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Evaluation of slum improvements
Pergamon
Cities, Vol. 13, No. 2, pp. 97-108, 1996 Copyright 0 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved
0264-2751(95)001294
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Evaluation of slum improvements Case study in Visakhapatnam,
India
Peter Abelson Department of Economics,
Macquarie University, Sydney, NSW2109,
Australia
The paper discusses how to evaluate slum improvement programmes and illustrates the discussion with a case study of improvements in 170 slums in Visakhapatnam, India. Valuation methods discussed include land and house prices, income changes, contingent valuation, switching values and cost-effectiveness. Drawing mostly on property prices but also on other valuation methods, the paper reports evaluations of the slum improvement programme as a whole in Visakhapatnam, improvements in 19 sample slums, and investment in health care, education and vocational training. As measured by willingness to pay values, the return on the whole programme was marginal. The results are sensitive to assumptions about recurrent maintenance expenditures. Returns to investments in individual slums showed significant economies of scale with higher returns in larger slums. There were high income returns to training and the educational courses were probably warranted on willingness to pay grounds. There was, however, little improvement in the health status of slum households. The paper concludes that notwithstanding government policies to target support to poor communities, willingness to pay valuations can usefully contribute to programme evaluations. Finally the paper suggests various areas of research that would improve evaluation procedures. Copyright 0 1996 Elsevier Science Ltd.
Economic rates of return are rarely estimated for social projects such as housing or health care schemes for low income households. As Lipton and Toye (1990) point out, evaluations of such projects tend rather to resemble laundry lists in which objectives are ticked off as they are (more or less) achieved. There are probably two main reasons why economic rates of return are not estimated. First, the benefits are difficult to quantify. Second, there is an understandable reluctance to estimate the benefits of poverty based projects using conventional economic (willingness to pay) criteria. However, in the absence of economic evaluations, the effectiveness of resources used by projects cannot be assessed. Consistent assessment of the relative rates of return of projects helps to establish priorities. If the returns on poverty oriented projects are lower than returns on other projects, governments can determine the appropriate level of subsidy (if any) for the low income communities. This paper discusses the evaluation of slum improvements taking Visakhapatnam as a case study. Visakhapatnam lies on the east coast of India, midway between Calcutta and Madras. Between
1971 and 1991 its population trebled from 360 000 to 1.05 million. This rapid increase reflected the city’s industrial growth as a major naval base and manufacturing centre, the poverty of the surrounding rural areas from which many migrate, and the high natural birth rate. There is a shortage of land in the city which is bounded by hills and sea. Population density is high, with 30 000 persons per km2 in much of the city. Despite some industrial prosperity, over 200 000 people live in nearly 200 officially designated slums with an average annual household income of 13 000 rupees per annum (&260). Half the adults in the slums are illiterate. Few slum households have private tap water and only half of the slums have public tap water. Under one in five slum houses has a toilet. In 1988, the Municipal Corporation of Visakhapatnam (MCV), supported by the UK Overseas Development Administration (ODA), started a major slum improvement programme. This included physical infrastructure improvements (roads, drainage, paths, street lighting), improved water supply, public toilets, community centres, primary health care services, and educational and training services.
97
A case study of Indian slum improvements: Table 1
Summary
of slum improvement
Civil infrastructure Roads/paths (bitumen) Roads/paths (concrete) Dustbins (large and small) Slum drains (all sizes) Gedda (town drain) improvements Culverts Community halls (large and small) Street lights Water pipelines Water fountains Bore wells Pilot water project Public latrines Public bathrooms
To September
$ no. m
98 91.5 95 815 129 107 871
m no.
25 905 92
no. no. m no.
139 955 4 999 99 267 1 315 37 7
IlO.
m no. no.
no. no. no. no. Unit
Socioeconomic programmes Pre-school education centres Non-formal education centres Adult education centres Reading rooms/libraries Short-term vocational
courses Nos. trained on courses Sewing centres Sources:
ODA
programme
Unit
Unit Health programme Primary health centres Trained delivery nurses Clean hut competitions Food and nutrition camps
P Abelson
1992
To March 1992 46 356 119 94 To March 1992
no.
106
no. no. no.
142 142 162
no.
27
no. no.
1 008 162
vocational
and Municipal
Council
of Visakhapatnam
Concurrently, the local Housing Corporation provided subsidised housing loans to one in four slum residents to improve their dwellings. The Visakhapatnam project was one of three major slum improvement programmes funded by the ODA in the second half of the 198Os, the other two being in Hyderabad and Indore. Concurrently the World Bank was financing physical infrastructure slum improvement programmes in Calcutta, Madras, Madhya Pradesh and Uttar Pradesh. The ODA embarked on the slum improvement programmes because it believed that the beneficiaries of the housing programmes it had been supporting in the early 1980s were often not from the economically weaker sections of the community. A crucial feature of the ODA financed programmes was the integrated approach which incorporates not only physical upgrading, but also community development, health and education inputs, and coordination of inputs by voluntary organizations. The paper starts by describing the slum improvement programme (SIP), the costs and main impacts.
98
I then briefly discuss approaches to benefit valuation. There follow evaluations of the the whole programme; the benefits in a sample of 19 slums; and returns to investment in education, vocational training and health services. The final section summarizes the main points.
Urban slum improvement Visakhapatnam
in
The slum improvement programme was a comprehensive attempt to improve the living conditions, health and incomes of 190 000 people living in 36 500 houses in 170 slums. Of these slums, 110 were on public land, 37 on private landlord property and 23 largely in private individual land ownership. Before the SIP, one in five dwellings were pucca houses with stabilized block walls and permanent roofs; one in ten were semipucca houses with mud walls and tiled roofs; seven in ten were kutcha houses with thatched roofs and various wall materials, including many pyramid shaped houses constructed entirely of thatched materials. In addition to the problems of low incomes and illiteracy, the incidence of gastrointestinal and respiratory diseases was high and one in five children suffered malnutrition. The components of the SIP are summarized in Table 1. Roads, paths, storm drains and multipurpose community halls were the main elements. Water, sanitation services and dustbins for garbage collection were smaller elements. Indian government targets of a public tap for each 20 households and a public toilet for each 10 households were not met. The ODA/MCV view was that community latrines are difficult to maintain, not popular, and a poor investment if space permits individual latrines. Primary health care is provided daily from 46 centres each serving about 5000 people. Services include health education, family planning, nutrition supplementation for pre-school children, immunization programmes, training of traditional birth attendants, treatment of minor ailments and provision of basic drugs. Socioeconomic services include daily non-formal education for pre-school activities, school dropouts, adult literacy courses, and vocational training in sewing, welding, repair services and various other trades. Land reforms were designed to give slum inhabitants security of tenure and a stake in the slum improvements and to provide an organized layout for each slum. The MCV purchased land in about 20 slums then in private ownership, planned the slums allowing 60 square yards per plot, and provided legal tenures (p&us) to the plots. The layouts were planned to minimize the loss of established pucca houses. However, the 60 square yard lot criterion sometimes reduced the dwellings in a slum. Pattus are usually permanent (free) land leases.
A case study Table 2
Direct ODA funded expenditures (1991-92 Rs) 1988 to September 1992 (Million rupees)
(%)
After September 1992 (Million rupees)
Engineering Health services Socio-economich Education’ Establishmentd Office costsC Training/evaluation/ computers Machinery
136.9 17.6 5.5 9.7 17.9 8.4
67.6 8.7 2.7 4.8 8.8 4.1
82.4 2.5 2.0 3.1 0.9 0.0
3.9 2.7
1.9 1.3
2.7 1.8
Total
202.6
99.9
95.4
“Based on annual inflation rate of 6%. “Income generating and cultural activities, including vocational training, revolving loans etc. ‘Nursery schools, non-formal education and adult literacy. ‘Staff overheads/permanent employees of MCV, including engineers and community development officers. ‘Office overheads in Visakhapatnam. Source: ODA.
Households are generally not allowed to sell puttus when they are used as security for Housing Corporation loans or for the first five years of the grant. The grants of pattas combined with lot size restrictions created considerable excess demand for lots in many slums (with owners, tenants and family members all demanding pattas). This placed considerable power and some monies in the hands of the slum neighbourhood committees who determined lot allocations (Prasada Rao and Rajesh Patnaik, 1992). Between 1990 and 1992, the Housing Corporation provided subsidized loans for pucca house construction to about 12 000 households in 71 slums. In 1992 the normal loan was Rsll 700 and a grant of RslOOO was also provided. Because construction of even a modest pucca house cost about Rs23 000, households had to find another RslO 000 or so. By late 1992 6000 houses had been completed. Maintenance of the SIP infrastructure is shared between the MCV and the slum communities. The MCV is responsible for replacing worn out asphalt roads, waste collection, and maintaining drains. The slum communities are responsible for cleaning drains and sewers, repairing road patches, replacing inspection chambers and manhole covers, repairing leakages in pipelines, and maintaining community centres. At the time of this study many slums had weak institutional and financing mechanisms for maintaining the improvements and, as we shall see, this is a substantial problem.
Programme costs Table 2 shows expenditures by major activity category in 1991-92 rupees. Engineering will account for 75% of expenditures when the projects are com-
of Indian
slum improvements:
P Abelson
plete. Relatively small parts of the budget were allocated to health and education services. The ODA also provided &lo0 000 per annum in offbudget management and advisory services. Total housing expenditure in the slums to September 1992 was an estimated Rs18.5 million. Annual maintenance expenditure for civil engineering projects is usually between 5 and 10% of capital expenditures. The higher percentage would probably be required to maintain the slum assets close to their original condition. In fact, there is already evidence of low maintenance. Some roads and drains constructed early in the SIP already require rehabilitation. One-third of the bore wells and water supplies to some public latrines were out of order within one year. Almost certainly, a low maintenance regime (anything less than 5% of capital costs) would lead to major asset deterioration in lo-15 years. To evaluate the SIP, we must consider whether the financial expenditures reflect resource costs. The main resource omitted is land. This is reasonable because (with or without the SIP) most land in the slums would be devoted to housing. Of course, land costs should be included when the land has a valuable alternative use, for example inner city commercial land. Also, when land shortage combined with the lot size rule causes the MCV to relocate households to other areas, the replacement costs should be added to project costs. However, these cases are exceptional and are not included below. Nor is any adjustment made below for the minor differences between financial and resource costs. Most expenditures were on local Indian goods and services; imports were minimal. The prices of some materials, for example cement prices before 1989, were controlled and did not fully represent the value of the materials to the Indian economy. No adjustment was made for possible discrepancies between wages and the opportunity costs of labour.
