SOUTH AFRICA: Monitoring Service Delivery in Johannesburg

Transcription

1 DRAFT SOUTH AFRICA: Monitoring Service Delivery in Johannesburg April 2002 Africa PREM1 Southern Africa Department The report was prepared by a team comprising of Vandana Chandra (Task Manager), Shashi Kolavalli and Richard Tomlinson (consultants), Barbara Piazza-Georgi (consultant, survey analysis), Werner Fourie (GIS consultant), and Bala Rajaratnam (consultant, sampling and statistics). The team wishes to thank the client, the City of Johannesburg, for its valuable partnership throughout the process; Ketso Gordon, former city manager, for his encouragement and interest at the start of the task, and Rasheed Seedat and Mayur Maganlal (CPU) for their continued support. It also wishes to thank Junaid Ahmad for providing the initial motivation and guidance throughout the process. It gratefully acknowledges financial support provided by the Dutch Trust Fund and the City of Johannesburg.

2 The findings, interpretations and conclusions expressed in this report are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, nor the countries that they represent and should not be attributed to them. ii

3 TABLE OF CONTENTS 1. Introduction and Objectives Commitment to Meeting Basic Needs 3 Basic service levels and housing... 4 Constitutional responsibilities and financing.. 5 City of Johannesburg s (CoJ s) strategy and vision 6 3. The Process 7 Initiating an information system. 8 Cluster survey.. 9 Identifying poorly served areas 9 Administrative regions 11 Sampling households.. 13 Survey instrument 13 Hpuseholds in poorly served areas Access to Housing and Basic Services.. 14 Status of service delivery in poorly served areas of Johannesburg. 15 Housing Toilets/sanitation. 17 Water 18 Waste removal. 20 Power supply.. 20 Community Priorities Public Infrastructure.. 21 Telephones 21 Transport 22 Health Services.. 22 Schools.. 22 Other public services: policing, ambulance and safety Employment Status Payment for Services / Cost Recovery.. 24 Free basic services. 25 Organization of delivery 25 Financing free services Agenda for Monitoring. 28 References. 33 Tables: 1 Definition of various levels of essential services in South Africa 4 2 Poorly served in Johannesburg (Percent) Proposed outcomes, performance areas and key performance indicators 30 Figures: 1 Sharing of toilets: Number of households that shared a toilet in poorly served areas Overcrowding of water sources: number of households that shared their water.. 19 Maps. 35 Map 1: Administrative areas of the city with selected Eas highlighted. 36 Map 2: Households in informal dwellings by administrative areas Map 3: Households without access to basic sanitation facilities 38 Map 4: Households without water piped into house or stand 39 iii

4 Map 5: Percentage of households without weekly garbage removal Map 6: Percentage of households without access to electricity. 41 Map 7: Location of residence and work. 42 Annexure Annex 1: Sampling design for poverty monitoring.. 43 Annes 2: The core service of municipal service delivery in Johannesburg Appendix 1 Table of weights corresponding to sample design. 61 Annex 3: Programming details.. 92 Annex 4: City of Johannesburg s objectives for service delivery and performance indicators.. 93 Annex 5: Status of access to various services 94 iv

5 1. INTRODUCTION AND OBJECTIVES The emphasis placed by the post-apartheid government on meeting the basic needs of its citizens and the role it assigns to local governments or municipalities to ensure that they are met have pressured local governments in South Africa to gear themselves to deliver services more effectively. Formerly white local authorities have become responsible for serving a larger number of households that include much of the poorer population. In 1990, Greater Johannesburg had 13 local government structures, which included formally constituted local government for whites and other structures for black, colored and Indian areas. In creating the Greater Johannesburg Metropolitan Council in 1995, these were amalgamated into one metropolitan council and four metropolitan local councils. With the redrawing of boundaries, as part of municipal transformation at the end of 2000, and local government elections in 2000, a new metropolitan government was created for the newly titled City of Johannesburg (CoJ). The new municipality includes both African townships with significant backlogs 1 in service delivery and some of the best-served rich suburbs. The municipality is seeking to reduce the backlogs in the provision of basic services such as water, sanitation, electricity, and roads to ensure that poor households in the previously disadvantaged areas experience an improved quality of life. It is also striving to facilitate economic growth to reduce poverty and to develop a broader economic base to take care of the basic needs of the poor. The World Bank has worked with the city in recent years to support its efforts in local economic development (LED) and improving service delivery. Our support to LED has sought to enable informed policy making at national and local levels to create jobs through sustained economic growth. As part of these efforts, a series of firm surveys were undertaken to identify the structural factors that constrain economic growth and job creation in the city. 2 With the support of French donor AFD, a service delivery study was undertaken in which 1996 Census information was used to map households without access to essential services water and sanitation, roads and storm water, electricity and waste removal (Tomlinson et al 2000). This Study provides a base map of access to services in different parts of GJMC and the geographical distribution of households without access to essential services. It also examined capital expenditures on service delivery at the central, provincial and local levels since Mapping access to services is useful for developing effective strategies to improve service delivery. An understanding of the spatial aspects of livelihood 1 Backlogs indicate the extent to which government lags behind in ensuring that all households have access to a basic level of services. They are usually expressed as the number of households that receive a deficient (less than basic) level of services. 2 The results of some of the surveys have been reported in Chandra et al 2000, 2001 and

6 opportunities and provision of services is particularly important in a country in which spatial segregation was a deliberate policy. At the national level, such mapping is essential, as poverty incidence is a criterion for the allocation of resources. 3 Useful information on access to some of the municipal services is collected in five-yearly population censuses. However, the absence of more frequent generation of information on access to essential services and relating such information with detailed information on capital expenditures on infrastructure development makes it difficult to effectively monitor service delivery, measure it against expenditures, and design strategies to improve it. To make systematic efforts to use available information and to generate the information required for improving decision-making regarding the organization of service delivery, the CoJ, with a grant from the British Department for International Development, sought assistance from the World Bank in establishing a service delivery monitoring system. The proposal was to improve access to basic services through improved governance facilitated by an information system that meets the needs of various stakeholders. The idea was to give political representatives and communities access to information to hold each other and service delivery units accountable, particularly as the new city administration is headed by an elected mayor. The challenge was to pilot a simple and affordable survey that focuses on poorly served areas, and to integrate the generation and use of information with political processes to give voice to the city s poor residents. In late 2001, the first survey to establish the system was conducted by Global Image, a Geographic Information System (GIS) services company, with the technical support of the World Bank. 4 The survey collected information on household access to basic services, their priorities, their expenditure on services, and educational and employment status of household members in poorly served areas of the city. The information generated by the survey has been made available for use by various stakeholders. The city will make the information available to the public on its website. Preliminary meetings were held with city planners and service providers to apprise them of the nature of the information collected through the survey and possible uses it can be put to. The city also proposes to organize brief training sessions for its staff on the use of this information. The plan is to disseminate the survey results among planners, policy makers and political representatives. People s Centers that the CoJ has developed in different regions of the city will serve as forums for making this information available to citizens. 3 Alderman et al (2000) provides useful information and methodologies for mapping poverty at regional levels in South Africa. This study utilizes relationships between certain socio-economic characteristics and expenditure (poverty) observed in Income Expenditure Survey (IES)/October Household Survey (OHS) to estimate the number of poor in different regions on the basis of the 1996 census. Though it provides useful information for targeting funds linked to levels of poverty to local governments, it is not dis-aggregated enough for local governments to monitor poverty within their boundaries. 4 The firm was also involved in the first mapping exercise. 2

7 OBJECTIVES This report makes use of the information collected through the survey to examine: 1) the level of and changes in access to basic services in poorly served areas vis-à-vis priorities and targets of the CoJ, 2) access to public infrastructure, 3) education and employment status of the population, and 4) payment for services, policies and financing of the delivery of basic services in the city. One of the objectives of this exercise is to demonstrate to various stakeholders how information generated through such surveys can be used to monitor and improve the effectiveness of service delivery through informed decision making. ORGANIZATION OF THE REPORT The rest of the report is organized into eight brief sections. In the next section, we discuss policies regarding the provision of basic services, their financing, and strategies and vision of the CoJ. In the third section, we describe the processes involved in establishing the information needs, sampling, conduct of the survey, and some salient features of households and neighborhoods surveyed. In section four, we present the status of access to housing and essential services i.e., water, sanitation, and waste removal. It includes an assessment of the present status of service delivery, a comparison of the latter with polices towards basic services, and a comparison with the situation in 1996, to the extent feasible. The fifth section deals with the delivery of other public services such as telephones, transport, schools and emergency and policing services. The sixth section briefly examines the education and employment status of respondents that give an indication of their economic status. Section seven focuses on payment for various services, cost recovery and financing of basic service delivery in CoJ. The final section offers the City an agenda for strengthening monitoring. 2. Commitment to Meeting Basic Needs Improving living conditions of the poor and reducing disparities are some of the challenges faced by the post-apartheid government of South Africa. The newly elected government s commitment to delivering housing and services was articulated in the Reconstruction and Development Programme (RDP) (1994, s ): The first priority is to begin to meet the basic needs of people jobs, land, housing, water, electricity, telecommunications, transport, a clean and healthy environment, nutrition, health care and social welfare. The new government aptly made the delivery of housing and basic services to all South Africans a priority. The commitment to make housing and service delivery a priority was founded on strategy development processes that were already in place. In the case of housing, the National Housing Forum (NHF) was established in 1992 to develop an alternative to the National Party government's racially based housing policy. This body, comprising of representatives of mass-based political groupings, labor, business community, building industry, financial institutions, civil society and development organizations, formulated a housing delivery approach in which the government facilitates a framework in which the private sector carries out delivery, (Tomlinson 2000). The NHF agreements became the 3

8 basis of the Housing White Paper, 1994, which remains at the core of the national housing policy (Rust and Rubenstein 1996). This policy has since been compiled into a single document known as the National Housing Code (2000). The difficulty with improving the delivery of municipal services was that there was no accurate information about how many households lacked the basic level of services envisaged as part of the basic needs strategy, what it would cost to provide this basic level of services, how the cost could be financed and how the services could actually be delivered. In 1993, the World Bank with financial support from USAID, undertook research in the metropolitan centers to quantify the backlogs and estimate the cost of overcoming the backlogs. In 1994, after the ANC came to power, the World Bank, together with a counterpart South African team and assistance from USAID, funded a municipal finance study and prepared the Municipal Infrastructure Investment Framework (MIIF). The Bank s empirical work provided the first cut database for the MIIF, which in addition to backlog and cost estimates, provided recommendations as to how services might be delivered and financed. Subsequently, the MIIF was debated considerably among government ministries and amended and updated by the South African team, with occasional assistance from USAID-funded international municipal finance experts. The MIIF envisaged a R 60 billion, ten-year investment program. However, the prevailing belief at the time was that the investment could not be financed solely by national government and that its associated projects could not be implemented and managed by most local governments. The MIIF charted the way for the outsourcing of service delivery and for raising private equity and loan capital for the delivery of services (Tomlinson 2002). BASIC SERVICE LEVELS AND HOUSING The current policy of the national government is that all South African citizens should have access to at least the basic level of essential services or better if they can afford them. Various service levels are defined in Table 1. Basic levels require communal water supply and refuse removal and individualized provision of electricity and sanitation. Table 1: Definition of various levels of essential services in South Africa Services Basic services Intermediate services Full services Water Communal standpipes Yard taps In house Sanitation Ventilated improved pit latrines (VIP s) Waterborne Waterborne Electricity 5/8 Amp supply Per-paid 20/30 Amp supply 60 Amp supply Roads Graded Gravel Paved Storm water Earth lined open channel Open channel, other linings Piped systems 4

9 channel linings Refuse removal Communal Curbside removal Curbside removal Source: Department of Provincial and Local Government (2001) With regard to housing, however, the level is not rigidly defined. South Africa s vision for housing is articulated in the Housing Act, 1997 which states that all citizens and permanent residents should have access to a) permanent residential structures with secure tenure, internal and external privacy and adequate protection against elements; and b) potable water, adequate sanitary facilities and domestic energy supply (CoJ, 2001: Vision 2030). The Vision does not clarify what adequate means except to state that it is measured by certain core factors such as security of tenure, availability of services, facilities and infrastructure, and so on. The Vision also recognizes an individual s right of choice in satisfying his or her housing needs. Given these broad principles, the criteria that the government uses for offering subsidies may be an indicator of what it considers to be adequate. The housing subsidy funds the acquisition and servicing of stands and the erection of top structures, whether directly by the developer or indirectly by the beneficiary through the People s Housing Process. To qualify, the beneficiaries need to have an income of less than R3,500 per month, and should not have owned a fixed residential property previously. The current subsidy is R16, The subsidy is typically spent on providing a serviced site. When top structures are included, houses have come to be known as RDP give-aways. When top structures are not included, the development is intended to take place through the Peoples Housing Process. CONSTITUTIONAL RESPONSIBILITIES AND FINANCING Constitutionally, all spheres of government - national, provincial and municipal - share the responsibility for ensuring access to basic services, but municipal governments have traditionally delivered essential municipal services. Under fiscal decentralization, in addition to making municipal services accessible to all citizens, municipalities are now charged with the provision of free basic services to poor citizens who cannot pay. National and provincial governments are concurrently responsible for providing education, health, welfare and housing. However, the national government is largely responsible for policy development and its financing. The provincial governments are responsible for ensuring that houses are delivered. They also have considerable leeway in interpreting housing policy. Provincial governments can be directly involved in the provision of housing or can do it through municipalities. In the case of Gauteng, for 5 Subsidy levels will be revised beginning April Households with a monthly income ranging from 0 to R1, 500 will receive R20, 300, but they must contribute R2, 479; households with income from R1, 501 to R2, 500 will receive R12, 700; and households with income from R2, 501 to R3, 500 will receive R7, 000. The households in the last two categories will contribute whatever is the balance of the purchase price. Single women with dependents, aged and disabled with incomes of less than R800 per month will receive R22, 800, ie., they are not required to come up with their contribution of R2, 479. The contribution of R2,479 is believed to be the difference between R20,300 and what it costs to deliver a 30m 2 top structure. 5

