,387 1,688,236 1,744,928 1,803,524 1,864,087 1,926,684 1,991,383 2,058,255 2,127,372VI 427,972 447,550 468,024 489,434 511,824 535,238 559,723 585,328 612,104 640,106 669,388

VII 1,379,954 1,441,194 1,505,151 1,571,946 1,641,706 1,714,562 1,790,651 1,870,116 1,953,108 2,039,783 2,130,305VIII 454,964 465,951 477,203 488,727 500,529 512,616 524,995 537,673 550,658 563,955 577,574

IX 1,529,407 1,596,322 1,666,165 1,739,063 1,815,151 1,894,568 1,977,460 2,063,978 2,154,282 2,248,537 2,346,915Total 17,292,117 17,734,544 18,192,889 18,667,646 19,159,333 19,668,492 20,195,685 20,741,508 21,306,581 21,891,551 22,497,090

Source: Census of India, 1991 * - SDS Estimates

Work-Force Distribution, Bangalore, 1991-2001 1991 1992* 1993* 1994* 1995* 1996* 1997* 1998* 1999* 2000* 2001* I 10,336 10,592 10,855 11,125 11,401 11,684 11,974 12,271 12,575 12,887 13,207 II 10,409 10,707 11,013 11,328 11,652 11,985 12,328 12,681 13,044 13,417 13,800 III 9,069 9,424 9,792 10,175 10,573 10,987 11,417 11,863 12,327 12,810 13,311 IV 4,182 4,789 5,485 6,281 7,193 8,237 9,433 10,802 12,371 14,167 16,224 Va 18,168 17,674 17,193 16,726 16,271 15,828 15,398 14,979 14,572 14,175 13,790 Vb 444,708 462,330 480,650 499,696 519,497 540,083 561,484 583,733 606,864 630,911 655,912 VI 124,129 135,242 147,351 160,543 174,916 190,577 207,639 226,229 246,483 268,551 292,594 VII 305,849 326,043 347,571 370,521 394,985 421,065 448,867 478,505 510,099 543,780 579,684 VIII 115,208 119,123 123,171 127,357 131,685 136,160 140,787 145,572 150,518 155,634 160,922 IX 320,055 331,640 343,645 356,084 368,973 382,329 396,169 410,509 425,369 440,766 456,721 Total 1,362,113 1,427,565 1,496,727 1,569,836 1,647,146 1,728,935 1,815,496 1,907,144 2,004,222 2,107,098 2,216,165

Source: Census of India, 1991 * - SDS Estimates

Karnataka Gross State Domestic Product (SDP), at Current Prices (in Rs. lakh)

Economic Sector

1991- 92

1992- 93

1993- 94

1994- 95

1995- 96

1996- 97

1997- 98*

1998- 99*

1999- 2000*

2000- 2001*

2001- 2002*

• Agriculture(I+II) 1008896 109704 1276797 1476256 1676446 1801560 2023332 2272404 2552137 2866305 3219147

•Livestock, Forestry, Fishing(III)

88676 95448 100684 119778 130043 147697 163589 181191 200687 222281 246198

•Mining & Quarrying(IV)

25099 22905 31584 34727 39807 4353398 48603 54256 60566 67610 75473

•Manufacturing(Va+Vb) 532837 532837 580917 669428 781607 860425 781607 1082211 1213670 136113 1526508

• Construction(VI) 163255 205148 217737 256359 276969 305549 346493 389146 437050 495615 562027 •Trade & Commerce(VII)

699756 742858 876880 1057102 1208373 1350817 1541012 1757987 2005512 2287888 2610023

• Transport(VIII) 181321 213643 257128 302985 356950 429194 509882 6057409 719619 854907 1015630 • Other Services(IX) 357815 393704 470891 534778 618877 722491 831443 956825 1101114 1267162 1458250

Source: Directorate of Economics and Statistics, Government of Karnataka

* S.D.S. Estimates

Step 2 & 3 Karnataka (2001-2002) Economic sector SDP

(in Rs. Lakh) No. of workers Productivity

per worker (in Rs. Lakh)

• Agriculture (I+II) 3,219,147 13,540,824 0.238 • Livestock, Forestry, Fishing (III) 246,198 731,585 0.337 • Mining & Quarrying (IV) 75,473 187,707 0.402 • Manufacturing (Va+Vb) 1,526,508 2,312,792 0.660 • Construction (VI) 562,027 669,388 0.840 • Trade & Commerce (VII) 2,610,023 2,130,305 1.225 • Transport (VIII) 1,015,630 577,574 1.758 • Other Services (IX) 1,458,250 2,346,915 0.621 Total 10,713,256 22,497,090 0.476

Step 4 Bangalore U.A. (2001-2002) Economic sector No. of Workers Productivity

Per Worker (in Rs. Lakh)

City Product (in Rs. Lakh)

• Agriculture (I+II) 27,007 0.238 6,427.666 • Livestock, Forestry, Fishing (III) 13,311 0.337 4,485.807 • Mining & Quarrying (IV) 16,224 0.402 6,522.048 • Manufacturing (Va+Vb) 669,702 0.660 442,003.32 • Construction (VI) 292,594 0.840 245,778.96 • Trade & Commerce (VII) 579,684 1.225 710,112.90 • Transport (VIII) 160,922 1.758 282,900.88 • Other Services (IX) 456,721 0.621 283,623.74 Total 2,216,165 0.894 Rs.1,981,855.3 Per Capita city product,2001-2002 City product (in Rs. Lakh) = 1,981,855.30= Rs.33,950.72 Bangalore U.A. Population 5,837,447

8. SDS Toolkit for Developing Municipal Performance Audit Indicators∗

Indicator: Water Consumption

Definition: Per Capita Water Availability for Domestic Consumption from Municipal Sources

Significance: Comparison with per capita daily water requirement norm brings out the adequacy or otherwise of the municipal service as well as the access to water as per the standards for good living.

Result:

2000

Data Variables: Data Variable Data 1. Total Water Supply Daily (in million litres) (A) 2. Transmission Leakages (in MLD) (B) 3. Water Available Daily for Domestic Consumption through Distribution

Network (in million litres) (C)

4. Water Supplied Daily through Lorries (in million litres) (D) 5. Water Available for Domestic Consumption (E=C+D) 5. City Population (F) 6. Per Capita Water Availability for Domestic Consumption (in litres)

(E*1000000/F)

∗ Society for Development Studies (2002), Institutional Development of Urban Local Bodies in Tamil Nadu: Development & Implementation of Planning, Monitoring and Evaluation Indicators

Methodology: Step 1: Estimation of Total Water Supply per Day

i. Method A Data recorded by meters installed at:

- Pumping Station - Infiltration Well - Treatment Plant - Entry Point in the City - Overhead Tank - Booster Station - Storage Point

ii. Method B

Records of Metro Water/TWAD iii. Method C

Data Variable Data 1. Capacity of Water Reservoirs (in million litres) (A) 2. Number of Fillings of the Reservoir per Day (B) 3. Total Water Supply per Day (in million litres) (A*B)

iv. Method D

Data Variable Data 1. Operational Capacity of the Water Pumps (in KL/Hour) (A) 2. Number of Hours Pumps are Operated in a Day (B) 3. Total Water Supply per Day (in million litres) (A*B) 4. Average Electricity Consumption per Pump per Hour 5. Annual Electricity Charges paid for Water Pumps

Step 2: Transmission Leakages Data Variable Data

i. Transmission Leakages from Supply Point to Storage Tanks 1. Total Water Supply (in Million litres) (As in Step 1) (A) 2. Total Water received at the Storage Tanks (in Million Litres) (B)* 3. Leakages in Water Supply (C=A-B) ii. Transmission Leakages in Distribution Network 4. Number of Water Connections – ½”(D) 5. Number of Water Connections – ¾” (E) 6. Outflow from Water Connection – ½” (litres/minute) (F) 7. Outflow from Water Connection – ¾” (litres/minute) (G) 8. Duration of Water Supply (in minutes) (H) 9. Total Water Supply (in million litres) I = [(D*F*H)+(E*G*H)/1000000) 10. Leakages in Water Distribution (in million litres) (J=B-I) iii. Total Water Leakages (K=C+J) Note: * - With no direct line feeding Data Source: Recording by Municipality Step 3: Daily Water Available for Domestic Consumption Data Variable Data 1. Total Water Supply Daily (in million litres) (A) 2. Total Water Leakages Daily (in million litres) (B) 3. Total Water Available Daily (in million litres) (C=A-B) 4. Total Water Supply Daily for Non-domestic Use (in million litres) (D) 5. Daily Domestic Consumption of Water (in million litres) (C-D) Data Source: Total Water Supply Daily As in Step 1 Water Leakages As in Step 2 Total Water Supply Daily for Non-domestic Use Municipal Meter Ledger Water Supplied Daily through Lorries Lorry Log Book – Trip Sheet

Step 4: Per Capita Availability of Water for Domestic Use (lpcd) Water available for domestic consumption divided by city population Data Source for City Population: As in Indicator 1

Municipal Performance

Norms Score

Chennai Corporation 90 lpcd (Result *10/90) Other Corporation 90 lpcd (Result *10/90) Municipalities 70 lpcd (Result *10/70)

Indicator: Solid Waste Generation Definition: Per Capita Solid Waste Generation per Day, in Kilograms Significance: The indicator is the starting point for a solid waste management plan and defines the

extent of services required in the city in terms of collection, transportation and disposal facilities. The indicator also helps in estimating the future demand for preparing a long term strategy.

Result:

2000

Data Variables & Methodology: No. of Waste

Collection Vehicles (Operational)

No. of Trips per Day per Vehicle

Avg. Qty. of Garbage Collected per Trip (in tonnes)

Garbage Collection per Day (in tonnes)

1. Daily Garbage Collection (in tonnes) i. Bullock Carts (A) (B) (C) D=A*B*C ii. Tri-cycle (E) (F) (G) H=E*F*G iii. Auto-rickshaws (I) (J) (K) L=I*J*K iv. Dumpers/Hunters (M) (N) (O) P=M*N*O v. Tractors (Q) (R) (S) T=Q*R*S vi. Big Lorries Tippers

(U) (V) (W) X=U*V*W

vii. Big Lorry Non Tippers

(Y) (Z) (AA) AB=Y*Z*AA

viii. Small Lorries Tippers

(AC) (AD) (AE) AF=AC*AD*AE

ix.Small Lorry Non Trippers

(AG) (AH) (AI) AJ=AG*AH*AI

x. Others (AK) (AL) (AM) (AN)=AK*AL*AM

Total AO=D+H+L+P+T+X+AB+AF+AJ+AN

2. Number of Households Covered (AP) 3. Average Household Size (AQ) 4. Population Covered AR=AP*AQ 5. Per Capita Solid Waste Generation per Day (in Kgs.)

(AS=U*1000/X)

Data Source: Number of Operational Waste Collection Vehicles - Lorry Log Book Number of Daily Trips - Lorry Log Books - Trip Sheet Average Quantity of Garbage Collected per Trip - Weigh Bridge Records - Collection Register Number of Households Covered - As in Indicator 27 Average Household Size - As in Indicator 1 Benchmark:

Waste Generation

Per Capita Daily Chennai Corporation 0.75 kg Other Corporation 0.50 kg Municipalities 0.45 kg

Stage IV: Sub-city Approach for Using Indicators

9. Information Based Strategies for Urban Management

Bangalore City Report∗

Introduction The development process for making indicators an effective planning and policy tool require further refinements, after a set of demand-supply (D-S) models are developed. The demand-supply models help to identify the key contributing factors, which need to be addressed. In the case of providing housing for the poor, for example, the D-S model suggests the need to address the issue of lowering the cost of the housing solution on the one hand, and improving the incomes, on the other. But to make these approaches effective and sustainable, it was recognized by Society for Development Studies (SDS), that the analysis at the city level has to be disaggregated to the sub-city level and the major governance and management problems of weak coordination and convergence of operations of the key stakeholders have to be addressed. SDS-World Bank Policy Research Programme Recognising these components of the indicator development process to make indicators an integrated part of the operational toolkit for planners and decision makers as well as for the citizen group, SDS has collaborated with the World Bank+ in developing information-based strategies for urban management.. There are at least two main objectives of urban management. These are (a) to provide an environment conducive to economic growth and job creation, and (b) to ensure delivery of services to all segments of the population to reduce inequalities in opportunity among residents. At the same time, urban managers need to maintain fiscal sustainability by ensuring that urban services are funded through an appropriate mix of taxes, user charges, other levies, and transfers. In achieving these goals, urban managers face a complex set of inherent and emergent challenges. To address these challenges, urban decision makers need to adopt inclusive policies by involving all partners and stakeholders -- central, state and local governments, the private sector and citizen groups. Policies need to be based on credible, locally relevant information that is accessible to everyone in the city. To further these goals, the Bangalore City Report presents initial results from a collaboration between a forward looking urban administration, a non-profit research organization with extensive experience in the urban sector, and a research team from an international organization. The goal of this collaboration is to demonstrate the utility of information-based urban management approaches in rapidly growing urban areas. This work is embedded in other activities aimed at increasing transparency of urban decision making and in strengthening the capacity of local administrators. In order to improve the effectiveness of local policies and programs, it is important to develop local capacity and appropriate incentives for the collection and use of spatially detailed data, and related analytic methods to identify, evaluate, and prioritize policy and planning issues. To address some of these issues, the Society for Development Studies (SDS) and the Development Research Group (DECRG) of the World Bank have initiated a policy research program to examine the prospects of public dissemination and use of spatially detailed data in decision making leading to improvements in quality of life in urban areas, particularly those operating in weak institutional settings.