Programme services and impacts Outputs of social programmes are rarely easy to value. In national income accounting, outputs are often measured by inputs, for example of health or education services. This approach would be selfdefeating in an evaluation study because benefits (outputs) would equal costs (inputs) by definition. Fortunately, various ODA commissioned studies of the SIP provide valuable output data. The main study was a household survey (in 1988 and 1991) by the Visakhapatnam Institute for Development and Planning Studies (IDPS, Sarveswara Rao and Ramachandrudu, 1992a). The IDPS selected 800 households (one-third of all .households) from 10 of the 45 slums improved in 1988-89. Just over 600 households were interviewed in both 1988 and 1991. This survey provides useful data on
99
A case study Table 3
of Indian
slum improvements:
P Abelson
Housing and environment 1988 No.
Households Owning house With l&d tenure With pucca houses Less than 100 ft’ More than 200 ft2 Average area ft’ With electricity With separate latrine With tap water Using dustbins Using drains Using traditional fuel Source: Household (1992a).
533 186 182
144 211 72 325 108 368 405
surveys;
% 87.1 30.4 29.7 34.8 17.3 34.5 11.8 53.1 17.7 60.1 66.2
Sarveswara
1991 No. 542 449 339
171 381 133 345 282 529 414
% 88.6 73.4 55.4 19.8 34.1 62.3 21.7 56.4 46.1 86.4 67.6
Change in % 1.5 43.0 25.6 - 15.0 16.8 18.8 27.8 9.9 3.3 28.4 26.3 -1.4
Rao and Ramachandrudu
changes in housing conditions and health and education status. In addition, the IDPS carried out a nutrition and morbidity survey of 80 households in 1988 and 1991 (Swarajyalakshmi et al, 1992); analysed the households moving in and out of the slums (Sarveswara Rao and Ramachandrudu, 1992b); and conducted a detailed review of the impacts of the SIP on the lives of 35 households in four slums (Prasada Rao and Rajesh Patnaik, 1992). The MCV also studied the impacts of the vocational training programme (Patrudu, 1992). Most of the surveys described slum households before and after the SIP. Such observations must be interpreted carefully. Some observed changes (for example rising incomes) may be due to causes other than SIP services. On the other hand, some impacts (for example improved health) may not be observable within three years. The surveys did not include slum areas without SIPS because there were no strictly comparable slums outside the programme. The following subsections summarize the impacts of the main programmes: they show some substantial improvements in living standards.
Housing and environment As shown in Table 3, many more slum households had secure land tenure in 1991 than in 1988. There were more pucca houses; house size and electricity supply increased significantly. Waste disposal practices improved with greater use of dustbins for solid wastes and drains (instead of roads or paths) for wastewater. Flood and fire risks were reduced. However, some important elements of environmental quality did not improve much. Households with access to protected (tap) drinking water increased only marginally. Nearly half the households still depend on well water which tends to be saline, may be polluted, and is often inaccessible. Few households have their own latrines. Communal lat-
100
rines were not properly maintained (Hodgson, 1992). Two-thirds of all households continue to use traditional polluting fuels (firewood, dung cakes and charcoal) for cooking.
Health care and status The health care services have been well utilized. In 1990-91, there were 172 000 attendances at the 46 primary health care centres, equivalent to nearly one attendance per slum resident. The proportion of fully immunized children (aged 1 to 3 years) rose from 25 to 83%. More women adopted permanent family planning methods. Pregnant women registered more quickly and institutional deliveries increased from 42 to 72%. Nevertheless, there was little immediate evidence of improvements in health status. In 1991-92, there were 42 still births and 7 maternal deaths during 2628 deliveries in the 170 project slums. The increase in institutional delivery did not reduce the probability of problems during delivery. There have been fewer postnatal problems, but whether this is due to the changed delivery process is not known. Between 1988 and 1991, reported diseases actually increased in the slums (Table 4). Among general diseases, malaria, typhoid, respiratory disorders and skin diseases increased significantly and gastroenteric disorders also rose. Among chronic diseases, cases of strokes, high blood pressure and diabetes all rose. On the other hand, unlike in 1988, there were no reported cases in 1991 of whooping cough or diphtheria, both of which are preventable by immunization. The increased morbidity incidence almost certainly reflected a city-wide trend, for example for malarTable 4
Health care and status
(%)
1991 (No.)
(%)
1988-91 Change in %
193 106 52 32
64.1 35.2 86.7 53.3
262 106 40 8
67.2 27.2 85.1 17.0
3.1 -8.0 -1.6 -36.3
Fully immunized (l-3 age) Persons sick, last 14 day& Persons chronically ill“
56 43 39
24.7 1.5 1.4
92 130 92
82.9 4.5 3.2
58.2 3.0 1.8
Households undercaloried” Children 20%+ underweight’
55 60
68.8 46.7
53 41
66.3 31.7
Category”
1988 (No.)
Tubectomiesh Vasectomies’ No delivery problems’ Post-natal consultations’
“The first eight rows are based on 612 households; rows are based on 80 households. bPercentages in couples. ‘Percentages of deliveries. dPercentages of all population. ePercentages of households. ‘Percentages of children from 0 to 5 years. Sources: (1992a);
Household surveys; Sarveswara Swarajyalakshmi et al (1992).
-2.5 -15.0
the last two
Rao and Ramachandrudu
A case study of Indian slum improvements: Table 5
Education,
employment,
income and assets
1988” (No.)
Category Children at pre-school Adult literacy Overall literacy Adults employed
68 769 1508 897
Household income/month (Rs) 1 163 Male income/month (Rs) 854 Female income/month (Rs) 381 Household expend./month (Rs) 816 Household assets 17 113 Household debt 5 723
1988-91 Change
1991 (%) (No.)
(%) in %
29.4 44.5 53.7 51.9
57.1 51.5 62.9 52.5
117 948 1818 967
27.6 7.0 9.2 0.6
1 348 926 380
15.2 8.4 -0.3
1 087 31 132 9 840
33.2 81.9 71.9
“All rupee figures in 1991 prices: the nominal 1988 values inflated by the consumer price index for Visakhapatnam. Source: Household (1992a).
surveys;
Sarveswara
are
Rao and Ramachandrudu
ia. It may also have reflected an increase in reporting. However, the incidence of malaria is three to four times higher in the slums than elsewhere (Hodgson, 1992). Moreover, several morbidities, such as respiratory infections and skin diseases, can be attributed to the ongoing poor slum environments - the poor water supply, inadequate sanitation, indoor air pollution, the lack of personal hygiene and still inadequate nutrition. In 1991, nearly all surveyed adults suffered vitamin and iron deficiencies, two-thirds suffered calorie deficiencies, and one-third suffered protein deficiencies. There had been no significant improvement since 1988 (Swarajyalakshmi et al, 1992). On the other hand, the nutritional well-being of children under 5 improved. The proportion of under-5s who were less than 80% of the relevant ICMR standard weight fell from fell from 47 to 32% in three years. And no under-2s were less than 80% of the relevant weight in 1991.
Education,
vocational training, and incomes
Education and training courses were well-attended: 80% of slum children (3-6 years) attended preschool in 1991; two-thirds attended ODA funded pre-schools. Of the 8000 school dropouts in the slums, 3500 enrolled for non-formal education (about half of those enrolled attend); 21 000 illiterates enrolled for adult literacy classes (about one-third of these attend). In the three years to end-1991, 746 people received vocational training in one of 15 trades and 3422 completed sewing courses. There are some positive output measures. For example, literacy rates improved (see Table 5). Vocational courses also improved employment and income prospects. Of a sample of 129 trainees, 26%
P Abelson
were employed before training and 59% after training, and average (nominal) wages rose by 37% 1992). Adult female employment in(Patrudu, creased from 21 to 25%, but male employment did not increase. Between 1988 and 1991, the income and wealth of slum households rose considerably. Average reported real household income rose by 15%; real household expenditure (excluding debt repayment) increased by 33% ; and the real value of gross household assets rose by 82%. These data must be interpreted cautiously. Sarveswara Rao and Ramachandrudu (1992a) suggest that respondents are likely to understate income by more than they overstate expenditure and that expenditure figures are therefore more reliable. However, reported income comfortably exceeded expenditure (see Table 5). This may be due to omissions of spending on consumer durables and debt repayments from expenditures.
Other impacts A SIP also affects households who move into, or out of, improved slums. Inmovers would usually gain from a SIP. Outmovers may be gainers who take advantage of increased wealth or losers who are pushed out by higher rents or new land arrangements. The former motives were more common according to Sarveswara Rao and Ramachandrudu (1992b). In-movers gave employment and lower rent as the main reasons. Adjacent neighbourhoods also benefit from the improved slums. In some adjacent areas, improvements followed directly after the slum upgrading. However, there is no record of these indirect effects.
Impacts by beneficiaries IDPS studies show that, in 1991 compared with 1988, about 50% of slum households had significantly higher income, 30% slightly lower income and 20% much lower income. However, the studies did not assess the effects of the SIP on incomes. Apart from households who lost tenure, most slum households gained from some aspect of the SIP. But it appears that the most powerful, established and better-off households gained more than others. Between 1988 and 1991, income differentials in the slums widened; landlords gained significant real rent increases; male earnings rose more than the female earnings; employed persons’ earnings rose more than the earnings of self-employed persons; and the relative position of the Scheduled castes deteriorated. The anthropological case studies confirmed that the more powerful families gained more land rights. On the other hand, less well-off households probably benefited more from the health care services and the nursery schools.
101
A case sludy of Indian slum improvements:
P Abelson
Table 6 measures
outputs
A summary
of project
inputs,
and benefit
Project inputs
Main project outputs
Civic infrastructure
Health; amenity; productivity$’ reduced flood damage Amenity; health; reduced fire damage Health; life expectancy; reduced health expenditures Productivity Productivity Amenity; health; productivity; reduced public facility expenditures elsewhere
Housing improvements Health/nutrition Education Vocational Community
training centres
Project outputs
Benefit measures
Improved
health
Improved
amenity
Improved
productivity
Improved
assets
Increased incomes (due to increased productivity); reduced health care expenditures; quality and value of life measures; increased property values Property values/rents; user payments and taxes Increased incomes; property values/rents Property values/rents; reduced public expenditures; participation rates
“Minor effect lighting.
related
to more
usable
road
spaces
and
street
General benefit valuation issues When slum improvements are formally evaluated, benefits are almost always based on estimated increases in land values or rents. For example, World Bank studies forecast that increased land values or rents would produce real rates of return of between 15 and 19% for slum improvement programmes in Madhya Pradesh, Uttar Pradesh and Calcutta and estimated an ex post 23% rate of return for slum improvements in Madras. The studies did not estimate other measures of benefits, for example for access to health care or water. Likewise the ODA’s slum improvement project in Hyderabad was justified by forecast land value increases as proxies for project benefits. This approach is open to criticism. In imperfect markets, land values may not accurately reflect household preferences. Clarke et al (1989) strongly criticized the use of land values in Hyderabad because the land value data were extremely unreliable. Lacking adequate data, planners may simply assume ex ante that, because the planned improvements are desirable, land values will rise to reflect these expenditures. But this process is circular and does not provide independent justification of proposed expenditures. To adopt the land value approach, three questions must be answered. What benefits do land values reflect? Are land value data adequate? And are there alternative valuation methods that would measure the benefits more precisely or draw on more reliable data? To provide a perspective for these
102
questions, Table 6 outlines the main relationships between the major SIP inputs and outputs and possible benefit measures. Under certain conditions, nearly all private benefits (including health, productivity and amenity benefits) would be reflected in increased land and house prices in the slums. These conditions are: All private benefits accrue only to residents of the slums. ?? All SIP benefits (including health benefits, reduced flood and fire damages etc) are fully perceived by potential buyers. ?? Slum property prices are not controlled; slum residents can sell or rent their properties to outsiders as well as to other slum residents. ?? Property transactions are costless. ??