10 example, the province itself intends to deliver housing. 6 The earlier predominant role of developers in providing RDP give-aways has been widely rejected. Municipalities are directly involved in the delivery of user fee services such as electricity, water and sanitation. They are also expected to provide public goods such as municipal and household infrastructure, streets and streetlights. Municipal transformation, as outlined in the 1998 White Paper on Local Government, was undertaken to make municipalities more accountable, financially sustainable and able to deliver critical services to all residents (RSA 2001). The process included the rationalization of municipal boundaries and organizational restructuring necessary to improve their capacity to deliver basic services. As the constitution assigns service delivery roles to the three spheres of government, nationally raised revenue is shared and allocated as entitlement to provincial and local governments. Revenue is shared through a system of intergovernmental transfers, consisting of both unconditional transfers (primarily equitable share) and conditional grants. The Equitable share subsidy is allocated on the basis of the number of households in poverty. In addition, local governments are also given transition grants to assist them with the costs associated with the redrawing of municipal boundaries and the organizational restructuring required for service delivery. Of the total transfers to municipalities in recent years, equitable share transfers constitute about 57 percent, municipal infrastructure transfers about 35 percent, and transfers for capacity development and restructuring constitute about 8 percent of the total (RSA 2001). The equitable share subsidy is formula driven and, in effect, targets municipalities with high levels of poverty and low levels of expenditure per household. 7 The Equitable share subsidy provides for an allocation of R151 per household in metropolitan areas. This amount is likely to be raised to R194 in In large part, the equitable share subsidy is intended to pay for the provision of services at a basic level. The equitable share and other transfers from national government account for less than 2 percent of the budget of large metropolitan municipalities like the CoJ. Since provincial governments are responsible for services such as health, education, welfare, provincial roads, and organizing housing delivery, which have little cost recovery potential, nearly 90 percent of their budget comes from transfers from national government. The municipalities on the other hand, raise nearly 90 percent of their expenses. CITY OF JOHANNESBURG S (COJ S) STRATEGY AND VISION Restructuring efforts at the CoJ began with the development of a medium term strategy, igoli 2002, to deal with the financial crisis and institutional problems confronting the city around The focus of this strategy was on a three-year revenue-led budget, credit control, institutional rationalization and partnerships with the private sector in the area of service delivery. It included a major restructuring of the city 6 Alternatively, in the Western Cape, municipalities are providing serviced sites. The beneficiaries are given the balance of subsidy to build their own housing. 7 It is targeted at households with expenditures below R800 per month. Income levels that qualify for the subsidy are also expected to be revised upwards. 6

11 administration to create independent service providers, a core administration, and a number of regional administrations to more effectively respond to the demand for services. During the preparation of igoli 2002 it was realized that a longer-term vision was needed. igoli 2010 represented the initial step in building the vision, but it has since been supplanted by igoli Even though superseded since, igoli 2010 provides the basis for the 2030 strategy (Tomlinson et al 2002). igoli 2010, which was completed at the end of 2000, just a little before the election of the new executive mayor, sought to position Johannesburg as a globally competitive African world-class city. The 2030 strategy (popularly called Vision 2030) draws largely on the empirical findings of a series of World Bank reports on local economic development produced in partnership with the CoJ during , and places greater emphasis on economic development. It calls for Johannesburg to become a world-class business location. One of the tenets of the Vision is that a better quality of life for its citizens is based fundamentally on the ability of the city to grow: the ability of the city to provide for services is related to its tax revenue base or growth (CoJ, 2001). The CoJ does not consider service delivery to be its greatest challenge to becoming a better city as nearly 96 percent of the households have access to basic water, 84 percent to sanitation, 85 percent to electricity and 85 percent to waste removal; and, only about 16 percent of the households receive less than basic service levels (CoJ 2001). The city finds further support for its Vision in a survey that suggests that the citizens are more concerned about joblessness than socio-economic backlogs. The Vision puts it as follows: It is the belief of the Johannesburg City Council that, by growing the economy of the city, and by basing our dreams of a better life for all our citizens firmly on economic growth, we can aim to confer on the citizens of the City the economic freedom equivalent to the political freedom they achieved in (City of Johannesburg, 2002, p. 115) As the city s primary concerns are focused on the nature of economic growth to be achieved and the strategy for achieving it, the city seeks to become substantially involved in the Blue IQ -led growth strategy of the Gauteng Provincial Government. Blue IQ is a R1.7 billion initiative to invest in 10 mega-projects in tourism, technology, transport and high value-added manufacturing, to create a 'smart' province. Through Blue IQ, Gauteng seeks to invigorate its economy by attracting some R100 billion in foreign direct investment in the next 10 years creating an environment in which local and foreign businesses can prosper and boost job creation opportunities for all South Africans. 8 The two key features of Blue IQ s growth strategy are the focus on a smart province, which leads to employment creation for the skilled, and the location of Blue IQ s intended 10 mega-projects, which steer growth to the area between the Johannesburg and Tshwane (Pretoria) Central Business Districts. 3. The Process In this section, we elaborate on the efforts to initiate a monitoring system including the mechanics of the first survey. The genesis for the monitoring system lies in 8 7

12 the World Bank s local economic development methodology developed for the CoJ in The latter sought to conceptualize an optimal role for a fiscally decentralized CoJ in the form of a regulator that would seek to alleviate poverty by applying a two-pronged strategy. The first prong would focus on reducing income-poverty through job creation by creating an enabling business environment for private sector investment and economic growth in Johannesburg. The second prong would address non-income poverty reduction by directly tracking the effects of local government expenditures on service delivery to poor households in the city. A monitoring system based on current information regarding the status of service delivery in poor households would allow sectoral expenditures to be better targeted towards poor neighborhoods. By establishing a one-to-one link between sectoral budget allocations and their impact on poor households, the monitoring system would keep municipal officials and the citizens of Johannesburg abreast of the city s progress towards Vision 2030 s goal of a world-class city. Initiating an information system Creating ownership of the process is critical to establishing an effective monitoring system: obtaining clarification on who monitors whom, how, for what purposes and what their incentives would be are some of the critical factors in making monitoring systems effective. The proposed monitoring system initiated by the city administration is expected to improve service delivery in the least well-served areas through interventions at two levels. First, availability of better information on the demand for services and the extent to which they are being delivered is expected to help administrators and independent service providers in improving the effectiveness of service delivery. However, this is based on the assumption that both regulators and service providers would have incentives to improve service delivery, particularly to those that are least well-served. It may be unreasonable to assume that it would happen without their downward accountability to communities. This is the reason the second intervention is important. Second, availability of information on the status of service delivery and expenditures to political representatives and communities could initiate political processes that make administrators and independent service delivery units more accountable to communities. An important assumption here is that transparency will help in building downward accountability. Over time, the communities and political representatives may be expected to demand information and ensure that monitoring initiated by city administrators is continued. The potential of information flows to improve service delivery can be maximized by generating demand for such information from various stakeholders. To ensure that the information system that is established is demand-driven, we consulted with various stakeholders including planners, service providers and political representatives to identify their information needs and to understand the role information could play in enhancing accountability. The consultations were useful in identifying a core information set that could be used to monitor the status of service delivery in the city, and one that would also be adequate for constructing some of the indicators that the city could use to assess the performance of independent service providers. 9 This process would eventually be used to 9 Although many service providers and their regulators are likely to collect their own information, the information collected through this survey could be potentially useful to them also. The only limitation is 8

13 establish the key performance indicators (KPIs) on the mayor s scorecard which would allow the CoJ to monitor its poverty alleviation efforts and serve as inputs into the business plan and contracts negotiated by the CoJ and the private utility service providers of services (water, sanitation, electricity and refuse removal, etc.). Another component of the information-based monitoring system is the regular reporting of fiscal information regarding trends in capital and current expenditures on services delivered to poorly served areas of the city as well as revenues collected for services delivered to these areas. If the information on service delivery generated by the monitoring system is indeed useful to the policymakers, then overtime, it should be reflected in a refocusing of service delivery expenditures to areas and sectors that need them most. Over time, the efficiency of the monitoring system should be mirrored in more targeted and efficient public expenditures on service delivery to poor areas matched by a corresponding improvement in the status of service delivery and non-income poverty in these areas. Cluster survey Among the several objectives in designing a monitoring mechanism were simplicity, affordability (time and financial costs), user friendliness and fiscal accountability. As one of the objectives was to pilot a simple and relatively inexpensive method for monitoring the delivery of basic services to the poor, the survey was to be limited to poorly served areas of the city. The spatial clustering of households receiving similar levels of service makes it feasible to identify areas that are poorly served and obtain reliable estimates with small samples. The first challenge was to identify poorly served areas that could be redefined as new information becomes available. Identifying poorly served areas The starting point for identifying clusters was the 1996 Census of the GJMC, which has household information on access to basic services. All Enumerator Areas (EAs) or basic units of sampling in which a significant percentage of households received less than basic services were identified and mapped using a geographical information system (GIS). These EAs are henceforth referred to as poorly served areas and are the focus of this study. This exercise revealed that the level of services received within EAs is homogenous 10 and EAs with similar levels of services are contiguously located or clustered on the ground. Therefore, it was feasible to identify EAs and clusters of EAs in which a significant proportion of the households did not have access to basic levels of services. Identification of poorly served EAs entailed defining what makes an EA poorly served in relation to a particular service, and developing an index or criteria for that the monitoring system will focus on poorly served areas (explained in following sections) of the city whereas the regulatory bodies may require information from the entire service area. However, to the extent that the mandate of independent service providers is to improve service delivery in poorly served areas, the information generated through this survey could be relevant to assess their performance. It is also possible to adapt and extend the survey methodology to cover the well-serviced areas of the CoJ. 10 Most EAs were either well served or poorly served, i.e., more than 75 percent of the households had access to basic services or less than 25 percent of the households did. There were few that fell between these ranges. 9

14 aggregating the level of provision of all essential services. The first problem was aggregating the level of provision of different services. Fortunately, since levels of various services enjoyed by households were correlated, there was no need to find a way to aggregate by creating an index with arbitrary weights. For example, the quality of housing and water services enjoyed by households was correlated (See Annex 1). Housing quality was also strongly correlated with access to electricity; and access to water was correlated with sanitation services. The level of access to water and quality of housing are, therefore, useful predictors of the level of other services enjoyed by households. Starting with the first problem, an EA was classified as poorly served if the proportion of households that received less than the basic level of services exceeded a certain level. However, there is no objective way to determine the cut-off point or the percent of households that should receive less than basic services for an EA to be categorized as poorly serviced. The cut-off level chosen determines the number of EAs that would be included in the survey, and therefore, the costs. For example, if EAs in which 5 percent or more of the households with less than acceptable housing are categorized as poorly served, as many as 45 percent of the EAs turn out to be poorly served. On the other hand, if the criterion is less stringent at 20 percent, only about 30 percent of the EAs would be classified as poorly served. So, there is a trade-off between being inclusive to include even EAs in which only a small proportion of the households experience less than basic services and the number of EAs to be included in the survey or survey costs. In the case of some services such as water, the trade-offs were not significant. In any case, the choice of a cut-off point was to strike a balance between the desire to monitor even the marginally under-served areas and the financial costs of doing so. The final selection of poorly served EAs was done to keep the number of households to be surveyed to less than 6,000. The selected EAs are poorly served in at least one of the three services: water, sanitation and electricity. The selection was an iterative process in which areas selected under different criteria were mapped to find a set of criteria that generated areas that overlapped considerably. One of these exercises was conducted in the presence of city planners who indicated whether the criteria selected identified areas that they perceive to be poorly served. The cut-off points chosen for classifying EAs as poorly serviced are 5 percent of the households with less than basic water and sanitation, 10 percent for electricity and 20 percent for housing. Sparsely populated areas on the outskirts of the city were excluded by selecting only those EAs in which household density is greater than 1 per hectare. This process yielded 921 EAs (approximately 20 percent of the EAs) and 125,409 households. The areas selected for the survey include 23.9 percent of 1996 population, 22.4 percent of households and 7.4 percent of the geographical area of CoJ. These areas are distributed all over the city (Map 1). As the results of this survey would be compared with those of the 1996 census and the results of other city-wide surveys, it is important to pay particular attention to a number of factors. One, the city s boundaries have changed since 1966; the most important change is that Midrand is now included in the CoJ. Two, the population of the city has increased, particularly in areas that were poorly served to begin with. Much of 10