∗ Abstract from SDS – World Bank Report (2002), Information-Based Strategies for Urban Management + The SDS partners are the Development Research Group (DECRG) of the World Bank

The Steering Committee, Bangalore Local Urban Observatory has identified revenue expansion from own sources by local bodies and improvement in water supply services as the key concerns for improving the quality of life in Bangalore. To address these issues, a comprehensive household survey has been carried out covering the Bangalore municipal (Bangalore Mahanagar Palike) area. The Bangalore Urban Survey is designed to be representative of the Bangalore municipal area. All households of the city are part of the sampling universe with the exception of residents of military cantonments and institutional population (e.g., prisons). The sample size was determined to be 3,000 households (final sample size is 2,905). To ensure that all parts of the city were covered by the sample, the number of households in each ward is proportional to the number of households in that ward according to the preliminary estimates of the Census of March 2001. The master sampling frame consists of the most recently available electoral rolls. Within each ward, the sample taken is completely random. A diagnosis of income-based poverty has some inherent shortcomings and leaves out many dimensions of deprivation, which are important for welfare analysis. Policy making requires relevant and accurate information on the causes and characteristics of poverty. For a city like Bangalore, which is relatively more developed than the most urban areas in the country, the classification of the poor by their income alone may not be sufficient unless other elements such as cost of living and the benefits from asset accumulation are explicitly considered. The Bangalore urban survey uses a welfare indicator based on a comprehensive measure of household consumption. Consumption includes non-monetary benefits such as agricultural production and the stream of services from owning assets. All these non-monetary benefits are translated into monetary values (Rupees in Indian context) to make benefits from different goods or services comparable and obtain an aggregate welfare measure. The sample survey points to the considerable variation in welfare levels across the city, at the levels of wards, and across various housing categories. Rapid population growth in Bangalore within the span of two decades has exposed the disparities between the affluent, the middle class and the relatively poor segments of the city. There exists considerable heterogeneity in welfare levels between classes living in close proximity to each other. The welfare analysis in the Report covers issues relating to housing conditions, access to habitat services and educational attainment. Rapid population growth in Bangalore presents a number of unprecedented challenges. While the city enjoys one of the highest per capita income in the country, growth potential has not filtered down to all city dwellers. The varying levels of welfare reflected in the household survey are therefore an indicator of the highly uneven pattern of growth. City managers in Bangalore may take solace in the fact that the standard of living in Bangalore is still higher than that of other Indian cities. However, the persistence of pockets of poverty is an issue of concern. Since the constituency of the poor is fluid, the dangers of it increasing over time cannot be negated. Employment in the informal sector has increased considerably, and given the wide disparity in consumption between the highest and the lowest quintile of households, the gap between communities is definitely on the increase. There is an urgent need to make delivery of basic services more efficient, so that the poor can be integrated into the mainstream economy. This not only increases their productivity, but also their chances of attaining a relatively higher quality of life. Providing adequate public services to a rapidly increasing urban population is a continuing challenge worldwide. As cities grow, especially in the developing world, the gap between demand and supply of basic urban services appears to be growing. The household survey data illustrate this scenario analytically for Bangalore. The sub-city level insights provided by this database are useful in examining the dominant causes for under-provision of services and evaluation of some possible options to address these gaps. The Report looks at the two central concerns with respect to water services in Bangalore. The first is the availability in relation to the requirements considered necessary for agreeable living conditions. The other is the distribution across the city’s area and across the income strata of the population, particularly the low-income and poorer sections. The principles of good governance, as elucidated in the UN Global Campaign on Good Urban Governance has highlighted equity as one of the critical requirements. In this study, as per the consensus of the

key city decision makers, the analysis of urban services is restricted to water delivery operations. However, the methodology developed for this purpose and the analytical framework used are equally relevant to other components of the urban infrastructure and services. The objectives of the analysis of the Bangalore Urban Household Survey data are to facilitate the design of strategies to provide people with adequate access to safe drinking water, reduce distributional inequities in terms of coverage and per capita availability, devise water conservation measures, and analyze households’ willingness to pay for service improvements, which may be used to review the tariff structure for cost recovery. Given the intermittent water supply that increases coping costs, and the wide gap between needs and supply, the survey examined the satisfaction level of households with their access to water and improvements desired. Households were asked to score various aspects of their current water supply situation on a scale of one to five (dissatisfied / somewhat dissatisfied / neutral / somewhat satisfied / satisfied). Following the questions on the satisfaction with current water supply and the desire for improvements in the services, respondents were asked what type of improvements they would like to obtain. Sample households were asked how much they would be willing to pay for the service improvement after determining the choice of the desired service improvement,. Rather than requesting a single price, the methodology is based on estimating a range across which households may be willing to pay for improvements. This enables estimation of the uncertainty associated with every response. This uncertainty can be due to many factors. These include an individual’s or household’s own characteristics and preferences ( tenure stability, education status, present and future incomes, or household size), incomplete information on the potential benefits of water supply, and the level of credibility of the service provider vis-à-vis the improvements assured. Importantly, households may also be unsure about their ability to pay increased charges in the future. Poorer households with fluctuating income may prefer inferior, but cheaper service access, rather than committing to higher payments. Initiatives originating in communities are currently not playing a major role in provision of water services in Bangalore. For low income urban areas in developing countries, community action has been suggested as an effective means for reducing shortcomings in access to services. Although the Bangalore Urban Household Survey does not ask specifically about contributions to the improvement of water supply services, a more general question was posed to assess the potential for such efforts. Property taxes are one of the main sources of local government ‘own source revenues’ and often account for 70-80 percent of local government finances. With rapid population growth, local governments are under considerable pressure to increase fiscal capacity for providing services. The situation is exacerbated by decentralization which is accompanied by additional expenditure responsibilities (more mandates for providing services, added administrative responsibilities) without concomitant increase in the resource base (fewer transfers, time lag between expenditure and revenue decentralization). Many local governments have initiated self-reporting (self assessment) of property tax information to reduce administrative costs associated with tax collection. Self assessment has several benefits which include reducing costs of administration and reporting, as well as reduce opportunities for informal agreements between home owners and appraisers which are a dead weight loss to the local government. Self assessment however can provide incentives to misreport if penalties for misreporting are not severe and accurate information on the assessed value of the property (either from annual value or capital value) is not easily available. However, in the presence of reliable exogenous (which are easy to monitor and difficult to misreport) information on housing and neighborhood characteristics, it is possible to evaluate the accuracy of self assessed taxes. Follow up appraisals can be carried out if there are significant variations between self reported and predicted assessments. In this perspective, the Self Assessment Scheme, introduced by Bangalore Mahanagar Palike in April 2000 for reporting property taxes is examined in the Report.

The Government of Karnataka has amended Section 109 of the Karnataka Municipal Corporation Act, 1976 permitting a move from the Annual Rental Value System to a Capital Value System for the purpose of property tax assessment. According to the capital value system, property tax will be levied on the total cost of the property. The capital value and annual value property tax systems are assessed in the Report in terms of revenue potential and equity considerations.

Quality of Life in Bangalore Sample Population Structure

1. The 2905 survey households represent a population of 13,453. The average size of sample households is 4.6 and is comparable to the average of 4.5 for the Bangalore municipal area (provisional results, Census 2001). The household size varies marginally across zones, and is the lowest in west zone and highest in east zone. The sub-city data on household size is an important input for assessing the habitat requirements, both in terms of living space and urban services

2. The sample households have a female male ratio of 875, which is lower than the average for Bangalore district (906, Census 2001). Although the Census 2001 data are not available for Bangalore municipal area, the municipal population would have a lower female-male ratio compared to the district population due to large existence of single male earning member migrant households. This is reflected in the population pyramid in Figure 1, which shows a much larger number of males in economically active age than females. These migrants come alone to the city, in search of employment, and stay with relatives initially. Although these single member households are using the urban services, the service providers do not incorporate their requirements in the city plans. Since many of the services are charged on a flat rate, the additional usage is a net revenue loss to the service providers.

Figure 1: Age and Sex Distribution of the Survey Sample Population

200

400

600

800

1000

0-4

10-14

20-24

30-34

40-44

50-54

60-64

70-74

80-84

200

400

600

800

1000

femalemale

200

400

600

800

1000 200

400

600

800

1000

0-4

10-14

20-24

30-34

40-44

50-54

60-64

70-74

80-84

200

400

600

800

1000

femalefemalemalemale

Number of persons

Age

gro

up

Source: Bangalore Urban Household Survey, 2001

Migration

3. The high economic growth of the city is attracting a large number of migrants who contribute to the continuing rapid population growth. The migrants form 14.3 percent of the sample population. There has been a spurt in migration rates lately due to the fast growth of computer hardware and software industries in the recent past; 16.6 percent of the migrants in the sample households have migrated in the past 5 years. Employment, marriage and displacement of the family are the three major reasons for migration to Bangalore (Table 1). Bangalore is attracting migrants not only from the southern region but also from other parts of the country. In fact, the other three southern states, Tamil Nadu, Andhra Pradesh and Kerala, contribute less than one-fourth of the migrant population in Bangalore.

Table 1: Migration patterns

Reason for Migration

Percent of households

Employment 27.1 Education 5.7 Business 3.6 Marriage 21.2 Moved with family 29.1 Natural disaster 0.3 Others 2.3 No response 10.7


4. Geographically, there is some indication that more recent migrants tend to live nearer to the outskirts of the city (Figure 2). These maps need to be interpreted with caution. The survey includes information only about when the household members moved to Bangalore and their current residence, but not whether the household has moved within the city since first arriving in Bangalore.

Figure 2: Geographic Patterns of Migration

Source: Bangalore Urban Household Survey, 2001 Note: Black points show the survey households in the relevant category that is mapped. For reference, all remaining survey households are shown as light gray points.

Welfare Measurement in the Bangalore Survey

5. The quality of life in Bangalore has deteriorated over the last decade despite high per capita city product, estimated at Rs. 33,951 in 2001 (BMRDA 2000), a 12 percent annual growth the last decade. The key indicators on human settlements show a larger concentration of population in slum settlements, declining per capita living space but improved access to urban basic services. Slum concentration (7.57 percent) has increased in the last decade but is still lower than in other Indian mega-cities. The declining quality of life with rising per capita city product is because of increasing income disparity between households in the lowest and highest income quintiles is estimated to have increased from 1:4.93 in 1991 to 1:13.60 in 2001. Households in Bangalore are largely dependent on monetised income. A very low proportion of the sample households (4.6 percent) has linkages with the agricultural sector - cultivation and/or ownership of land. In the case of these households, their well-being level is enhanced by agricultural income and/or the supply of home-grown crops, fruits and vegetables.

6. A diagnosis of poverty based on income alone, however, leaves out many dimensions of deprivation that are important from the standpoint of welfare analysis. Policy making requires relevant and accurate information on the causes and characteristics of poverty. Are households poor on account of a lack of educational opportunities, ill-health, or both, which reinforce a lower position in the labour market? Or, are they poor because they do not have adequate purchasing power relative to the cost of basic goods? The dimensions of poverty may change as an economy progresses. In the industrialised world, poverty does not arise due to the deprivation of a few basic necessities. Instead poverty is defined in relative rather than absolute terms. For a city

like Bangalore, which is relatively more developed, the classification of the poor by their income alone may not suffice unless other elements such as cost of living are taken into account. The fact that the number of poor households (categorised on the basis of BPL) has decreased in the last decade does not necessarily mean that welfare levels of communities have increased.

7. From a practical point of view, income is also not an ideal measure of welfare. Household members often have several income sources, some of which are informal. Others may receive non-monetary in-kind payments such as food or manufactured goods. Income also tends to fluctuate, especially for the self-employed and informal sector workers. Finally, people are hesitant to reveal their income in survey questionnaires. For this reason, the Bangalore urban household survey uses a welfare indicator based on a comprehensive measure of household consumption. Consumption includes non-monetary benefits such as agricultural production and the stream of services from owning assets such as televisions or cars. All of these non-monetary benefits are translated into Rupee values in order to make benefits from different goods or services comparable and obtain an aggregate consumption measure. Consumption also tends to vary less than income, since households need to buy food using savings even if there is a shortfall in income. In survey interviews, households are more willing to provide information on goods owned, received or purchased than on income. In most welfare studies, consumption is therefore the preferred indicator of living standards (Deaton 1997).

8. Total consumption in the survey instrument is divided into broadly two main streams: food and non-food. Detailed information relating to average fortnightly consumption of cereals and pulses, along with weekly expenditures on items such as vegetables and milk, provides the estimates for the average annualised food expenditures. A similar section on non-food comprising expenditures related to education, health, maintenance and repair of the house and other household support services, provides the estimate of the non-food component in the household’s annualised budget. A third section based on the ownership of assets yields an estimate of the value of the benefits realized from owning the asset. Based on the year of purchase and purchase price, this consumption component is estimated as the difference between the value of the asset at the beginning of the year minus the value at the end of the year. Ownership of assets, such as major kitchen appliances, televisions or vehicles, tends to be a good indicator of household welfare. By estimating and summing up Rupee values for each of these components, a consumption aggregate for each of the 2,905 sample households has been obtained. Dividing by household size yields the per capita consumption value which provides the basis for comparisons across households.1

The Welfare Profile for Bangalore

9. Given the difficulties in measuring consumption and similar welfare measures, these indicators are most often used for welfare rankings rather than to establish absolute measures. While actual per capita consumption estimates in Rupees are mentioned below, this report is mostly concerned with households’ welfare levels relative to each other. A common way of expressing this is to look, for instance, whether the housing conditions of the poorest 20 percent of households (the first, or lowest quintile) differ markedly from those of the richest 20 percent. The survey results have been tabulated in the Report by quintiles. Table 2 provides the quintile ranges of consumption for the sample households. This stratification reflects not just the variations between quintiles but also suggests the range of inequity in the welfare index. The continuum of welfare for households, measured 1 Per capita household consumption is used rather than consumption per adult equivalent because of the difficulties in determining suitable equivalency scales. A preferable approach is to test the sensitivity of analysis results to different choices of equivalency factors or household economies of scale (see for example Dreze and Srinivasan 1997).

by annual consumption per capita, extends from a minimum of Rs.3,951 to a maximum of Rs.2,71,390– an inequity of roughly 69 times between the lowest and the highest quintile household. The median level of welfare for Bangalore city is estimated at Rs. 25,927. That means half of the sample households have lower, and half have higher per capita consumption levels. The distribution of per capita household consumption in the sample is shown in Figure 3.

Table 2: Per Capita Household Consumption Estimates by Quintiles

Quintiles Minimum Maximum

First 3,951 16,203 Second 16,217 22,102 Third 22,104 29,751 Fourth 29,767 41,639 Fifth 41,649 2,71,390

Source : Bangalore Urban Household Survey, 2001

Figure 3: Distribution of Consumption Across the Sample Households


10. Households will spend different proportions of their disposable income on various consumption items. Figure 4 shows what percentage of total per capita consumption accounts for each of four major consumption components in each of five welfare groups. For example, among the poorest 20 percent of households, food represents a little over 45% of their total consumption, while non-food items represent only about 28%. For the wealthiest households, this pattern is reversed. Food represents only 20 percent of total consumption for the wealthiest households Wealthier households also spend proportionally more on housing and utilities and derive a higher share of their total welfare from the ownership of assets.