Even under these conditions, there would usually be some residual householder surpluses. For example, it is unlikely that the full benefits of health care and training programmes would be precisely capitalized in property values. Moreover, several conditions are unlikely to hold fully. In Visakhapatnam, some benefits accrue to non-slum residents; health benefits may not be fully appreciated; there are some official restrictions on sale of slum properties (which are often bypassed); and there are transaction costs. If the above conditions are not met fully, changes in slum property values do not reflect all the private benefits of SIPS. Also, there may be public benefits, for example savings in expenditure on asset maintenance. Turning to the second question, if property values are considered nevertheless to provide an approximate measure of most private SIP benefits to slum households, are adequate property data available? The answer depends on circumstances. However a few general observations may be made here. There are two basic data issues. First, even when property sale prices are registered with public bodies and officially available, the price data are almost always recorded manually and difficult to access. Data collection and interpretation generally requires a special research exercise. Property valuations, usually made for taxation purposes, are generally imprecise and sometimes unreliable. The second issue relates to the analysis. In time series analysis, changes in property prices in improved slums may be compared with changes in unimproved slums or non-slum areas. But there is a major presumption that any differential change is attributable to the SIPS and not to other factors such as changes in access to employment. If this presumption does not hold, the data and analysis requirements are much greater. In cross-section comparisons, there must again be a presumption that observed price differentials are due to the SIPS and not, say, to access differentials. Generally controls, and data, for other important independent variables are required.
A case study of Indian slum improvements: P Abelson Table 7
Summary
Slum improvement Engineering Education Socioeconomic Health Other Total ODA
of project costs (1992 million rupees)
Housing
After September 1992
Total
137 10 6 18 32 203
82 3 2 3 5 95
219 13 8 21 37 29X
12
5
programme
overheads”
Infrastructure
1988 to September 1992
improvements’
17 22 pa
maintenanceh 185
91
276
“f100 OOtl pa converted at current exchange rates and into 1992 Rs. ‘Assumes annual infrastructure maintenance is 10% of engineering costs, but this may not be affordable. ‘Assumes total improvements equivalent to 12 000 new houses at Rs23 000 per house. Source: ODA.
The main alternative valuation method is contingent valuation: asking slum and non-slum households what they would be willing to pay for a SIP. It has been shown that low income households can usefully be asked what they would be willing to pay for specified improvements, for example for water supply (Whittington et al, 1990, 1991). However, households may be unable or unwilling for various reasons to say what they would be prepared to pay for a SIP. First, many people would find it difficult to state what they would be willing to pay for such a complex package of disparate services as distinct from some particular components. Second, great care is required to obtain accurate answers, even if the respondent wishes to cooperate. For example, community development officers in Visakhapatnam considered that stated willingness to pay monthly contributions for public toilet use would be less than the equivalent daily amounts, Third, if households believe that they are entitled to the urban services in any case, or if they know that the services are provided under foreign aid, they may not cooperate wholeheartedly in a willingness to pay survey. Real changes in household incomes may measure some slum improvement benefits associated with education and training. Also, some health improvements and infrastructure benefits, such as improved open spaces for trading, may enhance productivity and incomes. However, amenity improvements, some health benefits, and some collective asset improvements, for example those due to reduced flood and fire risk, are not reflected in incomes. Travel cost analysis can be used to infer the value of services when there are significant differentials in both access costs and usage. Examples are water supply and health care services. However, this requires a special study of service use and travel costs.
When none of these benefit valuation methods can be used, the switching value method may be used. If total service costs and participation rates are known or forecast, unit service costs can be computed. A judgement whether unit value would exceed unit costs may then be made. Alternatively, SIPS are sometimes justified using a ‘cost-effectiveness’ criterion. A proposed SIP is said to be cost-effective if expenditure per household is in line with, or lower than, the norm in other SIPS. This presumes that SIP outputs are similar and that expenditure variations reflect differences in unit costs. However Clarke et al (1989) concluded that the cost variations within the Hyderabad SIP reflected political pressures to improve the quality of facilities. When outputs vary, benefits must be assessed; cost-effectiveness is not an adequate criterion. In conclusion, the total benefits (TB) of a SIP can be expressed as: TB =
@DLV
+ @DHS
+ PNSB
+ GB
(1)
where @DLL’ are changes in land values in the slums, @DHS are changes in householder surpluses of slum households that are not capitalized in land values, PNSB are private non-slum benefits, and GB are government benefits (increased user fees and taxes and expenditure savings). In unregulated property markets, land values (or house prices) can provide a measure of most SIP benefits to slum residents. But property price data are not always readily available and analysis of the determinants of property prices requires care. Also, some SIP benefits (for example health and education benefits) may not be reflected fully in property values. In these cases, other valuation methods, such as willingness to pay surveys, may provide supplementary information on the output values. Most benefits to non-slum households are likewise reflected in (non-slum) land prices but these are difficult to measure. Finally, assessments of the costs, benefits and rates of return to major components of public programmes are generally desirable. But, due to complementarities in the programme, the return to the whole SIP programme may be greater than the sum of the returns to the individual parts.
Evalution of the whole Visakhapatnam improvment programme
slum
Estimated project costs are shown in Table 7. Total capital expenditure on the SIP, including ODA overheads, was Rs315 million in 1992 Rs. Infrastructure maintenance could cost up to an additional Rs22 million per annum: with a 10% discount rate, the capital equivalent would be Rs220 million. Estimated housing expenditures totalled another Rs276 million. Turning to benefits, the evaluation below is based
103
A case study of Indian slum improvements: Table 8
Summary
P Abelson
of SIP costs and benefits (1992 Rs million)
~
Slum improvement programme costs SIP expenditures 198%93 ODA overhead expenditures Maintenance expenditures capitalised” Total costs
298 17 110 41.5
Slum improvement programme benefits Increased slum property values Slum household surpluses” Private benefits to non-slum households” Government benefits” Total benefits Social surplus or subsidy Including only SIP expenditures Including ODA and maintenance
270 27 21 27 351
expenditures
“Based on likely maintenance expenditures ‘Assumed to be equal to 10% of increased
53 -64
(set text). slum land values.
Source: ODA.
on property values, although income effects are also considered. Indicative figures for benefits to government and non-slum households are also given. However, detailed quantification is not possible from available data. There are no records of public spending in the slum areas. Moreover, valuing external benefits in one area due to improvements in another is difficult and rarely attempted. Visakhapatnam has an active housing market with between 1.5 and 2.0% of the housing stock sold each year. Recorded sales in the slums are lower due to restrictions on sales. But there are many unofficial (benami) sales where prices are known locally. Of course, houses sold under restrictions sell at a discount. Also, land sales are fewer than house sales. Therefore, land values must be inferred from house prices or based on valuation data. For this study, MCV officers collected official land valuation data for typical properties in 24 slum areas improved in 1988 or 1989 and valued in 1987 and 1990. In the three years to 1990, mean land values in the sample slums increased from Rs256 to Rs441 per square yard. Elsewhere in Visakhapatnam, land values rose by an estimated average 25%. It follows that slum land values would have risen to Rs320 without the SIP: the difference between Rs441 and Rs320 is attributable to the SIP. Applying a benefit of Rs121 per square yard to 36 500 houses, with an average 50 square yards per house, produces a total increase in land values of Rs221 million in 1990 Rs or Rs254 million in 1992 Rs. Municipal officers Z&O estimated the prices of pucca, semi-pucca and kutcha houses in 19 improved slums in 1992 with and without the SIP. The without SIP estimates were based on prices in comparable unimproved slums. The SIPS increased the average price of pukka houses by an estimated Rsll 400, semi-pukka houses by Rs7800 and kutcha houses by Rs4250 (see Table 9). Applying these increases to
104
the 36 500 houses in the 170 slums produces a total increase in house prices due to the SIP of Rs28.5 million in 1992 Rs. (This does not include value increments due to upgrading kutcha and semi-pucca houses to pucca houses.) The estimated benefits from the land value and house price calculations are reassuringly close. The researchers did not know how their figures would be used and could not massage the figures to produce these close results. Real slum household incomes rose by 15% between 1988 and 1991. This reflected a rise in adult female employment from 22 to 28%. Average real male income rose by 8.4% whereas the average real female income was constant. However, the impact of the SIP on slum incomes cannot be estimated because there are no comparable data on income changes in other parts of Visakhapatnam. Within Visakhapatnam, 1988-91 was a period of high economic activity, including much construction and a new steel plant. Therefore, the total value of the SIP cannot be derived from income changes. Table 8 summarizes total SIP costs and benefits. Annual maintenance expenditures are assumed here to be 5% of civil engineering capital expenditures and capitalized using a 10% discount rate. These maintenance expenditures would not maintain the assets adequately, but are realistic. The increase in slum property values shown is the mean of the land value and house price estimates. Importantly, it is assumed that slum household surpluses (benefits not capitalized into property values), private benefits to non-slum households, and government benefits would each be equal to 10% of increased slum property values. These judgements appear plausible, but lack empirical support. It was not possible to attempt to quantify these possible benefits in the time available to the writer in Visakhapatnam. As shown, estimated total benefits to slum households (including household surpluses) approximately equal SIP capital expenditures. Including assumed private benefits to non-slum households and government savings, there would be a surplus of Rs53 million over capital expenditures. (Because the benefits are capitalized in property prices, a precise discounting exercise is not made.) On the other hand, including ODA overheads and capitalized maintenance expenditures, there would be a total social deficit of Rs64 million. Maintenance costs critically affect this result. To gain perspective, we may note that SIP expenditure per slum household is R8300 without slum maintenance (and Rsll 600 with maintenance) compared with average slum household income of Rs16 000. Therefore, to pay for the capital and recurrent costs of the SIP, slum households would have to make a one-off payment of about threequarters of their annual income. If this is more than they would be willing to pay, the difference must be
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A case study of Indian slum improvements: Table 10
Evaluation
P Abelson
of training courses
VMC
Opp. costs
Overhead and other
Total
Increased real earnings/month due to training (1991 Rs) Persons Persons Weighted employed employed monthly Weighted before after” averageb annual averageb
410 375 400 1000 650 400 4675 400 1000 680 450
532 33x 0 300 589 612 1320 614 765 1042 45
205 18X 200 500 325 200 1000 200 500 340 22s
1147 900 600 1800 1564 1212 6995 1214 2265 2062 720
112 0 111 188 240 71 170 141 153 38 0
Average trainee costs (1991 Rs)
Trade
Number persons in survey
Printing, binding Cane weaving Food processing Motor rewinding Electrician, wiring Embroidery Welding, fabrication Motor servicing Motor driving Radio/TV servicing Wool knitting
20 5 16 4 14 13 10 16 s 19 7
“Assume earning RlSO/month informally before. “This allows for those who are not employed before assumed not to gain from the training. Source:
Patrudu
or after
the training
households
and
Evaluation of indivudual slum improvments The benefits of improvements in 19 sample slums, for which both engineering expenditure and property price data were available, are presented in Table 9. The following procedural points may be noted. Estimated total expenditures (in 1992 Rs) in each slum include estimates for health, education and other expenditures as well as engineering, but not ODA overheads or maintenance expenditures. Increases in land values and house prices (both in 1992 Rs) were based on land registration data and municipal officer estimates. To estimate the total land improved in each slum, 60 square yards are allowed per pucca house, 50 square yards per semi-pucca house, and 40 square yards per kutcha house. Drawing on the land valuations, the mean benefit-cost ratio (BCR) is 1.18. The improvements have a BCR above 1.0 in 10 slums and below 1.0 in 9 slums. The house price results indicate a mean BCR of 1.86. In this case, 13 slums show a BCR above one and six slums a BCR below one. These results suggest that the net benefits of slum projects vary considerably. Importantly, there are significant economies of scale. As shown in Equation (2), the net benefit in each slum (NBh , based on increased house prices less expenditures) is positively related to the number of houses (H) in each slum: NBh = -625 000 + 5174H (4.8) R2 = 0.58
106
course
I20 0 128 241 126 60 51 212 313 73 0
and for those
1441 0 1537 2892 1512 717 612 2539 3756 x79 0
employed
in other
126 0 2.56 161 97 59 9 20’) 166 43 0
trades
0.8 na 0.4 1.6 1.1 1.7 11.4 0.5 0.6 2.3 na
who were
(1992).