15 these is explained by migration from rural areas. Three, a number of informal settlements 11 have emerged since then. Finally and most importantly, the 2001 survey is relevant only for poorly served areas within various administrative regions. The proportion of population, households and geographical area in the city s 11 regions that can be characterized as poorly served are given in Table 2. The survey results given in the next section have been aggregated to regional levels for ease of presentation, although they may not reflect conditions in the regions. Table 2: Poorly served in Johannesburg (Percent) Administrative Region Population Households Area Alexandra Central Region Diepkloof / Meadowlands Diepsloot Doornkop / Soweto Ennerdale / Orange Farm Johannesburg South Midrand / Ivory Park Northcliff Roodepoort Sandton / Rosebank Administrative regions. As evident from Table 2 and Map 1, poorly served areas are spread all over the city, including some of the clearly richer administrative regions. The percentage share of the population of various regions included in the survey suggests that service delivery is particularly weak in regions of the city such as Diepsloot, Midrand / Ivory Park, and Alexandra in north/northeast; Ennerdale / Orange Farm, Doornkop / Soweto in south/southeast. The severity of lack of access to services, however, may be greater in some poorly served patches of otherwise well served administrative regions. As one would expect, there is a strong association between the status of service delivery and the racial composition of administrative regions, with a few exceptions. Nearly 45 percent of Johannesburg's population resides in Soweto and Diepkloof, contiguous areas sited south of Central Johannesburg. The residents of these two large areas in the city are almost entirely African. The combined population of the Soweto, Diepkloof, Johannesburg South and Orange Farm, four districts south of central Johannesburg is nearly 60 percent of the city's total population. Nearly 85 percent of the residents of these districts are African. Northern districts have a third of the city s population, and about 45 percent of them are African. The proportion falls to 37 percent if Alexandra, the township that withstood forced removal, is deleted from the calculations. 11 Informal settlements are those in which the residents lack ownership or permission to use the land on which their dwellings are built. Unlike illegal settlements, informal settlements are recognized in the sense that there is an attempt to provide them with basic services. 11

16 The nature of housing in different regions and the kinds of problems that households may face in accessing services vary. Formal townships such as Soweto were constructed by the then Johannesburg City Council to house blacks relocated from areas in the city. The Johannesburg City Council provided such townships with a "full" services level. Eventually central government forbade the construction of such townships, to prevent further black urbanization. Backyard shacks emerged during the 1980s in townships like Soweto, as government's ability to control the townships broke down. Often the occupants of these shacks were family members, but a large share of the occupants rented the shacks. Continued black urbanization has resulted in patches of informal settlements in the townships and large informal settlements adjacent to the townships. While backyard shack dwellers share the services available to the main house, the inhabitants of informal settlements obtain services as best they can. The government provides shared communal water connections and, where necessary, supplies water by tankers. Pit latrines and other forms of sanitation are also provided. Alexandra in the north east is a unique case, which through dint of a long struggle, managed to withstand forced removal. Due its location close to work opportunities in Sandton, Alexandra became a favored destination for migrants, both from South Africa and from elsewhere in Africa. This led to tremendously high densities and backlogs in service delivery, as plot owners rented space, mostly in the form of backyard shacks with shared water and sanitation. Informal settlements have grown rapidly, mostly due to the formation of new households and due to migration to the cities from rural areas. Except where the settlements are poorly located, for example, within river flood plains or on dolomitic land that has the potential for sinkholes, the upgrading of such settlements has become a prominent feature of the Gauteng Provincial Governments housing policy. Where settlements are poorly located, such as informal settlements in Alexandra along the banks of the Jukseki River, government seeks to relocate the inhabitants, but the difficulty is that land cheap enough to allow low-income housing is generally more than 25 kilometers from the settlements, which is considerably farther away from job opportunities and with poor access to education and health facilities. Johannesburg central and the immediately surrounding suburbs like Yeoville in Berea offer a different set of problems. As apartheid controls broke down during the 1980s, blacks rented premises in central Johannesburg and these suburbs. All too often unscrupulous property owners went out of their way to attract black renters and then increased rentals and rented properties in a manner that led to high levels of overcrowding. These landlords also often failed to maintain services levels and to pay rates, and eventually abandoned their buildings. Then too, black inhabitants often organized against paying for rents and services, sometimes in reaction to the behavior of the landlords and sometimes to evade these payments. The CoJ has introduced a policy of rehabilitating these buildings. In the interim, CBD apartments are now 100 percent black, with the proportion in the inner city suburbs still high, but declining with distance from the CBD. In all such areas, services are available at a full services level. 12

17 Sampling households An important consideration in sampling households was to enable the extrapolation of results to wards, the smallest political units that are represented in the city council. 12 Wards were, therefore, used as one of the bases for stratification. The other was homogeneity in access within EAs. Poorly serviced EAs identified in the process described above were categorized by the level of homogeneity in service delivery and the wards they belong to. A two-stage sampling of households was followed in which households were sampled from a sample of EAs. A 20 percent sample of EAs was chosen to represent different wards and level of homogeneity in service delivery. As stated earlier, this survey covers approximately 20 percent of the households in the city. Approximately, 5100 households were selected from 20 percent of these EAs. Households were systematically sampled in selected EAs, with the sample size depending on homogeneity within EAs. Fifteen households were sampled from EAs with a predominant service level and 25 in EAs without. We developed simple protocols for enumerators to select households using aerial photographs that were available with Global Image, the GIS firm managing the selection of households for the sample. One of them, for example, was to enter a crowded stand or a block of houses at four different points and sample every 7 th dwelling (Annex 1). 13 During the survey, each team of enumerators was given an aerial photograph of the areas it was surveying; enumerators were then required to mark the location of selected households on the photographs. These maps were scrutinized by the supervisors, who also used them to locate the households for verification of a sample of surveys conducted. The survey of 5100 households was completed in six weeks between mid August and end of September 2001 by a team of 20 enumerators. Survey instrument. A simple survey instrument was designed to collect information from households on access to basic services, household expenditures on services, their priorities, and educational and employment status of household members (Annex 2). Several drafts of the instrument were circulated for comments and also pre-tested. The survey was designed to take less than 30 minutes. One of the significant aspects of the survey was the use of hand-held computers or palms to capture information. Since of the objectives of the survey was to build local capacity to routinely undertake surveys, we decided to use advanced technology to simplify the process. Palms offered several advantages over recording information on paper. Internal consistency checks built into the palm programs drastically reduced errors that may be committed in eliciting and recording information. As questions were automatically prompted based on information that is fed into the computer, any mistakes that enumerators may make by not following the appropriate sequence and choice of questions were also eliminated. The palms also provided some control over enumerators as they automatically documented the times at which interviews were initiated and 12 They typically contain 40 EAs and 6,000 households. 13 This number was changed depending on the density of housing to get the required number of households in an each EA. 13

18 completed. Most importantly, as enumerators were required to transfer data to a server every other day, data verification could be done promptly and the data became available immediately for analysis, thus precluding the use of coders and data analysts. Thus the use of palms reduced the financial and time cost of conducting the surveys and gathering survey data substantially. The programming required for palms included designing the questionnaire with elaborate internal consistency checks and input-led prompting of questions; transfer of data to a server and transformation of data into excel format for immediate use (Annex 3). The programmed palms have been handed over to the city for use in the future. The excel database is accessible to city officials and other stakeholders in user-friendly drop down menu formats which permit the user to navigate through the entire survey database or to obtain custom-made tables in excel at the level of the 11 administrative regions, neighborhoods, or the city as a whole on the one hand, as well as by sector/service provided on the other. The CoJ is also planning to install the database on its website for easy access to the public. Households in poorly served areas. The survey indicates that the average family size is 3.8. Nearly 70 percent of the households have 4 or less members; nearly 12 percent have 5; 8 percent have 6, and less than 10 percent have more than 6 members. The shares of population falling into various age groups were as follows: 0 to 10 years percent; 11 to 20 years percent; 21 to 40 years percent; 41 to 65 years percent and above 65 years - 2 percent. The age composition of population in Alexandra and Central Johannesburg was significantly different from this pattern in that the percent of population aged 1 to 10 was much lower; that was made up with significantly higher population aged 21 to 40. As these areas are favorably located for jobs, they attract people of certain age. Of the population 15 years and older that was not going to school, 4.5 percent had no education, 15.4 percent had from 0 to 5th grade, 54.2 percent from 6 th to 11 th grade, 2.4 percent had technical training, 1.6 percent had diploma, 0.55 percent had Bachelor s degree and 0.14 percent had post graduate degrees. The neighborhoods with bettereducated population were Sandton, Roodepoort and Central Johannesburg. Of the 15 and older population not going to school, about 44 percent were working. Nearly two-thirds of the workers were in formal jobs. The proportion of those not working is lower than average in Roodepoort and Sandton, and higher in Diepkloof, Doornkoop, and Johannesburg South. A majority of the nearly 45 percent that were unemployed were actively looking for work; others would accept work if offered to them. The remaining 10 percent are made up of housewives, disabled, pensioners and those not willing to work. 4. Access to Housing and Basic Services Before examining the present status of service delivery in the poor areas of CoJ, a few clarifications are in order. Generally, there may not be correspondence between the objectives of the three spheres of government in relation to meeting basic needs. However, there may be greater congruence between the objectives of the national 14

19 government, which largely bears the cost of subsidies, and of municipalities that have to deliver the services. The provincial governments may seek to provide higher than the basic levels of service. The Gauteng Provincial Government, for example, has determined that all households should have at least an intermediate level of service within the next four years (GPG 2001). Local policies may vary. 14 Johannesburg has remained flexible, but it has often sought to provide full services level. Johannesburg s intensions are reflected by its use of Consolidated Municipal Infrastructure Programme grant funds for provision of bulk services that allow the delivery to households of a full services level. Even municipalities that face difficulties in meeting the objectives set by the national government may seek to provide full services for political reasons. New housing projects often provide for full service levels, as residents of neighboring better-off households may put considerable political pressure on their councilors to ensure that their neighborhoods are not downgraded after the addition of new housing projects. Also, there may be reluctance, given the country s history, to determine that anything less than full service levels is adequate for a population that has been historically denied access to decent housing and services. A limitation of the definitions of various levels of service given earlier is that they do not address congestion, that is, the number of households that share a certain facility. Although the objective is ultimately to provide non-shared facilities, in the short run, the best that can be done is to reduce congestion. The CoJ seems to have considered various standards to be achieved in the short and medium terms. For example, the sector papers prepared for igoli 2010 by the consultants indicate that the short term objective for the water sector utility should be to supply 20 to 30 liters per capita per day (lpcd) within a walking distance of 200 meters; and in medium term, to supply 50 to 60 lpcd on site (See Annex 4 for other services). Status of service delivery in poorly served areas of Johannesburg The 1996 Census of the Greater Johannesburg Metropolitan area indicated that 20 percent of the households lived in informal housing- representing a backlog of almost 154,472 (Tomlinson et al 1999). However, there was little correspondence between the number of households living in informal housing and housing backlogs estimated by the government, as the government estimates backlogs on the basis of number of people who qualify for a housing subsidy rather than on the basis of the number of households that lack adequate housing. With regard to water supply, 98 percent of the households had access to at least a basic level, but only 66 percent enjoyed full service. About 88 percent of households had either an intermediate or full sanitation service. About 86 percent of all households had electricity at the full service level. Below, the findings of the 2001 survey are presented for various administrative regions of the city. To reiterate, the results are relevant only for the poorly served identified within these regions. Wherever available, comparable figures on the status of service delivery in 1996 are also presented. Housing 14 Tshwane (Pretoria) for example seeks to provide a full services level. 15

20 As discussed earlier, it is not possible to clearly identify the proportion of individuals living in unacceptable housing, because the housing policy, without clearly defining what is adequate housing, recognizes the rights of individuals to make the choice. However, informal dwellings such as shacks whether located in backyards or on independent plots are usually considered to be less than adequate housing. Again, this may not hold good if individual choice is considered. For example, an estimated 40 percent of the residents of backyard shacks in Soweto may not be interested in improved housing (Beall et. al. 2002) 15. With these caveats in place, the housing situation is examined next. Nearly 65 percent of the households in the poorly served areas were living in informal dwellings (shacks in backyards and informal settlements) in The proportion living in informal dwellings has declined to about a one-half (51 percent) in There was a small decline (3 percent point) in backyard shacks and a nearly 10- point decline in other shacks. However, relative to 1996, the proportion of households living in shacks has increased in Diepkloof, Johannesburg South, Northcliff and Roodepoort (Annex 5: Tables 1.1 and 1.2). Changes in other housing categories are not all that clear because the categories are not comparable. The areas in which the proportion of households staying in informal dwellings is greater than one half are Diepkloof, Doornkop, Johannesburg South, and Northcliff (Map 2). The households that have not lived in their dwellings for a long period tend to be in informal dwellings suggesting, as one might expect, that new entrants are moving into informal housing. For example, households that have been living in their dwelling for less than a year are in other shacks (40 percent) and backyard shacks (19 percent). Only twelve percent each of such households were living in single and multiple households. Even among those who have been living in their present dwelling for 1 to 5 years, 55 percent were in shacks of one kind or the other. Only 20 percent were in single or multiple household formal houses. The extent of crowding is also an indicator of housing quality. Single and multiple household formal units had more rooms (other than bathrooms) compared to informal shacks. The survey confirms that those who live in multiple household units, for example, have more than 2 rooms excluding bathrooms while single households usually had more than 3 rooms, except in Johannesburg South. Backyard shacks averaged less than 1.5 rooms, and other shacks had a slightly higher average, but less than 2 rooms. Slightly less than 30 percent of the households have one or less person per room; about 57 percent have from more than 1 to 3 persons per room. Of the remaining 13 percent of households, 7.3 percent have 3.1 to 4 persons per room; 2.7 percent from 4.1 to 5 and 3.1 percent have more than 5 persons per room. This pattern is fairly consistent among neighborhoods except for higher proportion having less than 1 person per room in Roodepoort and Sandton. 15 One possible explanation for this preference lies in the house vs. job tradeoff facing a poor household in which working age adults often have to make the difficult choice between living in their own house in a neighborhood far away from the city and living in a backyard shack but being close to work. 16