11. A useful tool for representing the degree of inequality in a population is the so-called Lorenz curve. It plots the cumulative percentage of households against the cumulative percentage of consumption by those households. If every household had the same consumption, there would be perfect equality and the cumulative distribution would yield a straight line at a 45-degree angle (see Figure 5). The more the actual curve deviates from this straight line, the higher the inequality. For Bangalore we can see that, for instance, the poorest forty percent of sample households account for only 20 percent of total consumption (dotted lines in Figure 5). The often quoted Gini coefficient of inequality summarizes the information in a Lorenz curve graph. It is calculated as the area between the straight line and the curve as a fraction of the total area under the straight line. The further the line is from the straight line, the higher this fraction, and consequently the higher the Gini coefficient. The Gini for the Bangalore household survey is 0.32. This is identical to the Gini for all of India in 1992

according to the National Sample Survey. But inequality in the city of Bangalore is lower than in urban areas in India as a whole, which have a Gini of 0.38.

Figure 4: Shares of Consumption Components by Welfare Group


Figure 5: Lorenz curve of welfare distribution in Bangalore

Source: Bangalore Urban Household Survey, 2001xxxx

Figure 6: Geographic Distribution of Welfare Groups in Bangalore (Quintiles)

First Second

Third

Fourth Fifth


12. Figure 6 shows the geographic distribution of welfare groups in Bangalore. What is striking is that there is almost no striking clustering visible in these maps. Households in the highest welfare group live very close to households at the lower end of the welfare distribution. This is not to say that clusters of, for example, low income households do not exist. But these urban pockets of poverty, such as slums and squatter settlements, tend to be relatively small and located right next to areas with better-off residents.

13. At the level of planning zones, the east zone has the highest share of residents who are in the poorest consumption quintile (Table 3). Twenty three percent of households in that zone are among the poorest households in Bangalore while 17.8 percent of east zone households are among the wealthiest in Bangalore. In four of the wards in the east zone, more than half of the households are among the poorest, while in thirty other wards, the concentration of the poor ranged between 25 -50 percent. A review of the poorer households in the 4 wards - Bhashamnagara and Sevashrama in the West; Jagajivanaramnagara in the South and Benniganahalli in the East shows that 68 percent of the households reside either in notified squatter settlements or unauthorised revenue sites. The average family size was 6.4 with the majority of families living in either single or two room tenements. The west zone shows the largest share of households that are among the wealthiest twenty percent in Bangalore with 22 percent.

Table 3: Consumption Patterns across the City by Zone Across Quintiles (percentage distribution)

Consumption quintiles

Zones First Second Third Fourth Fifth Total

East 23.0 21.4 19.5 18.3 17.8 100.0 South 16.8 20.2 19.2 23.2 20.6 100.0 West 20.7 17.7 21.9 17.6 22.0 100.0


Vulnerability Issues

35. An important area of concern related to welfare is food security. Given the negligible share of agriculture in the city economy, and the small proportion of households having direct linkages with agriculture, the survey results provide limited insight on the issue of food security. The majority of households in Bangalore relied on private retail stores. A small percentage of ration card holders belonging mainly to the BPL category access the Public Distribution System. Less than one percent of households purchased all their provisions from the PDS. Most notably is that many households with higher per capita consumption were also accessing the PDS. Given the wide disparity in welfare levels, and the fact that different welfare groups have varying levels of defence against crisis situations, there is a need for safeguard mechanism for households on the verge of the poverty line. Any escalation in the prices of essential commodities hurts the poor most. They are more dependent on the PDS and city managers therefore need to ensure that the PDS system functions as intended and is not subject to misallocations of quotas to richer segments of the society.

36. About 7.5 percent of sample households are headed by females. Among these households a considerably larger proportion (30.9 percent) is in the poorest welfare group than in the better-off groups (Table 4). Among male headed households the distribution is close to the expected levels of one-fifth of households in each quintile.

Table 4: Welfare by Gender of Household Head

Consumption quintiles First Second Third Fourth Fifth Total

Male headed 19.2 20.1 20.2 20.2 20.3 100.0 Female headed 30.9 16.6 18.4 15.7 18.4 100.0


Housing Housing Categories

37. Housing and utility service delivery have been largely in the public domain in Bangalore. The inability of housing agencies to meet the habitat requirements of the people led to the evolution of informal housing development mechanisms. Revenue sites were agricultural lands that have been sub-divided and sold for residential use without conversion approvals from the planning agency. Vatarra are group homes that were initially built for industrial workers but are now part of the regular housing market. The weak land use

enforcement led to a large proliferation of sub-standard human settlements in the city and imposed a financial burden on the local government. The local bodies, under continuous public pressure for provision of urban basic services to the revenue sites, have extended trunk services to unserviced areas. But the recovery of the cost, through levies in the form of development charges on the house-owners, has been low. Land use data (1995) show that 35 percent of city area is under residential use.

38. Formal housing delivery mechanism, including public agencies, private sector and cooperatives, have been highly deficient in providing housing solutions to the people in Bangalore. The housing interventions by public agencies, Bangalore Development Authority (BDA), Karnataka Housing Board (KHB) and Bangalore Mahanagar Palike (BMP), have been in the form of developed sites and built up units. These account for 12.0 percent of the dwellings of the sample households. The interventions by other actors in the formal sector, private builders/developers, cooperative housing societies and employers, have been also limited and account for 10.6 percent of the housing units (Table 6).

Table 5: Distribution of housing categories

Housing category Percent of households

Non-notified squatter settlement 1.2 Notified squatter settlement 6.4 Resettlement 1.3 Unauthorized revenue site 55.9 Vatarra 2.1 BDA/KHB/BMP/EWS Plots 9.6 BDA/KHB/BMP/EWS Flats 1.1 Cooperative housing 2.1 Employee housing 3.3 Private builder 5.2 City Improvement Trust 11.7


39. The unauthorised revenue sites developed by private builders without planning permissions have provided affordable housing solutions to large sections of the population, particularly in the low- and middle-income segment of the market. More than one-half of the sample households are living in housing units constructed on unauthorised revenue sites. These sites were offered by land-owners without any basic services such as water, sewerage, electricity, access roads, etc. The housing was made habitable by the owners after they were given electricity connections by Karnataka Electricity Board (now Karnataka Power Transmission Corporation Limited - KPTCL) and by accessing ground water, either through individual or community action and construction of pit latrines. BMP is now regularizing these revenue sites on payment of development charges and providing municipal services. Revenue sites are also being increasingly brought into the property tax net.

40. The high migration rate is one of the causal factors for the development of revenue sites and squatter settlements. The survey data show that the west zone with a high concentration of migrants compared to other zones, has also a higher proportion of households living in squatter units and housing constructed on revenue sites (66.3 percent). The migrants constitute 17.2 percent of the population in the west zone as compared to 15.9 percent and 10.4 percent in south and east zones, respectively. The industrial and trading activities in the zone act as a pull factor for the migrants.

41. Figure 7 shows the geographic distribution of major groups of housing categories among the sample households. It shows clearly the very large importance of revenue sites all across the city area. Squatter settlements, in contrast, are localized in a few pockets. Privately provided housing is most prominent within an inner ring around the core area of the city.

Figure 7: Geographic Distribution of Housing Categories in the Sample2


Housing and Welfare

42. An inverse relationship exists between levels of welfare and the quality of the housing category. Approximately 76 percent of the households in the non-notified squatter settlements and over half in the notified squatter settlements are in the lowest quintile. Poor households are a smaller percentage in the BDA/KHB/BMP plots and flats and in private builder colonies. The linkages between housing type and welfare exist with respect to the variations in access to basic services. In non-notified and notified squatter settlements, resettlement sites and vatarras, the density and concentration of population tends to be greater, bringing more pressure to bear on the existing fragile network of services. Fights over water or toilet facilities are common phenomena. It is significant that in terms of incidence of crime, more than 20 percent of respondents in notified and non-notified squatter settlements and vatarras and 48 percent of respondents in resettlement sites, reported a perceived increase in levels of crime. While a possible increase in crime rates in the more affluent residential colonies is explicable in terms of income disparities, an increase in crime in the above mentioned housing categories is a reflection of the potential breakdown of social cohesion that can take place in the context of increasing marginalisation. Table 6 below presents welfare levels by housing category, while Table 7 shows differences in crime perception across housing types.

Table 6: Variation in Welfare Levels by Housing Type

Consumption quintiles (% share) Housing category Avg. con-

sumption per capita

First

Second

Third

Fourth

Fifth

Non-notified squatter settlement 13,140 76 20 - 4 - Notified squatter settlement 17,166 52 24 18 5 1 Resettlement 15,768 52 29 16 3 - Unauthorised revenue site 30,127 18 21 21 22 19

2 Squatter settlements include non-notified and notified squatter settlements; Public housing includes BDA/KHB/BMP/EWS plots and flats, cooperative housing, and employer housing; Private housing includes Vatarra, private builders and City Improvement Trust Board.

Vatarra 20,002 38 24 21 15 16 BDA/KHB/BMP/EWS plots 40,309 6 13 16 26 37 BDA/KHB/BMP/EWS/flats 36,806 18 12 18 27 24 Cooperative housing 34,069 8 18 21 28 24 Employer housing 25,818 31 19 19 20 11 Private Builders 47,129 3 10 22 20 45 City Improvement Trust Board 31,058 17 24 22 17 20


Table 7: Perception of Crime by Housing Type (percent)3

Housing Type Decreased considerably

Decreased somewhat

No change Increased somewhat

Increased considerably

Non-notified squatter settlement 2.9 2.9 73.5 17.6 2.9 Notified squatter settlement 5.9 9.1 63.2 17.3 4.3 Resettlement 2.6 7.9 44.7 34.2 10.5 Unauthorised revenue site 5.4 15.7 54.3 20.3 4.2 Vatarra 13.1 32.8 36.0 16.4 1.6 BDA/KHB/BMP/EWS plots 7.3 17.7 54.1 18.1 2.8 BDA/KHB/BMP/EWS/flats - 12.1 69.7 15.1 3.0 Cooperative housing - 18.0 57.4 9.8 14.7 Employer housing 12.8 20.2 44.7 17.0 5.3 Private Builders 6.5 16.4 48.7 18.4 9.9 City Improvement Trust Board 6.5 16.1 53.2 18.5 5.6

Total 6.0 15.8 54.0 19.3 4.8


43. The proportion of poorest households is considerably larger in informal housing categories (squatter settlements and resettlement colonies) compared to other housing types. More than three-fourth of the sample households in non-notified squatter settlements and more than one-half of those in notified squatter settlements and resettlement colonies are in the poorest consumption quintile. Government intervention in these settlements would directly contribute to poverty reduction in the city. The households living in dwellings constructed on revenue sites are almost equally distributed across the quintiles, corroborating the fact that these units have catered to the demand of all segments of the housing market.

44. The analysis by BMP property tax zones4 shows that a high proportion of households in the north zone are living in squatter settlements. This is because the wholesale trading activities located in the zone create a high demand for unskilled informal sector workers. West and South-west zones have a higher proportion of 3 The survey asked the respondent: “In your opinion, how has crime changed in this neighbourhood during the last five years?” 4 The six BMP property tax zones are north, west, south-west, south, east and north-east zones, although they do not follow ward or other easily defined geographic boundaries.

households living in housing constructed on revenue sites particularly in the wards on the periphery. Intervention by the service providers needs to be focused on these city zones for improving the access of households to habitat-related services.

45. Housing agencies, both in public and private sector, still consider housing to be largely supply driven and implement projects without an assessment of demand in different market segments. The high vacancy rate in the flats/apartments constructed in Bangalore may be because of the high preference of households for detached housing constructed on individual sites. The sample data show that a small proportion of households (3.5 percent) are living in flats/apartments. These households are largely concentrated in the north and east zones of the city. Larger congestion is evident in the dwellings located in the north and south-west zones of the city, both in terms of per capita living space and number of persons per room. This is partly because a higher proportion of households living in the north zone have put their dwellings to mixed use as is also the case in the west zone. House extensions to increase living space may not be feasible as these zones consist of highly congested areas.

10. Data Base at Sub-National Level for Decentralised Governance

Issues in the Local Monitoring Agenda∗

1. Participatory, Transparent and Accountable Governance The relationship between human development and good governance is now universally accepted. To quote the UNDP “Governance can not be sound unless it sustains human development”1. While the literature on governance has largely related to the national level, the need to focus on governance at the local level is emerging as a key urban management issue at the global level with projected scenarios of urban population crossing the 50 percent threshold in the new millennium. In the case of India, the Constitution (74th Amendment) Act has provided the legislative framework that has necessitated priority consideration being given to good governance at the city/town level while the debate is on as to what connotes good governance, the terms which come up most frequently are transparency, accountability, participatory, partnerships, demand-driven, people-oriented, and corruption-free. To meet most of these principles, a possible definition of good governance may be the mobilisation and utilisation of resources for meeting the development and operational requirements for optimal management of the city, which would also result in increasing the city product and promoting to its equitable distribution. Within this overall framework, the concern of planners, decision makers and city managers in India and other countries in the South, which are shifting from centralised planning and management to a decentralised approach, is how to bring about, within the

On the face of it, the achievements seem to be good. Women have been empowered, as also

the disadvantaged segments of the population through the implementation of the constitutional guarantee of their representation in legislative forums and positions of decisions making, capacity building inputs have been provided to the newly empowered personnel in city management, state governments have established State Finance Commissions, who have produced their documents and recommendations to devolve finance from the state to the local governments, among a range of such initiatives. The issue that has to be addressed at this stage, the gestation period of the decentralisation process, is: Has there been a marked difference in the conditions of the people, in the provision of services to the slum and squatter settlements, in the growth of city income/product, in the economic and financial viability of the city, in the proportion of people below the poverty line, in city level resource mobilisation, in the approach to devolution of funds from the higher tier governments, in the public expenditure pattern etc. At the same time, even if the constitutional amendment may have ensured formal participation of the hitherto deprived representatives of the people in the city management system. How effective has been the role of the newly empowered representatives. Has a participatory consultative process