made up by benefits to non-slum government, or by social subsidy.
The figure
295 0 261 388 402 211 345 335 553 263 0
Percent Payback rate of period return years
in parentheses
is the t-statistic.
(2)
Evaluation of education and health investments Evaluation of training Per capita costs and returns to 129 trainees in 11 training course are summarized in Table 10. The costs include the direct training costs of the courses paid by the MCV, a 50% mark-up to allow for MCV overhead costs, and the opportunity costs of trainees. The last cost was based on the numbers in employment before the courses and their pretraining earnings. The unemployed were also assumed to forgo informal earnings of Rs150 per month. Per capita returns are a weighted average of the increased income due to each course. Of the 129 trainees, 34 were employed before and after training. Following training, an extra 30 were employed in the trade for which they trained, another 12 were employed in other occupations, and 53 remained unemployed (though usually in informal income earning activities). For the evaluation, it is assumed conservatively that those who gained employment would lose their informal earnings and that the increased earnings of trainees employed in occupations for which they had not trained were not attributable to the training. Despite these conservative assumptions, there were high rates of return to training in 8 of the 11 courses. The returns to training in welding and fabrication (the most expensive course) are marginal. There were zero returns to the craft courses in cane weaving and knitting. Most of these results are strong ones which would not be sensitive to changes in assumptions. Evaluation of education By mid-1992, the MCV had spent
Rs8.9 million
on
A case study of Indian slum improvements:
education services (nursery schools, non-formal adult education and adult literacy classes). In the absence of detailed costs and results for these services, only broad unit costs and willingness to pay measures can be reported. From 1988-89 to 1991-92, the MCV spent Rs2.25 million per annum on the three educational services. There were about 1.0 million nursery school attendantes (of three hours) per annum; 0.5 million attendances (two hours) at non-formal education classes per annum; and 1 .O million attendances (one hour) at adult literacy classes per annum. The average cost for these 2.5 million education services was slightly under Rsl per attendance or Rs20 for a month of attendances. This represented about 1.5% of average household incomes. It is a matter for judgement whether these courses represented value for money as there is no evidence (one way or the other) on productivity changes due to the courses or on what the slum households would have been willing to pay for the courses.
Evaluation
of health care
Slum improvements would be expected to significantly improve the health of slum residents and reduce infectious diseases. However, as we saw above, apart from some improvements in the weight and health of young children there was little evidence of improved health status by 1992. It is possible that two to three years is too short a period over which to observe measurable improvement in health status, but I would not attach great weight to this possible explanation. Without physical measures of improved health status, there can be no meaningful evaluation of health improvements. Nor could conclusions be drawn about productivity because slum residents were not asked about their attendance at work. Turning to the unit cost approach, annual recurrent expenditure on health care services was Rs6.0 million, of which Rs4.0 million was spent on services from primary health care centres including provision of drugs. With an annual utilization rate of 170 000 attendances, the average cost per attendance was Rs24. Of this, medicines account for Rs13 and labour for Rsl 1. IDPS surveys suggest that the primary health care services are popular because they are convenient and free. However, the unit cost of Rs24 is well above the normal price (Rs5-10) for a consultation with a doctor, which deters many slum residents from visiting doctors. Also, several respondents expressed disappointment with the quality of services. Overall, it is doubtful whether slum residents would be willing to pay for more than a small part of the cost of the health services. This does not mean that the services should be withdrawn as subsidies are often provided for health care, especially for
poor households. However, the services the slums may not be cost-effective.
P Abelson
provided
in
Conclusions The Municipal Corporation of Visakhapatnam has spent about Rs300 million on improving 170 slums including both physical and socioeconomic programmes. At least RslO million per annum will be required to maintain the improvements. These expenditures will bring benefits to slum and non-slum households and savings to the government. Most slum household benefits are reflected in increased land and house prices, although some benefits may not be fully capitalized into property prices. The SIP increased total land values in the 170 slums by an estimated Rs254 million and house prices by Rs285 million. Given a perfect market and accurate data, these two increases would be the same. In addition, slum householder surpluses, private non-slum benefits and government savings may be conservatively expected to sum to at least onequarter of SIP costs. Therefore, the total value of social benefits would exceed capital expenditures. However, if maintenance expenditures and ODA overheads are taken into account, the estimated social deficit is about Rs60 million. These results are not surprising. It is not, after all, surprising that low income slum households and adjacent communities may not be willing to pay for all slum improvement and maintenance expenditures. But this is not a good reason to abandon willingness to pay as the basis for the evaluation and to abandon explicit estimates of the social subsidy component. The study also evaluated SIPS in 19 separate slums by comparing increases in local land and house prices with estimated local SIP expenditures excluding maintenance. The estimated rates of return varied considerably with two-thirds of the sample slums showing a BCR above one and the rest having a BCR below one. Economies of scale were significant with higher returns in the larger slums. The Visakhapatnam SIP is a comprehensive approach to slum improvements, which includes educational, training and health care services. Notwithstanding the holistic nature of the programme, we would like to know which parts produce the best, or worst, results. The analysis showed that there were high returns to most training courses and that slum households could be willing to pay for most of the educational services, which have a low unit cost. On the other hand, the health care services and improved slum infrastructure had made little impact on the health status of slum residents by 1992 and it is doubtful whether slum households would be willing to pay the direct health care costs. It may be too early to observe health improvements. However, the project concentrated on readily achievable roads
107
A case study of Indian slum improvements:
P Abelson
and drains. There was little improvement in slum water supply (partly because of a chronic city-wide water shortage) or in sanitary facilities (partly because public facilities were considered less effective than private ones). Although there are many more data on the impacts of the SIP in Visakhapatnam than is usual, gaps remain. Land valuation data are crude. Estimated house prices in the no-SIP scenario could not be based on clearly established control cases. There is little information on maintenance expenditures. Also, we were obliged to speculate on the extent to which benefits are capitalized in property prices, the benefits to non-slum households, and the savings to government. Nor, to the best of the writer knowledge, was any research done on the rank preferences of households for different services. These topics could usefully be the subject of more research in Visakhapatnam or elsewhere. Finally, there is concern about the sustainability of the slum improvements. The institutional arrangements for maintenance, including finance and taxation, in Visakhapatnam appear weak. This does not n,ecessarily invalidate the programme on welfare grounds but clearly the benefits would be greater if the improvements could be sustained.
Acknowledgements This study was funded by the UK Overseas Development Administration and administered by the Overseas Development Institute (ODI), London. I owe a special debt of gratitude to David Crapper (ODA) for organizing my field trip to India, for briefing me on the project, and for commenting in detail on a draft paper. In preparing this paper, I was also greatly assisted by Mr Patrudu and Mr Kumar from the Visakhapatnam Urban Community Develop-
108
ment Department and by Michael Slingsby (ODA). Jim Winpenny’s (ODI) comments on a draft paper were most helpful. However the views expressed in this paper are my own responsibility.
References Clarke, J, Jones, S, Pickford, J, Higginbottom, A and Bennett, C (1989) Evaluation Study of Hyderabad Slum Improvement Project Evaluation Report EV47.5, Overseas Development Administration, London Hodgson, K (1992) Report on Environmental Health and Sanita(ion Visit to Visakhapatnam Slum Improvement Project Universities of the North of England Consortium for International Activities Lipton, M and Toye, J (1990) Does Aid Work in India? Routledge, London Patrudu (1992) Vocational Training Courses - An Assessment Study Visakhapatnam Slum Improvement Project Prasada Rao, D L and Patnaik, Rajesh (1992) Anthropological Studies - Case Studies of Selected Families Evaluation Study of the Visakhapatnam Slum Improvement Project, Institute of Development and Planning Studies, Visakhapatnam Sarveswara Rao, B and Ramachandrudu, G (1992a) Report on the Household Survey Evaluation Study of the Visakhapatnam Slum Improvement Project, Institute of Development and Planning Studies. Visakhapatnam Sarveswara Rao, B (1992b) Report on Moved-out and Moved-in Households Evaluation Studv of the Visakhanatnam Slum Improvement Project, Institute of Development’and Planning Studies, Visakhapatnam Swarajyalakshmi, B, Sarveswara Rao, B, Narayana Raju, B and Ramachandrudu, G (1992) Report on Changes in Health Status, IY88-YI Evaluation Study of the Visakhapatnam Slum Improvement Project, Institute of Development and Planning Studies, Visakhapatnam Whittington, D, Briscoe, J, Mu, Xinming and Baram, W (1990) ‘Estimating the willingness to pay for water services in developing countries: a case study of the use of contingent valuation surveys in southern Haiti’ Economic Development and Cultural Change 38 293-3 11 Whittington, D, Lauria, D T and Mu, Xinming (1991) ‘A study of water vending and willingness to pay for water in Onitsha, Nigeria’ World Development 19 179-198
Cities, Vol. 13, No. 2, pp. 97-108, 1996 Copyright 0 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved
0264-2751(95)001294
0X4-2751/96
$15.00 + 0.00
Evaluation of slum improvements Case study in Visakhapatnam,
India
Peter Abelson Department of Economics,
Macquarie University, Sydney, NSW2109,
Australia
The paper discusses how to evaluate slum improvement programmes and illustrates the discussion with a case study of improvements in 170 slums in Visakhapatnam, India. Valuation methods discussed include land and house prices, income changes, contingent valuation, switching values and cost-effectiveness. Drawing mostly on property prices but also on other valuation methods, the paper reports evaluations of the slum improvement programme as a whole in Visakhapatnam, improvements in 19 sample slums, and investment in health care, education and vocational training. As measured by willingness to pay values, the return on the whole programme was marginal. The results are sensitive to assumptions about recurrent maintenance expenditures. Returns to investments in individual slums showed significant economies of scale with higher returns in larger slums. There were high income returns to training and the educational courses were probably warranted on willingness to pay grounds. There was, however, little improvement in the health status of slum households. The paper concludes that notwithstanding government policies to target support to poor communities, willingness to pay valuations can usefully contribute to programme evaluations. Finally the paper suggests various areas of research that would improve evaluation procedures. Copyright 0 1996 Elsevier Science Ltd.