21 Nearly 80 percent of the households included in the survey own the house in which they live. 16 Exceptions are Central Johannesburg (34 percent), Roodepoort (58 percent), and Sandton (75 percent) areas where large proportions of households pay rent. As nearly 80 percent of the households own their houses, and 8 percent who do not own do not pay rent for one reason or the other, nearly 90 percent of the households indicated that they do not have expenditures on housing. The only areas with a significant proportion of households renting accommodation and paying rent are Central Johannesburg (64 percent), Diepsloot (24 percent), Roodepoort (34 percent), and Sandton (20 percent). Rent payments are related to both housing type and neighborhood. Nearly 75 percent of those who stay in flats, mostly in Johannesburg central, pay rent. Others include those in other rooms (31 percent), backyard shacks (23 percent) that were established for rental purposes, and townhouses (36 percent). Less than 20 percent of residents of other housing types pay rent. Rents paid are obviously the highest for flats followed by townhouses and farms. In terms of area, rents are higher in Central Johannesburg, Sandton and Roodepoort. Of the 10 percent of households that do pay rent, for 7 percent the rent includes charges for 1 to 5 services, usually refuse removal, water and sanitation. Toilets/sanitation Nearly one half of the households manage with less than basic services defined as VIP or better (Annex 5: Table 2.1). The areas in which more than two-thirds of the households manage with less than basic sanitation facilities are Diepkloof, Dornkoop, Ennerdale, Johannesburg South and Northcliff (Map 3). Changes in access to sanitation since 1996 are not clear. The proportion of households with flush or private chemical toilets has not changed since 1996 in these areas. Pit and bucket toilets accounted for nearly 50 percent in Now pit and chemical toilets account for 44 percent. Other categories are not comparable. Access to sanitation facilities is strongly related to housing type. Nearly all the flats, rooms in hostels, and townhouses have full waterborne flush toilets, although especially in hostels they may be in poor condition because of lack of maintenance. Three-quarters of those in multiple households units and nearly two thirds of those in backyard shacks also have similar facilities. The remaining residents of backyard shacks and other rooms make use of pit latrines and chemical toilets that are provided on the streets. In only 13 percent of the cases, toilets are located inside the house; 62 percent outside the house, but on stand/site, and 25 percent outside the stand/site. The location of toilets is also related to housing type: flats, townhouses and to a limited extent, rooms in hostels and single household houses have toilets inside; nearly 45 percent of the shacks not in backyards use toilets outside their stand; for the rest, a toilet on stand is the predominant type. 16 Households living is shacks constructed by them in informal settlements indicate that they own their house although they may not have formal titles. 17

22 1 household 45% 2-5 households 19% 6-10 households 8% households 5% households 6% More than 50 households 17% Figure 1: Sharing of toilets: Number of households that shared a toilet in poorly served areas of Johannesburg, 2001 Though everyone has access to some kind of toilet, crowding is a problem (Figure 1). Only a little more than 40 percent of the households do not share their toilets with others. Less than 10 percent of the households in Alexandra enjoy such exclusive access, but nearly three quarters of the households in Ennerdale do. Nearly 6 percent of the households share a toilet with 20 to 50 households; and about 17 percent share it with more than 50 families. Sharing with a large number of households is more common in Alexandra, Diepkloof, Northcliff (where the proportions exceed 80 percent) and Roodepoort (Annex 5: Table 2.2). Sharing is also related to the type of toilets: one half of the chemical toilets that are usually provided on the streets are shared by more than 50 families. Other and septic tanks type of toilets are also shared by a large number of families. Sanitation delivery falls short of providing, in the short run, at least one toilet for every 7 dwellings as recommended by igoli 2010 (See Annex 4). Water About 15 percent of the households have a water connection to the house. Of these, 2 percent are admitted to be informal connections (the real figure is likely to be higher). Nearly one half of the households have access to a water source on their site or stand; about a quarter get water from public stands, and about 3 percent depend on water tankers. Residents in shacks in informal settlements depend a lot on communal taps (Annex 5: Table 3.1). If the national definition of basic communal standpipes - is 18

23 followed, nearly all the households have access to a basic or better level except in Johannesburg South. The CoJ s short run objective is to provide water within 200 meters, with less than 20 households sharing a standpipe. The 2001 survey showed that less than 10 percent of the households were more than 50 meters away from a water source. Only 1 percent had to walk more than 200m. The distance to a water source was slightly higher in Diepkloof, Doornkoop, Ennerdale and Johannesburg South. While this survey does not provide any indication of the quantity of water that is made available, in terms of distance from a water source, almost all the population is presently within 200 meters from a source, which is the short term objective of the city. However, there is considerable crowding at water sources (Figure 2). 1-5 households 53% 6-10 households 7% households 5% households 9% Over 50 households 26% Figure 2: Overcrowding at water sources: number of households that shared their water source, Johannesburg, 2001 Across all areas, one half of the households shared their source of water with 1 to 5 households, and a little more than one-fourth of the households shared with more than 50 households. However, in Diepkloof, Johannesburg South, Northcliff and Roodepoort more than 50 percent of households shared with more than 50 households (Annex 5: Table 3.2). Distance to the water source and the number of families with which a household shares water are, of course, related: sources farther off are shared with a larger number of households. In light of the CoJ s long term objective of providing water on site, more than 50 percent of the households in Diepkloof, Johannesburg South, Northcliff, and Roodepoort 19

24 need to have their services upgraded these households either do not have piped water in their house or their neighbor s stand (Map 4). It is not entirely clear whether overall access to water has improved or worsened since 1996 as some definitions may have changed. A smaller proportion of households has in-house connections now. Water piped into dwellings/houses has declined from 21 percent to 13 percent. However, water piped to one s or a neighbor s stand has increased from 23 percent to 56 percent. The use of public/communal taps has decreased from 49 percent to 25 percent. Waste removal The bulk of the households (69 percent) enjoys weekly removal of refuse bags from the curbside (Annex 5: Table 4). Many households use more than one means of garbage disposal. The basic level of service delivery is to organize communal clearing of solid waste at least once a week. The proportion of households without weekly bags removal or clearance of community dumps is relatively higher in Diepkloof, Ennerdale, Johannesburg South and Northcliff (Map 5) Garbage removal is particularly weak in Johannesburg South and Northcliff. Power supply Overall, a little more than a third of the households do not have access to electricity. In Diepkloof, Doornkop, Johannesburg South, Northcliff and Roodepoort, more than 50 percent of the households indicated that they do not have access to electricity in (Annex 5: Table 5.1 & Map ). 17 Most of the connections are said to be legal but about 5 percent of the households admit to having informal connections. Most of the informal connections were in Alexandra (15 percent), Diepsloot (15 percent), and Johannesburg South (9 percent). Nearly three-quarters of the households reported occasional interruptions in power supply; about 3 percent reported daily interruptions, mostly in Alexandra, Diepkloof, Diepsloot, Doornkop, Ennerdale and Midrand. Across all poorly served areas, access to electricity has improved somewhat since 1996; 42 percent of the households reported not using electricity for lighting in However, the proportion of households without access has increased in Diepkloof, Johannesburg South, Northcliff and Roodepoort. Most households consume more than one source of energy. Nearly three-quarters of the households use paraffin. 19 Only 3 percent have access to gas. One-fifth, use coal. Three-quarters use candles, as a source of light in the absence of electricity, and perhaps, as a cheaper source of light even when there is access to electricity. Less than 10 percent use wood. The difference in energy use patterns between those with and without access to electricity is that more of those without access to electricity use paraffin (98 percent vs 59 percent), candles for lighting (93 percent vs. 63 percent), coal for heating (27 percent vs. 17 percent) and obviously batteries. There is no difference in their use of gas. 17 Some of them may have lost access due to non-payment. 18 The Census makes a distinction between electricity used for lighting, cooking and heating. We have used information on lighting since all those with access to electricity presumably use it for lighting. 19 Even those who have access to electricity often use paraffin for cooking. 20

25 Monthly expenditures incurred on gas and electricity were relatively higher than on other sources. Household expenditure on ESKOM electricity was nearly R80 per month, and on gas more than R85. Monthly expenditure on paraffin was about R30, and about R45 on coal. Nearly 6 percent said that they do not spend anything on energy sources. Nearly 50 percent spend less than R200 per month. Including those who do not spend anything, nearly 84 percent of the households spend less than R200 per month. Those with access to electricity spend more on energy sources (R125/month) compared to those without access to electricity (R92/month), except the residents of Diepkloof. Community Priorities The households were also asked to rank their three most preferred services. 20 Housing was identified as the most important service by 44 percent of the households. It was followed by sanitation (16 percent) and water (16 percent) (Annex 5: Table 6). Housing is consistently rated as being most important in all the areas. Even in areas in which more than 90 percent of the households do not have electricity, housing appears as the number one priority. Close to a fifth of the population ranked water as being important in several administrative areas. The households were also asked to list three services whose delivery they felt had improved or worsened in the last five years. Slightly more than half of the households felt that none of the services had improved in the last five years. Sanitation was singled out by 11 percent of the households and electricity by 8 percent in all neighborhoods. There are significant differences between neighborhoods. But, it is noteworthy that despite substantial attention by government to improving housing, only households in Sandton, Alexandra and Midrand felt that housing had improved. In terms of deterioration, less than 30 percent of the households felt that none of the services had deteriorated. The services mentioned by most households as having deteriorated are policing and sanitation. Again, there are significant inter-neighborhood differences. Although housing was not mentioned as the most deteriorated service in any area, it was mentioned as the second or the third most deteriorated service in most neighborhoods. 5. Public Infrastructure In addition to essential services, municipalities are expected to provide public goods. Infrastructure such as health and roads for example, are provided by both provincial and municipal governments. This section considers the status of services that are more public in nature; although telephone services are delivered individually and therefore more private, their provision is also examined. Telephones 20 Although the objective of the survey was to elicit priorities on what the city should be doing, because of inconsistencies in how the question was presented to them, some respondents may have indicated the importance of various services to them regardless of the level enjoyed by them. 21

26 Only one in ten households has a landline at home. More than 30 percent have cell phones (Annex 5: Table 7.1). As there is considerable overlapping in these two categories, a little more than a third of the households either has a landline or a cell phone or both. About 2 percent of the households use their neighbor s phone; more than 80 percent use public phones. The long-term objective of the city is to connect everyone. Slightly more than a third of the households spend anywhere from R11 to R50 per month on telephones. About twenty percent each spend from R51 to R100 and R101 to R200 monthly. Transport Nearly 75 percent of the households have access to transport within 15 minutes of the house; another 22 percent within 30 minutes (Annex 5: Table 8). Seventy percent of them can reach a health facility within 30 minutes. Another 25 percent can reach it in 30 minutes to one hour. Other recent surveys suggest that the average trip to work takes 72 minutes and less than 1 percent of the population is stranded, that is, it cannot afford motorized traffic (CoJ 2001). Minibus is the most commonly used means of transport, availed by more than 90 percent of the households. One in five households uses trains (limited to some neighborhoods). Less than 25 percent spend less than R100 per month on transport; another 40 percent, from R101 to R300 per month. If the objective of the CoJ is to judge connectedness to transport by keeping transport expenditures to less than 10 percent of the household income, then the income of these households needs to be in the range of from R1, 000 to R3, 000 per month. Health services One in two households uses hospitals. More than 80 percent use clinics. One in four households sees private doctors. These numbers are not mutually exclusive. About 13 percent see traditional healers, and seventeen percent go to chemists. Nearly two-thirds feel that health facilities are satisfactory or good. Only a quarter of them feel that they are bad. Surprisingly, there are no differences in the rating of various kinds of facilities. Three significant complaints the households have regarding health facilities are: long wait (64 percent), no medicines (41 percent), and unprofessional service (31 percent). Schools Nearly 90 percent of the children go to government schools. About 8 percent go to private schools. Private school attendance is higher in Central Johannesburg (57 percent), Johannesburg South (21 percent), Northcliff, Roodepoort and Sandton (about 30 percent). Nearly 75 percent of the children take less than 30 minutes to reach their school. Twenty one percent take 30 minutes to one hour. Some of the significant problems the respondents mentioned about educational services they receive are overcrowding (25 percent) and lack of books (17 percent). Other public services: policing, ambulance and safety 22

27 About one in five households indicated that they experienced crime against their person or property in the last twelve months. Crime against their person was somewhat higher in Alexandra, Central Johannesburg and Diepsloot; against property, it was higher in Johannesburg central and Northcliff (Annex 5: Table 9). More than 60 percent of the households indicated that the police response to calls from their neighborhood was adequate. Satisfaction was low in Diepkloof (37 percent), Doornkop (34 percent), and Northcliff (8 percent). Police response, however, is not immediate in all neighborhoods. Only about 17 percent of the households indicated that the police arrives in less than 30 minutes; 18 percent indicated that they arrive in 30 minutes to an hour; 26 percent indicated that they take 2 to 4 hours; and 12 percent indicated that they do not come. In regard to ambulance services too, about two-thirds of the households indicated that they were satisfied with the service. Satisfaction was lower in Diepkloof (34 percent), Doornkop (39 percent), and Northcliff (10 percent). Slightly more than 50 percent of the households indicated they were satisfied with the fire service, with satisfaction low in the three neighborhoods identified above. Other significant problems mentioned were littering and public urination in all the neighborhoods, and overcrowding (relatively low in Ennerdale, Johannesburg South, Midrand and Sandton). 6. Employment Status Providing service at the level that is affordable for a household is an important consideration in improving service delivery. As mentioned earlier, political considerations often make it imperative for municipalities to provide higher than the affordable level of services. With respect to water for example, nearly 30 percent of the users are estimated to be receiving a higher than the affordable level of service (personal communication). The employment status of household members is an indication of a household s capacity to pay for services. To recap the employment status noted in an earlier section, about 44 percent of the population over 15 of age and not in school was working, with nearly two-thirds of them in formal jobs. Nearly 45 percent were unemployed. The proportion of the unemployed in the population exceeds 50 percent in Diepkloof, Doornkop and Johannesburg South (Annex 5: Table 10.1). Of those that are not working, nearly 45 percent are looking for work, and 35 percent would accept work. About 9 percent are pensioners or retired and about 5 percent are homemakers. This pattern holds good more or less for all the neighborhoods. Employment status is associated with the level of education: unemployment rates are lower for those with higher levels of education. This pattern only holds up to the bachelor s degree: while only a third of the graduates are unemployed, one half of the post-graduates are (Annex 5: Table 10.2). The jobs in which the respondents of this survey were employed in are distributed through out the city. Though three areas, Johannesburg north, central business district and south/vaal account for a little more than one half of the jobs, there is a fair amount of distribution over the entire municipality (Annex 5: Table 10.3). Most people also tend to 23