∗ Paper Prepared by SDS Team

really taken off, are the planning and management processes more transparent and are the decision-makers effectively accountable. Has there been an improvement in the quality of consultations. In effect, has the India initiative been able to usher in the process of participatory, transparent and accountable governance and provide a model to the rest of the world. The type of response that may be received on these issues will be indicative of the effectiveness of decentralised governance. 2. Sub-National and Sub-City Data Base and Decentralised Governance On most of these issues, the response is largely qualitative, not quantitative and that too, limited. There is a growing fear that rather then seeing a demand-driven approach to city planning and management emerging, consequent to the constitutional changes, there are evidences of a demand-driving process gathering momentum, which may not necessarily be in the spirit of participatory, consultative and accountable processes, the keystones of good decentralised governance. Unless the stakeholders know adequately the ground situation, with information and data, their participation in the consultative process remains an academic exercise. The rider with this is that as Information is Power, unless all have equal access to this instrument of power in decision-making, the “haves” will continue to dominate the process at the cost of the “haves not”. Equity in access to information is critical to effective decentralised governance. A more serious constraint is that the level of disaggregation of the available data is not adequate to facilitate an effective decentralised process. Some, but not all of the required data are available at city level and almost none at the sub-city level. This possibly explains the lack of proper assessment of the impact of the decentralisation process, a study of the changes in the scenario after the great step forward in 1994. To usher in an era of participatory, transparent and accountable governance, which decentralised governance is expected to herald, the major requirement is to promote and support sub-city planning, resource allocations, revenue generation and management processes. The key issue for supporting and enabling this type of decentralised and participatory governance is a body of data base at the sub-national level, which adequately addresses the priority city management concerns and provides the base for efficient mobilisation and utilisation of resources. Fiscal decentralisation is one key mechanism that may contribute to improved fiscal efficiency in resource mobilisation and public expenditure, but in absence of other interventions, the devolution process has limited potential. 3. Sub-National Data The need for developing a strong sub-city data base for effective decentralised governance on a priority basis is the rapidly accelerating phenomenon of cities within cities, a city with the best of urban services and a city deprived of minimum civic amenities. This ground situation should be recognised in developing the sub-national and sub-city level data base for efficient city fiscal relations, both in terms of changing the pattern of city investment and service flows and the mobilisation of financial resources from within the city through a mix of taxes, user charges and other fiscal instruments. These data are equally important for planning and managing other aspects of the city programmes. The level of disaggregation of sub-national data base at the city level may be undertaken at the level of city wards/administrative zones as well as for city areas recognised, for

example, a clusters of informal settlements, concentrations of the poor and of the rich within the city. In this context, the important data issues at the sub-national level that have to be addressed should be those that will facilitate the development and implementation of the city level programmes that addresses the key city concerns and assessment, project preparation, monitoring and impact assessment of city initiatives. The communities have to be involved and that would include all categories of communities in the city, the rich and the poor, the educated and the illiterate, the formal settlements households, the informal settlement households, the professionals, the working class, the residents in the inner city, the CBD, the urban fringe, among others. While disaggregation of data at all these levels may be an impractical task in the short term, the sub-city level data base development programme should include it in the long-term perspective. However, even in the short-term, some level of disaggregation is essential, and this may be decided through the consultative process within the city. A few illustrative areas of weak, deficient and inadequate data base at the city level, and where immediate action is necessary for good urban governance, including fiscal management, are: ⇒ For each major city service, data on. among others. * the reach out *availability *quality ⇒ For major city infrastructure, data on *age structure of the assets O&M (Operation and Maintenance) expenditure *capacity utilisation *leakages and wastages *new asset capital formation ⇒ For city economic health, data on *the city product *the income disparities (quintile distribution and by spatial distribution) * local resource base * city poverty line *people in below poverty line (BPL) category ⇒ For city efficiency in planning and management, data on *the informal economic activities *informal settlements *garbage collection *tax collections and avoidance *land use pattern * time required for building permissions.

On these key aspect of the city performance depends the viability and sustainability of city management. A study of only the income and expenditure of local governments and analysis of the elasticity and buoyancy of city taxes, the priority concern of fiscal analysis, would be inadequate to the task of efficient city/sub-national governance. Spatial issues have to be integrated into the social, economic and financial framework of urban governance and management. The city infrastructure assessment analysis and requirements, together with the assessed capacity of the citizens to pay for the basic services in different spatial locations in the city are equally, if not more, important than the traditional city-level (overlooking the sub-city dimensions) fiscal approach to decentralised governance. The absence of city data on city product is a major limitation to take several important planning decisions. Its limits the utility of the elasticity-buoyancy analysis of tax instruments, often used to modify tax rates, as also all other policy and planning decisions that are related to city income,

city productivity, distribution of city households by level of income. The absence of city poverty line estimates has been a major reason for the failure of urban poverty alleviation programmes at the city level to take off and is also a constraint to asses the true affordability of the poor to pay for urban services. Lack of data on subsidy component of the pricing of civic amenities and their distribution among the different income groups in the city limits the efficacy of multiple pricing practices for urban services. City infrastructure assets data base is another weak area. These assets have been deteriorating at a rapid rate and a large part of the investments were made 50 or more years ago, with no plough back for upgradation and renewal and the absence of regular O&M budgetary provisions. The result is reflected in local capacity utilisation and productivity, high operational cost, leakages and wastages. No city estimates are available on existing investment in city infrastructure assets and this becomes a constraint to estimate new investment needs. Data base on the increasing growth of the informal section is limited and weak and tends to under-estimate the city income and growth prospects. A few studies suggest that capital formation in the housing stock of the poor in informal settlements, aggregate investment in productive assets and output in informal sector are of magnitudes that cannot be overlooked from the perspective of revenue potential and directing the pattern of public expenditure in civic amenities. 4. Sub-city Data Another important issue is the generation of key data at the sub-city level, at the level of city wards and administrative districts. The Constitution (74th Amendment) Act envisages the “Ward Committees and other Committees to carry out the responsibilities conferred upon them including those in relation to the Twelfth Schedule”, which indicates illustrative municipal functions introduced in the Indian Constitution For efficient ward level planning and policy intervention, the pre-requisite is good quality data at the level of sub-city jurisdiction. Cities have not given attention to this issue, and this limitation emerged in the India Urban Indicators presented in the UN Habitat II Conference. Considerable data at the sub-city level are available in official records, often in the municipal corporations but no inventory is prepared on these data and no assessment has been made of their quality, reliability and frequency of generation. 5. Policy issues for Strong Sub-National Data Base The sub-national and sub-city data are required for effective implementation of activities at each stage of the city planning and policy cycle. These would include assessment of current city trends and status of programmes, assessment of the development needs, preparation of policies, strategies and action plans, monitoring the implementation and subsequent impact of the programmes and review of the programme. In this perspective, the key policy issues that arise are: ⇒ How do we generate information and data that truly bring out the local situation at sub-

national and sub-city level

⇒ How do we institutionalise a system that provides access to the information and data on a regular and equitable basis to all, at least cost or cost that all the stakeholders can afford.

⇒ How do we address to the specific requirements of each major stakeholder groups ⇒ How do we involve communities in the data development process ⇒ What types of capacity building initiatives are required ⇒ How do we transfer the indicators and sub-national and sub-city data to the planning and

policy system through a participatory process. To address these issues, a brief presentation is made of two recent initiatives, the UNCHS Global Observatory (GUO) Programme and the Community Sustainable Development Indicators (CSDI) Programme. The objectives of these initiatives fits into the sub-national and sub-city data requirements for effective decentralisation and the making of sub-national policy and there is potential to bring about convergence of some of the activities. While the GUO Programme is supported by the UNCHS, UNDP,World Bank and UK DFID, among others, the CSDI Programme is supported by the UK DFID. In both these programmes, SDS is a key partner. 6. Global Urban Observatory Initiative The experiences of the international community of researchers, policy makers and multi-lateral agencies who had contributed to the successful development of a comprehensive Global Urban Indicators Programme during 1994-96, in which 235 cities and towns from 110 countries had participated, has brought out the need to institutionalise the development and application of sub-national data activities. It was realised that if the activities of data generation and management at the sub-national level was to be undertaken on a continuing basis, an institutional system at the city level had to be installed, which would be ultimately owned and adopted by the local citizens and stakeholders. The generation of sub-national data will utilise a mix of the conventional process of data generation by official agencies and the participatory process for some of the areas which are not covered by the existing process or their data are not considered to be reliable or regularly generated. In this context, the UNCHS, the UN Agency for Cities, has conceived the GUO Programme, with a network of Local Urban Observatory (LUOs) at the city level and National Urban Observatory (NUOs) at the national level. There is provision to also establish Regional Urban Observatory (RUOs) in the five UN Regions of Asia-Pacific, Africa, Arab States, Caribbean-South America, and North America-Europe. In the next three years, the plan is to establish at least 125 LUOs and 25 NUOs. To make the GUO Programme sustainable, the activities include a major capacity building agenda to develop city level expertise among all the stakeholders. An Expert Group Meeting was held in the UNCHS in April 1999 to work out the capacity building agenda. A study of the key urban indicators on local governance brought out by the UNCHS are illustrative of the concerns that have generally attracted consideration at the sub-national level. They relate basically to employment in city government, the coverage and quality of civic amenities, housing and environmental management issues, the income and expenditure position of city governments, the debt conditions, contractual services, bringing out the public-private partnership. Some other modules have indicators that would provide a more extensive city perspective. The coverage may have to be enlarged to provide inputs for proper city governance.

The UNCHS Indicators brought out, however, clearly the non-availability of city level data on key aspects of city activities that directly have a bearing of city viability, growth and sustainability. These included, for example, city product, quintile distribution of income, poverty line, economic and financial data on the growing informal settlements and economic activities, which in many Indian and third world cities are estimated to be contributing to one-half or more of the incremental housing stock, economic employment and a considerable proportion of the city product. In fact, the contribution of this dynamic component of the city economy to the city product is not even attempted to be estimated and their potential to contribute to city financing is overlooked on the ground that these segments of the population are incapable of contributing to the city exchequer, though there is enough and growing evidence on this segment paying considerably more for use of key civic services than the segment of the population that is the focal point of revenue generation and consumption of highly subsidised civic services. The GUO network will go beyond facilitating and supporting the data development activities to also promoting and facilitating the application of the indicators and data to the city level planning and policy processes and providing a informed base, easily accessible to all, for consultative activities among the stakeholders. 7. Community Sustainable Development Indicators (CSDI) Programme An important partner in the development of sub-national data, a especially in the context of participatory decentralised governance, is the community. The need to have data that reflects their perceptions and requirements of city services and activities then becomes necessary. The existing top-down data generation process largely overlooks the concerns of this group. The need is to develop a ground-upwards process, where the people play a key role and governments, academics and researchers provide catalytic inputs, including capacity building support. The data base is developed on city concerns that are prioritised by the community through a consultative process. The community-driven consultative process brings out, by way of illustration, city concerns like poverty, income generation opportunities, access to credit, housing, water and sanitation and health care. The next stage is to facilitate the communities to decide on what type of information they perceive would bring out their problems sin its various dimensions, nd the interventions and support they perceive as important to address to their specific needs. For the latter also, the communities are motivated to identify the information that would effectively put across their development ideas. The community-driven process will remain an academic exercise unless the community is able to put across its concerns to city managers, planners and decision makers. An objective of the CSDI programme is to also facilitate the interaction between the community and the providers of the services, so that the demand-supply equilibrium may be established. The project is bring implemented in Delhi and a secondary town in West Bengal (Nabadwip). A community centre has been established in Delhi to facilitate transfer of capacity building inputs to the community (about 1000 households) and providing a platform for informed consultative processes. At the same time, the city government support has been mobilised, as also of the State government and the Planning Commission, to facilitate consultations between the community and the city government and bring about changes in the programmes that the governments have for the community. The

anticipated output is a community data base initiated planning and policy process that will produce a development plan of action that meets the community responses within the budgetary provisions of the city government and other resources that may be mobilised in due course.

11. Incorporating Geographic Information into MEASURE Surveys A Field Guide to GPS Data Collection∗

Introduction Researchers, policymakers, and program managers have long recognized geographic location as an important

factor in population and health outcomes. Knowing how the health of women and children may differ by where they live can lead to a better understanding of where and why events occur and how interventions can be implemented effectively. But demographic and health data collection has not traditionally included the detailed locational information needed to incorporate geography into complex analyses. To broaden the uses of its data, MEASURE has expanded the Demographic and Health Surveys (DHS) and Service Provider Assessments (SPA) to include geographic data. With this new locational information, MEASURE data can be analysed as part of a geographic information system (GIS) to gain new perspective on the health and well being of communities around the world.

Geographic information is made up of two components, locational and attribute. Location represents where on the earth the items of interest are located, while the attributes provide information about what is occurring there. Examples of geographic information might include the location of the center of a village, a household, or a health facility. Geographic data can be collected in three basic forms: points, lines or areas. An example of point data would be latitude / longitude readings from a GPS (global positioning system) unit, which might represent the center of a village, or the center of an administrative unit, or a household. Line data can be road networks, or rivers. Points and lines can also make up areas, sometimes known as polygons. Polygons can represent administrative or political units such as states, or provinces or they can also represent other non-political regions such as health clinic service areas, or areas prone to flooding during the rainy season. All geographic data regardless of whether is a point line or polygon must be able to be geographically located on the earth’s surface, or georeferenced. Attribute information such as the number of people in the household, maximum travel speed on a given road surface type, or population in a district can then be linked to its location. Coordinate Sys

By definitiis through the use oone familiar to mosequator as its startinthe equator have a ndegrees north latitudirection and uses ainternational conveneast of this line hav

∗ Abstract from RepUniversity of North

Points ,lines and polygon are stored as a series of x,y, coordinates. Points are a simple x,y location,
Point
teonf at pg eg

de liti

e p

or C

Roads, River

while lines and polygons are recorded as a series of x,y, locationsOnce the locations are recorded attributes cab be linked to the
Line
Adminstrative areas, service areas

objects

Polygon

m geographic dat coordinate systeople is latitudepoint. Positionsative value. Th

, while the Soutne known as theon, is a line thatositive longitud

t written by Livarolina at Chap

Village Centroid, or house

a must refer to a location or locations on the earth. The most common way to do this em. Though there are many different coordinate systems in use around the world, the and longitude. Latitude defines position in a north-south direction and uses the that are north of the equator have a positive latitude value while positions south of e earth’s pole represent the maximum values for latitude. The North Pole is at 90

h Pole is 90 degrees south latitude. Longitude defines position in an east-west Prime Meridian as its starting point. The Prime Meridian, as established by runs through the Greenwich Observatory in Greenwich England. Positions that are e coordinates while positions west of the line have negative values. On the other side

ia Montana, Macro International, John Spencer, Corolina Population Centre, el Hill, 2001, with support from USAID under the MEASURE programme.

of the planet from the Prime Meridian is the International Date Line. The International Date Line has a longitude of 180 degrees and is the maximum longitude value.