Economic rates of return are rarely estimated for social projects such as housing or health care schemes for low income households. As Lipton and Toye (1990) point out, evaluations of such projects tend rather to resemble laundry lists in which objectives are ticked off as they are (more or less) achieved. There are probably two main reasons why economic rates of return are not estimated. First, the benefits are difficult to quantify. Second, there is an understandable reluctance to estimate the benefits of poverty based projects using conventional economic (willingness to pay) criteria. However, in the absence of economic evaluations, the effectiveness of resources used by projects cannot be assessed. Consistent assessment of the relative rates of return of projects helps to establish priorities. If the returns on poverty oriented projects are lower than returns on other projects, governments can determine the appropriate level of subsidy (if any) for the low income communities. This paper discusses the evaluation of slum improvements taking Visakhapatnam as a case study. Visakhapatnam lies on the east coast of India, midway between Calcutta and Madras. Between
1971 and 1991 its population trebled from 360 000 to 1.05 million. This rapid increase reflected the city’s industrial growth as a major naval base and manufacturing centre, the poverty of the surrounding rural areas from which many migrate, and the high natural birth rate. There is a shortage of land in the city which is bounded by hills and sea. Population density is high, with 30 000 persons per km2 in much of the city. Despite some industrial prosperity, over 200 000 people live in nearly 200 officially designated slums with an average annual household income of 13 000 rupees per annum (&260). Half the adults in the slums are illiterate. Few slum households have private tap water and only half of the slums have public tap water. Under one in five slum houses has a toilet. In 1988, the Municipal Corporation of Visakhapatnam (MCV), supported by the UK Overseas Development Administration (ODA), started a major slum improvement programme. This included physical infrastructure improvements (roads, drainage, paths, street lighting), improved water supply, public toilets, community centres, primary health care services, and educational and training services.
97
A case study of Indian slum improvements: Table 1
Summary
of slum improvement
Civil infrastructure Roads/paths (bitumen) Roads/paths (concrete) Dustbins (large and small) Slum drains (all sizes) Gedda (town drain) improvements Culverts Community halls (large and small) Street lights Water pipelines Water fountains Bore wells Pilot water project Public latrines Public bathrooms
To September
$ no. m
98 91.5 95 815 129 107 871
m no.
25 905 92
no. no. m no.
139 955 4 999 99 267 1 315 37 7
IlO.
m no. no.
no. no. no. no. Unit
Socioeconomic programmes Pre-school education centres Non-formal education centres Adult education centres Reading rooms/libraries Short-term vocational
courses Nos. trained on courses Sewing centres Sources:
ODA
programme
Unit
Unit Health programme Primary health centres Trained delivery nurses Clean hut competitions Food and nutrition camps
P Abelson
1992
To March 1992 46 356 119 94 To March 1992
no.
106
no. no. no.
142 142 162
no.
27
no. no.
1 008 162
vocational
and Municipal
Council
of Visakhapatnam
Concurrently, the local Housing Corporation provided subsidised housing loans to one in four slum residents to improve their dwellings. The Visakhapatnam project was one of three major slum improvement programmes funded by the ODA in the second half of the 198Os, the other two being in Hyderabad and Indore. Concurrently the World Bank was financing physical infrastructure slum improvement programmes in Calcutta, Madras, Madhya Pradesh and Uttar Pradesh. The ODA embarked on the slum improvement programmes because it believed that the beneficiaries of the housing programmes it had been supporting in the early 1980s were often not from the economically weaker sections of the community. A crucial feature of the ODA financed programmes was the integrated approach which incorporates not only physical upgrading, but also community development, health and education inputs, and coordination of inputs by voluntary organizations. The paper starts by describing the slum improvement programme (SIP), the costs and main impacts.
98
I then briefly discuss approaches to benefit valuation. There follow evaluations of the the whole programme; the benefits in a sample of 19 slums; and returns to investment in education, vocational training and health services. The final section summarizes the main points.
Urban slum improvement Visakhapatnam
in
The slum improvement programme was a comprehensive attempt to improve the living conditions, health and incomes of 190 000 people living in 36 500 houses in 170 slums. Of these slums, 110 were on public land, 37 on private landlord property and 23 largely in private individual land ownership. Before the SIP, one in five dwellings were pucca houses with stabilized block walls and permanent roofs; one in ten were semipucca houses with mud walls and tiled roofs; seven in ten were kutcha houses with thatched roofs and various wall materials, including many pyramid shaped houses constructed entirely of thatched materials. In addition to the problems of low incomes and illiteracy, the incidence of gastrointestinal and respiratory diseases was high and one in five children suffered malnutrition. The components of the SIP are summarized in Table 1. Roads, paths, storm drains and multipurpose community halls were the main elements. Water, sanitation services and dustbins for garbage collection were smaller elements. Indian government targets of a public tap for each 20 households and a public toilet for each 10 households were not met. The ODA/MCV view was that community latrines are difficult to maintain, not popular, and a poor investment if space permits individual latrines. Primary health care is provided daily from 46 centres each serving about 5000 people. Services include health education, family planning, nutrition supplementation for pre-school children, immunization programmes, training of traditional birth attendants, treatment of minor ailments and provision of basic drugs. Socioeconomic services include daily non-formal education for pre-school activities, school dropouts, adult literacy courses, and vocational training in sewing, welding, repair services and various other trades. Land reforms were designed to give slum inhabitants security of tenure and a stake in the slum improvements and to provide an organized layout for each slum. The MCV purchased land in about 20 slums then in private ownership, planned the slums allowing 60 square yards per plot, and provided legal tenures (p&us) to the plots. The layouts were planned to minimize the loss of established pucca houses. However, the 60 square yard lot criterion sometimes reduced the dwellings in a slum. Pattus are usually permanent (free) land leases.
A case study Table 2
Direct ODA funded expenditures (1991-92 Rs) 1988 to September 1992 (Million rupees)
(%)
After September 1992 (Million rupees)
Engineering Health services Socio-economich Education’ Establishmentd Office costsC Training/evaluation/ computers Machinery
136.9 17.6 5.5 9.7 17.9 8.4
67.6 8.7 2.7 4.8 8.8 4.1
82.4 2.5 2.0 3.1 0.9 0.0
3.9 2.7
1.9 1.3
2.7 1.8
Total
202.6
99.9
95.4
“Based on annual inflation rate of 6%. “Income generating and cultural activities, including vocational training, revolving loans etc. ‘Nursery schools, non-formal education and adult literacy. ‘Staff overheads/permanent employees of MCV, including engineers and community development officers. ‘Office overheads in Visakhapatnam. Source: ODA.
Households are generally not allowed to sell puttus when they are used as security for Housing Corporation loans or for the first five years of the grant. The grants of pattas combined with lot size restrictions created considerable excess demand for lots in many slums (with owners, tenants and family members all demanding pattas). This placed considerable power and some monies in the hands of the slum neighbourhood committees who determined lot allocations (Prasada Rao and Rajesh Patnaik, 1992). Between 1990 and 1992, the Housing Corporation provided subsidized loans for pucca house construction to about 12 000 households in 71 slums. In 1992 the normal loan was Rsll 700 and a grant of RslOOO was also provided. Because construction of even a modest pucca house cost about Rs23 000, households had to find another RslO 000 or so. By late 1992 6000 houses had been completed. Maintenance of the SIP infrastructure is shared between the MCV and the slum communities. The MCV is responsible for replacing worn out asphalt roads, waste collection, and maintaining drains. The slum communities are responsible for cleaning drains and sewers, repairing road patches, replacing inspection chambers and manhole covers, repairing leakages in pipelines, and maintaining community centres. At the time of this study many slums had weak institutional and financing mechanisms for maintaining the improvements and, as we shall see, this is a substantial problem.
Programme costs Table 2 shows expenditures by major activity category in 1991-92 rupees. Engineering will account for 75% of expenditures when the projects are com-
of Indian
slum improvements:
P Abelson
plete. Relatively small parts of the budget were allocated to health and education services. The ODA also provided &lo0 000 per annum in offbudget management and advisory services. Total housing expenditure in the slums to September 1992 was an estimated Rs18.5 million. Annual maintenance expenditure for civil engineering projects is usually between 5 and 10% of capital expenditures. The higher percentage would probably be required to maintain the slum assets close to their original condition. In fact, there is already evidence of low maintenance. Some roads and drains constructed early in the SIP already require rehabilitation. One-third of the bore wells and water supplies to some public latrines were out of order within one year. Almost certainly, a low maintenance regime (anything less than 5% of capital costs) would lead to major asset deterioration in lo-15 years. To evaluate the SIP, we must consider whether the financial expenditures reflect resource costs. The main resource omitted is land. This is reasonable because (with or without the SIP) most land in the slums would be devoted to housing. Of course, land costs should be included when the land has a valuable alternative use, for example inner city commercial land. Also, when land shortage combined with the lot size rule causes the MCV to relocate households to other areas, the replacement costs should be added to project costs. However, these cases are exceptional and are not included below. Nor is any adjustment made below for the minor differences between financial and resource costs. Most expenditures were on local Indian goods and services; imports were minimal. The prices of some materials, for example cement prices before 1989, were controlled and did not fully represent the value of the materials to the Indian economy. No adjustment was made for possible discrepancies between wages and the opportunity costs of labour.