28 live close to work. For example, seventy percent of the jobs held by those living in Alexandra are in Johannesburg north i.e. mostly Sandton (Map 6); one half of the jobs held by those living in Ennerdale were in the south. It is not clear whether people find places to live close to where they find jobs or look for jobs close to wherever they are living, probably the latter. The time taken by workers to reach their jobs also does not appear to be unreasonable long. Slightly less than one half of the employed reach their workplace in less than 30 minutes. Another 38 percent require 30 minutes to 1 hour to reach their workplace. Travel times are higher for those living in Diepkloof, Doornkop and Midrand. 7. Payment for Services / Cost Recovery Households pay for services either directly, usually through a single payment for a bundle of services, or as part of rent. A little more than 7 percent of the households pay for services as part of rent. This proportion varies by type of housing: 70 percent living in flats do so; 8.5 percent in multiple homes; 16.2 percent in hostel rooms, 26 percent in rooms in other dwellings; 36 percent in townhouses, and 15.2 percent in backyard shacks. In all these cases, payments for services are made to landlords, who in turn may or may not be paying the service provider. Only a small proportion, from a quarter to a third, of the households make direct payments for services to the service providers. Most of them make a single payment for a bundle of services that includes water, sanitation and waste removal. The situation is the same when services are considered individually. For example, about two-thirds of the households do not pay for water and sanitation, and more than 85 percent of the households do not pay for waste removal. The situation is better with regard to electricity. Nearly three quarters of those who have access to electricity indicate that they pay the provider directly. Twelve percent of the households pay for electricity as part of their rent and the remaining 11 percent have illegal connections. Non-payment for electricity is much lower than in the case of other utilities. However, this survey only indicates whether people pay and who they pay to; whether they pay regularly is not known. Payment for services is associated with the type of housing. About 40 to 50 percent of the households in multiple-household dwellings, rooms, shacks and single households pay for services in one form or the other. The proportion of households that does not pay for water, sanitation and garbage removal is highest (in excess of 85 percent) among those in shacks not in the backyards. Payment for services is also associated with the level of services received (Annex 5: Table 11.1 to 11.3). For example, households that receive water piped in-house and piped in-stand, sanitation services in the form of full water-borne flush toilets, and weekly removal of refuse bags, have the lowest levels of non-payment for services. This is not to suggest that non payment for services can be explained by lack of willingness to pay for poor services. Superior services that are individualized also facilitate better billing and more effective collection. High levels of non-payment, although most prevalent in the worst-served areas of the city, have implications for the financial viability of service provision. It is not clear whether those who do not pay for services are also those who should be receiving free services because they cannot pay for 24

29 services. The next section examines policies relating to the free provision of services and their financing. Free Basic Services To reiterate, the national policy is to offer basic services, water, sanitation, electricity, and waste removal to all households (RDP 1994). Although the policy is often interpreted as the provision of free basic services to all, the intention of the policy is to target poor households that are currently unable to obtain access to basic services on a sustaining basis (RSA 2001). Therefore, the priority is to target people who do not have access and to provide free basic services to those who cannot pay. Municipalities have the flexibility to define basic services that they would like to provide free of cost. Presently, the consensus in the country is around a package that contains the following: 1) 6 kilolitres (kl) of drinking water per month, 2) Ventilated Improved Pit (VIP) latrine or better, at the household level, 3) 50 kilo watt hours (kwh) of electricity per month, and 4) household refuse disposal from a street container within 200 meters of the household (RSA 2001). The CoJ already offers 6 kl of drinking water free to all households beginning July Beginning July 2002, it will also offer 50 kwh of free electricity to all households. The city has already waived taxes on all properties worth less than R20,000. Although the free supply of water and electricity is not targeted to poor households, cross-subsidization effectively targets it to poor households. Organization of delivery As part of the restructuring to improve service delivery during , the CoJ created independent service providers such as Johannesburg Water for water and sanitation, City Power for electricity, and Pickitup for waste removal. These were departments of the city council that were corporatized into semi-autonomous companies, of which the city remains the sole shareholder. In electricity, there is more than one provider. Historically, electricity distribution in the Greater Johannesburg area was divided between the municipality and Eskom, the nationwide electricity company. The latter distributes electricity to areas such as Soweto, which were not part of the old Johannesburg municipality. Eskom still distributes power to more than half the area presently covered by Johannesburg s boundaries, including 60 percent of the low-income households. 22 In addition to these main corporatized utilities, the city also created about 11 other service providers such as the Johannesburg Roads Agency, Johannesburg City Parks, Johannesburg Zoo, Fresh Produce Market, Civic Theatre etc. collectively referred to as the Utilities, Agencies and Corporate Entities (UACs). While some of the corporate entities generate an income for the city from the service delivery rates charges, other UACs such as roads or Pickitup have to be fully subsidized. 21 Free supply of 6kl of water to all households was announced with a price hike in which all those who use more than 6 kl would experience a hike in charges. 22 The two utilities have different tariffs: Eskom charges up to 40 percent more to domestic customers and 40 percent less to industrial customers, with implications for location of industrial users. Who will supply power to CoJ and where is still being debated as the government also considers the Regional Electricity Distributor (RED) policy, whereby electricity distribution will be reorganized on a regional basis. 25

30 The utility companies created by the city and managed by private companies are expected to become financially independent, but they are regulated by the city. They have a number of management and financial links with the city. - The City regulates the utilities operations by approving their annual business plans; it also reviews their quarterly accounts and performance indicators as specified in service delivery contracts with the city. These arrangements were put into practice for the first time during ; - As the utilities are not able to raise loans in the open market, the city raises funds on their behalf; that is, their capital expenditures are also approved by the city; - As the sole shareholder, the city receives a fixed annual dividend from some of the utilities regardless of their financial performance; - As they are still in a transition to independent utilities, the city continues to collect revenues from a significant proportion of the clients/households. Electricity and water utilities bill only their 10-20,000 largest customers directly. 23 Financing free services The strategies adopted to finance free basic services vary. Consider the cases of electricity and water, two important services that traditionally served as sources of income for municipalities. Johannesburg Water estimates that the cost of providing free water is R80 million. 24 As indicated earlier, the bulk of this loss is being recovered through a hike in charges for those who use more water. In the case of electricity, on the other hand, the hike in charges that would be required to recover costs fully is considered to be too high. City Power estimates that the annual costs of providing free power will be R44 million. It will recover about R17 million through a rate hike, and expects the city to subsidize the balance of R 27 million. How the utilities that have been created to become financially independent, presumably as a step towards delivering services through concessions, are persuaded to extend services to poor areas and also provide free basic services is relevant for financing. The CoJ s contract management unit (CMU) influences the UACs agenda on a regular basis through a review of annual business plans and contracts. As stated above, it raises funds for them and also approves their capital expenditures. The city also retains the ability to cross subsidize services by mandating a transfer of dividends from utilities that traditionally served as sources of income. The two utilities, Johannesburg Water and City Power, are in a financially weak position even without the imposition of delivery of free services. Major utilities face significant problems in collecting their dues. Eskom, which services 60 percent of the low-income households in the city, suffers non-technical losses of up to 80 percent (CoJ 23 The city is obviously concerned about giving up consolidated billing, as it would lose the ability to use critical services such as electricity to collect other taxes and fees that the households may be less willing to pay. 24 Personal communication, CFO, Johannesburg Water. 26

31 2001). City Power reports technical losses of 9 percent (against international benchmarks of 6 percent) and non-technical losses of 18 percent (2001 Business Plan). Johannesburg Water, for example, cannot account for nearly 40 percent of the water; the bulk of its water losses take place in Soweto. Of the remaining 60 percent that can be billed, the payment recovery rate is about 70 percent. Some of the factors that contribute to poor recoveries relate to household ability and willingness to pay. A substantial section of the clients may be enjoying levels of service higher than what they can afford. For example, nearly 30 percent of the water customers are estimated to enjoy levels of services that are higher than they can afford. Many of the new low-income housing projects have been given full services, leading to consumption levels that the recipients cannot afford. In some areas such as Soweto, there is also a strong culture of non-payment for services, rooted in the Soweto Rent Boycott that began in mid-1986 (Tomlinson 1999). Some Soweto households have continued to not pay. A substantial portion of them may be too poor to pay. Fiscal outcomes for the first half of the year indicate that City Power is forecasting losses of Rand 349, 193 and 107 million respectively over the coming three years, , and The losses include dividends of Rand 226, 321 and 334 million that it is contractually obliged to make to the City. 25 Similarly, in the case of Johannesburg Water, after the utility pays the CoJ dividends of Rand 100, 120 and 140 million over respectively, it is estimated to lose Rand 117 million in , but is expected to make profits of Rand 16 and 86 million over the following two years. 26 In the case of Johannesburg Water for example, these projections are based on upgrading services to 56,000 households in informal settlements, a third of the estimated total backlog of 171,000 households in Johannesburg Water estimates the cost of eliminating the entire backlog to range between Rand 800 million to Rand 1 billion. The utility considers various spheres of government to be responsible for achieving universal coverage (Quarterly Reports and Business Plan). Problems in covering the current costs of providing services to even the present customer base raise questions regarding the fiscal affordability of the service delivery arrangements in place. The city faces financial challenges in covering the budgetary deficits of the utilities even with the present customer base, and also in ensuring that adequate investments are made to upgrade services in poorly served areas in which the bulk of the residents may not be able to pay for services. Starting with the fiscal year , the city s financial department clearly demarcated the core budget related with the general administration of the city from the UACs budget which remains the responsibility of the city as it is the sole shareholder. Its draft budget for core functions for the three years is Rand 3.6, 3.7 and 3.9 billion respectively. These figures include a net subsidy to the UACs of Rand 864 million in this fiscal year declining to Rand 772 million by The share of the core budget applied towards the financing of services delivered by the utilities to their present customer base will decrease from 24 percent in this fiscal year to 25 Figures taken from City Power s current budget document, published on the City of Johannesburg website. 26 As is the first year of this fiscal relationship between the city and the UACs, the proposed fiscal arrangements are still being tested. The first round of fiscal outcomes suggest that the two main utilities, Johannesburg Water and City Power may not be able to honor their dividend commitment. 27

32 20 percent in 2003/04. As the present transfers to the UACs are based on their business plans with respect to their existing customer base, and the plans do not include the costs of financing or a schedule for addressing the backlogs in service delivery, it is not clear what type of financing is required to cover these backlogs. While the utilities have plans to improve their financial efficiency through better collections and reductions in losses etc., their business plans do not seem to include a blue print for addressing service delivery backlogs and the more challenging task of undertaking the necessary capital expenditures required to maintain present levels of service delivery (Business Plans). While the fiscal sustainability of the expenditures on subsidies to the UACs remains unsettled, the case of at least one utility raises concerns: if Johannesburg Water s estimated cost of financing the backlogs is any indicator, it appears that this key utility alone would require Rand billion or about 25 percent of the current core budget to provide basic water services to the informal settlements. The sustainability of the city s expenditures on service delivery will also depend on how fast CoJ s revenues grow and the UACs ability to move towards self reliance. Presently, the city is reliant predominantly on its local economic tax base for its revenues. The latter are comprised of property rates(56 percent), regional services council levy (28 percent), equitable share transfers from national government (2 percent, rising to 3 percent by 2003/04), other income (10 percent) and other grants (5 percent). For the present fiscal year, some additional recurrent transfers and conditional grants 27 from national government are available for the city. However, in general, the city s budget is likely to be limited by its own local economic tax base. The UACs ability to bear any additional burden of service delivery is also limited by their budgets. Presently, their budgets range from Rand 6.1 billion in 2001/02 to Rand 7.4 billion in 2003/04; transfers from the core budget of the city currently amount to 13 percent of total expenditures declining to 10 percent by 2003/04. As noted earlier, the UACs are presently operating at a loss of about Rand 425 million. 8. Agenda for monitoring The survey of poorly served areas is an important step towards establishing a system to monitor the delivery of essential services, particularly to the poor. An noted earlier, the efforts to improve the delivery of essential services to the poor began with an exercise to examine the geographical distribution of services delivery backlogs, using the 1996 Census. This effort was part of an initiative to develop an information base essential for facilitating local economic development. Much more needs to be done to have an effective monitoring system in place. The relevant issues are what kinds of information would be collected through surveys in the future, how various stakeholders would use the information, how it may contribute to building accountability for improving the delivery of essential services to the poor and how its results, i.e. changes in the status of service delivery on the ground may help to improve the city s fiscal efficiency with respect to 27 For this FY, information from the National Treasury suggests that the CoJ has been allocated significantly larger resources. However, discussions with officials in both the National Treasury and the financial department of the city revealed that these transfers are conditional and granted in lieu of certain reforms; while the city is eligible for them, it is not automatically entitled to them. 28