Prior to GPS obtaining accurate positional data was difficult. To achieve high accuracy usually required

sophisticated survey equipment and a considerable amount of time and resources. While other alternatives may have been cheaper there was a substantial sacrifice in accuracy. With the advent of GPS technology however, geographic data can easily be collected accurately and cost-effectively. It should be noted; GPS is not the only source of geographic data though. Remotely sensed data such as air photos, satellite and radar images can provide land use and land cover data. Existing hand-drawn maps, such as those outlining census enumeration areas, can also be a source for geographic information.

GIS A key tool for maximizing the use of this type of data is a geographic information system, or GIS. Put simply,

GIS is a combination of computer hardware and software used to store, manipulate, analyze, and display geographic data. Using a GIS for data analysis inherently places importance on where events or phenomena occur, and why they occur there. Looking at things from this spatial perspective can often add valuable context to human activity. GIS facilitates this type of analysis by integrating common database operations, such as query and statistical analysis, with unique visualization and geographic analysis benefits that maps can provide. The powerful analytical capabilities of the software mean that attributes can be queried and more complex questions can be explored. For example, a dataset of health facilities containing their location, and attributes such as how many doctors and nurses are on staff, may be useful and of itself. But knowing the location of the communities they serve, and how easy or difficult it may be to reach the facilities might be more important. Knowing where the catchment population is and its relation to the facilities, and routes of travel can answer this kind of question. GIS gives users the chance to analyse these layers of information simultaneously. How is the Data Collected? What is GPS:

GPS (Global Positional System) is a satellite-based navigation system developed by the United States Department of Defense to provide a consistent and accurate method of determining location. While it was originally designed for military applications, GPS also provides commercial and recreational users with worldwide navigation coverage. A GPS receiver determines its position using a set of 24 satellites that orbit the earth. Each satellites’ position, as well as the current time, is transmitted via radio signals. The GPS unit receives these signals and uses them to calculate its position in

terms of latitude, longitude, and altitude.

Despite all the technology involved with GPS, there are many opportunities for error to be introduced in the coordinate. Even though most of the sources of errors are unavoidable, users should be aware of them and be prepared to take steps to minimize their impact. Some errors though are always present and are unavoidable by the user. These include errors caused by atmospheric conditions that bend and delay the GPS signal from the satellites. Errors can also arise from something known as multi-path interference that happens when the signals bounce off of buildings or other objects. The greatest source of error however is a factor of the positions of the satellites in the sky. Positional Dilution of Precision or PDOP refers to the spread of satellites in the sky and can be quantified by a number. The ideal condition is that the satellites are evenly distributed throughout the sky and not clustered in one quadrant of the sky.

Longitude

Latitude

of PDOP error is multiplicative. So in other words, if all other errors combine to introduce two meters of error, and there is a PDOP value of 7 the error present is 14 meters. While some GPS receivers will display a PDOP most recreational grade receivers do not. So users must rely on the display of satellite locations to make sure that satellites are evenly spread throughout the sky.

Prior to May of 2000, there was an additional source of error present in GPS coordinates that users had to concern themselves with. This was known as Selective Availability (S/A). For reasons of U.S. national security the U.S. Department of Defense (USDOD) intentionally degraded the accuracy of the GPS positions for non-military users. While S/A was in effect most users of GPS achieved a horizontal accuracy of 100 meters. In May of 2000, President Clinton discontinued S/A for most of the world. The DOD can reinstate S/A on a global or regional basis however if it feels U.S. national security is threatened. The effect of discontinuation of S/A is that horizontal accuracy of GPS has improved to 10 meters or less.

GPS Accuracy There are several types of GPS receivers in use. Survey grade receivers are the most accurate and the most

expensive. Typically these receivers have sub-centimeter accuracy and cost in the tens of thousands of dollars. Mapping grade receivers typically can produce sub-meter accuracy and cost between one thousand and five thousand dollars and are designed for high-end cartographic activity. Recreational grade GPS units are designed for use when hunting, hiking, or other recreational activities. Of the three types described, recreational receivers are particularly well suited for use in DHS and MEASURE projects. Costs typically are below two hundred dollars and positions recorded will be accurate to within 15 meters.

Understanding and defining the accuracy needs of the project is crucial to ensure successful GPS point collection. For most projects 10-15 meter accuracy is more than sufficient, and this level of accuracy can be achieved with nearly all recreational grade receivers. Some receivers provide the ability to improve accuracy through the use of point averaging. This technique works by collecting readings at one location over specified period of time and then calculating the mathematical average of the points. Thereby minimizing the effect of the errors present in the coordinates. While point averaging was developed to counter the effect of S/A, it still has merit today. Averaging points can smooth out the variation introduced due to the other errors present in the signal. Point averaging techniques however can improve accuracy to between 5 and 10 meters. The extra effort involved in achieving this level of accuracy is minimal so it is advisable to use point averaging when the option exists on the receiver. When the averaging feature is turned on, the unit starts collecting points. The user then pushes a button to stop the point collection, and all points collected within that time frame are averaged, yielding a more accurate point. Experiments have shown that averaging points over a period of time as little as three minutes can reduce error to as little as 5 meters. Collection of points over longer period of time can improve accuracy even further, however averaging points for greater than 10 minutes does not improve accuracy significantly. Since an error range of 5 to 15 meters is adequate for most MEASURE applications, averaging points for three minutes should be sufficient. Using GPS in MEASURE Projects

Collecting GPS data during a MEASURE DHS+ survey is simple and requires little additional work. GPS data can be collected within the existing framework of the survey, GPS data is free, many GPS receivers are inexpensive and survey staff can be trained quickly in their use. This section describes the benefits and drawbacks of GPS point collection as well as an overview of the steps required to add a GPS component to projects.

Benefits of Collecting GPS Data Benefits of GPS point collection are substantial. If latitude and longitude readings are taken for each sample

cluster (see Collection Approach), providing a set of point locations that are then linked to all of the household and individual level attributes contained in the full DHS dataset. Rather than constraining a geographic analysis to national or provincial levels, point data for the sample clusters can be aggregated to new units of analysis, such as climatic zones or ethnic regions, as in the example below. New variables can also be attributed to the point locations and used in multivariate analysis.

Drawbacks to GPS The accuracy of the GPS receivers’ calculated position depends on the strength and number of signals that it

receives. The receiver will always collect data from as many satellites as it can, and will choose the best four (angle and strength of signal) from which to compute a position. But obstacles such as buildings, mountains, and tree canopies can distort the signals and introduce error to the reading. Even more serious, user mistakes such as inaccurate or incomplete waypoint naming can be extremely difficult to rectify after the teams and GPS units have returned from the field.

Project Preparation Before beginning any project that will incorporate GPS, preliminary planning is essential. It is important to

coordinate equipment purchases, arrange for training and personnel needs as well as develop point collection protocols. The specifics of the project will determine how some decisions will be made however there are some things that will be common to all projects.

Personnel Requirements One of the advantages of GPS is that it can be done without hiring additional personnel. The details and number

of people will vary according to specific projects, however there needs to be a field team which will collect points in the field (either during the listing process or during the survey itself) and a Field GPS coordinator. The main responsibility of the Field GPS coordinator is to download the points from the GPS receivers, and resolve questions raised by the collection team members about point collection protocols. The Field GPS coordinator also addresses technical problems that the collection team is unable to resolve, and makes sure all necessary data is collected and team members are following the established point collection protocols. The coordinator will download points from the GPS receivers into a computer on a regular basis. This will allow the coordinator to determine points are being collected correctly and protocols are being followed.

Training Requirements In order to operate properly the equipment the field team must be trained in the basics of the unit, the point

collection protocols as well as simple troubleshooting techniques. In order to prevent a ‘black-box’ syndrome where the team does not understand the way the units work it is also helpful to cover the basics of how GPS operates. Lastly, the team should be given time to practice collecting points. This training can last from half a day to a full day, depending on the number of people and the specifics of the project. Its’ necessary therefore to include GPS training in the regular project training regime. If this requires obtaining additional space for GPS training this should be arranged early in the project planning stages. Since there needs to be a hands-on component to the training, the training site should have a parking lot

or other open space available where people can get a clear view of the sky. Detailed training materials are presented later in the packet.

Training Adequate training of personnel is crucial to promote an understanding of proper use of the GPS receivers and how

to troubleshoot problems that may occur in the field. It is important to identify a local staff person to serve as the GPS coordinator for the duration of the data collection and processing. The specific duties of a GPS coordinator are described below. This person should be someone who has existing knowledge and/or experience with GPS, or the willingness and ability to learn quickly. The GPS coordinator should be trained by the country monitor or designee. The training of data collectors can then be conducted by the GPS coordinator (in collaboration with the country monitor). Training of data collectors should focus on four aspects:

(1) An overview of how the Global Positioning System works (2) Introduction to the GPS unit, features and data collection procedure (3) Point collection protocols and troubleshooting (4) Hands on practice sessions

A presentation with notes is attached at the back of this manual, and an electronic copy is also provided. This can be used as-is for standard DHS or SPA data collection, or modified for other needs. (1) Overview of GPS

Everyone using the GPS receivers should have a basic understanding of the global positioning system and the underlying theory behind the technology. This allows users to fully understand how the system works and the importance of following proper protocols. Users who have an understanding of the workings of GPS will be more likely to recognize problems that may arise in the field and know the importance of resolving the problem. Typically this section of training need not last more than a half-hour and should cover the following topics:

A. History of GPS B. Description of the components of GPS (satellites, ground stations, receivers) C. Overview of how receiver calculates a position D. Errors that are present in coordinate and how to minimize them

(2) Introduction to the GPS Unit Everyone on the data collection teams should be introduced to the GPS unit in the training session. The basics of using the unit should be covered: on/off, initializing the unit and adjusting settings (datum, coordinate system, measurement units), acquiring a position, checking satellite coverage, marking a position with point averaging, changing the waypoint name, renaming and deleting waypoints, as well as adjusting contrast, light, and time/day. (3) Point Collection Protocols and Troubleshooting Well-defined point collection protocols are essential to the obtaining accurate positions. These protocols should explicitly describe how Ids are assigned and where points should be collected. This section of training should describe the protocols in detail, as well as provide guidance on solving problems that might be faced in the field such as replacing batteries, finding adequate satellite coverage in the sky. Typically this section of training should last 30 minutes. (4) Hands on Practice Session GPS technology is relatively simple to use, however it does require some practice for people to become proficient in the use of the receivers. Therefore it is vital that time be set aside in each training session for the GPS users to practice collecting points and filling out the point collection logs. This hands-on training must be conducted outside and should take at least 60 minutes.

GPS Coordinator The main responsibility of the GPS coordinator is to be responsible for downloading the points from the GPS

receivers, resolve questions raised by the collection team members about point collection protocols, address technical problems that the collection team is unable to resolve, make sure all necessary data is collected and ensure that team members are following the established point collection protocols. The coordinator will download points from the GPS

receivers into a computer on a regular basis. This will allow the coordinator to determine points are being collected correctly and protocols are being followed. Because of the advanced tasks the coordinator must perform, s/he will need additional training beyond that which is given to the collection teams. This training will include how to transfer points from the GPS receiver to a computer, as well as some more advanced training with the GPS units. At a minimum the coordinator should understand how to reinitialize the GPS unit, and modify the system settings (e.g. coordinate system, datum, measurement units). Project Checklist: Six months prior to any fieldwork: 1. Schedule data collection

1. During listing fieldwork: Collecting GPS data during this stage is preferable. Because there are typically fewer listing teams than interviewing teams, fewer units are required. Also, field supervisors are not preoccupied with the main survey, so GPS data collection is easier to incorporate during this stage of the survey.

2. During survey fieldwork: One unit per interviewing tam is required. Since field supervisors are very busy with other responsibilities, GPS data collection is more likely to be forgotten or lower on the priority list. The GPS coordinator must pay careful attention in surveys where data is collected during main survey fieldwork.

Three months prior to data collection: As soon as contract is signed, order hardware 2. Identify Hardware Needs: One GPS unit per team

1. Recreational Receivers ($100-$200 each) 1. One unit per team, plus 2 extra backup units 2. 8 extra AA batteries per GPS unit 3. 2 PC cables per survey for data downloading 4. 1 copy of data downloading software

Collection Approach GPS point collection can happen during the listing process or during the administration of the survey.

Measure/DHS+ fieldwork is generally divided into two phases: the listing and the main fieldwork. After the survey population has been stratified and enumeration areas identified, teams go out to the field to carry out the listing. In each enumeration area or cluster that has been selected, all of the households (or health facilities) must be identified on a sketch map. The final sample selection is drawn from the listed households. It is easiest for the teams to collect the GPS data during listing, when they are not occupied with the survey itself. In the listing and fieldwork phases, survey teams visit all of the clusters. However, it is strongly recommended that GPS data be gathered during the listing. There are typically fewer listing teams than interviewing teams, so it is cheaper to provided GPS units and training for the listing teams. In the case of DHS surveys, gathering GPS data during the listing also allows Macros’ data processing specialist to check the data during the first DP visit. Because GPS readings are to be gathered during the listing, the GPS units must be ordered as soon as the contract is signed. If the units do not arrive in time for the listing, GPS readings will have to be taken during the main fieldwork. This will require additional units (because there are more interviewing teams) and GPS training will have to be added to the main training. DHS surveys take place in countries where fieldwork conditions can be difficult or even extreme. It is not unusual for survey materiel to be damaged or lost. Although GPS units are usually rugged and reliable, they can be broken or lost. Two extra units should be ordered to serve as backups. During the listing, one GPS reading must be taken for each cluster. Although clusters are actually administrative (enumeration) areas, we collect only one point. Releasing one point for the cluster greatly reduces the chance of

compromising confidentiality of the respondents, but it is enough to allow the integration of multiple datasets for further analysis without having to collect one point for each household. Additionally, because of small number of cases per cluster (usually an average of 30 women interviewed), clusters must always be aggregated to produce estimates with acceptable confidence intervals. The DHS cluster point should always be taken at the center of the main village or settlement in the EA. If there is more than one settlement in the EA, one point should be taken for each settlement. If the EA is segmented, one point should also be taken for each segment. In cases where multiple points are taken for one EA, notes should be made on the paper form indicating which waypoint ID goes with which point on the sketch map. Symbols should be added to the sketch map indicating the location where the point was taken. DHS surveys typically conduct household interviews in 250 to 500 clusters. Each listing team usually visits 10 to 25 clusters, depending on the topography of the area and total number of clusters to be surveyed. It is unlikely that any one team would need to collect more than 500 points. The Gramin 12XL units have a maximum capacity of 500 stored points. If a team needs to collect more than that, special arrangements must be made. If the data cannot be downloaded to a laptop in the field, the unit will need to be returned to the central office. The GPS coordinator will need to download all the data from the unit and clear out the machines’ memory. Then the unit can be sent back to the field. Perhaps the greatest limitation for DHS data is the sampling scheme. DHS samples are drawn from population-based clusters. While they are an accurate representation of the population, the observations are clustered and this not randomly spread across geographic areas. Additionally, the number of observations needed to create a nationally and provincially representative sample is simply too small to represent small areas. However, methods of small area estimation and interpolation can overcome some of these limitations. Representations at areas different from original sample stratifications must be done with care. For health facility surveys, one reading is taken for each health facility. If community questionnaires are part of the SPA, one reading is also taken in each community. In facility data collection, the reading should be taken at the front door. If the door is covered and/or satellite coverage is insufficient to take a reading, the data collector should go to the road at the border of the yard or compound of the facility. The location should be relatively open, away from tall buildings and out from under tree canopy, in order to receive adequate satellite signals.