Programme services and impacts Outputs of social programmes are rarely easy to value. In national income accounting, outputs are often measured by inputs, for example of health or education services. This approach would be selfdefeating in an evaluation study because benefits (outputs) would equal costs (inputs) by definition. Fortunately, various ODA commissioned studies of the SIP provide valuable output data. The main study was a household survey (in 1988 and 1991) by the Visakhapatnam Institute for Development and Planning Studies (IDPS, Sarveswara Rao and Ramachandrudu, 1992a). The IDPS selected 800 households (one-third of all .households) from 10 of the 45 slums improved in 1988-89. Just over 600 households were interviewed in both 1988 and 1991. This survey provides useful data on
99
A case study Table 3
of Indian
slum improvements:
P Abelson
Housing and environment 1988 No.
Households Owning house With l&d tenure With pucca houses Less than 100 ft’ More than 200 ft2 Average area ft’ With electricity With separate latrine With tap water Using dustbins Using drains Using traditional fuel Source: Household (1992a).
533 186 182
144 211 72 325 108 368 405
surveys;
% 87.1 30.4 29.7 34.8 17.3 34.5 11.8 53.1 17.7 60.1 66.2
Sarveswara
1991 No. 542 449 339
171 381 133 345 282 529 414
% 88.6 73.4 55.4 19.8 34.1 62.3 21.7 56.4 46.1 86.4 67.6
Change in % 1.5 43.0 25.6 - 15.0 16.8 18.8 27.8 9.9 3.3 28.4 26.3 -1.4
Rao and Ramachandrudu
changes in housing conditions and health and education status. In addition, the IDPS carried out a nutrition and morbidity survey of 80 households in 1988 and 1991 (Swarajyalakshmi et al, 1992); analysed the households moving in and out of the slums (Sarveswara Rao and Ramachandrudu, 1992b); and conducted a detailed review of the impacts of the SIP on the lives of 35 households in four slums (Prasada Rao and Rajesh Patnaik, 1992). The MCV also studied the impacts of the vocational training programme (Patrudu, 1992). Most of the surveys described slum households before and after the SIP. Such observations must be interpreted carefully. Some observed changes (for example rising incomes) may be due to causes other than SIP services. On the other hand, some impacts (for example improved health) may not be observable within three years. The surveys did not include slum areas without SIPS because there were no strictly comparable slums outside the programme. The following subsections summarize the impacts of the main programmes: they show some substantial improvements in living standards.
Housing and environment As shown in Table 3, many more slum households had secure land tenure in 1991 than in 1988. There were more pucca houses; house size and electricity supply increased significantly. Waste disposal practices improved with greater use of dustbins for solid wastes and drains (instead of roads or paths) for wastewater. Flood and fire risks were reduced. However, some important elements of environmental quality did not improve much. Households with access to protected (tap) drinking water increased only marginally. Nearly half the households still depend on well water which tends to be saline, may be polluted, and is often inaccessible. Few households have their own latrines. Communal lat-
100
rines were not properly maintained (Hodgson, 1992). Two-thirds of all households continue to use traditional polluting fuels (firewood, dung cakes and charcoal) for cooking.
Health care and status The health care services have been well utilized. In 1990-91, there were 172 000 attendances at the 46 primary health care centres, equivalent to nearly one attendance per slum resident. The proportion of fully immunized children (aged 1 to 3 years) rose from 25 to 83%. More women adopted permanent family planning methods. Pregnant women registered more quickly and institutional deliveries increased from 42 to 72%. Nevertheless, there was little immediate evidence of improvements in health status. In 1991-92, there were 42 still births and 7 maternal deaths during 2628 deliveries in the 170 project slums. The increase in institutional delivery did not reduce the probability of problems during delivery. There have been fewer postnatal problems, but whether this is due to the changed delivery process is not known. Between 1988 and 1991, reported diseases actually increased in the slums (Table 4). Among general diseases, malaria, typhoid, respiratory disorders and skin diseases increased significantly and gastroenteric disorders also rose. Among chronic diseases, cases of strokes, high blood pressure and diabetes all rose. On the other hand, unlike in 1988, there were no reported cases in 1991 of whooping cough or diphtheria, both of which are preventable by immunization. The increased morbidity incidence almost certainly reflected a city-wide trend, for example for malarTable 4
Health care and status
(%)
1991 (No.)
(%)
1988-91 Change in %
193 106 52 32
64.1 35.2 86.7 53.3
262 106 40 8
67.2 27.2 85.1 17.0
3.1 -8.0 -1.6 -36.3
Fully immunized (l-3 age) Persons sick, last 14 day& Persons chronically ill“
56 43 39
24.7 1.5 1.4
92 130 92
82.9 4.5 3.2
58.2 3.0 1.8
Households undercaloried” Children 20%+ underweight’
55 60
68.8 46.7
53 41
66.3 31.7
Category”
1988 (No.)
Tubectomiesh Vasectomies’ No delivery problems’ Post-natal consultations’
“The first eight rows are based on 612 households; rows are based on 80 households. bPercentages in couples. ‘Percentages of deliveries. dPercentages of all population. ePercentages of households. ‘Percentages of children from 0 to 5 years. Sources: (1992a);
Household surveys; Sarveswara Swarajyalakshmi et al (1992).
-2.5 -15.0
the last two
Rao and Ramachandrudu
A case study of Indian slum improvements: Table 5
Education,
employment,
income and assets
1988” (No.)
Category Children at pre-school Adult literacy Overall literacy Adults employed
68 769 1508 897
Household income/month (Rs) 1 163 Male income/month (Rs) 854 Female income/month (Rs) 381 Household expend./month (Rs) 816 Household assets 17 113 Household debt 5 723
1988-91 Change
1991 (%) (No.)
(%) in %
29.4 44.5 53.7 51.9
57.1 51.5 62.9 52.5
117 948 1818 967
27.6 7.0 9.2 0.6
1 348 926 380
15.2 8.4 -0.3
1 087 31 132 9 840
33.2 81.9 71.9
“All rupee figures in 1991 prices: the nominal 1988 values inflated by the consumer price index for Visakhapatnam. Source: Household (1992a).
surveys;
Sarveswara
are
Rao and Ramachandrudu
ia. It may also have reflected an increase in reporting. However, the incidence of malaria is three to four times higher in the slums than elsewhere (Hodgson, 1992). Moreover, several morbidities, such as respiratory infections and skin diseases, can be attributed to the ongoing poor slum environments - the poor water supply, inadequate sanitation, indoor air pollution, the lack of personal hygiene and still inadequate nutrition. In 1991, nearly all surveyed adults suffered vitamin and iron deficiencies, two-thirds suffered calorie deficiencies, and one-third suffered protein deficiencies. There had been no significant improvement since 1988 (Swarajyalakshmi et al, 1992). On the other hand, the nutritional well-being of children under 5 improved. The proportion of under-5s who were less than 80% of the relevant ICMR standard weight fell from fell from 47 to 32% in three years. And no under-2s were less than 80% of the relevant weight in 1991.
Education,
vocational training, and incomes
Education and training courses were well-attended: 80% of slum children (3-6 years) attended preschool in 1991; two-thirds attended ODA funded pre-schools. Of the 8000 school dropouts in the slums, 3500 enrolled for non-formal education (about half of those enrolled attend); 21 000 illiterates enrolled for adult literacy classes (about one-third of these attend). In the three years to end-1991, 746 people received vocational training in one of 15 trades and 3422 completed sewing courses. There are some positive output measures. For example, literacy rates improved (see Table 5). Vocational courses also improved employment and income prospects. Of a sample of 129 trainees, 26%
P Abelson
were employed before training and 59% after training, and average (nominal) wages rose by 37% 1992). Adult female employment in(Patrudu, creased from 21 to 25%, but male employment did not increase. Between 1988 and 1991, the income and wealth of slum households rose considerably. Average reported real household income rose by 15%; real household expenditure (excluding debt repayment) increased by 33% ; and the real value of gross household assets rose by 82%. These data must be interpreted cautiously. Sarveswara Rao and Ramachandrudu (1992a) suggest that respondents are likely to understate income by more than they overstate expenditure and that expenditure figures are therefore more reliable. However, reported income comfortably exceeded expenditure (see Table 5). This may be due to omissions of spending on consumer durables and debt repayments from expenditures.
Other impacts A SIP also affects households who move into, or out of, improved slums. Inmovers would usually gain from a SIP. Outmovers may be gainers who take advantage of increased wealth or losers who are pushed out by higher rents or new land arrangements. The former motives were more common according to Sarveswara Rao and Ramachandrudu (1992b). In-movers gave employment and lower rent as the main reasons. Adjacent neighbourhoods also benefit from the improved slums. In some adjacent areas, improvements followed directly after the slum upgrading. However, there is no record of these indirect effects.
Impacts by beneficiaries IDPS studies show that, in 1991 compared with 1988, about 50% of slum households had significantly higher income, 30% slightly lower income and 20% much lower income. However, the studies did not assess the effects of the SIP on incomes. Apart from households who lost tenure, most slum households gained from some aspect of the SIP. But it appears that the most powerful, established and better-off households gained more than others. Between 1988 and 1991, income differentials in the slums widened; landlords gained significant real rent increases; male earnings rose more than the female earnings; employed persons’ earnings rose more than the earnings of self-employed persons; and the relative position of the Scheduled castes deteriorated. The anthropological case studies confirmed that the more powerful families gained more land rights. On the other hand, less well-off households probably benefited more from the health care services and the nursery schools.
101
A case sludy of Indian slum improvements:
P Abelson
Table 6 measures
outputs
A summary
of project
inputs,
and benefit
Project inputs
Main project outputs
Civic infrastructure
Health; amenity; productivity$’ reduced flood damage Amenity; health; reduced fire damage Health; life expectancy; reduced health expenditures Productivity Productivity Amenity; health; productivity; reduced public facility expenditures elsewhere
Housing improvements Health/nutrition Education Vocational Community
training centres
Project outputs
Benefit measures
Improved
health
Improved
amenity
Improved
productivity
Improved
assets
Increased incomes (due to increased productivity); reduced health care expenditures; quality and value of life measures; increased property values Property values/rents; user payments and taxes Increased incomes; property values/rents Property values/rents; reduced public expenditures; participation rates
“Minor effect lighting.
related
to more
usable
road
spaces
and
street
General benefit valuation issues When slum improvements are formally evaluated, benefits are almost always based on estimated increases in land values or rents. For example, World Bank studies forecast that increased land values or rents would produce real rates of return of between 15 and 19% for slum improvement programmes in Madhya Pradesh, Uttar Pradesh and Calcutta and estimated an ex post 23% rate of return for slum improvements in Madras. The studies did not estimate other measures of benefits, for example for access to health care or water. Likewise the ODA’s slum improvement project in Hyderabad was justified by forecast land value increases as proxies for project benefits. This approach is open to criticism. In imperfect markets, land values may not accurately reflect household preferences. Clarke et al (1989) strongly criticized the use of land values in Hyderabad because the land value data were extremely unreliable. Lacking adequate data, planners may simply assume ex ante that, because the planned improvements are desirable, land values will rise to reflect these expenditures. But this process is circular and does not provide independent justification of proposed expenditures. To adopt the land value approach, three questions must be answered. What benefits do land values reflect? Are land value data adequate? And are there alternative valuation methods that would measure the benefits more precisely or draw on more reliable data? To provide a perspective for these
102
questions, Table 6 outlines the main relationships between the major SIP inputs and outputs and possible benefit measures. Under certain conditions, nearly all private benefits (including health, productivity and amenity benefits) would be reflected in increased land and house prices in the slums. These conditions are: All private benefits accrue only to residents of the slums. ?? All SIP benefits (including health benefits, reduced flood and fire damages etc) are fully perceived by potential buyers. ?? Slum property prices are not controlled; slum residents can sell or rent their properties to outsiders as well as to other slum residents. ?? Property transactions are costless. ??