33 service delivery in the future. The immediate agenda needs to focus on: 1) encouraging the sharing and use of information among various organizations or units engaged in planning and delivery of essential services, and 2) disseminating information among political representatives and the public. The first could potentially lead to improved planning and increased accountability of service providers to the city. The second, through enhanced transparency, could make the city and the service providers more accountable to communities and their representatives. Carried to its logical end, one could hope that it might make representatives more accountable to their communities. The three sets of organizations or units of organizations engaged in planning and service delivery are the CoJ s Corporate Planning Unit (CPU), the Contract Monitoring Unit (CMU) and the UACs that are directly involved in service delivery. The CPU, which has the larger mandate to monitor the city s progress towards its service delivery goals, commissioned the first survey in partnership with the World Bank with external support from donors. The CPU performs a regulatory function on behalf of the city to ensure that the UACs meet the city s service delivery objectives. The first major challenge would be to get these three organizations to agree on the periodic collection and use of a core set of information, including measurable indicators, for planning and delivering services. To successfully achieve its objective of delivering essential services, the city needs to have in place a process that generates the required information, and decision making processes that effectively use the information. Doing it in a transparent manner will be more efficient. Business plans prepared by utilities ought to make use of such information. Information on access to services and their status collected periodically should drive strategy development and investments made to improve services to the poor. Naturally, the same information would also be the most appropriate for assessing the performance of relevant UACs, through appropriate indicators. Effective monitoring requires clear objectives and carefully chosen measurable indicators. While the desired final goals may be lofty and stated vaguely, it is useful to have practical intermediate outcomes as mileposts that are more clear. The CoJ is committed to meeting the objective of providing basic services to all as set by the national government. As the cluster survey shows, in certain services, the city does better than providing basic levels. However, the challenge faced by the city in extending basic services to everyone does not appear to be as insignificant as assumed in the Vision document. The igoli 2010 consultants recommended strategies with short and mid term goals; these seem relevant given the extent of sharing of facilities and overcrowding that the cluster survey suggests. The CoJ needs to clearly define its service delivery objectives. The choice of indicators is also critical. Indicators serve the dual purpose of helping the city to monitor its own progress in achieving the objectives and also in assessing and influencing the performance of UACs, when they are incorporated as an integral part of the CoJ-UAC service contracts. An important consideration is whether indicators that would otherwise be appropriate for assessing the performance of an organization delivering a service such as a UAC, would also be adequate when the objective is to ensure that the consumption of the services is targeted to certain sections 29

34 of the population, such as households in poorly served areas. A cursory examination of one set of proposed indicators for the CoJ in 2002 suggests that the choice of indicators that seems appropriate for monitoring CoJ s progress towards Vision 2030 is not suitable for tracking progress in the delivery of basic services to its communities in poorly served areas. A sample of these indicators is presented in Table 3. TABLE 3: PROPOSED OUTCOMES, PERFORMANCE AREAS AND KEY PERFORMANCE INDICATORS FOR A SELECT SET OF SERVICES DELIVERED BY COJ Service Outcomes Performance area Performance indicator Electricity Access to safe and reliable electricity Provide electrical infrastructure to all Percent of residents served supply for everyone residents Ensure a safe and reliable Uninterrupted power Water Waste removal Bus Housing Access to safe, reliable and affordable water and sanitation to everyone Access to a clean city and environmentally friendly waste management services Reliable and efficient and safe public bus transport Optimal living conditions for the city of Johannesburg Source: Draft consultants report for CPU, 2002 electrical supply Provide water and sanitation infrastructure to all residents Ensure safe and reliable supply of drinking water Provide affordable waster management services to all Provide a reliable public transport system Be a competitive supplier of public transport services Develop an integrated housing plan and develop mechanisms to enable housing services delivery Guide operation and maintenance of efficient and effective housing services delivery supply Percent of residents served Compliance with environmental standards and interruptions Percent of residents served Percent increase is extent of compliance to schedules Regional targets all add up to the central, department of housing targets Number of housing opportunities created in line with the housing development plan. In the case of electricity, water and waste removal for example, the key performance indicator is the percentage of households served. It is quite feasible for a service provider to register an improvement in the indicator without having improved 30

35 access to the section of the population that may have the least access, such as those in informal settlements. 28 It may be cheaper and more expedient to improve services in an area that is already well served compared to an area that is chronically underserved. An indicator is all the more meaningless when it is a mere count of units of services delivered, as in the case of housing. In the case of bus service for example, it is not clear whether the service seeks to pay any attention to meeting the needs in neglected neighborhoods. Compliance to schedules does not ensure that schedules are convenient for the poor or the buses ply routes that may be beneficial to the poor as well. The choice of indicators particularly for assessing the performance of UACs needs to be made on the basis of the nature of the contracts established with them. 29 The city influences the performance of service providers directly by approving their business plans, or limiting the scope of what they can and cannot do, and through the choice of indicators measures their performance. The two tools need to be complementary. While general solutions to problems identified above can be proposed, it is necessary to consider carefully all aspects of the contracts to identify performance indicators that contribute to building a consistent incentive structure. A general solution to prevent a particular section or geographical area of the city, such as poorly served areas, from being ignored is to estimate the indicator for various types of regions and subgroups. In the case of housing for example, where new housing is created, and who benefits from the housing created would have significant implications for whether the program has benefited those without access, although the creation of the new housing stock itself may be beneficial. Here, as in other cases, the performance indicator may relate to the outcome, although the service provider may not have control over the outcome. For example, even a substantial expansion in housing opportunities created may not increase the percentage of households residing in formal housing if the new housing location is not well connected to the workplace of the poor. But it helps to influence the organization to focus on the problem rather than on just the service it provides. The CPU has taken the leadership in establishing the monitoring system. As noted earlier, to avoid duplication between information generated by various departments and the cluster survey, the CPU organized meetings with the departments and the survey team prior to the launch of the survey. Follow up meetings and a seminar were also conducted upon survey completion in December 2001 to share preliminary results on the status of service delivery in the poorly served areas of the city. A second round of the dissemination process is now required to transfer the detailed information base, share final results with the departments and initiate further dialogue on monitorable indicators or KPIs. It will also be necessary for the CPU to lead the dissemination, make the database available on the council s website and incorporate feedback from the departments in preparation for the next round of the cluster survey. There is also need to build wider ownership for such surveys. A single survey cannot meet the needs of everyone, but a core set of issues can be identified.. 28 The feasibility of doing so depends on the extent to which coverage of services can be increased in areas that are already better served. 29 It many cases, it may be appropriate to have two sets of indicators, one to measure progress in achieving the objective and another to assess the performance of service providers. 31

36 A number of changes may also be made in the survey. In the first round, the monitoring system focused on poorly served areas. It may be expanded to cover better served areas, as maintaining the standard of service is a medium term objective, and information from the entire city may be needed to monitor the performance of UACs as well as tracking CoJ s progress towards the goals of Vision The focus may be changed to other services such as policing, cleanliness of streets etc. that are expected to need increased attention. It may also be necessary to introduce other forms of eliciting information such as mail-in surveys or web-based questionnaires from better served areas in which personal surveys may not be feasible. Another aspect of the service delivery monitoring system envisaged in the early stages of the cluster survey was to establish more direct links between progress in service delivery and the city s expenditures on service delivery. The objective was to improve fiscal transparency and accountability with respect to the delivery of services. The original plan included tracking the city s expenditures on service delivery from 1996 to the present year, especially on poorly serviced areas, and relating them to the status of service delivery on the ground. In an ideal world, the cluster survey could be used to inform the process and target budgetary allocations towards services that were underfinanced and areas and households that were underserved. However, with the recent utility reforms, i.e., the corporatization of the UACs in the last two to three years and proposals to make them fully independent in the near future, this aspect appears unnecessary. Given the present institutional arrangements, it may be more appropriate to track fiscal expenditures by linking the levels of the subsidy paid annually by the city to the UACs with the latter s business plans and performance, as measured by progress made in service delivery with respect to CoJ s objectives. If the CoJ, CPU and UACs indeed begin using the information collected by the cluster survey, then the latter could play an important role in enabling an informed fiscal process whereby survey information could influence the level of the subsidy paid to each service provider. The issue of integrating the monitoring system with the political process needs to be pursued further. The concept of the monitoring system was discussed with various members of the Greater Johannesburg Metropolitan Council, both politicians and bureaucrats, before the December 2000 local government elections. However, since there is now a new cabinet in place, it would be useful to re-engage the cabinet in a process of familiarizing its members with what is available as well as to elicit comments on how the monitoring system can be improved to play a useful role in the city s decision making process. 32

37 REFERENCES Alderman, Harold, Miri am Babita, Jean Lanjouw, Peter Lanjouw, Nthabiseng Makhatha, Amina Mohamed, Berk Ozler and Olivia Qaba. (2000). Combining census and survey data to construct a poverty map of South Africa, Statistics South Africa, Pretoria. Beall J., O. Crankshaw and S. Parnell. (2002). Uniting Divided Cities: Governance and Social Exclusion. London: Earthscan. Chandra, Vandana, Claire Marie-Noel and Jean-Pascal Nganou. (2002) Constraints to Growth in South Africa s Black Informal Sector, Report No. 3, Discussion Paper No. 17. Washington, D.C: The World Bank, Southern Africa Department. Chandra, Vandana, Lalita Moorty, Jean-Pascal Nganou, Bala Rajarathnam and Kendall Schaefer. (2001) Constraints to Growth and Employment in South Africa Report No. 2: Statistics from the Small, Medium and Micro Enterprise Survey. Discussion Paper No. 15. Washington, D.C: The World Bank, Southern Africa Department. Chandra, Vandana, Lalita Moorty, Bala Rajarathnam and Kendall Schaefer. (2000) Constraints to Growth and Employment in South Africa Report No. 1: Statistics from the Large Manufacturing Firm Survey. Discussion Paper No. 14. Washington, D.C: The World Bank, Southern Africa Department. City of Johannesburg (CoJ) (2001). Joburg 2030 full report. Johannesburg: Corporate Planning Unit. Department of Provincial and Local Government. (2001). The Revised Municipal Infrastructure Investment Framework: A Framework for Delivering Water and Sanitation, Energy, Roads and Stormwater Drainage and the Disposal of Solid Waste. Pretoria: Gauteng Provincial Government (GPG). (2001). Business Plan for the Elimination of the Backlog in Water and Sanitation Services in the Gauteng Province: High Level Business Plan. Johannesburg: Department of Development Planning and Local Government Guateng Provincial Government. (2001). Business Plan for the Elimination of the Backlog in Water and Sanitation services in Guateng Province: High Level Business Plan. Department of Development Planning and Local Government. Republic of South Africa (RSA). (2001). Intergovernmental Fiscal Review National Treasury ( Republic of South Africa??? A Vision for Housing in South Africa? Republic of South Africa. (2001). The Revised Municipal Infrastructure Investment Framework : A Framework for Delivering Water and Sanitation, Energy, Roads and Stormwater Drainage and the Disposal of Solid Waster. Pretoria:????? Rust, Kecia and Susan Rubenstein (eds.) (1996). A Mandate to Build: Developing a Consensus Around National Housing Policy in South Africa, Johannesburg: Raven Press. Tomlinson Richard. (2002). International best practice, enabling frameworks and the policy process: a South African case study, International Journal of Urban and Regional Research. (forthcoming) 33

38 Tomlinson, Mary (2000). South Africa's Housing Policy: Lessons from four years of delivery, Third World Planning Review 21,3, p. 284 Tomlinson, Richard, R Beauregard, L Brenner and X Mangeu. (2002). The Post-Apartheid Struggle for an Integrated Johannesburg in Tomlinson, R, R Beauregard, L Brenner and X Mangeu (eds) Emerging Johannesburg. New York: Routledge (forthcoming). Tomlinson, Richard. (1999). Ten years in the making: a history of metropolitan government in Johannesburg, Urban Forum Vol.10, #1, pp Tomlinson, Richard., Phil Sinnett, Bala Rajaratnam and Werner Fourie. (2000). Service Delivery, Equity and Efficiency in Greater Johannesburg. The World Bank and GJMC. (Draft) 34

39 Maps Map 1: Administrative areas of the city with selected EAs highlighted. Map 2: Households in informal dwellings by administrative areas Map 3: Households without access to basic sanitation facilities. Map 4: Households without water piped into house or stand. Map 5: Percent of households without weekly garbage removal Map 6: Percent of households without access to electricity Map 7: Location of residence and work 35

40 MAP 1: POORLY SERVED AREAS OF JOHANNESBURG 36

41 MAP 2: HOUSEHOLDS IN SHACKS (INFORMAL DWELLINGS) BY ADMINISTRATIVE AREAS 37

42 Map 3: Households without access to basic sanitation facilities. 38

43 Map 4: Households without water piped into house or stand. 39

44 Map 5: Percent of households without weekly garbage removal 40

45 Map 6: Percent of households without access to electricity 41

46 MAP 7: WORK LOCATION OF ALEXANDRIA RESIDENTS 42

47 ANNEX 1: NOTE ON THE SAMPLING DESIGN FOR THE POVERTY MONITORING SURVEY 1. Introduction, background and objectives One of the primary objectives of the cluster survey is to accurately estimate the status of social service delivery and infrastructure in Johannesburg. Data on the current status of service delivery on the ground is not available to the GJMC. The most recent data on service delivery is that of the population Census This data is now more than 4 years old and many changes have occurred since then. The majority of these changes have been primarily in previously disadvantaged areas - due to both local and provincial government programmes, migration and other factors. In order to effectively measure the current status of social service delivery on the ground, primary data has to be gathered in these areas (see Figure 1 below). Figure 1: Differential service delivery in Johannesburg and changes since 1996 Rich areas No real changes in service delivery in the last 4 years Stationary status quo - GJMA Poor areas Changes in service delivery in the last 4 years Need for measurement A complete enumeration of households, like the population Census of 1996, is the most accurate way of measuring the status of social service delivery in these areas. Given the cost of this approach the next best alternative is a sample survey which can do this - but at a fraction of the cost. The sample survey (hereafter referred to as the cluster survey ) will have to be designed in such a manner that it will accurately reflect the status of social service delivery on the ground. In the absence of any other information a simple random sample(s.r.s) provides the best means to achieve this. However given the nature of service delivery on the ground, additional information can be used to modify the sample design and therefore gather more accurate information. 43