Develop Data Management Protocols The reading for a cluster is stored in two places: on the GPS unit and on a paper form. GPS units can be broken or lost, and experience has shown that a hardcopy backup is essential. In addition, the paper form provides a backup should the data in the GPS unit be changed, deleted, or misidentified (i.e., the operator names the cluster incorrectly in the unit). The paper form is also where notes should be made in cases where multiple points are taken for one cluster. Each cluster should have its own GPS form to fill out.

Data Management Protocols for GPS Coordinator ! GPS coordinator clears out any existing waypoints in all units, and sets units (WGS84, hdd.ddddd, metric) ! GPS coordinator logs all units out (serial number and team number, other supplies) ! One point collected for each cluster or facility. All points averaged for 5 minutes. ! Waypoint naming convention strictly followed: use cluster or facility ID, alpha characters only when naming

scheme has been predetermined ! When listing complete, GPS coordinator downloads data from all units and sends to Macro. Detailed sample

file is also sent. ! Data is entered from paper forms, DP staff sends data entry file with lat/long and locational information back

to Macro.

Training Agenda

1. Introduction 2. Global Positioning System Lesson 3. GPS Receiver Lesson 4. Overview of Point Collection Protocols

5. Practice with GPS receiver

Timeline

1. During survey planning, determine when GPS data will be collected. 2. When contract is signed: GPS units and accessories ordered and sent to country (or to managing

institution), 2-3 months prior to data collection. 3. Country monitor identifies and trains local GPS coordinator before main (listing or survey) training. 4. GPS coordinator conducts ½ day GPS training with data collection staff. This training should take place

with the main listing or survey training, depending on when data will be collected. 5. GPS data is collected; data saved in units and recorded on paper forms as backup. 6. When collection is complete, all GPS units are returned to central office. GPS coordinator checks to

make sure a waypoint was collected for each cluster / facility. 7. GPS coordinator downloads all data and sends it to managing institution. 8. Paper form data is entered upon first DP staff visit. DP staff sends data entry file with all other locational

information to managing institution. DP staff checks to make sure lat/long entered for each cluster / facility.

Stage V: Using Indicatrs to Promote Sustainable Partnerships

12. An Operational Toolkit of Information-based Strategies Building Partnerships through Knowledge Infrastructure-led Coordination &

Convergence A Road Map towards Good Urban Management and Governance∗

Vinay D. Lall+

The Perspective

The most critical challenges for good urban management and governance in the new millenium

are bringing about time-efficiency and cost-efficiency at each stage of planning and management of urban programmes, both at the micro project level and at the aggregate town/city level. Within the terminology of “management” are a series of activities. Each of these activities are equally important and require the same level of attention as the end product. An effective demand-driven process at the planning stage is the first critical requirement for an efficient urban management system. Then comes the stage of effective monitoring of the implementation of the planned activity. This would provide a protective cover so that operational problems that might derail the scheduled completion of the project within the approved project cost and time horizons, are minimised and the responses to induct corrective measures, if required, are undertaken with the minimum delays and cost escalation. If these two critical preparatory and implementation functions of efficient management are put into place, the prospects of the anticipated outputs being delivered within the project time line and budget (given a margin of up to 10% escalation), are likely to be very high. Performance auditing, the final responsibility under the umbrella of urban management, should go beyond the attainment of the project outputs such as the creation of specified assets (numerical and financial value) and coverage (households, water connectivity, empowerment). Performance auditing should also evaluate the impact, immediate as well as over a period of years. This assessment is critical to know whether the creation of the assets or the delivery of the urban services has contributed to the improvement in the work, productivity and quality of life, as may be the case, of the direct user groups of the assets and services created and delivered. The performance auditing may also go a step further, to assess the impact at the secondary stage of the direct project partners, covering communities and groups, who are linked to the first stage or direct partners, through backward and forward linkages. If this is done, the output of the performance auditing exercise will be further strengthened. In effect, performance auditing would capture, thus, a whole range of economic and social products and impacts that might have emerged from the project. It would just not relate to input-output matrices of project inflows and

∗ An Abstract from Paper presented at Policy Research Seminar on Information-Based Strategies for Urban Management, April 19 – 20, 2002, Bangalore, Society for Development Studies + Director, Society for Development Studies (SDS), Regional Network for Knowledge Infrastructure in Asia-Pacific & Arab Regions. Research inputs were provided by SDS colleagues Ajay Suri, Pragya Rajoria and Moushumi Basu. Data base used is of the Bangalore Urban Household Survey, 2001, a joint SDS-World Bank policy research programme, undertaken in the context of the activities of the Bangalore Local Urban Observatory (BLUO)

direct out flows, as is often seen in an evaluation exercise, but would also take cognizance of the totality of the social costs and benefits. The Operational Principles

The basic requirement for undertaking a full length exercise to address the millenium challenges are, first, an extensive, strong and properly validated data base at the sub-city level and, second, a set of tools that are required to convert the data base into a powerful mechanism that would induct time and cost efficiency at each stage of the planning, production and management cycle. As is well known, a large number of agencies are involved in an urban project, be it in the area of urban transport, housing, municipal finances, water and sanitation, environment or urban poverty, the last being the focal goal of global and national development initiatives. The partners or stakeholders would be from within the government system, covering as in the case of India, the Urban Local government, State government and National government, the Private sector, the NGO and CBO sector and professional groups of researchers, trainers, among others. Apart from the organisations from within the country, often-urban projects have partners from outside the country.

Efficient urban management requires that each partner, who is a stakeholder in the project, is

committed, able to open his/her door to each other, share information on the components, interact among themselves at each stage and jointly contribute to attainment of the totality of goals and impacts rather than being concerned, as is now the case, with only “their” targets, goals and minimum outputs, so that they may claim that “their” project has been successful. SDS research in India and abroad as well as of other institutions have shown that be it in the case of poverty alleviation, housing, water supply, credit, empowerment, among others, the one “partner”, who is generally not so enthusiastic in the midst of success records of all other stakeholders, is invariably the partner who is the “recipient” of the project outputs. In several poverty alleviation programmes in India, it has been seen that more often than not, the recipient of poverty alleviation inputs may be momentary “lifted” above the poverty line and may enjoy new and good assets and services, be at the center of a group of partner admirers, but these evidences of success are not so evident a few years down the line. It would not be unusual to find “this recipient” having fallen back to the poverty status, the benchmark one before the poverty alleviation inputs were delivered. The performance auditing should cover a period, depending on the nature of the project, of five to five plus years after the project is completed. SDS Strategy

a. Data sharing requires a ‘win-win’ situation The guiding principle of SDS strategy, evolved from over more than 15 years of experience in

policy research, action projects, training and technical assistance missions in different parts of India and in more than 20 countries world-wide, is to bring to the forefront, the inter-dependency among stakeholders so that each may “open their door” to the other/others. SDS has realized through interactions with different groups of stakeholders at different stages of the government system, as well as among the private sector, NGO/CBO and researchers, that each of them are ready to share information with a “particular partner” but not with all. Also, each one is clear that the sharing will be only on the basis of mutual benefit; no free lunch or one-way gains. In the government system, the personality clash is often evident and personal relationships, rather than an institutional relationship, is often found to be the operational principle for partnerships. SDS studies have brought out evidences

in many of the urban sector programmes, as well as in rural programmes, that the right hand in the system often does not know what the left does, and each hand “protects” individual territories, creating monopolistic, or at best, duopolistic situations with respect to information sharing and operational activities. Partners will share information only if there is a mutual benefit.

b. Data Generator-User Links In a recent work that examined the data generation and sharing operations at the lowest level of

governance, in the course of work for the National Capital Region Planning Board in India for preparation of its Regional Plan - 2021, the phenomena of “desk-based” data generation and management was very much evident. The “possessor” of the data often does not know the value of the database and how it is to be used for planning and decision-making. The data are collected on a routine basis, on directions from a higher-level functionary, who also might not always know the reason for the data collection. There seems to be a rather long network established to generate data and the known linkage seems to be only with the immediate forward and backward functionary in the system.

The functions of using the data are invariably not at the level where data are generated but at a

much higher level in the government system. At the lowest level, the data “generator” is thus, mainly interested in “filling-in” periodic returns of “performance” of specified schemes, projects and programmes to be submitted to a higher up functionary in the administrative system. The thrust of the data work is to examine basically the coverage indicators of the specific project/programme, nothing more, nothing less. The nuances of validation of the data at ground level is not recognised and whatever data flows upwards from the ground level is rarely validated at the higher levels of the system. My own experience has been that the higher placed or distanced the data user functionary is in the long linkage system of data generator and final user, the less seems to the intention and/or capability to validate the data. In fact, skills to validate data do not exist and generally data validation function is limited to checking of sub-totals and totals.

I would not blame the higher-level functionary. This is to be expected to be the case when the

database is inadequate and one does not have much of a choice in the selection of the data required for planning or decision-making, and sometimes all the planning and decision making operations are undertaken on a fire fighting basis. The present project, through various ways, seeks to bring into focus this issue of lack of choice in a scenario of limited/ inadequate database.

c. Reliable Data is the Demand SDS experiences at various levels of the administrative system suggests that everything is not

yet lost. There is a definite interest among planners, administrators and decision-makers in the government system to improve the quality of the data, extend its coverage and actually use them as tools at all levels of government planning and decision-making. Many of them have complained that data development is a long process and one has to be sure of the quality of the data. They have often made the point that wrong data sends the wrong signals and leads to formulation of wrong policies, which is more dangerous than policy without data, where there are equal chances of taking the right decisions. There is an element of truth in this presumption. But that should also be the reason why the process of good data generation should be immediately initiated, so that down the line, planning and decision-making may become information-driven.

The process has been initiated to build not only a data base but a credible data base that would be seen as reliable by all city stakeholders. The process has been slow. For over a decade, international organizations have been mesmerized with the task of data development, through the urban indicators programme, with the focus on comparative ranking of cities. Such a comparative ranking may be found useful by the international organizations and researchers, but the cities, especially in the developing world, do not find these exercises so relevant. City managers and decision makers are just not interested in comparisons (a most uncomfortable activity, from the perspective of government officials, especially in the developing world). They have been disappointed with the focus on only data and indicators, as their need is indicators-based products that would bring about a change in their conditions.

The SDS-World Bank policy research programme attempts to develop a new approach to data

generation at the sub-city level and use the data to address critical city priority policy concerns. It introduces the role of validation of data and to test the utility of the data, and seeks to apply them to specific urban problems.

d. Breaking the Georgian Knot The really difficult task is going to be changing the mind set of “data owners” to share their

data with others. Addressing the challenges requires a good deal of tact, negotiation and persuasion. SDS experiences in the field have made us realize that no official will “effectively open his door” unless he/she would benefit, in terms of his/her own operations. We realized that possibly one way to make officials open up their doors is to demonstrate the “inter-dependency” among the partners. The officials have to be not only sensitized on the utility of indicators-driven approach to good urban management/governance, but also have to be demonstrated that the approach is workable. They have to be further enabled to recognize that within their own operational environment, their success, effectiveness and growth path will depend upon the success and growth of the others. This inter-dependency approach can only be demonstrated through an appropriate set of indicators among the partners, sectors, organizations and a demand-supply planning model.

e. Going beyond Performance Auditing–Indicator Products International development banks are more concerned with indicators to facilitate performance

auditing and urban management at the project level, and this is a step forward. One, however, is still not clear of their interest on the sustainability of the impact. I am of the view that performance auditing must not just look at the attainments of the project goals only in terms of some outputs but assess also if the impact is sustained much after the completion of the project.

In this context, SDS has been advocating the need, immediately after the UN Habitat II

Conference in 1996, that the focus in the indicators movement should shift from indicator compilation to application of indicators, and for that purpose, SDS had proposed at several national, regional and global fora that the global movement should now place priority attention to development of indicator application products, with case studies of applying indicators to address selected urban concerns, and on capacity building. A series of planning models were first developed and released by the Bangalore Local Urban Observatory (BLUO) in their publication and on their web site.5 These products brought 5 Government of Karnataka, 2000, Bangalore City Indicators Programme, Bangalore Metropolitan Region Development Authority (BMRDA), which hosts the BLUO (website : www. bangaloreluo.org)

out a few demand and supply relationships and was the first stage of the SDS movement to develop indicator products. Key inter-sector partners were identified. The second stage activity in SDS Indicators agenda is the development and testing of indicator products.

There are positive rays of hope among the planners and decision makers. They are now

recognizing the significance of good and reliable data at the sub-city level and the need to develop products that would facilitate the use of the new information. SDS has received requests for technical assistance from cities and governments in several countries to use their indicators as planning and policy tools. The SDS-World Bank policy research study would provide some products for this purpose.

The SDS-World Bank project has received extensive support and ownership of the government

at all the levels in the city of Bangalore and the State of Karnataka and from other key city partners. This was because of a change in mindset of the key government functionaries. The support was received in Bangalore right from the wards up to the Chief Executive Officials of all the planning and service delivery organizations within the government system at the city and state level. The project has been developed as a mega city model in Bangalore and the city issues that were studied were selected by city planners and decision makers. More than seven issues were short listed initially, but only the first two could be taken up, again in consultation with key city managers. These two issues are property taxation and pricing of water utility services.