Even under these conditions, there would usually be some residual householder surpluses. For example, it is unlikely that the full benefits of health care and training programmes would be precisely capitalized in property values. Moreover, several conditions are unlikely to hold fully. In Visakhapatnam, some benefits accrue to non-slum residents; health benefits may not be fully appreciated; there are some official restrictions on sale of slum properties (which are often bypassed); and there are transaction costs. If the above conditions are not met fully, changes in slum property values do not reflect all the private benefits of SIPS. Also, there may be public benefits, for example savings in expenditure on asset maintenance. Turning to the second question, if property values are considered nevertheless to provide an approximate measure of most private SIP benefits to slum households, are adequate property data available? The answer depends on circumstances. However a few general observations may be made here. There are two basic data issues. First, even when property sale prices are registered with public bodies and officially available, the price data are almost always recorded manually and difficult to access. Data collection and interpretation generally requires a special research exercise. Property valuations, usually made for taxation purposes, are generally imprecise and sometimes unreliable. The second issue relates to the analysis. In time series analysis, changes in property prices in improved slums may be compared with changes in unimproved slums or non-slum areas. But there is a major presumption that any differential change is attributable to the SIPS and not to other factors such as changes in access to employment. If this presumption does not hold, the data and analysis requirements are much greater. In cross-section comparisons, there must again be a presumption that observed price differentials are due to the SIPS and not, say, to access differentials. Generally controls, and data, for other important independent variables are required.
A case study of Indian slum improvements: P Abelson Table 7
Summary
Slum improvement Engineering Education Socioeconomic Health Other Total ODA
of project costs (1992 million rupees)
Housing
After September 1992
Total
137 10 6 18 32 203
82 3 2 3 5 95
219 13 8 21 37 29X
12
5
programme
overheads”
Infrastructure
1988 to September 1992
improvements’
17 22 pa
maintenanceh 185
91
276
“f100 OOtl pa converted at current exchange rates and into 1992 Rs. ‘Assumes annual infrastructure maintenance is 10% of engineering costs, but this may not be affordable. ‘Assumes total improvements equivalent to 12 000 new houses at Rs23 000 per house. Source: ODA.
The main alternative valuation method is contingent valuation: asking slum and non-slum households what they would be willing to pay for a SIP. It has been shown that low income households can usefully be asked what they would be willing to pay for specified improvements, for example for water supply (Whittington et al, 1990, 1991). However, households may be unable or unwilling for various reasons to say what they would be prepared to pay for a SIP. First, many people would find it difficult to state what they would be willing to pay for such a complex package of disparate services as distinct from some particular components. Second, great care is required to obtain accurate answers, even if the respondent wishes to cooperate. For example, community development officers in Visakhapatnam considered that stated willingness to pay monthly contributions for public toilet use would be less than the equivalent daily amounts, Third, if households believe that they are entitled to the urban services in any case, or if they know that the services are provided under foreign aid, they may not cooperate wholeheartedly in a willingness to pay survey. Real changes in household incomes may measure some slum improvement benefits associated with education and training. Also, some health improvements and infrastructure benefits, such as improved open spaces for trading, may enhance productivity and incomes. However, amenity improvements, some health benefits, and some collective asset improvements, for example those due to reduced flood and fire risk, are not reflected in incomes. Travel cost analysis can be used to infer the value of services when there are significant differentials in both access costs and usage. Examples are water supply and health care services. However, this requires a special study of service use and travel costs.
When none of these benefit valuation methods can be used, the switching value method may be used. If total service costs and participation rates are known or forecast, unit service costs can be computed. A judgement whether unit value would exceed unit costs may then be made. Alternatively, SIPS are sometimes justified using a ‘cost-effectiveness’ criterion. A proposed SIP is said to be cost-effective if expenditure per household is in line with, or lower than, the norm in other SIPS. This presumes that SIP outputs are similar and that expenditure variations reflect differences in unit costs. However Clarke et al (1989) concluded that the cost variations within the Hyderabad SIP reflected political pressures to improve the quality of facilities. When outputs vary, benefits must be assessed; cost-effectiveness is not an adequate criterion. In conclusion, the total benefits (TB) of a SIP can be expressed as: TB =
@DLV
+ @DHS
+ PNSB
+ GB
(1)
where @DLL’ are changes in land values in the slums, @DHS are changes in householder surpluses of slum households that are not capitalized in land values, PNSB are private non-slum benefits, and GB are government benefits (increased user fees and taxes and expenditure savings). In unregulated property markets, land values (or house prices) can provide a measure of most SIP benefits to slum residents. But property price data are not always readily available and analysis of the determinants of property prices requires care. Also, some SIP benefits (for example health and education benefits) may not be reflected fully in property values. In these cases, other valuation methods, such as willingness to pay surveys, may provide supplementary information on the output values. Most benefits to non-slum households are likewise reflected in (non-slum) land prices but these are difficult to measure. Finally, assessments of the costs, benefits and rates of return to major components of public programmes are generally desirable. But, due to complementarities in the programme, the return to the whole SIP programme may be greater than the sum of the returns to the individual parts.
Evalution of the whole Visakhapatnam improvment programme
slum
Estimated project costs are shown in Table 7. Total capital expenditure on the SIP, including ODA overheads, was Rs315 million in 1992 Rs. Infrastructure maintenance could cost up to an additional Rs22 million per annum: with a 10% discount rate, the capital equivalent would be Rs220 million. Estimated housing expenditures totalled another Rs276 million. Turning to benefits, the evaluation below is based
103
A case study of Indian slum improvements: Table 8
Summary
P Abelson
of SIP costs and benefits (1992 Rs million)
~
Slum improvement programme costs SIP expenditures 198%93 ODA overhead expenditures Maintenance expenditures capitalised” Total costs
298 17 110 41.5
Slum improvement programme benefits Increased slum property values Slum household surpluses” Private benefits to non-slum households” Government benefits” Total benefits Social surplus or subsidy Including only SIP expenditures Including ODA and maintenance
270 27 21 27 351
expenditures
“Based on likely maintenance expenditures ‘Assumed to be equal to 10% of increased
53 -64
(set text). slum land values.
Source: ODA.
on property values, although income effects are also considered. Indicative figures for benefits to government and non-slum households are also given. However, detailed quantification is not possible from available data. There are no records of public spending in the slum areas. Moreover, valuing external benefits in one area due to improvements in another is difficult and rarely attempted. Visakhapatnam has an active housing market with between 1.5 and 2.0% of the housing stock sold each year. Recorded sales in the slums are lower due to restrictions on sales. But there are many unofficial (benami) sales where prices are known locally. Of course, houses sold under restrictions sell at a discount. Also, land sales are fewer than house sales. Therefore, land values must be inferred from house prices or based on valuation data. For this study, MCV officers collected official land valuation data for typical properties in 24 slum areas improved in 1988 or 1989 and valued in 1987 and 1990. In the three years to 1990, mean land values in the sample slums increased from Rs256 to Rs441 per square yard. Elsewhere in Visakhapatnam, land values rose by an estimated average 25%. It follows that slum land values would have risen to Rs320 without the SIP: the difference between Rs441 and Rs320 is attributable to the SIP. Applying a benefit of Rs121 per square yard to 36 500 houses, with an average 50 square yards per house, produces a total increase in land values of Rs221 million in 1990 Rs or Rs254 million in 1992 Rs. Municipal officers Z&O estimated the prices of pucca, semi-pucca and kutcha houses in 19 improved slums in 1992 with and without the SIP. The without SIP estimates were based on prices in comparable unimproved slums. The SIPS increased the average price of pukka houses by an estimated Rsll 400, semi-pukka houses by Rs7800 and kutcha houses by Rs4250 (see Table 9). Applying these increases to
104
the 36 500 houses in the 170 slums produces a total increase in house prices due to the SIP of Rs28.5 million in 1992 Rs. (This does not include value increments due to upgrading kutcha and semi-pucca houses to pucca houses.) The estimated benefits from the land value and house price calculations are reassuringly close. The researchers did not know how their figures would be used and could not massage the figures to produce these close results. Real slum household incomes rose by 15% between 1988 and 1991. This reflected a rise in adult female employment from 22 to 28%. Average real male income rose by 8.4% whereas the average real female income was constant. However, the impact of the SIP on slum incomes cannot be estimated because there are no comparable data on income changes in other parts of Visakhapatnam. Within Visakhapatnam, 1988-91 was a period of high economic activity, including much construction and a new steel plant. Therefore, the total value of the SIP cannot be derived from income changes. Table 8 summarizes total SIP costs and benefits. Annual maintenance expenditures are assumed here to be 5% of civil engineering capital expenditures and capitalized using a 10% discount rate. These maintenance expenditures would not maintain the assets adequately, but are realistic. The increase in slum property values shown is the mean of the land value and house price estimates. Importantly, it is assumed that slum household surpluses (benefits not capitalized into property values), private benefits to non-slum households, and government benefits would each be equal to 10% of increased slum property values. These judgements appear plausible, but lack empirical support. It was not possible to attempt to quantify these possible benefits in the time available to the writer in Visakhapatnam. As shown, estimated total benefits to slum households (including household surpluses) approximately equal SIP capital expenditures. Including assumed private benefits to non-slum households and government savings, there would be a surplus of Rs53 million over capital expenditures. (Because the benefits are capitalized in property prices, a precise discounting exercise is not made.) On the other hand, including ODA overheads and capitalized maintenance expenditures, there would be a total social deficit of Rs64 million. Maintenance costs critically affect this result. To gain perspective, we may note that SIP expenditure per slum household is R8300 without slum maintenance (and Rsll 600 with maintenance) compared with average slum household income of Rs16 000. Therefore, to pay for the capital and recurrent costs of the SIP, slum households would have to make a one-off payment of about threequarters of their annual income. If this is more than they would be willing to pay, the difference must be
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A case study of Indian slum improvements: Table 10
Evaluation
P Abelson
of training courses
VMC
Opp. costs
Overhead and other
Total
Increased real earnings/month due to training (1991 Rs) Persons Persons Weighted employed employed monthly Weighted before after” averageb annual averageb
410 375 400 1000 650 400 4675 400 1000 680 450
532 33x 0 300 589 612 1320 614 765 1042 45
205 18X 200 500 325 200 1000 200 500 340 22s
1147 900 600 1800 1564 1212 6995 1214 2265 2062 720
112 0 111 188 240 71 170 141 153 38 0
Average trainee costs (1991 Rs)
Trade
Number persons in survey
Printing, binding Cane weaving Food processing Motor rewinding Electrician, wiring Embroidery Welding, fabrication Motor servicing Motor driving Radio/TV servicing Wool knitting
20 5 16 4 14 13 10 16 s 19 7
“Assume earning RlSO/month informally before. “This allows for those who are not employed before assumed not to gain from the training. Source:
Patrudu
or after
the training
households
and
Evaluation of indivudual slum improvments The benefits of improvements in 19 sample slums, for which both engineering expenditure and property price data were available, are presented in Table 9. The following procedural points may be noted. Estimated total expenditures (in 1992 Rs) in each slum include estimates for health, education and other expenditures as well as engineering, but not ODA overheads or maintenance expenditures. Increases in land values and house prices (both in 1992 Rs) were based on land registration data and municipal officer estimates. To estimate the total land improved in each slum, 60 square yards are allowed per pucca house, 50 square yards per semi-pucca house, and 40 square yards per kutcha house. Drawing on the land valuations, the mean benefit-cost ratio (BCR) is 1.18. The improvements have a BCR above 1.0 in 10 slums and below 1.0 in 9 slums. The house price results indicate a mean BCR of 1.86. In this case, 13 slums show a BCR above one and six slums a BCR below one. These results suggest that the net benefits of slum projects vary considerably. Importantly, there are significant economies of scale. As shown in Equation (2), the net benefit in each slum (NBh , based on increased house prices less expenditures) is positively related to the number of houses (H) in each slum: NBh = -625 000 + 5174H (4.8) R2 = 0.58
106
course
I20 0 128 241 126 60 51 212 313 73 0
and for those
1441 0 1537 2892 1512 717 612 2539 3756 x79 0
employed
in other
126 0 2.56 161 97 59 9 20’) 166 43 0
trades
0.8 na 0.4 1.6 1.1 1.7 11.4 0.5 0.6 2.3 na
who were
(1992).