48 2. The core features of municipal service delivery in Johannesburg Arguably the two most important features of social service delivery in Johannesburg are: 1. The overwhelming spatial homogeneity 2. The high correlation between the different services These two attributes have real implications in terms of the sampling design and strategy. If indeed there exist contiguous homogeneous belts, this implies an effective stratification tool which can be used to stratify areas into homogeneous zones. Sampling theory dictates that stratification into homogeneous strata can be effectively used to obtain more accurate population estimates from the sample. Alternatively viewed this means that smaller sample sizes within homogeneous strata can give the same level of precision that a s.r.s design Secondly, if services are highly correlated, this implies that in identifying poorer areas, using one or two service variables will suffice in identifying most of the relevant poor areas that need to be considered. Before proceeding to exploit these two features, their validity is confirmed below: A. High level of correlation between service-levels The Census 1996 data allows for exploratory analysis of the relationship between the four service-level variables water, electricity, sanitation and housing. An index was constructed for each of the four service-level variables as follows: A scale from 1 to 5 with 1 representing the highest level and 5 the lowest, was assigned to the different service quality within a service-delivery variable. Each enumerator area was then assigned an average service-level for each service variable based on this service quality ranking. The following matrix calculates the product moment Pearson correlation co-efficient for the 4 service variable available in the Census(Table 1) Table 1: Correlation matrix Average service levels within EAs Housing Electricity Sanitation Water Housing Electricity Sanitation Water

49 One can investigate the relationship between the service-levels in an alternate way. Instead of using a service-level index with ranks, it is also possible to define a more natural service-level (backlog) for the different enumerator areas. In particular, we can define the proportion of households within an enumerator area that have basic or below basic levels of service for each of the four service variables. This defines a 4x1 vector for each enumerator area with (p w, p e, p h, p s ) where the p s stand for the proportion of backlogs. The strength of the bivariate relationship between the 4 variables can be investigated by a simple correlation analysis as follows 30.. Table 2: Correlation matrix percentage of service levels within EAs Housing Electricity Sanitation Water Housing Electricity Sanitation Water Though the Pearson product moment correlation analysis has its limitations it can however be used to provide an approximate yardstick to measure the level of the relationship between service variables. The matrix of correlation co-efficients above confirms the relationships hypothesized earlier on. Noting that the Pearson product moment correlation analysis only measures the strength of a linear bivariate relationship the results above are significant (at the p- value of 0.001) for every correlation coefficient. An interesting finding from the analysis above is that water and housing turn out to be the strongest predictors of all others. This has a real implication in the identification of areas to be sampled. It implies that it is possible to use water to identify poorly serviced areas and then complement this with the housing variable to bring in areas into the cluster survey net - that is those which escaped the water criterion. This process circumvents constructing a composite service-level index and thus introducing subjective weights in the process of selecting areas. We confirm the predictive capability of the water and housing criterion later on using an alternative technique. Additionally, the correlation analysis indicates that electricity is most correlated with housing and sanitation with water. Water and housing are highly correlated (see diagram below) 30 Percentage service-levels indicate the proportion of households with either a basic or below basic service level 45

50 Figure : Structural relationships between services Electricity --- Housing Water --- Sanitation Therefore using water and housing act as proxies for all the four service variables. Naturally some subjective judgement should be used to bring in other areas which are not captured by the housing and water criterion (like the Joburg CBD etc...). B. The existence of homogeneous areas It was also hypothesized that areas in Johannesburg are primarily homogeneous with respect to service delivery. In order to prove this hypothesis, the level of backlog was quantified for the 4 service variables by considering the proportion of households within the enumerator area which had either a basic or a below basic level of service. Once the proportions were derived, the service-backlog continuum(between 0% and 100%) was discretized by creating 10 equal classes within the continuum, namely 0%-10% backlog, 10%-20% backlog.. up to 90%-100% backlog. Each EA was assigned to one of the service backlog classes depending on where its actual service backlog was. The following table provides the percentage of enumerator area within these classes for water and housing. Table 3: Distribution of service delivery levels: Water and Housing Service-backlog Housing Excludes formal backyard dwelling Includes formal backyard dwelling 0%-10% 82.4% 60.3% 29.6% 10%-20% 1.3% 8.0% 13.1% 20%-30% 1.1% 4.6% 12.0% 30%-40% 1.3% 4.1% 9.7% 40%-50% 1.1% 3.0% 6.6% 50%-60% 0.8% 2.3% 4.8% 60%-70% 0.6% 2.1% 3.6% 70%-80% 0.7% 1.4% 3.1% 80%-90% 1.0% 2.6% 3.0% 90%-100% 9.5% 11.8% 14.5% 46

51 The figures below illustrate the data in the tables above. Water - Service backlog distribution 100% 80% 60% % of EAs 40% 20% 0% < 10 % 10%-20% 20%-30% 30%-40% 40%-50% 50%-60% 60%-70% 70%-80% 80%-90% 90%-100% % water service backlog Housing backlog disitrbution 100% 80% 60% % of EAs 40% 20% 0% < 5% 5%-20% 20%-30% 30%-40% 40%-50% 50%-60% 60%-70% 70%-80% 80%-90% 90%-100% % of basic or below basic housing 47

52 Indeed, service delivery does appear homogeneous. There are relatively fewer EAs in the centre of the distributions. The homogeniety has its main implications in the sample design and sample size. A homogeneous stratum implies that accurate stratum estimates can be obtained from relatively smaller sample sizes. In particular, for a given stratum, a larger sample should be taken if If the stratum is larger The stratum is more heterogeneous (Tschuprow & Neyman, 19??) Precision in sample estimates is maximised if sample sizes within the strata are taken according to the following formula: N hs h n = h n where n = total sample size (5000??) N hs h n h = sample size in stratum h N h = stratum population total S h = stratum population variance (Tschuprow & Neyman, 19??) The above not only implies that larger sample are required in heterogeneous strata but it also provides a rough rule of thumb which we can quantify/calculate the sample sizes within the different strata. 3. Identification of Poorly serviced Areas Now that the most important features of service delivery in Johannesburg have been established, we can proceed to identify poorly serviced areas and devise a sampling strategy to sample these areas. 3.1 Defining a natural criteria to identify poorly serviced areas. Water and housing together can be used to identify poorly serviced areas as they are strong predictors of other service variables. A cut-off criterion based on selecting all EAs with more than a fixed % of households with either a basic or below basic level of service for water can be used as a starting point. A starting cut-off level of either 5% or 10% can be used. This criterion has the advantage that it is largely inclusive of any area which has less than 95% full or intermediate levels of service The listing of areas implied by this method has to be compared to the listing of areas drawn up when the first GIS exercise was performed in conjunction with the GJMC. 48

53 This criterion has the added advantage that it is not subjective. But however one needs to demonstrate that the 5% cut-off level is firstly robust and secondly that it manages to net all the EAs which would have been included if a service variable other than water or housing were used. We overcome this drawback later in the note. The 5% criteria has the distinct disadvantage that sparsely populated EAs can be misclassified as poorly serviced areas. For example a rural EA of 20 households with 2 shacks will be included in the class of poorly serviced areas. 49

54 3.2 The robustness of the 5% cut-off The two diagrams below illustrate the sensitivity of the 5% cut-off point for the water and housing variable. Sensitivity test - Housing 100% 80% % of EAs included 60% 40% 20% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Cut-off level Sensitivity test - Water % of EAs included % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Cut-off level 50

55 From the graphs above, the cut-off level for the housing variable seems rather sensitive over a large part of its domain. However the water cut-off level is appears quite robust after the 5% level. The housing variable attains a reasonable level of robustness at a cut-ff of about 20%. The sensitivity of the housing cut-off is partially explained by the inability of the housing variable to always measure poverty accurately. In Johannesburg this is due to three phenomena: 1. There are some areas in Johannesburg with formal services and informal housing for example orange farm. This is common in areas with Incremental housing programmes and areas subject to land programmes. In these areas intermediate services are provided but top structures for houses are not provided. Households in these areas tend to retain their informal dwellings/shacks. 2. Owners of formal dwellings sometimes rent out their formal houses and move into backyard shacks. This is used as a source of income. 3. It is also not uncommon for parents to move into backyard shacks and give up their formal dwellings to their children, spouses and grand children. The reasons above and sample size constraints require the use of a higher cut-off level for housing in order to identify the true poorly serviced areas. 3.3 The ability of water/housing to pick up poorly serviced areas Though water together with housing tends to pick up most of the poorly serviced areas. There is no guarantee that they will pick up all the poorly serviced areas in terms of electricity or sanitation. In order to ensure that all areas are indeed identified, all areas with 5% or more of basic and below basic service levels have been mapped for all the services using GIS techniques. This has also been done for the 10% cut-off level (See red maps in the following pages). A visual inspection confirms 32 (see maps below) that the water and housing service maps do indeed cover most of the areas deemed poorly serviced in terms of electricity and sanitation. This should not be a surprising results due to the extremely high correlation between the 4 service variables. 3.4 The exclusion of farm areas & sparsely populated areas A visual inspection of the maps demonstrates that farm areas and sparsely populated areas in the outskirts of Johannesburg are also picked up by the 5% criteria. From a service-delivery perspective, provision of bulk services to these areas is mostly infeasible and would not find itself as a priority for the GJMC. 32 These have to be compared to maps obtained as a result of earlier exercises 51

56 In order to exclude these areas, a population density criteria of 1 household per hectare was applied. As expected this tends to decrease the number of EAs which are selected: The green maps below indicate areas for the 4 service variables after applying the population density criteria using the following cut-off levels. Table 4: Identification of poorly services areas when individual services are used. Service Cut-off No of EAs No of households Water 5% Housing 20% Electricity 10% Sanitation 5% Due to the reasons explained earlier the housing variable does not always adequately manage to pick up poorly serviced areas. 3.5 Final criteria and the inclusion of other areas In order to take an all inclusive sample which identifies all possible poorly services areas in terms of all the 4 services a final criteria has to be defined. This can be defined in the following manner. Any area which is deemed as poorly serviced according to any of the 3 service variables (water, electricity and sanitation) is included in the final frame of areas (see yellow map) 33. This approach includes any area which is poorly serviced in any way and yet works around constructing a composite index. This approach yields a sample frame of 921 EAs and households. The mapping exercise together with the statistical analysis identifies poorly serviced areas in accordance with robust cut-off levels. However there are other areas which Joburg will inherit from the former Midrand municipality, like Ivory Park, that should be investigated. In addition to this other areas which have suffered decay in service delivery can also be included (Westbury ) The approach above, by taking advantage of geographical clustering, allows for the sampling of specified areas, which are fewer than if all EAs were included. 4. Sampling strategy 4.1 Introduction to the overall sample design The best sampling strategy in measuring poverty in the areas chosen above is not immediately evident. Any sampling strategy however needs to take the following into consideration: 33 Housing has been excluded due to the fact that it can be a misleading indicator (see section 3.2). It can however still be included if necessary. Its inclusion will mean a larger number of areas will be captured by the identification process explained above. 52

57 Obtain precise estimates which are statistically valid The sampling strategy the resulting estimates have to have the capability to be extrapolated to the different wards in the Unicity of Johannesburg - to have political interest The sampling strategy also needs to have adequate coverage given the roughly 5000 sample size and the associated cost constraint Given the nature of service delivery, stratified sample design where strata are homogeneous will bring about relatively high levels of precision in sample estimates. Secondly the need to cover the different wards simply translates into the wards becoming a variable of stratification. Thirdly, the cost constraint can be overcome by engaging traditional techniques normally used to overcome these namely a 2-stage cluster sampling. This entails random sampling (pps possibly) of EAs before sampling of households. The reader must bear in mind that 2-stage cluster sampling can yield estimates with higher sampling variability. With the above concepts in mind the poorly services areas defined above can be categorized by Ward and level of service. One type of categorization by the level of service into homogeneous and heterogeneous areas is to categorize all the areas into three groups: Type I, Type II and Type III. Type I areas include all EAs with less than 25% of household in the EAs with a basic or below basic level of service for water. Type II with service backlog between 25% and 75% and Type III with service backlog over 75%. Type I and type III areas are mainly homogeneous where the majority of household (over 75%) enjoy the same level of service. Type II areas are the heterogeneous areas where there is no predominant level of service. Water has been used as the main stratification variable here as it is a stronger predictor than the others 34. This categorization classifies EAs into homogeneous groups, which will facilitate the stratification process. It will also ensure adequate representation of all the different types of service areas (see table 5 below). 34 The use of water (only!) as a stratification variable will not lead to major inconsistencies as the stratification process is only meant to provide an approximate means to separate homogeneous areas from heterogeneous areas. 53

58 Table 5: Poorly serviced areas in Johannesburg by ward and type of service-level No of EAs No of households WARD Type I Type III Total Type I Type II Type III Total