However, the commitment to work together and share information was possible only after a

considerable preparatory work among the CEOs of city/state organizations, during the pre-project period, in the course of another programme to establish the BLUO, hosted in the Bangalore Metropolitan Region Development Authority (BMRDA). Thereafter the process has been unexpectedly smooth as the CEOs recognised the validity of the argument of having good sub-city data and information for their own operations, and secondly, sharing information among each other, as SDS was able to demonstrate inter-dependency among the organizations among whom information was to be shared.

The challenges of urban management and good governance in the developing countries is now

extending to secondary towns. To address their concerns, SDS-World Bank policy research agenda is developing a District Administration (Urban) Model. This model is being tested in Jaipur district in the Indian State of Rajasthan. This District includes Jaipur a city of 2 million, and 10 secondary towns with population ranging from 10,000 to 50,000. A comprehensive data base has been developed and apart from addressing the issues of property tax and water pricing, the local planners and decision-makers requested SDS to examine waste management. The District Administration model would build up indicator products for property taxation, water pricing and waste management. A third model to be developed would be a Regional Model, that would take into account a few contiguous urban and rural areas in 3-4 Indian States. A fourth model in the pipeline would be for two secondary cities in Kenya and the city stakeholders have identified poverty, crime, storm water drainage and water as critical priority concerns. Operational Model

The operational model seeks to build up the inter-dependency among sectors as well as among partners within the urban setting. SDS policy research has shown, for example, that revenues of the

housing board would improve and housing stock would get better prices if the habitat services are fully available in good quality; at the same time, the incomes of service providers of water, electricity, telephone would also improve, if the housing stock becomes habitable and people actually shift into the housing stock. To take a very specific case, water utility service providers would earn only the minimum meter rental income, if the connection has been made but there is no occupancy of the housing unit, as the major income from consumption of water is wholly dependent on actual occupancy of housing unit. The same relationship of occupied housing unit is evident in the case of the income generated by other utility service providers like telephone and electricity. In the case of property taxation, the city municipal council/ corporation will earn high property revenues if the housing unit is occupied and preferably if it is occupied on rental basis, whereas non-occupancy of housing unit reduces the property tax accruals, as tax rate on vacant units are generally lower than on occupied units.

Occupancy may depend upon the availability of habitat services. As such, linking property tax

revenues to improved habitat services, as has emerged in this study, is crucial. In such a scenario, a partnership may be developed to leverage financial resources among the partners for implementation of projects, as each of the partners would tend to benefit. This is the working principle of the SDS operational model.

Based on the inter-dependency factor, the coordination among partners and sectors can be

developed. However, coordination would not be sufficient for effective implementation. The critical issue is not only the coordination, linkage and partnership among the agencies, but it is also important to ensure that the resources flow in the right sequence, as may be required by the project, and at the right time, as well as in the required quantum. Quality of the resources has to be also of a level that is desired. There may be a certain amount of coordination among agencies but the convergence may not be appropriate. This lack of convergence is reflected, in the first place, in inputs flowing in a random manner. There are many development projects wherein credit was provided before skill was imparted or before a plot or skeletal housing unit was available; as a result, the total credit amount was not available when required, on the availability of the related inputs.

A third concern is the mobilisation of the local resources. No project can be sustainable if it is to be implemented only on the basis of external funding. Projects have to be owned by the people, by the group/community, whose interests have to be developed through the project interventions.

A study of budgets of urban local bodies in the developing world brings out a phenomena of

rising budget lapses or under-spending under some of the heads of expenditure. This is in the midst of an over all scarcity of funds.6 Often an explanation of delayed release of the funds or some operational constraint is provided for under-spending, but such a situation might not happen, if all the partners had a proper system of coordination and convergence.

A good system of information, with sub-city level details, will help to identify, sufficiently in advance, the budget heads under which funds are not likely to be used and also those heads where funds are urgently required. To facilitate proper coordination and convergence activities, as per the 6 Government of National Capital Territory of Delhi, for example, spent 39 per cent of its approved plan expenditure in 1998-99, 41 per cent in 1999-2000, and is expected to reach 60 per cent in 2000-01. During 2001-02, most of the Departments are likely to spend les than 50 per cent of their budgets, such as, NDMC, Technical Education, Education, Information & Technology, Transport, among others.

requirements, and to enable resources to be leveraged among partners, SDS has developed the Coordination-Convergence-Leveraging (CCL) Model for effective urban management. Applying the CCL The CCL Model is one of the indicators product that SDS considers as its most significant contribution to the development and promotion of the knowledge infrastructure movement. SDS considers it as a powerful potential tool for urban management and good governance. Once this product is found acceptable, all the indicators promoting organizations, especially the UN Habitat, World Bank, ADB, DFID, among others, would be able to induct it as part of their programmes. The Model transfers the Indicators from its position as a largely academic and comparative performance tool, into an operational tool that strengthens the planning, monitoring, performance auditing and decision-making process of all the key stakeholders that plan, develop and mange the urban infrastructure and governance system.

Demand for this tool has been evident in SDS extensive work in the Asia-pacific and Arab region as the UN HABITAT regional partner and also in some of the countries in the African region where SDS has been professionally associated in action projects, capacity building and technical assistance. The demand is now also evident in UN Habitat and the World Bank.

The requirements for applying the CCL model are: i. Assessment of the inter-dependency among the partners whose operations have to be

properly co-ordinated and converged. ii. Identification of the forward and backward linkages among the partners and their

operations in major production and service provisions iii. Analysis of the demand and supply factors of each partners iv. Identification of which inter-dependent stakeholders/sectors having high multiplier

impact potential in terms of production and service provision

Case Study

a. The Approach

The SDS-WB Bangalore case study has generated through its Bangalore Urban Household Survey, 2001, a wide set of data at the sub-city level for each of the city’s 100 wards. The data have been segmented also by income quintiles of the households and a few other parameters. This data base is used to explain the CCL model.

The guiding principle to adopting the CCL model is to first, a priori, examine the loss that

may accrue from poor or inadequate coordination and weak convergence of activities to all the partners in the delivery and consumption process, taking into account both the forward and backward linkages. Various types of losses arising through, price escalation; delays related to production/provision of services; asset/service not being available, usable, or inadequately accessible due to wastage, leakage and other operational deficiencies in the supply system. Often, the loss accrues when proper information is not available to cross-check given information, as for example, in the case

of self-assessment property tax system. The critical issue is the lack of proper sub-city data that would help to understand the ground situation.

At the second stage, the contributory factors have to be identified, which may lead to the loss.

In the case of a project under implementation, the loss would be of the time and cost over runs, and these may arise out of delays in provision of some inputs and/or their supply not being available in the proper sequence. Quality issues may also be a contributing factor. At other times, the loss may arise from the failure of a crosschecking mechanism, a good monitoring system. In the case of normal service production and delivery operations, the contributory factors to the loss may be leakages and wastages, as well as the failures in provision of some critical input required for the production and service operations.

b. Property Taxation—Towards Reform in the SAS The key operational constraint in a SAS, whether it is property tax or any other fiscal instrument, is the possibility of concealment of the actual tax base, by intention, accident or due to inadequate or poor knowledge of the valuation process. The problem becomes highly complex, when the assessee has no control or direct knowledge of the factors that determine the value or the value itself. While in the case of the income tax, corporate and personal, and the sales tax, for example, the assessee has the value determination capacity and knowledge of the tax base, in the case of property taxation, the assessee may not have the capacity or knowledge. The value determinants may not necessarily be within his operational range and external factors may be important, over which the assessee may have no control. In such a situation, a multi-component rate structure tends to further complicate the tax base determination. This system opens several avenues for mis-reporting. In the case of Bangalore, six parameters contribute towards the final incidence and it is desirable to limit the determinants to 2 or 3 most significant parameters, which may be open to minimal mis-reporting as well as capture the market value adequately. There is also a need to compress the parameters into a composite tax rate, which would further reduce the scope of mis-reporting. For this purpose, a weighting formula has to developed that would not only make the tax assessment process simple and transparent, but also provide minimal scope for mis-reporting. From among the parameters presently included in the present property tax system in Bangalore, I would recommend for inclusion of two, which have the minimal scope of under-reporting. These are costs of construction and occupancy status. While an element of differential in cost of construction (original value) will always exist, the median rate would be representative of the cost situation. In the case of occupancy status, census data would provide some norms, though there may be scope for mis-reporting on the rental value itself. Age is not relevant, as an old house, if sold in the market would get almost a comparable prices as a new one, as land is the most important component of housing cost. Across the mega cities and metros in the country, including Bangalore, there has been a significant change in the urban skyline and the urban form through rising demolition of large plotted units to be replaced by skyscrapers with high value space. In effect, old properties have a good market. Also, increasing proportion of old stock is being upgraded, especially from inside, even if the external part may not be changed much, though

that also is being changed and illegal extensions are also being made. There is no rationale to provide substantial depreciation to reduce the tax rate, as that discriminates against new properties. Housing typology and construction cost are also captured in the housing cost parameter.

c. High Elasticity-generating Property Tax Component The Bangalore Urban Household Survey sub-city data base has enabled the identification of a

factor that is emerging as a potential buoyancy generating one. This factor is anticipated to have considerable impact on property values in the process of urbanisation and possibly become more significant than location of the property or one-time cost of construction. It might also emerge as a major determinant of the rental value. This dynamic and volatile factor owes its origin to the widening gap between the needs and supply of key habitat services. Access to water, sanitation, waste collection, electricity are among the key services that are found to be always in “short supply” in rapidly growing urban agglomerations and Bangalore is no exception. Two similar sized properties, for example, in the same location but with access to different quality of services have different values in the perception of the property user/consumer. The key services are water, sanitation, wastes collection, telephone, electricity, and connectivity, among others. In this Study, water is taken as the variable to represent all these services.

In this context, lets take an example for coordinating the activities of two major city service

providing agencies, the Municipal Corporation (Bangalore Mahanagar Palike) and the water service provider (Bangalore Water Supplies and Sanitation Board). Looking at the impact that good habitat services has on property values and property tax revenues on the one hand, and on the other, good occupancy has on the water service provider revenues, some of the sub-city data from the Bangalore Urban Household Survey data base were examined to study a few critical potential inter-related parameters: property values, rentals, property tax collections, additional household expenditure to ensure proper storage of treated water. The analysis was done for each of the six property tax zones.

A Bangalorean has two major components of expenditure for alternative systems of water

storage and treatment due to inadequacy of water from the BWSSB. The expenditure on building the water storage facility has been largely incurred during the last 7 years. Investment year data were not generated for water treatment equipments and supplies, but as these expenses are largely with respect to “stored” water, which includes sources where water is not treated, the investment period is taken similar to that of storage facilities. It is estimated that the citizens of Bangalore have invested Rs 360 crores ($ 85 million) in building their own water storage facilities in the city and have invested another Rs. 60 crores ($ 13 million) in installing water treatment equipments within their homes, as they have to often take recourse to water from outside the regular government delivery system.

The fact that almost the whole of the water-related investments have been made in the last 5-

7 years, becomes a crucial indicator of the prospective scenario in the coming decades. The evidence brings out dramatically the need to strengthen the water producing and delivery system on the one hand, and on the other, the scope for rationalising water pricing, which would “protect” the consumer from additional home-based investments to improve the regularity and quality of household level water supply

The sub-city data show that the additional investment on water storage and treatment is spread out across all the property tax administrative zones (Table 5). While an average Bangalorean has invested Rs. 6,734, there are variations in the different administrative zones, reflected in the indictors of the lowest and highest investment: Rs. 5,719 in the North-East Zone and Rs.7,350 in the East Zone. These averages are based on the aggregation of the investments of property tax payers who have paid their tax at different rates, as per their respective tax zone. Similar variations are evident when the tax data are analysed by tax zones (Table 6). While in tax zones D-F, the average investment is around the average for the city, the amount rises considerably in tax zone C and very substantially in tax zones B and A, the high tax zones; however, as the number of tax payers in tax zones A, B and C is small, it is not advisable to generalize in their case.

It is significant to observe that the average annual expenditure of a household for water storage and treatment as percent of the basic property tax paid (excluding all the cess) is as high as 51.1 per cent for an average Bangalorean property tax payer. In terms of administrative zones, the rate ranges from a minimum of 42.0 per cent in the North-East Zone to as high as 60.8 per cent in the East Zone. In terms of tax zones, the rates vary from 43.4 per cent in zone F to 53.5 per cent in zone D. The proportions are lower if the average annual expenditure is related to the total property tax paid (inclusive of all cess) - 38.2 per cent for an average Bangalorean property tax payer. These variations are in spite of a relatively comparable household size (4.5 to 4.8).

These evidences provide the rationale for imposition of a cess for assured good quality water provision. The cess rate may be at a rate lower than expenditures made by the present property tax payers on their additional storage and treatment facilities. A 35 per cent rate on the property tax may be considered and termed as the “Efficiency” Cess” for provision of potable water. An “efficiency” norm in terms of LPHD (Litres per Household Daily) may be determined. A countervailing provision should be an Inefficiency Penalty, in the form of a rebate in the basic property tax rate, if the water is not supplied as per a pre-determined “efficiency norm”.

Table 1: Efficiency Cess on Property Tax, Administrative Zone-wise An Illustrative Coordination and Convergence Practice

North West South-West South East North-East Bangalore 1. Average Annual Property Tax (Rs.) Sample Size 118 149 144 270 209 146 1036 2928 2604 2110 2630 2315 2609 2521 Basic PT 2184 1942 1574 1962 1727 1946 1881 All Cess 744 661 536 668 588 663 640 2. Average Annual Water Storage &Treatment Cost (Rs) 951 998 848 1025 1050 817 962 3. Average Annual Water Storage &Treatment Cost as % of Annual Property tax PT+Cess 32.5 38.3 40.2 39.0 45.4 31.3 38.2 Basic PT 43.5 51.4 53.9 52.3 60.8 42.0 51.1 4. Average Household Size 4.5 4.6 4.5 4.6 4.7 4.8 4.6

Source: Estimated on basis of data base from Bangalore Urban Household Survey, 2001 Note: 1 These estimates on investments on water storage are higher than provided in Tables 22 and 23 in Chapter 4 of Bangalore City Report, mainly because these are restricted to owner occupied house and do not include the tenants. Invariably house owners invest more on water storage, as the property belongs to them.