made up by benefits to non-slum government, or by social subsidy.
The figure
295 0 261 388 402 211 345 335 553 263 0
Percent Payback rate of period return years
in parentheses
is the t-statistic.
(2)
Evaluation of education and health investments Evaluation of training Per capita costs and returns to 129 trainees in 11 training course are summarized in Table 10. The costs include the direct training costs of the courses paid by the MCV, a 50% mark-up to allow for MCV overhead costs, and the opportunity costs of trainees. The last cost was based on the numbers in employment before the courses and their pretraining earnings. The unemployed were also assumed to forgo informal earnings of Rs150 per month. Per capita returns are a weighted average of the increased income due to each course. Of the 129 trainees, 34 were employed before and after training. Following training, an extra 30 were employed in the trade for which they trained, another 12 were employed in other occupations, and 53 remained unemployed (though usually in informal income earning activities). For the evaluation, it is assumed conservatively that those who gained employment would lose their informal earnings and that the increased earnings of trainees employed in occupations for which they had not trained were not attributable to the training. Despite these conservative assumptions, there were high rates of return to training in 8 of the 11 courses. The returns to training in welding and fabrication (the most expensive course) are marginal. There were zero returns to the craft courses in cane weaving and knitting. Most of these results are strong ones which would not be sensitive to changes in assumptions. Evaluation of education By mid-1992, the MCV had spent
Rs8.9 million
on
A case study of Indian slum improvements:
education services (nursery schools, non-formal adult education and adult literacy classes). In the absence of detailed costs and results for these services, only broad unit costs and willingness to pay measures can be reported. From 1988-89 to 1991-92, the MCV spent Rs2.25 million per annum on the three educational services. There were about 1.0 million nursery school attendantes (of three hours) per annum; 0.5 million attendances (two hours) at non-formal education classes per annum; and 1 .O million attendances (one hour) at adult literacy classes per annum. The average cost for these 2.5 million education services was slightly under Rsl per attendance or Rs20 for a month of attendances. This represented about 1.5% of average household incomes. It is a matter for judgement whether these courses represented value for money as there is no evidence (one way or the other) on productivity changes due to the courses or on what the slum households would have been willing to pay for the courses.
Evaluation
of health care
Slum improvements would be expected to significantly improve the health of slum residents and reduce infectious diseases. However, as we saw above, apart from some improvements in the weight and health of young children there was little evidence of improved health status by 1992. It is possible that two to three years is too short a period over which to observe measurable improvement in health status, but I would not attach great weight to this possible explanation. Without physical measures of improved health status, there can be no meaningful evaluation of health improvements. Nor could conclusions be drawn about productivity because slum residents were not asked about their attendance at work. Turning to the unit cost approach, annual recurrent expenditure on health care services was Rs6.0 million, of which Rs4.0 million was spent on services from primary health care centres including provision of drugs. With an annual utilization rate of 170 000 attendances, the average cost per attendance was Rs24. Of this, medicines account for Rs13 and labour for Rsl 1. IDPS surveys suggest that the primary health care services are popular because they are convenient and free. However, the unit cost of Rs24 is well above the normal price (Rs5-10) for a consultation with a doctor, which deters many slum residents from visiting doctors. Also, several respondents expressed disappointment with the quality of services. Overall, it is doubtful whether slum residents would be willing to pay for more than a small part of the cost of the health services. This does not mean that the services should be withdrawn as subsidies are often provided for health care, especially for
poor households. However, the services the slums may not be cost-effective.
P Abelson
provided
in
Conclusions The Municipal Corporation of Visakhapatnam has spent about Rs300 million on improving 170 slums including both physical and socioeconomic programmes. At least RslO million per annum will be required to maintain the improvements. These expenditures will bring benefits to slum and non-slum households and savings to the government. Most slum household benefits are reflected in increased land and house prices, although some benefits may not be fully capitalized into property prices. The SIP increased total land values in the 170 slums by an estimated Rs254 million and house prices by Rs285 million. Given a perfect market and accurate data, these two increases would be the same. In addition, slum householder surpluses, private non-slum benefits and government savings may be conservatively expected to sum to at least onequarter of SIP costs. Therefore, the total value of social benefits would exceed capital expenditures. However, if maintenance expenditures and ODA overheads are taken into account, the estimated social deficit is about Rs60 million. These results are not surprising. It is not, after all, surprising that low income slum households and adjacent communities may not be willing to pay for all slum improvement and maintenance expenditures. But this is not a good reason to abandon willingness to pay as the basis for the evaluation and to abandon explicit estimates of the social subsidy component. The study also evaluated SIPS in 19 separate slums by comparing increases in local land and house prices with estimated local SIP expenditures excluding maintenance. The estimated rates of return varied considerably with two-thirds of the sample slums showing a BCR above one and the rest having a BCR below one. Economies of scale were significant with higher returns in the larger slums. The Visakhapatnam SIP is a comprehensive approach to slum improvements, which includes educational, training and health care services. Notwithstanding the holistic nature of the programme, we would like to know which parts produce the best, or worst, results. The analysis showed that there were high returns to most training courses and that slum households could be willing to pay for most of the educational services, which have a low unit cost. On the other hand, the health care services and improved slum infrastructure had made little impact on the health status of slum residents by 1992 and it is doubtful whether slum households would be willing to pay the direct health care costs. It may be too early to observe health improvements. However, the project concentrated on readily achievable roads
107
A case study of Indian slum improvements:
P Abelson
and drains. There was little improvement in slum water supply (partly because of a chronic city-wide water shortage) or in sanitary facilities (partly because public facilities were considered less effective than private ones). Although there are many more data on the impacts of the SIP in Visakhapatnam than is usual, gaps remain. Land valuation data are crude. Estimated house prices in the no-SIP scenario could not be based on clearly established control cases. There is little information on maintenance expenditures. Also, we were obliged to speculate on the extent to which benefits are capitalized in property prices, the benefits to non-slum households, and the savings to government. Nor, to the best of the writer knowledge, was any research done on the rank preferences of households for different services. These topics could usefully be the subject of more research in Visakhapatnam or elsewhere. Finally, there is concern about the sustainability of the slum improvements. The institutional arrangements for maintenance, including finance and taxation, in Visakhapatnam appear weak. This does not n,ecessarily invalidate the programme on welfare grounds but clearly the benefits would be greater if the improvements could be sustained.
Acknowledgements This study was funded by the UK Overseas Development Administration and administered by the Overseas Development Institute (ODI), London. I owe a special debt of gratitude to David Crapper (ODA) for organizing my field trip to India, for briefing me on the project, and for commenting in detail on a draft paper. In preparing this paper, I was also greatly assisted by Mr Patrudu and Mr Kumar from the Visakhapatnam Urban Community Develop-
108
ment Department and by Michael Slingsby (ODA). Jim Winpenny’s (ODI) comments on a draft paper were most helpful. However the views expressed in this paper are my own responsibility.
References Clarke, J, Jones, S, Pickford, J, Higginbottom, A and Bennett, C (1989) Evaluation Study of Hyderabad Slum Improvement Project Evaluation Report EV47.5, Overseas Development Administration, London Hodgson, K (1992) Report on Environmental Health and Sanita(ion Visit to Visakhapatnam Slum Improvement Project Universities of the North of England Consortium for International Activities Lipton, M and Toye, J (1990) Does Aid Work in India? Routledge, London Patrudu (1992) Vocational Training Courses - An Assessment Study Visakhapatnam Slum Improvement Project Prasada Rao, D L and Patnaik, Rajesh (1992) Anthropological Studies - Case Studies of Selected Families Evaluation Study of the Visakhapatnam Slum Improvement Project, Institute of Development and Planning Studies, Visakhapatnam Sarveswara Rao, B and Ramachandrudu, G (1992a) Report on the Household Survey Evaluation Study of the Visakhapatnam Slum Improvement Project, Institute of Development and Planning Studies. Visakhapatnam Sarveswara Rao, B (1992b) Report on Moved-out and Moved-in Households Evaluation Studv of the Visakhanatnam Slum Improvement Project, Institute of Development’and Planning Studies, Visakhapatnam Swarajyalakshmi, B, Sarveswara Rao, B, Narayana Raju, B and Ramachandrudu, G (1992) Report on Changes in Health Status, IY88-YI Evaluation Study of the Visakhapatnam Slum Improvement Project, Institute of Development and Planning Studies, Visakhapatnam Whittington, D, Briscoe, J, Mu, Xinming and Baram, W (1990) ‘Estimating the willingness to pay for water services in developing countries: a case study of the use of contingent valuation surveys in southern Haiti’ Economic Development and Cultural Change 38 293-3 11 Whittington, D, Lauria, D T and Mu, Xinming (1991) ‘A study of water vending and willingness to pay for water in Onitsha, Nigeria’ World Development 19 179-198