59 Total The table above is essentially the stratified sample design(and frame) by Ward and Type of service-area. The next task is to decide on the number of Enumerator areas to be chosen and the sample size within the respective EAs. The balance between the number of PSUs and the number of households within an EA is dictated by the trade-off between sampling and non-sampling errors. Recommendations in the literature (Grosh and Munoz, 1996; OHS 95, SSA) recommend a sample size of about within a PSU. If a sample size of approximately 20 is used, this allows for sampling approximately 250 EAs (250 = 5000/20). This amounts to a quarter of the EAs in the frame. This in turn implies that one in every 4 EA within a stratum is to be chosen randomly from the frame. Some of the strata have less than 4 EAs (see above) omitting these could mean ward level analysis will not be possible. Hence strata with only 1 or 2 EAs have their EAs sampled with probability 1 to ensure that these areas are indeed covered. This process introduces a higher number of EAs and hence the sample size of 5000 is exceeded. This can however be easily overcome by selecting 1 in every 5 EAs in each stratum. The heterogeneity in Type II service-areas can be accounted by sampling slightly higher in these areas. As a rule of thumb, 25 households can be sampled in Type II areas and 15 in Type I and III areas. 55

60 4.2 Implementation of the first sampling stage The table below provides the number of EAs to be sampled(randomly) within each strata according to the above algorithm. Note that the number of EAs to be sampled within a stratum have been rounded up to the nearest integer. Table 6: Number of EAs to be sample within the different strata (s.r.s in the first stage) Type I Type III

61 Total no of EAs No of hholds An alternative to the random sampling of PSUs is sampling according to PPS (probability proportional to size). PPS will allow a roughly self-weighted sample and will yield unbiased statistical estimates. There are several methods for carrying out PPS sampling in practice - the most commonly used one is termed systematic sampling with PPS. An important aspect of the sample design is the derivation of the weights - as this has to be performed in order to calculate Ward level and Administrative region level statistics. The first stage weights corresponding to the random sample design above can be calculated quite easily as the inverse of the sampling fraction (See Appendix 1 for first stage weights). The second stage weights can be calculated in a similar manner. 57

62 A further consideration in the choice of the number of EAs to be sampled is the need for spatial representativity. A GIS map for Johannesburg by Ward and type of service-area has been drawn-up (see attached map). The map picks up mainly eight areas that are poorly serviced according to the 1996 census: Diepsloot Alexandra Doornkop(North of Soweto) Protea South Orange Farm Kliptown, Dlamini, Chiawelo and Klipspruit Joburg CBD Other areas A closer look at the individual areas by the type of service-levels shows that service-level areas lie in contiguous belts (See Maps). This is true for all area except Alexandra. The existence of contiguous service belts with similar service levels implies that a lower number of EAs need to be sampled in these regions due to the overwhelming spatial homogeneity. Naturally, Alexandra needs special attention whereby a proportionately higher number of EAs can be sampled. 4.3 Implementation of the second sampling stage - sampling within an enumerator area There are various important issues that need to be addressed in the second sampling stage. These include the following: The type of sampling process within an Enumerator area The definition of households and the case of primary and secondary dwellings The case of non-responses These points are addressed below: The sampling process within an Enumerator area Once an enumerator area is chosen as a sampling unit in the first stage of the cluster sampling process, sample sizes within an EA are to be based on the level of heterogeneity in the EA (either a sample size of 15 or 25). In order to get scientifically valid estimates simple random sampling within an EA is recommended in the literature. In practice however, systematic sampling is usually carried out. In order to carry out sampling within an EA a frame of households in the EA is crucial. In other words a list of dwellings in each selected PSU is needed to determine which dwellings on the list will be visited in the survey. This listing is usually obtained from a Census or a previous survey. The South African population Census 1996 however does not allow for this. One option is to use aerial maps to obtain a spatial listing of the dwellings in the EA. The literature in sampling theory 58

63 suggests that household listing can be carried out either as a separate field operation conducted in all PSUs before the survey starts or by the survey teams themselves when they first arrive in each PSU. The first option(i.e the separate field exercise) is more expensive but more reliable. This is mainly because listing as a separate exercise is more reliable than listing as part of field work because staff that are specifically trained and devoted to listing are less likely to bias the sample by excluding the dwellings that are harder to reach (Grosh and Munoz, 1996). The complete list of dwelling (be it a field exercise or one using aerial maps) should be recorded in a standard form with one line per dwelling. The two most important characteristics of the list are that all dwellings within the PSU be included on it and that it allows the selected dwellings to be located easily. Fieldwork should always start with cartographic reconnaissance. The maps do not need to be very precise in terms of scale or the locations of the dwellings, but they should show the PSU boundaries and the landmarks used to split it into smaller areas. This helps to organise the daily work of the different enumerator areas. The dwellings to be visited within an EA/PSU are selected by systematic sampling (as compared to simple random sampling) from the PSU listings. A few extra dwellings are also selected to be used if replacements are needed in the field. If two or more households occupy a dwelling all the households are to be sampled. In homogeneous EAs (Type I & Type III) a simple/systematic random sampling need not be followed strictly and yet more precise estimates can be obtained. However, heterogeneous areas have to be handled more carefully The definition of households and the case of primary and secondary dwellings The basic analytical unit of the poverty information survey is the household. Many surveys define the household as a group of people who share a roof and a cooking pot 35. The practical implementation of sampling households makes it difficult to preserve the above definition of a household, because that would entail time-consuming interviewing throughout each PSU. In practice dwellings are listed instead of households. A dwelling is defined as a group of rooms or a single room occupied or intended for occupancy as separate living quarters by a family or some other group of persons living together, or by a person living alone (Kish, 1965). Besides the advantage of being shorter to complete, a listing of dwellings is more permanent than a listing of households. Strictly speaking, therefore the sample surveys such as the LSMS are samples of dwellings rather than of households, though the listing operation is still traditionally called household-listing. Some dwellings may be unoccupied and some may be occupied by one single household. (The average number of households per dwelling 35 For a discussion of the concept of a household and its variants, and details on the operational definitions used by various UN agencies see UNNHSCP(1989) 59

64 ranges from 0.9 to 1.1 in most countries.) If a dwelling with two households is selected in the sample, both are interviewed separately The case of non-response and household replacement Some households selected for the sample will not be interviewed because of one of the following reasons: The interviewer cannot locate the dwelling The dwelling is uninhabited The dwelling s residents are away from home and expected to remain so until after the end of the survey. The residents refuse to be interviewed. Non-responding households cannot be considered to be a random sample of households can introduce bias to the results. There is a lot of controversy about what should be done about non-response. The traditional approach to non-response has been to replace these by other carefully selected households. The idea is to replace households by near neighbors, which are likely to have similar socio-economic characteristics. The case of non-response should be referred to field supervisors. 60

65 APPENDIX 1 Table of weights corresponding to sample design (s.r.s in the first stage) Table A1: Weighting factors for each stratum Type I Type III

66

67 ANNEX 2: SURVEY INSTRUMENT Name Actions Main N.B. A User must have been enter before the 1 st two actions are possible. New Survey -> Basic 10 for a new survey Review Survey -> ReviewSurveys New User -> NewUser (not possible after a user has been created in normal operation) About -> Program name, version etc Name Actions ReviewSurveys Main Menu -> Main Examine -> allows viewing and navigating of the selected survey but only allows editing of the summary field 63

68 Name Actions New User Created a new User ID (attached to each survey) and associates it with a name and Palm ID. Menu Items About -> Progarm name, version etc Goto Form -> Not available in normal usage Goto Section -> allows navigation back to the start of an earlier section Cancel -> exits the current form. If editing the survey the survey data is lost. If reviewing the survey then the data is retained 64

69 Name Basic 10 Validation 7 digit EA Number, not == , Address at least 3 characters, race selected Next Household 10 Name Household 10 Validation All numbers >= 0 Next Housing 10 65

70 Name Housing 10 Validation House type selected, # rooms >= 1 Next Housing 20 Name Housing 20 Validation At least one house of own building type, no numbers > 0 without a house type Next Housing 30 66

71 Name Housing 30 Validation Number of households >= 1, number of years living in house indicated Next Housing 40 Name Housing 40 Validation Yes, No or Expecting selected Next No ->Housing 50 Yes/Expected -> Housing

72 Name Housing 50 Validation Yes or No selected Next Yes -> Housing 60 No -> Housing 80 Name Housing 60 Validation Cost > 0 How often and recipient indicated. Next Housing 70 68

73 Name Housing 70 Validation At least one tick field selected Next Housing 100 Name Housing 80 Validation Reason selected. If Other then text field must have a value Next Housing

74 Name Housing 90 Validation Yes or No selected Next Yes -> Housing 110 No -> Land 10 Name Housing 110 Validation Reason selected, if Other chosen than text field must have a value Next Land 10 70

75 Name Land 10 Validation Yes, No or Expecting selected Next No and Land Rent included in House Rent - > Services 10 No and Land Rent not included in House Rent -> Land 50 Yes/Expected -> Land 20 Name Land 20 Validation Yes or No selected Next Yes -> Land 30 No -> Land 40 71

76 Name Land 30 Validation Cost > 0, recipient and period indicated. If recipient is other then text field must have value Next Services 10 Name Land 40 Validation Reason must be selected. Other chosen text field must have a value Next Services 10 72

77 Name Land 50 Validation Reason selected. If other chosen then text field must have a value Next Services 10 Name Services 10 Validation Yes or No selected Next Yes -> Services 20 No -> Sanitation 10 73

78 Name Services 20 Validation Cost > 0, period and recipient selected. If recipient is other then text field has value Next Services 30 Name Services 30 Validation At least one field is selected (NB any fields other than Other selected in Housing 70 will not be available). If Other is chosen then text field must have a value. Next Sanitation 10 74

79 Name Sanitation 10 Validation Type selected, if Other chosen then text field must have a value Next Sanitation 20 Name Sanitation 20 Validation Location selected, # households indicated Next Sanitation included in rent or combined services -> Water 10 Otherwise -> Sanitation 30 75

80 Name Sanitation 30 Validation Cost >= 0, period indicated if cost > 0 Next Water 10 Name Water 10 Validation Source selected, households > 0 Next Water 20 76

81 Name Water 20 Validation Water location selected Next Water 30 Name Water 30 Validation At least one tick box selected, if Other selected then the text field must have a value Next If water included in rent or combined services -> Waste 10 Otherwise -> Water 40 77

82 Name Water 40 Validation Cost >= 0, If Cost > 0 period indicated Next Waste 10 Name Waste 10 Validation One box in each section selected Next If waste not included in house rent or combined services -> Waste 20 Otherwise Energy 10 78

83 Name Waste 20 Validation Cost >= 0, if Cost = 0 then Period selected Next Energy 10 Name Energy 10 Validation Any ticked service must have a cost >= 0 Any cost > 0 must have a period and a tick (periods do not not need a cost or a tick as they cannot be cleared). N.B. If house rent or combined services included electricity the cost fields will not be displayed Next Energy 20 79

84 Name Energy 20 Validation Any ticked service must have a cost >= 0 Any cost > 0 must have a period and a tick (periods do not not need a cost or a tick as they cannot be cleared). Next Energy 30 Name Energy 30 Validation Any ticked service must have a cost >= 0 Any cost > 0 must have a period and a tick (periods do not not need a cost or a tick as they cannot be cleared). Next If any electricity service is ticked in Energy 10 -> Energy 40 Otherwise -> Telephone 10 80

85 Name Energy 40 Validation Meter type selected, No, Daily or Occasionally selected Next Telephone 10 Name Telephone 10 Validation At least one tick field selected Next If Telephone not included in house rent or combined services -> Telephone 20 Otherwise -> ServicesConclusion 10 81

86 Name Telephone 20 Validation Cost >= 0, if Cost > 0 then period selected Next ServicesConclusion 10 Name ServicesConclusion 10 Validation 1 st, 2 nd, 3 rd all selected, no duplicates between them Next ServicesConclusion 20 82

87 Name ServicesConclusion 20 Validation Improved and Deteriorated both have value. Improved and Deteriorated are not the same unless they are both None. Next Transport 10 Name Transport 10 Validation Time to public transport and time to health facility both filled in. Next Transport 20 83

88 Name Transport 20 Validation Cost >= 0, if cost > 0 then period selected Next Education 10 Name Education 10 Validation Highest education level has been filled in, 0 <= School children <= people under 20 Next School Children = 0 -> Education 20 for 1 st child Otherwise Health 10 84

89 Name Education 20 Validation School run by, Travel time and suburb filled in. Next If travel time > 30 -> Education 30 Otherwise Eduction 40 Name Education 30 Validation At least one tick box selected, if Other selected then text field must have a value Next Education 40 85

90 Name Education 40 Validation At least one tick field filled in, if Other selected then text field must be filled in. Next If child # == School Children (from Education 10) -> Health 10 Otherwise -> Education 20 for next child Name Health 10 Validation At least box ticked, if Other ticked then text box must have a value Next Health 20 86

91 Name Health 20 Validation Rating selected Next Emergency 10 Name Emergency 10 Validation Yes or No selected for both questions Next Emergency 20 87

92 Name Emergency 20 Validation Response time selected for all questions Next Emergency 30 Name Emergency 30 Validation At least one tick box selected, if Other is selected then text box must have a value Next Work 10 88

93 Name Work 10 Validation Number of adults >= 0, < total adults less any children who must be over 10 Next Work 20 Name Work 20 Validation Work Status chosen Next Work Status is Not Working -> Work 40 Otherwise -> Work 30 89

94 Name Work 30 Validation Travel Time indicated, work suburb chosen Next Work 50 Name Work 40 Validation Unemployment reason chosen, if Other then text field has value Next Work 50 90

95 Name Work 50 Validation Education level chosen Next Summary 10 Name Summary 10 Validation None only form which can be update during review additional answers are added to the end of previous answers Next Main Menu 91