Table 2 : Efficiency Cess on Property Tax, Tax Zone-wise

An Illustrative Coordination and Convergence Practice

A B C D E F Bangalore 1 Average Annual Property Tax (Rs) Total Tax

22000 1910 6079 2354 2653 2375 2521

Basic PT 16412 1425 4535 1756 1979 1772 1881 All Cess 5588 485 1544 598 674 603 640 2 Average Annual Water Storage & Treatment Cost (Rs) 2857 2473 1293 939 863 770 962 3 Average Water Storage & Treatment Cost As % Of Annual Property Tax PT+Cess 13.0 129.5 21.3 39.9 32.5 32.4 38.2 Basic PT 17.4 173.6 28.5 53.5 43.6 43.4 51.1 4. Average Household Size 9 5 6.2 5.2 4.9 4.8 4.6 Note: 1.The sample size for A, B, and C tax zones is very small (0.1 to 0.9) and not adequate to make any general observation. 2. These estimates on investments on water storage are higher than provided in Tables 22 and 23 in Chapter 4 of Bangalore City Report, mainly because these are restricted to owner occupied house and do not include the tenants. Invariably house owners invest more on water storage, as the property belongs to them.

The penalty for non-provision of services should be linked to the services component and the

tax relief may go up to a considerable component of the 35 per cent cess levied for efficient services. A Citizen’s Monitoring and Service Performance Task Force may be constituted to periodically get the satisfaction report of the users and determine the penalty rate. The Task Force would include representatives of the varied mix of the city population, including the city manager,

service providers and user community, with an eminent representative of the User Community as the Chairperson. The suggestions made are relevant in the context of two plans of the BMP in the pipeline. First, to introduce capital concept, in place of the rental concept, for the tax base; and second, to introduce a cess on water at the rate of 20 per cent, making total cess 44 per cent (against 34 per cent presently and replacing education by water).

d. Coordination & Convergence in Property Tax and Effective Local Urban Governance

The rationale for linking the cess with property tax rather than with any individual service user charge is that under the Constitution 74th Amendment Act, the urban local body has to develop as the city manager and its management function entails the responsibility to keep the city clean, provide good quality services to its citizens and through the totality of its managerial operations, make the city an attractive investment destination. Its most powerful tool is probably the property tax. The city corporation has to take up the responsibility to coordinate the activities of all city service providers from within the government and outside and also ensure that the flow of services are in proper sequence and easily and regularly accessible. In effect, it becomes truly the city manager, in the spirit of the role that the urban local body should play. A few policy issues arise. Firstly, what happens if the assured services are not provided, secondly, how would the coordinating partners gain from the partnership, and thirdly, what would be their incentive to put in that extra effort (and expense) to ensure good quality services. As regards the first concern, the scheme of providing an addition revenue to the city manager for good services, should be accompanied by a penalty (rebate on property tax rate) for failure to provide the assured service. On the operational side, the city manager and partner service suppliers would have to come to an understanding, legally or informally, of an incentive and penalty provision among themselves. While the city manager would pass out a part of the incremental revenue through the Cess/Surcharge to the service providers, the latter will also have to “ pay for” their failure to meet their commitment.

In effect, the Cess/Surcharge becomes an additional parameter to determine the effective tax rate and also be a possible policy instrument to facilitate the CCL process of good urban management and governance. The prospects of the user community accepting this proposal seems to be very high, as is reflected in the Bangalore project data base on expenditure incurred by the people of Bangalore to manage, on their own, access to services which are not adequately available from the regular service providers. The Bangalore survey data on willingness to pay with respect to water services reinforces this possibility. The success of the proposed strategy to improve the resources of the city government and to provide good quality services and living and working environment to the people, as also contribute to the economic growth of the city by making it an attractive investment destination, is dependent upon developing effective coordination and convergence of activities and using city resources for its betterment, rather than depending on external avenues for resources.

e. Towards a Composite Property Tax The inclusion of the Efficiency Cess should not make the property tax system more

complicated. It is necessary to have not more than 2 or 3 parameters in the computation of the property tax base, while continuing the 2 rate structure, without any tax breaks through the tax rate route. A Composite Property tax rate may be considered. The weighting formula to work out a composite property tax rate rather than a multiple rate structure, is recommended to have the following weightage Construction Cost 50% Assured Services 35% (Efficiency Cess) Occupancy 15% (Highly under-reported component, with landlords

taking substantial advances, up to 2-3 years rental, and a token monthly rental, etc.)

The composite rate may be reviewed every 5 years by an Expert Committee that includes the

assessor/property tax collector, the tax payer, and professional experts and NGOs. In fact, norm may be worked out gradually for each of the three components.

f. Property Tax-Stimulating Revenues through Coordination & Convergence

The Efficiency Cess on the property tax, linked to provision of water is an illustrative example of the benefits of proper coordination and convergence of operations among city partners. While the property tax revenues of the municipal corporation will improve through this partnership, the revenue of the BSSWB will also improve on two fronts, first sharing a part of the Efficiency Cess, and second, through revenues on additional water consumption from their delivery system, attracting a large part of the water incomes that was going to other “informal” water service providers. Most important, the citizen will be the major beneficiary of this process.

Electricity provision is another strong candidate to be considered by the municipal corporation for generating more revenues though the CCL tool. People in Bangalore in particular, incur substantial investment on alternative energy sources (generators, UPS, etc) and a water typed coordination and convergence partnership would be useful to the people.

Housing is another potential candidate to test the CCL model. In recent years, the public and the private housing stock has remained unsold or unoccupied for long periods, due to non-access or poor quality of services of water, electricity, telephone, waste collection, poor connectivity, safety considerations, among others. A partnership between the housing provider and the provider of these services could ensure a better coordination in the provision of the services and the production of the housing stock. One may even go to the extent of the housing provider contributing to the development of some of the services, most critical to the sale and occupancy of the housing stock.

All these initiatives require a high level of coordination and convergence of activities among

several city partners within the government and sub-city information and indicators are the key operational instrument to initiate and forge partnerships. The urban local body in most of the urban centres has the responsibility of issuing the building permissions as well as the completion certificates, two critical bits of information on the forthcoming addition to the potential property tax base. The work

on the grant of these certificates and property tax assessment and collection is normally not within one department but spread over different departments located in the city. The CCL model of good governance or urban management has to be now adopted for testing among selected partners. The Seminar may examine the avenues of cooperation and partnership on the principles of inter-dependency and mutual benefits.

Using the valuable data on the property stock, the urban local body may take up some proactive initiatives with service providers to coordinate the provision of the services to be made available with minimum time gap after the receipt of the completion certificate. This will make the incremental property tax base functional at an early date.

Coordination & Convergence ---CAG’s Assessment of Need The Controller & Auditor General (CAG), Government of India has been entrusted a constitutional responsibility to examine the performance of government ministries and departments across the country and present its evaluation reports to the Parliament or the State Assembly. Every year the CAG selects a few ministries and departments for in-depth study. The thrust is on, among other issues, the expenditure or under-spending of allocated budgets, the monitoring of programmes and the impact or evaluation of the expenditures. The CAG assessments bring out the urgent need to consider the necessity of developing sub-city data in urban areas and introducing the CCL model for efficient governance. The latest CAG review (Union Government: Performance Appraisals, Civil 2002) covers three broad areas of urban governance and provides good evidence that strengthens the necessity of considering the application of the CCL model of urban management and good governance and building up sub-city data base. These illustrative governance areas are: water supply, health and education.

a. Water supply

The supply of clean drinking water is a basic necessity affecting the quality of life in cities.

State governments and Urban Local Bodies are responsible for providing this service through proper planning and implementation. Towards this goal, Government of India launched the Accelerated Urban Water Supply Programme (AUWSP) in 1994 to provide safe and adequate water supply to towns with population of less than 20,000. Against the 1,025 problem towns identified in 18 states, the CAG audit brings out that only one-fifth of the towns eventually got coverage under the final programme, 824 problem towns identified remained uncovered and no exercise was undertaken to identify problem towns in 5 states. In Sikkim, Assam, and Bihar, for example, none of the 98 problem towns identified were covered, while in the States of Gujarat, Rajasthan, Jammu & Kashmir, Karnataka and Arunachal Pradesh, the problem towns were not identified.

More significantly, the CAG assesses: “schemes were started without completion of necessary

groundwork resulting in large number of them remaining incomplete. There are numerous cases of diversion and retention of funds in deposits as well as misuse of resources. Water quality was suspect since no regular testing of water samples was done. Monitoring and review mechanism of the Union Government was deficient. It did not effectively track physical and financial progress of the schemes

being implemented by State Governments or suggest improvements. The Ministry did not undertake any evaluation study of the Programme to assess its impact.”

b. Health and Education: Major Road Blocks to Efficient Governance

Several tiers of health and education well-being often co-exist within the same city, making it imperative for city managers to pay special attention to the identification of high risk areas across the city. It has been generally observed that both health and education status is worse in poorer parts, calling thus for specific policy plans for such areas. The present approach to planning in both the sectors, unfortunately despite discussions on convergence and coordination follows a compartment-wise approach. Planning in health and education, even when they concentrate on a deprived segment such as urban slums, is disjointed. The health department for example, collects information related to only health, without exploring other crucial inputs influencing ill-health. Similarly, the education department too, collates information related to literacy, without linking up the active determinants discouraging communities from accessing education. The case studies of both sectors illustrates these lacunae.

To take the case of health for example, while there is an inherent multi-dimensionality to health, there exists no single framework both nationally and at the level of the states tackling health care. The State Department of Health and Family Welfare has several vertical sub-divisions, each of which looking into different aspects of health care, without necessarily following a coordinated approach. The Tuberculosis division, for example, looks after the incidence of TB, while similar other divisions exist for other major diseases. In the urban slum forms, typically the main hub of diseases, the causes are overlooked, such as poor sanitation, high population density, congestion, poor information base, and correspondingly low access to health facilities. At present, a particular slum is visited by officials of several divisions at different times to gather specific information on particular aspects related to their own work.

As regards several health sector programmes, the CAG audit draws attention to the lack of a singular framework, increasing therefore the chances of duplication of efforts both within the concerned division and the Ministry at large. Concerning the National Programme for Control of Blindness (NPCB), the CAG observes that “targets were set arbitrarily without taking into account the prevalence of blindness in the districts…[and] despite increase in absolute number of cataract surgeries, there was no corresponding decrease in the prevalence of blindness. Also relatively very less linkages were established leading to an improper sequencing of input flows.” Several examples are provided of ineffective monitoring for example, by operational staff, abuse and misuse of funds.

The CAG’s observations bring out clearly that limited coordination and convergence among government departments reduces in effect the potential reach of programmes, with large amounts of unutilised money enhancing the scope for misutilisation. The persistent cry over shortage of funds does seem to hold true. Several instances of large amounts of untilised funds confirm the above fact.

In the context of education, the CAG has assessed the functioning of the non-formal education (NFE) programme in operation since 1980. Bringing out the poor coverage, the CAG audit identifies the contributory factors as ineffective implementation of the Scheme, coupled with large-scale mismanagement of resources and absence of monitoring standards.. In the absence of benchmark survey of the potential out-of school children who could benefit from the Scheme, a series of half-

hearted measures, without community support and the strength of network were the principal reasons for the dismal performance of the scheme. An extreme illustration of the weak monitoring is evident from the example of grants of Rs. 24.74 crore being released to 8 States for opening night centres, when these centres were operational during daytime.

Development Agenda

The Toolkits are in the formative stage. The presence of a select group of high level professionals, toolkit producing researchers and trainers and toolkit user stakeholders from National, State and Local governments and urban service providers and management organisations, infrastructure financing and development organisations and donor agencies, presents an opportunity to share the outputs and get an assessment and feedback on the CCL model and related outputs. The fact is recognised now, that good information is critical at each stage of urban management and that the information should be at as much a disaggregated level as may be possible. But to use the information, toolkits like the ones presented in the Seminar are also required, something that had not yet been so well recognised.

The Agenda ahead may include the following components: 1. Further refinements in the Knowledge Infrastructure Toolkits 2. Testing the Knowledge Infrastructure Toolkits 3. Capacity Building Programmes on the Knowledge Infrastructure Toolkits at two levels

i. Senior Decision Makers, both from the Political and Administrative Leadership

ii. Operational Technicians who will develop and apply the Toolkits. These may be under two categories:

a. Sector Specialists b. Generalists

4. Partners and Collaborators for providing Training Infrastructure, Resource Persons, Trainers, Opportunities to apply the Toolkits, Financial Resources and developing and sustaining Networks.

An important development in the pipeline is a 2-stage capacity building programme to promote

the indicators-driven good governance model that we are developing, that seeks to attain better coordination and convergence among partners/stakeholders as well as city level resource leveraging. The model is based on the principle of inter-dependency among sectors and partners. The first stage of training will be a 3 days sensitization programme for Chief Executives and Heads of leading Urban Development Authorities and Municipalities as well as Senior Officials of Government of India and State Governments. The Programme would bring forth the concepts and develop an understanding to recognise the utility value of the sub-city information-driven approach and the inter-dependency planning and policy models, and provide the basic knowledge to use these tools for urban planning, management and decision making. An opportunity will be provided to interact with senior officials who have already been involved in the development process in Bangalore and Jaipur.

The second stage would be for mid-level officials, the technicians with responsibility for undertaking the task of developing good information at sub-city level and using them in the planning and decision-making process. This will be a 10 days programme, wherein the techniques to develop, validate and analyse the information and workout the planning models will be taught. Techniques and

processes to generate socio-economic data and geo-referencing household and ward level database, using global positioning system (GPS) system, will be brought out. Looking for Partners to Fine tune & Upscale Project Outputs

The SDS-WB policy research Team has brought out, in collaboration with the BLUO and key city partners, the role and utility value of good sub-city data. The information collected is an important resource and can be used to assess the relevance, performance and impact of government policy, scheme or development package. On the basis of the feedback, it may be possible to come out with modifications or even a new road map, along with building a case for a well accepted but not yet sufficiently successful approach to a participatory process among city stakeholders/partners. Good sub-city data also strengthens the rationale to not only improve management/governance of a programme, but hopefully, result in a more productive output, for example, to the urban local body through enhanced revenues, or to service providers through better reach out and incomes; a better satisfaction level to the citizens; an overall improvement in the Quality of Life; and bringing to the city the status of a desirable investment destination. The Seminar may examine the findings, suggest modifications, additions and any other exercise that would contribute to the utility value of the policy research outputs to the people in the cities, governments and international support organisations.

The Seminar may consider the indicator products and identify partners who would like to take these pilot study outputs to the next stage forward for extending the urban challenge areas, upscaling the range of activities and applying the techniques to more cities and urban centers. Partners may include potential cities, development agencies, governments, who would like to test the outputs within their locations and work areas, funding agencies that could upscale the research work, researchers and trainers who would like to work together with the Team, and NGOs who would like the communities to benefit from these tools.

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